diff --git a/.coveragerc b/.coveragerc
new file mode 100644
index 00000000..dfbea31b
--- /dev/null
+++ b/.coveragerc
@@ -0,0 +1,11 @@
+[run]
+omit =
+ tests/*
+ setup.py
+ */projects/*
+ */tests/*.py
+ */tools/*.py
+ */tutorials/*.py
+ */config.py
+source =
+ .
diff --git a/.dockerignore b/.dockerignore
new file mode 100644
index 00000000..f0c19b92
--- /dev/null
+++ b/.dockerignore
@@ -0,0 +1,19 @@
+__pycache__
+*.pyc
+*.pyo
+*.pyd
+.Python
+env
+pip-log.txt
+pip-delete-this-directory.txt
+.tox
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*,cover
+*.log
+.git
+**/*.atommic
+**/*.ckpt
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
new file mode 100644
index 00000000..a810c91d
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -0,0 +1,42 @@
+---
+name: Bug report
+about: Create a report to help us improve
+title: ''
+labels: bug
+assignees: ''
+
+---
+
+**Describe the bug**
+
+A clear and concise description of what the bug is.
+
+**Steps/Code to reproduce bug**
+
+Please list *minimal* steps or code snippet for us to be able to reproduce the bug.
+
+A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
+
+
+**Expected behavior**
+
+A clear and concise description of what you expected to happen.
+
+**Environment overview (please complete the following information)**
+
+ - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider - AWS, Azure, GCP, Collab)]
+ - Method of ATOMMIC install: [pip install or from source]. Please specify exact commands you used to install.
+ - If method of install is [Docker], provide `docker pull` & `docker run` commands used
+
+**Environment details**
+
+If docker image is used you don't need to specify these.
+Otherwise, please provide:
+- OS version
+- PyTorch version
+- Python version
+
+**Additional context**
+
+Add any other context about the problem here.
+Example: GPU model
diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
new file mode 100644
index 00000000..355e5539
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -0,0 +1,25 @@
+---
+name: Feature request
+about: Suggest an idea for this project
+title: ''
+labels: feature request
+assignees: ''
+
+---
+
+**Is your feature request related to a problem? Please describe.**
+
+A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+
+**Describe the solution you'd like**
+
+A clear and concise description of what you want to happen.
+Provide a code snippet on how new APIs/changes would be used by others.
+
+**Describe alternatives you've considered**
+
+A clear and concise description of any alternative solutions or features you've considered.
+
+**Additional context**
+
+Add any other context or screenshots about the feature request here.
diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
new file mode 100644
index 00000000..816f9328
--- /dev/null
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -0,0 +1,39 @@
+# What does this PR do ?
+
+Add a one line overview of what this PR aims to accomplish.
+
+**Collection**: [Note which collection this PR will affect]
+
+# Changelog
+- Add specific line by line info of high level changes in this PR.
+
+# Usage
+* You can potentially add a usage example below
+
+```python
+# Add a code snippet demonstrating how to use this
+```
+
+# Before your PR is "Ready for review"
+**Pre checks**:
+- [ ] Make sure you read and followed [Contributor guidelines](https://github.com/wdika/atommic/blob/main/CONTRIBUTING.md)
+- [ ] Did you write any new necessary tests?
+- [ ] Did you add or update any necessary documentation?
+- [ ] Does the PR affect components that are optional to install? (Ex: Numba, Pynini, Apex etc)
+ - [ ] Reviewer: Does the PR have correct import guards for all optional libraries?
+
+**PR Type**:
+- [ ] New Feature
+- [ ] Bugfix
+- [ ] Documentation
+
+If you haven't finished some of the above items you can still open "Draft" PR.
+
+
+## Who can review?
+
+Anyone in the community is free to review the PR once the checks have passed.
+[Contributor guidelines](https://github.com/wdika/atommic/blob/main/CONTRIBUTING.md) contains specific people who can review PRs to various areas.
+
+# Additional Information
+* Related to # (issue)
diff --git a/.github/labeler.yml b/.github/labeler.yml
new file mode 100644
index 00000000..c545f656
--- /dev/null
+++ b/.github/labeler.yml
@@ -0,0 +1,32 @@
+MTL:
+- atommic/collections/multitask/**/*
+- docs/source/api/multitask/**/*
+- tests/collections/multitask/**
+
+qMRI:
+- atommic/collections/quantitative/**/*
+- docs/source/api/quantitative/**/*
+- tests/collections/quantitative/**
+
+REC:
+- atommic/collections/reconstruction/**/*
+- docs/source/api/reconstruction/**/*
+- tests/collections/reconstruction/**
+
+SEG:
+- atommic/collections/segmentation/**/*
+- docs/source/api/segmentation/**/*
+- tests/collections/segmentation/**
+
+core:
+- atommic/core/**/*
+- tests/core/**
+
+common:
+- atommic/collections/common/**/*
+
+CI:
+- .github/**/*
+- Jenkinsfile
+- Dockerfile
+- ci.groovy
diff --git a/.github/workflows/changelog-build.yml b/.github/workflows/changelog-build.yml
new file mode 100644
index 00000000..beb9556a
--- /dev/null
+++ b/.github/workflows/changelog-build.yml
@@ -0,0 +1,47 @@
+name: 'Changelog Build (Release)'
+
+on:
+ push:
+ tags:
+ - '*'
+
+jobs:
+ changelog:
+ if: startsWith(github.ref, 'refs/tags/')
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/checkout@v4
+ with:
+ fetch-depth: 0 # Required due to the way Git works, without it this action won't be able to find any or the correct tags
+
+ - name: Get Previous tag
+ id: previous_tag
+ # git for-each-ref --sort=-creatordate --format '%(refname)' refs/tags ==> refs/tags/vX.Y.Z in descending order of date
+ # awk 'FNR == 2 {print substr($1, 11, length($1))}') ==> Selects the 2nd tag from the list, then strips the /refs/tags/ part of the tag
+ # set-output name=tag_name:: ==> Takes the clean tag vX.Y.Z and sets it to steps.previous_tag.outputs.tag_name
+ run: |
+ echo "::set-output name=tag_name::$(git for-each-ref --sort=-creatordate --format '%(refname)' refs/tags | awk 'FNR == 2 {print substr($1, 11, length($1))}')"
+ echo ${{ steps.previous_tag.outputs.tag_name }}
+
+ - name: Build Changelog
+ id: github_tag
+ uses: mikepenz/release-changelog-builder-action@v3.3.1
+ env:
+ GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+ with:
+ # Configuration file is setup with filters for domains
+ # owner:repo must point to current repo
+ # fromTag: Auto resolved from historical tag order (previous tag compared to current tag)
+ # toTag: Current tag reference
+ configuration: ".github/workflows/config/changelog-config.json"
+ owner: "wdika"
+ repo: "ATOMMIC"
+ ignorePreReleases: "false"
+ failOnError: "false"
+ fromTag: ${{ steps.previous_tag.outputs.tag_name }}
+ toTag: ${{ github.ref_name }}
+
+ - name: Print Changelog
+ run: |
+ echo "${{steps.github_tag.outputs.changelog}}"
+ echo "--- DONE ---"
diff --git a/.github/workflows/cherry-pick-release-commit.yml b/.github/workflows/cherry-pick-release-commit.yml
new file mode 100644
index 00000000..2037b6d9
--- /dev/null
+++ b/.github/workflows/cherry-pick-release-commit.yml
@@ -0,0 +1,28 @@
+name: Create PR to main with cherry-pick from release
+
+on:
+ pull_request_target:
+ branches:
+ - 'r*.*.*'
+ types: ["closed"]
+
+jobs:
+ cherry-pick-release-commit:
+ name: Cherry-pick release commit
+ runs-on: ubuntu-latest
+ steps:
+ - name: Checkout
+ uses: actions/checkout@v4
+ with:
+ fetch-depth: 0
+ - name: github-cherry-pick-action v1.0.3
+ uses: carloscastrojumo/github-cherry-pick-action@bb0869df47c27be4ae4c7a2d93d22827aa5a0054
+ with:
+ branch: main
+ labels: |
+ cherry-pick
+ reviewers: |
+ ${{ github.event.pull_request.user.login }}
+
+env:
+ GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/close-inactive-issue-pr.yml b/.github/workflows/close-inactive-issue-pr.yml
new file mode 100644
index 00000000..aad5ddcd
--- /dev/null
+++ b/.github/workflows/close-inactive-issue-pr.yml
@@ -0,0 +1,25 @@
+name: Stale-Close-Inactive-Issues-PRs
+on:
+ schedule:
+ - cron: "30 1 * * *"
+
+jobs:
+ close-issues:
+ runs-on: ubuntu-latest
+ permissions:
+ issues: write
+ pull-requests: write
+ steps:
+ - uses: actions/stale@v6
+ with:
+ operations-per-run: 100
+ days-before-issue-stale: 30
+ days-before-issue-close: 7
+ stale-issue-label: "stale"
+ stale-issue-message: "This issue is stale because it has been open for 30 days with no activity. Remove stale label or comment or this will be closed in 7 days."
+ close-issue-message: "This issue was closed because it has been inactive for 7 days since being marked as stale."
+ days-before-pr-stale: 14
+ days-before-pr-close: 7
+ stale-pr-message: "This PR is stale because it has been open for 14 days with no activity. Remove stale label or comment or update or this will be closed in 7 days."
+ close-pr-message: "This PR was closed because it has been inactive for 7 days since being marked as stale."
+ repo-token: ${{ secrets.GITHUB_TOKEN }}
diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
new file mode 100644
index 00000000..adaba9b0
--- /dev/null
+++ b/.github/workflows/codeql.yml
@@ -0,0 +1,75 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
+name: "CodeQL"
+
+on:
+ push:
+ branches: [ "main", "[rv][0-9]*", "gh-pages-src" ]
+ pull_request:
+ # The branches below must be a subset of the branches above
+ branches: [ "main" ]
+ schedule:
+ - cron: '19 1 * * 4'
+
+jobs:
+ analyze:
+ name: Analyze
+ runs-on: ubuntu-latest
+ permissions:
+ actions: read
+ contents: read
+ security-events: write
+
+ strategy:
+ fail-fast: false
+ matrix:
+ language: [ 'python' ]
+ # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python', 'ruby' ]
+ # Learn more about CodeQL language support at https://aka.ms/codeql-docs/language-support
+
+ steps:
+ - name: Checkout repository
+ uses: actions/checkout@v4
+
+ # Initializes the CodeQL tools for scanning.
+ - name: Initialize CodeQL
+ uses: github/codeql-action/init@v2
+ with:
+ languages: ${{ matrix.language }}
+ # If you wish to specify custom queries, you can do so here or in a config file.
+ # By default, queries listed here will override any specified in a config file.
+ # Prefix the list here with "+" to use these queries and those in the config file.
+
+ # Details on CodeQL's query packs refer to : https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+ queries: security-and-quality # security-extended,
+ config-file: ./.github/workflows/config/codeql.yml
+
+
+ # Autobuild attempts to build any compiled languages (C/C++, C#, Go, or Java).
+ # If this step fails, then you should remove it and run the build manually (see below)
+ - name: Autobuild
+ uses: github/codeql-action/autobuild@v2
+
+ # โน๏ธ Command-line programs to run using the OS shell.
+ # ๐ See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+
+ # If the Autobuild fails above, remove it and uncomment the following three lines.
+ # modify them (or add more) to build your code if your project, please refer to the EXAMPLE below for guidance.
+
+ # - run: |
+ # echo "Run, Build Application using script"
+ # ./location_of_script_within_repo/buildscript.sh
+
+ - name: Perform CodeQL Analysis
+ uses: github/codeql-action/analyze@v2
+ with:
+ category: "/language:${{matrix.language}}"
diff --git a/.github/workflows/config/changelog-config.json b/.github/workflows/config/changelog-config.json
new file mode 100644
index 00000000..beec3f59
--- /dev/null
+++ b/.github/workflows/config/changelog-config.json
@@ -0,0 +1,127 @@
+{
+ "categories": [
+ {
+ "title": "## MTL \n\nChangelog\n\n\n\n",
+ "labels": ["mtl", "multitask"],
+ "exclude_labels": ["cherry-pick"]
+ },
+ {
+ "title": "## qMRI \n\nChangelog\n\n\n\n",
+ "labels": ["qmri", "quantitative"],
+ "exclude_labels": ["cherry-pick"]
+ },
+ {
+ "title": "## REC\n\nChangelog\n\n\n\n",
+ "labels": ["rec", "reconstruction"],
+ "exclude_labels": ["cherry-pick"]
+ },
+ {
+ "title": "## SEG \n\nChangelog\n\n\n\n",
+ "labels": ["seg", "segmentation"],
+ "exclude_labels": ["cherry-pick"]
+ },
+ {
+ "title": "## ATOMMIC Tools \n\nChangelog\n\n\n\n",
+ "labels": ["tools"],
+ "exclude_labels": ["cherry-pick"]
+ },
+ {
+ "title": "## Export \n\nChangelog\n\n\n\n",
+ "labels": ["export"],
+ "exclude_labels": ["cherry-pick"]
+ },
+ {
+ "title": "## Documentation \n\nChangelog\n\n\n\n",
+ "labels": ["docs"],
+ "exclude_labels": ["cherry-pick"]
+ },
+ {
+ "title": "## Bugfixes \n\nChangelog\n\n\n\n",
+ "labels": ["bug"],
+ "exclude_labels": ["cherry-pick"]
+ },
+ {
+ "title": "## Cherrypick \n\nChangelog\n\n\n\n",
+ "labels": ["cherry-pick"],
+ "exclude_labels": ["cherry-pick"]
+ }
+ ],
+ "ignore_labels": [
+ "ignore"
+ ],
+ "sort": "ASC",
+ "template": "\n${{CHANGELOG}}\nUncategorized:\n${{UNCATEGORIZED}}\n\n",
+ "pr_template": "- ${{TITLE}} by @${{AUTHOR}} :: PR: #${{NUMBER}}",
+ "empty_template": "${{OWNER}}\n${{REPO}}\n${{FROM_TAG}}\n${{TO_TAG}}",
+ "label_extractor": [
+ {
+ "pattern": "(.*mtl.*)|(.*g2p.*)",
+ "target": "mtl",
+ "flags": "gimu",
+ "on_property": ["title", "body"]
+ },
+ {
+ "pattern": "(.*qmri.*)|(.*quantitative.*)",
+ "target": "qmri",
+ "flags": "gimu",
+ "on_property": ["title", "body"]
+ },
+ {
+ "pattern": "(.*rec.*)|(.*reconstruction.*)",
+ "target": "rec",
+ "flags": "gimu",
+ "on_property": ["title", "body"]
+ },
+ {
+ "pattern": "(.*seg.*)|(.*segmentation.*)",
+ "target": "seg",
+ "flags": "gimu",
+ "on_property": ["title", "body"]
+ },
+ {
+ "pattern": "(.*tools.*)",
+ "target": "tools",
+ "flags": "gimu",
+ "on_property": ["title", "body"]
+ },
+ {
+ "pattern": "(.*export.*)",
+ "target": "export",
+ "flags": "gimu",
+ "on_property": ["title", "body"]
+ },
+ {
+ "pattern": "(.*\\[x\\] Documentation.*)",
+ "target": "docs",
+ "flags": "gmu",
+ "on_property": ["title", "body"]
+ },
+ {
+ "pattern": "(.*\\[x\\] Bugfix.*)|(.*patch.*)",
+ "target": "bug",
+ "flags": "gmu",
+ "on_property": ["title", "body"]
+ },
+ {
+ "pattern": "(.*cherry-pick.*)|(.*cherrypick.*)",
+ "target": "cherrypick",
+ "flags": "gimu",
+ "on_property": ["title", "body"]
+ }
+ ],
+ "duplicate_filter": {
+ "pattern": ".+",
+ "on_property": "title",
+ "method": "match"
+ },
+ "transformers": [
+ ],
+ "max_tags_to_fetch": 100,
+ "max_pull_requests": 500,
+ "max_back_track_time_days": 365,
+ "exclude_merge_branches": [
+ ],
+ "tag_resolver": {
+ "method": "semver"
+ }
+}
diff --git a/.github/workflows/config/codeql.yml b/.github/workflows/config/codeql.yml
new file mode 100644
index 00000000..59258239
--- /dev/null
+++ b/.github/workflows/config/codeql.yml
@@ -0,0 +1,8 @@
+name: "CodeQL config"
+
+paths:
+ - .github/
+ - atommic/
+ - projects/
+ - tests/
+ - tools/
diff --git a/.github/workflows/coverage.yml b/.github/workflows/coverage.yml
new file mode 100644
index 00000000..16b24cc6
--- /dev/null
+++ b/.github/workflows/coverage.yml
@@ -0,0 +1,29 @@
+name: CodeCov
+on:
+ - push
+ - pull_request
+
+jobs:
+ build:
+ runs-on: ubuntu-latest
+ strategy:
+ matrix:
+ python-version: ["3.10"]
+ steps:
+ - uses: actions/checkout@v4
+ env:
+ CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }}
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v4
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip setuptools wheel
+ pip install coverage
+ pip install -e ".[dev]"
+ - name: Run Coverage
+ run: |
+ coverage run -m pytest --ignore=projects
+ - name: Upload Coverage to Codecov
+ uses: codecov/codecov-action@v1
diff --git a/.github/workflows/gh-docs.yml b/.github/workflows/gh-docs.yml
new file mode 100644
index 00000000..3ead89ff
--- /dev/null
+++ b/.github/workflows/gh-docs.yml
@@ -0,0 +1,35 @@
+name: gh-docs-build
+on:
+ push:
+ pull_request:
+ paths:
+ - "**"
+
+# Set the access for individual scopes
+permissions: write-all
+
+jobs:
+ deploy:
+ runs-on: ubuntu-latest
+
+ container:
+ image: squidfunk/mkdocs-material
+
+ steps:
+ - uses: actions/checkout@v4
+ if: github.event.repository.fork == false
+
+ - name: "Correct github config"
+ if: github.event.repository.fork == false
+ run: |
+ git config --global --add safe.directory "$GITHUB_WORKSPACE"
+ git config --global user.name "${GITHUB_ACTOR}"
+ git config --global user.email "${GITHUB_ACTOR}@users.noreply.${GITHUB_DOMAIN:-"github.com"}"
+ remote_repo="https://x-access-token:${GITHUB_TOKEN}@${GITHUB_DOMAIN:-"github.com"}/${GITHUB_REPOSITORY}.git"
+ echo "${remote_repo}"
+ git remote rm origin
+ git remote add origin "${remote_repo}"
+
+ - name: "Deploy Github Page"
+ continue-on-error: true
+ run: mkdocs gh-deploy --force
\ No newline at end of file
diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml
new file mode 100644
index 00000000..057208ed
--- /dev/null
+++ b/.github/workflows/labeler.yml
@@ -0,0 +1,14 @@
+name: "Pull Request Labeler"
+on:
+- pull_request_target
+
+jobs:
+ triage:
+ permissions:
+ contents: read
+ pull-requests: write
+ runs-on: ubuntu-latest
+ steps:
+ - uses: actions/labeler@v4
+ with:
+ repo-token: "${{ secrets.GITHUB_TOKEN }}"
diff --git a/.github/workflows/tox.yml b/.github/workflows/tox.yml
new file mode 100644
index 00000000..fb28e5e5
--- /dev/null
+++ b/.github/workflows/tox.yml
@@ -0,0 +1,26 @@
+name: Tox
+
+on:
+ - push
+ - pull_request
+
+jobs:
+ build:
+ runs-on: ubuntu-latest
+ strategy:
+ matrix:
+ python-version: ["3.10"]
+
+ steps:
+ - uses: actions/checkout@v4
+ - name: Set up Python ${{ matrix.python-version }}
+ uses: actions/setup-python@v4
+ with:
+ python-version: ${{ matrix.python-version }}
+ - name: Install dependencies
+ run: |
+ python -m pip install --upgrade pip setuptools wheel
+ pip install tox tox-gh-actions
+ python -m pip install -e ".[dev]"
+ - name: Test with tox
+ run: tox
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 00000000..6500cd24
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,159 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+# Usually these files are written by a python script from a template
+# before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+# For a library or package, you might want to ignore these files since the code is
+# intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+# However, in case of collaboration, if having platform-specific dependencies or dependencies
+# having no cross-platform support, pipenv may install dependencies that don't work, or not
+# install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+# This is especially recommended for binary packages to ensure reproducibility, and is more
+# commonly ignored for libraries.
+# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+# in version control.
+# https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+# and can be added to the global gitignore or merged into this file. For a more nuclear
+# option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 00000000..d4d3ac9c
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,35 @@
+default_language_version:
+ python: python3
+
+ci:
+ autofix_prs: true
+ autoupdate_commit_msg: '[pre-commit.ci] pre-commit suggestions'
+ autoupdate_schedule: quarterly
+
+repos:
+ - repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v4.3.0
+ hooks:
+ - id: trailing-whitespace
+ - id: end-of-file-fixer
+ - id: requirements-txt-fixer
+ - id: check-yaml
+ - id: check-json
+ - id: check-executables-have-shebangs
+ - id: check-ast
+ - id: check-case-conflict
+ - id: check-merge-conflict
+ - id: check-symlinks
+ - id: fix-encoding-pragma
+ args: [ '--pragma=# coding=utf-8' ]
+ - id: sort-simple-yaml
+ - repo: https://github.com/psf/black
+ rev: 23.3.0
+ hooks:
+ - id: black
+ name: Format code
+ additional_dependencies: ['click==8.0.2']
+ - repo: https://github.com/pre-commit/mirrors-mypy
+ rev: v0.982
+ hooks:
+ - id: mypy
diff --git a/.readthedocs.yml b/.readthedocs.yml
new file mode 100644
index 00000000..5df8f581
--- /dev/null
+++ b/.readthedocs.yml
@@ -0,0 +1,19 @@
+# coding=utf-8
+# __author__ = "Dimitris Karkalousos"
+
+# Required field.
+version: 2
+
+build:
+ os: ubuntu-22.04
+ tools:
+ python: "3.10"
+
+# Build documentation in the docs/ directory with Sphinx.
+sphinx:
+ configuration: docs/source/conf.py
+
+# Set the version of Python and requirements required to build your docs
+python:
+ install:
+ - requirements: requirements/requirements_docs.txt
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 00000000..adea03c7
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,19 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+ - family-names: Karkalousos
+ given-names: Dimitrios
+ orcid: https://orcid.org/0000-0001-5983-0322
+ - family-names: Isgum
+ given-names: Ivana
+ orcid: https://orcid.org/0000-0003-1869-5034
+ - family-names: Marquering
+ given-names: Henk
+ orcid: https://orcid.org/0000-0002-1414-6313
+ - family-names: Caan
+ given-names: Matthan
+ orcid: https://orcid.org/0000-0002-5162-8880
+title: "Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC)"
+repository-code: https://github.com/wdika/atommic
+version: 1.0.0
+date-released: 2023-12-01
diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md
new file mode 100644
index 00000000..34fdac92
--- /dev/null
+++ b/CODE_OF_CONDUCT.md
@@ -0,0 +1,77 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our community a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming, diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes, and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the overall community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or advances of any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email address, without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of acceptable behavior and will take appropriate and fair corrective action in response to any behavior that they deem inappropriate, threatening, offensive, or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, and will communicate reasons for moderation decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when an individual is officially representing the community in public spaces. Examples of representing our community include using an official e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be reported to the community leaders responsible for enforcement at d.karkalousos@amsterdamumc.nl. All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+**Community Impact**: Use of inappropriate language or other behavior deemed unprofessional or unwelcome in the community.
+
+**Consequence**: A private, written warning from community leaders, providing clarity around the nature of the violation and an explanation of why the behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+**Community Impact**: A violation through a single incident or series of actions.
+
+**Consequence**: A warning with consequences for continued behavior. No interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, for a specified period of time. This includes avoiding interactions in community spaces as well as external channels like social media. Violating these terms may lead to a temporary or permanent ban.
+
+### 3. Temporary Ban
+
+**Community Impact**: A serious violation of community standards, including sustained inappropriate behavior.
+
+**Consequence**: A temporary ban from any sort of interaction or public communication with the community for a specified period of time. No public or private interaction with the people involved, including unsolicited interaction with those enforcing the Code of Conduct, is allowed during this period. Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+**Community Impact**: Demonstrating a pattern of violation of community standards, including sustained inappropriate behavior, harassment of an individual, or aggression toward or disparagement of classes of individuals.
+
+**Consequence**: A permanent ban from any sort of public interaction within the community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant](https://www.contributor-covenant.org), version 2.0, available at https://www.contributor-covenant.org/version/2/0/code\_of\_conduct.html.
+
+Community Impact Guidelines were inspired by [Mozilla's code of conduct enforcement ladder](https://github.com/mozilla/diversity).
+
+For answers to common questions about this code of conduct, see the FAQ at https://www.contributor-covenant.org/faq. Translations are available at https://www.contributor-covenant.org/translations.
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 00000000..bc76833b
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,64 @@
+# Contributions are welcome!
+
+We do all of ATOMMIC's development in the open. Contributions from ATOMMIC community are welcome.
+
+
+# Pull Requests (PR) Guidelines
+
+**Send your PRs to the `main` branch**
+
+1) Make sure your PR does one thing. Have a clear answer to "What does this PR do?".
+2) Read General Principles and style guide below
+3) Make sure you sign your commits. E.g. use ``git commit -s`` when before your commit
+4) Make sure all unittests finish successfully before sending PR ``pytest`` or (if yor dev box does not have GPU) ``pytest --cpu`` from ATOMMIC's root folder
+5) Send your PR and request a review
+
+## Unit tests
+Quick tests (locally, while developing)
+```
+pytest
+# If you don't have a GPU do:
+# pytest --cpu
+```
+Full tests, including pre-trained model downloads
+```
+pytest --with_downloads
+```
+
+## Whom should you ask for review:
+Please ask @wdika to review your PRs. If you are not sure, please ask in the PR comments.
+
+Your pull requests must pass all checks and peer-review before they can be merged.
+
+# General principles
+1. **User-oriented**: make it easy for end users, even at the cost of writing more code in the background
+1. **Robust**: make it hard for users to make mistakes.
+1. **Well-tested**: please add simple, fast unittests. Consider adding CI tests for end-to-end functionality.
+1. **Reusable**: for every piece of code, think about how it can be reused in the future and make it easy to be reused.
+1. **Readable**: code should be easier to read.
+1. **Legal**: if you copy even one line of code from the Internet, make sure that the code allows the license that ATOMMIC supports. Give credit and link back to the code.
+1. **Sensible**: code should make sense. If you think a piece of code might be confusing, write comments.
+
+
+## Python style
+We use ``black`` as our style guide. To check whether your code will pass style check (from the ATOMMIC's repo folder) run:
+``python setup.py style`` and if it does not pass run ``python setup.py style --fix``.
+
+1. Include docstrings for every class and method exposed to the user.
+1. Use Python 3 type hints for every class and method exposed to the user.
+1. Avoid wild import: ``from X import *`` unless in ``X.py``, ``__all__`` is defined.
+1. Minimize the use of ``**kwargs``.
+1. ``RaiseError`` is preferred to ``assert``. Write: ```if X: raise Error``` instead of ```assert X```.
+1. Classes are preferred to standalone methods.
+1. Methods should be atommic. A method shouldn't be longer than 119 lines, e.g. can be fit into the computer screen without scrolling.
+1. If a method has arguments that don't fit into one line, each argument should be in its own line for readability.
+1. Add ``__init__.py`` for every folder.
+1. F-strings are preferred to formatted strings.
+1. Loggers are preferred to print. In ATOMMIC, you can use logger from ``from atommic.utils import logging``
+1. Private functions (functions start with ``_``) shouldn't be called outside its host file.
+1. If a comment lasts multiple lines, use ``'''`` instead of ``#``.
+
+# Collections
+Collection is a logical grouping of related Neural Modules. It is a grouping of modules that share a domain area or semantics.
+When contributing module to a collection, please make sure it belongs to that category.
+If you would like to start a new one and contribute back to the platform, you are very welcome to do so.
diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 00000000..4c3864cb
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,57 @@
+ARG BASE_IMAGE=nvcr.io/nvidia/pytorch:23.08-py3
+
+# build an image that includes only the atommic dependencies, ensures that dependencies
+# are included first for optimal caching, and useful for building a development
+# image (by specifying build target as `atommic-deps`)
+FROM ${BASE_IMAGE} as atommic-deps
+
+# Ensure apt-get won't prompt for selecting options
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update && \
+ apt-get upgrade -y && \
+ apt-get install -y --no-install-recommends \
+ libsndfile1 sox \
+ libfreetype6 \
+ swig \
+ rm -rf /var/lib/apt/lists/*
+
+WORKDIR /workspace/
+
+# install atommic dependencies
+WORKDIR /tmp/
+
+COPY requirements .
+RUN for f in "$(ls requirements*.txt)"; do pip3 install --disable-pip-version-check --no-cache-dir -r $f; done
+
+# copy atommic source into a scratch image
+FROM scratch as atommic-src
+COPY . .
+
+# start building the final container
+FROM atommic-deps as atommic
+ARG ATOMMIC_VERSION=1.0.0
+
+# Check that atommic_VERSION is set. Build will fail without this. Expose atommic and base container
+# version information as runtime environment variable for introspection purposes
+RUN /usr/bin/test -n "$ATOMMIC_VERSION" && \
+ /bin/echo "export ATOMMIC_VERSION=${ATOMMIC_VERSION}" >> /root/.bashrc && \
+ /bin/echo "export BASE_IMAGE=${BASE_IMAGE}" >> /root/.bashrc
+
+# Install ATOMMIC
+RUN --mount=from=atommic-src,target=/tmp/atommic cd /tmp/atommic && pip install --no-cache-dir ".[all]"
+
+# Check install
+RUN python -c "import atommic.collections.multitask.rs as atommic_mrs" && \
+ python -c "import atommic.collections.quantitative as atommic_qmri" && \
+ python -c "import atommic.collections.reconstruction as atommic_rec" && \
+ python -c "import atommic.collections.segmentation as atommic_seg"
+
+# copy projects/tools/tests into container for end user
+WORKDIR /workspace/atommic
+COPY projects /workspace/atommic/projects
+COPY tests /workspace/atommic/tests
+COPY tools /workspace/atommic/tools
+# COPY README.rst LICENSE /workspace/atommic/
+
+RUN printf "#!/bin/bash\njupyter lab --no-browser --allow-root --ip=0.0.0.0" >> start-jupyter.sh && \
+ chmod +x start-jupyter.sh \
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 00000000..261eeb9e
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,201 @@
+ Apache License
+ Version 2.0, January 2004
+ http://www.apache.org/licenses/
+
+ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+ 1. Definitions.
+
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diff --git a/README.md b/README.md
new file mode 100644
index 00000000..863c3686
--- /dev/null
+++ b/README.md
@@ -0,0 +1,187 @@
+# Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC)
+[](https://huggingface.co/wdika)
+[]()
+[](https://opensource.org/licenses/Apache-2.0)
+[](https://atommic.readthedocs.io/en/latest/?badge=latest)
+[](https://badge.fury.io/py/atommic)
+[](https://pypi.org/project/atommic/)
+[](https://pypi.org/project/atommic/)
+[]()
+
+
+
+
+# ๐ Introduction
+
+The [Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC)](https://github.com/wdika/atommic) is a
+toolbox for applying AI methods for **accelerated MRI reconstruction (REC)**, **MRI segmentation (SEG)**,
+**quantitative MR imaging (qMRI)**, as well as **multitask learning (MTL)**, i.e., performing multiple tasks
+simultaneously, such as reconstruction and segmentation. Each task is implemented in a separate collection, which
+consists of data loaders, transformations, models, metrics, and losses. **ATOMMIC** is designed to be modular and
+extensible, and it is easy to add new tasks, models, and datasets. **ATOMMIC** uses
+[PyTorch Lightning](https://www.pytorchlightning.ai/) for feasible high-performance multi-GPU/multi-node
+mixed-precision training.
+
+
+
+The schematic overview of **ATOMMIC** showcases the main components of the toolbox. First, we need an [MRI Dataset](README.md#mri-datasets) (e.g., **CC359**). Next, we need to define the high-level parameters, such as the [task and the model](../mri/collections.html), the [undersampling](../mri/undersampling.html), the [transforms](../mri/transforms.html), the [optimizer](../core/core.html#optimization), the [scheduler](../core/core.html#learning-rate-schedulers), the [loss](../mri/losses.html), the [trainer parameters](../core/core.html#training), and the [experiment manager](../core/exp_manager.html). All these parameters are defined in a `.yaml` file using [Hydra](https://hydra.cc/) and [OmegaConf](https://omegaconf.readthedocs.io/).
+
+The trained model is an `.atommic` [module](../core/export.html), exported with [ONNX](https://onnx.ai/) and [TorchScript](https://pytorch.org/docs/stable/jit.html) support, which can be used for inference. The `.atommic` module can also be uploaded on [HuggingFace](https://huggingface.co/). Pretrained models are available on our [HF](https://huggingface.co/wdika) account and can be downloaded and used for inference.
+
+## ๐ Quick Start Guide
+
+The best way to get started with ATOMMIC is to start with one of the [tutorials](tutorials.html):
+
+- [ATOMMIC Primer](https://github.com/wdika/atommic/tutorials/00_ATOMMIC_Primer.ipynb) - demonstrates how to use ATOMMIC.
+- [ATOMMIC MRI transforms](https://github.com/wdika/atommic/tutorials/01_ATOMMIC_MRI_transforms.ipynb) - demonstrates how to use ATOMMIC to undersample MRI data.
+- [ATOMMIC MRI undersampling](https://github.com/wdika/atommic/tutorials/02_ATOMMIC_MRI_undersampling.ipynb) - demonstrates how to use ATOMMIC to apply transforms to MRI data.
+- [ATOMMIC Upload Model on HuggingFace](https://github.com/wdika/atommic/tutorials/03_ATOMMIC_Upload_Model_On_HF.ipynb) - demonstrates how to upload a model on HuggingFace.
+
+You can also check the [projects](projects.html) page to see how to use ATOMMIC for specific tasks and public datasets.
+
+### **ATOMMIC paper is fully reproducible. Please check [here](projects/ATOMMIC_paper/README.md) for more information.**
+
+## ๐ค Training & Testing
+
+Training and testing models in **ATOMMIC** is intuitive and easy. You just need to properly configure the `.yaml`
+file and just run the following command:
+
+```bash
+atommic run -c path-to-config-file
+```
+
+## โ๏ธ Configuration
+
+1. Choose the **task** and the **model**, according to the [collections](../mri/collections.html).
+
+2. Choose the **dataset** and the **dataset parameters**, according to the [datasets](README.md#mri-datasets) or your own dataset.
+
+3. Choose the [undersampling](../mri/transforms.html).
+
+4. Choose the [transforms](../mri/transforms.html).
+
+5. Choose the [losses](../mri/losses.html).
+
+6. Choose the [optimizer](../core/core.html#optimization).
+
+7. Choose the [scheduler](../core/core.html#learning-rate-schedulers).
+
+8. Choose the [trainer parameters](../core/core.html#training).
+
+9. Choose the [experiment manager](../core/exp_manager.html).
+
+You can also check the [projects](projects.html) page to see how to configure the `.yaml` file for specific tasks.
+
+## ๐๏ธ Collections
+
+**ATOMMIC** is organized into [collections](../mri/collections.html), each of which implements a specific task. The following collections are currently available, implementing various models as listed:
+
+### MultiTask Learning (MTL)
+1. End-to-End Recurrent Attention Network (`SERANet`), 2. Image domain Deep Structured Low-Rank Network (`IDSLR`), 3. Image domain Deep Structured Low-Rank UNet (`IDSLRUNet`), 4. Multi-Task Learning for MRI Reconstruction and Segmentation (`MTLRS`), 5. Reconstruction Segmentation method using UNet (`RecSegUNet`), 6. Segmentation Network MRI (`SegNet`).
+
+### Quantitative MR Imaging (qMRI)
+1. Quantitative Recurrent Inference Machines (`qRIMBlock`), 2. Quantitative End-to-End Variational Network (`qVarNet`), 3. Quantitative Cascades of Independently Recurrent Inference Machines (`qCIRIM`).
+
+### MRI Reconstruction (REC)
+1. Cascades of Independently Recurrent Inference Machines (`CIRIM`), 2. Convolutional Recurrent Neural Networks (`CRNNet`), 3. Deep Cascade of Convolutional Neural Networks (`CascadeNet`), 4. Down-Up Net (`DUNet`), 5. End-to-End Variational Network (`VarNet`), 6. Independently Recurrent Inference Machines (`RIMBlock`), 7. Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (`JointICNet`), 8. `KIKINet`, 9. Learned Primal-Dual Net (`LPDNet`), 10. Model-based Deep Learning Reconstruction (`MoDL`), 11. `MultiDomainNet`, 12. `ProximalGradient`, 13. Recurrent Inference Machines (`RIMBlock`), 14. Recurrent Variational Network (`RecurrentVarNet`), 15. `UNet`, 16. Variable Splitting Network (`VSNet`), 17. `XPDNet`, 18. Zero-Filled reconstruction (`ZF`).
+
+### MRI Segmentation (SEG)
+1. `SegmentationAttentionUNet`, 2. `SegmentationDYNUNet`, 3. `SegmentationLambdaUNet`, 4. `SegmentationUNet`, 5. `Segmentation3DUNet`, 6. `SegmentationUNetR`, 7. `SegmentationVNet`.
+
+## MRI Datasets
+
+**ATOMMIC** supports public datasets, as well as private datasets. The following public datasets are supported natively:
+
+- [AHEAD](projects/MTL/ahead.html): Supports the `(qMRI)` and `(REC)` tasks.
+- [BraTS 2023 Adult Glioma](projects/SEG/brats2023adultglioma.html): Supports the `(SEG)` task.
+- [CC359](projects/REC/cc359.html): Supports the `(REC)` task.
+- [fastMRI Brains Multicoil](projects/REC/fastmribrainsmulticoil.html): Supports the `(REC)` task.
+- [fastMRI Knees Multicoil](projects/REC/fastmrikneesmulticoil.html): Supports the `(REC)` task.
+- [fastMRI Knees Singlecoil](projects/REC/fastmrikneessinglecoil.html): Supports the `(REC)` task.
+- [ISLES 2022 Sub Acute Stroke](projects/SEG/isles2022subacutestroke.html): Supports the `(SEG)` task.
+- [SKM-TEA](projects/REC/skmtea.html): Supports the `(REC)`, `(SEG)`, and `(MTL)` tasks.
+- [Stanford Knees](projects/REC/stanfordknees2019.html): Supports the `(REC)` task.
+
+## ๐ ๏ธ Installation
+
+**ATOMMIC** is best to be installed in a Conda environment.
+
+### ๐ Conda
+```
+conda create -n atommic python=3.10
+conda activate atommic
+```
+
+### ๐ฆ Pip
+Use this installation mode if you want the latest released version.
+
+```bash
+pip install atommic
+```
+
+### From source
+
+Use this installation mode if you are contributing to atommic.
+
+```bash
+git clone https://github.com/wdika/atommic
+cd atommic
+bash ./reinstall.sh
+```
+
+### ๐ณ Docker containers
+To build an atommic container with Dockerfile from a branch, please run
+
+```bash
+ DOCKER_BUILDKIT=1 docker build -f Dockerfile -t atommic:latest.
+```
+
+As [NeMo](https://github.com/NVIDIA/NeMo) suggests, if you choose to work with the `main` branch, use NVIDIA's PyTorch container version [21.05-py3](https://ngc.nvidia.com/containers/nvidia:pytorch/tags), then install from GitHub.
+
+```bash
+ docker run --gpus all -it --rm -v :/ATOMMIC --shm-size=8g \
+ -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \
+ stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:21.05-py3
+```
+
+## ๐ API Documentation
+
+[](https://atommic.readthedocs.io/en/latest/?badge=latest)
+
+Access the API Documentation [here](https://atommic.readthedocs.io/en/latest/index.html)
+
+## ๐ License
+
+**ATOMMIC** is under [](https://opensource.org/licenses/Apache-2.0)
+
+
+## ๐ Citation
+
+If you use ATOMMIC in your research, please cite as follows:
+
+```BibTeX
+@misc{atommic,
+ author = {Karkalousos Dimitrios, Isqum Ivana, Marquering Henk, Caan Matthan},
+ title = {ATOMMIC: Advanced Toolbox for Multitask Medical Imaging Consistency},
+ year = {2023},
+ url = {https://github.com/wdika/atommic},
+}
+```
+
+## ๐ References
+
+The following papers have used ATOMMIC:
+
+1. Karkalousos, D., Isgum, I., Marquering, H. & Caan, M.W.A.. (2024). MultiTask Learning for accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis. Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 227:991-1005 Available from https://proceedings.mlr.press/v227/karkalousos24a.html.
+
+2. Zhang, C., Karkalousos, D., Bazin, P. L., Coolen, B. F., Vrenken, H., Sonke, J. J., Forstmann, B. U., Poot, D. H. J., & Caan, M. W. A. (2022). A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine. NeuroImage, 264. [DOI](https://doi.org/10.1016/j.neuroimage.2022.119680)
+
+3. Karkalousos, D., Noteboom, S., Hulst, H. E., Vos, F. M., & Caan, M. W. A. (2022). Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction. Physics in Medicine & Biology. [DOI](https://doi.org/10.1088/1361-6560/AC6CC2)
+
+## ๐ง Contact
+
+For any questions, please contact Dimitris Karkalousos @ [d.karkalousos@amsterdamumc.nl](mailto:d.karkalousos@amsterdamumc.nl).
+
+## โ ๏ธ๐ Disclaimer & Acknowledgements
+
+> **Note:** ATOMMIC is built on top of [NeMo](https://github.com/NVIDIA/NeMo). NeMo is under Apache 2.0 license, so we are allowed to use it. We also assume that it is allowed to use the NeMo documentation, as long as we cite it and we always refer to the baselines everywhere and in the code and docs. ATOMMIC also includes implementations of reconstruction methods from [fastMRI](https://github.com/facebookresearch/fastMRI) and [DIRECT](https://github.com/NKI-AI/direct), and segmentation methods from [MONAI](https://github.com/Project-MONAI/MONAI), as well as other codebases which are always cited on the corresponding files. All methods in ATOMMIC are reimplemented and not called from the original libraries, allowing for full reproducibility, support, and easy extension. ATOMMIC is an open-source project under the Apache 2.0 license.
diff --git a/SECURITY.md b/SECURITY.md
new file mode 100644
index 00000000..88f067a7
--- /dev/null
+++ b/SECURITY.md
@@ -0,0 +1,20 @@
+# Security Policy
+
+## Supported Versions
+
+Use this section to tell people about which versions of your project are currently being supported with security
+updates.
+
+| Version | Supported |
+| ------- | ------------------ |
+| 5.1.x | :white_check_mark: |
+| 5.0.x | :x: |
+| 4.0.x | :white_check_mark: |
+| < 4.0 | :x: |
+
+## Reporting a Vulnerability
+
+Use this section to tell people how to report a vulnerability.
+
+Tell them where to go, how often they can expect to get an update on a reported vulnerability, what to expect if the
+vulnerability is accepted or declined, etc.
diff --git a/assets/atommic-logo.png b/assets/atommic-logo.png
new file mode 100644
index 00000000..6e9f093b
Binary files /dev/null and b/assets/atommic-logo.png differ
diff --git a/assets/atommic-schematic_overview.png b/assets/atommic-schematic_overview.png
new file mode 100644
index 00000000..2b86ffc6
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diff --git a/atommic/__init__.py b/atommic/__init__.py
new file mode 100644
index 00000000..30c4c993
--- /dev/null
+++ b/atommic/__init__.py
@@ -0,0 +1,16 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.package_info import ( # noqa: F401
+ __contact_emails__,
+ __contact_names__,
+ __description__,
+ __download_url__,
+ __homepage__,
+ __keywords__,
+ __license__,
+ __package_name__,
+ __repository_url__,
+ __shortversion__,
+ __version__,
+)
diff --git a/atommic/cli/__init__.py b/atommic/cli/__init__.py
new file mode 100644
index 00000000..dd39b4a5
--- /dev/null
+++ b/atommic/cli/__init__.py
@@ -0,0 +1,24 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+
+from atommic.cli.launch import register_cli_subcommand
+
+
+def main():
+ """Run the CLI."""
+ parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+ subparser = parser.add_subparsers(help="atommic commands.")
+ subparser.required = True
+ subparser.dest = "subcommand"
+
+ register_cli_subcommand(subparser)
+
+ args = parser.parse_args()
+ args.func(args)
+
+
+if __name__ == "__main__":
+ main()
diff --git a/atommic/cli/launch.py b/atommic/cli/launch.py
new file mode 100644
index 00000000..26a4fd72
--- /dev/null
+++ b/atommic/cli/launch.py
@@ -0,0 +1,181 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+
+import pytorch_lightning as pl
+import torch
+from omegaconf import DictConfig, OmegaConf
+
+from atommic.collections.multitask.rs.nn.idslr import IDSLR
+from atommic.collections.multitask.rs.nn.idslr_unet import IDSLRUNet
+from atommic.collections.multitask.rs.nn.mtlrs import MTLRS
+from atommic.collections.multitask.rs.nn.recseg_unet import RecSegUNet
+from atommic.collections.multitask.rs.nn.segnet import SegNet
+from atommic.collections.multitask.rs.nn.seranet import SERANet
+from atommic.collections.quantitative.nn.qcirim import qCIRIM
+from atommic.collections.quantitative.nn.qvarnet import qVarNet
+from atommic.collections.quantitative.nn.qzf import qZF
+from atommic.collections.reconstruction.nn.ccnn import CascadeNet
+from atommic.collections.reconstruction.nn.cirim import CIRIM
+from atommic.collections.reconstruction.nn.crnn import CRNNet
+from atommic.collections.reconstruction.nn.dunet import DUNet
+from atommic.collections.reconstruction.nn.jointicnet import JointICNet
+from atommic.collections.reconstruction.nn.kikinet import KIKINet
+from atommic.collections.reconstruction.nn.lpd import LPDNet
+from atommic.collections.reconstruction.nn.modl import MoDL
+from atommic.collections.reconstruction.nn.multidomainnet import MultiDomainNet
+from atommic.collections.reconstruction.nn.proximal_gradient import ProximalGradient
+from atommic.collections.reconstruction.nn.recurrentvarnet import RecurrentVarNet
+from atommic.collections.reconstruction.nn.unet import UNet
+from atommic.collections.reconstruction.nn.varnet import VarNet
+from atommic.collections.reconstruction.nn.vsnet import VSNet
+from atommic.collections.reconstruction.nn.xpdnet import XPDNet
+from atommic.collections.reconstruction.nn.zf import ZF
+from atommic.collections.segmentation.nn.attentionunet import SegmentationAttentionUNet
+from atommic.collections.segmentation.nn.dynunet import SegmentationDYNUNet
+from atommic.collections.segmentation.nn.lambdaunet import SegmentationLambdaUNet
+from atommic.collections.segmentation.nn.unet import SegmentationUNet
+from atommic.collections.segmentation.nn.unet3d import Segmentation3DUNet
+from atommic.collections.segmentation.nn.unetr import SegmentationUNetR
+from atommic.collections.segmentation.nn.vnet import SegmentationVNet
+from atommic.core.conf.hydra_runner import hydra_runner
+from atommic.utils import logging
+from atommic.utils.exp_manager import exp_manager
+
+
+def register_cli_subcommand(parser: argparse._SubParsersAction):
+ """Register parser for the launch command."""
+ parser_launch = parser.add_parser(
+ "run",
+ help="Launch atommic through cli given a configuration (yaml) file, e.g. atommic run -c /path/to/config.yaml",
+ )
+ parser_launch.add_argument(
+ "-c",
+ "--config-path",
+ required=True,
+ type=str,
+ help="Path to the configuration file.",
+ )
+ parser_launch.add_argument(
+ "-m",
+ "--multi-run",
+ action="store_true",
+ help="Hydra Multi-Run for hyperparameter optimization.",
+ )
+ parser_launch.set_defaults(func=main)
+
+
+@hydra_runner(config_path="../src", config_name="config")
+def main(cfg: DictConfig): # noqa: MC0001
+ """
+ Main function for training and running a model
+
+ Parameters
+ ----------
+ cfg : Configuration (yaml) file.
+ DictConfig
+ """
+ cfg = OmegaConf.load(f"{cfg.config_path}")
+
+ logging.info(f"Config: {OmegaConf.to_yaml(cfg)}")
+
+ trainer = pl.Trainer(**cfg.trainer)
+ exp_manager(trainer, cfg.get("exp_manager", None))
+
+ model_name = (cfg.model["model_name"]).upper()
+
+ if model_name == "CASCADENET":
+ model = CascadeNet(cfg.model, trainer=trainer)
+ elif model_name == "CIRIM":
+ model = CIRIM(cfg.model, trainer=trainer)
+ elif model_name == "CRNNET":
+ model = CRNNet(cfg.model, trainer=trainer)
+ elif model_name == "DUNET":
+ model = DUNet(cfg.model, trainer=trainer)
+ elif model_name in ("E2EVN", "VN"):
+ model = VarNet(cfg.model, trainer=trainer)
+ elif model_name == "IDSLR":
+ model = IDSLR(cfg.model, trainer=trainer)
+ elif model_name == "IDSLRUNET":
+ model = IDSLRUNet(cfg.model, trainer=trainer)
+ elif model_name == "JOINTICNET":
+ model = JointICNet(cfg.model, trainer=trainer)
+ elif model_name == "KIKINET":
+ model = KIKINet(cfg.model, trainer=trainer)
+ elif model_name == "LPDNET":
+ model = LPDNet(cfg.model, trainer=trainer)
+ elif model_name == "MODL":
+ model = MoDL(cfg.model, trainer=trainer)
+ elif model_name == "MTLRS":
+ model = MTLRS(cfg.model, trainer=trainer)
+ elif model_name == "MULTIDOMAINNET":
+ model = MultiDomainNet(cfg.model, trainer=trainer)
+ elif model_name == "PROXIMALGRADIENT":
+ model = ProximalGradient(cfg.model, trainer=trainer)
+ elif model_name == "QCIRIM":
+ model = qCIRIM(cfg.model, trainer=trainer)
+ elif model_name == "QVN":
+ model = qVarNet(cfg.model, trainer=trainer)
+ elif model_name == "QZF":
+ model = qZF(cfg.model, trainer=trainer)
+ elif model_name == "RECSEGNET":
+ model = RecSegUNet(cfg.model, trainer=trainer)
+ elif model_name == "RVN":
+ model = RecurrentVarNet(cfg.model, trainer=trainer)
+ elif model_name == "SEGMENTATIONATTENTIONUNET":
+ model = SegmentationAttentionUNet(cfg.model, trainer=trainer)
+ elif model_name == "SEGMENTATIONDYNUNET":
+ model = SegmentationDYNUNet(cfg.model, trainer=trainer)
+ elif model_name == "SEGMENTATIONLAMBDAUNET":
+ model = SegmentationLambdaUNet(cfg.model, trainer=trainer)
+ elif model_name == "SEGMENTATIONUNET":
+ model = SegmentationUNet(cfg.model, trainer=trainer)
+ elif model_name == "SEGMENTATIONUNETR":
+ model = SegmentationUNetR(cfg.model, trainer=trainer)
+ elif model_name == "SEGMENTATION3DUNET":
+ model = Segmentation3DUNet(cfg.model, trainer=trainer)
+ elif model_name == "SEGMENTATIONVNET":
+ model = SegmentationVNet(cfg.model, trainer=trainer)
+ elif model_name == "SEGNET":
+ model = SegNet(cfg.model, trainer=trainer)
+ elif model_name == "SERANET":
+ model = SERANet(cfg.model, trainer=trainer)
+ elif model_name == "UNET":
+ model = UNet(cfg.model, trainer=trainer)
+ elif model_name == "VSNET":
+ model = VSNet(cfg.model, trainer=trainer)
+ elif model_name == "XPDNET":
+ model = XPDNet(cfg.model, trainer=trainer)
+ elif model_name == "ZF":
+ model = ZF(cfg.model, trainer=trainer)
+ else:
+ raise NotImplementedError(f"{model_name} is not implemented in atommic.")
+
+ if cfg.get("pretrained", None):
+ checkpoint = cfg.get("checkpoint", None)
+ logging.info(f"Loading pretrained model from {checkpoint}")
+
+ # instantiate model
+ if checkpoint.endswith(".atommic"):
+ if "huggingface" in checkpoint:
+ _, state_dict = model.from_pretrained(checkpoint)
+ else:
+ _, state_dict = model.restore_from(checkpoint)
+ else:
+ state_dict = torch.load(checkpoint, map_location="cpu")["state_dict"]
+
+ model.load_state_dict(state_dict)
+
+ if cfg.get("mode", None) == "train":
+ logging.info("Validating")
+ trainer.validate(model)
+ logging.info("Training")
+ trainer.fit(model)
+ else:
+ logging.info("Testing")
+ trainer.test(model)
+
+
+if __name__ == "__main__":
+ main() # pylint: disable=no-value-for-parameter
diff --git a/atommic/collections/__init__.py b/atommic/collections/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/common/__init__.py b/atommic/collections/common/__init__.py
new file mode 100644
index 00000000..4bb04edc
--- /dev/null
+++ b/atommic/collections/common/__init__.py
@@ -0,0 +1,14 @@
+# coding=utf-8
+
+import atommic.collections.common.callbacks # noqa: F401
+from atommic.collections.common import data, losses, metrics, nn, parts # noqa: F401
+from atommic.package_info import __version__
+
+# Set collection version equal to atommic version.
+__version = __version__
+
+# Authorship.
+__author__ = "Dimitris Karkalousos"
+
+# Set collection name.
+__description__ = "Common MRI collection"
diff --git a/atommic/collections/common/callbacks/__init__.py b/atommic/collections/common/callbacks/__init__.py
new file mode 100644
index 00000000..870f50e6
--- /dev/null
+++ b/atommic/collections/common/callbacks/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.common.callbacks.callbacks import LogEpochTimeCallback # noqa: F401
+from atommic.collections.common.callbacks.ema import EMA # noqa: F401
diff --git a/atommic/collections/common/callbacks/callbacks.py b/atommic/collections/common/callbacks/callbacks.py
new file mode 100644
index 00000000..d5bbed3a
--- /dev/null
+++ b/atommic/collections/common/callbacks/callbacks.py
@@ -0,0 +1,75 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/callbacks/callbacks.py
+
+import time
+
+from pytorch_lightning.callbacks import Callback
+from pytorch_lightning.utilities import rank_zero_only
+
+
+class LogEpochTimeCallback(Callback):
+ """Simple callback that logs how long each epoch takes, in seconds, to a pytorch lightning log.
+
+ .. note::
+ Extends :class:`pytorch_lightning.callbacks.Callback`.
+
+ Examples
+ --------
+ >>> from pytorch_lightning import Trainer
+ >>> from pytorch_lightning.callbacks import ModelCheckpoint
+ >>> from pytorch_lightning.loggers import TensorBoardLogger
+ >>> from atommic.collections.common.callbacks.callbacks import LogEpochTimeCallback
+ >>> logger = TensorBoardLogger("tb_logs", name="my_model")
+ >>> checkpoint_callback = ModelCheckpoint(
+ ... dirpath="checkpoints",
+ ... filename="my_model-{epoch:02d}-{val_loss:.2f}",
+ ... save_top_k=3,
+ ... verbose=True,
+ ... monitor="val_loss",
+ ... mode="min",
+ ... )
+ >>> trainer = Trainer(
+ ... logger=logger,
+ ... callbacks=[LogEpochTimeCallback(), checkpoint_callback],
+ ... max_epochs=10,
+ ... gpus=1,
+ ... strategy="ddp_fork",
+ ... accelerator="gpu",
+ ... precision=16,
+ ... )
+ """
+
+ def __init__(self):
+ """Inits :class:`LogEpochTimeCallback`."""
+ super().__init__()
+ self.epoch_start = time.time()
+
+ @rank_zero_only
+ def on_train_epoch_start(self, trainer, pl_module):
+ """Called at the start of each epoch.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The trainer object.
+ pl_module : pytorch_lightning.LightningModule
+ The lightning module.
+ """
+ self.epoch_start = time.time()
+
+ @rank_zero_only
+ def on_train_epoch_end(self, trainer, pl_module):
+ """Called at the end of each epoch.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The trainer object.
+ pl_module : pytorch_lightning.LightningModule
+ The lightning module.
+ """
+ curr_time = time.time()
+ duration = curr_time - self.epoch_start
+ trainer.logger.log_metrics({"epoch_time": duration}, step=trainer.global_step)
diff --git a/atommic/collections/common/callbacks/ema.py b/atommic/collections/common/callbacks/ema.py
new file mode 100644
index 00000000..519e6a2e
--- /dev/null
+++ b/atommic/collections/common/callbacks/ema.py
@@ -0,0 +1,502 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/callbacks/ema.py
+
+import contextlib
+import copy
+import os
+import threading
+from typing import Any, Dict, Iterable, Optional
+
+import pytorch_lightning as pl
+import torch
+from pytorch_lightning import Callback
+from pytorch_lightning.utilities.exceptions import MisconfigurationException
+from pytorch_lightning.utilities.rank_zero import rank_zero_info
+
+
+class EMA(Callback):
+ """Implements Exponential Moving Averaging (EMA).
+
+ When training a model, this callback will maintain moving averages of the trained parameters.
+ When evaluating, we use the moving averages copy of the trained parameters.
+ When saving, we save an additional set of parameters with the prefix `ema`.
+
+ .. note::
+ Extends :class:`pytorch_lightning.callbacks.Callback`.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.callbacks.ema import EMA
+ >>> ema = EMA(decay=0.9999, validate_original_weights=False, every_n_steps=1, cpu_offload=False)
+ >>> trainer = Trainer(callbacks=[ema])
+ >>> trainer.fit(model)
+ >>> trainer.test(model)
+ """
+
+ def __init__(
+ self,
+ decay: float,
+ validate_original_weights: bool = False,
+ every_n_steps: int = 1,
+ cpu_offload: bool = False,
+ ):
+ """Inits :class:`EMA`.
+
+ Parameters
+ ----------
+ decay : float
+ The exponential decay used when calculating the moving average. Has to be between 0-1.
+ validate_original_weights : bool
+ Validate the original weights, as apposed to the EMA weights. Default is ``False``.
+ every_n_steps : int
+ Apply EMA every N steps. Default is ``1``.
+ cpu_offload : bool
+ Offload weights to CPU. Default is ``False``.
+ """
+ super().__init__()
+ if not 0 <= decay <= 1:
+ raise MisconfigurationException("EMA decay value must be between 0 and 1")
+ self.decay = decay
+ self.validate_original_weights = validate_original_weights
+ self.every_n_steps = every_n_steps
+ self.cpu_offload = cpu_offload
+
+ def on_fit_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+ """Initialize the EMA weights.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ pl_module : pl.LightningModule
+ PyTorch Lightning Module.
+ """
+ device = pl_module.device if not self.cpu_offload else torch.device("cpu")
+ trainer.optimizers = [
+ EMAOptimizer(
+ optim,
+ device=device,
+ decay=self.decay,
+ every_n_steps=self.every_n_steps,
+ current_step=trainer.global_step,
+ )
+ for optim in trainer.optimizers
+ if not isinstance(optim, EMAOptimizer)
+ ]
+
+ def on_validation_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+ """Swap the model weights.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ pl_module : pl.LightningModule
+ PyTorch Lightning Module.
+ """
+ if self._should_validate_ema_weights(trainer):
+ self.swap_model_weights(trainer)
+
+ def on_validation_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+ """Swap back the model weights.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ pl_module : pl.LightningModule
+ PyTorch Lightning Module.
+ """
+ if self._should_validate_ema_weights(trainer):
+ self.swap_model_weights(trainer)
+
+ def on_test_start(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+ """Swap the model weights.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ pl_module : pl.LightningModule
+ PyTorch Lightning Module.
+ """
+ if self._should_validate_ema_weights(trainer):
+ self.swap_model_weights(trainer)
+
+ def on_test_end(self, trainer: "pl.Trainer", pl_module: "pl.LightningModule") -> None:
+ """Swap back the model weights.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ pl_module : pl.LightningModule
+ PyTorch Lightning Module.
+ """
+ if self._should_validate_ema_weights(trainer):
+ self.swap_model_weights(trainer)
+
+ def _should_validate_ema_weights(self, trainer: "pl.Trainer") -> bool:
+ """Check if we should validate the EMA weights.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ """
+ return not self.validate_original_weights and self._ema_initialized(trainer)
+
+ def _ema_initialized(self, trainer: "pl.Trainer") -> bool:
+ """Check if EMA has been initialized.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ """
+ return any(isinstance(optimizer, EMAOptimizer) for optimizer in trainer.optimizers)
+
+ def swap_model_weights(self, trainer: "pl.Trainer", saving_ema_model: bool = False):
+ """Swaps the model weights with the EMA weights.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ saving_ema_model : bool, optional
+ Whether we are saving the EMA model. Default is ``False``.
+ """
+ for optimizer in trainer.optimizers:
+ assert isinstance(optimizer, EMAOptimizer)
+ optimizer.switch_main_parameter_weights(saving_ema_model)
+
+ @contextlib.contextmanager
+ def save_ema_model(self, trainer: "pl.Trainer"):
+ """Saves an EMA copy of the model + EMA optimizer states for resume.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ """
+ self.swap_model_weights(trainer, saving_ema_model=True)
+ try:
+ yield
+ finally:
+ self.swap_model_weights(trainer, saving_ema_model=False)
+
+ @contextlib.contextmanager
+ def save_original_optimizer_state(self, trainer: "pl.Trainer"):
+ """Saves the original optimizer states for resume.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ """
+ for optimizer in trainer.optimizers:
+ assert isinstance(optimizer, EMAOptimizer)
+ optimizer.save_original_optimizer_state = True
+ try:
+ yield
+ finally:
+ for optimizer in trainer.optimizers:
+ optimizer.save_original_optimizer_state = False
+
+ def on_load_checkpoint(
+ self, trainer: "pl.Trainer", pl_module: "pl.LightningModule", checkpoint: Dict[str, Any]
+ ) -> None:
+ """Restore EMA weights when loading a checkpoint.
+
+ Parameters
+ ----------
+ trainer : pl.Trainer
+ PyTorch Lightning Trainer.
+ pl_module : pl.LightningModule
+ PyTorch Lightning Module.
+ checkpoint : Dict[str, Any]
+ Checkpoint dictionary.
+ """
+ checkpoint_callback = trainer.checkpoint_callback
+
+ # Replace connector._ckpt_path with below to avoid calling into lightning's protected API
+ ckpt_path = trainer.ckpt_path
+
+ if ckpt_path and checkpoint_callback is not None and "atommic" in type(checkpoint_callback).__name__:
+ ext = checkpoint_callback.FILE_EXTENSION
+ if ckpt_path.endswith(f"-EMA{ext}"):
+ rank_zero_info(
+ "loading EMA based weights. The callback will treat the loaded EMA weights "
+ "as the main weights and create a new EMA copy when training."
+ )
+ return
+ ema_path = ckpt_path.replace(ext, f"-EMA{ext}")
+ if os.path.exists(ema_path):
+ ema_state_dict = torch.load(ema_path, map_location=torch.device("cpu"))
+
+ checkpoint["optimizer_states"] = ema_state_dict["optimizer_states"]
+ rank_zero_info("EMA state has been restored.")
+ else:
+ raise MisconfigurationException(
+ "Unable to find the associated EMA weights when re-loading, training "
+ f"will start with new EMA weights. Expected them to be at: {ema_path}",
+ )
+
+
+@torch.no_grad()
+def ema_update(ema_model_tuple, current_model_tuple, decay):
+ """Update EMA parameters.
+
+ Parameters
+ ----------
+ ema_model_tuple : tuple
+ EMA model parameters.
+ current_model_tuple : tuple
+ Current model parameters.
+ decay : float
+ Decay factor.
+ """
+ torch._foreach_mul_(ema_model_tuple, decay) # pylint: disable=protected-access
+ torch._foreach_add_(ema_model_tuple, current_model_tuple, alpha=(1.0 - decay)) # pylint: disable=protected-access
+
+
+def run_ema_update_cpu(ema_model_tuple, current_model_tuple, decay, pre_sync_stream=None):
+ """Run EMA update on CPU.
+
+ Parameters
+ ----------
+ ema_model_tuple : tuple
+ EMA model parameters.
+ current_model_tuple : tuple
+ Current model parameters.
+ decay : float
+ Decay factor.
+ pre_sync_stream : torch.cuda.Stream, optional
+ CUDA stream. Default is ``None``.
+ """
+ if pre_sync_stream is not None:
+ pre_sync_stream.synchronize()
+ ema_update(ema_model_tuple, current_model_tuple, decay)
+
+
+class EMAOptimizer(torch.optim.Optimizer):
+ r"""EMAOptimizer is a wrapper for torch.optim.Optimizer that computes Exponential Moving Average of parameters
+ registered in the optimizer.
+
+ EMA parameters are automatically updated after every step of the optimizer with the following formula:
+ $$ ema\_weight = ema\_weight + (1 - decay) * (training\_weight - ema\_weight) $$
+
+ To access EMA parameters, use ``swap_ema_weights()`` context manager to perform a temporary in-place swap of
+ regular parameters with EMA parameters.
+
+ .. note::
+ EMAOptimizer is not compatible with APEX AMP O2.
+ Extends :class:`torch.optim.Optimizer`.
+
+ Returns
+ -------
+ torch.optim.Optimizer
+ EMAOptimizer instance.
+
+ Examples
+ --------
+ >>> model = Model().to(device)
+ >>> opt = EMAOptimizer(opt, device, 0.9999)
+ >>> for epoch in range(n_epochs):
+ >>> training_loop(model, opt)
+ >>> regular_eval_accuracy = evaluate(model)
+ >>> with opt.swap_ema_weights():
+ >>> ema_eval_accuracy = evaluate(model)
+ """
+
+ def __init__(
+ self,
+ optimizer: torch.optim.Optimizer,
+ device: torch.device,
+ decay: float = 0.9999,
+ every_n_steps: int = 1,
+ current_step: int = 0,
+ stream: Optional[torch.cuda.Stream] = None,
+ ):
+ """Inits :class:`EMAOptimizer`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer to wrap.
+ device : torch.device
+ Device for EMA parameters.
+ decay : float
+ Decay factor. Default is ``0.9999``.
+ every_n_steps : int
+ Apply EMA every N steps. Default is ``1``.
+ current_step : int
+ Current step. Default is ``0``.
+ stream : torch.cuda.Stream, optional
+ CUDA stream. Default is ``None``.
+ """
+ self.optimizer = optimizer
+ self.decay = decay
+ self.device = device
+ self.current_step = current_step
+ self.every_n_steps = every_n_steps
+ self.save_original_optimizer_state = False
+
+ self.first_iteration = True
+ self.rebuild_ema_params = True
+ self.stream = stream
+ self.thread = None
+
+ self.ema_params: tuple = ()
+ self.in_saving_ema_model_context = False
+
+ def all_parameters(self) -> Iterable[torch.Tensor]:
+ """Returns an iterator over all parameters."""
+ return (param for group in self.param_groups for param in group["params"])
+
+ def step(self, closure=None, **kwargs): # pylint: disable=unused-argument
+ """Performs a single optimization step.
+
+ Parameters
+ ----------
+ closure : callable, optional
+ A closure that reevaluates the model and returns the loss, by default None.
+ **kwargs
+ Additional parameters.
+
+ Returns
+ -------
+ float
+ Loss.
+ """
+ self.join()
+
+ if self.first_iteration:
+ if any(p.is_cuda for p in self.all_parameters()):
+ self.stream = torch.cuda.Stream()
+
+ self.first_iteration = False
+
+ if self.rebuild_ema_params:
+ opt_params = list(self.all_parameters())
+
+ self.ema_params = self.ema_params + tuple(
+ copy.deepcopy(param.data.detach()).to(self.device) for param in opt_params[len(self.ema_params) :]
+ )
+ self.rebuild_ema_params = False
+
+ loss = self.optimizer.step(closure)
+
+ if self._should_update_at_step():
+ self.update()
+ self.current_step = self.current_step + 1
+ return loss
+
+ def _should_update_at_step(self) -> bool:
+ """Checks if EMA parameters should be updated at current step."""
+ return self.current_step % self.every_n_steps == 0
+
+ @torch.no_grad()
+ def update(self):
+ """Updates EMA parameters"""
+ if self.stream is not None:
+ self.stream.wait_stream(torch.cuda.current_stream())
+
+ with torch.cuda.stream(self.stream):
+ current_model_state = tuple(
+ param.data.to(self.device, non_blocking=True) for param in self.all_parameters()
+ )
+
+ if self.device.type == "cuda":
+ ema_update(self.ema_params, current_model_state, self.decay)
+
+ if self.device.type == "cpu":
+ self.thread = threading.Thread(
+ target=run_ema_update_cpu,
+ args=(
+ self.ema_params,
+ current_model_state,
+ self.decay,
+ self.stream,
+ ),
+ )
+ self.thread.start()
+
+ @staticmethod
+ def swap_tensors(tensor1, tensor2):
+ """Swaps the values of two tensors in-place."""
+ tmp = torch.empty_like(tensor1)
+ tmp.copy_(tensor1)
+ tensor1.copy_(tensor2)
+ tensor2.copy_(tmp)
+
+ def switch_main_parameter_weights(self, saving_ema_model: bool = False):
+ """Switches the main parameter weights with the EMA weights."""
+ self.join()
+ self.in_saving_ema_model_context = saving_ema_model
+ for param, ema_param in zip(self.all_parameters(), self.ema_params):
+ self.swap_tensors(param.data, ema_param)
+
+ @contextlib.contextmanager
+ def swap_ema_weights(self, enabled: bool = True):
+ """A context manager to in-place swap regular parameters with EMA parameters. It swaps back to the original
+ regular parameters on context manager exit.
+
+ Parameters
+ ----------
+ enabled : bool, optional
+ whether the swap should be performed, by default True
+ """
+ if enabled:
+ self.switch_main_parameter_weights()
+ try:
+ yield
+ finally:
+ if enabled:
+ self.switch_main_parameter_weights()
+
+ def __getattr__(self, name):
+ return getattr(self.optimizer, name)
+
+ def join(self):
+ """Wait for the EMA update to finish."""
+ if self.stream is not None:
+ self.stream.synchronize()
+
+ if self.thread is not None:
+ self.thread.join()
+
+ def state_dict(self):
+ """Return the optimizer state."""
+ self.join()
+
+ if self.save_original_optimizer_state:
+ return self.optimizer.state_dict()
+
+ # if we are in the context of saving an EMA model, the EMA weights are in the modules' actual weights
+ ema_params = self.ema_params if not self.in_saving_ema_model_context else list(self.all_parameters())
+ state_dict = {
+ "opt": self.optimizer.state_dict(),
+ "ema": ema_params,
+ "current_step": self.current_step,
+ "decay": self.decay,
+ "every_n_steps": self.every_n_steps,
+ }
+ return state_dict
+
+ def load_state_dict(self, state_dict):
+ """Load the optimizer state."""
+ self.join()
+ self.optimizer.load_state_dict(state_dict["opt"])
+ self.ema_params = tuple(param.to(self.device) for param in copy.deepcopy(state_dict["ema"]))
+ self.current_step = state_dict["current_step"]
+ self.decay = state_dict["decay"]
+ self.every_n_steps = state_dict["every_n_steps"]
+ self.rebuild_ema_params = False
+
+ def add_param_group(self, param_group):
+ """Add a param group to the :class:`Optimizer` s `param_groups`."""
+ self.optimizer.add_param_group(param_group)
+ self.rebuild_ema_params = True
diff --git a/atommic/collections/common/data/__init__.py b/atommic/collections/common/data/__init__.py
new file mode 100644
index 00000000..bc0a90aa
--- /dev/null
+++ b/atommic/collections/common/data/__init__.py
@@ -0,0 +1,14 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.common.data.mri_loader import MRIDataset # noqa: F401
+from atommic.collections.common.data.subsample import ( # noqa: F401
+ Equispaced1DMaskFunc,
+ Equispaced2DMaskFunc,
+ Gaussian1DMaskFunc,
+ Gaussian2DMaskFunc,
+ MaskFunc,
+ Poisson2DMaskFunc,
+ Random1DMaskFunc,
+ create_masker,
+)
diff --git a/atommic/collections/common/data/mri_loader.py b/atommic/collections/common/data/mri_loader.py
new file mode 100644
index 00000000..fac98ee1
--- /dev/null
+++ b/atommic/collections/common/data/mri_loader.py
@@ -0,0 +1,414 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import json
+import logging
+import os
+import random
+from pathlib import Path
+from typing import Callable, Dict, List, Optional, Sequence, Tuple, Union
+
+import h5py
+import numpy as np
+import yaml # type: ignore
+from defusedxml.ElementTree import fromstring
+from torch.utils.data import Dataset
+
+from atommic.collections.common.parts import utils
+
+
+def et_query(root: str, qlist: Sequence[str], namespace: str = "http://www.ismrm.org/ISMRMRD") -> str:
+ """Query an XML element for a list of attributes.
+
+ Parameters
+ ----------
+ root : str
+ The root element of the XML tree.
+ qlist : list
+ A list of strings, each of which is an attribute name.
+ namespace : str, optional
+ The namespace of the XML tree.
+
+ Returns
+ -------
+ str
+ A string containing the value of the last attribute in the list.
+ """
+ s = "."
+ prefix = "ismrmrd_namespace"
+ ns = {prefix: namespace}
+ for el in qlist:
+ s += f"//{prefix}:{el}"
+ value = root.find(s, ns) # type: ignore
+ if value is None:
+ return "0"
+ return str(value.text) # type: ignore
+
+
+class MRIDataset(Dataset):
+ """A generic class for loading an MRI dataset for any task.
+
+ .. note::
+ Extends :class:`torch.utils.data.Dataset`.
+ """
+
+ def __init__( # noqa: MC0001
+ self,
+ root: Union[str, Path, os.PathLike],
+ coil_sensitivity_maps_root: Union[str, Path, os.PathLike] = None,
+ mask_root: Union[str, Path, os.PathLike] = None,
+ noise_root: Union[str, Path, os.PathLike] = None,
+ initial_predictions_root: Union[str, Path, os.PathLike] = None,
+ dataset_format: str = None,
+ sample_rate: Optional[float] = None,
+ volume_sample_rate: Optional[float] = None,
+ use_dataset_cache: bool = False,
+ dataset_cache_file: Union[str, Path, os.PathLike] = None,
+ num_cols: Optional[Tuple[int]] = None,
+ consecutive_slices: int = 1,
+ data_saved_per_slice: bool = False,
+ n2r_supervised_rate: Optional[float] = 0.0,
+ complex_target: bool = False,
+ log_images_rate: Optional[float] = 1.0,
+ transform: Optional[Callable] = None,
+ **kwargs, # pylint: disable=unused-argument
+ ):
+ """Inits :class:`MRIDataset`.
+
+ Parameters
+ ----------
+ root : Union[str, Path, os.PathLike]
+ Path to the dataset.
+ coil_sensitivity_maps_root : Union[str, Path, os.PathLike], optional
+ Path to the coil sensitivities maps dataset, if stored separately.
+ mask_root : Union[str, Path, os.PathLike], optional
+ Path to stored masks, if stored separately.
+ noise_root : Union[str, Path, os.PathLike], optional
+ Path to stored noise, if stored separately (in json format).
+ initial_predictions_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the initial predictions. If provided, the initial predictions will be used
+ as the input of the reconstruction network. Default is ``None``.
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the slices should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ volume_sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the volumes should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ use_dataset_cache : bool, optional
+ Whether to cache dataset metadata. This is very useful for large datasets.
+ dataset_cache_file : Union[str, Path, os.PathLike, none], optional
+ A file in which to cache dataset information for faster load times. If not provided, the cache will be
+ stored in the dataset root.
+ num_cols : Optional[Tuple[int]], optional
+ If provided, only slices with the desired number of columns will be considered.
+ consecutive_slices : int, optional
+ An int (>0) that determine the amount of consecutive slices of the file to be loaded at the same time.
+ Default is ``1``, loading single slices.
+ data_saved_per_slice : bool, optional
+ Whether the data is saved per slice or per volume.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ complex_target : bool, optional
+ Whether to use a complex target or not. Default is ``False``.
+ log_images_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the slices should be logged as images. Default is
+ ``1.0``.
+ transform : Optional[Callable], optional
+ A sequence of callable objects that preprocesses the raw data into appropriate form. The transform function
+ should take ``kspace``, ``coil sensitivity maps``, ``quantitative maps``, ``mask``, ``initial prediction``,
+ ``target``, ``attributes``, ``filename``, and ``slice number`` as inputs. ``target`` may be null for test
+ data. Default is ``None``.
+ **kwargs
+ Additional keyword arguments.
+ """
+ super().__init__()
+ self.coil_sensitivity_maps_root = coil_sensitivity_maps_root
+ self.mask_root = mask_root
+
+ if str(noise_root).endswith(".json"):
+ with open(noise_root, "r") as f: # type: ignore # pylint: disable=unspecified-encoding
+ noise_root = [json.loads(line) for line in f.readlines()] # type: ignore
+ else:
+ noise_root = None
+
+ self.initial_predictions_root = initial_predictions_root
+ self.dataset_format = dataset_format
+
+ # set default sampling mode if none given
+ if not utils.is_none(sample_rate) and not utils.is_none(volume_sample_rate):
+ raise ValueError(
+ f"Both sample_rate {sample_rate} and volume_sample_rate {volume_sample_rate} are set. "
+ "Please set only one of them."
+ )
+
+ if sample_rate is None or sample_rate == "None":
+ sample_rate = 1.0
+
+ if volume_sample_rate is None or volume_sample_rate == "None":
+ volume_sample_rate = 1.0
+
+ self.dataset_cache_file = (
+ None if utils.is_none(dataset_cache_file) else Path(dataset_cache_file) # type: ignore
+ )
+
+ if self.dataset_cache_file is not None and self.dataset_cache_file.exists() and use_dataset_cache:
+ with open(self.dataset_cache_file, "rb") as f:
+ dataset_cache = yaml.safe_load(f)
+ else:
+ dataset_cache = {}
+
+ if consecutive_slices < 1:
+ raise ValueError(f"Consecutive slices {consecutive_slices} is out of range, must be > 0.")
+ self.consecutive_slices = consecutive_slices
+ self.complex_target = complex_target
+ self.transform = transform
+ self.data_saved_per_slice = data_saved_per_slice
+
+ self.recons_key = "reconstruction"
+ self.examples = []
+
+ # Check if our dataset is in the cache. If yes, use that metadata, if not, then regenerate the metadata.
+ if dataset_cache.get(root) is None or not use_dataset_cache:
+ if str(root).endswith(".json"):
+ with open(root, "r") as f: # pylint: disable=unspecified-encoding
+ examples = json.load(f)
+ files = [Path(example) for example in examples]
+ else:
+ files = list(Path(root).iterdir())
+
+ if n2r_supervised_rate != 0.0:
+ # randomly select a subset of files for N2R supervised loss based on n2r_supervised_rate
+ n2r_supervised_files = random.sample(
+ files, int(np.round(n2r_supervised_rate * len(files))) # type: ignore
+ )
+
+ for fname in sorted(files):
+ metadata, num_slices = self._retrieve_metadata(fname)
+ metadata["noise_levels"] = (
+ self.__parse_noise__(noise_root, fname) if noise_root is not None else [] # type: ignore
+ )
+ metadata["n2r_supervised"] = False
+ if n2r_supervised_rate != 0.0:
+ # Use lazy % formatting in logging
+ logging.info("%s files are selected for N2R supervised loss.", n2r_supervised_files)
+ if fname in n2r_supervised_files:
+ metadata["n2r_supervised"] = True
+
+ self.examples += [(fname, slice_ind, metadata) for slice_ind in range(num_slices)]
+
+ if dataset_cache.get(root) is None and use_dataset_cache:
+ dataset_cache[root] = self.examples
+ logging.info("Saving dataset cache to %s.", self.dataset_cache_file)
+ with open(self.dataset_cache_file, "wb") as f: # type: ignore
+ yaml.dump(dataset_cache, f)
+ else:
+ logging.info("Using dataset cache from %s.", self.dataset_cache_file)
+ self.examples = dataset_cache[root]
+
+ # subsample if desired
+ if sample_rate < 1.0: # sample by slice
+ random.shuffle(self.examples)
+ num_examples = round(len(self.examples) * sample_rate)
+ self.examples = self.examples[:num_examples]
+ elif volume_sample_rate < 1.0: # sample by volume
+ vol_names = sorted(list({f[0].stem for f in self.examples}))
+ random.shuffle(vol_names)
+ num_volumes = round(len(vol_names) * volume_sample_rate)
+ sampled_vols = vol_names[:num_volumes]
+ self.examples = [example for example in self.examples if example[0].stem in sampled_vols]
+
+ if num_cols and not utils.is_none(num_cols):
+ self.examples = [ex for ex in self.examples if ex[2]["encoding_size"][1] in num_cols]
+
+ self.indices_to_log = np.random.choice(
+ len(self.examples), int(log_images_rate * len(self.examples)), replace=False # type: ignore
+ )
+
+ def _retrieve_metadata(self, fname: Union[str, Path]) -> Tuple[Dict, int]:
+ """Retrieve metadata from a given file.
+
+ Parameters
+ ----------
+ fname : Union[str, Path]
+ Path to file.
+
+ Returns
+ -------
+ Tuple[Dict, int]
+ Metadata dictionary and number of slices in the file.
+
+ Examples
+ --------
+ >>> metadata, num_slices = _retrieve_metadata("file.h5")
+ >>> metadata
+ {'padding_left': 0, 'padding_right': 0, 'encoding_size': 0, 'recon_size': (0, 0)}
+ >>> num_slices
+ 1
+ """
+ with h5py.File(fname, "r") as hf:
+ if "ismrmrd_header" in hf:
+ et_root = fromstring(hf["ismrmrd_header"][()])
+
+ enc = ["encoding", "encodedSpace", "matrixSize"]
+ enc_size = (
+ int(et_query(et_root, enc + ["x"])),
+ int(et_query(et_root, enc + ["y"])),
+ int(et_query(et_root, enc + ["z"])),
+ )
+ rec = ["encoding", "reconSpace", "matrixSize"]
+ recon_size = (
+ int(et_query(et_root, rec + ["x"])),
+ int(et_query(et_root, rec + ["y"])),
+ int(et_query(et_root, rec + ["z"])),
+ )
+
+ params = ["encoding", "encodingLimits", "kspace_encoding_step_1"]
+ enc_limits_center = int(et_query(et_root, params + ["center"]))
+ enc_limits_max = int(et_query(et_root, params + ["maximum"])) + 1
+
+ padding_left = enc_size[1] // 2 - enc_limits_center
+ padding_right = padding_left + enc_limits_max
+ else:
+ padding_left = 0
+ padding_right = 0
+ enc_size = (0, 0, 0)
+ recon_size = (0, 0, 0)
+
+ if "kspace" in hf:
+ shape = hf["kspace"].shape
+ elif "reconstruction" in hf:
+ shape = hf["reconstruction"].shape
+ elif "target" in hf:
+ shape = hf["target"].shape
+ else:
+ raise ValueError(f"{fname} does not contain kspace, reconstruction, or target data.")
+
+ num_slices = 1 if self.data_saved_per_slice else shape[0]
+
+ metadata = {
+ "padding_left": padding_left,
+ "padding_right": padding_right,
+ "encoding_size": enc_size,
+ "recon_size": recon_size,
+ "num_slices": num_slices,
+ }
+
+ return metadata, num_slices
+
+ @staticmethod
+ def __parse_noise__(noise: str, fname: Path) -> List[str]:
+ """Parse noise type from filename.
+
+ Parameters
+ ----------
+ noise : str
+ json string of noise type.
+ fname : Path
+ Filename to parse noise type from.
+
+ Returns
+ -------
+ List[str]
+ List of noise values.
+ """
+ return [noise[i]["noise"] for i in range(len(noise)) if noise[i]["fname"] == fname.name] # type: ignore
+
+ def get_consecutive_slices(self, data: Dict, key: str, dataslice: int) -> np.ndarray:
+ """Get consecutive slices from a given data dictionary.
+
+ Parameters
+ ----------
+ data : dict
+ Data to extract slices from.
+ key : str
+ Key to extract slices from.
+ dataslice : int
+ Slice to index.
+
+ Returns
+ -------
+ np.ndarray
+ Array of consecutive slices. If ``self.consecutive_slices`` is > 1, then the array will have shape
+ ``(self.consecutive_slices, *data[key].shape[1:])``. Otherwise, the array will have shape
+ ``data[key].shape[1:]``.
+
+ Examples
+ --------
+ >>> data = {"kspace": np.random.rand(10, 640, 368)}
+ >>> from atommic.collections.common.data.mri_loader import MRIDataset
+ >>> MRIDataset.get_consecutive_slices(data, "kspace", 1).shape
+ (1, 640, 368)
+ >>> MRIDataset.get_consecutive_slices(data, "kspace", 5).shape
+ (5, 640, 368)
+ """
+ # read data
+ x = data[key]
+
+ if self.data_saved_per_slice:
+ x = np.expand_dims(x, axis=0)
+
+ if self.consecutive_slices == 1:
+ if x.shape[0] == 1:
+ return x[0]
+ if x.ndim != 2:
+ return x[dataslice]
+ return x
+
+ # get consecutive slices
+ num_slices = x.shape[0]
+
+ # If the number of consecutive slices is greater than or equal to the total slices, return the entire stack
+ if self.consecutive_slices >= num_slices:
+ # pad left and right with zero slices to match the desired number of slices
+ slices_to_add_start = (self.consecutive_slices - num_slices) // 2
+ slices_to_add_end = self.consecutive_slices - num_slices - slices_to_add_start
+ if slices_to_add_start > 0:
+ zero_slices = np.zeros((slices_to_add_start, *x.shape[1:]))
+ x = np.concatenate((zero_slices, x), axis=0)
+ if slices_to_add_end > 0:
+ zero_slices = np.zeros((slices_to_add_end, *x.shape[1:]))
+ x = np.concatenate((x, zero_slices), axis=0)
+ return x
+
+ # Calculate half of the consecutive slices to determine the middle position
+ half_slices = self.consecutive_slices // 2
+
+ # Determine the start and end slices based on the middle position
+ start_slice = dataslice - half_slices
+ end_slice = dataslice + half_slices + 1
+
+ # Handle edge cases
+ slices_to_add_start = 0
+ slices_to_add_end = 0
+ if start_slice < 0:
+ slices_to_add_start = abs(start_slice)
+ start_slice = 0
+
+ if end_slice > (num_slices - 1):
+ slices_to_add_end = end_slice - num_slices
+ extracted_slices = x[start_slice:]
+ else:
+ extracted_slices = x[start_slice:end_slice]
+
+ # Add slices to the start and end if needed
+ if slices_to_add_start > 0:
+ zero_slices = np.zeros((slices_to_add_start, *extracted_slices.shape[1:]))
+ extracted_slices = np.concatenate((zero_slices, extracted_slices), axis=0)
+ if slices_to_add_end > 0:
+ zero_slices = np.zeros((slices_to_add_end, *extracted_slices.shape[1:]))
+ extracted_slices = np.concatenate((extracted_slices, zero_slices), axis=0)
+
+ return extracted_slices
+
+ def __len__(self):
+ """Length of :class:`MRIDataset`."""
+ return len(self.examples)
+
+ def __getitem__(self, i: int):
+ """Get item from :class:`MRIDataset`."""
+ raise NotImplementedError
diff --git a/atommic/collections/common/data/subsample.py b/atommic/collections/common/data/subsample.py
new file mode 100644
index 00000000..24ce9179
--- /dev/null
+++ b/atommic/collections/common/data/subsample.py
@@ -0,0 +1,919 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import contextlib
+from typing import List, Optional, Sequence, Tuple, Union
+
+import numba as nb
+import numpy as np
+import torch
+
+
+@contextlib.contextmanager
+def temp_seed(rng: np.random, seed: Optional[Union[int, Tuple[int, ...]]]):
+ """Temporarily set the seed of a numpy random number generator.
+
+ Parameters
+ ----------
+ rng : np.random.Generator
+ The numpy random number generator to modify.
+ seed : Optional[Union[int, Tuple[int, ...]]], optional
+ The seed to set, by default None.
+ """
+ if seed is None:
+ try:
+ yield
+ finally:
+ pass
+ else:
+ state = rng.get_state()
+ rng.seed(seed)
+ try:
+ yield
+ finally:
+ rng.set_state(state)
+
+
+class MaskFunc:
+ """MaskFunc is an abstract base class for creating sub-sampling masks.
+
+ This class is used to create a mask for MRI data that can be used to randomly under-sample the k-space data. The
+ mask is created by retaining a specified fraction of the low-frequency columns and setting the rest to zero. The
+ fraction of low-frequency columns to retain and the amount of under-sampling can be specified at initialization.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.data.subsample import MaskFunc
+ >>> mask_func = MaskFunc(center_fractions=[0.08, 0.04], accelerations=[4, 8])
+ >>> mask_func.choose_acceleration()
+ (0.08, 4)
+ """
+
+ def __init__(self, center_fractions: Sequence[float], accelerations: Sequence[int]):
+ """Inits :class:`MaskFunc`.
+
+ Parameters
+ ----------
+ center_fractions : Sequence[float]
+ Fraction of low-frequency columns to be retained. If multiple values are provided, then one of these
+ numbers is chosen uniformly each time. For 2D setting this value corresponds to setting the
+ Full-Width-Half-Maximum.
+ accelerations : Sequence[int]
+ Amount of under-sampling. This should have the same length as center_fractions. If multiple values are
+ provided, then one of these is chosen uniformly each time.
+ """
+ super().__init__()
+ self.center_fractions = center_fractions
+ self.accelerations = accelerations
+ self.rng = np.random.RandomState()
+
+ def __call__(
+ self,
+ shape: Sequence[int],
+ seed: Optional[Union[int, Tuple[int, ...]]] = None,
+ partial_fourier_percentage: Optional[float] = 0.0,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, int]:
+ """Calls :class:`MaskFunc`.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the mask to be created. The shape should have at least 3 dimensions. Same as the shape of the
+ input k-space data.
+ seed : int or tuple of ints, optional
+ Seed for the random number generator. Default is ``None``.
+ partial_fourier_percentage : float, optional
+ Percentage of the low-frequency columns to be retained. Default is ``0.0``.
+ """
+ raise NotImplementedError
+
+ def choose_acceleration(self) -> Tuple[float, int]:
+ """Chooses an acceleration factor and center fractions from a list of multiple values.
+
+ Returns
+ -------
+ Tuple[float, int]
+ A tuple of the center fraction and the acceleration factor.
+ """
+ choice = self.rng.randint(0, len(self.accelerations))
+ center_fraction = self.center_fractions[choice]
+ acceleration = self.accelerations[choice]
+ return center_fraction, acceleration
+
+
+class Equispaced1DMaskFunc(MaskFunc):
+ r"""Equispaced1DMaskFunc creates a sub-sampling mask of a given shape.
+
+ The mask selects a subset of columns from the input k-space data. If the k-space data has N columns, the mask
+ picks out:
+
+ 1. N_low_freqs = (N * center_fraction) columns in the center corresponding to low-frequencies.
+
+ 2. The other columns are selected with equal spacing at a proportion that reaches the desired acceleration
+ rate taking into consideration the number of low frequencies. This ensures that the expected number of
+ columns selected is equal to (N / acceleration).
+
+
+ It is possible to use multiple center_fractions and accelerations, in which case one possible (center_fraction,
+ acceleration) is chosen uniformly at random each time the Equispaced1DMaskFunc object is called.
+
+ Note that this function may not give equispaced samples \
+ (documented in https://github.com/facebookresearch/fastMRI/issues/54), which will require modifications to
+ standard GRAPPA approaches. Nonetheless, this aspect of the function has been preserved to match the public
+ multicoil data.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.data.subsample import Equispaced1DMaskFunc
+ >>> mask_func = Equispaced1DMaskFunc(center_fractions=[0.08, 0.04], accelerations=[4, 8])
+ >>> mask_func.choose_acceleration()
+ (0.08, 4)
+ >>> kspace = torch.randn(1, 1, 640, 368)
+ >>> mask, acceleration = mask_func(kspace.shape)
+ >>> mask.shape
+ torch.Size([1, 1, 1, 368])
+ >>> acceleration
+ 4
+ """
+
+ def __call__(
+ self,
+ shape: Sequence[int],
+ seed: Optional[Union[int, Tuple[int, ...]]] = None,
+ partial_fourier_percentage: Optional[float] = 0.0,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, int]:
+ """Calls :class:`Equispaced1DMaskFunc`.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the mask to be created. The shape should have at least 3 dimensions. Same as the shape of the
+ input k-space data.
+ seed : int or tuple of ints, optional
+ Seed for the random number generator. Default is ``None``.
+ partial_fourier_percentage : float, optional
+ Percentage of the low-frequency columns to be retained. Default is ``0.0``.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, int]
+ A tuple of the generated mask and the acceleration factor.
+
+ Raises
+ ------
+ ValueError
+ If the `shape` parameter has less than 3 dimensions.
+ """
+ if len(shape) < 3:
+ raise ValueError("Shape should have 3 or more dimensions")
+
+ with temp_seed(self.rng, seed):
+ center_fraction, acceleration = self.choose_acceleration()
+ num_cols = shape[-2]
+ num_low_freqs = int(round(num_cols * center_fraction))
+
+ # create the mask
+ mask = np.zeros(num_cols, dtype=np.float32)
+ pad = torch.div((num_cols - num_low_freqs + 1), 2, rounding_mode="trunc").item()
+ mask[pad : pad + num_low_freqs] = True
+
+ # determine acceleration rate by adjusting for the number of low frequencies
+ adjusted_accel = (acceleration * (num_low_freqs - num_cols)) / (num_low_freqs * acceleration - num_cols)
+ offset = self.rng.randint(0, round(adjusted_accel))
+
+ accel_samples = np.arange(offset, num_cols - 1, adjusted_accel)
+ accel_samples = np.around(accel_samples).astype(np.uint)
+ mask[accel_samples] = True
+
+ # reshape the mask
+ mask_shape = [1 for _ in shape]
+ mask_shape[-2] = num_cols
+ mask = torch.from_numpy(mask.reshape(*mask_shape).astype(np.float32))
+
+ if partial_fourier_percentage != 0:
+ mask[:, : int(np.round(mask.shape[1] * partial_fourier_percentage))] = 0.0
+
+ return mask, acceleration
+
+
+class Equispaced2DMaskFunc(MaskFunc):
+ """Same as Equispaced1DMaskFunc, but for 2D k-space data.
+
+ .. note::
+ See ..class::`atommic.collections.common.data.subsample.Equispaced1DMaskFunc` for more details.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.data.subsample import Equispaced2DMaskFunc
+ >>> mask_func = Equispaced2DMaskFunc(center_fractions=[0.08, 0.04], accelerations=[4, 8])
+ >>> mask_func.choose_acceleration()
+ (0.08, 4)
+ >>> kspace = torch.randn(1, 1, 640, 368)
+ >>> mask, acceleration = mask_func(kspace.shape)
+ >>> mask.shape
+ torch.Size([1, 1, 640, 368])
+ >>> acceleration
+ 4
+ """
+
+ def __call__(
+ self,
+ shape: Sequence[int],
+ seed: Optional[Union[int, Tuple[int, ...]]] = None,
+ partial_fourier_percentage: Optional[float] = 0.0,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, int]:
+ """Calls :class:`Equispaced2DMaskFunc`.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the mask to be created. The shape should have at least 3 dimensions. Same as the shape of the
+ input k-space data.
+ seed : int or tuple of ints, optional
+ Seed for the random number generator. Default is ``None``.
+ partial_fourier_percentage : float, optional
+ Percentage of the low-frequency columns to be retained. Default is ``0.0``.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, int]
+ A tuple containing the mask and the acceleration factor.
+ The mask is a `torch.Tensor` of the same shape as the input shape.
+ The acceleration factor is an `int` that represents the number of samples taken in the k-space.
+
+ Raises
+ ------
+ ValueError
+ If the `shape` parameter has less than 3 dimensions.
+ """
+ if len(shape) < 3:
+ raise ValueError("Shape should have 3 or more dimensions")
+
+ with temp_seed(self.rng, seed):
+ center_fraction, acceleration = self.choose_acceleration()
+
+ acceleration = int(acceleration / 2)
+ center_fraction = center_fraction / 2
+
+ num_cols = shape[-2]
+ num_low_freqs = int(round(num_cols * center_fraction))
+
+ num_rows = shape[-3]
+ num_high_freqs = int(round(num_rows * center_fraction))
+
+ # create the mask
+ mask = np.zeros([num_rows, num_cols], dtype=np.float32)
+
+ pad_cols = torch.div((num_cols - num_low_freqs + 1), 2, rounding_mode="trunc").item()
+ pad_rows = torch.div((num_rows - num_high_freqs + 1), 2, rounding_mode="trunc").item()
+ mask[pad_rows : pad_rows + num_high_freqs, pad_cols : pad_cols + num_low_freqs] = True
+
+ for i in np.arange(0, num_rows, acceleration):
+ for j in np.arange(0, num_cols, acceleration):
+ mask[int(i), int(j)] = True
+
+ # reshape the mask
+ mask_shape = [1 for _ in shape]
+ mask_shape[-2] = num_cols
+ mask_shape[-3] = num_rows
+ mask = torch.from_numpy(mask.reshape(*mask_shape).astype(np.float32))
+
+ if partial_fourier_percentage != 0:
+ mask[:, : int(np.round(mask.shape[1] * partial_fourier_percentage))] = 0.0
+
+ return mask, acceleration * 2
+
+
+class Gaussian1DMaskFunc(MaskFunc):
+ """Same as Gaussian2DMaskFunc, but for 1D k-space data.
+
+ .. note::
+ See ..class::`atommic.collections.common.data.subsample.Gaussian2DMaskFunc` for more details.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.data.subsample import Gaussian1DMaskFunc
+ >>> mask_func = Gaussian1DMaskFunc(center_fractions=[0.7, 0.7], accelerations=[4, 8])
+ >>> mask_func.choose_acceleration()
+ (0.7, 4)
+ >>> kspace = torch.randn(1, 1, 640, 368)
+ >>> mask, acceleration = mask_func(kspace.shape)
+ >>> mask.shape
+ torch.Size([1, 1, 1, 368])
+ >>> acceleration
+ 4
+ """
+
+ def __call__(
+ self,
+ shape: Union[Sequence[int], np.ndarray],
+ seed: Optional[Union[int, Tuple[int, ...]]] = None,
+ partial_fourier_percentage: Optional[float] = 0.0,
+ center_scale: Optional[float] = 0.02,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, int]:
+ """Calls :class:`Gaussian1DMaskFunc`.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the mask to be created. The shape should have at least 3 dimensions. Same as the shape of the
+ input k-space data.
+ seed : int or tuple of ints, optional
+ Seed for the random number generator. Default is ``None``.
+ partial_fourier_percentage : float, optional
+ Percentage of the low-frequency columns to be retained. Default is ``0.0``.
+ center_scale : float, optional
+ For autocalibration purposes, data points near the k-space center will be fully sampled within an ellipse
+ of which the half-axes will be set to the given `center_scale` percentage of the fully sampled region.
+ Default is ``0.02``.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, int]
+ A tuple of the generated mask and the acceleration factor.
+ """
+ with temp_seed(self.rng, seed):
+ dims = [1 for _ in shape]
+ self.shape = tuple(shape[-3:-1])
+ self.shape = (self.shape[1], self.shape[0])
+ dims[-2] = self.shape[-2]
+
+ full_width_half_maximum, acceleration = self.choose_acceleration()
+
+ self.full_width_half_maximum = full_width_half_maximum
+ self.acceleration = acceleration
+ self.center_scale = center_scale
+
+ mask = self.gaussian_kspace()
+ mask[tuple(self.gaussian_coordinates())] = 1.0
+
+ mask = np.fft.ifftshift(np.fft.ifftshift(np.fft.ifftshift(mask, axes=0), axes=0), axes=(0, 1))
+
+ if partial_fourier_percentage != 0:
+ mask[:, : int(np.round(mask.shape[1] * partial_fourier_percentage))] = 0.0
+
+ mask = torch.from_numpy(np.transpose(mask, (1, 0))[0].reshape(*dims).astype(np.float32))
+
+ return mask, acceleration
+
+ def gaussian_kspace(self) -> np.ndarray:
+ """Creates a Gaussian sampled k-space center."""
+ scaled = int(self.shape[0] * self.center_scale)
+ center = np.ones((scaled, self.shape[1]))
+ top_scaled = torch.div((self.shape[0] - scaled), 2, rounding_mode="trunc").item()
+ bottom_scaled = self.shape[0] - scaled - top_scaled
+ top = np.zeros((top_scaled, self.shape[1]))
+ btm = np.zeros((bottom_scaled, self.shape[1]))
+ return np.concatenate((top, center, btm))
+
+ def gaussian_coordinates(self) -> Tuple[np.ndarray, np.ndarray]:
+ r"""Returns Gaussian sampled k-space coordinates.
+
+ Returns
+ -------
+ xsamples : np.ndarray
+ A 1D numpy array of x-coordinates.
+ ysamples : np.ndarray
+ A 1D numpy array of y-coordinates.
+
+ Notes
+ -----
+ The number of samples taken is determined by `n_sample` which is calculated as
+ `self.shape[0] / self.acceleration`. The selection of the samples is based on the probabilities calculated
+ from `gaussian_kernel`.
+ """
+ n_sample = int(self.shape[0] / self.acceleration)
+ idxs = np.random.choice(range(self.shape[0]), size=n_sample, replace=False, p=self.gaussian_kernel())
+ xsamples = np.concatenate([np.tile(i, self.shape[1]) for i in idxs])
+ ysamples = np.concatenate([range(self.shape[1]) for _ in idxs])
+ return xsamples, ysamples
+
+ def gaussian_kernel(self) -> np.ndarray:
+ r"""Creates a Gaussian sampled k-space kernel.
+
+ .. note::
+ The function calculates the Gaussian kernel by computing the sum of the exponential of the squared \
+ x-values divided by 2 times the square of the standard deviation. The standard deviation is calculated \
+ from the full width at half maximum (FWHM) of the Gaussian curve and is defined as the FWHM divided by \
+ the square root of 8 times the natural logarithm of 2. The FWHM and the kern_len are obtained from the \
+ `full_width_half_maximum` and `shape` attributes of the class respectively.
+
+ Returns
+ -------
+ ndarray
+ The Gaussian kernel.
+ """
+ kernel = 1
+ for kern_len in self.shape:
+ sigma = self.full_width_half_maximum / np.sqrt(8 * np.log(2))
+ x = np.linspace(-1.0, 1.0, kern_len)
+ g = np.exp(-(x**2 / (2 * sigma**2))) # noqa: F841
+ kernel = g
+ break
+ kernel = kernel / kernel.sum() # type: ignore
+ return kernel
+
+
+class Gaussian2DMaskFunc(MaskFunc):
+ """Creates a 2D sub-sampling mask of a given shape.
+
+ The sub-sampling mask is generated in k-space, with data points near the k-space center being fully sampled within
+ an ellipse. The half-axes of the ellipse are set to the `center_scale` percentage of the fully sampled region. The
+ remaining points are sampled according to a Gaussian distribution.
+
+ The center fractions act as Full-Width at Half-Maximum (FWHM) values.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.data.subsample import Gaussian2DMaskFunc
+ >>> mask_func = Gaussian2DMaskFunc(center_fractions=[0.7, 0.7], accelerations=[4, 8])
+ >>> mask_func.choose_acceleration()
+ (0.7, 4)
+ >>> kspace = torch.randn(1, 1, 640, 368)
+ >>> mask, acceleration = mask_func(kspace.shape)
+ >>> mask.shape
+ torch.Size([1, 1, 640, 368])
+ >>> acceleration
+ 4
+ """
+
+ def __call__(
+ self,
+ shape: Union[Sequence[int], np.ndarray],
+ seed: Optional[Union[int, Tuple[int, ...]]] = None,
+ partial_fourier_percentage: Optional[float] = 0.0,
+ center_scale: Optional[float] = 0.02,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, int]:
+ """Calls :class:`Gaussian2DMaskFunc`.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the mask to be created. The shape should have at least 3 dimensions. Same as the shape of the
+ input k-space data.
+ seed : int or tuple of ints, optional
+ Seed for the random number generator. Default is ``None``.
+ partial_fourier_percentage : float, optional
+ Percentage of the low-frequency columns to be retained. Default is ``0.0``.
+ center_scale : float, optional
+ For autocalibration purposes, data points near the k-space center will be fully sampled within an ellipse
+ of which the half-axes will be set to the given `center_scale` percentage of the fully sampled region.
+ Default is ``0.02``.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, int]
+ A tuple of the generated mask and the acceleration factor.
+
+ Raises
+ ------
+ ValueError
+ If the `shape` parameter has less than 3 dimensions.
+ """
+ with temp_seed(self.rng, seed):
+ dims = [1 for _ in shape]
+ self.shape = tuple(shape[-3:-1])
+ dims[-3:-1] = self.shape
+
+ full_width_half_maximum, acceleration = self.choose_acceleration()
+
+ self.full_width_half_maximum = full_width_half_maximum
+ self.acceleration = acceleration
+ self.center_scale = center_scale
+
+ mask = self.gaussian_kspace()
+ mask[tuple(self.gaussian_coordinates())] = 1.0
+
+ if partial_fourier_percentage != 0:
+ mask[: int(np.round(mask.shape[0] * partial_fourier_percentage)), :] = 0.0
+
+ mask = torch.from_numpy(mask.reshape(dims).astype(np.float32))
+
+ return mask, acceleration
+
+ def gaussian_kspace(self) -> np.ndarray:
+ """Creates a Gaussian sampled k-space center."""
+ a, b = self.center_scale * self.shape[0], self.center_scale * self.shape[1]
+ afocal, bfocal = self.shape[0] / 2, self.shape[1] / 2
+ xx, yy = np.mgrid[: self.shape[0], : self.shape[1]]
+ ellipse = np.power((xx - afocal) / a, 2) + np.power((yy - bfocal) / b, 2)
+ return (ellipse < 1).astype(float)
+
+ def gaussian_coordinates(self) -> List[Tuple[int, int]]:
+ r"""Returns Gaussian sampled k-space coordinates.
+
+ Returns
+ -------
+ xsamples : np.ndarray
+ A 1D numpy array of x-coordinates.
+ ysamples : np.ndarray
+ A 1D numpy array of y-coordinates.
+
+ Notes
+ -----
+ The number of samples taken is determined by `n_sample` which is calculated as \
+ `self.shape[0] / self.acceleration`. The selection of the samples is based on the probabilities calculated \
+ from `gaussian_kernel`.
+ """
+ n_sample = int(self.shape[0] * self.shape[1] / self.acceleration)
+ cartesian_prod = list(np.ndindex(self.shape))
+ kernel = self.gaussian_kernel()
+ idxs = np.random.choice(range(len(cartesian_prod)), size=n_sample, replace=False, p=kernel.flatten())
+ return list(zip(*list(map(cartesian_prod.__getitem__, idxs))))
+
+ def gaussian_kernel(self) -> np.ndarray:
+ r"""Creates a Gaussian sampled k-space kernel.
+
+ .. note::
+ The function calculates the Gaussian kernel by computing the sum of the exponential of the squared \
+ x-values divided by 2 times the square of the standard deviation. The standard deviation is calculated \
+ from the full width at half maximum (FWHM) of the Gaussian curve and is defined as the FWHM divided by \
+ the square root of 8 times the natural logarithm of 2. The FWHM and the kern_len are obtained from the \
+ `full_width_half_maximum` and `shape` attributes of the class respectively.
+
+ Returns
+ -------
+ ndarray
+ The Gaussian kernel.
+ """
+ kernels = []
+ for kern_len in self.shape:
+ sigma = self.full_width_half_maximum / np.sqrt(8 * np.log(2))
+ x = np.linspace(-1.0, 1.0, kern_len)
+ g = np.exp(-(x**2 / (2 * sigma**2)))
+ kernels.append(g)
+ kernel = np.sqrt(np.outer(kernels[0], kernels[1]))
+ kernel = kernel / kernel.sum()
+ return kernel
+
+
+class Poisson2DMaskFunc(MaskFunc):
+ r"""Generate variable-density Poisson-disc sampling pattern, as described in [1]_.
+
+ The function generates a variable density Poisson-disc sampling mask with density proportional to
+ :math:`1 / (1 + s |r|)`, where :math:`r` represents the k-space radius, and :math:`s` represents the slope. A
+ binary search is performed on the slope :math:`s` such that the resulting acceleration factor is close to the
+ prescribed acceleration factor `accel`. The parameter `tol` determines how much they can deviate.
+
+ References
+ ----------
+ .. [1] Bridson, Robert. "Fast Poisson disk sampling in arbitrary dimensions." SIGGRAPH sketches. 2007
+
+ .. note::
+ Taken and adapted from: https://github.com/mikgroup/sigpy/blob/master/sigpy/mri/samp.py
+ """
+
+ def __call__(
+ self,
+ shape: Union[Sequence[int], np.ndarray],
+ seed: Optional[Union[int, Tuple[int, ...]]] = None,
+ partial_fourier_percentage: Optional[float] = 0.0,
+ center_scale: Optional[float] = 0.02,
+ calib: Optional[Tuple[float, float]] = (0.0, 0.0),
+ crop_corner: bool = True,
+ max_attempts: int = 30,
+ tol: float = 0.3,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, int]:
+ """Calls :class:`Poisson2DMaskFunc`.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the mask to be created. The shape should have at least 3 dimensions. Same as the shape of the
+ input k-space data.
+ seed : int or tuple of ints, optional
+ Seed for the random number generator. Default is ``None``.
+ partial_fourier_percentage : float, optional
+ Percentage of the low-frequency columns to be retained. Default is ``0.0``.
+ center_scale : float, optional
+ For autocalibration purposes, data points near the k-space center will be fully sampled within an ellipse
+ of which the half-axes will be set to the given `center_scale` percentage of the fully sampled region.
+ Default is ``0.02``.
+ calib : Optional[Tuple[float, float]], optional
+ Defines the size of the calibration region, which is a square region in the center of k-space. The first
+ value defines the percentage of the center that is sampled, and the second value defines the size of the
+ calibration region in the center of k-space. Default is ``(0.0, 0.0)``.
+ crop_corner : bool, optional
+ If set to True, the center of the mask will be cropped to the size of the calibration region. Default is
+ ``True``.
+ max_attempts : int, optional
+ Maximum number of attempts to generate a mask with the desired acceleration factor. Default is ``30``.
+ tol : float, optional
+ Tolerance for the acceleration factor. Default is ``0.3``.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, int]
+ A tuple containing the mask and the acceleration factor.
+ The mask is a `torch.Tensor` of the same shape as the input shape.
+ The acceleration factor is an `int` that represents the number of samples taken in the k-space.
+ """
+ with temp_seed(self.rng, seed):
+ self.shape = tuple(shape[-3:-1])
+ self.center_scale = center_scale
+ _, self.acceleration = self.choose_acceleration()
+
+ ny, nx = self.shape
+ y, x = np.mgrid[:ny, :nx]
+ x = np.maximum(abs(x - nx / 2) - calib[-1] / 2, 0) # type: ignore
+ x /= x.max()
+ y = np.maximum(abs(y - ny / 2) - calib[-2] / 2, 0) # type: ignore
+ y /= y.max()
+
+ r = np.hypot(x, y)
+
+ slope_max = 40.0
+ slope_min = 0.0
+
+ d = max(nx, ny)
+
+ while slope_min < slope_max:
+ slope = (slope_max + slope_min) / 2
+ radius_x = np.clip((1 + r * slope) * nx / d, 1, None)
+ radius_y = np.clip((1 + r * slope) * ny / d, 1, None)
+
+ mask = self.generate_poisson_mask(nx, ny, max_attempts, radius_x, radius_y, calib)
+
+ if crop_corner:
+ mask *= r < 1
+
+ with np.errstate(divide="ignore", invalid="ignore"):
+ actual_acceleration = mask.size / np.sum(mask)
+
+ if abs(actual_acceleration - self.acceleration) < tol:
+ break
+ if actual_acceleration < self.acceleration:
+ slope_min = slope
+ else:
+ slope_max = slope
+
+ pattern1 = mask
+ pattern2 = self.centered_circle()
+ mask = np.logical_or(pattern1, pattern2)
+
+ if abs(actual_acceleration - self.acceleration) >= tol:
+ raise ValueError(f"Cannot generate mask to satisfy acceleration factor of {self.acceleration}.")
+
+ if partial_fourier_percentage != 0:
+ mask[:, : int(np.round(mask.shape[1] * partial_fourier_percentage))] = 0.0
+
+ mask = torch.from_numpy(mask.reshape(self.shape).astype(np.float32)).unsqueeze(0).unsqueeze(-1)
+
+ return mask, self.acceleration
+
+ def centered_circle(self) -> np.ndarray:
+ """Creates a boolean centered circle image using the center_scale as a radius.
+
+ Returns
+ -------
+ np.ndarray
+ A 2D array of type bool, where True values indicate the points inside the centered circle and False
+ values indicate the points outside the centered circle. The circle has its center at the center of the
+ input shape and its radius is determined by the `center_scale` attribute.
+ """
+ center_x = int((self.shape[0] - 1) / 2)
+ center_y = int((self.shape[1] - 1) / 2)
+
+ X, Y = np.indices(self.shape)
+ radius = int(self.shape[0] * self.center_scale)
+ radius_squared = radius**2
+ return ((X - center_x) ** 2 + (Y - center_y) ** 2) < radius_squared
+
+ @staticmethod
+ @nb.jit(nopython=True, cache=True)
+ def generate_poisson_mask(
+ nx: int,
+ ny: int,
+ max_attempts: int,
+ radius_x: np.ndarray,
+ radius_y: np.ndarray,
+ calib: Tuple[float, float],
+ ) -> np.ndarray:
+ """Generates a Poisson mask of shape `(ny, nx)` by placing points on the grid according to a Poisson
+ distribution.
+
+ Parameters
+ ----------
+ nx : int
+ The number of columns in the output mask.
+ ny : int
+ The number of rows in the output mask.
+ max_attempts : int
+ The maximum number of attempts to generate a mask with the desired acceleration factor.
+ radius_x : np.ndarray
+ An array of shape `(ny, nx)` representing the radius of the Poisson distribution in the x-direction.
+ radius_y : np.ndarray
+ An array of shape `(ny, nx)` representing the radius of the Poisson distribution in the y-direction.
+ calib : Tuple[float, float]
+ Defines the size of the calibration region. The calibration region is a square region in the center of
+ k-space. The first value defines the percentage of the center that is sampled. The second value defines
+ the size of the calibration region in the center of k-space.
+
+ Returns
+ -------
+ np.ndarray
+ A binary mask with points placed according to a Poisson distribution.
+ """
+ mask = np.zeros((ny, nx))
+
+ # Add calibration region
+ mask[
+ int(ny / 2 - calib[-2] / 2) : int(ny / 2 + calib[-2] / 2),
+ int(nx / 2 - calib[-1] / 2) : int(nx / 2 + calib[-1] / 2),
+ ] = 1
+
+ # initialize active list
+ pxs = np.empty(nx * ny, np.int32)
+ pys = np.empty(nx * ny, np.int32)
+ pxs[0] = np.random.randint(0, nx)
+ pys[0] = np.random.randint(0, ny)
+ num_actives = 1
+ while num_actives > 0:
+ i = np.random.randint(0, num_actives)
+ px = pxs[i]
+ py = pys[i]
+ rx = radius_x[py, px]
+ ry = radius_y[py, px]
+
+ # Attempt to generate point
+ done = False
+ k = 0
+ while not done and k < max_attempts:
+ # Generate point randomly from r and 2 * r
+ v = (np.random.random() * 3 + 1) ** 0.5
+ t = 2 * np.pi * np.random.random()
+ qx = px + v * rx * np.cos(t)
+ qy = py + v * ry * np.sin(t)
+
+ # Reject if outside grid or close to other points
+ if 0 <= qx < nx and 0 <= qy < ny:
+ startx = max(int(qx - rx), 0)
+ endx = min(int(qx + rx + 1), nx)
+ starty = max(int(qy - ry), 0)
+ endy = min(int(qy + ry + 1), ny)
+
+ done = True
+ for x in range(startx, endx):
+ for y in range(starty, endy):
+ if mask[y, x] == 1 and (
+ ((qx - x) / radius_x[y, x]) ** 2 + ((qy - y) / (radius_y[y, x])) ** 2 < 1
+ ):
+ done = False
+ break
+
+ k += 1
+
+ # Add point if done else remove from active list
+ if done:
+ pxs[num_actives] = qx
+ pys[num_actives] = qy
+ mask[int(qy), int(qx)] = 1
+ num_actives += 1
+ else:
+ pxs[i] = pxs[num_actives - 1]
+ pys[i] = pys[num_actives - 1]
+ num_actives -= 1
+
+ return mask
+
+
+class Random1DMaskFunc(MaskFunc):
+ r"""Random1DMaskFunc creates a sub-sampling mask of a given shape.
+
+ The mask selects a subset of columns from the input k-space data. If the k-space data has N columns, the mask
+ picks out:
+
+ 1. N_low_freqs = (N * center_fraction) columns in the center corresponding to low-frequencies.
+
+ 2. The other columns are selected uniformly at random with a probability equal to:
+ prob = (N / acceleration - N_low_freqs) / (N - N_low_freqs). This ensures that the expected number of columns
+ selected is equal to (N / acceleration).
+
+ It is possible to use multiple center_fractions and accelerations, in which case one possible (center_fraction,
+ acceleration) is chosen uniformly at random each time the Random1DMaskFunc object is called.
+
+ For example, if accelerations = [4, 8] and center_fractions = [0.08, 0.04], then there is a 50% probability that
+ 4-fold acceleration with 8% center fraction is selected and a 50% probability that 8-fold acceleration with 4%
+ center fraction is selected.
+ """
+
+ def __call__(
+ self,
+ shape: Sequence[int],
+ seed: Optional[Union[int, Tuple[int, ...]]] = None,
+ partial_fourier_percentage: Optional[float] = 0.0,
+ **kwargs,
+ ) -> Tuple[torch.Tensor, int]:
+ """Calls :class:`Random1DMaskFunc`.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the mask to be created. The shape should have at least 3 dimensions. Same as the shape of the
+ input k-space data.
+ seed : int or tuple of ints, optional
+ Seed for the random number generator. Default is ``None``.
+ partial_fourier_percentage : float, optional
+ Percentage of the low-frequency columns to be retained. Default is ``0.0``.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, int]
+ A tuple of the generated mask and the acceleration factor.
+
+ Raises
+ ------
+ ValueError
+ If the `shape` parameter has less than 3 dimensions.
+ """
+ if len(shape) < 3:
+ raise ValueError("Shape should have 3 or more dimensions")
+
+ with temp_seed(self.rng, seed):
+ num_cols = shape[-2]
+ center_fraction, acceleration = self.choose_acceleration()
+
+ # create the mask
+ num_low_freqs = int(round(num_cols * center_fraction))
+ prob = (num_cols / acceleration - num_low_freqs) / (num_cols - num_low_freqs)
+ mask = self.rng.uniform(size=num_cols) < prob
+ pad = torch.div((num_cols - num_low_freqs + 1), 2, rounding_mode="trunc").item()
+ mask[pad : pad + num_low_freqs] = True
+
+ # reshape the mask
+ mask_shape = [1 for _ in shape]
+ mask_shape[-2] = num_cols
+ mask = torch.from_numpy(mask.reshape(*mask_shape).astype(np.float32))
+
+ if partial_fourier_percentage != 0:
+ mask[:, : int(np.round(mask.shape[1] * partial_fourier_percentage))] = 0.0
+
+ return mask, acceleration
+
+
+def create_masker(
+ mask_type_str: str, center_fractions: Union[Sequence[float], float], accelerations: Union[Sequence[int], int]
+) -> MaskFunc:
+ """Creates a MaskFunc object based on the specified mask type.
+
+ Parameters
+ ----------
+ mask_type_str : str
+ The string representation of the mask type. Must be one of the following:
+ 'equispaced1d', 'equispaced2d', 'gaussian1d', 'gaussian2d', 'poisson2d', 'random1d'.
+ center_fractions : Sequence[float] or float
+ The center fractions for the mask.
+ accelerations : Sequence[int] or int
+ The accelerations for the mask.
+
+ Returns
+ -------
+ MaskFunc
+ A MaskFunc object that corresponds to the specified mask type.
+
+ Raises
+ ------
+ NotImplementedError
+ If the specified `mask_type_str` is not supported.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.data.subsample import create_masker
+ >>> create_masker("random1d", [0.5], [4])
+ Random1DMaskFunc([0.5], [4])
+ >>> create_masker("equispaced2d", [0.3, 0.7], [8, 6])
+ Equispaced2DMaskFunc([0.3, 0.7], [8, 6])
+ >>> create_masker("poisson2d", [0.3, 0.7], [8, 6])
+ Poisson2DMaskFunc([0.3, 0.7], [8, 6])
+ >>> create_masker("gaussian1d", [0.3, 0.7], [8, 6])
+ Gaussian1DMaskFunc([0.3, 0.7], [8, 6])
+ >>> create_masker("gaussian2d", [0.3, 0.7], [8, 6])
+ Gaussian2DMaskFunc([0.3, 0.7], [8, 6])
+ """
+ if isinstance(center_fractions, float):
+ center_fractions = [center_fractions]
+ if isinstance(accelerations, int):
+ accelerations = [accelerations]
+ if mask_type_str == "random1d":
+ return Random1DMaskFunc(center_fractions, accelerations)
+ if mask_type_str == "equispaced1d":
+ return Equispaced1DMaskFunc(center_fractions, accelerations)
+ if mask_type_str == "equispaced2d":
+ return Equispaced2DMaskFunc(center_fractions, accelerations)
+ if mask_type_str == "gaussian1d":
+ return Gaussian1DMaskFunc(center_fractions, accelerations)
+ if mask_type_str == "gaussian2d":
+ return Gaussian2DMaskFunc(center_fractions, accelerations)
+ if mask_type_str == "poisson2d":
+ return Poisson2DMaskFunc(center_fractions, accelerations)
+ raise NotImplementedError(f"{mask_type_str} not supported")
diff --git a/atommic/collections/common/losses/__init__.py b/atommic/collections/common/losses/__init__.py
new file mode 100644
index 00000000..bdf483a2
--- /dev/null
+++ b/atommic/collections/common/losses/__init__.py
@@ -0,0 +1,8 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.common.losses.aggregator import AggregatorLoss # noqa: F401
+from atommic.collections.common.losses.wasserstein import SinkhornDistance # noqa: F401
+
+VALID_RECONSTRUCTION_LOSSES = ["l1", "mse", "ssim", "noise_aware", "wasserstein"]
+VALID_SEGMENTATION_LOSSES = ["cross_entropy", "dice"]
diff --git a/atommic/collections/common/losses/aggregator.py b/atommic/collections/common/losses/aggregator.py
new file mode 100644
index 00000000..64f03d20
--- /dev/null
+++ b/atommic/collections/common/losses/aggregator.py
@@ -0,0 +1,65 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/losses/aggregator.py
+
+from typing import List
+
+import torch
+
+from atommic.core.classes.common import typecheck
+from atommic.core.classes.loss import Loss
+from atommic.core.neural_types.elements import LossType
+from atommic.core.neural_types.neural_type import NeuralType
+
+__all__ = ["AggregatorLoss"]
+
+
+class AggregatorLoss(Loss):
+ """Aggregates multiple losses into a single loss.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.losses.aggregator import AggregatorLoss
+ >>> loss = AggregatorLoss(num_inputs=2)
+ >>> loss(loss_1=torch.tensor(1.0), loss_2=torch.tensor(2.0))
+ tensor(3.)
+ """
+
+ @property
+ def input_types(self):
+ """Returns definitions of module input ports."""
+ return {f"loss_{str(i + 1)}": NeuralType(elements_type=LossType()) for i in range(self._num_losses)}
+
+ @property
+ def output_types(self):
+ """Returns definitions of module output ports."""
+ return {"loss": NeuralType(elements_type=LossType())}
+
+ def __init__(self, num_inputs: int = 2, weights: List[float] = None):
+ """Inits :class:`AggregatorLoss`.
+
+ Parameters
+ ----------
+ num_inputs : int
+ Number of losses to be summed.
+ weights : List[float]
+ Weights to be applied to each loss. If None, all losses are weighted equally.
+ """
+ super().__init__()
+ self._num_losses = num_inputs
+ if weights is not None and len(weights) != num_inputs:
+ raise ValueError("Length of weights should be equal to the number of inputs (num_inputs)")
+ self._weights = weights
+
+ @typecheck()
+ def forward(self, **kwargs):
+ """Computes the sum of the losses."""
+ values = [kwargs[x] for x in sorted(kwargs.keys())]
+ loss = torch.zeros_like(values[0])
+ for loss_idx, loss_value in enumerate(values):
+ if self._weights is not None:
+ loss = loss.add(loss_value, alpha=self._weights[loss_idx])
+ else:
+ loss = loss.add(loss_value)
+ return loss
diff --git a/atommic/collections/common/losses/wasserstein.py b/atommic/collections/common/losses/wasserstein.py
new file mode 100644
index 00000000..5f627d92
--- /dev/null
+++ b/atommic/collections/common/losses/wasserstein.py
@@ -0,0 +1,122 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from https://github.com/dfdazac/wassdistance/blob/master/layers.py
+
+import torch
+
+from atommic.core.classes.loss import Loss
+
+
+class SinkhornDistance(Loss):
+ r"""Given two empirical measures each with :math:`P_1` locations :math:`x\in\mathbb{R}^{D_1}` and :math:`P_2`
+ locations :math:`y\in\mathbb{R}^{D_2}`, outputs an approximation of the regularized OT cost for point clouds.
+ """
+
+ def __init__(self, eps=0.1, max_iter=100, reduction="mean"):
+ """Inits :class:`SinkhornDistance`.
+
+ Parameters
+ ----------
+ eps : float
+ Regularization coefficient. Default is ``0.1``.
+ max_iter : int
+ Maximum number of Sinkhorn iterations. Default is ``100``.
+ reduction : string, optional
+ Specifies the reduction to apply to the output:
+ 'none' | 'mean' | 'sum'. 'none': no reduction will be applied.
+ Default is ``mean``.
+ """
+ super().__init__()
+ self.eps = torch.tensor([eps])
+ self.max_iter = max_iter
+ self.reduction = reduction
+
+ def forward(self, x, y):
+ r"""Forward pass of the Sinkhorn algorithm.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ :math:`(N, P_1, D_1)`
+ y : torch.Tensor
+ :math:`(N, P_2, D_2)`
+
+ Returns
+ -------
+ torch.Tensor
+ The Sinkhorn distance between the two point clouds. Output shape :math:`(N)` or :math:`()`, depending on
+ `reduction`
+ """
+ self.eps = self.eps.clone().to(x.device)
+
+ # The Sinkhorn algorithm takes as input three variables :
+ C = self._cost_matrix(x, y) # Wasserstein cost function
+ x_points = x.shape[-2]
+ y_points = y.shape[-2]
+ if x.dim() == 2:
+ batch_size = 1
+ else:
+ batch_size = x.shape[0]
+
+ # both marginals are fixed with equal weights
+ mu = (
+ torch.empty(batch_size, x_points, dtype=torch.float, requires_grad=False)
+ .fill_(1.0 / x_points)
+ .squeeze()
+ .to(x.device)
+ )
+ nu = (
+ torch.empty(batch_size, y_points, dtype=torch.float, requires_grad=False)
+ .fill_(1.0 / y_points)
+ .squeeze()
+ .to(x.device)
+ )
+
+ u = torch.zeros_like(mu)
+ v = torch.zeros_like(nu)
+ # To check if algorithm terminates because of threshold or max iterations reached
+ actual_nits = 0
+ # Stopping criterion
+ thresh = 1e-1
+
+ # Sinkhorn iterations
+ for _ in range(self.max_iter):
+ u1 = u # useful to check the update
+ u = self.eps * (torch.log(mu + 1e-8) - torch.logsumexp(self.M(C, u, v), dim=-1)) + u
+ v = self.eps * (torch.log(nu + 1e-8) - torch.logsumexp(self.M(C, u, v).transpose(-2, -1), dim=-1)) + v
+ err = (u - u1).abs().sum(-1).mean()
+
+ actual_nits = actual_nits + 1
+ if err.item() < thresh:
+ break
+
+ U, V = u, v
+ # Transport plan pi = diag(a)*K*diag(b)
+ pi = torch.exp(self.M(C, U, V))
+ # Sinkhorn distance
+ cost = torch.sum(pi * C, dim=(-2, -1))
+
+ if self.reduction == "mean":
+ cost = cost.mean()
+ elif self.reduction == "sum":
+ cost = cost.sum()
+
+ return cost # , pi, C
+
+ def M(self, C, u, v):
+ r"""Modified cost for logarithmic updates $M_{ij} = (-c_{ij} + u_i + v_j) / \epsilon$"""
+ return (-C + u.unsqueeze(-1) + v.unsqueeze(-2)) / self.eps
+
+ @staticmethod
+ def _cost_matrix(x, y, p=2):
+ r"""Returns the matrix of $|x_i-y_j|^p$."""
+ x_col = x.unsqueeze(-2)
+ y_lin = y.unsqueeze(-3)
+ C = torch.sum((torch.abs(x_col - y_lin)) ** p, -1)
+ return C
+
+ @staticmethod
+ def ave(u, u1, tau):
+ r"""Barycenter subroutine, used by kinetic acceleration through extrapolation."""
+ return tau * u + (1 - tau) * u1
diff --git a/atommic/collections/common/metrics/__init__.py b/atommic/collections/common/metrics/__init__.py
new file mode 100644
index 00000000..f1362c27
--- /dev/null
+++ b/atommic/collections/common/metrics/__init__.py
@@ -0,0 +1,4 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.common.metrics.global_average_loss_metric import GlobalAverageLossMetric # noqa: F401
diff --git a/atommic/collections/common/metrics/global_average_loss_metric.py b/atommic/collections/common/metrics/global_average_loss_metric.py
new file mode 100644
index 00000000..f1ce3ba7
--- /dev/null
+++ b/atommic/collections/common/metrics/global_average_loss_metric.py
@@ -0,0 +1,80 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from:
+# https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/metrics/global_average_loss_metric.py
+
+import torch
+from torchmetrics import Metric
+
+__all__ = ["GlobalAverageLossMetric"]
+
+
+class GlobalAverageLossMetric(Metric):
+ """This class is for averaging loss across multiple processes if a distributed backend is used. True average is
+ computed not running average. It does not accumulate gradients so the averaged loss cannot be used for
+ optimization.
+
+ .. note::
+ If ``take_avg_loss`` is ``True``, the :meth:`update` method ``loss`` argument has to be a mean loss. If
+ ``take_avg_loss`` is ``False`` then the :meth:`update` method ``loss`` argument has to be a sum of losses. See
+ PyTorch Lightning Metrics for the metric usage instruction.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.metrics.global_average_loss_metric import GlobalAverageLossMetric
+ >>> metric = GlobalAverageLossMetric()
+ >>> metric.update(torch.tensor(1.0), torch.tensor(1))
+ >>> metric.update(torch.tensor(2.0), torch.tensor(1))
+ >>> metric.compute()
+ tensor(1.5000)
+ >>> metric.update(torch.tensor(3.0), torch.tensor(1))
+ >>> metric.compute()
+ tensor(2.0000)
+ """
+
+ full_state_update: bool = True
+
+ def __init__(self, dist_sync_on_step=False, process_group=None, take_avg_loss=True):
+ """Inits :class:`GlobalAverageLossMetric`.
+
+ Parameters
+ ----------
+ dist_sync_on_step : bool
+ Synchronize metric state across processes at each method :meth:`forward` call before returning the value at
+ the step. Default is ``False``.
+ process_group : Any, optional
+ Specify the process group on which synchronization is called. default: ``None`` (which selects the entire
+ world). Default is ``None``.
+ take_avg_loss : bool
+ If ``True`` values of :meth:`update` method ``loss`` argument has to be a mean loss. If ``False`` values of
+ :meth:`update` method ``loss`` argument has to be a sum of losses. Default is ``True``.
+ """
+ super().__init__(dist_sync_on_step=dist_sync_on_step, process_group=process_group)
+ self.add_state("loss_sum", torch.tensor(0.0, dtype=torch.float64), dist_reduce_fx='sum')
+ self.add_state("num_measurements", torch.tensor(0, dtype=torch.int64), dist_reduce_fx='sum')
+ self.take_avg_loss = take_avg_loss
+
+ def update(self, loss, num_measurements): # pylint: disable=arguments-differ
+ """Updates :attr:`loss_sum` and :attr:`num_measurements`.
+
+ Parameters
+ ----------
+ loss : torch.Tensor
+ A float zero dimensional ``torch.Tensor`` which is either sum or average of losses for processed examples.
+ See ``take_avg_loss`` parameter of :meth:`__init__`.
+ num_measurements : torch.Tensor
+ An integer zero dimensional ``torch.Tensor`` which contains a number of loss measurements. The sum or mean
+ of the results of these measurements are in the ``loss`` parameter.
+ """
+ if self.take_avg_loss:
+ self.loss_sum = self.loss_sum + loss.detach() * num_measurements
+ else:
+ self.loss_sum = self.loss_sum + loss.detach()
+ self.num_measurements = self.num_measurements + num_measurements
+
+ def compute(self):
+ """Returns mean loss."""
+ if self.num_measurements.eq(0):
+ return torch.tensor(float("nan"))
+ return self.loss_sum / self.num_measurements
diff --git a/atommic/collections/common/nn/__init__.py b/atommic/collections/common/nn/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/common/nn/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/common/nn/base.py b/atommic/collections/common/nn/base.py
new file mode 100644
index 00000000..a2579054
--- /dev/null
+++ b/atommic/collections/common/nn/base.py
@@ -0,0 +1,499 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from abc import ABC
+from typing import Dict, Optional, Sequence, Tuple
+
+import torch
+import wandb
+from omegaconf import DictConfig
+from pytorch_lightning import Trainer
+from torch import nn
+from torch.utils.data import DataLoader
+from torchmetrics.metric import Metric
+
+from atommic.collections.common.parts import utils
+from atommic.collections.common.parts.fft import ifft2
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.core.classes import modelPT
+from atommic.utils import model_utils
+
+wandb.require("service")
+
+__all__ = ["DistributedMetricSum", "BaseMRIModel", "BaseSensitivityModel"]
+
+
+# Taken and adapted from: https://github.com/facebookresearch/fastMRI/blob/main/fastmri/pl_modules/mri_module.py
+class DistributedMetricSum(Metric):
+ """A metric that sums the values of a metric across all workers.
+
+ Returns
+ -------
+ metric : torch.FloatTensor
+ The metric value.
+
+ Examples
+ --------
+ >>> metric = DistributedMetricSum()
+ >>> metric(torch.tensor(1.0))
+ >>> metric(torch.tensor(2.0))
+ >>> metric.compute()
+ tensor(3.)
+ """
+
+ full_state_update: bool = True
+
+ def __init__(self, dist_sync_on_step=True):
+ """Inits :class:`DistributedMetricSum`.
+
+ Parameters
+ ----------
+ dist_sync_on_step : bool
+ Synchronize metric state across processes at each ``forward()`` before returning the value at the step.
+ Default is ``True``.
+ """
+ super().__init__(dist_sync_on_step=dist_sync_on_step)
+ self.add_state("quantity", default=torch.tensor(0.0), dist_reduce_fx="sum")
+
+ # pylint: disable=arguments-differ
+ def update(self, batch: torch.Tensor):
+ """Update the metric with a batch of data."""
+ self.quantity += batch
+
+ def compute(self):
+ """Compute the metric value."""
+ return self.quantity
+
+
+class BaseMRIModel(modelPT.ModelPT, ABC):
+ """Base class for (any task performed on) MRI models."""
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`BaseMRIModel`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ The configuration file.
+ trainer : Trainer
+ The PyTorch Lightning trainer. Default is ``None``.
+ """
+ # Get global rank and total number of GPU workers for IterableDataset partitioning, if applicable
+ self.world_size = 1
+ if trainer is not None:
+ self.world_size = trainer.num_nodes * trainer.num_devices
+
+ cfg = model_utils.convert_model_config_to_dict_config(cfg)
+ cfg = model_utils.maybe_update_config_version(cfg)
+
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ # pylint: disable=arguments-differ
+ def training_step(self, batch: Dict[float, torch.Tensor], batch_idx: int) -> Dict[str, torch.Tensor]:
+ """Performs a training step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data.
+ batch_idx : int
+ Batch index.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary with the loss and the log.
+ """
+ raise NotImplementedError
+
+ def validation_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a validation step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data.
+ batch_idx : int
+ Batch index.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary with the loss and the log.
+ """
+ raise NotImplementedError
+
+ def test_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a test step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data.
+ batch_idx : int
+ Batch index.
+
+ Returns
+ -------
+ Tuple[str, int, torch.Tensor]
+ Tuple with the filename, the slice index and the prediction.
+ """
+ raise NotImplementedError
+
+ def log_image(self, name, image):
+ """Logs an image.
+
+ Parameters
+ ----------
+ name : str
+ Name of the image.
+ image : torch.Tensor
+ Image to log.
+ """
+ if image.dim() > 3:
+ image = image[0, 0, :, :].unsqueeze(0)
+ elif image.shape[0] != 1:
+ image = image.unsqueeze(0)
+
+ if ".h5" in name:
+ name = name.replace(".h5", "")
+
+ if "wandb" in self.logger.__module__.lower():
+ if image.is_cuda:
+ image = image.detach().cpu()
+ self.logger.experiment.log({name: wandb.Image(image.numpy())})
+
+ if "tensorboard" in self.logger.__module__.lower():
+ self.logger.experiment.add_image(name, image, global_step=self.global_step)
+
+ def on_validation_epoch_end(self):
+ """Called at the end of validation epoch to aggregate outputs."""
+ raise NotImplementedError
+
+ def on_test_epoch_end(self):
+ """Called at the end of test epoch to aggregate outputs and save predictions."""
+ raise NotImplementedError
+
+ def setup_training_data(self, train_data_config: Optional[DictConfig]):
+ """Setups the training data.
+
+ Parameters
+ ----------
+ train_data_config : Optional[DictConfig]
+ Training data configuration.
+
+ Returns
+ -------
+ train_data : torch.utils.data.DataLoader
+ Training data.
+ """
+ self._train_dl = self._setup_dataloader_from_config(cfg=train_data_config)
+
+ def setup_validation_data(self, val_data_config: Optional[DictConfig]):
+ """Setups the validation data.
+
+ Parameters
+ ----------
+ val_data_config : Optional[DictConfig]
+ Validation data configuration.
+
+ Returns
+ -------
+ val_data : torch.utils.data.DataLoader
+ Validation data.
+ """
+ self._validation_dl = self._setup_dataloader_from_config(cfg=val_data_config)
+
+ def setup_test_data(self, test_data_config: Optional[DictConfig]):
+ """Setups the test data.
+
+ Parameters
+ ----------
+ test_data_config : Optional[DictConfig]
+ Test data configuration.
+
+ Returns
+ -------
+ test_data : torch.utils.data.DataLoader
+ Test data.
+ """
+ self._test_dl = self._setup_dataloader_from_config(cfg=test_data_config)
+
+ @staticmethod
+ def _setup_dataloader_from_config(cfg: DictConfig) -> DataLoader:
+ """Setups the dataloader from the configuration (yaml) file.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration file.
+
+ Returns
+ -------
+ dataloader : torch.utils.data.DataLoader
+ Dataloader.
+ """
+ raise NotImplementedError
+
+
+class BaseSensitivityModel(nn.Module, ABC):
+ """Model for learning sensitivity estimation from k-space data [1]_. This model applies an IFFT to multichannel
+ k-space data and then a U-Net to the coil images to estimate coil sensitivities.
+
+ References
+ ----------
+ .. [1] Sriram A, Zbontar J, Murrell T, Defazio A, Zitnick CL, Yakubova N, Knoll F, Johnson P. End-to-end
+ variational networks for accelerated MRI reconstruction. InInternational Conference on Medical Image Computing
+ and Computer-Assisted Intervention 2020 Oct 4 (pp. 64-73). Springer, Cham.
+
+ Returns
+ -------
+ torch.Tensor
+ Estimated coil sensitivity maps.
+ """
+
+ def __init__(
+ self,
+ chans: int = 8,
+ num_pools: int = 4,
+ in_chans: int = 2,
+ out_chans: int = 2,
+ drop_prob: float = 0.0,
+ padding_size: int = 15,
+ mask_type: str = "2D",
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+ coil_dim: int = 1,
+ normalize: bool = True,
+ mask_center: bool = True,
+ ):
+ """Inits :class:`BaseSensitivityModel`.
+
+ Parameters
+ ----------
+ chans : int
+ Number of channels in the input k-space data. Default is ``8``.
+ num_pools : int
+ Number of U-Net downsampling/upsampling operations. Default is ``4``.
+ in_chans : int
+ Number of input channels to the U-Net. Default is ``2``.
+ out_chans : int
+ Number of output channels to the U-Net. Default is ``2``.
+ drop_prob : float
+ Dropout probability. Default is ``0.0``.
+ padding_size : int
+ Padding size for the U-Net. Default is ``15``.
+ mask_type : str
+ If kspace is undersampled, the undersampling mask type must be specified. Default is ``"2D"``.
+ fft_centered : bool
+ If ``True``, the input data is assumed to be centered. Default is ``False``.
+ fft_normalization : str
+ Normalization for the IFFT. Default is ``"backward"``.
+ spatial_dims : Sequence[int]
+ Spatial dimensions of the input data. Default is ``None``.
+ coil_dim : int
+ Dimension of the coils. Default is ``1``.
+ normalize : bool
+ If ``True``, the input data is normalized. Default is ``True``.
+ mask_center : bool
+ If ``True``, the center of the k-space is masked out. Default is ``True``.
+ """
+ super().__init__()
+
+ self.mask_type = mask_type
+
+ self.norm_unet = NormUnet(
+ chans,
+ num_pools,
+ in_chans=in_chans,
+ out_chans=out_chans,
+ drop_prob=drop_prob,
+ padding_size=padding_size,
+ normalize=normalize,
+ )
+
+ self.mask_center = mask_center
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+ self.normalize = normalize
+
+ @staticmethod
+ def chans_to_batch_dim(x: torch.Tensor) -> Tuple[torch.Tensor, int]:
+ """Combines the channel dimension with the batch dimension.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Tensor to stack the batch and channel dimensions.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, int]
+ Tuple of the converted tensor and the batch size.
+ """
+ batch_size, coils, height, width, complex_dim = x.shape
+ return x.view(batch_size * coils, 1, height, width, complex_dim), batch_size
+
+ @staticmethod
+ def batch_chans_to_chan_dim(x: torch.Tensor, batch_size: int) -> torch.Tensor:
+ """Splits the batch and channel dimensions into the channel dimension.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Tensor to split the batch and channel dimensions.
+ batch_size : int
+ Batch size.
+
+ Returns
+ -------
+ torch.Tensor
+ Converted tensor.
+ """
+ batch_size_coils, _, height, width, complex_dim = x.shape
+ coils = torch.div(batch_size_coils, batch_size, rounding_mode="trunc")
+ return x.view(batch_size, coils, height, width, complex_dim)
+
+ @staticmethod
+ def divide_root_sum_of_squares(x: torch.Tensor, coil_dim: int) -> torch.Tensor:
+ """Divide the input by the root of the sum of squares of the magnitude of each complex number.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor to divide.
+ coil_dim : int
+ Dimension of the coils.
+
+ Returns
+ -------
+ torch.Tensor
+ Normalized tensor by the root sum of squares.
+ """
+ return x / utils.rss_complex(x, dim=coil_dim).unsqueeze(-1).unsqueeze(coil_dim)
+
+ @staticmethod
+ def get_pad_and_num_low_freqs(
+ mask: torch.Tensor, num_low_frequencies: Optional[int] = None
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Get the padding to apply to the input to make it square and the number of low frequencies to keep.
+
+ Parameters
+ ----------
+ mask : torch.Tensor
+ Mask to use to determine the padding and number of low frequencies.
+ num_low_frequencies : Optional[int]
+ Number of low frequencies to keep. Default is ``None``.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, torch.Tensor]
+ Tuple of the padding to apply to the input and the number of low frequencies to keep.
+ """
+ if num_low_frequencies is None or num_low_frequencies == 0:
+ # get low frequency line locations and mask them out
+ squeezed_mask = mask[:, 0, 0, :, 0].to(torch.int8)
+ cent = torch.div(squeezed_mask.shape[1], 2, rounding_mode="trunc")
+ # running argmin returns the first non-zero
+ left = torch.argmin(squeezed_mask[:, :cent].flip(1), dim=1)
+ right = torch.argmin(squeezed_mask[:, cent:], dim=1)
+ num_low_frequencies_tensor = torch.max(
+ 2 * torch.min(left, right), torch.ones_like(left)
+ ) # force a symmetric center unless 1
+ else:
+ num_low_frequencies_tensor = num_low_frequencies * torch.ones(
+ mask.shape[0], dtype=mask.dtype, device=mask.device
+ )
+
+ pad = torch.div(mask.shape[-2] - num_low_frequencies_tensor + 1, 2, rounding_mode="trunc")
+
+ return pad, num_low_frequencies_tensor
+
+ def __call__(
+ self,
+ masked_kspace: torch.Tensor,
+ mask: torch.Tensor,
+ sensitivity_maps: Optional[torch.Tensor] = None,
+ num_low_frequencies: Optional[int] = None,
+ ) -> torch.Tensor:
+ """Calls :class:`BaseSensitivityModel`.
+
+ Parameters
+ ----------
+ masked_kspace : torch.Tensor
+ Subsampled k-space data of shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ sensitivity_maps : Optional[torch.Tensor]
+ Coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. Default is ``None``. If provided, the
+ sensitivity maps will be fine-tuned using a U-Net.
+ num_low_frequencies : Optional[int]
+ Number of low frequencies to keep. Default is ``None``.
+
+ Returns
+ -------
+ torch.Tensor
+ Estimated coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2].
+ """
+ return self.forward(masked_kspace, mask, sensitivity_maps, num_low_frequencies)
+
+ def forward(
+ self,
+ masked_kspace: torch.Tensor,
+ mask: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ num_low_frequencies: Optional[int] = None,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`BaseSensitivityModel`.
+
+ Parameters
+ ----------
+ masked_kspace : torch.Tensor
+ Subsampled k-space data of shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. If provided, the sensitivity maps will
+ be fine-tuned using a U-Net. If not provided, the sensitivity maps will be estimated from the input k-space
+ data. In the second case the sensitivity maps are just a torch.ones_like of the input k-space data, so the
+ mean of the sensitivity maps is 1. This is the default behavior of the model.
+ num_low_frequencies : Optional[int]
+ Number of low frequencies to keep. Default is ``None``.
+
+ Returns
+ -------
+ torch.Tensor
+ Estimated coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2].
+ """
+ if torch.mean(sensitivity_maps) != 1:
+ # If sensitivity maps are provided, fine-tune them using a U-Net.
+ initial_sensitivity_maps = sensitivity_maps.clone()
+ else:
+ if self.mask_center:
+ # get low frequency line locations and mask them out in the center of k-space
+ pad, num_low_freqs = self.get_pad_and_num_low_freqs(mask, num_low_frequencies)
+ masked_kspace = utils.batched_mask_center(
+ masked_kspace, pad, pad + num_low_freqs, mask_type=self.mask_type
+ )
+
+ initial_sensitivity_maps = ifft2(
+ masked_kspace,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ # stack batch and coil dimensions
+ initial_sensitivity_maps, batches = self.chans_to_batch_dim(initial_sensitivity_maps)
+
+ # estimate sensitivities
+ sensitivity_maps = self.batch_chans_to_chan_dim(self.norm_unet(initial_sensitivity_maps), batches)
+
+ # normalize
+ if self.normalize:
+ sensitivity_maps = self.divide_root_sum_of_squares(sensitivity_maps, self.coil_dim)
+
+ return sensitivity_maps
diff --git a/atommic/collections/common/parts/__init__.py b/atommic/collections/common/parts/__init__.py
new file mode 100644
index 00000000..7dfdc0d3
--- /dev/null
+++ b/atommic/collections/common/parts/__init__.py
@@ -0,0 +1,19 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.common.parts import fft # noqa: F401
+from atommic.collections.common.parts.transforms import ( # noqa: F401
+ N2R,
+ SSDU,
+ Composer,
+ Cropper,
+ EstimateCoilSensitivityMaps,
+ GeometricDecompositionCoilCompression,
+ Masker,
+ MRIDataTransforms,
+ NoisePreWhitening,
+ Normalizer,
+ SNREstimator,
+ ZeroFillingPadding,
+)
+from atommic.collections.common.parts.utils import * # noqa: F401
diff --git a/atommic/collections/common/parts/coil_sensitivity_maps.py b/atommic/collections/common/parts/coil_sensitivity_maps.py
new file mode 100644
index 00000000..8eb999e0
--- /dev/null
+++ b/atommic/collections/common/parts/coil_sensitivity_maps.py
@@ -0,0 +1,254 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/direct/algorithms/mri_algorithms.py
+
+from abc import ABC
+from typing import Callable, Optional, Sequence
+
+import numpy as np
+import torch
+
+from atommic.collections.common.parts.fft import ifft2
+from atommic.collections.common.parts.utils import crop_to_acs
+
+
+class MaximumEigenvaluePowerMethod(ABC):
+ """A class for solving the maximum eigenvalue problem using the Power Method.
+
+ The Power Method is an iterative algorithm that can be used to find the largest eigenvalue of a matrix. The
+ algorithm is initialized with a random vector and iteratively updates the vector by multiplying it with the matrix
+ and normalizing it. The algorithm converges to the eigenvector corresponding to the largest eigenvalue. The
+ largest eigenvalue is then estimated by taking the dot product of the eigenvector with the matrix.
+ """
+
+ def __init__(
+ self,
+ forward_operator: Callable,
+ norm_func: Optional[Callable] = None,
+ max_iter: int = 30,
+ ):
+ """Inits :class:`MaximumEigenvaluePowerMethod`.
+
+ Parameters
+ ----------
+ forward_operator : Callable
+ The forward operator for the problem.
+ norm_func : Callable, optional
+ An optional function for normalizing the eigenvector. Default is ``None``.
+ max_iter : int, optional
+ Maximum number of iterations to run the algorithm. Default is ``30``.
+ """
+ super().__init__()
+ self.forward_operator = forward_operator
+ self.norm_func = norm_func
+ self.max_iter = max_iter
+ self.iter = 0
+
+ def _update(self) -> None:
+ """Perform a single update step of the maximum eigenvalue guess and corresponding eigenvector."""
+ y = self.forward_operator(self.x) # type: ignore
+ if self.norm_func is None:
+ self.max_eig = (y * self.x.conj()).sum() / (self.x * self.x.conj()).sum() # type: ignore
+ else:
+ self.max_eig = self.norm_func(y)
+ self.x = y / self.max_eig
+
+ def _done(self) -> bool:
+ """Check if the algorithm is done."""
+ return self.iter >= self.max_iter
+
+ def _fit(self, x: torch.Tensor) -> None:
+ """Sets initial maximum eigenvector guess."""
+ self.x = x
+
+ def update(self) -> None:
+ """Update the algorithm's parameters and increment the iteration count."""
+ self._update()
+ self.iter += 1
+
+ def done(self) -> bool:
+ """Check if the algorithm has converged."""
+ return self._done()
+
+ def fit(self, *args, **kwargs) -> None:
+ """Fit the algorithm.
+
+ Parameters
+ ----------
+ *args : tuple
+ Tuple of arguments for `_fit` method.
+ **kwargs : dict
+ Keyword arguments for `_fit` method.
+ """
+ self._fit(*args, **kwargs)
+ while not self.done():
+ self.update()
+
+
+class EspiritCalibration(ABC, torch.nn.Module):
+ """Estimates sensitivity maps estimated with the ESPIRIT calibration method as described in [1]_.
+
+ We adapted code for ESPIRIT method adapted from [2]_.
+
+ References
+ ----------
+
+ .. [1] Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M. ESPIRiT--an eigenvalue
+ approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med. 2014 Mar;71(3):990-1001.
+ doi: 10.1002/mrm.24751. PMID: 23649942; PMCID: PMC4142121.
+ .. [2] https://github.com/mikgroup/sigpy/blob/1817ff849d34d7cbbbcb503a1b310e7d8f95c242/sigpy/mri/app.py#L388-L491
+ """
+
+ def __init__(
+ self,
+ threshold: float = 0.05,
+ kernel_size: int = 6,
+ crop: float = 0.95,
+ max_iter: int = 100,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = (-2, -1),
+ ):
+ """Inits :class:`EstimateSensitivityMap`.
+
+ Parameters
+ ----------
+ threshold: float, optional
+ Threshold for the calibration matrix. Default: 0.05.
+ kernel_size: int, optional
+ Kernel size for the calibration matrix. Default: 6.
+ crop: float, optional
+ Output eigenvalue cropping threshold. Default: 0.95.
+ max_iter: int, optional
+ Power method iterations. Default: 30.
+ fft_centered: bool, optional
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization: str, optional
+ Normalization to apply to the FFT. Default is ``"backward"``.
+ spatial_dims: Sequence[int], optional
+ Spatial dimensions of the input. Default is ``(-2, -1)``.
+ """
+ super().__init__()
+
+ self.threshold = threshold
+ self.kernel_size = kernel_size
+ self.crop = crop
+ self.max_iter = max_iter
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+
+ def calculate_sensitivity_map(self, acs_mask: torch.Tensor, kspace: torch.Tensor) -> torch.Tensor:
+ """Calculates sensitivity map given as input the `acs_mask` and the `k-space`.
+
+ Parameters
+ ----------
+ acs_mask : torch.Tensor
+ Autocalibration mask.
+ kspace : torch.Tensor
+ K-space.
+
+ Returns
+ -------
+ sensitivity_map : torch.Tensor
+ Coil sensitivity maps.
+ """
+ ndim = kspace.ndim - 2
+ spatial_size = kspace.shape[1:-1]
+
+ # Used in case the k-space is padded (e.g. for batches)
+ non_padded_dim = kspace.clone().sum(dim=tuple(range(1, kspace.ndim))).bool()
+
+ num_coils = non_padded_dim.sum()
+ acs_kspace_cropped = torch.view_as_complex(crop_to_acs(acs_mask.squeeze(), kspace[non_padded_dim]))
+
+ # Get calibration matrix.
+ calibration_matrix = (
+ torch.nn.functional.unfold(acs_kspace_cropped.unsqueeze(0), kernel_size=self.kernel_size, stride=1)
+ .transpose(1, 2)
+ .to(acs_kspace_cropped.device)
+ .reshape(
+ num_coils,
+ *(np.array(acs_kspace_cropped.shape[1:3]) - self.kernel_size + 1),
+ *([self.kernel_size] * ndim),
+ )
+ )
+ calibration_matrix = calibration_matrix.reshape(num_coils, -1, self.kernel_size**ndim)
+ calibration_matrix = calibration_matrix.permute(1, 0, 2)
+ calibration_matrix = calibration_matrix.reshape(-1, num_coils * self.kernel_size**ndim)
+
+ _, s, vh = torch.linalg.svd(calibration_matrix, full_matrices=True)
+ vh = torch.where(s > (self.threshold * s.max()), vh, torch.zeros_like(vh))
+
+ # Get kernels
+ num_kernels = vh.shape[0]
+ kernels = vh.reshape([num_kernels, num_coils] + [self.kernel_size] * ndim)
+
+ # Get covariance matrix in image domain
+ covariance = torch.zeros(
+ spatial_size[::-1] + (num_coils, num_coils),
+ dtype=kernels.dtype,
+ device=kernels.device,
+ )
+ for kernel in kernels:
+ pad_h, pad_w = (
+ spatial_size[0] - self.kernel_size,
+ spatial_size[1] - self.kernel_size,
+ )
+ pad = (pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2)
+ kernel_padded = torch.nn.functional.pad(kernel, pad)
+
+ img_kernel = ifft2(kernel_padded, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ img_kernel = torch.view_as_complex(img_kernel)
+
+ aH = img_kernel.permute(*torch.arange(img_kernel.ndim - 1, -1, -1)).unsqueeze(-1)
+ a = aH.transpose(-1, -2).conj()
+ covariance = covariance + aH @ a
+
+ covariance = covariance * (np.prod(spatial_size) / self.kernel_size**ndim)
+ sensitivity_map = torch.ones(
+ (*spatial_size[::-1], num_coils, 1),
+ dtype=kernels.dtype,
+ device=kernels.device,
+ )
+
+ def forward(x):
+ return covariance @ x
+
+ def normalize(x):
+ return (x.abs() ** 2).sum(dim=-2, keepdims=True) ** 0.5
+
+ power_method = MaximumEigenvaluePowerMethod(forward, max_iter=self.max_iter, norm_func=normalize)
+ power_method.fit(x=sensitivity_map)
+
+ temp_sensitivity_map = power_method.x.squeeze(-1)
+ temp_sensitivity_map = temp_sensitivity_map.permute(
+ *torch.arange(temp_sensitivity_map.ndim - 1, -1, -1)
+ ).squeeze(-1)
+ temp_sensitivity_map = temp_sensitivity_map * temp_sensitivity_map.conj() / temp_sensitivity_map.abs()
+
+ max_eig = power_method.max_eig.squeeze()
+ max_eig = max_eig.permute(*torch.arange(max_eig.ndim - 1, -1, -1))
+ temp_sensitivity_map = temp_sensitivity_map * (max_eig > self.crop)
+
+ sensitivity_map = torch.zeros_like(kspace, device=kspace.device, dtype=kspace.dtype)
+ sensitivity_map[non_padded_dim] = torch.view_as_real(temp_sensitivity_map)
+ return sensitivity_map
+
+ def forward(self, acs_mask: torch.Tensor, kspace: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`EspiritCalibration`.
+
+ Parameters
+ ----------
+ acs_mask : torch.Tensor
+ Autocalibration mask.
+ kspace : torch.Tensor
+ K-space.
+
+ Returns
+ -------
+ sensitivity_map : torch.Tensor
+ Coil sensitivity maps.
+ """
+ return self.calculate_sensitivity_map(acs_mask, kspace)
diff --git a/atommic/collections/common/parts/fft.py b/atommic/collections/common/parts/fft.py
new file mode 100644
index 00000000..18b4178b
--- /dev/null
+++ b/atommic/collections/common/parts/fft.py
@@ -0,0 +1,334 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import List, Sequence, Union
+
+import numpy as np
+import torch
+from omegaconf import ListConfig
+
+__all__ = ["fft2", "ifft2", "fftshift", "ifftshift"]
+
+
+def fft2(
+ x: torch.Tensor,
+ centered: bool = False,
+ normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+) -> torch.Tensor:
+ r"""Apply 2-dimensional Fast Fourier Transform.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Complex valued input data.
+ centered : bool
+ Whether to center the fft. If True, the fft will be shifted so that the zero frequency component is in the
+ center of the spectrum. Default is ``False``.
+ normalization : str
+ Normalization mode. For the forward transform (fft2()), these correspond to: \n
+ * ``forward`` - normalize by 1/n
+ * ``backward`` - no normalization
+ * ``ortho`` - normalize by 1/sqrt(n) (making the FFT orthonormal)
+
+ Where n = prod(s) is the logical FFT size.
+ Calling the backward transform (ifft2()) with the same normalization mode will apply an overall
+ normalization of 1/n between the two transforms. This is required to make ifft2() the exact inverse.
+ Default is ``backward`` (no normalization).
+ spatial_dims : Sequence[int]
+ Dimensions to apply the FFT. Default is the last two dimensions. If tensor is viewed as real, the last
+ dimension is assumed to be the complex dimension.
+
+ Returns
+ -------
+ torch.Tensor
+ The 2D FFT of the input.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.fft import fft2
+ >>> data = torch.randn(2, 3, 4, 5, 2)
+ >>> fft2(data).shape
+ torch.Size([2, 3, 4, 5, 2])
+ >>> fft2(data, centered=True, normalization="ortho", spatial_dims=[-3, -2]).shape
+ torch.Size([2, 3, 4, 5, 2])
+
+ .. note::
+ The PyTorch fft2 function does not support complex tensors. Therefore, the input is converted to a complex
+ tensor and then converted back to a real tensor. This is done by using the torch.view_as_complex and
+ torch.view_as_real functions. The input is assumed to be a real tensor with the last dimension being the
+ complex dimension.
+
+ The PyTorch fft2 function performs a separate fft, so fft2 is the same as fft(fft(data, dim=-2), dim=-1).
+
+ Source: https://pytorch.org/docs/stable/fft.html#torch.fft.fft2
+ """
+ if x.shape[-1] == 2:
+ x = torch.view_as_complex(x)
+
+ if spatial_dims is None:
+ spatial_dims = [-2, -1]
+ elif isinstance(spatial_dims, ListConfig):
+ spatial_dims = list(spatial_dims)
+
+ if centered:
+ x = ifftshift(x, dim=spatial_dims)
+
+ x = torch.fft.fft2(
+ x,
+ dim=spatial_dims,
+ norm=normalization if normalization.lower() != "none" else None,
+ )
+
+ if centered:
+ x = fftshift(x, dim=spatial_dims)
+
+ x = torch.view_as_real(x)
+
+ return x
+
+
+def ifft2(
+ x: torch.Tensor,
+ centered: bool = False,
+ normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+) -> torch.Tensor:
+ r"""Apply 2-dimensional Inverse Fast Fourier Transform.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Complex valued input data.
+ centered : bool
+ Whether to center the ifft. If True, the ifft will be shifted so that the zero frequency component is in the
+ center of the spectrum. Default is ``False``.
+ normalization : str
+ Normalization mode. For the backward transform (ifft2()), these correspond to: \n
+ * ``forward`` - normalize by 1/n
+ * ``backward`` - no normalization
+ * ``ortho`` - normalize by 1/sqrt(n) (making the IFFT orthonormal)
+
+ Where n = prod(s) is the logical IFFT size.
+ Calling the forward transform (fft2()) with the same normalization mode will apply an overall
+ normalization of 1/n between the two transforms. This is required to make fft2() the exact inverse.
+ Default is ``backward`` (no normalization).
+ spatial_dims : Sequence[int]
+ Dimensions to apply the IFFT. Default is the last two dimensions. If tensor is viewed as real, the last
+ dimension is assumed to be the complex dimension.
+
+ Returns
+ -------
+ torch.Tensor
+ The 2D IFFT of the input.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.fft import ifft2
+ >>> data = torch.randn(2, 3, 4, 5, 2)
+ >>> ifft2(data).shape
+ torch.Size([2, 3, 4, 5, 2])
+ >>> ifft2(data, centered=True, normalization="ortho", spatial_dims=[-3, -2]).shape
+ torch.Size([2, 3, 4, 5, 2])
+
+ .. note::
+ The PyTorch ifft2 function does not support complex tensors. Therefore, the input is converted to a complex
+ tensor and then converted back to a real tensor. This is done by using the torch.view_as_complex and
+ torch.view_as_real functions. The input is assumed to be a real tensor with the last dimension being the
+ complex dimension.
+
+ The PyTorch ifft2 function performs a separate ifft, so ifft2 is the same as ifft(ifft(data, dim=-2), dim=-1).
+
+ Source: https://pytorch.org/docs/stable/fft.html#torch.fft.ifft2
+ """
+ if x.shape[-1] == 2:
+ x = torch.view_as_complex(x)
+
+ if spatial_dims is None:
+ spatial_dims = [-2, -1]
+ elif isinstance(spatial_dims, ListConfig):
+ spatial_dims = list(spatial_dims)
+
+ if centered:
+ x = ifftshift(x, dim=spatial_dims)
+
+ x = torch.fft.ifft2(
+ x,
+ dim=spatial_dims,
+ norm=normalization if normalization.lower() != "none" else None,
+ )
+
+ if centered:
+ x = fftshift(x, dim=spatial_dims)
+
+ x = torch.view_as_real(x)
+
+ return x
+
+
+def roll_one_dim(x: torch.Tensor, shift: int, dim: int) -> torch.Tensor:
+ """Similar to roll but for only one dim.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ shift : int
+ Amount to roll.
+ dim : int
+ Which dimension to roll.
+
+ Returns
+ -------
+ torch.Tensor
+ The rolled tensor.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.fft import roll_one_dim
+ >>> data = torch.randn(2, 3, 4, 5)
+ >>> roll_one_dim(data, 1, 0).shape
+ torch.Size([2, 3, 4, 5])
+
+ .. note::
+ Source: https://github.com/facebookresearch/fastMRI/blob/main/fastmri/fftc.py
+ """
+ shift %= x.size(dim)
+ if shift == 0:
+ return x
+
+ left = x.narrow(dim, 0, x.size(dim) - shift)
+ right = x.narrow(dim, x.size(dim) - shift, shift)
+
+ return torch.cat((right, left), dim=dim)
+
+
+def roll(x: torch.Tensor, shift: List[int], dim: Union[List[int], Sequence[int]]) -> torch.Tensor:
+ """Similar to np.roll but applies to PyTorch Tensors.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ shift : List[int]
+ Amount to roll.
+ dim : Union[List[int], Sequence[int]]
+ Which dimension to roll.
+
+ Returns
+ -------
+ torch.Tensor
+ The rolled tensor.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.fft import roll
+ >>> data = torch.randn(2, 3, 4, 5)
+ >>> roll(data, [1, 2], [0, 1]).shape
+ torch.Size([2, 3, 4, 5])
+
+ .. note::
+ Source: https://github.com/facebookresearch/fastMRI/blob/main/fastmri/fftc.py
+ """
+ if len(shift) != len(dim):
+ raise ValueError("len(shift) must match len(dim)")
+
+ if isinstance(dim, ListConfig):
+ dim = list(dim)
+
+ for s, d in zip(shift, dim):
+ x = roll_one_dim(x, s, d)
+
+ return x
+
+
+def fftshift(x: torch.Tensor, dim: Union[List[int], Sequence[int]] = None) -> torch.Tensor:
+ """Similar to np.fft.fftshift but applies to PyTorch Tensors.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ dim : Union[List[int], Sequence[int]]
+ Which dimension to shift.
+
+ Returns
+ -------
+ torch.Tensor
+ The shifted tensor.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.fft import fftshift
+ >>> data = torch.randn(2, 3, 4, 5)
+ >>> fftshift(data).shape
+ torch.Size([2, 3, 4, 5])
+
+ .. note::
+ Source: https://github.com/facebookresearch/fastMRI/blob/main/fastmri/fftc.py
+ """
+ if dim is None:
+ # this weird code is necessary for torch.jit.script typing
+ dim = [0] * (x.dim())
+ for i in range(1, x.dim()):
+ dim[i] = i
+ elif isinstance(dim, ListConfig):
+ dim = list(dim)
+ elif not isinstance(dim, list):
+ dim = [dim] # type: ignore
+
+ # Also necessary for torch.jit.script
+ shift = [0] * len(dim)
+ for i, dim_num in enumerate(dim):
+ shift[i] = np.floor_divide(x.shape[dim_num], 2)
+
+ return roll(x, shift, dim)
+
+
+def ifftshift(x: torch.Tensor, dim: Union[List[int], Sequence[int]] = None) -> torch.Tensor:
+ """Similar to np.fft.ifftshift but applies to PyTorch Tensors.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ dim : Union[List[int], Sequence[int]]
+ Which dimension to shift.
+
+ Returns
+ -------
+ torch.Tensor
+ The shifted tensor.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.fft import ifftshift
+ >>> data = torch.randn(2, 3, 4, 5)
+ >>> ifftshift(data).shape
+ torch.Size([2, 3, 4, 5])
+
+ .. note::
+ Source: https://github.com/facebookresearch/fastMRI/blob/main/fastmri/fftc.py
+ """
+ if dim is None:
+ # this weird code is necessary for torch.jit.script typing
+ dim = [0] * (x.dim())
+ for i in range(1, x.dim()):
+ dim[i] = i
+ elif isinstance(dim, ListConfig):
+ dim = list(dim)
+ elif not isinstance(dim, list):
+ dim = [dim] # type: ignore
+
+ # Also necessary for torch.jit.script
+ shift = [0] * len(dim)
+ for i, dim_num in enumerate(dim):
+ shift[i] = np.floor_divide(x.shape[dim_num] + 1, 2)
+
+ return roll(x, shift, dim)
diff --git a/atommic/collections/common/parts/patch_utils.py b/atommic/collections/common/parts/patch_utils.py
new file mode 100644
index 00000000..1fc0883e
--- /dev/null
+++ b/atommic/collections/common/parts/patch_utils.py
@@ -0,0 +1,10 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/collections/common/parts/patch_utils.py
+
+from packaging import version
+
+# Library version globals
+TORCH_VERSION = None
+TORCH_VERSION_MIN = version.Version("1.12.0")
diff --git a/atommic/collections/common/parts/transforms.py b/atommic/collections/common/parts/transforms.py
new file mode 100644
index 00000000..3bb2947c
--- /dev/null
+++ b/atommic/collections/common/parts/transforms.py
@@ -0,0 +1,3266 @@
+# coding=utf-8
+from __future__ import annotations
+
+__author__ = "Dimitris Karkalousos"
+
+import os
+from math import sqrt
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
+
+import numpy as np
+import torch
+
+from atommic.collections.common.parts.coil_sensitivity_maps import EspiritCalibration
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import add_coil_dim_if_singlecoil, apply_mask, center_crop
+from atommic.collections.common.parts.utils import coil_combination_method as coil_combination_method_func
+from atommic.collections.common.parts.utils import is_none, reshape_fortran, rss, to_tensor
+from atommic.collections.motioncorrection.parts.motionsimulation import MotionSimulation
+
+__all__ = [
+ "Composer",
+ "Cropper",
+ "EstimateCoilSensitivityMaps",
+ "GeometricDecompositionCoilCompression",
+ "Masker",
+ "MRIDataTransforms",
+ "N2R",
+ "NoisePreWhitening",
+ "Normalizer",
+ "SNREstimator",
+ "SSDU",
+ "ZeroFillingPadding",
+]
+
+
+class Composer:
+ """Composes multiple transforms together.
+
+ Returns
+ -------
+ composed_data: torch.Tensor
+ Composed data.
+
+ Example
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.transforms import Composer, Masker, Normalizer
+ >>> data = torch.randn(1, 32, 320, 320, 2) 1j * torch.randn(1, 32, 320, 320, 2)
+ >>> print(torch.min(torch.abs(data)), torch.max(torch.abs(data)))
+ tensor(1e-06) tensor(1.4142)
+ >>> masker = Masker(mask_func="random", padding="reflection", seed=0)
+ >>> normalizer = Normalizer(normalization_type="max")
+ >>> composer = Composer([masker, normalizer])
+ >>> composed_data = composer(data)
+ >>> print(torch.min(torch.abs(composed_data)), torch.max(torch.abs(composed_data)))
+ tensor(0.) tensor(1.)
+ """
+
+ def __init__(self, transforms: Union[List[Callable], Callable, None]):
+ """Inits :class:`Composer`.
+
+ Parameters
+ ----------
+ transforms: list
+ List of transforms to compose.
+ """
+ self.transforms = transforms
+
+ def __call__(
+ self,
+ data: Union[torch.Tensor, List[torch.Tensor], None],
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> List[torch.Tensor] | torch.Tensor:
+ """Calls :class:`Composer`."""
+ for transform in self.transforms: # type: ignore
+ if not is_none(transform):
+ data = transform(data, apply_backward_transform, apply_forward_transform)
+ return data
+
+ def __repr__(self):
+ """Representation of :class:`Composer`."""
+ return f"Composed transforms: {self.transforms}"
+
+ def __str__(self):
+ """String representation of :class:`Composer`."""
+ return self.__repr__()
+
+
+class Cropper:
+ """Crops data to a given size.
+
+ Returns
+ -------
+ cropped_data : torch.Tensor
+ Cropped data.
+
+ Example
+ -------
+ >>> import torch
+ >>> from atommic.collections.common.parts.transforms import Cropper
+ >>> data = torch.randn(1, 15, 320, 320, 2)
+ >>> cropping = Cropper(cropping_size=(256, 256), spatial_dims=(-2, -1)) # don't account for complex dim
+ >>> cropped_data = cropping(data)
+ >>> cropped_data.shape
+ [1, 15, 256, 256, 2]
+ """
+
+ def __init__(
+ self,
+ cropping_size: Tuple,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = (-2, -1),
+ ):
+ """Inits :class:`Cropper`.
+
+ Parameters
+ ----------
+ cropping_size : tuple
+ Size of the cropped data.
+ fft_centered : bool
+ If True, the input is assumed to be centered in the frequency domain. Default is `False`.
+ fft_normalization : str
+ Normalization of the FFT. Default is `backward`.
+ spatial_dims : tuple
+ Spatial dimensions.
+ """
+ self.cropping_size = cropping_size
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+
+ def __call__(
+ self,
+ data: Union[torch.Tensor, List[torch.Tensor], None],
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> List[torch.Tensor] | torch.Tensor:
+ """Calls :class:`Cropper`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to crop.
+ apply_backward_transform : bool
+ Apply backward transform, i.e. Inverse Fast Fourier Transform. Default is ``False``.
+ apply_forward_transform : bool
+ Apply forward transform, i.e. Fast Fourier Transform. Default is ``False``.
+ """
+ if not is_none(data):
+ if isinstance(data, list) and len(data) > 0:
+ return [self.forward(d, apply_backward_transform, apply_forward_transform) for d in data]
+ if data.dim() > 1 and data.mean() != 1: # type: ignore
+ return self.forward(data, apply_backward_transform, apply_forward_transform)
+ return data
+
+ def __repr__(self):
+ """Representation of :class:`Cropper`."""
+ return f"Data will be cropped to size={self.cropping_size}."
+
+ def __str__(self):
+ """String representation of :class:`Cropper`."""
+ return self.__repr__()
+
+ def forward(
+ self,
+ data: torch.Tensor,
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`Cropper`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to crop.
+ apply_backward_transform : bool
+ Apply backward transform, i.e. Inverse Fast Fourier Transform. Default is ``False``.
+ apply_forward_transform : bool
+ Apply forward transform, i.e. Fast Fourier Transform. Default is ``False``.
+ """
+ if apply_backward_transform:
+ data = ifft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ elif apply_forward_transform:
+ data = fft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ is_complex = data.shape[-1] == 2
+ is_one = data.shape[-1] == 1
+ if is_complex:
+ data = torch.view_as_complex(data)
+ elif is_one:
+ data = data.squeeze(-1)
+
+ crop_size = (data.shape[self.spatial_dims[0]], data.shape[self.spatial_dims[1]])
+
+ # Check for smallest size against the target shape.
+ h = min(int(self.cropping_size[0]), crop_size[0])
+ w = min(int(self.cropping_size[1]), crop_size[1])
+
+ # Check for smallest size against the stored recon shape in data.
+ if crop_size[0] != 0:
+ h = h if h <= crop_size[0] else crop_size[0]
+ if crop_size[1] != 0:
+ w = w if w <= crop_size[1] else crop_size[1]
+
+ data = center_crop(data, (int(h), int(w)))
+
+ if is_complex:
+ data = torch.view_as_real(data)
+ elif is_one:
+ data = data.unsqueeze(-1)
+
+ if apply_backward_transform:
+ data = fft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ elif apply_forward_transform:
+ data = ifft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ return data
+
+
+class EstimateCoilSensitivityMaps:
+ r"""Data Transformer for training MRI reconstruction models.
+
+ Estimates sensitivity maps given masked k-space data using one of three methods:
+
+ * Unit: unit sensitivity map in case of single coil acquisition.
+ * RSS-estimate: sensitivity maps estimated by using the root-sum-of-squares of the autocalibration-signal.
+ * ESPIRIT: sensitivity maps estimated with the ESPIRIT method [Uecker2014]_.
+
+ References
+ ----------
+ .. [Uecker2014] Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M. ESPIRiT--an
+ eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med. 2014
+ Mar;71(3):990-1001. doi: 10.1002/mrm.24751. PMID: 23649942; PMCID: PMC4142121.
+
+ """
+
+ def __init__(
+ self,
+ coil_sensitivity_maps_type: str = "espirit",
+ gaussian_sigma: Optional[float] = None,
+ espirit_threshold: float = 0.05,
+ espirit_kernel_size: int = 6,
+ espirit_crop: float = 0.95,
+ espirit_max_iters: int = 30,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = (-2, -1),
+ coil_dim: int = 1,
+ ) -> None:
+ """Inits :class:`EstimateSensitivityMapModule`.
+
+ Parameters
+ ----------
+ type: str
+ Type of sensitivity map to estimate. One of "unit", "rss", "espirit". Default is ``"espirit"``.
+ gaussian_sigma: float, optional
+ If non-zero, acs_image well be calculated
+ espirit_threshold: float
+ Threshold for the calibration matrix when `type`=="espirit". Default: 0.05.
+ espirit_kernel_size: int
+ Kernel size for the calibration matrix when `type`=="espirit". Default: 6.
+ espirit_crop: float
+ Output eigenvalue cropping threshold when `type`=="espirit". Default: 0.95.
+ espirit_max_iters: int
+ Power method iterations when `type`=="espirit". Default: 30.
+ fft_centered: bool
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization: str
+ Normalization to apply to the FFT. Default is ``"backward"``.
+ spatial_dims: Sequence[int]
+ Spatial dimensions of the input. Default is ``(-2, -1)``.
+ coil_dim: int
+ Dimension corresponding to coil. Default: 1.
+ """
+ super().__init__()
+ self.coil_sensitivity_maps_type = coil_sensitivity_maps_type
+ if self.coil_sensitivity_maps_type not in ["unit", "rss", "espirit"]:
+ raise ValueError(
+ f"Expected type of map to be either `unit`, `rss`, `espirit`. Got {self.coil_sensitivity_maps_type}."
+ )
+
+ self.gaussian_sigma = gaussian_sigma
+ self.espirit_threshold = espirit_threshold
+ self.espirit_kernel_size = espirit_kernel_size
+ self.espirit_crop = espirit_crop
+ self.espirit_max_iters = espirit_max_iters
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+ self.coil_dim = coil_dim
+
+ # Espirit attributes
+ if self.coil_sensitivity_maps_type == "espirit":
+ self.espirit_calibrator = EspiritCalibration(
+ self.espirit_threshold,
+ self.espirit_kernel_size,
+ self.espirit_crop,
+ self.espirit_max_iters,
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ )
+
+ def calculate_acs_mask(self, kspace: torch.Tensor) -> torch.Tensor:
+ """Calculates the autocalibration (ACS) mask.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ K-space.
+ Returns
+ -------
+ acs_mask: torch.Tensor
+ Autocalibration mask.
+ """
+ # size of k-space
+ Nx = kspace.shape[-3]
+ Ny = kspace.shape[-2]
+
+ # create an empty mask
+ acs_mask = torch.zeros((Nx, Ny))
+
+ # define the indices for the center region
+ acs_start_x = int((Nx - self.espirit_kernel_size) / 2)
+ acs_start_y = int((Ny - self.espirit_kernel_size) / 2)
+
+ acs_end_x = int((Nx + self.espirit_kernel_size) / 2)
+ acs_end_y = int((Ny + self.espirit_kernel_size) / 2)
+
+ # set the center region to 1
+ acs_mask[acs_start_x:acs_end_x, acs_start_y:acs_end_y] = 1
+
+ # reshape acs_mask to kspace
+ acs_mask = acs_mask.unsqueeze(0).unsqueeze(-1)
+
+ return acs_mask
+
+ def estimate_acs_image(self, acs_mask: torch.Tensor, kspace: torch.Tensor, width_dim: int = -2) -> torch.Tensor:
+ """Estimates the autocalibration (ACS) image by sampling the k-space using the ACS mask.
+
+ Parameters
+ ----------
+ acs_mask : torch.Tensor
+ Autocalibration mask.
+ kspace : torch.Tensor
+ K-space.
+ width_dim: int
+ Dimension corresponding to width. Default: -2.
+
+ Returns
+ -------
+ acs_image: torch.Tensor
+ Estimate of the ACS image.
+ """
+ if self.gaussian_sigma == 0 or not self.gaussian_sigma:
+ kspace_acs = kspace * acs_mask + 0.0 # + 0.0 removes the sign of zeros.
+ else:
+ gaussian_mask = torch.linspace(-1, 1, kspace.size(width_dim), dtype=kspace.dtype)
+ gaussian_mask = torch.exp(-((gaussian_mask / self.gaussian_sigma) ** 2))
+ gaussian_mask_shape = torch.ones(len(kspace.shape)).int()
+ gaussian_mask_shape[width_dim] = kspace.size(width_dim)
+ gaussian_mask = gaussian_mask.reshape(tuple(gaussian_mask_shape))
+ kspace_acs = kspace * acs_mask * gaussian_mask + 0.0
+
+ # Get complex-valued data solution
+ # Shape (batch, coil, height, width, complex=2)
+ acs_image = ifft2(kspace_acs, self.fft_centered, self.fft_normalization, self.spatial_dims)
+
+ return acs_image
+
+ def __call__(self, kspace: torch.Tensor) -> torch.Tensor:
+ """Estimates sensitivity maps for the input sample."""
+ return self.forward(kspace)
+
+ def __repr__(self) -> str:
+ """Representation of :class:`EstimateCoilSensitivityMaps`."""
+ return f"Estimating coil sensitivity maps of {self.coil_sensitivity_maps_type} type."
+
+ def __str__(self) -> str:
+ """String representation of :class:`EstimateCoilSensitivityMaps`."""
+ return self.__repr__()
+
+ def forward(self, kspace: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`EstimateCoilSensitivityMaps`."""
+ acs_mask = self.calculate_acs_mask(kspace)
+
+ if self.coil_sensitivity_maps_type == "unit":
+ sensitivity_map = torch.zeros(kspace.shape).float()
+ # Assumes complex channel is last
+ # assert_complex(kspace, complex_last=True)
+ sensitivity_map[..., 0] = 1.0
+ # Shape (coil, height, width, complex=2)
+ sensitivity_map = sensitivity_map.to(kspace.device)
+ elif self.coil_sensitivity_maps_type == "rss":
+ # Shape (batch, coil, height, width, complex=2)
+ acs_image = self.estimate_acs_image(acs_mask, kspace)
+ # Shape (batch, height, width)
+ acs_image_rss = rss(acs_image, dim=self.coil_dim)
+ # Shape (batch, 1, height, width, 1)
+ acs_image_rss = acs_image_rss.unsqueeze(self.coil_dim)
+ # Shape (batch, coil, height, width, complex=2)
+ sensitivity_map = torch.where(
+ acs_image_rss == 0,
+ torch.tensor([0.0], dtype=acs_image.dtype).to(acs_image.device),
+ acs_image / acs_image_rss,
+ )
+ else:
+ sensitivity_map = self.espirit_calibrator(acs_mask, kspace)
+
+ sensitivity_map_norm = torch.sqrt((sensitivity_map**2).sum(-1).sum(self.coil_dim))
+ sensitivity_map_norm = sensitivity_map_norm.unsqueeze(self.coil_dim).unsqueeze(-1)
+
+ sensitivity_map = torch.where(
+ sensitivity_map_norm == 0,
+ torch.tensor([0.0], dtype=sensitivity_map.dtype).to(sensitivity_map.device),
+ sensitivity_map / sensitivity_map_norm,
+ )
+
+ return sensitivity_map
+
+
+class GeometricDecompositionCoilCompression:
+ """Geometric Decomposition Coil Compression in PyTorch, as presented in [Zhang2013]_.
+
+ References
+ ----------
+ .. [Zhang2013] Zhang, T., Pauly, J. M., Vasanawala, S. S., & Lustig, M. (2013). Coil compression for accelerated
+ imaging with Cartesian sampling. Magnetic Resonance in Medicine, 69(2), 571โ582.
+ https://doi.org/10.1002/mrm.24267
+
+ Returns
+ -------
+ torch.Tensor
+ Coil compressed data.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.transforms import GeometricDecompositionCoilCompression
+ >>> data = torch.randn([30, 100, 100], dtype=torch.complex64)
+ >>> gdcc = GeometricDecompositionCoilCompression(virtual_coils=10, calib_lines=24, spatial_dims=[-2, -1])
+ >>> gdcc(data).shape
+ torch.Size([10, 100, 100, 2])
+ """
+
+ def __init__(
+ self,
+ virtual_coils: int = None,
+ calib_lines: int = None,
+ align_data: bool = True,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = (-2, -1),
+ ):
+ """Inits :class:`GeometricDecompositionCoilCompression`.
+
+ Parameters
+ ----------
+ virtual_coils : int
+ Number of final-"virtual" coils.
+ calib_lines : int
+ Calibration lines to sample data points.
+ align_data : bool
+ Align data to the first calibration line. Default is ``True``.
+ fft_centered : bool
+ Whether to center the fft. Default is ``False``.
+ fft_normalization : str
+ FFT normalization. Default is ``"backward"``.
+ spatial_dims : Sequence[int]
+ Dimensions to apply the FFT. Default is ``None``.
+ """
+ super().__init__()
+ # TODO: account for multiple echo times
+ self.virtual_coils = virtual_coils
+ self.calib_lines = calib_lines
+ self.align_data = align_data
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+
+ def __call__(
+ self,
+ data: Union[torch.Tensor, None],
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> torch.Tensor:
+ """Calls :class:`GeometricDecompositionCoilCompression`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to apply coil compression.
+ apply_backward_transform : bool
+ Apply backward transform. Default is ``False``.
+ apply_forward_transform : bool
+ Apply forward transform. Default is ``False``.
+ """
+ if not is_none(data) and data.dim() > 1 and data.mean() != 1: # type: ignore
+ return self.forward(data, apply_backward_transform, apply_forward_transform)
+ return data
+
+ def __repr__(self):
+ """Representation of :class:`GeometricDecompositionCoilCompression`."""
+ return f"Coil Compression is applied reducing coils to {self.virtual_coils}."
+
+ def __str__(self):
+ """String representation of :class:`GeometricDecompositionCoilCompression`."""
+ return str(self.__repr__)
+
+ # pylint: disable=unused-argument
+ def forward(
+ self,
+ data: torch.Tensor,
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`GeometricDecompositionCoilCompression`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to apply coil compression.
+ apply_backward_transform : bool
+ Apply backward transform. Default is ``False``.
+ apply_forward_transform : bool
+ Apply forward transform. Default is ``False``.
+
+ Returns
+ -------
+ torch.Tensor
+ Coil compressed data.
+ """
+ if not self.virtual_coils:
+ raise ValueError("Number of virtual coils must be defined for geometric decomposition coil compression.")
+
+ if apply_forward_transform:
+ data = fft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ self.data = data
+ if self.data.shape[-1] == 2:
+ self.data = torch.view_as_complex(self.data)
+
+ curr_num_coils = self.data.shape[0]
+ if curr_num_coils < self.virtual_coils:
+ raise ValueError(
+ f"Tried to compress from {curr_num_coils} to {self.virtual_coils} coils, please select less coils."
+ )
+
+ self.data = self.data.permute(1, 2, 0)
+ self.init_data: torch.Tensor = self.data
+ self.fft_dim = [0, 1]
+
+ _, self.width, self.coils = self.data.shape
+
+ # TODO: figure out why this is happening for singlecoil data
+ # For singlecoil data, use no calibration lines equal to the no of coils.
+ if self.virtual_coils == 1:
+ self.calib_lines = self.data.shape[-1]
+
+ self.crop()
+ self.calculate_gcc()
+ if self.align_data:
+ self.align_compressed_coils()
+ rotated_compressed_data = self.rotate_and_compress(data_to_cc=self.aligned_data)
+ else:
+ rotated_compressed_data = self.rotate_and_compress(data_to_cc=self.unaligned_data)
+
+ rotated_compressed_data = torch.flip(rotated_compressed_data, dims=[1])
+ rotated_compressed_data = torch.view_as_real(rotated_compressed_data.permute(2, 0, 1))
+
+ if not apply_forward_transform:
+ rotated_compressed_data = fft2(
+ rotated_compressed_data,
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ )
+
+ return rotated_compressed_data.detach().clone()
+
+ def crop(self):
+ """Crop to the size of the calibration lines."""
+ s = torch.as_tensor([self.calib_lines, self.width, self.coils])
+
+ idx = [
+ torch.arange(
+ abs(int(self.data.shape[n] // 2 + torch.ceil(-s[n] / 2))),
+ abs(int(self.data.shape[n] // 2 + torch.ceil(s[n] / 2) + 1)),
+ )
+ for n in range(len(s))
+ ]
+
+ self.data = (
+ self.data[idx[0][0] : idx[0][-1], idx[1][0] : idx[1][-1], idx[2][0] : idx[2][-1]]
+ .unsqueeze(-2)
+ .permute(1, 0, 2, 3)
+ )
+
+ def calculate_gcc(self):
+ """Calculates Geometric Coil-Compression."""
+ ws = (self.virtual_coils // 2) * 2 + 1
+
+ Nx, Ny, Nz, Nc = self.data.shape
+
+ im = torch.view_as_complex(
+ ifft2(torch.view_as_real(self.data), self.fft_centered, self.fft_normalization, spatial_dims=0)
+ )
+
+ s = torch.as_tensor([Nx + ws - 1, Ny, Nz, Nc])
+ idx = [
+ torch.arange(
+ abs(int(im.shape[n] // 2 + torch.ceil((-s[n] / 2).clone().detach()))),
+ abs(int(im.shape[n] // 2 + torch.ceil((s[n] / 2).clone().detach())) + 1),
+ )
+ for n in range(len(s))
+ ]
+
+ zpim = torch.zeros((Nx + ws - 1, Ny, Nz, Nc)).type(im.dtype)
+ zpim[idx[0][0] : idx[0][-1], idx[1][0] : idx[1][-1], idx[2][0] : idx[2][-1], idx[3][0] : idx[3][-1]] = im
+
+ self.unaligned_data = torch.zeros((Nc, min(Nc, ws * Ny * Nz), Nx)).type(im.dtype)
+ for n in range(Nx):
+ tmpc = reshape_fortran(zpim[n : n + ws, :, :, :], (ws * Ny * Nz, Nc))
+ _, _, v = torch.svd(tmpc, some=False)
+ self.unaligned_data[:, :, n] = v
+
+ self.unaligned_data = self.unaligned_data[:, : self.virtual_coils, :]
+
+ def align_compressed_coils(self):
+ """Virtual Coil Alignment."""
+ self.aligned_data = self.unaligned_data
+
+ _, sy, nc = self.aligned_data.shape
+ ncc = sy
+
+ n0 = nc // 2
+
+ A00 = self.aligned_data[:, :ncc, n0 - 1]
+
+ A0 = A00
+ for n in range(n0, 0, -1):
+ A1 = self.aligned_data[:, :ncc, n - 1]
+ C = torch.conj(A1).T @ A0
+ u, _, v = torch.svd(C, some=False)
+ P = v @ torch.conj(u).T
+ self.aligned_data[:, :ncc, n - 1] = A1 @ torch.conj(P).T
+ A0 = self.aligned_data[:, :ncc, n - 1]
+
+ A0 = A00
+ for n in range(n0, nc):
+ A1 = self.aligned_data[:, :ncc, n]
+ C = torch.conj(A1).T @ A0
+ u, _, v = torch.svd(C, some=False)
+ P = v @ torch.conj(u).T
+ self.aligned_data[:, :ncc, n] = A1 @ torch.conj(P).T
+ A0 = self.aligned_data[:, :ncc, n]
+
+ def rotate_and_compress(self, data_to_cc):
+ """Uses compression matrices to project the data onto them -> rotate to the compressed space."""
+ _data = self.init_data.permute(1, 0, 2).unsqueeze(-2)
+ _ncc = data_to_cc.shape[1]
+
+ data_to_cc = data_to_cc.to(_data.device)
+
+ Nx, Ny, Nz, Nc = _data.shape
+ im = torch.view_as_complex(
+ ifft2(torch.view_as_real(_data), self.fft_centered, self.fft_normalization, spatial_dims=0)
+ )
+
+ ccdata = torch.zeros((Nx, Ny, Nz, _ncc)).type(_data.dtype).to(_data.device)
+ for n in range(Nx):
+ tmpc = im[n, :, :, :].squeeze().reshape(Ny * Nz, Nc)
+ ccdata[n, :, :, :] = (tmpc @ data_to_cc[:, :, n]).reshape(Ny, Nz, _ncc).unsqueeze(0)
+
+ ccdata = (
+ torch.view_as_complex(
+ fft2(torch.view_as_real(ccdata), self.fft_centered, self.fft_normalization, spatial_dims=0)
+ )
+ .permute(1, 0, 2, 3)
+ .squeeze()
+ )
+
+ # Singlecoil
+ if ccdata.dim() == 2:
+ ccdata = ccdata.unsqueeze(-1)
+
+ gcc = torch.zeros(ccdata.shape).type(ccdata.dtype)
+ for n in range(ccdata.shape[-1]):
+ gcc[:, :, n] = torch.view_as_complex(
+ ifft2(
+ torch.view_as_real(ccdata[:, :, n]), self.fft_centered, self.fft_normalization, self.spatial_dims
+ )
+ )
+
+ return gcc
+
+
+class Masker:
+ """Undersamples k-space data.
+
+ Returns
+ -------
+ Tuple[List[torch.Tensor], List[torch.Tensor], List[float]]
+ Masked data, mask, and acceleration factor. They are returned as a tuple of lists, where each list corresponds
+ to a different acceleration factor. If one acceleration factor is provided, the lists will be of length 1.
+
+ Example
+ -------
+ >>> import torch
+ >>> from atommic.collections.common.parts.transforms import Masker
+ >>> data = torch.randn(1, 15, 320, 320, 2)
+ >>> mask = torch.ones(320, 320)
+ >>> masker = Masker(mask_func=None, spatial_dims=(-2, -1), shift_mask=False, partial_fourier_percentage=0.0, \
+ center_scale=0.02, dimensionality=2, remask=True)
+ >>> masked_data = masker(data, mask, seed=None)
+ >>> masked_data[0][0].shape # masked data
+ [1, 15, 320, 320, 2]
+ >>> masked_data[1][0].shape # mask
+ [320, 320]
+ >>> masked_data[2][0] # acceleration factor
+ 10.0
+ """
+
+ def __init__(
+ self,
+ mask_func: Optional[Callable] = None,
+ spatial_dims: Sequence[int] = (-2, -1),
+ shift_mask: bool = False,
+ partial_fourier_percentage: float = 0.0,
+ center_scale: float = 0.02,
+ dimensionality: int = 2,
+ remask: bool = True,
+ dataset_format: str = None,
+ ):
+ """Inits :class:`Masker`.
+
+ Parameters
+ ----------
+ mask_func : callable, optional
+ Masker function. Default is `None`.
+ spatial_dims : tuple
+ Spatial dimensions. Default is `(-2, -1)`.
+ shift_mask : bool
+ Whether to shift the mask. Default is `False`.
+ partial_fourier_percentage : float
+ Whether to simulate half scan. Default is `0.0`, which means no half scan.
+ center_scale : float
+ Percentage of center to remain densely sampled. Default is `0.02`.
+ dimensionality : int
+ Dimensionality of the data. Default is `2`.
+ remask: bool
+ Whether to remask the data. If False, the mask will be generated only once. If True, the mask will be
+ enerated every time the transform is called. Default is `False`.
+ dataset_format : str, optional
+ The format of the dataset. Usefull if loading precomputed masks. For example, ``'custom_dataset'`` or
+ ``'public_dataset_name'``. Default is ``None``.
+ """
+ self.mask_func = mask_func
+ self.spatial_dims = spatial_dims
+ self.shift_mask = shift_mask
+ self.partial_fourier_percentage = partial_fourier_percentage
+ self.center_scale = center_scale
+ self.dimensionality = dimensionality
+ self.remask = remask
+ self.dataset_format = dataset_format
+
+ def __call__(
+ self,
+ data: torch.Tensor,
+ mask: Union[List, torch.Tensor, np.ndarray] = None,
+ padding: Optional[Tuple] = None,
+ seed: Optional[int] = None,
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> Tuple[
+ List[float | torch.Tensor | Any],
+ List[torch.Tensor | Any] | List[torch.Tensor | np.ndarray | None | Any],
+ List[int | torch.Tensor | Any],
+ ]:
+ """Calls :class:`Masker`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input k-space data to apply mask.
+ mask : Union[List, torch.Tensor, np.ndarray], optional
+ Mask to apply. Default is ``None``.
+ padding : Optional[Tuple], optional
+ Padding to apply. Default is ``None``.
+ seed : Optional[int], optional
+ Seed to apply. Default is ``None``.
+ apply_backward_transform : bool
+ Apply backward transform, i.e. inverse Fast Fourier Transform. Default is ``False``.
+ apply_forward_transform : bool
+ Apply forward transform, i.e. Fast Fourier Transform. Default is ``False``.
+ """
+ if self.dataset_format is not None and "skm-tea" in self.dataset_format.lower():
+ if not is_none(self.mask_func) and not isinstance(mask, np.ndarray):
+ # if skm-tea dataset, then the mask is already computed and loaded
+ accelerations = list(self.mask_func[0].accelerations) # type: ignore
+ self.acc = []
+ masks = []
+ for i in range(len(accelerations)): # pylint: disable=consider-using-enumerate
+ self.acc.append(accelerations[i])
+ masks.append(mask[str(accelerations[i])]) # type: ignore
+ mask = masks
+ else:
+ mask = None
+
+ # Check if mask is precomputed or not.
+ if not is_none(mask):
+ if isinstance(mask, list):
+ if len(mask) == 0:
+ mask = None
+ elif mask.ndim == 0: # type: ignore
+ mask = None
+
+ if not is_none(mask) and isinstance(mask, list) and len(mask) > 0:
+ self.__type__ = "Masks are precomputed and loaded."
+ elif not is_none(mask) and not isinstance(mask, list) and mask.ndim != 0 and len(mask) > 0: # type: ignore
+ self.__type__ = "Mask is either precomputed and loaded or data are prospectively undersampled."
+ elif isinstance(self.mask_func, list):
+ self.__type__ = "Multiple accelerations are provided and masks are generated on the fly."
+ else:
+ self.__type__ = "A single acceleration is provided and mask is generated on the fly."
+ return self.forward(data, mask, padding, seed)
+
+ def __repr__(self) -> str:
+ """Representation of :class:`Masker`."""
+ return f"{self.__type__}"
+
+ def __str__(self) -> str:
+ """String representation of :class:`Masker`."""
+ return self.__repr__()
+
+ def forward( # noqa: MC0001
+ self,
+ data: torch.Tensor,
+ mask: Union[List, torch.Tensor, np.ndarray] = None,
+ padding: Optional[Tuple] = None,
+ seed: Optional[int] = None,
+ ) -> Tuple[
+ List[float | torch.Tensor | Any],
+ List[torch.Tensor | Any] | List[torch.Tensor | np.ndarray | None | Any],
+ List[int | torch.Tensor | Any],
+ ]:
+ """Forward pass of :class:`Masker`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input k-space data to apply mask.
+ mask : Union[List, torch.Tensor, np.ndarray], optional
+ Mask to apply. Default is ``None``.
+ padding : Optional[Tuple], optional
+ Padding to apply. Default is ``None``.
+ seed : Optional[int], optional
+ Seed to apply. Default is ``None``.
+ """
+ is_complex = data.shape[-1] == 2
+
+ spatial_dims = tuple(x - 1 for x in self.spatial_dims) if is_complex else self.spatial_dims
+
+ if not is_none(mask) and isinstance(mask, list) and len(mask) > 0:
+ masked_data = []
+ masks = []
+ accelerations = []
+ for i, m in enumerate(mask):
+ if list(m.shape) == [data.shape[spatial_dims[0]], data.shape[spatial_dims[1]]]:
+ if isinstance(m, np.ndarray):
+ m = torch.from_numpy(m)
+ m = m.unsqueeze(0).unsqueeze(-1)
+
+ if not is_none(padding[0]) and padding[0] != 0: # type: ignore
+ m[:, :, : padding[0]] = 0 # type: ignore
+ m[:, :, padding[1] :] = 0 # type: ignore
+
+ if self.shift_mask:
+ m = torch.fft.fftshift(m, dim=(spatial_dims[0], spatial_dims[1]))
+
+ m = m.to(torch.float32)
+
+ masked_data.append(data * m + 0.0)
+ masks.append(m)
+
+ if self.dataset_format is not None and "skm-tea" in self.dataset_format.lower():
+ accelerations.append(float(self.acc[i]))
+ else:
+ accelerations.append(np.round(m.squeeze(0).squeeze(-1).numpy().size / m.numpy().sum()))
+
+ elif not is_none(mask) and not isinstance(mask, list) and mask.ndim != 0 and len(mask) > 0: # type: ignore
+ if isinstance(mask, np.ndarray):
+ mask = torch.from_numpy(mask)
+ mask = mask.unsqueeze(0).unsqueeze(-1)
+
+ if not is_none(padding) and padding[0] != 0: # type: ignore
+ mask[:, :, : padding[0]] = 0 # type: ignore
+ mask[:, :, padding[1] :] = 0 # type: ignore
+
+ if mask.shape[-3] != data.shape[-3] or mask.shape[-2] != data.shape[-2]:
+ mask = center_crop(mask.squeeze(-1), (data.shape[-3], data.shape[-2])).unsqueeze(-1)
+
+ if self.shift_mask:
+ mask = torch.fft.fftshift(mask, dim=(spatial_dims[0], spatial_dims[1]))
+
+ masked_data = [data * mask + 0.0]
+ masks = [mask]
+ accelerations = [np.round(mask.squeeze(0).squeeze(-1).numpy().size / mask.numpy().sum())]
+
+ elif isinstance(self.mask_func, list):
+ masked_data = []
+ masks = []
+ accelerations = []
+ for m in self.mask_func:
+ if self.dimensionality == 2:
+ _masked_data, _mask, _accelerations = apply_mask(
+ data,
+ m,
+ seed,
+ padding,
+ shift=self.shift_mask,
+ partial_fourier_percentage=self.partial_fourier_percentage,
+ center_scale=self.center_scale,
+ )
+
+ elif self.dimensionality == 3:
+ _masked_data = []
+ _masks = []
+ _accelerations = []
+ j_mask = None
+ for j in range(data.shape[0]):
+ j_masked_data, j_mask, j_acc = apply_mask(
+ data[j],
+ m,
+ seed,
+ padding,
+ shift=self.shift_mask,
+ partial_fourier_percentage=self.partial_fourier_percentage,
+ center_scale=self.center_scale,
+ existing_mask=j_mask if not self.remask else None,
+ )
+ _masked_data.append(j_masked_data)
+ _masks.append(j_mask)
+ _accelerations.append(j_acc)
+ _masked_data = torch.stack(_masked_data, dim=0)
+ _mask = torch.stack(_masks, dim=0)
+ _accelerations = torch.stack(_accelerations, dim=0)
+ else:
+ raise ValueError(f"Unsupported data dimensionality {self.dimensionality}D.")
+ masked_data.append(_masked_data)
+ masks.append(_mask)
+ accelerations.append(_accelerations)
+
+ elif not is_none(self.mask_func):
+ masked_data, masks, accelerations = apply_mask( # type: ignore
+ data,
+ self.mask_func[0], # type: ignore
+ seed,
+ padding,
+ shift=self.shift_mask,
+ partial_fourier_percentage=self.partial_fourier_percentage,
+ center_scale=self.center_scale,
+ )
+ masked_data = [masked_data]
+ masks = [masks]
+ accelerations = [accelerations] # type: ignore
+
+ else:
+ masked_data = [data]
+ masks = [torch.empty([])]
+ accelerations = [torch.empty([])]
+
+ return masked_data, masks, accelerations
+
+
+class N2R:
+ """Generates Noise to Reconstruction (N2R) sampling masks, as presented in [Desai2022]_.
+
+ References
+ ----------
+ .. [Desai2022] AD Desai, BM Ozturkler, CM Sandino, et al. Noise2Recon: Enabling Joint MRI Reconstruction and
+ Denoising with Semi-Supervised and Self-Supervised Learning. ArXiv 2022. https://arxiv.org/abs/2110.00075
+
+ Returns
+ -------
+ sampling_mask_noise : torch.Tensor
+ Sampling mask with noise. The shape should be (1, nx, ny, 1).
+ """
+
+ def __init__(
+ self,
+ probability: float = 0.0,
+ std_devs: Tuple[float, float] = (0.0, 0.0),
+ rhos: Tuple[float, float] = (0.0, 0.0),
+ use_mask: bool = True,
+ ):
+ """Inits :class:`N2R`.
+
+ Parameters
+ ----------
+ probability : float, optional
+ Probability of sampling. Default is ``0.0``.
+ std_devs : Tuple[float, float], optional
+ Standard deviations of the Gaussian noise. Default is ``(0.0, 0.0)``.
+ rhos: Tuple[float, float], optional
+ Rho values for the Gaussian noise. Default is ``(0.0, 0.0)``.
+ use_mask : bool, optional
+ Whether to use the mask. Default is ``True``.
+ """
+ self.probability = probability
+ self.std_devs = std_devs
+ self.rhos = rhos
+ self.use_mask = use_mask
+
+ def __call__(self, data: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+ """Calls :class:`N2R`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data. The shape should be (nc, nx, ny).
+ mask : torch.Tensor
+ Input mask. The shape should be (nx, ny).
+
+ Returns
+ -------
+ sampling_mask_noise : torch.Tensor
+ Sampling mask with noise. The shape should be (1, nx, ny, 1).
+ """
+ mask = mask.squeeze(0).squeeze(-1)
+ # if mask is 1D, repeat it for nx
+ if mask.shape[0] == 1:
+ mask = mask.repeat_interleave(data.shape[1], 0)
+ return self.forward(mask)
+
+ def __repr__(self):
+ """Representation of :class:`N2R`."""
+ return (
+ f"N2R(probability={self.probability}, std_devs={self.std_devs}, rhos={self.rhos}, "
+ f"use_mask={self.use_mask})"
+ )
+
+ def __str__(self):
+ """String representation of :class:`N2R`."""
+ return self.__repr__()
+
+ def forward(self, mask: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`N2R`.
+
+ Parameters
+ ----------
+ mask : torch.Tensor
+ Input mask. The shape should be (nx, ny).
+
+ Returns
+ -------
+ sampling_mask_noise : torch.Tensor
+ Sampling mask with noise. The shape should be (1, nx, ny, 1).
+ """
+ _rand = torch.rand(1).item()
+
+ if _rand >= self.probability:
+ return torch.ones_like(mask).unsqueeze(0).unsqueeze(-1)
+
+ rhos = (
+ self._rand_range(*self.rhos)
+ if not is_none(self.rhos) and self.rhos[0] != 0.0 and self.rhos[1] != 0.0
+ else None
+ )
+
+ if not self.use_mask:
+ mask = torch.ones(mask.shape)
+
+ std_devs = (
+ self._rand_range(*self.std_devs)
+ if not is_none(self.std_devs) and self.std_devs[0] != 0.0 and self.std_devs[1] != 0.0
+ else 1e-6 # add a small number to avoid division by zero
+ )
+ gen = torch.Generator(device=mask.device).manual_seed(int(_rand * 1e10))
+ noise = std_devs * torch.randn(mask.shape + (2,), generator=gen, device=mask.device)
+ if noise.shape[-1] == 2:
+ noise = torch.view_as_complex(noise)
+
+ if rhos is not None and rhos != 1:
+ shape = mask.shape
+ mask = mask.view(-1)
+ # TODO: this doesn't work if the matrix is > 2*24 in size.
+ num_valid = torch.sum(mask)
+ weights = mask / num_valid
+ samples = torch.multinomial(weights, int((1 - rhos) * num_valid), replacement=False, generator=gen)
+ mask[samples] = 0
+ mask = mask.view(shape)
+
+ if mask is not None:
+ noise = noise * mask
+
+ return torch.abs(noise).to(mask).unsqueeze(0).unsqueeze(-1)
+
+ @staticmethod
+ def _rand_range(low, high, size: int = None) -> float:
+ """Uniform float random number between [low, high).
+
+ Parameters
+ ----------
+ low : float
+ Lower bound.
+ high : float
+ Upper bound.
+ size : int, optional
+ Number of samples. Default is ``None``.
+
+ Returns
+ -------
+ val : float
+ A uniformly sampled number in range [low, high).
+ """
+ if size is None:
+ size = 1
+ if low > high:
+ high, low = low, high
+ if high - low == 0:
+ return low
+ return (low + (high - low) * torch.rand(size)).cpu().item()
+
+
+class NoisePreWhitening:
+ """Applies noise pre-whitening / coil decorrelation.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.transforms import NoisePreWhitening
+ >>> data = torch.randn([30, 100, 100], dtype=torch.complex64)
+ >>> data = torch.view_as_real(data)
+ >>> data.mean()
+ tensor(-0.0011)
+ >>> noise_prewhitening = NoisePreWhitening(find_patch_size=True, scale_factor=1.0)
+ >>> noise_prewhitening(data).mean()
+ tensor(-0.0023)
+ """
+
+ def __init__(
+ self,
+ find_patch_size: bool = True,
+ patch_size: List[int] = None,
+ scale_factor: float = 1.0,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = (-2, -1),
+ ):
+ """Inits :class:`NoisePreWhitening`.
+
+ Parameters
+ ----------
+ find_patch_size : bool
+ Find optimal patch size (automatically) to calculate psi. If False, patch_size must be defined.
+ Default is ``True``.
+ patch_size : list of ints
+ Define patch size to calculate psi, [x_start, x_end, y_start, y_end].
+ scale_factor : float
+ Applied on the noise covariance matrix. Used to adjust for effective noise bandwidth and difference in
+ sampling rate between noise calibration and actual measurement.
+ scale_factor = (T_acq_dwell/T_noise_dwell)*NoiseReceiverBandwidthRatio
+ Default is ``1.0``.
+ fft_centered : bool
+ If True, the zero-frequency component is located at the center of the spectrum.
+ Default is ``False``.
+ fft_normalization : str
+ Normalization mode. Options are ``"backward"``, ``"ortho"``, ``"forward"``.
+ Default is ``"backward"``.
+ spatial_dims : sequence of ints
+ Spatial dimensions of the input data.
+ """
+ super().__init__()
+ # TODO: account for multiple echo times
+ self.find_patch_size = find_patch_size
+ self.patch_size = patch_size
+ self.scale_factor = scale_factor
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+
+ def __call__(
+ self,
+ data: torch.Tensor,
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> torch.Tensor:
+ """Calls :class:`NoisePreWhitening`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to apply coil compression.
+ apply_backward_transform : bool
+ Apply backward transform. Default is ``False``.
+ apply_forward_transform : bool
+ Apply forward transform. Default is ``False``.
+ """
+ return self.forward(data, apply_backward_transform, apply_forward_transform)
+
+ def __repr__(self):
+ """Representation of :class:`NoisePreWhitening`."""
+ return f"Noise pre-whitening is applied with patch size {self.patch_size}."
+
+ def __str__(self):
+ """String representation of :class:`NoisePreWhitening`."""
+ return str(self.__repr__)
+
+ # pylint: disable=unused-argument
+ def forward(
+ self,
+ data: torch.Tensor,
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`NoisePreWhitening`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to apply noise pre-whitening.
+ apply_backward_transform : bool
+ Apply backward transform before noise pre-whitening.
+ apply_forward_transform : bool
+ Apply forward transform before noise pre-whitening.
+
+ Returns
+ -------
+ torch.Tensor
+ Noise pre-whitened data.
+ """
+ if apply_forward_transform:
+ data = fft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ if data.shape[-1] != 2:
+ data = torch.view_as_real(data)
+
+ if self.find_patch_size:
+ patch = self.find_optimal_patch_size(data)
+ noise = data[:, patch[0] : patch[1], patch[2] : patch[3]]
+ elif not is_none(self.patch_size):
+ noise = data[
+ :,
+ self.patch_size[0] : self.patch_size[1], # type: ignore
+ self.patch_size[-2] : self.patch_size[-1], # type: ignore
+ ]
+ else:
+ raise ValueError(
+ "No patch size has been defined, while find_patch_size is False for noise prewhitening. Please define "
+ "a patch size or set find_patch_size to True."
+ )
+ noise_int = torch.reshape(noise, (noise.shape[0], int(torch.numel(noise) / noise.shape[0])))
+
+ deformation_matrix = (1 / (float(noise_int.shape[1]) - 1)) * torch.mm(noise_int, torch.conj(noise_int).t())
+ # ensure that the matrix is positive definite
+ deformation_matrix = deformation_matrix + torch.eye(deformation_matrix.shape[0]) * 1e-6
+ psi = torch.linalg.inv(torch.linalg.cholesky(deformation_matrix)) * sqrt(2) * sqrt(self.scale_factor)
+
+ data = torch.reshape(
+ torch.mm(psi, torch.reshape(data, (data.shape[0], int(torch.numel(data) / data.shape[0])))), data.shape
+ )
+
+ if apply_forward_transform:
+ data = ifft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ return data.detach().clone()
+
+ @staticmethod
+ def find_optimal_patch_size(data: torch.Tensor, min_noise: float = 1e10) -> List[int]:
+ """Find optimal patch size for noise pre-whitening.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to find optimal patch size.
+ min_noise : float
+ Minimum noise value. It is inversely proportional to the noise level. Default is ``1e10``.
+
+ Returns
+ -------
+ List[int]
+ Optimal patch size, [x_start, x_end, y_start, y_end].
+ """
+ if data.shape[-1] == 2:
+ data = torch.view_as_complex(data)
+ best_patch = []
+ for patch_length in [10, 20, 30, 40, 50]:
+ for patch_start_x in range(0, data.shape[-2] - patch_length, 10):
+ for patch_start_y in range(0, data.shape[-1] - patch_length, 10):
+ patch = torch.abs(
+ rss(
+ data[
+ :,
+ patch_start_x : patch_start_x + patch_length,
+ patch_start_y : patch_start_y + patch_length,
+ ],
+ )
+ )
+ noise = torch.sqrt(
+ torch.sum(torch.abs(patch - torch.mean(patch)) ** 2) / (len(torch.flatten(patch)) - 1)
+ )
+ if noise < min_noise:
+ min_noise = noise
+ best_patch = [
+ patch_start_x,
+ patch_start_x + patch_length,
+ patch_start_y,
+ patch_start_y + patch_length,
+ ]
+ return best_patch
+
+
+class Normalizer:
+ """Normalizes data given a normalization type.
+
+ Returns
+ -------
+ normalized_data: torch.Tensor
+ Normalized data to range according to the normalization type.
+
+ Example
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.transforms import Normalizer
+ >>> data = torch.randn(1, 32, 320, 320, 2) 1j * torch.randn(1, 32, 320, 320, 2)
+ >>> print(torch.min(torch.abs(data)), torch.max(torch.abs(data)))
+ tensor(1e-06) tensor(1.4142)
+ >>> normalizer = Normalizer(normalization_type="max")
+ >>> normalized_data = normalizer(data)
+ >>> print(torch.min(torch.abs(data)), torch.max(torch.abs(data)))
+ tensor(0.) tensor(1.)
+ """
+
+ def __init__(
+ self,
+ normalization_type: Optional[str] = None,
+ kspace_normalization: bool = False,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = (-2, -1),
+ ):
+ """Inits :class:`Normalizer`.
+
+ Parameters
+ ----------
+ normalization_type: str, optional
+ Normalization type. It can be one of the following:
+ - "max": normalize data by its maximum value.
+ - "mean": normalize data by its mean value.
+ - "minmax": normalize data by its minimum and maximum values.
+ - None: do not normalize data. It can be useful to verify FFT normalization.
+ Default is `None`.
+ kspace_normalization: str, optional
+ Normalize in k-space.
+ fft_centered: bool, optional
+ If True, the FFT will be centered. Default is `False`. Should be set for complex data normalization.
+ fft_normalization: str, optional
+ FFT normalization type. It can be one of the following:
+ - "backward": normalize the FFT by the number of elements in the input.
+ - "ortho": normalize the FFT by the number of elements in the input and the square root of the product of
+ the sizes of the input dimensions.
+ - "forward": normalize the FFT by the square root of the number of elements in the input.
+ Default is "backward".
+ spatial_dims: tuple, optional
+ Spatial dimensions. Default is `(-2, -1)`.
+ """
+ self.normalization_type = normalization_type
+ self.kspace_normalization = kspace_normalization
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+
+ def __call__(
+ self,
+ data: Union[torch.Tensor, List[torch.Tensor], None],
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> Union[torch.Tensor, List[torch.Tensor], None]:
+ """Calls :class:`Normalizer`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to apply coil compression.
+ apply_backward_transform : bool
+ Apply backward transform. Default is ``False``.
+ apply_forward_transform : bool
+ Apply forward transform. Default is ``False``.
+ """
+ if not is_none(data):
+ if isinstance(data, list) and len(data) > 0:
+ return [self.forward(d, apply_backward_transform, apply_forward_transform) for d in data]
+ if data.dim() > 1 and data.mean() != 1: # type: ignore
+ return self.forward(data, apply_backward_transform, apply_forward_transform)
+ return data, None
+
+ def __repr__(self):
+ """Representation of :class:`Normalizer`."""
+ return f"Normalization type is set to {self.normalization_type}."
+
+ def __str__(self):
+ """String representation of :class:`Normalizer`."""
+ return self.__repr__()
+
+ def forward(
+ self,
+ data: torch.Tensor,
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> Tuple[torch.Tensor, Dict]:
+ """Forward pass of :class:`Normalizer`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data.
+ apply_backward_transform : bool, optional
+ If True, apply backward transform. Default is ``False``.
+ apply_forward_transform : bool, optional
+ If True, apply forward transform. Default is ``False``.
+
+ Returns
+ -------
+ data : torch.Tensor
+ Normalized data.
+ attrs : dict
+ Normalization attributes.
+ """
+ if self.kspace_normalization and apply_backward_transform:
+ apply_backward_transform = False
+
+ if apply_backward_transform:
+ data = ifft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ elif apply_forward_transform:
+ data = fft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ if data.shape[-1] == 2:
+ data = torch.view_as_complex(data)
+
+ attrs = {
+ "min": torch.min(torch.abs(data)),
+ "max": torch.max(torch.abs(data)),
+ "mean": torch.mean(torch.abs(data)),
+ "std": torch.std(torch.abs(data)),
+ "var": torch.var(torch.abs(data)),
+ }
+
+ if self.normalization_type == "max":
+ data = data / torch.max(torch.abs(data))
+ elif self.normalization_type == "minmax":
+ min_value = torch.min(torch.abs(data))
+ data = (data - min_value) / (torch.max(torch.abs(data)) - min_value)
+ elif self.normalization_type == "mean_std":
+ data = data - torch.mean(torch.abs(data))
+ data = data / torch.std(torch.abs(data))
+ elif self.normalization_type == "mean_var":
+ data = data - torch.mean(torch.abs(data))
+ data = data / torch.var(torch.abs(data))
+ elif self.normalization_type == "grayscale":
+ data = data - torch.min(torch.abs(data))
+ data = data / torch.max(torch.abs(data))
+ data = data * 255
+ elif is_none(self.normalization_type) or self.normalization_type == "fft":
+ pass
+ else:
+ raise ValueError(f"Normalization type {self.normalization_type} is not supported.")
+
+ if torch.is_complex(data):
+ data = torch.view_as_real(data)
+
+ if apply_backward_transform:
+ data = fft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ elif apply_forward_transform:
+ data = ifft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ return data, attrs
+
+
+class SNREstimator:
+ """Estimates Signal-to-Noise Ratio.
+
+ Returns
+ -------
+ snr : float
+ Estimated SNR.
+
+ Example
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.transforms import SNREstimator
+ >>> data = torch.randn(1, 32, 320, 320, 2) 1j * torch.randn(1, 32, 320, 320, 2)
+ >>> print(torch.min(torch.abs(data)), torch.max(torch.abs(data)))
+ tensor(1e-06) tensor(1.4142)
+ >>> snr_estimator = SNREstimator()
+ >>> snr_estimator(data)
+ 3.2
+ """
+
+ def __init__(
+ self,
+ patch_size: List[int],
+ apply_ifft: bool = True,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = (-2, -1),
+ coil_dim: int = 1,
+ multicoil: bool = True,
+ ):
+ """Inits :class:`SNREstimator`.
+
+ Parameters
+ ----------
+ patch_size : list of ints
+ Define patch size to calculate noise.
+ x_start, x_end, y_start, y_end
+ apply_ifft: bool
+ If data in k-space go to imspace
+ fft_centered: bool
+ If True, apply centered FFT
+ fft_normalization : str
+ Type of FFT normalization
+ spatial_dims : tuple of ints
+ Spatial dimensions
+ coil_dim : int
+ Coil dimension
+ multicoil : bool
+ If True, multicoil data. Else single coil data.
+ """
+ super().__init__()
+ self.patch_size = patch_size
+ self.apply_ifft = apply_ifft
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+ self.coil_dim = coil_dim
+ self.multicoil = multicoil
+
+ def __call__(self, data):
+ """Calls :class:`SNREstimator`."""
+ if not self.patch_size:
+ return data
+
+ is_complex = torch.is_complex(data)
+ if data.shape[-1] != 2 and is_complex:
+ data = torch.view_as_real(data)
+
+ if not self.multicoil:
+ data = torch.unsqueeze(data, self.coil_dim)
+
+ imspace = (
+ ifft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if self.apply_ifft
+ else data
+ )
+
+ if is_complex:
+ imspace = torch.view_as_complex(imspace)
+
+ rss_eta = torch.abs(rss(imspace, dim=self.coil_dim)).detach().cpu().numpy()
+
+ # TODO: numpy funcs need to be replaced from torch funcs
+ # pylint: disable=import-outside-toplevel
+ from skimage.filters import threshold_otsu
+ from skimage.morphology import convex_hull_image
+
+ signal = torch.mean(
+ torch.from_numpy(np.nonzero(convex_hull_image(np.where(rss_eta > threshold_otsu(rss_eta), 1, 0)))[0]).to(
+ dtype=torch.float32
+ )
+ )
+
+ kspace_patch = torch.abs(
+ rss(
+ fft2(
+ imspace,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )[:, self.patch_size[0] : self.patch_size[1], self.patch_size[-2] : self.patch_size[-1]],
+ dim=self.coil_dim,
+ )
+ )
+
+ noise = torch.sqrt(
+ torch.sum(torch.abs(kspace_patch - torch.mean(kspace_patch)) ** 2) / (len(torch.flatten(kspace_patch)) - 1)
+ )
+
+ return (signal / noise).item() if not torch.isnan(signal) and not torch.isnan(noise) else 0
+
+
+class SSDU:
+ """Generates Self-Supervised Data Undersampling (SSDU) masks, as presented in [Yaman2020]_.
+
+ References
+ ----------
+ .. [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uฤurbil, K, Akรงakaya, M. Self-supervised
+ learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson
+ Med. 2020; 84: 3172โ3191. https://doi.org/10.1002/mrm.28378
+
+ Returns
+ -------
+ loss_mask: torch.Tensor
+ Loss mask.
+ training_mask: torch.Tensor
+ Training mask.
+ """
+
+ def __init__(
+ self,
+ mask_type: str = "Gaussian",
+ rho: float = 0.4,
+ acs_block_size: Sequence[int] = (4, 4),
+ gaussian_std_scaling_factor: float = 4.0,
+ outer_kspace_fraction: float = 0.0,
+ export_and_reuse_masks: bool = False,
+ ):
+ """Inits :class:`SSDU`.
+
+ Parameters
+ ----------
+ mask_type: str, optional
+ Mask type. It can be one of the following:
+ - "Gaussian": Gaussian sampling.
+ - "Uniform": Uniform sampling.
+ Default is "Gaussian".
+ rho: float, optional
+ Split ratio for training and loss masks. Default is ``0.4``.
+ acs_block_size: Sequence[int], optional
+ Keeps a small acs region fully-sampled for training masks, if there is no acs region. The small acs block
+ should be set to zero. Default is ``(4, 4)``.
+ gaussian_std_scaling_factor: float, optional
+ Scaling factor for standard deviation of the Gaussian noise. If Uniform is select this factor is ignored.
+ Default is ``4.0``.
+ outer_kspace_fraction: float, optional
+ Fraction of the outer k-space region to be kept/unmasked. Default is ``0.0``.
+ export_and_reuse_masks: bool, optional
+ If ``True``, the generated masks are exported to the tmp directory and reused in the next call. This
+ option is useful when the data is too large to be stored in memory. Default is ``False``.
+ """
+ if mask_type not in ["Gaussian", "Uniform"]:
+ raise ValueError(f"SSDU mask type {mask_type} is not supported.")
+ self.mask_type = mask_type
+ self.rho = rho
+ self.acs_block_size = acs_block_size
+ self.gaussian_std_scaling_factor = gaussian_std_scaling_factor
+ self.outer_kspace_fraction = outer_kspace_fraction
+ self.export_and_reuse_masks = export_and_reuse_masks
+
+ def __call__(self, data: torch.Tensor, mask: torch.Tensor, fname: str) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Calls :class:`SSDU`."""
+ return self.forward(mask, fname)
+
+ def __repr__(self):
+ """Representation of :class:`SSDU`."""
+ return f"SSDU type is set to {self.mask_type}."
+
+ def __str__(self):
+ """String representation of :class:`SSDU`."""
+ return self.__repr__()
+
+ def forward(self, mask: torch.Tensor, fname: str) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Forward pass of :class:`SSDU`.
+
+ Parameters
+ ----------
+ mask : torch.Tensor
+ Mask tensor.
+ fname : str
+ File name to save the generated masks.
+
+ Returns
+ -------
+ train_mask : torch.Tensor
+ Training mask.
+ loss_mask : torch.Tensor
+ Loss mask.
+ """
+ if self.export_and_reuse_masks:
+ # check if masks are already generated
+ precomputed_masks = self.__exists__(fname, (mask.shape[0], mask.shape[1]))
+ if precomputed_masks is not None:
+ return precomputed_masks[0], precomputed_masks[1]
+
+ if self.mask_type == "Gaussian":
+ _mask = self.__gaussian_sampling__(mask).type(torch.float32)
+ else:
+ _mask = self.__uniform_sampling__(mask).type(torch.float32)
+
+ train_mask = torch.where(mask == 1, 1 - _mask, mask)
+ loss_mask = torch.where(mask == 1, _mask, mask)
+
+ # TODO: should we add the acs region to ensure linearity in FFT?
+ # train_mask = torch.where(self.__find_acs_region__(train_mask) == 1, 1, train_mask)
+ # loss_mask = torch.where(self.__find_acs_region__(mask) == 1, 1, loss_mask)
+
+ if self.outer_kspace_fraction > 0:
+ train_mask = self.__apply_outer_kspace_unmask__(train_mask)
+ loss_mask = self.__apply_outer_kspace_unmask__(loss_mask)
+
+ if self.export_and_reuse_masks:
+ # save masks
+ self.__export__(torch.stack([train_mask, loss_mask], dim=0), fname)
+
+ return train_mask, loss_mask
+
+ @staticmethod
+ def __find_acs_region__(mask: torch.Tensor) -> torch.Tensor: # noqa: MC0001
+ """Find the acs region.
+
+ Parameters
+ ----------
+ mask : torch.Tensor
+ Sampling mask.
+
+ Returns
+ -------
+ torch.Tensor
+ ACS region.
+ """
+ center = (mask.shape[0] // 2, mask.shape[1] // 2)
+
+ # find the size of the acs region, start from the center and go left to find contiguous 1s
+ acs_region = torch.zeros_like(mask)
+ for i in range(center[0], 0, -1):
+ if mask[i, center[1]] == 1:
+ acs_region[i, :] = 1
+ else:
+ break
+
+ # go right
+ for i in range(center[0], mask.shape[0]):
+ if mask[i, center[1]] == 1:
+ acs_region[i, :] = 1
+ else:
+ break
+
+ # go up
+ for i in range(center[1], 0, -1):
+ if mask[center[0], i] == 1:
+ acs_region[:, i] = 1
+ else:
+ break
+
+ # go down
+ for i in range(center[1], mask.shape[1]):
+ if mask[center[0], i] == 0:
+ acs_region[:, i] = 1
+ else:
+ break
+
+ # keep only the acs region
+ # take only the first row and stop when you find a 1
+ left = 0
+ for i in range(acs_region.shape[0]):
+ if acs_region[i, 0] == 1:
+ left = i
+ break
+
+ # take only the last row and stop when you find a 1
+ right = 0
+ for i in range(acs_region.shape[0] - 1, 0, -1):
+ if acs_region[i, 0] == 1:
+ right = i
+ break
+
+ # take only the first column and stop when you find a 1
+ up = 0
+ for i in range(acs_region.shape[1]):
+ if acs_region[0, i] == 1:
+ up = i
+ break
+
+ # take only the last column and stop when you find a 1
+ down = 0
+ for i in range(acs_region.shape[1] - 1, 0, -1):
+ if acs_region[0, i] == 1:
+ down = i
+ break
+
+ acs_region = torch.zeros_like(mask)
+ acs_region[left:right, up:down] = 1
+
+ # keep only the part of the acs region that is in the mask
+ return acs_region * mask
+
+ def __gaussian_sampling__(self, mask: torch.Tensor) -> torch.Tensor:
+ """Applies Gaussian sampling."""
+ nrow, ncol = mask.shape[0], mask.shape[1]
+ center_kx = nrow // 2
+ center_ky = ncol // 2
+
+ tmp_mask = mask.clone()
+ tmp_mask[
+ center_kx - self.acs_block_size[0] // 2 : center_kx + self.acs_block_size[0] // 2,
+ center_ky - self.acs_block_size[1] // 2 : center_ky + self.acs_block_size[1] // 2,
+ ] = 0
+
+ _mask = torch.zeros_like(mask)
+ count = 0
+
+ total = int(torch.ceil(torch.sum(mask[:]) * self.rho))
+
+ while count <= total:
+ indx = int(np.round(np.random.normal(loc=center_kx, scale=(nrow - 1) / self.gaussian_std_scaling_factor)))
+ indy = int(np.round(np.random.normal(loc=center_ky, scale=(ncol - 1) / self.gaussian_std_scaling_factor)))
+
+ if 0 <= indx < nrow and 0 <= indy < ncol and tmp_mask[indx, indy] == 1 and _mask[indx, indy] != 1:
+ _mask[indx, indy] = 1
+ count = count + 1
+
+ return _mask
+
+ def __uniform_sampling__(self, mask: torch.Tensor) -> torch.Tensor:
+ """Applies uniform sampling."""
+ nrow, ncol = mask.shape[0], mask.shape[1]
+ center_kx = nrow // 2
+ center_ky = ncol // 2
+
+ tmp_mask = mask.clone()
+ tmp_mask[
+ center_kx - self.acs_block_size[0] // 2 : center_kx + self.acs_block_size[0] // 2,
+ center_ky - self.acs_block_size[1] // 2 : center_ky + self.acs_block_size[1] // 2,
+ ] = 0
+
+ _mask = tmp_mask.view(-1) if tmp_mask.is_contiguous() else tmp_mask.reshape(-1)
+
+ num_valid = torch.sum(_mask)
+ ind = torch.multinomial(_mask / num_valid, int(self.rho * num_valid), replacement=False)
+ _mask[ind] = 0
+
+ return _mask.view(mask.shape)
+
+ @staticmethod
+ def __find_center_ind__(data: torch.Tensor, dims: tuple = (1, 2, 3)) -> int:
+ """Calculates the center of the k-space.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data. The shape should be (nx, ny, nc).
+ dims : tuple, optional
+ Dimensions to calculate the norm. Default is ``(1, 2, 3)``.
+
+ Returns
+ -------
+ center_ind : int
+ The center of the k-space
+ """
+ for dim in dims:
+ data = torch.linalg.norm(data, dim=dim, keepdims=True)
+ return torch.argsort(data.squeeze())[-1:]
+
+ def __apply_outer_kspace_unmask__(self, mask: torch.Tensor) -> torch.Tensor:
+ """Applies outer k-space (un)mask.
+
+ Parameters
+ ----------
+ mask : torch.Tensor
+ Input mask. The shape should be (nx, ny).
+
+ Returns
+ -------
+ mask : torch.Tensor
+ Output mask. The shape should be (nx, ny).
+ """
+ mask_out = int(mask.shape[1] * self.outer_kspace_fraction)
+ mask[:, 0:mask_out] = torch.ones((mask.shape[0], mask_out))
+ mask[:, mask.shape[1] - mask_out : mask.shape[1]] = torch.ones((mask.shape[0], mask_out))
+ return mask
+
+ @staticmethod
+ def __exists__(fname: str, shape: Tuple) -> Union[np.ndarray, None]:
+ """Checks if the sampling mask exists.
+
+ Parameters
+ ----------
+ fname : str
+ Filename to save the sampling mask.
+ shape : tuple
+ Shape of the sampling mask.
+
+ Returns
+ -------
+ exists : bool
+ True if the sampling mask exists.
+ """
+ if ".h5" in fname:
+ fname = fname.replace(".h5", ".npy")
+ else:
+ fname = fname + ".npy"
+ # set path to the tmp directory of the home directory
+ path = os.path.join(os.path.expanduser("~"), "tmp", fname)
+ spatial_dims = (2, 3)
+ if os.path.exists(path):
+ masks = np.load(path)
+ if masks.ndim == 3:
+ spatial_dims = (1, 2)
+ if (masks.shape[spatial_dims[0]], masks.shape[spatial_dims[1]]) == shape:
+ return torch.from_numpy(masks)
+ return None
+
+ @staticmethod
+ def __export__(mask: torch.Tensor, fname: str) -> None:
+ """Exports the sampling mask to a numpy file.
+
+ Parameters
+ ----------
+ mask : torch.Tensor
+ Sampling mask. The shape should be (1, nx, ny, 1).
+ fname : str
+ Filename to save the sampling mask.
+ """
+ if ".h5" in fname:
+ fname = fname.replace(".h5", ".npy")
+ else:
+ fname = fname + ".npy"
+ # set path to the tmp directory of the home directory
+ path = os.path.join(os.path.expanduser("~"), "tmp", fname)
+ np.save(path, mask.cpu().numpy())
+
+
+class ZeroFillingPadding:
+ """Zero-Filling padding in k-space -> changes the Field-of-View (FoV) in image space.
+
+ Returns
+ -------
+ zero_filled_data : torch.Tensor
+ Zero filled data.
+ spatial_dims : tuple
+ Spatial dimensions.
+
+ Example
+ -------
+ >>> import torch
+ >>> from atommic.collections.common.parts.transforms import ZeroFillingPadding
+ >>> data = torch.randn(1, 15, 320, 320, 2)
+ >>> zero_filling = ZeroFillingPadding(zero_filling_size=(400, 400), spatial_dims=(-2, -1))
+ >>> zero_filled_data = zero_filling(data)
+ >>> zero_filled_data.shape
+ [1, 15, 400, 400, 2]
+ """
+
+ def __init__(
+ self,
+ zero_filling_size: Tuple,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = (-2, -1),
+ ):
+ """Inits :class:`ZeroFillingPadding`.
+
+ Parameters
+ ----------
+ zero_filling_size : tuple
+ Size of the zero filled data.
+ fft_centered : bool, optional
+ If True, the FFT will be centered. Default is ``False``. Should be set for complex data normalization.
+ fft_normalization : str, optional
+ FFT normalization type. It can be one of the following:
+ spatial_dims : tuple, optional
+ Spatial dimensions. Default is ``(-2, -1)``.
+ """
+ self.zero_filling_size = zero_filling_size
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+
+ def __call__(
+ self,
+ data: Union[torch.Tensor, None],
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> torch.Tensor:
+ """Calls :class:`ZeroFillingPadding`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to crop.
+ apply_backward_transform : bool
+ Apply backward transform, i.e. Inverse Fast Fourier Transform. Default is ``False``.
+ apply_forward_transform : bool
+ Apply forward transform, i.e. Fast Fourier Transform. Default is ``False``.
+ """
+ if not is_none(data) and data.dim() > 1 and data.mean() != 1: # type: ignore
+ return self.forward(data, apply_backward_transform, apply_forward_transform)
+ return data
+
+ def __repr__(self) -> str:
+ """Representation of :class:`ZeroFillingPadding`."""
+ return f"Zero-Filling will be applied to data with size {self.zero_filling_size}."
+
+ def __str__(self) -> str:
+ """String representation of :class:`ZeroFillingPadding`."""
+ return self.__repr__()
+
+ def forward(
+ self,
+ data: torch.Tensor,
+ apply_backward_transform: bool = False,
+ apply_forward_transform: bool = False,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`ZeroFillingPadding`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input data to crop.
+ apply_backward_transform : bool
+ Apply backward transform, i.e. Inverse Fast Fourier Transform. Default is ``False``.
+ apply_forward_transform : bool
+ Apply forward transform, i.e. Fast Fourier Transform. Default is ``False``.
+ """
+ if apply_backward_transform:
+ data = ifft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ elif apply_forward_transform:
+ data = fft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ is_complex = data.shape[-1] == 2
+
+ if is_complex:
+ data = torch.view_as_complex(data)
+
+ padding_top = np.floor_divide(abs(int(self.zero_filling_size[0]) - data.shape[self.spatial_dims[0]]), 2)
+ padding_bottom = padding_top
+ padding_left = np.floor_divide(abs(int(self.zero_filling_size[1]) - data.shape[self.spatial_dims[1]]), 2)
+ padding_right = padding_left
+
+ data = torch.nn.functional.pad(
+ data, pad=(padding_left, padding_right, padding_top, padding_bottom), mode="constant", value=0
+ )
+
+ if is_complex:
+ data = torch.view_as_real(data)
+
+ if apply_backward_transform:
+ data = fft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ elif apply_forward_transform:
+ data = ifft2(
+ data,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ return data
+
+
+class MRIDataTransforms:
+ """Generic class to apply transforms for MRI data."""
+
+ def __init__(
+ self,
+ dataset_format: str = None,
+ apply_prewhitening: bool = False,
+ find_patch_size: bool = True,
+ prewhitening_scale_factor: float = 1.0,
+ prewhitening_patch_start: int = 10,
+ prewhitening_patch_length: int = 30,
+ apply_gcc: bool = False,
+ gcc_virtual_coils: int = 10,
+ gcc_calib_lines: int = 24,
+ gcc_align_data: bool = True,
+ apply_random_motion: bool = False,
+ random_motion_type: str = "gaussian",
+ random_motion_percentage: Sequence[int] = (10, 20),
+ random_motion_angle: int = 10,
+ random_motion_translation: int = 10,
+ random_motion_center_percentage: float = 0.02,
+ random_motion_num_segments: int = 8,
+ random_motion_random_num_segments: bool = True,
+ random_motion_non_uniform: bool = False,
+ estimate_coil_sensitivity_maps: bool = False,
+ coil_sensitivity_maps_type: str = "ESPIRiT",
+ coil_sensitivity_maps_gaussian_sigma: float = 0.0,
+ coil_sensitivity_maps_espirit_threshold: float = 0.05,
+ coil_sensitivity_maps_espirit_kernel_size: int = 6,
+ coil_sensitivity_maps_espirit_crop: float = 0.95,
+ coil_sensitivity_maps_espirit_max_iters: int = 30,
+ coil_combination_method: str = "SENSE",
+ dimensionality: int = 2,
+ mask_func: Optional[Callable] = None,
+ shift_mask: bool = False,
+ mask_center_scale: float = 0.02,
+ partial_fourier_percentage: float = 0.0,
+ remask: bool = False,
+ ssdu: bool = False,
+ ssdu_mask_type: str = "Gaussian",
+ ssdu_rho: float = 0.4,
+ ssdu_acs_block_size: Sequence[int] = (4, 4),
+ ssdu_gaussian_std_scaling_factor: float = 4.0,
+ ssdu_outer_kspace_fraction: float = 0.0,
+ ssdu_export_and_reuse_masks: bool = False,
+ n2r: bool = False,
+ n2r_supervised_rate: float = 0.0,
+ n2r_probability: float = 0.0,
+ n2r_std_devs: Optional[Tuple[float, float]] = None,
+ n2r_rhos: Optional[Tuple[float, float]] = None,
+ n2r_use_mask: bool = False,
+ unsupervised_masked_target: bool = False,
+ crop_size: Optional[Tuple[int, int]] = None,
+ kspace_crop: bool = False,
+ crop_before_masking: bool = True,
+ kspace_zero_filling_size: Optional[Tuple] = None,
+ normalize_inputs: bool = True,
+ normalization_type: str = "max",
+ kspace_normalization: bool = False,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+ coil_dim: int = 0,
+ consecutive_slices: int = 1, # pylint: disable=unused-argument
+ use_seed: bool = True,
+ ):
+ """Inits :class:`MRIDataTransforms`.
+
+ Parameters
+ ----------
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ Default is ``None``.
+ apply_prewhitening : bool, optional
+ Apply prewhitening. If ``True`` then the prewhitening arguments are used. Default is ``False``.
+ find_patch_size : bool, optional
+ Find optimal patch size (automatically) to calculate psi. If False, patch_size must be defined.
+ Default is ``True``.
+ prewhitening_scale_factor : float, optional
+ Prewhitening scale factor. Default is ``1.0``.
+ prewhitening_patch_start : int, optional
+ Prewhitening patch start. Default is ``10``.
+ prewhitening_patch_length : int, optional
+ Prewhitening patch length. Default is ``30``.
+ apply_gcc : bool, optional
+ Apply Geometric Decomposition Coil Compression. If ``True`` then the GCC arguments are used.
+ Default is ``False``.
+ gcc_virtual_coils : int, optional
+ GCC virtual coils. Default is ``10``.
+ gcc_calib_lines : int, optional
+ GCC calibration lines. Default is ``24``.
+ gcc_align_data : bool, optional
+ GCC align data. Default is ``True``.
+ apply_random_motion : bool, optional
+ Simulate random motion in k-space. Default is ``False``.
+ random_motion_type : str, optional
+ Random motion type. It can be one of the following: ``piecewise_transient``, ``piecewise_constant``,
+ ``gaussian``. Default is ``gaussian``.
+ random_motion_percentage : Sequence[int], optional
+ Random motion percentage. For example, 10%-20% motion can be defined as ``(10, 20)``.
+ Default is ``(10, 10)``.
+ random_motion_angle : float, optional
+ Random motion angle. Default is ``10.0``.
+ random_motion_translation : float, optional
+ Random motion translation. Default is ``10.0``.
+ random_motion_center_percentage : float, optional
+ Random motion center percentage. Default is ``0.0``.
+ random_motion_num_segments : int, optional
+ Random motion number of segments to partition the k-space. Default is ``8``.
+ random_motion_random_num_segments : bool, optional
+ Whether to randomly generate the number of segments. Default is ``True``.
+ random_motion_non_uniform : bool, optional
+ Random motion non-uniform sampling. Default is ``False``.
+ estimate_coil_sensitivity_maps : bool, optional
+ Automatically estimate coil sensitivity maps. Default is ``False``. If ``True`` then the coil sensitivity
+ maps arguments are used. Note that this is different from the ``estimate_coil_sensitivity_maps_with_nn``
+ argument, which uses a neural network to estimate the coil sensitivity maps. The
+ ``estimate_coil_sensitivity_maps`` estimates the coil sensitivity maps with methods such as ``ESPIRiT``,
+ ``RSS`` or ``UNit``. ``ESPIRiT`` is the ``Eigenvalue to Self-Consistent Parallel Imaging Reconstruction
+ Technique`` method. ``RSS`` is the ``Root Sum of Squares`` method. ``UNit`` returns a uniform coil
+ sensitivity map.
+ coil_sensitivity_maps_type : str, optional
+ Coil sensitivity maps type. It can be one of the following: ``ESPIRiT``, ``RSS`` or ``UNit``. Default is
+ ``ESPIRiT``.
+ coil_sensitivity_maps_gaussian_sigma : float, optional
+ Coil sensitivity maps Gaussian sigma. Default is ``0.0``.
+ coil_sensitivity_maps_espirit_threshold : float, optional
+ Coil sensitivity maps ESPRIT threshold. Default is ``0.05``.
+ coil_sensitivity_maps_espirit_kernel_size : int, optional
+ Coil sensitivity maps ESPRIT kernel size. Default is ``6``.
+ coil_sensitivity_maps_espirit_crop : float, optional
+ Coil sensitivity maps ESPRIT crop. Default is ``0.95``.
+ coil_sensitivity_maps_espirit_max_iters : int, optional
+ Coil sensitivity maps ESPRIT max iterations. Default is ``30``.
+ coil_combination_method : str, optional
+ Coil combination method. Default is ``"SENSE"``.
+ dimensionality : int, optional
+ Dimensionality. Default is ``2``.
+ mask_func : Optional[Callable], optional
+ Mask function to retrospectively undersample the k-space. Default is ``None``.
+ shift_mask : bool, optional
+ Whether to shift the mask. This needs to be set alongside with the ``fft_centered`` argument.
+ Default is ``False``.
+ mask_center_scale : Optional[float], optional
+ Center scale of the mask. This defines how much densely sampled will be the center of k-space.
+ Default is ``0.02``.
+ partial_fourier_percentage : float, optional
+ Whether to simulate a half scan. Default is ``0.0``.
+ remask : bool, optional
+ Use the same mask. Default is ``False``.
+ ssdu : bool, optional
+ Whether to apply Self-Supervised Data Undersampling (SSDU) masks. Default is ``False``.
+ ssdu_mask_type: str, optional
+ Mask type. It can be one of the following:
+ - "Gaussian": Gaussian sampling.
+ - "Uniform": Uniform sampling.
+ Default is "Gaussian".
+ ssdu_rho: float, optional
+ Split ratio for training and loss masks. Default is ``0.4``.
+ ssdu_acs_block_size: tuple, optional
+ Keeps a small acs region fully-sampled for training masks, if there is no acs region. The small acs block
+ should be set to zero. Default is ``(4, 4)``.
+ ssdu_gaussian_std_scaling_factor: float, optional
+ Scaling factor for standard deviation of the Gaussian noise. If Uniform is select this factor is ignored.
+ Default is ``4.0``.
+ ssdu_outer_kspace_fraction: float, optional
+ Fraction of the outer k-space to be kept/unmasked. Default is ``0.0``.
+ ssdu_export_and_reuse_masks: bool, optional
+ Whether to export and reuse the masks. Default is ``False``.
+ n2r : bool, optional
+ Whether to apply Noise to Reconstruction (N2R) masks. Default is ``False``.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ n2r_probability : float, optional
+ Probability of applying N2R. Default is ``0.0``.
+ n2r_std_devs : Optional[Tuple[float, float]], optional
+ Standard deviations for the noise. Default is ``(0.0, 0.0)``.
+ n2r_rhos : Optional[Tuple[float, float]], optional
+ Rho values for the noise. Default is ``(0.0, 0.0)``.
+ n2r_use_mask : bool, optional
+ Whether to use a mask for N2R. Default is ``False``.
+ unsupervised_masked_target : bool, optional
+ Whether to use the masked initial estimation for unsupervised learning. Default is ``False``.
+ crop_size : Optional[Tuple[int, int]], optional
+ Center crop size. It applies cropping in image space. Default is ``None``.
+ kspace_crop : bool, optional
+ Whether to crop in k-space. Default is ``False``.
+ crop_before_masking : bool, optional
+ Whether to crop before masking. Default is ``True``.
+ kspace_zero_filling_size : Optional[Tuple], optional
+ Whether to apply zero filling in k-space. Default is ``None``.
+ normalize_inputs : bool, optional
+ Whether to normalize the inputs. Default is ``True``.
+ normalization_type : str, optional
+ Normalization type. Can be ``max`` or ``mean`` or ``minmax``. Default is ``max``.
+ kspace_normalization : bool, optional
+ Whether to normalize the k-space. Default is ``False``.
+ fft_centered : bool, optional
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization : str, optional
+ FFT normalization. Default is ``"backward"``.
+ spatial_dims : Sequence[int], optional
+ Spatial dimensions. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``0``, meaning that the coil dimension is the first dimension before applying
+ batch.
+ consecutive_slices : int, optional
+ Consecutive slices. Default is ``1``.
+ use_seed : bool, optional
+ Whether to use seed. Default is ``True``.
+ """
+ super().__init__()
+
+ self.dataset_format = dataset_format
+
+ self.coil_combination_method = coil_combination_method
+ self.kspace_crop = kspace_crop
+ self.crop_before_masking = crop_before_masking
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim - 1 if dimensionality == 2 else coil_dim
+
+ self.prewhitening = (
+ NoisePreWhitening(
+ find_patch_size=find_patch_size,
+ patch_size=[
+ prewhitening_patch_start,
+ prewhitening_patch_length + prewhitening_patch_start,
+ prewhitening_patch_start,
+ prewhitening_patch_length + prewhitening_patch_start,
+ ],
+ scale_factor=prewhitening_scale_factor,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if apply_prewhitening
+ else None
+ )
+
+ self.gcc = (
+ GeometricDecompositionCoilCompression(
+ virtual_coils=gcc_virtual_coils,
+ calib_lines=gcc_calib_lines,
+ align_data=gcc_align_data,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if apply_gcc
+ else None
+ )
+
+ self.random_motion = (
+ MotionSimulation(
+ motion_type=random_motion_type,
+ angle=random_motion_angle,
+ translation=random_motion_translation,
+ center_percentage=random_motion_center_percentage,
+ motion_percentage=random_motion_percentage,
+ num_segments=random_motion_num_segments,
+ random_num_segments=random_motion_random_num_segments,
+ non_uniform=random_motion_non_uniform,
+ spatial_dims=self.spatial_dims,
+ )
+ if apply_random_motion
+ else None
+ )
+
+ self.coil_sensitivity_maps_estimator = (
+ EstimateCoilSensitivityMaps(
+ coil_sensitivity_maps_type=coil_sensitivity_maps_type.lower(),
+ gaussian_sigma=coil_sensitivity_maps_gaussian_sigma,
+ espirit_threshold=coil_sensitivity_maps_espirit_threshold,
+ espirit_kernel_size=coil_sensitivity_maps_espirit_kernel_size,
+ espirit_crop=coil_sensitivity_maps_espirit_crop,
+ espirit_max_iters=coil_sensitivity_maps_espirit_max_iters,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ )
+ if estimate_coil_sensitivity_maps
+ else None
+ )
+
+ self.kspace_zero_filling = (
+ ZeroFillingPadding(
+ zero_filling_size=kspace_zero_filling_size, # type: ignore
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if not is_none(kspace_zero_filling_size)
+ else None
+ )
+
+ self.shift_mask = shift_mask
+ self.masking = Masker(
+ mask_func=mask_func,
+ spatial_dims=self.spatial_dims,
+ shift_mask=shift_mask,
+ partial_fourier_percentage=partial_fourier_percentage,
+ center_scale=mask_center_scale,
+ dimensionality=dimensionality,
+ remask=remask,
+ dataset_format=self.dataset_format,
+ )
+
+ self.ssdu = ssdu
+ self.ssdu_masking = (
+ SSDU(
+ mask_type=ssdu_mask_type,
+ rho=ssdu_rho,
+ acs_block_size=ssdu_acs_block_size,
+ gaussian_std_scaling_factor=ssdu_gaussian_std_scaling_factor,
+ outer_kspace_fraction=ssdu_outer_kspace_fraction,
+ export_and_reuse_masks=ssdu_export_and_reuse_masks,
+ )
+ if self.ssdu
+ else None
+ )
+
+ self.n2r = n2r
+ self.n2r_supervised_rate = n2r_supervised_rate
+ self.n2r_masking = (
+ N2R(
+ probability=n2r_probability,
+ std_devs=n2r_std_devs, # type: ignore
+ rhos=n2r_rhos, # type: ignore
+ use_mask=n2r_use_mask,
+ )
+ if self.n2r
+ else None
+ )
+
+ self.unsupervised_masked_target = unsupervised_masked_target
+
+ self.cropping = (
+ Cropper(
+ cropping_size=crop_size, # type: ignore
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if not is_none(crop_size)
+ else None
+ )
+
+ self.normalization_type = normalization_type
+ self.normalization = (
+ Normalizer(
+ normalization_type=self.normalization_type,
+ kspace_normalization=kspace_normalization,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if normalize_inputs
+ else None
+ )
+
+ self.prewhitening = Composer([self.prewhitening]) # type: ignore
+ self.coils_shape_transforms = Composer(
+ [
+ self.gcc, # type: ignore
+ self.kspace_zero_filling, # type: ignore
+ ]
+ )
+ self.cropping = Composer([self.cropping]) # type: ignore
+ self.random_motion = Composer([self.random_motion]) # type: ignore
+ self.normalization = Composer([self.normalization]) # type: ignore
+
+ self.use_seed = use_seed
+
+ def __call__(
+ self,
+ kspace: np.ndarray,
+ sensitivity_map: np.ndarray,
+ mask: np.ndarray,
+ initial_prediction: np.ndarray,
+ target: np.ndarray,
+ attrs: Dict,
+ fname: str,
+ slice_idx: int,
+ ) -> Tuple[
+ Union[torch.Tensor, List[torch.Tensor]],
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.Tensor,
+ Union[List[torch.Tensor], torch.Tensor],
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.tensor,
+ str,
+ int,
+ Union[List[Union[float, torch.Tensor, Any]]],
+ Dict,
+ ]:
+ """Calls :class:`MRIDataTransforms`.
+
+ Parameters
+ ----------
+ kspace : np.ndarray
+ The fully-sampled kspace, if exists. Otherwise, the subsampled kspace.
+ sensitivity_map : np.ndarray
+ The coil sensitivity map.
+ mask : np.ndarray
+ The subsampling mask, if exists, meaning that the data are either prospectively undersampled or the mask is
+ stored and loaded.
+ initial_prediction : np.ndarray
+ The initial prediction, if exists. Otherwise, it will be estimated with the chosen coil combination method.
+ target : np.ndarray
+ The target, if exists. Otherwise, it will be estimated with the chosen coil combination method.
+ attrs : Dict
+ The attributes, if stored in the data.
+ fname : str
+ The file name.
+ slice_idx : int
+ The slice index.
+ """
+ kspace, masked_kspace, mask, kspace_pre_normalization_vars, acc = self.__process_kspace__( # type: ignore
+ kspace, mask, attrs, fname
+ )
+ sensitivity_map, sensitivity_pre_normalization_vars = self.__process_coil_sensitivities_map__(
+ sensitivity_map, kspace
+ )
+
+ if self.n2r and len(masked_kspace) > 1: # type: ignore
+ prediction, prediction_pre_normalization_vars = self.__initialize_prediction__(
+ initial_prediction, masked_kspace[0], sensitivity_map # type: ignore
+ )
+ if isinstance(masked_kspace, list) and not masked_kspace[1][0].dim() < 2: # type: ignore
+ noise_prediction, noise_prediction_pre_normalization_vars = self.__initialize_prediction__(
+ None, masked_kspace[1], sensitivity_map # type: ignore
+ )
+ else:
+ noise_prediction = torch.tensor([])
+ noise_prediction_pre_normalization_vars = None
+ prediction = [prediction, noise_prediction]
+ else:
+ prediction, prediction_pre_normalization_vars = self.__initialize_prediction__(
+ initial_prediction, masked_kspace, sensitivity_map # type: ignore
+ )
+ noise_prediction_pre_normalization_vars = None
+
+ if self.unsupervised_masked_target:
+ target, target_pre_normalization_vars = prediction, prediction_pre_normalization_vars
+ else:
+ target, target_pre_normalization_vars = self.__initialize_prediction__(
+ None if self.ssdu else target, kspace, sensitivity_map
+ )
+
+ attrs.update(
+ self.__parse_normalization_vars__(
+ kspace_pre_normalization_vars, # type: ignore
+ sensitivity_pre_normalization_vars,
+ prediction_pre_normalization_vars,
+ noise_prediction_pre_normalization_vars,
+ target_pre_normalization_vars,
+ )
+ )
+ attrs["fname"] = fname
+ attrs["slice_idx"] = slice_idx
+
+ return (
+ kspace,
+ masked_kspace, # type: ignore
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ fname,
+ slice_idx,
+ acc, # type: ignore
+ attrs,
+ )
+
+ def __repr__(self) -> str:
+ """Representation of :class:`MRIDataTransforms`."""
+ return (
+ f"Preprocessing transforms initialized for {self.__class__.__name__}: "
+ f"prewhitening = {self.prewhitening}, "
+ f"masking = {self.masking}, "
+ f"SSDU masking = {self.ssdu_masking}, "
+ f"kspace zero-filling = {self.kspace_zero_filling}, "
+ f"cropping = {self.cropping}, "
+ f"normalization = {self.normalization}, "
+ )
+
+ def __str__(self) -> str:
+ """String representation of :class:`MRIDataTransforms`."""
+ return self.__repr__()
+
+ def __process_kspace__( # noqa: MC0001
+ self, kspace: np.ndarray, mask: Union[np.ndarray, None], attrs: Dict, fname: str
+ ) -> Tuple[torch.Tensor, Union[List[torch.Tensor], torch.Tensor], Union[List[torch.Tensor], torch.Tensor], int]:
+ """Apply the preprocessing transforms to the kspace.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The kspace.
+ mask : torch.Tensor
+ The mask, if None, the mask is generated.
+ attrs : Dict
+ The attributes, if stored in the file.
+ fname : str
+ The file name.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, Union[List[torch.Tensor], torch.Tensor], Union[List[torch.Tensor], torch.Tensor], int]
+ The transformed (fully-sampled) kspace, the masked kspace, the mask, the attributes and the acceleration
+ factor.
+ """
+ kspace = to_tensor(kspace)
+ kspace = add_coil_dim_if_singlecoil(kspace, dim=self.coil_dim)
+
+ kspace = self.coils_shape_transforms(kspace, apply_backward_transform=True)
+ kspace = self.prewhitening(kspace) # type: ignore
+
+ if self.crop_before_masking:
+ kspace = self.cropping(kspace, apply_backward_transform=not self.kspace_crop) # type: ignore
+
+ masked_kspace, mask, acc = self.masking(
+ self.random_motion(kspace), # type: ignore
+ mask,
+ (
+ attrs["padding_left"] if "padding_left" in attrs else 0,
+ attrs["padding_right"] if "padding_right" in attrs else 0,
+ ),
+ tuple(map(ord, fname)) if self.use_seed else None, # type: ignore
+ )
+
+ if not self.crop_before_masking:
+ kspace = self.cropping(kspace, apply_backward_transform=not self.kspace_crop) # type: ignore
+ masked_kspace = self.cropping(masked_kspace, apply_backward_transform=not self.kspace_crop) # type: ignore
+ mask = self.cropping(mask) # type: ignore
+
+ init_kspace = kspace
+ init_masked_kspace = masked_kspace
+ init_mask = mask
+
+ if isinstance(kspace, list):
+ kspaces = []
+ pre_normalization_vars = []
+ for i in range(len(kspace)): # pylint: disable=consider-using-enumerate
+ if not is_none(self.normalization.__repr__()):
+ _kspace, _pre_normalization_vars = self.normalization( # type: ignore
+ kspace[i], apply_backward_transform=True
+ )
+ else:
+ _kspace = kspace[i]
+ is_complex = _kspace.shape[-1] == 2
+ if is_complex:
+ _kspace = torch.view_as_complex(_kspace)
+ _pre_normalization_vars = {
+ "min": torch.min(torch.abs(_kspace)),
+ "max": torch.max(torch.abs(_kspace)),
+ "mean": torch.mean(torch.abs(_kspace)),
+ "std": torch.std(torch.abs(_kspace)),
+ "var": torch.var(torch.abs(_kspace)),
+ }
+ if is_complex:
+ _kspace = torch.view_as_real(_kspace)
+ kspaces.append(_kspace)
+ pre_normalization_vars.append(_pre_normalization_vars)
+ kspace = kspaces
+ else:
+ if not is_none(self.normalization.__repr__()):
+ kspace, pre_normalization_vars = self.normalization( # type: ignore
+ kspace, apply_backward_transform=True
+ )
+ else:
+ is_complex = kspace.shape[-1] == 2
+ if is_complex:
+ kspace = torch.view_as_complex(kspace)
+ pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(kspace)),
+ "max": torch.max(torch.abs(kspace)),
+ "mean": torch.mean(torch.abs(kspace)),
+ "std": torch.std(torch.abs(kspace)),
+ "var": torch.var(torch.abs(kspace)),
+ }
+ if is_complex:
+ kspace = torch.view_as_real(kspace)
+
+ if isinstance(masked_kspace, list):
+ masked_kspaces = []
+ masked_pre_normalization_vars = []
+ for i in range(len(masked_kspace)): # pylint: disable=consider-using-enumerate
+ if not is_none(self.normalization.__repr__()):
+ _masked_kspace, _masked_pre_normalization_vars = self.normalization( # type: ignore
+ masked_kspace[i], apply_backward_transform=True
+ )
+ else:
+ _masked_kspace = masked_kspace[i]
+ is_complex = _masked_kspace.shape[-1] == 2
+ if is_complex:
+ _masked_kspace = torch.view_as_complex(_masked_kspace)
+ _masked_pre_normalization_vars = {
+ "min": torch.min(torch.abs(_masked_kspace)),
+ "max": torch.max(torch.abs(_masked_kspace)),
+ "mean": torch.mean(torch.abs(_masked_kspace)),
+ "std": torch.std(torch.abs(_masked_kspace)),
+ "var": torch.var(torch.abs(_masked_kspace)),
+ }
+ if is_complex:
+ _masked_kspace = torch.view_as_real(_masked_kspace)
+ masked_kspaces.append(_masked_kspace)
+ masked_pre_normalization_vars.append(_masked_pre_normalization_vars)
+ masked_kspace = masked_kspaces
+ else:
+ if not is_none(self.normalization.__repr__()):
+ masked_kspace, masked_pre_normalization_vars = self.normalization(
+ masked_kspace, apply_backward_transform=True
+ )
+ else:
+ is_complex = masked_kspace.shape[-1] == 2
+ if is_complex:
+ masked_kspace = torch.view_as_complex(masked_kspace)
+ masked_pre_normalization_vars = {
+ "min": torch.min(torch.abs(masked_kspace)),
+ "max": torch.max(torch.abs(masked_kspace)),
+ "mean": torch.mean(torch.abs(masked_kspace)),
+ "std": torch.std(torch.abs(masked_kspace)),
+ "var": torch.var(torch.abs(masked_kspace)),
+ }
+ if is_complex:
+ masked_kspace = torch.view_as_real(masked_kspace)
+
+ if self.ssdu:
+ kspace, masked_kspace, mask = self.__self_supervised_data_undersampling__( # type: ignore
+ kspace, masked_kspace, mask, fname
+ )
+
+ n2r_pre_normalization_vars = None
+ if self.n2r and (not attrs["n2r_supervised"] or self.ssdu):
+ n2r_masked_kspace, n2r_mask = self.__noise_to_reconstruction__(init_kspace, init_masked_kspace, init_mask)
+
+ if self.ssdu:
+ if isinstance(mask, list):
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ if init_mask[i].dim() != mask[i][0].dim(): # type: ignore
+ # find dimensions == 1 in mask[i][0] and add them to init_mask
+ unitary_dims = [j for j in range(mask[i][0].dim()) if mask[i][0].shape[j] == 1]
+ # unsqueeze init_mask to the index of the unitary dimensions
+ for j in unitary_dims:
+ init_mask[i] = init_mask[i].unsqueeze(j) # type: ignore
+ masked_kspace[i] = init_masked_kspace[i]
+ mask[i][0] = init_mask[i]
+ else:
+ if init_mask.dim() != mask[0].dim(): # type: ignore
+ # find dimensions == 1 in mask[0] and add them to init_mask
+ unitary_dims = [j for j in range(mask[0].dim()) if mask[0].shape[j] == 1]
+ # unsqueeze init_mask to the index of the unitary dimensions
+ for j in unitary_dims:
+ init_mask = init_mask.unsqueeze(j) # type: ignore
+ masked_kspace = init_masked_kspace
+ mask[0] = init_mask
+
+ if "None" not in self.normalization.__repr__():
+ if isinstance(masked_kspace, list):
+ masked_kspaces = []
+ masked_pre_normalization_vars = []
+ for i in range(len(masked_kspace)): # pylint: disable=consider-using-enumerate
+ _masked_kspace, _masked_pre_normalization_vars = self.normalization( # type: ignore
+ masked_kspace[i], apply_backward_transform=True
+ )
+ masked_kspaces.append(_masked_kspace)
+ masked_pre_normalization_vars.append(_masked_pre_normalization_vars)
+ masked_kspace = masked_kspaces
+ else:
+ masked_kspace, masked_pre_normalization_vars = self.normalization( # type: ignore
+ masked_kspace, apply_backward_transform=True
+ )
+ if isinstance(n2r_masked_kspace, list):
+ n2r_masked_kspaces = []
+ n2r_pre_normalization_vars = []
+ for i in range(len(n2r_masked_kspace)): # pylint: disable=consider-using-enumerate
+ _n2r_masked_kspace, _n2r_pre_normalization_vars = self.normalization( # type: ignore
+ n2r_masked_kspace[i], apply_backward_transform=True
+ )
+ n2r_masked_kspaces.append(_n2r_masked_kspace)
+ n2r_pre_normalization_vars.append(_n2r_pre_normalization_vars)
+ n2r_masked_kspace = n2r_masked_kspaces
+ else:
+ n2r_masked_kspace, n2r_pre_normalization_vars = self.normalization( # type: ignore
+ n2r_masked_kspace, apply_backward_transform=True
+ )
+ else:
+ masked_pre_normalization_vars = None # type: ignore
+ n2r_pre_normalization_vars = None # type: ignore
+
+ masked_kspace = [masked_kspace, n2r_masked_kspace]
+ mask = [mask, n2r_mask]
+
+ if self.normalization_type == "grayscale":
+ if isinstance(mask, list):
+ masks = []
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ _mask, _ = self.normalization(mask[i], apply_backward_transform=False) # type: ignore
+ masks.append(_mask)
+ mask = masks
+ else:
+ mask, _ = self.normalization(mask, apply_backward_transform=False) # type: ignore
+
+ pre_normalization_vars = { # type: ignore
+ "kspace_pre_normalization_vars": pre_normalization_vars,
+ "masked_kspace_pre_normalization_vars": masked_pre_normalization_vars,
+ "noise_masked_kspace_pre_normalization_vars": n2r_pre_normalization_vars,
+ }
+
+ return kspace, masked_kspace, mask, pre_normalization_vars, acc # type: ignore
+
+ def __noise_to_reconstruction__(
+ self,
+ kspace: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ mask: Union[List, torch.Tensor],
+ ) -> Tuple[Union[List, torch.Tensor], Union[List, torch.Tensor]]:
+ """Apply the noise-to-reconstruction transform.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The fully-sampled kspace.
+ masked_kspace : torch.Tensor
+ The undersampled kspace.
+ mask : Union[List, torch.Tensor]
+ The undersampling mask.
+
+ Returns
+ -------
+ n2r_masked_kspace : Union[List, torch.Tensor]
+ The noise-to-reconstruction undersampled kspace.
+ n2r_mask : Union[List, torch.Tensor]
+ The noise-to-reconstruction mask.
+ """
+ if isinstance(mask, list):
+ n2r_masked_kspaces = []
+ n2r_masks = []
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ n2r_mask = self.n2r_masking(kspace, mask[i]) # type: ignore # pylint: disable=not-callable
+ n2r_masks.append(n2r_mask)
+ n2r_masked_kspaces.append(masked_kspace[i] * n2r_mask + 0.0)
+ n2r_mask = n2r_masks
+ n2r_masked_kspace = n2r_masked_kspaces
+ else:
+ n2r_mask = self.n2r_masking(kspace, mask) # type: ignore # pylint: disable=not-callable
+ n2r_masked_kspace = masked_kspace * n2r_mask + 0.0
+ return n2r_masked_kspace, n2r_mask
+
+ def __self_supervised_data_undersampling__( # noqa: MC0001
+ self,
+ kspace: torch.Tensor,
+ masked_kspace: Union[List, torch.Tensor],
+ mask: Union[List, torch.Tensor],
+ fname: str,
+ ) -> Tuple[
+ List[float | Any] | float | Any,
+ List[float | Any] | float | Any,
+ List[List[torch.Tensor | Any]] | List[torch.Tensor | Any],
+ ]:
+ """Self-supervised data undersampling.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The fully-sampled kspace.
+ masked_kspace : Union[List, torch.Tensor]
+ The undersampled kspace.
+ mask : Union[List, torch.Tensor]
+ The undersampling mask.
+ fname : str
+ The filename of the current sample.
+
+ Returns
+ -------
+ kspace : torch.Tensor
+ The kspace with the loss mask applied.
+ masked_kspace : torch.Tensor
+ The kspace with the train mask applied.
+ mask : list, [torch.Tensor, torch.Tensor]
+ The train and loss masks.
+ """
+ if isinstance(mask, list):
+ kspaces = []
+ masked_kspaces = []
+ masks = []
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ is_1d = mask[i].squeeze().dim() == 1
+ if self.shift_mask:
+ mask[i] = torch.fft.fftshift(mask[i].squeeze(-1), dim=(-2, -1)).unsqueeze(-1)
+ mask[i] = mask[i].squeeze()
+ if is_1d:
+ mask[i] = mask[i].unsqueeze(0).repeat_interleave(kspace.shape[1], dim=0)
+ train_mask, loss_mask = self.ssdu_masking( # type: ignore # pylint: disable=not-callable
+ kspace, mask[i], fname
+ )
+ if self.shift_mask:
+ train_mask = torch.fft.fftshift(train_mask, dim=(0, 1))
+ loss_mask = torch.fft.fftshift(loss_mask, dim=(0, 1))
+ if is_1d:
+ train_mask = train_mask.unsqueeze(0).unsqueeze(-1)
+ loss_mask = loss_mask.unsqueeze(0).unsqueeze(-1)
+ else:
+ # find unitary dims in mask
+ dims = [i for i, x in enumerate(mask[i].shape) if x == 1]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ if train_mask.dim() != kspace.dim():
+ # find dims != to any train_mask dim
+ dims = [i for i, x in enumerate(kspace.shape) if x not in train_mask.shape]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ kspaces.append(kspace * loss_mask + 0.0)
+ masked_kspaces.append(masked_kspace[i] * train_mask + 0.0)
+ masks.append([train_mask, loss_mask])
+ kspace = kspaces
+ masked_kspace = masked_kspaces
+ mask = masks
+ else:
+ is_1d = mask.squeeze().dim() == 1
+ if self.shift_mask:
+ mask = torch.fft.fftshift(mask.squeeze(-1), dim=(-2, -1)).unsqueeze(-1)
+ mask = mask.squeeze()
+ if is_1d:
+ mask = mask.unsqueeze(0).repeat_interleave(kspace.shape[1], dim=0)
+ train_mask, loss_mask = self.ssdu_masking( # type: ignore # pylint: disable=not-callable
+ kspace, mask, fname
+ )
+ if self.shift_mask:
+ train_mask = torch.fft.fftshift(train_mask, dim=(0, 1))
+ loss_mask = torch.fft.fftshift(loss_mask, dim=(0, 1))
+ if is_1d:
+ train_mask = train_mask.unsqueeze(0).unsqueeze(-1)
+ loss_mask = loss_mask.unsqueeze(0).unsqueeze(-1)
+ else:
+ # find unitary dims in mask
+ dims = [i for i, x in enumerate(mask.shape) if x == 1]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ if train_mask.dim() != kspace.dim():
+ # find dims != to any train_mask dim
+ dims = [i for i, x in enumerate(kspace.shape) if x not in train_mask.shape]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ kspace = kspace * loss_mask + 0.0
+ masked_kspace = masked_kspace * train_mask + 0.0
+ mask = [train_mask, loss_mask]
+ return kspace, masked_kspace, mask
+
+ def __process_coil_sensitivities_map__(
+ self, sensitivity_map: np.ndarray, kspace: torch.Tensor
+ ) -> Union[torch.Tensor, Dict]:
+ """Preprocesses the coil sensitivities map.
+
+ Parameters
+ ----------
+ sensitivity_map : np.ndarray
+ The coil sensitivities map.
+ kspace : torch.Tensor
+ The kspace.
+
+ Returns
+ -------
+ List[torch.Tensor, Dict]
+ The preprocessed coil sensitivities map and the normalization variables.
+ """
+ # This condition is necessary in case of auto estimation of sense maps.
+ if self.coil_sensitivity_maps_estimator is not None:
+ sensitivity_map = self.coil_sensitivity_maps_estimator(kspace)
+ elif sensitivity_map is not None and sensitivity_map.size != 0:
+ sensitivity_map = to_tensor(sensitivity_map)
+ sensitivity_map = self.coils_shape_transforms(sensitivity_map, apply_forward_transform=True)
+ sensitivity_map = self.cropping(sensitivity_map, apply_forward_transform=self.kspace_crop) # type: ignore
+ else:
+ # If no sensitivity map is provided, either the data is singlecoil or the sense net is used.
+ # Initialize the sensitivity map to 1 to assure for the singlecoil case.
+ sensitivity_map = torch.ones_like(kspace) if not isinstance(kspace, list) else torch.ones_like(kspace[0])
+
+ if not is_none(self.normalization.__repr__()):
+ sensitivity_map, pre_normalization_vars = self.normalization( # type: ignore
+ sensitivity_map, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ is_complex = sensitivity_map.shape[-1] == 2
+ if is_complex:
+ sensitivity_map = torch.view_as_complex(sensitivity_map)
+ pre_normalization_vars = {
+ "min": torch.min(torch.abs(sensitivity_map)),
+ "max": torch.max(torch.abs(sensitivity_map)),
+ "mean": torch.mean(torch.abs(sensitivity_map)),
+ "std": torch.std(torch.abs(sensitivity_map)),
+ "var": torch.var(torch.abs(sensitivity_map)),
+ }
+ if is_complex:
+ sensitivity_map = torch.view_as_real(sensitivity_map)
+ return sensitivity_map, pre_normalization_vars
+
+ def __initialize_prediction__(
+ self, prediction: Union[np.ndarray, None], kspace: torch.Tensor, sensitivity_map: torch.Tensor
+ ) -> Tuple[Union[List[torch.Tensor], torch.Tensor], Dict]:
+ """Predicts a coil-combined image.
+
+ Parameters
+ ----------
+ prediction : np.ndarray
+ The initial estimation, if None, the prediction is initialized.
+ kspace : torch.Tensor
+ The kspace.
+ sensitivity_map : torch.Tensor
+ The sensitivity map.
+
+ Returns
+ -------
+ Tuple[Union[List[torch.Tensor], torch.Tensor], Dict]
+ The initialized prediction, either a list of coil-combined images or a single coil-combined image and the
+ pre-normalization variables (min, max, mean, std).
+ """
+ if is_none(prediction) or prediction.ndim < 2 or isinstance(kspace, list): # type: ignore
+ if isinstance(kspace, list):
+ prediction = []
+ pre_normalization_vars = []
+ for y in kspace:
+ pred = coil_combination_method_func(
+ ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_map,
+ method=self.coil_combination_method,
+ dim=self.coil_dim,
+ )
+ pred = self.cropping(pred, apply_forward_transform=self.kspace_crop) # type: ignore
+ if not is_none(self.normalization.__repr__()):
+ pred, _pre_normalization_vars = self.normalization( # type: ignore
+ pred, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ if pred.shape[-1] == 2:
+ pred = torch.view_as_complex(pred)
+ _pre_normalization_vars = {
+ "min": torch.min(torch.abs(pred)),
+ "max": torch.max(torch.abs(pred)),
+ "mean": torch.mean(torch.abs(pred)),
+ "std": torch.std(torch.abs(pred)),
+ "var": torch.var(torch.abs(pred)),
+ }
+ prediction.append(pred)
+ pre_normalization_vars.append(_pre_normalization_vars)
+ if prediction[0].shape[-1] != 2 and torch.is_complex(prediction[0]):
+ prediction = [torch.view_as_real(x) for x in prediction]
+ else:
+ prediction = coil_combination_method_func(
+ ifft2(kspace, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_map,
+ method=self.coil_combination_method,
+ dim=self.coil_dim,
+ )
+ prediction = self.cropping(prediction, apply_forward_transform=self.kspace_crop) # type: ignore
+ if not is_none(self.normalization.__repr__()):
+ prediction, pre_normalization_vars = self.normalization( # type: ignore
+ prediction, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ if prediction.shape[-1] == 2:
+ prediction = torch.view_as_complex(prediction)
+ pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(prediction)),
+ "max": torch.max(torch.abs(prediction)),
+ "mean": torch.mean(torch.abs(prediction)),
+ "std": torch.std(torch.abs(prediction)),
+ "var": torch.var(torch.abs(prediction)),
+ }
+ if prediction.shape[-1] != 2 and torch.is_complex(prediction):
+ prediction = torch.view_as_real(prediction)
+ else:
+ if isinstance(prediction, np.ndarray):
+ prediction = to_tensor(prediction)
+ prediction = self.cropping(prediction, apply_forward_transform=self.kspace_crop) # type: ignore
+ if not is_none(self.normalization.__repr__()):
+ prediction, pre_normalization_vars = self.normalization( # type: ignore
+ prediction, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ if prediction.shape[-1] == 2: # type: ignore
+ prediction = torch.view_as_complex(prediction)
+ pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(prediction)),
+ "max": torch.max(torch.abs(prediction)),
+ "mean": torch.mean(torch.abs(prediction)),
+ "std": torch.std(torch.abs(prediction)),
+ "var": torch.var(torch.abs(prediction)),
+ }
+ if prediction.shape[-1] != 2 and torch.is_complex(prediction):
+ prediction = torch.view_as_real(prediction)
+ return prediction, pre_normalization_vars # type: ignore
+
+ def __parse_normalization_vars__( # noqa: MC0001
+ self, kspace_vars, sensitivity_vars, prediction_vars, noise_prediction_vars, target_vars
+ ) -> Dict:
+ """
+ Parses the normalization variables and returns a unified dictionary.
+
+ Parameters
+ ----------
+ kspace_vars : Dict
+ The kspace normalization variables.
+ sensitivity_vars : Dict
+ The sensitivity map normalization variables.
+ prediction_vars : Dict
+ The prediction normalization variables.
+ noise_prediction_vars : Union[Dict, None]
+ The noise prediction normalization variables.
+ target_vars : Dict
+ The target normalization variables.
+
+ Returns
+ -------
+ Dict
+ The normalization variables.
+ """
+ normalization_vars = {}
+
+ masked_kspace_vars = kspace_vars["masked_kspace_pre_normalization_vars"]
+ if isinstance(masked_kspace_vars, list):
+ if masked_kspace_vars[0] is not None:
+ for i, masked_kspace_var in enumerate(masked_kspace_vars):
+ normalization_vars[f"masked_kspace_min_{i}"] = masked_kspace_var["min"]
+ normalization_vars[f"masked_kspace_max_{i}"] = masked_kspace_var["max"]
+ normalization_vars[f"masked_kspace_mean_{i}"] = masked_kspace_var["mean"]
+ normalization_vars[f"masked_kspace_std_{i}"] = masked_kspace_var["std"]
+ normalization_vars[f"masked_kspace_var_{i}"] = masked_kspace_var["var"]
+ else:
+ if masked_kspace_vars is not None:
+ normalization_vars["masked_kspace_min"] = masked_kspace_vars["min"]
+ normalization_vars["masked_kspace_max"] = masked_kspace_vars["max"]
+ normalization_vars["masked_kspace_mean"] = masked_kspace_vars["mean"]
+ normalization_vars["masked_kspace_std"] = masked_kspace_vars["std"]
+ normalization_vars["masked_kspace_var"] = masked_kspace_vars["var"]
+
+ noise_masked_kspace_vars = kspace_vars["noise_masked_kspace_pre_normalization_vars"]
+ if noise_masked_kspace_vars is not None:
+ if isinstance(noise_masked_kspace_vars, list):
+ if noise_masked_kspace_vars[0] is not None:
+ for i, noise_masked_kspace_var in enumerate(noise_masked_kspace_vars):
+ normalization_vars[f"noise_masked_kspace_min_{i}"] = noise_masked_kspace_var["min"]
+ normalization_vars[f"noise_masked_kspace_max_{i}"] = noise_masked_kspace_var["max"]
+ normalization_vars[f"noise_masked_kspace_mean_{i}"] = noise_masked_kspace_var["mean"]
+ normalization_vars[f"noise_masked_kspace_std_{i}"] = noise_masked_kspace_var["std"]
+ normalization_vars[f"noise_masked_kspace_var_{i}"] = noise_masked_kspace_var["var"]
+ else:
+ if noise_masked_kspace_vars is not None:
+ normalization_vars["noise_masked_kspace_min"] = noise_masked_kspace_vars["min"]
+ normalization_vars["noise_masked_kspace_max"] = noise_masked_kspace_vars["max"]
+ normalization_vars["noise_masked_kspace_mean"] = noise_masked_kspace_vars["mean"]
+ normalization_vars["noise_masked_kspace_std"] = noise_masked_kspace_vars["std"]
+ normalization_vars["noise_masked_kspace_var"] = noise_masked_kspace_vars["var"]
+
+ kspace_vars = kspace_vars["kspace_pre_normalization_vars"]
+ if isinstance(kspace_vars, list):
+ if kspace_vars[0] is not None:
+ for i, kspace_var in enumerate(kspace_vars):
+ normalization_vars[f"kspace_min_{i}"] = kspace_var["min"]
+ normalization_vars[f"kspace_max_{i}"] = kspace_var["max"]
+ normalization_vars[f"kspace_mean_{i}"] = kspace_var["mean"]
+ normalization_vars[f"kspace_std_{i}"] = kspace_var["std"]
+ normalization_vars[f"kspace_var_{i}"] = kspace_var["var"]
+ else:
+ if kspace_vars is not None:
+ normalization_vars["kspace_min"] = kspace_vars["min"]
+ normalization_vars["kspace_max"] = kspace_vars["max"]
+ normalization_vars["kspace_mean"] = kspace_vars["mean"]
+ normalization_vars["kspace_std"] = kspace_vars["std"]
+ normalization_vars["kspace_var"] = kspace_vars["var"]
+
+ if sensitivity_vars is not None:
+ normalization_vars["sensitivity_maps_min"] = sensitivity_vars["min"]
+ normalization_vars["sensitivity_maps_max"] = sensitivity_vars["max"]
+ normalization_vars["sensitivity_maps_mean"] = sensitivity_vars["mean"]
+ normalization_vars["sensitivity_maps_std"] = sensitivity_vars["std"]
+ normalization_vars["sensitivity_maps_var"] = sensitivity_vars["var"]
+
+ if isinstance(prediction_vars, list):
+ if prediction_vars[0] is not None:
+ for i, prediction_var in enumerate(prediction_vars):
+ normalization_vars[f"prediction_min_{i}"] = prediction_var["min"]
+ normalization_vars[f"prediction_max_{i}"] = prediction_var["max"]
+ normalization_vars[f"prediction_mean_{i}"] = prediction_var["mean"]
+ normalization_vars[f"prediction_std_{i}"] = prediction_var["std"]
+ normalization_vars[f"prediction_var_{i}"] = prediction_var["var"]
+ else:
+ if prediction_vars is not None:
+ normalization_vars["prediction_min"] = prediction_vars["min"]
+ normalization_vars["prediction_max"] = prediction_vars["max"]
+ normalization_vars["prediction_mean"] = prediction_vars["mean"]
+ normalization_vars["prediction_std"] = prediction_vars["std"]
+ normalization_vars["prediction_var"] = prediction_vars["var"]
+
+ if noise_prediction_vars is not None:
+ if isinstance(noise_prediction_vars, list):
+ for i, noise_prediction_var in enumerate(noise_prediction_vars):
+ normalization_vars[f"noise_prediction_min_{i}"] = noise_prediction_var["min"]
+ normalization_vars[f"noise_prediction_max_{i}"] = noise_prediction_var["max"]
+ normalization_vars[f"noise_prediction_mean_{i}"] = noise_prediction_var["mean"]
+ normalization_vars[f"noise_prediction_std_{i}"] = noise_prediction_var["std"]
+ normalization_vars[f"noise_prediction_var_{i}"] = noise_prediction_var["var"]
+ else:
+ normalization_vars["noise_prediction_min"] = noise_prediction_vars["min"]
+ normalization_vars["noise_prediction_max"] = noise_prediction_vars["max"]
+ normalization_vars["noise_prediction_mean"] = noise_prediction_vars["mean"]
+ normalization_vars["noise_prediction_std"] = noise_prediction_vars["std"]
+ normalization_vars["noise_prediction_var"] = noise_prediction_vars["var"]
+
+ if isinstance(target_vars, list):
+ if target_vars[0] is not None:
+ for i, target_var in enumerate(target_vars):
+ normalization_vars[f"target_min_{i}"] = target_var["min"]
+ normalization_vars[f"target_max_{i}"] = target_var["max"]
+ normalization_vars[f"target_mean_{i}"] = target_var["mean"]
+ normalization_vars[f"target_std_{i}"] = target_var["std"]
+ normalization_vars[f"target_var_{i}"] = target_var["var"]
+ else:
+ if target_vars is not None:
+ normalization_vars["target_min"] = target_vars["min"]
+ normalization_vars["target_max"] = target_vars["max"]
+ normalization_vars["target_mean"] = target_vars["mean"]
+ normalization_vars["target_std"] = target_vars["std"]
+ normalization_vars["target_var"] = target_vars["var"]
+
+ return normalization_vars
diff --git a/atommic/collections/common/parts/utils.py b/atommic/collections/common/parts/utils.py
new file mode 100644
index 00000000..f513cc42
--- /dev/null
+++ b/atommic/collections/common/parts/utils.py
@@ -0,0 +1,1097 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from https://github.com/facebookresearch/fastMRI
+
+from pathlib import Path
+from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
+
+import h5py
+import numpy as np
+import torch
+
+__all__ = [
+ "add_coil_dim_if_singlecoil",
+ "apply_mask",
+ "batched_mask_center",
+ "center_crop",
+ "center_crop_to_smallest",
+ "check_stacked_complex",
+ "coil_combination_method",
+ "complex_abs",
+ "complex_abs_sq",
+ "complex_center_crop",
+ "complex_conj",
+ "complex_mul",
+ "crop_to_acs",
+ "expand_op",
+ "is_none",
+ "mask_center",
+ "normalize_inplace",
+ "parse_list_and_keep_last",
+ "reshape_fortran",
+ "rnn_weights_init",
+ "rss",
+ "rss_complex",
+ "save_predictions",
+ "sense",
+ "to_tensor",
+ "unnormalize",
+ "zero_nan_inf",
+]
+
+
+def add_coil_dim_if_singlecoil(x: torch.tensor, dim: int = 0) -> torch.tensor:
+ """
+ Add dummy coil dimension if single coil data.
+
+ Parameters
+ ----------
+ x : torch.tensor
+ The input data.
+ dim : int
+ The dimension to add coil dimension. Default is ``0``.
+
+ Returns
+ -------
+ torch.tensor
+ The input data with coil dimension added if single coil.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.utils import add_coil_dim_if_singlecoil
+ >>> data = torch.rand(10, 10)
+ >>> data.shape
+ (10, 10)
+ >>> add_coil_dim_if_singlecoil(data).shape
+ (1, 10, 10)
+ >>> add_coil_dim_if_singlecoil(data, dim=-1).shape
+ (10, 10, 1)
+ """
+ if len(x.shape) >= 4:
+ return x
+ return torch.unsqueeze(x, dim=dim)
+
+
+def apply_mask(
+ x: torch.Tensor,
+ mask_func: Callable,
+ seed: Optional[Union[int, Tuple[int, ...]]] = None,
+ padding: Optional[Sequence[int]] = None,
+ shift: bool = False,
+ partial_fourier_percentage: Optional[float] = 0.0,
+ center_scale: Optional[float] = 0.02,
+ existing_mask: Optional[torch.Tensor] = None,
+) -> Tuple[Any, Any, int]:
+ """
+ Retrospectively accelerate/subsample k-space data by applying a mask to the input data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The input k-space data. This should have at least 3 dimensions, where dimensions -3 and -2 are the spatial
+ dimensions, and the final dimension has size 2 (for complex values).
+ mask_func : Callable
+ A function that takes a shape (tuple of ints) and a random number seed and returns a mask.
+ seed : Optional[Union[int, Tuple[int, ...]]], optional
+ Seed for the random number generator. Default is ``None``.
+ padding : Optional[Sequence[int]], optional
+ Padding value to apply for mask. Default is ``None``.
+ shift : bool, optional
+ Toggle to shift mask when subsampling. Applicable on 2D data. Default is ``False``.
+ partial_fourier_percentage : Optional[float], optional
+ Percentage of kspace to be dropped. Default is ``0.0``.
+ center_scale : Optional[float], optional
+ Scale of the center of the mask. Applicable on Gaussian masks. Default is ``0.02``.
+ existing_mask : Optional[torch.Tensor], optional
+ When given, use this mask instead of generating a new one. Default is ``None``.
+
+ Returns
+ -------
+ Tuple[Any, Any, int]
+ Tuple containing the masked k-space data, the mask, and the acceleration factor.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import apply_mask
+ >>> import torch
+ >>> data = torch.tensor([[[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]], \
+ [[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]]])
+ >>> data.shape
+ torch.Size([2, 2, 3, 2])
+ >>> mask = torch.tensor([[[1., 1., 1.], [1., 1., 1.]], [[1., 1., 1.], [1., 1., 1.]]])
+ >>> mask.shape
+ torch.Size([2, 2, 3])
+ >>> apply_mask(data, mask)
+ (tensor([[[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]],
+ [[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]]]),
+ tensor([[[1., 1., 1.], [1., 1., 1.]], [[1., 1., 1.], [1., 1., 1.]]]),
+ 6)
+ >>> masked_data, subsampling_mask, acceleration_factor = apply_mask(data, mask)
+ >>> masked_data.shape
+ torch.Size([2, 2, 3, 2])
+ >>> subsampling_mask.shape
+ torch.Size([2, 2, 3])
+ >>> acceleration_factor
+ 6
+ >>> apply_mask(data, mask, padding=[1, 2], shift=True)
+ (tensor([[[[0., 0.], [0., 0.], [0., 0.]], [[1., 1.], [2., 2.], [3., 3.]]],
+ [[[0., 0.], [0., 0.], [0., 0.]], [[1., 1.], [2., 2.], [3., 3.]]]]),
+ tensor([[[0., 0., 0.], [1., 1., 1.]], [[0., 0., 0.], [1., 1., 1.]]]),
+ 3)
+ >>> masked_data, subsampling_mask, acceleration_factor = apply_mask(data, mask, padding=[1, 2], shift=True)
+ >>> masked_data.shape
+ torch.Size([2, 2, 3, 2])
+ >>> subsampling_mask.shape
+ torch.Size([2, 2, 3])
+ >>> acceleration_factor
+ 3
+ """
+ shape = np.array(x.shape)
+ shape[:-3] = 1
+
+ if existing_mask is None:
+ mask, acc = mask_func(shape, seed, partial_fourier_percentage=partial_fourier_percentage, scale=center_scale)
+ else:
+ mask = existing_mask
+ acc = mask.size / mask.sum()
+
+ mask = mask.to(x.device)
+
+ if padding is not None and (padding[0] > 0 or padding[1] > 0):
+ mask[..., : padding[0], :] = 0
+ mask[..., padding[1] :, :] = 0 # padding value inclusive on right of zeros
+
+ if shift:
+ mask = torch.fft.fftshift(mask, dim=(1, 2))
+
+ masked_x = x * mask + 0.0 # the + 0.0 removes the sign of the zeros
+
+ return masked_x, mask, acc
+
+
+def batched_mask_center(
+ x: torch.Tensor, mask_from: torch.Tensor, mask_to: torch.Tensor, mask_type: str = "2D"
+) -> torch.Tensor:
+ """
+ Initializes a mask with the center filled in. Can operate with different masks for each batch element.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The input image or batch of images. This should have at least 3 dimensions, where dimensions -3 and -2 are the
+ spatial dimensions, and the final dimension has size 1 (for real values).
+ mask_from : torch.Tensor
+ Part of center to start filling.
+ mask_to : torch.Tensor
+ Part of center to end filling.
+ mask_type : str, optional
+ Type of mask to apply. Can be either ``1D`` or ``2D``. Default is ``2D``.
+
+ Returns
+ -------
+ torch.Tensor
+ The masked image or batch of images with filled center.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import batched_mask_center
+ >>> import torch
+ >>> data = torch.randn(1, 32, 320, 320)
+ >>> batched_mask_center(data, torch.tensor([140]), torch.tensor([180]))
+ """
+ if mask_from.shape != mask_to.shape:
+ raise ValueError("mask_from and mask_to must match shapes.")
+ if mask_from.ndim != 1:
+ raise ValueError("mask_from and mask_to must have 1 dimension.")
+ if mask_from.shape[0] not in (1, x.shape[0]) or x.shape[0] != mask_to.shape[0]:
+ raise ValueError("mask_from and mask_to must have batch_size length.")
+
+ if mask_from.shape[0] == 1:
+ mask = mask_center(x, int(mask_from), int(mask_to), mask_type=mask_type)
+ else:
+ mask = torch.zeros_like(x)
+ for i, (start, end) in enumerate(zip(mask_from, mask_to)):
+ mask[i, :, :, start:end] = x[i, :, :, start:end]
+
+ return mask
+
+
+def center_crop(x: torch.Tensor, shape: Tuple[int, int]) -> torch.Tensor:
+ """
+ Apply a center crop to the input complex image or batch of complex images or real image or batch of real images
+ without a complex dimension.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The input tensor to be center cropped. It should have at least 2 dimensions and the cropping is applied along
+ the last two dimensions.
+ shape : Tuple[int, int]
+ The output shape. The shape should be smaller than the corresponding dimensions of data.
+
+ Returns
+ -------
+ torch.Tensor
+ The center cropped image or batch of images.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import center_crop
+ >>> import torch
+ >>> data = torch.tensor([[[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]], [[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]]])
+ >>> data.shape
+ torch.Size([2, 2, 3])
+ >>> center_crop(data, (1, 2))
+ tensor([[[2.+2.j, 3.+3.j]], [[2.+2.j, 3.+3.j]]])
+ >>> center_crop(data, (1, 2)).shape
+ torch.Size([2, 1, 2])
+ """
+ if not (0 < shape[0] <= x.shape[-2] and 0 < shape[1] <= x.shape[-1]):
+ raise ValueError("Invalid shapes.")
+
+ w_from = torch.div((x.shape[-2] - shape[0]), 2, rounding_mode="trunc")
+ h_from = torch.div((x.shape[-1] - shape[1]), 2, rounding_mode="trunc")
+ w_to = w_from + shape[0]
+ h_to = h_from + shape[1]
+
+ return x[..., w_from:w_to, h_from:h_to]
+
+
+def center_crop_to_smallest(
+ x: Union[torch.Tensor, np.ndarray], y: Union[torch.Tensor, np.ndarray]
+) -> Tuple[Union[torch.Tensor, np.ndarray], Union[torch.Tensor, np.ndarray]]:
+ """
+ Apply a center crop on the larger image to the size of the smaller.
+
+ The minimum is taken over dim=-1 and dim=-2. If x is smaller than y at dim=-1 and y is smaller than x at dim=-2,
+ then the returned dimension will be a mixture of the two.
+
+ Parameters
+ ----------
+ x : torch.Tensor or np.ndarray
+ The first image.
+ y : torch.Tensor or np.ndarray
+ The second image.
+
+ Returns
+ -------
+ Tuple[torch.Tensor or np.ndarray, torch.Tensor or np.ndarray]
+ Tuple of x and y, cropped to the minimum size.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import center_crop_to_smallest
+ >>> import torch
+ >>> data1 = torch.tensor([[[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]], [[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]]])
+ >>> data2 = torch.tensor([[[1+1j, 2+2j, 3+3j, 4+4j, 5+5j], [1+1j, 2+2j, 3+3j, 4+4j, 5+5j]], \
+ [[1+1j, 2+2j, 3+3j, 4+4j, 5+5j], [1+1j, 2+2j, 3+3j, 4+4j, 5+5j], [1+1j, 2+2j, 3+3j, 4+4j, 5+5j]]])
+ >>> data1.shape
+ torch.Size([2, 2, 3])
+ >>> data2.shape
+ torch.Size([2, 3, 5])
+ >>> center_crop_to_smallest(data1, data2)
+ (tensor([[[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]], [[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]]]), \
+ tensor([[[1.+1.j, 2.+2.j, 3.+3.j], [1.+1.j, 2.+2.j, 3.+3.j]], \
+ [[1.+1.j, 2.+2.j, 3.+3.j], [1.+1.j, 2.+2.j, 3.+3.j]]]))
+ >>> center_crop_to_smallest(data1, data2)[0].shape
+ torch.Size([2, 2, 3])
+ >>> center_crop_to_smallest(data1, data2)[1].shape
+ torch.Size([2, 2, 3])
+ >>> center_crop_to_smallest(data2, data1)
+ (tensor([[[1.+1.j, 2.+2.j, 3.+3.j], [1.+1.j, 2.+2.j, 3.+3.j]], \
+ [[1.+1.j, 2.+2.j, 3.+3.j], [1.+1.j, 2.+2.j, 3.+3.j]]]), \
+ tensor([[[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]], [[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]]]))
+ >>> center_crop_to_smallest(data2, data1)[0].shape
+ torch.Size([2, 2, 3])
+ >>> center_crop_to_smallest(data2, data1)[1].shape
+ torch.Size([2, 2, 3])
+ """
+ smallest_width = min(x.shape[-1], y.shape[-1])
+ smallest_height = min(x.shape[-2], y.shape[-2])
+ x = center_crop(x, (smallest_height, smallest_width))
+ y = center_crop(y, (smallest_height, smallest_width))
+ return x, y
+
+
+def check_stacked_complex(x: torch.Tensor) -> torch.Tensor:
+ """
+ Check if tensor is stacked complex (real & imaginary parts stacked along last dim) and convert it to a combined
+ complex tensor.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Tensor to check.
+
+ Returns
+ -------
+ torch.Tensor
+ Tensor with stacked complex converted to combined complex.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import check_stacked_complex
+ >>> import torch
+ >>> data = torch.tensor([1+1j, 2+2j, 3+3j])
+ >>> data.shape
+ torch.Size([3])
+ >>> data = torch.view_as_real(data)
+ >>> data.shape
+ >>> check_stacked_complex(data)
+ tensor([1.+1.j, 2.+2.j, 3.+3.j])
+ >>> check_stacked_complex(data).shape
+ torch.Size([3])
+ >>> data = torch.tensor([1+1j, 2+2j, 3+3j])
+ >>> data.shape
+ torch.Size([3])
+ >>> check_stacked_complex(data)
+ tensor([1.+1.j, 2.+2.j, 3.+3.j])
+ >>> check_stacked_complex(data).shape
+ torch.Size([3])
+ """
+ return torch.view_as_complex(x) if x.shape[-1] == 2 else x
+
+
+def coil_combination_method(
+ x: torch.Tensor, sensitivity_maps: torch.Tensor, method: str = "SENSE", dim: int = 0
+) -> torch.Tensor:
+ """
+ Selects the coil combination method.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The tensor to coil-combine.
+ sensitivity_maps : torch.Tensor
+ The coil sensitivity maps.
+ method : str, optional
+ The coil combination method to use. Options are ``"SENSE"``, ``"RSS"``, ``"RSS_COMPLEX"``.
+ Default is ``"SENSE"``.
+ dim : int, optional
+ The dimension to coil-combine along. Default is ``0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Coil-combined tensor with the selected method applied.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import coil_combination_method
+ >>> import torch
+ >>> data = torch.tensor([[[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]], \
+ [[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]]])
+ >>> data.shape
+ torch.Size([2, 2, 3, 2])
+ >>> coil_sensitivity_maps = torch.tensor([[[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]], \
+ [[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]]])
+ >>> coil_sensitivity_maps.shape
+ torch.Size([2, 2, 3, 2])
+ >>> coil_combination_method(data, coil_sensitivity_maps, method="SENSE")
+ tensor([[[2.8284, 2.8284],
+ [5.6569, 5.6569],
+ [8.4853, 8.4853]],
+ [[2.8284, 2.8284],
+ [5.6569, 5.6569],
+ [8.4853, 8.4853]]])
+ >>> coil_combination_method(data, coil_sensitivity_maps, method="SENSE").shape
+ torch.Size([2, 3, 2])
+ >>> coil_combination_method(data, coil_sensitivity_maps, method="RSS")
+ tensor([[[1.4142, 1.4142],
+ [2.8284, 2.8284],
+ [4.2426, 4.2426]],
+ [[1.4142, 1.4142],
+ [2.8284, 2.8284],
+ [4.2426, 4.2426]]])
+ >>> coil_combination_method(data, coil_sensitivity_maps, method="RSS").shape
+ torch.Size([2, 3, 2])
+ >>> coil_combination_method(data, coil_sensitivity_maps, method="RSS_COMPLEX")
+ tensor([[[1.4142, 1.4142],
+ [2.8284, 2.8284],
+ [4.2426, 4.2426]],
+ [[1.4142, 1.4142],
+ [2.8284, 2.8284],
+ [4.2426, 4.2426]]])
+ >>> coil_combination_method(data, coil_sensitivity_maps, method="RSS_COMPLEX").shape
+ torch.Size([2, 3, 2])
+ """
+ if method == "SENSE":
+ return sense(x, sensitivity_maps, dim)
+ if method == "RSS":
+ return rss(x, dim)
+ if method == "RSS_COMPLEX":
+ return rss_complex(x, dim)
+ raise ValueError("Output type not supported.")
+
+
+def complex_abs(x: torch.Tensor) -> torch.Tensor:
+ """
+ Compute the absolute value of a complex valued input tensor.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Complex tensor. The last dimension must be of size 2.
+
+ Returns
+ -------
+ torch.Tensor
+ Absolute value of complex tensor.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import complex_abs
+ >>> import torch
+ >>> data = torch.tensor([1+1j, 2+2j, 3+3j])
+ >>> complex_abs(data)
+ tensor([1.4142, 2.8284, 4.2426])
+ """
+ if x.shape[-1] != 2:
+ if torch.is_complex(x):
+ x = torch.view_as_real(x)
+ else:
+ raise ValueError("Tensor does not have separate complex dim.")
+ return (x**2).sum(dim=-1)
+
+
+def complex_abs_sq(x: torch.Tensor) -> torch.Tensor:
+ """
+ Compute the squared absolute value of a complex tensor.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Complex tensor. The last dimension must be of size 2.
+
+ Returns
+ -------
+ torch.Tensor
+ Squared absolute value of complex tensor.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import complex_abs_sq
+ >>> import torch
+ >>> data = torch.tensor([1+1j, 2+2j, 3+3j])
+ >>> complex_abs_sq(data)
+ tensor([2., 8., 18.])
+ """
+ if x.shape[-1] != 2:
+ if torch.is_complex(x):
+ x = torch.view_as_real(x)
+ else:
+ raise ValueError("Tensor does not have separate complex dim.")
+ return (x**2).sum(dim=-1).sqrt()
+
+
+def complex_center_crop(x: torch.Tensor, shape: Tuple[int, int]) -> torch.Tensor:
+ """
+ Apply a center crop to the input image or batch of complex images.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The input tensor to be center cropped. It should have at least 3 dimensions and the cropping is applied along
+ the last two dimensions.
+ shape : Tuple[int, int]
+ The output shape. The shape should be smaller than the corresponding dimensions of data.
+
+ Returns
+ -------
+ torch.Tensor
+ The complex center cropped image or batch of images.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import complex_center_crop
+ >>> import torch
+ >>> data = torch.tensor([[[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]], \
+ [[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]]])
+ >>> data.shape
+ torch.Size([2, 2, 3, 2])
+ >>> complex_center_crop(data, (1, 2))
+ tensor([[[[2., 2.]]],
+ [[[2., 2.]]]])
+ >>> complex_center_crop(data, (1, 2)).shape
+ torch.Size([2, 1, 1, 2])
+ """
+ if not (0 < shape[0] <= x.shape[-3] and 0 < shape[1] <= x.shape[-2]):
+ raise ValueError("Invalid shapes.")
+
+ w_from = torch.div((x.shape[-3] - shape[0]), 2, rounding_mode="trunc")
+ h_from = torch.div((x.shape[-2] - shape[1]), 2, rounding_mode="trunc")
+ w_to = w_from + shape[0]
+ h_to = h_from + shape[1]
+
+ return x[..., w_from:w_to, h_from:h_to, :]
+
+
+def complex_conj(x: torch.Tensor) -> torch.Tensor:
+ """
+ Complex conjugate.
+
+ This applies the complex conjugate assuming that the input array has the last dimension as the complex dimension.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Complex tensor to apply the complex conjugate to. The last dimension must be of size 2.
+
+ Returns
+ -------
+ torch.Tensor
+ Result of complex conjugate.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import complex_conj
+ >>> import torch
+ >>> data = torch.tensor([1+1j, 2+2j, 3+3j])
+ >>> complex_conj(data)
+ tensor([1.-1.j, 2.-2.j, 3.-3.j])
+ """
+ if x.shape[-1] != 2:
+ raise ValueError("Tensor does not have separate complex dim.")
+ return torch.stack((x[..., 0], -x[..., 1]), dim=-1)
+
+
+def complex_mul(x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
+ """
+ Complex multiplication.
+
+ This multiplies two complex tensors assuming that they are both stored as real arrays with the last dimension
+ being the complex dimension.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ First complex tensor to multiply. The last dimension must be of size 2.
+ y : torch.Tensor
+ Second complex tensor to multiply. The last dimension must be of size 2.
+
+ Returns
+ -------
+ torch.Tensor
+ Result of complex multiplication.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import complex_mul
+ >>> import torch
+ >>> datax = torch.tensor([1+1j, 2+2j, 3+3j])
+ >>> datay = torch.tensor([4+4j, 5+5j, 6+6j])
+ >>> complex_mul(datax, datay)
+ tensor([[-7.+20.j],
+ [-4.+16.j],
+ [-1.+12.j]])
+ """
+ if not x.shape[-1] == y.shape[-1] == 2:
+ raise ValueError("Tensors do not have separate complex dim.")
+ re = x[..., 0] * y[..., 0] - x[..., 1] * y[..., 1]
+ im = x[..., 0] * y[..., 1] + x[..., 1] * y[..., 0]
+ return torch.stack((re, im), dim=-1)
+
+
+def crop_to_acs(acs_mask: torch.Tensor, kspace: torch.Tensor) -> torch.Tensor:
+ r"""Crops k-space to autocalibration region given the acs_mask.
+
+ Parameters
+ ----------
+ acs_mask : torch.Tensor
+ Autocalibration mask of shape (height, width).
+ kspace : torch.Tensor
+ K-space of shape (coil, height, width, *).
+
+ Returns
+ -------
+ torch.Tensor
+ Cropped k-space of shape (coil, height, width, *), where height and width are the new dimensions derived from
+ the acs_mask.
+ """
+ nonzero_idxs = torch.nonzero(acs_mask)
+ x, y = nonzero_idxs[..., 0], nonzero_idxs[..., 1]
+ xl, xr = x.min(), x.max()
+ yl, yr = y.min(), y.max()
+ return kspace[:, xl : xr + 1, yl : yr + 1, :]
+
+
+def expand_op(x: torch.Tensor, sensitivity_maps: torch.Tensor, dim: int = 1) -> torch.Tensor:
+ """
+ Expand a coil-combined image to a multi-coil image.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The coil-combined image.
+ sensitivity_maps : torch.Tensor
+ The sensitivity maps.
+ dim : int
+ The coil dimension to expand. Default is ``1``.
+
+ Returns
+ -------
+ torch.Tensor
+ The multi-coil image.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.utils import expand_op
+ >>> data = torch.rand(1, 200, 200, 2)
+ >>> sens = torch.rand(1, 30, 200, 200, 2)
+ >>> expand_op(data, sens).shape
+ (1, 30, 200, 200, 2)
+ """
+ return torch.unsqueeze(x, dim=dim) * sensitivity_maps
+
+
+def is_none(x: Union[Any, None]) -> bool:
+ """
+ Check if input is None or "None".
+
+ Parameters
+ ----------
+ x : Union[Any, None]
+ Input to check.
+
+ Returns
+ -------
+ bool
+ True if x is None or "None", False otherwise.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import is_none
+ >>> is_none(None)
+ True
+ >>> is_none("None")
+ True
+ """
+ return x is None or str(x).lower() == "none" or "none" in str(x).lower()
+
+
+def mask_center(
+ x: torch.Tensor, mask_from: Optional[int], mask_to: Optional[int], mask_type: str = "2D"
+) -> torch.Tensor:
+ """
+ Apply a center crop to the input real image or batch of real images.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The input image or batch of images. This should have at least 3 dimensions, where dimensions -3 and -2 are the
+ spatial dimensions, and the final dimension has size 1 (for real values).
+ mask_from : Optional[int]
+ Part of center to start filling.
+ mask_to : Optional[int]
+ Part of center to end filling.
+ mask_type : str, optional
+ Type of mask to apply. Can be either ``1D`` or ``2D``. Default is ``2D``.
+
+ Returns
+ -------
+ torch.Tensor
+ The masked image or batch of images with filled center.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import mask_center
+ >>> import torch
+ >>> data = torch.tensor([[[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]], \
+ [[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]]])
+ >>> data.shape
+ torch.Size([2, 2, 3, 2])
+ >>> mask_center(data, 1, 2)
+ tensor([[[[0., 0.], [1., 1.], [0., 0.]], [[0., 0.], [1., 1.], [0., 0.]]],
+ [[[0., 0.], [1., 1.], [0., 0.]], [[0., 0.], [1., 1.], [0., 0.]]]])
+ >>> mask_center(data, 1, 2).shape
+ torch.Size([2, 2, 3, 2])
+ """
+ mask = torch.zeros_like(x)
+
+ if isinstance(mask_from, list):
+ mask_from = mask_from[0]
+
+ if isinstance(mask_to, list):
+ mask_to = mask_to[0]
+
+ if mask_type == "1D":
+ mask[:, :, :, mask_from:mask_to] = x[:, :, :, mask_from:mask_to]
+ elif mask_type == "2D":
+ mask[:, :, mask_from:mask_to] = x[:, :, mask_from:mask_to]
+ else:
+ raise ValueError(f"Unknown mask type {mask_type}")
+
+ return mask
+
+
+def normalize_inplace(x: torch.Tensor, normalization_type: str = "max") -> torch.Tensor:
+ """
+ Normalize the input data inplace. This is different from the ``Normalizer`` transformation that normalizes the data
+ in a non batch-wise manner.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The input data.
+ normalization_type : str
+ The normalization type. Default is ``"max"``.
+
+ Returns
+ -------
+ torch.Tensor
+ The unnormalized data.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.utils import normalize_inplace
+ >>> data = torch.rand(1, 200, 200, 2)
+ >>> attrs = {"max": 164.4672133, "min": 0.000279681}
+ >>> normalize_inplace(data, attrs).shape
+ (1, 200, 200, 2)
+ """
+ if normalization_type == "max":
+ return x / torch.max(torch.abs(x))
+ if normalization_type == "minmax":
+ min_value = torch.min(torch.abs(x))
+ return (x - min_value) / (torch.max(torch.abs(x)) - min_value)
+ if normalization_type == "mean_std":
+ x = x - torch.mean(torch.abs(x))
+ return x / torch.std(torch.abs(x))
+ if normalization_type == "mean_var":
+ x = x - torch.mean(torch.abs(x))
+ return x / torch.var(torch.abs(x))
+ if normalization_type == "grayscale":
+ x = x - torch.min(torch.abs(x))
+ x = x / torch.max(torch.abs(x))
+ return x * 255.0
+ return x
+
+
+def parse_list_and_keep_last(x: Union[Any, List[Any]]) -> List[Any]:
+ """Parse a list of values and keep the last one, until the last value is not a list."""
+ if isinstance(x, list):
+ while isinstance(x, list):
+ x = x[-1]
+ return x
+
+
+def reshape_fortran(x, shape) -> torch.Tensor:
+ """
+ Reshapes a tensor in Fortran order. Taken from https://stackoverflow.com/a/63964246
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor to be reshaped.
+ shape : Sequence[int]
+ Shape to reshape the tensor to.
+
+ Returns
+ -------
+ torch.Tensor
+ Reshaped tensor.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import reshape_fortran
+ >>> import torch
+ >>> data = torch.tensor([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
+ >>> data.shape
+ torch.Size([2, 2, 3])
+ >>> reshape_fortran(data, (3, 2, 2))
+ tensor([[[ 1, 7],
+ [ 4, 10]],
+ [[ 2, 8],
+ [ 5, 11]],
+ [[ 3, 9],
+ [ 6, 12]]])
+ >>> reshape_fortran(data, (3, 2, 2)).shape
+ torch.Size([3, 2, 2])
+ """
+ return x.permute(*reversed(range(len(x.shape)))).reshape(*reversed(shape)).permute(*reversed(range(len(shape))))
+
+
+def rnn_weights_init(module: torch.nn.Module, std_init_range: float = 0.02, xavier: bool = True):
+ r"""Initialize weights in Recurrent Neural Network.
+
+ Parameters
+ ----------
+ module : torch.nn.Module
+ Module to initialize.
+ std_init_range : float
+ Standard deviation of normal initializer. Default is ``0.02``.
+ xavier : bool
+ If True, xavier initializer will be used in Linear layers as in [Vaswani2017]_. Otherwise, normal initializer
+ will be used. Default is ``True``.
+
+ References
+ ----------
+ .. [Vaswani2017] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser ล, Polosukhin I. Attention
+ is all you need. Advances in neural information processing systems. 2017;30.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.utils import rnn_weights_init
+ >>> rnn = torch.nn.GRU(10, 20, 2)
+ >>> rnn.apply(rnn_weights_init)
+ GRU(10, 20, num_layers=2)
+ """
+ if isinstance(module, torch.nn.Linear):
+ if xavier:
+ torch.nn.init.xavier_uniform_(module.weight)
+ else:
+ torch.nn.init.normal_(module.weight, mean=0.0, std=std_init_range)
+ if module.bias is not None:
+ torch.nn.init.constant_(module.bias, 0.0)
+ elif isinstance(module, torch.nn.Embedding):
+ torch.nn.init.normal_(module.weight, mean=0.0, std=std_init_range)
+ elif isinstance(module, torch.nn.LayerNorm):
+ torch.nn.init.constant_(module.weight, 1.0)
+ torch.nn.init.constant_(module.bias, 0.0)
+
+
+def rss(x: torch.Tensor, dim: int = 0) -> torch.Tensor:
+ """
+ Compute the Root Sum of Squares (RSS).
+
+ RSS is computed assuming that dim is the coil dimension.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Tensor to apply the RSS transform to.
+ dim : int, optional
+ Dimension to apply the RSS transform to. Default is ``0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Coil-combined tensor with RSS applied.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import rss
+ >>> import torch
+ >>> data = torch.tensor([[[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]], \
+ [[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]]])
+ >>> data.shape
+ torch.Size([2, 2, 3, 2])
+ >>> rss(data)
+ tensor([[[2.8284, 2.8284],
+ [5.6569, 5.6569],
+ [8.4853, 8.4853]],
+ [[2.8284, 2.8284],
+ [5.6569, 5.6569],
+ [8.4853, 8.4853]]])
+ >>> rss(data).shape
+ torch.Size([2, 3, 2])
+ """
+ return torch.sqrt((x**2).sum(dim))
+
+
+def rss_complex(x: torch.Tensor, dim: int = 0) -> torch.Tensor:
+ """
+ Compute the Root Sum of Squares (RSS) for complex inputs.
+
+ RSS is computed assuming that dim is the coil dimension.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Tensor to apply the RSS transform to.
+ dim : int, optional
+ Dimension to apply the RSS transform to. Default is ``0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Coil-combined tensor with RSS applied.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import rss_complex
+ >>> import torch
+ >>> data = torch.tensor([[[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]], [[1+1j, 2+2j, 3+3j], [1+1j, 2+2j, 3+3j]]])
+ >>> data.shape
+ torch.Size([2, 2, 3])
+ >>> rss_complex(data, dim=0)
+ tensor([[1.4142, 2.8284, 4.2426],
+ [1.4142, 2.8284, 4.2426]])
+ >>> rss_complex(data, dim=0).shape
+ torch.Size([2, 3])
+ """
+ return torch.sqrt(complex_abs_sq(x).sum(dim))
+
+
+def save_predictions(
+ predictions: Dict[str, np.ndarray], out_dir: Path, key: str = "reconstructions", file_format: str = "h5"
+) -> None:
+ """
+ Save predictions to selected format.
+
+ Parameters
+ ----------
+ predictions : Dict[str, np.ndarray]
+ A dictionary mapping input filenames to corresponding predictions.
+ out_dir : Path
+ The output directory to save the predictions to.
+ key : str, optional
+ The key to save the predictions under. Default is ``reconstructions``.
+ file_format : str, optional
+ The format to save the predictions in. Default is ``h5``.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import save_predictions
+ >>> import numpy as np
+ >>> from pathlib import Path
+ >>> data = {"test.h5": np.array([[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]])}
+ >>> data["test.h5"].shape
+ (2, 3, 2)
+ >>> output_directory = Path("predictions")
+ >>> save_predictions(data, output_directory, key="reconstructions", file_format="h5")
+ >>> save_predictions(data, output_directory, key="segmentations", file_format="h5")
+ """
+ if file_format != "h5":
+ raise ValueError(f"Output format {file_format} is not supported.")
+ out_dir.mkdir(exist_ok=True, parents=True)
+ for fname, preds in predictions.items():
+ with h5py.File(out_dir / fname, "w") as hf:
+ hf.create_dataset(key, data=preds)
+
+
+def sense(x: torch.Tensor, sensitivity_maps: torch.Tensor, dim: int = 0) -> torch.Tensor:
+ """
+ Coil-combination according to the SENSitivity Encoding (SENSE) method [Pruessmann1999]_.
+
+ References
+ ----------
+ .. [Pruessmann1999] Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI.
+ Magn Reson Med 1999; 42:952-962.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The tensor to coil-combine.
+ sensitivity_maps : torch.Tensor
+ The coil sensitivity maps.
+ dim : int, optional
+ The dimension to coil-combine along. Default is ``0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Coil-combined tensor with SENSE applied.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import sense
+ >>> import torch
+ >>> data = torch.tensor([[[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]], \
+ [[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]]])
+ >>> data.shape
+ torch.Size([2, 2, 3, 2])
+ >>> coil_sensitivity_maps = torch.tensor([[[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]], \
+ [[[1., 1.], [2., 2.], [3., 3.]], [[1., 1.], [2., 2.], [3., 3.]]]])
+ >>> coil_sensitivity_maps.shape
+ torch.Size([2, 2, 3, 2])
+ >>> sense(data, coil_sensitivity_maps)
+ tensor([[[2.8284, 2.8284],
+ [5.6569, 5.6569],
+ [8.4853, 8.4853]],
+ [[2.8284, 2.8284],
+ [5.6569, 5.6569],
+ [8.4853, 8.4853]]])
+ >>> sense(data, coil_sensitivity_maps).shape
+ torch.Size([2, 3, 2])
+ """
+ return complex_mul(x, complex_conj(sensitivity_maps)).sum(dim)
+
+
+def to_tensor(x: np.ndarray) -> torch.Tensor:
+ """
+ Converts a numpy array to a torch tensor. For complex arrays, the real and imaginary parts are stacked along the
+ last dimension.
+
+ Parameters
+ ----------
+ x : np.ndarray
+ Input numpy array to be converted to torch.
+
+ Returns
+ -------
+ torch.Tensor
+ Torch tensor version of input.
+
+ Examples
+ --------
+ >>> from atommic.collections.common.parts.utils import to_tensor
+ >>> import numpy as np
+ >>> data = np.array([[1+1j, 2+2j, 3+3j], [4+4j, 5+5j, 6+6j]])
+ >>> data.shape
+ (2, 3)
+ >>> to_tensor(data)
+ tensor([[[1., 1.],
+ [2., 2.],
+ [3., 3.]],
+ [[4., 4.],
+ [5., 5.],
+ [6., 6.]]], dtype=torch.float64)
+ >>> to_tensor(data).shape
+ torch.Size([2, 3, 2])
+ """
+ if np.iscomplexobj(x):
+ x = np.stack((x.real, x.imag), axis=-1)
+ return torch.from_numpy(x)
+
+
+def unnormalize(x: torch.Tensor, attrs: Dict, normalization_type: str = "max") -> torch.Tensor:
+ """
+ Unnormalize the input data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ The input data.
+ attrs : Dict
+ The attributes of the input data.
+ normalization_type : str
+ The normalization type. Default is ``"max"``.
+
+ Returns
+ -------
+ torch.Tensor
+ The unnormalized data.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.common.parts.utils import unnormalize
+ >>> data = torch.rand(1, 200, 200, 2)
+ >>> attrs = {"max": 1.0, "min": 0.0}
+ >>> unnormalize(data, attrs).shape
+ (1, 200, 200, 2)
+ """
+ if normalization_type == "max":
+ return x * attrs["max"]
+ if normalization_type == "minmax":
+ return x * (attrs["max"] - attrs["min"]) + attrs["min"]
+ if normalization_type == "mean_std":
+ return x * attrs["std"] + attrs["mean"]
+ if normalization_type == "mean_var":
+ return x * attrs["var"] + attrs["mean"]
+ if normalization_type == "grayscale":
+ return x / 255.0
+ return x
+
+
+def zero_nan_inf(x):
+ """If x is nan or inf, return 0."""
+ if torch.isnan(x).any() or torch.isinf(x).any():
+ x = torch.tensor(0.0)
+ return x
diff --git a/atommic/collections/motioncorrection/__init__.py b/atommic/collections/motioncorrection/__init__.py
new file mode 100644
index 00000000..a7912f9b
--- /dev/null
+++ b/atommic/collections/motioncorrection/__init__.py
@@ -0,0 +1,13 @@
+# coding=utf-8
+
+from atommic.collections.motioncorrection import parts # noqa: F401
+from atommic.package_info import __version__
+
+# Set collection version equal to atommic version.
+__version = __version__
+
+# Authorship.
+__author__ = "Dimitris Karkalousos"
+
+# Set collection name.
+__description__ = "MRI motion correction collection."
diff --git a/atommic/collections/motioncorrection/parts/__init__.py b/atommic/collections/motioncorrection/parts/__init__.py
new file mode 100644
index 00000000..a0a26bd6
--- /dev/null
+++ b/atommic/collections/motioncorrection/parts/__init__.py
@@ -0,0 +1,4 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.motioncorrection.parts.motionsimulation import MotionSimulation # noqa: F401
diff --git a/atommic/collections/motioncorrection/parts/motionsimulation.py b/atommic/collections/motioncorrection/parts/motionsimulation.py
new file mode 100644
index 00000000..8b9b07ad
--- /dev/null
+++ b/atommic/collections/motioncorrection/parts/motionsimulation.py
@@ -0,0 +1,440 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from https://github.com/bduffy0/motion-correction/blob/master/layer/motion_sim.py
+
+import math
+import random
+from typing import Any, Dict, Optional, Sequence, Tuple
+
+import torch
+
+from atommic.collections.common.parts import utils
+
+
+def get_center_rect(image: torch.tensor, center_percentage: float = 0.02, dim: int = 0) -> torch.tensor:
+ """Get a center rectangle of a given dimension.
+
+ Parameters
+ ----------
+ image : torch.tensor
+ The image to get the center rectangle from.
+ center_percentage : float
+ The percentage of the image to take as the center rectangle.
+ dim : int
+ The dimension to take the center rectangle from.
+
+ Returns
+ -------
+ torch.tensor
+ The center rectangle.
+ """
+ shape = (image[0].item(), image[1].item())
+ mask = torch.zeros(shape)
+ half_pct = center_percentage / 2
+ center = [int(x / 2) for x in shape]
+ mask = torch.swapaxes(mask, 0, dim)
+ mask[:, center[1] - math.ceil(shape[1] * half_pct) : math.ceil(center[1] + shape[1] * half_pct)] = 1
+ mask = torch.swapaxes(mask, 0, dim)
+ return mask
+
+
+def segment_array_by_locs(shape: Sequence[int], locations: Sequence[int]) -> torch.tensor:
+ """Generate a segmentation mask based on a list of locations.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the array to segment.
+ locations : Sequence[int]
+ The locations to segment the array into.
+
+ Returns
+ -------
+ torch.tensor
+ The segmentation mask.
+ """
+ mask_out = torch.zeros(torch.prod(shape), dtype=int)
+ for i in range(len(locations) - 1):
+ loc = [locations[i], locations[i + 1]]
+ mask_out[loc[0] : loc[1]] = i + 1
+ return mask_out.reshape(shape)
+
+
+def segments_to_random_indices(shape: Sequence[int], seg_lengths: Sequence[int]) -> torch.tensor:
+ """Generate a segmentation mask based on a list of locations.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the array to segment.
+ seg_lengths : Sequence[int]
+ The lengths of the segments to generate.
+
+ Returns
+ -------
+ torch.tensor
+ The segmentation mask.
+ """
+ random_indices = torch.randint(low=0, high=shape, size=(sum(seg_lengths),)).sort()[0]
+ seg_mask = torch.zeros(shape).type(torch.int)
+ seg_new_indices = torch.cumsum(torch.tensor(seg_lengths), 0).tolist()
+ seg_new_indices = [0] + seg_new_indices
+ for i in range(len(seg_new_indices) - 1):
+ seg_mask[random_indices[seg_new_indices[i] : seg_new_indices[i + 1]]] = i + 1
+ return seg_mask
+
+
+def segments_to_random_blocks(shape: Sequence[int], seg_lengths: Sequence[int]) -> torch.tensor:
+ """Generate a segmentation mask based on a list of locations.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the array to segment.
+ seg_lengths : Sequence[int]
+ The lengths of the segments to generate.
+
+ Returns
+ -------
+ torch.tensor
+ The segmentation mask.
+ """
+ seg_mask = torch.zeros(shape).type(torch.int)
+ seg_lengths_sorted = sorted(seg_lengths, reverse=True)
+ for i, seg_len in enumerate(seg_lengths_sorted):
+ loc = torch.randint(low=0, high=seg_mask.size()[0], size=(1,))
+ while (sum(seg_mask[loc : loc + seg_len]) != 0) or (loc + seg_len > seg_mask.size()[0]):
+ loc = torch.randint(low=0, high=seg_mask.size()[0], size=(1,))
+ seg_mask[loc : loc + seg_len] = i + 1
+ return seg_mask
+
+
+def create_rand_partition(im_length: int, num_segments: int):
+ """Create a random partition of an array.
+
+ Parameters
+ ----------
+ im_length : int
+ The length of the array to partition.
+ num_segments : int
+ The number of segments to partition the array into.
+
+ Returns
+ -------
+ list
+ The partition locations.
+ """
+ rand_segment_locs = sorted(list(torch.randint(im_length, size=(num_segments,))))
+ rand_segment_locs[0] = 0
+ rand_segment_locs[-1] = None
+ return rand_segment_locs
+
+
+def create_rotation_matrix_3d(angles: Sequence[float]) -> torch.tensor:
+ """Create a 3D rotation matrix.
+
+ Parameters
+ ----------
+ angles : Sequence[float]
+ The angles to rotate the matrix by.
+
+ Returns
+ -------
+ torch.tensor
+ The rotation matrix.
+ """
+ mat1 = torch.FloatTensor(
+ [
+ [1.0, 0.0, 0.0],
+ [0.0, math.cos(angles[0]), math.sin(angles[0])],
+ [0.0, -math.sin(angles[0]), math.cos(angles[0])],
+ ]
+ )
+ mat2 = torch.FloatTensor(
+ [
+ [math.cos(angles[1]), 0.0, -math.sin(angles[1])],
+ [0.0, 1.0, 0.0],
+ [math.sin(angles[1]), 0.0, math.cos(angles[1])],
+ ]
+ )
+ mat3 = torch.FloatTensor(
+ [
+ [math.cos(angles[2]), math.sin(angles[2]), 0.0],
+ [-math.sin(angles[2]), math.cos(angles[2]), 0.0],
+ [0.0, 0.0, 1.0],
+ ]
+ )
+ return (mat1 @ mat2) @ mat3
+
+
+def translate_kspace(freq_domain: torch.tensor, translations: torch.tensor) -> torch.tensor:
+ """Translate a k-space array.
+
+ Parameters
+ ----------
+ freq_domain : torch.tensor
+ The k-space array to translate.
+ translations : torch.tensor
+ The translations to apply to the k-space array.
+
+ Returns
+ -------
+ torch.tensor
+ The translated k-space array.
+ """
+ lin_spaces = [torch.linspace(-0.5, 0.5, x) for x in freq_domain.shape[:-1]]
+ meshgrids = torch.meshgrid(*lin_spaces, indexing="ij")
+ grid_coords = torch.stack([mg.flatten() for mg in meshgrids], 0)
+ phase_shift = torch.multiply(grid_coords, translations).sum(axis=0) # phase shift is added
+ exp_phase_shift = torch.exp(-2j * math.pi * phase_shift).to(freq_domain.device)
+ motion_kspace = torch.view_as_real(
+ torch.multiply(exp_phase_shift, torch.view_as_complex(freq_domain).flatten()).reshape(freq_domain.shape[:-1])
+ )
+
+ return motion_kspace
+
+
+class MotionSimulation:
+ """Simulates random translations and rotations in the frequency domain.
+
+ Examples
+ --------
+ >>> from atommic.collections.motioncorrection.parts import MotionSimulation
+ >>> import torch
+ >>> motion_simulation = MotionSimulation()
+ >>> kspace = torch.randn(1, 1, 256, 256, 2)
+ >>> motion_kspace = motion_simulation(kspace)
+ >>> motion_kspace.shape
+ torch.Size([1, 1, 256, 256, 2])
+ """
+
+ def __init__(
+ self,
+ motion_type: str = "piecewise_transient",
+ angle: float = 0,
+ translation: float = 10,
+ center_percentage: float = 0.02,
+ motion_percentage: Sequence[float] = (15, 20),
+ num_segments: int = 8,
+ random_num_segments: bool = False,
+ non_uniform: bool = False,
+ spatial_dims: Sequence[int] = (-2, -1),
+ ):
+ """Inits :class:`MotionSimulation`.
+
+ Parameters
+ ----------
+ motion_type : str
+ The motion_type of motion to simulate.
+ angle : float
+ The angle to rotate the k-space array by.
+ translation : float
+ The translation to apply to the k-space array.
+ center_percentage : float
+ The percentage of the k-space array to center the motion.
+ motion_percentage : Sequence[float]
+ The percentage of the k-space array to apply the motion.
+ num_segments : int
+ The number of segments to partition the k-space array into.
+ random_num_segments : bool
+ Whether to randomly generate the number of segments.
+ non_uniform : bool
+ Whether to use non-uniform sampling.
+ spatial_dims : Sequence[int]
+ The spatial dimensions to apply the motion to.
+ """
+ self.motion_type = motion_type
+ self.angle, self.translation = angle, translation
+ self.center_percentage = center_percentage
+
+ if motion_percentage[1] == motion_percentage[0]:
+ motion_percentage[1] += 1 # type: ignore
+ elif motion_percentage[1] < motion_percentage[0]:
+ raise ValueError("Uniform is not defined when low>= high.")
+
+ self.motion_percentage = motion_percentage
+
+ self.spatial_dims = spatial_dims
+ self._spatial_dims = random.choice(spatial_dims)
+
+ self.num_segments = num_segments
+ self.random_num_segments = random_num_segments
+
+ if non_uniform:
+ raise NotImplementedError("NUFFT is not implemented. This is a feature to be added in the future.")
+
+ self.trajectory = None
+ self.params: Dict[Any, Any] = {}
+
+ def _calc_dimensions(self, shape):
+ """Calculate the dimensions to apply the motion to.
+
+ Parameters
+ ----------
+ shape : Sequence[int]
+ The shape of the image.
+
+ Returns
+ -------
+ Sequence[int]
+ The dimensions to apply the motion to.
+ """
+ pe_dims = [0, 1, 2]
+ pe_dims.pop(self._spatial_dims)
+ self.phase_encoding_dims = pe_dims
+ shape = list(shape)
+ if shape[-1] == 2:
+ shape = shape[:-1]
+ self.shape = shape.copy()
+ shape.pop(self._spatial_dims)
+ self.phase_encoding_shape = torch.tensor(shape)
+ self.num_phase_encoding_steps = self.phase_encoding_shape[0] * self.phase_encoding_shape[1]
+ self._spatial_dims = len(self.shape) - 1 if self._spatial_dims == -1 else self._spatial_dims
+
+ def _generate_random_segments(self):
+ """Generate random segments."""
+ pct_corrupt = torch.distributions.Uniform(*[x / 100 for x in self.motion_percentage]).sample((1, 1))
+
+ corrupt_matrix_shape = torch.tensor([int(x * math.sqrt(pct_corrupt)) for x in self.phase_encoding_shape])
+
+ if torch.prod(corrupt_matrix_shape) == 0:
+ corrupt_matrix_shape = [1, 1]
+
+ if self.motion_type in {"gaussian"}:
+ num_segments = torch.prod(corrupt_matrix_shape)
+ else:
+ if not self.random_num_segments:
+ num_segments = self.num_segments
+ else:
+ num_segments = random.randint(1, self.num_segments)
+
+ # segment a smaller vector occupying pct_corrupt percent of the space
+ if self.motion_type in {"piecewise_transient", "piecewise_constant"}:
+ seg_locs = create_rand_partition(torch.prod(corrupt_matrix_shape), num_segments=num_segments)
+ else:
+ seg_locs = list(range(num_segments))
+
+ rand_segmentation = segment_array_by_locs(shape=torch.prod(corrupt_matrix_shape), locations=seg_locs)
+
+ seg_lengths = [(rand_segmentation == seg_num).sum() for seg_num in torch.unique(rand_segmentation)]
+
+ # assign segments to a vector with same number of elements as pe-steps
+ if self.motion_type in {"piecewise_transient", "gaussian"}:
+ seg_vector = segments_to_random_indices(torch.prod(self.phase_encoding_shape), seg_lengths)
+ else:
+ seg_vector = segments_to_random_blocks(torch.prod(self.phase_encoding_shape), seg_lengths)
+
+ # reshape to phase encoding shape with a random order
+ reshape_order = random.choice(["F", "C"])
+
+ if reshape_order == "F":
+ seg_array = utils.reshape_fortran(
+ seg_vector, (self.phase_encoding_shape[0].item(), self.phase_encoding_shape[1].item())
+ )
+ else:
+ seg_array = seg_vector.reshape((self.phase_encoding_shape[0].item(), self.phase_encoding_shape[1].item()))
+
+ self.order = reshape_order
+
+ # mask center k-space
+ mask_not_including_center = (
+ get_center_rect(
+ self.phase_encoding_shape,
+ center_percentage=self.center_percentage,
+ dim=1 if reshape_order == "C" else 0,
+ )
+ == 0
+ )
+
+ self.seg_array = seg_array * mask_not_including_center
+ self.num_segments = num_segments
+
+ def _get_motion_trajectory(self, translation_rotation=None, random_segments=True):
+ """Obtain a motion trajectory.
+
+ Returns
+ -------
+ torch.tensor
+ The random trajectory.
+ """
+
+ if random_segments:
+ self._generate_random_segments()
+ else:
+ raise NotImplementedError("Custom segments (masks) not supported")
+
+ if not translation_rotation:
+ translations, rotations = self._simulate_random_trajectory()
+ else:
+ (translations, rotations) = translation_rotation
+ if translations.shape[0] != self.num_segments:
+ translations = torch.cat((torch.tensor([[0, 0, 0]]), translations), dim=0)
+ if rotations.shape[0] != self.num_segments:
+ rotations = torch.cat((torch.tensor([[0, 0, 0]]), rotations), dim=0)
+
+ # if segment==0, then no motion
+ translations[0, :] = 0
+ rotations[0, :] = 0
+
+ # lookup values for each segment
+ translations_pe = [translations[:, i][self.seg_array.long()] for i in range(3)]
+ rotations_pe = [rotations[:, i][self.seg_array.long()] for i in range(3)]
+
+ # reshape and convert to radians
+ translations = torch.stack(
+ [torch.broadcast_to(x.unsqueeze(self._spatial_dims), self.shape) for x in translations_pe], 0
+ )
+ rotations = torch.stack(
+ [torch.broadcast_to(x.unsqueeze(self._spatial_dims), self.shape) for x in rotations_pe], 0
+ )
+
+ rotations = rotations * (math.pi / 180.0) # convert to radians
+
+ self.translations = translations.reshape(3, -1)
+ self.rotations = rotations.reshape(3, -1).reshape(3, -1)
+
+ def _simulate_random_trajectory(self):
+ """Simulate a random trajectory."""
+ # generate random translations and rotations
+ rand_translations = torch.distributions.normal.Normal(loc=0, scale=self.translation).sample(
+ (self.num_segments, 3)
+ )
+ rand_rotations = torch.distributions.normal.Normal(loc=0, scale=self.angle).sample((self.num_segments, 3))
+ return rand_translations, rand_rotations
+
+ def forward(
+ self,
+ kspace,
+ translations_rotations: Optional[Tuple[torch.Tensor, torch.Tensor]] = None,
+ apply_backward_transform: bool = False, # pylint: disable=unused-argument
+ apply_forward_transform: bool = False, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`MotionSimulation`.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The kspace to apply the motion to.
+ translations_rotations : Optional[Tuple[torch.Tensor, torch.Tensor]]
+ The translations and rotations to apply to the kspace. If None, a random trajectory is generated.
+ apply_backward_transform : bool
+ Placeholder for the backward transform. Generalizes the Composer, but not used.
+ apply_forward_transform : bool
+ Placeholder for the forward transform. Generalizes the Composer, but not used.
+
+ Returns
+ -------
+ torch.Tensor
+ The kspace with the motion applied.
+ """
+ self._calc_dimensions(kspace.shape)
+ self._get_motion_trajectory(translations_rotations)
+
+ motion_kspace = translate_kspace(freq_domain=kspace, translations=self.translations)
+
+ return motion_kspace
+
+ def __call__(self, *args, **kwargs):
+ """Call :class:`MotionSimulation`."""
+ return self.forward(*args, **kwargs)
diff --git a/atommic/collections/multitask/__init__.py b/atommic/collections/multitask/__init__.py
new file mode 100644
index 00000000..eff3fc92
--- /dev/null
+++ b/atommic/collections/multitask/__init__.py
@@ -0,0 +1,13 @@
+# coding=utf-8
+
+from atommic.collections.multitask import rs # noqa: F401
+from atommic.package_info import __version__
+
+# Set collection version equal to atommic version.
+__version = __version__
+
+# Authorship.
+__author__ = "Dimitris Karkalousos"
+
+# Set collection name.
+__description__ = "MRI multitask learning collection."
diff --git a/atommic/collections/multitask/rs/__init__.py b/atommic/collections/multitask/rs/__init__.py
new file mode 100644
index 00000000..4aeec9ac
--- /dev/null
+++ b/atommic/collections/multitask/rs/__init__.py
@@ -0,0 +1,13 @@
+# coding=utf-8
+
+from atommic.collections.multitask.rs import data, nn, parts # noqa: F401
+from atommic.package_info import __version__
+
+# Set collection version equal to atommic version.
+__version = __version__
+
+# Authorship.
+__author__ = "Dimitris Karkalousos"
+
+# Set collection name.
+__description__ = "Accelerated-MRI reconstruction and MRI segmentation using multitask learning collection."
diff --git a/atommic/collections/multitask/rs/data/__init__.py b/atommic/collections/multitask/rs/data/__init__.py
new file mode 100644
index 00000000..2e7a3cf9
--- /dev/null
+++ b/atommic/collections/multitask/rs/data/__init__.py
@@ -0,0 +1,4 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.multitask.rs.data.mrirs_loader import RSMRIDataset # noqa: F401
diff --git a/atommic/collections/multitask/rs/data/mrirs_loader.py b/atommic/collections/multitask/rs/data/mrirs_loader.py
new file mode 100644
index 00000000..5a8c80e5
--- /dev/null
+++ b/atommic/collections/multitask/rs/data/mrirs_loader.py
@@ -0,0 +1,532 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import os
+import warnings
+from pathlib import Path
+from typing import Callable, Optional, Tuple, Union
+
+import h5py
+import nibabel as nib
+import numpy as np
+
+from atommic.collections.common.data.mri_loader import MRIDataset
+from atommic.collections.common.parts.utils import is_none
+
+
+class RSMRIDataset(MRIDataset):
+ """A dataset class for accelerated-MRI reconstruction and MRI segmentation.
+
+ Examples
+ --------
+ >>> from atommic.collections.multitask.rs.data.mrirs_loader import RSMRIDataset
+ >>> dataset = RSMRIDataset(root='data/train', sample_rate=0.1)
+ >>> print(len(dataset))
+ 100
+ >>> kspace, imspace, coil_sensitivities, mask, initial_prediction, segmentation_labels, attrs, filename, \
+ slice_num = dataset[0]
+ >>> print(kspace.shape)
+ np.array([30, 640, 368])
+
+ .. note::
+ Extends :class:`atommic.collections.common.data.MRIDataset`.
+ """
+
+ def __init__(
+ self,
+ root: Union[str, Path, os.PathLike],
+ coil_sensitivity_maps_root: Union[str, Path, os.PathLike] = None,
+ mask_root: Union[str, Path, os.PathLike] = None,
+ noise_root: Union[str, Path, os.PathLike] = None,
+ initial_predictions_root: Union[str, Path, os.PathLike] = None,
+ dataset_format: str = None,
+ sample_rate: Optional[float] = None,
+ volume_sample_rate: Optional[float] = None,
+ use_dataset_cache: bool = False,
+ dataset_cache_file: Union[str, Path, os.PathLike] = None,
+ num_cols: Optional[Tuple[int]] = None,
+ consecutive_slices: int = 1,
+ data_saved_per_slice: bool = False,
+ n2r_supervised_rate: Optional[float] = 0.0,
+ complex_target: bool = False,
+ log_images_rate: Optional[float] = 1.0,
+ transform: Optional[Callable] = None,
+ segmentations_root: Union[str, Path, os.PathLike] = None,
+ segmentation_classes: int = 2,
+ segmentation_classes_to_remove: Optional[Tuple[int]] = None,
+ segmentation_classes_to_combine: Optional[Tuple[int]] = None,
+ segmentation_classes_to_separate: Optional[Tuple[int]] = None,
+ segmentation_classes_thresholds: Optional[Tuple[float]] = None,
+ complex_data: bool = True,
+ **kwargs,
+ ):
+ """Inits :class:`RSMRIDataset`.
+
+ Parameters
+ ----------
+ root : Union[str, Path, os.PathLike]
+ Path to the dataset.
+ sense_root : Union[str, Path, os.PathLike], optional
+ Path to the coil sensitivities maps dataset, if stored separately.
+ mask_root : Union[str, Path, os.PathLike], optional
+ Path to stored masks, if stored separately.
+ noise_root : Union[str, Path, os.PathLike], optional
+ Path to stored noise, if stored separately (in json format).
+ initial_predictions_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the initial predictions. If provided, the initial predictions will be used
+ as the input of the reconstruction network. Default is ``None``.
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ Default is ``None``.
+ sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the slices should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ volume_sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the volumes should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ use_dataset_cache : bool, optional
+ Whether to cache dataset metadata. This is very useful for large datasets.
+ dataset_cache_file : Union[str, Path, os.PathLike], optional
+ A file in which to cache dataset information for faster load times.
+ num_cols : Optional[Tuple[int]], optional
+ If provided, only slices with the desired number of columns will be considered.
+ consecutive_slices : int, optional
+ An int (>0) that determine the amount of consecutive slices of the file to be loaded at the same time.
+ Default is ``1``, loading single slices.
+ data_saved_per_slice : bool, optional
+ Whether the data is saved per slice or per volume.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ complex_target : bool, optional
+ Whether to use a complex target or not. Default is ``False``.
+ log_images_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be logged as images. Default is
+ ``1.0``.
+ transform : Optional[Callable], optional
+ A sequence of callable objects that preprocesses the raw data into appropriate form. The transform function
+ should take ``kspace``, ``coil sensitivity maps``, ``mask``, ``initial prediction``, ``segmentation``,
+ ``target``, ``attributes``, ``filename``, and ``slice number`` as inputs. ``target`` may be null for test
+ data. Default is ``None``.
+ segmentations_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the segmentations.
+ segmentation_classes : int, optional
+ The number of segmentation classes. Default is ``2``.
+ segmentation_classes_to_remove : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to remove. For example, if the dataset contains segmentation classes
+ 0, 1, 2, 3, and 4, and you want to remove classes 1 and 3, set this to ``(1, 3)``. Default is ``None``.
+ segmentation_classes_to_combine : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to combine. For example, if the dataset contains segmentation classes
+ 0, 1, 2, 3, and 4, and you want to combine classes 1 and 3, set this to ``(1, 3)``. Default is ``None``.
+ segmentation_classes_to_separate : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to separate. For example, if the dataset contains segmentation classes
+ 0, 1, 2, 3, and 4, and you want to separate class 1 into 2 classes, set this to ``(1, 2)``.
+ Default is ``None``.
+ segmentation_classes_thresholds : Optional[Tuple[float]], optional
+ A tuple of thresholds for the segmentation classes. For example, if the dataset contains segmentation
+ classes 0, 1, 2, 3, and 4, and you want to set the threshold for class 1 to 0.5, set this to
+ ``(0.5, 0.5, 0.5, 0.5, 0.5)``. Default is ``None``.
+ complex_data : bool, optional
+ Whether the data is complex. If ``False``, the data is assumed to be magnitude only. Default is ``True``.
+ **kwargs : dict
+ Additional keyword arguments.
+ """
+ super().__init__(
+ root,
+ coil_sensitivity_maps_root,
+ mask_root,
+ noise_root,
+ initial_predictions_root,
+ dataset_format,
+ sample_rate,
+ volume_sample_rate,
+ use_dataset_cache,
+ dataset_cache_file,
+ num_cols,
+ consecutive_slices,
+ data_saved_per_slice,
+ n2r_supervised_rate,
+ complex_target,
+ log_images_rate,
+ transform,
+ **kwargs,
+ )
+
+ self.segmentations_root = segmentations_root
+
+ # Create random number generator used for consecutive slice selection and set consecutive slice amount
+ self.consecutive_slices = consecutive_slices
+ self.segmentation_classes = segmentation_classes
+ self.segmentation_classes_to_remove = segmentation_classes_to_remove
+ self.segmentation_classes_to_combine = segmentation_classes_to_combine
+ self.segmentation_classes_to_separate = segmentation_classes_to_separate
+ self.segmentation_classes_thresholds = segmentation_classes_thresholds
+ self.complex_data = complex_data
+
+ def process_segmentation_labels(self, segmentation_labels: np.ndarray) -> np.ndarray: # noqa: MC0001
+ """Processes segmentation labels to remove, combine, and separate classes.
+
+ Parameters
+ ----------
+ segmentation_labels : np.ndarray
+ The segmentation labels. The shape should be (num_slices, height, width) or (height, width).
+
+ Returns
+ -------
+ np.ndarray
+ The processed segmentation labels.
+ """
+ # find the dimension with the segmentation classes
+ segmentation_labels_dim = segmentation_labels.ndim - 1
+ for dim in range(segmentation_labels.ndim):
+ if segmentation_labels.shape[dim] == self.segmentation_classes:
+ segmentation_labels_dim = dim
+
+ # move it to the last dimension
+ segmentation_labels = np.moveaxis(segmentation_labels, segmentation_labels_dim, -1)
+
+ # if we have a single slice, add a new dimension
+ if segmentation_labels.ndim == 2:
+ segmentation_labels = np.expand_dims(segmentation_labels, axis=0)
+
+ # check if we need to remove any classes, e.g. background
+ if self.segmentation_classes_to_remove is not None:
+ segmentation_labels = np.delete(segmentation_labels, self.segmentation_classes_to_remove, axis=-1)
+
+ # check if we need to combine any classes, e.g. White Matter and Gray Matter
+ if self.segmentation_classes_to_combine is not None:
+ segmentation_labels_to_combine = np.sum(
+ segmentation_labels[..., self.segmentation_classes_to_combine], axis=-1, keepdims=True
+ )
+ segmentation_labels_to_keep = np.delete(segmentation_labels, self.segmentation_classes_to_combine, axis=-1)
+
+ if self.segmentation_classes_to_remove is not None and 0 in self.segmentation_classes_to_remove:
+ # if background is removed, we can stack the combined labels with the rest straight away
+ segmentation_labels = np.concatenate(
+ [segmentation_labels_to_combine, segmentation_labels_to_keep], axis=-1
+ )
+ else:
+ # if background is not removed, we need to add it back as new background channel
+ segmentation_labels = np.concatenate(
+ [segmentation_labels[..., 0:1], segmentation_labels_to_combine, segmentation_labels_to_keep],
+ axis=-1,
+ )
+
+ # check if we need to separate any classes, e.g. pathologies from White Matter and Gray Matter
+ if self.segmentation_classes_to_separate is not None:
+ for x in self.segmentation_classes_to_separate:
+ segmentation_class_to_separate = segmentation_labels[..., x]
+ for i in range(segmentation_labels.shape[-1]):
+ if i == x:
+ continue
+ segmentation_labels[..., i][segmentation_class_to_separate > 0] = 0
+
+ # threshold probability maps if any threshold is given
+ if self.segmentation_classes_thresholds is not None:
+ for i, voxel_thres in enumerate(self.segmentation_classes_thresholds):
+ if voxel_thres is not None:
+ segmentation_labels[..., i][segmentation_labels[..., i] < voxel_thres] = 0
+ segmentation_labels[..., i][segmentation_labels[..., i] >= voxel_thres] = 1
+
+ if self.consecutive_slices == 1:
+ # bring the segmentation classes dimension back to the first dimension
+ segmentation_labels = np.moveaxis(segmentation_labels, -1, 0)
+ elif self.consecutive_slices > 1:
+ # bring the segmentation classes dimension back to the second dimension
+ segmentation_labels = np.moveaxis(segmentation_labels, -1, 1)
+
+ return segmentation_labels
+
+ def __getitem__(self, i: int): # noqa: MC0001
+ """Get item from :class:`RSMRIDataset`."""
+ fname, dataslice, metadata = self.examples[i]
+ with h5py.File(fname, "r") as hf:
+ if self.complex_data:
+ kspace = self.get_consecutive_slices(hf, "kspace", dataslice).astype(np.complex64)
+
+ sensitivity_map = np.array([])
+ if "sensitivity_map" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "sensitivity_map", dataslice).astype(
+ np.complex64
+ )
+ elif "maps" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "maps", dataslice).astype(np.complex64)
+ elif self.coil_sensitivity_maps_root is not None and self.coil_sensitivity_maps_root != "None":
+ coil_sensitivity_maps_root = self.coil_sensitivity_maps_root
+ split_dir = str(fname).split("/")
+ # check if exists
+ if not os.path.exists(Path(f"{coil_sensitivity_maps_root}/{split_dir[-2]}/{fname.name}")):
+ # find to what depth the coil_sensitivity_maps_root directory is nested
+ for j in range(len(split_dir)):
+ # get the coil_sensitivity_maps_root directory name
+ coil_sensitivity_maps_root = Path(f"{self.coil_sensitivity_maps_root}/{split_dir[-j]}/")
+ if os.path.exists(coil_sensitivity_maps_root / Path(split_dir[-2]) / fname.name):
+ break
+ # load coil sensitivity maps
+ with h5py.File(Path(coil_sensitivity_maps_root) / Path(split_dir[-2]) / fname.name, "r") as sf:
+ if "sensitivity_map" in sf or "sensitivity_map" in next(iter(sf.keys())):
+ sensitivity_map = (
+ self.get_consecutive_slices(sf, "sensitivity_map", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+
+ mask = None
+ if "mask" in hf:
+ mask = np.asarray(self.get_consecutive_slices(hf, "mask", dataslice))
+ if mask.ndim == 3:
+ mask = mask[dataslice]
+ elif self.mask_root is not None and self.mask_root != "None":
+ with h5py.File(Path(self.mask_root) / fname.name, "r") as mf:
+ mask = np.asarray(self.get_consecutive_slices(mf, "mask", dataslice))
+
+ imspace = np.empty([])
+
+ elif not self.complex_data:
+ if "reconstruction_rss" in hf:
+ imspace = self.get_consecutive_slices(hf, "reconstruction_rss", dataslice)
+ elif "reconstruction_sense" in hf:
+ imspace = self.get_consecutive_slices(hf, "reconstruction_sense", dataslice)
+ elif "reconstruction" in hf:
+ imspace = self.get_consecutive_slices(hf, "reconstruction", dataslice)
+ elif "target" in hf:
+ imspace = self.get_consecutive_slices(hf, "target", dataslice)
+ else:
+ raise ValueError(
+ "Complex data has not been selected but no reconstruction data found in file. "
+ "Only 'reconstruction' key is supported."
+ )
+ kspace = np.empty([])
+ sensitivity_map = np.array([])
+ mask = np.empty([])
+
+ segmentation_labels = np.empty([])
+ if self.segmentations_root is not None and self.segmentations_root != "None":
+ with h5py.File(Path(self.segmentations_root) / fname.name, "r") as sf:
+ segmentation_labels = np.asarray(self.get_consecutive_slices(sf, "segmentation", dataslice))
+ segmentation_labels = self.process_segmentation_labels(segmentation_labels)
+ elif "segmentation" in hf:
+ segmentation_labels = np.asarray(self.get_consecutive_slices(hf, "segmentation", dataslice))
+ segmentation_labels = self.process_segmentation_labels(segmentation_labels)
+
+ initial_prediction = np.empty([])
+ if not is_none(self.initial_predictions_root):
+ with h5py.File(Path(self.initial_predictions_root) / fname.name, "r") as ipf: # type: ignore
+ if "reconstruction" in hf:
+ initial_prediction = (
+ self.get_consecutive_slices(ipf, "reconstruction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ initial_prediction = (
+ self.get_consecutive_slices(ipf, "initial_prediction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ else:
+ if "reconstruction" in hf:
+ initial_prediction = (
+ self.get_consecutive_slices(hf, "reconstruction", dataslice).squeeze().astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ initial_prediction = (
+ self.get_consecutive_slices(hf, "initial_prediction", dataslice).squeeze().astype(np.complex64)
+ )
+
+ attrs = dict(hf.attrs)
+
+ # get noise level for current slice, if metadata["noise_levels"] is not empty
+ if "noise_levels" in metadata and len(metadata["noise_levels"]) > 0:
+ metadata["noise"] = metadata["noise_levels"][dataslice]
+ else:
+ metadata["noise"] = 1.0
+
+ attrs.update(metadata)
+
+ if sensitivity_map.shape != kspace.shape and sensitivity_map.ndim > 1:
+ if sensitivity_map.ndim == 3:
+ sensitivity_map = np.transpose(sensitivity_map, (2, 0, 1))
+ elif sensitivity_map.ndim == 4:
+ sensitivity_map = np.transpose(sensitivity_map, (0, 3, 1, 2))
+ else:
+ raise ValueError(
+ f"Sensitivity map has invalid dimensions {sensitivity_map.shape} compared to kspace {kspace.shape}"
+ )
+
+ attrs["log_image"] = bool(dataslice in self.indices_to_log)
+
+ return (
+ (
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
+
+
+class SKMTEARSMRIDataset(RSMRIDataset):
+ """Supports the SKM-TEA dataset for multitask accelerated MRI reconstruction and MRI segmentation.
+
+ .. note::
+ Extends :class:`atommic.collections.multitask.rs.data.mrirs_loader.RSMRIDataset`.
+ """
+
+ def __getitem__(self, i: int): # noqa: MC0001
+ """Get item from :class:`SKMTEARSMRIDataset`."""
+ if not is_none(self.dataset_format):
+ dataset_format = self.dataset_format.lower() # type: ignore
+ masking = "default"
+ if "custom_masking" in dataset_format:
+ masking = "custom"
+ dataset_format = dataset_format.replace("custom_masking", "").strip("_")
+ else:
+ dataset_format = None
+ masking = "custom"
+
+ fname, dataslice, metadata = self.examples[i]
+ with h5py.File(fname, "r") as hf:
+ kspace = self.get_consecutive_slices(hf, "kspace", dataslice).astype(np.complex64)
+
+ if not is_none(dataset_format) and dataset_format == "skm-tea-echo1":
+ kspace = kspace[:, :, 0, :]
+ elif not is_none(dataset_format) and dataset_format == "skm-tea-echo2":
+ kspace = kspace[:, :, 1, :]
+ elif not is_none(dataset_format) and dataset_format == "skm-tea-echo1+echo2":
+ kspace = kspace[:, :, 0, :] + kspace[:, :, 1, :]
+ elif not is_none(dataset_format) and dataset_format == "skm-tea-echo1+echo2-mc":
+ kspace = np.concatenate([kspace[:, :, 0, :], kspace[:, :, 1, :]], axis=-1)
+ else:
+ warnings.warn(
+ f"Dataset format {dataset_format} is either not supported or set to None. "
+ "Using by default only the first echo."
+ )
+ kspace = kspace[:, :, 0, :]
+
+ kspace = kspace[48:-48, 40:-40]
+
+ sensitivity_map = self.get_consecutive_slices(hf, "maps", dataslice).astype(np.complex64)
+ sensitivity_map = sensitivity_map[..., 0]
+
+ sensitivity_map = sensitivity_map[48:-48, 40:-40]
+
+ if masking == "custom":
+ mask = np.array([])
+ else:
+ masks = hf["masks"]
+ mask = {}
+ for key, val in masks.items():
+ mask[key.split("_")[-1].split(".")[0]] = np.asarray(val)
+
+ # get the file format of the segmentation files
+ segmentation_labels = nib.load(
+ Path(self.segmentations_root) / Path(str(fname.name.split(".")[0]) + ".nii.gz") # type: ignore
+ ).get_fdata()
+
+ # get a slice
+ segmentation_labels = self.get_consecutive_slices({"seg": segmentation_labels}, "seg", dataslice)
+
+ # Get the segmentation labels. They are valued as follows:
+ # 0: Patellar Cartilage
+ patellar_cartilage = np.zeros_like(segmentation_labels)
+ patellar_cartilage[segmentation_labels == 1] = 1
+ # 1: Femoral Cartilage
+ femoral_cartilage = np.zeros_like(segmentation_labels)
+ femoral_cartilage[segmentation_labels == 2] = 1
+ # 2: Lateral Tibial Cartilage
+ lateral_tibial_cartilage = np.zeros_like(segmentation_labels)
+ lateral_tibial_cartilage[segmentation_labels == 3] = 1
+ # 3: Medial Tibial Cartilage
+ medial_tibial_cartilage = np.zeros_like(segmentation_labels)
+ medial_tibial_cartilage[segmentation_labels == 4] = 1
+ # 4: Lateral Meniscus
+ lateral_meniscus = np.zeros_like(segmentation_labels)
+ lateral_meniscus[segmentation_labels == 5] = 1
+ # 5: Medial Meniscus
+ medial_meniscus = np.zeros_like(segmentation_labels)
+ medial_meniscus[segmentation_labels == 6] = 1
+ # combine Lateral Tibial Cartilage and Medial Tibial Cartilage
+ tibial_cartilage = lateral_tibial_cartilage + medial_tibial_cartilage
+ # combine Lateral Meniscus and Medial Meniscus
+ medial_meniscus = lateral_meniscus + medial_meniscus
+
+ if self.consecutive_slices > 1:
+ segmentation_labels_dim = 1
+ else:
+ segmentation_labels_dim = 0
+
+ # stack the labels in the last dimension
+ segmentation_labels = np.stack(
+ [patellar_cartilage, femoral_cartilage, tibial_cartilage, medial_meniscus],
+ axis=segmentation_labels_dim,
+ )
+
+ # TODO: This is hardcoded on the SKM-TEA side, how to generalize this?
+ # We need to crop the segmentation labels in the frequency domain to reduce the FOV.
+ segmentation_labels = np.fft.fftshift(np.fft.fft2(segmentation_labels))
+ segmentation_labels = segmentation_labels[:, 48:-48, 40:-40]
+ segmentation_labels = np.fft.ifft2(np.fft.ifftshift(segmentation_labels)).real
+
+ imspace = np.empty([])
+
+ initial_prediction = np.empty([])
+ attrs = dict(hf.attrs)
+
+ # get noise level for current slice, if metadata["noise_levels"] is not empty
+ if "noise_levels" in metadata and len(metadata["noise_levels"]) > 0:
+ metadata["noise"] = metadata["noise_levels"][dataslice]
+ else:
+ metadata["noise"] = 1.0
+
+ attrs.update(metadata)
+
+ kspace = np.transpose(kspace, (2, 0, 1))
+ sensitivity_map = np.transpose(sensitivity_map.squeeze(), (2, 0, 1))
+
+ attrs["log_image"] = bool(dataslice in self.indices_to_log)
+
+ return (
+ (
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
diff --git a/atommic/collections/multitask/rs/nn/__init__.py b/atommic/collections/multitask/rs/nn/__init__.py
new file mode 100644
index 00000000..3ba6fb41
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/__init__.py
@@ -0,0 +1,10 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.multitask.rs.nn.idslr import IDSLR # noqa: F401
+from atommic.collections.multitask.rs.nn.idslr_unet import IDSLRUNet # noqa: F401
+from atommic.collections.multitask.rs.nn.mtlrs import MTLRS # noqa: F401
+from atommic.collections.multitask.rs.nn.mtlrs_base.mtlrs_block import MTLRSBlock # noqa: F401
+from atommic.collections.multitask.rs.nn.recseg_unet import RecSegUNet # noqa: F401
+from atommic.collections.multitask.rs.nn.segnet import SegNet # noqa: F401
+from atommic.collections.multitask.rs.nn.seranet import SERANet # noqa: F401
diff --git a/atommic/collections/multitask/rs/nn/base.py b/atommic/collections/multitask/rs/nn/base.py
new file mode 100644
index 00000000..5a0b506a
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/base.py
@@ -0,0 +1,1859 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import os
+import warnings
+from abc import ABC
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Tuple, Union
+
+import h5py
+import numpy as np
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch.nn import L1Loss, MSELoss
+from torch.utils.data import DataLoader
+
+# do not import BaseMRIModel, BaseSensitivityModel, and DistributedMetricSum directly to avoid circular imports
+import atommic.collections.common as atommic_common
+from atommic.collections.common.data.subsample import create_masker
+from atommic.collections.common.losses import VALID_RECONSTRUCTION_LOSSES, VALID_SEGMENTATION_LOSSES
+from atommic.collections.common.losses.aggregator import AggregatorLoss
+from atommic.collections.common.losses.wasserstein import SinkhornDistance
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import (
+ check_stacked_complex,
+ coil_combination_method,
+ complex_abs,
+ complex_abs_sq,
+ expand_op,
+ is_none,
+ unnormalize,
+)
+from atommic.collections.multitask.rs.data import mrirs_loader
+from atommic.collections.multitask.rs.parts.transforms import RSMRIDataTransforms
+from atommic.collections.reconstruction.losses.na import NoiseAwareLoss
+from atommic.collections.reconstruction.losses.ssim import SSIMLoss
+from atommic.collections.reconstruction.metrics import mse, nmse, psnr, ssim
+from atommic.collections.segmentation.losses.cross_entropy import CrossEntropyLoss
+from atommic.collections.segmentation.losses.dice import Dice
+
+__all__ = ["BaseMRIReconstructionSegmentationModel"]
+
+
+class BaseMRIReconstructionSegmentationModel(atommic_common.nn.base.BaseMRIModel, ABC): # type: ignore
+ """Base class of all (multitask) MRI reconstruction and MRI segmentation models."""
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None): # noqa: MC0001
+ """Inits :class:`BaseMRIReconstructionSegmentationModel`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ The configuration file.
+ trainer : Trainer
+ The PyTorch Lightning trainer. Default is ``None``.
+ """
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ # Initialize the Fast-Fourier Transform parameters.
+ self.fft_centered = cfg_dict.get("fft_centered", False)
+ self.fft_normalization = cfg_dict.get("fft_normalization", "backward")
+ self.spatial_dims = cfg_dict.get("spatial_dims", None)
+ self.coil_dim = cfg_dict.get("coil_dim", 1)
+
+ # Initialize the dimensionality of the data. It can be 2D or 2.5D -> meaning 2D with > 1 slices or 3D.
+ self.dimensionality = cfg_dict.get("dimensionality", 2)
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+
+ # Initialize the coil combination method. It can be either "SENSE" or "RSS" (root-sum-of-squares) or
+ # "RSS-complex" (root-sum-of-squares of the complex-valued data).
+ self.coil_combination_method = cfg_dict.get("coil_combination_method", "SENSE")
+
+ # Refers to Self-Supervised Data Undersampling (SSDU). If True, then the model is trained with only
+ # undersampled data.
+ self.ssdu = cfg_dict.get("ssdu", False)
+
+ # Refers to Noise-to-Recon. If True, then the model can either be trained with only undersampled data or with
+ # both undersampled and (a percentage of) fully-sampled data.
+ self.n2r = cfg_dict.get("n2r", False)
+
+ # Initialize the sensitivity network if cfg_dict.get("estimate_coil_sensitivity_maps_with_nn") is True.
+ self.estimate_coil_sensitivity_maps_with_nn = cfg_dict.get("estimate_coil_sensitivity_maps_with_nn", False)
+
+ self.use_reconstruction_module = cfg_dict.get("use_reconstruction_module")
+ if self.use_reconstruction_module:
+ # Initialize loss related parameters.
+ self.kspace_reconstruction_loss = cfg_dict.get("kspace_reconstruction_loss", False)
+ self.n2r_loss_weight = cfg_dict.get("n2r_loss_weight", 1.0) if self.n2r else 1.0
+ self.reconstruction_losses = {}
+ reconstruction_loss = cfg_dict.get("reconstruction_loss")
+ reconstruction_losses_ = {}
+ if reconstruction_loss is not None:
+ for k, v in reconstruction_loss.items():
+ if k not in VALID_RECONSTRUCTION_LOSSES:
+ raise ValueError(
+ f"Reconstruction loss {k} is not supported. Please choose one of the following: "
+ f"{VALID_RECONSTRUCTION_LOSSES}."
+ )
+ if v is None or v == 0.0:
+ warnings.warn(
+ f"The weight of reconstruction loss {k} is set to 0.0. This loss will not be used."
+ )
+ else:
+ reconstruction_losses_[k] = v
+ else:
+ # Default reconstruction loss is L1.
+ reconstruction_losses_["l1"] = 1.0
+ if sum(reconstruction_losses_.values()) != 1.0:
+ warnings.warn("Sum of reconstruction losses weights is not 1.0. Adjusting weights to sum up to 1.0.")
+ total_weight = sum(reconstruction_losses_.values())
+ reconstruction_losses_ = {k: v / total_weight for k, v in reconstruction_losses_.items()}
+ for name in VALID_RECONSTRUCTION_LOSSES:
+ if name in reconstruction_losses_:
+ if name == "ssim":
+ if self.ssdu:
+ raise ValueError("SSIM loss is not supported for SSDU.")
+ self.reconstruction_losses[name] = SSIMLoss()
+ elif name == "mse":
+ self.reconstruction_losses[name] = MSELoss()
+ elif name == "wasserstein":
+ self.reconstruction_losses[name] = SinkhornDistance()
+ elif name == "noise_aware":
+ self.reconstruction_losses[name] = NoiseAwareLoss()
+ elif name == "l1":
+ self.reconstruction_losses[name] = L1Loss()
+ # replace losses names by 'loss_1', 'loss_2', etc. to properly iterate in the aggregator loss
+ self.reconstruction_losses = {f"loss_{i+1}": v for i, v in enumerate(self.reconstruction_losses.values())}
+ self.total_reconstruction_losses = len(self.reconstruction_losses)
+ self.total_reconstruction_loss_weight = cfg_dict.get("total_reconstruction_loss_weight", 1.0)
+
+ # Initialize the reconstruction metrics.
+ self.reconstruction_loss_weight = cfg_dict.get("reconstruction_loss_weight", 1.0)
+
+ # Set normalization parameters for logging
+ self.unnormalize_loss_inputs = cfg_dict.get("unnormalize_loss_inputs", False)
+ self.unnormalize_log_outputs = cfg_dict.get("unnormalize_log_outputs", False)
+ self.normalization_type = cfg_dict.get("normalization_type", "max")
+
+ # Refers to cascading or iterative reconstruction methods.
+ self.accumulate_predictions = cfg_dict.get("accumulate_predictions", False)
+
+ # Refers to the type of the complex-valued data. It can be either "stacked" or "complex_abs" or
+ # "complex_sqrt_abs".
+ self.complex_valued_type = cfg_dict.get("complex_valued_type", "stacked")
+
+ # Set normalization parameters for logging
+ self.unnormalize_loss_inputs = cfg_dict.get("unnormalize_loss_inputs", False)
+ self.unnormalize_log_outputs = cfg_dict.get("unnormalize_log_outputs", False)
+ self.normalization_type = cfg_dict.get("normalization_type", "max")
+ self.normalize_segmentation_output = cfg_dict.get("normalize_segmentation_output", True)
+
+ # Whether to log multiple modalities, e.g. T1, T2, and FLAIR will be stacked and logged.
+ self.log_multiple_modalities = cfg_dict.get("log_multiple_modalities", False)
+
+ # Set threshold for segmentation classes. If None, no thresholding is applied.
+ self.segmentation_classes_thresholds = cfg_dict.get("segmentation_classes_thresholds", None)
+ self.segmentation_activation = cfg_dict.get("segmentation_activation", None)
+
+ # Initialize loss related parameters.
+ self.segmentation_losses = {}
+ segmentation_loss = cfg_dict.get("segmentation_loss")
+ segmentation_losses_ = {}
+ if segmentation_loss is not None:
+ for k, v in segmentation_loss.items():
+ if k not in VALID_SEGMENTATION_LOSSES:
+ raise ValueError(
+ f"Segmentation loss {k} is not supported. Please choose one of the following: "
+ f"{VALID_SEGMENTATION_LOSSES}."
+ )
+ if v is None or v == 0.0:
+ warnings.warn(f"The weight of segmentation loss {k} is set to 0.0. This loss will not be used.")
+ else:
+ segmentation_losses_[k] = v
+ else:
+ # Default segmentation loss is Dice.
+ segmentation_losses_["dice"] = 1.0
+ if sum(segmentation_losses_.values()) != 1.0:
+ warnings.warn("Sum of segmentation losses weights is not 1.0. Adjusting weights to sum up to 1.0.")
+ total_weight = sum(segmentation_losses_.values())
+ segmentation_losses_ = {k: v / total_weight for k, v in segmentation_losses_.items()}
+ for name in VALID_SEGMENTATION_LOSSES:
+ if name in segmentation_losses_:
+ if name == "cross_entropy":
+ cross_entropy_loss_classes_weight = torch.tensor(
+ cfg_dict.get("cross_entropy_loss_classes_weight", 0.0)
+ )
+ self.segmentation_losses[name] = CrossEntropyLoss(
+ num_samples=cfg_dict.get("cross_entropy_loss_num_samples", 50),
+ ignore_index=cfg_dict.get("cross_entropy_loss_ignore_index", -100),
+ reduction=cfg_dict.get("cross_entropy_loss_reduction", "none"),
+ label_smoothing=cfg_dict.get("cross_entropy_loss_label_smoothing", 0.0),
+ weight=cross_entropy_loss_classes_weight,
+ )
+ elif name == "dice":
+ self.segmentation_losses[name] = Dice(
+ include_background=cfg_dict.get("dice_loss_include_background", False),
+ to_onehot_y=cfg_dict.get("dice_loss_to_onehot_y", False),
+ sigmoid=cfg_dict.get("dice_loss_sigmoid", True),
+ softmax=cfg_dict.get("dice_loss_softmax", False),
+ other_act=cfg_dict.get("dice_loss_other_act", None),
+ squared_pred=cfg_dict.get("dice_loss_squared_pred", False),
+ jaccard=cfg_dict.get("dice_loss_jaccard", False),
+ flatten=cfg_dict.get("dice_loss_flatten", False),
+ reduction=cfg_dict.get("dice_loss_reduction", "mean"),
+ smooth_nr=cfg_dict.get("dice_loss_smooth_nr", 1e-5),
+ smooth_dr=cfg_dict.get("dice_loss_smooth_dr", 1e-5),
+ batch=cfg_dict.get("dice_loss_batch", False),
+ )
+ self.segmentation_losses = {f"loss_{i+1}": v for i, v in enumerate(self.segmentation_losses.values())}
+ self.total_segmentation_losses = len(self.segmentation_losses)
+ self.total_segmentation_loss_weight = cfg_dict.get("total_segmentation_loss_weight", 1.0)
+
+ # Set the metrics
+ cross_entropy_metric_num_samples = cfg_dict.get("cross_entropy_metric_num_samples", 50)
+ cross_entropy_metric_ignore_index = cfg_dict.get("cross_entropy_metric_ignore_index", -100)
+ cross_entropy_metric_reduction = cfg_dict.get("cross_entropy_metric_reduction", "none")
+ cross_entropy_metric_label_smoothing = cfg_dict.get("cross_entropy_metric_label_smoothing", 0.0)
+ cross_entropy_metric_classes_weight = torch.tensor(cfg_dict.get("cross_entropy_metric_classes_weight", 0.0))
+ dice_metric_include_background = cfg_dict.get("dice_metric_include_background", False)
+ dice_metric_to_onehot_y = cfg_dict.get("dice_metric_to_onehot_y", False)
+ dice_metric_sigmoid = cfg_dict.get("dice_metric_sigmoid", True)
+ dice_metric_softmax = cfg_dict.get("dice_metric_softmax", False)
+ dice_metric_other_act = cfg_dict.get("dice_metric_other_act", None)
+ dice_metric_squared_pred = cfg_dict.get("dice_metric_squared_pred", False)
+ dice_metric_jaccard = cfg_dict.get("dice_metric_jaccard", False)
+ dice_metric_flatten = cfg_dict.get("dice_metric_flatten", False)
+ dice_metric_reduction = cfg_dict.get("dice_metric_reduction", "mean")
+ dice_metric_smooth_nr = cfg_dict.get("dice_metric_smooth_nr", 1e-5)
+ dice_metric_smooth_dr = cfg_dict.get("dice_metric_smooth_dr", 1e-5)
+ dice_metric_batch = cfg_dict.get("dice_metric_batch", True)
+
+ # Initialize the module
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ if self.estimate_coil_sensitivity_maps_with_nn:
+ self.coil_sensitivity_maps_nn = atommic_common.nn.base.BaseSensitivityModel( # type: ignore
+ cfg_dict.get("coil_sensitivity_maps_nn_chans", 8),
+ cfg_dict.get("coil_sensitivity_maps_nn_pools", 4),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ mask_type=cfg_dict.get("coil_sensitivity_maps_nn_mask_type", "2D"),
+ normalize=cfg_dict.get("coil_sensitivity_maps_nn_normalize", True),
+ mask_center=cfg_dict.get("coil_sensitivity_maps_nn_mask_center", True),
+ )
+
+ if self.use_reconstruction_module:
+ # Set aggregation loss
+ self.total_reconstruction_loss = AggregatorLoss(
+ num_inputs=self.total_reconstruction_losses, weights=list(reconstruction_losses_.values())
+ )
+
+ self.MSE = atommic_common.nn.base.DistributedMetricSum() # type: ignore
+ self.NMSE = atommic_common.nn.base.DistributedMetricSum() # type: ignore
+ self.SSIM = atommic_common.nn.base.DistributedMetricSum() # type: ignore
+ self.PSNR = atommic_common.nn.base.DistributedMetricSum() # type: ignore
+ self.TotExamples = atommic_common.nn.base.DistributedMetricSum() # type: ignore
+
+ self.mse_vals_reconstruction: Dict = defaultdict(dict)
+ self.nmse_vals_reconstruction: Dict = defaultdict(dict)
+ self.ssim_vals_reconstruction: Dict = defaultdict(dict)
+ self.psnr_vals_reconstruction: Dict = defaultdict(dict)
+
+ if not is_none(cross_entropy_metric_classes_weight) and cross_entropy_metric_classes_weight != 0.0:
+ self.cross_entropy_metric = CrossEntropyLoss(
+ num_samples=cross_entropy_metric_num_samples,
+ ignore_index=cross_entropy_metric_ignore_index,
+ reduction=cross_entropy_metric_reduction,
+ label_smoothing=cross_entropy_metric_label_smoothing,
+ weight=cross_entropy_metric_classes_weight,
+ )
+ else:
+ self.cross_entropy_metric = None # type: ignore
+ self.dice_metric = Dice(
+ include_background=dice_metric_include_background,
+ to_onehot_y=dice_metric_to_onehot_y,
+ sigmoid=dice_metric_sigmoid,
+ softmax=dice_metric_softmax,
+ other_act=dice_metric_other_act,
+ squared_pred=dice_metric_squared_pred,
+ jaccard=dice_metric_jaccard,
+ flatten=dice_metric_flatten,
+ reduction=dice_metric_reduction,
+ smooth_nr=dice_metric_smooth_nr,
+ smooth_dr=dice_metric_smooth_dr,
+ batch=dice_metric_batch,
+ )
+
+ # Set aggregation loss
+ self.total_segmentation_loss = AggregatorLoss(
+ num_inputs=self.total_segmentation_losses, weights=list(segmentation_losses_.values())
+ )
+
+ # Set distributed metrics
+ self.CROSS_ENTROPY = atommic_common.nn.base.DistributedMetricSum() # type: ignore
+ self.DICE = atommic_common.nn.base.DistributedMetricSum() # type: ignore
+ self.cross_entropy_vals: Dict = defaultdict(dict)
+ self.dice_vals: Dict = defaultdict(dict)
+ self.TotExamples = atommic_common.nn.base.DistributedMetricSum() # type: ignore
+
+ def __abs_output__(self, x: torch.Tensor) -> torch.Tensor:
+ """Converts the input to absolute value."""
+ if x.shape[-1] == 2 or torch.is_complex(x):
+ if torch.is_complex(x):
+ x = torch.view_as_real(x)
+ if self.complex_valued_type == "stacked":
+ x = check_stacked_complex(x)
+ elif self.complex_valued_type == "complex_abs":
+ x = complex_abs(x)
+ elif self.complex_valued_type == "complex_sqrt_abs":
+ x = complex_abs_sq(x)
+ return x
+
+ def __unnormalize_for_loss_or_log__(
+ self,
+ target: torch.Tensor,
+ prediction: torch.Tensor,
+ sensitivity_maps: Union[torch.Tensor, None],
+ attrs: Dict,
+ r: int,
+ batch_idx: int = 1,
+ ) -> Tuple[torch.Tensor, torch.Tensor, Union[torch.Tensor, None]]:
+ """
+ Unnormalizes the data for computing the loss or logging.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : torch.Tensor
+ Prediction data of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor or None
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2] or None.
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ batch_idx : int
+ Batch index. Default is ``1``.
+
+ Returns
+ -------
+ target : torch.Tensor
+ Unnormalized target data.
+ prediction : torch.Tensor
+ Unnormalized prediction data.
+ sensitivity_maps : torch.Tensor
+ Unnormalized sensitivity maps.
+ """
+ if self.n2r and not attrs["n2r_supervised"][batch_idx]:
+ target = unnormalize(
+ target,
+ {
+ "min": attrs["prediction_min"][batch_idx]
+ if "prediction_min" in attrs
+ else attrs[f"prediction_min_{r}"][batch_idx],
+ "max": attrs["prediction_max"][batch_idx]
+ if "prediction_max" in attrs
+ else attrs[f"prediction_max_{r}"][batch_idx],
+ "mean": attrs["prediction_mean"][batch_idx]
+ if "prediction_mean" in attrs
+ else attrs[f"prediction_mean_{r}"][batch_idx],
+ "std": attrs["prediction_std"][batch_idx]
+ if "prediction_std" in attrs
+ else attrs[f"prediction_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction = unnormalize(
+ prediction,
+ {
+ "min": attrs["noise_prediction_min"][batch_idx]
+ if "noise_prediction_min" in attrs
+ else attrs[f"noise_prediction_min_{r}"][batch_idx],
+ "max": attrs["noise_prediction_max"][batch_idx]
+ if "noise_prediction_max" in attrs
+ else attrs[f"noise_prediction_max_{r}"][batch_idx],
+ attrs["noise_prediction_mean"][batch_idx]
+ if "noise_prediction_mean" in attrs
+ else "mean": attrs[f"noise_prediction_mean_{r}"][batch_idx],
+ attrs["noise_prediction_std"][batch_idx]
+ if "noise_prediction_std" in attrs
+ else "std": attrs[f"noise_prediction_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ else:
+ target = unnormalize(
+ target,
+ {
+ "min": attrs["target_min"][batch_idx]
+ if "target_min" in attrs
+ else attrs[f"target_min_{r}"][batch_idx],
+ "max": attrs["target_max"][batch_idx]
+ if "target_max" in attrs
+ else attrs[f"target_max_{r}"][batch_idx],
+ "mean": attrs["target_mean"][batch_idx]
+ if "target_mean" in attrs
+ else attrs[f"target_mean_{r}"][batch_idx],
+ "std": attrs["target_std"][batch_idx]
+ if "target_std" in attrs
+ else attrs[f"target_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction = unnormalize(
+ prediction,
+ {
+ "min": attrs["prediction_min"][batch_idx]
+ if "prediction_min" in attrs
+ else attrs[f"prediction_min_{r}"][batch_idx],
+ "max": attrs["prediction_max"][batch_idx]
+ if "prediction_max" in attrs
+ else attrs[f"prediction_max_{r}"][batch_idx],
+ "mean": attrs["prediction_mean"][batch_idx]
+ if "prediction_mean" in attrs
+ else attrs[f"prediction_mean_{r}"][batch_idx],
+ "std": attrs["prediction_std"][batch_idx]
+ if "prediction_std" in attrs
+ else attrs[f"prediction_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+
+ if sensitivity_maps is not None:
+ sensitivity_maps = unnormalize(
+ sensitivity_maps,
+ {
+ "min": attrs["sensitivity_maps_min"][batch_idx],
+ "max": attrs["sensitivity_maps_max"][batch_idx],
+ "mean": attrs["sensitivity_maps_mean"][batch_idx],
+ "std": attrs["sensitivity_maps_std"][batch_idx],
+ },
+ self.normalization_type,
+ )
+
+ return target, prediction, sensitivity_maps
+
+ def process_reconstruction_loss(
+ self,
+ target: torch.Tensor,
+ prediction: Union[list, torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ attrs: Union[Dict, torch.Tensor],
+ r: Union[int, torch.Tensor],
+ loss_func: torch.nn.Module,
+ ) -> torch.Tensor:
+ """Processes the reconstruction loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ loss_func : torch.nn.Module
+ Loss function. Default is ``torch.nn.L1Loss()``.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if target.shape[-1] != 2 and torch.is_complex(target):
+ target = torch.view_as_real(target)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ target = expand_op(target, sensitivity_maps, self.coil_dim)
+ # Transform to k-space.
+ target = fft2(target, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target / torch.max(torch.abs(target)))
+ elif not self.unnormalize_loss_inputs:
+ target = self.__abs_output__(target / torch.max(torch.abs(target)))
+
+ def compute_reconstruction_loss(t, p, s):
+ if self.unnormalize_loss_inputs:
+ # we do the unnormalization here to avoid explicitly iterating through list of predictions, which
+ # might be a list of lists.
+ t, p, s = self.__unnormalize_for_loss_or_log__(t, p, s, attrs, r)
+
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if p.shape[-1] != 2 and torch.is_complex(p):
+ p = torch.view_as_real(p)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ p = expand_op(p, s, self.coil_dim)
+ # Transform to k-space.
+ p = fft2(p, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # If SSDU is used, then apply the mask to the prediction to enforce data consistency.
+ if self.ssdu:
+ p = p * mask
+ # Ensure loss inputs are both viewed in the same way.
+ p = self.__abs_output__(p / torch.max(torch.abs(p)))
+ elif not self.unnormalize_loss_inputs:
+ p = self.__abs_output__(p / torch.max(torch.abs(p)))
+
+ if "ssim" in str(loss_func).lower():
+ p = torch.abs(p / torch.max(torch.abs(p)))
+ t = torch.abs(t / torch.max(torch.abs(t)))
+
+ return loss_func(
+ t,
+ p,
+ data_range=torch.tensor([max(torch.max(t).item(), torch.max(p).item())]).unsqueeze(dim=0).to(t),
+ )
+
+ return loss_func(t, p)
+
+ return compute_reconstruction_loss(target, prediction, sensitivity_maps)
+
+ def process_segmentation_loss(self, target: torch.Tensor, prediction: torch.Tensor, attrs: Dict) -> Dict:
+ """Processes the segmentation loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, nr_classes, n_x, n_y].
+ prediction : torch.Tensor
+ Prediction of shape [batch_size, nr_classes, n_x, n_y].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+
+ Returns
+ -------
+ Dict
+ Dictionary containing the (multiple) loss values. For example, if the cross entropy loss and the dice loss
+ are used, the dictionary will contain the keys ``cross_entropy_loss``, ``dice_loss``, and
+ (combined) ``segmentation_loss``.
+ """
+ if self.unnormalize_loss_inputs:
+ target, prediction, _ = self.__unnormalize_for_loss_or_log__(target, prediction, None, attrs, attrs["r"])
+ losses = {}
+ for name, loss_func in self.segmentation_losses.items():
+ loss = loss_func(target, prediction)
+ if isinstance(loss, tuple):
+ # In case of the dice loss, the loss is a tuple of the form (dice, dice loss)
+ loss = loss[1]
+ losses[name] = loss
+ return self.total_segmentation_loss(**losses) * self.total_segmentation_loss_weight
+
+ def __compute_loss__(
+ self,
+ predictions_reconstruction: Union[list, torch.Tensor],
+ predictions_reconstruction_n2r: Union[list, torch.Tensor],
+ target_reconstruction: torch.Tensor,
+ predictions_segmentation: Union[list, torch.Tensor],
+ target_segmentation: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ ssdu_loss_mask: torch.Tensor,
+ attrs: Dict,
+ r: int,
+ ) -> torch.Tensor:
+ """Computes the reconstruction loss.
+
+ Parameters
+ ----------
+ predictions_reconstruction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ predictions_reconstruction_n2r : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2], if Noise-to-Recon is used. Otherwise, None.
+ target_reconstruction : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ predictions_segmentation : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, nr_classes, n_x, n_y].
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ ssdu_loss_mask : torch.Tensor
+ SSDU loss mask of shape [batch_size, 1, n_x, n_y, 1]. It will be used if self.ssdu is True, to enforce
+ data consistency on the prediction.
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ if self.consecutive_slices > 1:
+ batch_size, slices = target_segmentation.shape[:2]
+ target_segmentation = target_segmentation.reshape(batch_size * slices, *target_segmentation.shape[2:])
+
+ segmentation_loss = self.process_segmentation_loss(target_segmentation, predictions_segmentation, attrs)
+
+ if self.use_reconstruction_module:
+ if predictions_reconstruction_n2r is not None and not attrs["n2r_supervised"]:
+ # Noise-to-Recon with/without SSDU
+ target = predictions_reconstruction
+ predictions_reconstruction = predictions_reconstruction_n2r
+ weight = self.n2r_loss_weight
+ else:
+ # Supervised learning or Noise-to-Recon with SSDU
+ target = target_reconstruction
+ weight = 1.0
+ losses = {}
+ for name, loss_func in self.reconstruction_losses.items():
+ losses[name] = (
+ self.process_reconstruction_loss(
+ target,
+ predictions_reconstruction,
+ sensitivity_maps,
+ ssdu_loss_mask,
+ attrs,
+ r,
+ loss_func=loss_func,
+ )
+ * weight
+ )
+ reconstruction_loss = self.total_reconstruction_loss(**losses)
+ else:
+ reconstruction_loss = torch.tensor(0.0)
+
+ loss = (
+ self.total_segmentation_loss_weight * segmentation_loss
+ + self.total_reconstruction_loss_weight * reconstruction_loss
+ )
+
+ if self.accumulate_predictions:
+ loss = sum(list(loss))
+
+ return loss
+
+ def __compute_and_log_metrics_and_outputs__( # noqa: MC0001
+ self,
+ predictions_reconstruction: Union[list, torch.Tensor],
+ target_reconstruction: torch.Tensor,
+ predictions_segmentation: Union[list, torch.Tensor],
+ target_segmentation: torch.Tensor,
+ attrs: Dict,
+ ):
+ """Computes the metrics and logs the outputs.
+
+ Parameters
+ ----------
+ predictions_reconstruction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ target_reconstruction : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ predictions_segmentation : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, nr_classes, n_x, n_y].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ """
+ if isinstance(predictions_reconstruction, list):
+ while isinstance(predictions_reconstruction, list):
+ predictions_reconstruction = predictions_reconstruction[-1]
+
+ if isinstance(predictions_segmentation, list):
+ while isinstance(predictions_segmentation, list):
+ predictions_segmentation = predictions_segmentation[-1]
+
+ if self.consecutive_slices > 1:
+ # reshape the target and prediction to [batch_size, self.consecutive_slices, nr_classes, n_x, n_y]
+ batch_size = target_segmentation.shape[0] // self.consecutive_slices
+ target_segmentation = target_segmentation.reshape(
+ batch_size, self.consecutive_slices, *target_segmentation.shape[1:]
+ )
+ target_reconstruction = target_reconstruction.reshape(
+ batch_size, self.consecutive_slices, *target_reconstruction.shape[2:]
+ )
+ predictions_segmentation = predictions_segmentation.reshape(
+ batch_size, self.consecutive_slices, *predictions_segmentation.shape[2:]
+ )
+ predictions_reconstruction = predictions_reconstruction.reshape(
+ batch_size, self.consecutive_slices, *predictions_reconstruction.shape[1:]
+ )
+ target_segmentation = target_segmentation[:, self.consecutive_slices // 2]
+ target_reconstruction = target_reconstruction[:, self.consecutive_slices // 2]
+ predictions_segmentation = predictions_segmentation[:, self.consecutive_slices // 2]
+ predictions_reconstruction = predictions_reconstruction[:, self.consecutive_slices // 2]
+
+ fname = attrs["fname"]
+ slice_idx = attrs["slice_idx"]
+
+ # Iterate over the batch and log the target and predictions.
+ for _batch_idx_ in range(target_segmentation.shape[0]):
+ output_predictions_reconstruction = predictions_reconstruction[_batch_idx_]
+ output_target_reconstruction = target_reconstruction[_batch_idx_]
+ output_predictions_segmentation = predictions_segmentation[_batch_idx_]
+ output_target_segmentation = target_segmentation[_batch_idx_]
+
+ if self.unnormalize_log_outputs:
+ # Unnormalize target and predictions with pre normalization values. This is only for logging purposes.
+ # For the loss computation, the self.unnormalize_loss_inputs flag is used.
+ (
+ output_target_segmentation,
+ output_predictions_segmentation,
+ _,
+ ) = self.__unnormalize_for_loss_or_log__( # type: ignore
+ output_target_segmentation,
+ output_predictions_segmentation,
+ None,
+ attrs,
+ attrs["r"],
+ batch_idx=_batch_idx_,
+ )
+ (
+ output_target_reconstruction,
+ output_predictions_reconstruction,
+ _,
+ ) = self.__unnormalize_for_loss_or_log__( # type: ignore
+ output_target_reconstruction,
+ output_predictions_reconstruction,
+ None,
+ attrs,
+ attrs["r"],
+ batch_idx=_batch_idx_,
+ )
+
+ output_predictions_reconstruction = output_predictions_reconstruction.detach().cpu()
+ output_target_reconstruction = output_target_reconstruction.detach().cpu()
+ output_target_segmentation = output_target_segmentation.detach().cpu()
+ output_predictions_segmentation = output_predictions_segmentation.detach().cpu()
+
+ # Normalize target and predictions to [0, 1] for logging.
+ if torch.is_complex(output_target_reconstruction) and output_target_reconstruction.shape[-1] != 2:
+ output_target_reconstruction = torch.view_as_real(output_target_reconstruction)
+ if output_target_reconstruction.shape[-1] == 2:
+ output_target_reconstruction = complex_abs(output_target_reconstruction)
+ output_target_reconstruction = output_target_reconstruction / torch.max(
+ torch.abs(output_target_reconstruction)
+ )
+ output_target_reconstruction = output_target_reconstruction.detach().cpu()
+
+ if (
+ torch.is_complex(output_predictions_reconstruction)
+ and output_predictions_reconstruction.shape[-1] != 2
+ ):
+ output_predictions_reconstruction = torch.view_as_real(output_predictions_reconstruction)
+ if output_predictions_reconstruction.shape[-1] == 2:
+ output_predictions_reconstruction = complex_abs(output_predictions_reconstruction)
+ output_predictions_reconstruction = output_predictions_reconstruction / torch.max(
+ torch.abs(output_predictions_reconstruction)
+ )
+ output_predictions_reconstruction = output_predictions_reconstruction.detach().cpu()
+
+ # Log target and predictions, if log_image is True for this slice.
+ if attrs["log_image"][_batch_idx_]:
+ key = f"{fname[_batch_idx_]}_slice_{int(slice_idx[_batch_idx_])}"
+
+ if self.log_multiple_modalities:
+ # concatenate the reconstruction predictions for logging
+ output_target_reconstruction = torch.cat(
+ [output_target_reconstruction[i] for i in range(output_target_reconstruction.shape[0])], dim=-1
+ )
+
+ if self.use_reconstruction_module:
+ self.log_image(
+ f"{key}/a/reconstruction/target/predictions/error",
+ torch.cat(
+ [
+ output_target_reconstruction,
+ output_predictions_reconstruction,
+ torch.abs(output_target_reconstruction - output_predictions_reconstruction),
+ ],
+ dim=-1,
+ ),
+ )
+
+ # concatenate the segmentation classes for logging
+ target_segmentation_class = torch.cat(
+ [output_target_segmentation[i] for i in range(output_target_segmentation.shape[0])], dim=-1
+ )
+ output_predictions_segmentation_class = torch.cat(
+ [output_predictions_segmentation[i] for i in range(output_predictions_segmentation.shape[0])],
+ dim=-1,
+ )
+ self.log_image(f"{key}/b/segmentation/target", target_segmentation_class)
+ self.log_image(f"{key}/c/segmentation/predictions", output_predictions_segmentation_class)
+ self.log_image(
+ f"{key}/d/segmentation/error",
+ torch.abs(target_segmentation_class - output_predictions_segmentation_class),
+ )
+
+ # Compute metrics and log them.
+ output_predictions_reconstruction = output_predictions_reconstruction.numpy()
+ output_target_reconstruction = output_target_reconstruction.numpy()
+
+ self.mse_vals_reconstruction[fname[_batch_idx_]][str(slice_idx[_batch_idx_].item())] = torch.tensor(
+ mse(output_target_reconstruction, output_predictions_reconstruction)
+ ).view(1)
+ self.nmse_vals_reconstruction[fname[_batch_idx_]][str(slice_idx[_batch_idx_].item())] = torch.tensor(
+ nmse(output_target_reconstruction, output_predictions_reconstruction)
+ ).view(1)
+
+ max_value = max(np.max(output_target_reconstruction), np.max(output_predictions_reconstruction)) - min(
+ np.min(output_target_reconstruction), np.min(output_predictions_reconstruction)
+ )
+
+ self.ssim_vals_reconstruction[fname[_batch_idx_]][str(slice_idx[_batch_idx_].item())] = torch.tensor(
+ ssim(output_target_reconstruction, output_predictions_reconstruction, maxval=max_value)
+ ).view(1)
+ self.psnr_vals_reconstruction[fname[_batch_idx_]][str(slice_idx[_batch_idx_].item())] = torch.tensor(
+ psnr(output_target_reconstruction, output_predictions_reconstruction, maxval=max_value)
+ ).view(1)
+
+ if self.cross_entropy_metric is not None:
+ self.cross_entropy_vals[fname[_batch_idx_]][
+ str(slice_idx[_batch_idx_].item())
+ ] = self.cross_entropy_metric(
+ output_target_segmentation.to(self.device),
+ output_predictions_segmentation.to(self.device),
+ )
+
+ dice_score, _ = self.dice_metric(output_target_segmentation, output_predictions_segmentation)
+ self.dice_vals[fname[_batch_idx_]][str(slice_idx[_batch_idx_].item())] = dice_score
+
+ def __check_noise_to_recon_inputs__(
+ self, y: torch.Tensor, mask: torch.Tensor, initial_prediction: torch.Tensor, attrs: Dict
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ r"""Checks if Noise-to-Recon [1] is used.
+
+ References
+ ----------
+ .. [1] Desai, AD, Ozturkler, BM, Sandino, CM, et al. Noise2Recon: Enabling SNR-robust MRI reconstruction with
+ semi-supervised and self-supervised learning. Magn Reson Med. 2023; 90(5): 2052-2070. doi: 10.1002/mrm.29759
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2].
+ attrs : Dict
+ Attributes dictionary. Even though Noise-to-Recon is an unsupervised method, a percentage of the data might
+ be used for supervised learning. In this case, the ``attrs["n2r_supervised"]`` will be True. So we know
+ which data are used for supervised learning and which for unsupervised.
+
+ Returns
+ -------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2].
+ n2r_y : torch.Tensor
+ Subsampled k-space data for Noise-to-Recon. Shape [batch_size, n_coils, n_x, n_y, 2].
+ n2r_mask : torch.Tensor
+ Sampling mask for Noise-to-Recon. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_initial_prediction : torch.Tensor
+ Initial prediction for Noise-to-Recon. Shape [batch_size, n_x, n_y, 2].
+ """
+ if self.n2r and (not attrs["n2r_supervised"].all() or self.ssdu):
+ y, n2r_y = y
+ mask, n2r_mask = mask
+ initial_prediction, n2r_initial_prediction = initial_prediction
+ else:
+ n2r_y = None
+ n2r_mask = None
+ n2r_initial_prediction = None
+ return y, mask, initial_prediction, n2r_y, n2r_mask, n2r_initial_prediction
+
+ def __process_unsupervised_inputs__(
+ self,
+ n2r_y: torch.Tensor,
+ mask: torch.Tensor,
+ n2r_mask: torch.Tensor,
+ n2r_initial_prediction: torch.Tensor,
+ attrs: Dict,
+ r: int,
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Process inputs if Noise-to-Recon and/or SSDU are used.
+
+ Parameters
+ ----------
+ n2r_y : Union[List[torch.Tensor], torch.Tensor]
+ Noise-to-Recon subsampled k-space data, if Noise-to-Recon is used. If multiple accelerations are used, then
+ it is a list of torch.Tensor. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_mask : Union[List[torch.Tensor], torch.Tensor]
+ Noise-to-Recon sampling mask, if Noise-to-Recon is used. If multiple accelerations are used, then
+ it is a list of torch.Tensor. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_initial_prediction : Union[List[torch.Tensor], torch.Tensor]
+ Noise-to-Recon initial prediction, if Noise-to-Recon is used. If multiple accelerations are used, then
+ it is a list of torch.Tensor. Shape [batch_size, n_x, n_y, 2].
+ attrs : Dict
+ Attributes dictionary. Even though Noise-to-Recon is an unsupervised method, a percentage of the data might
+ be used for supervised learning. In this case, the ``attrs["n2r_supervised"]`` will be True. So we know
+ which data are used for supervised learning and which for unsupervised.
+ r : int
+ Random index used to select the acceleration.
+
+ Returns
+ -------
+ n2r_y : torch.Tensor
+ Noise-to-Recon subsampled k-space data, if Noise-to-Recon is used. If multiple accelerations are used, then
+ one factor is randomly selected. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_mask : torch.Tensor
+ Noise-to-Recon sampling mask, if Noise-to-Recon is used. If multiple accelerations are used, then one
+ factor is randomly selected. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_initial_prediction : torch.Tensor
+ Noise-to-Recon initial prediction, if Noise-to-Recon is used. If multiple accelerations are used, then one
+ factor is randomly selected. Shape [batch_size, n_x, n_y, 2].
+ loss_mask : torch.Tensor
+ SSDU loss mask, if SSDU is used. Shape [batch_size, 1, n_x, n_y, 1].
+ """
+ if self.n2r and (not attrs["n2r_supervised"].all() or self.ssdu):
+ # Noise-to-Recon with/without SSDU.
+
+ if isinstance(n2r_y, list):
+ # Check multiple accelerations for Noise-to-Recon
+ n2r_y = n2r_y[r]
+ if n2r_mask is not None:
+ n2r_mask = n2r_mask[r]
+ n2r_initial_prediction = n2r_initial_prediction[r]
+
+ # Check if SSDU is used
+ if self.ssdu:
+ mask, loss_mask = mask
+ else:
+ loss_mask = torch.ones_like(mask)
+
+ # Ensure that the mask has the same number of dimensions as the input mask.
+ if n2r_mask.dim() < mask.dim():
+ n2r_mask = None
+ elif self.ssdu and not self.n2r:
+ # SSDU without Noise-to-Recon.
+ mask, loss_mask = mask
+ else:
+ loss_mask = torch.ones_like(mask)
+
+ return n2r_y, n2r_mask, n2r_initial_prediction, mask, loss_mask
+
+ @staticmethod
+ def __process_inputs__(
+ kspace: Union[List, torch.Tensor],
+ y: Union[List, torch.Tensor],
+ mask: Union[List, torch.Tensor],
+ initial_prediction: Union[List, torch.Tensor],
+ target: Union[List, torch.Tensor],
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int]:
+ """Processes lists of inputs to torch.Tensor. In the case where multiple accelerations are used, then the
+ inputs are lists. This function converts the lists to torch.Tensor by randomly selecting one acceleration. If
+ only one acceleration is used, then the inputs are torch.Tensor and are returned as is.
+
+ Parameters
+ ----------
+ kspace : Union[List, torch.Tensor]
+ Full k-space data of length n_accelerations or shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ y : Union[List, torch.Tensor]
+ Subsampled k-space data of length n_accelerations or shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ mask : Union[List, torch.Tensor]
+ Sampling mask of length n_accelerations or shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction : Union[List, torch.Tensor]
+ Initial prediction of length n_accelerations or shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ target : Union[List, torch.Tensor]
+ Target data of length n_accelerations or shape [batch_size, n_x, n_y, 2].
+
+ Returns
+ -------
+ kspace : torch.Tensor
+ Full k-space data of shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ y : torch.Tensor
+ Subsampled k-space data of shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction : torch.Tensor
+ Initial prediction of shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ r : int
+ Random index used to select the acceleration.
+ """
+ if isinstance(y, list):
+ r = np.random.randint(len(y))
+ y = y[r]
+ mask = mask[r]
+ initial_prediction = initial_prediction[r]
+ else:
+ r = 0
+ if isinstance(kspace, list):
+ kspace = kspace[r]
+ target = target[r]
+ elif isinstance(target, list):
+ target = target[r]
+ return kspace, y, mask, initial_prediction, target, r
+
+ def inference_step( # noqa: MC0001
+ self,
+ kspace: torch.Tensor,
+ y: Union[List[torch.Tensor], torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ mask: Union[List[torch.Tensor], torch.Tensor],
+ initial_prediction_reconstruction: Union[List, torch.Tensor],
+ target_reconstruction: torch.Tensor,
+ target_segmentation: torch.Tensor,
+ fname: str,
+ slice_idx: int,
+ acceleration: float,
+ attrs: Dict,
+ ):
+ """Performs an inference step, i.e., computes the predictions of the model.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ Fully sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ y : Union[List[torch.Tensor], torch.Tensor]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : Union[List[torch.Tensor], torch.Tensor]
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if Unsupervised
+ Learning methods are used, it contains their masks. Shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction_reconstruction : Union[List, torch.Tensor]
+ Initial reconstruction prediction. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_x, n_y, 2].
+ target_reconstruction : torch.Tensor
+ Target reconstruction data. Shape [batch_size, n_x, n_y].
+ target_segmentation : torch.Tensor
+ Target segmentation data. Shape [batch_size, n_x, n_y].
+ fname : str
+ File name.
+ slice_idx : int
+ Slice index.
+ acceleration : float
+ Acceleration factor of the sampling mask, randomly selected if multiple accelerations are used.
+ attrs : Dict
+ Attributes dictionary.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary of processed inputs and model's predictions, with keys:
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask, randomly selected if multiple accelerations are used.
+ 'predictions_reconstruction' : Union[List[torch.Tensor], torch.Tensor]
+ Model's predictions. If accumulate predictions is True, then it is a list of torch.Tensor.
+ Shape [batch_size, n_x, n_y, 2].
+ 'predictions_reconstruction_n2r' : Union[List[torch.Tensor], torch.Tensor]
+ Model's predictions for Noise-to-Recon, if Noise-to-Recon is used. If accumulate predictions is
+ True, then it is a list of torch.Tensor. Shape [batch_size, n_x, n_y, 2].
+ 'target_reconstruction' : torch.Tensor
+ Target data. Shape [batch_size, n_x, n_y].
+ 'target_segmentation' : torch.Tensor
+ Target segmentation data. Shape [batch_size, n_x, n_y].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'loss_mask' : torch.Tensor
+ SSDU loss mask, if SSDU is used. Shape [batch_size, 1, n_x, n_y, 1].
+ 'attrs' : dict
+ Attributes dictionary.
+ 'r' : int
+ Random index used for selected acceleration.
+ """
+ # Check if Noise-to-Recon is used
+ (
+ y,
+ mask,
+ initial_prediction_reconstruction,
+ n2r_y,
+ n2r_mask,
+ n2r_initial_prediction_reconstruction,
+ ) = self.__check_noise_to_recon_inputs__(y, mask, initial_prediction_reconstruction, attrs)
+
+ # Process inputs to randomly select one acceleration factor, in case multiple accelerations are used.
+ kspace, y, mask, initial_prediction_reconstruction, target_reconstruction, r = self.__process_inputs__(
+ kspace, y, mask, initial_prediction_reconstruction, target_reconstruction
+ )
+
+ # Process inputs if Noise-to-Recon and/or SSDU are used.
+ n2r_y, n2r_mask, n2r_initial_prediction_reconstruction, mask, loss_mask = self.__process_unsupervised_inputs__(
+ n2r_y, mask, n2r_mask, n2r_initial_prediction_reconstruction, attrs, r
+ )
+
+ # Check if a network is used for coil sensitivity maps estimation.
+ if self.estimate_coil_sensitivity_maps_with_nn:
+ # Estimate coil sensitivity maps with a network.
+ sensitivity_maps = self.coil_sensitivity_maps_nn(kspace, mask, sensitivity_maps)
+ # (Re-)compute the initial prediction with the estimated sensitivity maps. This also means that the
+ # self.coil_combination_method is set to "SENSE", since in "RSS" the sensitivity maps are not used.
+ initial_prediction_reconstruction = coil_combination_method(
+ ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+ if n2r_initial_prediction_reconstruction is not None:
+ n2r_initial_prediction_reconstruction = coil_combination_method(
+ ifft2(n2r_y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+
+ # Model forward pass
+ predictions_reconstruction, predictions_segmentation = self.forward(
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ attrs["noise"],
+ )
+
+ if not is_none(self.segmentation_classes_thresholds):
+ for class_idx, thres in enumerate(self.segmentation_classes_thresholds):
+ if self.segmentation_activation == "sigmoid":
+ if isinstance(predictions_segmentation, list):
+ cond = [torch.sigmoid(pred[:, class_idx]) for pred in predictions_segmentation]
+ else:
+ cond = torch.sigmoid(predictions_segmentation[:, class_idx])
+ elif self.segmentation_activation == "softmax":
+ if isinstance(predictions_segmentation, list):
+ cond = [torch.softmax(pred[:, class_idx], dim=1) for pred in predictions_segmentation]
+ else:
+ cond = torch.softmax(predictions_segmentation[:, class_idx], dim=1)
+ else:
+ if isinstance(predictions_segmentation, list):
+ cond = [pred[:, class_idx] for pred in predictions_segmentation]
+ else:
+ cond = predictions_segmentation[:, class_idx]
+
+ if isinstance(predictions_segmentation, list):
+ for idx, pred in enumerate(predictions_segmentation):
+ predictions_segmentation[idx][:, class_idx] = torch.where(
+ cond[idx] >= thres,
+ predictions_segmentation[idx][:, class_idx],
+ torch.zeros_like(predictions_segmentation[idx][:, class_idx]),
+ )
+ else:
+ predictions_segmentation[:, class_idx] = torch.where(
+ cond >= thres,
+ predictions_segmentation[:, class_idx],
+ torch.zeros_like(predictions_segmentation[:, class_idx]),
+ )
+
+ # Noise-to-Recon forward pass, if Noise-to-Recon is used.
+ predictions_reconstruction_n2r = None
+ if self.n2r and n2r_mask is not None:
+ predictions_reconstruction_n2r = self.forward(
+ n2r_y,
+ sensitivity_maps,
+ n2r_mask,
+ n2r_initial_prediction_reconstruction,
+ target_reconstruction,
+ attrs["noise"],
+ )
+
+ # Get acceleration factor from acceleration list, if multiple accelerations are used. Or if batch size > 1.
+ if isinstance(acceleration, list):
+ if acceleration[0].shape[0] > 1:
+ acceleration[0] = acceleration[0][0]
+ acceleration = np.round(acceleration[r].item())
+ else:
+ if acceleration.shape[0] > 1: # type: ignore
+ acceleration = acceleration[0] # type: ignore
+ acceleration = np.round(acceleration.item()) # type: ignore
+
+ # Pass r to the attrs dictionary, so that it can be used in unnormalize_for_loss_or_log if needed.
+ attrs["r"] = r
+
+ return {
+ "fname": fname,
+ "slice_idx": slice_idx,
+ "acceleration": acceleration,
+ "predictions_reconstruction": predictions_reconstruction,
+ "predictions_reconstruction_n2r": predictions_reconstruction_n2r,
+ "predictions_segmentation": predictions_segmentation,
+ "target_reconstruction": target_reconstruction,
+ "target_segmentation": target_segmentation,
+ "sensitivity_maps": sensitivity_maps,
+ "loss_mask": loss_mask,
+ "attrs": attrs,
+ "r": r,
+ }
+
+ def training_step(self, batch: Dict[float, torch.Tensor], batch_idx: int) -> Dict[str, torch.Tensor]:
+ """Performs a training step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'kspace' : List of torch.Tensor
+ Fully-sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'y' : Union[torch.Tensor, None]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'mask' : Union[torch.Tensor, None]
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if
+ Unsupervised Learning methods, like Noise-to-Recon or SSDU, are used, then it is a list of
+ torch.Tensor with masks for each method. Shape [batch_size, 1, n_x, n_y, 1].
+ 'initial_prediction_reconstruction': torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2].
+ 'target_reconstruction': torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y].
+ 'target_segmentation': Union[torch.Tensor, None]
+ Target segmentation. Shape [batch_size, n_x, n_y].
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+ batch_idx : int
+ Batch index.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary of loss and log.
+ """
+ (
+ kspace,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ target_segmentation,
+ fname,
+ slice_idx,
+ acceleration,
+ attrs,
+ ) = batch
+
+ outputs = self.inference_step(
+ kspace,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ target_segmentation,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration,
+ attrs, # type: ignore
+ )
+
+ # Compute loss
+ train_loss = self.__compute_loss__(
+ outputs["predictions_reconstruction"],
+ outputs["predictions_reconstruction_n2r"],
+ outputs["target_reconstruction"],
+ outputs["predictions_segmentation"],
+ outputs["target_segmentation"],
+ outputs["sensitivity_maps"],
+ outputs["loss_mask"],
+ outputs["attrs"],
+ outputs["r"],
+ )
+
+ # Log loss for the chosen acceleration factor and the learning rate in the selected logger.
+ logs = {
+ f'train_loss_{outputs["acceleration"]}x': train_loss.item(),
+ "lr": self._optimizer.param_groups[0]["lr"], # type: ignore
+ }
+
+ self.log(
+ "train_joint_loss",
+ train_loss,
+ on_step=True,
+ on_epoch=True,
+ prog_bar=True,
+ logger=True,
+ batch_size=target_segmentation.shape[0], # type: ignore
+ sync_dist=True,
+ )
+
+ return {"loss": train_loss, "log": logs}
+
+ def validation_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a validation step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'kspace' : List of torch.Tensor
+ Fully-sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'y' : Union[torch.Tensor, None]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'mask' : Union[torch.Tensor, None]
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if
+ Unsupervised Learning methods, like Noise-to-Recon or SSDU, are used, then it is a list of
+ torch.Tensor with masks for each method. Shape [batch_size, 1, n_x, n_y, 1].
+ 'initial_prediction_reconstruction': torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2].
+ 'target_reconstruction': torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y].
+ 'target_segmentation': Union[torch.Tensor, None]
+ Target segmentation. Shape [batch_size, n_x, n_y].
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+ batch_idx : int
+ Batch index.
+ """
+ (
+ kspace,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ target_segmentation,
+ fname,
+ slice_idx,
+ acceleration,
+ attrs,
+ ) = batch
+
+ outputs = self.inference_step(
+ kspace,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ target_segmentation,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration,
+ attrs, # type: ignore
+ )
+
+ predictions_reconstruction = outputs["predictions_reconstruction"]
+ predictions_reconstruction_n2r = outputs["predictions_reconstruction_n2r"]
+ target_reconstruction = outputs["target_reconstruction"]
+ predictions_segmentation = outputs["predictions_segmentation"]
+ target_segmentation = outputs["target_segmentation"]
+
+ # Compute loss
+ val_loss = self.__compute_loss__(
+ predictions_reconstruction,
+ predictions_reconstruction_n2r,
+ target_reconstruction,
+ predictions_segmentation,
+ target_segmentation,
+ outputs["sensitivity_maps"],
+ outputs["loss_mask"],
+ outputs["attrs"],
+ outputs["r"],
+ )
+
+ self.validation_step_outputs.append({"val_loss": val_loss})
+
+ # Compute metrics and log them and log outputs.
+ self.__compute_and_log_metrics_and_outputs__(
+ predictions_reconstruction,
+ target_reconstruction,
+ predictions_segmentation,
+ target_segmentation,
+ outputs["attrs"],
+ )
+
+ def test_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a test step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'kspace' : List of torch.Tensor
+ Fully-sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'y' : Union[torch.Tensor, None]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'mask' : Union[torch.Tensor, None]
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if
+ Unsupervised Learning methods, like Noise-to-Recon or SSDU, are used, then it is a list of
+ torch.Tensor with masks for each method. Shape [batch_size, 1, n_x, n_y, 1].
+ 'initial_prediction_reconstruction': torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2].
+ 'target_reconstruction': torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y].
+ 'target_segmentation': Union[torch.Tensor, None]
+ Target segmentation. Shape [batch_size, n_x, n_y].
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+ batch_idx : int
+ Batch index.
+ """
+ (
+ kspace,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ target_segmentation,
+ fname,
+ slice_idx,
+ acceleration,
+ attrs,
+ ) = batch
+
+ outputs = self.inference_step(
+ kspace,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ target_segmentation,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration,
+ attrs, # type: ignore
+ )
+
+ predictions_reconstruction = outputs["predictions_reconstruction"]
+ predictions_segmentation = outputs["predictions_segmentation"]
+
+ # Compute metrics and log them and log outputs.
+ self.__compute_and_log_metrics_and_outputs__(
+ predictions_reconstruction,
+ outputs["target_reconstruction"],
+ predictions_segmentation,
+ outputs["target_segmentation"],
+ outputs["attrs"],
+ )
+
+ if isinstance(predictions_segmentation, list):
+ while isinstance(predictions_segmentation, list):
+ predictions_segmentation = predictions_segmentation[-1]
+
+ predictions_segmentation = predictions_segmentation.detach().cpu().numpy()
+
+ if self.use_reconstruction_module:
+ if isinstance(predictions_reconstruction, list):
+ while isinstance(predictions_reconstruction, list):
+ predictions_reconstruction = predictions_reconstruction[-1]
+
+ # If "16" or "16-mixed" fp is used, ensure complex type will be supported when saving the predictions.
+ predictions_reconstruction = (
+ torch.view_as_complex(torch.view_as_real(predictions_reconstruction).type(torch.float32))
+ .detach()
+ .cpu()
+ .numpy()
+ )
+
+ predictions = (
+ (predictions_segmentation, predictions_reconstruction)
+ if self.use_reconstruction_module
+ else (predictions_segmentation, predictions_segmentation)
+ )
+
+ self.test_step_outputs.append([str(fname[0]), slice_idx, predictions]) # type: ignore
+
+ def on_validation_epoch_end(self): # noqa: MC0001
+ """Called at the end of validation epoch to aggregate outputs.
+
+ Returns
+ -------
+ metrics : dict
+ Dictionary of metrics.
+ """
+ self.log("val_loss", torch.stack([x["val_loss"] for x in self.validation_step_outputs]).mean(), sync_dist=True)
+
+ # Log metrics.
+ if self.cross_entropy_metric is not None:
+ cross_entropy_vals = defaultdict(dict)
+ for k, v in self.cross_entropy_vals.items():
+ cross_entropy_vals[k].update(v)
+
+ dice_vals = defaultdict(dict)
+ for k, v in self.dice_vals.items():
+ dice_vals[k].update(v)
+
+ metrics_segmentation = {"Cross_Entropy": 0, "DICE": 0}
+
+ if self.use_reconstruction_module:
+ mse_vals_reconstruction = defaultdict(dict)
+ nmse_vals_reconstruction = defaultdict(dict)
+ ssim_vals_reconstruction = defaultdict(dict)
+ psnr_vals_reconstruction = defaultdict(dict)
+
+ for k, v in self.mse_vals_reconstruction.items():
+ mse_vals_reconstruction[k].update(v)
+ for k, v in self.nmse_vals_reconstruction.items():
+ nmse_vals_reconstruction[k].update(v)
+ for k, v in self.ssim_vals_reconstruction.items():
+ ssim_vals_reconstruction[k].update(v)
+ for k, v in self.psnr_vals_reconstruction.items():
+ psnr_vals_reconstruction[k].update(v)
+
+ metrics_reconstruction = {"MSE": 0, "NMSE": 0, "SSIM": 0, "PSNR": 0}
+
+ local_examples = 0
+ for fname in dice_vals:
+ local_examples += 1
+ if self.cross_entropy_metric is not None:
+ metrics_segmentation["Cross_Entropy"] = metrics_segmentation["Cross_Entropy"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in cross_entropy_vals[fname].items()])
+ )
+ metrics_segmentation["DICE"] = metrics_segmentation["DICE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in dice_vals[fname].items()])
+ )
+
+ if self.use_reconstruction_module:
+ metrics_reconstruction["MSE"] = metrics_reconstruction["MSE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_reconstruction[fname].items()])
+ )
+ metrics_reconstruction["NMSE"] = metrics_reconstruction["NMSE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_reconstruction[fname].items()])
+ )
+ metrics_reconstruction["SSIM"] = metrics_reconstruction["SSIM"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_reconstruction[fname].items()])
+ )
+ metrics_reconstruction["PSNR"] = metrics_reconstruction["PSNR"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_reconstruction[fname].items()])
+ )
+
+ # reduce across ddp via sum
+ if self.cross_entropy_metric is not None:
+ metrics_segmentation["Cross_Entropy"] = self.CROSS_ENTROPY(metrics_segmentation["Cross_Entropy"])
+ metrics_segmentation["DICE"] = self.DICE(metrics_segmentation["DICE"])
+
+ if self.use_reconstruction_module:
+ metrics_reconstruction["MSE"] = self.MSE(metrics_reconstruction["MSE"])
+ metrics_reconstruction["NMSE"] = self.NMSE(metrics_reconstruction["NMSE"])
+ metrics_reconstruction["SSIM"] = self.SSIM(metrics_reconstruction["SSIM"])
+ metrics_reconstruction["PSNR"] = self.PSNR(metrics_reconstruction["PSNR"])
+
+ tot_examples = self.TotExamples(torch.tensor(local_examples))
+
+ for metric, value in metrics_segmentation.items():
+ self.log(f"val_metrics/{metric}", value / tot_examples, prog_bar=True, sync_dist=True)
+ if self.use_reconstruction_module:
+ for metric, value in metrics_reconstruction.items():
+ self.log(f"val_metrics/{metric}", value / tot_examples, prog_bar=True, sync_dist=True)
+
+ def on_test_epoch_end(self): # noqa: MC0001
+ """Called at the end of test epoch to aggregate outputs, log metrics and save predictions.
+
+ Returns
+ -------
+ metrics : dict
+ Dictionary of metrics.
+ """
+ # Log metrics.
+ if self.cross_entropy_metric is not None:
+ cross_entropy_vals = defaultdict(dict)
+ for k, v in self.cross_entropy_vals.items():
+ cross_entropy_vals[k].update(v)
+
+ dice_vals = defaultdict(dict)
+ for k, v in self.dice_vals.items():
+ dice_vals[k].update(v)
+
+ metrics_segmentation = {"Cross_Entropy": 0, "DICE": 0}
+
+ if self.use_reconstruction_module:
+ mse_vals_reconstruction = defaultdict(dict)
+ nmse_vals_reconstruction = defaultdict(dict)
+ ssim_vals_reconstruction = defaultdict(dict)
+ psnr_vals_reconstruction = defaultdict(dict)
+
+ for k, v in self.mse_vals_reconstruction.items():
+ mse_vals_reconstruction[k].update(v)
+ for k, v in self.nmse_vals_reconstruction.items():
+ nmse_vals_reconstruction[k].update(v)
+ for k, v in self.ssim_vals_reconstruction.items():
+ ssim_vals_reconstruction[k].update(v)
+ for k, v in self.psnr_vals_reconstruction.items():
+ psnr_vals_reconstruction[k].update(v)
+
+ metrics_reconstruction = {"MSE": 0, "NMSE": 0, "SSIM": 0, "PSNR": 0}
+
+ local_examples = 0
+ for fname in dice_vals:
+ local_examples += 1
+ if self.cross_entropy_metric is not None:
+ metrics_segmentation["Cross_Entropy"] = metrics_segmentation["Cross_Entropy"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in cross_entropy_vals[fname].items()])
+ )
+ metrics_segmentation["DICE"] = metrics_segmentation["DICE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in dice_vals[fname].items()])
+ )
+
+ if self.use_reconstruction_module:
+ metrics_reconstruction["MSE"] = metrics_reconstruction["MSE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_reconstruction[fname].items()])
+ )
+ metrics_reconstruction["NMSE"] = metrics_reconstruction["NMSE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_reconstruction[fname].items()])
+ )
+ metrics_reconstruction["SSIM"] = metrics_reconstruction["SSIM"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_reconstruction[fname].items()])
+ )
+ metrics_reconstruction["PSNR"] = metrics_reconstruction["PSNR"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_reconstruction[fname].items()])
+ )
+
+ # reduce across ddp via sum
+ if self.cross_entropy_metric is not None:
+ metrics_segmentation["Cross_Entropy"] = self.CROSS_ENTROPY(metrics_segmentation["Cross_Entropy"])
+ metrics_segmentation["DICE"] = self.DICE(metrics_segmentation["DICE"])
+
+ if self.use_reconstruction_module:
+ metrics_reconstruction["MSE"] = self.MSE(metrics_reconstruction["MSE"])
+ metrics_reconstruction["NMSE"] = self.NMSE(metrics_reconstruction["NMSE"])
+ metrics_reconstruction["SSIM"] = self.SSIM(metrics_reconstruction["SSIM"])
+ metrics_reconstruction["PSNR"] = self.PSNR(metrics_reconstruction["PSNR"])
+
+ tot_examples = self.TotExamples(torch.tensor(local_examples))
+
+ for metric, value in metrics_segmentation.items():
+ self.log(f"test_metrics/{metric}", value / tot_examples, prog_bar=True, sync_dist=True)
+ if self.use_reconstruction_module:
+ for metric, value in metrics_reconstruction.items():
+ self.log(f"test_metrics/{metric}", value / tot_examples, prog_bar=True, sync_dist=True)
+
+ segmentations = defaultdict(list)
+ for fname, slice_num, output in self.test_step_outputs:
+ segmentations_pred, _ = output
+ segmentations[fname].append((slice_num, segmentations_pred))
+
+ for fname in segmentations:
+ segmentations[fname] = np.stack([out for _, out in sorted(segmentations[fname])])
+
+ if self.consecutive_slices > 1:
+ # iterate over the slices and always keep the middle slice
+ for fname in segmentations:
+ segmentations[fname] = segmentations[fname][:, self.consecutive_slices // 2]
+
+ if self.use_reconstruction_module:
+ reconstructions = defaultdict(list)
+ for fname, slice_num, output in self.test_step_outputs:
+ _, reconstructions_pred = output
+ reconstructions[fname].append((slice_num, reconstructions_pred))
+
+ for fname in reconstructions:
+ reconstructions[fname] = np.stack([out for _, out in sorted(reconstructions[fname])])
+
+ if self.consecutive_slices > 1:
+ # iterate over the slices and always keep the middle slice
+ for fname in reconstructions:
+ reconstructions[fname] = reconstructions[fname][:, self.consecutive_slices // 2]
+ else:
+ reconstructions = None
+
+ if "wandb" in self.logger.__module__.lower():
+ out_dir = Path(os.path.join(self.logger.save_dir, "predictions"))
+ else:
+ out_dir = Path(os.path.join(self.logger.log_dir, "predictions"))
+ out_dir.mkdir(exist_ok=True, parents=True)
+
+ if reconstructions is not None:
+ for (fname, segmentations_pred), (_, reconstructions_pred) in zip(
+ segmentations.items(), reconstructions.items()
+ ):
+ with h5py.File(out_dir / fname, "w") as hf:
+ hf.create_dataset("segmentation", data=segmentations_pred)
+ hf.create_dataset("reconstruction", data=reconstructions_pred)
+ else:
+ for fname, segmentations_pred in segmentations.items():
+ with h5py.File(out_dir / fname, "w") as hf:
+ hf.create_dataset("segmentation", data=segmentations_pred)
+
+ @staticmethod
+ def _setup_dataloader_from_config(cfg: DictConfig) -> DataLoader:
+ """Setups the dataloader from the configuration (yaml) file.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration file.
+
+ Returns
+ -------
+ dataloader : torch.utils.data.DataLoader
+ Dataloader.
+ """
+ mask_root = cfg.get("mask_path", None)
+ mask_args = cfg.get("mask_args", None)
+ shift_mask = mask_args.get("shift_mask", False)
+ mask_type = mask_args.get("type", None)
+
+ mask_func = None
+ mask_center_scale = 0.02
+
+ if is_none(mask_root) and not is_none(mask_type):
+ accelerations = mask_args.get("accelerations", [1])
+ accelerations = list(accelerations)
+ if len(accelerations) == 1:
+ accelerations = accelerations * 2
+ center_fractions = mask_args.get("center_fractions", [1])
+ center_fractions = list(center_fractions)
+ if len(center_fractions) == 1:
+ center_fractions = center_fractions * 2
+ mask_center_scale = mask_args.get("center_scale", 0.02)
+
+ mask_func = [create_masker(mask_type, center_fractions, accelerations)]
+
+ complex_data = cfg.get("complex_data", True)
+
+ dataset_format = cfg.get("dataset_format", None)
+ if dataset_format.lower() in (
+ "skm-tea-echo1",
+ "skm-tea-echo2",
+ "skm-tea-echo1+echo2",
+ "skm-tea-echo1+echo2-mc",
+ ):
+ dataloader = mrirs_loader.SKMTEARSMRIDataset
+ else:
+ dataloader = mrirs_loader.RSMRIDataset
+
+ dataset = dataloader(
+ root=cfg.get("data_path"),
+ coil_sensitivity_maps_root=cfg.get("coil_sensitivity_maps_path", None),
+ mask_root=mask_root,
+ noise_root=cfg.get("noise_path", None),
+ initial_predictions_root=cfg.get("initial_predictions_path"),
+ dataset_format=dataset_format,
+ sample_rate=cfg.get("sample_rate", 1.0),
+ volume_sample_rate=cfg.get("volume_sample_rate", None),
+ use_dataset_cache=cfg.get("use_dataset_cache", False),
+ dataset_cache_file=cfg.get("dataset_cache_file", None),
+ num_cols=cfg.get("num_cols", None),
+ consecutive_slices=cfg.get("consecutive_slices", 1),
+ data_saved_per_slice=cfg.get("data_saved_per_slice", False),
+ n2r_supervised_rate=cfg.get("n2r_supervised_rate", 0.0),
+ complex_target=cfg.get("complex_target", False),
+ log_images_rate=cfg.get("log_images_rate", 1.0),
+ transform=RSMRIDataTransforms(
+ complex_data=complex_data,
+ dataset_format=dataset_format,
+ apply_prewhitening=cfg.get("apply_prewhitening", False),
+ find_patch_size=cfg.get("find_patch_size", False),
+ prewhitening_scale_factor=cfg.get("prewhitening_scale_factor", 1.0),
+ prewhitening_patch_start=cfg.get("prewhitening_patch_start", 10),
+ prewhitening_patch_length=cfg.get("prewhitening_patch_length", 30),
+ apply_gcc=cfg.get("apply_gcc", False),
+ gcc_virtual_coils=cfg.get("gcc_virtual_coils", 10),
+ gcc_calib_lines=cfg.get("gcc_calib_lines", 10),
+ gcc_align_data=cfg.get("gcc_align_data", False),
+ apply_random_motion=cfg.get("apply_random_motion", False),
+ random_motion_type=cfg.get("random_motion_type", "gaussian"),
+ random_motion_percentage=cfg.get("random_motion_percentage", [10, 10]),
+ random_motion_angle=cfg.get("random_motion_angle", 10),
+ random_motion_translation=cfg.get("random_motion_translation", 10),
+ random_motion_center_percentage=cfg.get("random_motion_center_percentage", 0.02),
+ random_motion_num_segments=cfg.get("random_motion_num_segments", 8),
+ random_motion_random_num_segments=cfg.get("random_motion_random_num_segments", True),
+ random_motion_non_uniform=cfg.get("random_motion_non_uniform", False),
+ estimate_coil_sensitivity_maps=cfg.get("estimate_coil_sensitivity_maps", False),
+ coil_sensitivity_maps_type=cfg.get("coil_sensitivity_maps_type", "espirit"),
+ coil_sensitivity_maps_gaussian_sigma=cfg.get("coil_sensitivity_maps_gaussian_sigma", 0.0),
+ coil_sensitivity_maps_espirit_threshold=cfg.get("coil_sensitivity_maps_espirit_threshold", 0.05),
+ coil_sensitivity_maps_espirit_kernel_size=cfg.get("coil_sensitivity_maps_espirit_kernel_size", 6),
+ coil_sensitivity_maps_espirit_crop=cfg.get("coil_sensitivity_maps_espirit_crop", 0.95),
+ coil_sensitivity_maps_espirit_max_iters=cfg.get("coil_sensitivity_maps_espirit_max_iters", 30),
+ coil_combination_method=cfg.get("coil_combination_method", "SENSE"),
+ dimensionality=cfg.get("dimensionality", 2),
+ mask_func=mask_func,
+ shift_mask=shift_mask,
+ mask_center_scale=mask_center_scale,
+ remask=cfg.get("remask", False),
+ ssdu=cfg.get("ssdu", False),
+ ssdu_mask_type=cfg.get("ssdu_mask_type", "Gaussian"),
+ ssdu_rho=cfg.get("ssdu_rho", 0.4),
+ ssdu_acs_block_size=cfg.get("ssdu_acs_block_size", (4, 4)),
+ ssdu_gaussian_std_scaling_factor=cfg.get("ssdu_gaussian_std_scaling_factor", 4.0),
+ ssdu_outer_kspace_fraction=cfg.get("ssdu_outer_kspace_fraction", 0.0),
+ ssdu_export_and_reuse_masks=cfg.get("ssdu_export_and_reuse_masks", False),
+ n2r=cfg.get("n2r", False),
+ n2r_supervised_rate=cfg.get("n2r_supervised_rate", 0.0),
+ n2r_probability=cfg.get("n2r_probability", 0.5),
+ n2r_std_devs=cfg.get("n2r_std_devs", (0.0, 0.0)),
+ n2r_rhos=cfg.get("n2r_rhos", (0.4, 0.4)),
+ n2r_use_mask=cfg.get("n2r_use_mask", True),
+ unsupervised_masked_target=cfg.get("unsupervised_masked_target", False),
+ crop_size=cfg.get("crop_size", None),
+ kspace_crop=cfg.get("kspace_crop", False),
+ crop_before_masking=cfg.get("crop_before_masking", False),
+ kspace_zero_filling_size=cfg.get("kspace_zero_filling_size", None),
+ normalize_inputs=cfg.get("normalize_inputs", True),
+ normalization_type=cfg.get("normalization_type", "max"),
+ kspace_normalization=cfg.get("kspace_normalization", False),
+ fft_centered=cfg.get("fft_centered", False),
+ fft_normalization=cfg.get("fft_normalization", "backward"),
+ spatial_dims=cfg.get("spatial_dims", None),
+ coil_dim=cfg.get("coil_dim", 1),
+ consecutive_slices=cfg.get("consecutive_slices", 1),
+ use_seed=cfg.get("use_seed", True),
+ ),
+ segmentations_root=cfg.get("segmentations_path"),
+ segmentation_classes=cfg.get("segmentation_classes", 2),
+ segmentation_classes_to_remove=cfg.get("segmentation_classes_to_remove", None),
+ segmentation_classes_to_combine=cfg.get("segmentation_classes_to_combine", None),
+ segmentation_classes_to_separate=cfg.get("segmentation_classes_to_separate", None),
+ segmentation_classes_thresholds=cfg.get("segmentation_classes_thresholds", None),
+ complex_data=complex_data,
+ )
+ if cfg.shuffle:
+ sampler = torch.utils.data.RandomSampler(dataset)
+ else:
+ sampler = torch.utils.data.SequentialSampler(dataset)
+
+ return torch.utils.data.DataLoader(
+ dataset=dataset,
+ batch_size=cfg.get("batch_size", 1),
+ sampler=sampler,
+ num_workers=cfg.get("num_workers", 4),
+ pin_memory=cfg.get("pin_memory", False),
+ drop_last=cfg.get("drop_last", False),
+ )
diff --git a/atommic/collections/multitask/rs/nn/idslr.py b/atommic/collections/multitask/rs/nn/idslr.py
new file mode 100644
index 00000000..e6a988c4
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/idslr.py
@@ -0,0 +1,248 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import List, Tuple, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import coil_combination_method
+from atommic.collections.multitask.rs.nn.base import BaseMRIReconstructionSegmentationModel
+from atommic.collections.multitask.rs.nn.idslr_base.idslr_block import DC, UnetDecoder, UnetEncoder
+from atommic.core.classes.common import typecheck
+
+__all__ = ["IDSLR"]
+
+
+class IDSLR(BaseMRIReconstructionSegmentationModel):
+ """Implementation of the Image domain Deep Structured Low-Rank network, as presented in [Pramanik2021]_.
+
+ References
+ ----------
+ .. [Pramanik2021] Pramanik A, Wu X, Jacob M. Joint calibrationless reconstruction and segmentation of parallel
+ MRI. arXiv preprint arXiv:2105.09220. 2021 May 19.
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`IDSLR`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer object. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.input_channels = cfg_dict.get("input_channels", 2)
+ if self.input_channels == 0:
+ raise ValueError("Segmentation module input channels cannot be 0.")
+ reconstruction_out_chans = cfg_dict.get("reconstruction_module_output_channels", 2)
+ self.segmentation_out_chans = cfg_dict.get("segmentation_module_output_channels", 1)
+ chans = cfg_dict.get("channels", 32)
+ num_pools = cfg_dict.get("num_pools", 4)
+ drop_prob = cfg_dict.get("drop_prob", 0.0)
+ normalize = cfg_dict.get("normalize", True)
+ padding = cfg_dict.get("padding", True)
+ padding_size = cfg_dict.get("padding_size", 11)
+ self.norm_groups = cfg_dict.get("norm_groups", 2)
+ self.num_iters = cfg_dict.get("num_iters", 5)
+
+ self.reconstruction_encoder = UnetEncoder(
+ chans=chans,
+ num_pools=num_pools,
+ in_chans=self.input_channels,
+ drop_prob=drop_prob,
+ normalize=normalize,
+ padding=padding,
+ padding_size=padding_size,
+ norm_groups=self.norm_groups,
+ )
+ self.reconstruction_decoder = UnetDecoder(
+ chans=chans,
+ num_pools=num_pools,
+ out_chans=reconstruction_out_chans,
+ drop_prob=drop_prob,
+ normalize=normalize,
+ padding=padding,
+ padding_size=padding_size,
+ norm_groups=self.norm_groups,
+ )
+ self.segmentation_decoder = UnetDecoder(
+ chans=chans,
+ num_pools=num_pools,
+ out_chans=self.segmentation_out_chans,
+ drop_prob=drop_prob,
+ normalize=normalize,
+ padding=padding,
+ padding_size=padding_size,
+ norm_groups=self.norm_groups,
+ )
+
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+ self.magnitude_input = cfg_dict.get("magnitude_input", True)
+ self.normalize_segmentation_output = cfg_dict.get("normalize_segmentation_output", True)
+
+ self.dc = DC()
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ init_reconstruction_pred: torch.Tensor,
+ target_reconstruction: torch.Tensor, # pylint: disable=unused-argument
+ hx: torch.Tensor = None, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> Tuple[Union[List, torch.Tensor], torch.Tensor]:
+ """Forward pass of :class:`IDSLR`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ init_reconstruction_pred : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2]
+ target_reconstruction : torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y, 2]
+ hx : torch.Tensor, optional
+ Initial hidden state for the RNN. Default is ``None``.
+ sigma : float, optional
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ Tuple[Union[List, torch.Tensor], torch.Tensor]
+ Tuple containing the predicted reconstruction and segmentation.
+ """
+ if self.consecutive_slices > 1:
+ batch, slices = y.shape[:2]
+ y = y.reshape(y.shape[0] * y.shape[1], *y.shape[2:])
+ sensitivity_maps = sensitivity_maps.reshape(
+ sensitivity_maps.shape[0] * sensitivity_maps.shape[1],
+ *sensitivity_maps.shape[2:],
+ )
+ mask = mask.reshape(mask.shape[0] * mask.shape[1], *mask.shape[2:])
+
+ # In case of deviating number of coils, we need to pad up to maximum number of coils == number of input \
+ # channels for the reconstruction module
+ num_coils = y.shape[1]
+ if num_coils * 2 != self.input_channels:
+ num_coils_to_add = (self.input_channels - num_coils * 2) // 2
+ dummy_coil_data = torch.zeros_like(torch.movedim(y, self.coil_dim, 0)[0]).unsqueeze(self.coil_dim)
+ for _ in range(num_coils_to_add):
+ y = torch.cat([y, dummy_coil_data], dim=self.coil_dim)
+ sensitivity_maps = torch.cat([sensitivity_maps, dummy_coil_data], dim=self.coil_dim)
+
+ y_prediction = y.clone()
+ for _ in range(self.num_iters):
+ init_reconstruction_pred = ifft2(
+ y_prediction, self.fft_centered, self.fft_normalization, self.spatial_dims
+ )
+ output = self.reconstruction_encoder(init_reconstruction_pred)
+ reconstruction_encoder_prediction, _, padding_size, _, _ = (
+ output[0].copy(),
+ output[1],
+ output[2],
+ output[3],
+ output[4],
+ )
+ reconstruction_decoder_prediction = self.reconstruction_decoder(*output)
+ reconstruction_decoder_prediction = reconstruction_decoder_prediction + init_reconstruction_pred
+ reconstruction_decoder_prediction_kspace = fft2(
+ reconstruction_decoder_prediction, self.fft_centered, self.fft_normalization, self.spatial_dims
+ )
+ y_prediction = self.dc(reconstruction_decoder_prediction_kspace, y, mask)
+
+ pred_reconstruction = self.process_intermediate_pred(y_prediction, sensitivity_maps, True)
+
+ pred_segmentation_input = reconstruction_encoder_prediction
+ if self.magnitude_input:
+ pred_segmentation_input = [torch.abs(x) for x in pred_segmentation_input]
+
+ pred_segmentation = self.segmentation_decoder(pred_segmentation_input, iscomplex=False, pad_sizes=padding_size)
+ pred_segmentation = self.process_final_segmentation(pred_segmentation)
+
+ if self.normalize_segmentation_output:
+ pred_segmentation = (pred_segmentation - pred_segmentation.min()) / (
+ pred_segmentation.max() - pred_segmentation.min()
+ )
+
+ pred_segmentation = torch.abs(pred_segmentation)
+
+ if self.consecutive_slices > 1:
+ pred_reconstruction = pred_reconstruction.view([batch, slices, *pred_reconstruction.shape[1:]])
+ pred_segmentation = pred_segmentation.view([batch, slices, *pred_segmentation.shape[1:]])
+
+ return pred_reconstruction, pred_segmentation
+
+ def process_intermediate_pred(
+ self,
+ prediction: Union[list, torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ do_coil_combination: bool = False,
+ ) -> torch.Tensor:
+ """Processes the intermediate prediction.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ Intermediate prediction. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ do_coil_combination : bool
+ Whether to do coil combination. In this case the prediction is in k-space. Default is ``False``.
+
+ Returns
+ -------
+ torch.Tensor, shape [batch_size, n_x, n_y, 2]
+ Processed prediction.
+ """
+ # Take the last time step of the prediction
+ if do_coil_combination:
+ prediction = ifft2(
+ prediction,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ prediction = coil_combination_method(
+ prediction, sensitivity_maps, method=self.coil_combination_method, dim=self.coil_dim
+ )
+ prediction = torch.view_as_complex(prediction)
+ return prediction
+
+ def process_final_segmentation(self, prediction: torch.Tensor) -> torch.Tensor:
+ """Processes the final segmentation prediction.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ Final segmentation prediction. Shape [batch_size, n_classes, n_x, n_y, 2]
+
+ Returns
+ -------
+ torch.Tensor
+ Processed prediction. Shape [batch_size, n_classes, n_x, n_y]
+ """
+ if prediction.shape[-1] == 2:
+ prediction = torch.view_as_complex(prediction)
+ if prediction.shape[1] != self.segmentation_out_chans and prediction.shape[1] != 2 and prediction.dim() == 5:
+ prediction = prediction.squeeze(1)
+ if prediction.shape[1] != self.segmentation_out_chans:
+ prediction = prediction.permute(0, 3, 1, 2)
+ prediction = torch.abs(prediction)
+ if self.normalize_segmentation_output:
+ prediction = prediction / torch.max(prediction)
+ return prediction
diff --git a/atommic/collections/multitask/rs/nn/idslr_base/__init__.py b/atommic/collections/multitask/rs/nn/idslr_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/idslr_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/multitask/rs/nn/idslr_base/idslr_block.py b/atommic/collections/multitask/rs/nn/idslr_base/idslr_block.py
new file mode 100644
index 00000000..da9b0f88
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/idslr_base/idslr_block.py
@@ -0,0 +1,316 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import math
+from typing import List, Tuple
+
+import torch
+from torch import Tensor, nn
+
+from atommic.collections.reconstruction.nn.unet_base.unet_block import ConvBlock, TransposeConvBlock
+
+
+class DC(nn.Module):
+ """IDSLR Data consistency block, as presented."""
+
+ def __init__(self):
+ """Inits :class:`DC`."""
+ super().__init__()
+ self.dc_weight = nn.Parameter(torch.ones(1))
+
+ def forward(self, prediction_kspace: Tensor, reference_kspace: Tensor, mask: Tensor) -> Tensor:
+ """Forward pass of :class:`DC`.
+
+ Parameters
+ ----------
+ prediction_kspace : torch.Tensor
+ Prediction k-space. Shape: (batch, channels, height, width, complex)
+ reference_kspace : torch.Tensor
+ Reference k-space. Shape: (batch, channels, height, width, complex)
+ mask : torch.Tensor
+ Subsampling mask. Shape: (batch, channels, height, width, 1)
+
+ Returns
+ -------
+ torch.Tensor
+ Data consistency k-space. Shape: (batch, channels, height, width, complex)
+ """
+ return torch.div(
+ torch.view_as_complex(reference_kspace) + self.dc_weight * torch.view_as_complex(prediction_kspace),
+ mask.squeeze(-1) + torch.complex(self.dc_weight, torch.zeros_like(self.dc_weight)),
+ )
+
+
+class UnetEncoder(nn.Module):
+ """UNet Encoder block, according to the implementation of the NormUnet."""
+
+ def __init__(
+ self,
+ chans: int,
+ num_pools: int,
+ in_chans: int = 2,
+ drop_prob: float = 0.0,
+ normalize: bool = True,
+ padding: bool = True,
+ padding_size: int = 15,
+ norm_groups: int = 2,
+ ):
+ """Inits :class:`UnetEncoder`.
+
+ Parameters
+ ----------
+ chans : int
+ Number of channels in the first layer.
+ num_pools : int
+ Number of down-sampling layers.
+ in_chans : int, optional
+ Number of input channels. Default is ``2``.
+ drop_prob : float, optional
+ Dropout probability. Default is ``0.0``.
+ normalize : bool, optional
+ Whether to normalize the input. Default is ``True``.
+ padding : bool, optional
+ Whether to pad the input. Default is ``True``.
+ padding_size : int, optional
+ Padding size. Default is ``15``.
+ norm_groups : int, optional
+ Number of groups for group normalization. Default is ``2``.
+ """
+ super().__init__()
+
+ self.in_chans = in_chans
+ self.chans = chans
+ self.num_pools = num_pools
+ self.drop_prob = drop_prob
+ self.normalize = normalize
+ self.padding = padding
+ self.padding_size = padding_size
+ self.norm_groups = norm_groups
+
+ self.down_sample_layers = torch.nn.ModuleList([ConvBlock(in_chans, chans, drop_prob)])
+ ch = chans
+ for _ in range(num_pools - 1):
+ self.down_sample_layers.append(ConvBlock(ch, ch * 2, drop_prob))
+ ch = ch * 2
+ self.conv = ConvBlock(ch, ch * 2, drop_prob)
+
+ @staticmethod
+ def complex_to_chan_dim(x: torch.Tensor) -> torch.Tensor:
+ """Converts the last dimension of the input to complex."""
+ b, c, h, w, two = x.shape
+ if two != 2:
+ raise AssertionError
+ return x.permute(0, 4, 1, 2, 3).reshape(b, 2 * c, h, w)
+
+ def norm(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Normalizes the input."""
+ # group norm
+ b, c, h, w = x.shape
+ x = x.reshape(b, self.norm_groups, -1)
+ mean = x.mean(-1, keepdim=True)
+ std = x.std(-1, keepdim=True)
+
+ x = (x - mean) / std
+ x = x.reshape(b, c, h, w)
+ return x, mean, std
+
+ def pad(self, x: torch.Tensor) -> Tuple[torch.Tensor, Tuple[List[int], List[int], int, int]]:
+ """Pads the input with zeros to make it square."""
+ _, _, h, w = x.shape
+ w_mult = ((w - 1) | self.padding_size) + 1
+ h_mult = ((h - 1) | self.padding_size) + 1
+ w_pad = [math.floor((w_mult - w) / 2), math.ceil((w_mult - w) / 2)]
+ h_pad = [math.floor((h_mult - h) / 2), math.ceil((h_mult - h) / 2)]
+ x = torch.nn.functional.pad(x, w_pad + h_pad)
+ return x, (h_pad, w_pad, h_mult, w_mult)
+
+ def forward(
+ self, x: torch.Tensor
+ ) -> Tuple[List[torch.Tensor], bool, Tuple[List[int], List[int], int, int], torch.Tensor, torch.Tensor]:
+ """Forward pass of :class:`UnetEncoder`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data. Shape: (batch, channels, height, width, complex)
+
+ Returns
+ -------
+ List[torch.Tensor]
+ List of down-sampled layers.
+ bool
+ Whether the input was complex.
+ Tuple[List[int], List[int], int, int]
+ Padding sizes.
+ torch.Tensor
+ Mean of the input.
+ torch.Tensor
+ Standard deviation of the input.
+ """
+ iscomplex = False
+ if x.shape[-1] == 2:
+ x = self.complex_to_chan_dim(x)
+ iscomplex = True
+
+ if self.normalize:
+ x, mean, std = self.norm(x)
+ else:
+ mean = 1.0
+ std = 1.0
+
+ if self.padding:
+ x, pad_sizes = self.pad(x)
+ else:
+ pad_sizes = None
+
+ stack = []
+ output = x
+
+ # apply down-sampling layers
+ for layer in self.down_sample_layers:
+ output = layer(output)
+ stack.append(output)
+ output = torch.nn.functional.avg_pool2d(output, kernel_size=2, stride=2, padding=0)
+
+ output = self.conv(output)
+ stack.append(output)
+
+ return stack, iscomplex, pad_sizes, mean, std # type: ignore
+
+
+class UnetDecoder(nn.Module):
+ """UNet Encoder block, according to the implementation of the NormUnet."""
+
+ def __init__(
+ self,
+ chans: int,
+ num_pools: int,
+ out_chans: int = 2,
+ drop_prob: float = 0.0,
+ normalize: bool = True,
+ padding: bool = True,
+ padding_size: int = 15,
+ norm_groups: int = 2,
+ ):
+ """Inits :class:`UnetDecoder`.
+
+ Parameters
+ ----------
+ chans : int
+ Number of channels in the first layer.
+ num_pools : int
+ Number of down-sampling layers.
+ in_chans : int, optional
+ Number of input channels. Default is ``2``.
+ drop_prob : float, optional
+ Dropout probability. Default is ``0.0``.
+ normalize : bool, optional
+ Whether to normalize the input. Default is ``True``.
+ padding : bool, optional
+ Whether to pad the input. Default is ``True``.
+ padding_size : int, optional
+ Padding size. Default is ``15``.
+ norm_groups : int, optional
+ Number of groups for group normalization. Default is ``2``.
+ """
+ super().__init__()
+
+ self.out_chans = out_chans
+ self.chans = chans
+ self.num_pools = num_pools
+ self.drop_prob = drop_prob
+ self.normalize = normalize
+ self.padding = padding
+ self.padding_size = padding_size
+ self.norm_groups = norm_groups
+
+ ch = chans * (2 ** (num_pools - 1))
+ self.up_conv = torch.nn.ModuleList()
+ self.up_transpose_conv = torch.nn.ModuleList()
+ for _ in range(num_pools - 1):
+ self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch))
+ self.up_conv.append(ConvBlock(ch * 2, ch, drop_prob))
+ ch = ch // 2
+
+ self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch))
+ self.up_conv.append(
+ torch.nn.Sequential(
+ ConvBlock(ch * 2, ch, drop_prob),
+ torch.nn.Conv2d(ch, self.out_chans, kernel_size=1, stride=1),
+ )
+ )
+
+ @staticmethod
+ def chan_complex_to_last_dim(x: torch.Tensor) -> torch.Tensor:
+ """Converts the last dimension of the input to complex."""
+ b, c2, h, w = x.shape
+ if c2 % 2 != 0:
+ raise AssertionError
+ c = torch.div(c2, 2, rounding_mode="trunc")
+ return x.view(b, 2, c, h, w).permute(0, 2, 3, 4, 1).contiguous()
+
+ @staticmethod
+ def unpad(x: torch.Tensor, h_pad: List[int], w_pad: List[int], h_mult: int, w_mult: int) -> torch.Tensor:
+ """Unpads the input."""
+ return x[..., h_pad[0] : h_mult - h_pad[1], w_pad[0] : w_mult - w_pad[1]]
+
+ def unnorm(self, x: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) -> torch.Tensor:
+ """Unnormalizes the input."""
+ b, c, h, w = x.shape
+ input_data = x.reshape(b, self.norm_groups, -1)
+ return (input_data * std + mean).reshape(b, c, h, w)
+
+ def forward(
+ self,
+ x_stack: List[torch.Tensor],
+ iscomplex: bool = False,
+ pad_sizes: Tuple[List[int], List[int], int, int] = None,
+ mean: torch.Tensor = None,
+ std: torch.Tensor = None,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`UnetDecoder`.
+
+ Parameters
+ ----------
+ x_stack : List[torch.Tensor]
+ List of tensors from the encoder.
+ iscomplex : bool, optional
+ Whether the input is complex. Default is ``False``.
+ pad_sizes : Tuple[List[int], List[int], int, int], optional
+ Padding sizes. Default is ``None``.
+ mean : torch.Tensor, optional
+ Mean of the input. Default is ``None``.
+ std : torch.Tensor, optional
+ Standard deviation of the input. Default is ``None``.
+
+ Returns
+ -------
+ torch.Tensor
+ Output of the network.
+ """
+ output = x_stack.pop()
+ # apply up-sampling layers
+ for transpose_conv, conv in zip(self.up_transpose_conv, self.up_conv):
+ downsample_layer = x_stack.pop()
+ output = transpose_conv(output)
+
+ # reflect pad on the right/bottom if needed to handle odd input dimensions
+ padding = [0, 0, 0, 0]
+ if output.shape[-1] != downsample_layer.shape[-1]:
+ padding[1] = 1 # padding right
+ if output.shape[-2] != downsample_layer.shape[-2]:
+ padding[3] = 1 # padding bottom
+ if torch.sum(torch.tensor(padding)) != 0:
+ output = torch.nn.functional.pad(output, padding, "reflect")
+
+ output = torch.cat([output, downsample_layer], dim=1)
+ output = conv(output)
+
+ if self.padding:
+ output = self.unpad(output, *pad_sizes) # type: ignore
+ if self.normalize and mean is not None and std is not None:
+ output = self.unnorm(output, mean, std)
+ if iscomplex:
+ output = self.chan_complex_to_last_dim(output)
+
+ return output
diff --git a/atommic/collections/multitask/rs/nn/idslr_unet.py b/atommic/collections/multitask/rs/nn/idslr_unet.py
new file mode 100644
index 00000000..9dfcf375
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/idslr_unet.py
@@ -0,0 +1,187 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import List, Tuple, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import coil_combination_method
+from atommic.collections.multitask.rs.nn.base import BaseMRIReconstructionSegmentationModel
+from atommic.collections.multitask.rs.nn.idslr_base.idslr_block import DC, UnetDecoder, UnetEncoder
+from atommic.collections.reconstruction.nn.unet_base.unet_block import Unet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["IDSLRUNet"]
+
+
+class IDSLRUNet(BaseMRIReconstructionSegmentationModel):
+ r"""Implementation of the Image domain Deep Structured Low-Rank network using a UNet (and not only the decoder
+ part) as segmentation model, as presented in [Pramanik2021]_.
+
+ References
+ ----------
+ .. [Pramanik2021] Pramanik A, Wu X, Jacob M. Joint calibrationless reconstruction and segmentation of parallel
+ MRI. arXiv preprint arXiv:2105.09220. 2021 May 19.
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`IDSLRUNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer object. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.input_channels = cfg_dict.get("input_channels", 2)
+ if self.input_channels == 0:
+ raise ValueError("Segmentation module input channels cannot be 0.")
+ reconstruction_out_chans = cfg_dict.get("reconstruction_module_output_channels", 2)
+ segmentation_out_chans = cfg_dict.get("segmentation_module_output_channels", 1)
+ chans = cfg_dict.get("channels", 32)
+ num_pools = cfg_dict.get("num_pools", 4)
+ drop_prob = cfg_dict.get("drop_prob", 0.0)
+ normalize = cfg_dict.get("normalize", False)
+ padding = cfg_dict.get("padding", False)
+ padding_size = cfg_dict.get("padding_size", 11)
+ self.norm_groups = cfg_dict.get("norm_groups", 2)
+ self.num_iters = cfg_dict.get("num_iters", 5)
+
+ self.reconstruction_encoder = UnetEncoder(
+ chans=chans,
+ num_pools=num_pools,
+ in_chans=self.input_channels,
+ drop_prob=drop_prob,
+ normalize=normalize,
+ padding=padding,
+ padding_size=padding_size,
+ norm_groups=self.norm_groups,
+ )
+ self.reconstruction_decoder = UnetDecoder(
+ chans=chans,
+ num_pools=num_pools,
+ out_chans=reconstruction_out_chans,
+ drop_prob=drop_prob,
+ normalize=normalize,
+ padding=padding,
+ padding_size=padding_size,
+ norm_groups=self.norm_groups,
+ )
+
+ self.segmentation_module = Unet(
+ in_chans=reconstruction_out_chans,
+ out_chans=segmentation_out_chans,
+ chans=chans,
+ num_pool_layers=num_pools,
+ drop_prob=drop_prob,
+ )
+
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+ self.magnitude_input = cfg_dict.get("magnitude_input", True)
+ self.normalize_segmentation_output = cfg_dict.get("normalize_segmentation_output", True)
+
+ self.dc = DC()
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ init_reconstruction_pred: torch.Tensor,
+ target_reconstruction: torch.Tensor, # pylint: disable=unused-argument
+ hx: torch.Tensor = None, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> Tuple[Union[List, torch.Tensor], torch.Tensor]:
+ """Forward pass of :class:`IDSLRUNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ init_reconstruction_pred : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2]
+ target_reconstruction : torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y, 2]
+ hx : torch.Tensor, optional
+ Initial hidden state for the RNN. Default is ``None``.
+ sigma : float, optional
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ Tuple[Union[List, torch.Tensor], torch.Tensor]
+ Tuple containing the predicted reconstruction and segmentation.
+ """
+ if self.consecutive_slices > 1:
+ batch, slices = y.shape[:2]
+ y = y.reshape(y.shape[0] * y.shape[1], *y.shape[2:])
+ sensitivity_maps = sensitivity_maps.reshape(
+ sensitivity_maps.shape[0] * sensitivity_maps.shape[1],
+ *sensitivity_maps.shape[2:],
+ )
+ mask = mask.reshape(mask.shape[0] * mask.shape[1], *mask.shape[2:])
+
+ # In case of deviating number of coils, we need to pad up to maximum number of coils == number of input \
+ # channels for the reconstruction module
+ num_coils = y.shape[1]
+ if num_coils * 2 != self.input_channels:
+ num_coils_to_add = (self.input_channels - num_coils * 2) // 2
+ dummy_coil_data = torch.zeros_like(torch.movedim(y, self.coil_dim, 0)[0]).unsqueeze(self.coil_dim)
+ for _ in range(num_coils_to_add):
+ y = torch.cat([y, dummy_coil_data], dim=self.coil_dim)
+ sensitivity_maps = torch.cat([sensitivity_maps, dummy_coil_data], dim=self.coil_dim)
+
+ y_prediction = y.clone()
+ for _ in range(self.num_iters):
+ init_reconstruction_pred = ifft2(
+ y_prediction, self.fft_centered, self.fft_normalization, self.spatial_dims
+ )
+ output = self.reconstruction_encoder(init_reconstruction_pred)
+ reconstruction_decoder_prediction = self.reconstruction_decoder(*output)
+ reconstruction_decoder_prediction = reconstruction_decoder_prediction + init_reconstruction_pred
+ reconstruction_decoder_prediction_kspace = fft2(
+ reconstruction_decoder_prediction, self.fft_centered, self.fft_normalization, self.spatial_dims
+ )
+ y_prediction = self.dc(reconstruction_decoder_prediction_kspace, y, mask)
+
+ pred_reconstruction = ifft2(y_prediction, self.fft_centered, self.fft_normalization, self.spatial_dims)
+
+ b, c, h, w, _ = pred_reconstruction.shape
+ pred_segmentation_input = pred_reconstruction.permute(0, 4, 1, 2, 3).reshape(b, 2 * c, h, w)
+
+ if self.magnitude_input:
+ pred_segmentation_input = torch.abs(pred_segmentation_input)
+
+ pred_segmentation = self.segmentation_module(pred_segmentation_input)
+
+ if self.normalize_segmentation_output:
+ pred_segmentation = (pred_segmentation - pred_segmentation.min()) / (
+ pred_segmentation.max() - pred_segmentation.min()
+ )
+
+ pred_segmentation = torch.abs(pred_segmentation)
+
+ pred_reconstruction = coil_combination_method(
+ pred_reconstruction, sensitivity_maps, method=self.coil_combination_method, dim=self.coil_dim
+ )
+ pred_reconstruction = torch.view_as_complex(pred_reconstruction)
+
+ if self.consecutive_slices > 1:
+ pred_reconstruction = pred_reconstruction.view([batch, slices, *pred_reconstruction.shape[1:]])
+ pred_segmentation = pred_segmentation.view([batch, slices, *pred_segmentation.shape[1:]])
+
+ return pred_reconstruction, pred_segmentation
diff --git a/atommic/collections/multitask/rs/nn/mtlrs.py b/atommic/collections/multitask/rs/nn/mtlrs.py
new file mode 100644
index 00000000..81daa2d4
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/mtlrs.py
@@ -0,0 +1,308 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Dict, List, Tuple, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import fft2
+from atommic.collections.common.parts.utils import expand_op
+from atommic.collections.multitask.rs.nn.base import BaseMRIReconstructionSegmentationModel
+from atommic.collections.multitask.rs.nn.mtlrs_base.mtlrs_block import MTLRSBlock
+from atommic.core.classes.common import typecheck
+
+__all__ = ["MTLRS"]
+
+
+class MTLRS(BaseMRIReconstructionSegmentationModel):
+ """Implementation of the Multi-Task Learning for MRI Reconstruction and Segmentation (MTLRS) model, as presented
+ in [Karkalousos2023]_.
+
+ References
+ ----------
+ .. [Karkalousos2023] Karkalousos, D., Iลกgum, I., Marquering, H., Caan, M. W. A., (2023). MultiTask Learning for
+ accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis. In Proceedings of
+ Machine Learning Research (Vol. 078).
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`MTLRS`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer object. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.reconstruction_module_recurrent_filters = cfg_dict.get("reconstruction_module_recurrent_filters")
+ self.reconstruction_module_time_steps = cfg_dict.get("reconstruction_module_time_steps")
+ self.reconstruction_module_num_cascades = cfg_dict.get("reconstruction_module_num_cascades")
+ self.reconstruction_module_accumulate_predictions = cfg_dict.get(
+ "reconstruction_module_accumulate_predictions"
+ )
+ conv_dim = cfg_dict.get("reconstruction_module_conv_dim")
+ reconstruction_module_params = {
+ "num_cascades": self.reconstruction_module_num_cascades,
+ "time_steps": self.reconstruction_module_time_steps,
+ "no_dc": cfg_dict.get("reconstruction_module_no_dc"),
+ "keep_prediction": cfg_dict.get("reconstruction_module_keep_prediction"),
+ "dimensionality": cfg_dict.get("reconstruction_module_dimensionality"),
+ "recurrent_layer": cfg_dict.get("reconstruction_module_recurrent_layer"),
+ "conv_filters": cfg_dict.get("reconstruction_module_conv_filters"),
+ "conv_kernels": cfg_dict.get("reconstruction_module_conv_kernels"),
+ "conv_dilations": cfg_dict.get("reconstruction_module_conv_dilations"),
+ "conv_bias": cfg_dict.get("reconstruction_module_conv_bias"),
+ "recurrent_filters": self.reconstruction_module_recurrent_filters,
+ "recurrent_kernels": cfg_dict.get("reconstruction_module_recurrent_kernels"),
+ "recurrent_dilations": cfg_dict.get("reconstruction_module_recurrent_dilations"),
+ "recurrent_bias": cfg_dict.get("reconstruction_module_recurrent_bias"),
+ "depth": cfg_dict.get("reconstruction_module_depth"),
+ "conv_dim": conv_dim,
+ "pretrained": cfg_dict.get("pretrained"),
+ "accumulate_predictions": self.reconstruction_module_accumulate_predictions,
+ }
+
+ self.segmentation_module_output_channels = cfg_dict.get("segmentation_module_output_channels", 2)
+ segmentation_module_params = {
+ "segmentation_module": cfg_dict.get("segmentation_module"),
+ "output_channels": self.segmentation_module_output_channels,
+ "channels": cfg_dict.get("segmentation_module_channels", 64),
+ "pooling_layers": cfg_dict.get("segmentation_module_pooling_layers", 2),
+ "dropout": cfg_dict.get("segmentation_module_dropout", 0.0),
+ "temporal_kernel": cfg_dict.get("segmentation_module_temporal_kernel", 1),
+ "activation": cfg_dict.get("segmentation_module_activation", "elu"),
+ "bias": cfg_dict.get("segmentation_module_bias", False),
+ "conv_dim": conv_dim,
+ }
+
+ self.coil_dim = cfg_dict.get("coil_dim", 1)
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+
+ self.rs_cascades = cfg_dict.get("joint_reconstruction_segmentation_module_cascades", 1)
+ self.rs_module = torch.nn.ModuleList(
+ [
+ MTLRSBlock(
+ reconstruction_module_params=reconstruction_module_params,
+ segmentation_module_params=segmentation_module_params,
+ input_channels=cfg_dict.get("segmentation_module_input_channels", 2),
+ magnitude_input=cfg_dict.get("magnitude_input", False),
+ fft_centered=cfg_dict.get("fft_centered", False),
+ fft_normalization=cfg_dict.get("fft_normalization", "backward"),
+ spatial_dims=cfg_dict.get("spatial_dims", (-2, -1)),
+ coil_dim=self.coil_dim,
+ dimensionality=cfg_dict.get("dimensionality", 2),
+ consecutive_slices=self.consecutive_slices,
+ coil_combination_method=cfg_dict.get("coil_combination_method", "SENSE"),
+ normalize_segmentation_output=cfg_dict.get("normalize_segmentation_output", True),
+ )
+ for _ in range(self.rs_cascades)
+ ]
+ )
+
+ self.task_adaption_type = cfg_dict.get("task_adaption_type", "multi_task_learning")
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ init_reconstruction_pred: torch.Tensor,
+ target_reconstruction: torch.Tensor,
+ hx: torch.Tensor = None,
+ sigma: float = 1.0,
+ ) -> Tuple[Union[List, torch.Tensor], torch.Tensor]:
+ """Forward pass of :class:`MTLRS`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ init_reconstruction_pred : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2]
+ target_reconstruction : torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y, 2]
+ hx : torch.Tensor, optional
+ Initial hidden state for the RNN. Default is ``None``.
+ sigma : float, optional
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ Tuple[Union[List, torch.Tensor], torch.Tensor]
+ Tuple containing the predicted reconstruction and segmentation.
+ """
+ pred_reconstructions = []
+ for cascade in self.rs_module:
+ pred_reconstruction, pred_segmentation, hx = cascade(
+ y=y,
+ sensitivity_maps=sensitivity_maps,
+ mask=mask,
+ init_reconstruction_pred=init_reconstruction_pred,
+ target_reconstruction=target_reconstruction,
+ hx=hx,
+ sigma=sigma,
+ )
+ pred_reconstructions.append(pred_reconstruction)
+ init_reconstruction_pred = pred_reconstruction[-1][-1]
+
+ if self.task_adaption_type == "multi_task_learning":
+ hidden_states = [
+ torch.cat(
+ [torch.abs(init_reconstruction_pred.unsqueeze(self.coil_dim) * pred_segmentation)]
+ * (f // self.segmentation_module_output_channels),
+ dim=self.coil_dim,
+ )
+ for f in self.reconstruction_module_recurrent_filters
+ if f != 0
+ ]
+
+ if self.consecutive_slices > 1:
+ hx = [x.unsqueeze(1) for x in hx]
+
+ # Check if the concatenated hidden states are the same size as the hidden state of the RNN
+ if hidden_states[0].shape[self.coil_dim] != hx[0].shape[self.coil_dim]:
+ prev_hidden_states = hidden_states
+ hidden_states = []
+ for hs in prev_hidden_states:
+ new_hidden_state = hs
+ for _ in range(hx[0].shape[1] - prev_hidden_states[0].shape[1]):
+ new_hidden_state = torch.cat(
+ [new_hidden_state, torch.zeros_like(hx[0][:, 0, :, :]).unsqueeze(self.coil_dim)],
+ dim=self.coil_dim,
+ )
+ hidden_states.append(new_hidden_state)
+
+ hx = [hx[i] + hidden_states[i] for i in range(len(hx))]
+
+ init_reconstruction_pred = torch.view_as_real(init_reconstruction_pred)
+
+ return pred_reconstructions, pred_segmentation
+
+ def process_reconstruction_loss( # noqa: MC0001
+ self,
+ target: torch.Tensor,
+ prediction: Union[List[List[torch.Tensor]], List[torch.Tensor], torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ attrs: Union[Dict, torch.Tensor],
+ r: Union[int, torch.Tensor],
+ loss_func: torch.nn.Module,
+ ) -> torch.Tensor:
+ """Processes the reconstruction loss for the CIRIM model. It differs from the base class in that it can handle
+ multiple cascades and time steps.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ mask : torch.Tensor
+ Mask of shape [batch_size, n_x, n_y, 2]. It will be used if self.ssdu is True, to enforce data consistency
+ on the prediction.
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ loss_func : torch.nn.Module
+ Loss function. Must be one of {torch.nn.L1Loss(), torch.nn.MSELoss(),
+ atommic.collections.reconstruction.losses.ssim.SSIMLoss()}. Default is ``torch.nn.L1Loss()``.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if target.shape[-1] != 2 and torch.is_complex(target):
+ target = torch.view_as_real(target)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ target = expand_op(target, sensitivity_maps, self.coil_dim)
+ # Transform to k-space.
+ target = fft2(target, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target / torch.max(torch.abs(target)))
+ elif not self.unnormalize_loss_inputs:
+ target = self.__abs_output__(target / torch.max(torch.abs(target)))
+
+ def compute_reconstruction_loss(t, p, s):
+ if self.unnormalize_loss_inputs:
+ # we do the unnormalization here to avoid explicitly iterating through list of predictions, which
+ # might be a list of lists.
+ t, p, s = self.__unnormalize_for_loss_or_log__(t, p, s, attrs, r)
+
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if p.shape[-1] != 2 and torch.is_complex(p):
+ p = torch.view_as_real(p)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ p = expand_op(p, s, self.coil_dim)
+ # Transform to k-space.
+ p = fft2(p, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # If SSDU is used, then apply the mask to the prediction to enforce data consistency.
+ if self.ssdu:
+ p = p * mask
+ # Ensure loss inputs are both viewed in the same way.
+ p = self.__abs_output__(p / torch.max(torch.abs(p)))
+ elif not self.unnormalize_loss_inputs:
+ p = self.__abs_output__(p / torch.max(torch.abs(p)))
+
+ if "ssim" in str(loss_func).lower():
+ p = torch.abs(p / torch.max(torch.abs(p)))
+ t = torch.abs(t / torch.max(torch.abs(t)))
+
+ return loss_func(
+ t,
+ p,
+ data_range=torch.tensor([max(torch.max(t).item(), torch.max(p).item())]).unsqueeze(dim=0).to(t),
+ )
+
+ return loss_func(t, p)
+
+ if self.accumulate_predictions:
+ rs_cascades_weights = torch.logspace(-1, 0, steps=len(prediction)).to(target.device)
+ rs_cascades_loss = []
+ for rs_cascade_pred in prediction:
+ cascades_weights = torch.logspace(-1, 0, steps=len(rs_cascade_pred)).to(target.device)
+ cascades_loss = []
+ for cascade_pred in rs_cascade_pred:
+ time_steps_weights = torch.logspace(-1, 0, steps=self.time_steps).to(target.device)
+ time_steps_loss = [
+ compute_reconstruction_loss(target, time_step_pred, sensitivity_maps)
+ for time_step_pred in cascade_pred
+ ]
+ cascade_loss = sum(x * w for x, w in zip(time_steps_loss, time_steps_weights)) / self.time_steps
+ cascades_loss.append(cascade_loss)
+ rs_cascade_loss = sum(x * w for x, w in zip(cascades_loss, cascades_weights)) / len(rs_cascade_pred)
+ rs_cascades_loss.append(rs_cascade_loss)
+ loss = sum(x * w for x, w in zip(rs_cascades_loss, rs_cascades_weights)) / len(prediction)
+ else:
+ # keep the last prediction of the last cascade of the last rs cascade
+ prediction = prediction[-1][-1][-1]
+ loss = compute_reconstruction_loss(target, prediction, sensitivity_maps)
+ return loss
diff --git a/atommic/collections/multitask/rs/nn/mtlrs_base/__init__.py b/atommic/collections/multitask/rs/nn/mtlrs_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/mtlrs_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/multitask/rs/nn/mtlrs_base/mtlrs_block.py b/atommic/collections/multitask/rs/nn/mtlrs_base/mtlrs_block.py
new file mode 100644
index 00000000..800999c2
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/mtlrs_base/mtlrs_block.py
@@ -0,0 +1,328 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import math
+from typing import Dict, List, Optional, Tuple, Union
+
+import torch
+
+from atommic.collections.common.parts.utils import rnn_weights_init
+from atommic.collections.reconstruction.nn.rim_base import rim_block
+from atommic.collections.reconstruction.nn.rim_base.conv_layers import ConvNonlinear
+from atommic.collections.reconstruction.nn.unet_base.unet_block import Unet
+from atommic.collections.segmentation.nn.attentionunet_base.attentionunet_block import AttentionUnet
+from atommic.collections.segmentation.nn.lambdaunet_base.lambdaunet_block import LambdaBlock
+from atommic.collections.segmentation.nn.vnet_base.vnet_block import VNet
+
+__all__ = ["MTLRSBlock"]
+
+
+class MTLRSBlock(torch.nn.Module):
+ """Implementation of a Multi-Task Learning for MRI Reconstruction and Segmentation (MTLRS) block, as presented in
+ [Karkalousos2023]_.
+
+ References
+ ----------
+ .. [Karkalousos2023] Karkalousos, D., Iลกgum, I., Marquering, H., Caan, M. W. A., (2023). MultiTask Learning for
+ accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis. In Proceedings of
+ Machine Learning Research (Vol. 078).
+ """
+
+ def __init__(
+ self,
+ reconstruction_module_params: Dict,
+ segmentation_module_params: Dict,
+ input_channels: int,
+ magnitude_input: bool = True,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ dimensionality: int = 2,
+ consecutive_slices: int = 1,
+ coil_combination_method: str = "SENSE",
+ normalize_segmentation_output: bool = True,
+ ):
+ """Inits :class:`MTLRSBlock`.
+
+ Parameters
+ ----------
+ reconstruction_module_params : Dict
+ Parameters for the reconstruction module.
+ segmentation_module_params : Dict
+ Parameters for the segmentation module.
+ input_channels : int
+ Number of input channels.
+ magnitude_input : bool
+ Whether the input is magnitude or complex. Default is ``True``.
+ fft_centered : bool
+ Whether the FFT is centered. Default is ``False``.
+ fft_normalization : str
+ Normalization of the FFT. Default is ``"backward"``.
+ spatial_dims : Tuple[int, int]
+ Spatial dimensions of the input. Default is ``None``.
+ coil_dim : int
+ Coil dimension of the input. Default is ``1``.
+ dimensionality : int
+ Dimensionality of the input. Default is ``2``.
+ consecutive_slices : int
+ Number of consecutive slices to be used. Default is ``1``.
+ coil_combination_method : str
+ Coil combination method. Default is ``"SENSE"``.
+ normalize_segmentation_output : bool
+ Whether to normalize the segmentation output. Default is ``True`` .
+ """
+ super().__init__()
+
+ # General parameters
+ self.input_channels = input_channels
+ self.magnitude_input = magnitude_input
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+ self.coil_dim = coil_dim
+ self.dimensionality = dimensionality
+ if self.dimensionality != 2:
+ raise NotImplementedError(f"Currently only 2D is supported for segmentation, got {self.dimensionality}D.")
+ self.consecutive_slices = consecutive_slices
+ self.coil_combination_method = coil_combination_method
+
+ # Reconstruction module parameters
+ self.reconstruction_module_params = reconstruction_module_params
+ self.reconstruction_module_recurrent_filters = self.reconstruction_module_params["recurrent_filters"]
+ self.reconstruction_module_time_steps = 8 * math.ceil(self.reconstruction_module_params["time_steps"] / 8)
+ self.no_dc = self.reconstruction_module_params["no_dc"]
+ self.keep_prediction = self.reconstruction_module_params["keep_prediction"]
+ self.reconstruction_module_dimensionality = self.reconstruction_module_params["dimensionality"]
+ reconstruction_module_consecutive_slices = (
+ self.consecutive_slices if self.reconstruction_module_dimensionality == 3 else 1
+ )
+ self.reconstruction_module = torch.nn.ModuleList(
+ [
+ rim_block.RIMBlock(
+ recurrent_layer=self.reconstruction_module_params["recurrent_layer"],
+ conv_filters=self.reconstruction_module_params["conv_filters"],
+ conv_kernels=self.reconstruction_module_params["conv_kernels"],
+ conv_dilations=self.reconstruction_module_params["conv_dilations"],
+ conv_bias=self.reconstruction_module_params["conv_bias"],
+ recurrent_filters=self.reconstruction_module_recurrent_filters,
+ recurrent_kernels=self.reconstruction_module_params["recurrent_kernels"],
+ recurrent_dilations=self.reconstruction_module_params["recurrent_dilations"],
+ recurrent_bias=self.reconstruction_module_params["recurrent_bias"],
+ depth=self.reconstruction_module_params["depth"],
+ time_steps=self.reconstruction_module_time_steps,
+ conv_dim=self.reconstruction_module_params["conv_dim"],
+ no_dc=self.no_dc,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim - 1,
+ dimensionality=self.reconstruction_module_dimensionality,
+ consecutive_slices=reconstruction_module_consecutive_slices,
+ coil_combination_method=self.coil_combination_method,
+ )
+ for _ in range(self.reconstruction_module_params["num_cascades"])
+ ]
+ )
+ # Keep estimation through the cascades if keep_prediction is True or re-estimate it if False.
+ self.reconstruction_module_keep_prediction = self.reconstruction_module_params["keep_prediction"]
+ # initialize weights if not using pretrained cirim
+ if not self.reconstruction_module_params["pretrained"]:
+ std_init_range = 1 / self.reconstruction_module_recurrent_filters[0] ** 0.5
+ self.reconstruction_module.apply(lambda module: rnn_weights_init(module, std_init_range))
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+ self.accumulate_predictions = self.reconstruction_module_params["accumulate_predictions"]
+
+ # Segmentation module parameters
+ self.segmentation_module_params = segmentation_module_params
+ segmentation_module = self.segmentation_module_params["segmentation_module"]
+ self.segmentation_module_output_channels = self.segmentation_module_params["output_channels"]
+ if segmentation_module.lower() == "unet":
+ segmentation_module = Unet(
+ in_chans=self.input_channels,
+ out_chans=self.segmentation_module_output_channels,
+ chans=self.segmentation_module_params["channels"],
+ num_pool_layers=self.segmentation_module_params["pooling_layers"],
+ drop_prob=self.segmentation_module_params["dropout"],
+ )
+ elif segmentation_module.lower() == "attentionunet":
+ segmentation_module = AttentionUnet(
+ in_chans=self.input_channels,
+ out_chans=self.segmentation_module_output_channels,
+ chans=self.segmentation_module_params["channels"],
+ num_pool_layers=self.segmentation_module_params["pooling_layers"],
+ drop_prob=self.segmentation_module_params["dropout"],
+ )
+ elif segmentation_module.lower() == "lambdaunet":
+ segmentation_module = LambdaBlock(
+ in_chans=self.input_channels,
+ out_chans=self.segmentation_module_output_channels,
+ drop_prob=self.segmentation_module_params["dropout"],
+ temporal_kernel=self.segmentation_module_params["temporal_kernel"],
+ num_slices=self.consecutive_slices,
+ )
+ elif segmentation_module.lower() == "vnet":
+ segmentation_module = VNet(
+ in_chans=self.input_channels,
+ out_chans=self.segmentation_module_output_channels,
+ act=self.segmentation_module_params["activation"],
+ drop_prob=self.segmentation_module_params["dropout"],
+ bias=self.segmentation_module_params["bias"],
+ )
+ elif segmentation_module.lower() == "convlayer":
+ segmentation_module = torch.nn.Sequential(
+ ConvNonlinear(
+ self.input_channels,
+ self.segmentation_module_output_channels,
+ conv_dim=self.segmentation_module_params["conv_dim"],
+ kernel_size=3,
+ dilation=1,
+ bias=False,
+ nonlinear=None, # No nonlinear activation
+ )
+ )
+ else:
+ raise ValueError(f"Segmentation module {segmentation_module} not implemented.")
+ self.segmentation_module = segmentation_module
+
+ self.normalize_segmentation_output = normalize_segmentation_output
+
+ def forward( # noqa: MC0001
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ init_reconstruction_pred: torch.Tensor,
+ target_reconstruction: torch.Tensor, # pylint: disable=unused-argument
+ hx: torch.Tensor = None,
+ sigma: float = 1.0,
+ ) -> Tuple[Union[List, torch.Tensor], torch.Tensor]:
+ """Forward pass of :class:`MTLRSBlock`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ init_reconstruction_pred : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2]
+ target_reconstruction : torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y, 2]
+ hx : torch.Tensor, optional
+ Initial hidden state for the RNN. Default is ``None``.
+ sigma : float, optional
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ Tuple[Union[List, torch.Tensor], torch.Tensor]
+ Tuple containing the predicted reconstruction and segmentation.
+ """
+ if self.consecutive_slices > 1 and self.reconstruction_module_dimensionality == 2:
+ # Do per slice reconstruction
+ pred_reconstruction_slices = []
+ for slice_idx in range(self.consecutive_slices):
+ y_slice = y[:, slice_idx, ...]
+ prediction_slice = y_slice.clone()
+ sensitivity_maps_slice = sensitivity_maps[:, slice_idx, ...]
+ mask_slice = mask[:, 0, ...]
+ init_reconstruction_pred_slice = init_reconstruction_pred[:, slice_idx, ...]
+ _pred_reconstruction_slice = (
+ None
+ if init_reconstruction_pred_slice is None or init_reconstruction_pred_slice.dim() < 4
+ else init_reconstruction_pred_slice
+ )
+ cascades_predictions = []
+ for i, cascade in enumerate(self.reconstruction_module):
+ # Forward pass through the cascades
+ prediction_slice, hx = cascade(
+ prediction_slice,
+ y_slice,
+ sensitivity_maps_slice,
+ mask_slice,
+ _pred_reconstruction_slice,
+ hx,
+ sigma,
+ keep_prediction=False if i == 0 else self.keep_prediction,
+ )
+ time_steps_predictions = [torch.view_as_complex(pred) for pred in prediction_slice]
+ cascades_predictions.append(torch.stack(time_steps_predictions, dim=0))
+ pred_reconstruction_slices.append(torch.stack(cascades_predictions, dim=0))
+ preds = torch.stack(pred_reconstruction_slices, dim=3)
+
+ cascades_predictions = [
+ [
+ preds[cascade_prediction, time_step_prediction, ...]
+ for time_step_prediction in range(preds.shape[1])
+ ]
+ for cascade_prediction in range(preds.shape[0])
+ ]
+ else:
+ prediction = y.clone()
+ _pred_reconstruction = (
+ None
+ if init_reconstruction_pred is None or init_reconstruction_pred.dim() < 4
+ else init_reconstruction_pred
+ )
+ sigma = 1.0
+ cascades_predictions = []
+ for i, cascade in enumerate(self.reconstruction_module):
+ # Forward pass through the cascades
+ prediction, hx = cascade(
+ prediction,
+ y,
+ sensitivity_maps,
+ mask,
+ _pred_reconstruction,
+ hx,
+ sigma,
+ keep_prediction=False if i == 0 else self.keep_prediction,
+ )
+ time_steps_predictions = [torch.view_as_complex(pred) for pred in prediction]
+ cascades_predictions.append(time_steps_predictions)
+ pred_reconstruction = cascades_predictions
+
+ _pred_reconstruction = pred_reconstruction
+ if isinstance(_pred_reconstruction, list):
+ _pred_reconstruction = _pred_reconstruction[-1]
+ if isinstance(_pred_reconstruction, list):
+ _pred_reconstruction = _pred_reconstruction[-1]
+ if _pred_reconstruction.shape[-1] != 2:
+ _pred_reconstruction = torch.view_as_real(_pred_reconstruction)
+ if self.consecutive_slices > 1 and _pred_reconstruction.dim() == 5:
+ _pred_reconstruction = _pred_reconstruction.reshape(
+ _pred_reconstruction.shape[0] * _pred_reconstruction.shape[1],
+ *_pred_reconstruction.shape[2:],
+ )
+ if _pred_reconstruction.shape[-1] == 2:
+ if self.input_channels == 1:
+ _pred_reconstruction = torch.view_as_complex(_pred_reconstruction).unsqueeze(1)
+ if self.magnitude_input:
+ _pred_reconstruction = torch.abs(_pred_reconstruction)
+ elif self.input_channels == 2:
+ if self.magnitude_input:
+ raise ValueError("Magnitude input is not supported for 2-channel input.")
+ _pred_reconstruction = _pred_reconstruction.permute(0, 3, 1, 2)
+ else:
+ raise ValueError(f"The input channels must be either 1 or 2. Found: {self.input_channels}")
+ else:
+ _pred_reconstruction = _pred_reconstruction.unsqueeze(1)
+
+ pred_segmentation = self.segmentation_module(torch.abs(_pred_reconstruction))
+
+ if self.normalize_segmentation_output:
+ pred_segmentation = (pred_segmentation - pred_segmentation.min()) / (
+ pred_segmentation.max() - pred_segmentation.min()
+ )
+
+ pred_segmentation = torch.abs(pred_segmentation)
+
+ if self.consecutive_slices > 1:
+ # get batch size and number of slices from y, because if the reconstruction module is used they will
+ # not be saved before
+ pred_segmentation = pred_segmentation.view([y.shape[0], y.shape[1], *pred_segmentation.shape[1:]])
+
+ return pred_reconstruction, pred_segmentation, hx # type: ignore
diff --git a/atommic/collections/multitask/rs/nn/recseg_unet.py b/atommic/collections/multitask/rs/nn/recseg_unet.py
new file mode 100644
index 00000000..dc6b3fc9
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/recseg_unet.py
@@ -0,0 +1,148 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import List, Tuple, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.multitask.rs.nn.base import BaseMRIReconstructionSegmentationModel
+from atommic.collections.reconstruction.nn.unet_base.unet_block import Unet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["RecSegUNet"]
+
+
+class RecSegUNet(BaseMRIReconstructionSegmentationModel):
+ """Implementation of the Reconstruction Segmentation method using UNets for both the reconstruction and
+ segmentation as presented in [Sui2021]_.
+
+ References
+ ----------
+ .. [Sui2021] Sui, B, Lv, J, Tong, X, Li, Y, Wang, C. Simultaneous image reconstruction and lesion segmentation in
+ accelerated MRI using multitasking learning. Med Phys. 2021; 48: 7189โ 7198. https://doi.org/10.1002/mp.15213
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`RecSegUNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer object. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.input_channels = cfg_dict.get("input_channels", 2)
+ if self.input_channels == 0:
+ raise ValueError("Segmentation module input channels cannot be 0.")
+ if self.input_channels > 2:
+ raise ValueError(f"Segmentation module input channels must be either 1 or 2. Found: {self.input_channels}")
+
+ reconstruction_module_output_channels = cfg_dict.get("reconstruction_module_output_channels", 1)
+
+ self.reconstruction_module = Unet(
+ in_chans=self.input_channels,
+ out_chans=reconstruction_module_output_channels,
+ chans=cfg_dict.get("reconstruction_module_channels", 64),
+ num_pool_layers=cfg_dict.get("reconstruction_module_pooling_layers", 2),
+ drop_prob=cfg_dict.get("reconstruction_module_dropout", 0.0),
+ )
+
+ self.segmentation_module = Unet(
+ in_chans=reconstruction_module_output_channels,
+ out_chans=cfg_dict.get("segmentation_module_output_channels", 1),
+ chans=cfg_dict.get("segmentation_module_channels", 64),
+ num_pool_layers=cfg_dict.get("segmentation_module_pooling_layers", 2),
+ drop_prob=cfg_dict.get("segmentation_module_dropout", 0.0),
+ )
+
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+ self.magnitude_input = cfg_dict.get("magnitude_input", True)
+ self.normalize_segmentation_output = cfg_dict.get("normalize_segmentation_output", True)
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor, # pylint: disable=unused-argument
+ sensitivity_maps: torch.Tensor, # pylint: disable=unused-argument
+ mask: torch.Tensor, # pylint: disable=unused-argument
+ init_reconstruction_pred: torch.Tensor,
+ target_reconstruction: torch.Tensor, # pylint: disable=unused-argument
+ hx: torch.Tensor = None, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> Tuple[Union[List, torch.Tensor], torch.Tensor]:
+ """Forward pass of :class:`RecSegUNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ init_reconstruction_pred : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2]
+ target_reconstruction : torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y, 2]
+ hx : torch.Tensor, optional
+ Initial hidden state for the RNN. Default is ``None``.
+ sigma : float, optional
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ Tuple[Union[List, torch.Tensor], torch.Tensor]
+ Tuple containing the predicted reconstruction and segmentation.
+ """
+ if self.consecutive_slices > 1:
+ batch, slices = init_reconstruction_pred.shape[:2]
+ init_reconstruction_pred = init_reconstruction_pred.reshape(
+ init_reconstruction_pred.shape[0] * init_reconstruction_pred.shape[1],
+ *init_reconstruction_pred.shape[2:],
+ )
+
+ if init_reconstruction_pred.shape[-1] == 2:
+ if self.input_channels == 1:
+ init_reconstruction_pred = torch.view_as_complex(init_reconstruction_pred).unsqueeze(1)
+ if self.magnitude_input:
+ init_reconstruction_pred = torch.abs(init_reconstruction_pred)
+ elif self.input_channels == 2:
+ if self.magnitude_input:
+ raise ValueError("Magnitude input is not supported for 2-channel input.")
+ init_reconstruction_pred = init_reconstruction_pred.permute(0, 3, 1, 2)
+ else:
+ raise ValueError(f"The input channels must be either 1 or 2. Found: {self.input_channels}")
+ else:
+ if init_reconstruction_pred.dim() == 3:
+ init_reconstruction_pred = init_reconstruction_pred.unsqueeze(1)
+
+ pred_reconstruction = self.reconstruction_module(torch.abs(init_reconstruction_pred))
+
+ if self.magnitude_input:
+ pred_reconstruction = torch.abs(pred_reconstruction)
+
+ pred_segmentation = self.segmentation_module(pred_reconstruction)
+
+ if self.normalize_segmentation_output:
+ pred_segmentation = (pred_segmentation - pred_segmentation.min()) / (
+ pred_segmentation.max() - pred_segmentation.min()
+ )
+
+ pred_segmentation = torch.abs(pred_segmentation)
+
+ pred_reconstruction = pred_reconstruction.squeeze(1)
+
+ if self.consecutive_slices > 1:
+ pred_reconstruction = pred_reconstruction.view([batch, slices, *pred_reconstruction.shape[1:]])
+ pred_segmentation = pred_segmentation.view([batch, slices, *pred_segmentation.shape[1:]])
+
+ return pred_reconstruction, pred_segmentation
diff --git a/atommic/collections/multitask/rs/nn/segnet.py b/atommic/collections/multitask/rs/nn/segnet.py
new file mode 100644
index 00000000..c6532336
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/segnet.py
@@ -0,0 +1,293 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Any, Dict, Tuple, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch import nn
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import coil_combination_method
+from atommic.collections.multitask.rs.nn.base import BaseMRIReconstructionSegmentationModel
+from atommic.collections.multitask.rs.nn.idslr_base.idslr_block import DC, UnetDecoder, UnetEncoder
+from atommic.collections.reconstruction.nn.rim_base.conv_layers import ConvNonlinear
+from atommic.core.classes.common import typecheck
+
+__all__ = ["SegNet"]
+
+
+class SegNet(BaseMRIReconstructionSegmentationModel):
+ """Implementation of the Segmentation Network MRI, as described in, as presented in [Sun2019]_.
+
+ References
+ ----------
+ .. [Sun2019] Sun, L., Fan, Z., Ding, X., Huang, Y., Paisley, J. (2019). Joint CS-MRI Reconstruction and
+ Segmentation with a Unified Deep Network. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information
+ Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham.
+ https://doi.org/10.1007/978-3-030-20351-1_38
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`SegNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer object. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.use_reconstruction_module = cfg_dict.get("use_reconstruction_module", True)
+
+ self.dimensionality = cfg_dict.get("dimensionality", 2)
+ if self.dimensionality != 2:
+ raise NotImplementedError(f"Currently only 2D is supported for segmentation, got {self.dimensionality}D.")
+
+ self.input_channels = cfg_dict.get("input_channels", 2)
+ reconstruction_out_chans = cfg_dict.get("reconstruction_module_output_channels", 2)
+ segmentation_out_chans = cfg_dict.get("segmentation_module_output_channels", 1)
+ chans = cfg_dict.get("channels", 32)
+ num_pools = cfg_dict.get("num_pools", 4)
+ drop_prob = cfg_dict.get("drop_prob", 0.0)
+ normalize = cfg_dict.get("normalize", False)
+ padding = cfg_dict.get("padding", False)
+ padding_size = cfg_dict.get("padding_size", 11)
+ self.norm_groups = cfg_dict.get("norm_groups", 2)
+ num_cascades = cfg_dict.get("num_cascades", 5)
+
+ self.reconstruction_encoder = nn.ModuleList(
+ [
+ UnetEncoder(
+ chans=chans,
+ num_pools=num_pools,
+ in_chans=self.input_channels,
+ drop_prob=drop_prob,
+ normalize=normalize,
+ padding=padding,
+ padding_size=padding_size,
+ norm_groups=self.norm_groups,
+ )
+ for _ in range(num_cascades)
+ ]
+ )
+ self.reconstruction_decoder = nn.ModuleList(
+ [
+ UnetDecoder(
+ chans=chans,
+ num_pools=num_pools,
+ out_chans=reconstruction_out_chans,
+ drop_prob=drop_prob,
+ normalize=normalize,
+ padding=padding,
+ padding_size=padding_size,
+ norm_groups=self.norm_groups,
+ )
+ for _ in range(num_cascades)
+ ]
+ )
+ self.segmentation_decoder = nn.ModuleList(
+ [
+ UnetDecoder(
+ chans=chans,
+ num_pools=num_pools,
+ out_chans=segmentation_out_chans,
+ drop_prob=drop_prob,
+ normalize=normalize,
+ padding=padding,
+ padding_size=padding_size,
+ norm_groups=self.norm_groups,
+ )
+ for _ in range(num_cascades)
+ ]
+ )
+
+ self.segmentation_final_layer = torch.nn.Sequential(
+ ConvNonlinear(
+ segmentation_out_chans * num_cascades,
+ segmentation_out_chans,
+ conv_dim=cfg_dict.get("segmentation_final_layer_conv_dim", 2),
+ kernel_size=cfg_dict.get("segmentation_final_layer_kernel_size", 3),
+ dilation=cfg_dict.get("segmentation_final_layer_dilation", 1),
+ bias=cfg_dict.get("segmentation_final_layer_bias", False),
+ nonlinear=cfg_dict.get("segmentation_final_layer_nonlinear", "relu"),
+ )
+ )
+
+ self.magnitude_input = cfg_dict.get("magnitude_input", True)
+ self.normalize_segmentation_output = cfg_dict.get("normalize_segmentation_output", True)
+
+ self.dc = DC()
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ init_reconstruction_pred: torch.Tensor,
+ target_reconstruction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> Tuple[Any, Any]:
+ """Forward pass of :class:`SegNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ init_reconstruction_pred : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2]
+ target_reconstruction : torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ Tuple[Union[List, torch.Tensor], torch.Tensor]
+ Tuple containing the predicted reconstruction and segmentation.
+ """
+ if self.consecutive_slices > 1:
+ batch, slices = y.shape[0], y.shape[1]
+ y = y.reshape(y.shape[0] * y.shape[1], *y.shape[2:])
+ sensitivity_maps = sensitivity_maps.reshape(
+ sensitivity_maps.shape[0] * sensitivity_maps.shape[1],
+ *sensitivity_maps.shape[2:],
+ )
+ mask = mask.reshape(mask.shape[0] * mask.shape[1], *mask.shape[2:])
+
+ # In case of deviating number of coils, we need to pad up to maximum number of coils == number of input \
+ # channels for the reconstruction module
+ num_coils = y.shape[1]
+ if num_coils * 2 != self.input_channels:
+ num_coils_to_add = (self.input_channels - num_coils * 2) // 2
+ dummy_coil_data = torch.zeros_like(torch.movedim(y, self.coil_dim, 0)[0]).unsqueeze(self.coil_dim)
+ for _ in range(num_coils_to_add):
+ y = torch.cat([y, dummy_coil_data], dim=self.coil_dim)
+ sensitivity_maps = torch.cat([sensitivity_maps, dummy_coil_data], dim=self.coil_dim)
+
+ y_prediction = y.clone()
+ pred_segmentations = []
+ for re, rd, sd in zip(self.reconstruction_encoder, self.reconstruction_decoder, self.segmentation_decoder):
+ init_reconstruction_pred = ifft2(
+ y_prediction, self.fft_centered, self.fft_normalization, self.spatial_dims
+ )
+ output = re(init_reconstruction_pred)
+ reconstruction_encoder_prediction, padding_size = output[0].copy(), output[2]
+
+ pred_segmentation_input = reconstruction_encoder_prediction
+ if self.magnitude_input:
+ pred_segmentation_input = [torch.abs(x) for x in pred_segmentation_input]
+
+ pred_segmentations.append(sd(pred_segmentation_input, iscomplex=False, pad_sizes=padding_size))
+ reconstruction_decoder_prediction = rd(*output)
+ reconstruction_decoder_prediction_kspace = fft2(
+ reconstruction_decoder_prediction,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ y_prediction = self.dc(reconstruction_decoder_prediction_kspace, y, mask)
+
+ pred_reconstruction = self.process_intermediate_pred(y_prediction, sensitivity_maps, True)
+
+ pred_segmentation = self.segmentation_final_layer(torch.cat(pred_segmentations, dim=1))
+
+ if self.normalize_segmentation_output:
+ pred_segmentation = (pred_segmentation - pred_segmentation.min()) / (
+ pred_segmentation.max() - pred_segmentation.min()
+ )
+
+ pred_segmentation = torch.abs(pred_segmentation)
+
+ pred_segmentations.append(pred_segmentation)
+
+ if self.consecutive_slices > 1:
+ # get batch size and number of slices from y, because if the reconstruction module is used they will not
+ # be saved before
+ pred_reconstruction = pred_reconstruction.view([batch, slices, *pred_reconstruction.shape[1:]])
+ pred_segmentations = [x.view([batch, slices, *x.shape[1:]]) for x in pred_segmentations]
+
+ return pred_reconstruction, pred_segmentations
+
+ def process_segmentation_loss(self, target: torch.Tensor, prediction: torch.Tensor, attrs: Dict) -> Dict:
+ """Processes the segmentation loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, nr_classes, n_x, n_y].
+ prediction : torch.Tensor
+ Prediction of shape [batch_size, nr_classes, n_x, n_y].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+
+ Returns
+ -------
+ Dict
+ Dictionary containing the (multiple) loss values. For example, if the cross entropy loss and the dice loss
+ are used, the dictionary will contain the keys ``cross_entropy_loss``, ``dice_loss``, and
+ (combined) ``segmentation_loss``.
+ """
+ if self.unnormalize_loss_inputs:
+ target, prediction = self.__unnormalize_for_loss_or_log__( # type: ignore
+ target, prediction, None, attrs, attrs["r"]
+ )
+ losses = {}
+ for name, loss_func in self.segmentation_losses.items():
+ cascades_loss = []
+ for i in range(len(prediction)): # pylint: disable=consider-using-enumerate
+ loss = loss_func(target, prediction[i])
+ if isinstance(loss, tuple):
+ # In case of the dice loss, the loss is a tuple of the form (dice, dice loss)
+ loss = loss[1]
+ cascades_loss.append(loss)
+ losses[name] = torch.stack(cascades_loss).mean().to(target.device)
+ return self.total_segmentation_loss(**losses) * self.total_segmentation_loss_weight
+
+ def process_intermediate_pred(
+ self,
+ prediction: Union[list, torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ do_coil_combination: bool = False,
+ ) -> torch.Tensor:
+ """Processes the intermediate prediction.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ Intermediate prediction. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ do_coil_combination : bool
+ Whether to do coil combination. In this case the prediction is in k-space. Default is ``False``.
+
+ Returns
+ -------
+ torch.Tensor, shape [batch_size, n_x, n_y, 2]
+ Processed prediction.
+ """
+ # Take the last time step of the prediction
+ if do_coil_combination:
+ prediction = ifft2(
+ prediction,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ prediction = coil_combination_method(
+ prediction, sensitivity_maps, method=self.coil_combination_method, dim=self.coil_dim
+ )
+ prediction = torch.view_as_complex(prediction)
+ return prediction
diff --git a/atommic/collections/multitask/rs/nn/seranet.py b/atommic/collections/multitask/rs/nn/seranet.py
new file mode 100644
index 00000000..86792a20
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/seranet.py
@@ -0,0 +1,246 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import List, Tuple, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts import coil_combination_method
+from atommic.collections.multitask.rs.nn.base import BaseMRIReconstructionSegmentationModel
+from atommic.collections.multitask.rs.nn.seranet_base.convlstm_unet import ConvLSTMNormUnet
+from atommic.collections.multitask.rs.nn.seranet_base.seranet_block import (
+ SERANetReconstructionBlock,
+ SERANetRecurrentBlock,
+)
+from atommic.collections.reconstruction.nn.ccnn_base.ccnn_block import CascadeNetBlock, Conv2d
+from atommic.collections.reconstruction.nn.unet_base.unet_block import Unet
+from atommic.collections.segmentation.nn.attentionunet_base.attentionunet_block import AttentionGate
+from atommic.core.classes.common import typecheck
+
+__all__ = ["SERANet"]
+
+
+class SERANet(BaseMRIReconstructionSegmentationModel):
+ """Implementation of the End-to-End Recurrent Attention Network as presented in [Huang2019]_.
+
+ References
+ ----------
+ .. [Huang2019] Huang, Q., Chen, X., Metaxas, D., Nadar, M.S. (2019). Brain Segmentation from k-Space with
+ End-to-End Recurrent Attention Network. In: , et al. Medical Image Computing and Computer Assisted
+ Intervention โ MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham.
+ https://doi.org/10.1007/978-3-030-32248-9_31
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`SERANet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer object. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.input_channels = cfg_dict.get("input_channels", 2)
+ if self.input_channels == 0:
+ raise ValueError("Segmentation module input channels cannot be 0.")
+ if self.input_channels > 2:
+ raise ValueError(f"Segmentation module input channels must be either 1 or 2. Found: {self.input_channels}")
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+
+ reconstruction_module = cfg_dict.get("reconstruction_module", "unet")
+ reconstruction_module_output_channels = cfg_dict.get("reconstruction_module_output_channels", 1)
+ if reconstruction_module.lower() == "unet":
+ regularizer = Unet(
+ in_chans=self.input_channels,
+ out_chans=reconstruction_module_output_channels,
+ chans=cfg_dict.get("reconstruction_module_channels", 64),
+ num_pool_layers=cfg_dict.get("reconstruction_module_pooling_layers", 2),
+ drop_prob=cfg_dict.get("reconstruction_module_dropout", 0.0),
+ )
+ elif reconstruction_module.lower() == "cascadenet":
+ regularizer = torch.nn.ModuleList(
+ [
+ CascadeNetBlock(
+ Conv2d(
+ in_channels=self.input_channels,
+ out_channels=reconstruction_module_output_channels,
+ hidden_channels=cfg_dict.get("reconstruction_module_hidden_channels", 64),
+ n_convs=cfg_dict.get("reconstruction_module_n_convs", 2),
+ batchnorm=cfg_dict.get("reconstruction_module_batchnorm", True),
+ ),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim if self.consecutive_slices == 1 else self.coil_dim - 1,
+ no_dc=True,
+ )
+ for _ in range(cfg_dict.get("reconstruction_module_num_cascades", 5))
+ ]
+ )
+ else:
+ raise ValueError(f"Unknown reconstruction module: {reconstruction_module} for SERANet")
+
+ self.reconstruction_module = SERANetReconstructionBlock(
+ num_reconstruction_blocks=cfg_dict.get("reconstruction_module_num_blocks", 3),
+ reconstruction_model=regularizer,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim if self.consecutive_slices == 1 else self.coil_dim - 1,
+ coil_combination_method=self.coil_combination_method,
+ )
+ self.segmentation_module_input_channels = cfg_dict.get("segmentation_module_input_channels", 2)
+ segmentation_module_output_channels = cfg_dict.get("segmentation_module_output_channels", 1)
+ self.segmentation_module = ConvLSTMNormUnet(
+ in_chans=self.segmentation_module_input_channels,
+ out_chans=segmentation_module_output_channels,
+ chans=cfg_dict.get("segmentation_module_channels", 64),
+ num_pools=cfg_dict.get("segmentation_module_pooling_layers", 2),
+ drop_prob=cfg_dict.get("segmentation_module_dropout", 0.0),
+ )
+ self.recurrent_module = SERANetRecurrentBlock(
+ num_iterations=cfg_dict.get("recurrent_module_iterations", 3),
+ attention_model=AttentionGate(
+ in_chans_x=self.segmentation_module_input_channels * 2,
+ in_chans_g=segmentation_module_output_channels,
+ out_chans=segmentation_module_output_channels,
+ ),
+ unet_model=ConvLSTMNormUnet(
+ in_chans=self.segmentation_module_input_channels * 2,
+ out_chans=segmentation_module_output_channels,
+ chans=cfg_dict.get("recurrent_module_attention_channels", 64),
+ num_pools=cfg_dict.get("recurrent_module_attention_pooling_layers", 2),
+ drop_prob=cfg_dict.get("recurrent_module_attention_dropout", 0.0),
+ ),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ self.magnitude_input = cfg_dict.get("magnitude_input", True)
+ self.normalize_segmentation_output = cfg_dict.get("normalize_segmentation_output", True)
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ init_reconstruction_pred: torch.Tensor,
+ target_reconstruction: torch.Tensor, # pylint: disable=unused-argument
+ hx: torch.Tensor = None, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> Tuple[Union[List, torch.Tensor], torch.Tensor]:
+ """Forward pass of :class:`SERANet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ init_reconstruction_pred : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2]
+ target_reconstruction : torch.Tensor
+ Target reconstruction. Shape [batch_size, n_x, n_y, 2]
+ hx : torch.Tensor, optional
+ Initial hidden state for the RNN. Default is ``None``.
+ sigma : float, optional
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ Tuple[Union[List, torch.Tensor], torch.Tensor]
+ Tuple containing the predicted reconstruction and segmentation.
+ """
+ if self.consecutive_slices > 1:
+ batch, slices = init_reconstruction_pred.shape[:2]
+ init_reconstruction_pred = init_reconstruction_pred.reshape(
+ init_reconstruction_pred.shape[0] * init_reconstruction_pred.shape[1],
+ *init_reconstruction_pred.shape[2:],
+ )
+ y = y.reshape(y.shape[0] * y.shape[1], *y.shape[2:])
+ mask = mask.reshape(mask.shape[0] * mask.shape[1], *mask.shape[2:])
+ sensitivity_maps = sensitivity_maps.reshape(
+ sensitivity_maps.shape[0] * sensitivity_maps.shape[1],
+ *sensitivity_maps.shape[2:],
+ )
+
+ if init_reconstruction_pred.shape[-1] == 2:
+ if self.input_channels == 1:
+ init_reconstruction_pred = torch.view_as_complex(init_reconstruction_pred).unsqueeze(1)
+ if self.magnitude_input:
+ init_reconstruction_pred = torch.abs(init_reconstruction_pred)
+ elif self.input_channels == 2:
+ if self.magnitude_input:
+ raise ValueError("Magnitude input is not supported for 2-channel input.")
+ init_reconstruction_pred = init_reconstruction_pred.permute(0, 3, 1, 2)
+ else:
+ raise ValueError(f"The input channels must be either 1 or 2. Found: {self.input_channels}")
+ else:
+ if init_reconstruction_pred.dim() == 3:
+ init_reconstruction_pred = init_reconstruction_pred.unsqueeze(1)
+
+ reconstruction = self.reconstruction_module(init_reconstruction_pred, y, sensitivity_maps, mask)
+
+ if len(reconstruction) > 1:
+ pred_reconstruction = reconstruction[-2]
+ else:
+ pred_reconstruction = reconstruction[-1]
+
+ segmentation = reconstruction[-1]
+
+ if segmentation.shape[-1] == 2:
+ segmentation = torch.abs(torch.view_as_complex(segmentation))
+
+ # In case of deviating number of coils, we need to pad up to maximum number of coils == number of input \
+ # channels for the reconstruction module
+ num_coils = segmentation.shape[1]
+ if num_coils != self.segmentation_module_input_channels:
+ num_coils_to_add = self.segmentation_module_input_channels - num_coils
+ dummy_segmentation_coil_data = torch.zeros_like(
+ torch.movedim(segmentation, self.coil_dim, 0)[0]
+ ).unsqueeze(self.coil_dim)
+ dummy_coil_data = torch.zeros_like(torch.movedim(pred_reconstruction, self.coil_dim, 0)[0]).unsqueeze(
+ self.coil_dim
+ )
+ for _ in range(num_coils_to_add):
+ segmentation = torch.cat([segmentation, dummy_segmentation_coil_data], dim=self.coil_dim)
+ pred_reconstruction = torch.cat([pred_reconstruction, dummy_coil_data], dim=self.coil_dim)
+ y = torch.cat([y, dummy_coil_data], dim=self.coil_dim)
+ sensitivity_maps = torch.cat([sensitivity_maps, dummy_coil_data], dim=self.coil_dim)
+
+ segmentation = self.segmentation_module(segmentation)
+
+ pred_segmentation = self.recurrent_module(pred_reconstruction, segmentation, y, sensitivity_maps, mask)
+
+ if self.normalize_segmentation_output:
+ pred_segmentation = (pred_segmentation - pred_segmentation.min()) / (
+ pred_segmentation.max() - pred_segmentation.min()
+ )
+
+ pred_segmentation = torch.abs(pred_segmentation)
+
+ pred_reconstruction = coil_combination_method(
+ pred_reconstruction,
+ sensitivity_maps,
+ method=self.coil_combination_method,
+ dim=self.coil_dim if self.consecutive_slices == 1 else self.coil_dim - 1,
+ )
+ pred_reconstruction = torch.view_as_complex(pred_reconstruction)
+
+ if self.consecutive_slices > 1:
+ pred_reconstruction = pred_reconstruction.view([batch, slices, *pred_reconstruction.shape[1:]])
+ pred_segmentation = pred_segmentation.view([batch, slices, *pred_segmentation.shape[1:]])
+
+ return pred_reconstruction, pred_segmentation
diff --git a/atommic/collections/multitask/rs/nn/seranet_base/__init__.py b/atommic/collections/multitask/rs/nn/seranet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/seranet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/multitask/rs/nn/seranet_base/convlstm.py b/atommic/collections/multitask/rs/nn/seranet_base/convlstm.py
new file mode 100644
index 00000000..02278a26
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/seranet_base/convlstm.py
@@ -0,0 +1,247 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/ndrplz/ConvLSTM_pytorch/blob/master/convlstm.py
+
+from typing import Any, List, Optional, Tuple
+
+import torch
+from torch import Tensor, nn
+
+
+class ConvLSTMCell(nn.Module):
+ """A Convolutional Long Short-Term Memory (LSTM) cell."""
+
+ def __init__(self, input_dim: int, hidden_dim: int, kernel_size: Tuple[int, int], bias: bool = True):
+ """Inits :class:`ConvLSTMCell`.
+
+ Parameters
+ ----------
+ input_dim: int
+ Number of channels of input tensor.
+ hidden_dim: int
+ Number of channels of hidden state.
+ kernel_size: (int, int)
+ Size of the convolutional kernel.
+ bias: bool
+ Whether to add the bias. Default is ``True``.
+ """
+ super().__init__()
+
+ self.input_dim = input_dim
+ self.hidden_dim = hidden_dim
+
+ self.kernel_size = kernel_size
+ self.padding = kernel_size[0] // 2, kernel_size[1] // 2
+ self.bias = bias
+
+ self.conv = nn.Conv2d(
+ in_channels=self.input_dim + self.hidden_dim,
+ out_channels=4 * self.hidden_dim,
+ kernel_size=self.kernel_size,
+ padding=self.padding,
+ bias=self.bias,
+ )
+
+ def forward(self, input_tensor: torch.Tensor, cur_state: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Forward pass of :class:`ConvLSTMCell`.
+
+ Parameters
+ ----------
+ input_tensor: torch.Tensor
+ Input tensor. Shape [batch_size, input_dim, height, width]
+ cur_state: torch.Tensor
+ Current state of the hidden state. Shape [batch_size, hidden_dim, height, width]
+
+ Returns
+ -------
+ Tuple[torch.Tensor, torch.Tensor]
+ Tuple of the next hidden state and the cell state. Shape [batch_size, hidden_dim, height, width]
+ """
+ h_cur, c_cur = cur_state
+
+ # concatenate along channel axis
+ combined = torch.cat([input_tensor, h_cur], dim=1)
+
+ combined_conv = self.conv(combined)
+ cc_i, cc_f, cc_o, cc_g = torch.split(combined_conv, self.hidden_dim, dim=1)
+ i = torch.sigmoid(cc_i)
+ f = torch.sigmoid(cc_f)
+ o = torch.sigmoid(cc_o)
+ g = torch.tanh(cc_g)
+
+ c_next = f * c_cur + i * g
+ h_next = o * torch.tanh(c_next)
+
+ return h_next, c_next
+
+ def init_hidden(self, batch_size: int, image_size: Tuple[int, int]) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Initializes the hidden state.
+
+ Parameters
+ ----------
+ batch_size: int
+ Batch size.
+ image_size: Tuple[int, int]
+ Size of the image. Shape [height, width]
+
+ Returns
+ -------
+ Tuple[torch.Tensor, torch.Tensor]
+ Tuple of the next hidden state and the cell state. Shape [batch_size, hidden_dim, height, width]
+ """
+ height, width = image_size
+ return (
+ torch.zeros(batch_size, self.hidden_dim, height, width, device=self.conv.weight.device),
+ torch.zeros(batch_size, self.hidden_dim, height, width, device=self.conv.weight.device),
+ )
+
+
+class ConvLSTM(nn.Module):
+ """A Convolutional Long Short-Term Memory (LSTM) block."""
+
+ def __init__(
+ self,
+ input_dim: int,
+ hidden_dim: int,
+ kernel_size: Any,
+ num_layers: int,
+ batch_first: bool = False,
+ bias: bool = True,
+ return_all_layers: bool = False,
+ ):
+ """Inits :class:`ConvLSTM`.
+
+ Parameters
+ ----------
+ input_dim: int
+ Number of channels of input tensor.
+ hidden_dim: int
+ Number of channels of hidden state.
+ kernel_size: Any
+ Size of the convolutional kernel.
+ num_layers: int
+ Number of layers.
+ batch_first: bool
+ Whether the first dimension corresponds to the batch size. Default is ``False``.
+ bias: bool
+ Whether to add the bias. Default is ``True``.
+ return_all_layers: bool
+ Whether to return all layers or just the last layer. Default is ``False``.
+ """
+ super().__init__()
+
+ kernel_size = (kernel_size, kernel_size)
+
+ self._check_kernel_size_consistency(kernel_size)
+
+ # Make sure that both `kernel_size` and `hidden_dim` are lists having len == num_layers
+ kernel_size = self._extend_for_multilayer(kernel_size, num_layers)
+ hidden_dim = self._extend_for_multilayer(hidden_dim, num_layers)
+ if not len(kernel_size) == len(hidden_dim) == num_layers: # type: ignore
+ raise ValueError("Inconsistent list length.")
+
+ self.input_dim = input_dim
+ self.hidden_dim = hidden_dim
+ self.kernel_size = kernel_size
+ self.num_layers = num_layers
+ self.batch_first = batch_first
+ self.bias = bias
+ self.return_all_layers = return_all_layers
+
+ cell_list = []
+ for i in range(0, self.num_layers):
+ cur_input_dim = self.input_dim if i == 0 else self.hidden_dim[i - 1] # type: ignore
+ cell_list.append(
+ ConvLSTMCell(
+ input_dim=cur_input_dim,
+ hidden_dim=self.hidden_dim[i], # type: ignore
+ kernel_size=self.kernel_size[i],
+ bias=self.bias,
+ )
+ )
+
+ self.cell_list = nn.ModuleList(cell_list)
+
+ def forward(
+ self, input_tensor: torch.Tensor, hidden_state: Optional[List[Tuple[torch.Tensor, torch.Tensor]]] = None
+ ) -> tuple[list[Tensor], list[list[Any]]]:
+ """Forward pass of :class:`ConvLSTM`.
+
+ Parameters
+ ----------
+ input_tensor : torch.Tensor
+ Input tensor of shape [batch_size, seq_len, channels, height, width] if ``batch_first`` is ``False``.
+ Otherwise, the shape should be [seq_len, batch_size, channels, height, width].
+ hidden_state : Optional[List[Tuple[torch.Tensor, torch.Tensor]]]
+ List of tuples of the hidden state and the cell state for each layer. The shape of each tensor should be
+ [batch_size, hidden_dim, height, width]. If ``None``, the hidden state will be initialized to zero.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, List[Tuple[torch.Tensor, torch.Tensor]]]
+ Tuple of the output tensor and the list of the hidden state and the cell state for each layer. The shape
+ of the output tensor is [batch_size, seq_len, hidden_dim, height, width] if ``batch_first`` is ``False``.
+ Otherwise, the shape should be [seq_len, batch_size, hidden_dim, height, width]. The shape of each tensor
+ in the list is [batch_size, hidden_dim, height, width].
+ """
+ if not self.batch_first:
+ # (t, b, c, h, w) -> (b, t, c, h, w)
+ input_tensor = input_tensor.permute(1, 0, 2, 3, 4)
+
+ b, _, _, h, w = input_tensor.size()
+
+ # Implement stateful ConvLSTM
+ if hidden_state is not None:
+ raise NotImplementedError()
+
+ # Since the init is done in forward. Can send image size here
+ hidden_state = self._init_hidden(batch_size=b, image_size=(h, w))
+
+ layer_output_list = []
+ last_state_list = []
+
+ seq_len = input_tensor.size(1)
+ cur_layer_input = input_tensor
+
+ for layer_idx in range(self.num_layers):
+ h, c = hidden_state[layer_idx]
+ output_inner = []
+ for t in range(seq_len):
+ h, c = self.cell_list[layer_idx](input_tensor=cur_layer_input[:, t, :, :, :], cur_state=[h, c])
+ output_inner.append(h)
+
+ layer_output = torch.stack(output_inner, dim=1)
+ cur_layer_input = layer_output
+
+ layer_output_list.append(layer_output)
+ last_state_list.append([h, c])
+
+ if not self.return_all_layers:
+ layer_output_list = layer_output_list[-1:]
+ last_state_list = last_state_list[-1:]
+
+ return layer_output_list, last_state_list
+
+ def _init_hidden(self, batch_size, image_size):
+ """Initialize the hidden state."""
+ init_states = []
+ for i in range(self.num_layers):
+ init_states.append(self.cell_list[i].init_hidden(batch_size, image_size))
+ return init_states
+
+ @staticmethod
+ def _check_kernel_size_consistency(kernel_size):
+ """Check the kernel size consistency."""
+ if not (
+ isinstance(kernel_size, tuple)
+ or (isinstance(kernel_size, list) and all(isinstance(elem, tuple) for elem in kernel_size))
+ ):
+ raise ValueError("`kernel_size` must be tuple or list of tuples")
+
+ @staticmethod
+ def _extend_for_multilayer(param, num_layers):
+ """Extend parameter for multilayer."""
+ if not isinstance(param, list):
+ param = [param] * num_layers
+ return param
diff --git a/atommic/collections/multitask/rs/nn/seranet_base/convlstm_unet.py b/atommic/collections/multitask/rs/nn/seranet_base/convlstm_unet.py
new file mode 100644
index 00000000..ac134241
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/seranet_base/convlstm_unet.py
@@ -0,0 +1,148 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import math
+from typing import List, Tuple
+
+import torch
+
+from atommic.collections.multitask.rs.nn.seranet_base.convlstm import ConvLSTM
+from atommic.collections.reconstruction.nn.unet_base.unet_block import Unet
+
+
+class ConvLSTMNormUnet(torch.nn.Module):
+ """Normalized U-Net with additional Convolutional LSTM input layer model.
+
+ This is the same as a regular U-Net, but with normalization applied to the input before the U-Net. This keeps the
+ values more numerically stable during training.
+ """
+
+ def __init__(
+ self,
+ chans: int,
+ num_pools: int,
+ in_chans: int = 2,
+ out_chans: int = 2,
+ drop_prob: float = 0.0,
+ padding_size: int = 15,
+ normalize: bool = True,
+ norm_groups: int = 2,
+ ):
+ """Inits :class:`ConvLSTMNormUnet`.
+
+ Parameters
+ ----------
+ chans : int
+ Number of output channels of the first convolution layer.
+ num_pools : int
+ Number of down-sampling and up-sampling layers.
+ in_chans : int
+ Number of channels in the input to the U-Net model. Default is ``2``.
+ out_chans : int
+ Number of channels in the output to the U-Net model. Default is ``2``.
+ drop_prob : float
+ Dropout probability. Default is ``0.0``.
+ padding_size: int
+ Size of the padding. Default is ``15``.
+ normalize: bool
+ Whether to normalize the input. Default is ``True``.
+ norm_groups: int
+ Number of groups to use for group normalization. Default is ``2``.
+ """
+ super().__init__()
+ self.convlstm = ConvLSTM(in_chans, chans, kernel_size=3, num_layers=1)
+ self.unet = Unet(
+ in_chans=chans, out_chans=out_chans, chans=chans, num_pool_layers=num_pools, drop_prob=drop_prob
+ )
+ self.padding_size = padding_size
+ self.normalize = normalize
+ self.norm_groups = norm_groups
+
+ @staticmethod
+ def complex_to_chan_dim(x: torch.Tensor) -> torch.Tensor:
+ """Convert the last dimension of the input to complex."""
+ b, c, h, w, two = x.shape
+ if two != 2:
+ raise AssertionError
+ return x.permute(0, 4, 1, 2, 3).reshape(b, 2 * c, h, w)
+
+ @staticmethod
+ def chan_complex_to_last_dim(x: torch.Tensor) -> torch.Tensor:
+ """Convert the last dimension of the input to complex."""
+ b, c2, h, w = x.shape
+ if c2 % 2 != 0:
+ raise AssertionError
+ c = torch.div(c2, 2, rounding_mode="trunc")
+ return x.view(b, 2, c, h, w).permute(0, 2, 3, 4, 1).contiguous()
+
+ def norm(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Normalize the input."""
+ # group norm
+ b, c, h, w = x.shape
+
+ x = x.reshape(b, self.norm_groups, -1)
+
+ mean = x.mean(-1, keepdim=True)
+ std = x.std(-1, keepdim=True)
+
+ x = (x - mean) / std
+
+ x = x.reshape(b, c, h, w)
+
+ return x, mean, std
+
+ def unnorm(self, x: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) -> torch.Tensor:
+ """Unnormalize the input."""
+ b, c, h, w = x.shape
+ input_data = x.reshape(b, self.norm_groups, -1)
+ return (input_data * std + mean).reshape(b, c, h, w)
+
+ def pad(self, x: torch.Tensor) -> Tuple[torch.Tensor, Tuple[List[int], List[int], int, int]]:
+ """Pad the input with zeros to make it square."""
+ _, _, h, w = x.shape
+ w_mult = ((w - 1) | self.padding_size) + 1
+ h_mult = ((h - 1) | self.padding_size) + 1
+ w_pad = [math.floor((w_mult - w) / 2), math.ceil((w_mult - w) / 2)]
+ h_pad = [math.floor((h_mult - h) / 2), math.ceil((h_mult - h) / 2)]
+ x = torch.nn.functional.pad(x, w_pad + h_pad)
+
+ return x, (h_pad, w_pad, h_mult, w_mult)
+
+ @staticmethod
+ def unpad(x: torch.Tensor, h_pad: List[int], w_pad: List[int], h_mult: int, w_mult: int) -> torch.Tensor:
+ """Unpad the input."""
+ return x[..., h_pad[0] : h_mult - h_pad[1], w_pad[0] : w_mult - w_pad[1]]
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`ConvLSTMNormUnet`."""
+ iscomplex = False
+ if x.shape[-1] == 2:
+ x = self.complex_to_chan_dim(x)
+ iscomplex = True
+
+ mean = 1.0
+ std = 1.0
+
+ if self.normalize:
+ x, mean, std = self.norm(x)
+
+ x, pad_sizes = self.pad(x)
+
+ x, _ = self.convlstm(x.unsqueeze(0))
+ x = x[0]
+ if x.shape[0] == 1:
+ x = x.squeeze(0)
+ elif x.shape[1] == 1:
+ x = x.squeeze(1)
+ else:
+ raise AssertionError
+ x = self.unet(x)
+ x = self.unpad(x, *pad_sizes)
+
+ if self.normalize:
+ x = self.unnorm(x, mean, std)
+
+ if iscomplex:
+ x = self.chan_complex_to_last_dim(x)
+
+ return x
diff --git a/atommic/collections/multitask/rs/nn/seranet_base/seranet_block.py b/atommic/collections/multitask/rs/nn/seranet_base/seranet_block.py
new file mode 100644
index 00000000..0cecb5af
--- /dev/null
+++ b/atommic/collections/multitask/rs/nn/seranet_base/seranet_block.py
@@ -0,0 +1,349 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Optional, Tuple
+
+import torch
+from torch import nn
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import coil_combination_method
+
+
+class SERANetDC(nn.Module):
+ """SERANet Data consistency block, as presented in [Huang2019]_.
+
+ References
+ ----------
+ .. [Huang2019] Huang, Q., Chen, X., Metaxas, D., Nadar, M.S. (2019). Brain Segmentation from k-Space with
+ End-to-End Recurrent Attention Network. In: , et al. Medical Image Computing and Computer Assisted
+ Intervention โ MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham.
+ https://doi.org/10.1007/978-3-030-32248-9_31
+ """
+
+ def __init__(self, fft_centered: bool, fft_normalization: str, spatial_dims: Tuple[int, ...]):
+ """Inits :class:`SERANetDC`.
+
+ Parameters
+ ----------
+ fft_centered: bool
+ Whether to center the fft.
+ fft_normalization: str
+ Normalization to apply to the fft.
+ spatial_dims: Tuple[int, ...]
+ Spatial dimensions.
+ """
+ super().__init__()
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+ self.dc_weight = nn.Parameter(torch.ones(1))
+
+ def forward(
+ self,
+ prediction: torch.Tensor,
+ prev_prediction: torch.Tensor,
+ reference_kspace: torch.Tensor,
+ mask: torch.Tensor,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`SERANetDC`.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ Prediction. Shape: (batch, channels, height, width, complex)
+ prev_prediction : torch.Tensor
+ Previous prediction. Shape: (batch, channels, height, width, complex)
+ reference_kspace : torch.Tensor
+ Reference k-space. Shape: (batch, channels, height, width, complex)
+ mask : torch.Tensor
+ Subsampling mask. Shape: (batch, channels, height, width, 1)
+
+ Returns
+ -------
+ torch.Tensor
+ Data consistency k-space. Shape: (batch, channels, height, width, complex)
+ """
+ prediction = fft2(prediction.float(), self.fft_centered, self.fft_normalization, self.spatial_dims).to(
+ prediction
+ )
+ if prediction.dim() < reference_kspace.dim():
+ prediction = prediction.unsqueeze(1)
+ zero = torch.zeros_like(prediction)
+ soft_dc = torch.where(mask.bool(), prediction - reference_kspace, zero) * self.dc_weight
+ return prev_prediction - soft_dc - prediction
+
+
+class SERANetReconstructionBlock(torch.nn.Module):
+ """Reconstruction Model block for End-to-End Recurrent Attention Network, as presented in [Huang2019]_.
+
+ This model applies a combination of soft data consistency with the input model as a regularizer. A series of these
+ blocks can be stacked to form the full variational network.
+
+ References
+ ----------
+ .. [Huang2019] Huang, Q., Chen, X., Metaxas, D., Nadar, M.S. (2019). Brain Segmentation from k-Space with
+ End-to-End Recurrent Attention Network. In: , et al. Medical Image Computing and Computer Assisted
+ Intervention โ MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham.
+ https://doi.org/10.1007/978-3-030-32248-9_31
+ """
+
+ def __init__(
+ self,
+ num_reconstruction_blocks: int,
+ reconstruction_model: torch.nn.Module,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ coil_combination_method: str = "SENSE",
+ ):
+ """Inits :class:`SERANetReconstructionBlock`.
+
+ Parameters
+ ----------
+ num_reconstruction_blocks : int
+ Number of reconstruction blocks.
+ reconstruction_model : torch.nn.Module
+ Reconstruction model.
+ fft_centered : bool, optional
+ Whether to center the fft. Default is ``False``.
+ fft_normalization : str, optional
+ The normalization of the fft. Default is ``"backward"``.
+ spatial_dims : Tuple[int, int], optional
+ The spatial dimensions of the data. Default is ``None``.
+ coil_dim : int, optional
+ The dimension of the coil dimension. Default is ``1``.
+ coil_combination_method : str, optional
+ The coil combination method. Default is ``"SENSE"``.
+ """
+ super().__init__()
+ self.reconstruction_module = torch.nn.ModuleList(
+ [reconstruction_model for _ in range(num_reconstruction_blocks)]
+ )
+ self.model_name = self.reconstruction_module[0].__class__.__name__.lower()
+ if self.model_name == "modulelist":
+ self.model_name = self.reconstruction_module[0][0].__class__.__name__.lower()
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+ self.coil_combination_method = coil_combination_method
+
+ self.reconstruction_module_dc = torch.nn.ModuleList(
+ [
+ SERANetDC(
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims, # type: ignore
+ )
+ for _ in range(num_reconstruction_blocks)
+ ]
+ )
+
+ def forward(
+ self,
+ prediction: torch.Tensor,
+ ref_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`SERANetReconstructionBlock`.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ Prediction. Shape: [batch, channels, height, width, 2]
+ ref_kspace : torch.Tensor
+ Reference k-space. Shape: [batch, channels, height, width, 2]
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps. Shape: [batch, coils, height, width, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape: [batch, 1, height, width, 1]
+
+ Returns
+ -------
+ torch.Tensor
+ Reconstruction. Shape: [batch, height, width, 2]
+ """
+ pred_reconstruction = []
+ prev_reconstruction = ref_kspace.clone()
+ for recon_block, dc_block in zip(self.reconstruction_module, self.reconstruction_module_dc):
+ reconstruction = self.step(recon_block, prediction, ref_kspace, sensitivity_maps, mask)
+ reconstruction = dc_block(reconstruction, prev_reconstruction, ref_kspace, mask)
+ prev_reconstruction = reconstruction
+ reconstruction = ifft2(reconstruction, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ pred_reconstruction.append(reconstruction)
+ return pred_reconstruction
+
+ def step(
+ self,
+ block: torch.nn.Module,
+ pred: torch.Tensor,
+ ref_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ ) -> torch.Tensor:
+ """Step of the reconstruction block.
+
+ Parameters
+ ----------
+ block : torch.nn.Module
+ The block to apply.
+ pred : torch.Tensor
+ Prediction. Shape: [batch, height, width, 2]
+ ref_kspace : torch.Tensor
+ Reference k-space. Shape: [batch, channels, height, width, 2]
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps. Shape: [batch, coils, height, width, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape: [batch, 1, height, width, 1]
+
+ Returns
+ -------
+ torch.Tensor
+ Reconstruction. Shape: [batch, height, width, 2]
+ """
+ if self.model_name == "unet":
+ reconstruction = block(pred).permute(0, 2, 3, 1)
+ reconstruction = torch.view_as_real(
+ reconstruction[..., 0].float() + 1j * reconstruction[..., 1].float()
+ ).to(reconstruction)
+ elif "cascadenet" in self.model_name:
+ reconstruction = ref_kspace.clone()
+ for cascade in block:
+ reconstruction = cascade(reconstruction, ref_kspace, sensitivity_maps, mask)
+ reconstruction = torch.view_as_complex(
+ coil_combination_method(
+ ifft2(
+ reconstruction,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ sensitivity_maps,
+ method=self.coil_combination_method,
+ dim=self.coil_dim,
+ )
+ )
+ else:
+ reconstruction = pred.clone()
+
+ return reconstruction
+
+
+class SERANetRecurrentBlock(torch.nn.Module):
+ """RecurrentModel block for End-to-End Recurrent Attention Network, as presented in [1]_.
+
+ This model applies a combination of soft data consistency with the input model as a regularizer.
+ A series of these blocks can be stacked to form the full variational network.
+
+ References
+ ----------
+
+ .. [1] Pramanik A, Wu X, Jacob M. Joint calibrationless reconstruction and segmentation of parallel MRI. arXiv
+ preprint arXiv:2105.09220. 2021 May 19.
+ """
+
+ def __init__(
+ self,
+ num_iterations: int,
+ attention_model: torch.nn.Module,
+ unet_model: torch.nn.Module,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ ):
+ """Inits :class:`SERANetRecurrentBlock`.
+
+ Parameters
+ ----------
+ num_iterations : int
+ Number of iterations for the recurrent block.
+ attention_model : torch.nn.Module
+ Attention model.
+ unet_model : torch.nn.Module
+ Unet model.
+ fft_centered : bool, optional
+ Whether to center the fft. Default is ``False``.
+ fft_normalization : str, optional
+ The normalization of the fft. Default is ``"backward"``.
+ spatial_dims : Tuple[int, int], optional
+ The spatial dimensions of the data. Default is ``None``.
+ coil_dim : int, optional
+ The dimension of the coil dimension. Default is ``1``.
+ """
+ super().__init__()
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+
+ self.num_iterations = num_iterations
+ self.recurrent_module_unet = unet_model
+ self.recurrent_module_attention = attention_model
+ self.recurrent_module_dc = SERANetDC(
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims, # type: ignore
+ )
+
+ def forward(
+ self,
+ pred_reconstruction: torch.Tensor,
+ pred_segmentation: torch.Tensor,
+ ref_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor, # pylint: disable=unused-argument
+ mask: torch.Tensor,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`SERANetRecurrentBlock`.
+
+ Parameters
+ ----------
+ pred_reconstruction : torch.Tensor
+ Prediction. Shape: [batch, height, width, 2]
+ pred_segmentation : torch.Tensor
+ Prediction. Shape: [batch, num_classes, height, width]
+ ref_kspace : torch.Tensor
+ Reference k-space. Shape: [batch, channels, height, width, 2]
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps. Shape: [batch, coils, height, width, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape: [batch, 1, height, width, 1]
+
+ Returns
+ -------
+ torch.Tensor
+ Reconstruction. Shape: [batch, height, width, 2]
+ """
+ attention_map = pred_segmentation.clone() # TODO: remove this
+ prev_prediction = ref_kspace.clone()
+ for _ in range(self.num_iterations):
+ attention_map = self.chan_complex_to_last_dim(
+ self.recurrent_module_attention(
+ self.complex_to_chan_dim(pred_reconstruction), attention_map * pred_segmentation
+ )
+ )
+ attention_map = self.recurrent_module_dc(attention_map, prev_prediction, ref_kspace, mask)
+ prev_prediction = attention_map
+ attention_map = self.recurrent_module_unet(self.complex_to_chan_dim(attention_map))
+ return attention_map
+
+ @staticmethod
+ def complex_to_chan_dim(x: torch.Tensor) -> torch.Tensor:
+ """Convert the last dimension of the input to complex."""
+ b, c, h, w, two = x.shape
+ if two != 2:
+ raise AssertionError
+ return x.permute(0, 4, 1, 2, 3).reshape(b, 2 * c, h, w)
+
+ @staticmethod
+ def chan_complex_to_last_dim(x: torch.Tensor) -> torch.Tensor:
+ """Convert the last dimension of the input to complex."""
+ b, c2, h, w = x.shape
+ if c2 % 2 != 0:
+ raise AssertionError
+ c = torch.div(c2, 2, rounding_mode="trunc")
+ return x.view(b, 2, c, h, w).permute(0, 2, 3, 4, 1).contiguous()
diff --git a/atommic/collections/multitask/rs/parts/__init__.py b/atommic/collections/multitask/rs/parts/__init__.py
new file mode 100644
index 00000000..acc43ef9
--- /dev/null
+++ b/atommic/collections/multitask/rs/parts/__init__.py
@@ -0,0 +1,4 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.multitask.rs.parts.transforms import RSMRIDataTransforms # noqa: F401
diff --git a/atommic/collections/multitask/rs/parts/transforms.py b/atommic/collections/multitask/rs/parts/transforms.py
new file mode 100644
index 00000000..d10abda9
--- /dev/null
+++ b/atommic/collections/multitask/rs/parts/transforms.py
@@ -0,0 +1,1264 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
+
+import numpy as np
+import torch
+
+from atommic.collections.common.parts.fft import ifft2
+from atommic.collections.common.parts.transforms import (
+ N2R,
+ SSDU,
+ Composer,
+ Cropper,
+ EstimateCoilSensitivityMaps,
+ GeometricDecompositionCoilCompression,
+ Masker,
+ NoisePreWhitening,
+ Normalizer,
+ ZeroFillingPadding,
+)
+from atommic.collections.common.parts.utils import add_coil_dim_if_singlecoil
+from atommic.collections.common.parts.utils import coil_combination_method as coil_combination_method_func
+from atommic.collections.common.parts.utils import is_none, to_tensor
+from atommic.collections.motioncorrection.parts.motionsimulation import MotionSimulation
+
+__all__ = ["RSMRIDataTransforms"]
+
+
+class RSMRIDataTransforms:
+ """Data transforms for accelerated-MRI reconstruction and MRI segmentation.
+
+ Returns
+ -------
+ RSMRIDataTransforms
+ Preprocessed data for accelerated-MRI reconstruction and MRI segmentation.
+ """
+
+ def __init__(
+ self,
+ complex_data: bool = True,
+ dataset_format: str = None,
+ apply_prewhitening: bool = False,
+ find_patch_size: bool = True,
+ prewhitening_scale_factor: float = 1.0,
+ prewhitening_patch_start: int = 10,
+ prewhitening_patch_length: int = 30,
+ apply_gcc: bool = False,
+ gcc_virtual_coils: int = 10,
+ gcc_calib_lines: int = 24,
+ gcc_align_data: bool = True,
+ apply_random_motion: bool = False,
+ random_motion_type: str = "gaussian",
+ random_motion_percentage: Sequence[int] = (10, 10),
+ random_motion_angle: int = 10,
+ random_motion_translation: int = 10,
+ random_motion_center_percentage: float = 0.02,
+ random_motion_num_segments: int = 8,
+ random_motion_random_num_segments: bool = True,
+ random_motion_non_uniform: bool = False,
+ estimate_coil_sensitivity_maps: bool = False,
+ coil_sensitivity_maps_type: str = "ESPIRiT",
+ coil_sensitivity_maps_gaussian_sigma: float = 0.0,
+ coil_sensitivity_maps_espirit_threshold: float = 0.05,
+ coil_sensitivity_maps_espirit_kernel_size: int = 6,
+ coil_sensitivity_maps_espirit_crop: float = 0.95,
+ coil_sensitivity_maps_espirit_max_iters: int = 30,
+ coil_combination_method: str = "SENSE",
+ dimensionality: int = 2,
+ mask_func: Optional[List] = None,
+ shift_mask: bool = False,
+ mask_center_scale: Optional[float] = 0.02,
+ partial_fourier_percentage: float = 0.0,
+ remask: bool = False,
+ ssdu: bool = False,
+ ssdu_mask_type: str = "Gaussian",
+ ssdu_rho: float = 0.4,
+ ssdu_acs_block_size: Sequence[int] = (4, 4),
+ ssdu_gaussian_std_scaling_factor: float = 4.0,
+ ssdu_outer_kspace_fraction: float = 0.0,
+ ssdu_export_and_reuse_masks: bool = False,
+ n2r: bool = False,
+ n2r_supervised_rate: float = 0.0,
+ n2r_probability: float = 0.0,
+ n2r_std_devs: Tuple[float, float] = None,
+ n2r_rhos: Tuple[float, float] = None,
+ n2r_use_mask: bool = False,
+ unsupervised_masked_target: bool = False,
+ crop_size: Optional[Tuple[int, int]] = None,
+ kspace_crop: bool = False,
+ crop_before_masking: bool = True,
+ kspace_zero_filling_size: Optional[Tuple] = None,
+ normalize_inputs: bool = True,
+ normalization_type: str = "max",
+ kspace_normalization: bool = False,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+ coil_dim: int = 0,
+ consecutive_slices: int = 1, # pylint: disable=unused-argument
+ use_seed: bool = True,
+ ):
+ """Inits :class:`RSMRIDataTransforms`.
+
+ Parameters
+ ----------
+ complex_data : bool, optional
+ Whether to use complex data. If ``False`` the data are assumed to be magnitude only. Default is ``True``.
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ Default is ``None``.
+ apply_prewhitening : bool, optional
+ Apply prewhitening. If ``True`` then the prewhitening arguments are used. Default is ``False``.
+ find_patch_size : bool, optional
+ Find optimal patch size (automatically) to calculate psi. If False, patch_size must be defined.
+ Default is ``True``.
+ prewhitening_scale_factor : float, optional
+ Prewhitening scale factor. Default is ``1.0``.
+ prewhitening_patch_start : int, optional
+ Prewhitening patch start. Default is ``10``.
+ prewhitening_patch_length : int, optional
+ Prewhitening patch length. Default is ``30``.
+ apply_gcc : bool, optional
+ Apply Geometric Decomposition Coil Compression. If ``True`` then the GCC arguments are used.
+ Default is ``False``.
+ gcc_virtual_coils : int, optional
+ GCC virtual coils. Default is ``10``.
+ gcc_calib_lines : int, optional
+ GCC calibration lines. Default is ``24``.
+ gcc_align_data : bool, optional
+ GCC align data. Default is ``True``.
+ apply_random_motion : bool, optional
+ Simulate random motion in k-space. Default is ``False``.
+ random_motion_type : str, optional
+ Random motion type. It can be one of the following: ``piecewise_transient``, ``piecewise_constant``,
+ ``gaussian``. Default is ``gaussian``.
+ random_motion_percentage : Sequence[int], optional
+ Random motion percentage. For example, 10%-20% motion can be defined as ``(10, 20)``.
+ Default is ``(10, 10)``.
+ random_motion_angle : float, optional
+ Random motion angle. Default is ``10.0``.
+ random_motion_translation : float, optional
+ Random motion translation. Default is ``10.0``.
+ random_motion_center_percentage : float, optional
+ Random motion center percentage. Default is ``0.0``.
+ random_motion_num_segments : int, optional
+ Random motion number of segments to partition the k-space. Default is ``8``.
+ random_motion_random_num_segments : bool, optional
+ Whether to randomly generate the number of segments. Default is ``True``.
+ random_motion_non_uniform : bool, optional
+ Random motion non-uniform sampling. Default is ``False``.
+ estimate_coil_sensitivity_maps : bool, optional
+ Automatically estimate coil sensitivity maps. Default is ``False``. If ``True`` then the coil sensitivity
+ maps arguments are used. Note that this is different from the ``estimate_coil_sensitivity_maps_with_nn``
+ argument, which uses a neural network to estimate the coil sensitivity maps. The
+ ``estimate_coil_sensitivity_maps`` estimates the coil sensitivity maps with methods such as ``ESPIRiT``,
+ ``RSS`` or ``UNit``. ``ESPIRiT`` is the ``Eigenvalue to Self-Consistent Parallel Imaging Reconstruction
+ Technique`` method. ``RSS`` is the ``Root Sum of Squares`` method. ``UNit`` returns a uniform coil
+ sensitivity map.
+ coil_sensitivity_maps_type : str, optional
+ Coil sensitivity maps type. It can be one of the following: ``ESPIRiT``, ``RSS`` or ``UNit``. Default is
+ ``ESPIRiT``.
+ coil_sensitivity_maps_gaussian_sigma : float, optional
+ Coil sensitivity maps Gaussian sigma. Default is ``0.0``.
+ coil_sensitivity_maps_espirit_threshold : float, optional
+ Coil sensitivity maps ESPRIT threshold. Default is ``0.05``.
+ coil_sensitivity_maps_espirit_kernel_size : int, optional
+ Coil sensitivity maps ESPRIT kernel size. Default is ``6``.
+ coil_sensitivity_maps_espirit_crop : float, optional
+ Coil sensitivity maps ESPRIT crop. Default is ``0.95``.
+ coil_sensitivity_maps_espirit_max_iters : int, optional
+ Coil sensitivity maps ESPRIT max iterations. Default is ``30``.
+ coil_combination_method : str, optional
+ Coil combination method. Default is ``"SENSE"``.
+ dimensionality : int, optional
+ Dimensionality. Default is ``2``.
+ mask_func : Optional[List["MaskFunc"]], optional
+ Mask function to retrospectively undersample the k-space. Default is ``None``.
+ shift_mask : bool, optional
+ Whether to shift the mask. This needs to be set alongside with the ``fft_centered`` argument.
+ Default is ``False``.
+ mask_center_scale : Optional[float], optional
+ Center scale of the mask. This defines how much densely sampled will be the center of k-space.
+ Default is ``0.02``.
+ partial_fourier_percentage : float, optional
+ Whether to simulate a half scan. Default is ``0.0``.
+ remask : bool, optional
+ Use the same mask. Default is ``False``.
+ ssdu : bool, optional
+ Whether to apply Self-Supervised Data Undersampling (SSDU) masks. Default is ``False``.
+ ssdu_mask_type: str, optional
+ Mask type. It can be one of the following:
+ - "Gaussian": Gaussian sampling.
+ - "Uniform": Uniform sampling.
+ Default is "Gaussian".
+ ssdu_rho: float, optional
+ Split ratio for training and loss masks. Default is ``0.4``.
+ ssdu_acs_block_size: tuple, optional
+ Keeps a small acs region fully-sampled for training masks, if there is no acs region. The small acs block
+ should be set to zero. Default is ``(4, 4)``.
+ ssdu_gaussian_std_scaling_factor: float, optional
+ Scaling factor for standard deviation of the Gaussian noise. If Uniform is select this factor is ignored.
+ Default is ``4.0``.
+ ssdu_outer_kspace_fraction: float, optional
+ Fraction of the outer k-space to be kept/unmasked. Default is ``0.0``.
+ ssdu_export_and_reuse_masks: bool, optional
+ Whether to export and reuse the masks. Default is ``False``.
+ n2r : bool, optional
+ Whether to apply Noise to Reconstruction (N2R) masks. Default is ``False``.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ n2r_probability : float, optional
+ Probability of applying N2R. Default is ``0.0``.
+ n2r_std_devs : Tuple[float, float], optional
+ Standard deviations for the noise. Default is ``(0.0, 0.0)``.
+ n2r_rhos : Tuple[float, float], optional
+ Rho values for the noise. Default is ``(0.0, 0.0)``.
+ n2r_use_mask : bool, optional
+ Whether to use a mask for N2R. Default is ``False``.
+ unsupervised_masked_target : bool, optional
+ Whether to use the masked initial estimation for unsupervised learning. Default is ``False``.
+ crop_size : Optional[Tuple[int, int]], optional
+ Center crop size. It applies cropping in image space. Default is ``None``.
+ kspace_crop : bool, optional
+ Whether to crop in k-space. Default is ``False``.
+ crop_before_masking : bool, optional
+ Whether to crop before masking. Default is ``True``.
+ kspace_zero_filling_size : Optional[Tuple], optional
+ Whether to apply zero filling in k-space. Default is ``None``.
+ normalize_inputs : bool, optional
+ Whether to normalize the inputs. Default is ``True``.
+ normalization_type : str, optional
+ Normalization type. Can be ``max`` or ``mean`` or ``minmax``. Default is ``max``.
+ kspace_normalization : bool, optional
+ Whether to normalize the k-space. Default is ``False``.
+ fft_centered : bool, optional
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization : str, optional
+ FFT normalization. Default is ``"backward"``.
+ spatial_dims : Sequence[int], optional
+ Spatial dimensions. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``0``, meaning that the coil dimension is the first dimension before applying
+ batch.
+ consecutive_slices : int, optional
+ Consecutive slices. Default is ``1``.
+ use_seed : bool, optional
+ Whether to use seed. Default is ``True``.
+ """
+ self.complex_data = complex_data
+
+ self.dataset_format = dataset_format
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim - 1 if dimensionality == 2 and not is_none(coil_dim) else coil_dim
+
+ if not self.complex_data:
+ if not is_none(coil_combination_method):
+ raise ValueError("Coil combination method for non-complex data should be None.")
+ if not is_none(mask_func):
+ raise ValueError("Mask function for non-complex data should be None.")
+ self.kspace_crop = kspace_crop
+ if self.kspace_crop:
+ raise ValueError("K-space crop for non-complex data should be False.")
+ if not is_none(kspace_zero_filling_size):
+ raise ValueError("K-space zero filling size for non-complex data should be None.")
+ if not is_none(coil_dim):
+ raise ValueError("Coil dimension for non-complex data should be None.")
+ if apply_prewhitening:
+ raise ValueError("Prewhitening for non-complex data cannot be applied.")
+ if apply_gcc:
+ raise ValueError("GCC for non-complex data cannot be applied.")
+ if apply_random_motion:
+ raise ValueError("Random motion for non-complex data cannot be applied.")
+ else:
+ self.prewhitening = (
+ NoisePreWhitening(
+ find_patch_size=find_patch_size,
+ patch_size=[
+ prewhitening_patch_start,
+ prewhitening_patch_length + prewhitening_patch_start,
+ prewhitening_patch_start,
+ prewhitening_patch_length + prewhitening_patch_start,
+ ],
+ scale_factor=prewhitening_scale_factor,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if apply_prewhitening
+ else None
+ )
+
+ self.gcc = (
+ GeometricDecompositionCoilCompression(
+ virtual_coils=gcc_virtual_coils,
+ calib_lines=gcc_calib_lines,
+ align_data=gcc_align_data,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if apply_gcc
+ else None
+ )
+
+ self.random_motion = (
+ MotionSimulation(
+ motion_type=random_motion_type,
+ angle=random_motion_angle,
+ translation=random_motion_translation,
+ center_percentage=random_motion_center_percentage,
+ motion_percentage=random_motion_percentage,
+ num_segments=random_motion_num_segments,
+ random_num_segments=random_motion_random_num_segments,
+ non_uniform=random_motion_non_uniform,
+ spatial_dims=self.spatial_dims,
+ )
+ if apply_random_motion
+ else None
+ )
+
+ self.coil_sensitivity_maps_estimator = (
+ EstimateCoilSensitivityMaps(
+ coil_sensitivity_maps_type=coil_sensitivity_maps_type.lower(),
+ gaussian_sigma=coil_sensitivity_maps_gaussian_sigma,
+ espirit_threshold=coil_sensitivity_maps_espirit_threshold,
+ espirit_kernel_size=coil_sensitivity_maps_espirit_kernel_size,
+ espirit_crop=coil_sensitivity_maps_espirit_crop,
+ espirit_max_iters=coil_sensitivity_maps_espirit_max_iters,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ )
+ if estimate_coil_sensitivity_maps
+ else None
+ )
+
+ self.kspace_zero_filling = (
+ ZeroFillingPadding(
+ zero_filling_size=kspace_zero_filling_size, # type: ignore
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if not is_none(kspace_zero_filling_size)
+ else None
+ )
+
+ self.shift_mask = shift_mask
+ self.masking = Masker(
+ mask_func=mask_func, # type: ignore
+ spatial_dims=self.spatial_dims,
+ shift_mask=shift_mask,
+ partial_fourier_percentage=partial_fourier_percentage,
+ center_scale=mask_center_scale, # type: ignore
+ dimensionality=dimensionality,
+ remask=remask,
+ dataset_format=self.dataset_format,
+ )
+
+ self.ssdu = ssdu
+ self.ssdu_masking = (
+ SSDU(
+ mask_type=ssdu_mask_type,
+ rho=ssdu_rho,
+ acs_block_size=ssdu_acs_block_size,
+ gaussian_std_scaling_factor=ssdu_gaussian_std_scaling_factor,
+ outer_kspace_fraction=ssdu_outer_kspace_fraction,
+ export_and_reuse_masks=ssdu_export_and_reuse_masks,
+ )
+ if self.ssdu
+ else None
+ )
+
+ self.n2r = n2r
+ self.n2r_supervised_rate = n2r_supervised_rate
+ self.n2r_masking = (
+ N2R(
+ probability=n2r_probability,
+ std_devs=n2r_std_devs, # type: ignore
+ rhos=n2r_rhos, # type: ignore
+ use_mask=n2r_use_mask,
+ )
+ if self.n2r
+ else None
+ )
+
+ self.unsupervised_masked_target = unsupervised_masked_target
+
+ self.kspace_crop = kspace_crop
+ self.crop_before_masking = crop_before_masking
+
+ self.coil_combination_method = coil_combination_method
+
+ self.prewhitening = Composer([self.prewhitening]) # type: ignore
+ self.coils_shape_transforms = Composer(
+ [
+ self.gcc, # type: ignore
+ self.kspace_zero_filling, # type: ignore
+ ]
+ )
+ self.random_motion = Composer([self.random_motion]) # type: ignore
+
+ self.cropping = (
+ Cropper(
+ cropping_size=crop_size, # type: ignore
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if not is_none(crop_size)
+ else None
+ )
+
+ self.normalization_type = normalization_type
+ self.normalization = (
+ Normalizer(
+ normalization_type=self.normalization_type,
+ kspace_normalization=kspace_normalization,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if normalize_inputs
+ else None
+ )
+
+ self.crop_normalize = Composer(
+ [
+ self.cropping, # type: ignore
+ self.normalization, # type: ignore
+ ]
+ )
+
+ self.cropping = Composer([self.cropping]) # type: ignore
+ self.normalization = Composer([self.normalization]) # type: ignore
+
+ self.use_seed = use_seed
+
+ def __call__(
+ self,
+ kspace: np.ndarray,
+ imspace: np.ndarray,
+ sensitivity_map: np.ndarray,
+ mask: np.ndarray,
+ initial_prediction_reconstruction: np.ndarray,
+ segmentation_labels: np.ndarray,
+ attrs: Dict,
+ fname: str,
+ slice_idx: int,
+ ) -> Tuple[
+ Union[torch.Tensor, List[torch.Tensor]],
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.Tensor,
+ Union[List[torch.Tensor], torch.Tensor],
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.tensor,
+ torch.tensor,
+ str,
+ int,
+ Union[List[Union[float, torch.Tensor, Any]]],
+ Dict,
+ ]:
+ """Calls :class:`RSMRIDataTransforms`.
+
+ Parameters
+ ----------
+ kspace : np.ndarray
+ The fully-sampled kspace, if exists. Otherwise, the subsampled kspace.
+ imspace : np.ndarray
+ The image space for segmentation, if exists.
+ sensitivity_map : np.ndarray
+ The coil sensitivity map.
+ mask : np.ndarray
+ The subsampling mask, if exists, meaning that the data are either prospectively undersampled or the mask is
+ stored and loaded.
+ initial_prediction_reconstruction : np.ndarray
+ The initial prediction, if exists. Otherwise, it will be estimated with the chosen coil combination method.
+ segmentation_labels : np.ndarray
+ The segmentation labels.
+ attrs : Dict
+ The attributes, if stored in the data.
+ fname : str
+ The file name.
+ slice_idx : int
+ The slice index.
+ """
+ initial_prediction_reconstruction = (
+ to_tensor(initial_prediction_reconstruction)
+ if initial_prediction_reconstruction is not None and initial_prediction_reconstruction.size != 0
+ else torch.tensor([])
+ )
+
+ if not self.complex_data:
+ kspace = torch.empty([])
+ kspace_pre_normalization_vars = None
+ sensitivity_map = torch.empty([])
+ sensitivity_pre_normalization_vars = None
+ masked_kspace = torch.empty([])
+ mask = torch.empty([])
+ acc = torch.empty([])
+ (
+ initial_prediction_reconstruction,
+ initial_prediction_pre_normalization_vars,
+ ) = self.__initialize_prediction__(imspace, kspace, sensitivity_map)
+
+ if "min" in attrs:
+ initial_prediction_pre_normalization_vars["min"] = attrs["min"]
+ if "max" in attrs:
+ initial_prediction_pre_normalization_vars["max"] = attrs["max"]
+ if "mean" in attrs:
+ initial_prediction_pre_normalization_vars["mean"] = attrs["mean"]
+ if "std" in attrs:
+ initial_prediction_pre_normalization_vars["std"] = attrs["std"]
+
+ noise_prediction_pre_normalization_vars = None
+ target_reconstruction = initial_prediction_reconstruction
+ target_pre_normalization_vars = initial_prediction_pre_normalization_vars
+ else:
+ kspace, masked_kspace, mask, kspace_pre_normalization_vars, acc = self.__process_kspace__( # type: ignore
+ kspace, mask, attrs, fname
+ )
+ sensitivity_map, sensitivity_pre_normalization_vars = self.__process_coil_sensitivities_map__(
+ sensitivity_map, kspace
+ )
+ target_reconstruction, target_pre_normalization_vars = self.__initialize_prediction__(
+ torch.empty([]), kspace, sensitivity_map
+ )
+ target_prediction_pre_normalization_vars = None
+ if self.n2r and len(masked_kspace) > 1:
+ (
+ initial_prediction_reconstruction,
+ initial_prediction_pre_normalization_vars,
+ ) = self.__initialize_prediction__(
+ initial_prediction_reconstruction, masked_kspace[0], sensitivity_map
+ )
+ if isinstance(masked_kspace, list) and not masked_kspace[1][0].dim() < 2:
+ noise_prediction, noise_prediction_pre_normalization_vars = self.__initialize_prediction__(
+ None, masked_kspace[1], sensitivity_map
+ )
+ else:
+ noise_prediction = torch.tensor([])
+ noise_prediction_pre_normalization_vars = None
+ initial_prediction_reconstruction = [initial_prediction_reconstruction, noise_prediction]
+ else:
+ (
+ initial_prediction_reconstruction,
+ initial_prediction_pre_normalization_vars,
+ ) = self.__initialize_prediction__(initial_prediction_reconstruction, masked_kspace, sensitivity_map)
+ noise_prediction_pre_normalization_vars = None
+
+ if self.unsupervised_masked_target:
+ target_reconstruction, target_prediction_pre_normalization_vars = (
+ initial_prediction_reconstruction,
+ noise_prediction_pre_normalization_vars,
+ )
+ else:
+ target_reconstruction, target_prediction_pre_normalization_vars = self.__initialize_prediction__(
+ None if self.ssdu else target_prediction_pre_normalization_vars, kspace, sensitivity_map
+ )
+
+ if not is_none(segmentation_labels) and segmentation_labels.ndim > 1:
+ segmentation_labels = self.cropping(torch.from_numpy(segmentation_labels)) # type: ignore
+ else:
+ segmentation_labels = torch.empty([])
+
+ # if segmentation_labels is Bool type, convert to float
+ if segmentation_labels.dtype == torch.bool:
+ segmentation_labels = segmentation_labels.float()
+ segmentation_labels = torch.abs(segmentation_labels)
+
+ attrs.update(
+ self.__parse_normalization_vars__(
+ kspace_pre_normalization_vars,
+ sensitivity_pre_normalization_vars,
+ initial_prediction_pre_normalization_vars,
+ noise_prediction_pre_normalization_vars,
+ target_pre_normalization_vars,
+ )
+ )
+ attrs["fname"] = fname
+ attrs["slice_idx"] = slice_idx
+
+ return (
+ kspace,
+ masked_kspace,
+ sensitivity_map,
+ mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ segmentation_labels,
+ fname,
+ slice_idx,
+ acc,
+ attrs,
+ )
+
+ def __repr__(self) -> str:
+ """Representation of :class:`RSMRIDataTransforms`."""
+ return (
+ f"Preprocessing transforms initialized for {self.__class__.__name__}: "
+ f"prewhitening = {self.prewhitening}, "
+ f"masking = {self.masking}, "
+ f"SSDU masking = {self.ssdu_masking}, "
+ f"kspace zero-filling = {self.kspace_zero_filling}, "
+ f"cropping = {self.cropping}, "
+ f"normalization = {self.normalization}, "
+ )
+
+ def __str__(self) -> str:
+ """String representation of :class:`RSMRIDataTransforms`."""
+ return self.__repr__()
+
+ def __process_kspace__( # noqa: MC0001
+ self, kspace: np.ndarray, mask: Union[np.ndarray, None], attrs: Dict, fname: str
+ ) -> Tuple[torch.Tensor, Union[List[torch.Tensor], torch.Tensor], Union[List[torch.Tensor], torch.Tensor], int]:
+ """Apply the preprocessing transforms to the kspace.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The kspace.
+ mask : torch.Tensor
+ The mask, if None, the mask is generated.
+ attrs : Dict
+ The attributes, if stored in the file.
+ fname : str
+ The file name.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, Union[List[torch.Tensor], torch.Tensor], Union[List[torch.Tensor], torch.Tensor], int]
+ The transformed (fully-sampled) kspace, the masked kspace, the mask, the attributes and the acceleration
+ factor.
+ """
+ kspace = to_tensor(kspace)
+ kspace = add_coil_dim_if_singlecoil(kspace, dim=self.coil_dim)
+
+ kspace = self.coils_shape_transforms(kspace, apply_backward_transform=True)
+ kspace = self.prewhitening(kspace) # type: ignore
+
+ if self.crop_before_masking:
+ kspace = self.cropping(kspace, apply_backward_transform=not self.kspace_crop) # type: ignore
+
+ masked_kspace, mask, acc = self.masking(
+ self.random_motion(kspace), # type: ignore
+ mask,
+ (
+ attrs["padding_left"] if "padding_left" in attrs else 0,
+ attrs["padding_right"] if "padding_right" in attrs else 0,
+ ),
+ tuple(map(ord, fname)) if self.use_seed else None, # type: ignore
+ )
+
+ if not self.crop_before_masking:
+ kspace = self.cropping(kspace, apply_backward_transform=not self.kspace_crop) # type: ignore
+ masked_kspace = self.cropping(masked_kspace, apply_backward_transform=not self.kspace_crop) # type: ignore
+ mask = self.cropping(mask) # type: ignore
+
+ init_kspace = kspace
+ init_masked_kspace = masked_kspace
+ init_mask = mask
+
+ if isinstance(kspace, list):
+ kspaces = []
+ pre_normalization_vars = []
+ for i in range(len(kspace)): # pylint: disable=consider-using-enumerate
+ if not is_none(self.normalization.__repr__()):
+ _kspace, _pre_normalization_vars = self.normalization( # type: ignore
+ kspace[i], apply_backward_transform=True
+ )
+ else:
+ _kspace = kspace[i]
+ is_complex = _kspace.shape[-1] == 2
+ if is_complex:
+ _kspace = torch.view_as_complex(_kspace)
+ _pre_normalization_vars = {
+ "min": torch.min(torch.abs(_kspace)),
+ "max": torch.max(torch.abs(_kspace)),
+ "mean": torch.mean(torch.abs(_kspace)),
+ "std": torch.std(torch.abs(_kspace)),
+ "var": torch.var(torch.abs(_kspace)),
+ }
+ if is_complex:
+ _kspace = torch.view_as_real(_kspace)
+ kspaces.append(_kspace)
+ pre_normalization_vars.append(_pre_normalization_vars)
+ kspace = kspaces
+ else:
+ if not is_none(self.normalization.__repr__()):
+ kspace, pre_normalization_vars = self.normalization( # type: ignore
+ kspace, apply_backward_transform=True
+ )
+ else:
+ is_complex = kspace.shape[-1] == 2
+ if is_complex:
+ kspace = torch.view_as_complex(kspace)
+ pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(kspace)),
+ "max": torch.max(torch.abs(kspace)),
+ "mean": torch.mean(torch.abs(kspace)),
+ "std": torch.std(torch.abs(kspace)),
+ "var": torch.var(torch.abs(kspace)),
+ }
+ if is_complex:
+ kspace = torch.view_as_real(kspace)
+
+ if isinstance(masked_kspace, list):
+ masked_kspaces = []
+ masked_pre_normalization_vars = []
+ for i in range(len(masked_kspace)): # pylint: disable=consider-using-enumerate
+ if not is_none(self.normalization.__repr__()):
+ _masked_kspace, _masked_pre_normalization_vars = self.normalization( # type: ignore
+ masked_kspace[i], apply_backward_transform=True
+ )
+ else:
+ _masked_kspace = masked_kspace[i]
+ is_complex = _masked_kspace.shape[-1] == 2
+ if is_complex:
+ _masked_kspace = torch.view_as_complex(_masked_kspace)
+ _masked_pre_normalization_vars = {
+ "min": torch.min(torch.abs(_masked_kspace)),
+ "max": torch.max(torch.abs(_masked_kspace)),
+ "mean": torch.mean(torch.abs(_masked_kspace)),
+ "std": torch.std(torch.abs(_masked_kspace)),
+ "var": torch.var(torch.abs(_masked_kspace)),
+ }
+ if is_complex:
+ _masked_kspace = torch.view_as_real(_masked_kspace)
+ masked_kspaces.append(_masked_kspace)
+ masked_pre_normalization_vars.append(_masked_pre_normalization_vars)
+ masked_kspace = masked_kspaces
+ else:
+ if not is_none(self.normalization.__repr__()):
+ masked_kspace, masked_pre_normalization_vars = self.normalization(
+ masked_kspace, apply_backward_transform=True
+ )
+ else:
+ is_complex = masked_kspace.shape[-1] == 2
+ if is_complex:
+ masked_kspace = torch.view_as_complex(masked_kspace)
+ masked_pre_normalization_vars = {
+ "min": torch.min(torch.abs(masked_kspace)),
+ "max": torch.max(torch.abs(masked_kspace)),
+ "mean": torch.mean(torch.abs(masked_kspace)),
+ "std": torch.std(torch.abs(masked_kspace)),
+ "var": torch.var(torch.abs(masked_kspace)),
+ }
+ if is_complex:
+ masked_kspace = torch.view_as_real(masked_kspace)
+
+ if self.ssdu:
+ kspace, masked_kspace, mask = self.__self_supervised_data_undersampling__( # type: ignore
+ kspace, masked_kspace, mask, fname
+ )
+
+ n2r_pre_normalization_vars = None
+ if self.n2r and (not attrs["n2r_supervised"] or self.ssdu):
+ n2r_masked_kspace, n2r_mask = self.__noise_to_reconstruction__(init_kspace, init_masked_kspace, init_mask)
+
+ if self.ssdu:
+ if isinstance(mask, list):
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ if init_mask[i].dim() != mask[i][0].dim(): # type: ignore
+ # find dimensions == 1 in mask[i][0] and add them to init_mask
+ unitary_dims = [j for j in range(mask[i][0].dim()) if mask[i][0].shape[j] == 1]
+ # unsqueeze init_mask to the index of the unitary dimensions
+ for j in unitary_dims:
+ init_mask[i] = init_mask[i].unsqueeze(j) # type: ignore
+ masked_kspace[i] = init_masked_kspace[i]
+ mask[i][0] = init_mask[i]
+ else:
+ if init_mask.dim() != mask[0].dim(): # type: ignore
+ # find dimensions == 1 in mask[0] and add them to init_mask
+ unitary_dims = [j for j in range(mask[0].dim()) if mask[0].shape[j] == 1]
+ # unsqueeze init_mask to the index of the unitary dimensions
+ for j in unitary_dims:
+ init_mask = init_mask.unsqueeze(j) # type: ignore
+ masked_kspace = init_masked_kspace
+ mask[0] = init_mask
+
+ if "None" not in self.normalization.__repr__():
+ if isinstance(masked_kspace, list):
+ masked_kspaces = []
+ masked_pre_normalization_vars = []
+ for i in range(len(masked_kspace)): # pylint: disable=consider-using-enumerate
+ _masked_kspace, _masked_pre_normalization_vars = self.normalization( # type: ignore
+ masked_kspace[i], apply_backward_transform=True
+ )
+ masked_kspaces.append(_masked_kspace)
+ masked_pre_normalization_vars.append(_masked_pre_normalization_vars)
+ masked_kspace = masked_kspaces
+ else:
+ masked_kspace, masked_pre_normalization_vars = self.normalization( # type: ignore
+ masked_kspace, apply_backward_transform=True
+ )
+ if isinstance(n2r_masked_kspace, list):
+ n2r_masked_kspaces = []
+ n2r_pre_normalization_vars = []
+ for i in range(len(n2r_masked_kspace)): # pylint: disable=consider-using-enumerate
+ _n2r_masked_kspace, _n2r_pre_normalization_vars = self.normalization( # type: ignore
+ n2r_masked_kspace[i], apply_backward_transform=True
+ )
+ n2r_masked_kspaces.append(_n2r_masked_kspace)
+ n2r_pre_normalization_vars.append(_n2r_pre_normalization_vars)
+ n2r_masked_kspace = n2r_masked_kspaces
+ else:
+ n2r_masked_kspace, n2r_pre_normalization_vars = self.normalization( # type: ignore
+ n2r_masked_kspace, apply_backward_transform=True
+ )
+ else:
+ masked_pre_normalization_vars = None # type: ignore
+ n2r_pre_normalization_vars = None # type: ignore
+
+ masked_kspace = [masked_kspace, n2r_masked_kspace]
+ mask = [mask, n2r_mask]
+
+ if self.normalization_type == "grayscale":
+ if isinstance(mask, list):
+ masks = []
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ _mask, _ = self.normalization(mask[i], apply_backward_transform=False) # type: ignore
+ masks.append(_mask)
+ mask = masks
+ else:
+ mask, _ = self.normalization(mask, apply_backward_transform=False) # type: ignore
+
+ pre_normalization_vars = { # type: ignore
+ "kspace_pre_normalization_vars": pre_normalization_vars,
+ "masked_kspace_pre_normalization_vars": masked_pre_normalization_vars,
+ "noise_masked_kspace_pre_normalization_vars": n2r_pre_normalization_vars,
+ }
+
+ return kspace, masked_kspace, mask, pre_normalization_vars, acc # type: ignore
+
+ def __noise_to_reconstruction__(
+ self,
+ kspace: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ mask: Union[List, torch.Tensor],
+ ) -> Tuple[Union[List, torch.Tensor], Union[List, torch.Tensor]]:
+ """Apply the noise-to-reconstruction transform.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The fully-sampled kspace.
+ masked_kspace : torch.Tensor
+ The undersampled kspace.
+ mask : Union[List, torch.Tensor]
+ The undersampling mask.
+
+ Returns
+ -------
+ n2r_masked_kspace : Union[List, torch.Tensor]
+ The noise-to-reconstruction undersampled kspace.
+ n2r_mask : Union[List, torch.Tensor]
+ The noise-to-reconstruction mask.
+ """
+ if isinstance(mask, list):
+ n2r_masked_kspaces = []
+ n2r_masks = []
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ n2r_mask = self.n2r_masking(kspace, mask[i]) # type: ignore # pylint: disable=not-callable
+ n2r_masks.append(n2r_mask)
+ n2r_masked_kspaces.append(masked_kspace[i] * n2r_mask + 0.0)
+ n2r_mask = n2r_masks
+ n2r_masked_kspace = n2r_masked_kspaces
+ else:
+ n2r_mask = self.n2r_masking(kspace, mask) # type: ignore # pylint: disable=not-callable
+ n2r_masked_kspace = masked_kspace * n2r_mask + 0.0
+ return n2r_masked_kspace, n2r_mask
+
+ def __self_supervised_data_undersampling__( # noqa: MC0001
+ self,
+ kspace: torch.Tensor,
+ masked_kspace: Union[List, torch.Tensor],
+ mask: Union[List, torch.Tensor],
+ fname: str,
+ ) -> Tuple[
+ List[float | Any] | float | Any,
+ List[float | Any] | float | Any,
+ List[List[torch.Tensor | Any]] | List[torch.Tensor | Any],
+ ]:
+ """Self-supervised data undersampling.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The fully-sampled kspace.
+ masked_kspace : Union[List, torch.Tensor]
+ The undersampled kspace.
+ mask : Union[List, torch.Tensor]
+ The undersampling mask.
+ fname : str
+ The filename of the current sample.
+
+ Returns
+ -------
+ kspace : torch.Tensor
+ The kspace with the loss mask applied.
+ masked_kspace : torch.Tensor
+ The kspace with the train mask applied.
+ mask : list, [torch.Tensor, torch.Tensor]
+ The train and loss masks.
+ """
+ if isinstance(mask, list):
+ kspaces = []
+ masked_kspaces = []
+ masks = []
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ is_1d = mask[i].squeeze().dim() == 1
+ if self.shift_mask:
+ mask[i] = torch.fft.fftshift(mask[i].squeeze(-1), dim=(-2, -1)).unsqueeze(-1)
+ mask[i] = mask[i].squeeze()
+ if is_1d:
+ mask[i] = mask[i].unsqueeze(0).repeat_interleave(kspace.shape[1], dim=0)
+ train_mask, loss_mask = self.ssdu_masking( # type: ignore # pylint: disable=not-callable
+ kspace, mask[i], fname
+ )
+ if self.shift_mask:
+ train_mask = torch.fft.fftshift(train_mask, dim=(0, 1))
+ loss_mask = torch.fft.fftshift(loss_mask, dim=(0, 1))
+ if is_1d:
+ train_mask = train_mask.unsqueeze(0).unsqueeze(-1)
+ loss_mask = loss_mask.unsqueeze(0).unsqueeze(-1)
+ else:
+ # find unitary dims in mask
+ dims = [i for i, x in enumerate(mask[i].shape) if x == 1]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ if train_mask.dim() != kspace.dim():
+ # find dims != to any train_mask dim
+ dims = [i for i, x in enumerate(kspace.shape) if x not in train_mask.shape]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ kspaces.append(kspace * loss_mask + 0.0)
+ masked_kspaces.append(masked_kspace[i] * train_mask + 0.0)
+ masks.append([train_mask, loss_mask])
+ kspace = kspaces
+ masked_kspace = masked_kspaces
+ mask = masks
+ else:
+ is_1d = mask.squeeze().dim() == 1
+ if self.shift_mask:
+ mask = torch.fft.fftshift(mask.squeeze(-1), dim=(-2, -1)).unsqueeze(-1)
+ mask = mask.squeeze()
+ if is_1d:
+ mask = mask.unsqueeze(0).repeat_interleave(kspace.shape[1], dim=0)
+ train_mask, loss_mask = self.ssdu_masking( # type: ignore # pylint: disable=not-callable
+ kspace, mask, fname
+ )
+ if self.shift_mask:
+ train_mask = torch.fft.fftshift(train_mask, dim=(0, 1))
+ loss_mask = torch.fft.fftshift(loss_mask, dim=(0, 1))
+ if is_1d:
+ train_mask = train_mask.unsqueeze(0).unsqueeze(-1)
+ loss_mask = loss_mask.unsqueeze(0).unsqueeze(-1)
+ else:
+ # find unitary dims in mask
+ dims = [i for i, x in enumerate(mask.shape) if x == 1]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ if train_mask.dim() != kspace.dim():
+ # find dims != to any train_mask dim
+ dims = [i for i, x in enumerate(kspace.shape) if x not in train_mask.shape]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ kspace = kspace * loss_mask + 0.0
+ masked_kspace = masked_kspace * train_mask + 0.0
+ mask = [train_mask, loss_mask]
+ return kspace, masked_kspace, mask
+
+ def __process_coil_sensitivities_map__(
+ self, sensitivity_map: np.ndarray, kspace: torch.Tensor
+ ) -> Union[torch.Tensor, Dict]:
+ """Preprocesses the coil sensitivities map.
+
+ Parameters
+ ----------
+ sensitivity_map : np.ndarray
+ The coil sensitivities map.
+ kspace : torch.Tensor
+ The kspace.
+
+ Returns
+ -------
+ List[torch.Tensor, Dict]
+ The preprocessed coil sensitivities map and the normalization variables.
+ """
+ # This condition is necessary in case of auto estimation of sense maps.
+ if self.coil_sensitivity_maps_estimator is not None:
+ sensitivity_map = self.coil_sensitivity_maps_estimator(kspace)
+ elif sensitivity_map is not None and sensitivity_map.size != 0:
+ sensitivity_map = to_tensor(sensitivity_map)
+ sensitivity_map = self.coils_shape_transforms(sensitivity_map, apply_forward_transform=True)
+ sensitivity_map = self.cropping(sensitivity_map, apply_forward_transform=self.kspace_crop) # type: ignore
+ else:
+ # If no sensitivity map is provided, either the data is singlecoil or the sense net is used.
+ # Initialize the sensitivity map to 1 to assure for the singlecoil case.
+ sensitivity_map = torch.ones_like(kspace) if not isinstance(kspace, list) else torch.ones_like(kspace[0])
+
+ if not is_none(self.normalization.__repr__()):
+ sensitivity_map, pre_normalization_vars = self.normalization( # type: ignore
+ sensitivity_map, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ is_complex = sensitivity_map.shape[-1] == 2
+ if is_complex:
+ sensitivity_map = torch.view_as_complex(sensitivity_map)
+ pre_normalization_vars = {
+ "min": torch.min(torch.abs(sensitivity_map)),
+ "max": torch.max(torch.abs(sensitivity_map)),
+ "mean": torch.mean(torch.abs(sensitivity_map)),
+ "std": torch.std(torch.abs(sensitivity_map)),
+ "var": torch.var(torch.abs(sensitivity_map)),
+ }
+ if is_complex:
+ sensitivity_map = torch.view_as_real(sensitivity_map)
+ return sensitivity_map, pre_normalization_vars
+
+ def __initialize_prediction__(
+ self, prediction: Union[np.ndarray, None], kspace: torch.Tensor, sensitivity_map: torch.Tensor
+ ) -> Tuple[Union[List[torch.Tensor], torch.Tensor], Dict]:
+ """Predicts a coil-combined image.
+
+ Parameters
+ ----------
+ prediction : np.ndarray
+ The initial estimation, if None, the prediction is initialized.
+ kspace : torch.Tensor
+ The kspace.
+ sensitivity_map : torch.Tensor
+ The sensitivity map.
+
+ Returns
+ -------
+ Tuple[Union[List[torch.Tensor], torch.Tensor], Dict]
+ The initialized prediction, either a list of coil-combined images or a single coil-combined image and the
+ pre-normalization variables (min, max, mean, std).
+ """
+ if is_none(prediction) or prediction.ndim < 2 or isinstance(kspace, list): # type: ignore
+ if isinstance(kspace, list):
+ prediction = []
+ pre_normalization_vars = []
+ for y in kspace:
+ pred = coil_combination_method_func(
+ ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_map,
+ method=self.coil_combination_method,
+ dim=self.coil_dim,
+ )
+ pred = self.cropping(pred, apply_forward_transform=self.kspace_crop) # type: ignore
+ if not is_none(self.normalization.__repr__()):
+ pred, _pre_normalization_vars = self.normalization( # type: ignore
+ pred, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ if pred.shape[-1] == 2:
+ pred = torch.view_as_complex(pred)
+ _pre_normalization_vars = {
+ "min": torch.min(torch.abs(pred)),
+ "max": torch.max(torch.abs(pred)),
+ "mean": torch.mean(torch.abs(pred)),
+ "std": torch.std(torch.abs(pred)),
+ "var": torch.var(torch.abs(pred)),
+ }
+ prediction.append(pred)
+ pre_normalization_vars.append(_pre_normalization_vars)
+ if prediction[0].shape[-1] != 2 and torch.is_complex(prediction[0]):
+ prediction = [torch.view_as_real(x) for x in prediction]
+ else:
+ prediction = coil_combination_method_func(
+ ifft2(kspace, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_map,
+ method=self.coil_combination_method,
+ dim=self.coil_dim,
+ )
+ prediction = self.cropping(prediction, apply_forward_transform=self.kspace_crop) # type: ignore
+ if not is_none(self.normalization.__repr__()):
+ prediction, pre_normalization_vars = self.normalization( # type: ignore
+ prediction, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ if prediction.shape[-1] == 2:
+ prediction = torch.view_as_complex(prediction)
+ pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(prediction)),
+ "max": torch.max(torch.abs(prediction)),
+ "mean": torch.mean(torch.abs(prediction)),
+ "std": torch.std(torch.abs(prediction)),
+ "var": torch.var(torch.abs(prediction)),
+ }
+ if prediction.shape[-1] != 2 and torch.is_complex(prediction):
+ prediction = torch.view_as_real(prediction)
+ else:
+ if isinstance(prediction, np.ndarray):
+ prediction = to_tensor(prediction)
+ prediction = self.cropping(prediction, apply_forward_transform=self.kspace_crop) # type: ignore
+ if not is_none(self.normalization.__repr__()):
+ prediction, pre_normalization_vars = self.normalization( # type: ignore
+ prediction, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ if prediction.shape[-1] == 2: # type: ignore
+ prediction = torch.view_as_complex(prediction)
+ pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(prediction)),
+ "max": torch.max(torch.abs(prediction)),
+ "mean": torch.mean(torch.abs(prediction)),
+ "std": torch.std(torch.abs(prediction)),
+ "var": torch.var(torch.abs(prediction)),
+ }
+ if prediction.shape[-1] != 2 and torch.is_complex(prediction):
+ prediction = torch.view_as_real(prediction)
+ return prediction, pre_normalization_vars # type: ignore
+
+ def __parse_normalization_vars__( # noqa: MC0001
+ self, kspace_vars, sensitivity_vars, prediction_vars, noise_prediction_vars, target_vars
+ ) -> Dict:
+ """
+ Parses the normalization variables and returns a unified dictionary.
+
+ Parameters
+ ----------
+ kspace_vars : Dict
+ The kspace normalization variables.
+ sensitivity_vars : Dict
+ The sensitivity map normalization variables.
+ prediction_vars : Dict
+ The prediction normalization variables.
+ noise_prediction_vars : Union[Dict, None]
+ The noise prediction normalization variables.
+ target_vars : Dict
+ The target normalization variables.
+
+ Returns
+ -------
+ Dict
+ The normalization variables.
+ """
+ normalization_vars = {}
+
+ if self.complex_data:
+ masked_kspace_vars = kspace_vars["masked_kspace_pre_normalization_vars"]
+ if isinstance(masked_kspace_vars, list):
+ if masked_kspace_vars[0] is not None:
+ for i, masked_kspace_var in enumerate(masked_kspace_vars):
+ normalization_vars[f"masked_kspace_min_{i}"] = masked_kspace_var["min"]
+ normalization_vars[f"masked_kspace_max_{i}"] = masked_kspace_var["max"]
+ normalization_vars[f"masked_kspace_mean_{i}"] = masked_kspace_var["mean"]
+ normalization_vars[f"masked_kspace_std_{i}"] = masked_kspace_var["std"]
+ normalization_vars[f"masked_kspace_var_{i}"] = masked_kspace_var["var"]
+ else:
+ if masked_kspace_vars is not None:
+ normalization_vars["masked_kspace_min"] = masked_kspace_vars["min"]
+ normalization_vars["masked_kspace_max"] = masked_kspace_vars["max"]
+ normalization_vars["masked_kspace_mean"] = masked_kspace_vars["mean"]
+ normalization_vars["masked_kspace_std"] = masked_kspace_vars["std"]
+ normalization_vars["masked_kspace_var"] = masked_kspace_vars["var"]
+
+ noise_masked_kspace_vars = kspace_vars["noise_masked_kspace_pre_normalization_vars"]
+ if noise_masked_kspace_vars is not None:
+ if isinstance(noise_masked_kspace_vars, list):
+ if noise_masked_kspace_vars[0] is not None:
+ for i, noise_masked_kspace_var in enumerate(noise_masked_kspace_vars):
+ normalization_vars[f"noise_masked_kspace_min_{i}"] = noise_masked_kspace_var["min"]
+ normalization_vars[f"noise_masked_kspace_max_{i}"] = noise_masked_kspace_var["max"]
+ normalization_vars[f"noise_masked_kspace_mean_{i}"] = noise_masked_kspace_var["mean"]
+ normalization_vars[f"noise_masked_kspace_std_{i}"] = noise_masked_kspace_var["std"]
+ normalization_vars[f"noise_masked_kspace_var_{i}"] = noise_masked_kspace_var["var"]
+ else:
+ if noise_masked_kspace_vars is not None:
+ normalization_vars["noise_masked_kspace_min"] = noise_masked_kspace_vars["min"]
+ normalization_vars["noise_masked_kspace_max"] = noise_masked_kspace_vars["max"]
+ normalization_vars["noise_masked_kspace_mean"] = noise_masked_kspace_vars["mean"]
+ normalization_vars["noise_masked_kspace_std"] = noise_masked_kspace_vars["std"]
+ normalization_vars["noise_masked_kspace_var"] = noise_masked_kspace_vars["var"]
+
+ kspace_vars = kspace_vars["kspace_pre_normalization_vars"]
+ if isinstance(kspace_vars, list):
+ if kspace_vars[0] is not None:
+ for i, kspace_var in enumerate(kspace_vars):
+ normalization_vars[f"kspace_min_{i}"] = kspace_var["min"]
+ normalization_vars[f"kspace_max_{i}"] = kspace_var["max"]
+ normalization_vars[f"kspace_mean_{i}"] = kspace_var["mean"]
+ normalization_vars[f"kspace_std_{i}"] = kspace_var["std"]
+ normalization_vars[f"kspace_var_{i}"] = kspace_var["var"]
+ else:
+ if kspace_vars is not None:
+ normalization_vars["kspace_min"] = kspace_vars["min"]
+ normalization_vars["kspace_max"] = kspace_vars["max"]
+ normalization_vars["kspace_mean"] = kspace_vars["mean"]
+ normalization_vars["kspace_std"] = kspace_vars["std"]
+ normalization_vars["kspace_var"] = kspace_vars["var"]
+
+ if sensitivity_vars is not None:
+ normalization_vars["sensitivity_maps_min"] = sensitivity_vars["min"]
+ normalization_vars["sensitivity_maps_max"] = sensitivity_vars["max"]
+ normalization_vars["sensitivity_maps_mean"] = sensitivity_vars["mean"]
+ normalization_vars["sensitivity_maps_std"] = sensitivity_vars["std"]
+ normalization_vars["sensitivity_maps_var"] = sensitivity_vars["var"]
+
+ if isinstance(prediction_vars, list):
+ if prediction_vars[0] is not None:
+ for i, prediction_var in enumerate(prediction_vars):
+ normalization_vars[f"prediction_min_{i}"] = prediction_var["min"]
+ normalization_vars[f"prediction_max_{i}"] = prediction_var["max"]
+ normalization_vars[f"prediction_mean_{i}"] = prediction_var["mean"]
+ normalization_vars[f"prediction_std_{i}"] = prediction_var["std"]
+ normalization_vars[f"prediction_var_{i}"] = prediction_var["var"]
+ else:
+ if prediction_vars is not None:
+ normalization_vars["prediction_min"] = prediction_vars["min"]
+ normalization_vars["prediction_max"] = prediction_vars["max"]
+ normalization_vars["prediction_mean"] = prediction_vars["mean"]
+ normalization_vars["prediction_std"] = prediction_vars["std"]
+ normalization_vars["prediction_var"] = prediction_vars["var"]
+
+ if noise_prediction_vars is not None:
+ if isinstance(noise_prediction_vars, list):
+ for i, noise_prediction_var in enumerate(noise_prediction_vars):
+ normalization_vars[f"noise_prediction_min_{i}"] = noise_prediction_var["min"]
+ normalization_vars[f"noise_prediction_max_{i}"] = noise_prediction_var["max"]
+ normalization_vars[f"noise_prediction_mean_{i}"] = noise_prediction_var["mean"]
+ normalization_vars[f"noise_prediction_std_{i}"] = noise_prediction_var["std"]
+ normalization_vars[f"noise_prediction_var_{i}"] = noise_prediction_var["var"]
+ else:
+ normalization_vars["noise_prediction_min"] = noise_prediction_vars["min"]
+ normalization_vars["noise_prediction_max"] = noise_prediction_vars["max"]
+ normalization_vars["noise_prediction_mean"] = noise_prediction_vars["mean"]
+ normalization_vars["noise_prediction_std"] = noise_prediction_vars["std"]
+ normalization_vars["noise_prediction_var"] = noise_prediction_vars["var"]
+
+ if isinstance(target_vars, list):
+ if target_vars[0] is not None:
+ for i, target_var in enumerate(target_vars):
+ normalization_vars[f"target_min_{i}"] = target_var["min"]
+ normalization_vars[f"target_max_{i}"] = target_var["max"]
+ normalization_vars[f"target_mean_{i}"] = target_var["mean"]
+ normalization_vars[f"target_std_{i}"] = target_var["std"]
+ normalization_vars[f"target_var_{i}"] = target_var["var"]
+ else:
+ if target_vars is not None:
+ normalization_vars["target_min"] = target_vars["min"]
+ normalization_vars["target_max"] = target_vars["max"]
+ normalization_vars["target_mean"] = target_vars["mean"]
+ normalization_vars["target_std"] = target_vars["std"]
+ normalization_vars["target_var"] = target_vars["var"]
+
+ return normalization_vars
diff --git a/atommic/collections/quantitative/__init__.py b/atommic/collections/quantitative/__init__.py
new file mode 100644
index 00000000..51a1770f
--- /dev/null
+++ b/atommic/collections/quantitative/__init__.py
@@ -0,0 +1,13 @@
+# coding=utf-8
+
+from atommic.collections.quantitative import data, nn, parts # noqa: F401
+from atommic.package_info import __version__
+
+# Set collection version equal to atommic version.
+__version = __version__
+
+# Authorship.
+__author__ = "Dimitris Karkalousos"
+
+# Set collection name.
+__description__ = "Collection of quantitative MRI data and models."
diff --git a/atommic/collections/quantitative/data/__init__.py b/atommic/collections/quantitative/data/__init__.py
new file mode 100644
index 00000000..4d30b121
--- /dev/null
+++ b/atommic/collections/quantitative/data/__init__.py
@@ -0,0 +1,4 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.quantitative.data.qmri_loader import AHEADqMRIDataset, qMRIDataset # noqa: F401
diff --git a/atommic/collections/quantitative/data/qmri_loader.py b/atommic/collections/quantitative/data/qmri_loader.py
new file mode 100644
index 00000000..572f10fc
--- /dev/null
+++ b/atommic/collections/quantitative/data/qmri_loader.py
@@ -0,0 +1,297 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import os
+import re
+import warnings
+from pathlib import Path
+from typing import Callable, Optional, Tuple, Union
+
+import h5py
+import numpy as np
+
+from atommic.collections.common.data.mri_loader import MRIDataset
+from atommic.collections.common.parts.utils import is_none
+
+
+class qMRIDataset(MRIDataset):
+ """A dataset class for quantitative MRI.
+
+ .. note::
+ Extends :class:`atommic.collections.common.data.MRIDataset`.
+ """
+
+ def __init__(
+ self,
+ root: Union[str, Path, os.PathLike],
+ coil_sensitivity_maps_root: Union[str, Path, os.PathLike] = None,
+ mask_root: Union[str, Path, os.PathLike] = None,
+ noise_root: Union[str, Path, os.PathLike] = None,
+ initial_predictions_root: Union[str, Path, os.PathLike] = None,
+ dataset_format: str = None,
+ sample_rate: Optional[float] = None,
+ volume_sample_rate: Optional[float] = None,
+ use_dataset_cache: bool = False,
+ dataset_cache_file: Union[str, Path, os.PathLike] = None,
+ num_cols: Optional[Tuple[int]] = None,
+ consecutive_slices: int = 1,
+ data_saved_per_slice: bool = False,
+ n2r_supervised_rate: Optional[float] = 0.0,
+ complex_target: bool = False,
+ log_images_rate: Optional[float] = 1.0,
+ transform: Optional[Callable] = None,
+ sequence: str = None,
+ segmentation_mask_root: Union[str, Path, os.PathLike] = None,
+ kspace_scaling_factor: float = 1.0,
+ **kwargs,
+ ):
+ """Inits :class:`qMRIDataset`.
+
+ Parameters
+ ----------
+ root : Union[str, Path, os.PathLike]
+ Path to the dataset.
+ sense_root : Union[str, Path, os.PathLike], optional
+ Path to the coil sensitivities maps dataset, if stored separately.
+ mask_root : Union[str, Path, os.PathLike], optional
+ Path to stored masks, if stored separately.
+ noise_root : Union[str, Path, os.PathLike], optional
+ Path to stored noise, if stored separately (in json format).
+ initial_predictions_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the initial predictions. If provided, the initial predictions will be used
+ as the input of the reconstruction network. Default is ``None``.
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ Default is ``None``.
+ sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the slices should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ volume_sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the volumes should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ use_dataset_cache : bool, optional
+ Whether to cache dataset metadata. This is very useful for large datasets.
+ dataset_cache_file : Union[str, Path, os.PathLike], optional
+ A file in which to cache dataset information for faster load times.
+ num_cols : Optional[Tuple[int]], optional
+ If provided, only slices with the desired number of columns will be considered.
+ consecutive_slices : int, optional
+ An int (>0) that determine the amount of consecutive slices of the file to be loaded at the same time.
+ Default is ``1``, loading single slices.
+ data_saved_per_slice : bool, optional
+ Whether the data is saved per slice or per volume.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ complex_target : bool, optional
+ Whether to use a complex target or not. Default is ``False``.
+ log_images_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be logged as images. Default is
+ ``1.0``.
+ transform : Optional[Callable], optional
+ A sequence of callable objects that preprocesses the raw data into appropriate form. The transform function
+ should take ``kspace``, ``coil sensitivity maps``, ``quantitative maps``, ``mask``, ``initial prediction``,
+ ``target``, ``attributes``, ``filename``, and ``slice number`` as inputs. ``target`` may be null for test
+ data. Default is ``None``.
+ sequence : str, optional
+ Sequence of the dataset. For example, ``MEGRE`` or ``FUTURE_SEQUENCES``.
+ segmentation_mask_root : Union[str, Path, os.PathLike], optional
+ Path to stored segmentation masks, if stored separately.
+ kspace_scaling_factor : float, optional
+ A float that scales the kspace. Default is ``1.0``.
+ """
+ super().__init__(
+ root,
+ coil_sensitivity_maps_root,
+ mask_root,
+ noise_root,
+ initial_predictions_root,
+ dataset_format,
+ sample_rate,
+ volume_sample_rate,
+ use_dataset_cache,
+ dataset_cache_file,
+ num_cols,
+ consecutive_slices,
+ data_saved_per_slice,
+ n2r_supervised_rate,
+ complex_target,
+ log_images_rate,
+ transform,
+ **kwargs,
+ )
+ if sequence not in ("MEGRE", "FUTURE_SEQUENCES"):
+ warnings.warn(
+ 'Sequence should be either "MEGRE" or "FUTURE_SEQUENCES". '
+ f'Found {sequence}. If you are using this dataset for reconstruction, ignore this warning.'
+ 'If you are using this dataset for quantitative mapping, please use the correct sequence.'
+ )
+ self.sequence = sequence
+ self.segmentation_mask_root = segmentation_mask_root
+ self.kspace_scaling_factor = kspace_scaling_factor
+
+ def __getitem__(self, i: int): # noqa: MC0001
+ """Get item from :class:`qMRIDataset`."""
+ raise NotImplementedError
+
+
+class AHEADqMRIDataset(qMRIDataset):
+ """Supports the AHEAD dataset for quantitative MRI.
+
+ .. note::
+ Extends :class:`atommic.collections.quantitative.data.qMRIDataset`.
+ """
+
+ def __getitem__(self, i: int): # noqa: MC0001
+ """Get item from :class:`AHEADqMRIDataset`."""
+ fname, dataslice, metadata = self.examples[i]
+ with h5py.File(fname, "r") as hf:
+ kspace = self.get_consecutive_slices(hf, "kspace", dataslice).astype(np.complex64)
+
+ kspace = kspace / self.kspace_scaling_factor
+
+ if "sensitivity_map" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "sensitivity_map", dataslice).astype(np.complex64)
+ elif not is_none(self.coil_sensitivity_maps_root):
+ coil_sensitivity_maps_root = self.coil_sensitivity_maps_root
+ split_dir = str(fname).split("/")
+ # check if exists
+ if not os.path.exists(Path(f"{coil_sensitivity_maps_root}/{split_dir[-2]}/{fname.name}")):
+ # find to what depth the coil_sensitivity_maps_root directory is nested
+ for j in range(len(split_dir)):
+ # get the coil_sensitivity_maps_root directory name
+ coil_sensitivity_maps_root = Path(f"{self.coil_sensitivity_maps_root}/{split_dir[-j]}/")
+ if os.path.exists(coil_sensitivity_maps_root / Path(split_dir[-2]) / fname.name):
+ break
+
+ with h5py.File(
+ Path(coil_sensitivity_maps_root) / Path(split_dir[-2]) / fname.name, "r" # type: ignore
+ ) as sf:
+ if "sensitivity_map" in sf or "sensitivity_map" in next(iter(sf.keys())):
+ sensitivity_map = (
+ self.get_consecutive_slices(sf, "sensitivity_map", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ else:
+ sensitivity_map = np.array([])
+
+ if "mask" in hf:
+ mask = np.asarray(self.get_consecutive_slices(hf, "mask", dataslice))
+ if mask.ndim == 3:
+ mask = mask[dataslice]
+ elif not is_none(self.mask_root):
+ with h5py.File(Path(self.mask_root) / fname.name, "r") as mf: # type: ignore
+ mask = np.asarray(self.get_consecutive_slices(mf, "mask", dataslice))
+ else:
+ mask = np.empty([])
+
+ if "anatomy_mask" in hf:
+ anatomy_mask = np.asarray(self.get_consecutive_slices(hf, "anatomy_mask", dataslice))
+ if anatomy_mask.ndim == 3:
+ anatomy_mask = anatomy_mask[dataslice]
+ elif not is_none(self.segmentation_mask_root):
+ with h5py.File(Path(self.segmentation_mask_root) / fname.name, "r") as mf: # type: ignore
+ anatomy_mask = np.asarray(self.get_consecutive_slices(mf, "anatomy_mask", dataslice))
+ else:
+ anatomy_mask = np.empty([])
+
+ mask = [mask, anatomy_mask]
+
+ prediction = np.empty([])
+ if not is_none(self.initial_predictions_root):
+ with h5py.File(Path(self.initial_predictions_root) / fname.name, "r") as ipf: # type: ignore
+ rkey = "reconstruction" if "reconstruction" in ipf else "initial_prediction"
+ prediction = self.get_consecutive_slices(ipf, rkey, dataslice).squeeze().astype(np.complex64)
+ elif "reconstruction" in hf or "initial_prediction" in hf:
+ rkey = "reconstruction" if "reconstruction" in hf else "initial_prediction"
+ prediction = self.get_consecutive_slices(hf, rkey, dataslice).squeeze().astype(np.complex64)
+
+ # find key containing "reconstruction_"
+ rkey = re.findall(r"reconstruction_(.*)", str(hf.keys())) # type: ignore
+ self.recons_key = "reconstruction_" + rkey[0] if rkey else "target"
+ if "reconstruction_rss" in self.recons_key:
+ self.recons_key = "reconstruction_rss"
+ elif "reconstruction_sense" in hf:
+ self.recons_key = "reconstruction_sense"
+
+ if self.complex_target:
+ target = None
+ else:
+ # find key containing "reconstruction_"
+ rkey = re.findall(r"reconstruction_(.*)", str(hf.keys())) # type: ignore
+ self.recons_key = "reconstruction_" + rkey[0] if rkey else "target"
+ if "reconstruction_rss" in self.recons_key:
+ self.recons_key = "reconstruction_rss"
+ elif "reconstruction_sense" in hf:
+ self.recons_key = "reconstruction_sense"
+ target = self.get_consecutive_slices(hf, self.recons_key, dataslice) if self.recons_key in hf else None
+
+ attrs = dict(hf.attrs)
+
+ # get noise level for current slice, if metadata["noise_levels"] is not empty
+ if "noise_levels" in metadata and len(metadata["noise_levels"]) > 0:
+ metadata["noise"] = metadata["noise_levels"][dataslice]
+ else:
+ metadata["noise"] = 1.0
+
+ attrs.update(metadata)
+
+ if self.data_saved_per_slice:
+ # arbitrary slice number for logging purposes
+ dataslice = str(fname.name) # type: ignore
+ if "h5" in dataslice: # type: ignore
+ dataslice = dataslice.split(".h5")[0] # type: ignore
+ dataslice = int(dataslice.split("_")[-1]) # type: ignore
+
+ attrs["log_image"] = bool(dataslice in self.indices_to_log) if not self.data_saved_per_slice else True
+
+ if not is_none(self.sequence):
+ return (
+ (
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
+ return (
+ (
+ kspace,
+ sensitivity_map,
+ np.empty([]),
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ sensitivity_map,
+ np.empty([]),
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
diff --git a/atommic/collections/quantitative/nn/__init__.py b/atommic/collections/quantitative/nn/__init__.py
new file mode 100644
index 00000000..854ab46c
--- /dev/null
+++ b/atommic/collections/quantitative/nn/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.quantitative.nn.qcirim import qCIRIM # noqa: F401
+from atommic.collections.quantitative.nn.qvarnet import qVarNet # noqa: F401
diff --git a/atommic/collections/quantitative/nn/base.py b/atommic/collections/quantitative/nn/base.py
new file mode 100644
index 00000000..de4f4402
--- /dev/null
+++ b/atommic/collections/quantitative/nn/base.py
@@ -0,0 +1,2658 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import os
+import warnings
+from abc import ABC
+from collections import defaultdict
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import h5py
+import numpy as np
+import torch
+from numpy import ndarray
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch import Tensor
+from torch.nn import L1Loss, MSELoss
+from torch.utils.data import DataLoader
+
+# do not import BaseMRIModel and BaseSensitivityModel directly to avoid circular imports
+import atommic.collections.common as atommic_common
+from atommic.collections.common.data.subsample import create_masker
+from atommic.collections.common.losses import VALID_RECONSTRUCTION_LOSSES
+from atommic.collections.common.losses.aggregator import AggregatorLoss
+from atommic.collections.common.losses.wasserstein import SinkhornDistance
+from atommic.collections.common.parts import fft, utils
+from atommic.collections.quantitative.data.qmri_loader import AHEADqMRIDataset
+from atommic.collections.quantitative.parts.transforms import qMRIDataTransforms
+from atommic.collections.reconstruction.losses.na import NoiseAwareLoss
+from atommic.collections.reconstruction.losses.ssim import SSIMLoss
+from atommic.collections.reconstruction.metrics.reconstruction_metrics import mse, nmse, psnr, ssim
+from atommic.collections.reconstruction.nn.base import DistributedMetricSum
+
+__all__ = ["BaseqMRIReconstructionModel", "SignalForwardModel"]
+
+
+class BaseqMRIReconstructionModel(atommic_common.nn.base.BaseMRIModel, ABC):
+ """Base class of all quantitative MRIReconstruction models."""
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`BaseqMRIReconstructionModel`.
+
+ Parameters
+ ----------
+ cfg: DictConfig
+ The configuration file.
+ trainer: Trainer
+ The PyTorch Lightning trainer.
+ """
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ # Initialize the Fast-Fourier Transform parameters.
+ self.fft_centered = cfg_dict.get("fft_centered", False)
+ self.fft_normalization = cfg_dict.get("fft_normalization", "backward")
+ self.spatial_dims = cfg_dict.get("spatial_dims", None)
+ self.coil_dim = cfg_dict.get("coil_dim", 1)
+
+ # Initialize the dimensionality of the data. It can be 2D or 2.5D -> meaning 2D with > 1 slices or 3D.
+ self.dimensionality = cfg_dict.get("dimensionality", 2)
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+
+ # Initialize the coil combination method. It can be either "SENSE" or "RSS" (root-sum-of-squares) or
+ # "RSS-complex" (root-sum-of-squares of the complex-valued data).
+ self.coil_combination_method = cfg_dict.get("coil_combination_method", "SENSE")
+
+ # Refers to Self-Supervised Data Undersampling (SSDU). If True, then the model is trained with only
+ # undersampled data.
+ self.ssdu = cfg_dict.get("ssdu", False)
+
+ # Refers to Noise-to-Recon. If True, then the model can either be trained with only undersampled data or with
+ # both undersampled and (a percentage of) fully-sampled data.
+ self.n2r = cfg_dict.get("n2r", False)
+
+ # Initialize the sensitivity network if cfg_dict.get("estimate_coil_sensitivity_maps_with_nn") is True.
+ self.estimate_coil_sensitivity_maps_with_nn = cfg_dict.get("estimate_coil_sensitivity_maps_with_nn", False)
+
+ # Initialize loss related parameters.
+ self.kspace_quantitative_loss = cfg_dict.get("kspace_quantitative_loss", False)
+ self.n2r_loss_weight = cfg_dict.get("n2r_loss_weight", 1.0) if self.n2r else 1.0
+ self.quantitative_losses = {}
+ quantitative_loss = cfg_dict.get("quantitative_loss")
+ quantitative_losses_ = {}
+ if quantitative_loss is not None:
+ for k, v in quantitative_loss.items():
+ if k not in VALID_RECONSTRUCTION_LOSSES:
+ raise ValueError(
+ f"Quantitative loss {k} is not supported. Please choose one of the following: "
+ f"{VALID_RECONSTRUCTION_LOSSES}."
+ )
+ if v is None or v == 0.0:
+ warnings.warn(f"The weight of quantitative loss {k} is set to 0.0. This loss will not be used.")
+ else:
+ quantitative_losses_[k] = v
+ else:
+ # Default quantitative loss is L1.
+ quantitative_losses_["l1"] = 1.0
+ if sum(quantitative_losses_.values()) != 1.0:
+ warnings.warn("Sum of quantitative losses weights is not 1.0. Adjusting weights to sum up to 1.0.")
+ total_weight = sum(quantitative_losses_.values())
+ quantitative_losses_ = {k: v / total_weight for k, v in quantitative_losses_.items()}
+ for name in VALID_RECONSTRUCTION_LOSSES:
+ if name in quantitative_losses_:
+ if name == "ssim":
+ if self.ssdu:
+ raise ValueError("SSIM loss is not supported for SSDU.")
+ self.quantitative_losses[name] = SSIMLoss()
+ elif name == "mse":
+ self.quantitative_losses[name] = MSELoss()
+ elif name == "wasserstein":
+ self.quantitative_losses[name] = SinkhornDistance()
+ elif name == "noise_aware":
+ self.quantitative_losses[name] = NoiseAwareLoss()
+ elif name == "l1":
+ self.quantitative_losses[name] = L1Loss()
+ # replace losses names by 'loss_1', 'loss_2', etc. to properly iterate in the aggregator loss
+ self.quantitative_losses = {f"loss_{i+1}": v for i, v in enumerate(self.quantitative_losses.values())}
+ self.total_quantitative_losses = len(self.quantitative_losses)
+ self.total_quantitative_loss_weight = cfg_dict.get("total_quantitative_loss_weight", 1.0)
+ self.total_quantitative_reconstruction_loss_weight = cfg_dict.get(
+ "total_quantitative_reconstruction_loss_weight", 1.0
+ )
+ quantitative_parameters_regularization_factors = cfg_dict.get("quantitative_parameters_regularization_factors")
+ self.quantitative_parameters_regularization_factors = {
+ "R2star": quantitative_parameters_regularization_factors[0]["R2star"],
+ "S0": quantitative_parameters_regularization_factors[1]["S0"],
+ "B0": quantitative_parameters_regularization_factors[2]["B0"],
+ "phi": quantitative_parameters_regularization_factors[3]["phi"],
+ }
+
+ # Set normalization parameters for logging
+ self.unnormalize_loss_inputs = cfg_dict.get("unnormalize_loss_inputs", False)
+ self.unnormalize_log_outputs = cfg_dict.get("unnormalize_log_outputs", False)
+ self.normalization_type = cfg_dict.get("normalization_type", "max")
+
+ # Refers to cascading or iterative reconstruction methods.
+ self.accumulate_predictions = cfg_dict.get("accumulate_predictions", False)
+
+ # Refers to the type of the complex-valued data. It can be either "stacked" or "complex_abs" or
+ # "complex_sqrt_abs".
+ self.complex_valued_type = cfg_dict.get("complex_valued_type", "stacked")
+
+ # Initialize the module
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ if self.estimate_coil_sensitivity_maps_with_nn:
+ self.coil_sensitivity_maps_nn = atommic_common.nn.base.BaseSensitivityModel(
+ cfg_dict.get("coil_sensitivity_maps_nn_chans", 8),
+ cfg_dict.get("coil_sensitivity_maps_nn_pools", 4),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ mask_type=cfg_dict.get("coil_sensitivity_maps_nn_mask_type", "2D"),
+ normalize=cfg_dict.get("coil_sensitivity_maps_nn_normalize", True),
+ mask_center=cfg_dict.get("coil_sensitivity_maps_nn_mask_center", True),
+ )
+
+ # Set aggregation loss
+ self.total_quantitative_loss = AggregatorLoss(
+ num_inputs=self.total_quantitative_losses, weights=list(quantitative_losses_.values())
+ )
+
+ self.MSE = DistributedMetricSum()
+ self.NMSE = DistributedMetricSum()
+ self.SSIM = DistributedMetricSum()
+ self.PSNR = DistributedMetricSum()
+ self.TotExamples = DistributedMetricSum()
+
+ # Set evaluation metrics dictionaries
+ self.mse_vals_reconstruction: Dict = defaultdict(dict)
+ self.nmse_vals_reconstruction: Dict = defaultdict(dict)
+ self.ssim_vals_reconstruction: Dict = defaultdict(dict)
+ self.psnr_vals_reconstruction: Dict = defaultdict(dict)
+
+ self.mse_vals_R2star: Dict = defaultdict(dict)
+ self.nmse_vals_R2star: Dict = defaultdict(dict)
+ self.ssim_vals_R2star: Dict = defaultdict(dict)
+ self.psnr_vals_R2star: Dict = defaultdict(dict)
+
+ self.mse_vals_S0: Dict = defaultdict(dict)
+ self.nmse_vals_S0: Dict = defaultdict(dict)
+ self.ssim_vals_S0: Dict = defaultdict(dict)
+ self.psnr_vals_S0: Dict = defaultdict(dict)
+
+ self.mse_vals_B0: Dict = defaultdict(dict)
+ self.nmse_vals_B0: Dict = defaultdict(dict)
+ self.ssim_vals_B0: Dict = defaultdict(dict)
+ self.psnr_vals_B0: Dict = defaultdict(dict)
+
+ self.mse_vals_phi: Dict = defaultdict(dict)
+ self.nmse_vals_phi: Dict = defaultdict(dict)
+ self.ssim_vals_phi: Dict = defaultdict(dict)
+ self.psnr_vals_phi: Dict = defaultdict(dict)
+
+ def __abs_output__(self, x: torch.Tensor) -> torch.Tensor:
+ """Converts the input to absolute value."""
+ if x.shape[-1] == 2 or torch.is_complex(x):
+ if torch.is_complex(x):
+ x = torch.view_as_real(x)
+ if self.complex_valued_type == "stacked":
+ x = utils.check_stacked_complex(x)
+ elif self.complex_valued_type == "utils.complex_abs":
+ x = utils.complex_abs(x)
+ elif self.complex_valued_type == "complex_sqrt_abs":
+ x = utils.complex_abs_sq(x)
+ return x
+
+ def __unnormalize_for_loss_or_log__(
+ self,
+ target: torch.Tensor,
+ prediction: torch.Tensor,
+ sensitivity_maps: Union[torch.Tensor, None],
+ attrs: Dict,
+ r: int,
+ batch_idx: int = 1,
+ ) -> Tuple[torch.Tensor, torch.Tensor, Union[torch.Tensor, None]]:
+ """
+ Unnormalizes the data for computing the loss or logging.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : torch.Tensor
+ Prediction data of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor or None
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2] or None.
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ batch_idx : int
+ Batch index. Default is ``1``.
+
+ Returns
+ -------
+ target : torch.Tensor
+ Unnormalized target data.
+ prediction : torch.Tensor
+ Unnormalized prediction data.
+ sensitivity_maps : torch.Tensor
+ Unnormalized sensitivity maps.
+ """
+ if self.n2r and not attrs["n2r_supervised"][batch_idx]:
+ target = utils.unnormalize(
+ target,
+ {
+ "min": attrs["prediction_min"][batch_idx]
+ if "prediction_min" in attrs
+ else attrs[f"prediction_min_{r}"][batch_idx],
+ "max": attrs["prediction_max"][batch_idx]
+ if "prediction_max" in attrs
+ else attrs[f"prediction_max_{r}"][batch_idx],
+ "mean": attrs["prediction_mean"][batch_idx]
+ if "prediction_mean" in attrs
+ else attrs[f"prediction_mean_{r}"][batch_idx],
+ "std": attrs["prediction_std"][batch_idx]
+ if "prediction_std" in attrs
+ else attrs[f"prediction_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction = utils.unnormalize(
+ prediction,
+ {
+ "min": attrs["noise_prediction_min"][batch_idx]
+ if "noise_prediction_min" in attrs
+ else attrs[f"noise_prediction_min_{r}"][batch_idx],
+ "max": attrs["noise_prediction_max"][batch_idx]
+ if "noise_prediction_max" in attrs
+ else attrs[f"noise_prediction_max_{r}"][batch_idx],
+ attrs["noise_prediction_mean"][batch_idx]
+ if "noise_prediction_mean" in attrs
+ else "mean": attrs[f"noise_prediction_mean_{r}"][batch_idx],
+ attrs["noise_prediction_std"][batch_idx]
+ if "noise_prediction_std" in attrs
+ else "std": attrs[f"noise_prediction_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ else:
+ target = utils.unnormalize(
+ target,
+ {
+ "min": attrs["target_min"][batch_idx]
+ if "target_min" in attrs
+ else attrs[f"target_min_{r}"][batch_idx],
+ "max": attrs["target_max"][batch_idx]
+ if "target_max" in attrs
+ else attrs[f"target_max_{r}"][batch_idx],
+ "mean": attrs["target_mean"][batch_idx]
+ if "target_mean" in attrs
+ else attrs[f"target_mean_{r}"][batch_idx],
+ "std": attrs["target_std"][batch_idx]
+ if "target_std" in attrs
+ else attrs[f"target_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction = utils.unnormalize(
+ prediction,
+ {
+ "min": attrs["prediction_min"][batch_idx]
+ if "prediction_min" in attrs
+ else attrs[f"prediction_min_{r}"][batch_idx],
+ "max": attrs["prediction_max"][batch_idx]
+ if "prediction_max" in attrs
+ else attrs[f"prediction_max_{r}"][batch_idx],
+ "mean": attrs["prediction_mean"][batch_idx]
+ if "prediction_mean" in attrs
+ else attrs[f"prediction_mean_{r}"][batch_idx],
+ "std": attrs["prediction_std"][batch_idx]
+ if "prediction_std" in attrs
+ else attrs[f"prediction_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+
+ if sensitivity_maps is not None:
+ sensitivity_maps = utils.unnormalize(
+ sensitivity_maps,
+ {
+ "min": attrs["sensitivity_maps_min"][batch_idx],
+ "max": attrs["sensitivity_maps_max"][batch_idx],
+ "mean": attrs["sensitivity_maps_mean"][batch_idx],
+ "std": attrs["sensitivity_maps_std"][batch_idx],
+ },
+ self.normalization_type,
+ )
+
+ return target, prediction, sensitivity_maps
+
+ def __unnormalize_qmaps_for_loss_or_log__(
+ self,
+ target_R2star_map: torch.Tensor,
+ prediction_R2star_map: torch.Tensor,
+ target_S0_map: torch.Tensor,
+ prediction_S0_map: torch.Tensor,
+ target_B0_map: torch.Tensor,
+ prediction_B0_map: torch.Tensor,
+ target_phi_map: torch.Tensor,
+ prediction_phi_map: torch.Tensor,
+ attrs: Dict,
+ r: int,
+ batch_idx: int = 1,
+ ) -> Tuple[
+ torch.Tensor,
+ torch.Tensor,
+ torch.Tensor,
+ torch.Tensor,
+ torch.Tensor,
+ torch.Tensor,
+ torch.Tensor,
+ torch.Tensor,
+ ]:
+ """
+ Unnormalizes the quantitative maps for computing the loss or logging.
+
+ Parameters
+ ----------
+ target_R2star_map : torch.Tensor
+ Target R2star map of shape [batch_size, n_x, n_y].
+ prediction_R2star_map : torch.Tensor
+ Prediction R2star map of shape [batch_size, n_x, n_y].
+ target_S0_map : torch.Tensor
+ Target S0 map of shape [batch_size, n_x, n_y].
+ prediction_S0_map : torch.Tensor
+ Prediction S0 map of shape [batch_size, n_x, n_y].
+ target_B0_map : torch.Tensor
+ Target B0 map of shape [batch_size, n_x, n_y].
+ prediction_B0_map : torch.Tensor
+ Prediction B0 map of shape [batch_size, n_x, n_y].
+ target_phi_map : torch.Tensor
+ Target phi map of shape [batch_size, n_x, n_y].
+ prediction_phi_map : torch.Tensor
+ Prediction phi map of shape [batch_size, n_x, n_y].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ batch_idx : int
+ Batch index. Default is ``1``.
+
+ Returns
+ -------
+ target_R2star_map : torch.Tensor
+ Unnormalized target R2star map.
+ prediction_R2star_map : torch.Tensor
+ Unnormalized prediction R2star map.
+ target_S0_map : torch.Tensor
+ Unnormalized target S0 map.
+ prediction_S0_map : torch.Tensor
+ Unnormalized prediction S0 map.
+ target_B0_map : torch.Tensor
+ Unnormalized target B0 map.
+ prediction_B0_map : torch.Tensor
+ Unnormalized prediction B0 map.
+ target_phi_map : torch.Tensor
+ Unnormalized target phi map.
+ prediction_phi_map : torch.Tensor
+ Unnormalized prediction phi map.
+ """
+ target_R2star_map = utils.unnormalize(
+ target_R2star_map,
+ {
+ "min": attrs["R2star_map_target_min"][batch_idx]
+ if "target_min" in attrs
+ else attrs[f"R2star_map_target_min_{r}"][batch_idx],
+ "max": attrs["R2star_map_target_max"][batch_idx]
+ if "R2star_map_target_max" in attrs
+ else attrs[f"R2star_map_target_max_{r}"][batch_idx],
+ "mean": attrs["R2star_map_target_mean"][batch_idx]
+ if "R2star_map_target_mean" in attrs
+ else attrs[f"R2star_map_target_mean_{r}"][batch_idx],
+ "std": attrs["R2star_map_target_std"][batch_idx]
+ if "R2star_map_target_std" in attrs
+ else attrs[f"R2star_map_target_std_{r}"][batch_idx],
+ "var": attrs["R2star_map_target_var"][batch_idx]
+ if "R2star_map_target_var" in attrs
+ else attrs[f"R2star_map_target_var_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction_R2star_map = utils.unnormalize(
+ prediction_R2star_map,
+ {
+ "min": attrs["R2star_map_init_min"][batch_idx]
+ if "R2star_map_init_min" in attrs
+ else attrs[f"R2star_map_init_min_{r}"][batch_idx],
+ "max": attrs["R2star_map_init_max"][batch_idx]
+ if "R2star_map_init_max" in attrs
+ else attrs[f"R2star_map_init_max_{r}"][batch_idx],
+ "mean": attrs["R2star_map_init_mean"][batch_idx]
+ if "R2star_map_init_mean" in attrs
+ else attrs[f"R2star_map_init_mean_{r}"][batch_idx],
+ "std": attrs["R2star_map_init_std"][batch_idx]
+ if "R2star_map_init_std" in attrs
+ else attrs[f"R2star_map_init_std_{r}"][batch_idx],
+ "var": attrs["R2star_map_init_var"][batch_idx]
+ if "R2star_map_init_var" in attrs
+ else attrs[f"R2star_map_init_var_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ target_S0_map = utils.unnormalize(
+ target_S0_map,
+ {
+ "min": attrs["S0_map_target_min"][batch_idx]
+ if "S0_map_target_min" in attrs
+ else attrs[f"S0_map_target_min_{r}"][batch_idx],
+ "max": attrs["S0_map_target_max"][batch_idx]
+ if "S0_map_target_max" in attrs
+ else attrs[f"S0_map_target_max_{r}"][batch_idx],
+ "mean": attrs["S0_map_target_mean"][batch_idx]
+ if "S0_map_target_mean" in attrs
+ else attrs[f"S0_map_target_mean_{r}"][batch_idx],
+ "std": attrs["S0_map_target_std"][batch_idx]
+ if "S0_map_target_std" in attrs
+ else attrs[f"S0_map_target_std_{r}"][batch_idx],
+ "var": attrs["S0_map_target_var"][batch_idx]
+ if "S0_map_target_var" in attrs
+ else attrs[f"S0_map_target_var_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction_S0_map = utils.unnormalize(
+ prediction_S0_map,
+ {
+ "min": attrs["S0_map_init_min"][batch_idx]
+ if "S0_map_init_min" in attrs
+ else attrs[f"S0_map_init_min_{r}"][batch_idx],
+ "max": attrs["S0_map_init_max"][batch_idx]
+ if "S0_map_init_max" in attrs
+ else attrs[f"S0_map_init_max_{r}"][batch_idx],
+ "mean": attrs["S0_map_init_mean"][batch_idx]
+ if "S0_map_init_mean" in attrs
+ else attrs[f"S0_map_init_mean_{r}"][batch_idx],
+ "std": attrs["S0_map_init_std"][batch_idx]
+ if "S0_map_init_std" in attrs
+ else attrs[f"S0_map_init_std_{r}"][batch_idx],
+ "var": attrs["S0_map_init_var"][batch_idx]
+ if "S0_map_init_var" in attrs
+ else attrs[f"S0_map_init_var_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ target_B0_map = utils.unnormalize(
+ target_B0_map,
+ {
+ "min": attrs["B0_map_target_min"][batch_idx]
+ if "B0_map_target_min" in attrs
+ else attrs[f"B0_map_target_min_{r}"][batch_idx],
+ "max": attrs["B0_map_target_max"][batch_idx]
+ if "B0_map_target_max" in attrs
+ else attrs[f"B0_map_target_max_{r}"][batch_idx],
+ "mean": attrs["B0_map_target_mean"][batch_idx]
+ if "B0_map_target_mean" in attrs
+ else attrs[f"B0_map_target_mean_{r}"][batch_idx],
+ "std": attrs["B0_map_target_std"][batch_idx]
+ if "B0_map_target_std" in attrs
+ else attrs[f"B0_map_target_std_{r}"][batch_idx],
+ "var": attrs["B0_map_target_var"][batch_idx]
+ if "B0_map_target_var" in attrs
+ else attrs[f"B0_map_target_var_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction_B0_map = utils.unnormalize(
+ prediction_B0_map,
+ {
+ "min": attrs["B0_map_init_min"][batch_idx]
+ if "B0_map_init_min" in attrs
+ else attrs[f"B0_map_init_min_{r}"][batch_idx],
+ "max": attrs["B0_map_init_max"][batch_idx]
+ if "B0_map_init_max" in attrs
+ else attrs[f"B0_map_init_max_{r}"][batch_idx],
+ "mean": attrs["B0_map_init_mean"][batch_idx]
+ if "B0_map_init_mean" in attrs
+ else attrs[f"B0_map_init_mean_{r}"][batch_idx],
+ "std": attrs["B0_map_init_std"][batch_idx]
+ if "B0_map_init_std" in attrs
+ else attrs[f"B0_map_init_std_{r}"][batch_idx],
+ "var": attrs["B0_map_init_var"][batch_idx]
+ if "B0_map_init_var" in attrs
+ else attrs[f"B0_map_init_var_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ target_phi_map = utils.unnormalize(
+ target_phi_map,
+ {
+ "min": attrs["phi_map_target_min"][batch_idx]
+ if "phi_map_target_min" in attrs
+ else attrs[f"phi_map_target_min_{r}"][batch_idx],
+ "max": attrs["phi_map_target_max"][batch_idx]
+ if "phi_map_target_max" in attrs
+ else attrs[f"phi_map_target_max_{r}"][batch_idx],
+ "mean": attrs["phi_map_target_mean"][batch_idx]
+ if "phi_map_target_mean" in attrs
+ else attrs[f"phi_map_target_mean_{r}"][batch_idx],
+ "std": attrs["phi_map_target_std"][batch_idx]
+ if "phi_map_target_std" in attrs
+ else attrs[f"phi_map_target_std_{r}"][batch_idx],
+ "var": attrs["phi_map_target_var"][batch_idx]
+ if "phi_map_target_var" in attrs
+ else attrs[f"phi_map_target_var_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction_phi_map = utils.unnormalize(
+ prediction_phi_map,
+ {
+ "min": attrs["phi_map_init_min"][batch_idx]
+ if "phi_map_init_min" in attrs
+ else attrs[f"phi_map_init_min_{r}"][batch_idx],
+ "max": attrs["phi_map_init_max"][batch_idx]
+ if "phi_map_init_max" in attrs
+ else attrs[f"phi_map_init_max_{r}"][batch_idx],
+ "mean": attrs["phi_map_init_mean"][batch_idx]
+ if "phi_map_init_mean" in attrs
+ else attrs[f"phi_map_init_mean_{r}"][batch_idx],
+ "std": attrs["phi_map_init_std"][batch_idx]
+ if "phi_map_init_std" in attrs
+ else attrs[f"phi_map_init_std_{r}"][batch_idx],
+ "var": attrs["phi_map_init_var"][batch_idx]
+ if "phi_map_init_var" in attrs
+ else attrs[f"phi_map_init_var_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+
+ return (
+ target_R2star_map,
+ prediction_R2star_map,
+ target_S0_map,
+ prediction_S0_map,
+ target_B0_map,
+ prediction_B0_map,
+ target_phi_map,
+ prediction_phi_map,
+ )
+
+ def process_quantitative_loss(
+ self,
+ target: torch.Tensor,
+ prediction: Union[list, torch.Tensor],
+ anatomy_mask: torch.Tensor,
+ quantitative_map: str,
+ loss_func: torch.nn.Module,
+ ) -> torch.Tensor:
+ """Processes the quantitative loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ anatomy_mask : torch.Tensor
+ Mask of specified anatomy, e.g. brain. Shape [n_x, n_y].
+ quantitative_map : str
+ Type of quantitative map to regularize the loss. Must be one of {"R2star", "S0", "B0", "phi"}.
+ loss_func : torch.nn.Module
+ Loss function. Default is ``torch.nn.L1Loss()``.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ if isinstance(prediction, list):
+ while isinstance(prediction, list):
+ prediction = prediction[-1]
+
+ target = torch.abs(self.__abs_output__(target / torch.max(torch.abs(target))))
+ prediction = torch.abs(self.__abs_output__(prediction / torch.max(torch.abs(prediction))))
+ anatomy_mask = torch.abs(anatomy_mask).to(target)
+
+ if "ssim" in str(loss_func).lower():
+ return (
+ loss_func(
+ target * anatomy_mask,
+ prediction * anatomy_mask,
+ data_range=torch.tensor(
+ [max(torch.max(target * anatomy_mask).item(), torch.max(prediction * anatomy_mask).item())]
+ )
+ .unsqueeze(dim=0)
+ .to(target),
+ )
+ * self.quantitative_parameters_regularization_factors[quantitative_map]
+ )
+
+ return (
+ loss_func(target * anatomy_mask, prediction * anatomy_mask)
+ / self.quantitative_parameters_regularization_factors[quantitative_map]
+ )
+
+ def process_reconstruction_loss( # noqa: MC0001
+ self,
+ target: torch.Tensor,
+ prediction: Union[list, torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ attrs: Dict,
+ r: int,
+ loss_func: torch.nn.Module,
+ ) -> torch.Tensor:
+ """Processes the reconstruction loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ loss_func : torch.nn.Module
+ Loss function. Default is ``torch.nn.L1Loss()``.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ if isinstance(prediction, list):
+ while isinstance(prediction, list):
+ prediction = prediction[-1]
+
+ if self.unnormalize_loss_inputs:
+ target, prediction, sensitivity_maps = self.__unnormalize_for_loss_or_log__(
+ target, prediction, sensitivity_maps, attrs, r
+ )
+
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_quantitative_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if target.shape[-1] != 2 and torch.is_complex(target):
+ target = torch.view_as_real(target)
+ if prediction.shape[-1] != 2 and torch.is_complex(prediction):
+ prediction = torch.view_as_real(prediction)
+
+ # Transform to k-space.
+ target = fft.fft2(target, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ prediction = fft.fft2(prediction, self.fft_centered, self.fft_normalization, self.spatial_dims)
+
+ target = self.__abs_output__(target / torch.max(torch.abs(target)))
+ prediction = self.__abs_output__(prediction / torch.max(torch.abs(prediction)))
+ elif not self.unnormalize_loss_inputs:
+ target = self.__abs_output__(target / torch.max(torch.abs(target)))
+ prediction = self.__abs_output__(prediction / torch.max(torch.abs(prediction)))
+
+ prediction = torch.abs(prediction / torch.max(torch.abs(prediction)))
+ target = torch.abs(target / torch.max(torch.abs(target)))
+
+ return torch.mean(
+ torch.tensor([loss_func(target[:, echo], prediction[:, echo]) for echo in range(target.shape[1])])
+ )
+
+ def __compute_loss__(
+ self,
+ target_reconstruction: torch.Tensor,
+ prediction_reconstruction: Union[list, torch.Tensor],
+ R2star_map_prediction: torch.Tensor,
+ R2star_map_target: torch.Tensor,
+ S0_map_prediction: torch.Tensor,
+ S0_map_target: torch.Tensor,
+ B0_map_prediction: torch.Tensor,
+ B0_map_target: torch.Tensor,
+ phi_map_prediction: torch.Tensor,
+ phi_map_target: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ anatomy_mask: torch.Tensor,
+ attrs: Dict,
+ r: int,
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Computes the quantitative loss.
+
+ Parameters
+ ----------
+ target_reconstruction : torch.Tensor
+ Reconstruction target data of shape [batch_size, n_x, n_y, 2].
+ prediction_reconstruction : Union[list, torch.Tensor]
+ Reconstruction prediction(s) of shape [batch_size, n_x, n_y, 2].
+ R2star_map_prediction : torch.Tensor
+ R2* map prediction of shape [batch_size, n_x, n_y].
+ R2star_map_target : torch.Tensor
+ R2* map target of shape [batch_size, n_x, n_y].
+ S0_map_prediction : torch.Tensor
+ S0 map prediction of shape [batch_size, n_x, n_y].
+ S0_map_target : torch.Tensor
+ S0 map target of shape [batch_size, n_x, n_y].
+ B0_map_prediction : torch.Tensor
+ B0 map prediction of shape [batch_size, n_x, n_y].
+ B0_map_target : torch.Tensor
+ B0 map target of shape [batch_size, n_x, n_y].
+ phi_map_prediction : torch.Tensor
+ Phi map prediction of shape [batch_size, n_x, n_y].
+ phi_map_target : torch.Tensor
+ Phi map target of shape [batch_size, n_x, n_y].
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ anatomy_mask : torch.Tensor
+ Mask of specified anatomy, e.g. brain. Shape [n_x, n_y].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+
+ Returns
+ -------
+ lossR2star : torch.Tensor
+ R2* loss.
+ lossS0 : torch.Tensor
+ S0 loss.
+ lossB0 : torch.Tensor
+ B0 loss.
+ lossPhi : torch.Tensor
+ Phi loss.
+ quantitative_loss : torch.Tensor
+ Reconstruction loss.
+ loss : torch.Tensor
+ Total loss.
+ """
+ if self.unnormalize_loss_inputs:
+ (
+ R2star_map_target,
+ R2star_map_prediction,
+ S0_map_target,
+ S0_map_prediction,
+ B0_map_target,
+ B0_map_prediction,
+ phi_map_target,
+ phi_map_prediction,
+ ) = self.__unnormalize_qmaps_for_loss_or_log__(
+ R2star_map_target,
+ R2star_map_prediction,
+ S0_map_target,
+ S0_map_prediction,
+ B0_map_target,
+ B0_map_prediction,
+ phi_map_target,
+ phi_map_prediction,
+ attrs,
+ attrs["r"],
+ R2star_map_target.shape[0],
+ )
+
+ lossesR2star = {}
+ lossesS0 = {}
+ lossesB0 = {}
+ lossesPhi = {}
+ for name, loss_func in self.quantitative_losses.items():
+ lossesR2star[name] = self.process_quantitative_loss(
+ R2star_map_target, R2star_map_prediction, anatomy_mask, "R2star", loss_func
+ )
+ lossesS0[name] = self.process_quantitative_loss(
+ S0_map_target, S0_map_prediction, anatomy_mask, "S0", loss_func
+ )
+ lossesB0[name] = self.process_quantitative_loss(
+ B0_map_target, B0_map_prediction, anatomy_mask, "B0", loss_func
+ )
+ lossesPhi[name] = self.process_quantitative_loss(
+ phi_map_target, phi_map_prediction, anatomy_mask, "phi", loss_func
+ )
+
+ lossR2star = self.total_quantitative_loss(**lossesR2star)
+ lossS0 = self.total_quantitative_loss(**lossesS0)
+ lossB0 = self.total_quantitative_loss(**lossesB0)
+ lossPhi = self.total_quantitative_loss(**lossesPhi)
+ qmpas_loss = [lossR2star, lossS0, lossB0, lossPhi]
+
+ total_quantitative_loss = sum(qmpas_loss) / len(qmpas_loss) * self.total_quantitative_loss_weight
+
+ # Reconstruction loss, if self.use_reconstruction_module is True, then the loss is accumulated.
+ if self.use_reconstruction_module:
+ losses = {}
+ for name, loss_func in self.quantitative_losses.items():
+ losses[name] = self.process_reconstruction_loss(
+ target_reconstruction,
+ prediction_reconstruction,
+ sensitivity_maps,
+ attrs,
+ r,
+ loss_func,
+ )
+ quantitative_reconstruction_loss = (
+ self.total_quantitative_loss(**losses) * self.total_quantitative_reconstruction_loss_weight
+ )
+ else:
+ quantitative_reconstruction_loss = torch.tensor(0.0).to(R2star_map_target)
+
+ total_quantitative_loss = total_quantitative_loss + quantitative_reconstruction_loss
+
+ return lossR2star, lossS0, lossB0, lossPhi, quantitative_reconstruction_loss, total_quantitative_loss
+
+ def __compute_and_log_metrics_and_outputs__( # pylint: disable=too-many-statements
+ self,
+ prediction_R2star_map: Union[list, torch.Tensor],
+ prediction_S0_map: Union[list, torch.Tensor],
+ prediction_B0_map: Union[list, torch.Tensor],
+ prediction_phi_map: Union[list, torch.Tensor],
+ prediction_reconstruction: Union[list, torch.Tensor],
+ target_R2star_map: Union[list, torch.Tensor],
+ target_S0_map: Union[list, torch.Tensor],
+ target_B0_map: Union[list, torch.Tensor],
+ target_phi_map: Union[list, torch.Tensor],
+ target_reconstruction: Union[list, torch.Tensor],
+ anatomy_mask: torch.Tensor,
+ attrs: Dict,
+ fname: str,
+ slice_idx: int,
+ acceleration: float,
+ ):
+ """Computes the metrics and logs the outputs.
+
+ Parameters
+ ----------
+ prediction_R2star_map : Union[list, torch.Tensor]
+ R2* map prediction(s) of shape [batch_size, n_x, n_y].
+ prediction_S0_map : Union[list, torch.Tensor]
+ S0 map prediction(s) of shape [batch_size, n_x, n_y].
+ prediction_B0_map : Union[list, torch.Tensor]
+ B0 map prediction(s) of shape [batch_size, n_x, n_y].
+ prediction_phi_map : Union[list, torch.Tensor]
+ Phi map prediction(s) of shape [batch_size, n_x, n_y].
+ prediction_reconstruction : Union[list, torch.Tensor]
+ Reconstruction prediction(s) of shape [batch_size, n_x, n_y, 2].
+ target_R2star_map : Union[list, torch.Tensor]
+ R2* map target(s) of shape [batch_size, n_x, n_y].
+ target_S0_map : Union[list, torch.Tensor]
+ S0 map target(s) of shape [batch_size, n_x, n_y].
+ target_B0_map : Union[list, torch.Tensor]
+ B0 map target(s) of shape [batch_size, n_x, n_y].
+ target_phi_map : Union[list, torch.Tensor]
+ Phi map target(s) of shape [batch_size, n_x, n_y].
+ target_reconstruction : Union[list, torch.Tensor]
+ Reconstruction target(s) of shape [batch_size, n_x, n_y, 2].
+ anatomy_mask : torch.Tensor
+ Mask of specified anatomy, e.g. brain. Shape [n_x, n_y].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ fname : str
+ File name.
+ slice_idx : int
+ Slice index.
+ acceleration : float
+ Acceleration factor.
+ """
+
+ # if predictions are lists, e.g. in case of cascades or time steps or both, unpack them and keep the last one
+ def unpack_if_list_and_abs(x):
+ if isinstance(x, list):
+ while isinstance(x, list):
+ x = x[-1]
+ x = self.__abs_output__(x)
+ if x.dim() == 3:
+ x = x.unsqueeze(1)
+ return x
+
+ # Add dummy dimensions to target and predictions for logging.
+ prediction_R2star_map = unpack_if_list_and_abs(prediction_R2star_map) * anatomy_mask
+ prediction_S0_map = unpack_if_list_and_abs(prediction_S0_map) * anatomy_mask
+ prediction_B0_map = unpack_if_list_and_abs(prediction_B0_map) * anatomy_mask
+ prediction_phi_map = unpack_if_list_and_abs(prediction_phi_map) * anatomy_mask
+ target_R2star_map = unpack_if_list_and_abs(target_R2star_map) * anatomy_mask
+ target_S0_map = unpack_if_list_and_abs(target_S0_map) * anatomy_mask
+ target_B0_map = unpack_if_list_and_abs(target_B0_map) * anatomy_mask
+ target_phi_map = unpack_if_list_and_abs(target_phi_map) * anatomy_mask
+ if self.use_reconstruction_module:
+ prediction_reconstruction = unpack_if_list_and_abs(prediction_reconstruction)
+ target_reconstruction = unpack_if_list_and_abs(target_reconstruction)
+
+ # Iterate over the batch and log the target and predictions.
+ for _batch_idx_ in range(target_R2star_map.shape[0]):
+ output_target_R2star_map = target_R2star_map[_batch_idx_]
+ output_prediction_R2star_map = prediction_R2star_map[_batch_idx_]
+ output_target_S0_map = target_S0_map[_batch_idx_]
+ output_prediction_S0_map = prediction_S0_map[_batch_idx_]
+ output_target_B0_map = target_B0_map[_batch_idx_]
+ output_prediction_B0_map = prediction_B0_map[_batch_idx_]
+ output_target_phi_map = target_phi_map[_batch_idx_]
+ output_prediction_phi_map = prediction_phi_map[_batch_idx_]
+ if self.use_reconstruction_module:
+ output_target_reconstruction = target_reconstruction[_batch_idx_]
+ output_prediction_reconstruction = prediction_reconstruction[_batch_idx_]
+
+ if self.unnormalize_log_outputs:
+ (
+ output_target_R2star_map,
+ output_prediction_R2star_map,
+ output_target_S0_map,
+ output_prediction_S0_map,
+ output_target_B0_map,
+ output_prediction_B0_map,
+ output_target_phi_map,
+ output_prediction_phi_map,
+ ) = self.__unnormalize_qmaps_for_loss_or_log__(
+ output_target_R2star_map,
+ output_prediction_R2star_map,
+ output_target_S0_map,
+ output_prediction_S0_map,
+ output_target_B0_map,
+ output_prediction_B0_map,
+ output_target_phi_map,
+ output_prediction_phi_map,
+ attrs,
+ attrs["r"],
+ _batch_idx_,
+ )
+
+ output_target_R2star_map = (
+ torch.abs(output_target_R2star_map / torch.max(torch.abs(output_target_R2star_map))).detach().cpu()
+ )
+ output_prediction_R2star_map = (
+ torch.abs(output_prediction_R2star_map / torch.max(torch.abs(output_prediction_R2star_map)))
+ .detach()
+ .cpu()
+ )
+ output_target_S0_map = (
+ torch.abs(output_target_S0_map / torch.max(torch.abs(output_target_S0_map))).detach().cpu()
+ )
+ output_prediction_S0_map = (
+ torch.abs(output_prediction_S0_map / torch.max(torch.abs(output_prediction_S0_map))).detach().cpu()
+ )
+ output_target_B0_map = (
+ torch.abs(output_target_B0_map / torch.max(torch.abs(output_target_B0_map))).detach().cpu()
+ )
+ output_prediction_B0_map = (
+ torch.abs(output_prediction_B0_map / torch.max(torch.abs(output_prediction_B0_map))).detach().cpu()
+ )
+ output_target_phi_map = (
+ torch.abs(output_target_phi_map / torch.max(torch.abs(output_target_phi_map))).detach().cpu()
+ )
+ output_prediction_phi_map = (
+ torch.abs(output_prediction_phi_map / torch.max(torch.abs(output_prediction_phi_map))).detach().cpu()
+ )
+
+ if self.use_reconstruction_module:
+ output_target_reconstruction = (
+ torch.abs(output_target_reconstruction / torch.max(torch.abs(output_target_reconstruction)))
+ .detach()
+ .cpu()
+ )
+ output_prediction_reconstruction = (
+ torch.abs(
+ output_prediction_reconstruction / torch.max(torch.abs(output_prediction_reconstruction))
+ )
+ .detach()
+ .cpu()
+ )
+
+ slice_num = int(slice_idx[_batch_idx_].item()) # type: ignore
+
+ # Log target and predictions, if log_image is True for this slice.
+ if attrs["log_image"][_batch_idx_]:
+ # if consecutive slices, select the middle slice
+ if self.consecutive_slices > 1:
+ output_target_R2star_map = output_target_R2star_map[self.consecutive_slices // 2]
+ output_prediction_R2star_map = output_prediction_R2star_map[self.consecutive_slices // 2]
+ output_target_S0_map = output_target_S0_map[self.consecutive_slices // 2]
+ output_prediction_S0_map = output_prediction_S0_map[self.consecutive_slices // 2]
+ output_target_B0_map = output_target_B0_map[self.consecutive_slices // 2]
+ output_prediction_B0_map = output_prediction_B0_map[self.consecutive_slices // 2]
+ output_target_phi_map = output_target_phi_map[self.consecutive_slices // 2]
+ output_prediction_phi_map = output_prediction_phi_map[self.consecutive_slices // 2]
+
+ key = f"{fname[_batch_idx_]}_slice_{int(slice_idx[_batch_idx_])}-Acc={acceleration}x" # type: ignore
+
+ output_target_qmaps = torch.cat(
+ [output_target_R2star_map, output_target_S0_map, output_target_B0_map, output_target_phi_map],
+ dim=-1,
+ )
+ output_prediction_qmaps = torch.cat(
+ [
+ output_prediction_R2star_map,
+ output_prediction_S0_map,
+ output_prediction_B0_map,
+ output_prediction_phi_map,
+ ],
+ dim=-1,
+ )
+
+ self.log_image(f"{key}/qmaps/target", output_target_qmaps)
+ self.log_image(f"{key}/qmaps/reconstruction", output_prediction_qmaps)
+ self.log_image(f"{key}/qmaps/error", output_target_qmaps - output_prediction_qmaps)
+
+ if self.use_reconstruction_module:
+ output_target_reconstruction_echoes = torch.cat(
+ [output_target_reconstruction[i] for i in range(output_target_reconstruction.shape[0])], dim=-1
+ )
+ output_prediction_reconstruction_echoes = torch.cat(
+ [
+ output_prediction_reconstruction[i]
+ for i in range(output_prediction_reconstruction.shape[0])
+ ],
+ dim=-1,
+ )
+
+ self.log_image(f"{key}/reconstruction/target", output_target_reconstruction_echoes)
+ self.log_image(f"{key}/reconstruction/prediction", output_prediction_reconstruction_echoes)
+ self.log_image(
+ f"{key}/reconstruction/error",
+ torch.abs(output_target_reconstruction_echoes - output_prediction_reconstruction_echoes),
+ )
+
+ if self.use_reconstruction_module:
+ output_target_reconstruction = output_target_reconstruction.unsqueeze(1).numpy()
+ output_prediction_reconstruction = output_prediction_reconstruction.unsqueeze(1).numpy()
+
+ # compute metrics per echo time
+ mses = []
+ nmses = []
+ ssims = []
+ psnrs = []
+ for echo_time in range(output_target_reconstruction.shape[0]):
+ echo_output_target_reconstruction = output_target_reconstruction[echo_time, ...]
+ echo_output_prediction_reconstruction = output_prediction_reconstruction[echo_time, ...]
+
+ echo_output_target_reconstruction = np.abs(
+ echo_output_target_reconstruction / np.max(np.abs(echo_output_target_reconstruction))
+ )
+ echo_output_prediction_reconstruction = np.abs(
+ echo_output_prediction_reconstruction / np.max(np.abs(echo_output_prediction_reconstruction))
+ )
+
+ mses.append(
+ torch.tensor(
+ mse(echo_output_target_reconstruction, echo_output_prediction_reconstruction)
+ ).view(1)
+ )
+ nmses.append(
+ torch.tensor(
+ nmse(echo_output_target_reconstruction, echo_output_prediction_reconstruction)
+ ).view(1)
+ )
+
+ max_value = max(
+ np.max(echo_output_target_reconstruction), np.max(echo_output_prediction_reconstruction)
+ ) - min(np.min(echo_output_target_reconstruction), np.min(echo_output_prediction_reconstruction))
+
+ ssims.append(
+ torch.tensor(
+ ssim(
+ echo_output_target_reconstruction,
+ echo_output_prediction_reconstruction,
+ max_value,
+ )
+ ).view(1)
+ )
+ psnrs.append(
+ torch.tensor(
+ psnr(
+ echo_output_target_reconstruction,
+ echo_output_prediction_reconstruction,
+ max_value,
+ )
+ ).view(1)
+ )
+
+ self.mse_vals_reconstruction[fname[_batch_idx_]][str(slice_num)] = torch.tensor(mses).mean()
+ self.nmse_vals_reconstruction[fname[_batch_idx_]][str(slice_num)] = torch.tensor(nmses).mean()
+ self.ssim_vals_reconstruction[fname[_batch_idx_]][str(slice_num)] = torch.tensor(ssims).mean()
+ self.psnr_vals_reconstruction[fname[_batch_idx_]][str(slice_num)] = torch.tensor(psnrs).mean()
+ else:
+ self.mse_vals_reconstruction[fname[_batch_idx_]][str(slice_num)] = torch.tensor(0).view(1)
+ self.nmse_vals_reconstruction[fname[_batch_idx_]][str(slice_num)] = torch.tensor(0).view(1)
+ self.ssim_vals_reconstruction[fname[_batch_idx_]][str(slice_num)] = torch.tensor(0).view(1)
+ self.psnr_vals_reconstruction[fname[_batch_idx_]][str(slice_num)] = torch.tensor(0).view(1)
+
+ # compute metrics for quantitative maps
+ output_target_R2star_map = output_target_R2star_map.numpy()
+ output_prediction_R2star_map = output_prediction_R2star_map.numpy()
+ output_target_S0_map = output_target_S0_map.numpy()
+ output_prediction_S0_map = output_prediction_S0_map.numpy()
+ output_target_B0_map = output_target_B0_map.numpy()
+ output_prediction_B0_map = output_prediction_B0_map.numpy()
+ output_target_phi_map = output_target_phi_map.numpy()
+ output_prediction_phi_map = output_prediction_phi_map.numpy()
+
+ self.mse_vals_R2star[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ mse(output_target_R2star_map, output_prediction_R2star_map)
+ ).view(1)
+ self.nmse_vals_R2star[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ nmse(output_target_R2star_map, output_prediction_R2star_map)
+ ).view(1)
+
+ max_value = max(np.max(output_target_R2star_map), np.max(output_prediction_R2star_map)) - min(
+ np.min(output_target_R2star_map), np.min(output_prediction_R2star_map)
+ )
+
+ self.ssim_vals_R2star[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ ssim(output_target_R2star_map, output_prediction_R2star_map, maxval=max_value)
+ ).view(1)
+ self.psnr_vals_R2star[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ psnr(output_target_R2star_map, output_prediction_R2star_map, maxval=max_value)
+ ).view(1)
+
+ self.mse_vals_S0[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ mse(output_target_S0_map, output_prediction_S0_map)
+ ).view(1)
+ self.nmse_vals_S0[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ nmse(output_target_S0_map, output_prediction_S0_map)
+ ).view(1)
+
+ max_value = max(np.max(output_target_S0_map), np.max(output_prediction_S0_map)) - min(
+ np.min(output_target_S0_map), np.min(output_prediction_S0_map)
+ )
+
+ self.ssim_vals_S0[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ ssim(output_target_S0_map, output_prediction_S0_map, maxval=max_value)
+ ).view(1)
+ self.psnr_vals_S0[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ psnr(output_target_S0_map, output_prediction_S0_map, maxval=max_value)
+ ).view(1)
+
+ self.mse_vals_B0[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ mse(output_target_B0_map, output_prediction_B0_map)
+ ).view(1)
+ self.nmse_vals_B0[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ nmse(output_target_B0_map, output_prediction_B0_map)
+ ).view(1)
+
+ max_value = max(np.max(output_target_B0_map), np.max(output_prediction_B0_map)) - min(
+ np.min(output_target_B0_map), np.min(output_prediction_B0_map)
+ )
+
+ self.ssim_vals_B0[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ ssim(output_target_B0_map, output_prediction_B0_map, maxval=max_value)
+ ).view(1)
+ self.psnr_vals_B0[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ psnr(output_target_B0_map, output_prediction_B0_map, maxval=max_value)
+ ).view(1)
+
+ self.mse_vals_phi[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ mse(output_target_phi_map, output_prediction_phi_map)
+ ).view(1)
+ self.nmse_vals_phi[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ nmse(output_target_phi_map, output_prediction_phi_map)
+ ).view(1)
+ self.ssim_vals_phi[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ ssim(output_target_phi_map, output_prediction_phi_map, maxval=max_value)
+ ).view(1)
+ self.psnr_vals_phi[fname[_batch_idx_]][str(slice_num)] = torch.tensor(
+ psnr(output_target_phi_map, output_prediction_phi_map, maxval=max_value)
+ ).view(1)
+
+ @staticmethod
+ def __process_inputs__(
+ R2star_map_init: Union[list, torch.Tensor],
+ S0_map_init: Union[list, torch.Tensor],
+ B0_map_init: Union[list, torch.Tensor],
+ phi_map_init: Union[list, torch.Tensor],
+ kspace: Union[list, torch.Tensor],
+ y: Union[list, torch.Tensor],
+ mask: Union[list, torch.Tensor],
+ initial_prediction_reconstruction: Union[List, torch.Tensor],
+ target_reconstruction: Union[list, torch.Tensor],
+ ) -> tuple[
+ Union[Union[list, Tensor], Any],
+ Union[Union[list, Tensor], Any],
+ Union[Union[list, Tensor], Any],
+ Union[Union[list, Tensor], Any],
+ Union[Tensor, Any],
+ Union[Tensor, Any],
+ Union[Tensor, Any],
+ Union[Tensor, Any],
+ Union[Tensor, Any],
+ Union[int, ndarray],
+ ]:
+ """Processes lists of inputs to torch.Tensor. In the case where multiple accelerations are used, then the
+ inputs are lists. This function converts the lists to torch.Tensor by randomly selecting one acceleration. If
+ only one acceleration is used, then the inputs are torch.Tensor and are returned as is.
+
+ Parameters
+ ----------
+ R2star_map_init : Union[list, torch.Tensor]
+ R2* map of length n_accelerations or shape [batch_size, n_x, n_y].
+ S0_map_init : Union[list, torch.Tensor]
+ S0 map of length n_accelerations or shape [batch_size, n_x, n_y].
+ B0_map_init : Union[list, torch.Tensor]
+ B0 map of length n_accelerations or shape [batch_size, n_x, n_y].
+ phi_map_init : Union[list, torch.Tensor]
+ Phi map of length n_accelerations or shape [batch_size, n_x, n_y].
+ kspace : Union[list, torch.Tensor]
+ Full k-space data of length n_accelerations or shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ y : Union[list, torch.Tensor]
+ Subsampled k-space data of length n_accelerations or shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ mask : Union[list, torch.Tensor]
+ Sampling mask of length n_accelerations or shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction_reconstruction : Union[List, torch.Tensor]
+ Initial reconstruction prediction. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_x, n_y, 2].
+ target_reconstruction : torch.Tensor
+ Target reconstruction data. Shape [batch_size, n_x, n_y, 2].
+
+ Returns
+ -------
+ R2star_map_init : torch.Tensor
+ R2* map of shape [batch_size, n_x, n_y].
+ S0_map_init : torch.Tensor
+ S0 map of shape [batch_size, n_x, n_y].
+ B0_map_init : torch.Tensor
+ B0 map of shape [batch_size, n_x, n_y].
+ phi_map_init : torch.Tensor
+ Phi map of shape [batch_size, n_x, n_y].
+ kspace : torch.Tensor
+ Full k-space data of shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ y : torch.Tensor
+ Subsampled k-space data of shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ r : int
+ Random index used to select the acceleration.
+ """
+ if isinstance(y, list):
+ r = np.random.randint(len(y))
+ R2star_map_init = R2star_map_init[r]
+ S0_map_init = S0_map_init[r]
+ B0_map_init = B0_map_init[r]
+ phi_map_init = phi_map_init[r]
+ y = y[r]
+ mask = mask[r]
+ initial_prediction_reconstruction = initial_prediction_reconstruction[r]
+ else:
+ r = 0
+ if isinstance(kspace, list):
+ kspace = kspace[r]
+ target_reconstruction = target_reconstruction[r]
+ elif isinstance(target_reconstruction, list):
+ target_reconstruction = target_reconstruction[r]
+ return (
+ R2star_map_init,
+ S0_map_init,
+ B0_map_init,
+ phi_map_init,
+ kspace,
+ y,
+ mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ r,
+ )
+
+ def inference_step(
+ self,
+ R2star_map_initial_prediction: torch.Tensor,
+ S0_map_initial_prediction: torch.Tensor,
+ B0_map_initial_prediction: torch.Tensor,
+ phi_map_initial_prediction: torch.Tensor,
+ TEs: Union[List[torch.Tensor], torch.Tensor],
+ kspace: torch.Tensor,
+ y: Union[List[torch.Tensor], torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ sampling_mask: Union[List[torch.Tensor], torch.Tensor],
+ anatomy_mask: Union[List[torch.Tensor], torch.Tensor],
+ initial_prediction_reconstruction: Union[List, torch.Tensor],
+ target_reconstruction: torch.Tensor,
+ fname: str,
+ slice_idx: int,
+ acceleration: float,
+ attrs: Dict,
+ ):
+ """Performs an inference step, i.e., computes the predictions of the model.
+
+ Parameters
+ ----------
+ R2star_map_initial_prediction : torch.Tensor
+ Initial R2* map prediction. Shape [batch_size, n_x, n_y].
+ S0_map_initial_prediction : torch.Tensor
+ Initial S0 map prediction. Shape [batch_size, n_x, n_y].
+ B0_map_initial_prediction : torch.Tensor
+ Initial B0 map prediction. Shape [batch_size, n_x, n_y].
+ phi_map_initial_prediction : torch.Tensor
+ Initial phi map prediction. Shape [batch_size, n_x, n_y].
+ TEs : Union[List[torch.Tensor], torch.Tensor]
+ Echo times. If multiple echoes are used, then it is a list of torch.Tensor. Shape [batch_size, n_echoes].
+ kspace : torch.Tensor
+ Fully sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ y : Union[List[torch.Tensor], torch.Tensor]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ sampling_mask : Union[List[torch.Tensor], torch.Tensor]
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if Unsupervised
+ Learning methods are used, it contains their masks. Shape [batch_size, 1, n_x, n_y, 1].
+ anatomy_mask : Union[List[torch.Tensor], torch.Tensor]
+ Mask of specified anatomy, e.g. brain. Shape [n_x, n_y].
+ initial_prediction_reconstruction : Union[List, torch.Tensor]
+ Initial reconstruction prediction. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_x, n_y, 2].
+ target_reconstruction : torch.Tensor
+ Target reconstruction data. Shape [batch_size, n_x, n_y, 2].
+ fname : str
+ File name.
+ slice_idx : int
+ Slice index.
+ acceleration : float
+ Acceleration factor of the sampling mask, randomly selected if multiple accelerations are used.
+ attrs : Dict
+ Attributes dictionary.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary of loss and log.
+ """
+ # Process inputs to randomly select one acceleration factor, in case multiple accelerations are used.
+ (
+ R2star_map_initial_prediction,
+ S0_map_initial_prediction,
+ B0_map_initial_prediction,
+ phi_map_initial_prediction,
+ kspace,
+ y,
+ sampling_mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ r,
+ ) = self.__process_inputs__(
+ R2star_map_initial_prediction,
+ S0_map_initial_prediction,
+ B0_map_initial_prediction,
+ phi_map_initial_prediction,
+ kspace,
+ y,
+ sampling_mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ )
+
+ # Check if a network is used for coil sensitivity maps estimation.
+ if self.estimate_coil_sensitivity_maps_with_nn:
+ # Estimate coil sensitivity maps with a network.
+ sensitivity_maps = self.coil_sensitivity_maps_nn(kspace, sampling_mask, sensitivity_maps)
+ # (Re-)compute the initial prediction with the estimated sensitivity maps. This also means that the
+ # self.coil_combination_method is set to "SENSE", since in "RSS" the sensitivity maps are not used.
+ initial_prediction_reconstruction = utils.coil_combination_method(
+ fft.ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+
+ # Model forward pass
+ predictions = self.forward(
+ R2star_map_initial_prediction,
+ S0_map_initial_prediction,
+ B0_map_initial_prediction,
+ phi_map_initial_prediction,
+ TEs.tolist()[0], # type: ignore
+ y,
+ sensitivity_maps,
+ initial_prediction_reconstruction,
+ torch.ones_like(anatomy_mask),
+ sampling_mask,
+ attrs["noise"],
+ )
+
+ # Get acceleration factor from acceleration list, if multiple accelerations are used. Or if batch size > 1.
+ if isinstance(acceleration, list):
+ if acceleration[0].shape[0] > 1:
+ acceleration[0] = acceleration[0][0]
+ acceleration = np.round(acceleration[r].item())
+ else:
+ if acceleration.shape[0] > 1: # type: ignore
+ acceleration = acceleration[0] # type: ignore
+ acceleration = np.round(acceleration.item()) # type: ignore
+
+ return {
+ "fname": fname,
+ "slice_idx": slice_idx,
+ "acceleration": acceleration,
+ "prediction_reconstruction": predictions[0],
+ "prediction_R2star_map": predictions[1],
+ "prediction_S0_map": predictions[2],
+ "prediction_B0_map": predictions[3],
+ "prediction_phi_map": predictions[4],
+ "initial_prediction_reconstruction": initial_prediction_reconstruction,
+ "target_reconstruction": target_reconstruction,
+ "sensitivity_maps": sensitivity_maps,
+ "r": r,
+ }
+
+ def training_step(self, batch: Dict[float, torch.Tensor], batch_idx: int) -> Dict[str, torch.Tensor]:
+ """Performs a training step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'R2star_map_init' : List of torch.Tensor
+ R2* initial map. Shape [batch_size, n_x, n_y].
+ 'R2star_map_target' : torch.Tensor
+ R2* target map. Shape [batch_size, n_x, n_y].
+ 'S0_map_init' : List of torch.Tensor
+ S0 initial map. Shape [batch_size, n_x, n_y].
+ 'S0_map_target' : torch.Tensor
+ S0 target map. Shape [batch_size, n_x, n_y].
+ 'B0_map_init' : List of torch.Tensor
+ B0 initial map. Shape [batch_size, n_x, n_y].
+ 'B0_map_target' : torch.Tensor
+ B0 target map. Shape [batch_size, n_x, n_y].
+ 'phi_map_init' : List of torch.Tensor
+ Phi initial map. Shape [batch_size, n_x, n_y].
+ 'phi_map_target' : torch.Tensor
+ Phi target map. Shape [batch_size, n_x, n_y].
+ 'TEs' : List of float
+ Echo times. If multiple echoes are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_echoes].
+ 'kspace' : List of torch.Tensor
+ Fully-sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'y' : Union[torch.Tensor, None]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'mask' : List of torch.Tensor
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if
+ Unsupervised Learning methods, like Noise-to-Recon or SSDU, are used, then it is a list of
+ torch.Tensor with masks for each method. Shape [batch_size, 1, n_x, n_y, 1].
+ 'anatomy_mask' : torch.Tensor
+ Mask of specified anatomy, e.g. brain. Shape [n_x, n_y].
+ 'initial_prediction' : Union[torch.Tensor, None]
+ Initial prediction. Shape [batch_size, n_x, n_y, 2] or None.
+ 'target' : Union[torch.Tensor, None]
+ Target data. Shape [batch_size, n_x, n_y] or None.
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+ batch_idx : int
+ Batch index.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary of loss and log.
+ """
+ (
+ R2star_map_init,
+ R2star_map_target,
+ S0_map_init,
+ S0_map_target,
+ B0_map_init,
+ B0_map_target,
+ phi_map_init,
+ phi_map_target,
+ TEs,
+ kspace,
+ y,
+ sensitivity_maps,
+ sampling_mask,
+ anatomy_mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ fname,
+ slice_idx,
+ acceleration,
+ attrs,
+ ) = batch
+
+ outputs = self.inference_step(
+ R2star_map_init,
+ S0_map_init,
+ B0_map_init,
+ phi_map_init,
+ TEs,
+ kspace,
+ y,
+ sensitivity_maps,
+ sampling_mask,
+ anatomy_mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration,
+ attrs, # type: ignore
+ )
+
+ # Compute loss
+ lossR2star, lossS0, lossB0, lossPhi, quantitative_reconstruction_loss, train_loss = self.__compute_loss__(
+ outputs["target_reconstruction"],
+ outputs["prediction_reconstruction"],
+ outputs["prediction_R2star_map"],
+ R2star_map_target,
+ outputs["prediction_S0_map"],
+ S0_map_target,
+ outputs["prediction_B0_map"],
+ B0_map_target,
+ outputs["prediction_phi_map"],
+ phi_map_target,
+ outputs["sensitivity_maps"],
+ anatomy_mask,
+ attrs, # type: ignore
+ outputs["r"],
+ )
+
+ acceleration = np.round(outputs['acceleration'])
+ tensorboard_logs = {
+ f"train_loss_{acceleration}x": train_loss.item(),
+ f"loss_reconstruction_{acceleration}x": quantitative_reconstruction_loss.item(),
+ f"loss_R2star_{acceleration}x": lossR2star.item(),
+ f"loss_S0_{acceleration}x": lossS0.item(),
+ f"loss_B0_{acceleration}x": lossB0.item(),
+ f"loss_phi_{acceleration}x": lossPhi.item(),
+ "lr": self._optimizer.param_groups[0]["lr"], # type: ignore
+ }
+
+ self.log(
+ "train_loss",
+ train_loss,
+ on_step=True,
+ on_epoch=True,
+ prog_bar=True,
+ logger=True,
+ batch_size=R2star_map_target.shape[0], # type: ignore
+ sync_dist=True,
+ )
+ if self.use_reconstruction_module:
+ self.log(
+ "quantitative_reconstruction_loss",
+ quantitative_reconstruction_loss,
+ on_step=True,
+ on_epoch=True,
+ prog_bar=True,
+ logger=True,
+ batch_size=R2star_map_target.shape[0], # type: ignore
+ sync_dist=True,
+ )
+ self.log(
+ "train_R2star_loss",
+ lossR2star,
+ on_step=True,
+ on_epoch=True,
+ prog_bar=True,
+ logger=True,
+ batch_size=R2star_map_target.shape[0], # type: ignore
+ sync_dist=True,
+ )
+ self.log(
+ "train_S0_loss",
+ lossS0,
+ on_step=True,
+ on_epoch=True,
+ prog_bar=True,
+ logger=True,
+ batch_size=R2star_map_target.shape[0], # type: ignore
+ sync_dist=True,
+ )
+ self.log(
+ "train_B0_loss",
+ lossB0,
+ on_step=True,
+ on_epoch=True,
+ prog_bar=True,
+ logger=True,
+ batch_size=R2star_map_target.shape[0], # type: ignore
+ sync_dist=True,
+ )
+ self.log(
+ "train_phi_loss",
+ lossPhi,
+ on_step=True,
+ on_epoch=True,
+ prog_bar=True,
+ logger=True,
+ batch_size=R2star_map_target.shape[0], # type: ignore
+ sync_dist=True,
+ )
+
+ return {"loss": train_loss, "log": tensorboard_logs}
+
+ def validation_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a validation step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'R2star_map_init' : List of torch.Tensor
+ R2* initial map. Shape [batch_size, n_x, n_y].
+ 'R2star_map_target' : torch.Tensor
+ R2* target map. Shape [batch_size, n_x, n_y].
+ 'S0_map_init' : List of torch.Tensor
+ S0 initial map. Shape [batch_size, n_x, n_y].
+ 'S0_map_target' : torch.Tensor
+ S0 target map. Shape [batch_size, n_x, n_y].
+ 'B0_map_init' : List of torch.Tensor
+ B0 initial map. Shape [batch_size, n_x, n_y].
+ 'B0_map_target' : torch.Tensor
+ B0 target map. Shape [batch_size, n_x, n_y].
+ 'phi_map_init' : List of torch.Tensor
+ Phi initial map. Shape [batch_size, n_x, n_y].
+ 'phi_map_target' : torch.Tensor
+ Phi target map. Shape [batch_size, n_x, n_y].
+ 'TEs' : List of float
+ Echo times. If multiple echoes are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_echoes].
+ 'kspace' : List of torch.Tensor
+ Fully-sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'y' : Union[torch.Tensor, None]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'mask' : List of torch.Tensor
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if
+ Unsupervised Learning methods, like Noise-to-Recon or SSDU, are used, then it is a list of
+ torch.Tensor with masks for each method. Shape [batch_size, 1, n_x, n_y, 1].
+ 'anatomy_mask' : torch.Tensor
+ Mask of specified anatomy, e.g. brain. Shape [n_x, n_y].
+ 'initial_prediction' : Union[torch.Tensor, None]
+ Initial prediction. Shape [batch_size, n_x, n_y, 2] or None.
+ 'target' : Union[torch.Tensor, None]
+ Target data. Shape [batch_size, n_x, n_y] or None.
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+ batch_idx : int
+ Batch index.
+ """
+ (
+ R2star_map_init,
+ R2star_map_target,
+ S0_map_init,
+ S0_map_target,
+ B0_map_init,
+ B0_map_target,
+ phi_map_init,
+ phi_map_target,
+ TEs,
+ kspace,
+ y,
+ sensitivity_maps,
+ sampling_mask,
+ anatomy_mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ fname,
+ slice_idx,
+ acceleration,
+ attrs,
+ ) = batch
+
+ outputs = self.inference_step(
+ R2star_map_init,
+ S0_map_init,
+ B0_map_init,
+ phi_map_init,
+ TEs,
+ kspace,
+ y,
+ sensitivity_maps,
+ sampling_mask,
+ anatomy_mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration,
+ attrs, # type: ignore
+ )
+
+ target_reconstruction = outputs["target_reconstruction"]
+ prediction_reconstruction = outputs["prediction_reconstruction"]
+ prediction_R2star_map = outputs["prediction_R2star_map"]
+ prediction_S0_map = outputs["prediction_S0_map"]
+ prediction_B0_map = outputs["prediction_B0_map"]
+ prediction_phi_map = outputs["prediction_phi_map"]
+ acceleration = outputs["acceleration"]
+
+ # Compute loss
+ _, _, _, _, _, val_loss = self.__compute_loss__(
+ target_reconstruction,
+ prediction_reconstruction,
+ prediction_R2star_map,
+ R2star_map_target,
+ prediction_S0_map,
+ S0_map_target,
+ prediction_B0_map,
+ B0_map_target,
+ prediction_phi_map,
+ phi_map_target,
+ outputs["sensitivity_maps"],
+ anatomy_mask,
+ attrs, # type: ignore
+ outputs["r"],
+ )
+ self.validation_step_outputs.append({"val_loss": val_loss})
+
+ attrs["r"] = outputs["r"] # type: ignore
+
+ # Compute metrics and log them and log outputs.
+ self.__compute_and_log_metrics_and_outputs__(
+ prediction_R2star_map,
+ prediction_S0_map,
+ prediction_B0_map,
+ prediction_phi_map,
+ prediction_reconstruction,
+ R2star_map_target,
+ S0_map_target,
+ B0_map_target,
+ phi_map_target,
+ target_reconstruction,
+ anatomy_mask,
+ attrs, # type: ignore
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration, # type: ignore
+ )
+
+ def test_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a test step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'R2star_map_init' : List of torch.Tensor
+ R2* initial map. Shape [batch_size, n_x, n_y].
+ 'R2star_map_target' : torch.Tensor
+ R2* target map. Shape [batch_size, n_x, n_y].
+ 'S0_map_init' : List of torch.Tensor
+ S0 initial map. Shape [batch_size, n_x, n_y].
+ 'S0_map_target' : torch.Tensor
+ S0 target map. Shape [batch_size, n_x, n_y].
+ 'B0_map_init' : List of torch.Tensor
+ B0 initial map. Shape [batch_size, n_x, n_y].
+ 'B0_map_target' : torch.Tensor
+ B0 target map. Shape [batch_size, n_x, n_y].
+ 'phi_map_init' : List of torch.Tensor
+ Phi initial map. Shape [batch_size, n_x, n_y].
+ 'phi_map_target' : torch.Tensor
+ Phi target map. Shape [batch_size, n_x, n_y].
+ 'TEs' : List of float
+ Echo times. If multiple echoes are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_echoes].
+ 'kspace' : List of torch.Tensor
+ Fully-sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'y' : Union[torch.Tensor, None]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'mask' : List of torch.Tensor
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if
+ Unsupervised Learning methods, like Noise-to-Recon or SSDU, are used, then it is a list of
+ torch.Tensor with masks for each method. Shape [batch_size, 1, n_x, n_y, 1].
+ 'anatomy_mask' : torch.Tensor
+ Mask of specified anatomy, e.g. brain. Shape [n_x, n_y].
+ 'initial_prediction' : Union[torch.Tensor, None]
+ Initial prediction. Shape [batch_size, n_x, n_y, 2] or None.
+ 'target' : Union[torch.Tensor, None]
+ Target data. Shape [batch_size, n_x, n_y] or None.
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+ batch_idx : int
+ Batch index.
+ """
+ (
+ R2star_map_init,
+ R2star_map_target,
+ S0_map_init,
+ S0_map_target,
+ B0_map_init,
+ B0_map_target,
+ phi_map_init,
+ phi_map_target,
+ TEs,
+ kspace,
+ y,
+ sensitivity_maps,
+ sampling_mask,
+ anatomy_mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ fname,
+ slice_idx,
+ acceleration,
+ attrs,
+ ) = batch
+
+ outputs = self.inference_step(
+ R2star_map_init,
+ S0_map_init,
+ B0_map_init,
+ phi_map_init,
+ TEs,
+ kspace,
+ y,
+ sensitivity_maps,
+ sampling_mask,
+ anatomy_mask,
+ initial_prediction_reconstruction,
+ target_reconstruction,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration,
+ attrs, # type: ignore
+ )
+
+ target_reconstruction = outputs["target_reconstruction"]
+ prediction_reconstruction = outputs["prediction_reconstruction"]
+ prediction_R2star_map = outputs["prediction_R2star_map"]
+ prediction_S0_map = outputs["prediction_S0_map"]
+ prediction_B0_map = outputs["prediction_B0_map"]
+ prediction_phi_map = outputs["prediction_phi_map"]
+ acceleration = outputs["acceleration"]
+
+ # Compute metrics and log them and log outputs.
+ self.__compute_and_log_metrics_and_outputs__(
+ prediction_R2star_map,
+ prediction_S0_map,
+ prediction_B0_map,
+ prediction_phi_map,
+ prediction_reconstruction,
+ R2star_map_target,
+ S0_map_target,
+ B0_map_target,
+ phi_map_target,
+ target_reconstruction,
+ anatomy_mask,
+ attrs, # type: ignore
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration, # type: ignore
+ )
+
+ if self.accumulate_predictions:
+ if self.use_reconstruction_module:
+ while isinstance(prediction_reconstruction, list):
+ prediction_reconstruction = prediction_reconstruction[-1]
+
+ while isinstance(prediction_R2star_map, list):
+ prediction_R2star_map = prediction_R2star_map[-1]
+ while isinstance(prediction_S0_map, list):
+ prediction_S0_map = prediction_S0_map[-1]
+ while isinstance(prediction_B0_map, list):
+ prediction_B0_map = prediction_B0_map[-1]
+ while isinstance(prediction_phi_map, list):
+ prediction_phi_map = prediction_phi_map[-1]
+
+ if self.use_reconstruction_module:
+ # If "16" or "16-mixed" fp is used, ensure complex type will be supported when saving the predictions.
+ prediction_reconstruction = (
+ torch.view_as_complex(torch.view_as_real(prediction_reconstruction).type(torch.float32))
+ .detach()
+ .cpu()
+ .numpy()
+ )
+
+ prediction_qmaps = (
+ torch.stack([prediction_R2star_map, prediction_S0_map, prediction_B0_map, prediction_phi_map], dim=0)
+ .detach()
+ .cpu()
+ .numpy()
+ )
+
+ predictions = (
+ (prediction_qmaps, prediction_reconstruction)
+ if self.use_reconstruction_module
+ else (prediction_qmaps, prediction_qmaps)
+ )
+
+ self.test_step_outputs.append([str(fname[0]), slice_idx, predictions]) # type: ignore
+
+ def on_validation_epoch_end(self): # noqa: MC0001
+ """Called at the end of validation epoch to aggregate outputs.
+
+ Returns
+ -------
+ metrics : dict
+ Dictionary of metrics.
+ """
+ self.log("val_loss", torch.stack([x["val_loss"] for x in self.validation_step_outputs]).mean())
+
+ # Log metrics.
+ # Taken from: https://github.com/facebookresearch/fastMRI/blob/main/fastmri/pl_modules/mri_module.py
+ mse_vals_R2star = defaultdict(dict)
+ nmse_vals_R2star = defaultdict(dict)
+ ssim_vals_R2star = defaultdict(dict)
+ psnr_vals_R2star = defaultdict(dict)
+
+ mse_vals_S0 = defaultdict(dict)
+ nmse_vals_S0 = defaultdict(dict)
+ ssim_vals_S0 = defaultdict(dict)
+ psnr_vals_S0 = defaultdict(dict)
+
+ mse_vals_B0 = defaultdict(dict)
+ nmse_vals_B0 = defaultdict(dict)
+ ssim_vals_B0 = defaultdict(dict)
+ psnr_vals_B0 = defaultdict(dict)
+
+ mse_vals_phi = defaultdict(dict)
+ nmse_vals_phi = defaultdict(dict)
+ ssim_vals_phi = defaultdict(dict)
+ psnr_vals_phi = defaultdict(dict)
+
+ for k, v in self.mse_vals_R2star.items():
+ mse_vals_R2star[k].update(v)
+ for k, v in self.nmse_vals_R2star.items():
+ nmse_vals_R2star[k].update(v)
+ for k, v in self.ssim_vals_R2star.items():
+ ssim_vals_R2star[k].update(v)
+ for k, v in self.psnr_vals_R2star.items():
+ psnr_vals_R2star[k].update(v)
+
+ for k, v in self.mse_vals_S0.items():
+ mse_vals_S0[k].update(v)
+ for k, v in self.nmse_vals_S0.items():
+ nmse_vals_S0[k].update(v)
+ for k, v in self.ssim_vals_S0.items():
+ ssim_vals_S0[k].update(v)
+ for k, v in self.psnr_vals_S0.items():
+ psnr_vals_S0[k].update(v)
+
+ for k, v in self.mse_vals_B0.items():
+ mse_vals_B0[k].update(v)
+ for k, v in self.nmse_vals_B0.items():
+ nmse_vals_B0[k].update(v)
+ for k, v in self.ssim_vals_B0.items():
+ ssim_vals_B0[k].update(v)
+ for k, v in self.psnr_vals_B0.items():
+ psnr_vals_B0[k].update(v)
+
+ for k, v in self.mse_vals_phi.items():
+ mse_vals_phi[k].update(v)
+ for k, v in self.nmse_vals_phi.items():
+ nmse_vals_phi[k].update(v)
+ for k, v in self.ssim_vals_phi.items():
+ ssim_vals_phi[k].update(v)
+ for k, v in self.psnr_vals_phi.items():
+ psnr_vals_phi[k].update(v)
+
+ if self.use_reconstruction_module:
+ mse_vals_reconstruction = defaultdict(dict)
+ nmse_vals_reconstruction = defaultdict(dict)
+ ssim_vals_reconstruction = defaultdict(dict)
+ psnr_vals_reconstruction = defaultdict(dict)
+
+ for k, v in self.mse_vals_reconstruction.items():
+ mse_vals_reconstruction[k].update(v)
+ for k, v in self.nmse_vals_reconstruction.items():
+ nmse_vals_reconstruction[k].update(v)
+ for k, v in self.ssim_vals_reconstruction.items():
+ ssim_vals_reconstruction[k].update(v)
+ for k, v in self.psnr_vals_reconstruction.items():
+ psnr_vals_reconstruction[k].update(v)
+
+ # apply means across image volumes
+ metrics = {
+ "MSE": {"R2star": 0, "S0": 0, "B0": 0, "phi": 0, "reconstruction": 0},
+ "NMSE": {"R2star": 0, "S0": 0, "B0": 0, "phi": 0, "reconstruction": 0},
+ "SSIM": {"R2star": 0, "S0": 0, "B0": 0, "phi": 0, "reconstruction": 0},
+ "PSNR": {"R2star": 0, "S0": 0, "B0": 0, "phi": 0, "reconstruction": 0},
+ }
+ local_examples = 0
+ for fname in mse_vals_R2star:
+ local_examples += 1
+ metrics["MSE"]["R2star"] = metrics["MSE"]["R2star"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_R2star[fname].items()])
+ )
+ metrics["NMSE"]["R2star"] = metrics["NMSE"]["R2star"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_R2star[fname].items()])
+ )
+ metrics["SSIM"]["R2star"] = metrics["SSIM"]["R2star"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_R2star[fname].items()])
+ )
+ metrics["PSNR"]["R2star"] = metrics["PSNR"]["R2star"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_R2star[fname].items()])
+ )
+
+ metrics["MSE"]["S0"] = metrics["MSE"]["S0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_S0[fname].items()])
+ )
+ metrics["NMSE"]["S0"] = metrics["NMSE"]["S0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_S0[fname].items()])
+ )
+ metrics["SSIM"]["S0"] = metrics["SSIM"]["S0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_S0[fname].items()])
+ )
+ metrics["PSNR"]["S0"] = metrics["PSNR"]["S0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_S0[fname].items()])
+ )
+
+ metrics["MSE"]["B0"] = metrics["MSE"]["B0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_B0[fname].items()])
+ )
+ metrics["NMSE"]["B0"] = metrics["NMSE"]["B0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_B0[fname].items()])
+ )
+ metrics["SSIM"]["B0"] = metrics["SSIM"]["B0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_B0[fname].items()])
+ )
+ metrics["PSNR"]["B0"] = metrics["PSNR"]["B0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_B0[fname].items()])
+ )
+
+ metrics["MSE"]["phi"] = metrics["MSE"]["phi"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_phi[fname].items()])
+ )
+ metrics["NMSE"]["phi"] = metrics["NMSE"]["phi"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_phi[fname].items()])
+ )
+ metrics["SSIM"]["phi"] = metrics["SSIM"]["phi"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_phi[fname].items()])
+ )
+ metrics["PSNR"]["phi"] = metrics["PSNR"]["phi"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_phi[fname].items()])
+ )
+
+ if self.use_reconstruction_module:
+ metrics["MSE"]["reconstruction"] = metrics["MSE"]["reconstruction"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_reconstruction[fname].items()])
+ )
+ metrics["NMSE"]["reconstruction"] = metrics["NMSE"]["reconstruction"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_reconstruction[fname].items()])
+ )
+ metrics["SSIM"]["reconstruction"] = metrics["SSIM"]["reconstruction"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_reconstruction[fname].items()])
+ )
+ metrics["PSNR"]["reconstruction"] = metrics["PSNR"]["reconstruction"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_reconstruction[fname].items()])
+ )
+
+ # reduce across ddp via sum
+ metrics["MSE"]["R2star"] = self.MSE(metrics["MSE"]["R2star"])
+ metrics["NMSE"]["R2star"] = self.NMSE(metrics["NMSE"]["R2star"])
+ metrics["SSIM"]["R2star"] = self.SSIM(metrics["SSIM"]["R2star"])
+ metrics["PSNR"]["R2star"] = self.PSNR(metrics["PSNR"]["R2star"])
+
+ metrics["MSE"]["S0"] = self.MSE(metrics["MSE"]["S0"])
+ metrics["NMSE"]["S0"] = self.NMSE(metrics["NMSE"]["S0"])
+ metrics["SSIM"]["S0"] = self.SSIM(metrics["SSIM"]["S0"])
+ metrics["PSNR"]["S0"] = self.PSNR(metrics["PSNR"]["S0"])
+
+ metrics["MSE"]["B0"] = self.MSE(metrics["MSE"]["B0"])
+ metrics["NMSE"]["B0"] = self.NMSE(metrics["NMSE"]["B0"])
+ metrics["SSIM"]["B0"] = self.SSIM(metrics["SSIM"]["B0"])
+ metrics["PSNR"]["B0"] = self.PSNR(metrics["PSNR"]["B0"])
+
+ metrics["MSE"]["phi"] = self.MSE(metrics["MSE"]["phi"])
+ metrics["NMSE"]["phi"] = self.NMSE(metrics["NMSE"]["phi"])
+ metrics["SSIM"]["phi"] = self.SSIM(metrics["SSIM"]["phi"])
+ metrics["PSNR"]["phi"] = self.PSNR(metrics["PSNR"]["phi"])
+
+ if self.use_reconstruction_module:
+ metrics["MSE"]["reconstruction"] = self.MSE(metrics["MSE"]["reconstruction"])
+ metrics["NMSE"]["reconstruction"] = self.NMSE(metrics["NMSE"]["reconstruction"])
+ metrics["SSIM"]["reconstruction"] = self.SSIM(metrics["SSIM"]["reconstruction"])
+ metrics["PSNR"]["reconstruction"] = self.PSNR(metrics["PSNR"]["reconstruction"])
+
+ tot_examples = self.TotExamples(torch.tensor(local_examples))
+
+ for metric, value in metrics.items():
+ self.log(f"val_metrics/{metric}_R2star", value["R2star"] / tot_examples, prog_bar=True, sync_dist=True)
+ self.log(f"val_metrics/{metric}_S0", value["S0"] / tot_examples, prog_bar=True, sync_dist=True)
+ self.log(f"val_metrics/{metric}_B0", value["B0"] / tot_examples, prog_bar=True, sync_dist=True)
+ self.log(f"val_metrics/{metric}_phi", value["phi"] / tot_examples, prog_bar=True, sync_dist=True)
+ if self.use_reconstruction_module:
+ self.log(
+ f"val_metrics/{metric}_Reconstruction",
+ value["reconstruction"] / tot_examples,
+ prog_bar=True,
+ sync_dist=True,
+ )
+
+ def on_test_epoch_end(self): # noqa: MC0001
+ """Called at the end of test epoch to aggregate outputs, log metrics and save predictions.
+
+ Returns
+ -------
+ metrics : dict
+ Dictionary of metrics.
+ """
+ # Log metrics.
+ # Taken from: https://github.com/facebookresearch/fastMRI/blob/main/fastmri/pl_modules/mri_module.py
+ mse_vals_R2star = defaultdict(dict)
+ nmse_vals_R2star = defaultdict(dict)
+ ssim_vals_R2star = defaultdict(dict)
+ psnr_vals_R2star = defaultdict(dict)
+
+ mse_vals_S0 = defaultdict(dict)
+ nmse_vals_S0 = defaultdict(dict)
+ ssim_vals_S0 = defaultdict(dict)
+ psnr_vals_S0 = defaultdict(dict)
+
+ mse_vals_B0 = defaultdict(dict)
+ nmse_vals_B0 = defaultdict(dict)
+ ssim_vals_B0 = defaultdict(dict)
+ psnr_vals_B0 = defaultdict(dict)
+
+ mse_vals_phi = defaultdict(dict)
+ nmse_vals_phi = defaultdict(dict)
+ ssim_vals_phi = defaultdict(dict)
+ psnr_vals_phi = defaultdict(dict)
+
+ for k, v in self.mse_vals_R2star.items():
+ mse_vals_R2star[k].update(v)
+ for k, v in self.nmse_vals_R2star.items():
+ nmse_vals_R2star[k].update(v)
+ for k, v in self.ssim_vals_R2star.items():
+ ssim_vals_R2star[k].update(v)
+ for k, v in self.psnr_vals_R2star.items():
+ psnr_vals_R2star[k].update(v)
+
+ for k, v in self.mse_vals_S0.items():
+ mse_vals_S0[k].update(v)
+ for k, v in self.nmse_vals_S0.items():
+ nmse_vals_S0[k].update(v)
+ for k, v in self.ssim_vals_S0.items():
+ ssim_vals_S0[k].update(v)
+ for k, v in self.psnr_vals_S0.items():
+ psnr_vals_S0[k].update(v)
+
+ for k, v in self.mse_vals_B0.items():
+ mse_vals_B0[k].update(v)
+ for k, v in self.nmse_vals_B0.items():
+ nmse_vals_B0[k].update(v)
+ for k, v in self.ssim_vals_B0.items():
+ ssim_vals_B0[k].update(v)
+ for k, v in self.psnr_vals_B0.items():
+ psnr_vals_B0[k].update(v)
+
+ for k, v in self.mse_vals_phi.items():
+ mse_vals_phi[k].update(v)
+ for k, v in self.nmse_vals_phi.items():
+ nmse_vals_phi[k].update(v)
+ for k, v in self.ssim_vals_phi.items():
+ ssim_vals_phi[k].update(v)
+ for k, v in self.psnr_vals_phi.items():
+ psnr_vals_phi[k].update(v)
+
+ if self.use_reconstruction_module:
+ mse_vals_reconstruction = defaultdict(dict)
+ nmse_vals_reconstruction = defaultdict(dict)
+ ssim_vals_reconstruction = defaultdict(dict)
+ psnr_vals_reconstruction = defaultdict(dict)
+
+ for k, v in self.mse_vals_reconstruction.items():
+ mse_vals_reconstruction[k].update(v)
+ for k, v in self.nmse_vals_reconstruction.items():
+ nmse_vals_reconstruction[k].update(v)
+ for k, v in self.ssim_vals_reconstruction.items():
+ ssim_vals_reconstruction[k].update(v)
+ for k, v in self.psnr_vals_reconstruction.items():
+ psnr_vals_reconstruction[k].update(v)
+
+ # apply means across image volumes
+ metrics = {
+ "MSE": {"R2star": 0, "S0": 0, "B0": 0, "phi": 0, "reconstruction": 0},
+ "NMSE": {"R2star": 0, "S0": 0, "B0": 0, "phi": 0, "reconstruction": 0},
+ "SSIM": {"R2star": 0, "S0": 0, "B0": 0, "phi": 0, "reconstruction": 0},
+ "PSNR": {"R2star": 0, "S0": 0, "B0": 0, "phi": 0, "reconstruction": 0},
+ }
+ local_examples = 0
+ for fname in mse_vals_R2star:
+ local_examples += 1
+ metrics["MSE"]["R2star"] = metrics["MSE"]["R2star"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_R2star[fname].items()])
+ )
+ metrics["NMSE"]["R2star"] = metrics["NMSE"]["R2star"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_R2star[fname].items()])
+ )
+ metrics["SSIM"]["R2star"] = metrics["SSIM"]["R2star"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_R2star[fname].items()])
+ )
+ metrics["PSNR"]["R2star"] = metrics["PSNR"]["R2star"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_R2star[fname].items()])
+ )
+
+ metrics["MSE"]["S0"] = metrics["MSE"]["S0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_S0[fname].items()])
+ )
+ metrics["NMSE"]["S0"] = metrics["NMSE"]["S0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_S0[fname].items()])
+ )
+ metrics["SSIM"]["S0"] = metrics["SSIM"]["S0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_S0[fname].items()])
+ )
+ metrics["PSNR"]["S0"] = metrics["PSNR"]["S0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_S0[fname].items()])
+ )
+
+ metrics["MSE"]["B0"] = metrics["MSE"]["B0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_B0[fname].items()])
+ )
+ metrics["NMSE"]["B0"] = metrics["NMSE"]["B0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_B0[fname].items()])
+ )
+ metrics["SSIM"]["B0"] = metrics["SSIM"]["B0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_B0[fname].items()])
+ )
+ metrics["PSNR"]["B0"] = metrics["PSNR"]["B0"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_B0[fname].items()])
+ )
+
+ metrics["MSE"]["phi"] = metrics["MSE"]["phi"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_phi[fname].items()])
+ )
+ metrics["NMSE"]["phi"] = metrics["NMSE"]["phi"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_phi[fname].items()])
+ )
+ metrics["SSIM"]["phi"] = metrics["SSIM"]["phi"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_phi[fname].items()])
+ )
+ metrics["PSNR"]["phi"] = metrics["PSNR"]["phi"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_phi[fname].items()])
+ )
+
+ if self.use_reconstruction_module:
+ metrics["MSE"]["reconstruction"] = metrics["MSE"]["reconstruction"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in mse_vals_reconstruction[fname].items()])
+ )
+ metrics["NMSE"]["reconstruction"] = metrics["NMSE"]["reconstruction"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals_reconstruction[fname].items()])
+ )
+ metrics["SSIM"]["reconstruction"] = metrics["SSIM"]["reconstruction"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals_reconstruction[fname].items()])
+ )
+ metrics["PSNR"]["reconstruction"] = metrics["PSNR"]["reconstruction"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals_reconstruction[fname].items()])
+ )
+
+ # reduce across ddp via sum
+ metrics["MSE"]["R2star"] = self.MSE(metrics["MSE"]["R2star"])
+ metrics["NMSE"]["R2star"] = self.NMSE(metrics["NMSE"]["R2star"])
+ metrics["SSIM"]["R2star"] = self.SSIM(metrics["SSIM"]["R2star"])
+ metrics["PSNR"]["R2star"] = self.PSNR(metrics["PSNR"]["R2star"])
+
+ metrics["MSE"]["S0"] = self.MSE(metrics["MSE"]["S0"])
+ metrics["NMSE"]["S0"] = self.NMSE(metrics["NMSE"]["S0"])
+ metrics["SSIM"]["S0"] = self.SSIM(metrics["SSIM"]["S0"])
+ metrics["PSNR"]["S0"] = self.PSNR(metrics["PSNR"]["S0"])
+
+ metrics["MSE"]["B0"] = self.MSE(metrics["MSE"]["B0"])
+ metrics["NMSE"]["B0"] = self.NMSE(metrics["NMSE"]["B0"])
+ metrics["SSIM"]["B0"] = self.SSIM(metrics["SSIM"]["B0"])
+ metrics["PSNR"]["B0"] = self.PSNR(metrics["PSNR"]["B0"])
+
+ metrics["MSE"]["phi"] = self.MSE(metrics["MSE"]["phi"])
+ metrics["NMSE"]["phi"] = self.NMSE(metrics["NMSE"]["phi"])
+ metrics["SSIM"]["phi"] = self.SSIM(metrics["SSIM"]["phi"])
+ metrics["PSNR"]["phi"] = self.PSNR(metrics["PSNR"]["phi"])
+
+ if self.use_reconstruction_module:
+ metrics["MSE"]["reconstruction"] = self.MSE(metrics["MSE"]["reconstruction"])
+ metrics["NMSE"]["reconstruction"] = self.NMSE(metrics["NMSE"]["reconstruction"])
+ metrics["SSIM"]["reconstruction"] = self.SSIM(metrics["SSIM"]["reconstruction"])
+ metrics["PSNR"]["reconstruction"] = self.PSNR(metrics["PSNR"]["reconstruction"])
+
+ tot_examples = self.TotExamples(torch.tensor(local_examples))
+
+ for metric, value in metrics.items():
+ self.log(f"test_metrics/{metric}_R2star", value["R2star"] / tot_examples, prog_bar=True, sync_dist=True)
+ self.log(f"test_metrics/{metric}_S0", value["S0"] / tot_examples, prog_bar=True, sync_dist=True)
+ self.log(f"test_metrics/{metric}_B0", value["B0"] / tot_examples, prog_bar=True, sync_dist=True)
+ self.log(f"test_metrics/{metric}_phi", value["phi"] / tot_examples, prog_bar=True, sync_dist=True)
+ if self.use_reconstruction_module:
+ self.log(
+ f"test_metrics/{metric}_Reconstruction",
+ value["reconstruction"] / tot_examples,
+ prog_bar=True,
+ sync_dist=True,
+ )
+
+ qmaps = defaultdict(list)
+ for fname, slice_num, output in self.test_step_outputs:
+ qmaps_pred, _ = output
+ qmaps[fname].append((slice_num, qmaps_pred))
+
+ for fname in qmaps:
+ qmaps[fname] = np.stack([out for _, out in sorted(qmaps[fname])])
+
+ if self.consecutive_slices > 1:
+ # iterate over the slices and always keep the middle slice
+ for fname in qmaps:
+ qmaps[fname] = qmaps[fname][:, self.consecutive_slices // 2]
+
+ if self.use_reconstruction_module:
+ reconstructions = defaultdict(list)
+ for fname, slice_num, output in self.test_step_outputs:
+ _, reconstructions_pred = output
+ reconstructions[fname].append((slice_num, reconstructions_pred))
+
+ for fname in reconstructions:
+ reconstructions[fname] = np.stack([out for _, out in sorted(reconstructions[fname])])
+
+ if self.consecutive_slices > 1:
+ # iterate over the slices and always keep the middle slice
+ for fname in reconstructions:
+ reconstructions[fname] = reconstructions[fname][:, self.consecutive_slices // 2]
+ else:
+ reconstructions = None
+
+ if "wandb" in self.logger.__module__.lower():
+ out_dir = Path(os.path.join(self.logger.save_dir, "predictions"))
+ else:
+ out_dir = Path(os.path.join(self.logger.log_dir, "predictions"))
+ out_dir.mkdir(exist_ok=True, parents=True)
+
+ if reconstructions is not None:
+ for (fname, qmaps_pred), (_, reconstructions_pred) in zip(qmaps.items(), reconstructions.items()):
+ with h5py.File(out_dir / fname, "w") as hf:
+ hf.create_dataset("qmaps", data=qmaps_pred)
+ hf.create_dataset("reconstruction", data=reconstructions_pred)
+ else:
+ for fname, qmaps_pred in qmaps.items():
+ with h5py.File(out_dir / fname, "w") as hf:
+ hf.create_dataset("qmaps", data=qmaps_pred)
+
+ @staticmethod
+ def _setup_dataloader_from_config(cfg: DictConfig) -> DataLoader:
+ """Setups the dataloader from the configuration (yaml) file.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration file.
+
+ Returns
+ -------
+ dataloader : torch.utils.data.DataLoader
+ Dataloader.
+ """
+ mask_root = cfg.get("mask_path", None)
+ mask_args = cfg.get("mask_args", None)
+ shift_mask = mask_args.get("shift_mask", False)
+ mask_type = mask_args.get("type", None)
+
+ mask_func = None
+ mask_center_scale = 0.02
+
+ if utils.is_none(mask_root) and not utils.is_none(mask_type):
+ accelerations = mask_args.get("accelerations", [1])
+ accelerations = list(accelerations)
+ if len(accelerations) == 1:
+ accelerations = accelerations * 2
+ center_fractions = mask_args.get("center_fractions", [1])
+ center_fractions = list(center_fractions)
+ if len(center_fractions) == 1:
+ center_fractions = center_fractions * 2
+ mask_center_scale = mask_args.get("center_scale", 0.02)
+
+ mask_func = [create_masker(mask_type, center_fractions, accelerations)]
+
+ dataset_format = cfg.get("dataset_format", None)
+ if dataset_format.lower() == "ahead":
+ dataloader = AHEADqMRIDataset
+ else:
+ raise ValueError(
+ f"Dataset format {dataset_format} not supported. "
+ "At the moment only the AHEAD is supported for quantitative MRI."
+ )
+
+ dataset = dataloader(
+ root=cfg.get("data_path"),
+ coil_sensitivity_maps_root=cfg.get("coil_sensitivity_maps_path", None),
+ mask_root=mask_root,
+ noise_root=cfg.get("noise_path", None),
+ initial_predictions_root=cfg.get("initial_predictions_path"),
+ dataset_format=dataset_format,
+ sample_rate=cfg.get("sample_rate", 1.0),
+ volume_sample_rate=cfg.get("volume_sample_rate", None),
+ use_dataset_cache=cfg.get("use_dataset_cache", False),
+ dataset_cache_file=cfg.get("dataset_cache_file", None),
+ num_cols=cfg.get("num_cols", None),
+ consecutive_slices=cfg.get("consecutive_slices", 1),
+ data_saved_per_slice=cfg.get("data_saved_per_slice", False),
+ n2r_supervised_rate=cfg.get("n2r_supervised_rate", 0.0),
+ complex_target=cfg.get("complex_target", False),
+ log_images_rate=cfg.get("log_images_rate", 1.0),
+ transform=qMRIDataTransforms(
+ TEs=cfg.get("TEs"),
+ precompute_quantitative_maps=cfg.get("precompute_quantitative_maps"),
+ qmaps_scaling_factor=cfg.get("qmaps_scaling_factor"),
+ dataset_format=dataset_format,
+ apply_prewhitening=cfg.get("apply_prewhitening", False),
+ find_patch_size=cfg.get("find_patch_size", False),
+ prewhitening_scale_factor=cfg.get("prewhitening_scale_factor", 1.0),
+ prewhitening_patch_start=cfg.get("prewhitening_patch_start", 10),
+ prewhitening_patch_length=cfg.get("prewhitening_patch_length", 30),
+ apply_gcc=cfg.get("apply_gcc", False),
+ gcc_virtual_coils=cfg.get("gcc_virtual_coils", 10),
+ gcc_calib_lines=cfg.get("gcc_calib_lines", 10),
+ gcc_align_data=cfg.get("gcc_align_data", False),
+ apply_random_motion=cfg.get("apply_random_motion", False),
+ random_motion_type=cfg.get("random_motion_type", "gaussian"),
+ random_motion_percentage=cfg.get("random_motion_percentage", [10, 10]),
+ random_motion_angle=cfg.get("random_motion_angle", 10),
+ random_motion_translation=cfg.get("random_motion_translation", 10),
+ random_motion_center_percentage=cfg.get("random_motion_center_percentage", 0.02),
+ random_motion_num_segments=cfg.get("random_motion_num_segments", 8),
+ random_motion_random_num_segments=cfg.get("random_motion_random_num_segments", True),
+ random_motion_non_uniform=cfg.get("random_motion_non_uniform", False),
+ estimate_coil_sensitivity_maps=cfg.get("estimate_coil_sensitivity_maps", False),
+ coil_sensitivity_maps_type=cfg.get("coil_sensitivity_maps_type", "espirit"),
+ coil_sensitivity_maps_gaussian_sigma=cfg.get("coil_sensitivity_maps_gaussian_sigma", 0.0),
+ coil_sensitivity_maps_espirit_threshold=cfg.get("coil_sensitivity_maps_espirit_threshold", 0.05),
+ coil_sensitivity_maps_espirit_kernel_size=cfg.get("coil_sensitivity_maps_espirit_kernel_size", 6),
+ coil_sensitivity_maps_espirit_crop=cfg.get("coil_sensitivity_maps_espirit_crop", 0.95),
+ coil_sensitivity_maps_espirit_max_iters=cfg.get("coil_sensitivity_maps_espirit_max_iters", 30),
+ coil_combination_method=cfg.get("coil_combination_method", "SENSE"),
+ dimensionality=cfg.get("dimensionality", 2),
+ mask_func=mask_func,
+ shift_mask=shift_mask,
+ mask_center_scale=mask_center_scale,
+ remask=cfg.get("remask", False),
+ ssdu=cfg.get("ssdu", False),
+ ssdu_mask_type=cfg.get("ssdu_mask_type", "Gaussian"),
+ ssdu_rho=cfg.get("ssdu_rho", 0.4),
+ ssdu_acs_block_size=cfg.get("ssdu_acs_block_size", (4, 4)),
+ ssdu_gaussian_std_scaling_factor=cfg.get("ssdu_gaussian_std_scaling_factor", 4.0),
+ ssdu_outer_kspace_fraction=cfg.get("ssdu_outer_kspace_fraction", 0.0),
+ ssdu_export_and_reuse_masks=cfg.get("ssdu_export_and_reuse_masks", False),
+ n2r=cfg.get("n2r", False),
+ n2r_supervised_rate=cfg.get("n2r_supervised_rate", 0.0),
+ n2r_probability=cfg.get("n2r_probability", 0.5),
+ n2r_std_devs=cfg.get("n2r_std_devs", (0.0, 0.0)),
+ n2r_rhos=cfg.get("n2r_rhos", (0.4, 0.4)),
+ n2r_use_mask=cfg.get("n2r_use_mask", True),
+ unsupervised_masked_target=cfg.get("unsupervised_masked_target", False),
+ crop_size=cfg.get("crop_size", None),
+ kspace_crop=cfg.get("kspace_crop", False),
+ crop_before_masking=cfg.get("crop_before_masking", False),
+ kspace_zero_filling_size=cfg.get("kspace_zero_filling_size", None),
+ normalize_inputs=cfg.get("normalize_inputs", True),
+ normalization_type=cfg.get("normalization_type", "max"),
+ kspace_normalization=cfg.get("kspace_normalization", False),
+ fft_centered=cfg.get("fft_centered", False),
+ fft_normalization=cfg.get("fft_normalization", "backward"),
+ spatial_dims=cfg.get("spatial_dims", None),
+ coil_dim=cfg.get("coil_dim", 1),
+ consecutive_slices=cfg.get("consecutive_slices", 1),
+ use_seed=cfg.get("use_seed", True),
+ ),
+ sequence=cfg.get("sequence", None),
+ segmentation_mask_root=cfg.get("segmentation_mask_path", None),
+ kspace_scaling_factor=cfg.get("kspace_scaling_factor", 1),
+ )
+ if cfg.shuffle:
+ sampler = torch.utils.data.RandomSampler(dataset)
+ else:
+ sampler = torch.utils.data.SequentialSampler(dataset)
+
+ return torch.utils.data.DataLoader(
+ dataset=dataset,
+ batch_size=cfg.get("batch_size", 1),
+ sampler=sampler,
+ num_workers=cfg.get("num_workers", 4),
+ pin_memory=cfg.get("pin_memory", False),
+ drop_last=cfg.get("drop_last", False),
+ )
+
+
+class SignalForwardModel:
+ """Defines a signal forward model based on sequence."""
+
+ def __init__(self, sequence: Union[str, None] = None):
+ """Inits :class:`SignalForwardModel`.
+
+ Parameters
+ ----------
+ sequence : str
+ Sequence name.
+ """
+ super().__init__()
+ self.sequence = sequence.lower() if isinstance(sequence, str) else None
+ self.scaling = 1e-3
+
+ def __call__(
+ self,
+ R2star_map: torch.Tensor,
+ S0_map: torch.Tensor,
+ B0_map: torch.Tensor,
+ phi_map: torch.Tensor,
+ TEs: Optional[List] = None,
+ ):
+ """Calls :class:`SignalForwardModel`.
+
+ Parameters
+ ----------
+ R2star_map : torch.Tensor
+ R2* map of shape [batch_size, n_x, n_y].
+ S0_map : torch.Tensor
+ S0 map of shape [batch_size, n_x, n_y].
+ B0_map : torch.Tensor
+ B0 map of shape [batch_size, n_x, n_y].
+ phi_map : torch.Tensor
+ phi map of shape [batch_size, n_x, n_y].
+ TEs : list of float, optional
+ List of echo times.
+ """
+ if TEs is None:
+ TEs = torch.Tensor([3.0, 11.5, 20.0, 28.5])
+ if self.sequence == "megre":
+ return self.MEGRESignalModel(R2star_map, S0_map, B0_map, phi_map, TEs)
+ if self.sequence == "megre_no_phase":
+ return self.MEGRENoPhaseSignalModel(R2star_map, S0_map, TEs)
+ raise ValueError(
+ "Only MEGRE and MEGRE no phase are supported are signal forward model at the moment. "
+ f"Found {self.sequence}"
+ )
+
+ def MEGRESignalModel(
+ self,
+ R2star_map: torch.Tensor,
+ S0_map: torch.Tensor,
+ B0_map: torch.Tensor,
+ phi_map: torch.Tensor,
+ TEs: Optional[List] = None,
+ ):
+ """MEGRE forward model.
+
+ Parameters
+ ----------
+ R2star_map : torch.Tensor
+ R2* map of shape [batch_size, n_x, n_y].
+ S0_map : torch.Tensor
+ S0 map of shape [batch_size, n_x, n_y].
+ B0_map : torch.Tensor
+ B0 map of shape [batch_size, n_x, n_y].
+ phi_map : torch.Tensor
+ phi map of shape [batch_size, n_x, n_y].
+ TEs : list of float, optional
+ List of echo times.
+ """
+ S0_map_real = S0_map
+ S0_map_imag = phi_map
+
+ def first_term(i):
+ """First term of the MEGRE signal model."""
+ return torch.exp(-TEs[i] * self.scaling * R2star_map)
+
+ def second_term(i):
+ """Second term of the MEGRE signal model."""
+ return torch.cos(B0_map * self.scaling * -TEs[i])
+
+ def third_term(i):
+ """Third term of the MEGRE signal model."""
+ return torch.sin(B0_map * self.scaling * -TEs[i])
+
+ pred = torch.stack(
+ [
+ torch.stack(
+ (
+ S0_map_real * first_term(i) * second_term(i) - S0_map_imag * first_term(i) * third_term(i),
+ S0_map_real * first_term(i) * third_term(i) + S0_map_imag * first_term(i) * second_term(i),
+ ),
+ -1,
+ )
+ for i in range(len(TEs)) # type: ignore
+ ],
+ 1,
+ )
+ pred = torch.where(torch.isnan(pred), torch.zeros_like(pred), pred)
+ return torch.view_as_real(pred[..., 0] + 1j * pred[..., 1])
+
+ def MEGRENoPhaseSignalModel(
+ self,
+ R2star_map: torch.Tensor,
+ S0_map: torch.Tensor,
+ TEs: Optional[List] = None,
+ ):
+ """MEGRE no phase forward model.
+
+ Parameters
+ ----------
+ R2star_map : torch.Tensor
+ R2* map of shape [batch_size, n_x, n_y].
+ S0_map : torch.Tensor
+ S0 map of shape [batch_size, n_x, n_y].
+ TEs : list of float, optional
+ List of echo times.
+ """
+ pred = torch.stack(
+ [
+ torch.stack(
+ (
+ S0_map * torch.exp(-TEs[i] * self.scaling * R2star_map), # type: ignore
+ S0_map * torch.exp(-TEs[i] * self.scaling * R2star_map), # type: ignore
+ ),
+ -1,
+ )
+ for i in range(len(TEs)) # type: ignore
+ ],
+ 1,
+ )
+ pred = torch.where(torch.isnan(pred), torch.zeros_like(pred), pred)
+ return torch.view_as_real(pred[..., 0] + 1j * pred[..., 1])
diff --git a/atommic/collections/quantitative/nn/qcirim.py b/atommic/collections/quantitative/nn/qcirim.py
new file mode 100644
index 00000000..36c5a3ec
--- /dev/null
+++ b/atommic/collections/quantitative/nn/qcirim.py
@@ -0,0 +1,484 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import math
+from typing import Dict, List, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch import Tensor
+
+from atommic.collections.common.parts.fft import fft2
+from atommic.collections.common.parts.utils import coil_combination_method, rnn_weights_init
+from atommic.collections.quantitative.nn.base import BaseqMRIReconstructionModel, SignalForwardModel
+from atommic.collections.quantitative.nn.qrim_base.qrim_block import qRIMBlock
+from atommic.collections.quantitative.parts.transforms import R2star_B0_S0_phi_mapping
+from atommic.collections.reconstruction.nn.rim_base.rim_block import RIMBlock
+from atommic.core.classes.common import typecheck
+
+__all__ = ["qCIRIM"]
+
+
+class qCIRIM(BaseqMRIReconstructionModel):
+ """Implementation of the quantitative Recurrent Inference Machines (qRIM), as presented in [Zhang2022]_.
+
+ Also implements the qCIRIM model, which is a qRIM model with cascades.
+
+ References
+ ----------
+ .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW.
+ A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent
+ inference machine. NeuroImage. 2022 Dec 1;264:119680.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`qCIRIM`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ Trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+ quantitative_module_dimensionality = cfg_dict.get("quantitative_module_dimensionality")
+ if quantitative_module_dimensionality != 2:
+ raise ValueError(
+ f"Only 2D is currently supported for qMRI models.Found {quantitative_module_dimensionality}"
+ )
+
+ quantitative_module_no_dc = cfg_dict.get("quantitative_module_no_dc")
+ if not quantitative_module_no_dc:
+ raise ValueError("qCIRIM does not support explicit DC component.")
+
+ self.reconstruction_module = torch.nn.ModuleList([])
+
+ self.use_reconstruction_module = cfg_dict.get("use_reconstruction_module")
+ if self.use_reconstruction_module:
+ self.reconstruction_module_recurrent_filters = cfg_dict.get("reconstruction_module_recurrent_filters")
+ self.reconstruction_module_time_steps = 8 * math.ceil(cfg_dict.get("reconstruction_module_time_steps") / 8)
+ self.reconstruction_module_no_dc = cfg_dict.get("reconstruction_module_no_dc")
+ self.reconstruction_module_num_cascades = cfg_dict.get("reconstruction_module_num_cascades")
+
+ for _ in range(self.reconstruction_module_num_cascades):
+ self.reconstruction_module.append(
+ RIMBlock(
+ recurrent_layer=cfg_dict.get("reconstruction_module_recurrent_layer"),
+ conv_filters=cfg_dict.get("reconstruction_module_conv_filters"),
+ conv_kernels=cfg_dict.get("reconstruction_module_conv_kernels"),
+ conv_dilations=cfg_dict.get("reconstruction_module_conv_dilations"),
+ conv_bias=cfg_dict.get("reconstruction_module_conv_bias"),
+ recurrent_filters=self.reconstruction_module_recurrent_filters,
+ recurrent_kernels=cfg_dict.get("reconstruction_module_recurrent_kernels"),
+ recurrent_dilations=cfg_dict.get("reconstruction_module_recurrent_dilations"),
+ recurrent_bias=cfg_dict.get("reconstruction_module_recurrent_bias"),
+ depth=cfg_dict.get("reconstruction_module_depth"),
+ time_steps=self.reconstruction_module_time_steps,
+ conv_dim=cfg_dict.get("reconstruction_module_conv_dim"),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim - 1,
+ dimensionality=cfg_dict.get("reconstruction_module_dimensionality"),
+ coil_combination_method=self.coil_combination_method,
+ )
+ )
+
+ # Keep estimation through the cascades if keep_prediction is True or re-estimate it if False.
+ self.reconstruction_module_keep_prediction = cfg_dict.get("reconstruction_module_keep_prediction")
+
+ # initialize weights if not using pretrained cirim
+ if not cfg_dict.get("pretrained", False):
+ std_init_range = 1 / self.reconstruction_module_recurrent_filters[0] ** 0.5
+ self.reconstruction_module.apply(lambda module: rnn_weights_init(module, std_init_range))
+
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+ self.reconstruction_module_accumulate_predictions = cfg_dict.get(
+ "reconstruction_module_accumulate_predictions"
+ )
+
+ self.quantitative_maps_scaling_factor = cfg_dict.get("quantitative_maps_scaling_factor")
+
+ quantitative_module_num_cascades = cfg_dict.get("quantitative_module_num_cascades")
+ self.quantitative_module = torch.nn.ModuleList(
+ [
+ qRIMBlock(
+ recurrent_layer=cfg_dict.get("quantitative_module_recurrent_layer"),
+ conv_filters=cfg_dict.get("quantitative_module_conv_filters"),
+ conv_kernels=cfg_dict.get("quantitative_module_conv_kernels"),
+ conv_dilations=cfg_dict.get("quantitative_module_conv_dilations"),
+ conv_bias=cfg_dict.get("quantitative_module_conv_bias"),
+ recurrent_filters=cfg_dict.get("quantitative_module_recurrent_filters"),
+ recurrent_kernels=cfg_dict.get("quantitative_module_recurrent_kernels"),
+ recurrent_dilations=cfg_dict.get("quantitative_module_recurrent_dilations"),
+ recurrent_bias=cfg_dict.get("quantitative_module_recurrent_bias"),
+ depth=cfg_dict.get("quantitative_module_depth"),
+ time_steps=cfg_dict.get("quantitative_module_time_steps"),
+ conv_dim=cfg_dict.get("quantitative_module_conv_dim"),
+ linear_forward_model=SignalForwardModel(
+ sequence=cfg_dict.get("quantitative_module_signal_forward_model_sequence")
+ ),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ coil_combination_method=self.coil_combination_method,
+ dimensionality=quantitative_module_dimensionality,
+ )
+ for _ in range(quantitative_module_num_cascades)
+ ]
+ )
+ self.quantitative_maps_regularization_factors = cfg_dict.get(
+ "quantitative_maps_regularization_factors", [150.0, 150.0, 1000.0, 150.0]
+ )
+
+ self.accumulate_predictions = cfg_dict.get("quantitative_module_accumulate_predictions")
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ R2star_map_init: torch.Tensor,
+ S0_map_init: torch.Tensor,
+ B0_map_init: torch.Tensor,
+ phi_map_init: torch.Tensor,
+ TEs: List,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ initial_prediction: torch.Tensor,
+ anatomy_mask: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ sigma: float = 1.0,
+ ) -> Union[List[List[Tensor]], List[Tensor]]:
+ """Forward pass of :class:`qCIRIM`.
+
+ Parameters
+ ----------
+ R2star_map_init : torch.Tensor
+ Initial R2* map of shape [batch_size, n_x, n_y].
+ S0_map_init : torch.Tensor
+ Initial S0 map of shape [batch_size, n_x, n_y].
+ B0_map_init : torch.Tensor
+ Initial B0 map of shape [batch_size, n_x, n_y].
+ phi_map_init : torch.Tensor
+ Initial phase map of shape [batch_size, n_x, n_y].
+ TEs : List
+ List of echo times.
+ y : torch.Tensor
+ Subsampled k-space data of shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2].
+ initial_prediction : torch.Tensor
+ Initial prediction of shape [batch_size, n_x, n_y, 2].
+ anatomy_mask : torch.Tensor
+ Brain mask of shape [batch_size, 1, n_x, n_y, 1].
+ sampling_mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ sigma : float
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ List of list of torch.Tensor or torch.Tensor
+ If self.accumulate_loss is True, returns a list of all intermediate predictions.
+ If False, returns the final estimate.
+ """
+ if self.use_reconstruction_module:
+ cascades_echoes_reconstruction_predictions = []
+ sigma = 1.0
+ for echo in range(y.shape[1]):
+ reconstruction_prediction = y[:, echo, ...].clone()
+ initial_reconstruction_prediction_echo = None
+ hx = None
+ cascades_reconstruction_predictions = []
+ for i, cascade in enumerate(self.reconstruction_module):
+ # Forward pass through the cascades
+ reconstruction_prediction, hx = cascade(
+ reconstruction_prediction,
+ y[:, echo, ...],
+ sensitivity_maps,
+ sampling_mask[:, 0, ...],
+ initial_reconstruction_prediction_echo if i == 0 else reconstruction_prediction,
+ hx,
+ sigma,
+ keep_prediction=False if i == 0 else self.reconstruction_module_keep_prediction,
+ )
+ cascades_reconstruction_predictions.append(
+ [torch.view_as_complex(x) for x in reconstruction_prediction]
+ )
+ reconstruction_prediction = reconstruction_prediction[-1]
+ cascades_echoes_reconstruction_predictions.append(cascades_reconstruction_predictions)
+
+ # cascades_echoes_reconstruction_predictions is of length n_echoes, len(self.reconstruction_module),
+ # self.reconstruction_module_time_steps. We want to concatenate the echoes for each cascade and time step.
+ reconstruction_prediction = []
+ for cascade in range(len(cascades_echoes_reconstruction_predictions[0])):
+ reconstruction_prediction_cascade = []
+ for time_step in range(len(cascades_echoes_reconstruction_predictions[0][cascade])):
+ reconstruction_prediction_time_step = []
+ for echo in range( # pylint: disable=consider-using-enumerate
+ len(cascades_echoes_reconstruction_predictions)
+ ):
+ reconstruction_prediction_time_step.append(
+ cascades_echoes_reconstruction_predictions[echo][cascade][time_step]
+ )
+ reconstruction_prediction_time_step = torch.stack(reconstruction_prediction_time_step, dim=1)
+ if reconstruction_prediction_time_step.shape[-1] != 2: # type: ignore
+ reconstruction_prediction_time_step = torch.view_as_real(reconstruction_prediction_time_step)
+ reconstruction_prediction_cascade.append(reconstruction_prediction_time_step)
+ reconstruction_prediction.append(reconstruction_prediction_cascade)
+
+ final_reconstruction_prediction = reconstruction_prediction[-1][-1]
+ if not self.reconstruction_module_accumulate_predictions:
+ reconstruction_prediction = final_reconstruction_prediction
+
+ y = fft2(
+ coil_combination_method(
+ final_reconstruction_prediction.unsqueeze(self.coil_dim),
+ sensitivity_maps.unsqueeze(self.coil_dim - 1),
+ method=self.coil_combination_method,
+ dim=self.coil_dim - 1,
+ ),
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ )
+
+ R2star_maps_init = []
+ S0_maps_init = []
+ B0_maps_init = []
+ phi_maps_init = []
+ for batch_idx in range(final_reconstruction_prediction.shape[0]):
+ R2star_map_init, S0_map_init, B0_map_init, phi_map_init = R2star_B0_S0_phi_mapping(
+ final_reconstruction_prediction[batch_idx],
+ TEs,
+ anatomy_mask,
+ scaling_factor=self.quantitative_maps_scaling_factor,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ R2star_maps_init.append(R2star_map_init.squeeze(0))
+ S0_maps_init.append(S0_map_init.squeeze(0))
+ B0_maps_init.append(B0_map_init.squeeze(0))
+ phi_maps_init.append(phi_map_init.squeeze(0))
+ R2star_map_init = torch.stack(R2star_maps_init, dim=0).to(y)
+ S0_map_init = torch.stack(S0_maps_init, dim=0).to(y)
+ B0_map_init = torch.stack(B0_maps_init, dim=0).to(y)
+ phi_map_init = torch.stack(phi_maps_init, dim=0).to(y)
+ else:
+ reconstruction_prediction = initial_prediction.clone()
+
+ R2star_map_init = R2star_map_init / self.quantitative_maps_regularization_factors[0]
+ S0_map_init = S0_map_init / self.quantitative_maps_regularization_factors[1]
+ B0_map_init = B0_map_init / self.quantitative_maps_regularization_factors[2]
+ phi_map_init = phi_map_init / self.quantitative_maps_regularization_factors[3]
+
+ qmaps_prediction = torch.stack([R2star_map_init, S0_map_init, B0_map_init, phi_map_init], dim=1)
+ hx = None
+ cascades_R2star_maps_prediction = []
+ cascades_S0_maps_prediction = []
+ cascades_B0_maps_prediction = []
+ cascades_phi_maps_prediction = []
+ for i, cascade in enumerate(self.quantitative_module):
+ # Forward pass through the cascades
+ qmaps_prediction, hx = cascade(qmaps_prediction, y, sensitivity_maps, sampling_mask, TEs, hx)
+ # Keep the intermediate predictions
+ for qmaps_pred in qmaps_prediction:
+ cascades_R2star_maps_prediction.append(
+ qmaps_pred[:, 0, ...] * self.quantitative_maps_regularization_factors[0]
+ )
+ cascades_S0_maps_prediction.append(
+ qmaps_pred[:, 1, ...] * self.quantitative_maps_regularization_factors[1]
+ )
+ cascades_B0_maps_prediction.append(
+ qmaps_pred[:, 2, ...] * self.quantitative_maps_regularization_factors[2]
+ )
+ cascades_phi_maps_prediction.append(
+ qmaps_pred[:, 3, ...] * self.quantitative_maps_regularization_factors[3]
+ )
+ # Keep the final prediction for the next cascade
+ qmaps_prediction = qmaps_prediction[-1]
+
+ if not self.accumulate_predictions:
+ reconstruction_prediction = (
+ reconstruction_prediction[-1][-1] if self.use_reconstruction_module else torch.empty([])
+ )
+ cascades_R2star_maps_prediction = cascades_R2star_maps_prediction[-1][-1]
+ cascades_S0_maps_prediction = cascades_S0_maps_prediction[-1][-1]
+ cascades_B0_maps_prediction = cascades_B0_maps_prediction[-1][-1]
+ cascades_phi_maps_prediction = cascades_phi_maps_prediction[-1][-1]
+
+ return [
+ reconstruction_prediction,
+ cascades_R2star_maps_prediction,
+ cascades_S0_maps_prediction,
+ cascades_B0_maps_prediction,
+ cascades_phi_maps_prediction,
+ ]
+
+ def process_quantitative_loss(
+ self,
+ target: torch.Tensor,
+ prediction: Union[list, torch.Tensor],
+ anatomy_mask: torch.Tensor,
+ quantitative_map: str,
+ loss_func: torch.nn.Module,
+ ) -> torch.Tensor:
+ """Processes the quantitative loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ anatomy_mask : torch.Tensor
+ Mask of specified anatomy, e.g. brain. Shape [n_x, n_y].
+ quantitative_map : str
+ Type of quantitative map to regularize the loss. Must be one of {"R2star", "S0", "B0", "phi"}.
+ loss_func : torch.nn.Module
+ Loss function. Default is ``torch.nn.L1Loss()``.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ target = torch.abs(self.__abs_output__(target / torch.max(torch.abs(target))))
+ anatomy_mask = torch.abs(anatomy_mask).to(target)
+
+ def compute_quantitative_loss(t, p, m):
+ p = torch.abs(self.__abs_output__(p / torch.max(torch.abs(p))))
+
+ if "ssim" in str(loss_func).lower():
+ return (
+ loss_func(
+ t * m,
+ p * m,
+ data_range=torch.tensor([max(torch.max(t * m).item(), torch.max(p * m).item())])
+ .unsqueeze(dim=0)
+ .to(t),
+ )
+ * self.quantitative_parameters_regularization_factors[quantitative_map]
+ )
+ return loss_func(t * m, p * m) / self.quantitative_parameters_regularization_factors[quantitative_map]
+
+ if self.accumulate_predictions:
+ cascades_loss = []
+ for cascade_pred in prediction:
+ time_steps_loss = [
+ compute_quantitative_loss(target, time_step_pred, anatomy_mask) for time_step_pred in cascade_pred
+ ]
+ cascades_loss.append(torch.sum(torch.stack(time_steps_loss, dim=0)) / len(prediction))
+ loss = sum(cascades_loss) / len(self.quantitative_module)
+ else:
+ loss = compute_quantitative_loss(target, prediction, anatomy_mask)
+ return loss
+
+ def process_reconstruction_loss( # noqa: MC0001
+ self,
+ target: torch.Tensor,
+ prediction: Union[list, torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ attrs: Dict,
+ r: int,
+ loss_func: torch.nn.Module,
+ ) -> torch.Tensor:
+ """Processes the reconstruction loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ loss_func : torch.nn.Module
+ Loss function. Must be one of {torch.nn.L1Loss(), torch.nn.MSELoss(),
+ atommic.collections.reconstruction.losses.ssim.SSIMLoss()}. Default is ``torch.nn.L1Loss()``.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ if self.unnormalize_loss_inputs:
+ for cascade in range(len(prediction)): # pylint: disable=consider-using-enumerate
+ for time_steps in range(len(prediction[cascade])):
+ target, prediction[cascade][time_steps], sensitivity_maps = self.__unnormalize_for_loss_or_log__(
+ target, prediction[cascade][time_steps], sensitivity_maps, attrs, r
+ )
+
+ # If kspace reconstruction loss is used, the target needs to be transformed in k-space.
+ if self.kspace_quantitative_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if target.shape[-1] != 2 and torch.is_complex(target):
+ target = torch.view_as_real(target)
+
+ # Transform to k-space.
+ target = fft2(target, self.fft_centered, self.fft_normalization, self.spatial_dims)
+
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target / torch.max(torch.abs(target)))
+
+ for cascade in range(len(prediction)): # pylint: disable=consider-using-enumerate
+ for time_steps in range(len(prediction[cascade])):
+ if prediction[cascade][time_steps].shape[-1] != 2 and torch.is_complex(
+ prediction[cascade][time_steps]
+ ):
+ prediction[cascade][time_steps] = torch.view_as_real(prediction[cascade][time_steps])
+ prediction[cascade][time_steps] = fft2(
+ prediction[cascade][time_steps],
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ )
+ prediction[cascade][time_steps] = self.__abs_output__(
+ prediction[cascade][time_steps] / torch.max(torch.abs(prediction[cascade][time_steps]))
+ )
+
+ elif not self.unnormalize_loss_inputs:
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target / torch.max(torch.abs(target)))
+ for cascade in range(len(prediction)): # pylint: disable=consider-using-enumerate
+ for time_steps in range(len(prediction[cascade])):
+ prediction[cascade][time_steps] = self.__abs_output__(
+ prediction[cascade][time_steps] / torch.max(torch.abs(prediction[cascade][time_steps]))
+ )
+
+ def compute_reconstruction_loss(t, p):
+ p = torch.abs(p / torch.max(torch.abs(p)))
+ t = torch.abs(t / torch.max(torch.abs(t)))
+ return torch.mean(torch.tensor([loss_func(t[:, echo], p[:, echo]) for echo in range(t.shape[1])]))
+
+ if self.reconstruction_module_accumulate_predictions:
+ cascades_loss = []
+ for cascade_prediction in prediction:
+ cascade_time_steps_loss = [
+ compute_reconstruction_loss(target, cascade_prediction_time_step_prediction).mean()
+ for cascade_prediction_time_step_prediction in cascade_prediction
+ ]
+ cascade_loss = [
+ x
+ * torch.logspace(-1, 0, steps=self.reconstruction_module_time_steps).to(cascade_time_steps_loss[0])
+ for x in cascade_time_steps_loss
+ ]
+ cascades_loss.append(sum(sum(cascade_loss) / len(self.reconstruction_module)))
+ loss = sum(cascades_loss) / len(prediction)
+ else:
+ loss = compute_reconstruction_loss(target, prediction)
+ return loss
diff --git a/atommic/collections/quantitative/nn/qrim_base/__init__.py b/atommic/collections/quantitative/nn/qrim_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/quantitative/nn/qrim_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/quantitative/nn/qrim_base/qrim_block.py b/atommic/collections/quantitative/nn/qrim_base/qrim_block.py
new file mode 100644
index 00000000..2bdd20d7
--- /dev/null
+++ b/atommic/collections/quantitative/nn/qrim_base/qrim_block.py
@@ -0,0 +1,231 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Any, List, Optional, Tuple, Union
+
+import torch
+
+from atommic.collections.quantitative.nn.base import SignalForwardModel
+from atommic.collections.quantitative.nn.qrim_base.utils import analytical_log_likelihood_gradient
+from atommic.collections.reconstruction.nn.rim_base import conv_layers, rnn_cells
+
+
+class qRIMBlock(torch.nn.Module):
+ """qRIMBlock extends a block of Recurrent Inference Machines (RIMs) as presented in [Zhang2022]_.
+
+ References
+ ----------
+ .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW.
+ A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent
+ inference machine. NeuroImage. 2022 Dec 1;264:119680.
+ """
+
+ def __init__(
+ self,
+ recurrent_layer=None,
+ conv_filters=None,
+ conv_kernels=None,
+ conv_dilations=None,
+ conv_bias=None,
+ recurrent_filters=None,
+ recurrent_kernels=None,
+ recurrent_dilations=None,
+ recurrent_bias=None,
+ depth: int = 2,
+ time_steps: int = 8,
+ conv_dim: int = 2,
+ linear_forward_model=None,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ coil_combination_method: str = "SENSE",
+ dimensionality: int = 2, # pylint: disable=unused-argument
+ ):
+ """Inits :class:`qRIMBlock`.
+
+ Parameters
+ ----------
+ recurrent_layer : torch.nn.Module, optional
+ Recurrent layer. Default is ``None``.
+ conv_filters : int, optional
+ Number of filters in the convolutional layers. Default is ``None``.
+ conv_kernels : int, optional
+ Kernel size of the convolutional layers. Default is ``None``.
+ conv_dilations : int, optional
+ Dilation of the convolutional layers. Default is ``None``.
+ conv_bias : bool, optional
+ Bias of the convolutional layers. Default is ``None``.
+ recurrent_filters : int, optional
+ Number of filters in the recurrent layers. Default is ``None``.
+ recurrent_kernels : int, optional
+ Kernel size of the recurrent layers. Default is ``None``.
+ recurrent_dilations : int, optional
+ Dilation of the recurrent layers. Default is ``None``.
+ recurrent_bias : bool, optional
+ Bias of the recurrent layers. Default is ``None``.
+ depth : int, optional
+ Number of RIM layers. Default is ``2``.
+ time_steps : int, optional
+ Number of time steps. Default is ``8``.
+ conv_dim : int, optional
+ Dimension of the convolutional layers. Default is ``2``.
+ linear_forward_model : SignalForwardModel, optional
+ Linear forward model. Default is ``None``.
+ fft_centered : bool, optional
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization : str, optional
+ Normalization of the FFT. Default is ``"backward"``.
+ spatial_dims : tuple, optional
+ Spatial dimensions of the input. Default is ``None``.
+ coil_dim : int, optional
+ Coils dimension of the input. Default is ``1``.
+ coil_combination_method : str, optional
+ Method to combine the coils. Default is ``"SENSE"``.
+ dimensionality : int, optional
+ Dimensionality of the input. Default is ``2``.
+ """
+ super().__init__()
+
+ self.linear_forward_model = (
+ SignalForwardModel(sequence="MEGRE") if linear_forward_model is None else linear_forward_model
+ )
+
+ self.input_size = depth * 4
+ self.time_steps = time_steps
+
+ self.layers = torch.nn.ModuleList()
+ for (
+ (conv_features, conv_k_size, conv_dilation, l_conv_bias, nonlinear),
+ (rnn_features, rnn_k_size, rnn_dilation, rnn_bias, rnn_type),
+ ) in zip(
+ zip(conv_filters, conv_kernels, conv_dilations, conv_bias, ["relu", "relu", None]),
+ zip(
+ recurrent_filters,
+ recurrent_kernels,
+ recurrent_dilations,
+ recurrent_bias,
+ [recurrent_layer, recurrent_layer, None],
+ ),
+ ):
+ conv_layer = None
+
+ if conv_features != 0:
+ conv_layer = conv_layers.ConvNonlinear(
+ self.input_size,
+ conv_features,
+ conv_dim=conv_dim,
+ kernel_size=conv_k_size,
+ dilation=conv_dilation,
+ bias=l_conv_bias,
+ nonlinear=nonlinear,
+ )
+ self.input_size = conv_features
+
+ if rnn_features != 0 and rnn_type is not None:
+ if rnn_type.upper() == "GRU":
+ rnn_type = rnn_cells.ConvGRUCell
+ elif rnn_type.upper() == "MGU":
+ rnn_type = rnn_cells.ConvMGUCell
+ elif rnn_type.upper() == "INDRNN":
+ rnn_type = rnn_cells.IndRNNCell
+ else:
+ raise ValueError("Please specify a proper recurrent layer type.")
+
+ rnn_layer = rnn_type(
+ self.input_size,
+ rnn_features,
+ conv_dim=conv_dim,
+ kernel_size=rnn_k_size,
+ dilation=rnn_dilation,
+ bias=rnn_bias,
+ )
+
+ self.input_size = rnn_features
+
+ self.layers.append(conv_layers.ConvRNNStack(conv_layer, rnn_layer))
+
+ self.final_layer = torch.nn.Sequential(conv_layer)
+
+ self.recurrent_filters = recurrent_filters
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+ self.coil_combination_method = coil_combination_method
+
+ def forward(
+ self,
+ prediction: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ TEs: List,
+ hx: torch.Tensor = None,
+ ) -> Tuple[Any, Union[list, torch.Tensor, None]]:
+ """Forward pass of :class:`qRIMBlock`.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ Initial prediction of the quantitative maps.
+ masked_kspace : torch.Tensor
+ Subsampled k-space of shape [batch_size, n_coils, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2].
+ sampling_mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ TEs : List
+ List of echo times.
+ hx : torch.Tensor, optional
+ Initial hidden state. If None, it will be initialized with zeros. Default is ``None``.
+
+ Returns
+ -------
+ Tuple[Any, Union[list, torch.Tensor, None]]
+ Tuple containing the prediction and the hidden state. The prediction is a list of the predicted
+ quantitative maps of shape [batch_size, n_echoes, n_coils, n_x, n_y] and the hidden state is a list
+ of the hidden states of the recurrent layers.
+ """
+ batch_size = masked_kspace.shape[0]
+
+ if hx is None:
+ hx = [
+ prediction.new_zeros((prediction.size(0), f, *prediction.size()[2:])).to(masked_kspace)
+ for f in self.recurrent_filters
+ if f != 0
+ ]
+
+ predictions = []
+ for _ in range(self.time_steps):
+ grad_prediction = torch.zeros_like(prediction)
+ for idx in range(batch_size):
+ grad_prediction[idx] = (
+ analytical_log_likelihood_gradient(
+ self.linear_forward_model,
+ prediction[idx],
+ TEs,
+ sensitivity_maps[idx],
+ masked_kspace[idx],
+ sampling_mask[idx],
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ coil_combination_method=self.coil_combination_method,
+ ).contiguous()
+ / 100
+ )
+ grad_prediction = torch.cat([grad_prediction, prediction], dim=self.coil_dim - 1).to(masked_kspace)
+ for h, convrnn in enumerate(self.layers):
+ hx[h] = convrnn(grad_prediction, hx[h])
+ grad_prediction = hx[h]
+ grad_prediction = self.final_layer(grad_prediction)
+ prediction = prediction + grad_prediction
+ prediction_tmp = prediction[:, 0, :, :]
+ prediction_tmp[prediction_tmp < 0] = 0
+ prediction[:, 0, :, :] = prediction_tmp
+ predictions.append(prediction)
+
+ return predictions, hx
diff --git a/atommic/collections/quantitative/nn/qrim_base/utils.py b/atommic/collections/quantitative/nn/qrim_base/utils.py
new file mode 100644
index 00000000..88d3f2f4
--- /dev/null
+++ b/atommic/collections/quantitative/nn/qrim_base/utils.py
@@ -0,0 +1,167 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import List, Sequence
+
+import torch
+
+from atommic.collections.common.parts import fft, utils
+from atommic.collections.quantitative.nn.base import SignalForwardModel
+
+
+def expand_op(x: torch.Tensor, sensitivity_maps: torch.Tensor) -> torch.Tensor:
+ """Expands a coil-combined image to multicoil.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Coil-combined image.
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ Multicoil image.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.quantitative.nn.qrim_base.utils import expand_op
+ >>> data = torch.randn(1, 1, 320, 320, 2)
+ >>> coil_sensitivity_maps = torch.randn(1, 32, 320, 320, 2)
+ >>> expand_op(data, coil_sensitivity_maps).shape
+ torch.Size([1, 32, 320, 320, 2])
+ """
+ x = utils.complex_mul(x, sensitivity_maps)
+ if torch.isnan(x).any():
+ x = torch.where(torch.isnan(x), torch.zeros_like(x), x)
+ return x
+
+
+def analytical_log_likelihood_gradient(
+ linear_forward_model: SignalForwardModel,
+ prediction: torch.Tensor,
+ TEs: List,
+ sensitivity_maps: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ fft_centered: bool,
+ fft_normalization: str,
+ spatial_dims: Sequence[int],
+ coil_dim: int,
+ coil_combination_method: str = "SENSE",
+ scaling: float = 1e-3,
+) -> torch.Tensor:
+ """Computes the analytical gradient of the log-likelihood function.
+
+ Parameters
+ ----------
+ linear_forward_model: SignalForwardModel
+ Signal forward model to use.
+ prediction : torch.Tensor
+ Current prediction of the quantitative maps.
+ TEs : List
+ List of echo times.
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2].
+ masked_kspace : torch.Tensor
+ Data of shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ sampling_mask : torch.Tensor
+ Mask of the sampling of shape [batch_size, 1, n_x, n_y, 1].
+ fft_centered : bool
+ If True, the FFT is centered.
+ fft_normalization : str
+ Normalization of the FFT.
+ spatial_dims : Sequence[int]
+ Spatial dimensions of the input.
+ coil_dim : int
+ Coil dimension.
+ coil_combination_method : str, optional
+ Coil combination method. Default is ``"SENSE"``.
+ scaling : float, optional
+ Scaling factor. Default is ``1e-3``.
+
+ Returns
+ -------
+ torch.Tensor
+ Analytical gradient of the log-likelihood function.
+ """
+ nr_TEs = len(TEs)
+
+ R2star_map, S0_map, B0_map, phi_map = prediction[0], prediction[1], prediction[2], prediction[3]
+
+ R2star_map = R2star_map.unsqueeze(0)
+ S0_map = S0_map.unsqueeze(0)
+ B0_map = B0_map.unsqueeze(0)
+ phi_map = phi_map.unsqueeze(0)
+
+ pred = linear_forward_model(R2star_map, S0_map, B0_map, phi_map, TEs)
+
+ S0_map_real = S0_map
+ S0_map_imag = phi_map
+
+ pred_kspace = fft.fft2(
+ expand_op(pred.unsqueeze(coil_dim), sensitivity_maps.unsqueeze(0).unsqueeze(coil_dim - 1)),
+ fft_centered,
+ fft_normalization,
+ spatial_dims,
+ )
+
+ diff_data = (pred_kspace - masked_kspace) * sampling_mask
+ diff_data_inverse = utils.coil_combination_method(
+ fft.ifft2(diff_data, fft_centered, fft_normalization, spatial_dims),
+ sensitivity_maps.unsqueeze(0).unsqueeze(coil_dim - 1),
+ method=coil_combination_method,
+ dim=coil_dim,
+ )
+
+ def first_term(i):
+ """First term of the gradient."""
+ return torch.exp(-TEs[i] * scaling * R2star_map)
+
+ def second_term(i):
+ """Second term of the gradient."""
+ return torch.cos(B0_map * scaling * -TEs[i])
+
+ def third_term(i):
+ """Third term of the gradient."""
+ return torch.sin(B0_map * scaling * -TEs[i])
+
+ S0_part_der = torch.stack(
+ [torch.stack((first_term(i) * second_term(i), -first_term(i) * third_term(i)), -1) for i in range(nr_TEs)], 1
+ )
+
+ R2str_part_der = torch.stack(
+ [
+ torch.stack(
+ (
+ -TEs[i] * scaling * first_term(i) * (S0_map_real * second_term(i) - S0_map_imag * third_term(i)),
+ -TEs[i] * scaling * first_term(i) * (-S0_map_real * third_term(i) - S0_map_imag * second_term(i)),
+ ),
+ -1,
+ )
+ for i in range(nr_TEs)
+ ],
+ 1,
+ )
+
+ S0_map_real_grad = (
+ diff_data_inverse[..., 0] * S0_part_der[..., 0] - diff_data_inverse[..., 1] * S0_part_der[..., 1]
+ )
+ S0_map_imag_grad = (
+ diff_data_inverse[..., 0] * S0_part_der[..., 1] + diff_data_inverse[..., 1] * S0_part_der[..., 0]
+ )
+ R2star_map_real_grad = (
+ diff_data_inverse[..., 0] * R2str_part_der[..., 0] - diff_data_inverse[..., 1] * R2str_part_der[..., 1]
+ )
+ R2star_map_imag_grad = (
+ diff_data_inverse[..., 0] * R2str_part_der[..., 1] + diff_data_inverse[..., 1] * R2str_part_der[..., 0]
+ )
+
+ S0_map_grad = torch.stack([S0_map_real_grad, S0_map_imag_grad], -1).squeeze()
+ S0_map_grad = torch.mean(S0_map_grad, 0)
+ R2star_map_grad = torch.stack([R2star_map_real_grad, R2star_map_imag_grad], -1).squeeze()
+ R2star_map_grad = torch.mean(R2star_map_grad, 0)
+
+ return torch.stack([R2star_map_grad[..., 0], S0_map_grad[..., 0], R2star_map_grad[..., 1], S0_map_grad[..., 1]], 0)
diff --git a/atommic/collections/quantitative/nn/qvarnet.py b/atommic/collections/quantitative/nn/qvarnet.py
new file mode 100644
index 00000000..7b3a6b53
--- /dev/null
+++ b/atommic/collections/quantitative/nn/qvarnet.py
@@ -0,0 +1,246 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import List, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch import Tensor
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import coil_combination_method
+from atommic.collections.quantitative.nn.base import BaseqMRIReconstructionModel, SignalForwardModel
+from atommic.collections.quantitative.nn.qvarnet_base.qvarnet_block import qVarNetBlock
+from atommic.collections.quantitative.parts.transforms import R2star_B0_S0_phi_mapping
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.collections.reconstruction.nn.varnet_base.varnet_block import VarNetBlock
+from atommic.core.classes.common import typecheck
+
+__all__ = ["qVarNet"]
+
+
+class qVarNet(BaseqMRIReconstructionModel):
+ """Implementation of the quantitative End-to-end Variational Network (qVN), as presented in [Zhang2022]_.
+
+ References
+ ----------
+ .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW.
+ A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent
+ inference machine. NeuroImage. 2022 Dec 1;264:119680.
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ # init superclass
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+ quantitative_module_dimensionality = cfg_dict.get("quantitative_module_dimensionality")
+ if quantitative_module_dimensionality != 2:
+ raise ValueError(
+ f"Only 2D is currently supported for qMRI models.Found {quantitative_module_dimensionality}"
+ )
+
+ self.reconstruction_module = torch.nn.ModuleList([])
+
+ self.use_reconstruction_module = cfg_dict.get("use_reconstruction_module")
+ if self.use_reconstruction_module:
+ self.reconstruction_module_num_cascades = cfg_dict.get("reconstruction_module_num_cascades")
+ self.reconstruction_module_no_dc = cfg_dict.get("reconstruction_module_no_dc")
+
+ for _ in range(self.reconstruction_module_num_cascades):
+ self.reconstruction_module.append(
+ VarNetBlock(
+ NormUnet(
+ chans=cfg_dict.get("reconstruction_module_channels"),
+ num_pools=cfg_dict.get("reconstruction_module_pooling_layers"),
+ in_chans=cfg_dict.get("reconstruction_module_in_channels"),
+ out_chans=cfg_dict.get("reconstruction_module_out_channels"),
+ padding_size=cfg_dict.get("reconstruction_module_padding_size"),
+ normalize=cfg_dict.get("reconstruction_module_normalize"),
+ ),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim - 1,
+ no_dc=self.reconstruction_module_no_dc,
+ )
+ )
+
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+ self.reconstruction_module_accumulate_predictions = cfg_dict.get(
+ "reconstruction_module_accumulate_predictions"
+ )
+
+ quantitative_module_num_cascades = cfg_dict.get("quantitative_module_num_cascades")
+ self.quantitative_module = torch.nn.ModuleList(
+ [
+ qVarNetBlock(
+ NormUnet(
+ chans=cfg_dict.get("quantitative_module_channels"),
+ num_pools=cfg_dict.get("quantitative_module_pooling_layers"),
+ in_chans=cfg_dict.get("quantitative_module_in_channels"),
+ out_chans=cfg_dict.get("quantitative_module_out_channels"),
+ padding_size=cfg_dict.get("quantitative_module_padding_size"),
+ normalize=cfg_dict.get("quantitative_module_normalize"),
+ ),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ no_dc=cfg_dict.get("quantitative_module_no_dc"),
+ linear_forward_model=SignalForwardModel(
+ sequence=cfg_dict.get("quantitative_module_signal_forward_model_sequence")
+ ),
+ )
+ for _ in range(quantitative_module_num_cascades)
+ ]
+ )
+
+ self.quantitative_maps_regularization_factors = cfg_dict.get(
+ "quantitative_maps_regularization_factors", [150.0, 150.0, 1000.0, 150.0]
+ )
+
+ self.accumulate_predictions = cfg_dict.get("quantitative_module_accumulate_predictions")
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ R2star_map_init: torch.Tensor,
+ S0_map_init: torch.Tensor,
+ B0_map_init: torch.Tensor,
+ phi_map_init: torch.Tensor,
+ TEs: List,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ initial_prediction: torch.Tensor,
+ anatomy_mask: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> Union[List[List[Tensor]], List[Tensor]]:
+ """
+ Forward pass of the network.
+
+ Parameters
+ ----------
+ R2star_map_init : torch.Tensor
+ Initial R2* map of shape [batch_size, n_x, n_y].
+ S0_map_init : torch.Tensor
+ Initial S0 map of shape [batch_size, n_x, n_y].
+ B0_map_init : torch.Tensor
+ Initial B0 map of shape [batch_size, n_x, n_y].
+ phi_map_init : torch.Tensor
+ Initial phase map of shape [batch_size, n_x, n_y].
+ TEs : List
+ List of echo times.
+ y : torch.Tensor
+ Subsampled k-space data of shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2].
+ initial_prediction : torch.Tensor
+ Initial prediction of shape [batch_size, n_x, n_y, 2].
+ anatomy_mask : torch.Tensor
+ Brain mask of shape [batch_size, 1, n_x, n_y, 1].
+ sampling_mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ sigma : float
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ List of list of torch.Tensor or torch.Tensor
+ If self.accumulate_loss is True, returns a list of all intermediate predictions.
+ If False, returns the final estimate.
+ """
+ if self.use_reconstruction_module:
+ cascades_echoes_predictions = []
+ for echo in range(y.shape[1]):
+ prediction = y[:, echo, ...].clone()
+ for cascade in self.reconstruction_module:
+ # Forward pass through the cascades
+ prediction = cascade(prediction, y[:, echo, ...], sensitivity_maps, sampling_mask.squeeze(1))
+ reconstruction_prediction = ifft2(
+ prediction,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ reconstruction_prediction = coil_combination_method(
+ reconstruction_prediction,
+ sensitivity_maps,
+ method=self.coil_combination_method,
+ dim=self.coil_dim - 1,
+ )
+ cascades_echoes_predictions.append(torch.view_as_complex(reconstruction_prediction))
+
+ reconstruction_prediction = torch.stack(cascades_echoes_predictions, dim=1)
+ if reconstruction_prediction.shape[-1] != 2:
+ reconstruction_prediction = torch.view_as_real(reconstruction_prediction)
+
+ y = fft2(
+ coil_combination_method(
+ reconstruction_prediction.unsqueeze(self.coil_dim),
+ sensitivity_maps.unsqueeze(self.coil_dim - 1),
+ method=self.coil_combination_method,
+ dim=self.coil_dim - 1,
+ ),
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ )
+
+ R2star_maps_init = []
+ S0_maps_init = []
+ B0_maps_init = []
+ phi_maps_init = []
+ for batch_idx in range(reconstruction_prediction.shape[0]):
+ R2star_map_init, S0_map_init, B0_map_init, phi_map_init = R2star_B0_S0_phi_mapping(
+ reconstruction_prediction[batch_idx],
+ TEs,
+ anatomy_mask,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ R2star_maps_init.append(R2star_map_init.squeeze(0))
+ S0_maps_init.append(S0_map_init.squeeze(0))
+ B0_maps_init.append(B0_map_init.squeeze(0))
+ phi_maps_init.append(phi_map_init.squeeze(0))
+ R2star_map_init = torch.stack(R2star_maps_init, dim=0).to(y)
+ S0_map_init = torch.stack(S0_maps_init, dim=0).to(y)
+ B0_map_init = torch.stack(B0_maps_init, dim=0).to(y)
+ phi_map_init = torch.stack(phi_maps_init, dim=0).to(y)
+ else:
+ reconstruction_prediction = initial_prediction.clone()
+
+ R2star_map_pred = R2star_map_init / self.quantitative_maps_regularization_factors[0]
+ S0_map_pred = S0_map_init / self.quantitative_maps_regularization_factors[1]
+ B0_map_pred = B0_map_init / self.quantitative_maps_regularization_factors[2]
+ phi_map_pred = phi_map_init / self.quantitative_maps_regularization_factors[3]
+
+ prediction = torch.stack([R2star_map_pred, S0_map_pred, B0_map_pred, phi_map_pred], dim=1)
+ for cascade in self.quantitative_module:
+ # Forward pass through the cascades
+ prediction = cascade(
+ prediction,
+ y,
+ sensitivity_maps,
+ sampling_mask,
+ TEs,
+ )
+
+ R2star_map_pred, S0_map_pred, B0_map_pred, phi_map_pred = (
+ prediction[:, 0, ...] * self.quantitative_maps_regularization_factors[0],
+ prediction[:, 1, ...] * self.quantitative_maps_regularization_factors[1],
+ prediction[:, 2, ...] * self.quantitative_maps_regularization_factors[2],
+ prediction[:, 3, ...] * self.quantitative_maps_regularization_factors[3],
+ )
+
+ return [
+ reconstruction_prediction if self.use_reconstruction_module else torch.empty([]),
+ R2star_map_pred,
+ S0_map_pred,
+ B0_map_pred,
+ phi_map_pred,
+ ]
diff --git a/atommic/collections/quantitative/nn/qvarnet_base/__init__.py b/atommic/collections/quantitative/nn/qvarnet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/quantitative/nn/qvarnet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/quantitative/nn/qvarnet_base/qvarnet_block.py b/atommic/collections/quantitative/nn/qvarnet_base/qvarnet_block.py
new file mode 100644
index 00000000..8ffedde1
--- /dev/null
+++ b/atommic/collections/quantitative/nn/qvarnet_base/qvarnet_block.py
@@ -0,0 +1,150 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import List, Optional, Tuple
+
+import torch
+
+from atommic.collections.common.parts import utils
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.quantitative.nn.base import SignalForwardModel
+
+
+class qVarNetBlock(torch.nn.Module):
+ """Implementation of the quantitative End-to-end Variational Network (qVN), as presented in [Zhang2022]_.
+
+ References
+ ----------
+ .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW.
+ A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent
+ inference machine. NeuroImage. 2022 Dec 1;264:119680.
+ """
+
+ def __init__(
+ self,
+ model: torch.nn.Module,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ no_dc: bool = False,
+ linear_forward_model=None,
+ ):
+ """Inits :class:`qVarNetBlock`.
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Model to apply soft data consistency.
+ fft_centered : bool, optional
+ Whether to center the fft. Default is ``False``.
+ fft_normalization : str, optional
+ The normalization of the fft. Default is ``backward``.
+ spatial_dims : tuple, optional
+ The spatial dimensions of the data. Default is ``None``.
+ coil_dim : int, optional
+ The dimension of the coils. Default is ``1``.
+ no_dc : bool, optional
+ Whether to not apply the DC component. Default is ``False``.
+ linear_forward_model : torch.nn.Module, optional
+ Linear forward model. Default is ``None``.
+ """
+ super().__init__()
+
+ self.linear_forward_model = (
+ SignalForwardModel(sequence="MEGRE") if linear_forward_model is None else linear_forward_model
+ )
+
+ self.model = model
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+ self.no_dc = no_dc
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+
+ def sens_expand(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with coil-combined data to get multicoil data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ Expanded multicoil data.
+ """
+ return fft2(
+ utils.complex_mul(x, sens_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ def sens_reduce(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with multicoil data to get coil-combined data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ SENSE coil-combined reconstruction.
+ """
+ x = ifft2(x, centered=self.fft_centered, normalization=self.fft_normalization, spatial_dims=self.spatial_dims)
+ return utils.complex_mul(x, utils.complex_conj(sens_maps)).sum(dim=self.coil_dim)
+
+ def forward(
+ self,
+ prediction: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ TEs: List,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`qVarNetBlock`.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ Initial prediction of the quantitative maps.
+ masked_kspace : torch.Tensor
+ Subsampled k-space of shape [batch_size, n_coils, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2].
+ sampling_mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ TEs : List
+ List of echo times.
+
+ Returns
+ -------
+ torch.Tensor
+ Reconstructed image of shape [batch_size, n_coils, n_x, n_y, 2].
+ """
+ initial_prediction = self.linear_forward_model(
+ prediction[:, 0, ...].unsqueeze(0), # R2*
+ prediction[:, 1, ...].unsqueeze(0), # S0
+ prediction[:, 2, ...].unsqueeze(0), # B0
+ prediction[:, 3, ...].unsqueeze(0), # phi
+ TEs,
+ )
+ initial_prediction_kspace = self.sens_expand(initial_prediction, sensitivity_maps.unsqueeze(self.coil_dim - 1))
+ soft_dc = (initial_prediction_kspace - masked_kspace) * sampling_mask * self.dc_weight
+ initial_prediction = self.sens_reduce(soft_dc, sensitivity_maps.unsqueeze(self.coil_dim - 1)).to(masked_kspace)
+
+ prediction = torch.view_as_real(prediction + torch.view_as_complex(self.model(initial_prediction)))
+ prediction_tmp = prediction[:, 0, ...]
+ prediction_tmp[prediction_tmp < 0] = 0
+ prediction[:, 0, ...] = prediction_tmp
+
+ return torch.abs(torch.view_as_complex(prediction))
diff --git a/atommic/collections/quantitative/nn/qzf.py b/atommic/collections/quantitative/nn/qzf.py
new file mode 100644
index 00000000..088be4be
--- /dev/null
+++ b/atommic/collections/quantitative/nn/qzf.py
@@ -0,0 +1,111 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import List, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch import Tensor
+
+from atommic.collections.common.parts.fft import ifft2
+from atommic.collections.common.parts.utils import coil_combination_method
+from atommic.collections.quantitative.nn.base import BaseqMRIReconstructionModel
+from atommic.core.classes.common import typecheck
+
+__all__ = ["qZF"]
+
+
+class qZF(BaseqMRIReconstructionModel):
+ """Abstract class for returning the initial estimates of the quantitative maps."""
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ # init superclass
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+ quantitative_module_dimensionality = cfg_dict.get("quantitative_module_dimensionality")
+ if quantitative_module_dimensionality != 2:
+ raise ValueError(
+ f"Only 2D is currently supported for qMRI models.Found {quantitative_module_dimensionality}"
+ )
+
+ self.use_reconstruction_module = cfg_dict.get("use_reconstruction_module")
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ R2star_map_init: torch.Tensor,
+ S0_map_init: torch.Tensor,
+ B0_map_init: torch.Tensor,
+ phi_map_init: torch.Tensor,
+ TEs: List, # pylint: disable=unused-argument
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ initial_prediction: torch.Tensor,
+ anatomy_mask: torch.Tensor, # pylint: disable=unused-argument
+ sampling_mask: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> Union[List[List[Tensor]], List[Tensor]]:
+ """
+ Forward pass of the network.
+
+ Parameters
+ ----------
+ R2star_map_init : torch.Tensor
+ Initial R2* map of shape [batch_size, n_x, n_y].
+ S0_map_init : torch.Tensor
+ Initial S0 map of shape [batch_size, n_x, n_y].
+ B0_map_init : torch.Tensor
+ Initial B0 map of shape [batch_size, n_x, n_y].
+ phi_map_init : torch.Tensor
+ Initial phase map of shape [batch_size, n_x, n_y].
+ TEs : List
+ List of echo times.
+ y : torch.Tensor
+ Subsampled k-space data of shape [batch_size, n_echoes, n_coils, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2].
+ initial_prediction : torch.Tensor
+ Initial prediction of shape [batch_size, n_x, n_y, 2].
+ anatomy_mask : torch.Tensor
+ Brain mask of shape [batch_size, 1, n_x, n_y, 1].
+ sampling_mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ sigma : float
+ Standard deviation of the noise. Default is ``1.0``.
+
+ Returns
+ -------
+ List of list of torch.Tensor or torch.Tensor
+ If self.accumulate_loss is True, returns a list of all intermediate predictions.
+ If False, returns the final estimate.
+ """
+ if self.use_reconstruction_module:
+ reconstruction_prediction = torch.stack(
+ [
+ torch.view_as_complex(
+ coil_combination_method(
+ ifft2(
+ y[:, echo, ...].clone(), self.fft_centered, self.fft_normalization, self.spatial_dims
+ ),
+ sensitivity_maps,
+ method=self.coil_combination_method,
+ dim=self.coil_dim - 1,
+ )
+ )
+ for echo in range(y.shape[1])
+ ],
+ dim=1,
+ )
+ else:
+ reconstruction_prediction = initial_prediction
+
+ return [
+ reconstruction_prediction,
+ R2star_map_init,
+ S0_map_init,
+ B0_map_init,
+ phi_map_init,
+ ]
diff --git a/atommic/collections/quantitative/parts/__init__.py b/atommic/collections/quantitative/parts/__init__.py
new file mode 100644
index 00000000..04610c4c
--- /dev/null
+++ b/atommic/collections/quantitative/parts/__init__.py
@@ -0,0 +1,4 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.quantitative.parts.transforms import qMRIDataTransforms # noqa: F401
diff --git a/atommic/collections/quantitative/parts/transforms.py b/atommic/collections/quantitative/parts/transforms.py
new file mode 100644
index 00000000..99a31e66
--- /dev/null
+++ b/atommic/collections/quantitative/parts/transforms.py
@@ -0,0 +1,2080 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import math
+from typing import Any, Dict, List, Optional, Sequence, Tuple, Union
+
+import numpy as np
+import torch
+from skimage.restoration import unwrap_phase
+from torch.nn import functional as F
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.transforms import (
+ N2R,
+ SSDU,
+ Composer,
+ Cropper,
+ EstimateCoilSensitivityMaps,
+ GeometricDecompositionCoilCompression,
+ Masker,
+ NoisePreWhitening,
+ Normalizer,
+ ZeroFillingPadding,
+)
+from atommic.collections.common.parts.utils import add_coil_dim_if_singlecoil
+from atommic.collections.common.parts.utils import coil_combination_method as coil_combination_method_func
+from atommic.collections.common.parts.utils import is_none, sense, to_tensor
+from atommic.collections.motioncorrection.parts.motionsimulation import MotionSimulation
+
+__all__ = ["qMRIDataTransforms"]
+
+
+class qMRIDataTransforms:
+ """Data transforms for quantitative MRI.
+
+ Returns
+ -------
+ qMRIDataTransforms
+ Preprocessed data for quantitative MRI.
+ """
+
+ def __init__(
+ self,
+ TEs: Optional[List[float]],
+ precompute_quantitative_maps: bool = True,
+ qmaps_scaling_factor: float = 1.0,
+ dataset_format: str = None,
+ apply_prewhitening: bool = False,
+ find_patch_size: bool = True,
+ prewhitening_scale_factor: float = 1.0,
+ prewhitening_patch_start: int = 10,
+ prewhitening_patch_length: int = 30,
+ apply_gcc: bool = False,
+ gcc_virtual_coils: int = 10,
+ gcc_calib_lines: int = 24,
+ gcc_align_data: bool = True,
+ apply_random_motion: bool = False,
+ random_motion_type: str = "gaussian",
+ random_motion_percentage: Sequence[int] = (10, 10),
+ random_motion_angle: int = 10,
+ random_motion_translation: int = 10,
+ random_motion_center_percentage: float = 0.02,
+ random_motion_num_segments: int = 8,
+ random_motion_random_num_segments: bool = True,
+ random_motion_non_uniform: bool = False,
+ estimate_coil_sensitivity_maps: bool = False,
+ coil_sensitivity_maps_type: str = "ESPIRiT",
+ coil_sensitivity_maps_gaussian_sigma: float = 0.0,
+ coil_sensitivity_maps_espirit_threshold: float = 0.05,
+ coil_sensitivity_maps_espirit_kernel_size: int = 6,
+ coil_sensitivity_maps_espirit_crop: float = 0.95,
+ coil_sensitivity_maps_espirit_max_iters: int = 30,
+ coil_combination_method: str = "SENSE",
+ dimensionality: int = 2,
+ mask_func: Optional[List] = None,
+ shift_mask: bool = False,
+ mask_center_scale: Optional[float] = 0.02,
+ partial_fourier_percentage: float = 0.0,
+ remask: bool = False,
+ ssdu: bool = False,
+ ssdu_mask_type: str = "Gaussian",
+ ssdu_rho: float = 0.4,
+ ssdu_acs_block_size: Sequence[int] = (4, 4),
+ ssdu_gaussian_std_scaling_factor: float = 4.0,
+ ssdu_outer_kspace_fraction: float = 0.0,
+ ssdu_export_and_reuse_masks: bool = False,
+ n2r: bool = False,
+ n2r_supervised_rate: float = 0.0,
+ n2r_probability: float = 0.0,
+ n2r_std_devs: Tuple[float, float] = None,
+ n2r_rhos: Tuple[float, float] = None,
+ n2r_use_mask: bool = False,
+ unsupervised_masked_target: bool = False,
+ crop_size: Optional[Tuple[int, int]] = None,
+ kspace_crop: bool = False,
+ crop_before_masking: bool = True,
+ kspace_zero_filling_size: Optional[Tuple] = None,
+ normalize_inputs: bool = True,
+ normalization_type: str = "max",
+ kspace_normalization: bool = False,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+ coil_dim: int = 0,
+ consecutive_slices: int = 1, # pylint: disable=unused-argument
+ use_seed: bool = True,
+ ):
+ """Inits :class:`qMRIDataTransforms`.
+
+ Parameters
+ ----------
+ TEs : Optional[List[float]]
+ Echo times.
+ precompute_quantitative_maps : bool, optional
+ Precompute quantitative maps. Default is ``True``.
+ qmaps_scaling_factor : float, optional
+ Quantitative maps scaling factor. Default is ``1e-3``.
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ Default is ``None``.
+ apply_prewhitening : bool, optional
+ Apply prewhitening. If ``True`` then the prewhitening arguments are used. Default is ``False``.
+ find_patch_size : bool, optional
+ Find optimal patch size (automatically) to calculate psi. If False, patch_size must be defined.
+ Default is ``True``.
+ prewhitening_scale_factor : float, optional
+ Prewhitening scale factor. Default is ``1.0``.
+ prewhitening_patch_start : int, optional
+ Prewhitening patch start. Default is ``10``.
+ prewhitening_patch_length : int, optional
+ Prewhitening patch length. Default is ``30``.
+ apply_gcc : bool, optional
+ Apply Geometric Decomposition Coil Compression. If ``True`` then the GCC arguments are used.
+ Default is ``False``.
+ gcc_virtual_coils : int, optional
+ GCC virtual coils. Default is ``10``.
+ gcc_calib_lines : int, optional
+ GCC calibration lines. Default is ``24``.
+ gcc_align_data : bool, optional
+ GCC align data. Default is ``True``.
+ apply_random_motion : bool, optional
+ Simulate random motion in k-space. Default is ``False``.
+ random_motion_type : str, optional
+ Random motion type. It can be one of the following: ``piecewise_transient``, ``piecewise_constant``,
+ ``gaussian``. Default is ``gaussian``.
+ random_motion_percentage : Sequence[int], optional
+ Random motion percentage. For example, 10%-20% motion can be defined as ``(10, 20)``.
+ Default is ``(10, 10)``.
+ random_motion_angle : float, optional
+ Random motion angle. Default is ``10.0``.
+ random_motion_translation : float, optional
+ Random motion translation. Default is ``10.0``.
+ random_motion_center_percentage : float, optional
+ Random motion center percentage. Default is ``0.0``.
+ random_motion_num_segments : int, optional
+ Random motion number of segments to partition the k-space. Default is ``8``.
+ random_motion_random_num_segments : bool, optional
+ Whether to randomly generate the number of segments. Default is ``True``.
+ random_motion_non_uniform : bool, optional
+ Random motion non-uniform sampling. Default is ``False``.
+ estimate_coil_sensitivity_maps : bool, optional
+ Automatically estimate coil sensitivity maps. Default is ``False``. If ``True`` then the coil sensitivity
+ maps arguments are used. Note that this is different from the ``estimate_coil_sensitivity_maps_with_nn``
+ argument, which uses a neural network to estimate the coil sensitivity maps. The
+ ``estimate_coil_sensitivity_maps`` estimates the coil sensitivity maps with methods such as ``ESPIRiT``,
+ ``RSS`` or ``UNit``. ``ESPIRiT`` is the ``Eigenvalue to Self-Consistent Parallel Imaging Reconstruction
+ Technique`` method. ``RSS`` is the ``Root Sum of Squares`` method. ``UNit`` returns a uniform coil
+ sensitivity map.
+ coil_sensitivity_maps_type : str, optional
+ Coil sensitivity maps type. It can be one of the following: ``ESPIRiT``, ``RSS`` or ``UNit``. Default is
+ ``ESPIRiT``.
+ coil_sensitivity_maps_gaussian_sigma : float, optional
+ Coil sensitivity maps Gaussian sigma. Default is ``0.0``.
+ coil_sensitivity_maps_espirit_threshold : float, optional
+ Coil sensitivity maps ESPRIT threshold. Default is ``0.05``.
+ coil_sensitivity_maps_espirit_kernel_size : int, optional
+ Coil sensitivity maps ESPRIT kernel size. Default is ``6``.
+ coil_sensitivity_maps_espirit_crop : float, optional
+ Coil sensitivity maps ESPRIT crop. Default is ``0.95``.
+ coil_sensitivity_maps_espirit_max_iters : int, optional
+ Coil sensitivity maps ESPRIT max iterations. Default is ``30``.
+ coil_combination_method : str, optional
+ Coil combination method. Default is ``"SENSE"``.
+ dimensionality : int, optional
+ Dimensionality. Default is ``2``.
+ mask_func : Optional[List["MaskFunc"]], optional
+ Mask function to retrospectively undersample the k-space. Default is ``None``.
+ shift_mask : bool, optional
+ Whether to shift the mask. This needs to be set alongside with the ``fft_centered`` argument.
+ Default is ``False``.
+ mask_center_scale : Optional[float], optional
+ Center scale of the mask. This defines how much densely sampled will be the center of k-space.
+ Default is ``0.02``.
+ partial_fourier_percentage : float, optional
+ Whether to simulate a half scan. Default is ``0.0``.
+ remask : bool, optional
+ Use the same mask. Default is ``False``.
+ ssdu : bool, optional
+ Whether to apply Self-Supervised Data Undersampling (SSDU) masks. Default is ``False``.
+ ssdu_mask_type: str, optional
+ Mask type. It can be one of the following:
+ - "Gaussian": Gaussian sampling.
+ - "Uniform": Uniform sampling.
+ Default is "Gaussian".
+ ssdu_rho: float, optional
+ Split ratio for training and loss masks. Default is ``0.4``.
+ ssdu_acs_block_size: tuple, optional
+ Keeps a small acs region fully-sampled for training masks, if there is no acs region. The small acs block
+ should be set to zero. Default is ``(4, 4)``.
+ ssdu_gaussian_std_scaling_factor: float, optional
+ Scaling factor for standard deviation of the Gaussian noise. If Uniform is select this factor is ignored.
+ Default is ``4.0``.
+ ssdu_outer_kspace_fraction: float, optional
+ Fraction of the outer k-space to be kept/unmasked. Default is ``0.0``.
+ ssdu_export_and_reuse_masks: bool, optional
+ Whether to export and reuse the masks. Default is ``False``.
+ n2r : bool, optional
+ Whether to apply Noise to Reconstruction (N2R) masks. Default is ``False``.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ n2r_probability : float, optional
+ Probability of applying N2R. Default is ``0.0``.
+ n2r_std_devs : Tuple[float, float], optional
+ Standard deviations for the noise. Default is ``(0.0, 0.0)``.
+ n2r_rhos : Tuple[float, float], optional
+ Rho values for the noise. Default is ``(0.0, 0.0)``.
+ n2r_use_mask : bool, optional
+ Whether to use a mask for N2R. Default is ``False``.
+ unsupervised_masked_target : bool, optional
+ Whether to use the masked initial estimation for unsupervised learning. Default is ``False``.
+ crop_size : Optional[Tuple[int, int]], optional
+ Center crop size. It applies cropping in image space. Default is ``None``.
+ kspace_crop : bool, optional
+ Whether to crop in k-space. Default is ``False``.
+ crop_before_masking : bool, optional
+ Whether to crop before masking. Default is ``True``.
+ kspace_zero_filling_size : Optional[Tuple], optional
+ Whether to apply zero filling in k-space. Default is ``None``.
+ normalize_inputs : bool, optional
+ Whether to normalize the inputs. Default is ``True``.
+ normalization_type : str, optional
+ Normalization type. Can be ``max`` or ``mean`` or ``minmax``. Default is ``max``.
+ kspace_normalization : bool, optional
+ Whether to normalize the k-space. Default is ``False``.
+ fft_centered : bool, optional
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization : str, optional
+ FFT normalization. Default is ``"backward"``.
+ spatial_dims : Sequence[int], optional
+ Spatial dimensions. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``0``, meaning that the coil dimension is the first dimension before applying
+ batch.
+ consecutive_slices : int, optional
+ Consecutive slices. Default is ``1``.
+ use_seed : bool, optional
+ Whether to use seed. Default is ``True``.
+ """
+ super().__init__()
+
+ if not precompute_quantitative_maps:
+ raise ValueError(
+ "Loading quantitative maps from disk is not supported yet. "
+ "Please set precompute_quantitative_maps to True."
+ )
+ self.precompute_quantitative_maps = precompute_quantitative_maps
+
+ if TEs is None:
+ raise ValueError("Please specify echo times (TEs).")
+ self.TEs = TEs
+ self.qmaps_scaling_factor = qmaps_scaling_factor
+
+ self.dataset_format = dataset_format
+
+ self.coil_combination_method = coil_combination_method
+ self.kspace_crop = kspace_crop
+ self.crop_before_masking = crop_before_masking
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim - 1 if dimensionality == 2 else coil_dim
+
+ self.prewhitening = (
+ NoisePreWhitening(
+ find_patch_size=find_patch_size,
+ patch_size=[
+ prewhitening_patch_start,
+ prewhitening_patch_length + prewhitening_patch_start,
+ prewhitening_patch_start,
+ prewhitening_patch_length + prewhitening_patch_start,
+ ],
+ scale_factor=prewhitening_scale_factor,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if apply_prewhitening
+ else None
+ )
+
+ self.gcc = (
+ GeometricDecompositionCoilCompression(
+ virtual_coils=gcc_virtual_coils,
+ calib_lines=gcc_calib_lines,
+ align_data=gcc_align_data,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if apply_gcc
+ else None
+ )
+
+ self.random_motion = (
+ MotionSimulation(
+ motion_type=random_motion_type,
+ angle=random_motion_angle,
+ translation=random_motion_translation,
+ center_percentage=random_motion_center_percentage,
+ motion_percentage=random_motion_percentage,
+ num_segments=random_motion_num_segments,
+ random_num_segments=random_motion_random_num_segments,
+ non_uniform=random_motion_non_uniform,
+ spatial_dims=self.spatial_dims,
+ )
+ if apply_random_motion
+ else None
+ )
+
+ self.coil_sensitivity_maps_estimator = (
+ EstimateCoilSensitivityMaps(
+ coil_sensitivity_maps_type=coil_sensitivity_maps_type.lower(),
+ gaussian_sigma=coil_sensitivity_maps_gaussian_sigma,
+ espirit_threshold=coil_sensitivity_maps_espirit_threshold,
+ espirit_kernel_size=coil_sensitivity_maps_espirit_kernel_size,
+ espirit_crop=coil_sensitivity_maps_espirit_crop,
+ espirit_max_iters=coil_sensitivity_maps_espirit_max_iters,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ )
+ if estimate_coil_sensitivity_maps
+ else None
+ )
+
+ self.kspace_zero_filling = (
+ ZeroFillingPadding(
+ zero_filling_size=kspace_zero_filling_size, # type: ignore
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if not is_none(kspace_zero_filling_size)
+ else None
+ )
+
+ self.shift_mask = shift_mask
+ self.masking = Masker(
+ mask_func=mask_func, # type: ignore
+ spatial_dims=self.spatial_dims,
+ shift_mask=self.shift_mask,
+ partial_fourier_percentage=partial_fourier_percentage,
+ center_scale=mask_center_scale, # type: ignore
+ dimensionality=dimensionality,
+ remask=remask,
+ dataset_format=self.dataset_format,
+ )
+
+ self.ssdu = ssdu
+ self.ssdu_masking = (
+ SSDU(
+ mask_type=ssdu_mask_type,
+ rho=ssdu_rho,
+ acs_block_size=ssdu_acs_block_size,
+ gaussian_std_scaling_factor=ssdu_gaussian_std_scaling_factor,
+ outer_kspace_fraction=ssdu_outer_kspace_fraction,
+ export_and_reuse_masks=ssdu_export_and_reuse_masks,
+ )
+ if self.ssdu
+ else None
+ )
+
+ self.n2r = n2r
+ self.n2r_supervised_rate = n2r_supervised_rate
+ self.n2r_masking = (
+ N2R(
+ probability=n2r_probability,
+ std_devs=n2r_std_devs, # type: ignore
+ rhos=n2r_rhos, # type: ignore
+ use_mask=n2r_use_mask,
+ )
+ if self.n2r
+ else None
+ )
+
+ self.unsupervised_masked_target = unsupervised_masked_target
+
+ self.cropping = (
+ Cropper(
+ cropping_size=crop_size, # type: ignore
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if not is_none(crop_size)
+ else None
+ )
+
+ self.normalization_type = normalization_type
+ self.normalization = (
+ Normalizer(
+ normalization_type=self.normalization_type,
+ kspace_normalization=kspace_normalization,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ if normalize_inputs
+ else None
+ )
+
+ self.prewhitening = Composer([self.prewhitening]) # type: ignore
+ self.coils_shape_transforms = Composer(
+ [
+ self.gcc, # type: ignore
+ self.kspace_zero_filling, # type: ignore
+ ]
+ )
+ self.crop_normalize = Composer(
+ [
+ self.cropping, # type: ignore
+ self.normalization, # type: ignore
+ ]
+ )
+ self.cropping = Composer([self.cropping]) # type: ignore
+ self.random_motion = Composer([self.random_motion]) # type: ignore
+ self.normalization = Composer([self.normalization]) # type: ignore
+
+ self.use_seed = use_seed
+
+ def __call__(
+ self,
+ kspace: np.ndarray,
+ sensitivity_map: np.ndarray,
+ mask: np.ndarray,
+ initial_prediction: np.ndarray,
+ target: np.ndarray,
+ attrs: Dict,
+ fname: str,
+ slice_idx: int,
+ ) -> Tuple[
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.Tensor,
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.Tensor,
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.Tensor,
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.Tensor,
+ torch.Tensor,
+ torch.Tensor,
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.Tensor,
+ Union[List[torch.Tensor], torch.Tensor],
+ torch.Tensor,
+ Union[List[torch.Tensor], torch.Tensor],
+ Union[List[torch.Tensor], torch.Tensor],
+ str,
+ int,
+ List[Union[float, torch.Tensor, Any]],
+ Dict,
+ ]:
+ """Calls :class:`qMRIDataTransforms`.
+
+ Parameters
+ ----------
+ kspace : np.ndarray
+ The fully-sampled kspace, if exists. Otherwise, the subsampled kspace.
+ sensitivity_map : np.ndarray
+ The coil sensitivity map.
+ mask : np.ndarray
+ The subsampling mask, if exists, meaning that the data are either prospectively undersampled or the mask is
+ stored and loaded. It can be a list of masks, with the subsampling, the brain, and the head mask.
+ initial_prediction : np.ndarray
+ The initial prediction, if exists. Otherwise, it will be estimated with the chosen coil combination method.
+ target : np.ndarray
+ The target, if exists. Otherwise, it will be estimated with the chosen coil combination method.
+ attrs : Dict
+ The attributes, if stored in the data.
+ fname : str
+ The file name.
+ slice_idx : int
+ The slice index.
+
+ Returns
+ -------
+ The transformed data.
+ """
+ mask, anatomy_mask = mask
+
+ if mask.ndim <= 1:
+ mask = None
+
+ kspace, masked_kspace, mask, kspace_pre_normalization_vars, acc = self.__process_kspace__( # type: ignore
+ kspace, mask, attrs, fname
+ )
+ sensitivity_map, sensitivity_pre_normalization_vars = self.__process_coil_sensitivities_map__(
+ sensitivity_map, kspace
+ )
+
+ if self.n2r and len(masked_kspace) > 1: # type: ignore
+ prediction, prediction_pre_normalization_vars = self.__initialize_prediction__(
+ initial_prediction, masked_kspace[0], sensitivity_map # type: ignore
+ )
+ if isinstance(masked_kspace, list) and not masked_kspace[1][0].dim() < 2: # type: ignore
+ noise_prediction, noise_prediction_pre_normalization_vars = self.__initialize_prediction__(
+ None, masked_kspace[1], sensitivity_map # type: ignore
+ )
+ else:
+ noise_prediction = torch.tensor([])
+ noise_prediction_pre_normalization_vars = None
+ prediction = [prediction, noise_prediction]
+ else:
+ prediction, prediction_pre_normalization_vars = self.__initialize_prediction__(
+ initial_prediction, masked_kspace, sensitivity_map # type: ignore
+ )
+ noise_prediction_pre_normalization_vars = None
+
+ if self.unsupervised_masked_target:
+ target, target_pre_normalization_vars = prediction, prediction_pre_normalization_vars
+ else:
+ target, target_pre_normalization_vars = self.__initialize_prediction__(
+ None if self.ssdu else target, kspace, sensitivity_map
+ )
+
+ if anatomy_mask.ndim != 0:
+ anatomy_mask = self.cropping(torch.from_numpy(anatomy_mask)) # type: ignore
+
+ (
+ R2star_map_target,
+ R2star_map_target_pre_normalization_vars,
+ S0_map_target,
+ S0_map_target_pre_normalization_vars,
+ B0_map_target,
+ B0_map_target_pre_normalization_vars,
+ phi_map_target,
+ phi_map_target_pre_normalization_vars,
+ ) = self.__compute_quantitative_maps__(
+ kspace, sensitivity_map, None, anatomy_mask
+ ) # type: ignore
+ (
+ R2star_map_init,
+ R2star_map_init_pre_normalization_vars,
+ S0_map_init,
+ S0_map_init_pre_normalization_vars,
+ B0_map_init,
+ B0_map_init_pre_normalization_vars,
+ phi_map_init,
+ phi_map_init_pre_normalization_vars,
+ ) = self.__compute_quantitative_maps__( # type: ignore
+ masked_kspace, sensitivity_map, prediction, anatomy_mask # type: ignore
+ )
+
+ attrs.update(
+ self.__parse_normalization_vars__(
+ kspace_pre_normalization_vars, # type: ignore
+ sensitivity_pre_normalization_vars,
+ prediction_pre_normalization_vars,
+ noise_prediction_pre_normalization_vars,
+ target_pre_normalization_vars,
+ R2star_map_init_pre_normalization_vars, # type: ignore
+ R2star_map_target_pre_normalization_vars,
+ S0_map_init_pre_normalization_vars, # type: ignore
+ S0_map_target_pre_normalization_vars,
+ B0_map_init_pre_normalization_vars, # type: ignore
+ B0_map_target_pre_normalization_vars,
+ phi_map_init_pre_normalization_vars, # type: ignore
+ phi_map_target_pre_normalization_vars,
+ )
+ )
+ attrs["fname"] = fname
+ attrs["slice_idx"] = slice_idx
+
+ return (
+ R2star_map_init,
+ R2star_map_target,
+ S0_map_init,
+ S0_map_target,
+ B0_map_init,
+ B0_map_target,
+ phi_map_init,
+ phi_map_target,
+ torch.tensor(self.TEs),
+ kspace,
+ masked_kspace, # type: ignore
+ sensitivity_map,
+ mask,
+ anatomy_mask,
+ prediction,
+ target,
+ fname,
+ slice_idx,
+ acc, # type: ignore
+ attrs,
+ )
+
+ def __repr__(self) -> str:
+ """Representation of :class:`qMRIDataTransforms`."""
+ return (
+ f"Preprocessing transforms initialized for {self.__class__.__name__}: "
+ f"precompute_quantitative_maps = {self.precompute_quantitative_maps}, "
+ f"prewhitening = {self.prewhitening}, "
+ f"masking = {self.masking}, "
+ f"SSDU masking = {self.ssdu_masking}, "
+ f"kspace zero-filling = {self.kspace_zero_filling}, "
+ f"cropping = {self.cropping}, "
+ f"normalization = {self.normalization}, "
+ )
+
+ def __str__(self) -> str:
+ """String representation of :class:`qMRIDataTransforms`."""
+ return self.__repr__()
+
+ def __process_kspace__( # noqa: MC0001
+ self, kspace: np.ndarray, mask: Union[np.ndarray, None], attrs: Dict, fname: str
+ ) -> Tuple[torch.Tensor, Union[List[torch.Tensor], torch.Tensor], Union[List[torch.Tensor], torch.Tensor], int]:
+ """Apply the preprocessing transforms to the kspace.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The kspace.
+ mask : torch.Tensor
+ The mask, if None, the mask is generated.
+ attrs : Dict
+ The attributes, if stored in the file.
+ fname : str
+ The file name.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, Union[List[torch.Tensor], torch.Tensor], Union[List[torch.Tensor], torch.Tensor], int]
+ The transformed (fully-sampled) kspace, the masked kspace, the mask, the attributes and the acceleration
+ factor.
+ """
+ kspace = to_tensor(kspace)
+ kspace = add_coil_dim_if_singlecoil(kspace, dim=self.coil_dim)
+
+ kspace_echoes = []
+ for ke in range(kspace.shape[0]):
+ kspace_echo = kspace[ke]
+ kspace_echo = self.coils_shape_transforms(kspace_echo, apply_backward_transform=True)
+ kspace_echo = self.prewhitening(kspace_echo) # type: ignore
+ kspace_echoes.append(kspace_echo)
+ kspace = torch.stack(kspace_echoes, dim=0)
+
+ if self.crop_before_masking:
+ kspace = self.cropping(kspace, apply_backward_transform=not self.kspace_crop) # type: ignore
+
+ kspace = torch.stack([self.random_motion(kspace[ke]) for ke in range(kspace.shape[0])], dim=0) # type: ignore
+
+ masked_kspace, mask, acc = self.masking(
+ kspace,
+ mask,
+ (
+ attrs["padding_left"] if "padding_left" in attrs else 0,
+ attrs["padding_right"] if "padding_right" in attrs else 0,
+ ),
+ tuple(map(ord, fname)) if self.use_seed else None, # type: ignore
+ )
+
+ if not self.crop_before_masking:
+ kspace = self.cropping(kspace, apply_backward_transform=not self.kspace_crop) # type: ignore
+ masked_kspace = self.cropping(masked_kspace, apply_backward_transform=not self.kspace_crop) # type: ignore
+ mask = self.cropping(mask) # type: ignore
+
+ init_kspace = kspace
+ init_masked_kspace = masked_kspace
+ init_mask = mask
+
+ if isinstance(kspace, list):
+ kspaces = []
+ pre_normalization_vars = []
+ for i in range(len(kspace)): # pylint: disable=consider-using-enumerate
+ if not is_none(self.normalization.__repr__()):
+ _kspace, _pre_normalization_vars = self.normalization( # type: ignore
+ kspace[i], apply_backward_transform=True
+ )
+ else:
+ _kspace = kspace[i]
+ is_complex = _kspace.shape[-1] == 2
+ if is_complex:
+ _kspace = torch.view_as_complex(_kspace)
+ _pre_normalization_vars = {
+ "min": torch.min(torch.abs(_kspace)),
+ "max": torch.max(torch.abs(_kspace)),
+ "mean": torch.mean(torch.abs(_kspace)),
+ "std": torch.std(torch.abs(_kspace)),
+ "var": torch.var(torch.abs(_kspace)),
+ }
+ if is_complex:
+ _kspace = torch.view_as_real(_kspace)
+ kspaces.append(_kspace)
+ pre_normalization_vars.append(_pre_normalization_vars)
+ kspace = kspaces
+ else:
+ if not is_none(self.normalization.__repr__()):
+ kspace, pre_normalization_vars = self.normalization( # type: ignore
+ kspace, apply_backward_transform=True
+ )
+ else:
+ is_complex = kspace.shape[-1] == 2
+ if is_complex:
+ kspace = torch.view_as_complex(kspace)
+ pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(kspace)),
+ "max": torch.max(torch.abs(kspace)),
+ "mean": torch.mean(torch.abs(kspace)),
+ "std": torch.std(torch.abs(kspace)),
+ "var": torch.var(torch.abs(kspace)),
+ }
+ if is_complex:
+ kspace = torch.view_as_real(kspace)
+
+ if isinstance(masked_kspace, list):
+ masked_kspaces = []
+ masked_pre_normalization_vars = []
+ for i in range(len(masked_kspace)): # pylint: disable=consider-using-enumerate
+ if not is_none(self.normalization.__repr__()):
+ _masked_kspace, _masked_pre_normalization_vars = self.normalization( # type: ignore
+ masked_kspace[i], apply_backward_transform=True
+ )
+ else:
+ _masked_kspace = masked_kspace[i]
+ is_complex = _masked_kspace.shape[-1] == 2
+ if is_complex:
+ _masked_kspace = torch.view_as_complex(_masked_kspace)
+ _masked_pre_normalization_vars = {
+ "min": torch.min(torch.abs(_masked_kspace)),
+ "max": torch.max(torch.abs(_masked_kspace)),
+ "mean": torch.mean(torch.abs(_masked_kspace)),
+ "std": torch.std(torch.abs(_masked_kspace)),
+ "var": torch.var(torch.abs(_masked_kspace)),
+ }
+ if is_complex:
+ _masked_kspace = torch.view_as_real(_masked_kspace)
+ masked_kspaces.append(_masked_kspace)
+ masked_pre_normalization_vars.append(_masked_pre_normalization_vars)
+ masked_kspace = masked_kspaces
+ else:
+ if not is_none(self.normalization.__repr__()):
+ masked_kspace, masked_pre_normalization_vars = self.normalization(
+ masked_kspace, apply_backward_transform=True
+ )
+ else:
+ is_complex = masked_kspace.shape[-1] == 2
+ if is_complex:
+ masked_kspace = torch.view_as_complex(masked_kspace)
+ masked_pre_normalization_vars = {
+ "min": torch.min(torch.abs(masked_kspace)),
+ "max": torch.max(torch.abs(masked_kspace)),
+ "mean": torch.mean(torch.abs(masked_kspace)),
+ "std": torch.std(torch.abs(masked_kspace)),
+ "var": torch.var(torch.abs(masked_kspace)),
+ }
+ if is_complex:
+ masked_kspace = torch.view_as_real(masked_kspace)
+
+ if self.ssdu:
+ kspace, masked_kspace, mask = self.__self_supervised_data_undersampling__( # type: ignore
+ kspace, masked_kspace, mask, fname
+ )
+
+ n2r_pre_normalization_vars = None
+ if self.n2r and (not attrs["n2r_supervised"] or self.ssdu):
+ n2r_masked_kspace, n2r_mask = self.__noise_to_reconstruction__(init_kspace, init_masked_kspace, init_mask)
+
+ if self.ssdu:
+ if isinstance(mask, list):
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ if init_mask[i].dim() != mask[i][0].dim(): # type: ignore
+ # find dimensions == 1 in mask[i][0] and add them to init_mask
+ unitary_dims = [j for j in range(mask[i][0].dim()) if mask[i][0].shape[j] == 1]
+ # unsqueeze init_mask to the index of the unitary dimensions
+ for j in unitary_dims:
+ init_mask[i] = init_mask[i].unsqueeze(j) # type: ignore
+ masked_kspace[i] = init_masked_kspace[i]
+ mask[i][0] = init_mask[i]
+ else:
+ if init_mask.dim() != mask[0].dim(): # type: ignore
+ # find dimensions == 1 in mask[0] and add them to init_mask
+ unitary_dims = [j for j in range(mask[0].dim()) if mask[0].shape[j] == 1]
+ # unsqueeze init_mask to the index of the unitary dimensions
+ for j in unitary_dims:
+ init_mask = init_mask.unsqueeze(j) # type: ignore
+ masked_kspace = init_masked_kspace
+ mask[0] = init_mask
+
+ if "None" not in self.normalization.__repr__():
+ if isinstance(masked_kspace, list):
+ masked_kspaces = []
+ masked_pre_normalization_vars = []
+ for i in range(len(masked_kspace)): # pylint: disable=consider-using-enumerate
+ _masked_kspace, _masked_pre_normalization_vars = self.normalization( # type: ignore
+ masked_kspace[i], apply_backward_transform=True
+ )
+ masked_kspaces.append(_masked_kspace)
+ masked_pre_normalization_vars.append(_masked_pre_normalization_vars)
+ masked_kspace = masked_kspaces
+ else:
+ masked_kspace, masked_pre_normalization_vars = self.normalization( # type: ignore
+ masked_kspace, apply_backward_transform=True
+ )
+ if isinstance(n2r_masked_kspace, list):
+ n2r_masked_kspaces = []
+ n2r_pre_normalization_vars = []
+ for i in range(len(n2r_masked_kspace)): # pylint: disable=consider-using-enumerate
+ _n2r_masked_kspace, _n2r_pre_normalization_vars = self.normalization( # type: ignore
+ n2r_masked_kspace[i], apply_backward_transform=True
+ )
+ n2r_masked_kspaces.append(_n2r_masked_kspace)
+ n2r_pre_normalization_vars.append(_n2r_pre_normalization_vars)
+ n2r_masked_kspace = n2r_masked_kspaces
+ else:
+ n2r_masked_kspace, n2r_pre_normalization_vars = self.normalization( # type: ignore
+ n2r_masked_kspace, apply_backward_transform=True
+ )
+ else:
+ masked_pre_normalization_vars = None # type: ignore
+ n2r_pre_normalization_vars = None # type: ignore
+
+ masked_kspace = [masked_kspace, n2r_masked_kspace]
+ mask = [mask, n2r_mask]
+
+ if self.normalization_type == "grayscale":
+ if isinstance(mask, list):
+ masks = []
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ _mask, _ = self.normalization(mask[i], apply_backward_transform=False) # type: ignore
+ masks.append(_mask)
+ mask = masks
+ else:
+ mask, _ = self.normalization(mask, apply_backward_transform=False) # type: ignore
+
+ pre_normalization_vars = { # type: ignore
+ "kspace_pre_normalization_vars": pre_normalization_vars,
+ "masked_kspace_pre_normalization_vars": masked_pre_normalization_vars,
+ "noise_masked_kspace_pre_normalization_vars": n2r_pre_normalization_vars,
+ }
+
+ return kspace, masked_kspace, mask, pre_normalization_vars, acc # type: ignore
+
+ def __noise_to_reconstruction__(
+ self,
+ kspace: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ mask: Union[List, torch.Tensor],
+ ) -> Tuple[Union[List, torch.Tensor], Union[List, torch.Tensor]]:
+ """Apply the noise-to-reconstruction transform.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The fully-sampled kspace.
+ masked_kspace : torch.Tensor
+ The undersampled kspace.
+ mask : Union[List, torch.Tensor]
+ The undersampling mask.
+
+ Returns
+ -------
+ n2r_masked_kspace : Union[List, torch.Tensor]
+ The noise-to-reconstruction undersampled kspace.
+ n2r_mask : Union[List, torch.Tensor]
+ The noise-to-reconstruction mask.
+ """
+ if isinstance(mask, list):
+ n2r_masked_kspaces = []
+ n2r_masks = []
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ n2r_mask = self.n2r_masking(kspace, mask[i]) # type: ignore # pylint: disable=not-callable
+ n2r_masks.append(n2r_mask)
+ n2r_masked_kspaces.append(masked_kspace[i] * n2r_mask + 0.0)
+ n2r_mask = n2r_masks
+ n2r_masked_kspace = n2r_masked_kspaces
+ else:
+ n2r_mask = self.n2r_masking(kspace, mask) # type: ignore # pylint: disable=not-callable
+ n2r_masked_kspace = masked_kspace * n2r_mask + 0.0
+ return n2r_masked_kspace, n2r_mask
+
+ def __self_supervised_data_undersampling__( # noqa: MC0001
+ self,
+ kspace: torch.Tensor,
+ masked_kspace: Union[List, torch.Tensor],
+ mask: Union[List, torch.Tensor],
+ fname: str,
+ ) -> Tuple[
+ List[float | Any] | float | Any,
+ List[float | Any] | float | Any,
+ List[List[torch.Tensor | Any]] | List[torch.Tensor | Any],
+ ]:
+ """Self-supervised data undersampling.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ The fully-sampled kspace.
+ masked_kspace : Union[List, torch.Tensor]
+ The undersampled kspace.
+ mask : Union[List, torch.Tensor]
+ The undersampling mask.
+ fname : str
+ The filename of the current sample.
+
+ Returns
+ -------
+ kspace : torch.Tensor
+ The kspace with the loss mask applied.
+ masked_kspace : torch.Tensor
+ The kspace with the train mask applied.
+ mask : list, [torch.Tensor, torch.Tensor]
+ The train and loss masks.
+ """
+ if isinstance(mask, list):
+ kspaces = []
+ masked_kspaces = []
+ masks = []
+ for i in range(len(mask)): # pylint: disable=consider-using-enumerate
+ is_1d = mask[i].squeeze().dim() == 1
+ if self.shift_mask:
+ mask[i] = torch.fft.fftshift(mask[i].squeeze(-1), dim=(-2, -1)).unsqueeze(-1)
+ mask[i] = mask[i].squeeze()
+ if is_1d:
+ mask[i] = mask[i].unsqueeze(0).repeat_interleave(kspace.shape[1], dim=0)
+ train_mask, loss_mask = self.ssdu_masking( # type: ignore # pylint: disable=not-callable
+ kspace, mask[i], fname
+ )
+ if self.shift_mask:
+ train_mask = torch.fft.fftshift(train_mask, dim=(0, 1))
+ loss_mask = torch.fft.fftshift(loss_mask, dim=(0, 1))
+ if is_1d:
+ train_mask = train_mask.unsqueeze(0).unsqueeze(-1)
+ loss_mask = loss_mask.unsqueeze(0).unsqueeze(-1)
+ else:
+ # find unitary dims in mask
+ dims = [i for i, x in enumerate(mask[i].shape) if x == 1]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ if train_mask.dim() != kspace.dim():
+ # find dims != to any train_mask dim
+ dims = [i for i, x in enumerate(kspace.shape) if x not in train_mask.shape]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ kspaces.append(kspace * loss_mask + 0.0)
+ masked_kspaces.append(masked_kspace[i] * train_mask + 0.0)
+ masks.append([train_mask, loss_mask])
+ kspace = kspaces
+ masked_kspace = masked_kspaces
+ mask = masks
+ else:
+ is_1d = mask.squeeze().dim() == 1
+ if self.shift_mask:
+ mask = torch.fft.fftshift(mask.squeeze(-1), dim=(-2, -1)).unsqueeze(-1)
+ mask = mask.squeeze()
+ if is_1d:
+ mask = mask.unsqueeze(0).repeat_interleave(kspace.shape[1], dim=0)
+ train_mask, loss_mask = self.ssdu_masking( # type: ignore # pylint: disable=not-callable
+ kspace, mask, fname
+ )
+ if self.shift_mask:
+ train_mask = torch.fft.fftshift(train_mask, dim=(0, 1))
+ loss_mask = torch.fft.fftshift(loss_mask, dim=(0, 1))
+ if is_1d:
+ train_mask = train_mask.unsqueeze(0).unsqueeze(-1)
+ loss_mask = loss_mask.unsqueeze(0).unsqueeze(-1)
+ else:
+ # find unitary dims in mask
+ dims = [i for i, x in enumerate(mask.shape) if x == 1]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ if train_mask.dim() != kspace.dim():
+ # find dims != to any train_mask dim
+ dims = [i for i, x in enumerate(kspace.shape) if x not in train_mask.shape]
+ # unsqueeze to broadcast
+ for d in dims:
+ train_mask = train_mask.unsqueeze(d)
+ loss_mask = loss_mask.unsqueeze(d)
+ kspace = kspace * loss_mask + 0.0
+ masked_kspace = masked_kspace * train_mask + 0.0
+ mask = [train_mask, loss_mask]
+ return kspace, masked_kspace, mask
+
+ def __process_coil_sensitivities_map__(self, sensitivity_map: np.ndarray, kspace: torch.Tensor) -> torch.Tensor:
+ """Preprocesses the coil sensitivities map.
+
+ Parameters
+ ----------
+ sensitivity_map : np.ndarray
+ The coil sensitivities map.
+ kspace : torch.Tensor
+ The kspace.
+
+ Returns
+ -------
+ torch.Tensor
+ The preprocessed coil sensitivities map.
+ """
+ # This condition is necessary in case of auto estimation of sense maps.
+ if self.coil_sensitivity_maps_estimator is not None:
+ sensitivity_map = self.coil_sensitivity_maps_estimator(kspace)
+ elif sensitivity_map is not None and sensitivity_map.size != 0:
+ sensitivity_map = to_tensor(sensitivity_map)
+ sensitivity_map = self.coils_shape_transforms(sensitivity_map, apply_forward_transform=True)
+ sensitivity_map = self.cropping(sensitivity_map, apply_forward_transform=self.kspace_crop) # type: ignore
+ else:
+ # If no sensitivity map is provided, either the data is singlecoil or the sense net is used.
+ # Initialize the sensitivity map to 1 to assure for the singlecoil case.
+ sensitivity_map = torch.ones_like(kspace) if not isinstance(kspace, list) else torch.ones_like(kspace[0])
+ if not is_none(self.normalization.__repr__()):
+ sensitivity_map, pre_normalization_vars = self.normalization( # type: ignore
+ sensitivity_map, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ is_complex = sensitivity_map.shape[-1] == 2
+ if is_complex:
+ sensitivity_map = torch.view_as_complex(sensitivity_map)
+ pre_normalization_vars = {
+ "min": torch.min(torch.abs(sensitivity_map)),
+ "max": torch.max(torch.abs(sensitivity_map)),
+ "mean": torch.mean(torch.abs(sensitivity_map)),
+ "std": torch.std(torch.abs(sensitivity_map)),
+ "var": torch.var(torch.abs(sensitivity_map)),
+ }
+ if is_complex:
+ sensitivity_map = torch.view_as_real(sensitivity_map)
+ return sensitivity_map, pre_normalization_vars
+
+ def __compute_quantitative_maps__(
+ self,
+ kspace: torch.Tensor,
+ sensitivity_map: torch.Tensor,
+ prediction: Union[torch.Tensor, None],
+ anatomy_mask: torch.Tensor,
+ ) -> Tuple[
+ List[Any] | Any,
+ List[Dict[str, torch.Tensor]],
+ List[Any] | Any,
+ List[Dict[str, torch.Tensor]],
+ List[Any] | Any,
+ List[Dict[str, torch.Tensor]],
+ List[Any] | Any,
+ List[Dict[str, torch.Tensor]],
+ ]:
+ """Compute quantitative maps from the masked kspace data.
+
+ Parameters
+ ----------
+ kspace: torch.Tensor
+ Kspace data.
+ sensitivity_map: torch.Tensor
+ Sensitivity maps.
+ prediction: Union[torch.Tensor, None]
+ Initial prediction or None.
+ anatomy_mask: torch.Tensor
+ Brain mask.
+
+ Returns
+ -------
+ R2star_map: torch.Tensor
+ Computed R2* map.
+ R2star_map_pre_normalization_vars: Dict[str, torch.Tensor]
+ Computed R2* map pre-normalization variables.
+ S0_map: torch.Tensor
+ Computed S0 map.
+ S0_map_pre_normalization_vars: Dict[str, torch.Tensor]
+ Computed S0 map pre-normalization variables.
+ B0_map: torch.Tensor
+ Computed B0 map.
+ B0_map_pre_normalization_vars: Dict[str, torch.Tensor]
+ Computed B0 map pre-normalization variables.
+ phi_map: torch.Tensor
+ Computed phi map.
+ phi_map_pre_normalization_vars: Dict[str, torch.Tensor]
+ Computed phi map pre-normalization variables.
+ """
+ R2star_maps = []
+ R2star_map_pre_normalization_vars = []
+ S0_maps = []
+ S0_map_pre_normalization_vars = []
+ B0_maps = []
+ B0_map_pre_normalization_vars = []
+ phi_maps = []
+ phi_map_pre_normalization_vars = []
+ if isinstance(kspace, list):
+ for i, _ in enumerate(kspace):
+ R2star_map, S0_map, B0_map, phi_map = R2star_B0_S0_phi_mapping(
+ prediction[i] if isinstance(prediction, list) else prediction,
+ self.TEs,
+ anatomy_mask,
+ scaling_factor=self.qmaps_scaling_factor,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ R2star_maps.append(R2star_map)
+ R2star_map_pre_normalization_vars.append(
+ {
+ "min": torch.min(torch.abs(R2star_map)),
+ "max": torch.max(torch.abs(R2star_map)),
+ "mean": torch.mean(torch.abs(R2star_map)),
+ "std": torch.std(torch.abs(R2star_map)),
+ "var": torch.var(torch.abs(R2star_map)),
+ }
+ )
+ S0_maps.append(S0_map)
+ S0_map_pre_normalization_vars.append(
+ {
+ "min": torch.min(torch.abs(S0_map)),
+ "max": torch.max(torch.abs(S0_map)),
+ "mean": torch.mean(torch.abs(S0_map)),
+ "std": torch.std(torch.abs(S0_map)),
+ "var": torch.var(torch.abs(S0_map)),
+ }
+ )
+ B0_maps.append(B0_map)
+ B0_map_pre_normalization_vars.append(
+ {
+ "min": torch.min(torch.abs(B0_map)),
+ "max": torch.max(torch.abs(B0_map)),
+ "mean": torch.mean(torch.abs(B0_map)),
+ "std": torch.std(torch.abs(B0_map)),
+ "var": torch.var(torch.abs(B0_map)),
+ }
+ )
+ phi_maps.append(phi_map)
+ phi_map_pre_normalization_vars.append(
+ {
+ "min": torch.min(torch.abs(phi_map)),
+ "max": torch.max(torch.abs(phi_map)),
+ "mean": torch.mean(torch.abs(phi_map)),
+ "std": torch.std(torch.abs(phi_map)),
+ "var": torch.var(torch.abs(phi_map)),
+ }
+ )
+
+ R2star_map = R2star_maps
+ S0_map = S0_maps
+ B0_map = B0_maps
+ phi_map = phi_maps
+ else:
+ if prediction is None:
+ prediction = sense(
+ ifft2(
+ kspace,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ sensitivity_map.unsqueeze(0),
+ dim=self.coil_dim,
+ )
+
+ R2star_map, S0_map, B0_map, phi_map = R2star_B0_S0_phi_mapping(
+ prediction,
+ self.TEs,
+ anatomy_mask,
+ scaling_factor=self.qmaps_scaling_factor,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ R2star_map_pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(R2star_map)),
+ "max": torch.max(torch.abs(R2star_map)),
+ "mean": torch.mean(torch.abs(R2star_map)),
+ "std": torch.std(torch.abs(R2star_map)),
+ "var": torch.var(torch.abs(R2star_map)),
+ }
+ S0_map_pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(S0_map)),
+ "max": torch.max(torch.abs(S0_map)),
+ "mean": torch.mean(torch.abs(S0_map)),
+ "std": torch.std(torch.abs(S0_map)),
+ "var": torch.var(torch.abs(S0_map)),
+ }
+ B0_map_pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(B0_map)),
+ "max": torch.max(torch.abs(B0_map)),
+ "mean": torch.mean(torch.abs(B0_map)),
+ "std": torch.std(torch.abs(B0_map)),
+ "var": torch.var(torch.abs(B0_map)),
+ }
+ phi_map_pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(phi_map)),
+ "max": torch.max(torch.abs(phi_map)),
+ "mean": torch.mean(torch.abs(phi_map)),
+ "std": torch.std(torch.abs(phi_map)),
+ "var": torch.var(torch.abs(phi_map)),
+ }
+
+ return (
+ R2star_map,
+ R2star_map_pre_normalization_vars,
+ S0_map,
+ S0_map_pre_normalization_vars,
+ B0_map,
+ B0_map_pre_normalization_vars,
+ phi_map,
+ phi_map_pre_normalization_vars,
+ )
+
+ def __initialize_prediction__(
+ self, prediction: Union[torch.Tensor, np.ndarray, None], kspace: torch.Tensor, sensitivity_map: torch.Tensor
+ ) -> Union[List[torch.Tensor], torch.Tensor]:
+ """Predicts a coil-combined image.
+
+ Parameters
+ ----------
+ prediction : np.ndarray
+ The initial estimation, if None, the prediction is initialized.
+ kspace : torch.Tensor
+ The kspace.
+ sensitivity_map : torch.Tensor
+ The sensitivity map.
+
+ Returns
+ -------
+ Union[List[torch.Tensor], torch.Tensor]
+ The initialized prediction, either a list of coil-combined images or a single coil-combined image.
+ """
+ if is_none(prediction) or prediction.ndim < 2: # type: ignore
+ if isinstance(kspace, list):
+ prediction = []
+ pre_normalization_vars = []
+ for y in kspace:
+ pred = coil_combination_method_func(
+ ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_map,
+ method=self.coil_combination_method,
+ dim=self.coil_dim,
+ )
+ pred = self.cropping(pred, apply_forward_transform=self.kspace_crop) # type: ignore
+ if not is_none(self.normalization.__repr__()):
+ pred, _pre_normalization_vars = self.normalization( # type: ignore
+ pred, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ if pred.shape[-1] == 2:
+ pred = torch.view_as_complex(pred)
+ _pre_normalization_vars = {
+ "min": torch.min(torch.abs(pred)),
+ "max": torch.max(torch.abs(pred)),
+ "mean": torch.mean(torch.abs(pred)),
+ "std": torch.std(torch.abs(pred)),
+ "var": torch.var(torch.abs(pred)),
+ }
+ prediction.append(pred)
+ pre_normalization_vars.append(_pre_normalization_vars)
+ if prediction[0].shape[-1] != 2 and torch.is_complex(prediction[0]):
+ prediction = [torch.view_as_real(x) for x in prediction]
+ else:
+ prediction = coil_combination_method_func(
+ ifft2(kspace, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_map,
+ method=self.coil_combination_method,
+ dim=self.coil_dim,
+ )
+ prediction = self.cropping(prediction, apply_forward_transform=self.kspace_crop) # type: ignore
+ if not is_none(self.normalization.__repr__()):
+ prediction, pre_normalization_vars = self.normalization( # type: ignore
+ prediction, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ if prediction.shape[-1] == 2:
+ prediction = torch.view_as_complex(prediction)
+ pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(prediction)),
+ "max": torch.max(torch.abs(prediction)),
+ "mean": torch.mean(torch.abs(prediction)),
+ "std": torch.std(torch.abs(prediction)),
+ "var": torch.var(torch.abs(prediction)),
+ }
+ if prediction.shape[-1] != 2 and torch.is_complex(prediction):
+ prediction = torch.view_as_real(prediction)
+ else:
+ if isinstance(prediction, np.ndarray):
+ prediction = to_tensor(prediction)
+ prediction = self.cropping(prediction, apply_forward_transform=self.kspace_crop) # type: ignore
+ if not is_none(self.normalization.__repr__()):
+ prediction, pre_normalization_vars = self.normalization( # type: ignore
+ prediction, apply_forward_transform=self.kspace_crop
+ )
+ else:
+ if prediction.shape[-1] == 2: # type: ignore
+ prediction = torch.view_as_complex(prediction)
+ pre_normalization_vars = { # type: ignore
+ "min": torch.min(torch.abs(prediction)),
+ "max": torch.max(torch.abs(prediction)),
+ "mean": torch.mean(torch.abs(prediction)),
+ "std": torch.std(torch.abs(prediction)),
+ "var": torch.var(torch.abs(prediction)),
+ }
+ if prediction.shape[-1] != 2 and torch.is_complex(prediction):
+ prediction = torch.view_as_real(prediction)
+
+ return prediction, pre_normalization_vars
+
+ @staticmethod
+ def __parse_normalization_vars__( # noqa: MC0001
+ kspace_vars,
+ sensitivity_vars,
+ prediction_vars,
+ noise_prediction_vars,
+ target_vars,
+ R2star_maps_init_vars,
+ R2star_map_target_vars,
+ S0_map_init_vars,
+ S0_map_target_vars,
+ B0_map_init_vars,
+ B0_map_target_vars,
+ phi_map_init_vars,
+ phi_map_target_vars,
+ ) -> Dict:
+ """Parses the normalization variables and returns a unified dictionary.
+
+ Parameters
+ ----------
+ kspace_vars : Dict
+ The kspace normalization variables.
+ sensitivity_vars : Dict
+ The sensitivity map normalization variables.
+ prediction_vars : Dict
+ The prediction normalization variables.
+ noise_prediction_vars : Union[Dict, None]
+ The noise prediction normalization variables.
+ target_vars : Dict
+ The target normalization variables.
+ R2star_maps_init_vars : Dict
+ The R2* maps initialization normalization variables.
+ R2star_map_target_vars : Dict
+ The R2* maps target normalization variables.
+ S0_map_init_vars : Dict
+ The S0 maps initialization normalization variables.
+ S0_map_target_vars : Dict
+ The S0 maps target normalization variables.
+ B0_map_init_vars : Dict
+ The B0 maps initialization normalization variables.
+ B0_map_target_vars : Dict
+ The B0 maps target normalization variables.
+ phi_map_init_vars : Dict
+ The phi maps initialization normalization variables.
+ phi_map_target_vars : Dict
+ The phi maps target normalization variables.
+
+ Returns
+ -------
+ Dict
+ The normalization variables.
+ """
+ normalization_vars = {}
+
+ masked_kspace_vars = kspace_vars["masked_kspace_pre_normalization_vars"]
+ if isinstance(masked_kspace_vars, list):
+ if masked_kspace_vars[0] is not None:
+ for i, masked_kspace_var in enumerate(masked_kspace_vars):
+ normalization_vars[f"masked_kspace_min_{i}"] = masked_kspace_var["min"]
+ normalization_vars[f"masked_kspace_max_{i}"] = masked_kspace_var["max"]
+ normalization_vars[f"masked_kspace_mean_{i}"] = masked_kspace_var["mean"]
+ normalization_vars[f"masked_kspace_std_{i}"] = masked_kspace_var["std"]
+ normalization_vars[f"masked_kspace_var_{i}"] = masked_kspace_var["var"]
+ else:
+ if masked_kspace_vars is not None:
+ normalization_vars["masked_kspace_min"] = masked_kspace_vars["min"]
+ normalization_vars["masked_kspace_max"] = masked_kspace_vars["max"]
+ normalization_vars["masked_kspace_mean"] = masked_kspace_vars["mean"]
+ normalization_vars["masked_kspace_std"] = masked_kspace_vars["std"]
+ normalization_vars["masked_kspace_var"] = masked_kspace_vars["var"]
+
+ noise_masked_kspace_vars = kspace_vars["noise_masked_kspace_pre_normalization_vars"]
+ if noise_masked_kspace_vars is not None:
+ if isinstance(noise_masked_kspace_vars, list):
+ if noise_masked_kspace_vars[0] is not None:
+ for i, noise_masked_kspace_var in enumerate(noise_masked_kspace_vars):
+ normalization_vars[f"noise_masked_kspace_min_{i}"] = noise_masked_kspace_var["min"]
+ normalization_vars[f"noise_masked_kspace_max_{i}"] = noise_masked_kspace_var["max"]
+ normalization_vars[f"noise_masked_kspace_mean_{i}"] = noise_masked_kspace_var["mean"]
+ normalization_vars[f"noise_masked_kspace_std_{i}"] = noise_masked_kspace_var["std"]
+ normalization_vars[f"noise_masked_kspace_var_{i}"] = noise_masked_kspace_var["var"]
+ else:
+ if noise_masked_kspace_vars is not None:
+ normalization_vars["noise_masked_kspace_min"] = noise_masked_kspace_vars["min"]
+ normalization_vars["noise_masked_kspace_max"] = noise_masked_kspace_vars["max"]
+ normalization_vars["noise_masked_kspace_mean"] = noise_masked_kspace_vars["mean"]
+ normalization_vars["noise_masked_kspace_std"] = noise_masked_kspace_vars["std"]
+ normalization_vars["noise_masked_kspace_var"] = noise_masked_kspace_vars["var"]
+
+ kspace_vars = kspace_vars["kspace_pre_normalization_vars"]
+ if isinstance(kspace_vars, list):
+ if kspace_vars[0] is not None:
+ for i, kspace_var in enumerate(kspace_vars):
+ normalization_vars[f"kspace_min_{i}"] = kspace_var["min"]
+ normalization_vars[f"kspace_max_{i}"] = kspace_var["max"]
+ normalization_vars[f"kspace_mean_{i}"] = kspace_var["mean"]
+ normalization_vars[f"kspace_std_{i}"] = kspace_var["std"]
+ normalization_vars[f"kspace_var_{i}"] = kspace_var["var"]
+ else:
+ if kspace_vars is not None:
+ normalization_vars["kspace_min"] = kspace_vars["min"]
+ normalization_vars["kspace_max"] = kspace_vars["max"]
+ normalization_vars["kspace_mean"] = kspace_vars["mean"]
+ normalization_vars["kspace_std"] = kspace_vars["std"]
+ normalization_vars["kspace_var"] = kspace_vars["var"]
+
+ if sensitivity_vars is not None:
+ normalization_vars["sensitivity_maps_min"] = sensitivity_vars["min"]
+ normalization_vars["sensitivity_maps_max"] = sensitivity_vars["max"]
+ normalization_vars["sensitivity_maps_mean"] = sensitivity_vars["mean"]
+ normalization_vars["sensitivity_maps_std"] = sensitivity_vars["std"]
+ normalization_vars["sensitivity_maps_var"] = sensitivity_vars["var"]
+
+ if isinstance(prediction_vars, list):
+ if prediction_vars[0] is not None:
+ for i, prediction_var in enumerate(prediction_vars):
+ normalization_vars[f"prediction_min_{i}"] = prediction_var["min"]
+ normalization_vars[f"prediction_max_{i}"] = prediction_var["max"]
+ normalization_vars[f"prediction_mean_{i}"] = prediction_var["mean"]
+ normalization_vars[f"prediction_std_{i}"] = prediction_var["std"]
+ normalization_vars[f"prediction_var_{i}"] = prediction_var["var"]
+ else:
+ if prediction_vars is not None:
+ normalization_vars["prediction_min"] = prediction_vars["min"]
+ normalization_vars["prediction_max"] = prediction_vars["max"]
+ normalization_vars["prediction_mean"] = prediction_vars["mean"]
+ normalization_vars["prediction_std"] = prediction_vars["std"]
+ normalization_vars["prediction_var"] = prediction_vars["var"]
+
+ if noise_prediction_vars is not None:
+ if isinstance(noise_prediction_vars, list):
+ for i, noise_prediction_var in enumerate(noise_prediction_vars):
+ normalization_vars[f"noise_prediction_min_{i}"] = noise_prediction_var["min"]
+ normalization_vars[f"noise_prediction_max_{i}"] = noise_prediction_var["max"]
+ normalization_vars[f"noise_prediction_mean_{i}"] = noise_prediction_var["mean"]
+ normalization_vars[f"noise_prediction_std_{i}"] = noise_prediction_var["std"]
+ normalization_vars[f"noise_prediction_var_{i}"] = noise_prediction_var["var"]
+ else:
+ normalization_vars["noise_prediction_min"] = noise_prediction_vars["min"]
+ normalization_vars["noise_prediction_max"] = noise_prediction_vars["max"]
+ normalization_vars["noise_prediction_mean"] = noise_prediction_vars["mean"]
+ normalization_vars["noise_prediction_std"] = noise_prediction_vars["std"]
+ normalization_vars["noise_prediction_var"] = noise_prediction_vars["var"]
+
+ if isinstance(target_vars, list):
+ if target_vars[0] is not None:
+ for i, target_var in enumerate(target_vars):
+ normalization_vars[f"target_min_{i}"] = target_var["min"]
+ normalization_vars[f"target_max_{i}"] = target_var["max"]
+ normalization_vars[f"target_mean_{i}"] = target_var["mean"]
+ normalization_vars[f"target_std_{i}"] = target_var["std"]
+ normalization_vars[f"target_var_{i}"] = target_var["var"]
+ else:
+ if target_vars is not None:
+ normalization_vars["target_min"] = target_vars["min"]
+ normalization_vars["target_max"] = target_vars["max"]
+ normalization_vars["target_mean"] = target_vars["mean"]
+ normalization_vars["target_std"] = target_vars["std"]
+ normalization_vars["target_var"] = target_vars["var"]
+
+ if isinstance(R2star_maps_init_vars, list):
+ if R2star_maps_init_vars[0] is not None:
+ for i, R2star_map_init_var in enumerate(R2star_maps_init_vars):
+ normalization_vars[f"R2star_map_init_min_{i}"] = R2star_map_init_var["min"]
+ normalization_vars[f"R2star_map_init_max_{i}"] = R2star_map_init_var["max"]
+ normalization_vars[f"R2star_map_init_mean_{i}"] = R2star_map_init_var["mean"]
+ normalization_vars[f"R2star_map_init_std_{i}"] = R2star_map_init_var["std"]
+ normalization_vars[f"R2star_map_init_var_{i}"] = R2star_map_init_var["var"]
+ else:
+ if R2star_maps_init_vars is not None:
+ normalization_vars["R2star_map_init_min"] = R2star_maps_init_vars["min"]
+ normalization_vars["R2star_map_init_max"] = R2star_maps_init_vars["max"]
+ normalization_vars["R2star_map_init_mean"] = R2star_maps_init_vars["mean"]
+ normalization_vars["R2star_map_init_std"] = R2star_maps_init_vars["std"]
+ normalization_vars["R2star_map_init_var"] = R2star_maps_init_vars["var"]
+
+ if isinstance(R2star_map_target_vars, list):
+ if R2star_map_target_vars[0] is not None:
+ for i, R2star_map_target_var in enumerate(R2star_map_target_vars):
+ normalization_vars[f"R2star_map_target_min_{i}"] = R2star_map_target_var["min"]
+ normalization_vars[f"R2star_map_target_max_{i}"] = R2star_map_target_var["max"]
+ normalization_vars[f"R2star_map_target_mean_{i}"] = R2star_map_target_var["mean"]
+ normalization_vars[f"R2star_map_target_std_{i}"] = R2star_map_target_var["std"]
+ normalization_vars[f"R2star_map_target_var_{i}"] = R2star_map_target_var["var"]
+ else:
+ if R2star_map_target_vars is not None:
+ normalization_vars["R2star_map_target_min"] = R2star_map_target_vars["min"]
+ normalization_vars["R2star_map_target_max"] = R2star_map_target_vars["max"]
+ normalization_vars["R2star_map_target_mean"] = R2star_map_target_vars["mean"]
+ normalization_vars["R2star_map_target_std"] = R2star_map_target_vars["std"]
+ normalization_vars["R2star_map_target_var"] = R2star_map_target_vars["var"]
+
+ if isinstance(S0_map_init_vars, list):
+ if S0_map_init_vars[0] is not None:
+ for i, S0_map_init_var in enumerate(S0_map_init_vars):
+ normalization_vars[f"S0_map_init_min_{i}"] = S0_map_init_var["min"]
+ normalization_vars[f"S0_map_init_max_{i}"] = S0_map_init_var["max"]
+ normalization_vars[f"S0_map_init_mean_{i}"] = S0_map_init_var["mean"]
+ normalization_vars[f"S0_map_init_std_{i}"] = S0_map_init_var["std"]
+ normalization_vars[f"S0_map_init_var_{i}"] = S0_map_init_var["var"]
+ else:
+ if S0_map_init_vars is not None:
+ normalization_vars["S0_map_init_min"] = S0_map_init_vars["min"]
+ normalization_vars["S0_map_init_max"] = S0_map_init_vars["max"]
+ normalization_vars["S0_map_init_mean"] = S0_map_init_vars["mean"]
+ normalization_vars["S0_map_init_std"] = S0_map_init_vars["std"]
+ normalization_vars["S0_map_init_var"] = S0_map_init_vars["var"]
+
+ if isinstance(S0_map_target_vars, list):
+ if S0_map_target_vars[0] is not None:
+ for i, S0_map_target_var in enumerate(S0_map_target_vars):
+ normalization_vars[f"S0_map_target_min_{i}"] = S0_map_target_var["min"]
+ normalization_vars[f"S0_map_target_max_{i}"] = S0_map_target_var["max"]
+ normalization_vars[f"S0_map_target_mean_{i}"] = S0_map_target_var["mean"]
+ normalization_vars[f"S0_map_target_std_{i}"] = S0_map_target_var["std"]
+ normalization_vars[f"S0_map_target_var_{i}"] = S0_map_target_var["var"]
+ else:
+ if S0_map_target_vars is not None:
+ normalization_vars["S0_map_target_min"] = S0_map_target_vars["min"]
+ normalization_vars["S0_map_target_max"] = S0_map_target_vars["max"]
+ normalization_vars["S0_map_target_mean"] = S0_map_target_vars["mean"]
+ normalization_vars["S0_map_target_std"] = S0_map_target_vars["std"]
+ normalization_vars["S0_map_target_var"] = S0_map_target_vars["var"]
+
+ if isinstance(B0_map_init_vars, list):
+ if B0_map_init_vars[0] is not None:
+ for i, B0_map_init_var in enumerate(B0_map_init_vars):
+ normalization_vars[f"B0_map_init_min_{i}"] = B0_map_init_var["min"]
+ normalization_vars[f"B0_map_init_max_{i}"] = B0_map_init_var["max"]
+ normalization_vars[f"B0_map_init_mean_{i}"] = B0_map_init_var["mean"]
+ normalization_vars[f"B0_map_init_std_{i}"] = B0_map_init_var["std"]
+ normalization_vars[f"B0_map_init_var_{i}"] = B0_map_init_var["var"]
+ else:
+ if B0_map_init_vars is not None:
+ normalization_vars["B0_map_init_min"] = B0_map_init_vars["min"]
+ normalization_vars["B0_map_init_max"] = B0_map_init_vars["max"]
+ normalization_vars["B0_map_init_mean"] = B0_map_init_vars["mean"]
+ normalization_vars["B0_map_init_std"] = B0_map_init_vars["std"]
+ normalization_vars["B0_map_init_var"] = B0_map_init_vars["var"]
+
+ if isinstance(B0_map_target_vars, list):
+ if B0_map_target_vars[0] is not None:
+ for i, B0_map_target_var in enumerate(B0_map_target_vars):
+ normalization_vars[f"B0_map_target_min_{i}"] = B0_map_target_var["min"]
+ normalization_vars[f"B0_map_target_max_{i}"] = B0_map_target_var["max"]
+ normalization_vars[f"B0_map_target_mean_{i}"] = B0_map_target_var["mean"]
+ normalization_vars[f"B0_map_target_std_{i}"] = B0_map_target_var["std"]
+ normalization_vars[f"B0_map_target_var_{i}"] = B0_map_target_var["var"]
+ else:
+ if B0_map_target_vars is not None:
+ normalization_vars["B0_map_target_min"] = B0_map_target_vars["min"]
+ normalization_vars["B0_map_target_max"] = B0_map_target_vars["max"]
+ normalization_vars["B0_map_target_mean"] = B0_map_target_vars["mean"]
+ normalization_vars["B0_map_target_std"] = B0_map_target_vars["std"]
+ normalization_vars["B0_map_target_var"] = B0_map_target_vars["var"]
+
+ if isinstance(phi_map_init_vars, list):
+ if phi_map_init_vars[0] is not None:
+ for i, phi_map_init_var in enumerate(phi_map_init_vars):
+ normalization_vars[f"phi_map_init_min_{i}"] = phi_map_init_var["min"]
+ normalization_vars[f"phi_map_init_max_{i}"] = phi_map_init_var["max"]
+ normalization_vars[f"phi_map_init_mean_{i}"] = phi_map_init_var["mean"]
+ normalization_vars[f"phi_map_init_std_{i}"] = phi_map_init_var["std"]
+ normalization_vars[f"phi_map_init_var_{i}"] = phi_map_init_var["var"]
+ else:
+ if phi_map_init_vars is not None:
+ normalization_vars["phi_map_init_min"] = phi_map_init_vars["min"]
+ normalization_vars["phi_map_init_max"] = phi_map_init_vars["max"]
+ normalization_vars["phi_map_init_mean"] = phi_map_init_vars["mean"]
+ normalization_vars["phi_map_init_std"] = phi_map_init_vars["std"]
+ normalization_vars["phi_map_init_var"] = phi_map_init_vars["var"]
+
+ if isinstance(phi_map_target_vars, list):
+ if phi_map_target_vars[0] is not None:
+ for i, phi_map_target_var in enumerate(phi_map_target_vars):
+ normalization_vars[f"phi_map_target_min_{i}"] = phi_map_target_var["min"]
+ normalization_vars[f"phi_map_target_max_{i}"] = phi_map_target_var["max"]
+ normalization_vars[f"phi_map_target_mean_{i}"] = phi_map_target_var["mean"]
+ normalization_vars[f"phi_map_target_std_{i}"] = phi_map_target_var["std"]
+ normalization_vars[f"phi_map_target_var_{i}"] = phi_map_target_var["var"]
+ else:
+ if phi_map_target_vars is not None:
+ normalization_vars["phi_map_target_min"] = phi_map_target_vars["min"]
+ normalization_vars["phi_map_target_max"] = phi_map_target_vars["max"]
+ normalization_vars["phi_map_target_mean"] = phi_map_target_vars["mean"]
+ normalization_vars["phi_map_target_std"] = phi_map_target_vars["std"]
+ normalization_vars["phi_map_target_var"] = phi_map_target_vars["var"]
+
+ return normalization_vars
+
+
+class GaussianSmoothing(torch.nn.Module):
+ """Apply gaussian smoothing on a 1d, 2d or 3d tensor. Filtering is performed separately for each channel in the
+ input using a depthwise convolution.
+ """
+
+ def __init__(
+ self,
+ channels: int,
+ kernel_size: Union[List[int], int],
+ sigma: float,
+ dim: int = 2,
+ shift: bool = True,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+ ):
+ """Inits :class:`GaussianSmoothing`.
+
+ Parameters
+ ----------
+ channels : int
+ Number of channels in the input tensor.
+ kernel_size : Union[Optional[List[int]], int]
+ Gaussian kernel size.
+ sigma : float
+ Gaussian kernel standard deviation.
+ dim : int
+ Number of dimensions in the input tensor.
+ shift : bool
+ If True, the gaussian kernel is centered at (kernel_size - 1) / 2.
+ fft_centered : bool
+ Whether to center the FFT for a real- or complex-valued input.
+ fft_normalization : str
+ Whether to normalize the FFT output (None, "ortho", "backward", "forward", "none").
+ spatial_dims : Sequence[int]
+ Spatial dimensions to keep in the FFT.
+ """
+ super().__init__()
+
+ self.shift = shift
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+
+ if isinstance(kernel_size, int):
+ kernel_size = [kernel_size] * dim
+
+ if isinstance(sigma, float):
+ sigma = [sigma] * dim # type: ignore
+
+ # The gaussian kernel is the product of the gaussian function of each dimension.
+ kernel = 1
+ for size, std, mgrid in zip(
+ kernel_size,
+ sigma,
+ torch.meshgrid([torch.arange(size, dtype=torch.float32) for size in kernel_size], indexing="ij"),
+ ): # type: ignore
+ tmp_kernel = 1 / (std * math.sqrt(2 * math.pi)) * torch.exp(-(((mgrid - (size - 1) / 2) / std) ** 2) / 2)
+ kernel = kernel * tmp_kernel
+
+ # Make sure sum of values in gaussian kernel equals 1.
+ kernel = kernel / torch.sum(kernel)
+
+ # Reshape to depthwise convolutional weight
+ kernel = kernel.view(1, 1, *kernel.size()) # type: ignore
+ kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) # type: ignore
+
+ self.register_buffer("weight", kernel)
+ self.groups = channels
+
+ if dim == 1:
+ self.conv = F.conv1d
+ elif dim == 2:
+ self.conv = F.conv2d
+ elif dim == 3:
+ self.conv = F.conv3d
+ else:
+ raise RuntimeError(f"Only 1, 2 and 3 dimensions are supported. Received {dim}.")
+
+ def forward(self, data: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`GaussianSmoothing`.
+
+ Parameters
+ ----------
+ data : torch.Tensor
+ Input to apply gaussian filter on.
+
+ Returns
+ -------
+ torch.Tensor
+ Filtered output.
+ """
+ if self.shift:
+ data = data.permute(0, 2, 3, 1)
+ data = ifft2(
+ torch.fft.fftshift(
+ fft2(
+ torch.view_as_real(data[..., 0] + 1j * data[..., 1]),
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ ),
+ ),
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ ).permute(0, 3, 1, 2)
+
+ x = self.conv(data, weight=self.weight.to(data), groups=self.groups).to(data).detach()
+
+ if self.shift:
+ x = x.permute(0, 2, 3, 1)
+ x = ifft2(
+ torch.fft.fftshift(
+ fft2(
+ torch.view_as_real(x[..., 0] + 1j * x[..., 1]),
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ ),
+ ),
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ ).permute(0, 3, 1, 2)
+
+ return x
+
+
+class LeastSquaresFitting:
+ """Differentiable least square fitting in PyTorch."""
+
+ def __init__(self, device):
+ """Inits :class:`LeastSquaresFitting`."""
+ super().__init__()
+ self.device = device
+
+ @staticmethod
+ def lsqrt(A: torch.Tensor, Y: torch.Tensor, reg_factor: float = 0.0) -> torch.Tensor:
+ """Differentiable least square solution.
+
+ Parameters
+ ----------
+ A : torch.Tensor
+ Input matrix.
+ Y : torch.Tensor
+ Echo times matrix.
+ reg_factor : float
+ Regularization parameter.
+
+ Returns
+ -------
+ torch.Tensor
+ Least square solution.
+ """
+ q, r = torch.qr(A)
+ return torch.inverse(r) @ q.permute(0, 2, 1) @ Y + reg_factor
+
+ @staticmethod
+ def lsqrt_pinv(
+ A: torch.Tensor, Y: torch.Tensor, reg_factor: float = 0.0 # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Differentiable inverse least square solution.
+
+ Parameters
+ ----------
+ A : torch.Tensor
+ Input matrix.
+ Y : torch.Tensor
+ Echo times matrix.
+ reg_factor : float
+ Regularization parameter.
+
+ Returns
+ -------
+ torch.Tensor
+ Inverse least square solution.
+ """
+ if Y.dim() == 2:
+ return torch.matmul(torch.inverse(Y), A)
+ return torch.bmm(
+ torch.matmul(
+ torch.inverse(torch.matmul(torch.conj(Y).permute(0, 2, 1), Y)), torch.conj(Y).permute(0, 2, 1)
+ ),
+ A,
+ )[..., 0]
+
+
+def R2star_B0_S0_phi_mapping(
+ prediction: torch.Tensor,
+ TEs: Union[Optional[List[float]], float],
+ anatomy_mask: torch.Tensor,
+ scaling_factor: float = 1e-3,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+):
+ """Maps the prediction to R2*, B0, and S0 maps.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ The prediction of the model.
+ TEs : Union[Optional[List[float]], float]
+ The TEs of the images.
+ anatomy_mask : torch.Tensor
+ The anatomy mask of the images.
+ scaling_factor : float
+ The scaling factor to apply to the prediction.
+ fft_centered : bool
+ Whether to center the FFT for a real- or complex-valued input.
+ fft_normalization : str
+ Whether to normalize the FFT output (None, "ortho", "backward", "forward", "none").
+ spatial_dims : Sequence[int]
+ Spatial dimensions to keep in the FFT.
+
+ Returns
+ -------
+ R2star : torch.Tensor
+ The R2* map.
+ B0 : torch.Tensor
+ The B0 map.
+ S0 : torch.Tensor
+ The S0 map.
+ phi : torch.Tensor
+ The phi map.
+ """
+ R2star_map = R2star_mapping(prediction, TEs, scaling_factor=scaling_factor)
+ B0_map = -B0_phi_mapping(
+ prediction,
+ TEs,
+ anatomy_mask,
+ scaling_factor=scaling_factor,
+ fft_centered=fft_centered,
+ fft_normalization=fft_normalization,
+ spatial_dims=spatial_dims,
+ )[0]
+ S0_map, phi_map = S0_mapping(
+ prediction,
+ TEs,
+ R2star_map,
+ B0_map,
+ scaling_factor=scaling_factor,
+ fft_centered=fft_centered,
+ fft_normalization=fft_normalization,
+ spatial_dims=spatial_dims,
+ )
+
+ return R2star_map, S0_map, B0_map, phi_map
+
+
+def R2star_mapping(prediction: torch.Tensor, TEs: Union[Optional[List[float]], float], scaling_factor: float = 1e-3):
+ """R2* map and S0 map estimation for multi-echo GRE from stored magnitude image files acquired at multiple TEs.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ The prediction of the model.
+ TEs : Union[Optional[List[float]], float]
+ The TEs of the images.
+ scaling_factor : float
+ The scaling factor.
+
+ Returns
+ -------
+ R2star : torch.Tensor
+ The R2* map.
+ S0 : torch.Tensor
+ The S0 map.
+ """
+ prediction = torch.view_as_complex(prediction)
+ prediction = torch.abs(prediction / torch.max(torch.abs(prediction))) + 1e-8
+
+ prediction_flatten = torch.flatten(prediction, start_dim=1, end_dim=-1).detach().cpu()
+ log_prediction_flatten = torch.log(prediction_flatten)
+ sqrt_prediction_flatten = torch.sqrt(prediction_flatten)
+
+ TEs = torch.tensor(TEs).to(prediction_flatten)
+ TEs = TEs * scaling_factor # type: ignore
+
+ R2star_map = torch.zeros([prediction_flatten.shape[1]])
+ for i in range(prediction_flatten.shape[1]):
+ R2star_map[i], _ = torch.from_numpy(
+ np.polyfit(TEs, log_prediction_flatten[:, i], 1, w=sqrt_prediction_flatten[:, i])
+ ).to(prediction)
+
+ R2star_map = torch.reshape(-R2star_map, prediction.shape[1:4])
+ return R2star_map
+
+
+def B0_phi_mapping(
+ prediction: torch.Tensor,
+ TEs: Union[Optional[List[float]], float],
+ anatomy_mask: torch.Tensor,
+ scaling_factor: float = 1e-3,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+):
+ """B0 map and Phi map estimation for multi-echo GRE from stored magnitude image files acquired at multiple TEs.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ The prediction of the model.
+ TEs : Union[Optional[List[float]], float]
+ The TEs of the images.
+ anatomy_mask : torch.Tensor
+ The anatomy mask of the images.
+ scaling_factor : float
+ The scaling factor.
+ fft_centered : bool
+ Whether to center the FFT for a real- or complex-valued input.
+ fft_normalization : str
+ Whether to normalize the FFT output (None, "ortho", "backward", "forward", "none").
+ spatial_dims : Sequence[int]
+ Spatial dimensions to keep in the FFT.
+
+ Returns
+ -------
+ B0 : torch.Tensor
+ The B0 map.
+ phi : torch.Tensor
+ The phi map.
+ """
+ lsq = LeastSquaresFitting(device=prediction.device)
+
+ TEnotused = 3 # if fully_sampled else 3
+ TEs = torch.tensor(TEs)
+
+ # brain_mask is used only for descale of phase difference (so that phase_diff is in between -2pi and 2pi)
+ anatomy_mask_descale = anatomy_mask
+ shape = prediction.shape
+
+ # apply gaussian blur with radius r to
+ smoothing = GaussianSmoothing(
+ channels=2,
+ kernel_size=9,
+ sigma=scaling_factor,
+ dim=2,
+ fft_centered=fft_centered,
+ fft_normalization=fft_normalization,
+ spatial_dims=spatial_dims,
+ )
+ prediction = prediction.unsqueeze(1).permute([0, 1, 4, 2, 3]) # add a dummy batch dimension
+ for i in range(prediction.shape[0]):
+ prediction[i] = smoothing(F.pad(prediction[i], (4, 4, 4, 4), mode="reflect"))
+ prediction = prediction.permute([0, 1, 3, 4, 2]).squeeze(1)
+
+ prediction = ifft2(
+ torch.fft.fftshift(fft2(prediction, fft_centered, fft_normalization, spatial_dims), dim=(1, 2)),
+ fft_centered,
+ fft_normalization,
+ spatial_dims,
+ )
+
+ phase = torch.angle(torch.view_as_complex(prediction))
+
+ # unwrap phases
+ phase_unwrapped = torch.zeros_like(phase)
+
+ body_part_mask = anatomy_mask.clone()
+ body_part_mask_np = np.invert(body_part_mask.cpu().detach().numpy() > 0.5)
+
+ # loop over echo times
+ for i in range(phase.shape[0]):
+ phase_unwrapped[i] = torch.from_numpy(
+ unwrap_phase(np.ma.array(phase[i].detach().cpu().numpy(), mask=body_part_mask_np)).data
+ ).to(prediction)
+
+ phase_diff_set = []
+ TE_diff = []
+
+ # obtain phase differences and TE differences
+ for i in range(phase_unwrapped.shape[0] - TEnotused):
+ phase_diff_set.append(torch.flatten(phase_unwrapped[i + 1] - phase_unwrapped[i]))
+ phase_diff_set[i] = (
+ phase_diff_set[i]
+ - torch.round(
+ torch.abs(
+ torch.sum(phase_diff_set[i] * torch.flatten(anatomy_mask_descale))
+ / torch.sum(anatomy_mask_descale)
+ / 2
+ / np.pi
+ )
+ )
+ * 2
+ * np.pi
+ )
+ TE_diff.append(TEs[i + 1] - TEs[i]) # type: ignore
+
+ phase_diff_set = torch.stack(phase_diff_set, 0)
+ TE_diff = torch.stack(TE_diff, 0).to(prediction)
+
+ # least squares fitting to obtain phase map
+ B0_map_tmp = lsq.lsqrt_pinv(
+ phase_diff_set.unsqueeze(2).permute(1, 0, 2), TE_diff.unsqueeze(1) * scaling_factor # type: ignore
+ )
+ B0_map = B0_map_tmp.reshape(shape[-3], shape[-2])
+ B0_map = B0_map * torch.abs(body_part_mask)
+
+ # obtain phi map
+ phi_map = (phase_unwrapped[0] - scaling_factor * TEs[0] * B0_map).squeeze(0) # type: ignore
+
+ return B0_map.squeeze(0).to(prediction), phi_map.to(prediction)
+
+
+def S0_mapping(
+ prediction: torch.Tensor,
+ TEs: Union[Optional[List[float]], float],
+ R2star_map: torch.Tensor,
+ B0_map: torch.Tensor,
+ scaling_factor: float = 1e-3,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+):
+ """Complex S0 mapping.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ The prediction of the model.
+ TEs : Union[Optional[List[float]], float]
+ The TEs of the images.
+ R2star_map : torch.Tensor
+ The R2* map.
+ B0_map : torch.Tensor
+ The B0 map.
+ scaling_factor : float
+ The scaling factor.
+ fft_centered : bool
+ Whether to center the FFT for a real- or complex-valued input.
+ fft_normalization : str
+ Whether to normalize the FFT output (None, "ortho", "backward", "forward", "none").
+ spatial_dims : Sequence[int]
+ Spatial dimensions to keep in the FFT.
+
+ Returns
+ -------
+ S0 : torch.Tensor
+ The S0 map.
+ """
+ lsq = LeastSquaresFitting(device=prediction.device)
+
+ prediction = torch.view_as_complex(prediction)
+ prediction_flatten = prediction.reshape(prediction.shape[0], -1)
+
+ TEs = torch.tensor(TEs).to(prediction)
+
+ R2star_B0_complex_map = R2star_map.to(prediction) + 1j * B0_map.to(prediction)
+ R2star_B0_complex_map_flatten = R2star_B0_complex_map.flatten()
+
+ TEs_r2 = TEs[0:4].unsqueeze(1) * -R2star_B0_complex_map_flatten # type: ignore
+
+ S0_map = lsq.lsqrt_pinv(
+ prediction_flatten.permute(1, 0).unsqueeze(2), torch.exp(scaling_factor * TEs_r2.permute(1, 0).unsqueeze(2))
+ )
+
+ S0_map = torch.view_as_real(S0_map.reshape(prediction.shape[1:]))
+
+ S0_map = ifft2(
+ torch.fft.fftshift(fft2(S0_map, fft_centered, fft_normalization, spatial_dims), dim=(0, 1)),
+ fft_centered,
+ fft_normalization,
+ spatial_dims,
+ )
+
+ S0_map = torch.view_as_complex(S0_map).squeeze(-1)
+
+ return torch.abs(S0_map), torch.angle(S0_map)
diff --git a/atommic/collections/reconstruction/__init__.py b/atommic/collections/reconstruction/__init__.py
new file mode 100644
index 00000000..4493c9ba
--- /dev/null
+++ b/atommic/collections/reconstruction/__init__.py
@@ -0,0 +1,13 @@
+# coding=utf-8
+
+from atommic.collections.reconstruction import data, losses, metrics, nn, parts # noqa: F401
+from atommic.package_info import __version__
+
+# Set collection version equal to atommic version.
+__version = __version__
+
+# Authorship.
+__author__ = "Dimitris Karkalousos"
+
+# Set collection name.
+__description__ = "Reconstruction MRI models collection"
diff --git a/atommic/collections/reconstruction/data/__init__.py b/atommic/collections/reconstruction/data/__init__.py
new file mode 100644
index 00000000..39326e6a
--- /dev/null
+++ b/atommic/collections/reconstruction/data/__init__.py
@@ -0,0 +1,9 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.reconstruction.data.mri_reconstruction_loader import ( # noqa: F401
+ CC359ReconstructionMRIDataset,
+ ReconstructionMRIDataset,
+ SKMTEAReconstructionMRIDataset,
+ StanfordKneesReconstructionMRIDataset,
+)
diff --git a/atommic/collections/reconstruction/data/mri_reconstruction_loader.py b/atommic/collections/reconstruction/data/mri_reconstruction_loader.py
new file mode 100644
index 00000000..ef602ee6
--- /dev/null
+++ b/atommic/collections/reconstruction/data/mri_reconstruction_loader.py
@@ -0,0 +1,858 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from https://github.com/facebookresearch/fastMRI
+
+import json
+import logging
+import os
+import random
+import re
+import warnings
+from pathlib import Path
+from typing import Callable, Dict, List, Optional, Tuple, Union
+
+import h5py
+import numpy as np
+import yaml # type: ignore
+from defusedxml.ElementTree import fromstring
+from torch.utils.data import Dataset
+
+from atommic.collections.common.data.mri_loader import MRIDataset, et_query
+from atommic.collections.common.parts.utils import is_none
+
+
+class ReconstructionMRIDataset(MRIDataset):
+ """A dataset class for accelerated MRI reconstruction.
+
+ Examples
+ --------
+ >>> from atommic.collections.reconstruction.data.mri_reconstruction_loader import ReconstructionMRIDataset
+ >>> dataset = ReconstructionMRIDataset(root='data/train', sample_rate=0.1)
+ >>> print(len(dataset))
+ 100
+ >>> kspace, coil_sensitivities, mask, initial_prediction, target, attrs, filename, slice_num = dataset[0]
+ >>> print(kspace.shape)
+ np.array([30, 640, 368])
+
+ .. note::
+ Extends :class:`atommic.collections.common.data.mri_loader.MRIDataset`.
+ """
+
+ def __getitem__(self, i: int): # noqa: MC0001
+ """Get item from :class:`ReconstructionMRIDataset`."""
+ fname, dataslice, metadata = self.examples[i]
+ with h5py.File(fname, "r") as hf:
+ min_val = hf["min"][()] if "min" in hf else None
+ max_val = hf["max"][()] if "max" in hf else None
+ mean_val = hf["mean"][()] if "mean" in hf else None
+ std_val = hf["std"][()] if "std" in hf else None
+
+ kspace = self.get_consecutive_slices(hf, "kspace", dataslice).astype(np.complex64)
+
+ sensitivity_map = np.array([])
+ if "sensitivity_map" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "sensitivity_map", dataslice).astype(np.complex64)
+ elif "maps" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "maps", dataslice).astype(np.complex64)
+ elif self.coil_sensitivity_maps_root is not None and self.coil_sensitivity_maps_root != "None":
+ coil_sensitivity_maps_root = self.coil_sensitivity_maps_root
+ split_dir = str(fname).split("/")
+ for j in range(len(split_dir)):
+ coil_sensitivity_maps_root = Path(f"{self.coil_sensitivity_maps_root}/{split_dir[-j]}/")
+ if os.path.exists(coil_sensitivity_maps_root / Path(split_dir[-2]) / fname.name):
+ break
+ with h5py.File(Path(coil_sensitivity_maps_root) / Path(split_dir[-2]) / fname.name, "r") as sf:
+ if "sensitivity_map" in sf or "sensitivity_map" in next(iter(sf.keys())):
+ sensitivity_map = (
+ self.get_consecutive_slices(sf, "sensitivity_map", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+
+ mask = None
+ if "mask" in hf:
+ mask = np.asarray(self.get_consecutive_slices(hf, "mask", dataslice))
+ if mask.ndim == 3:
+ mask = mask[dataslice]
+ elif self.mask_root is not None and self.mask_root != "None":
+ with h5py.File(Path(self.mask_root) / fname.name, "r") as mf:
+ mask = np.asarray(self.get_consecutive_slices(mf, "mask", dataslice))
+
+ prediction = np.empty([])
+ if not is_none(self.initial_predictions_root):
+ with h5py.File(Path(self.initial_predictions_root) / fname.name, "r") as ipf: # type: ignore
+ if "reconstruction" in hf:
+ prediction = (
+ self.get_consecutive_slices(ipf, "reconstruction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ prediction = (
+ self.get_consecutive_slices(ipf, "initial_prediction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ else:
+ if "reconstruction" in hf:
+ prediction = (
+ self.get_consecutive_slices(hf, "reconstruction", dataslice).squeeze().astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ prediction = (
+ self.get_consecutive_slices(hf, "initial_prediction", dataslice).squeeze().astype(np.complex64)
+ )
+
+ if self.complex_target:
+ target = None
+ else:
+ # find key containing "reconstruction_"
+ rkey = re.findall(r"reconstruction_(.*)", str(hf.keys()))
+ self.recons_key = "reconstruction_" + rkey[0] if rkey else "target"
+ if "reconstruction_rss" in self.recons_key:
+ self.recons_key = "reconstruction_rss"
+ elif "reconstruction_sense" in hf:
+ self.recons_key = "reconstruction_sense"
+ target = self.get_consecutive_slices(hf, self.recons_key, dataslice) if self.recons_key in hf else None
+
+ attrs = dict(hf.attrs)
+
+ # get noise level for current slice, if metadata["noise_levels"] is not empty
+ if "noise_levels" in metadata and len(metadata["noise_levels"]) > 0:
+ metadata["noise"] = metadata["noise_levels"][dataslice]
+ else:
+ metadata["noise"] = 1.0
+
+ attrs.update(metadata)
+
+ if sensitivity_map.shape != kspace.shape and sensitivity_map.ndim > 1:
+ if sensitivity_map.ndim == 3:
+ sensitivity_map = np.transpose(sensitivity_map, (2, 0, 1))
+ elif sensitivity_map.ndim == 4:
+ sensitivity_map = np.transpose(sensitivity_map, (0, 3, 1, 2))
+ else:
+ raise ValueError(
+ f"Sensitivity map has invalid dimensions {sensitivity_map.shape} compared to kspace {kspace.shape}"
+ )
+
+ attrs["log_image"] = bool(dataslice in self.indices_to_log)
+
+ if min_val is not None:
+ attrs["min"] = min_val
+ if max_val is not None:
+ attrs["max"] = max_val
+ if mean_val is not None:
+ attrs["mean"] = mean_val
+ if std_val is not None:
+ attrs["std"] = std_val
+
+ return (
+ (
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
+
+
+class CC359ReconstructionMRIDataset(Dataset):
+ """Supports the CC359 dataset for accelerated MRI reconstruction.
+
+ .. note::
+ Similar to :class:`atommic.collections.common.data.mri_loader.MRIDataset`. It does not extend it because we
+ need to override the ``__init__`` and ``__getitem__`` methods.
+ """
+
+ def __init__( # noqa: MC0001
+ self,
+ root: Union[str, Path, os.PathLike],
+ coil_sensitivity_maps_root: Union[str, Path, os.PathLike] = None,
+ mask_root: Union[str, Path, os.PathLike] = None,
+ noise_root: Union[str, Path, os.PathLike] = None,
+ initial_predictions_root: Union[str, Path, os.PathLike] = None,
+ dataset_format: str = None,
+ sample_rate: Optional[float] = None,
+ volume_sample_rate: Optional[float] = None,
+ use_dataset_cache: bool = False,
+ dataset_cache_file: Union[str, Path, os.PathLike] = None,
+ num_cols: Optional[Tuple[int]] = None,
+ consecutive_slices: int = 1,
+ data_saved_per_slice: bool = False,
+ n2r_supervised_rate: Optional[float] = 0.0,
+ complex_target: bool = False,
+ log_images_rate: Optional[float] = 1.0,
+ transform: Optional[Callable] = None,
+ **kwargs, # pylint: disable=unused-argument
+ ):
+ """Inits :class:`CC359ReconstructionMRIDataset`.
+
+ Parameters
+ ----------
+ root : Union[str, Path, os.PathLike]
+ Path to the dataset.
+ coil_sensitivity_maps_root : Union[str, Path, os.PathLike], optional
+ Path to the coil sensitivities maps dataset, if stored separately.
+ mask_root : Union[str, Path, os.PathLike], optional
+ Path to stored masks, if stored separately.
+ noise_root : Union[str, Path, os.PathLike], optional
+ Path to stored noise, if stored separately (in json format).
+ initial_predictions_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the initial predictions. If provided, the initial predictions will be used
+ as the input of the reconstruction network. Default is ``None``.
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the slices should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ volume_sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the volumes should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ use_dataset_cache : bool, optional
+ Whether to cache dataset metadata. This is very useful for large datasets.
+ dataset_cache_file : Union[str, Path, os.PathLike, none], optional
+ A file in which to cache dataset information for faster load times. If not provided, the cache will be
+ stored in the dataset root.
+ num_cols : Optional[Tuple[int]], optional
+ If provided, only slices with the desired number of columns will be considered.
+ consecutive_slices : int, optional
+ An int (>0) that determine the amount of consecutive slices of the file to be loaded at the same time.
+ Default is ``1``, loading single slices.
+ data_saved_per_slice : bool, optional
+ Whether the data is saved per slice or per volume.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ complex_target : bool, optional
+ Whether to use a complex target or not. Default is ``False``.
+ log_images_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the slices should be logged as images. Default is
+ ``1.0``.
+ transform : Optional[Callable], optional
+ A sequence of callable objects that preprocesses the raw data into appropriate form. The transform function
+ should take ``kspace``, ``coil sensitivity maps``, ``quantitative maps``, ``mask``, ``initial prediction``,
+ ``target``, ``attributes``, ``filename``, and ``slice number`` as inputs. ``target`` may be null for test
+ data. Default is ``None``.
+ **kwargs
+ Additional keyword arguments.
+ """
+ super().__init__()
+ self.coil_sensitivity_maps_root = coil_sensitivity_maps_root
+ self.mask_root = mask_root
+
+ if str(noise_root).endswith(".json"):
+ with open(noise_root, "r") as f: # type: ignore # pylint: disable=unspecified-encoding
+ noise_root = [json.loads(line) for line in f.readlines()] # type: ignore
+ else:
+ noise_root = None
+
+ self.initial_predictions_root = initial_predictions_root
+ self.dataset_format = dataset_format
+
+ # set default sampling mode if none given
+ if not is_none(sample_rate) and not is_none(volume_sample_rate):
+ raise ValueError(
+ f"Both sample_rate {sample_rate} and volume_sample_rate {volume_sample_rate} are set. "
+ "Please set only one of them."
+ )
+
+ if sample_rate is None or sample_rate == "None":
+ sample_rate = 1.0
+
+ if volume_sample_rate is None or volume_sample_rate == "None":
+ volume_sample_rate = 1.0
+
+ self.dataset_cache_file = None if is_none(dataset_cache_file) else Path(dataset_cache_file) # type: ignore
+
+ if self.dataset_cache_file is not None and self.dataset_cache_file.exists() and use_dataset_cache:
+ with open(self.dataset_cache_file, "rb") as f:
+ dataset_cache = yaml.safe_load(f)
+ else:
+ dataset_cache = {}
+
+ if consecutive_slices < 1:
+ raise ValueError(f"Consecutive slices {consecutive_slices} is out of range, must be > 0.")
+ self.consecutive_slices = consecutive_slices
+ self.complex_target = complex_target
+ self.transform = transform
+ self.data_saved_per_slice = data_saved_per_slice
+
+ self.recons_key = "reconstruction"
+ self.examples = []
+
+ # Check if our dataset is in the cache. If yes, use that metadata, if not, then regenerate the metadata.
+ if dataset_cache.get(root) is None or not use_dataset_cache:
+ if str(root).endswith(".json"):
+ with open(root, "r") as f: # pylint: disable=unspecified-encoding
+ examples = json.load(f)
+ files = [Path(example) for example in examples]
+ else:
+ files = list(Path(root).iterdir())
+
+ if n2r_supervised_rate != 0.0:
+ # randomly select a subset of files for N2R supervised loss based on n2r_supervised_rate
+ n2r_supervised_files = random.sample(
+ files, int(np.round(n2r_supervised_rate * len(files))) # type: ignore
+ )
+
+ for fname in sorted(files):
+ metadata, num_slices = self._retrieve_metadata(fname)
+ metadata["noise_levels"] = (
+ self.__parse_noise__(noise_root, fname) if noise_root is not None else [] # type: ignore
+ )
+ metadata["n2r_supervised"] = False
+ if n2r_supervised_rate != 0.0:
+ # Use lazy % formatting in logging
+ logging.info("%s files are selected for N2R supervised loss.", n2r_supervised_files)
+ if fname in n2r_supervised_files:
+ metadata["n2r_supervised"] = True
+
+ if not is_none(num_slices) and not is_none(consecutive_slices):
+ num_slices = num_slices - (consecutive_slices - 1)
+
+ # Specific to CC359 dataset, we need to remove the first and last 50 slices
+ self.examples += [
+ (fname, slice_ind, metadata) for slice_ind in range(num_slices) if 50 < slice_ind < num_slices - 50
+ ]
+
+ if dataset_cache.get(root) is None and use_dataset_cache:
+ dataset_cache[root] = self.examples
+ logging.info("Saving dataset cache to %s.", self.dataset_cache_file)
+ with open(self.dataset_cache_file, "wb") as f: # type: ignore
+ yaml.dump(dataset_cache, f)
+ else:
+ logging.info("Using dataset cache from %s.", self.dataset_cache_file)
+ self.examples = dataset_cache[root]
+
+ # subsample if desired
+ if sample_rate < 1.0: # sample by slice
+ random.shuffle(self.examples)
+ num_examples = round(len(self.examples) * sample_rate)
+ self.examples = self.examples[:num_examples]
+ elif volume_sample_rate < 1.0: # sample by volume
+ vol_names = sorted(list({f[0].stem for f in self.examples}))
+ random.shuffle(vol_names)
+ num_volumes = round(len(vol_names) * volume_sample_rate)
+ sampled_vols = vol_names[:num_volumes]
+ self.examples = [example for example in self.examples if example[0].stem in sampled_vols]
+
+ if num_cols and not is_none(num_cols):
+ self.examples = [ex for ex in self.examples if ex[2]["encoding_size"][1] in num_cols]
+
+ self.indices_to_log = np.random.choice(
+ len(self.examples), int(log_images_rate * len(self.examples)), replace=False # type: ignore
+ )
+
+ def _retrieve_metadata(self, fname: Union[str, Path]) -> Tuple[Dict, int]:
+ """Retrieve metadata from a given file.
+
+ Parameters
+ ----------
+ fname : Union[str, Path]
+ Path to file.
+
+ Returns
+ -------
+ Tuple[Dict, int]
+ Metadata dictionary and number of slices in the file.
+
+ Examples
+ --------
+ >>> metadata, num_slices = _retrieve_metadata("file.h5")
+ >>> metadata
+ {'padding_left': 0, 'padding_right': 0, 'encoding_size': 0, 'recon_size': (0, 0)}
+ >>> num_slices
+ 1
+ """
+ with h5py.File(fname, "r") as hf:
+ if "ismrmrd_header" in hf:
+ et_root = fromstring(hf["ismrmrd_header"][()])
+
+ enc = ["encoding", "encodedSpace", "matrixSize"]
+ enc_size = (
+ int(et_query(et_root, enc + ["x"])),
+ int(et_query(et_root, enc + ["y"])),
+ int(et_query(et_root, enc + ["z"])),
+ )
+ rec = ["encoding", "reconSpace", "matrixSize"]
+ recon_size = (
+ int(et_query(et_root, rec + ["x"])),
+ int(et_query(et_root, rec + ["y"])),
+ int(et_query(et_root, rec + ["z"])),
+ )
+
+ params = ["encoding", "encodingLimits", "kspace_encoding_step_1"]
+ enc_limits_center = int(et_query(et_root, params + ["center"]))
+ enc_limits_max = int(et_query(et_root, params + ["maximum"])) + 1
+
+ padding_left = enc_size[1] // 2 - enc_limits_center
+ padding_right = padding_left + enc_limits_max
+ else:
+ padding_left = 0
+ padding_right = 0
+ enc_size = (0, 0, 0)
+ recon_size = (0, 0, 0)
+
+ if "kspace" in hf:
+ shape = hf["kspace"].shape
+ elif "reconstruction" in hf:
+ shape = hf["reconstruction"].shape
+ elif "target" in hf:
+ shape = hf["target"].shape
+ else:
+ raise ValueError(f"{fname} does not contain kspace, reconstruction, or target data.")
+
+ num_slices = 1 if self.data_saved_per_slice else shape[0]
+
+ metadata = {
+ "padding_left": padding_left,
+ "padding_right": padding_right,
+ "encoding_size": enc_size,
+ "recon_size": recon_size,
+ "num_slices": num_slices,
+ }
+
+ return metadata, num_slices
+
+ @staticmethod
+ def __parse_noise__(noise: str, fname: Path) -> List[str]:
+ """Parse noise type from filename.
+
+ Parameters
+ ----------
+ noise : str
+ json string of noise type.
+ fname : Path
+ Filename to parse noise type from.
+
+ Returns
+ -------
+ List[str]
+ List of noise values.
+ """
+ return [noise[i]["noise"] for i in range(len(noise)) if noise[i]["fname"] == fname.name] # type: ignore
+
+ def get_consecutive_slices(self, data: Dict, key: str, dataslice: int) -> np.ndarray:
+ """Get consecutive slices from a given data dictionary.
+
+ Parameters
+ ----------
+ data : dict
+ Data to extract slices from.
+ key : str
+ Key to extract slices from.
+ dataslice : int
+ Slice to index.
+
+ Returns
+ -------
+ np.ndarray
+ Array of consecutive slices. If ``self.consecutive_slices`` is > 1, then the array will have shape
+ ``(self.consecutive_slices, *data[key].shape[1:])``. Otherwise, the array will have shape
+ ``data[key].shape[1:]``.
+
+ Examples
+ --------
+ >>> data = {"kspace": np.random.rand(10, 640, 368)}
+ >>> from atommic.collections.common.data.mri_loader import MRIDataset
+ >>> MRIDataset.get_consecutive_slices(data, "kspace", 1).shape
+ (1, 640, 368)
+ >>> MRIDataset.get_consecutive_slices(data, "kspace", 5).shape
+ (5, 640, 368)
+ """
+ # read data
+ x = data[key]
+
+ if self.data_saved_per_slice:
+ x = np.expand_dims(x, axis=0)
+
+ if self.consecutive_slices == 1:
+ if x.shape[0] == 1:
+ return x[0]
+ if x.ndim != 2:
+ return x[dataslice]
+ return x
+
+ # get consecutive slices
+ num_slices = x.shape[0]
+
+ # If the number of consecutive slices is greater than or equal to the total slices, return the entire stack
+ if self.consecutive_slices >= num_slices:
+ # pad left and right with zero slices to match the desired number of slices
+ slices_to_add_start = (self.consecutive_slices - num_slices) // 2
+ slices_to_add_end = self.consecutive_slices - num_slices - slices_to_add_start
+ if slices_to_add_start > 0:
+ zero_slices = np.zeros((slices_to_add_start, *x.shape[1:]))
+ x = np.concatenate((zero_slices, x), axis=0)
+ if slices_to_add_end > 0:
+ zero_slices = np.zeros((slices_to_add_end, *x.shape[1:]))
+ x = np.concatenate((x, zero_slices), axis=0)
+ return x
+
+ # Calculate half of the consecutive slices to determine the middle position
+ half_slices = self.consecutive_slices // 2
+
+ # Determine the start and end slices based on the middle position
+ start_slice = dataslice - half_slices
+ end_slice = dataslice + half_slices + 1
+
+ # Handle edge cases
+ slices_to_add_start = 0
+ slices_to_add_end = 0
+ if start_slice < 0:
+ slices_to_add_start = abs(start_slice)
+ start_slice = 0
+
+ if end_slice > (num_slices - 1):
+ slices_to_add_end = end_slice - num_slices
+ extracted_slices = x[start_slice:]
+ else:
+ extracted_slices = x[start_slice:end_slice]
+
+ # Add slices to the start and end if needed
+ if slices_to_add_start > 0:
+ zero_slices = np.zeros((slices_to_add_start, *extracted_slices.shape[1:]))
+ extracted_slices = np.concatenate((zero_slices, extracted_slices), axis=0)
+ if slices_to_add_end > 0:
+ zero_slices = np.zeros((slices_to_add_end, *extracted_slices.shape[1:]))
+ extracted_slices = np.concatenate((extracted_slices, zero_slices), axis=0)
+
+ return extracted_slices
+
+ def __len__(self):
+ """Length of :class:`MRIDataset`."""
+ return len(self.examples)
+
+ def __getitem__(self, i: int): # noqa: MC0001
+ """Get item from :class:`CC359ReconstructionMRIDataset`."""
+ fname, dataslice, metadata = self.examples[i]
+ with h5py.File(fname, "r") as hf:
+ kspace = self.get_consecutive_slices(hf, "kspace", dataslice).astype(np.complex64)
+
+ kspace = np.transpose(kspace[..., ::2] + 1j * kspace[..., 1::2], (2, 0, 1))
+
+ sensitivity_map = np.array([])
+ if "sensitivity_map" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "sensitivity_map", dataslice).astype(np.complex64)
+ elif "maps" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "maps", dataslice).astype(np.complex64)
+ elif self.coil_sensitivity_maps_root is not None and self.coil_sensitivity_maps_root != "None":
+ coil_sensitivity_maps_root = self.coil_sensitivity_maps_root
+ split_dir = str(fname).split("/")
+ for j in range(len(split_dir)):
+ coil_sensitivity_maps_root = Path(f"{self.coil_sensitivity_maps_root}/{split_dir[-j]}/")
+ if os.path.exists(coil_sensitivity_maps_root / Path(split_dir[-2]) / fname.name):
+ break
+ with h5py.File(Path(coil_sensitivity_maps_root) / Path(split_dir[-2]) / fname.name, "r") as sf:
+ if "sensitivity_map" in sf or "sensitivity_map" in next(iter(sf.keys())):
+ sensitivity_map = (
+ self.get_consecutive_slices(sf, "sensitivity_map", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+
+ if self.mask_root is not None and self.mask_root != "None":
+ mask = []
+ with h5py.File(Path(self.mask_root) / fname.name, "r") as mf:
+ for key in mf.keys():
+ mask.append(np.asarray(self.get_consecutive_slices(mf, key, dataslice)))
+ else:
+ mask = None
+
+ prediction = np.empty([])
+ if not is_none(self.initial_predictions_root):
+ with h5py.File(Path(self.initial_predictions_root) / fname.name, "r") as ipf: # type: ignore
+ if "reconstruction" in hf:
+ prediction = (
+ self.get_consecutive_slices(ipf, "reconstruction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ prediction = (
+ self.get_consecutive_slices(ipf, "initial_prediction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ else:
+ if "reconstruction" in hf:
+ prediction = (
+ self.get_consecutive_slices(hf, "reconstruction", dataslice).squeeze().astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ prediction = (
+ self.get_consecutive_slices(hf, "initial_prediction", dataslice).squeeze().astype(np.complex64)
+ )
+
+ if self.complex_target:
+ target = None
+ else:
+ # find key containing "reconstruction_"
+ rkey = re.findall(r"reconstruction_(.*)", str(hf.keys()))
+ self.recons_key = "reconstruction_" + rkey[0] if rkey else "target"
+ if "reconstruction_rss" in self.recons_key:
+ self.recons_key = "reconstruction_rss"
+ elif "reconstruction_sense" in hf:
+ self.recons_key = "reconstruction_sense"
+ target = self.get_consecutive_slices(hf, self.recons_key, dataslice) if self.recons_key in hf else None
+
+ attrs = dict(hf.attrs)
+
+ # get noise level for current slice, if metadata["noise_levels"] is not empty
+ if "noise_levels" in metadata and len(metadata["noise_levels"]) > 0:
+ metadata["noise"] = metadata["noise_levels"][dataslice]
+ else:
+ metadata["noise"] = 1.0
+
+ attrs.update(metadata)
+
+ if sensitivity_map.shape != kspace.shape and sensitivity_map.ndim > 1:
+ if sensitivity_map.ndim == 3:
+ sensitivity_map = np.transpose(sensitivity_map, (2, 0, 1))
+ elif sensitivity_map.ndim == 4:
+ sensitivity_map = np.transpose(sensitivity_map, (0, 3, 1, 2))
+ else:
+ raise ValueError(
+ f"Sensitivity map has invalid dimensions {sensitivity_map.shape} compared to kspace {kspace.shape}"
+ )
+
+ attrs["log_image"] = bool(dataslice in self.indices_to_log)
+
+ return (
+ (
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
+
+
+class SKMTEAReconstructionMRIDataset(MRIDataset):
+ """Supports the SKM-TEA dataset for accelerated MRI reconstruction.
+
+ .. note::
+ Extends :class:`atommic.collections.reconstruction.data.mri_reconstruction_loader.ReconstructionMRIDataset`.
+ """
+
+ def __getitem__(self, i: int): # noqa: MC0001
+ """Get item from :class:`SKMTEAReconstructionMRIDataset`."""
+ if not is_none(self.dataset_format):
+ dataset_format = self.dataset_format.lower() # type: ignore
+ masking = "default"
+ if "custom_masking" in dataset_format:
+ masking = "custom"
+ dataset_format = dataset_format.replace("custom_masking", "").strip("_")
+ else:
+ dataset_format = None
+ masking = "custom"
+
+ fname, dataslice, metadata = self.examples[i]
+ with h5py.File(fname, "r") as hf:
+ kspace = self.get_consecutive_slices(hf, "kspace", dataslice).astype(np.complex64)
+
+ if not is_none(dataset_format) and dataset_format == "skm-tea-echo1":
+ kspace = kspace[:, :, 0, :]
+ elif not is_none(dataset_format) and dataset_format == "skm-tea-echo2":
+ kspace = kspace[:, :, 1, :]
+ elif not is_none(dataset_format) and dataset_format == "skm-tea-echo1+echo2":
+ kspace = kspace[:, :, 0, :] + kspace[:, :, 1, :]
+ elif not is_none(dataset_format) and dataset_format == "skm-tea-echo1+echo2-mc":
+ kspace = np.concatenate([kspace[:, :, 0, :], kspace[:, :, 1, :]], axis=-1)
+ else:
+ warnings.warn(
+ f"Dataset format {dataset_format} is either not supported or set to None. "
+ "Using by default only the first echo."
+ )
+ kspace = kspace[:, :, 0, :]
+
+ kspace = kspace[48:-48, 40:-40]
+
+ sensitivity_map = self.get_consecutive_slices(hf, "maps", dataslice).astype(np.complex64)
+ sensitivity_map = sensitivity_map[..., 0]
+
+ sensitivity_map = sensitivity_map[48:-48, 40:-40]
+
+ if masking == "custom":
+ mask = np.array([])
+ else:
+ masks = hf["masks"]
+ mask = {}
+ for key, val in masks.items():
+ mask[key.split("_")[-1].split(".")[0]] = np.asarray(val)
+
+ prediction = np.empty([])
+ if not is_none(self.initial_predictions_root):
+ if "reconstruction" in hf:
+ with h5py.File(Path(self.initial_predictions_root) / fname.name, "r") as ipf: # type: ignore
+ prediction = (
+ self.get_consecutive_slices(ipf, "reconstruction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ with h5py.File(Path(self.initial_predictions_root) / fname.name, "r") as ipf: # type: ignore
+ prediction = (
+ self.get_consecutive_slices(ipf, "initial_prediction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ else:
+ if "reconstruction" in hf:
+ prediction = (
+ self.get_consecutive_slices(hf, "reconstruction", dataslice).squeeze().astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ prediction = (
+ self.get_consecutive_slices(hf, "initial_prediction", dataslice).squeeze().astype(np.complex64)
+ )
+
+ if self.complex_target:
+ target = None
+ else:
+ # find key containing "reconstruction_"
+ self.recons_key = "target"
+ target = self.get_consecutive_slices(hf, self.recons_key, dataslice) if self.recons_key in hf else None
+
+ attrs = dict(hf.attrs)
+
+ # get noise level for current slice, if metadata["noise_levels"] is not empty
+ if "noise_levels" in metadata and len(metadata["noise_levels"]) > 0:
+ metadata["noise"] = metadata["noise_levels"][dataslice]
+ else:
+ metadata["noise"] = 1.0
+
+ attrs.update(metadata)
+
+ kspace = np.transpose(kspace, (2, 0, 1))
+ sensitivity_map = np.transpose(sensitivity_map.squeeze(), (2, 0, 1))
+
+ attrs["log_image"] = bool(dataslice in self.indices_to_log)
+
+ return (
+ (
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
+
+
+class StanfordKneesReconstructionMRIDataset(MRIDataset):
+ """Supports the Stanford Knees 2019 dataset for accelerated MRI reconstruction.
+
+ .. note::
+ Extends :class:`atommic.collections.reconstruction.data.mri_reconstruction_loader.ReconstructionMRIDataset`.
+ """
+
+ def __getitem__(self, i: int):
+ """Get item from :class:`StanfordKneesReconstructionMRIDataset`."""
+ fname, dataslice, metadata = self.examples[i]
+ with h5py.File(fname, "r") as hf:
+ kspace = self.get_consecutive_slices(hf, "kspace", dataslice).astype(np.complex64)
+
+ attrs = dict(hf.attrs)
+
+ sensitivity_map = np.array([])
+ if "sensitivity_map" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "sensitivity_map", dataslice).astype(np.complex64)
+ elif "maps" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "maps", dataslice).astype(np.complex64)
+ elif self.coil_sensitivity_maps_root is not None and self.coil_sensitivity_maps_root != "None":
+ coil_sensitivity_maps_root = self.coil_sensitivity_maps_root
+ split_dir = str(fname).split("/")
+ for j in range(len(split_dir)):
+ coil_sensitivity_maps_root = Path(f"{self.coil_sensitivity_maps_root}/{split_dir[-j]}/")
+ if os.path.exists(coil_sensitivity_maps_root / Path(split_dir[-2]) / fname.name):
+ break
+ with h5py.File(Path(coil_sensitivity_maps_root) / Path(split_dir[-2]) / fname.name, "r") as sf:
+ if "sensitivity_map" in sf or "sensitivity_map" in next(iter(sf.keys())):
+ sensitivity_map = (
+ self.get_consecutive_slices(sf, "sensitivity_map", dataslice).squeeze().astype(np.complex64)
+ )
+
+ # get noise level for current slice, if metadata["noise_levels"] is not empty
+ metadata["noise"] = (
+ metadata["noise_levels"][dataslice]
+ if "noise_levels" in metadata and len(metadata["noise_levels"]) > 0
+ else 1.0
+ )
+ attrs.update(metadata)
+ attrs["log_image"] = bool(dataslice in self.indices_to_log)
+
+ mask = None
+ prediction = None
+ target = np.array([])
+
+ return (
+ (
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ sensitivity_map,
+ mask,
+ prediction,
+ target,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
diff --git a/atommic/collections/reconstruction/losses/__init__.py b/atommic/collections/reconstruction/losses/__init__.py
new file mode 100644
index 00000000..c082c969
--- /dev/null
+++ b/atommic/collections/reconstruction/losses/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.reconstruction.losses.na import NoiseAwareLoss # noqa: F401
+from atommic.collections.reconstruction.losses.ssim import SSIMLoss # noqa: F401
diff --git a/atommic/collections/reconstruction/losses/na.py b/atommic/collections/reconstruction/losses/na.py
new file mode 100644
index 00000000..3ab34152
--- /dev/null
+++ b/atommic/collections/reconstruction/losses/na.py
@@ -0,0 +1,60 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+import torch.nn.functional as F
+
+from atommic.core.classes.loss import Loss
+
+
+class NoiseAwareLoss(Loss):
+ """Computes the Noise Aware loss between two tensors.
+
+ .. note::
+ Extends :class:`atommic.core.classes.loss.Loss`.
+
+ Examples
+ --------
+ >>> from atommic.collections.reconstruction.losses.na import NoiseAwareLoss
+ >>> import torch
+ >>> loss = NoiseAwareLoss(win_size=7, k1=0.01, k2=0.03)
+ >>> loss(X=torch.rand(1, 1, 256, 256), Y=torch.rand(1, 1, 256, 256))
+ tensor(0.0872)
+ """
+
+ def forward(
+ self, target: torch.Tensor, pred: torch.Tensor, mask: torch.Tensor = None, sigma: float = 0.0
+ ) -> torch.Tensor:
+ """Forward pass of :class:`NoiseAwareLoss`.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ The target tensor.
+ pred : torch.Tensor
+ The predicted tensor.
+ mask : torch.Tensor
+ The mask tensor. If None, all pixels are considered.
+ sigma : float
+ The noise level.
+ """
+ pred = pred.to(target.dtype)
+ if mask is None:
+ mask = torch.ones_like(target)
+ mask = mask.to(target.dtype)
+
+ # Compute the mean squared error
+ mse = F.mse_loss(target, pred, reduction="none")
+
+ # Compute the noise variance at each pixel
+ sigma = torch.median(torch.abs(target - pred)) / 0.6745
+
+ noise_var = sigma**2 / (1 - mask + 1e-8)
+
+ # Compute the noise aware loss
+ loss = mse / (2 * noise_var) + torch.log(
+ 2 * noise_var * torch.sqrt(torch.tensor([2 * 3.1415926535])).to(target.device)
+ )
+ loss = loss.mean()
+
+ return loss
diff --git a/atommic/collections/reconstruction/losses/ssim.py b/atommic/collections/reconstruction/losses/ssim.py
new file mode 100644
index 00000000..67d6ed9c
--- /dev/null
+++ b/atommic/collections/reconstruction/losses/ssim.py
@@ -0,0 +1,83 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from https://github.com/facebookresearch/fastMRI
+
+import torch
+import torch.nn.functional as F
+
+from atommic.core.classes.loss import Loss
+
+
+class SSIMLoss(Loss):
+ """Computes the (1-) SSIM loss between two tensors.
+
+ Examples
+ --------
+ >>> from atommic.collections.reconstruction.losses.ssim import SSIMLoss
+ >>> import torch
+ >>> loss = SSIMLoss(win_size=7, k1=0.01, k2=0.03)
+ >>> loss(X=torch.rand(1, 1, 256, 256), Y=torch.rand(1, 1, 256, 256), data_range=torch.tensor([1.]))
+ tensor(0.9872)
+ """
+
+ def __init__(self, win_size: int = 7, k1: float = 0.01, k2: float = 0.03):
+ """Inits :class:`SSIMLoss`.
+
+ Parameters
+ ----------
+ win_size : int, optional
+ Window size for SSIM calculation.
+ k1 : float, optional
+ k1 parameter for SSIM calculation.
+ k2 : float, optional
+ k2 parameter for SSIM calculation.
+ """
+ super().__init__()
+ self.win_size = win_size
+ self.k1, self.k2 = k1, k2
+ self.register_buffer("w", torch.ones(1, 1, win_size, win_size) / win_size**2)
+ NP = win_size**2
+ self.cov_norm = NP / (NP - 1)
+
+ def forward(self, X: torch.Tensor, Y: torch.Tensor, data_range: torch.Tensor = None):
+ """Forward pass of :class:`SSIMLoss`.
+
+ Parameters
+ ----------
+ X : torch.Tensor
+ First input tensor.
+ Y : torch.Tensor
+ Second input tensor.
+ data_range : torch.Tensor
+ Data range of the input tensors. If ``None``, it is computed as the maximum range of the input tensors.
+ Default is ``None``.
+ """
+ if not isinstance(self.w, torch.Tensor): # type: ignore # pylint: disable=access-member-before-definition
+ raise AssertionError
+
+ # This is necessary to first assign self.w to CUDA and then in case of fp32 to avoid RuntimeError: Inference
+ # tensors cannot be saved for backward.
+ self.w = self.w.to(Y).clone() # type: ignore
+
+ if data_range is None:
+ data_range = torch.tensor([max(X.max() - X.min(), Y.max() - Y.min())]).to(Y)
+ if isinstance(data_range, int):
+ data_range = torch.tensor([data_range]).to(Y)
+
+ data_range = data_range[:, None, None, None]
+ C1 = (self.k1 * data_range) ** 2
+ C2 = (self.k2 * data_range) ** 2
+ ux = F.conv2d(X, self.w)
+ uy = F.conv2d(Y, self.w)
+ uxx = F.conv2d(X * X, self.w)
+ uyy = F.conv2d(Y * Y, self.w)
+ uxy = F.conv2d(X * Y, self.w)
+ vx = self.cov_norm * (uxx - ux * ux)
+ vy = self.cov_norm * (uyy - uy * uy)
+ vxy = self.cov_norm * (uxy - ux * uy)
+ A1, A2, B1, B2 = (2 * ux * uy + C1, 2 * vxy + C2, ux**2 + uy**2 + C1, vx + vy + C2)
+ D = B1 * B2
+ S = (A1 * A2) / D
+
+ return 1 - S.mean()
diff --git a/atommic/collections/reconstruction/metrics/__init__.py b/atommic/collections/reconstruction/metrics/__init__.py
new file mode 100644
index 00000000..000bb905
--- /dev/null
+++ b/atommic/collections/reconstruction/metrics/__init__.py
@@ -0,0 +1,4 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.reconstruction.metrics.reconstruction_metrics import mse, nmse, psnr, ssim # noqa: F401
diff --git a/atommic/collections/reconstruction/metrics/reconstruction_metrics.py b/atommic/collections/reconstruction/metrics/reconstruction_metrics.py
new file mode 100644
index 00000000..c691f9e6
--- /dev/null
+++ b/atommic/collections/reconstruction/metrics/reconstruction_metrics.py
@@ -0,0 +1,235 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from https://github.com/facebookresearch/fastMRI
+
+import numpy as np
+from runstats import Statistics
+from skimage.metrics import peak_signal_noise_ratio, structural_similarity
+
+
+def mse(x: np.ndarray, y: np.ndarray, maxval: np.ndarray = None) -> float: # pylint: disable=unused-argument
+ """Computes Mean Squared Error (MSE).
+
+ Parameters
+ ----------
+ x : np.ndarray
+ Target image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D images,
+ the first dimension should be 1.
+ y : np.ndarray
+ Predicted image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+ maxval : np.ndarray
+ Maximum value of the images. If None, it is computed from the images. If the images are normalized, maxval
+ should be 1.
+
+ Returns
+ -------
+ float
+ Mean Squared Error.
+
+ Examples
+ --------
+ >>> from atommic.collections.reconstruction.metrics.reconstruction_metrics import mse
+ >>> import numpy as np
+ >>> datax = np.random.rand(3, 100, 100)
+ >>> datay = np.random.rand(3, 100, 100)
+ >>> mse(datax, datay)
+ 0.17035991151556373
+ """
+ return np.mean((x - y) ** 2)
+
+
+def nmse(x: np.ndarray, y: np.ndarray, maxval: np.ndarray = None) -> float: # pylint: disable=unused-argument
+ """Computes Normalized Mean Squared Error (NMSE).
+
+ Parameters
+ ----------
+ x : np.ndarray
+ Target image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D images,
+ the first dimension should be 1.
+ y : np.ndarray
+ Predicted image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+ maxval : np.ndarray
+ Maximum value of the images. If None, it is computed from the images. If the images are normalized, maxval
+ should be 1.
+
+ Returns
+ -------
+ float
+ Normalized Mean Squared Error.
+
+ Examples
+ --------
+ >>> from atommic.collections.reconstruction.metrics.reconstruction_metrics import nmse
+ >>> import numpy as np
+ >>> datax = np.random.rand(3, 100, 100)
+ >>> datay = np.random.rand(3, 100, 100)
+ >>> nmse(datax, datay)
+ 0.5001060028222054
+ """
+ return np.linalg.norm(x - y) ** 2 / np.linalg.norm(x) ** 2
+
+
+def psnr(x: np.ndarray, y: np.ndarray, maxval: np.ndarray = None) -> float:
+ """Computes Peak Signal to Noise Ratio (PSNR).
+
+ Parameters
+ ----------
+ x : np.ndarray
+ Target image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D images,
+ the first dimension should be 1.
+ y : np.ndarray
+ Predicted image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+ maxval : np.ndarray
+ Maximum value of the images. If None, it is computed from the images. If the images are normalized, maxval
+ should be 1.
+
+ Returns
+ -------
+ float
+ Peak Signal to Noise Ratio.
+
+ Examples
+ --------
+ >>> from atommic.collections.reconstruction.metrics.reconstruction_metrics import psnr
+ >>> import numpy as np
+ >>> datax = np.random.rand(3, 100, 100)
+ >>> datay = np.random.rand(3, 100, 100)
+ >>> psnr(datax, datay)
+ 7.6700572264458
+
+ .. note::
+ x and y must be normalized to the same range, e.g. [0, 1].
+
+ The PSNR is computed using the scikit-image implementation of the PSNR metric.
+ Source: https://scikit-image.org/docs/dev/api/skimage.metrics.html#skimage.metrics.peak_signal_noise_ratio
+ """
+ maxval = max(np.max(x) - np.min(x), np.max(y) - np.min(y)) if maxval is None else maxval
+ return peak_signal_noise_ratio(x, y, data_range=maxval)
+
+
+def ssim(x: np.ndarray, y: np.ndarray, maxval: np.ndarray = None) -> float:
+ """Computes Structural Similarity Index Measure (SSIM).
+
+ Parameters
+ ----------
+ x : np.ndarray
+ Target image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D images,
+ the first dimension should be 1.
+ y : np.ndarray
+ Predicted image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+ maxval : np.ndarray
+ Maximum value of the images. If None, it is computed from the images. If the images are normalized, maxval
+ should be 1.
+
+ Returns
+ -------
+ float
+ Structural Similarity Index Measure.
+
+ Examples
+ --------
+ >>> from atommic.collections.reconstruction.metrics.reconstruction_metrics import ssim
+ >>> import numpy as np
+ >>> datax = np.random.rand(3, 100, 100)
+ >>> datay = datax * 0.5
+ >>> ssim(datax, datay)
+ 0.01833040155119426
+
+ .. note::
+ x and y must be normalized to the same range, e.g. [0, 1].
+
+ The SSIM is computed using the scikit-image implementation of the SSIM metric.
+ Source: https://scikit-image.org/docs/dev/api/skimage.metrics.html#skimage.metrics.structural_similarity
+ """
+ if x.ndim == 2:
+ x = x[np.newaxis, :, :]
+ if y.ndim == 2:
+ y = y[np.newaxis, :, :]
+ if x.ndim != 3:
+ raise ValueError("Unexpected number of dimensions in ground truth.")
+ if x.ndim != y.ndim:
+ raise ValueError("Ground truth dimensions does not match prediction dimensions.")
+
+ maxval = max(np.max(x) - np.min(x), np.max(y) - np.min(y)) if maxval is None else maxval
+ maxval = max(maxval, 1)
+ ssim_score = sum(
+ structural_similarity(x[slice_num], y[slice_num], data_range=maxval) for slice_num in range(x.shape[0])
+ )
+ return ssim_score / x.shape[0]
+
+
+METRIC_FUNCS = {"MSE": mse, "NMSE": nmse, "PSNR": psnr, "SSIM": ssim}
+
+
+class ReconstructionMetrics:
+ r"""Maintains running statistics for a given collection of reconstruction metrics.
+
+ Examples
+ --------
+ >>> from atommic.collections.reconstruction.metrics.reconstruction_metrics import ReconstructionMetrics
+ >>> import numpy as np
+ >>> datax = np.random.rand(3, 100, 100)
+ >>> datay = np.random.rand(3, 100, 100)
+ >>> metrics = ReconstructionMetrics(METRIC_FUNCS, 'output', 'method')
+ >>> metrics.push(datax, datay)
+ >>> metrics.means()
+ {'MSE': 0.17035991151556373, 'NMSE': 0.5001060028222054, 'PSNR': 7.6700572264458, 'SSIM': 0.01833040155119426}
+ >>> metrics.__repr__()
+ 'MSE = 0.1704 +/- 0.01072 NMSE = 0.5001 +/- 0.01636 PSNR = 7.67 +/- 0.319 SSIM = 0.01833 +/- 0.03527\n'
+ """
+
+ def __init__(self, metric_funcs):
+ """Inits :class:`ReconstructionMetrics`.
+
+ Parameters
+ ----------
+ metric_funcs : dict
+ A dict where the keys are metric names and the values are Python functions for evaluating that metric.
+ """
+ self.metrics_scores = {metric: Statistics() for metric in metric_funcs}
+
+ def push(self, x, y, maxval=None):
+ """Pushes a new batch of metrics to the running statistics.
+
+ Parameters
+ ----------
+ x : np.ndarray
+ Target image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+ y : np.ndarray
+ Predicted image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+ maxval : np.ndarray
+ Maximum value of the images. If None, it is computed from the images. If the images are normalized, maxval
+ should be 1. Default is ``None``.
+
+ Returns
+ -------
+ dict
+ A dict where the keys are metric names and the values are the computed metric scores.
+ """
+ for metric, func in METRIC_FUNCS.items():
+ self.metrics_scores[metric].push(func(x, y, maxval=maxval))
+
+ def means(self):
+ """Mean of the means of each metric."""
+ return {metric: stat.mean() for metric, stat in self.metrics_scores.items()}
+
+ def stddevs(self):
+ """Standard deviation of the means of each metric."""
+ return {metric: stat.stddev() for metric, stat in self.metrics_scores.items()}
+
+ def __repr__(self):
+ """Representation of the metrics."""
+ means = self.means()
+ stddevs = self.stddevs()
+ metric_names = sorted(list(means))
+
+ res = " ".join(f"{name} = {means[name]:.4g} +/- {2 * stddevs[name]:.4g}" for name in metric_names) + "\n"
+
+ return res
diff --git a/atommic/collections/reconstruction/nn/__init__.py b/atommic/collections/reconstruction/nn/__init__.py
new file mode 100644
index 00000000..43e69857
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/__init__.py
@@ -0,0 +1,19 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.reconstruction.nn.ccnn import CascadeNet # noqa: F401
+from atommic.collections.reconstruction.nn.cirim import CIRIM # noqa: F401
+from atommic.collections.reconstruction.nn.crnn import CRNNet # noqa: F401
+from atommic.collections.reconstruction.nn.dunet import DUNet # noqa: F401
+from atommic.collections.reconstruction.nn.jointicnet import JointICNet # noqa: F401
+from atommic.collections.reconstruction.nn.kikinet import KIKINet # noqa: F401
+from atommic.collections.reconstruction.nn.lpd import LPDNet # noqa: F401
+from atommic.collections.reconstruction.nn.modl import MoDL # noqa: F401
+from atommic.collections.reconstruction.nn.multidomainnet import MultiDomainNet # noqa: F401
+from atommic.collections.reconstruction.nn.proximal_gradient import ProximalGradient # noqa: F401
+from atommic.collections.reconstruction.nn.recurrentvarnet import RecurrentVarNet # noqa: F401
+from atommic.collections.reconstruction.nn.unet import UNet # noqa: F401
+from atommic.collections.reconstruction.nn.varnet import VarNet # noqa: F401
+from atommic.collections.reconstruction.nn.vsnet import VSNet # noqa: F401
+from atommic.collections.reconstruction.nn.xpdnet import XPDNet # noqa: F401
+from atommic.collections.reconstruction.nn.zf import ZF # noqa: F401
diff --git a/atommic/collections/reconstruction/nn/base.py b/atommic/collections/reconstruction/nn/base.py
new file mode 100644
index 00000000..eab69851
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/base.py
@@ -0,0 +1,1419 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import os
+import warnings
+from abc import ABC
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, List, Tuple, Union
+
+import h5py
+import numpy as np
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch.nn import L1Loss, MSELoss
+from torch.utils.data import DataLoader
+
+from atommic.collections.common.data.subsample import create_masker
+from atommic.collections.common.losses import VALID_RECONSTRUCTION_LOSSES, AggregatorLoss, SinkhornDistance
+from atommic.collections.common.nn.base import BaseMRIModel, BaseSensitivityModel, DistributedMetricSum
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import (
+ check_stacked_complex,
+ coil_combination_method,
+ complex_abs,
+ complex_abs_sq,
+ expand_op,
+ is_none,
+ parse_list_and_keep_last,
+ unnormalize,
+)
+from atommic.collections.quantitative.data import AHEADqMRIDataset
+from atommic.collections.reconstruction.data.mri_reconstruction_loader import (
+ CC359ReconstructionMRIDataset,
+ ReconstructionMRIDataset,
+ SKMTEAReconstructionMRIDataset,
+ StanfordKneesReconstructionMRIDataset,
+)
+from atommic.collections.reconstruction.losses.na import NoiseAwareLoss
+from atommic.collections.reconstruction.losses.ssim import SSIMLoss
+from atommic.collections.reconstruction.metrics.reconstruction_metrics import mse, nmse, psnr, ssim
+from atommic.collections.reconstruction.parts.transforms import ReconstructionMRIDataTransforms
+
+__all__ = ["BaseMRIReconstructionModel"]
+
+
+class BaseMRIReconstructionModel(BaseMRIModel, ABC):
+ """Base class of all MRI reconstruction models."""
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`BaseMRIReconstructionModel`.
+
+ Parameters
+ ----------
+ cfg: DictConfig
+ The configuration file.
+ trainer: Trainer
+ The PyTorch Lightning trainer.
+ """
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ # Initialize the Fast-Fourier Transform parameters.
+ self.fft_centered = cfg_dict.get("fft_centered", False)
+ self.fft_normalization = cfg_dict.get("fft_normalization", "backward")
+ self.spatial_dims = cfg_dict.get("spatial_dims", None)
+ self.coil_dim = cfg_dict.get("coil_dim", 1)
+
+ # Initialize the dimensionality of the data. It can be 2D or 2.5D -> meaning 2D with > 1 slices or 3D.
+ self.dimensionality = cfg_dict.get("dimensionality", 2)
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+ self.num_echoes = cfg_dict.get("num_echoes", 0)
+
+ # Initialize the coil combination method. It can be either "SENSE" or "RSS" (root-sum-of-squares) or
+ # "RSS-complex" (root-sum-of-squares of the complex-valued data).
+ self.coil_combination_method = cfg_dict.get("coil_combination_method", "SENSE")
+
+ # Refers to Self-Supervised Data Undersampling (SSDU). If True, then the model is trained with only
+ # undersampled data.
+ self.ssdu = cfg_dict.get("ssdu", False)
+
+ # Refers to Noise-to-Recon. If True, then the model can either be trained with only undersampled data or with
+ # both undersampled and (a percentage of) fully-sampled data.
+ self.n2r = cfg_dict.get("n2r", False)
+
+ # Initialize the sensitivity network if cfg_dict.get("estimate_coil_sensitivity_maps_with_nn") is True.
+ self.estimate_coil_sensitivity_maps_with_nn = cfg_dict.get("estimate_coil_sensitivity_maps_with_nn", False)
+
+ # Initialize loss related parameters.
+ self.kspace_reconstruction_loss = cfg_dict.get("kspace_reconstruction_loss", False)
+ self.n2r_loss_weight = cfg_dict.get("n2r_loss_weight", 1.0) if self.n2r else 1.0
+ self.reconstruction_losses = {}
+ reconstruction_loss = cfg_dict.get("reconstruction_loss")
+ reconstruction_losses_ = {}
+ if reconstruction_loss is not None:
+ for k, v in reconstruction_loss.items():
+ if k not in VALID_RECONSTRUCTION_LOSSES:
+ raise ValueError(
+ f"Reconstruction loss {k} is not supported. Please choose one of the following: "
+ f"{VALID_RECONSTRUCTION_LOSSES}."
+ )
+ if v is None or v == 0.0:
+ warnings.warn(f"The weight of reconstruction loss {k} is set to 0.0. This loss will not be used.")
+ else:
+ reconstruction_losses_[k] = v
+ else:
+ # Default reconstruction loss is L1.
+ reconstruction_losses_["l1"] = 1.0
+ if sum(reconstruction_losses_.values()) != 1.0:
+ warnings.warn("Sum of reconstruction losses weights is not 1.0. Adjusting weights to sum up to 1.0.")
+ total_weight = sum(reconstruction_losses_.values())
+ reconstruction_losses_ = {k: v / total_weight for k, v in reconstruction_losses_.items()}
+ for name in VALID_RECONSTRUCTION_LOSSES:
+ if name in reconstruction_losses_:
+ if name == "ssim":
+ if self.ssdu:
+ raise ValueError("SSIM loss is not supported for SSDU.")
+ self.reconstruction_losses[name] = SSIMLoss()
+ elif name == "mse":
+ self.reconstruction_losses[name] = MSELoss()
+ elif name == "wasserstein":
+ self.reconstruction_losses[name] = SinkhornDistance()
+ elif name == "noise_aware":
+ self.reconstruction_losses[name] = NoiseAwareLoss()
+ elif name == "l1":
+ self.reconstruction_losses[name] = L1Loss()
+ # replace losses names by 'loss_1', 'loss_2', etc. to properly iterate in the aggregator loss
+ self.reconstruction_losses = {f"loss_{i+1}": v for i, v in enumerate(self.reconstruction_losses.values())}
+ self.total_reconstruction_losses = len(self.reconstruction_losses)
+ self.total_reconstruction_loss_weight = cfg_dict.get("total_reconstruction_loss_weight", 1.0)
+
+ # Set normalization parameters for logging
+ self.unnormalize_loss_inputs = cfg_dict.get("unnormalize_loss_inputs", False)
+ self.unnormalize_log_outputs = cfg_dict.get("unnormalize_log_outputs", False)
+ self.normalization_type = cfg_dict.get("normalization_type", "max")
+
+ # Refers to cascading or iterative reconstruction methods.
+ self.accumulate_predictions = cfg_dict.get("accumulate_predictions", False)
+
+ # Refers to the type of the complex-valued data. It can be either "stacked" or "complex_abs" or
+ # "complex_sqrt_abs".
+ self.complex_valued_type = cfg_dict.get("complex_valued_type", "stacked")
+
+ # Initialize the module
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ if self.estimate_coil_sensitivity_maps_with_nn:
+ self.coil_sensitivity_maps_nn = BaseSensitivityModel(
+ cfg_dict.get("coil_sensitivity_maps_nn_chans", 8),
+ cfg_dict.get("coil_sensitivity_maps_nn_pools", 4),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ mask_type=cfg_dict.get("coil_sensitivity_maps_nn_mask_type", "2D"),
+ normalize=cfg_dict.get("coil_sensitivity_maps_nn_normalize", True),
+ mask_center=cfg_dict.get("coil_sensitivity_maps_nn_mask_center", True),
+ )
+
+ # Set aggregation loss
+ self.total_reconstruction_loss = AggregatorLoss(
+ num_inputs=self.total_reconstruction_losses, weights=list(reconstruction_losses_.values())
+ )
+
+ # Set distributed metrics
+ self.MSE = DistributedMetricSum()
+ self.NMSE = DistributedMetricSum()
+ self.SSIM = DistributedMetricSum()
+ self.PSNR = DistributedMetricSum()
+ self.TotExamples = DistributedMetricSum()
+
+ # Set evaluation metrics dictionaries
+ self.mse_vals: Dict = defaultdict(dict)
+ self.nmse_vals: Dict = defaultdict(dict)
+ self.ssim_vals: Dict = defaultdict(dict)
+ self.psnr_vals: Dict = defaultdict(dict)
+
+ def __abs_output__(self, x: torch.Tensor) -> torch.Tensor:
+ """Converts the input to absolute value."""
+ if isinstance(x, list):
+ while isinstance(x, list):
+ x = x[-1]
+ if x.shape[-1] == 2 or torch.is_complex(x):
+ if torch.is_complex(x):
+ x = torch.view_as_real(x)
+ if self.complex_valued_type == "stacked":
+ x = torch.abs(check_stacked_complex(x))
+ elif self.complex_valued_type == "complex_abs":
+ x = complex_abs(x)
+ elif self.complex_valued_type == "complex_sqrt_abs":
+ x = complex_abs_sq(x)
+ return x
+
+ def __unnormalize_for_loss_or_log__(
+ self,
+ target: torch.Tensor,
+ prediction: torch.Tensor,
+ sensitivity_maps: Union[torch.Tensor, None],
+ attrs: Dict,
+ r: int,
+ batch_idx: int = 1,
+ ) -> Tuple[torch.Tensor, torch.Tensor, Union[torch.Tensor, None]]:
+ """Unnormalizes the data for computing the loss or logging.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : torch.Tensor
+ Prediction data of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor or None
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2] or None.
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ batch_idx : int
+ Batch index. Default is ``1``.
+
+ Returns
+ -------
+ target : torch.Tensor
+ Unnormalized target data.
+ prediction : torch.Tensor
+ Unnormalized prediction data.
+ sensitivity_maps : torch.Tensor
+ Unnormalized sensitivity maps.
+ """
+ if self.n2r and not attrs["n2r_supervised"][batch_idx]:
+ target = unnormalize(
+ target,
+ {
+ "min": attrs["prediction_min"][batch_idx]
+ if "prediction_min" in attrs
+ else attrs[f"prediction_min_{r}"][batch_idx],
+ "max": attrs["prediction_max"][batch_idx]
+ if "prediction_max" in attrs
+ else attrs[f"prediction_max_{r}"][batch_idx],
+ "mean": attrs["prediction_mean"][batch_idx]
+ if "prediction_mean" in attrs
+ else attrs[f"prediction_mean_{r}"][batch_idx],
+ "std": attrs["prediction_std"][batch_idx]
+ if "prediction_std" in attrs
+ else attrs[f"prediction_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction = unnormalize(
+ prediction,
+ {
+ "min": attrs["noise_prediction_min"][batch_idx]
+ if "noise_prediction_min" in attrs
+ else attrs[f"noise_prediction_min_{r}"][batch_idx],
+ "max": attrs["noise_prediction_max"][batch_idx]
+ if "noise_prediction_max" in attrs
+ else attrs[f"noise_prediction_max_{r}"][batch_idx],
+ attrs["noise_prediction_mean"][batch_idx]
+ if "noise_prediction_mean" in attrs
+ else "mean": attrs[f"noise_prediction_mean_{r}"][batch_idx],
+ attrs["noise_prediction_std"][batch_idx]
+ if "noise_prediction_std" in attrs
+ else "std": attrs[f"noise_prediction_std_{r}"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ else:
+ min_val = attrs["target_min"] if "target_min" in attrs else attrs[f"target_min_{r}"]
+ max_val = attrs["target_max"] if "target_max" in attrs else attrs[f"target_max_{r}"]
+ mean_val = attrs["target_mean"] if "target_mean" in attrs else attrs[f"target_mean_{r}"]
+ std_val = attrs["target_std"] if "target_std" in attrs else attrs[f"target_std_{r}"]
+ if isinstance(min_val, list):
+ min_val = min_val[batch_idx]
+ if isinstance(max_val, list):
+ max_val = max_val[batch_idx]
+ if isinstance(mean_val, list):
+ mean_val = mean_val[batch_idx]
+ if isinstance(std_val, list):
+ std_val = std_val[batch_idx]
+
+ target = unnormalize(
+ target, {"min": min_val, "max": max_val, "mean": mean_val, "std": std_val}, self.normalization_type
+ )
+
+ min_val = attrs["prediction_min"] if "prediction_min" in attrs else attrs[f"prediction_min_{r}"]
+ max_val = attrs["prediction_max"] if "prediction_max" in attrs else attrs[f"prediction_max_{r}"]
+ mean_val = attrs["prediction_mean"] if "prediction_mean" in attrs else attrs[f"prediction_mean_{r}"]
+ std_val = attrs["prediction_std"] if "prediction_std" in attrs else attrs[f"prediction_std_{r}"]
+ if isinstance(min_val, list):
+ min_val = min_val[batch_idx]
+ if isinstance(max_val, list):
+ max_val = max_val[batch_idx]
+ if isinstance(mean_val, list):
+ mean_val = mean_val[batch_idx]
+ if isinstance(std_val, list):
+ std_val = std_val[batch_idx]
+
+ prediction = unnormalize(
+ prediction, {"min": min_val, "max": max_val, "mean": mean_val, "std": std_val}, self.normalization_type
+ )
+
+ if sensitivity_maps is not None and "sensitivity_maps_min" in attrs:
+ sensitivity_maps = unnormalize(
+ sensitivity_maps,
+ {
+ "min": attrs["sensitivity_maps_min"][batch_idx],
+ "max": attrs["sensitivity_maps_max"][batch_idx],
+ "mean": attrs["sensitivity_maps_mean"][batch_idx],
+ "std": attrs["sensitivity_maps_std"][batch_idx],
+ },
+ self.normalization_type,
+ )
+
+ return target, prediction, sensitivity_maps
+
+ def process_reconstruction_loss(
+ self,
+ target: torch.Tensor,
+ prediction: Union[list, torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ attrs: Union[Dict, torch.Tensor],
+ r: Union[int, torch.Tensor],
+ loss_func: torch.nn.Module,
+ ) -> torch.Tensor:
+ """Processes the reconstruction loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ loss_func : torch.nn.Module
+ Loss function. Default is ``torch.nn.L1Loss()``.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if target.shape[-1] != 2 and torch.is_complex(target):
+ target = torch.view_as_real(target)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ target = expand_op(target, sensitivity_maps, self.coil_dim)
+ # Transform to k-space.
+ target = fft2(target, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target)
+ elif not self.unnormalize_loss_inputs:
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target / torch.max(torch.abs(target)))
+
+ def compute_reconstruction_loss(t, p, s):
+ if self.unnormalize_loss_inputs:
+ # we do the unnormalization here to avoid explicitly iterating through list of predictions, which
+ # might be a list of lists.
+ t, p, s = self.__unnormalize_for_loss_or_log__(t, p, s, attrs, r)
+
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if p.shape[-1] != 2 and torch.is_complex(p):
+ p = torch.view_as_real(p)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ p = expand_op(p, s, self.coil_dim)
+ # Transform to k-space.
+ p = fft2(p, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # If SSDU is used, then apply the mask to the prediction to enforce data consistency.
+ if self.ssdu:
+ p = p * mask
+ # Ensure loss inputs are both viewed in the same way.
+ p = self.__abs_output__(p / torch.max(torch.abs(p)))
+ elif not self.unnormalize_loss_inputs:
+ p = self.__abs_output__(p / torch.max(torch.abs(p)))
+
+ if "ssim" in str(loss_func).lower():
+ p = torch.abs(p / torch.max(torch.abs(p)))
+ t = torch.abs(t / torch.max(torch.abs(t)))
+
+ return loss_func(
+ t,
+ p,
+ data_range=torch.tensor([max(torch.max(t).item(), torch.max(p).item())]).unsqueeze(dim=0).to(t),
+ )
+
+ return loss_func(t, p)
+
+ if self.num_echoes > 0:
+ return torch.mean(
+ torch.stack(
+ [
+ compute_reconstruction_loss(
+ target[echo].unsqueeze(0), prediction[echo].unsqueeze(0), sensitivity_maps
+ )
+ for echo in range(target.shape[0])
+ ]
+ )
+ ).to(target)
+
+ return compute_reconstruction_loss(target, prediction, sensitivity_maps)
+
+ def __compute_loss__(
+ self,
+ target: torch.Tensor,
+ predictions: Union[list, torch.Tensor],
+ predictions_n2r: Union[list, torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ ssdu_loss_mask: torch.Tensor,
+ attrs: Union[Dict, torch.Tensor],
+ r: Union[int, torch.Tensor],
+ ) -> torch.Tensor:
+ """Computes the reconstruction loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ predictions : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ predictions_n2r : Union[list, torch.Tensor]
+ Noise-to-Recon prediction(s) of shape [batch_size, n_x, n_y, 2], if Noise-to-Recon is used.
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ ssdu_loss_mask : torch.Tensor
+ SSDU loss mask of shape [batch_size, 1, n_x, n_y, 1]. It will be used if self.ssdu is True, to enforce
+ data consistency on the prediction.
+ attrs : Union[Dict, torch.Tensor]
+ Attributes of the data with pre normalization values.
+ r : Union[int, torch.Tensor]
+ The selected acceleration factor.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ Reconstruction loss.
+ """
+ if predictions_n2r is not None and not attrs["n2r_supervised"]:
+ # Noise-to-Recon with/without SSDU
+ target = predictions
+ predictions = predictions_n2r
+ weight = self.n2r_loss_weight
+ else:
+ # Supervised learning or Noise-to-Recon with SSDU
+ weight = 1.0
+ losses = {}
+ for name, loss_func in self.reconstruction_losses.items():
+ losses[name] = (
+ self.process_reconstruction_loss(
+ target, predictions, sensitivity_maps, ssdu_loss_mask, attrs, r, loss_func=loss_func
+ )
+ * weight
+ )
+ return self.total_reconstruction_loss(**losses) * self.total_reconstruction_loss_weight
+
+ def __compute_and_log_metrics_and_outputs__(
+ self,
+ target: torch.Tensor,
+ predictions: Union[List[List[torch.Tensor]], List[torch.Tensor], torch.Tensor],
+ attrs: Union[Dict, torch.Tensor],
+ r: Union[int, torch.Tensor],
+ fname: Union[str, torch.Tensor],
+ slice_idx: Union[int, torch.Tensor],
+ acceleration: Union[float, torch.Tensor],
+ ):
+ """Computes the metrics and logs the outputs.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y].
+ predictions : Union[List[List[torch.Tensor]], List[torch.Tensor], torch.Tensor]
+ Prediction data of shape [batch_size, n_x, n_y, 2]. It can be a list or list of lists if iterative and/or
+ cascading reconstruction methods are used.
+ attrs : Union[Dict, torch.Tensor]
+ Attributes of the data with pre normalization values.
+ r : Union[int, torch.Tensor]
+ The selected acceleration factor.
+ fname : Union[str, torch.Tensor]
+ File name.
+ slice_idx : Union[int, torch.Tensor]
+ Slice index.
+ acceleration : Union[float, torch.Tensor]
+ Acceleration factor.
+ """
+ while isinstance(predictions, list):
+ predictions = predictions[-1]
+
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target)
+ predictions = self.__abs_output__(predictions)
+
+ # Check if multiple echoes are used.
+ if self.num_echoes > 1:
+ # find the batch size
+ batch_size = target.shape[0] / self.num_echoes
+ # reshape to [batch_size, num_echoes, n_x, n_y]
+ target = target.reshape((int(batch_size), self.num_echoes, *target.shape[1:]))
+ predictions = predictions.reshape((int(batch_size), self.num_echoes, *predictions.shape[1:]))
+ # concatenate the echoes in the last dim
+ target = torch.cat([target[:, i, ...] for i in range(self.num_echoes)], dim=-1)
+ predictions = torch.cat([predictions[:, i, ...] for i in range(self.num_echoes)], dim=-1)
+
+ # Add dummy dimensions to target and predictions for logging.
+ target = target.unsqueeze(1)
+ predictions = predictions.unsqueeze(1)
+
+ # Iterate over the batch and log the target and predictions.
+ for _batch_idx_ in range(target.shape[0]):
+ output_target = target[_batch_idx_]
+ output_predictions = predictions[_batch_idx_]
+
+ if self.unnormalize_log_outputs:
+ # Unnormalize target and predictions with pre normalization values. This is only for logging purposes.
+ # For the loss computation, the self.unnormalize_loss_inputs flag is used.
+ output_target, output_predictions, _ = self.__unnormalize_for_loss_or_log__(
+ output_target, output_predictions, None, attrs, r, _batch_idx_
+ )
+
+ # Normalize target and predictions to [0, 1] for logging.
+ if torch.is_complex(output_target) and output_target.shape[-1] != 2:
+ output_target = torch.view_as_real(output_target)
+ if output_target.shape[-1] == 2:
+ output_target = torch.view_as_complex(output_target)
+ output_target = torch.abs(output_target / torch.max(torch.abs(output_target))).detach().cpu()
+
+ if torch.is_complex(output_predictions) and output_predictions.shape[-1] != 2:
+ output_predictions = torch.view_as_real(output_predictions)
+ if output_predictions.shape[-1] == 2:
+ output_predictions = torch.view_as_complex(output_predictions)
+ output_predictions = (
+ torch.abs(output_predictions / torch.max(torch.abs(output_predictions))).detach().cpu()
+ )
+
+ # Log target and predictions, if log_image is True for this slice.
+ if attrs["log_image"][_batch_idx_]:
+ # if consecutive slices, select the middle slice
+ if self.consecutive_slices > 1:
+ output_target = output_target[self.consecutive_slices // 2]
+ output_predictions = output_predictions[self.consecutive_slices // 2]
+
+ key = f"{fname[_batch_idx_]}_slice_{int(slice_idx[_batch_idx_])}-Acc={acceleration}x" # type: ignore
+ self.log_image(f"{key}/target", output_target)
+ self.log_image(f"{key}/reconstruction", output_predictions)
+ self.log_image(f"{key}/error", torch.abs(output_target - output_predictions))
+
+ # Compute metrics and log them.
+ output_target = output_target.numpy()
+ output_predictions = output_predictions.numpy()
+ self.mse_vals[fname[_batch_idx_]][str(slice_idx[_batch_idx_].item())] = torch.tensor( # type: ignore
+ mse(output_target, output_predictions)
+ ).view(1)
+ self.nmse_vals[fname[_batch_idx_]][str(slice_idx[_batch_idx_].item())] = torch.tensor( # type: ignore
+ nmse(output_target, output_predictions)
+ ).view(1)
+
+ max_value = max(np.max(output_target), np.max(output_predictions)) - min(
+ np.min(output_target), np.min(output_predictions)
+ )
+ max_value = max(max_value, attrs['max']) if 'max' in attrs else max_value
+
+ self.ssim_vals[fname[_batch_idx_]][str(slice_idx[_batch_idx_].item())] = torch.tensor( # type: ignore
+ ssim(output_target, output_predictions, maxval=max_value)
+ ).view(1)
+ self.psnr_vals[fname[_batch_idx_]][str(slice_idx[_batch_idx_].item())] = torch.tensor( # type: ignore
+ psnr(output_target, output_predictions, maxval=max_value)
+ ).view(1)
+
+ def __check_noise_to_recon_inputs__(
+ self, y: torch.Tensor, mask: torch.Tensor, initial_prediction: torch.Tensor, attrs: Dict
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ r"""Checks if Noise-to-Recon [1] is used.
+
+ References
+ ----------
+ .. [1] Desai, AD, Ozturkler, BM, Sandino, CM, et al. Noise2Recon: Enabling SNR-robust MRI reconstruction with
+ semi-supervised and self-supervised learning. Magn Reson Med. 2023; 90(5): 2052-2070. doi: 10.1002/mrm.29759
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2].
+ attrs : Dict
+ Attributes dictionary. Even though Noise-to-Recon is an unsupervised method, a percentage of the data might
+ be used for supervised learning. In this case, the ``attrs["n2r_supervised"]`` will be True. So we know
+ which data are used for supervised learning and which for unsupervised.
+
+ Returns
+ -------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2].
+ n2r_y : torch.Tensor
+ Subsampled k-space data for Noise-to-Recon. Shape [batch_size, n_coils, n_x, n_y, 2].
+ n2r_mask : torch.Tensor
+ Sampling mask for Noise-to-Recon. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_initial_prediction : torch.Tensor
+ Initial prediction for Noise-to-Recon. Shape [batch_size, n_x, n_y, 2].
+ """
+ if self.n2r and (not attrs["n2r_supervised"].all() or self.ssdu):
+ y, n2r_y = y
+ mask, n2r_mask = mask
+ initial_prediction, n2r_initial_prediction = initial_prediction
+ else:
+ n2r_y = None
+ n2r_mask = None
+ n2r_initial_prediction = None
+ return y, mask, initial_prediction, n2r_y, n2r_mask, n2r_initial_prediction
+
+ def __process_unsupervised_inputs__(
+ self,
+ n2r_y: torch.Tensor,
+ mask: torch.Tensor,
+ n2r_mask: torch.Tensor,
+ n2r_initial_prediction: torch.Tensor,
+ attrs: Dict,
+ r: int,
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Process inputs if Noise-to-Recon and/or SSDU are used.
+
+ Parameters
+ ----------
+ n2r_y : Union[List[torch.Tensor], torch.Tensor]
+ Noise-to-Recon subsampled k-space data, if Noise-to-Recon is used. If multiple accelerations are used, then
+ it is a list of torch.Tensor. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_mask : Union[List[torch.Tensor], torch.Tensor]
+ Noise-to-Recon sampling mask, if Noise-to-Recon is used. If multiple accelerations are used, then
+ it is a list of torch.Tensor. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_initial_prediction : Union[List[torch.Tensor], torch.Tensor]
+ Noise-to-Recon initial prediction, if Noise-to-Recon is used. If multiple accelerations are used, then
+ it is a list of torch.Tensor. Shape [batch_size, n_x, n_y, 2].
+ attrs : Dict
+ Attributes dictionary. Even though Noise-to-Recon is an unsupervised method, a percentage of the data might
+ be used for supervised learning. In this case, the ``attrs["n2r_supervised"]`` will be True. So we know
+ which data are used for supervised learning and which for unsupervised.
+ r : int
+ Random index used to select the acceleration.
+
+ Returns
+ -------
+ n2r_y : torch.Tensor
+ Noise-to-Recon subsampled k-space data, if Noise-to-Recon is used. If multiple accelerations are used, then
+ one factor is randomly selected. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_mask : torch.Tensor
+ Noise-to-Recon sampling mask, if Noise-to-Recon is used. If multiple accelerations are used, then one
+ factor is randomly selected. Shape [batch_size, 1, n_x, n_y, 1].
+ n2r_initial_prediction : torch.Tensor
+ Noise-to-Recon initial prediction, if Noise-to-Recon is used. If multiple accelerations are used, then one
+ factor is randomly selected. Shape [batch_size, n_x, n_y, 2].
+ loss_mask : torch.Tensor
+ SSDU loss mask, if SSDU is used. Shape [batch_size, 1, n_x, n_y, 1].
+ """
+ if self.n2r and (not attrs["n2r_supervised"].all() or self.ssdu):
+ # Noise-to-Recon with/without SSDU.
+
+ if isinstance(n2r_y, list):
+ # Check multiple accelerations for Noise-to-Recon
+ n2r_y = n2r_y[r]
+ if n2r_mask is not None:
+ n2r_mask = n2r_mask[r]
+ n2r_initial_prediction = n2r_initial_prediction[r]
+
+ # Check if SSDU is used
+ if self.ssdu:
+ mask, loss_mask = mask
+ else:
+ loss_mask = torch.ones_like(mask)
+
+ # Ensure that the mask has the same number of dimensions as the input mask.
+ if n2r_mask.dim() < mask.dim():
+ n2r_mask = None
+ elif self.ssdu and not self.n2r and len(mask) == 2: # SSDU without Noise-to-Recon.
+ mask, loss_mask = mask
+ else:
+ loss_mask = torch.ones_like(mask)
+
+ return n2r_y, n2r_mask, n2r_initial_prediction, mask, loss_mask
+
+ @staticmethod
+ def __process_inputs__(
+ kspace: Union[List, torch.Tensor],
+ y: Union[List, torch.Tensor],
+ mask: Union[List, torch.Tensor],
+ initial_prediction: Union[List, torch.Tensor],
+ target: Union[List, torch.Tensor],
+ ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor, int]:
+ """Processes lists of inputs to torch.Tensor. In the case where multiple accelerations are used, then the
+ inputs are lists. This function converts the lists to torch.Tensor by randomly selecting one acceleration. If
+ only one acceleration is used, then the inputs are torch.Tensor and are returned as is.
+
+ Parameters
+ ----------
+ kspace : Union[List, torch.Tensor]
+ Full k-space data of length n_accelerations or shape [batch_size, n_coils, n_x, n_y, 2].
+ y : Union[List, torch.Tensor]
+ Subsampled k-space data of length n_accelerations or shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : Union[List, torch.Tensor]
+ Sampling mask of length n_accelerations or shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction : Union[List, torch.Tensor]
+ Initial prediction of length n_accelerations or shape [batch_size, n_coils, n_x, n_y, 2].
+ target : Union[List, torch.Tensor]
+ Target data of length n_accelerations or shape [batch_size, n_x, n_y, 2].
+
+ Returns
+ -------
+ kspace : torch.Tensor
+ Full k-space data of shape [batch_size, n_coils, n_x, n_y, 2].
+ y : torch.Tensor
+ Subsampled k-space data of shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : torch.Tensor
+ Sampling mask of shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction : torch.Tensor
+ Initial prediction of shape [batch_size, n_coils, n_x, n_y, 2].
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ r : int
+ Random index used to select the acceleration.
+ """
+ if isinstance(y, list):
+ r = np.random.randint(len(y))
+ y = y[r]
+ mask = mask[r]
+ initial_prediction = initial_prediction[r]
+ else:
+ r = 0
+ if isinstance(kspace, list):
+ kspace = kspace[r]
+ target = target[r]
+ elif isinstance(target, list):
+ target = target[r]
+ return kspace, y, mask, initial_prediction, target, r
+
+ def inference_step(
+ self,
+ kspace: torch.Tensor,
+ y: Union[List[torch.Tensor], torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ mask: Union[List[torch.Tensor], torch.Tensor],
+ initial_prediction: Union[List, torch.Tensor],
+ target: torch.Tensor,
+ fname: str,
+ slice_idx: int,
+ acceleration: float,
+ attrs: Dict,
+ ):
+ """Performs an inference step, i.e., computes the predictions of the model.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ Fully sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ y : Union[List[torch.Tensor], torch.Tensor]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ mask : Union[List[torch.Tensor], torch.Tensor]
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if Unsupervised
+ Learning methods are used, it contains their masks. Shape [batch_size, 1, n_x, n_y, 1].
+ initial_prediction : Union[List, torch.Tensor]
+ Initial prediction. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_x, n_y, 2].
+ target : torch.Tensor
+ Target data. Shape [batch_size, n_x, n_y, 2].
+ fname : str
+ File name.
+ slice_idx : int
+ Slice index.
+ acceleration : float
+ Acceleration factor of the sampling mask, randomly selected if multiple accelerations are used.
+ attrs : Dict
+ Attributes dictionary.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary of processed inputs and model's predictions, with keys:
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask, randomly selected if multiple accelerations are used.
+ 'predictions' : Union[List[torch.Tensor], torch.Tensor]
+ Model's predictions. If accumulate predictions is True, then it is a list of torch.Tensor.
+ Shape [batch_size, n_x, n_y, 2].
+ 'predictions_n2r' : Union[List[torch.Tensor], torch.Tensor]
+ Model's predictions for Noise-to-Recon, if Noise-to-Recon is used. If accumulate predictions is
+ True, then it is a list of torch.Tensor. Shape [batch_size, n_x, n_y, 2].
+ 'target' : torch.Tensor
+ Target data. Shape [batch_size, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'loss_mask' : torch.Tensor
+ SSDU loss mask, if SSDU is used. Shape [batch_size, 1, n_x, n_y, 1].
+ 'attrs' : dict
+ Attributes dictionary.
+ 'r' : int
+ Random index used for selected acceleration.
+ """
+ # Check if Noise-to-Recon is used
+ y, mask, initial_prediction, n2r_y, n2r_mask, n2r_initial_prediction = self.__check_noise_to_recon_inputs__(
+ y, mask, initial_prediction, attrs
+ )
+
+ # Process inputs to randomly select one acceleration factor, in case multiple accelerations are used.
+ kspace, y, mask, initial_prediction, target, r = self.__process_inputs__(
+ kspace, y, mask, initial_prediction, target
+ )
+
+ # Process inputs if Noise-to-Recon and/or SSDU are used.
+ n2r_y, n2r_mask, n2r_initial_prediction, mask, loss_mask = self.__process_unsupervised_inputs__(
+ n2r_y, mask, n2r_mask, n2r_initial_prediction, attrs, r
+ )
+
+ # Check if multiple echoes are used.
+ if self.num_echoes > 1:
+ # stack the echoes along the batch dimension
+ kspace = kspace.view(-1, *kspace.shape[2:])
+ y = y.view(-1, *y.shape[2:])
+ mask = mask.view(-1, *mask.shape[2:])
+ initial_prediction = initial_prediction.view(-1, *initial_prediction.shape[2:])
+ target = target.view(-1, *target.shape[2:])
+ sensitivity_maps = torch.repeat_interleave(sensitivity_maps, repeats=kspace.shape[0], dim=0)
+
+ # Check if a network is used for coil sensitivity maps estimation.
+ if self.estimate_coil_sensitivity_maps_with_nn:
+ # Estimate coil sensitivity maps with a network.
+ sensitivity_maps = self.coil_sensitivity_maps_nn(kspace, mask, sensitivity_maps)
+ # (Re-)compute the initial prediction with the estimated sensitivity maps. This also means that the
+ # self.coil_combination_method is set to "SENSE", since in "RSS" the sensitivity maps are not used.
+ initial_prediction = coil_combination_method(
+ ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+ if n2r_initial_prediction is not None:
+ n2r_initial_prediction = coil_combination_method(
+ ifft2(n2r_y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+
+ # Forward pass
+ predictions = self.forward(y, sensitivity_maps, mask, initial_prediction, attrs["noise"])
+
+ # Noise-to-Recon forward pass, if Noise-to-Recon is used.
+ predictions_n2r = None
+ if self.n2r and n2r_mask is not None:
+ predictions_n2r = self.forward(n2r_y, sensitivity_maps, n2r_mask, n2r_initial_prediction, attrs["noise"])
+
+ # Get acceleration factor from acceleration list, if multiple accelerations are used. Or if batch size > 1.
+ if isinstance(acceleration, list):
+ if acceleration[0].shape[0] > 1:
+ acceleration[0] = acceleration[0][0]
+ acceleration = np.round(acceleration[r].item())
+ else:
+ if acceleration.shape[0] > 1: # type: ignore
+ acceleration = acceleration[0] # type: ignore
+ acceleration = np.round(acceleration.item()) # type: ignore
+
+ return {
+ "fname": fname,
+ "slice_idx": slice_idx,
+ "acceleration": acceleration,
+ "predictions": predictions,
+ "predictions_n2r": predictions_n2r,
+ "target": target,
+ "sensitivity_maps": sensitivity_maps,
+ "loss_mask": loss_mask,
+ "attrs": attrs,
+ "r": r,
+ }
+
+ def training_step(self, batch: Dict[float, torch.Tensor], batch_idx: int) -> Dict[str, torch.Tensor]:
+ """Performs a training step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'kspace' : List of torch.Tensor
+ Fully-sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'y' : Union[torch.Tensor, None]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'mask' : Union[torch.Tensor, None]
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor. Also, if
+ Unsupervised Learning methods, like Noise-to-Recon or SSDU, are used, then it is a list of
+ torch.Tensor with masks for each method. Shape [batch_size, 1, n_x, n_y, 1].
+ 'initial_prediction' : Union[torch.Tensor, None]
+ Initial prediction. Shape [batch_size, n_x, n_y, 2] or None.
+ 'target' : Union[torch.Tensor, None]
+ Target data. Shape [batch_size, n_x, n_y] or None.
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+ batch_idx : int
+ Batch index.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary of loss and log.
+ """
+ kspace, y, sensitivity_maps, mask, initial_prediction, target, fname, slice_idx, acceleration, attrs = batch
+
+ outputs = self.inference_step(
+ kspace,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction,
+ target,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration,
+ attrs, # type: ignore
+ )
+
+ target = outputs["target"]
+
+ # Compute loss
+ train_loss = self.__compute_loss__(
+ target,
+ outputs["predictions"],
+ outputs["predictions_n2r"],
+ outputs["sensitivity_maps"],
+ outputs["loss_mask"],
+ outputs["attrs"],
+ outputs["r"],
+ )
+
+ # Log loss for the chosen acceleration factor and the learning rate in the selected logger.
+ logs = {
+ f'train_loss_{outputs["acceleration"]}x': train_loss.item(),
+ "lr": self._optimizer.param_groups[0]["lr"], # type: ignore
+ }
+
+ # In case of Noise-to-Recon or SSDU, the target is a list.
+ if isinstance(target, list):
+ while isinstance(target, list):
+ target = target[-1]
+
+ # Log train loss.
+ self.log(
+ "reconstruction_loss",
+ train_loss,
+ on_step=True,
+ on_epoch=True,
+ prog_bar=True,
+ logger=True,
+ batch_size=target.shape[0], # type: ignore
+ sync_dist=True,
+ )
+
+ return {"loss": train_loss, "log": logs}
+
+ def validation_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a validation step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data. List for multiple acceleration factors. Dict[str, torch.Tensor], with keys:
+ 'kspace' : List of torch.Tensor
+ Fully-sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'y' : Union[torch.Tensor, None]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'mask' : Union[torch.Tensor, None]
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor.. Also, if
+ Unsupervised Learning methods, like Noise-to-Recon or SSDU, are used, then it is a list of
+ torch.Tensor with masks for each method. Shape [batch_size, 1, n_x, n_y, 1].
+ 'initial_prediction' : Union[torch.Tensor, None]
+ Initial prediction. Shape [batch_size, n_x, n_y, 2] or None.
+ 'target' : Union[torch.Tensor, None]
+ Target data. Shape [batch_size, n_x, n_y] or None.
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+ batch_idx : int
+ Batch index.
+ """
+ kspace, y, sensitivity_maps, mask, initial_prediction, target, fname, slice_idx, acceleration, attrs = batch
+
+ outputs = self.inference_step(
+ kspace,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction,
+ target,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration,
+ attrs, # type: ignore
+ )
+
+ fname = outputs["fname"]
+ slice_idx = outputs["slice_idx"]
+ acceleration = outputs["acceleration"]
+ target = outputs["target"]
+ predictions = outputs["predictions"]
+ attrs = outputs["attrs"]
+ r = outputs["r"]
+
+ # Compute loss
+ val_loss = self.__compute_loss__(
+ target,
+ predictions,
+ outputs["predictions_n2r"],
+ outputs["sensitivity_maps"],
+ outputs["loss_mask"],
+ attrs,
+ r,
+ )
+ self.validation_step_outputs.append({"val_loss": val_loss})
+
+ # Compute metrics and log them and log outputs.
+ self.__compute_and_log_metrics_and_outputs__(
+ target,
+ predictions,
+ attrs,
+ r,
+ fname,
+ slice_idx,
+ acceleration,
+ )
+
+ def test_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a test step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data. List for multiple acceleration factors. Dict[str, torch.Tensor], with keys,
+ 'kspace' : List of torch.Tensor
+ Fully-sampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'y' : Union[torch.Tensor, None]
+ Subsampled k-space data. If multiple accelerations are used, then it is a list of torch.Tensor.
+ Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'sensitivity_maps' : torch.Tensor
+ Coils sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2].
+ 'mask' : Union[torch.Tensor, None]
+ Sampling mask. If multiple accelerations are used, then it is a list of torch.Tensor.. Also, if
+ Unsupervised Learning methods, like Noise-to-Recon or SSDU, are used, then it is a list of
+ torch.Tensor with masks for each method. Shape [batch_size, 1, n_x, n_y, 1].
+ 'initial_prediction' : Union[torch.Tensor, None]
+ Initial prediction. Shape [batch_size, n_x, n_y, 2] or None.
+ 'target' : Union[torch.Tensor, None]
+ Target data. Shape [batch_size, n_x, n_y] or None.
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+ batch_idx : int
+ Batch index.
+ """
+ kspace, y, sensitivity_maps, mask, initial_prediction, target, fname, slice_idx, acceleration, attrs = batch
+
+ outputs = self.inference_step(
+ kspace,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction,
+ target,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ acceleration,
+ attrs, # type: ignore
+ )
+
+ fname = outputs["fname"]
+ slice_idx = outputs["slice_idx"]
+ acceleration = outputs["acceleration"]
+ target = outputs["target"]
+ predictions = outputs["predictions"]
+ attrs = outputs["attrs"]
+ r = outputs["r"]
+
+ # Compute metrics and log them and log outputs.
+ self.__compute_and_log_metrics_and_outputs__(
+ target,
+ predictions,
+ attrs,
+ r,
+ fname,
+ slice_idx,
+ acceleration,
+ )
+
+ if self.accumulate_predictions:
+ predictions = parse_list_and_keep_last(predictions)
+
+ # If "16" or "16-mixed" fp is used, ensure complex type will be supported when saving the predictions.
+ if predictions.shape[-1] == 2:
+ predictions = torch.view_as_complex(predictions.type(torch.float32))
+ else:
+ predictions = torch.view_as_complex(torch.view_as_real(predictions).type(torch.float32))
+ predictions = predictions.detach().cpu().numpy()
+
+ self.test_step_outputs.append([fname, slice_idx, predictions])
+
+ def on_validation_epoch_end(self):
+ """Called at the end of validation epoch to aggregate outputs."""
+ self.log("val_loss", torch.stack([x["val_loss"] for x in self.validation_step_outputs]).mean(), sync_dist=True)
+
+ # Initialize metrics.
+ mse_vals = defaultdict(dict)
+ nmse_vals = defaultdict(dict)
+ ssim_vals = defaultdict(dict)
+ psnr_vals = defaultdict(dict)
+ for k, v in self.mse_vals.items():
+ mse_vals[k].update(v)
+ for k, v in self.nmse_vals.items():
+ nmse_vals[k].update(v)
+ for k, v in self.ssim_vals.items():
+ ssim_vals[k].update(v)
+ for k, v in self.psnr_vals.items():
+ psnr_vals[k].update(v)
+
+ # Parse metrics and log them.
+ metrics = {
+ "MSE": 0,
+ "NMSE": 0,
+ "SSIM": 0,
+ "PSNR": 0,
+ }
+ local_examples = 0
+ for fname in mse_vals:
+ local_examples += 1
+ metrics["MSE"] = metrics["MSE"] + torch.mean(torch.cat([v.view(-1) for _, v in mse_vals[fname].items()]))
+ metrics["NMSE"] = metrics["NMSE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals[fname].items()])
+ )
+ metrics["SSIM"] = metrics["SSIM"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals[fname].items()])
+ )
+ metrics["PSNR"] = metrics["PSNR"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals[fname].items()])
+ )
+
+ # reduce across ddp via sum
+ metrics["MSE"] = self.MSE(metrics["MSE"])
+ metrics["NMSE"] = self.NMSE(metrics["NMSE"])
+ metrics["SSIM"] = self.SSIM(metrics["SSIM"])
+ metrics["PSNR"] = self.PSNR(metrics["PSNR"])
+ tot_examples = self.TotExamples(torch.tensor(local_examples))
+
+ for metric, value in metrics.items():
+ self.log(f"val_metrics/{metric}", value / tot_examples, prog_bar=True, sync_dist=True)
+
+ def on_test_epoch_end(self):
+ """Called at the end of test epoch to aggregate outputs, log metrics and save predictions."""
+ # Initialize metrics.
+ mse_vals = defaultdict(dict)
+ nmse_vals = defaultdict(dict)
+ ssim_vals = defaultdict(dict)
+ psnr_vals = defaultdict(dict)
+
+ for k, v in self.mse_vals.items():
+ mse_vals[k].update(v)
+ for k, v in self.nmse_vals.items():
+ nmse_vals[k].update(v)
+ for k, v in self.ssim_vals.items():
+ ssim_vals[k].update(v)
+ for k, v in self.psnr_vals.items():
+ psnr_vals[k].update(v)
+
+ # apply means across image volumes
+ metrics = {
+ "MSE": 0,
+ "NMSE": 0,
+ "SSIM": 0,
+ "PSNR": 0,
+ }
+ local_examples = 0
+ for fname in mse_vals:
+ local_examples += 1
+ metrics["MSE"] = metrics["MSE"] + torch.mean(torch.cat([v.view(-1) for _, v in mse_vals[fname].items()]))
+ metrics["NMSE"] = metrics["NMSE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in nmse_vals[fname].items()])
+ )
+ metrics["SSIM"] = metrics["SSIM"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in ssim_vals[fname].items()])
+ )
+ metrics["PSNR"] = metrics["PSNR"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in psnr_vals[fname].items()])
+ )
+
+ # reduce across ddp via sum
+ metrics["MSE"] = self.MSE(metrics["MSE"])
+ metrics["NMSE"] = self.NMSE(metrics["NMSE"])
+ metrics["SSIM"] = self.SSIM(metrics["SSIM"])
+ metrics["PSNR"] = self.PSNR(metrics["PSNR"])
+ tot_examples = self.TotExamples(torch.tensor(local_examples))
+
+ for metric, value in metrics.items():
+ self.log(f"test_metrics/{metric}", value / tot_examples, prog_bar=True, sync_dist=True)
+
+ # Save predictions.
+ reconstructions = defaultdict(list)
+ for fname, slice_num, output in self.test_step_outputs:
+ reconstructions[fname].append((slice_num, output))
+
+ for fname in reconstructions:
+ reconstructions[fname] = np.stack([out for _, out in sorted(reconstructions[fname])])
+
+ if self.consecutive_slices > 1:
+ # iterate over the slices and always keep the middle slice
+ for fname in reconstructions:
+ reconstructions[fname] = reconstructions[fname][:, self.consecutive_slices // 2]
+
+ if "wandb" in self.logger.__module__.lower():
+ out_dir = Path(os.path.join(self.logger.save_dir, "reconstructions"))
+ else:
+ out_dir = Path(os.path.join(self.logger.log_dir, "reconstructions"))
+ out_dir.mkdir(exist_ok=True, parents=True)
+
+ for fname, recons in reconstructions.items():
+ with h5py.File(out_dir / fname[0], "w") as hf:
+ hf.create_dataset("reconstruction", data=recons)
+
+ @staticmethod
+ def _setup_dataloader_from_config(cfg: DictConfig) -> DataLoader:
+ """Setups the dataloader from the configuration (yaml) file.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration file.
+
+ Returns
+ -------
+ dataloader : torch.utils.data.DataLoader
+ Dataloader.
+ """
+ # Get mask parameters.
+ mask_root = cfg.get("mask_path", None)
+ mask_args = cfg.get("mask_args", None)
+ shift_mask = mask_args.get("shift_mask", False)
+ mask_type = mask_args.get("type", None)
+
+ mask_func = None
+ mask_center_scale = 0.02
+
+ if is_none(mask_root) and not is_none(mask_type):
+ accelerations = mask_args.get("accelerations", [1])
+ accelerations = list(accelerations)
+ if len(accelerations) == 1:
+ accelerations = accelerations * 2
+ center_fractions = mask_args.get("center_fractions", [1])
+ center_fractions = list(center_fractions)
+ if len(center_fractions) == 1:
+ center_fractions = center_fractions * 2
+ mask_center_scale = mask_args.get("center_scale", 0.02)
+
+ mask_func = [create_masker(mask_type, center_fractions, accelerations)]
+
+ dataset_format = cfg.get("dataset_format", None)
+ if dataset_format.lower() == "cc359":
+ dataloader = CC359ReconstructionMRIDataset
+ elif dataset_format.lower() == "stanford_knees":
+ dataloader = StanfordKneesReconstructionMRIDataset
+ elif dataset_format.lower() in (
+ "skm-tea-echo1",
+ "skm-tea-echo2",
+ "skm-tea-echo1+echo2",
+ "skm-tea-echo1+echo2-mc",
+ ):
+ dataloader = SKMTEAReconstructionMRIDataset
+ elif dataset_format.lower() == "ahead":
+ dataloader = AHEADqMRIDataset
+ else:
+ dataloader = ReconstructionMRIDataset
+
+ # Get dataset.
+ dataset = dataloader(
+ root=cfg.get("data_path"),
+ coil_sensitivity_maps_root=cfg.get("coil_sensitivity_maps_path", None),
+ mask_root=mask_root,
+ noise_root=cfg.get("noise_path", None),
+ initial_predictions_root=cfg.get("initial_predictions_path"),
+ dataset_format=dataset_format,
+ sample_rate=cfg.get("sample_rate", 1.0),
+ volume_sample_rate=cfg.get("volume_sample_rate", None),
+ use_dataset_cache=cfg.get("use_dataset_cache", False),
+ dataset_cache_file=cfg.get("dataset_cache_file", None),
+ num_cols=cfg.get("num_cols", None),
+ consecutive_slices=cfg.get("consecutive_slices", 1),
+ data_saved_per_slice=cfg.get("data_saved_per_slice", False),
+ n2r_supervised_rate=cfg.get("n2r_supervised_rate", 0.0),
+ complex_target=cfg.get("complex_target", False),
+ log_images_rate=cfg.get("log_images_rate", 1.0),
+ transform=ReconstructionMRIDataTransforms(
+ dataset_format=dataset_format,
+ apply_prewhitening=cfg.get("apply_prewhitening", False),
+ find_patch_size=cfg.get("find_patch_size", False),
+ prewhitening_scale_factor=cfg.get("prewhitening_scale_factor", 1.0),
+ prewhitening_patch_start=cfg.get("prewhitening_patch_start", 10),
+ prewhitening_patch_length=cfg.get("prewhitening_patch_length", 30),
+ apply_gcc=cfg.get("apply_gcc", False),
+ gcc_virtual_coils=cfg.get("gcc_virtual_coils", 10),
+ gcc_calib_lines=cfg.get("gcc_calib_lines", 10),
+ gcc_align_data=cfg.get("gcc_align_data", False),
+ apply_random_motion=cfg.get("apply_random_motion", False),
+ random_motion_type=cfg.get("random_motion_type", "gaussian"),
+ random_motion_percentage=cfg.get("random_motion_percentage", [10, 10]),
+ random_motion_angle=cfg.get("random_motion_angle", 10),
+ random_motion_translation=cfg.get("random_motion_translation", 10),
+ random_motion_center_percentage=cfg.get("random_motion_center_percentage", 0.02),
+ random_motion_num_segments=cfg.get("random_motion_num_segments", 8),
+ random_motion_random_num_segments=cfg.get("random_motion_random_num_segments", True),
+ random_motion_non_uniform=cfg.get("random_motion_non_uniform", False),
+ estimate_coil_sensitivity_maps=cfg.get("estimate_coil_sensitivity_maps", False),
+ coil_sensitivity_maps_type=cfg.get("coil_sensitivity_maps_type", "espirit"),
+ coil_sensitivity_maps_gaussian_sigma=cfg.get("coil_sensitivity_maps_gaussian_sigma", 0.0),
+ coil_sensitivity_maps_espirit_threshold=cfg.get("coil_sensitivity_maps_espirit_threshold", 0.05),
+ coil_sensitivity_maps_espirit_kernel_size=cfg.get("coil_sensitivity_maps_espirit_kernel_size", 6),
+ coil_sensitivity_maps_espirit_crop=cfg.get("coil_sensitivity_maps_espirit_crop", 0.95),
+ coil_sensitivity_maps_espirit_max_iters=cfg.get("coil_sensitivity_maps_espirit_max_iters", 30),
+ coil_combination_method=cfg.get("coil_combination_method", "SENSE"),
+ dimensionality=cfg.get("dimensionality", 2),
+ mask_func=mask_func, # type: ignore
+ shift_mask=shift_mask,
+ mask_center_scale=mask_center_scale,
+ remask=cfg.get("remask", False),
+ ssdu=cfg.get("ssdu", False),
+ ssdu_mask_type=cfg.get("ssdu_mask_type", "Gaussian"),
+ ssdu_rho=cfg.get("ssdu_rho", 0.4),
+ ssdu_acs_block_size=cfg.get("ssdu_acs_block_size", (4, 4)),
+ ssdu_gaussian_std_scaling_factor=cfg.get("ssdu_gaussian_std_scaling_factor", 4.0),
+ ssdu_outer_kspace_fraction=cfg.get("ssdu_outer_kspace_fraction", 0.0),
+ ssdu_export_and_reuse_masks=cfg.get("ssdu_export_and_reuse_masks", False),
+ n2r=cfg.get("n2r", False),
+ n2r_supervised_rate=cfg.get("n2r_supervised_rate", 0.0),
+ n2r_probability=cfg.get("n2r_probability", 0.5),
+ n2r_std_devs=cfg.get("n2r_std_devs", (0.0, 0.0)),
+ n2r_rhos=cfg.get("n2r_rhos", (0.4, 0.4)),
+ n2r_use_mask=cfg.get("n2r_use_mask", True),
+ unsupervised_masked_target=cfg.get("unsupervised_masked_target", False),
+ crop_size=cfg.get("crop_size", None),
+ kspace_crop=cfg.get("kspace_crop", False),
+ crop_before_masking=cfg.get("crop_before_masking", False),
+ kspace_zero_filling_size=cfg.get("kspace_zero_filling_size", None),
+ normalize_inputs=cfg.get("normalize_inputs", True),
+ normalization_type=cfg.get("normalization_type", "max"),
+ kspace_normalization=cfg.get("kspace_normalization", False),
+ fft_centered=cfg.get("fft_centered", False),
+ fft_normalization=cfg.get("fft_normalization", "backward"),
+ spatial_dims=cfg.get("spatial_dims", None),
+ coil_dim=cfg.get("coil_dim", 1),
+ consecutive_slices=cfg.get("consecutive_slices", 1),
+ use_seed=cfg.get("use_seed", True),
+ ),
+ )
+ if cfg.shuffle:
+ sampler = torch.utils.data.RandomSampler(dataset)
+ else:
+ sampler = torch.utils.data.SequentialSampler(dataset)
+
+ return torch.utils.data.DataLoader(
+ dataset=dataset,
+ batch_size=cfg.get("batch_size", 1),
+ sampler=sampler,
+ num_workers=cfg.get("num_workers", 4),
+ pin_memory=cfg.get("pin_memory", False),
+ drop_last=cfg.get("drop_last", False),
+ )
diff --git a/atommic/collections/reconstruction/nn/ccnn.py b/atommic/collections/reconstruction/nn/ccnn.py
new file mode 100644
index 00000000..a4029385
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/ccnn.py
@@ -0,0 +1,103 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import ifft2
+from atommic.collections.common.parts.utils import check_stacked_complex, coil_combination_method
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.ccnn_base.ccnn_block import CascadeNetBlock, Conv2d
+from atommic.core.classes.common import typecheck
+
+__all__ = ["CascadeNet"]
+
+
+class CascadeNet(BaseMRIReconstructionModel):
+ """Implementation of the Deep Cascade of Convolutional Neural Networks, as presented in [Schlemper2017]_.
+
+ References
+ ----------
+ .. [Schlemper2017] Schlemper, J., Caballero, J., Hajnal, J. V., Price, A., & Rueckert, D., A Deep Cascade of
+ Convolutional Neural Networks for MR Image Reconstruction. Information Processing in Medical Imaging (IPMI),
+ 2017.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`CascadeNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ # Cascades of CascadeCNN blocks
+ self.reconstruction_module = torch.nn.ModuleList(
+ [
+ CascadeNetBlock(
+ Conv2d(
+ in_channels=2,
+ out_channels=2,
+ hidden_channels=cfg_dict.get("hidden_channels"),
+ n_convs=cfg_dict.get("n_convs"),
+ batchnorm=cfg_dict.get("batchnorm"),
+ ),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ no_dc=cfg_dict.get("no_dc"),
+ )
+ for _ in range(cfg_dict.get("num_cascades"))
+ ]
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`CascadeNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ prediction = y.clone()
+ for cascade in self.reconstruction_module:
+ prediction = cascade(prediction, y, sensitivity_maps, mask)
+ return check_stacked_complex(
+ coil_combination_method(
+ ifft2(prediction, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+ )
diff --git a/atommic/collections/reconstruction/nn/ccnn_base/__init__.py b/atommic/collections/reconstruction/nn/ccnn_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/ccnn_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/ccnn_base/ccnn_block.py b/atommic/collections/reconstruction/nn/ccnn_base/ccnn_block.py
new file mode 100644
index 00000000..dc0132c1
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/ccnn_base/ccnn_block.py
@@ -0,0 +1,188 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Optional, Tuple
+
+import torch
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import complex_conj, complex_mul
+
+
+class Conv2d(torch.nn.Module):
+ """Implementation of a simple cascade of 2D convolutions. If batchnorm is set to True, batch normalization layer is
+ applied after each convolution.
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ hidden_channels: int,
+ n_convs: int = 3,
+ activation: torch.nn.Module = torch.nn.PReLU(),
+ batchnorm: bool = False,
+ ):
+ """Inits :class:`Conv2d`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ hidden_channels : int
+ Number of hidden channels.
+ n_convs : int, optional
+ Number of convolutional layers. Default is ``3``.
+ activation : torch.nn.Module, optional
+ Activation function. Default is ``nn.PReLU()``.
+ batchnorm : bool, optional
+ If True a batch normalization layer is applied after every convolution. Default is ``False``.
+ """
+ super().__init__()
+
+ self.conv = []
+ for idx in range(n_convs):
+ self.conv.append(
+ torch.nn.Conv2d(
+ in_channels if idx == 0 else hidden_channels,
+ hidden_channels if idx != n_convs - 1 else out_channels,
+ kernel_size=3,
+ padding=1,
+ )
+ )
+ if batchnorm:
+ self.conv.append(
+ torch.nn.BatchNorm2d(hidden_channels if idx != n_convs - 1 else out_channels, eps=1e-4)
+ )
+ if idx != n_convs - 1:
+ self.conv.append(activation)
+ self.conv = torch.nn.Sequential(*self.conv)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`Conv2d`."""
+ if x.dim() == 5:
+ x = x.squeeze(1)
+ if x.shape[-1] == 2:
+ x = x.permute(0, 3, 1, 2)
+ return self.conv(x) # type: ignore
+
+
+class CascadeNetBlock(torch.nn.Module):
+ """Model block for CascadeNet & Convolution Recurrent Neural Network."""
+
+ def __init__(
+ self,
+ model: torch.nn.Module,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ no_dc: bool = False,
+ ):
+ """Inits :class:`CascadeNetBlock`.
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Model to apply soft data consistency.
+ fft_centered : bool, optional
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization : str, optional
+ Whether to normalize the FFT. Default is ``"backward"``.
+ spatial_dims : Tuple[int, int], optional
+ Spatial dimensions of the input. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ no_dc : bool, optional
+ Flag to disable the soft data consistency. Default is ``False``.
+ """
+ super().__init__()
+
+ self.model = model
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+ self.no_dc = no_dc
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+
+ def sens_expand(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with coil-combined data to get multicoil data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ Expanded multicoil data.
+ """
+ return fft2(
+ complex_mul(x, sens_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ def sens_reduce(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with multicoil data to get coil-combined data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ SENSE coil-combined reconstruction.
+ """
+ x = ifft2(x, centered=self.fft_centered, normalization=self.fft_normalization, spatial_dims=self.spatial_dims)
+ return complex_mul(x, complex_conj(sens_maps)).sum(dim=self.coil_dim)
+
+ def forward(
+ self,
+ pred: torch.Tensor,
+ ref_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`CascadeNetBlock`.
+
+ Parameters
+ ----------
+ pred : torch.Tensor
+ Predicted k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ ref_kspace : torch.Tensor
+ Reference k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+
+ Returns
+ -------
+ torch.Tensor
+ Reconstructed image. Shape [batch_size, n_x, n_y, 2]
+ """
+ zero = torch.zeros(1, 1, 1, 1, 1).to(pred)
+ soft_dc = torch.where(mask.bool(), pred - ref_kspace, zero) * self.dc_weight
+
+ prediction = self.sens_reduce(pred, sensitivity_maps)
+ prediction = self.model(prediction.squeeze(self.coil_dim).permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
+ if prediction.dim() < sensitivity_maps.dim():
+ prediction = prediction.unsqueeze(1)
+ prediction = self.sens_expand(prediction, sensitivity_maps)
+
+ if not self.no_dc:
+ prediction = pred - soft_dc - prediction
+
+ return prediction
diff --git a/atommic/collections/reconstruction/nn/cirim.py b/atommic/collections/reconstruction/nn/cirim.py
new file mode 100644
index 00000000..8db932d9
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/cirim.py
@@ -0,0 +1,259 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import math
+from typing import Dict, List, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import fft2
+from atommic.collections.common.parts.utils import check_stacked_complex, expand_op
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.rim_base.rim_block import RIMBlock
+from atommic.core.classes.common import typecheck
+
+__all__ = ["CIRIM"]
+
+
+class CIRIM(BaseMRIReconstructionModel):
+ """Implementation of the Cascades of Independently Recurrent Inference Machines, as presented in
+ [Karkalousos2022]_.
+
+ References
+ ----------
+ .. [Karkalousos2022] Karkalousos D, Noteboom S, Hulst HE, Vos FM, Caan MWA. Assessment of data consistency through
+ cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction.
+ Phys Med Biol. 2022 Jun 8;67(12). doi: 10.1088/1361-6560/ac6cc2. PMID: 35508147.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`CIRIM`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ # make time-steps size divisible by 8 for fast fp16 training
+ self.time_steps = 8 * math.ceil(cfg_dict.get("time_steps") / 8)
+ self.no_dc = cfg_dict.get("no_dc")
+ self.reconstruction_module = torch.nn.ModuleList(
+ [
+ RIMBlock(
+ recurrent_layer=cfg_dict.get("recurrent_layer"),
+ conv_filters=cfg_dict.get("conv_filters"),
+ conv_kernels=cfg_dict.get("conv_kernels"),
+ conv_dilations=cfg_dict.get("conv_dilations"),
+ conv_bias=cfg_dict.get("conv_bias"),
+ recurrent_filters=cfg_dict.get("recurrent_filters"),
+ recurrent_kernels=cfg_dict.get("recurrent_kernels"),
+ recurrent_dilations=cfg_dict.get("recurrent_dilations"),
+ recurrent_bias=cfg_dict.get("recurrent_bias"),
+ depth=cfg_dict.get("depth"),
+ time_steps=self.time_steps,
+ conv_dim=cfg_dict.get("conv_dim"),
+ no_dc=self.no_dc,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ dimensionality=cfg_dict.get("dimensionality"),
+ coil_combination_method=self.coil_combination_method,
+ )
+ for _ in range(cfg_dict.get("num_cascades"))
+ ]
+ )
+
+ # Keep estimation through the cascades if keep_prediction is True or re-estimate it if False.
+ self.keep_prediction = cfg_dict.get("keep_prediction")
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor,
+ sigma: float = 1.0,
+ ) -> Union[List[List[torch.Tensor]], List[torch.Tensor], torch.Tensor]:
+ """Forward pass of :class:`CIRIM`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ List of torch.Tensor
+ List of the intermediate predictions for each cascade. Shape [batch_size, n_x, n_y].
+ """
+ prediction = y.clone()
+ initial_prediction = None if initial_prediction is None or initial_prediction.dim() < 4 else initial_prediction
+ hx = None
+ cascades_predictions = []
+ for i, cascade in enumerate(self.reconstruction_module):
+ # Forward pass through the cascades
+ prediction, hx = cascade(
+ prediction,
+ y,
+ sensitivity_maps,
+ mask,
+ initial_prediction if i == 0 else prediction,
+ hx,
+ sigma,
+ keep_prediction=False if i == 0 else self.keep_prediction,
+ )
+ cascades_predictions.append([check_stacked_complex(p) for p in prediction])
+ prediction = prediction[-1]
+ return cascades_predictions
+
+ def process_reconstruction_loss( # noqa: MC0001
+ self,
+ target: torch.Tensor,
+ prediction: Union[List[List[torch.Tensor]], List[torch.Tensor], torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ attrs: Union[Dict, torch.Tensor],
+ r: Union[int, torch.Tensor],
+ loss_func: torch.nn.Module,
+ ) -> torch.Tensor:
+ """Processes the reconstruction loss for the CIRIM model. It differs from the base class in that it can handle
+ multiple cascades and time steps.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ mask : torch.Tensor
+ Mask of shape [batch_size, n_x, n_y, 2]. It will be used if self.ssdu is True, to enforce data consistency
+ on the prediction.
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ loss_func : torch.nn.Module
+ Loss function. Must be one of {torch.nn.L1Loss(), torch.nn.MSELoss(),
+ atommic.collections.reconstruction.losses.ssim.SSIMLoss()}. Default is ``torch.nn.L1Loss()``.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if target.shape[-1] != 2 and torch.is_complex(target):
+ target = torch.view_as_real(target)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ target = expand_op(target, sensitivity_maps, self.coil_dim)
+ # Transform to k-space.
+ target = fft2(target, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target)
+ elif not self.unnormalize_loss_inputs:
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target)
+ # Normalize inputs to [0, 1]
+ target = torch.abs(target / torch.max(torch.abs(target)))
+
+ def compute_reconstruction_loss(t, p, s):
+ if self.unnormalize_loss_inputs:
+ # we do the unnormalization here to avoid explicitly iterating through list of predictions, which
+ # might be a list of lists.
+ t, p, s = self.__unnormalize_for_loss_or_log__(t, p, s, attrs, r)
+
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if p.shape[-1] != 2 and torch.is_complex(p):
+ p = torch.view_as_real(p)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ p = expand_op(p, s, self.coil_dim)
+ # Transform to k-space.
+ p = fft2(p, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # If SSDU is used, then apply the mask to the prediction to enforce data consistency.
+ if self.ssdu:
+ p = p * mask
+ # Ensure loss inputs are both viewed in the same way.
+ p = self.__abs_output__(p)
+ elif not self.unnormalize_loss_inputs:
+ p = self.__abs_output__(p)
+ # Normalize inputs to [0, 1]
+ p = torch.abs(p / torch.max(torch.abs(p)))
+
+ if "ssim" in str(loss_func).lower():
+ return loss_func(
+ t.unsqueeze(dim=self.coil_dim),
+ p.unsqueeze(dim=self.coil_dim),
+ data_range=torch.tensor(
+ [max(torch.max(t).item(), torch.max(p).item()) - min(torch.min(t).item(), torch.min(p).item())]
+ )
+ .unsqueeze(dim=0)
+ .to(t.device),
+ )
+
+ return loss_func(t, p)
+
+ if self.accumulate_predictions:
+ cascades_weights = torch.logspace(-1, 0, steps=len(prediction)).to(target.device)
+ cascades_loss = []
+ for cascade_pred in prediction:
+ time_steps_weights = torch.logspace(-1, 0, steps=self.time_steps).to(target.device)
+ if self.num_echoes > 0:
+ time_steps_loss = [
+ torch.mean(
+ torch.stack(
+ [
+ compute_reconstruction_loss(
+ target[echo].unsqueeze(0), time_step_pred[echo].unsqueeze(0), sensitivity_maps
+ )
+ for echo in range(target.shape[0])
+ ]
+ )
+ ).to(target)
+ for time_step_pred in cascade_pred
+ ]
+ else:
+ time_steps_loss = [
+ compute_reconstruction_loss(target, time_step_pred, sensitivity_maps)
+ for time_step_pred in cascade_pred
+ ]
+ cascade_loss = sum(x * w for x, w in zip(time_steps_loss, time_steps_weights)) / self.time_steps
+ cascades_loss.append(cascade_loss)
+ loss = sum(x * w for x, w in zip(cascades_loss, cascades_weights)) / len(prediction)
+ else:
+ # keep the last prediction of the last cascade
+ prediction = prediction[-1][-1]
+ loss = compute_reconstruction_loss(target, prediction, sensitivity_maps)
+
+ return loss
diff --git a/atommic/collections/reconstruction/nn/crnn.py b/atommic/collections/reconstruction/nn/crnn.py
new file mode 100644
index 00000000..59f8ce5e
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/crnn.py
@@ -0,0 +1,244 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Dict, List, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import check_stacked_complex, coil_combination_method, expand_op
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.crnn_base.crnn_block import GRUConv2d, RecurrentConvolutionalNetBlock
+from atommic.core.classes.common import typecheck
+
+__all__ = ["CRNNet"]
+
+
+class CRNNet(BaseMRIReconstructionModel):
+ """Implementation of the Convolutional Recurrent Neural Network, as presented in [Qin2019]_.
+
+ References
+ ----------
+ .. [Qin2019] C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V. Hajnal and D. Rueckert, "Convolutional
+ Recurrent Neural Networks for Dynamic MR Image Reconstruction," in IEEE Transactions on Medical Imaging, vol.
+ 38, no. 1, pp. 280-290, Jan. 2019, doi: 10.1109/TMI.2018.2863670.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`CRNNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.no_dc = cfg_dict.get("no_dc")
+ self.num_iterations = cfg_dict.get("num_iterations")
+
+ self.reconstruction_module = RecurrentConvolutionalNetBlock(
+ GRUConv2d(
+ in_channels=2,
+ out_channels=2,
+ hidden_channels=cfg_dict.get("hidden_channels"),
+ n_convs=cfg_dict.get("n_convs"),
+ batchnorm=cfg_dict.get("batchnorm"),
+ ),
+ num_iterations=self.num_iterations,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ no_dc=self.no_dc,
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> List[torch.Tensor]:
+ """Forward pass of :class:`CRNNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ List of torch.Tensor
+ List of the intermediate predictions for each cascade. Shape [batch_size, n_x, n_y].
+ """
+ predictions = self.reconstruction_module(y, sensitivity_maps, mask)
+ return [self.process_intermediate_pred(x, sensitivity_maps) for x in predictions]
+
+ def process_intermediate_pred(
+ self, prediction: Union[List, torch.Tensor], sensitivity_maps: torch.Tensor
+ ) -> torch.Tensor:
+ """Process the intermediate prediction.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ Intermediate prediction. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+
+ Returns
+ -------
+ torch.Tensor
+ Processed prediction. Shape [batch_size, n_x, n_y].
+ """
+ return check_stacked_complex(
+ coil_combination_method(
+ ifft2(
+ prediction,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+ )
+
+ def process_reconstruction_loss(
+ self,
+ target: torch.Tensor,
+ prediction: Union[list, torch.Tensor],
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ attrs: Dict,
+ r: int,
+ loss_func: torch.nn.Module,
+ ) -> torch.Tensor:
+ """Processes the reconstruction loss for the CRNNet model. It differs from the base class in that it uses the
+ intermediate predictions to compute the loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : Union[list, torch.Tensor]
+ Prediction(s) of shape [batch_size, n_x, n_y, 2].
+ sensitivity_maps : torch.Tensor
+ Sensitivity maps of shape [batch_size, n_coils, n_x, n_y, 2]. It will be used if self.ssdu is True, to
+ expand the target and prediction to multiple coils.
+ mask : torch.Tensor
+ Mask of shape [batch_size, n_x, n_y, 2]. It will be used if self.ssdu is True, to enforce data consistency
+ on the prediction.
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ r : int
+ The selected acceleration factor.
+ loss_func : torch.nn.Module
+ Loss function. Must be one of {torch.nn.L1Loss(), torch.nn.MSELoss(),
+ atommic.collections.reconstruction.losses.ssim.SSIMLoss()}. Default is ``torch.nn.L1Loss()``.
+
+ Returns
+ -------
+ loss: torch.FloatTensor
+ If self.accumulate_loss is True, returns an accumulative result of all intermediate losses.
+ Otherwise, returns the loss of the last intermediate loss.
+ """
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if target.shape[-1] != 2 and torch.is_complex(target):
+ target = torch.view_as_real(target)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ target = expand_op(target, sensitivity_maps, self.coil_dim)
+ # Transform to k-space.
+ target = fft2(target, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target)
+ elif not self.unnormalize_loss_inputs:
+ # Ensure loss inputs are both viewed in the same way.
+ target = self.__abs_output__(target)
+ # Normalize inputs to [0, 1]
+ target = torch.abs(target / torch.max(torch.abs(target)))
+
+ def compute_reconstruction_loss(t, p, s):
+ if self.unnormalize_loss_inputs:
+ # we do the unnormalization here to avoid explicitly iterating through list of predictions, which
+ # might be a list of lists.
+ t, p, s = self.__unnormalize_for_loss_or_log__(t, p, s, attrs, r)
+
+ # If kspace reconstruction loss is used, the target needs to be transformed to k-space.
+ if self.kspace_reconstruction_loss:
+ # If inputs are complex, then they need to be viewed as real.
+ if p.shape[-1] != 2 and torch.is_complex(p):
+ p = torch.view_as_real(p)
+ # If SSDU is used, then the coil-combined inputs need to be expanded to multiple coils using the
+ # sensitivity maps.
+ if self.ssdu:
+ p = expand_op(p, s, self.coil_dim)
+ # Transform to k-space.
+ p = fft2(p, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ # If SSDU is used, then apply the mask to the prediction to enforce data consistency.
+ if self.ssdu:
+ p = p * mask
+ # Ensure loss inputs are both viewed in the same way.
+ p = self.__abs_output__(p)
+ elif not self.unnormalize_loss_inputs:
+ p = self.__abs_output__(p)
+ # Normalize inputs to [0, 1]
+ p = torch.abs(p / torch.max(torch.abs(p)))
+
+ if "ssim" in str(loss_func).lower():
+ return loss_func(
+ t.unsqueeze(dim=self.coil_dim),
+ p.unsqueeze(dim=self.coil_dim),
+ data_range=torch.tensor(
+ [max(torch.max(t).item(), torch.max(p).item()) - min(torch.min(t).item(), torch.min(p).item())]
+ )
+ .unsqueeze(dim=0)
+ .to(t.device),
+ )
+ return loss_func(t, p)
+
+ iterations_weights = torch.logspace(-1, 0, steps=self.num_iterations).to(target.device)
+ if self.num_echoes > 0:
+ iterations_loss = [
+ torch.mean(
+ torch.stack(
+ [
+ compute_reconstruction_loss(
+ target[echo].unsqueeze(0), iteration_pred[echo].unsqueeze(0), sensitivity_maps
+ )
+ for echo in range(target.shape[0])
+ ]
+ )
+ ).to(target)
+ for iteration_pred in prediction
+ ]
+ else:
+ iterations_loss = [
+ compute_reconstruction_loss(target, iteration_pred, sensitivity_maps) for iteration_pred in prediction
+ ]
+ loss = sum(x * w for x, w in zip(iterations_loss, iterations_weights)) / self.num_iterations
+ return loss
diff --git a/atommic/collections/reconstruction/nn/crnn_base/__init__.py b/atommic/collections/reconstruction/nn/crnn_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/crnn_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/crnn_base/crnn_block.py b/atommic/collections/reconstruction/nn/crnn_base/crnn_block.py
new file mode 100644
index 00000000..8db859ae
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/crnn_base/crnn_block.py
@@ -0,0 +1,276 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Any, List, Optional, Tuple, Union
+
+import torch
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import complex_conj, complex_mul
+from atommic.collections.reconstruction.nn.rim_base.conv_layers import ConvNonlinear
+from atommic.collections.reconstruction.nn.rim_base.rnn_cells import ConvGRUCell
+
+
+class GRUConv2d(torch.nn.Module):
+ """Implementation of a GRU followed by a number of 2D convolutions inspired by [Qin2019]_.
+
+ References
+ ----------
+ .. [Qin2019] C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V. Hajnal and D. Rueckert, "Convolutional
+ Recurrent Neural Networks for Dynamic MR Image Reconstruction," in IEEE Transactions on Medical Imaging, vol.
+ 38, no. 1, pp. 280-290, Jan. 2019, doi: 10.1109/TMI.2018.2863670.
+ """
+
+ def __init__(
+ self,
+ in_channels,
+ out_channels,
+ hidden_channels,
+ n_convs=3,
+ activation="ReLU",
+ batchnorm=False, # pylint: disable=unused-argument
+ ):
+ """Inits :class:`GRUConv2d`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ hidden_channels : int
+ Number of hidden channels.
+ n_convs : int, optional
+ Number of convolutional layers. Default is ``3``.
+ activation : torch.nn.Module, optional
+ Activation function. Default is ``nn.ReLU()``.
+ batchnorm : bool, optional
+ If True a batch normalization layer is applied after every convolution. Default is ``False``.
+ """
+ super().__init__()
+
+ self.layers = torch.nn.ModuleList()
+ self.layers.append(
+ ConvGRUCell(
+ in_channels,
+ hidden_channels,
+ conv_dim=2,
+ kernel_size=3,
+ dilation=1,
+ bias=False,
+ )
+ )
+ for _ in range(n_convs):
+ self.layers.append(
+ ConvNonlinear(
+ hidden_channels,
+ hidden_channels,
+ conv_dim=2,
+ kernel_size=3,
+ dilation=1,
+ bias=False,
+ nonlinear=activation,
+ )
+ )
+ self.layers.append(
+ torch.nn.Sequential(
+ ConvNonlinear(
+ hidden_channels,
+ out_channels,
+ conv_dim=2,
+ kernel_size=3,
+ dilation=1,
+ bias=False,
+ nonlinear=activation,
+ )
+ )
+ )
+
+ self.hidden_channels = hidden_channels
+
+ def forward(self, x, hx: Optional[torch.Tensor] = None):
+ """Forward pass of :class:`GRUConv2d`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor.
+ hx : torch.Tensor, optional
+ Hidden state. Default is ``None``.
+
+ Returns
+ -------
+ torch.Tensor
+ Convoluted output.
+ """
+ if hx is None:
+ hx = x.new_zeros((x.size(0), self.hidden_channels, *x.size()[2:]))
+
+ for i, layer in enumerate(self.layers):
+ x = layer(x, hx) if i == 0 else layer(x)
+ return x
+
+
+class DataConsistencyLayer(torch.nn.Module):
+ """Data consistency layer for the CRNN, inspired by [Qin2019]_.
+
+ References
+ ----------
+ .. [Qin2019] C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V. Hajnal and D. Rueckert, "Convolutional
+ Recurrent Neural Networks for Dynamic MR Image Reconstruction," in IEEE Transactions on Medical Imaging, vol.
+ 38, no. 1, pp. 280-290, Jan. 2019, doi: 10.1109/TMI.2018.2863670.
+ """
+
+ def __init__(self):
+ """Initializes the data consistency layer."""
+ super().__init__()
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+
+ def forward(self, pred_kspace: torch.Tensor, ref_kspace: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`DataConsistencyLayer`.
+
+ Parameters
+ ----------
+ pred_kspace : torch.Tensor
+ Predicted k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ ref_kspace : torch.Tensor
+ Reference k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ """
+ zero = torch.zeros(1, 1, 1, 1, 1).to(pred_kspace)
+ return torch.where(mask.bool(), pred_kspace - ref_kspace, zero) * self.dc_weight
+
+
+class RecurrentConvolutionalNetBlock(torch.nn.Module):
+ """Model block for Recurrent Convolution Neural Network inspired by [Qin2019]_.
+
+ References
+ ----------
+ .. [Qin2019] C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V. Hajnal and D. Rueckert, "Convolutional
+ Recurrent Neural Networks for Dynamic MR Image Reconstruction," in IEEE Transactions on Medical Imaging, vol.
+ 38, no. 1, pp. 280-290, Jan. 2019, doi: 10.1109/TMI.2018.2863670.
+ """
+
+ def __init__(
+ self,
+ model: torch.nn.Module,
+ num_iterations: int = 10,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ no_dc: bool = False,
+ ):
+ """Inits :class:`RecurrentConvolutionalNetBlock`.
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Model to apply soft data consistency.
+ num_iterations : int, optional
+ Number of iterations. Default is ``10``.
+ fft_centered : bool, optional
+ Whether to use centered FFT. Default is ``False``.
+ fft_normalization : str, optional
+ Whether to use normalized FFT. Default is ``"backward"``.
+ spatial_dims : tuple, optional
+ Spatial dimensions of the input. Default is ``None``.
+ coil_dim : int, optional
+ Dimension of the coil. Default is ``1``.
+ no_dc : bool, optional
+ Whether to remove the DC component. Default is ``False``.
+ """
+ super().__init__()
+
+ self.model = model
+ self.num_iterations = num_iterations
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+ self.no_dc = no_dc
+
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+
+ def sens_expand(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with coil-combined data to get multicoil data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ Expanded multicoil data.
+ """
+ return fft2(
+ complex_mul(x, sens_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ def sens_reduce(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with multicoil data to get coil-combined data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ SENSE coil-combined reconstruction.
+ """
+ x = ifft2(x, centered=self.fft_centered, normalization=self.fft_normalization, spatial_dims=self.spatial_dims)
+ return complex_mul(x, complex_conj(sens_maps)).sum(dim=self.coil_dim)
+
+ def forward(
+ self,
+ ref_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ ) -> List[Union[torch.Tensor, Any]]:
+ """Forward pass of :class:`RecurrentConvolutionalNetBlock`.
+
+ Parameters
+ ----------
+ ref_kspace : torch.Tensor
+ Reference k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+
+ Returns
+ -------
+ torch.Tensor
+ Reconstructed image. Shape [batch_size, n_x, n_y, 2]
+ """
+ zero = torch.zeros(1, 1, 1, 1, 1).to(ref_kspace)
+ pred = ref_kspace.clone()
+
+ preds = []
+ for _ in range(self.num_iterations):
+ soft_dc = torch.where(mask.bool(), pred - ref_kspace, zero) * self.dc_weight
+
+ prediction = self.sens_reduce(pred, sensitivity_maps)
+ prediction = self.model(prediction.permute(0, 3, 1, 2)).permute(0, 2, 3, 1) + prediction
+ prediction = self.sens_expand(prediction.unsqueeze(self.coil_dim), sensitivity_maps)
+
+ if not self.no_dc:
+ prediction = pred - soft_dc - prediction
+
+ pred = prediction
+
+ preds.append(prediction)
+
+ return preds
diff --git a/atommic/collections/reconstruction/nn/crossdomain_base/__init__.py b/atommic/collections/reconstruction/nn/crossdomain_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/crossdomain_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/crossdomain_base/crossdomain_block.py b/atommic/collections/reconstruction/nn/crossdomain_base/crossdomain_block.py
new file mode 100644
index 00000000..ad82260f
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/crossdomain_base/crossdomain_block.py
@@ -0,0 +1,368 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/direct/nn/crossdomain/crossdomain.py
+
+from typing import Optional, Tuple, Union
+
+import torch
+from torch import nn
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import complex_conj, complex_mul
+
+
+class MultiCoil(nn.Module):
+ """Makes the forward pass of multi-coil data of shape (N, N_coils, H, W, C) to a model.
+
+ If coil_to_batch is set to True, coil dimension is moved to the batch dimension. Otherwise, it passes to the model
+ each coil-data individually.
+ """
+
+ def __init__(self, model: nn.Module, coil_dim: int = 1, coil_to_batch: bool = False):
+ """Inits :class:`MultiCoil`.
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Any nn.Module that takes as input with 4D data (N, H, W, C). Typically, a convolutional-like model.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ coil_to_batch : bool, optional
+ If True batch and coil dimensions are merged when forwarded by the model and unmerged when outputted.
+ Otherwise, input is forwarded to the model per coil. Default is ``False``.
+ """
+ super().__init__()
+ self.model = model
+ self.coil_to_batch = coil_to_batch
+ self.coil_dim = coil_dim
+
+ def _compute_model_per_coil(self, data: torch.Tensor) -> torch.Tensor:
+ """Computes the model per coil."""
+ output = []
+ for idx in range(data.size(self.coil_dim)):
+ subselected_data = data.select(self.coil_dim, idx)
+ if subselected_data.shape[-1] == 2 and subselected_data.dim() == 4:
+ output.append(self.model(subselected_data.permute(0, 3, 1, 2)))
+ else:
+ output.append(self.model(subselected_data.unsqueeze(self.coil_dim)).squeeze(self.coil_dim))
+ output = torch.stack(output, dim=self.coil_dim)
+ return output
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`MultiCoil`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data of shape (N, N_coils, H, W, C).
+
+ Returns
+ -------
+ torch.Tensor
+ Output data of shape (N, N_coils, H, W, C).
+ """
+ if self.coil_to_batch:
+ x = x.clone()
+
+ batch, coil, channels, height, width = x.size()
+ x = x.reshape(batch * coil, channels, height, width).contiguous()
+ x = self.model(x).permute(0, 2, 3, 1)
+ x = x.reshape(batch, coil, height, width, -1).permute(0, 1, 4, 2, 3)
+ else:
+ x = self._compute_model_per_coil(x).contiguous()
+
+ return x
+
+
+class CrossDomainNetwork(nn.Module):
+ """Based on KIKINet implementation. Modified to work with multi-coil k-space data, as presented in [Taejoon2018]_.
+
+ This performs optimisation in both, k-space ("K") and image ("I") domains according to domain_sequence.
+
+ References
+ ----------
+ .. [Taejoon2018] Eo, Taejoon, et al. โKIKI-Net: Cross-Domain Convolutional Neural Networks for Reconstructing
+ Undersampled Magnetic Resonance Images.โ Magnetic Resonance in Medicine, vol. 80, no. 5, Nov. 2018, pp.
+ 2188โ201. PubMed, https://doi.org/10.1002/mrm.27201.
+ """
+
+ def __init__(
+ self,
+ image_model_list: nn.Module,
+ kspace_model_list: Optional[Union[nn.Module, None]] = None,
+ domain_sequence: str = "KIKI",
+ image_buffer_size: int = 1,
+ kspace_buffer_size: int = 1,
+ normalize_image: bool = False, # pylint: disable=unused-argument
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ ):
+ """Inits :class:`CrossDomainNetwork`.
+
+ Parameters
+ ----------
+ image_model_list : torch.nn.Module
+ Image domain model list.
+ kspace_model_list : torch.nn.Module, optional
+ K-space domain model list. If set to None, a correction step is applied. Default is ``None``.
+ domain_sequence : str, optional
+ Domain sequence. Default is ``"KIKI"``.
+ image_buffer_size : int, optional
+ Image buffer size. Default is ``1``.
+ kspace_buffer_size : int, optional
+ K-space buffer size. Default is ``1``.
+ normalize_image : bool, optional
+ Whether to normalize the image. Default is ``False``.
+ fft_centered : bool, optional
+ Whether to use centered FFT. Default is ``False``.
+ fft_normalization : str, optional
+ Whether to normalize the FFT. Default is ``"backward"``.
+ spatial_dims : Tuple[int, int], optional
+ Spatial dimensions of the input. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ """
+ super().__init__()
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+
+ domain_sequence = list(domain_sequence.strip()) # type: ignore
+ if not set(domain_sequence).issubset({"K", "I"}):
+ raise ValueError(f"Invalid domain sequence. Got {domain_sequence}. Should only contain 'K' and 'I'.")
+ if kspace_model_list is not None and len(kspace_model_list) != domain_sequence.count("K"):
+ raise ValueError("K-space domain steps do not match k-space model list length.")
+ if len(image_model_list) != domain_sequence.count("I"):
+ raise ValueError("Image domain steps do not match image model list length.")
+
+ self.domain_sequence = domain_sequence
+ self.kspace_model_list = kspace_model_list
+ self.kspace_buffer_size = kspace_buffer_size
+ self.image_model_list = image_model_list
+ self.image_buffer_size = image_buffer_size
+
+ def kspace_correction(
+ self,
+ block_idx: int,
+ image_buffer: torch.Tensor,
+ kspace_buffer: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ sensitivity_map: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ ) -> torch.Tensor:
+ """Performs k-space correction.
+
+ Parameters
+ ----------
+ block_idx : int
+ Block index.
+ image_buffer : torch.Tensor
+ Image buffer.
+ kspace_buffer : torch.Tensor
+ K-space buffer.
+ sampling_mask : torch.Tensor
+ Subsampling mask.
+ sensitivity_map : torch.Tensor
+ Coil sensitivity maps.
+ masked_kspace : torch.Tensor
+ Subsampled k-space.
+
+ Returns
+ -------
+ torch.Tensor
+ K-space buffer.
+ """
+ forward_buffer = [
+ self._forward_operator(image.clone(), sampling_mask, sensitivity_map)
+ for image in torch.split(image_buffer, 2, -1)
+ ]
+ forward_buffer = torch.cat(forward_buffer, -1)
+
+ kspace_buffer = torch.cat([kspace_buffer, forward_buffer, masked_kspace], -1)
+
+ if self.kspace_model_list is not None:
+ kspace_buffer = self.kspace_model_list[block_idx](kspace_buffer.permute(0, 1, 4, 2, 3)).permute(
+ 0, 1, 3, 4, 2
+ )
+ else:
+ kspace_buffer = kspace_buffer[..., :2] - kspace_buffer[..., 2:4]
+
+ return kspace_buffer
+
+ def image_correction(
+ self,
+ block_idx: int,
+ image_buffer: torch.Tensor,
+ kspace_buffer: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ sensitivity_map: torch.Tensor,
+ ) -> torch.Tensor:
+ """Performs image space correction.
+
+ Parameters
+ ----------
+ block_idx : int
+ Block index.
+ image_buffer : torch.Tensor
+ Image buffer.
+ kspace_buffer : torch.Tensor
+ K-space buffer.
+ sampling_mask : torch.Tensor
+ Subsampling mask.
+ sensitivity_map : torch.Tensor
+ Coil sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ Image buffer.
+ """
+ backward_buffer = [
+ self._backward_operator(kspace.clone(), sampling_mask, sensitivity_map)
+ for kspace in torch.split(kspace_buffer, 2, -1)
+ ]
+ backward_buffer = torch.cat(backward_buffer, -1)
+ image_buffer = torch.cat([image_buffer, backward_buffer], -1).permute(0, 3, 1, 2)
+ image_buffer = self.image_model_list[block_idx](image_buffer).permute(0, 2, 3, 1)
+ return image_buffer
+
+ def _forward_operator(
+ self,
+ image: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ sensitivity_map: torch.Tensor,
+ ) -> torch.Tensor:
+ """Custom forward operator for the cross-domain correction.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Image space. Shape [batch, coils, height, width, 2].
+ sampling_mask : torch.Tensor
+ Subsampling mask. Shape [batch, 1, height, width, 1].
+ sensitivity_map : torch.Tensor
+ Coil sensitivity maps. Shape [batch, coils, height, width, 2].
+
+ Returns
+ -------
+ torch.Tensor
+ K-space prediction. Shape [batch, coils, height, width, 2].
+ """
+ return torch.where(
+ sampling_mask == 0,
+ torch.tensor([0.0], dtype=image.dtype).to(image.device),
+ fft2(
+ complex_mul(image.unsqueeze(self.coil_dim), sensitivity_map),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ).type(image.type()),
+ )
+
+ def _backward_operator(
+ self,
+ kspace: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ sensitivity_map: torch.Tensor,
+ ) -> torch.Tensor:
+ """Custom backward operator for the cross-domain correction.
+
+ Parameters
+ ----------
+ kspae : torch.Tensor
+ K-space. Shape [batch, coils, height, width, 2].
+ sampling_mask : torch.Tensor
+ Subsampling mask. Shape [batch, 1, height, width, 1].
+ sensitivity_map : torch.Tensor
+ Coil sensitivity maps. Shape [batch, coils, height, width, 2].
+
+ Returns
+ -------
+ torch.Tensor
+ Image space prediction. Shape [batch, coils, height, width, 2].
+ """
+ kspace = torch.where(sampling_mask == 0, torch.tensor([0.0], dtype=kspace.dtype).to(kspace.device), kspace)
+ return (
+ complex_mul(
+ ifft2(
+ kspace.float(),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ complex_conj(sensitivity_map),
+ )
+ .sum(self.coil_dim)
+ .type(kspace.type())
+ )
+
+ @staticmethod
+ def crop_to_shape(x: torch.Tensor, shape: tuple) -> torch.Tensor:
+ r"""Crops ``x`` to specified shape.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor with shape ('\'*, H, W).
+ shape : tuple
+ Crop shape corresponding to H, W.
+
+ Returns
+ -------
+ torch.Tensor
+ Cropped tensor.
+ """
+ h, w = x.shape[1:3]
+ if h > shape[0]:
+ x = x[:, : shape[0], :, :]
+ if w > shape[1]:
+ x = x[:, :, : shape[1], :]
+ return x
+
+ def forward(
+ self,
+ masked_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ ) -> torch.Tensor:
+ """Forward pass of :class:`CrossDomainNetwork`.
+
+ Parameters
+ ----------
+ masked_kspace : torch.Tensor
+ Subsampled k-space. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sampling_mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+
+ Returns
+ -------
+ torch.Tensor
+ Reconstructed image. Shape [batch_size, n_x, n_y, 2]
+ """
+ input_image = self._backward_operator(masked_kspace, sampling_mask, sensitivity_maps)
+
+ image_buffer = torch.cat([input_image] * self.image_buffer_size, -1).to(masked_kspace.device)
+ kspace_buffer = torch.cat([masked_kspace] * self.kspace_buffer_size, -1).to(masked_kspace.device)
+
+ kspace_block_idx, image_block_idx = 0, 0
+ for block_domain in self.domain_sequence:
+ if block_domain == "K":
+ kspace_buffer = self.kspace_correction(
+ kspace_block_idx, image_buffer, kspace_buffer, sampling_mask, sensitivity_maps, masked_kspace
+ )
+ kspace_block_idx = kspace_block_idx + 1
+ else:
+ image_buffer = self.image_correction(
+ image_block_idx, image_buffer, kspace_buffer, sampling_mask, sensitivity_maps
+ )
+ image_buffer = self.crop_to_shape(image_buffer, sensitivity_maps.shape[2:4])
+ image_block_idx = image_block_idx + 1
+
+ return image_buffer[..., :2]
diff --git a/atommic/collections/reconstruction/nn/didn_base/__init__.py b/atommic/collections/reconstruction/nn/didn_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/didn_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/didn_base/didn_block.py b/atommic/collections/reconstruction/nn/didn_base/didn_block.py
new file mode 100644
index 00000000..104226e3
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/didn_base/didn_block.py
@@ -0,0 +1,370 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/direct/nn/didn/didn.py
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class Subpixel(nn.Module):
+ """Subpixel convolution layer for up-scaling of low resolution features at super-resolution as presented in
+ [Songhyun2019]_.
+
+ References
+ ----------
+ .. [Songhyun2019] Yu, Songhyun, et al. โDeep Iterative Down-Up CNN for Image Denoising.โ 2019 IEEE/CVF Conference
+ on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2095โ103. IEEE Xplore,
+ https://doi.org/10.1109/CVPRW.2019.00262.
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ upscale_factor: int,
+ kernel_size: int,
+ padding: int = 0,
+ ):
+ """Inits :class:`Subpixel`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ upscale_factor : int
+ Subpixel upscale factor.
+ kernel_size : int
+ Convolution kernel size.
+ padding : int, optional
+ Padding size. Default is ``0``.
+ """
+ super().__init__()
+ self.conv = nn.Conv2d(
+ in_channels, out_channels * upscale_factor**2, kernel_size=kernel_size, padding=padding
+ )
+ self.pixelshuffle = nn.PixelShuffle(upscale_factor)
+
+ def forward(self, x):
+ """Computes Subpixel convolution."""
+ return self.pixelshuffle(self.conv(x))
+
+
+class ReconBlock(nn.Module):
+ """Reconstruction Block of DIDN model as presented in [Songhyun2019]_.
+
+ References
+ ----------
+ .. [Songhyun2019] Yu, Songhyun, et al. โDeep Iterative Down-Up CNN for Image Denoising.โ 2019 IEEE/CVF Conference
+ on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2095โ103. IEEE Xplore,
+ https://doi.org/10.1109/CVPRW.2019.00262.
+ """
+
+ def __init__(self, in_channels: int, num_convs: int):
+ """Inits :class:`ReconBlock`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ num_convs : int
+ Number of convolution blocks.
+ """
+ super().__init__()
+ self.convs = nn.ModuleList(
+ [
+ nn.Sequential(
+ *[
+ nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, padding=1),
+ nn.PReLU(),
+ ]
+ )
+ for _ in range(num_convs - 1)
+ ]
+ )
+ self.convs.append(nn.Conv2d(in_channels=in_channels, out_channels=in_channels, kernel_size=3, padding=1))
+ self.num_convs = num_convs
+
+ def forward(self, input_data: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`ReconBlock`.
+
+ Parameters
+ ----------
+ input_data : torch.Tensor
+ Input data.
+ """
+ output = input_data.clone()
+ for idx in range(self.num_convs):
+ output = self.convs[idx](output)
+
+ return input_data + output
+
+
+class DUB(nn.Module):
+ r"""Down-Up Block of DIDN model as presented in [Songhyun2019]_.
+
+ References
+ ----------
+ .. [Songhyun2019] Yu, Songhyun, et al. โDeep Iterative Down-Up CNN for Image Denoising.โ 2019 IEEE/CVF Conference
+ on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2095โ103. IEEE Xplore,
+ https://doi.org/10.1109/CVPRW.2019.00262.
+
+ """
+
+ def __init__(self, in_channels: int, out_channels: int):
+ """Inits :class:`DUB`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ """
+ super().__init__()
+
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+
+ # Scale 1
+ self.conv1_1 = nn.Sequential(*[nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1), nn.PReLU()] * 2)
+ self.down1 = nn.Conv2d(in_channels, in_channels * 2, kernel_size=3, stride=2, padding=1)
+ # Scale 2
+ self.conv2_1 = nn.Sequential(
+ *[nn.Conv2d(in_channels * 2, in_channels * 2, kernel_size=3, padding=1), nn.PReLU()]
+ )
+ self.down2 = nn.Conv2d(in_channels * 2, in_channels * 4, kernel_size=3, stride=2, padding=1)
+ # Scale 3
+ self.conv3_1 = nn.Sequential(
+ *[
+ nn.Conv2d(in_channels * 4, in_channels * 4, kernel_size=3, padding=1),
+ nn.PReLU(),
+ ]
+ )
+ self.up1 = nn.Sequential(*[Subpixel(in_channels * 4, in_channels * 2, 2, 1, 0)])
+ # Scale 2
+ self.conv_agg_1 = nn.Conv2d(in_channels * 4, in_channels * 2, kernel_size=1)
+ self.conv2_2 = nn.Sequential(
+ *[
+ nn.Conv2d(in_channels * 2, in_channels * 2, kernel_size=3, padding=1),
+ nn.PReLU(),
+ ]
+ )
+ self.up2 = nn.Sequential(*[Subpixel(in_channels * 2, in_channels, 2, 1, 0)])
+ # Scale 1
+ self.conv_agg_2 = nn.Conv2d(in_channels * 2, in_channels, kernel_size=1)
+ self.conv1_2 = nn.Sequential(*[nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1), nn.PReLU()] * 2)
+ self.conv_out = nn.Sequential(*[nn.Conv2d(in_channels, in_channels, kernel_size=3, padding=1), nn.PReLU()])
+
+ @staticmethod
+ def pad(x: torch.Tensor) -> torch.Tensor:
+ """Pads input to height and width dimensions if odd.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor.
+
+ Returns
+ -------
+ torch.Tensor
+ Padded tensor.
+ """
+ padding = [0, 0, 0, 0]
+
+ if x.shape[-2] % 2 != 0:
+ padding[3] = 1 # Padding right - width
+ if x.shape[-1] % 2 != 0:
+ padding[1] = 1 # Padding bottom - height
+ if sum(padding) != 0:
+ x = F.pad(x, padding, "reflect")
+ return x
+
+ @staticmethod
+ def crop_to_shape(x: torch.Tensor, shape: tuple) -> torch.Tensor:
+ r"""Crops ``x`` to specified shape.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor with shape ('\'*, H, W).
+ shape : tuple
+ Crop shape corresponding to H, W.
+
+ Returns
+ -------
+ torch.Tensor
+ Cropped tensor.
+ """
+ h, w = x.shape[-2:]
+ if h > shape[0]:
+ x = x[:, :, : shape[0], :]
+ if w > shape[1]:
+ x = x[:, :, :, : shape[1]]
+ return x
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`DUB`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor.
+
+ Returns
+ -------
+ torch.Tensor
+ DUB output.
+ """
+ x1 = self.pad(x.clone())
+ x1 = x1 + self.conv1_1(x1)
+ x2 = self.down1(x1)
+ x2 = x2 + self.conv2_1(x2)
+ out = self.down2(x2)
+ out = out + self.conv3_1(out)
+ out = self.up1(out)
+ out = torch.cat([x2, self.crop_to_shape(out, x2.shape[-2:])], dim=1)
+ out = self.conv_agg_1(out)
+ out = out + self.conv2_2(out)
+ out = self.up2(out)
+ out = torch.cat([x1, self.crop_to_shape(out, x1.shape[-2:])], dim=1)
+ out = self.conv_agg_2(out)
+ out = out + self.conv1_2(out)
+ out = x + self.crop_to_shape(self.conv_out(out), x.shape[-2:])
+ return out
+
+
+class DIDN(nn.Module):
+ r"""Deep Iterative Down-up convolutional Neural network (DIDN), as presented in [Songhyun2019]_.
+
+ References
+ ----------
+ .. [Songhyun2019] Yu, Songhyun, et al. โDeep Iterative Down-Up CNN for Image Denoising.โ 2019 IEEE/CVF Conference
+ on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2095โ103. IEEE Xplore,
+ https://doi.org/10.1109/CVPRW.2019.00262.
+
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ hidden_channels: int = 128,
+ num_dubs: int = 6,
+ num_convs_recon: int = 9,
+ skip_connection: bool = False,
+ ):
+ """Inits :class:`DIDN`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ hidden_channels : int, optional
+ Number of hidden channels. First convolution out_channels. Default: 128.
+ num_dubs : int, optional
+ Number of DUB networks. Default: 6.
+ num_convs_recon : int, optional
+ Number of ReconBlock convolutions. Default: 9.
+ skip_connection : bool, optional
+ Use skip connection. Default: False.
+ """
+ super().__init__()
+ self.conv_in = nn.Sequential(
+ *[nn.Conv2d(in_channels=in_channels, out_channels=hidden_channels, kernel_size=3, padding=1), nn.PReLU()]
+ )
+ self.down = nn.Conv2d(
+ in_channels=hidden_channels,
+ out_channels=hidden_channels,
+ kernel_size=3,
+ stride=2,
+ padding=1,
+ )
+ self.dubs = nn.ModuleList(
+ [DUB(in_channels=hidden_channels, out_channels=hidden_channels) for _ in range(num_dubs)]
+ )
+ self.recon_block = ReconBlock(in_channels=hidden_channels, num_convs=num_convs_recon)
+ self.recon_agg = nn.Conv2d(in_channels=hidden_channels * num_dubs, out_channels=hidden_channels, kernel_size=1)
+ self.conv = nn.Sequential(
+ *[
+ nn.Conv2d(
+ in_channels=hidden_channels,
+ out_channels=hidden_channels,
+ kernel_size=3,
+ padding=1,
+ ),
+ nn.PReLU(),
+ ]
+ )
+ self.up2 = Subpixel(hidden_channels, hidden_channels, 2, 1)
+ self.conv_out = nn.Conv2d(
+ in_channels=hidden_channels,
+ out_channels=out_channels,
+ kernel_size=3,
+ padding=1,
+ )
+ self.num_dubs = num_dubs
+ self.skip_connection = (in_channels == out_channels) and skip_connection
+
+ @staticmethod
+ def crop_to_shape(x: torch.Tensor, shape: tuple) -> torch.Tensor:
+ r"""Crops ``x`` to specified shape.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor with shape ('\'*, H, W).
+ shape : tuple
+ Crop shape corresponding to H, W.
+
+ Returns
+ -------
+ torch.Tensor
+ Cropped tensor.
+ """
+ h, w = x.shape[-2:]
+
+ if h > shape[0]:
+ x = x[:, :, : shape[0], :]
+ if w > shape[1]:
+ x = x[:, :, :, : shape[1]]
+ return x
+
+ def forward(self, x: torch.Tensor, channel_dim: int = 1) -> torch.Tensor:
+ r"""Forward pass of :class:`DIDN`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor with shape ('\'*, C, H, W).
+ channel_dim : int, optional
+ Channel dimension. Default is ``1``.
+
+ Returns
+ -------
+ torch.Tensor
+ Output tensor with shape ('\'*, C, H, W).
+ """
+ out = self.conv_in(x)
+ out = self.down(out)
+
+ dub_outs = []
+ for dub in self.dubs:
+ out = dub(out)
+ dub_outs.append(out)
+
+ out = [self.recon_block(dub_out) for dub_out in dub_outs]
+ out = self.recon_agg(torch.cat(out, dim=channel_dim))
+ out = self.conv(out)
+ out = self.up2(out)
+ out = self.conv_out(out)
+ out = self.crop_to_shape(out, x.shape[-2:])
+
+ if self.skip_connection:
+ out = x + out
+ return out
diff --git a/atommic/collections/reconstruction/nn/dunet.py b/atommic/collections/reconstruction/nn/dunet.py
new file mode 100644
index 00000000..a660d1a8
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/dunet.py
@@ -0,0 +1,149 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.utils import check_stacked_complex, coil_combination_method
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.didn_base.didn_block import DIDN
+from atommic.collections.reconstruction.nn.sigmanet_base.dc_layers import (
+ DataGDLayer,
+ DataIDLayer,
+ DataProxCGLayer,
+ DataVSLayer,
+)
+from atommic.collections.reconstruction.nn.sigmanet_base.sensitivity_net import SensitivityNetwork
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["DUNet"]
+
+
+class DUNet(BaseMRIReconstructionModel):
+ """Implementation of the Down-Up NET, inspired by [Hammernik2021]_.
+
+ References
+ ----------
+ .. [Hammernik2021] Hammernik, K, Schlemper, J, Qin, C, et al. Systematic valuation of iterative deep neural
+ networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination. Magn Reson Med.
+ 2021; 86: 1859โ 1872. https://doi.org/10.1002/mrm.28827
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`DUNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ reg_model_architecture = cfg_dict.get("reg_model_architecture")
+ if reg_model_architecture == "DIDN":
+ reg_model = DIDN(
+ in_channels=cfg_dict.get("in_channels", 2),
+ out_channels=cfg_dict.get("out_channels", 2),
+ hidden_channels=cfg_dict.get("didn_hidden_channels"),
+ num_dubs=cfg_dict.get("didn_num_dubs"),
+ num_convs_recon=cfg_dict.get("didn_num_convs_recon"),
+ )
+ elif reg_model_architecture in ["UNET", "NORMUNET"]:
+ reg_model = NormUnet(
+ cfg_dict.get("unet_num_filters"),
+ cfg_dict.get("unet_num_pool_layers"),
+ in_chans=cfg_dict.get("in_channels", 2),
+ out_chans=cfg_dict.get("out_channels", 2),
+ drop_prob=cfg_dict.get("unet_dropout_probability"),
+ padding_size=cfg_dict.get("unet_padding_size"),
+ normalize=cfg_dict.get("unet_normalize"),
+ )
+ else:
+ raise NotImplementedError(
+ "DUNET is currently implemented for reg_model_architecture == 'DIDN' or 'UNet'."
+ f"Got reg_model_architecture == {reg_model_architecture}."
+ )
+
+ data_consistency_term = cfg_dict.get("data_consistency_term")
+
+ if data_consistency_term == "GD":
+ dc_layer = DataGDLayer(
+ lambda_init=cfg_dict.get("data_consistency_lambda_init"),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ elif data_consistency_term == "PROX":
+ dc_layer = DataProxCGLayer(
+ lambda_init=cfg_dict.get("data_consistency_lambda_init"),
+ iterations=cfg_dict.get("data_consistency_iterations", 10),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ elif data_consistency_term == "VS":
+ dc_layer = DataVSLayer(
+ alpha_init=cfg_dict.get("data_consistency_alpha_init", 1.0),
+ beta_init=cfg_dict.get("data_consistency_beta_init", 1.0),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ else:
+ dc_layer = DataIDLayer()
+
+ self.reconstruction_module = SensitivityNetwork(
+ cfg_dict.get("num_iter"),
+ reg_model,
+ dc_layer,
+ shared_params=cfg_dict.get("shared_params"),
+ save_space=False,
+ reset_cache=False,
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor,
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`DUNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ return check_stacked_complex(
+ coil_combination_method(
+ self.reconstruction_module(initial_prediction, y, sensitivity_maps, mask),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+ )
diff --git a/atommic/collections/reconstruction/nn/jointicnet.py b/atommic/collections/reconstruction/nn/jointicnet.py
new file mode 100644
index 00000000..5e295d3a
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/jointicnet.py
@@ -0,0 +1,291 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.nn.base import BaseSensitivityModel
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import (
+ check_stacked_complex,
+ coil_combination_method,
+ complex_conj,
+ complex_mul,
+)
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["JointICNet"]
+
+
+class JointICNet(BaseMRIReconstructionModel):
+ """Implementation of the Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (Joint-ICNet),
+ as presented in [Jun2021]_.
+
+ References
+ ----------
+ .. [Jun2021] Jun, Yohan, et al. โJoint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network
+ (Joint-ICNet) for Fast MRI.โ 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE,
+ 2021, pp. 5266โ75. DOI.org (Crossref), https://doi.org/10.1109/CVPR46437.2021.00523.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`JointICNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.num_iter = cfg_dict.get("num_iter")
+
+ self.kspace_model = NormUnet(
+ cfg_dict.get("kspace_unet_num_filters"),
+ cfg_dict.get("kspace_unet_num_pool_layers"),
+ in_chans=2,
+ out_chans=2,
+ drop_prob=cfg_dict.get("kspace_unet_dropout_probability"),
+ padding_size=cfg_dict.get("kspace_unet_padding_size"),
+ normalize=cfg_dict.get("kspace_unet_normalize"),
+ )
+
+ self.image_model = NormUnet(
+ cfg_dict.get("imspace_unet_num_filters"),
+ cfg_dict.get("imspace_unet_num_pool_layers"),
+ in_chans=2,
+ out_chans=2,
+ drop_prob=cfg_dict.get("imspace_unet_dropout_probability"),
+ padding_size=cfg_dict.get("imspace_unet_padding_size"),
+ normalize=cfg_dict.get("imspace_unet_normalize"),
+ )
+
+ self.sens_net = BaseSensitivityModel(
+ cfg_dict.get("sens_unet_num_filters"),
+ cfg_dict.get("sens_unet_num_pool_layers"),
+ mask_center=cfg_dict.get("sens_unet_mask_center"),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ mask_type=cfg_dict.get("coil_sensitivity_maps_nn_mask_type", "2D"),
+ drop_prob=cfg_dict.get("sens_unet_dropout_probability"),
+ padding_size=cfg_dict.get("sens_unet_padding_size"),
+ normalize=cfg_dict.get("sens_unet_normalize"),
+ )
+
+ self.conv_out = torch.nn.Conv2d(in_channels=2, out_channels=2, kernel_size=1)
+
+ self.reg_param_I = torch.nn.Parameter(torch.ones(self.num_iter))
+ self.reg_param_F = torch.nn.Parameter(torch.ones(self.num_iter))
+ self.reg_param_C = torch.nn.Parameter(torch.ones(self.num_iter))
+
+ self.lr_image = torch.nn.Parameter(torch.ones(self.num_iter))
+ self.lr_sens = torch.nn.Parameter(torch.ones(self.num_iter))
+
+ def update_C(
+ self,
+ idx: int,
+ DC_sens: torch.Tensor,
+ image: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ y: torch.Tensor,
+ mask: torch.Tensor,
+ ) -> torch.Tensor:
+ r"""Updates the coil sensitivity maps.
+
+ .. math::
+ C = (1 - 2 * '\'lambda_{k}^{C} * ni_{k}) * C_{k}
+
+ C = 2 * '\'lambda_{k}^{C} * ni_{k} * D_{C}(F^-1(b))
+
+ A(x_{k}) = M * F * (C * x_{k})
+
+ C = 2 * ni_{k} * F^-1(M.T * (M * F * (C * x_{k}) - b)) * x_{k}^*
+
+ Parameters
+ ----------
+ idx : int
+ The current iteration index.
+ DC_sens : torch.Tensor
+ The initial coil sensitivity maps. Shape [batch_size, num_coils, num_sens_maps, num_rows, num_cols].
+ image : torch.Tensor
+ The predicted image. Shape [batch_size, num_coils, num_rows, num_cols].
+ sensitivity_maps : torch.Tensor
+ The coil sensitivity maps. Shape [batch_size, num_coils, num_sens_maps, num_rows, num_cols].
+ y : torch.Tensor
+ The subsampled k-space data. Shape [batch_size, num_coils, num_rows, num_cols].
+ mask : torch.Tensor
+ The subsampled mask. Shape [batch_size, 1, num_rows, num_cols].
+
+ Returns
+ -------
+ sensitivity_maps : torch.Tensor
+ The updated coil sensitivity maps. Shape [batch_size, num_coils, num_sens_maps, num_rows, num_cols].
+ """
+ # (1 - 2 * lambda_{k}^{C} * ni_{k}) * C_{k}
+ sense_term_1 = (1 - 2 * self.reg_param_C[idx] * self.lr_sens[idx]) * sensitivity_maps
+ # 2 * lambda_{k}^{C} * ni_{k} * D_{C}(F^-1(b))
+ sense_term_2 = 2 * self.reg_param_C[idx] * self.lr_sens[idx] * DC_sens
+ # A(x_{k}) = M * F * (C * x_{k})
+ sense_term_3_A = fft2(
+ complex_mul(image.unsqueeze(self.coil_dim), sensitivity_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ sense_term_3_A = torch.where(mask == 0, torch.tensor([0.0], dtype=y.dtype).to(y.device), sense_term_3_A)
+ # 2 * ni_{k} * F^-1(M.T * (M * F * (C * x_{k}) - b)) * x_{k}^*
+ sense_term_3_mask = torch.where(
+ mask == 1,
+ torch.tensor([0.0], dtype=y.dtype).to(y.device),
+ sense_term_3_A - y,
+ )
+
+ sense_term_3_backward = ifft2(
+ sense_term_3_mask,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ sense_term_3 = 2 * self.lr_sens[idx] * sense_term_3_backward * complex_conj(image).unsqueeze(self.coil_dim)
+ sensitivity_maps = sense_term_1 + sense_term_2 - sense_term_3
+ return sensitivity_maps
+
+ def update_X(
+ self,
+ idx: int,
+ image: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ y: torch.Tensor,
+ mask: torch.Tensor,
+ ) -> torch.Tensor:
+ r"""Updates the image.
+
+ .. math::
+ x_{k} = (1 - 2 * '\'lamdba_{{k}_{I}} * mi_{k} - 2 * '\'lamdba_{{k}_{F}} * mi_{k}) * x_{k}
+
+ x_{k} = 2 * mi_{k} * ('\'lambda_{{k}_{I}} * D_I(x_{k}) + '\'lambda_{{k}_{F}} * F^-1(D_F(f)))
+
+ A(x{k} - b) = M * F * (C * x{k}) - b
+
+ x_{k} = 2 * mi_{k} * A^* * (A(x{k} - b))
+
+ Parameters
+ ----------
+ idx : int
+ The current iteration index.
+ image : torch.Tensor
+ The predicted image. Shape [batch_size, num_coils, num_rows, num_cols].
+ sensitivity_maps : torch.Tensor
+ The coil sensitivity maps. Shape [batch_size, num_coils, num_sens_maps, num_rows, num_cols].
+ y : torch.Tensor
+ The subsampled k-space data. Shape [batch_size, num_coils, num_rows, num_cols].
+ mask : torch.Tensor
+ The subsampling mask. Shape [batch_size, 1, num_rows, num_cols].
+
+ Returns
+ -------
+ image : torch.Tensor
+ The updated image. Shape [batch_size, num_coils, num_rows, num_cols].
+ """
+ # (1 - 2 * lamdba_{k}_{I} * mi_{k} - 2 * lamdba_{k}_{F} * mi_{k}) * x_{k}
+ image_term_1 = (
+ 1 - 2 * self.reg_param_I[idx] * self.lr_image[idx] - 2 * self.reg_param_F[idx] * self.lr_image[idx]
+ ) * image
+ # D_I(x_{k})
+ image_term_2_DI = self.image_model(image.unsqueeze(self.coil_dim)).squeeze(self.coil_dim).contiguous()
+ image_term_2_DF = ifft2(
+ self.kspace_model(
+ fft2(
+ image,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ).unsqueeze(self.coil_dim)
+ )
+ .squeeze(self.coil_dim)
+ .contiguous(),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ # 2 * mi_{k} * (lambda_{k}_{I} * D_I(x_{k}) + lambda_{k}_{F} * F^-1(D_F(f)))
+ image_term_2 = (
+ 2
+ * self.lr_image[idx]
+ * (self.reg_param_I[idx] * image_term_2_DI + self.reg_param_F[idx] * image_term_2_DF)
+ )
+ # A(x{k}) - b) = M * F * (C * x{k}) - b
+ image_term_3_A = fft2(
+ complex_mul(image.unsqueeze(self.coil_dim), sensitivity_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ image_term_3_A = torch.where(mask == 0, torch.tensor([0.0], dtype=y.dtype).to(y.device), image_term_3_A) - y
+ # 2 * mi_{k} * A^* * (A(x{k}) - b))
+ image_term_3_Aconj = complex_mul(
+ ifft2(
+ image_term_3_A,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ complex_conj(sensitivity_maps),
+ ).sum(self.coil_dim)
+ image_term_3 = 2 * self.lr_image[idx] * image_term_3_Aconj
+ image = image_term_1 + image_term_2 - image_term_3
+ return image
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`JointICNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ DC_sens = self.sens_net(y, mask, sensitivity_maps)
+ sensitivity_maps = DC_sens.clone()
+ image = coil_combination_method(
+ ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+ for idx in range(self.num_iter):
+ sensitivity_maps = self.update_C(idx, DC_sens, image, sensitivity_maps, y, mask)
+ image = self.update_X(idx, image, sensitivity_maps, y, mask)
+ return check_stacked_complex(image)
diff --git a/atommic/collections/reconstruction/nn/kikinet.py b/atommic/collections/reconstruction/nn/kikinet.py
new file mode 100644
index 00000000..4cd28037
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/kikinet.py
@@ -0,0 +1,194 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import check_stacked_complex, complex_conj, complex_mul
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.ccnn_base.ccnn_block import Conv2d
+from atommic.collections.reconstruction.nn.crossdomain_base.crossdomain_block import MultiCoil
+from atommic.collections.reconstruction.nn.didn_base.didn_block import DIDN
+from atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block import MWCNN
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["KIKINet"]
+
+
+class KIKINet(BaseMRIReconstructionModel):
+ """Based on KIKINet implementation. Modified to work with multi-coil k-space data, as presented in [Taejoon2018]_.
+
+ References
+ ----------
+ .. [Taejoon2018] Eo, Taejoon, et al. โKIKI-Net: Cross-Domain Convolutional Neural Networks for Reconstructing
+ Undersampled Magnetic Resonance Images.โ Magnetic Resonance in Medicine, vol. 80, no. 5, Nov. 2018, pp.
+ 2188โ201. PubMed, https://doi.org/10.1002/mrm.27201.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`KIKINet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.num_iter = cfg_dict.get("num_iter")
+ self.no_dc = cfg_dict.get("no_dc")
+
+ kspace_model_architecture = cfg_dict.get("kspace_model_architecture")
+
+ if kspace_model_architecture == "CONV":
+ kspace_model = Conv2d(
+ in_channels=cfg_dict.get("kspace_in_channels", 2),
+ out_channels=cfg_dict.get("kspace_out_channels", 2),
+ hidden_channels=cfg_dict.get("kspace_conv_hidden_channels"),
+ n_convs=cfg_dict.get("kspace_conv_n_convs"),
+ batchnorm=cfg_dict.get("kspace_conv_batchnorm"),
+ )
+ elif kspace_model_architecture == "DIDN":
+ kspace_model = DIDN(
+ in_channels=cfg_dict.get("kspace_in_channels", 2),
+ out_channels=cfg_dict.get("kspace_out_channels", 2),
+ hidden_channels=cfg_dict.get("kspace_didn_hidden_channels"),
+ num_dubs=cfg_dict.get("kspace_didn_num_dubs"),
+ num_convs_recon=cfg_dict.get("kspace_didn_num_convs_recon"),
+ )
+ elif kspace_model_architecture in ["UNET", "NORMUNET"]:
+ kspace_model = NormUnet(
+ cfg_dict.get("kspace_unet_num_filters"),
+ cfg_dict.get("kspace_unet_num_pool_layers"),
+ in_chans=cfg_dict.get("kspace_in_channels", 2),
+ out_chans=cfg_dict.get("kspace_out_channels", 2),
+ drop_prob=cfg_dict.get("kspace_unet_dropout_probability"),
+ padding_size=cfg_dict.get("kspace_unet_padding_size"),
+ normalize=cfg_dict.get("kspace_unet_normalize"),
+ )
+ else:
+ raise NotImplementedError(
+ "KIKINet is currently implemented for kspace_model_architecture == 'CONV' or 'DIDN' or 'UNet'."
+ f"Got kspace_model_architecture == {kspace_model_architecture}."
+ )
+
+ image_model_architecture = cfg_dict.get("imspace_model_architecture")
+
+ if image_model_architecture == "MWCNN":
+ image_model = MWCNN(
+ input_channels=cfg_dict.get("imspace_in_channels", 2),
+ first_conv_hidden_channels=cfg_dict.get("image_mwcnn_hidden_channels"),
+ num_scales=cfg_dict.get("image_mwcnn_num_scales"),
+ bias=cfg_dict.get("image_mwcnn_bias"),
+ batchnorm=cfg_dict.get("image_mwcnn_batchnorm"),
+ )
+ elif image_model_architecture in ["UNET", "NORMUNET"]:
+ image_model = NormUnet(
+ cfg_dict.get("imspace_unet_num_filters"),
+ cfg_dict.get("imspace_unet_num_pool_layers"),
+ in_chans=cfg_dict.get("imspace_in_channels", 2),
+ out_chans=cfg_dict.get("imspace_out_channels", 2),
+ drop_prob=cfg_dict.get("imspace_unet_dropout_probability"),
+ padding_size=cfg_dict.get("imspace_unet_padding_size"),
+ normalize=cfg_dict.get("imspace_unet_normalize"),
+ )
+ else:
+ raise NotImplementedError(
+ "KIKINet is currently implemented only with image_model_architecture == 'MWCNN' or 'UNet'."
+ f"Got {image_model_architecture}."
+ )
+
+ self.image_model_list = torch.nn.ModuleList([image_model] * self.num_iter)
+ self.kspace_model_list = torch.nn.ModuleList([MultiCoil(kspace_model, coil_dim=1)] * self.num_iter)
+
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`KIKINet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ kspace = y.clone()
+ zero = torch.zeros(1, 1, 1, 1, 1).to(kspace)
+
+ for idx in range(self.num_iter):
+ soft_dc = torch.where(mask.bool(), kspace - y, zero) * self.dc_weight
+
+ kspace = self.kspace_model_list[idx](kspace)
+ if kspace.shape[-1] != 2:
+ kspace = kspace.permute(0, 1, 3, 4, 2).to(y)
+ # this is necessary, but why?
+ kspace = torch.view_as_real(kspace[..., 0] + 1j * kspace[..., 1])
+
+ image = complex_mul(
+ ifft2(
+ kspace,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ complex_conj(sensitivity_maps),
+ ).sum(self.coil_dim)
+ image = self.image_model_list[idx](image.unsqueeze(self.coil_dim)).squeeze(self.coil_dim)
+
+ if not self.no_dc:
+ image = fft2(
+ complex_mul(image.unsqueeze(self.coil_dim), sensitivity_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ).type(image.type())
+ image = kspace - soft_dc - image
+ image = complex_mul(
+ ifft2(
+ image,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ complex_conj(sensitivity_maps),
+ ).sum(self.coil_dim)
+
+ if idx < self.num_iter - 1:
+ kspace = fft2(
+ complex_mul(image.unsqueeze(self.coil_dim), sensitivity_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ).type(image.type())
+
+ return check_stacked_complex(image)
diff --git a/atommic/collections/reconstruction/nn/lpd.py b/atommic/collections/reconstruction/nn/lpd.py
new file mode 100644
index 00000000..0e21a0a3
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/lpd.py
@@ -0,0 +1,194 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import complex_conj, complex_mul
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.ccnn_base.ccnn_block import Conv2d
+from atommic.collections.reconstruction.nn.didn_base.didn_block import DIDN
+from atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block import MWCNN
+from atommic.collections.reconstruction.nn.primaldualnet_base.primaldualnet_block import DualNet, PrimalNet
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["LPDNet"]
+
+
+class LPDNet(BaseMRIReconstructionModel):
+ """Implementation of the Learned Primal Dual network, inspired by [Adler2018]_.
+
+ References
+ ----------
+ .. [Adler2018] Adler, Jonas, and Ozan รktem. โLearned Primal-Dual Reconstruction.โ IEEE Transactions on Medical
+ Imaging, vol. 37, no. 6, June 2018, pp. 1322โ32. arXiv.org, https://doi.org/10.1109/TMI.2018.2799231.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`LPDNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.dimensionality = cfg_dict.get("dimensionality", 2)
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+
+ self.num_iter = cfg_dict.get("num_iter")
+ self.num_primal = cfg_dict.get("num_primal")
+ self.num_dual = cfg_dict.get("num_dual")
+
+ primal_model_architecture = cfg_dict.get("primal_model_architecture")
+
+ if primal_model_architecture == "MWCNN":
+ primal_model = torch.nn.Sequential(
+ *[
+ MWCNN(
+ input_channels=cfg_dict.get("primal_in_channels") * (self.num_primal + 1),
+ first_conv_hidden_channels=cfg_dict.get("primal_mwcnn_hidden_channels"),
+ num_scales=cfg_dict.get("primal_mwcnn_num_scales"),
+ bias=cfg_dict.get("primal_mwcnn_bias"),
+ batchnorm=cfg_dict.get("primal_mwcnn_batchnorm"),
+ ),
+ torch.nn.Conv2d(
+ cfg_dict.get("primal_out_channels") * (self.num_primal + 1),
+ cfg_dict.get("primal_out_channels") * self.num_primal,
+ kernel_size=1,
+ ),
+ ]
+ )
+ elif primal_model_architecture in ["UNET", "NORMUNET"]:
+ primal_model = NormUnet(
+ cfg_dict.get("primal_unet_num_filters"),
+ cfg_dict.get("primal_unet_num_pool_layers"),
+ in_chans=cfg_dict.get("primal_in_channels") * (self.num_primal + 1),
+ out_chans=cfg_dict.get("primal_out_channels") * self.num_primal,
+ drop_prob=cfg_dict.get("primal_unet_dropout_probability"),
+ padding_size=cfg_dict.get("primal_unet_padding_size"),
+ normalize=cfg_dict.get("primal_unet_normalize"),
+ )
+ else:
+ raise NotImplementedError(
+ "LPDNet is currently implemented for primal_model_architecture == 'CONV' or 'UNet'."
+ f"Got primal_model_architecture == {primal_model_architecture}."
+ )
+
+ dual_model_architecture = cfg_dict.get("dual_model_architecture")
+
+ if dual_model_architecture == "CONV":
+ dual_model = Conv2d(
+ in_channels=cfg_dict.get("dual_in_channels") * (self.num_dual + 2),
+ out_channels=cfg_dict.get("dual_out_channels") * self.num_dual,
+ hidden_channels=cfg_dict.get("kspace_conv_hidden_channels"),
+ n_convs=cfg_dict.get("kspace_conv_n_convs"),
+ batchnorm=cfg_dict.get("kspace_conv_batchnorm"),
+ )
+ elif dual_model_architecture == "DIDN":
+ dual_model = DIDN(
+ in_channels=cfg_dict.get("dual_in_channels") * (self.num_dual + 2),
+ out_channels=cfg_dict.get("dual_out_channels") * self.num_dual,
+ hidden_channels=cfg_dict.get("kspace_didn_hidden_channels"),
+ num_dubs=cfg_dict.get("kspace_didn_num_dubs"),
+ num_convs_recon=cfg_dict.get("kspace_didn_num_convs_recon"),
+ )
+ elif dual_model_architecture in ["UNET", "NORMUNET"]:
+ dual_model = NormUnet(
+ cfg_dict.get("dual_unet_num_filters"),
+ cfg_dict.get("dual_unet_num_pool_layers"),
+ in_chans=cfg_dict.get("dual_in_channels") * (self.num_dual + 2),
+ out_chans=cfg_dict.get("dual_out_channels") * self.num_dual,
+ drop_prob=cfg_dict.get("dual_unet_dropout_probability"),
+ padding_size=cfg_dict.get("dual_unet_padding_size"),
+ normalize=cfg_dict.get("dual_unet_normalize"),
+ )
+ else:
+ raise NotImplementedError(
+ "LPDNet is currently implemented for dual_model_architecture == 'CONV' or 'DIDN' or 'UNet'."
+ f"Got dual_model_architecture == {dual_model_architecture}."
+ )
+
+ self.primal_net = torch.nn.ModuleList(
+ [PrimalNet(self.num_primal, primal_architecture=primal_model) for _ in range(self.num_iter)]
+ )
+ self.dual_net = torch.nn.ModuleList(
+ [DualNet(self.num_dual, dual_architecture=dual_model) for _ in range(self.num_iter)]
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor,
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`LPDNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ dual_buffer = torch.cat([y] * self.num_dual, -1).to(y.device)
+ primal_buffer = torch.cat([initial_prediction] * self.num_primal, -1).to(y.device)
+
+ for idx in range(self.num_iter):
+ # Dual
+ f_2 = primal_buffer[..., 2:4].clone()
+ f_2 = torch.where(
+ mask == 0,
+ torch.tensor([0.0], dtype=f_2.dtype).to(f_2.device),
+ fft2(
+ complex_mul(f_2.unsqueeze(self.coil_dim), sensitivity_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ).type(f_2.type()),
+ )
+ dual_buffer = self.dual_net[idx](dual_buffer, f_2, y)
+
+ # Primal
+ h_1 = dual_buffer[..., 0:2].clone()
+ # needed for python3.9
+ h_1 = torch.view_as_real(h_1[..., 0] + 1j * h_1[..., 1])
+ h_1 = complex_mul(
+ ifft2(
+ torch.where(mask == 0, torch.tensor([0.0], dtype=h_1.dtype).to(h_1.device), h_1),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ complex_conj(sensitivity_maps),
+ ).sum(self.coil_dim)
+ primal_buffer = self.primal_net[idx](primal_buffer, h_1)
+
+ primal_buffer = primal_buffer[..., 0:2]
+
+ return torch.view_as_real(primal_buffer[..., 0] + 1j * primal_buffer[..., 1])
diff --git a/atommic/collections/reconstruction/nn/modl.py b/atommic/collections/reconstruction/nn/modl.py
new file mode 100644
index 00000000..2ea7a83a
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/modl.py
@@ -0,0 +1,103 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch import nn
+
+from atommic.collections.common.parts.utils import check_stacked_complex
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.modl_base.modl_block import ConjugateGradient, ResidualNetwork
+from atommic.core.classes.common import typecheck
+
+__all__ = ["MoDL"]
+
+
+class MoDL(BaseMRIReconstructionModel):
+ """Implementation of the MoDL: Model Based Deep Learning Architecture for Inverse Problems.
+
+ Adjusted to optionally perform a data consistency step (Conjugate Gradient), as presented in [Aggarwal2018]_,
+ [Yaman2020]_. If dc is set to False, the network will perform a simple residual learning step.
+
+ References
+ ----------
+ .. [Aggarwal2018] MoDL: Model Based Deep Learning Architecture for Inverse Problems by H.K. Aggarwal, M.P Mani, and
+ Mathews Jacob in IEEE Transactions on Medical Imaging, 2018
+
+ .. [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uฤurbil, K, Akรงakaya, M. Self-supervised
+ learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson
+ Med. 2020; 84: 3172โ 3191. https://doi.org/10.1002/mrm.28378
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`MoDL`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.unrolled_iterations = cfg_dict.get("unrolled_iterations", 10)
+ self.reconstruction_module = ResidualNetwork(
+ nb_res_blocks=cfg_dict.get("residual_blocks", 15),
+ channels=cfg_dict.get("channels", 64),
+ regularization_factor=cfg_dict.get("regularization_factor", 0.1),
+ )
+ self.dc = cfg_dict.get("conjugate_gradient_dc", False)
+ if self.dc:
+ self.mu = nn.Parameter(torch.Tensor([cfg_dict.get("penalization_weight")]), requires_grad=True)
+ self.dc_block = ConjugateGradient(
+ cfg_dict.get("conjugate_gradient_iterations", 10),
+ self.mu,
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ self.coil_combination_method,
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor, # pylint: disable=unused-argument
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor,
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`MoDL`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ x = initial_prediction.clone()
+ for _ in range(self.unrolled_iterations):
+ x = self.reconstruction_module(x.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
+ if self.dc:
+ x = self.dc_block(initial_prediction + self.mu * x, sensitivity_maps, mask)
+ return check_stacked_complex(x)
diff --git a/atommic/collections/reconstruction/nn/modl_base/__init__.py b/atommic/collections/reconstruction/nn/modl_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/modl_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/modl_base/modl_block.py b/atommic/collections/reconstruction/nn/modl_base/modl_block.py
new file mode 100644
index 00000000..9f826ff9
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/modl_base/modl_block.py
@@ -0,0 +1,220 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Sequence
+
+import torch
+from torch import nn
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import coil_combination_method
+
+
+class ResidualNetwork(nn.Module):
+ """Residual Network, as presented in [Aggarwal2018]_, [Yaman2020]_.
+
+ References
+ ----------
+ [Aggarwal2018] MoDL: Model Based Deep Learning Architecture for Inverse Problems by H.K. Aggarwal, M.P Mani, and
+ Mathews Jacob in IEEE Transactions on Medical Imaging, 2018
+
+ [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uฤurbil, K, Akรงakaya, M. Self-supervised learning of
+ physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson Med. 2020; 84:
+ 3172โ 3191. https://doi.org/10.1002/mrm.28378
+ """
+
+ def __init__(self, nb_res_blocks: int = 15, channels: int = 64, regularization_factor: float = 0.1):
+ """Inits :class:`ResidualNetwork`.
+
+ Parameters
+ ----------
+ nb_res_blocks : int, optional
+ Number of residual blocks. Default is ``15``.
+ channels : int, optional
+ Number of channels. Default is ``64``.
+ regularization_factor : float, optional
+ Regularization factor. Default is ``0.1``.
+ """
+ super().__init__()
+ self.relu = nn.ReLU(inplace=True)
+ self.conv1 = nn.Conv2d(2, channels, kernel_size=3, stride=1, padding="same", bias=False)
+ self.layers1 = nn.ModuleList()
+ self.layers2 = nn.ModuleList()
+ for _ in range(1, nb_res_blocks + 1):
+ self.layers1.append(nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding="same", bias=False))
+ self.layers2.append(nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding="same", bias=False))
+ self.last_layer = nn.Conv2d(channels, channels, kernel_size=3, stride=1, padding="same", bias=False)
+ self.final_layer = nn.Conv2d(channels, 2, kernel_size=3, stride=1, padding="same", bias=False)
+ self.scaling = torch.tensor([regularization_factor]).type(torch.float32)
+ self.__weights_initialization__()
+
+ def __weights_initialization__(self):
+ """Initializes the weights of the network."""
+ for m in self.modules():
+ if isinstance(m, nn.Conv2d):
+ nn.init.xavier_normal_(m.weight)
+ if m.bias is not None:
+ nn.init.zeros_(m.bias)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`ResidualNetwork`."""
+ out = self.conv1(x)
+ x = out
+ for i in range(len(self.layers1)): # pylint: disable=consider-using-enumerate
+ x = self.scaling.to(x.device) * self.layers2[i](self.relu(self.layers1[i](x))) + x
+ x = self.last_layer(x)
+ x = x + out
+ return self.final_layer(x)
+
+
+class ConjugateGradient(nn.Module):
+ """Conjugate Gradient algorithm for solving the linear system of equations, as presented in [Aggarwal2018]_,
+ [Yaman2020]_.
+
+ References
+ ----------
+ [Aggarwal2018] MoDL: Model Based Deep Learning Architecture for Inverse Problems by H.K. Aggarwal, M.P Mani, and
+ Mathews Jacob in IEEE Transactions on Medical Imaging, 2018
+
+ [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uฤurbil, K, Akรงakaya, M. Self-supervised learning of
+ physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson Med. 2020; 84:
+ 3172โ 3191. https://doi.org/10.1002/mrm.28378
+ """
+
+ def __init__(
+ self,
+ CG_Iter: int = 10,
+ mu: nn.Parameter = nn.Parameter(torch.tensor([0.05]).type(torch.float32)),
+ fft_centered: bool = False,
+ fft_normalization: str = "ortho",
+ spatial_dims: Sequence[int] = (-2, -1),
+ coil_dim: int = 1,
+ coil_combination_method: str = "SENSE",
+ ):
+ """Inits :class:`ConjugateGradient`.
+
+ Parameters
+ ----------
+ CG_Iter : int, optional
+ Number of CG iterations. Default is ``10``.
+ mu : torch.nn.Parameter, optional
+ Regularization parameter. Default is ``0.05``.
+ fft_centered : bool, optional
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization : str, optional
+ Normalization type of the FFT. Default is ``"ortho"``.
+ spatial_dims : Sequence[int], optional
+ Spatial dimensions of the input. Default is ``(-2, -1)``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ """
+ super().__init__()
+ self.CG_Iter = CG_Iter
+ self.mu = mu
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims
+ self.coil_dim = coil_dim
+ self.coil_combination_method = coil_combination_method
+
+ def EhE_Op(
+ self, prediction: torch.Tensor, sens_maps: torch.Tensor, mask: torch.Tensor # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """This function calculates the product of the operator EhE with a given vector.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ The input vector.
+ sens_maps : torch.Tensor
+ The sensitivity maps.
+ mask : torch.Tensor
+ The undersampling mask.
+
+ Returns
+ -------
+ torch.Tensor
+ Data consistency term.
+ """
+ masked_kspace = fft2(
+ prediction.unsqueeze(self.coil_dim) * sens_maps,
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ )
+ masked_kspace = torch.view_as_real(masked_kspace[..., 0] + 1j * masked_kspace[..., 1])
+ image_space = ifft2(masked_kspace, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ pred = coil_combination_method(
+ image_space, torch.view_as_real(sens_maps), self.coil_combination_method, self.coil_dim
+ )
+ pred = torch.view_as_real(pred[..., 0] + 1j * pred[..., 1])
+ return torch.view_as_complex(pred) + self.mu * prediction
+
+ def proximal_gradient(
+ self,
+ rsold: torch.Tensor,
+ x: torch.Tensor,
+ r: torch.Tensor,
+ p: torch.Tensor,
+ sens_maps: torch.Tensor,
+ mask: torch.Tensor,
+ ) -> torch.Tensor:
+ """Proximal gradient descent.
+
+ Parameters
+ ----------
+ rsold : torch.Tensor
+ The residual.
+ x : torch.Tensor
+ The current estimate.
+ r : torch.Tensor
+ The current residual.
+ p : torch.Tensor
+ The current search direction.
+ sens_maps : torch.Tensor
+ The sensitivity maps.
+ mask : torch.Tensor
+ The undersampling mask.
+
+ Returns
+ -------
+ torch.Tensor
+ The new residual, the new estimate, the new residual, and the new search direction.
+ """
+ Ap = self.EhE_Op(p, sens_maps, mask)
+ alpha = rsold / torch.sum(torch.conj(p) * Ap)
+ alpha = alpha + 0j
+ x = x + alpha * p
+ r = r - alpha * Ap
+ rsnew = torch.sum(torch.conj(r) * r)
+ beta = rsnew / rsold
+ beta = beta + 0j
+ p = r + beta * p
+ return rsnew, x, r, p
+
+ def forward(self, rhs: torch.Tensor, sens_maps: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`ConjugateGradient`.
+
+ Parameters
+ ----------
+ rhs : torch.Tensor
+ The right-hand side of the linear system.
+ sens_maps : torch.Tensor
+ The sensitivity maps.
+ mask : torch.Tensor
+ The undersampling mask.
+
+ Returns
+ -------
+ torch.Tensor
+ The solution of the linear system.
+ """
+ rhs = torch.view_as_complex(rhs)
+ sens_maps = torch.view_as_complex(sens_maps)
+ x = torch.zeros_like(rhs)
+ i, r, p = 0, rhs, rhs
+ rsold = torch.sum(torch.conj(r) * r)
+ while i < self.CG_Iter:
+ rsold, x, r, p = self.proximal_gradient(rsold, x, r, p, sens_maps, mask)
+ i = i + 1
+ return torch.view_as_real(x)
diff --git a/atommic/collections/reconstruction/nn/multidomainnet.py b/atommic/collections/reconstruction/nn/multidomainnet.py
new file mode 100644
index 00000000..cfd8c491
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/multidomainnet.py
@@ -0,0 +1,117 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import ifft2
+from atommic.collections.common.parts.utils import check_stacked_complex, coil_combination_method
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.multidomainnet_base.multidomainnet_block import (
+ MultiDomainUnet2d,
+ StandardizationLayer,
+)
+from atommic.core.classes.common import typecheck
+
+__all__ = ["MultiDomainNet"]
+
+
+class MultiDomainNet(BaseMRIReconstructionModel):
+ """Feature-level multi-domain module. Inspired by AIRS Medical submission to the FastMRI 2020 challenge."""
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`MultiDomainNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.num_cascades = cfg_dict.get("num_cascades")
+
+ standardization = cfg_dict["standardization"]
+ if standardization:
+ self.standardization = StandardizationLayer(self.coil_dim, -1)
+
+ self.reconstruction_module = MultiDomainUnet2d(
+ # if standardization, in_channels is 4 due to standardized input
+ in_channels=4 if standardization else 2,
+ out_channels=2,
+ num_filters=cfg_dict["num_filters"],
+ num_pool_layers=cfg_dict["num_pool_layers"],
+ dropout_probability=cfg_dict["dropout_probability"],
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ )
+
+ def _compute_model_per_coil(self, model: torch.nn.Module, data: torch.Tensor) -> torch.Tensor:
+ """Computes the model per coil.
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ The model to be computed.
+ data : torch.Tensor
+ The data to be computed. Shape [batch_size, n_coils, n_x, n_y, 2].
+
+ Returns
+ -------
+ torch.Tensor
+ The computed output. Shape [batch_size, n_coils, n_x, n_y, 2].
+ """
+ output = []
+ for idx in range(data.size(self.coil_dim)):
+ subselected_data = data.select(self.coil_dim, idx)
+ output.append(model(subselected_data))
+ output = torch.stack(output, dim=self.coil_dim)
+ return output
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor, # pylint: disable=unused-argument
+ initial_prediction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`MultiDomainNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ image = ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims)
+ if hasattr(self, "standardization"):
+ image = self.standardization(image, sensitivity_maps)
+ prediction = self._compute_model_per_coil(self.reconstruction_module, image.permute(0, 1, 4, 2, 3)).permute(
+ 0, 1, 3, 4, 2
+ )
+ return check_stacked_complex(
+ coil_combination_method(prediction, sensitivity_maps, self.coil_combination_method, self.coil_dim)
+ )
diff --git a/atommic/collections/reconstruction/nn/multidomainnet_base/__init__.py b/atommic/collections/reconstruction/nn/multidomainnet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/multidomainnet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/multidomainnet_base/multidomainnet_block.py b/atommic/collections/reconstruction/nn/multidomainnet_base/multidomainnet_block.py
new file mode 100644
index 00000000..bceef88b
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/multidomainnet_base/multidomainnet_block.py
@@ -0,0 +1,495 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from:https://github.com/NKI-AI/direct/blob/main/direct/nn/multidomainnet/multidomain.py
+
+from typing import Optional, Sequence, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import complex_conj, complex_mul
+
+
+class MultiDomainConv2d(nn.Module):
+ """Multi-domain convolution layer."""
+
+ def __init__(
+ self,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+ coil_dim: int = 1, # pylint: disable=unused-argument
+ in_channels: int = 4,
+ out_channels: int = 4,
+ **kwargs,
+ ):
+ """Inits :class:`MultiDomainConv2d`.
+
+ Parameters
+ ----------
+ fft_centered : bool, optional
+ If True, the FFT is centered. Default is ``False``.
+ fft_normalization : str, optional
+ Normalization of the FFT. Default is ``"backward"``.
+ spatial_dims : Sequence[int], optional
+ Spatial dimensions. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ in_channels : int, optional
+ Number of input channels. Default is ``4``.
+ out_channels : int, optional
+ Number of output channels. Default is ``4``.
+ """
+ super().__init__()
+
+ self.image_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels // 2, **kwargs)
+ self.kspace_conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels // 2, **kwargs)
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = 1
+
+ def forward(self, image: torch.Tensor) -> torch.Tensor:
+ """Forward method of :class:`MultiDomainConv2d`."""
+ kspace = [
+ fft2(im, centered=self.fft_centered, normalization=self.fft_normalization, spatial_dims=self.spatial_dims)
+ for im in torch.split(image.permute(0, 2, 3, 1).contiguous(), 2, -1)
+ ]
+ kspace = torch.cat(kspace, -1).permute(0, 3, 1, 2)
+ kspace = self.kspace_conv(kspace)
+
+ backward = [
+ ifft2(
+ ks.float(),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ).type(image.type())
+ for ks in torch.split(kspace.permute(0, 2, 3, 1).contiguous(), 2, -1) # type: ignore
+ ]
+ backward = torch.cat(backward, -1).permute(0, 3, 1, 2)
+ image = self.image_conv(image)
+ image = torch.cat([image, backward], dim=self.coil_dim)
+ return image
+
+
+class MultiDomainConvTranspose2d(nn.Module):
+ """Multi-Domain convolutional transpose layer."""
+
+ def __init__(
+ self,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+ coil_dim: int = 1, # pylint: disable=unused-argument
+ in_channels: int = 4,
+ out_channels: int = 4,
+ **kwargs,
+ ):
+ """Inits :class:`MultiDomainConvTranspose2d`.
+
+ Parameters
+ ----------
+ fft_centered : bool, optional
+ If True, the FFT is centered. Default is ``False``.
+ fft_normalization : str, optional
+ Normalization of the FFT. Default is ``"backward"``.
+ spatial_dims : Sequence[int], optional
+ Spatial dimensions. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ in_channels : int, optional
+ Number of input channels. Default is ``4``.
+ out_channels : int, optional
+ Number of output channels. Default is ``4``.
+ """
+ super().__init__()
+ self.image_conv = nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels // 2, **kwargs)
+ self.kspace_conv = nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels // 2, **kwargs)
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = 1
+
+ def forward(self, image: torch.Tensor) -> torch.Tensor:
+ """Forward method of :class:`MultiDomainConvTranspose2d`."""
+ kspace = [
+ fft2(im, centered=self.fft_centered, normalization=self.fft_normalization, spatial_dims=self.spatial_dims)
+ for im in torch.split(image.permute(0, 2, 3, 1).contiguous(), 2, -1)
+ ]
+ kspace = torch.cat(kspace, -1).permute(0, 3, 1, 2)
+ kspace = self.kspace_conv(kspace)
+
+ backward = [
+ ifft2(
+ ks.float(),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ).type(image.type())
+ for ks in torch.split(kspace.permute(0, 2, 3, 1).contiguous(), 2, -1) # type: ignore
+ ]
+ backward = torch.cat(backward, -1).permute(0, 3, 1, 2)
+
+ image = self.image_conv(image)
+ return torch.cat([image, backward], dim=self.coil_dim)
+
+
+class MultiDomainConvBlock(nn.Module):
+ """A multi-domain convolutional block that consists of two multi-domain convolution layers each followed by
+ instance normalization, LeakyReLU activation and dropout.
+ """
+
+ def __init__(
+ self,
+ fft_centered: bool = False,
+ fft_normalization: str = "backwar",
+ spatial_dims: Sequence[int] = None,
+ coil_dim: int = 1,
+ in_channels: int = 4,
+ out_channels: int = 4,
+ dropout_probability: float = 0.0,
+ ):
+ """Inits :class:`MultiDomainConvBlock`.
+
+ Parameters
+ ----------
+ fft_centered : bool, optional
+ If True, the FFT is centered. Default is ``False``.
+ fft_normalization : str, optional
+ Normalization of the FFT. Default is ``"backward"``.
+ spatial_dims : Sequence[int], optional
+ Spatial dimensions. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ in_channels : int, optional
+ Number of input channels. Default is ``4``.
+ out_channels : int, optional
+ Number of output channels. Default is ``4``.
+ """
+ super().__init__()
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.dropout_probability = dropout_probability
+
+ self.layers = nn.Sequential(
+ MultiDomainConv2d(
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ in_channels,
+ out_channels,
+ kernel_size=3,
+ padding=1,
+ bias=False,
+ ),
+ nn.InstanceNorm2d(out_channels),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Dropout2d(dropout_probability),
+ MultiDomainConv2d(
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ out_channels,
+ out_channels,
+ kernel_size=3,
+ padding=1,
+ bias=False,
+ ),
+ nn.InstanceNorm2d(out_channels),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Dropout2d(dropout_probability),
+ )
+
+ def forward(self, _input: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`MultiDomainConvBlock`."""
+ return self.layers(_input)
+
+ def __repr__(self):
+ """Representation of :class:`MultiDomainConvBlock`."""
+ return (
+ f"MultiDomainConvBlock(in_channels={self.in_channels}, out_channels={self.out_channels}, "
+ f"dropout_probability={self.dropout_probability})"
+ )
+
+
+class TransposeMultiDomainConvBlock(nn.Module):
+ """A Transpose Convolutional Block that consists of one convolution transpose layers followed by instance
+ normalization and LeakyReLU activation.
+ """
+
+ def __init__(
+ self,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Sequence[int] = None,
+ coil_dim: int = 1,
+ in_channels: int = 4,
+ out_channels: int = 4,
+ ):
+ """Inits :class:`TransposeMultiDomainConvBlock`.
+
+ Parameters
+ ----------
+ fft_centered : bool, optional
+ If True, the FFT is centered. Default is ``False``.
+ fft_normalization : str, optional
+ Normalization of the FFT. Default is ``"backward"``.
+ spatial_dims : Sequence[int], optional
+ Spatial dimensions. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ in_channels : int, optional
+ Number of input channels. Default is ``4``.
+ out_channels : int, optional
+ Number of output channels. Default is ``4``.
+ """
+ super().__init__()
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.layers = nn.Sequential(
+ MultiDomainConvTranspose2d(
+ fft_centered,
+ fft_normalization,
+ spatial_dims,
+ coil_dim,
+ in_channels,
+ out_channels,
+ kernel_size=2,
+ stride=2,
+ bias=False,
+ ),
+ nn.InstanceNorm2d(out_channels),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ )
+
+ def forward(self, input_data: torch.Tensor) -> torch.Tensor:
+ """Forward method of :class:`TransposeMultiDomainConvBlock`."""
+ return self.layers(input_data)
+
+ def __repr__(self):
+ """Representation of :class:`TransposeMultiDomainConvBlock`."""
+ return f"MultiDomainConvBlock(in_channels={self.in_channels}, out_channels={self.out_channels})"
+
+
+class StandardizationLayer(nn.Module):
+ r"""Multi-channel data standardization method. Inspired by AIRS model submission to the Fast MRI 2020 challenge.
+ Given individual coil images :math:`'\'{x_i'\'}_{i=1}^{N_c}` and sensitivity coil maps
+ :math:`'\'{S_i'\'}_{i=1}^{N_c}` it returns
+
+ .. math::
+
+ [(x_{sense}, {x_{res}}_1), ..., (x_{sense}, {x_{res}}_{N_c})]
+
+ where
+
+ :math:`{x_{res}}_i = xi - S_i X x_{sense}` and
+
+ :math:`x_{sense} = '\'sum_{i=1}^{N_c} {S_i}^{*} X x_i`.
+ """
+
+ def __init__(self, coil_dim: int = 1, channel_dim: int = -1):
+ """Inits :class:`StandardizationLayer`.
+
+ Parameters
+ ----------
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ channel_dim : int, optional
+ Channel dimension. Default is ``-1``.
+ """
+ super().__init__()
+ self.coil_dim = coil_dim
+ self.channel_dim = channel_dim
+
+ def forward(self, coil_images: torch.Tensor, sensitivity_map: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`StandardizationLayer`."""
+ combined_image = complex_mul(coil_images, complex_conj(sensitivity_map)).sum(self.coil_dim)
+ residual_image = combined_image.unsqueeze(self.coil_dim) - complex_mul(
+ combined_image.unsqueeze(self.coil_dim), sensitivity_map
+ )
+ return torch.cat(
+ [
+ torch.cat(
+ [combined_image, residual_image.select(self.coil_dim, idx)],
+ self.channel_dim,
+ ).unsqueeze(self.coil_dim)
+ for idx in range(coil_images.size(self.coil_dim))
+ ],
+ self.coil_dim,
+ )
+
+
+class MultiDomainUnet2d(nn.Module):
+ """Unet modification to be used with Multi-domain network as in AIRS Medical submission to the Fast MRI 2020
+ challenge.
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ num_filters: int,
+ num_pool_layers: int,
+ dropout_probability: float,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ ):
+ """Inits :class:`MultiDomainUnet2d`.
+
+ Parameters
+ ----------
+ in_channels : int, optional
+ Number of input channels.
+ out_channels : int, optional
+ Number of output channels.
+ num_filters : int, optional
+ Number of filters.
+ num_pool_layers : int, optional
+ Number of pooling layers.
+ dropout_probability : float, optional
+ Dropout probability.
+ fft_centered : bool, optional
+ If True, the FFT is centered. Default is ``False``.
+ fft_normalization : str, optional
+ Normalization of the FFT. Default is ``"backward"``.
+ spatial_dims : Sequence[int], optional
+ Spatial dimensions. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ """
+ super().__init__()
+
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.num_filters = num_filters
+ self.num_pool_layers = num_pool_layers
+ self.dropout_probability = dropout_probability
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+
+ self.down_sample_layers = nn.ModuleList(
+ [
+ MultiDomainConvBlock(
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ in_channels,
+ num_filters,
+ dropout_probability,
+ )
+ ]
+ )
+ ch = num_filters
+ for _ in range(num_pool_layers - 1):
+ self.down_sample_layers += [
+ MultiDomainConvBlock(
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ ch,
+ ch * 2,
+ dropout_probability,
+ )
+ ]
+ ch = ch * 2
+ self.conv = MultiDomainConvBlock(
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ ch,
+ ch * 2,
+ dropout_probability,
+ )
+
+ self.up_conv = nn.ModuleList()
+ self.up_transpose_conv = nn.ModuleList()
+ for _ in range(num_pool_layers - 1):
+ self.up_transpose_conv += [
+ TransposeMultiDomainConvBlock(
+ self.fft_centered, self.fft_normalization, self.spatial_dims, self.coil_dim, ch * 2, ch
+ )
+ ]
+ self.up_conv += [
+ MultiDomainConvBlock(
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ ch * 2,
+ ch,
+ dropout_probability,
+ )
+ ]
+ ch = ch // 2
+
+ self.up_transpose_conv += [
+ TransposeMultiDomainConvBlock(
+ self.fft_centered, self.fft_normalization, self.spatial_dims, self.coil_dim, ch * 2, ch
+ )
+ ]
+ self.up_conv += [
+ nn.Sequential(
+ MultiDomainConvBlock(
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ ch * 2,
+ ch,
+ dropout_probability,
+ ),
+ nn.Conv2d(ch, self.out_channels, kernel_size=1, stride=1),
+ )
+ ]
+
+ def forward(self, input_data: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`MultiDomainUnet2d`."""
+ stack = []
+ output = input_data
+
+ # Apply down-sampling layers
+ for layer in self.down_sample_layers:
+ output = layer(output)
+ stack.append(output)
+ output = F.avg_pool2d(output, kernel_size=2, stride=2, padding=0)
+
+ output = self.conv(output)
+
+ # Apply up-sampling layers
+ for transpose_conv, conv in zip(self.up_transpose_conv, self.up_conv):
+ downsample_layer = stack.pop()
+ output = transpose_conv(output)
+
+ # Reflect pad on the right/bottom if needed to handle odd input dimensions.
+ padding = [0, 0, 0, 0]
+ if output.shape[-1] != downsample_layer.shape[-1]:
+ padding[1] = 1 # Padding right
+ if output.shape[-2] != downsample_layer.shape[-2]:
+ padding[3] = 1 # Padding bottom
+ if sum(padding) != 0:
+ output = F.pad(output, padding, "reflect")
+
+ output = torch.cat([output, downsample_layer], dim=1)
+ output = conv(output)
+
+ return output
diff --git a/atommic/collections/reconstruction/nn/mwcnn_base/__init__.py b/atommic/collections/reconstruction/nn/mwcnn_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/mwcnn_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/mwcnn_base/mwcnn_block.py b/atommic/collections/reconstruction/nn/mwcnn_base/mwcnn_block.py
new file mode 100644
index 00000000..51bc110b
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/mwcnn_base/mwcnn_block.py
@@ -0,0 +1,463 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/direct/nn/mwcnn/mwcnn.py
+
+from collections import OrderedDict
+from typing import Optional, Tuple
+
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+
+class DWT(nn.Module):
+ """2D Discrete Wavelet Transform as presented in [Liu2018]_.
+
+ References
+ ----------
+ .. [Liu2018] Liu, Pengju, et al. โMulti-Level Wavelet-CNN for Image Restoration.โ ArXiv:1805.07071 [Cs], May 2018.
+ arXiv.org, http://arxiv.org/abs/1805.07071.
+ """
+
+ def __init__(self):
+ """Inits :class:`DWT`."""
+ super().__init__()
+ self.requires_grad = False
+
+ @staticmethod
+ def forward(x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`DWT`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor.
+
+ Returns
+ -------
+ torch.Tensor
+ DWT of `x`.
+ """
+ x01 = x[:, :, 0::2, :] / 2
+ x02 = x[:, :, 1::2, :] / 2
+ x1 = x01[:, :, :, 0::2]
+ x2 = x02[:, :, :, 0::2]
+ x3 = x01[:, :, :, 1::2]
+ x4 = x02[:, :, :, 1::2]
+ x_LL = x1 + x2 + x3 + x4
+ x_HL = -x1 - x2 + x3 + x4
+ x_LH = -x1 + x2 - x3 + x4
+ x_HH = x1 - x2 - x3 + x4
+
+ return torch.cat((x_LL, x_HL, x_LH, x_HH), 1)
+
+
+class IWT(nn.Module):
+ """2D Inverse Wavelet Transform as presented in [Liu2018]_.
+
+ References
+ ----------
+ .. [Liu2018] Liu, Pengju, et al. โMulti-Level Wavelet-CNN for Image Restoration.โ ArXiv:1805.07071 [Cs], May 2018.
+ arXiv.org, http://arxiv.org/abs/1805.07071.
+ """
+
+ def __init__(self):
+ """Inits :class:`IWT`."""
+ super().__init__()
+ self.requires_grad = False
+ self._r = 2
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`IWT`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor.
+
+ Returns
+ -------
+ torch.Tensor
+ IWT of `x`.
+ """
+ batch, in_channel, in_height, in_width = x.size()
+ out_channel, out_height, out_width = int(in_channel / (self._r**2)), self._r * in_height, self._r * in_width
+
+ x1 = x[:, 0:out_channel, :, :] / 2
+ x2 = x[:, out_channel : out_channel * 2, :, :] / 2
+ x3 = x[:, out_channel * 2 : out_channel * 3, :, :] / 2
+ x4 = x[:, out_channel * 3 : out_channel * 4, :, :] / 2
+
+ h = torch.zeros([batch, out_channel, out_height, out_width], dtype=x.dtype).to(x.device)
+
+ h[:, :, 0::2, 0::2] = x1 - x2 - x3 + x4
+ h[:, :, 1::2, 0::2] = x1 - x2 + x3 - x4
+ h[:, :, 0::2, 1::2] = x1 + x2 - x3 - x4
+ h[:, :, 1::2, 1::2] = x1 + x2 + x3 + x4
+
+ return h
+
+
+class ConvBlock(nn.Module):
+ """Convolution Block for MWCNN as presented in [Liu2018]_.
+
+ References
+ ----------
+ .. [Liu2018] Liu, Pengju, et al. โMulti-Level Wavelet-CNN for Image Restoration.โ ArXiv:1805.07071 [Cs], May 2018.
+ arXiv.org, http://arxiv.org/abs/1805.07071.
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: int,
+ bias: bool = True,
+ batchnorm: bool = False,
+ activation: nn.Module = nn.ReLU(True),
+ scale: Optional[float] = 1.0,
+ ):
+ """Inits :class:`ConvBlock`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ kernel_size : int
+ Kernel size of convolution.
+ bias : bool, optional
+ Use convolution bias. Default is ``True``.
+ batchnorm : bool, optional
+ Use batch normalization. Default is ``False``.
+ activation : torch.nn.Module, optional
+ Activation function. Default is ``nn.ReLU(True)``.
+ scale : float, optional
+ Scale factor for convolution. Default is ``1.0``.
+ """
+ super().__init__()
+
+ net = [
+ nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ bias=bias,
+ padding=kernel_size // 2,
+ )
+ ]
+
+ if batchnorm:
+ net.append(nn.BatchNorm2d(num_features=out_channels, eps=1e-4, momentum=0.95))
+ net.append(activation)
+
+ self.net = nn.Sequential(*net)
+ self.scale = scale
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`ConvBlock`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input with shape (N, C, H, W).
+
+ Returns
+ -------
+ torch.Tensor
+ Output with shape (N, C', H', W').
+ """
+ return self.net(x) * self.scale
+
+
+class DilatedConvBlock(nn.Module):
+ """Double dilated Convolution Block fpr MWCNN as presented in [Liu2018]_.
+
+ References
+ ----------
+ .. [Liu2018] Liu, Pengju, et al. โMulti-Level Wavelet-CNN for Image Restoration.โ ArXiv:1805.07071 [Cs], May 2018.
+ arXiv.org, http://arxiv.org/abs/1805.07071.
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ dilations: Tuple[int, int],
+ kernel_size: int,
+ out_channels: Optional[int] = None,
+ bias: bool = True,
+ batchnorm: bool = False,
+ activation: nn.Module = nn.ReLU(True),
+ scale: Optional[float] = 1.0,
+ ):
+ """Inits :class:`DilatedConvBlock`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ dilations : Tuple[int, int]
+ Dilations for first and second convolution.
+ kernel_size : int
+ Kernel size of convolution.
+ out_channels : int
+ Number of output channels.
+ bias : bool, optional
+ Use convolution bias. Default is ``True``.
+ batchnorm : bool, optional
+ Use batch normalization. Default is ``False``.
+ activation : torch.nn.Module, optional
+ Activation function. Default is ``nn.ReLU(True)``.
+ scale : float, optional
+ Scale factor for convolution. Default is ``1.0``.
+ """
+ super().__init__()
+ net = [
+ nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=in_channels,
+ kernel_size=kernel_size,
+ bias=bias,
+ dilation=dilations[0],
+ padding=kernel_size // 2 + dilations[0] - 1,
+ )
+ ]
+
+ if batchnorm:
+ net.append(nn.BatchNorm2d(num_features=in_channels, eps=1e-4, momentum=0.95))
+ net.append(activation)
+ if out_channels is None:
+ out_channels = in_channels
+ net.append(
+ nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ bias=bias,
+ dilation=dilations[1],
+ padding=kernel_size // 2 + dilations[1] - 1,
+ )
+ )
+ if batchnorm:
+ net.append(nn.BatchNorm2d(num_features=in_channels, eps=1e-4, momentum=0.95))
+ net.append(activation)
+
+ self.net = nn.Sequential(*net)
+ self.scale = scale
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`DilatedConvBlock`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input with shape (N, C, H, W).
+
+ Returns
+ -------
+ torch.Tensor
+ Output with shape (N, C', H', W').
+ """
+ return self.net(x) * self.scale
+
+
+class MWCNN(nn.Module):
+ r"""Multi-level Wavelet CNN (MWCNN) implementation as presented in [Liu2018]_.
+
+ References
+ ----------
+ .. [Liu2018] Liu, Pengju, et al. โMulti-Level Wavelet-CNN for Image Restoration.โ ArXiv:1805.07071 [Cs], May 2018.
+ arXiv.org, http://arxiv.org/abs/1805.07071.
+
+ """
+
+ def __init__(
+ self,
+ input_channels: int,
+ first_conv_hidden_channels: int,
+ num_scales: int = 4,
+ bias: bool = True,
+ batchnorm: bool = False,
+ activation: nn.Module = nn.ReLU(True),
+ ):
+ """Inits :class:`MWCNN`.
+
+ Parameters
+ ----------
+ input_channels : int
+ Number of input channels.
+ first_conv_hidden_channels : int
+ Number of hidden channels in first convolution.
+ num_scales : int, optional
+ Number of scales. Default is ``4``.
+ bias : bool, optional
+ Use convolution bias. Default is ``True``.
+ batchnorm : bool, optional
+ Use batch normalization. Default is ``False``.
+ activation : torch.nn.Module, optional
+ Activation function. Default is ``nn.ReLU(True)``.
+ """
+ super().__init__()
+ self._kernel_size = 3
+ self.DWT = DWT()
+ self.IWT = IWT()
+
+ self.down = nn.ModuleList()
+ for idx in range(num_scales):
+ in_channels = input_channels if idx == 0 else first_conv_hidden_channels * 2 ** (idx + 1)
+ out_channels = first_conv_hidden_channels * 2**idx
+ dilations = (2, 1) if idx != num_scales - 1 else (2, 3)
+ self.down.append(
+ nn.Sequential(
+ OrderedDict(
+ [
+ (
+ f"convblock{idx}",
+ ConvBlock(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=self._kernel_size,
+ bias=bias,
+ batchnorm=batchnorm,
+ activation=activation,
+ ),
+ ),
+ (
+ f"dilconvblock{idx}",
+ DilatedConvBlock(
+ in_channels=out_channels,
+ dilations=dilations,
+ kernel_size=self._kernel_size,
+ bias=bias,
+ batchnorm=batchnorm,
+ activation=activation,
+ ),
+ ),
+ ]
+ )
+ )
+ )
+ self.up = nn.ModuleList()
+ for idx in range(num_scales)[::-1]:
+ in_channels = first_conv_hidden_channels * 2**idx
+ out_channels = input_channels if idx == 0 else first_conv_hidden_channels * 2 ** (idx + 1)
+ dilations = (2, 1) if idx != num_scales - 1 else (3, 2)
+ self.up.append(
+ nn.Sequential(
+ OrderedDict(
+ [
+ (
+ f"invdilconvblock{num_scales - 2 - idx}",
+ DilatedConvBlock(
+ in_channels=in_channels,
+ dilations=dilations,
+ kernel_size=self._kernel_size,
+ bias=bias,
+ batchnorm=batchnorm,
+ activation=activation,
+ ),
+ ),
+ (
+ f"invconvblock{num_scales - 2 - idx}",
+ ConvBlock(
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=self._kernel_size,
+ bias=bias,
+ batchnorm=batchnorm,
+ activation=activation,
+ ),
+ ),
+ ]
+ )
+ )
+ )
+ self.num_scales = num_scales
+
+ @staticmethod
+ def pad(x: torch.Tensor) -> torch.Tensor:
+ """Pads input to height and width dimensions if odd.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor.
+
+ Returns
+ -------
+ torch.Tensor
+ Padded tensor.
+ """
+ padding = [0, 0, 0, 0]
+
+ if x.shape[-2] % 2 != 0:
+ padding[3] = 1 # Padding right - width
+ if x.shape[-1] % 2 != 0:
+ padding[1] = 1 # Padding bottom - height
+ if sum(padding) != 0:
+ x = F.pad(x, padding, "reflect")
+ return x
+
+ @staticmethod
+ def crop_to_shape(x: torch.Tensor, shape: tuple) -> torch.Tensor:
+ r"""Crops ``x`` to specified shape.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor with shape ('\'*, H, W).
+ shape : tuple
+ Crop shape corresponding to H, W.
+
+ Returns
+ -------
+ torch.Tensor
+ Cropped tensor.
+ """
+ h, w = x.shape[-2:]
+ if h > shape[0]:
+ x = x[:, :, : shape[0], :]
+ if w > shape[1]:
+ x = x[:, :, :, : shape[1]]
+ return x
+
+ def forward(self, input_tensor: torch.Tensor, res: bool = False) -> torch.Tensor:
+ r"""Forward pass of :class:`MWCNN`.
+
+ Parameters
+ ----------
+ input_tensor : torch.Tensor
+ Input tensor with shape ('\'*, C, H, W).
+ res : bool, optional
+ If True, residual connection is applied to the output. Default is ``False``.
+
+ Returns
+ -------
+ torch.Tensor
+ Output tensor with shape ('\'*, C, H, W).
+ """
+ res_values = []
+ x = self.pad(input_tensor.clone())
+ for idx in range(self.num_scales):
+ if idx == 0:
+ x = self.pad(self.down[idx](x))
+ res_values.append(x)
+ elif idx == self.num_scales - 1:
+ x = self.down[idx](self.DWT(x))
+ else:
+ x = self.pad(self.down[idx](self.DWT(x)))
+ res_values.append(x)
+
+ for idx in range(self.num_scales):
+ if idx != self.num_scales - 1:
+ x = (
+ self.crop_to_shape(self.IWT(self.up[idx](x)), res_values[self.num_scales - 2 - idx].shape[-2:])
+ + res_values[self.num_scales - 2 - idx]
+ )
+ else:
+ x = self.crop_to_shape(self.up[idx](x), input_tensor.shape[-2:])
+ if res:
+ x = x + input_tensor
+ return x
diff --git a/atommic/collections/reconstruction/nn/primaldualnet_base/__init__.py b/atommic/collections/reconstruction/nn/primaldualnet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/primaldualnet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/primaldualnet_base/primaldualnet_block.py b/atommic/collections/reconstruction/nn/primaldualnet_base/primaldualnet_block.py
new file mode 100644
index 00000000..09fc6316
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/primaldualnet_base/primaldualnet_block.py
@@ -0,0 +1,113 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/direct/nn/lpd/lpd.py
+
+import torch
+from torch import nn
+
+
+class DualNet(nn.Module):
+ """Dual Network for Learned Primal Dual Network."""
+
+ def __init__(self, num_dual, **kwargs):
+ """Inits :class:`DualNet`.
+
+ Parameters
+ ----------
+ num_dual : int
+ Number of dual for LPD algorithm.
+ """
+ super().__init__()
+ if kwargs.get("dual_architecture") is None:
+ n_hidden = kwargs.get("n_hidden")
+ if n_hidden is None:
+ raise ValueError("n_hidden is required for DualNet")
+
+ self.dual_block = nn.Sequential(
+ *[
+ nn.Conv2d(2 * (num_dual + 2), n_hidden, kernel_size=3, padding=1),
+ nn.PReLU(),
+ nn.Conv2d(n_hidden, n_hidden, kernel_size=3, padding=1),
+ nn.PReLU(),
+ nn.Conv2d(n_hidden, 2 * num_dual, kernel_size=3, padding=1),
+ ]
+ )
+ else:
+ self.dual_block = kwargs.get("dual_architecture")
+
+ @staticmethod
+ def compute_model_per_coil(model: nn.Module, data: torch.Tensor) -> torch.Tensor:
+ """Computes model per coil.
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Model to be computed.
+ data : torch.Tensor
+ Input data.
+
+ Returns
+ -------
+ torch.Tensor
+ Multicoil output.
+ """
+ output = []
+ for idx in range(data.size(1)):
+ subselected_data = data.select(1, idx)
+ output.append(model(subselected_data))
+ output = torch.stack(output, dim=1)
+ return output
+
+ def forward(self, h: torch.Tensor, forward_f: torch.Tensor, g: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`DualNet`."""
+ inp = torch.cat([h, forward_f, g], dim=-1).permute(0, 1, 4, 2, 3)
+ return self.compute_model_per_coil(self.dual_block, inp).permute(0, 1, 3, 4, 2)
+
+
+class PrimalNet(nn.Module):
+ """Primal Network for Learned Primal Dual Network."""
+
+ def __init__(self, num_primal, **kwargs):
+ """Inits :class:`PrimalNet`.
+
+ Parameters
+ ----------
+ num_primal : int
+ Number of primal for LPD algorithm.
+ """
+ super().__init__()
+
+ if kwargs.get("primal_architecture") is None:
+ n_hidden = kwargs.get("n_hidden")
+ if n_hidden is None:
+ raise ValueError("Missing argument n_hidden.")
+ self.primal_block = nn.Sequential(
+ *[
+ nn.Conv2d(2 * (num_primal + 1), n_hidden, kernel_size=3, padding=1),
+ nn.PReLU(),
+ nn.Conv2d(n_hidden, n_hidden, kernel_size=3, padding=1),
+ nn.PReLU(),
+ nn.Conv2d(n_hidden, 2 * num_primal, kernel_size=3, padding=1),
+ ]
+ )
+ else:
+ self.primal_block = kwargs.get("primal_architecture")
+
+ def forward(self, f: torch.Tensor, backward_h: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`PrimalNet`.
+
+ Parameters
+ ----------
+ f : torch.Tensor
+ Forward function.
+ backward_h : torch.Tensor
+ Backward function.
+
+ Returns
+ -------
+ torch.Tensor
+ Primal function.
+ """
+ inp = torch.cat([f, backward_h], dim=-1).permute(0, 3, 1, 2)
+ return self.primal_block(inp).permute(0, 2, 3, 1)
diff --git a/atommic/collections/reconstruction/nn/proximal_gradient.py b/atommic/collections/reconstruction/nn/proximal_gradient.py
new file mode 100644
index 00000000..722c6f40
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/proximal_gradient.py
@@ -0,0 +1,85 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch import nn
+
+from atommic.collections.common.parts.utils import check_stacked_complex
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.modl_base.modl_block import ConjugateGradient
+from atommic.core.classes.common import typecheck
+
+__all__ = ["ProximalGradient"]
+
+
+class ProximalGradient(BaseMRIReconstructionModel):
+ """Implementation of the Proximal/Conjugate Gradient, according to [Aggarwal2018]_, [Yaman2020]_.
+
+ References
+ ----------
+ .. [Aggarwal2018] MoDL: Model Based Deep Learning Architecture for Inverse Problems by H.K. Aggarwal, M.P Mani, and
+ Mathews Jacob in IEEE Transactions on Medical Imaging, 2018
+
+ .. [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uฤurbil, K, Akรงakaya, M. Self-supervised
+ learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson
+ Med. 2020; 84: 3172โ 3191. https://doi.org/10.1002/mrm.28378
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`ProximalGradient`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+ self.mu = nn.Parameter(torch.Tensor([cfg_dict.get("penalization_weight")]), requires_grad=True)
+ self.dc_block = ConjugateGradient(
+ cfg_dict.get("conjugate_gradient_iterations", 10),
+ self.mu,
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ self.coil_combination_method,
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor, # pylint: disable=unused-argument
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor,
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`ProximalGradient`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ return check_stacked_complex(self.dc_block(initial_prediction, sensitivity_maps, mask))
diff --git a/atommic/collections/reconstruction/nn/recurrentvarnet.py b/atommic/collections/reconstruction/nn/recurrentvarnet.py
new file mode 100644
index 00000000..c581e6ee
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/recurrentvarnet.py
@@ -0,0 +1,179 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import math
+from typing import Optional
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import check_stacked_complex, coil_combination_method, rnn_weights_init
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.recurrentvarnet_base.recurrentvarnet_block import (
+ RecurrentInit,
+ RecurrentVarNetBlock,
+)
+from atommic.core.classes.common import typecheck
+
+__all__ = ["RecurrentVarNet"]
+
+
+class RecurrentVarNet(BaseMRIReconstructionModel):
+ """Implementation of the Recurrent Variational Network implementation, as presented in [Yiasemis2021]_.
+
+ References
+ ----------
+ .. [Yiasemis2021] Yiasemis, George, et al. โRecurrent Variational Network: A Deep Learning Inverse Problem Solver
+ Applied to the Task of Accelerated MRI Reconstruction.โ ArXiv:2111.09639 [Physics], Nov. 2021. arXiv.org,
+ http://arxiv.org/abs/2111.09639.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`RecurrentVarNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.in_channels = cfg_dict.get("in_channels")
+ self.recurrent_hidden_channels = cfg_dict.get("recurrent_hidden_channels")
+ self.recurrent_num_layers = cfg_dict.get("recurrent_num_layers")
+ self.no_parameter_sharing = cfg_dict.get("no_parameter_sharing")
+
+ # make time-steps size divisible by 8 for fast fp16 training
+ self.num_steps = 8 * math.ceil(cfg_dict.get("num_steps") / 8)
+
+ self.learned_initializer = cfg_dict.get("learned_initializer")
+ self.initializer_initialization = cfg_dict.get("initializer_initialization")
+ self.initializer_channels = cfg_dict.get("initializer_channels")
+ self.initializer_dilations = cfg_dict.get("initializer_dilations")
+
+ if (
+ self.learned_initializer
+ and self.initializer_initialization is not None
+ and self.initializer_channels is not None
+ and self.initializer_dilations is not None
+ ):
+ if self.initializer_initialization not in [
+ "sense",
+ "input_image",
+ "zero_filled",
+ ]:
+ raise ValueError(
+ "Unknown initializer_initialization. Expected `sense`, `'input_image` or `zero_filled`."
+ f"Got {self.initializer_initialization}."
+ )
+ self.initializer = RecurrentInit(
+ self.in_channels,
+ self.recurrent_hidden_channels,
+ channels=self.initializer_channels,
+ dilations=self.initializer_dilations,
+ depth=self.recurrent_num_layers,
+ multiscale_depth=cfg_dict.get("initializer_multiscale"),
+ )
+ else:
+ self.initializer = None # type: ignore
+
+ self.block_list: torch.nn.Module = torch.nn.ModuleList()
+ for _ in range(self.num_steps if self.no_parameter_sharing else 1):
+ self.block_list.append(
+ RecurrentVarNetBlock(
+ in_channels=self.in_channels,
+ hidden_channels=self.recurrent_hidden_channels,
+ num_layers=self.recurrent_num_layers,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ )
+ )
+
+ std_init_range = 1 / self.recurrent_hidden_channels**0.5
+
+ # initialize weights if not using pretrained cirim
+ if not cfg_dict.get("pretrained", False):
+ self.block_list.apply(lambda module: rnn_weights_init(module, std_init_range))
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor,
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`RecurrentVarNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ previous_state: Optional[torch.Tensor] = None
+
+ if self.initializer is not None:
+ if self.initializer_initialization in ("sense", "input_image"):
+ initializer_input_image = initial_prediction.unsqueeze(self.coil_dim)
+ elif self.initializer_initialization == "zero_filled":
+ initializer_input_image = ifft2(
+ y,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ previous_state = self.initializer(
+ fft2(
+ initializer_input_image,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ .sum(1)
+ .permute(0, 3, 1, 2)
+ )
+
+ kspace_prediction = y.clone()
+ for step in range(self.num_steps):
+ block = self.block_list[step] if self.no_parameter_sharing else self.block_list[0]
+ kspace_prediction, previous_state = block(
+ kspace_prediction,
+ y,
+ mask,
+ sensitivity_maps,
+ previous_state,
+ )
+
+ return check_stacked_complex(
+ coil_combination_method(
+ ifft2(kspace_prediction, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+ )
diff --git a/atommic/collections/reconstruction/nn/recurrentvarnet_base/__init__.py b/atommic/collections/reconstruction/nn/recurrentvarnet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/recurrentvarnet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/recurrentvarnet_base/recurrentvarnet_block.py b/atommic/collections/reconstruction/nn/recurrentvarnet_base/recurrentvarnet_block.py
new file mode 100644
index 00000000..67c06cfa
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/recurrentvarnet_base/recurrentvarnet_block.py
@@ -0,0 +1,378 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/direct/nn/recurrentvarnet/recurrentvarnet.py
+
+from typing import List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import complex_conj, complex_mul
+
+
+class Conv2dGRU(nn.Module):
+ """2D Convolutional Gated Recurrent Unit."""
+
+ def __init__(
+ self,
+ in_channels: int,
+ hidden_channels: int,
+ out_channels: Optional[int] = None,
+ num_layers: int = 2,
+ gru_kernel_size=1,
+ orthogonal_initialization: bool = True,
+ instance_norm: bool = False,
+ dense_connect: int = 0,
+ replication_padding: bool = True,
+ ):
+ """Inits :class:`Conv2dGRU`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ hidden_channels : int
+ Number of hidden channels.
+ out_channels : int, optional
+ Number of output channels. If None, same as in_channels.
+ num_layers : int, optional
+ Number of layers. Default is ``2``.
+ gru_kernel_size : int, optional
+ Size of the GRU kernel. Default is ``1``.
+ orthogonal_initialization : bool, optional
+ Orthogonal initialization is used if set to True. Default is ``True``.
+ instance_norm : bool, optional
+ Instance norm is used if set to True. Default is ``False``.
+ dense_connect : int, optional
+ Number of dense connections. Default is ``0``.
+ replication_padding : bool, optional
+ If set to true replication padding is applied. Default is ``True``.
+ """
+ super().__init__()
+
+ if out_channels is None:
+ out_channels = in_channels
+
+ self.num_layers = num_layers
+ self.hidden_channels = hidden_channels
+ self.dense_connect = dense_connect
+
+ self.reset_gates = nn.ModuleList([])
+ self.update_gates = nn.ModuleList([])
+ self.out_gates = nn.ModuleList([])
+ self.conv_blocks = nn.ModuleList([])
+
+ # Create convolutional blocks
+ for idx in range(num_layers + 1):
+ in_ch = in_channels if idx == 0 else (1 + min(idx, dense_connect)) * hidden_channels
+ out_ch = hidden_channels if idx < num_layers else out_channels
+ padding = 0 if replication_padding else (2 if idx == 0 else 1)
+ block = []
+ if replication_padding:
+ if idx == 1:
+ block.append(nn.ReplicationPad2d(2))
+ else:
+ block.append(nn.ReplicationPad2d(2 if idx == 0 else 1))
+ block.append(
+ nn.Conv2d(
+ in_channels=in_ch,
+ out_channels=out_ch,
+ kernel_size=5 if idx == 0 else 3,
+ dilation=(2 if idx == 1 else 1),
+ padding=padding,
+ )
+ )
+ self.conv_blocks.append(nn.Sequential(*block))
+
+ # Create GRU blocks
+ for _ in range(num_layers):
+ for gru_part in [self.reset_gates, self.update_gates, self.out_gates]:
+ block = []
+ if instance_norm:
+ block.append(nn.InstanceNorm2d(2 * hidden_channels))
+ block.append(
+ nn.Conv2d(
+ in_channels=2 * hidden_channels,
+ out_channels=hidden_channels,
+ kernel_size=gru_kernel_size,
+ padding=gru_kernel_size // 2,
+ )
+ )
+ gru_part.append(nn.Sequential(*block))
+
+ if orthogonal_initialization:
+ for reset_gate, update_gate, out_gate in zip(self.reset_gates, self.update_gates, self.out_gates):
+ nn.init.orthogonal_(reset_gate[-1].weight)
+ nn.init.orthogonal_(update_gate[-1].weight)
+ nn.init.orthogonal_(out_gate[-1].weight)
+ nn.init.constant_(reset_gate[-1].bias, -1.0)
+ nn.init.constant_(update_gate[-1].bias, 0.0)
+ nn.init.constant_(out_gate[-1].bias, 0.0)
+
+ def forward(
+ self,
+ cell_input: torch.Tensor,
+ previous_state: torch.Tensor,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Forward pass of :class:`Conv2dGRU`.
+
+ Parameters
+ ----------
+ cell_input : torch.Tensor
+ Input tensor.
+ previous_state : torch.Tensor
+ Previous hidden state.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, torch.Tensor]
+ Tuple of output tensor and new hidden state.
+ """
+ new_states: List[torch.Tensor] = []
+ conv_skip: List[torch.Tensor] = []
+
+ if previous_state is None:
+ batch_size, spatial_size = cell_input.size(0), (cell_input.size(2), cell_input.size(3))
+ state_size = [batch_size, self.hidden_channels] + list(spatial_size) + [self.num_layers]
+ previous_state = torch.zeros(*state_size, dtype=cell_input.dtype).to(cell_input.device)
+
+ for idx in range(self.num_layers):
+ if conv_skip:
+ cell_input = F.relu(
+ self.conv_blocks[idx](torch.cat([*conv_skip[-self.dense_connect :], cell_input], dim=1)),
+ inplace=True,
+ )
+ else:
+ cell_input = F.relu(self.conv_blocks[idx](cell_input), inplace=True)
+ if self.dense_connect > 0:
+ conv_skip.append(cell_input)
+
+ stacked_inputs = torch.cat([cell_input, previous_state[:, :, :, :, idx]], dim=1)
+
+ update = torch.sigmoid(self.update_gates[idx](stacked_inputs))
+ reset = torch.sigmoid(self.reset_gates[idx](stacked_inputs))
+ delta = torch.tanh(
+ self.out_gates[idx](torch.cat([cell_input, previous_state[:, :, :, :, idx] * reset], dim=1))
+ )
+ cell_input = previous_state[:, :, :, :, idx] * (1 - update) + delta * update
+ new_states.append(cell_input)
+ cell_input = F.relu(cell_input, inplace=False)
+ if conv_skip:
+ out = self.conv_blocks[self.num_layers](torch.cat([*conv_skip[-self.dense_connect :], cell_input], dim=1))
+ else:
+ out = self.conv_blocks[self.num_layers](cell_input)
+
+ return out, torch.stack(new_states, dim=-1)
+
+
+class RecurrentInit(nn.Module):
+ """Recurrent State Initializer (RSI) module of Recurrent Variational Network as presented in [Yiasemis2021]_.
+
+ References
+ ----------
+ .. [Yiasemis2021] Yiasemis, George, et al. โRecurrent Variational Network: A Deep Learning Inverse Problem Solver
+ Applied to the Task of Accelerated MRI Reconstruction.โ ArXiv:2111.09639 [Physics], Nov. 2021. arXiv.org,
+ http://arxiv.org/abs/2111.09639.
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ channels: Tuple[int, ...],
+ dilations: Tuple[int, ...],
+ depth: int = 2,
+ multiscale_depth: int = 1,
+ ):
+ """Inits :class:`RecurrentInit`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Input channels.
+ out_channels : int
+ Number of hidden channels of the recurrent unit of RecurrentVarNet Block.
+ channels : Tuple[int, ...]
+ Channels :math:`n_d` in the convolutional layers of initializer.
+ dilations : Tuple[int, ...]
+ Dilations :math:`p` of the convolutional layers of the initializer.
+ depth : int, optional
+ RecurrentVarNet Block number of layers :math:`n_l`. Default is ``2``.
+ multiscale_depth : int, optional
+ Number of feature layers to aggregate for the output, if 1, multi-scale context aggregation is disabled.
+ Default is ``1``.
+ """
+ super().__init__()
+
+ self.conv_blocks = nn.ModuleList()
+ self.out_blocks = nn.ModuleList()
+ self.depth = depth
+ self.multiscale_depth = multiscale_depth
+ tch = in_channels
+ for curr_channels, curr_dilations in zip(channels, dilations):
+ block = [
+ nn.ReplicationPad2d(curr_dilations),
+ nn.Conv2d(tch, curr_channels, 3, padding=0, dilation=curr_dilations),
+ ]
+ tch = curr_channels
+ self.conv_blocks.append(nn.Sequential(*block))
+ tch = np.sum(channels[-multiscale_depth:])
+ for _ in range(depth):
+ block = [nn.Conv2d(tch, out_channels, 1, padding=0)]
+ self.out_blocks.append(nn.Sequential(*block))
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`RecurrentInit`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Initialization for RecurrentInit.
+
+ Returns
+ -------
+ torch.Tensor
+ Initial recurrent hidden state from input `x`.
+ """
+ features = []
+ for block in self.conv_blocks:
+ x = F.relu(block(x), inplace=True)
+ if self.multiscale_depth > 1:
+ features.append(x)
+ if self.multiscale_depth > 1:
+ x = torch.cat(features[-self.multiscale_depth :], dim=1)
+ output_list = []
+ for block in self.out_blocks:
+ y = F.relu(block(x), inplace=True)
+ output_list.append(y)
+ return torch.stack(output_list, dim=-1)
+
+
+class RecurrentVarNetBlock(nn.Module):
+ r"""Recurrent Variational Network Block :math:`'\'mathcal{H}_{'\'theta_{t}}` as presented in [Yiasemis2021]_.
+
+ References
+ ----------
+ .. [Yiasemis2021] Yiasemis, George, et al. โRecurrent Variational Network: A Deep Learning Inverse Problem Solver
+ Applied to the Task of Accelerated MRI Reconstruction.โ ArXiv:2111.09639 [Physics], Nov. 2021. arXiv.org,
+ http://arxiv.org/abs/2111.09639.
+ """
+
+ def __init__(
+ self,
+ in_channels: int = 2,
+ hidden_channels: int = 64,
+ num_layers: int = 4,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ ):
+ """Inits :class:`RecurrentVarNetBlock`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Input channels. Default is ``2`` for complex data.
+ hidden_channels : int
+ Number of hidden channels of the recurrent unit of RecurrentVarNet Block. Default is ``64``.
+ num_layers : int
+ Number of layers of :math:`n_l` recurrent unit. Default is ``4``.
+ fft_centered : bool
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization : str
+ Whether to normalize the FFT. Default is ``"backward"``.
+ spatial_dims : Tuple[int, int], optional
+ Spatial dimensions of the input. Default is ``None``.
+ coil_dim : int
+ Coil dimension of the input. Default is ``1``.
+ """
+ super().__init__()
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+
+ self.learning_rate = nn.Parameter(torch.tensor([1.0])) # :math:`\alpha_t`
+ self.regularizer = Conv2dGRU(
+ in_channels=in_channels,
+ hidden_channels=hidden_channels,
+ num_layers=num_layers,
+ replication_padding=True,
+ ) # Recurrent Unit of RecurrentVarNet Block :math:`\mathcal{H}_{\theta_t}`
+
+ def forward(
+ self,
+ current_kspace: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ sampling_mask: torch.Tensor,
+ sensitivity_map: torch.Tensor,
+ hidden_state: Union[None, torch.Tensor],
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Forward pass of :class:`RecurrentVarNetBlock`.
+
+ Parameters
+ ----------
+ current_kspace : torch.Tensor
+ Current k-space prediction. Shape [batch_size, n_coil, height, width, 2].
+ masked_kspace : torch.Tensor
+ Subsampled k-space. Shape [batch_size, n_coil, height, width, 2].
+ sampling_mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, height, width, 1].
+ sensitivity_map : torch.Tensor
+ Coil sensitivities. Shape [batch_size, n_coil, height, width, 2].
+ hidden_state : Union[None, torch.Tensor]
+ ConvGRU hidden state. Shape [batch_size, n_l, height, width, hidden_channels].
+
+ Returns
+ -------
+ new_kspace : torch.Tensor
+ New k-space prediction. Shape [batch_size, n_coil, height, width, 2].
+ new_hidden_state : list of torch.Tensor
+ New ConvGRU hidden state. Shape [batch_size, n_l, height, width, hidden_channels].
+ """
+ kspace_error = torch.where(
+ sampling_mask == 0,
+ torch.tensor([0.0], dtype=masked_kspace.dtype).to(masked_kspace.device),
+ current_kspace - masked_kspace,
+ )
+
+ recurrent_term = torch.cat(
+ [
+ complex_mul(
+ ifft2(
+ kspace,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ complex_conj(sensitivity_map),
+ ).sum(self.coil_dim)
+ for kspace in torch.split(current_kspace, 2, -1)
+ ],
+ dim=-1,
+ ).permute(0, 3, 1, 2)
+
+ recurrent_term, hidden_state = self.regularizer(recurrent_term, hidden_state) # :math:`w_t`, :math:`h_{t+1}`
+ recurrent_term = recurrent_term.permute(0, 2, 3, 1)
+
+ recurrent_term = torch.cat(
+ [
+ fft2(
+ complex_mul(image.unsqueeze(self.coil_dim), sensitivity_map),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ for image in torch.split(recurrent_term, 2, -1)
+ ],
+ dim=-1,
+ )
+
+ new_kspace = current_kspace - self.learning_rate * kspace_error + recurrent_term
+
+ return new_kspace, hidden_state
diff --git a/atommic/collections/reconstruction/nn/rim_base/__init__.py b/atommic/collections/reconstruction/nn/rim_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/rim_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/rim_base/conv_layers.py b/atommic/collections/reconstruction/nn/rim_base/conv_layers.py
new file mode 100644
index 00000000..4e3d7125
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/rim_base/conv_layers.py
@@ -0,0 +1,147 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Union
+
+import torch
+from torch import nn
+
+
+class ConvRNNStack(nn.Module):
+ """A stack of convolutional RNNs."""
+
+ def __init__(self, convs, rnn):
+ """Inits :class:`ConvRNNStack`.
+
+ Parameters
+ ----------
+ convs : list of torch.nn.Module
+ List of convolutional layers.
+ rnn : torch.nn.Module
+ RNN layer.
+ """
+ super().__init__()
+ self.convs = convs
+ self.rnn = rnn
+
+ def forward(self, x: torch.Tensor, hidden: torch.Tensor = None) -> torch.Tensor:
+ """Forward p pass of :class:`ConvRNNStack`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor of shape [batch_size, seq_len, input_size].
+ hidden : torch.Tensor
+ Initial hidden state of shape [num_layers * num_directions, batch_size, hidden_size].
+
+ Returns
+ -------
+ torch.Tensor
+ Output tensor of shape [batch_size, seq_len, hidden_size].
+ """
+ return self.rnn(self.convs(x), hidden)
+
+
+class ConvNonlinear(nn.Module):
+ """A convolutional layer with nonlinearity."""
+
+ def __init__(
+ self,
+ input_size: int,
+ features: int,
+ conv_dim: int,
+ kernel_size: int,
+ dilation: int,
+ bias: bool,
+ nonlinear: Union[str, None] = "ReLU",
+ ):
+ """Inits :class:`ConvNonlinear`.
+
+ Parameters
+ ----------
+ input_size : int
+ Number of input channels.
+ features : int
+ Number of output channels.
+ conv_dim : int
+ Number of dimensions of the convolutional layer.
+ kernel_size : int
+ Size of the convolutional kernel.
+ dilation : int
+ Dilation of the convolutional kernel.
+ bias : bool
+ Whether to use bias.
+ nonlinear : str, optional
+ Nonlinearity of the convolutional layer. Default is ``"ReLU"``.
+ """
+ super().__init__()
+
+ self.input_size = input_size
+ self.features = features
+ self.kernel_size = kernel_size
+ self.dilation = dilation
+ self.bias = bias
+ self.conv_dim = conv_dim
+ self.conv_class = self.determine_conv_class(conv_dim)
+
+ if nonlinear is not None and nonlinear.upper() == "RELU":
+ self.nonlinear = torch.nn.ReLU()
+ elif nonlinear is not None and nonlinear.upper() == "LEAKYRELU":
+ self.nonlinear = torch.nn.LeakyReLU()
+ elif nonlinear is None:
+ self.nonlinear = lambda x: x
+ else:
+ raise ValueError("Please specify a proper nonlinearity")
+
+ self.padding = [
+ torch.nn.ReplicationPad1d(torch.div(dilation * (kernel_size - 1), 2, rounding_mode="trunc").item()),
+ torch.nn.ReplicationPad2d(torch.div(dilation * (kernel_size - 1), 2, rounding_mode="trunc").item()),
+ torch.nn.ReplicationPad3d(torch.div(dilation * (kernel_size - 1), 2, rounding_mode="trunc").item()),
+ ][conv_dim - 1]
+
+ self.conv_layer = self.conv_class(
+ in_channels=input_size,
+ out_channels=features,
+ kernel_size=kernel_size,
+ padding=0, # TODO: check if this is correct
+ dilation=dilation,
+ bias=bias,
+ )
+
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ """Resets the parameters of the convolutional layer."""
+ torch.nn.init.kaiming_normal_(self.conv_layer.weight, nonlinearity="relu")
+
+ if self.conv_layer.bias is not None:
+ nn.init.zeros_(self.conv_layer.bias)
+
+ @staticmethod
+ def determine_conv_class(n_dim: int) -> nn.Module:
+ """Determines the convolutional layer class."""
+ if n_dim == 1:
+ return nn.Conv1d
+ if n_dim == 2:
+ return nn.Conv2d
+ if n_dim == 3:
+ return nn.Conv3d
+ raise ValueError(f"Convolution of: {n_dim} dims is not implemented")
+
+ def extra_repr(self):
+ """Extra information about the layer."""
+ s = "{input_size}, {features}"
+ if "bias" in self.__dict__ and self.bias is not True:
+ s += ", bias={bias}"
+ if "nonlinear" in self.__dict__ and self.nonlinear != "tanh":
+ s += ", nonlinearity={nonlinear}"
+ return s.format(**self.__dict__)
+
+ def check_forward_input(self, _input: torch.Tensor) -> torch.Tensor:
+ """Checks input for correct size and shape."""
+ if _input.size(1) != self.input_size:
+ raise RuntimeError(f"input has inconsistent input_size: got {_input.size(1)}, expected {self.input_size}")
+
+ def forward(self, _input: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`ConvNonlinear`."""
+ return self.nonlinear(self.conv_layer(self.padding(_input)))
diff --git a/atommic/collections/reconstruction/nn/rim_base/rim_block.py b/atommic/collections/reconstruction/nn/rim_base/rim_block.py
new file mode 100644
index 00000000..d8de49d0
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/rim_base/rim_block.py
@@ -0,0 +1,300 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Any, Optional, Tuple, Union
+
+import torch
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import coil_combination_method, complex_mul
+from atommic.collections.reconstruction.nn.rim_base import conv_layers, rim_utils, rnn_cells
+
+
+class RIMBlock(torch.nn.Module):
+ """RIMBlock is a block of Recurrent Inference Machines (RIMs) as presented in [Lonning19]_.
+
+ References
+ ----------
+ .. [Lonning19] Lonning19 K, Putzky P, Sonke JJ, Reneman L, Caan MW, Welling M. Recurrent inference machines for
+ reconstructing heterogeneous MRI data. Medical image analysis. 2019 Apr 1;53:64-78.
+
+ """
+
+ def __init__(
+ self,
+ recurrent_layer=None,
+ conv_filters=None,
+ conv_kernels=None,
+ conv_dilations=None,
+ conv_bias=None,
+ recurrent_filters=None,
+ recurrent_kernels=None,
+ recurrent_dilations=None,
+ recurrent_bias=None,
+ depth: int = 2,
+ time_steps: int = 8,
+ conv_dim: int = 2,
+ no_dc: bool = True,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ dimensionality: int = 2,
+ consecutive_slices: int = 1,
+ coil_combination_method: str = "SENSE",
+ ):
+ """Inits :class:`RIMBlock`.
+
+ Parameters
+ ----------
+ recurrent_layer : torch.nn.Module
+ Type of the recurrent layer. It can be ``GRU``, ``MGU``, ``IndRNN``. Check ``rnn_cells`` for more details.
+ conv_filters : list of int
+ Number of filters in the convolutional layers.
+ conv_kernels : list of int
+ Kernel size of the convolutional layers.
+ conv_dilations : list of int
+ Dilation of the convolutional layers.
+ conv_bias : list of bool
+ Bias of the convolutional layers.
+ recurrent_filters : list of int
+ Number of filters in the recurrent layers.
+ recurrent_kernels : list of int
+ Kernel size of the recurrent layers.
+ recurrent_dilations : list of int
+ Dilation of the recurrent layers.
+ recurrent_bias : list of bool
+ Bias of the recurrent layers.
+ depth : int
+ Number of sequence of convolutional and recurrent layers. Default is ``2``.
+ time_steps : int
+ Number of recurrent time steps. Default is ``8``.
+ conv_dim : int
+ Dimension of the convolutional layers. Default is ``2``.
+ no_dc : bool
+ If ``True`` the DC component is not used. Default is ``True``.
+ fft_centered : bool
+ If ``True`` the FFT is centered. Default is ``False``.
+ fft_normalization : str
+ Normalization of the FFT. Default is ``"backward"``.
+ spatial_dims : tuple of int
+ Spatial dimensions of the input. Default is ``None``.
+ coil_dim : int
+ Coil dimension of the input. Default is ``1``.
+ dimensionality : int
+ Dimensionality of the input. Default is ``2``.
+ consecutive_slices : int
+ Number of consecutive slices. Default is ``1``.
+ coil_combination_method : str
+ Coil combination method. Default is ``"SENSE"``.
+ """
+ super().__init__()
+
+ self.input_size = depth * 2
+ self.time_steps = time_steps
+
+ self.layers = torch.nn.ModuleList()
+ for (
+ (conv_features, conv_k_size, conv_dilation, l_conv_bias, nonlinear),
+ (rnn_features, rnn_k_size, rnn_dilation, rnn_bias, rnn_type),
+ ) in zip(
+ zip(conv_filters, conv_kernels, conv_dilations, conv_bias, ["relu", "relu", None]),
+ zip(
+ recurrent_filters,
+ recurrent_kernels,
+ recurrent_dilations,
+ recurrent_bias,
+ [recurrent_layer, recurrent_layer, None],
+ ),
+ ):
+ conv_layer = None
+
+ if conv_features != 0:
+ conv_layer = conv_layers.ConvNonlinear(
+ self.input_size,
+ conv_features,
+ conv_dim=conv_dim,
+ kernel_size=conv_k_size,
+ dilation=conv_dilation,
+ bias=l_conv_bias,
+ nonlinear=nonlinear,
+ )
+ self.input_size = conv_features
+
+ if rnn_features != 0 and rnn_type is not None:
+ if rnn_type.upper() == "GRU":
+ rnn_type = rnn_cells.ConvGRUCell
+ elif rnn_type.upper() == "MGU":
+ rnn_type = rnn_cells.ConvMGUCell
+ elif rnn_type.upper() == "INDRNN":
+ rnn_type = rnn_cells.IndRNNCell
+ else:
+ raise ValueError("Please specify a proper recurrent layer type.")
+
+ rnn_layer = rnn_type(
+ self.input_size,
+ rnn_features,
+ conv_dim=conv_dim,
+ kernel_size=rnn_k_size,
+ dilation=rnn_dilation,
+ bias=rnn_bias,
+ )
+
+ self.input_size = rnn_features
+
+ self.layers.append(conv_layers.ConvRNNStack(conv_layer, rnn_layer))
+
+ self.final_layer = torch.nn.Sequential(conv_layer)
+
+ self.recurrent_filters = recurrent_filters
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+
+ self.no_dc = no_dc
+
+ if not self.no_dc:
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+ self.zero = torch.zeros(1, 1, 1, 1, 1)
+
+ self.dimensionality = dimensionality
+ self.consecutive_slices = consecutive_slices
+ self.coil_combination_method = coil_combination_method
+
+ def forward(
+ self,
+ y: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ prediction: torch.Tensor = None,
+ hx: torch.Tensor = None,
+ sigma: float = 1.0,
+ keep_prediction: bool = False,
+ ) -> Tuple[Any, Union[list, torch.Tensor, None]]:
+ """Forward pass of :class:`RIMBlock`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Predicted k-space. Shape: ``[batch, coils, height, width, 2]``.
+ masked_kspace : torch.Tensor
+ Subsampled k-space. Shape: ``[batch, coils, height, width, 2]``.
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape: ``[batch, coils, height, width, 2]``.
+ mask : torch.Tensor
+ Subsampling mask. Shape: ``[batch, coils, height, width, 2]``.
+ prediction : torch.Tensor, optional
+ Initial (zero-filled) prediction. Shape: ``[batch, coils, height, width, 2]``.
+ hx : torch.Tensor, optional
+ Initial prediction for the hidden state. Shape: ``[batch, coils, height, width, 2]``.
+ sigma : float, optional
+ Noise level. Default is ``1.0``.
+ keep_prediction : bool, optional
+ Whether to keep the prediction. Default is ``False``.
+
+ Returns
+ -------
+ Tuple[Any, Union[list, torch.Tensor, None]]
+ Reconstructed image and hidden states.
+ """
+ batch = masked_kspace.shape[0]
+ if self.dimensionality == 3 or self.consecutive_slices > 1:
+ # 3D pred.shape = [batch, slices, coils, height, width, 2] -> [batch * slices, coils, height, width, 2]
+ slices = masked_kspace.shape[1]
+ y = y.reshape([batch * slices, *y.shape[2:]])
+ masked_kspace = masked_kspace.reshape([batch * slices, *masked_kspace.shape[2:]])
+ mask = mask.reshape([batch * slices, *mask.shape[2:]])
+ sensitivity_maps = sensitivity_maps.reshape([batch * slices, *sensitivity_maps.shape[2:]])
+ else:
+ # 2D pred.shape = [batch, coils, height, width, 2]
+ slices = 1
+
+ if hx is None or (not isinstance(hx, list) and hx.dim() < 3):
+ hx = [
+ masked_kspace.new_zeros((masked_kspace.size(0), f, *masked_kspace.size()[2:-1]))
+ for f in self.recurrent_filters
+ if f != 0
+ ]
+
+ if prediction is None or prediction.ndim < 3:
+ prediction = (
+ y
+ if keep_prediction
+ else coil_combination_method(
+ ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ method=self.coil_combination_method,
+ dim=self.coil_dim,
+ )
+ )
+
+ if (self.consecutive_slices > 1 or self.dimensionality == 3) and prediction.dim() == 5:
+ prediction = prediction.reshape([batch * slices, *prediction.shape[2:]])
+
+ predictions = []
+ for _ in range(self.time_steps):
+ log_likelihood_gradient_prediction = rim_utils.log_likelihood_gradient(
+ prediction,
+ masked_kspace,
+ sensitivity_maps,
+ mask,
+ sigma,
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ self.coil_dim,
+ ).contiguous()
+
+ if self.consecutive_slices > 1 or self.dimensionality == 3:
+ log_likelihood_gradient_prediction = log_likelihood_gradient_prediction.view(
+ [
+ batch * slices,
+ 4,
+ log_likelihood_gradient_prediction.shape[2],
+ log_likelihood_gradient_prediction.shape[3],
+ ]
+ ).permute(1, 0, 2, 3)
+
+ for h, convrnn in enumerate(self.layers):
+ hx[h] = convrnn(log_likelihood_gradient_prediction, hx[h])
+ if self.consecutive_slices > 1 or self.dimensionality == 3:
+ hx[h] = hx[h].squeeze(0)
+ log_likelihood_gradient_prediction = hx[h]
+
+ log_likelihood_gradient_prediction = self.final_layer(log_likelihood_gradient_prediction)
+
+ if self.dimensionality == 2:
+ log_likelihood_gradient_prediction = log_likelihood_gradient_prediction.permute(0, 2, 3, 1)
+ elif self.dimensionality == 3:
+ log_likelihood_gradient_prediction = log_likelihood_gradient_prediction.permute(1, 2, 3, 0)
+ for h in range(len(hx)): # pylint: disable=consider-using-enumerate
+ hx[h] = hx[h].permute(1, 0, 2, 3)
+
+ prediction = prediction + log_likelihood_gradient_prediction
+
+ predictions.append(prediction)
+
+ if self.consecutive_slices > 1 or self.dimensionality == 3:
+ for i, pred in enumerate(predictions):
+ predictions[i] = pred.reshape([batch, slices, *pred.shape[1:]])
+
+ if self.no_dc:
+ return predictions, hx
+
+ soft_dc = torch.where(mask, y - masked_kspace, self.zero.to(masked_kspace)) * self.dc_weight
+ current_kspace = [
+ masked_kspace
+ - soft_dc
+ - fft2(
+ complex_mul(e.unsqueeze(self.coil_dim), sensitivity_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ for e in predictions
+ ]
+
+ return current_kspace, hx
diff --git a/atommic/collections/reconstruction/nn/rim_base/rim_utils.py b/atommic/collections/reconstruction/nn/rim_base/rim_utils.py
new file mode 100644
index 00000000..c7dd3ab7
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/rim_base/rim_utils.py
@@ -0,0 +1,82 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Sequence
+
+import torch
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+
+
+def log_likelihood_gradient(
+ prediction: torch.Tensor,
+ masked_kspace: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ sigma: float,
+ fft_centered: bool,
+ fft_normalization: str,
+ spatial_dims: Sequence[int],
+ coil_dim: int,
+) -> torch.Tensor:
+ """Computes the gradient of the log-likelihood function.
+
+ Parameters
+ ----------
+ prediction : torch.Tensor
+ Initial guess for the reconstruction. Shape [batch_size, height, width, 2].
+ masked_kspace : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, coils, height, width, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, coils, height, width, 2].
+ mask : torch.Tensor
+ Subsampling mask. Shape [batch_size, 1, height, width, 1].
+ sigma : float
+ Noise level.
+ fft_centered : bool
+ Whether to center the FFT.
+ fft_normalization : str
+ Whether to normalize the FFT.
+ spatial_dims : Sequence[int]
+ Spatial dimensions of the data.
+ coil_dim : int
+ Dimension of the coil.
+
+ Returns
+ -------
+ torch.Tensor
+ Gradient of the log-likelihood function. Shape [batch_size, 4, height, width]. 4 is the stacked real and
+ imaginary parts of the prediction and the real and imaginary parts of the gradient.
+ """
+ if coil_dim == 0:
+ coil_dim += 1
+
+ prediction_real, prediction_imaginary = map(lambda x: torch.unsqueeze(x, coil_dim), prediction.chunk(2, -1))
+ sensitivity_maps_real, sensitivity_maps_imaginary = sensitivity_maps.chunk(2, -1)
+
+ re_se = prediction_real * sensitivity_maps_real - prediction_imaginary * sensitivity_maps_imaginary
+ im_se = prediction_real * sensitivity_maps_imaginary + prediction_imaginary * sensitivity_maps_real
+ pred = torch.cat((re_se, im_se), -1)
+
+ pred = fft2(pred, centered=fft_centered, normalization=fft_normalization, spatial_dims=spatial_dims)
+
+ pred = ifft2(
+ mask * (pred - masked_kspace),
+ centered=fft_centered,
+ normalization=fft_normalization,
+ spatial_dims=spatial_dims,
+ )
+ pred_real, pred_imag = pred.chunk(2, -1)
+
+ if isinstance(sigma, torch.Tensor):
+ sigma = sigma.item()
+
+ sigma = max(sigma, 1.0) # TODO: check if we need this
+
+ re_out = torch.sum(pred_real * sensitivity_maps_real + pred_imag * sensitivity_maps_imaginary, coil_dim) / sigma
+ im_out = torch.sum(pred_imag * sensitivity_maps_real - pred_real * sensitivity_maps_imaginary, coil_dim) / sigma
+
+ prediction_real = prediction_real.squeeze(coil_dim)
+ prediction_imaginary = prediction_imaginary.squeeze(coil_dim)
+
+ return torch.cat((prediction_real, prediction_imaginary, re_out, im_out), -1).permute(0, 3, 1, 2)
diff --git a/atommic/collections/reconstruction/nn/rim_base/rnn_cells.py b/atommic/collections/reconstruction/nn/rim_base/rnn_cells.py
new file mode 100644
index 00000000..608e7fcf
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/rim_base/rnn_cells.py
@@ -0,0 +1,497 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from torch import nn
+
+
+class ConvGRUCellBase(nn.Module):
+ """Base class for Convolutional Gated Recurrent Unit (GRU) cells."""
+
+ def __init__(
+ self,
+ input_size: int,
+ hidden_size: int,
+ conv_dim: int,
+ kernel_size: int,
+ dilation: int,
+ bias: bool = True,
+ ):
+ """Inits :class:`ConvGRUCellBase`.
+
+ Parameters
+ ----------
+ input_size : int
+ Number of input channels.
+ hidden_size : int
+ Number of hidden channels.
+ conv_dim : int
+ Number of dimensions of the convolutional layer.
+ kernel_size : int
+ Size of the convolutional kernel.
+ dilation : int
+ Dilation of the convolutional kernel.
+ bias : bool
+ Whether to use bias. Default is ``True``.
+ """
+ super().__init__()
+
+ self.input_size = input_size
+ self.hidden_size = hidden_size
+ self.bias = bias
+ self.conv_dim = conv_dim
+ self.conv_class = self.determine_conv_class(conv_dim)
+
+ self.ih = nn.Conv2d(
+ input_size,
+ 3 * hidden_size,
+ kernel_size,
+ padding=torch.div(dilation * (kernel_size - 1), 2, rounding_mode="trunc").item(),
+ dilation=dilation,
+ bias=bias,
+ )
+ self.hh = nn.Conv2d(
+ hidden_size,
+ 3 * hidden_size,
+ kernel_size,
+ padding=torch.div(dilation * (kernel_size - 1), 2, rounding_mode="trunc").item(),
+ dilation=dilation,
+ bias=False,
+ )
+
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ """Initialize parameters following the way proposed in the paper."""
+ self.ih.weight.data = self.orthotogonalize_weights(self.ih.weight.data)
+ self.hh.weight.data = self.orthotogonalize_weights(self.hh.weight.data)
+
+ if self.bias is True:
+ nn.init.zeros_(self.ih.bias)
+
+ @staticmethod
+ def orthotogonalize_weights(weights: torch.Tensor, chunks: int = 1) -> torch.Tensor:
+ """Orthogonalize the weights of a convolutional layer."""
+ return torch.cat([nn.init.orthogonal_(w) for w in weights.chunk(chunks, 0)], 0)
+
+ @staticmethod
+ def determine_conv_class(n_dim: int) -> nn.Module:
+ """Determine the convolutional class to use."""
+ if n_dim == 1:
+ return nn.Conv1d
+ if n_dim == 2:
+ return nn.Conv2d
+ if n_dim == 3:
+ return nn.Conv3d
+ raise NotImplementedError("No convolution of this dimensionality implemented")
+
+ def extra_repr(self):
+ """Extra information to be printed when printing the model."""
+ s = "{input_size}, {hidden_size}"
+ if "bias" in self.__dict__ and self.bias is not True:
+ s += ", bias={bias}"
+ if "nonlinearity" in self.__dict__ and self.nonlinearity != "tanh":
+ s += ", nonlinearity={nonlinearity}"
+ return s.format(**self.__dict__)
+
+ def check_forward_input(self, _input: torch.Tensor):
+ """Check forward input."""
+ if _input.size(1) != self.input_size:
+ raise RuntimeError(f"input has inconsistent input_size: got {_input.size(1)}, expected {self.input_size}")
+
+ def check_forward_hidden(self, _input: torch.Tensor, hx: torch.Tensor, hidden_label: str = ""):
+ """Check forward hidden."""
+ if _input.size(0) != hx.size(0):
+ raise RuntimeError(
+ f"Input batch size {_input.size(0)} doesn't match hidden{hidden_label} batch size {hx.size(0)}"
+ )
+
+ if hx.size(1) != self.hidden_size:
+ raise RuntimeError(
+ f"hidden{hidden_label} has inconsistent hidden_size: got {hx.size(1)}, expected {self.hidden_size}"
+ )
+
+
+class ConvGRUCell(ConvGRUCellBase):
+ """A Convolutional GRU cell."""
+
+ def __init__(
+ self,
+ input_size: int,
+ hidden_size: int,
+ conv_dim: int,
+ kernel_size: int,
+ dilation: int,
+ bias: bool = True,
+ ):
+ """Inits :class:`ConvGRUCell`.
+
+ Parameters
+ ----------
+ input_size : int
+ Number of input channels.
+ hidden_size : int
+ Number of hidden channels.
+ conv_dim : int
+ Number of dimensions of the convolutional layer.
+ kernel_size : int
+ Size of the convolutional kernel.
+ dilation : int
+ Dilation of the convolutional kernel.
+ bias : bool
+ Whether to use bias. Default is ``True``.
+ """
+ super().__init__(input_size, hidden_size, conv_dim, kernel_size, dilation, bias)
+ self.conv_dim = conv_dim
+
+ def forward(self, _input: torch.Tensor, hx: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`ConvGRUCell`."""
+ if self.conv_dim == 3:
+ _input = _input.unsqueeze(0)
+ hx = hx.permute(1, 0, 2, 3).unsqueeze(0)
+
+ ih = self.ih(_input).chunk(3, 1)
+ hh = self.hh(hx).chunk(3, 1)
+
+ r = torch.sigmoid(ih[0] + hh[0])
+ z = torch.sigmoid(ih[1] + hh[1])
+ n = torch.tanh(ih[2] + r * hh[2])
+
+ hx = n * (1 - z) + z * hx
+
+ return hx
+
+
+class ConvMGUCellBase(nn.Module):
+ """Base class for a Convolutional Minimal Gated Unit cell, as presented in [Zhou2016]_.
+
+ References
+ ----------
+ .. [Zhou2016] Zhou GB, Wu J, Zhang CL, Zhou ZH. Minimal gated unit for recurrent neural networks. International
+ Journal of Automation and Computing. 2016 Jun;13(3):226-34.
+ """
+
+ def __init__(
+ self,
+ input_size: int,
+ hidden_size: int,
+ conv_dim: int,
+ kernel_size: int,
+ dilation: int,
+ bias: bool = True,
+ ):
+ """Inits :class:`ConvMGUCellBase`.
+
+ Parameters
+ ----------
+ input_size : int
+ Number of input channels.
+ hidden_size : int
+ Number of hidden channels.
+ conv_dim : int
+ Number of dimensions of the convolutional layer.
+ kernel_size : int
+ Size of the convolutional kernel.
+ dilation : int
+ Dilation of the convolutional kernel.
+ bias : bool
+ Whether to use bias. Default is ``True``.
+ """
+ super().__init__()
+
+ self.input_size = input_size
+ self.hidden_size = hidden_size
+ self.bias = bias
+ self.conv_dim = conv_dim
+ self.conv_class = self.determine_conv_class(conv_dim)
+
+ self.ih = nn.Conv2d(
+ input_size,
+ 2 * hidden_size,
+ kernel_size,
+ padding=torch.div(dilation * (kernel_size - 1), 2, rounding_mode="trunc").item(),
+ dilation=dilation,
+ bias=bias,
+ )
+ self.hh = nn.Conv2d(
+ hidden_size,
+ 2 * hidden_size,
+ kernel_size,
+ padding=torch.div(dilation * (kernel_size - 1), 2, rounding_mode="trunc").item(),
+ dilation=dilation,
+ bias=False,
+ )
+
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ """Reset the parameters."""
+ self.ih.weight.data = self.orthotogonalize_weights(self.ih.weight.data)
+ self.hh.weight.data = self.orthotogonalize_weights(self.hh.weight.data)
+
+ nn.init.xavier_uniform_(self.ih.weight, nn.init.calculate_gain("relu"))
+ nn.init.xavier_uniform_(self.hh.weight)
+
+ if self.bias is True:
+ nn.init.zeros_(self.ih.bias)
+
+ @staticmethod
+ def orthotogonalize_weights(weights: torch.Tensor, chunks: int = 1) -> torch.Tensor:
+ """Orthogonalize the weights."""
+ return torch.cat([nn.init.orthogonal_(w) for w in weights.chunk(chunks, 0)], 0)
+
+ @staticmethod
+ def determine_conv_class(n_dim: int) -> nn.Module:
+ """Determine the convolutional class."""
+ if n_dim == 1:
+ return nn.Conv1d
+ if n_dim == 2:
+ return nn.Conv2d
+ if n_dim == 3:
+ return nn.Conv3d
+ raise ValueError(f"Convolution of: {n_dim} dims is not implemented")
+
+ def extra_repr(self):
+ """Extra information about the ConvMGUCellBase."""
+ s = "{input_size}, {hidden_size}"
+ if "bias" in self.__dict__ and self.bias is not True:
+ s += ", bias={bias}"
+ if "nonlinearity" in self.__dict__ and self.nonlinearity != "tanh":
+ s += ", nonlinearity={nonlinearity}"
+ return s.format(**self.__dict__)
+
+ def check_forward_input(self, _input: torch.Tensor):
+ """Check the forward input."""
+ if _input.size(1) != self.input_size:
+ raise RuntimeError(f"input has inconsistent input_size: got {_input.size(1)}, expected {self.input_size}")
+
+ def check_forward_hidden(self, _input: torch.Tensor, hx: torch.Tensor, hidden_label: str = ""):
+ """Check the forward hidden."""
+ if _input.size(0) != hx.size(0):
+ raise RuntimeError(
+ f"Input batch size {_input.size(0)} doesn't match hidden{hidden_label} batch size {hx.size(0)}"
+ )
+
+ if hx.size(1) != self.hidden_size:
+ raise RuntimeError(
+ f"hidden{hidden_label} has inconsistent hidden_size: got {hx.size(1)}, expected {self.hidden_size}"
+ )
+
+
+class ConvMGUCell(ConvMGUCellBase):
+ """Base class for a Convolutional Minimal Gated Unit cell, as presented in [Zhou2016]_.
+
+ References
+ ----------
+ .. [Zhou2016] Zhou GB, Wu J, Zhang CL, Zhou ZH. Minimal gated unit for recurrent neural networks. International
+ Journal of Automation and Computing. 2016 Jun;13(3):226-34.
+ """
+
+ def __init__(
+ self,
+ input_size: int,
+ hidden_size: int,
+ conv_dim: int,
+ kernel_size: int,
+ dilation: int,
+ bias: bool = True,
+ ):
+ """Inits :class:`ConvMGUCell`.
+
+ Parameters
+ ----------
+ input_size : int
+ Number of input channels.
+ hidden_size : int
+ Number of hidden channels.
+ conv_dim : int
+ Number of dimensions of the convolutional layer.
+ kernel_size : int
+ Size of the convolutional kernel.
+ dilation : int
+ Dilation of the convolutional kernel.
+ bias : bool
+ Whether to use bias. Default is ``True``.
+ """
+ super().__init__(input_size, hidden_size, conv_dim, kernel_size, dilation, bias)
+ self.conv_dim = conv_dim
+
+ def forward(self, _input: torch.Tensor, hx: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`ConvMGUCell`."""
+ if self.conv_dim == 3:
+ _input = _input.unsqueeze(0)
+ hx = hx.permute(1, 0, 2, 3).unsqueeze(0)
+
+ ih = self.ih(_input).chunk(2, dim=1)
+ hh = self.hh(hx).chunk(2, dim=1)
+
+ f = torch.sigmoid(ih[0] + hh[0])
+ c = torch.tanh(ih[1] + f * hh[1])
+
+ return c + f * (hx - c)
+
+
+class IndRNNCellBase(nn.Module):
+ """Base class for Independently RNN cells as presented in [Li2018]_.
+
+ References
+ ----------
+ .. [Li2018] Li, S. et al. (2018) Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN,
+ Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (1), pp.
+ 5457โ5466. doi: 10.1109/CVPR.2018.00572.
+ """
+
+ def __init__(
+ self,
+ input_size: int,
+ hidden_size: int,
+ conv_dim: int,
+ kernel_size: int,
+ dilation: int,
+ bias: bool = True,
+ ):
+ """Inits :class:`IndRNNCellBase`.
+
+ Parameters
+ ----------
+ input_size : int
+ Number of input channels.
+ hidden_size : int
+ Number of hidden channels.
+ conv_dim : int
+ Number of dimensions of the convolutional layer.
+ kernel_size : int
+ Size of the convolutional kernel.
+ dilation : int
+ Dilation of the convolutional kernel.
+ bias : bool
+ Whether to use bias. Default is ``True``.
+ """
+ super().__init__()
+
+ self.input_size = input_size
+ self.hidden_size = hidden_size
+ self.kernel_size = kernel_size
+ self.bias = bias
+ self.conv_dim = conv_dim
+ self.conv_class = self.determine_conv_class(conv_dim)
+
+ self.ih = self.conv_class(
+ input_size,
+ hidden_size,
+ kernel_size,
+ padding=torch.div(dilation * (kernel_size - 1), 2, rounding_mode="trunc").item(),
+ dilation=dilation,
+ bias=bias,
+ )
+
+ if self.conv_dim == 2:
+ self.hh = nn.Parameter(
+ nn.init.normal_(torch.empty(1, hidden_size, 1, 1), std=1.0 / (hidden_size * (1 + kernel_size**2)))
+ )
+ elif self.conv_dim == 3:
+ self.hh = nn.Parameter(
+ nn.init.normal_(torch.empty(1, hidden_size, 1, 1, 1), std=1.0 / (hidden_size * (1 + kernel_size**2)))
+ )
+
+ self.reset_parameters()
+
+ def reset_parameters(self):
+ """Reset the parameters."""
+ self.ih.weight.data = self.orthotogonalize_weights(self.ih.weight.data)
+
+ nn.init.normal_(self.ih.weight, std=1.0 / (self.hidden_size * (1 + self.kernel_size**2)))
+
+ if self.bias is True:
+ nn.init.zeros_(self.ih.bias)
+
+ @staticmethod
+ def orthotogonalize_weights(weights: torch.Tensor, chunks: int = 1) -> torch.Tensor:
+ """Orthogonalize the weights."""
+ return torch.cat([nn.init.orthogonal_(w) for w in weights.chunk(chunks, 0)], 0)
+
+ @staticmethod
+ def determine_conv_class(n_dim: int) -> nn.Module:
+ """Determine the convolutional class."""
+ if n_dim == 1:
+ return nn.Conv1d
+ if n_dim == 2:
+ return nn.Conv2d
+ if n_dim == 3:
+ return nn.Conv3d
+ raise NotImplementedError("No convolution of this dimensionality implemented")
+
+ def extra_repr(self):
+ """Extra information about the module, used for printing."""
+ s = "{input_size}, {hidden_size}"
+ if "bias" in self.__dict__ and self.bias is not True:
+ s += ", bias={bias}"
+ if "nonlinearity" in self.__dict__ and self.nonlinearity != "tanh":
+ s += ", nonlinearity={nonlinearity}"
+ return s.format(**self.__dict__)
+
+ def check_forward_input(self, _input: torch.Tensor):
+ """Check forward input."""
+ if _input.size(1) != self.input_size:
+ raise RuntimeError(f"input has inconsistent input_size: got {_input.size(1)}, expected {self.input_size}")
+
+ def check_forward_hidden(self, _input, hx, hidden_label=""):
+ """Check forward hidden."""
+ if _input.size(0) != hx.size(0):
+ raise RuntimeError(
+ f"Input batch size {_input.size(0)} doesn't match hidden{hidden_label} batch size {hx.size(0)}"
+ )
+
+ if hx.size(1) != self.hidden_size:
+ raise RuntimeError(
+ f"hidden{hidden_label} has inconsistent hidden_size: got {hx.size(1)}, expected {self.hidden_size}"
+ )
+
+
+class IndRNNCell(IndRNNCellBase):
+ """Base class for Independently RNN cells as presented in [Li2018]_.
+
+ References
+ ----------
+ .. [Li2018] Li, S. et al. (2018) Independently Recurrent Neural Network (IndRNN): Building A Longer and Deeper RNN,
+ Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (1), pp.
+ 5457โ5466. doi: 10.1109/CVPR.2018.00572.
+ """
+
+ def __init__(
+ self,
+ input_size: int,
+ hidden_size: int,
+ conv_dim: int,
+ kernel_size: int,
+ dilation: int,
+ bias: bool = True,
+ ):
+ """Inits :class:`IndRNNCell`.
+
+ Parameters
+ ----------
+ input_size : int
+ Number of input channels.
+ hidden_size : int
+ Number of hidden channels.
+ conv_dim : int
+ Number of dimensions of the convolutional layer.
+ kernel_size : int
+ Size of the convolutional kernel.
+ dilation : int
+ Dilation of the convolutional kernel.
+ bias : bool
+ Whether to use bias. Default is ``True``.
+ """
+ super().__init__(input_size, hidden_size, conv_dim, kernel_size, dilation, bias)
+ self.conv_dim = conv_dim
+
+ def forward(self, _input: torch.Tensor, hx: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`IndRNNCell`."""
+ if self.conv_dim == 3:
+ # TODO: Check if this is correct
+ _input = _input.unsqueeze(0)
+ hx = hx.permute(1, 0, 2, 3).unsqueeze(0)
+
+ return nn.ReLU()(self.ih(_input) + self.hh * hx)
diff --git a/atommic/collections/reconstruction/nn/sigmanet_base/__init__.py b/atommic/collections/reconstruction/nn/sigmanet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/sigmanet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/sigmanet_base/dc_layers.py b/atommic/collections/reconstruction/nn/sigmanet_base/dc_layers.py
new file mode 100644
index 00000000..25110fad
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/sigmanet_base/dc_layers.py
@@ -0,0 +1,587 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from:
+# https://github.com/khammernik/sigmanet/blob/master/reconstruction/common/mytorch/models/datalayer.py
+
+from typing import Any, List, Optional, Tuple
+
+import torch
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import complex_abs, complex_conj, complex_mul
+
+
+class ConjugateGradient(torch.autograd.Function):
+ """Conjugate Gradient solver for the prox of the data term."""
+
+ @staticmethod
+ def complexDot(data1: torch.Tensor, data2: torch.Tensor) -> torch.Tensor:
+ """Complex dot product of two tensors."""
+ nBatch = data1.shape[0]
+ mult = complex_mul(data1, complex_conj(data2))
+ re, im = torch.unbind(mult, dim=-1)
+ return torch.stack([torch.sum(re.view(nBatch, -1), dim=-1), torch.sum(im.view(nBatch, -1), dim=-1)], -1)
+
+ @staticmethod
+ def solve(x0: torch.Tensor, M: torch.Tensor, tol: float, max_iter: int) -> torch.Tensor:
+ """Solve the linear system Mx=b using conjugate gradient.
+
+ Parameters
+ ----------
+ x0 : torch.Tensor
+ Initial guess. Shape [batch_size, height, width, 2].
+ M : torch.Tensor
+ Linear operator. Shape [batch_size, height, width, 2].
+ tol : float
+ Tolerance for the stopping criterion.
+ max_iter : int
+ Maximum number of iterations.
+ """
+ nBatch = x0.shape[0]
+ x = torch.zeros(x0.shape).to(x0.device)
+ r = x0.clone()
+ p = x0.clone()
+ x0x0 = (x0.pow(2)).view(nBatch, -1).sum(-1)
+ rr = torch.stack([(r.pow(2)).view(nBatch, -1).sum(-1), torch.zeros(nBatch).to(x0.device)], dim=-1)
+
+ it = 0
+ while torch.min(rr[..., 0] / x0x0) > tol and it < max_iter:
+ it = it + 1
+ q = M(p)
+
+ data1 = rr
+ data2 = ConjugateGradient.complexDot(p, q)
+
+ re1, im1 = torch.unbind(data1, -1)
+ re2, im2 = torch.unbind(data2, -1)
+ alpha = torch.stack([re1 * re2 + im1 * im2, im1 * re2 - re1 * im2], -1) / complex_abs(data2) ** 2
+
+ x = x + complex_mul(alpha.reshape(nBatch, 1, 1, 1, -1), p.clone())
+ r = r - complex_mul(alpha.reshape(nBatch, 1, 1, 1, -1), q.clone())
+ rr_new = torch.stack([(r.pow(2)).view(nBatch, -1).sum(-1), torch.zeros(nBatch).to(x0.device)], dim=-1)
+ beta = torch.stack([rr_new[..., 0] / rr[..., 0], torch.zeros(nBatch).to(x0.device)], dim=-1)
+ p = r.clone() + complex_mul(beta.reshape(nBatch, 1, 1, 1, -1), p)
+ rr = rr_new.clone()
+ return x
+
+ # pylint: disable=arguments-differ
+ @staticmethod
+ def forward(
+ ctx: torch.autograd.function,
+ z: torch.Tensor,
+ _lambda: torch.Tensor,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ tol: float,
+ max_iter: int,
+ fft_centered: bool,
+ fft_normalization: str,
+ spatial_dims: List[int],
+ ) -> torch.Tensor:
+ """Forward pass of :class:`ConjugateGradient`.
+
+ Parameters
+ ----------
+ ctx : torch.autograd.function
+ Context object.
+ z : torch.Tensor
+ Input image. Shape [batch_size, height, width, 2].
+ _lambda : torch.Tensor
+ Regularization parameter.
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, num_coils, height, width, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, num_coils, height, width, 2].
+ mask : torch.Tensor
+ Subsampling mask. Shape [batch_size, 1, height, width, 1].
+ tol : float
+ Tolerance for the stopping criterion.
+ max_iter : int
+ Maximum number of iterations.
+ fft_centered : bool
+ Whether to center the FFT.
+ fft_normalization : str
+ FFT normalization.
+ spatial_dims : List[int]
+ Spatial dimensions.
+
+ Returns
+ -------
+ torch.Tensor
+ Output image. Shape [batch_size, height, width, 2].
+ """
+ ctx.tol = tol
+ ctx.max_iter = max_iter
+ ctx.fft_centered = fft_centered
+ ctx.fft_normalization = fft_normalization
+ ctx.spatial_dims = spatial_dims
+
+ def A(x):
+ """Adjoint of the forward operator."""
+ x = (
+ fft2(
+ complex_mul(x.expand_as(sensitivity_maps), sensitivity_maps),
+ centered=fft_centered,
+ normalization=fft_normalization,
+ spatial_dims=spatial_dims,
+ )
+ * mask
+ )
+ return torch.sum(x, dim=-4, keepdim=True)
+
+ def AT(x):
+ """AT = A^H"""
+ return torch.sum(
+ complex_mul(
+ ifft2(x * mask, centered=fft_centered, normalization=fft_normalization, spatial_dims=spatial_dims),
+ complex_conj(sensitivity_maps),
+ ),
+ dim=(-5),
+ )
+
+ def M(p):
+ r"""M = A^H A + lambda I"""
+ return _lambda * AT(A(p)) + p
+
+ x0 = _lambda * AT(y) + z
+ ctx.save_for_backward(AT(y), x0, sensitivity_maps, mask, _lambda)
+
+ return ConjugateGradient.solve(x0, M, ctx.tol, ctx.max_iter)
+
+ # pylint: disable=arguments-differ
+ @staticmethod
+ def backward(
+ ctx: torch.autograd.function, grad_x: torch.Tensor
+ ) -> tuple[torch.Tensor, Any, None, None, None, None, None, None, None, None]:
+ """Backward pass of the conjugate gradient solver.
+
+ Parameters
+ ----------
+ ctx : torch.autograd.function
+ Context object.
+ grad_x : torch.Tensor
+ Gradient of the output image. Shape [batch_size, height, width, 2].
+
+ Returns
+ -------
+ Tuple[torch.Tensor, ...]
+ Gradient of the input image, regularization parameter, ...
+ """
+ ATy, rhs, sensitivity_maps, mask, _lambda = ctx.saved_tensors
+
+ def A(x):
+ """Adjoint of the forward operator."""
+ x = (
+ fft2(
+ complex_mul(x.expand_as(sensitivity_maps), sensitivity_maps),
+ centered=ctx.fft_centered,
+ normalization=ctx.fft_normalization,
+ spatial_dims=ctx.spatial_dims,
+ )
+ * mask
+ )
+ return torch.sum(x, dim=-4, keepdim=True)
+
+ def AT(x):
+ """AT = A^H"""
+ return torch.sum(
+ complex_mul(
+ ifft2(
+ x * mask,
+ centered=ctx.fft_centered,
+ normalization=ctx.fft_normalization,
+ spatial_dims=ctx.spatial_dims,
+ ),
+ complex_conj(sensitivity_maps),
+ ),
+ dim=(-5),
+ )
+
+ def M(p):
+ r"""M = A^H A + lambda I"""
+ return _lambda * AT(A(p)) + p
+
+ Qe = ConjugateGradient.solve(grad_x, M, ctx.tol, ctx.max_iter)
+ QQe = ConjugateGradient.solve(Qe, M, ctx.tol, ctx.max_iter)
+
+ grad_z = Qe
+
+ grad_lambda = (
+ complex_mul(
+ ifft2(
+ Qe, centered=ctx.fft_centered, normalization=ctx.fft_normalization, spatial_dims=ctx.spatial_dims
+ ),
+ complex_conj(ATy),
+ ).sum()
+ - complex_mul(
+ ifft2(
+ QQe, centered=ctx.fft_centered, normalization=ctx.fft_normalization, spatial_dims=ctx.spatial_dims
+ ),
+ complex_conj(rhs),
+ ).sum()
+ )
+
+ return grad_z, grad_lambda, None, None, None, None, None, None, None, None
+
+
+class DataIDLayer(torch.nn.Module):
+ """Placeholder for the identity data layer."""
+
+ def __init__(self, *args, **kwargs):
+ """Inits :class:`DataIDLayer`."""
+ super().__init__()
+
+
+class DataGDLayer(torch.nn.Module):
+ """DataLayer computing the gradient on the L2 data term."""
+
+ def __init__(
+ self,
+ lambda_init: float,
+ learnable: bool = True,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ ):
+ """Inits :class:`DataGDLayer`.
+
+ Parameters
+ ----------
+ lambda_init : float
+ Initial value of data term weight lambda.
+ learnable : bool
+ If True, the data term weight lambda is learnable. Default is ``True``.
+ fft_centered : bool
+ If True, the FFT is centered. Default is ``False``.
+ fft_normalization : str
+ If "ortho", the FFT is normalized. Default is ``"backward"``.
+ spatial_dims : tuple of int
+ If not None, the spatial dimensions of the FFT. Default is ``None``.
+ """
+ super().__init__()
+ self.lambda_init = lambda_init
+ self.data_weight = torch.nn.Parameter(torch.Tensor(1))
+ self.data_weight.data = torch.tensor(
+ lambda_init,
+ dtype=self.data_weight.dtype,
+ )
+ self.data_weight.requires_grad = learnable
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+
+ def forward(
+ self, x: torch.Tensor, y: torch.Tensor, sensitivity_maps: torch.Tensor, mask: torch.Tensor
+ ) -> torch.Tensor:
+ """Forward pass of :class:`DataGDLayer`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Prediction. Shape [batch_size, num_coils, height, width, 2].
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, num_coils, height, width, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, num_coils, height, width, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, num_channels, height, width, 1].
+
+ Returns
+ -------
+ torch.Tensor
+ Data loss term.
+ """
+ A_x_y = (
+ torch.sum(
+ fft2(
+ complex_mul(x.expand_as(sensitivity_maps), sensitivity_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+ * mask,
+ -4,
+ keepdim=True,
+ )
+ - y
+ )
+ gradD_x = torch.sum(
+ complex_mul(
+ ifft2(
+ A_x_y * mask,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ complex_conj(sensitivity_maps),
+ ),
+ dim=(-5),
+ )
+ return x - self.data_weight * gradD_x
+
+
+class DataProxCGLayer(torch.nn.Module):
+ """Solving the prox wrt. data term using Conjugate Gradient as presented in [Aggarwal2018]_.
+
+ References
+ ----------
+ .. [Aggarwal2018] Aggarwal HK, Mani MP, Jacob M. MoDL: Model-based deep learning architecture for inverse
+ problems. IEEE transactions on medical imaging. 2018 Aug 13;38(2):394-405.
+ """
+
+ def __init__(
+ self,
+ lambda_init: float,
+ tol: float = 1e-6,
+ iterations: int = 10,
+ learnable: bool = True,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ ):
+ """Inits :class:`DataProxCGLayer`.
+
+ Parameters
+ ----------
+ lambda_init : float
+ Initial value of data term weight lambda.
+ tol : float
+ Tolerance for the Conjugate Gradient solver. Default is ``1e-6``.
+ iterations : int
+ Number of iterations for the Conjugate Gradient solver. Default is ``10``.
+ learnable : bool
+ If True, the data term weight lambda is learnable. Default is ``True``.
+ fft_centered : bool
+ If True, the FFT is centered. Default is ``False``.
+ fft_normalization : str
+ FFT normalization. Default is ``"backward"``.
+ spatial_dims : tuple of int
+ Spatial dimensions of the FFT. Default is ``None``.
+ """
+ super().__init__()
+
+ self._lambda = torch.nn.Parameter(torch.Tensor(1))
+ self._lambda.data = torch.tensor(lambda_init)
+ self._lambda_init = lambda_init
+ self._lambda.requires_grad = learnable
+
+ self.tol = tol
+ self.iter = iterations
+
+ self.op = ConjugateGradient
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+
+ def forward(self, x: torch.Tensor, y: torch.Tensor, sensitivity_maps: torch.Tensor, mask: torch.Tensor):
+ """Forward pass of :class:`DataProxCGLayer`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Prediction. Shape [batch_size, num_coils, height, width, 2].
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, num_coils, height, width, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, num_coils, height, width, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, num_channels, height, width, 1].
+
+ Returns
+ -------
+ torch.Tensor
+ Data loss term.
+ """
+ return self.op.apply(
+ x,
+ self._lambda,
+ y,
+ sensitivity_maps,
+ mask,
+ self.tol,
+ self.iter,
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ )
+
+ def set_learnable(self, flag: bool):
+ """Set the learnability of the data term weight lambda."""
+ self._lambda.requires_grad = flag
+
+
+class DataVSLayer(torch.nn.Module):
+ """DataLayer using variable splitting formulation."""
+
+ def __init__(
+ self,
+ alpha_init: float = 1.0,
+ beta_init: float = 1.0,
+ learnable: bool = True,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ ):
+ """Inits :class:`DataVSLayer`.
+
+ Parameters
+ ----------
+ alpha_init : float
+ Initial value for the regularization parameter alpha.
+ beta_init : float
+ Initial value for the regularization parameter beta.
+ learnable : bool
+ If True, the data term weight lambda is learnable. Default is ``True``.
+ fft_centered : bool
+ If True, the FFT is centered. Default is ``False``.
+ fft_normalization : str
+ If "ortho", the FFT is normalized. Default is ``"backward"``.
+ spatial_dims : tuple of int
+ If not None, the spatial dimensions of the FFT. Default is ``None``.
+ """
+ super().__init__()
+ self.alpha = torch.nn.Parameter(torch.Tensor(1))
+ self.alpha.data = torch.tensor(alpha_init, dtype=self.alpha.dtype)
+
+ self.beta = torch.nn.Parameter(torch.Tensor(1))
+ self.beta.data = torch.tensor(beta_init, dtype=self.beta.dtype)
+
+ self.learnable = learnable
+ self.set_learnable(learnable)
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+
+ def forward(
+ self, x: torch.Tensor, y: torch.Tensor, sensitivity_maps: torch.Tensor, mask: torch.Tensor
+ ) -> torch.Tensor:
+ """Forward pass of :class:`DataVSLayer`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Prediction. Shape [batch_size, num_coils, height, width, 2].
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, num_coils, height, width, 2].
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, num_coils, height, width, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, num_channels, height, width, 1].
+
+ Returns
+ -------
+ torch.Tensor
+ Data loss term.
+ """
+ A_x = torch.sum(
+ fft2(
+ complex_mul(x.expand_as(sensitivity_maps), sensitivity_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ -4,
+ keepdim=True,
+ )
+ k_dc = (1 - mask) * A_x + mask * (self.alpha * A_x + (1 - self.alpha) * y)
+ x_dc = torch.sum(
+ complex_mul(
+ ifft2(
+ k_dc,
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ ),
+ complex_conj(sensitivity_maps),
+ ),
+ dim=(-5),
+ )
+ return self.beta * x + (1 - self.beta) * x_dc
+
+ def set_learnable(self, flag: bool):
+ """Set the learnable flag of the parameters.
+
+ Parameters
+ ----------
+ flag : bool
+ If True, the parameters are learnable.
+ """
+ self.learnable = flag
+ self.alpha.requires_grad = self.learnable
+ self.beta.requires_grad = self.learnable
+
+
+class DCLayer(torch.nn.Module):
+ """Data Consistency layer from DC-CNN, apply for single coil mainly."""
+
+ def __init__(
+ self,
+ lambda_init: float,
+ learnable: bool = True,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ ):
+ """Inits :class:`DCLayer`.
+
+ Parameters
+ ----------
+ lambda_init : float
+ Initial value of data term weight lambda.
+ learnable : bool
+ If True, the data term weight lambda is learnable. Default is ``True``.
+ fft_centered : bool
+ If True, the FFT is centered. Default is ``False``.
+ fft_normalization : str
+ If "ortho", the FFT is normalized. Default is ``"backward"``.
+ spatial_dims : tuple of int
+ If not None, the spatial dimensions of the FFT. Default is ``None``.
+ """
+ super().__init__()
+ self.lambda_ = torch.nn.Parameter(torch.Tensor(1))
+ self.lambda_.data = torch.tensor(lambda_init, dtype=self.lambda_.dtype)
+
+ self.learnable = learnable
+ self.set_learnable(learnable)
+
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+
+ def forward(self, x: torch.Tensor, y: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`DCLayer`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Prediction. Shape [batch_size, num_coils, height, width, 2].
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, num_coils, height, width, 2].
+ mask : torch.Tensor
+ Sampling mask. Shape [batch_size, 1, num_channels, height, width, 1].
+
+ Returns
+ -------
+ torch.Tensor
+ Data loss term.
+ """
+ A_x = fft2(x, centered=self.fft_centered, normalization=self.fft_normalization, spatial_dims=self.spatial_dims)
+ k_dc = (1 - mask) * A_x + mask * (self.lambda_ * A_x + (1 - self.lambda_) * y)
+ return ifft2(
+ k_dc, centered=self.fft_centered, normalization=self.fft_normalization, spatial_dims=self.spatial_dims
+ )
+
+ def set_learnable(self, flag: bool):
+ """Set the learnable flag of the parameters.
+
+ Parameters
+ ----------
+ flag : bool
+ If True, the parameters are learnable.
+ """
+ self.learnable = flag
+ self.lambda_.requires_grad = self.learnable
diff --git a/atommic/collections/reconstruction/nn/sigmanet_base/sensitivity_net.py b/atommic/collections/reconstruction/nn/sigmanet_base/sensitivity_net.py
new file mode 100644
index 00000000..c4ebb5de
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/sigmanet_base/sensitivity_net.py
@@ -0,0 +1,326 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from:
+# https://github.com/khammernik/sigmanet/blob/master/reconstruction/common/mytorch/models/sn.py
+
+import numpy as np
+import torch
+
+
+def matrix_invert(xx, xy, yx, yy):
+ """Invert a 2x2 matrix."""
+ det = xx * yy - xy * yx
+ return yy.div(det), -xy.div(det), -yx.div(det), xx.div(det)
+
+
+class ComplexInstanceNorm(torch.nn.Module):
+ """Complex instance normalization layer, as presented in [Chiheb2017]_.
+
+ References
+ ----------
+ .. [Chiheb2017] Deep complex networks, T Chiheb, O Bilaniuk, D Serdyuk - โฆ Conference on Learning Representations,
+ 2017, https://arxiv.org/abs/1705.09792
+ """
+
+ def __init__(self):
+ """Inits :class:`ComplexInstanceNorm`."""
+ super().__init__()
+ self.mean = 0
+ self.cov_xx_half = 1 / np.sqrt(2)
+ self.cov_xy_half = 0
+ self.cov_yx_half = 0
+ self.cov_yy_half = 1 / np.sqrt(2)
+
+ def complex_instance_norm(self, x):
+ """Operates on images x of size [nBatch, nSmaps, nFE, nPE, 2]"""
+ x_combined = torch.sum(x, dim=1, keepdim=True)
+ mean = x_combined.mean(dim=(1, 2, 3), keepdim=True)
+ x_m = x - mean
+ self.mean = mean
+ self.complex_pseudocovariance(x_m)
+
+ def complex_pseudocovariance(self, data):
+ """Data variable hast to be already mean-free! Operates on images x of size [nBatch, nSmaps, nFE, nPE, 2]"""
+ if data.size(-1) != 2:
+ raise AssertionError
+ shape = data.shape
+
+ # compute number of elements
+ N = shape[2] * shape[3]
+
+ # separate real/imaginary channel
+ re, im = torch.unbind(data, dim=-1)
+
+ # dimensions is now length of original shape - 1 (because channels are separated)
+ dim = list(range(1, len(shape) - 1))
+
+ # compute covariance entries. cxy = cyx
+ cxx = (re * re).sum(dim=dim, keepdim=True) / (N - 1)
+ cyy = (im * im).sum(dim=dim, keepdim=True) / (N - 1)
+ cxy = (re * im).sum(dim=dim, keepdim=True) / (N - 1)
+
+ # Eigenvalue decomposition C = V*S*inv(V)
+ s1 = (cxx + cyy) / 2 - torch.sqrt((cxx + cyy) ** 2 / 4 - cxx * cyy + cxy**2)
+ s2 = (cxx + cyy) / 2 + torch.sqrt((cxx + cyy) ** 2 / 4 - cxx * cyy + cxy**2)
+
+ # compute eigenvectors
+ v1x = s1 - cyy
+ v1y = cxy
+ v2x = s2 - cyy
+ v2y = cxy
+
+ # normalize eigenvectors
+ norm1 = torch.sqrt(torch.sum(v1x * v1x + v1y * v1y, dim=dim, keepdim=True))
+ norm2 = torch.sqrt(torch.sum(v2x * v2x + v2y * v2y, dim=dim, keepdim=True))
+
+ v1x = v1x.div(norm1)
+ v1y = v1y.div(norm1)
+
+ v2x = v2x.div(norm2)
+ v2y = v2y.div(norm2)
+
+ # now we need the sqrt of the covariance matrix.
+ # C^{-0.5} = V * sqrt(S) * inv(V)
+ det = v1x * v2y - v2x * v1y
+ s1 = torch.sqrt(s1).div(det)
+ s2 = torch.sqrt(s2).div(det)
+
+ self.cov_xx_half = v1x * v2y * s1 - v1y * v2x * s2
+ self.cov_yy_half = v1x * v2y * s2 - v1y * v2x * s1
+ self.cov_xy_half = v1x * v2x * (s2 - s1)
+ self.cov_yx_half = v1y * v2y * (s1 - s2)
+
+ def forward(self, _input):
+ """Forward pass of :class:`ComplexInstanceNorm`."""
+ return self.normalize(_input)
+
+ def set_normalization(self, _input):
+ """Set the normalization parameters for a given _input."""
+ mean = torch.tensor([torch.mean(_input).item()]).to(_input)
+ self.complex_pseudocovariance(_input - mean)
+ self.mean = mean.unsqueeze(1).unsqueeze(1).unsqueeze(1)
+ self.cov_xx_half = self.cov_xx_half.view(-1, 1, 1, 1)
+ self.cov_xy_half = self.cov_xy_half.view(-1, 1, 1, 1)
+ self.cov_yx_half = self.cov_yx_half.view(-1, 1, 1, 1)
+ self.cov_yy_half = self.cov_yy_half.view(-1, 1, 1, 1)
+
+ def normalize(self, x):
+ """Normalize the _input x."""
+ x_m = x - self.mean
+ re, im = torch.unbind(x_m, dim=-1)
+
+ cov_xx_half_inv, cov_xy_half_inv, cov_yx_half_inv, cov_yy_half_inv = matrix_invert(
+ self.cov_xx_half, self.cov_xy_half, self.cov_yx_half, self.cov_yy_half
+ )
+ x_norm_re = cov_xx_half_inv * re + cov_xy_half_inv * im
+ x_norm_im = cov_yx_half_inv * re + cov_yy_half_inv * im
+ img = torch.stack([x_norm_re, x_norm_im], dim=-1)
+ # img = img.clamp(-6, 6)
+ return img
+
+ def unnormalize(self, x):
+ """Unnormalize the _input x."""
+ re, im = torch.unbind(x, dim=-1)
+ x_unnorm_re = self.cov_xx_half * re + self.cov_xy_half * im
+ x_unnorm_im = self.cov_yx_half * re + self.cov_yy_half * im
+ return torch.stack([x_unnorm_re, x_unnorm_im], dim=-1) + self.mean
+
+
+class ComplexNormWrapper(torch.nn.Module):
+ """Wrapper for complex normalization."""
+
+ def __init__(self, model):
+ """Inits :class:`ComplexNormWrapper`.
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Model to be wrapped.
+ """
+ super().__init__()
+ self.model = model
+ self.complex_instance_norm = ComplexInstanceNorm()
+
+ def forward(self, _input):
+ """Forward pass of :class:`ComplexNormWrapper`."""
+ # compute complex instance norm on sample of size [nBatch, nSmaps, nFE, nPE, 2]
+ self.complex_instance_norm.set_normalization(_input)
+ output = self.complex_instance_norm.normalize(_input)
+
+ # re-shape data from [nBatch, nSmaps, nFE, nPE, 2] to [nBatch*nSmaps, 2, nFE, nPE]
+ shp = output.shape
+ output = output.view(shp[0] * shp[1], *shp[2:]).permute(0, 3, 1, 2)
+
+ # apply denoising
+ output = self.model(output)
+
+ # re-shape data from [nBatch*nSmaps, 2, nFE, nPE]
+ # to [nBatch, nSmaps, nFE, nPE, 2]
+ output = output.permute(0, 2, 3, 1).view(*shp)
+ # unnormalize
+ output = self.complex_instance_norm.unnormalize(output)
+ return output
+
+
+class SensitivityNetwork(torch.nn.Module):
+ """Sensitivity network with data term based on forward and adjoint containing the sensitivity maps"""
+
+ def __init__(
+ self,
+ num_iter,
+ model,
+ datalayer,
+ shared_params=True,
+ save_space=False,
+ reset_cache=False,
+ ):
+ """Init :class:`SensitivityNetwork`.
+
+ Parameters
+ ----------
+ num_iter : int
+ Number of iterations.
+ model : torch.nn.Module
+ Model to be used for the forward and adjoint.
+ datalayer : torch.nn.Module
+ Data layer to be used for the forward and adjoint.
+ shared_params : bool, optional
+ If True, the parameters of the model are shared between the forward and adjoint. Default is ``True``.
+ save_space : bool, optional
+ If True, the adjoint is computed in the forward pass. Default is ``False``.
+ reset_cache : bool, optional
+ If True, the adjoint is computed in the forward pass. Default is ``False``.
+ """
+ super().__init__()
+
+ self.shared_params = shared_params
+ self.num_iter = 1 if self.shared_params else num_iter
+ self.num_iter_total = num_iter
+ self.is_trainable = [True] * num_iter
+
+ # setup the modules
+ self.gradR = torch.nn.ModuleList([ComplexNormWrapper(model) for _ in range(self.num_iter)])
+ self.gradD = torch.nn.ModuleList([datalayer for _ in range(self.num_iter)])
+
+ self.save_space = save_space
+ self.reset_cache = reset_cache
+
+ def forward(self, x, y, smaps, mask):
+ """Forward pass of :class:`SensitivityNetwork`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ _input data.
+ y : torch.Tensor
+ Subsampled k-space data.
+ smaps : torch.Tensor
+ Coil sensitivity maps.
+ mask : torch.Tensor
+ Sampling mask.
+ """
+ if self.save_space:
+ return self.forward_save_space(x, y, smaps, mask)
+
+ x_all = [x]
+ x_half_all = []
+ if self.shared_params:
+ num_iter = self.num_iter_total
+ else:
+ num_iter = min(np.where(self.is_trainable)[0][-1] + 1, self.num_iter)
+
+ for i in range(num_iter):
+ x_thalf = x - self.gradR[i % self.num_iter](x)
+ x = self.gradD[i % self.num_iter](x_thalf, y, smaps, mask)
+ x_all.append(x)
+ x_half_all.append(x_thalf)
+
+ return x_all[-1]
+
+ def forward_save_space(self, x, y, smaps, mask):
+ """Forward pass of :class:`SensitivityNetwork` with saving space.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ _input data.
+ y : torch.Tensor
+ Subsampled k-space data.
+ smaps : torch.Tensor
+ Coil sensitivity maps.
+ mask : torch.Tensor
+ Sampling mask.
+ """
+ if self.shared_params:
+ num_iter = self.num_iter_total
+ else:
+ num_iter = min(np.where(self.is_trainable)[0][-1] + 1, self.num_iter)
+
+ for i in range(num_iter):
+ x_thalf = x - self.gradR[i % self.num_iter](x)
+ x = self.gradD[i % self.num_iter](x_thalf, y, smaps, mask)
+
+ # would run out of memory at test time if this is False for some cases
+ if self.reset_cache:
+ torch.cuda.empty_cache()
+ torch.backends.cuda.cufft_plan_cache.clear()
+
+ return x
+
+ def freeze(self, i):
+ """freeze parameter of cascade i"""
+ for param in self.gradR[i].parameters():
+ param.require_grad_ = False
+ self.is_trainable[i] = False
+
+ def unfreeze(self, i):
+ """freeze parameter of cascade i"""
+ for param in self.gradR[i].parameters():
+ param.require_grad_ = True
+ self.is_trainable[i] = True
+
+ def freeze_all(self):
+ """freeze parameter of cascade i"""
+ for i in range(self.num_iter):
+ self.freeze(i)
+
+ def unfreeze_all(self):
+ """freeze parameter of cascade i"""
+ for i in range(self.num_iter):
+ self.unfreeze(i)
+
+ def copy_params(self, src_i, trg_j):
+ """copy i-th cascade net parameters to j-th cascade net parameters"""
+ src_params = self.gradR[src_i].parameters()
+ trg_params = self.gradR[trg_j].parameters()
+
+ for trg_param, src_param in zip(trg_params, src_params):
+ trg_param.data.copy_(src_param.data)
+
+ def stage_training_init(self):
+ """set stage training flag to True"""
+ self.freeze_all()
+ self.unfreeze(0)
+ print(self.is_trainable)
+
+ def stage_training_transition_i(self, copy=False):
+ """set stage training flag to True"""
+ if self.shared_params:
+ return
+
+ # if all unlocked, don't do anything
+ if not np.all(self.is_trainable):
+ for i in range(self.num_iter):
+ # if last cascade is reached, unlock all
+ if i == self.num_iter - 1:
+ self.unfreeze_all()
+ break
+
+ # freeze current i, unlock next. copy parameter if specified
+ if self.is_trainable[i]:
+ self.freeze(i)
+ self.unfreeze(i + 1)
+ if copy:
+ self.copy_params(i, i + 1)
+ break
diff --git a/atommic/collections/reconstruction/nn/unet.py b/atommic/collections/reconstruction/nn/unet.py
new file mode 100644
index 00000000..c39bc3bd
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/unet.py
@@ -0,0 +1,88 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["UNet"]
+
+
+class UNet(BaseMRIReconstructionModel):
+ """Implementation of the UNet, as presented in [Ronneberger2015]_.
+
+ References
+ ----------
+ .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical
+ image segmentation. In International Conference on Medical image computing and computer-assisted intervention,
+ pages 234โ241. Springer, 2015.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`UNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.in_channels = cfg_dict.get("in_channels", 2)
+ self.reconstruction_module = NormUnet(
+ chans=cfg_dict.get("channels"),
+ num_pools=cfg_dict.get("pooling_layers"),
+ in_chans=self.in_channels,
+ out_chans=cfg_dict.get("out_channels", 2),
+ padding_size=cfg_dict.get("padding_size", 11),
+ drop_prob=cfg_dict.get("dropout", 0.0),
+ normalize=cfg_dict.get("normalize", True),
+ norm_groups=cfg_dict.get("norm_groups", 2),
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor, # pylint: disable=unused-argument
+ sensitivity_maps: torch.Tensor, # pylint: disable=unused-argument
+ mask: torch.Tensor, # pylint: disable=unused-argument
+ initial_prediction: torch.Tensor,
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`UNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ if self.in_channels == 1 and initial_prediction.shape[-1] == 2:
+ initial_prediction = torch.abs(torch.view_as_complex(initial_prediction))
+ prediction = self.reconstruction_module(initial_prediction.unsqueeze(self.coil_dim)).squeeze(self.coil_dim)
+ if self.in_channels == 2:
+ prediction = torch.view_as_complex(prediction)
+ return prediction
diff --git a/atommic/collections/reconstruction/nn/unet_base/__init__.py b/atommic/collections/reconstruction/nn/unet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/unet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/unet_base/unet_block.py b/atommic/collections/reconstruction/nn/unet_base/unet_block.py
new file mode 100644
index 00000000..9d63f60b
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/unet_base/unet_block.py
@@ -0,0 +1,338 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from https://github.com/facebookresearch/fastMRI
+
+import math
+from typing import List, Tuple
+
+import torch
+
+
+class NormUnet(torch.nn.Module):
+ """Normalized U-Net model.
+
+ This is the same as a regular U-Net, but with normalization applied to the input before the U-Net. This keeps the
+ values more numerically stable during training.
+ """
+
+ def __init__(
+ self,
+ chans: int,
+ num_pools: int,
+ in_chans: int = 2,
+ out_chans: int = 2,
+ drop_prob: float = 0.0,
+ padding_size: int = 15,
+ normalize: bool = True,
+ norm_groups: int = 2,
+ ):
+ """Inits :class:`NormUnet`.
+
+ Parameters
+ ----------
+ chans : int
+ Number of output channels of the first convolution layer.
+ num_pools : int
+ Number of down-sampling and up-sampling layers.
+ in_chans : int, optional
+ Number of channels in the input to the U-Net model. Default is ``2``.
+ out_chans : int, optional
+ Number of channels in the output to the U-Net model. Default is ``2``.
+ drop_prob : float, optional
+ Dropout probability. Default is ``0.0``.
+ padding_size : int, optional
+ Size of the padding. Default is ``15``.
+ normalize : bool, optional
+ Whether to normalize the input. Default is ``True``.
+ norm_groups : int, optional
+ Number of groups to use for group normalization. Default is ``2``.
+ """
+ super().__init__()
+ self.unet = Unet(
+ in_chans=in_chans, out_chans=out_chans, chans=chans, num_pool_layers=num_pools, drop_prob=drop_prob
+ )
+ self.padding_size = padding_size
+ self.normalize = normalize
+ self.norm_groups = norm_groups
+
+ @staticmethod
+ def complex_to_chan_dim(x: torch.Tensor) -> torch.Tensor:
+ """Convert the last dimension of the input to complex."""
+ b, c, h, w, two = x.shape
+ if two != 2:
+ raise AssertionError
+ return x.permute(0, 4, 1, 2, 3).reshape(b, 2 * c, h, w)
+
+ @staticmethod
+ def chan_complex_to_last_dim(x: torch.Tensor) -> torch.Tensor:
+ """Convert the last dimension of the input to complex."""
+ b, c2, h, w = x.shape
+ if c2 % 2 != 0:
+ raise AssertionError
+ c = torch.div(c2, 2, rounding_mode="trunc")
+ return x.view(b, 2, c, h, w).permute(0, 2, 3, 4, 1).contiguous()
+
+ def norm(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Normalize the input."""
+ # group norm
+ b, c, h, w = x.shape
+
+ x = x.reshape(b, self.norm_groups, -1)
+
+ mean = x.mean(-1, keepdim=True)
+ std = x.std(-1, keepdim=True)
+
+ x = (x - mean) / std
+
+ x = x.reshape(b, c, h, w)
+
+ return x, mean, std
+
+ def unnorm(self, x: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) -> torch.Tensor:
+ """Unnormalize the input."""
+ b, c, h, w = x.shape
+ input_data = x.reshape(b, self.norm_groups, -1)
+ return (input_data * std + mean).reshape(b, c, h, w)
+
+ def pad(self, x: torch.Tensor) -> Tuple[torch.Tensor, Tuple[List[int], List[int], int, int]]:
+ """Pad the input with zeros to make it square."""
+ _, _, h, w = x.shape
+ w_mult = ((w - 1) | self.padding_size) + 1
+ h_mult = ((h - 1) | self.padding_size) + 1
+ w_pad = [math.floor((w_mult - w) / 2), math.ceil((w_mult - w) / 2)]
+ h_pad = [math.floor((h_mult - h) / 2), math.ceil((h_mult - h) / 2)]
+ # TODO: fix this type when PyTorch fixes theirs
+ # the documentation lies - this actually takes a list
+ # https://github.com/pytorch/pytorch/blob/master/torch/nn/functional.py#L3457
+ # https://github.com/pytorch/pytorch/pull/16949
+ x = torch.nn.functional.pad(x, w_pad + h_pad)
+
+ return x, (h_pad, w_pad, h_mult, w_mult)
+
+ @staticmethod
+ def unpad(x: torch.Tensor, h_pad: List[int], w_pad: List[int], h_mult: int, w_mult: int) -> torch.Tensor:
+ """Unpad the input."""
+ return x[..., h_pad[0] : h_mult - h_pad[1], w_pad[0] : w_mult - w_pad[1]]
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`NormUnet`."""
+ iscomplex = False
+ if x.shape[-1] == 2:
+ x = self.complex_to_chan_dim(x)
+ iscomplex = True
+
+ mean = 1.0
+ std = 1.0
+
+ if self.normalize:
+ x, mean, std = self.norm(x)
+
+ if self.padding_size > 0:
+ x, pad_sizes = self.pad(x)
+
+ x = self.unet(x)
+
+ if self.padding_size > 0:
+ x = self.unpad(x, *pad_sizes)
+
+ if self.normalize:
+ x = self.unnorm(x, mean, std)
+
+ if iscomplex:
+ x = self.chan_complex_to_last_dim(x)
+
+ return x
+
+
+class Unet(torch.nn.Module):
+ """U-Net model, as presented in [Ronneberger2015]_.
+
+ References
+ ----------
+ .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical
+ image segmentation. In International Conference on Medical image computing and computer-assisted intervention,
+ pages 234โ241. Springer, 2015.
+ """
+
+ def __init__(
+ self, in_chans: int, out_chans: int, chans: int = 32, num_pool_layers: int = 4, drop_prob: float = 0.0
+ ):
+ """Inits :class:`Unet`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of channels in the input to the U-Net model.
+ out_chans : int
+ Number of channels in the output to the U-Net model.
+ chans : int
+ Number of output channels of the first convolution layer. Default is ``32``.
+ num_pool_layers : int
+ Number of down-sampling and up-sampling layers. Default is ``4``.
+ drop_prob : float
+ Dropout probability. Default is ``0.0``.
+ """
+ super().__init__()
+
+ self.in_chans = in_chans
+ self.out_chans = out_chans
+ self.chans = chans
+ self.num_pool_layers = num_pool_layers
+ self.drop_prob = drop_prob
+
+ self.down_sample_layers = torch.nn.ModuleList([ConvBlock(in_chans, chans, drop_prob)])
+ ch = chans
+ for _ in range(num_pool_layers - 1):
+ self.down_sample_layers.append(ConvBlock(ch, ch * 2, drop_prob))
+ ch = ch * 2
+ self.conv = ConvBlock(ch, ch * 2, drop_prob)
+
+ self.up_conv = torch.nn.ModuleList()
+ self.up_transpose_conv = torch.nn.ModuleList()
+ for _ in range(num_pool_layers - 1):
+ self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch))
+ self.up_conv.append(ConvBlock(ch * 2, ch, drop_prob))
+ ch = ch // 2
+
+ self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch))
+ self.up_conv.append(
+ torch.nn.Sequential(
+ ConvBlock(ch * 2, ch, drop_prob), torch.nn.Conv2d(ch, self.out_chans, kernel_size=1, stride=1)
+ )
+ )
+
+ def forward(self, image: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`Unet`.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input tensor of shape `(N, in_chans, H, W)`.
+
+ Returns
+ -------
+ torch.Tensor
+ Output tensor of shape `(N, out_chans, H, W)`.
+ """
+ stack = []
+ output = image
+
+ # apply down-sampling layers
+ for layer in self.down_sample_layers:
+ output = layer(output)
+ stack.append(output)
+ output = torch.nn.functional.avg_pool2d(output, kernel_size=2, stride=2, padding=0)
+
+ output = self.conv(output)
+
+ # apply up-sampling layers
+ for transpose_conv, conv in zip(self.up_transpose_conv, self.up_conv):
+ downsample_layer = stack.pop()
+ output = transpose_conv(output)
+
+ # reflect pad on the right/bottom if needed to handle odd input dimensions
+ padding = [0, 0, 0, 0]
+ if output.shape[-1] != downsample_layer.shape[-1]:
+ padding[1] = 1 # padding right
+ if output.shape[-2] != downsample_layer.shape[-2]:
+ padding[3] = 1 # padding bottom
+ if torch.sum(torch.tensor(padding)) != 0:
+ output = torch.nn.functional.pad(output, padding, "reflect")
+
+ output = torch.cat([output, downsample_layer], dim=1)
+ output = conv(output)
+
+ return output
+
+
+class ConvBlock(torch.nn.Module):
+ """A Convolutional Block that consists of two convolution layers each followed by instance normalization, LeakyReLU
+ activation and dropout.
+ """
+
+ def __init__(self, in_chans: int, out_chans: int, drop_prob: float):
+ """Inits :class:`ConvBlock`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of channels in the input.
+ out_chans : int
+ Number of channels in the output.
+ drop_prob : float
+ Dropout probability.
+ """
+ super().__init__()
+
+ self.in_chans = in_chans
+ self.out_chans = out_chans
+ self.drop_prob = drop_prob
+
+ self.layers = torch.nn.Sequential(
+ torch.nn.Conv2d(in_chans, out_chans, kernel_size=3, padding=1, bias=False),
+ torch.nn.InstanceNorm2d(out_chans),
+ torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ torch.nn.Dropout2d(drop_prob),
+ torch.nn.Conv2d(out_chans, out_chans, kernel_size=3, padding=1, bias=False),
+ torch.nn.InstanceNorm2d(out_chans),
+ torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ torch.nn.Dropout2d(drop_prob),
+ )
+
+ def forward(self, image: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`ConvBlock`.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input tensor of shape `(N, in_chans, H, W)`.
+
+ Returns
+ -------
+ torch.Tensor
+ Output tensor of shape `(N, out_chans, H, W)`.
+ """
+ return self.layers(image)
+
+
+class TransposeConvBlock(torch.nn.Module):
+ """A Transpose Convolutional Block that consists of one convolution transpose layers followed by instance
+ normalization and LeakyReLU activation.
+ """
+
+ def __init__(self, in_chans: int, out_chans: int):
+ """Inits :class:`TransposeConvBlock`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of channels in the input.
+ out_chans : int
+ Number of channels in the output.
+ """
+ super().__init__()
+
+ self.in_chans = in_chans
+ self.out_chans = out_chans
+
+ self.layers = torch.nn.Sequential(
+ torch.nn.ConvTranspose2d(in_chans, out_chans, kernel_size=2, stride=2, bias=False),
+ torch.nn.InstanceNorm2d(out_chans),
+ torch.nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ )
+
+ def forward(self, image: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`TransposeConvBlock`.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input tensor of shape `(N, in_chans, H, W)`.
+
+ Returns
+ -------
+ torch.Tensor
+ Output tensor of shape `(N, out_chans, H*2, W*2)`.
+ """
+ return self.layers(image)
diff --git a/atommic/collections/reconstruction/nn/varnet.py b/atommic/collections/reconstruction/nn/varnet.py
new file mode 100644
index 00000000..dd49d4ba
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/varnet.py
@@ -0,0 +1,110 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import ifft2
+from atommic.collections.common.parts.utils import check_stacked_complex, coil_combination_method
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.collections.reconstruction.nn.varnet_base.varnet_block import VarNetBlock
+from atommic.core.classes.common import typecheck
+
+__all__ = ["VarNet"]
+
+
+class VarNet(BaseMRIReconstructionModel):
+ """Implementation of the End-to-end Variational Network (VN), as presented in [Sriram2020]_.
+
+ References
+ ----------
+ .. [Sriram2020] Sriram A, Zbontar J, Murrell T, Defazio A, Zitnick CL, Yakubova N, Knoll F, Johnson P. End-to-end
+ variational networks for accelerated MRI reconstruction. InInternational Conference on Medical Image Computing
+ and Computer-Assisted Intervention 2020 Oct 4 (pp. 64-73). Springer, Cham.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`VarNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.no_dc = cfg_dict.get("no_dc")
+ self.num_cascades = cfg_dict.get("num_cascades")
+
+ # Cascades of VN blocks
+ self.cascades = torch.nn.ModuleList(
+ [
+ VarNetBlock(
+ NormUnet(
+ chans=cfg_dict.get("channels", 18),
+ num_pools=cfg_dict.get("pooling_layers", 4),
+ in_chans=cfg_dict.get("in_chans", 2),
+ out_chans=cfg_dict.get("out_chans", 2),
+ drop_prob=cfg_dict.get("dropout", 0.0),
+ padding_size=cfg_dict.get("padding_size", 11),
+ normalize=cfg_dict.get("normalize", True),
+ ),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ no_dc=self.no_dc,
+ )
+ for _ in range(self.num_cascades)
+ ]
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`VarNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ prediction = y.clone()
+ for cascade in self.cascades:
+ prediction = cascade(prediction, y, sensitivity_maps, mask)
+ return check_stacked_complex(
+ coil_combination_method(
+ ifft2(prediction, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+ )
diff --git a/atommic/collections/reconstruction/nn/varnet_base/__init__.py b/atommic/collections/reconstruction/nn/varnet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/varnet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/varnet_base/varnet_block.py b/atommic/collections/reconstruction/nn/varnet_base/varnet_block.py
new file mode 100644
index 00000000..eada823c
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/varnet_base/varnet_block.py
@@ -0,0 +1,126 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Optional, Tuple
+
+import torch
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import complex_conj, complex_mul
+
+
+class VarNetBlock(torch.nn.Module):
+ """Block for end-to-end variational network.
+
+ This model applies a combination of soft data consistency with the input model as a regularizer. A series of these
+ blocks can be stacked to form the full variational network.
+ """
+
+ def __init__(
+ self,
+ model: torch.nn.Module,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ no_dc: bool = False,
+ ):
+ """Inits :class:`VarNetBlock`.
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Model to apply soft data consistency.
+ fft_centered : bool, optional
+ Whether to center the FFT. Default is ``False``.
+ fft_normalization : str, optional
+ Whether to normalize the FFT. Default is ``"backward"``.
+ spatial_dims : Tuple[int, int], optional
+ Spatial dimensions of the input. Default is ``None``.
+ coil_dim : int, optional
+ Coil dimension. Default is ``1``.
+ no_dc : bool, optional
+ Flag to disable the soft data consistency. Default is ``False``.
+ """
+ super().__init__()
+
+ self.model = model
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+ self.no_dc = no_dc
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+
+ def sens_expand(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with coil-combined data to get multicoil data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ Expanded multicoil data.
+ """
+ return fft2(
+ complex_mul(x, sens_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ def sens_reduce(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with multicoil data to get coil-combined data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ SENSE coil-combined reconstruction.
+ """
+ x = ifft2(x, centered=self.fft_centered, normalization=self.fft_normalization, spatial_dims=self.spatial_dims)
+ return complex_mul(x, complex_conj(sens_maps)).sum(dim=self.coil_dim, keepdim=True)
+
+ def forward(
+ self, pred: torch.Tensor, ref_kspace: torch.Tensor, sensitivity_maps: torch.Tensor, mask: torch.Tensor
+ ) -> torch.Tensor:
+ """Forward pass of :class:`VarNetBlock`.
+
+ Parameters
+ ----------
+ pred : torch.Tensor
+ Predicted k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ ref_kspace : torch.Tensor
+ Reference k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+
+ Returns
+ -------
+ torch.Tensor
+ Reconstructed image. Shape [batch_size, n_x, n_y, 2]
+ """
+ zero = torch.zeros(1, 1, 1, 1, 1).to(pred)
+ soft_dc = torch.where(mask.bool(), pred - ref_kspace, zero) * self.dc_weight
+
+ prediction = self.sens_reduce(pred, sensitivity_maps)
+ prediction = self.model(prediction)
+ prediction = self.sens_expand(prediction, sensitivity_maps)
+
+ if not self.no_dc:
+ prediction = pred - soft_dc - prediction
+
+ return prediction
diff --git a/atommic/collections/reconstruction/nn/vsnet.py b/atommic/collections/reconstruction/nn/vsnet.py
new file mode 100644
index 00000000..f1a0c5ce
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/vsnet.py
@@ -0,0 +1,143 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import ifft2
+from atommic.collections.common.parts.utils import check_stacked_complex, coil_combination_method
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.ccnn_base.ccnn_block import Conv2d
+from atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block import MWCNN
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.collections.reconstruction.nn.vsnet_base.vsnet_block import (
+ DataConsistencyLayer,
+ VSNetBlock,
+ WeightedAverageTerm,
+)
+from atommic.core.classes.common import typecheck
+
+__all__ = ["VSNet"]
+
+
+class VSNet(BaseMRIReconstructionModel):
+ """Implementation of the Variable-Splitting Net, as presented in [Duan2019]_.
+
+ References
+ ----------
+ .. [Duan2019] Duan, J. et al. (2019) Vs-net: Variable splitting network for accelerated parallel MRI
+ reconstruction, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial
+ Intelligence and Lecture Notes in Bioinformatics), 11767 LNCS, pp. 713โ722. doi: 10.1007/978-3-030-32251-9_78.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`VSNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ num_cascades = cfg_dict.get("num_cascades")
+ self.num_cascades = cfg_dict.get("num_cascades")
+
+ image_model_architecture = cfg_dict.get("imspace_model_architecture")
+ if image_model_architecture == "CONV":
+ image_model = Conv2d(
+ in_channels=cfg_dict.get("imspace_in_channels", 2),
+ out_channels=cfg_dict.get("imspace_out_channels", 2),
+ hidden_channels=cfg_dict.get("imspace_conv_hidden_channels"),
+ n_convs=cfg_dict.get("imspace_conv_n_convs"),
+ batchnorm=cfg_dict.get("imspace_conv_batchnorm"),
+ )
+ elif image_model_architecture == "MWCNN":
+ image_model = MWCNN(
+ input_channels=cfg_dict.get("imspace_in_channels", 2),
+ first_conv_hidden_channels=cfg_dict.get("image_mwcnn_hidden_channels"),
+ num_scales=cfg_dict.get("image_mwcnn_num_scales"),
+ bias=cfg_dict.get("image_mwcnn_bias"),
+ batchnorm=cfg_dict.get("image_mwcnn_batchnorm"),
+ )
+ elif image_model_architecture in ["UNET", "NORMUNET"]:
+ image_model = NormUnet(
+ cfg_dict.get("imspace_unet_num_filters"),
+ cfg_dict.get("imspace_unet_num_pool_layers"),
+ in_chans=cfg_dict.get("imspace_in_channels", 2),
+ out_chans=cfg_dict.get("imspace_out_channels", 2),
+ drop_prob=cfg_dict.get("imspace_unet_dropout_probability"),
+ padding_size=cfg_dict.get("imspace_unet_padding_size"),
+ normalize=cfg_dict.get("imspace_unet_normalize"),
+ )
+ else:
+ raise NotImplementedError(
+ "VSNet is currently implemented only with image_model_architecture == 'MWCNN' or 'UNet'."
+ f"Got {image_model_architecture}."
+ )
+
+ image_model = torch.nn.ModuleList([image_model] * num_cascades)
+ data_consistency_model = torch.nn.ModuleList([DataConsistencyLayer()] * num_cascades)
+ weighted_average_model = torch.nn.ModuleList([WeightedAverageTerm()] * num_cascades)
+
+ self.reconstruction_module = VSNetBlock(
+ denoiser_block=image_model,
+ data_consistency_block=data_consistency_model,
+ weighted_average_block=weighted_average_model,
+ num_cascades=num_cascades,
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`VSNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ return check_stacked_complex(
+ coil_combination_method(
+ ifft2(
+ self.reconstruction_module(y, sensitivity_maps, mask),
+ self.fft_centered,
+ self.fft_normalization,
+ self.spatial_dims,
+ ),
+ sensitivity_maps,
+ self.coil_combination_method,
+ self.coil_dim,
+ )
+ )
diff --git a/atommic/collections/reconstruction/nn/vsnet_base/__init__.py b/atommic/collections/reconstruction/nn/vsnet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/vsnet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/reconstruction/nn/vsnet_base/vsnet_block.py b/atommic/collections/reconstruction/nn/vsnet_base/vsnet_block.py
new file mode 100644
index 00000000..94881525
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/vsnet_base/vsnet_block.py
@@ -0,0 +1,168 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from typing import Optional, Tuple
+
+import torch
+
+from atommic.collections.common.parts.fft import fft2, ifft2
+from atommic.collections.common.parts.utils import complex_conj, complex_mul
+
+
+class DataConsistencyLayer(torch.nn.Module):
+ """Data consistency layer for the VSNet.
+
+ This layer is used to ensure that the output of the VSNet is the same as the input.
+ """
+
+ def __init__(self):
+ """Inits :class:`DataConsistencyLayer`."""
+ super().__init__()
+ self.dc_weight = torch.nn.Parameter(torch.ones(1))
+
+ def forward(self, pred_kspace: torch.Tensor, ref_kspace: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`DataConsistencyLayer`.
+
+ Parameters
+ ----------
+ pred_kspace : torch.Tensor
+ Predicted k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ ref_kspace : torch.Tensor
+ Reference k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ """
+ return ((1 - mask) * pred_kspace + mask * ref_kspace) * self.dc_weight
+
+
+class WeightedAverageTerm(torch.nn.Module):
+ """Weighted average term for the VSNet."""
+
+ def __init__(self):
+ """Inits :class:`WeightedAverageTerm`."""
+ super().__init__()
+ self.param = torch.nn.Parameter(torch.ones(1))
+
+ def forward(self, x: torch.Tensor, Sx: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`WeightedAverageTerm`."""
+ return self.param * x + (1 - self.param) * Sx
+
+
+class VSNetBlock(torch.nn.Module):
+ """Model block for the Variable-Splitting Network inspired by [Duan2019]_.
+
+ References
+ ----------
+ .. [Duan2019] Duan, J. et al. (2019) Vs-net: Variable splitting network for accelerated parallel MRI
+ reconstruction, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial
+ Intelligence and Lecture Notes in Bioinformatics), 11767 LNCS, pp. 713โ722. doi: 10.1007/978-3-030-32251-9_78.
+ """
+
+ def __init__(
+ self,
+ denoiser_block: torch.nn.ModuleList,
+ data_consistency_block: torch.nn.ModuleList,
+ weighted_average_block: torch.nn.ModuleList,
+ num_cascades: int = 8,
+ fft_centered: bool = False,
+ fft_normalization: str = "backward",
+ spatial_dims: Optional[Tuple[int, int]] = None,
+ coil_dim: int = 1,
+ ):
+ """Inits :class:`VSNetBlock`.
+
+ Parameters
+ ----------
+ denoiser_block : torch.nn.ModuleList
+ Model to apply denoising.
+ data_consistency_block : torch.nn.ModuleList
+ Model to apply data consistency.
+ weighted_average_block : torch.nn.ModuleList
+ Model to apply weighted average.
+ num_cascades : int, optional
+ Number of cascades. Default is ``8``.
+ fft_centered : bool, optional
+ Whether to center the fft. Default is ``False``.
+ fft_normalization : str, optional
+ The normalization of the fft. Default is ``"backward"``.
+ spatial_dims : tuple, optional
+ The spatial dimensions of the data. Default is ``None``.
+ coil_dim : int, optional
+ The dimension of the coil. Default is ``1``.
+ """
+ super().__init__()
+
+ self.denoiser_block = denoiser_block
+ self.data_consistency_block = data_consistency_block
+ self.weighted_average_block = weighted_average_block
+ self.num_cascades = num_cascades
+ self.fft_centered = fft_centered
+ self.fft_normalization = fft_normalization
+ self.spatial_dims = spatial_dims if spatial_dims is not None else [-2, -1]
+ self.coil_dim = coil_dim
+
+ def sens_expand(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with coil-combined data to get multicoil data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ Expanded multicoil data.
+ """
+ return fft2(
+ complex_mul(x, sens_maps),
+ centered=self.fft_centered,
+ normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ )
+
+ def sens_reduce(self, x: torch.Tensor, sens_maps: torch.Tensor) -> torch.Tensor:
+ """Combines the sensitivity maps with multicoil data to get coil-combined data.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input data.
+ sens_maps : torch.Tensor
+ Coil Sensitivity maps.
+
+ Returns
+ -------
+ torch.Tensor
+ SENSE coil-combined reconstruction.
+ """
+ x = ifft2(x, centered=self.fft_centered, normalization=self.fft_normalization, spatial_dims=self.spatial_dims)
+ return complex_mul(x, complex_conj(sens_maps)).sum(dim=self.coil_dim)
+
+ def forward(self, kspace: torch.Tensor, sensitivity_maps: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`VSNetBlock`.
+
+ Parameters
+ ----------
+ kspace : torch.Tensor
+ Reference k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+
+ Returns
+ -------
+ torch.Tensor
+ Reconstructed image. Shape [batch_size, n_x, n_y, 2]
+ """
+ for idx in range(self.num_cascades):
+ pred = self.sens_reduce(kspace, sensitivity_maps)
+ pred = self.denoiser_block[idx](pred.permute(0, 3, 1, 2)).permute(0, 2, 3, 1)
+ pred = self.sens_expand(pred, sensitivity_maps)
+ sx = self.data_consistency_block[idx](pred, kspace, mask)
+ sx = self.sens_reduce(sx, sensitivity_maps)
+ kspace = self.weighted_average_block[idx](kspace + pred, sx)
+ return kspace
diff --git a/atommic/collections/reconstruction/nn/xpdnet.py b/atommic/collections/reconstruction/nn/xpdnet.py
new file mode 100644
index 00000000..41210e68
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/xpdnet.py
@@ -0,0 +1,203 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import warnings
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.collections.reconstruction.nn.ccnn_base.ccnn_block import Conv2d
+from atommic.collections.reconstruction.nn.crossdomain_base.crossdomain_block import CrossDomainNetwork, MultiCoil
+from atommic.collections.reconstruction.nn.didn_base.didn_block import DIDN
+from atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block import MWCNN
+from atommic.collections.reconstruction.nn.unet_base.unet_block import NormUnet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["XPDNet"]
+
+
+class XPDNet(BaseMRIReconstructionModel):
+ """Implementation of the XPDNet, as presented in [Ramzi2021]_.
+
+ References
+ ----------
+ .. [Ramzi2021] Ramzi, Zaccharie, et al. โXPDNet for MRI Reconstruction: An Application to the 2020 FastMRI
+ Challenge. ArXiv:2010.07290 [Physics, Stat], July 2021. arXiv.org, http://arxiv.org/abs/2010.07290.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`XPDNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ num_primal = cfg_dict.get("num_primal")
+ num_dual = cfg_dict.get("num_dual")
+ num_iter = cfg_dict.get("num_iter")
+
+ kspace_model_architecture = cfg_dict.get("kspace_model_architecture")
+ dual_conv_hidden_channels = cfg_dict.get("dual_conv_hidden_channels", 64)
+ dual_conv_num_dubs = cfg_dict.get("dual_conv_num_dubs", 2)
+ dual_conv_batchnorm = cfg_dict.get("dual_conv_batchnorm", True)
+ dual_didn_hidden_channels = cfg_dict.get("dual_didn_hidden_channels", 64)
+ dual_didn_num_dubs = cfg_dict.get("dual_didn_num_dubs", 2)
+ dual_didn_num_convs_recon = cfg_dict.get("dual_didn_num_convs_recon", True)
+
+ if cfg_dict.get("use_primal_only"):
+ kspace_model_list = None
+ num_dual = 1
+ elif kspace_model_architecture == "CONV":
+ kspace_model_list = torch.nn.ModuleList(
+ [
+ MultiCoil(
+ Conv2d(
+ cfg_dict.get("kspace_in_channels") * (num_dual + num_primal + 1),
+ cfg_dict.get("kspace_out_channels") * num_dual,
+ dual_conv_hidden_channels,
+ dual_conv_num_dubs,
+ batchnorm=dual_conv_batchnorm,
+ )
+ )
+ for _ in range(num_iter)
+ ]
+ )
+ elif kspace_model_architecture == "DIDN":
+ kspace_model_list = torch.nn.ModuleList(
+ [
+ MultiCoil(
+ DIDN(
+ in_channels=cfg_dict.get("kspace_in_channels") * (num_dual + num_primal + 1),
+ out_channels=cfg_dict.get("kspace_out_channels") * num_dual,
+ hidden_channels=dual_didn_hidden_channels,
+ num_dubs=dual_didn_num_dubs,
+ num_convs_recon=dual_didn_num_convs_recon,
+ )
+ )
+ for _ in range(num_iter)
+ ]
+ )
+ elif kspace_model_architecture in ["UNET", "NORMUNET"]:
+ kspace_model_list = torch.nn.ModuleList(
+ [
+ MultiCoil(
+ NormUnet(
+ cfg_dict.get("kspace_unet_num_filters"),
+ cfg_dict.get("kspace_unet_num_pool_layers"),
+ in_chans=cfg_dict.get("kspace_in_channels") * (num_dual + num_primal + 1),
+ out_chans=cfg_dict.get("kspace_out_channels") * num_dual,
+ drop_prob=cfg_dict.get("kspace_unet_dropout_probability"),
+ padding_size=cfg_dict.get("kspace_unet_padding_size"),
+ normalize=cfg_dict.get("kspace_unet_normalize"),
+ ),
+ coil_to_batch=True,
+ )
+ for _ in range(num_iter)
+ ]
+ )
+ else:
+ raise NotImplementedError(
+ "XPDNet is currently implemented for kspace_model_architecture == 'CONV' or 'DIDN'."
+ f"Got kspace_model_architecture == {kspace_model_architecture}."
+ )
+
+ image_model_architecture = cfg_dict.get("image_model_architecture")
+ mwcnn_hidden_channels = cfg_dict.get("mwcnn_hidden_channels", 16)
+ mwcnn_num_scales = cfg_dict.get("mwcnn_num_scales", 2)
+ mwcnn_bias = cfg_dict.get("mwcnn_bias", True)
+ mwcnn_batchnorm = cfg_dict.get("mwcnn_batchnorm", True)
+
+ if image_model_architecture == "MWCNN":
+ image_model_list = torch.nn.ModuleList(
+ [
+ torch.nn.Sequential(
+ MWCNN(
+ input_channels=cfg_dict.get("imspace_in_channels") * (num_primal + num_dual),
+ first_conv_hidden_channels=mwcnn_hidden_channels,
+ num_scales=mwcnn_num_scales,
+ bias=mwcnn_bias,
+ batchnorm=mwcnn_batchnorm,
+ ),
+ torch.nn.Conv2d(2 * (num_primal + num_dual), 2 * num_primal, kernel_size=3, padding=1),
+ )
+ for _ in range(num_iter)
+ ]
+ )
+ elif image_model_architecture in ["UNET", "NORMUNET"]:
+ image_model_list = torch.nn.ModuleList(
+ [
+ NormUnet(
+ cfg_dict.get("imspace_unet_num_filters"),
+ cfg_dict.get("imspace_unet_num_pool_layers"),
+ in_chans=cfg_dict.get("imspace_in_channels") * (num_primal + num_dual),
+ out_chans=cfg_dict.get("imspace_out_channels") * num_primal,
+ drop_prob=cfg_dict.get("imspace_unet_dropout_probability"),
+ padding_size=cfg_dict.get("imspace_unet_padding_size"),
+ normalize=cfg_dict.get("imspace_unet_normalize"),
+ )
+ for _ in range(num_iter)
+ ]
+ )
+ else:
+ raise NotImplementedError(f"Image model architecture {image_model_architecture} not found for XPDNet.")
+
+ self.num_cascades = cfg_dict.get("num_cascades")
+
+ self.reconstruction_module = CrossDomainNetwork(
+ image_model_list=image_model_list,
+ kspace_model_list=kspace_model_list,
+ domain_sequence="KI" * num_iter,
+ image_buffer_size=num_primal,
+ kspace_buffer_size=num_dual,
+ normalize_image=cfg_dict.get("normalize_image"),
+ fft_centered=self.fft_centered,
+ fft_normalization=self.fft_normalization,
+ spatial_dims=self.spatial_dims,
+ coil_dim=self.coil_dim,
+ )
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor,
+ initial_prediction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`XPDNet`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ prediction = self.reconstruction_module(y, sensitivity_maps, mask)
+ # filter UserWarning: ComplexHalf support is experimental and many operators don't support it yet.
+ # TODO: remove this when PyTorch fixes the issue.
+ warnings.filterwarnings("ignore", category=UserWarning)
+ return torch.view_as_real(prediction[..., 0] + 1j * prediction[..., 1])
diff --git a/atommic/collections/reconstruction/nn/zf.py b/atommic/collections/reconstruction/nn/zf.py
new file mode 100644
index 00000000..23b6b304
--- /dev/null
+++ b/atommic/collections/reconstruction/nn/zf.py
@@ -0,0 +1,76 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from omegaconf import DictConfig
+from pytorch_lightning import Trainer
+
+from atommic.collections.common.parts.fft import ifft2
+from atommic.collections.common.parts.utils import check_stacked_complex, coil_combination_method
+from atommic.collections.reconstruction.nn.base import BaseMRIReconstructionModel
+from atommic.core.classes.common import typecheck
+
+__all__ = ["ZF"]
+
+
+class ZF(BaseMRIReconstructionModel):
+ """Zero-Filled reconstruction using either root-sum-of-squares (RSS) or SENSE (SENSitivity Encoding, as presented
+ in [Pruessmann1999]_).
+
+ References
+ ----------
+ .. [Pruessmann1999] Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI.
+ Magn Reson Med 1999; 42:952-962.
+
+ """
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`ZF`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer. Default is ``None``.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ # pylint: disable=arguments-differ
+ @typecheck()
+ def forward(
+ self,
+ y: torch.Tensor,
+ sensitivity_maps: torch.Tensor,
+ mask: torch.Tensor, # pylint: disable=unused-argument
+ initial_prediction: torch.Tensor, # pylint: disable=unused-argument
+ sigma: float = 1.0, # pylint: disable=unused-argument
+ ) -> torch.Tensor:
+ """Forward pass of :class:`ZF`.
+
+ Parameters
+ ----------
+ y : torch.Tensor
+ Subsampled k-space data. Shape [batch_size, n_coils, n_x, n_y, 2]
+ sensitivity_maps : torch.Tensor
+ Coil sensitivity maps. Shape [batch_size, n_coils, n_x, n_y, 2]
+ mask : torch.Tensor
+ Subsampling mask. Shape [1, 1, n_x, n_y, 1]
+ initial_prediction : torch.Tensor
+ Initial prediction. Shape [batch_size, n_x, n_y, 2]
+ sigma : float
+ Noise level. Default is ``1.0``.
+
+ Returns
+ -------
+ torch.Tensor
+ Prediction of the final cascade. Shape [batch_size, n_x, n_y]
+ """
+ return check_stacked_complex(
+ coil_combination_method(
+ ifft2(y, self.fft_centered, self.fft_normalization, self.spatial_dims),
+ sensitivity_maps,
+ self.coil_combination_method.upper(),
+ self.coil_dim,
+ )
+ )
diff --git a/atommic/collections/reconstruction/parts/__init__.py b/atommic/collections/reconstruction/parts/__init__.py
new file mode 100644
index 00000000..c858a2e2
--- /dev/null
+++ b/atommic/collections/reconstruction/parts/__init__.py
@@ -0,0 +1,4 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.reconstruction.parts.transforms import ReconstructionMRIDataTransforms # noqa: F401
diff --git a/atommic/collections/reconstruction/parts/transforms.py b/atommic/collections/reconstruction/parts/transforms.py
new file mode 100644
index 00000000..1d0d26ce
--- /dev/null
+++ b/atommic/collections/reconstruction/parts/transforms.py
@@ -0,0 +1,14 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.common.parts.transforms import MRIDataTransforms
+
+__all__ = ["ReconstructionMRIDataTransforms"]
+
+
+class ReconstructionMRIDataTransforms(MRIDataTransforms):
+ """Transforms for the accelerated-MRI reconstruction task.
+
+ .. note::
+ Extends :class:`atommic.collections.common.parts.transforms.MRIDataTransforms`.
+ """
diff --git a/atommic/collections/segmentation/__init__.py b/atommic/collections/segmentation/__init__.py
new file mode 100644
index 00000000..3d42a64d
--- /dev/null
+++ b/atommic/collections/segmentation/__init__.py
@@ -0,0 +1,13 @@
+# coding=utf-8
+
+from atommic.collections.segmentation import data, losses, metrics, nn, parts # noqa: F401
+from atommic.package_info import __version__
+
+# Set collection version equal to atommic version.
+__version = __version__
+
+# Authorship.
+__author__ = "Dimitris Karkalousos"
+
+# Set collection name.
+__description__ = "MRI Segmentation collection"
diff --git a/atommic/collections/segmentation/data/__init__.py b/atommic/collections/segmentation/data/__init__.py
new file mode 100644
index 00000000..56f39303
--- /dev/null
+++ b/atommic/collections/segmentation/data/__init__.py
@@ -0,0 +1,9 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.segmentation.data.mri_segmentation_loader import ( # noqa: F401
+ BraTS2023AdultGliomaSegmentationMRIDataset,
+ ISLES2022SubAcuteStrokeSegmentationMRIDataset,
+ SegmentationMRIDataset,
+ SKMTEASegmentationMRIDataset,
+)
diff --git a/atommic/collections/segmentation/data/mri_segmentation_loader.py b/atommic/collections/segmentation/data/mri_segmentation_loader.py
new file mode 100644
index 00000000..a5058b2e
--- /dev/null
+++ b/atommic/collections/segmentation/data/mri_segmentation_loader.py
@@ -0,0 +1,1303 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import json
+import logging
+import os
+import random
+from pathlib import Path
+from typing import Callable, Dict, Optional, Tuple, Union
+
+import h5py
+import nibabel as nib
+import numpy as np
+import yaml # type: ignore
+from nibabel.filebasedimages import FileBasedImage
+from torch.utils.data import Dataset
+
+from atommic.collections.common.data.mri_loader import MRIDataset
+from atommic.collections.common.parts.utils import is_none
+
+
+class SegmentationMRIDataset(MRIDataset):
+ """A dataset class for MRI segmentation.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.data.mri_segmentation_loader import SegmentationMRIDataset
+ >>> dataset = SegmentationMRIDataset(root='data/train', sample_rate=0.1)
+ >>> print(len(dataset))
+ 100
+ >>> kspace, imspace, coil_sensitivities, mask, initial_prediction, segmentation_labels, attrs, filename, \
+ slice_num = dataset[0]
+ >>> print(kspace.shape)
+ np.array([30, 640, 368])
+
+ .. note::
+ Extends :class:`atommic.collections.common.data.mri_loader.MRIDataset`.
+ """
+
+ def __init__(
+ self,
+ root: Union[str, Path, os.PathLike],
+ coil_sensitivity_maps_root: Union[str, Path, os.PathLike] = None,
+ mask_root: Union[str, Path, os.PathLike] = None,
+ noise_root: Union[str, Path, os.PathLike] = None,
+ initial_predictions_root: Union[str, Path, os.PathLike] = None,
+ dataset_format: str = None,
+ sample_rate: Optional[float] = None,
+ volume_sample_rate: Optional[float] = None,
+ use_dataset_cache: bool = False,
+ dataset_cache_file: Union[str, Path, os.PathLike] = None,
+ num_cols: Optional[Tuple[int]] = None,
+ consecutive_slices: int = 1,
+ data_saved_per_slice: bool = False,
+ n2r_supervised_rate: Optional[float] = 0.0,
+ complex_target: bool = False,
+ log_images_rate: Optional[float] = 1.0,
+ transform: Optional[Callable] = None,
+ segmentations_root: Union[str, Path, os.PathLike] = None,
+ segmentation_classes: int = 2,
+ segmentation_classes_to_remove: Optional[Tuple[int]] = None,
+ segmentation_classes_to_combine: Optional[Tuple[int]] = None,
+ segmentation_classes_to_separate: Optional[Tuple[int]] = None,
+ segmentation_classes_thresholds: Optional[Tuple[float]] = None,
+ complex_data: bool = True,
+ **kwargs,
+ ):
+ """Inits :class:`SegmentationMRIDataset`.
+
+ Parameters
+ ----------
+ root : Union[str, Path, os.PathLike]
+ Path to the dataset.
+ sense_root : Union[str, Path, os.PathLike], optional
+ Path to the coil sensitivities maps dataset, if stored separately.
+ mask_root : Union[str, Path, os.PathLike], optional
+ Path to stored masks, if stored separately.
+ noise_root : Union[str, Path, os.PathLike], optional
+ Path to stored noise, if stored separately (in json format).
+ initial_predictions_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the initial predictions. If provided, the initial predictions will be used
+ as the input of the reconstruction network. Default is ``None``.
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ Default is ``None``.
+ sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the slices should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ volume_sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the volumes should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ use_dataset_cache : bool, optional
+ Whether to cache dataset metadata. This is very useful for large datasets.
+ dataset_cache_file : Union[str, Path, os.PathLike], optional
+ A file in which to cache dataset information for faster load times.
+ num_cols : Optional[Tuple[int]], optional
+ If provided, only slices with the desired number of columns will be considered.
+ consecutive_slices : int, optional
+ An int (>0) that determine the amount of consecutive slices of the file to be loaded at the same time.
+ Default is ``1``, loading single slices.
+ data_saved_per_slice : bool, optional
+ Whether the data is saved per slice or per volume.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ complex_target : bool, optional
+ Whether the target is complex. Default is ``False``.
+ log_images_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be logged as images. Default is
+ ``1.0``.
+ transform : Optional[Callable], optional
+ A sequence of callable objects that preprocesses the raw data into appropriate form. The transform function
+ should take ``kspace``, ``coil sensitivity maps``, ``mask``, ``initial prediction``, ``segmentation``,
+ ``target``, ``attributes``, ``filename``, and ``slice number`` as inputs. ``target`` may be null for test
+ data. Default is ``None``.
+ segmentations_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the segmentations.
+ segmentation_classes : int, optional
+ The number of segmentation classes. Default is ``2``.
+ segmentation_classes_to_remove : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to remove. For example, if the dataset contains segmentation classes
+ 0, 1, 2,
+ 3, and 4, and you want to remove classes 1 and 3, set this to ``(1, 3)``. Default is ``None``.
+ segmentation_classes_to_combine : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to combine. For example, if the dataset contains segmentation classes
+ 0, 1, 2, 3, and 4, and you want to combine classes 1 and 3, set this to ``(1, 3)``. Default is ``None``.
+ segmentation_classes_to_separate : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to separate. For example, if the dataset contains segmentation classes
+ 0, 1, 2, 3, and 4, and you want to separate class 1 into 2 classes, set this to ``(1, 2)``.
+ Default is ``None``.
+ segmentation_classes_thresholds : Optional[Tuple[float]], optional
+ A tuple of thresholds for the segmentation classes. For example, if the dataset contains segmentation
+ classes 0, 1, 2, 3, and 4, and you want to set the threshold for class 1 to 0.5, set this to
+ ``(0.5, 0.5, 0.5, 0.5, 0.5)``. Default is ``None``.
+ complex_data : bool, optional
+ Whether the data is complex. If ``False``, the data is assumed to be magnitude only. Default is ``True``.
+ **kwargs : dict
+ Additional keyword arguments.
+ """
+ super().__init__(
+ root,
+ coil_sensitivity_maps_root,
+ mask_root,
+ noise_root,
+ initial_predictions_root,
+ dataset_format,
+ sample_rate,
+ volume_sample_rate,
+ use_dataset_cache,
+ dataset_cache_file,
+ num_cols,
+ consecutive_slices,
+ data_saved_per_slice,
+ n2r_supervised_rate,
+ complex_target,
+ log_images_rate,
+ transform,
+ **kwargs,
+ )
+
+ self.segmentations_root = segmentations_root
+ self.consecutive_slices = consecutive_slices
+ self.segmentation_classes = segmentation_classes
+ self.segmentation_classes_to_remove = segmentation_classes_to_remove
+ self.segmentation_classes_to_combine = segmentation_classes_to_combine
+ self.segmentation_classes_to_separate = segmentation_classes_to_separate
+ self.segmentation_classes_thresholds = segmentation_classes_thresholds
+ self.complex_data = complex_data
+
+ def process_segmentation_labels(self, segmentation_labels: np.ndarray) -> np.ndarray: # noqa: MC0001
+ """Process segmentation labels to remove, combine, and separate classes.
+
+ Parameters
+ ----------
+ segmentation_labels : np.ndarray
+ The segmentation labels. The shape should be (num_slices, height, width) or (height, width).
+
+ Returns
+ -------
+ np.ndarray
+ The processed segmentation labels.
+ """
+ # find the dimension with the segmentation classes
+ segmentation_labels_dim = segmentation_labels.ndim - 1
+ for dim in range(segmentation_labels.ndim):
+ if segmentation_labels.shape[dim] == self.segmentation_classes:
+ segmentation_labels_dim = dim
+
+ # move it to the last dimension
+ segmentation_labels = np.moveaxis(segmentation_labels, segmentation_labels_dim, -1)
+
+ # if we have a single slice, add a new dimension
+ if segmentation_labels.ndim == 2:
+ segmentation_labels = np.expand_dims(segmentation_labels, axis=0)
+
+ # check if we need to remove any classes, e.g. background
+ if self.segmentation_classes_to_remove is not None:
+ segmentation_labels = np.delete(segmentation_labels, self.segmentation_classes_to_remove, axis=-1)
+
+ # check if we need to combine any classes, e.g. White Matter and Gray Matter
+ if self.segmentation_classes_to_combine is not None:
+ segmentation_labels_to_combine = np.sum(
+ segmentation_labels[..., self.segmentation_classes_to_combine], axis=-1, keepdims=True
+ )
+ segmentation_labels_to_keep = np.delete(segmentation_labels, self.segmentation_classes_to_combine, axis=-1)
+
+ if self.segmentation_classes_to_remove is not None and 0 in self.segmentation_classes_to_remove:
+ # if background is removed, we can stack the combined labels with the rest straight away
+ segmentation_labels = np.concatenate(
+ [segmentation_labels_to_combine, segmentation_labels_to_keep], axis=-1
+ )
+ else:
+ # if background is not removed, we need to add it back as new background channel
+ segmentation_labels = np.concatenate(
+ [segmentation_labels[..., 0:1], segmentation_labels_to_combine, segmentation_labels_to_keep],
+ axis=-1,
+ )
+
+ # check if we need to separate any classes, e.g. pathologies from White Matter and Gray Matter
+ if self.segmentation_classes_to_separate is not None:
+ for x in self.segmentation_classes_to_separate:
+ segmentation_class_to_separate = segmentation_labels[..., x]
+ for i in range(segmentation_labels.shape[-1]):
+ if i == x:
+ continue
+ segmentation_labels[..., i][segmentation_class_to_separate > 0] = 0
+
+ # threshold probability maps if any threshold is given
+ if self.segmentation_classes_thresholds is not None:
+ for i, voxel_thres in enumerate(self.segmentation_classes_thresholds):
+ if voxel_thres is not None:
+ segmentation_labels[..., i][segmentation_labels[..., i] < voxel_thres] = 0
+ segmentation_labels[..., i][segmentation_labels[..., i] >= voxel_thres] = 1
+
+ if self.consecutive_slices == 1:
+ # bring the segmentation classes dimension back to the first dimension
+ segmentation_labels = np.moveaxis(segmentation_labels, -1, 0)
+ elif self.consecutive_slices > 1:
+ # bring the segmentation classes dimension back to the second dimension
+ segmentation_labels = np.moveaxis(segmentation_labels, -1, 1)
+
+ return segmentation_labels
+
+ def __getitem__(self, i: int): # noqa: MC0001
+ """Get item from :class:`SegmentationMRIDataset`."""
+ fname, dataslice, metadata = self.examples[i]
+ with h5py.File(fname, "r") as hf:
+ if self.complex_data:
+ kspace = self.get_consecutive_slices(hf, "kspace", dataslice).astype(np.complex64)
+
+ sensitivity_map = np.array([])
+ if "sensitivity_map" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "sensitivity_map", dataslice).astype(
+ np.complex64
+ )
+ elif "maps" in hf:
+ sensitivity_map = self.get_consecutive_slices(hf, "maps", dataslice).astype(np.complex64)
+ elif self.coil_sensitivity_maps_root is not None and self.coil_sensitivity_maps_root != "None":
+ coil_sensitivity_maps_root = self.coil_sensitivity_maps_root
+ split_dir = str(fname).split("/")
+ # check if exists
+ if not os.path.exists(Path(f"{coil_sensitivity_maps_root}/{split_dir[-2]}/{fname.name}")):
+ # find to what depth the coil_sensitivity_maps_root directory is nested
+ for j in range(len(split_dir)):
+ # get the coil_sensitivity_maps_root directory name
+ coil_sensitivity_maps_root = Path(f"{self.coil_sensitivity_maps_root}/{split_dir[-j]}/")
+ if os.path.exists(coil_sensitivity_maps_root / Path(split_dir[-2]) / fname.name):
+ break
+ # load coil sensitivity maps
+ with h5py.File(Path(coil_sensitivity_maps_root) / Path(split_dir[-2]) / fname.name, "r") as sf:
+ if "sensitivity_map" in sf or "sensitivity_map" in next(iter(sf.keys())):
+ sensitivity_map = (
+ self.get_consecutive_slices(sf, "sensitivity_map", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+
+ mask = None
+ if "mask" in hf:
+ mask = np.asarray(self.get_consecutive_slices(hf, "mask", dataslice))
+ if mask.ndim == 3:
+ mask = mask[dataslice]
+ elif self.mask_root is not None and self.mask_root != "None":
+ with h5py.File(Path(self.mask_root) / fname.name, "r") as mf:
+ mask = np.asarray(self.get_consecutive_slices(mf, "mask", dataslice))
+
+ imspace = np.empty([])
+
+ elif not self.complex_data:
+ if "reconstruction" in hf:
+ imspace = self.get_consecutive_slices(hf, "reconstruction", dataslice)
+ elif "target" in hf:
+ imspace = self.get_consecutive_slices(hf, "target", dataslice)
+ else:
+ raise ValueError(
+ "Complex data has not been selected but no reconstruction or target data found in file. "
+ "Only 'reconstruction' and 'target' keys are supported."
+ )
+ kspace = np.empty([])
+ sensitivity_map = np.array([])
+ mask = np.empty([])
+
+ segmentation_labels = np.empty([])
+ if self.segmentations_root is not None and self.segmentations_root != "None":
+ with h5py.File(Path(self.segmentations_root) / fname.name, "r") as sf:
+ segmentation_labels = np.asarray(self.get_consecutive_slices(sf, "segmentation", dataslice))
+ segmentation_labels = self.process_segmentation_labels(segmentation_labels)
+ elif "segmentation" in hf:
+ segmentation_labels = np.asarray(self.get_consecutive_slices(hf, "segmentation", dataslice))
+ segmentation_labels = self.process_segmentation_labels(segmentation_labels)
+
+ initial_prediction = np.empty([])
+ if not is_none(self.initial_predictions_root):
+ with h5py.File(Path(self.initial_predictions_root) / fname.name, "r") as ipf: # type: ignore
+ if "reconstruction" in hf:
+ initial_prediction = (
+ self.get_consecutive_slices(ipf, "reconstruction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ initial_prediction = (
+ self.get_consecutive_slices(ipf, "initial_prediction", dataslice)
+ .squeeze()
+ .astype(np.complex64)
+ )
+ else:
+ if "reconstruction" in hf:
+ initial_prediction = (
+ self.get_consecutive_slices(hf, "reconstruction", dataslice).squeeze().astype(np.complex64)
+ )
+ elif "initial_prediction" in hf:
+ initial_prediction = (
+ self.get_consecutive_slices(hf, "initial_prediction", dataslice).squeeze().astype(np.complex64)
+ )
+
+ attrs = dict(hf.attrs)
+
+ # get noise level for current slice, if metadata["noise_levels"] is not empty
+ if "noise_levels" in metadata and len(metadata["noise_levels"]) > 0:
+ metadata["noise"] = metadata["noise_levels"][dataslice]
+ else:
+ metadata["noise"] = 1.0
+
+ attrs.update(metadata)
+
+ if sensitivity_map.shape != kspace.shape and sensitivity_map.ndim > 1:
+ if sensitivity_map.ndim == 3:
+ sensitivity_map = np.transpose(sensitivity_map, (2, 0, 1))
+ elif sensitivity_map.ndim == 4:
+ sensitivity_map = np.transpose(sensitivity_map, (0, 3, 1, 2))
+ else:
+ raise ValueError(
+ f"Sensitivity map has invalid dimensions {sensitivity_map.shape} compared to kspace {kspace.shape}"
+ )
+
+ attrs["log_image"] = bool(dataslice in self.indices_to_log)
+
+ return (
+ (
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
+
+
+class BraTS2023AdultGliomaSegmentationMRIDataset(Dataset):
+ """Supports the BraTS2023AdultGlioma dataset for MRI segmentation.
+
+ .. note::
+ Extends :class:`torch.utils.data.Dataset`.
+ """
+
+ def __init__(
+ self,
+ root: Union[str, Path, os.PathLike],
+ coil_sensitivity_maps_root: Union[str, Path, os.PathLike] = None, # pylint: disable=unused-argument
+ mask_root: Union[str, Path, os.PathLike] = None, # pylint: disable=unused-argument
+ noise_root: Union[str, Path, os.PathLike] = None, # pylint: disable=unused-argument
+ initial_predictions_root: Union[str, Path, os.PathLike] = None,
+ dataset_format: str = None,
+ sample_rate: Optional[float] = None,
+ volume_sample_rate: Optional[float] = None,
+ use_dataset_cache: bool = False,
+ dataset_cache_file: Union[str, Path, os.PathLike] = None,
+ num_cols: Optional[Tuple[int]] = None,
+ consecutive_slices: int = 1,
+ data_saved_per_slice: bool = False,
+ n2r_supervised_rate: Optional[float] = 0.0, # pylint: disable=unused-argument
+ complex_target: bool = False,
+ log_images_rate: Optional[float] = 1.0,
+ transform: Optional[Callable] = None,
+ segmentations_root: Union[str, Path, os.PathLike] = None,
+ segmentation_classes: int = 2,
+ segmentation_classes_to_remove: Optional[Tuple[int]] = None,
+ segmentation_classes_to_combine: Optional[Tuple[int]] = None,
+ segmentation_classes_to_separate: Optional[Tuple[int]] = None,
+ segmentation_classes_thresholds: Optional[Tuple[float]] = None,
+ complex_data: bool = True,
+ **kwargs, # pylint: disable=unused-argument
+ ):
+ """Inits :class:`BraTS2023AdultGliomaSegmentationMRIDataset`.
+
+ Parameters
+ ----------
+ root : Union[str, Path, os.PathLike]
+ Path to the dataset.
+ sense_root : Union[str, Path, os.PathLike], optional
+ Path to the coil sensitivities maps dataset, if stored separately.
+ mask_root : Union[str, Path, os.PathLike], optional
+ Path to stored masks, if stored separately.
+ noise_root : Union[str, Path, os.PathLike], optional
+ Path to stored noise, if stored separately (in json format).
+ initial_predictions_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the initial predictions. If provided, the initial predictions will be used
+ as the input of the reconstruction network. Default is ``None``.
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ Default is ``None``.
+ sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the slices should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ volume_sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the volumes should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ use_dataset_cache : bool, optional
+ Whether to cache dataset metadata. This is very useful for large datasets.
+ dataset_cache_file : Union[str, Path, os.PathLike], optional
+ A file in which to cache dataset information for faster load times.
+ num_cols : Optional[Tuple[int]], optional
+ If provided, only slices with the desired number of columns will be considered.
+ consecutive_slices : int, optional
+ An int (>0) that determine the amount of consecutive slices of the file to be loaded at the same time.
+ Default is ``1``, loading single slices.
+ data_saved_per_slice : bool, optional
+ Whether the data is saved per slice or per volume.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ complex_target : bool, optional
+ Whether the target is complex. Default is ``False``.
+ log_images_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be logged as images. Default is
+ ``1.0``.
+ transform : Optional[Callable], optional
+ A sequence of callable objects that preprocesses the raw data into appropriate form. The transform function
+ should take ``kspace``, ``coil sensitivity maps``, ``mask``, ``initial prediction``, ``segmentation``,
+ ``target``, ``attributes``, ``filename``, and ``slice number`` as inputs. ``target`` may be null for test
+ data. Default is ``None``.
+ segmentations_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the segmentations.
+ segmentation_classes : int, optional
+ The number of segmentation classes. Default is ``2``.
+ segmentation_classes_to_remove : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to remove. For example, if the dataset contains segmentation classes
+ 0, 1, 2,
+ 3, and 4, and you want to remove classes 1 and 3, set this to ``(1, 3)``. Default is ``None``.
+ segmentation_classes_to_combine : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to combine. For example, if the dataset contains segmentation classes
+ 0, 1, 2, 3, and 4, and you want to combine classes 1 and 3, set this to ``(1, 3)``. Default is ``None``.
+ segmentation_classes_to_separate : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to separate. For example, if the dataset contains segmentation classes
+ 0, 1, 2, 3, and 4, and you want to separate class 1 into 2 classes, set this to ``(1, 2)``.
+ Default is ``None``.
+ segmentation_classes_thresholds : Optional[Tuple[float]], optional
+ A tuple of thresholds for the segmentation classes. For example, if the dataset contains segmentation
+ classes 0, 1, 2, 3, and 4, and you want to set the threshold for class 1 to 0.5, set this to
+ ``(0.5, 0.5, 0.5, 0.5, 0.5)``. Default is ``None``.
+ complex_data : bool, optional
+ Whether the data is complex. If ``False``, the data is assumed to be magnitude only. Default is ``True``.
+ **kwargs : dict
+ Additional keyword arguments.
+ """
+ super().__init__()
+ self.initial_predictions_root = initial_predictions_root
+ self.dataset_format = dataset_format
+
+ # set default sampling mode if none given
+ if not is_none(sample_rate) and not is_none(volume_sample_rate):
+ raise ValueError(
+ f"Both sample_rate {sample_rate} and volume_sample_rate {volume_sample_rate} are set. "
+ "Please set only one of them."
+ )
+
+ if sample_rate is None or sample_rate == "None":
+ sample_rate = 1.0
+
+ if volume_sample_rate is None or volume_sample_rate == "None":
+ volume_sample_rate = 1.0
+
+ self.dataset_cache_file = None if is_none(dataset_cache_file) else Path(dataset_cache_file) # type: ignore
+
+ if self.dataset_cache_file is not None and self.dataset_cache_file.exists() and use_dataset_cache:
+ with open(self.dataset_cache_file, "rb") as f:
+ dataset_cache = yaml.safe_load(f)
+ else:
+ dataset_cache = {}
+
+ if consecutive_slices < 1:
+ raise ValueError(f"Consecutive slices {consecutive_slices} is out of range, must be > 0.")
+ self.consecutive_slices = consecutive_slices
+ self.complex_target = complex_target
+ self.transform = transform
+ self.data_saved_per_slice = data_saved_per_slice
+
+ self.examples = []
+
+ # Check if our dataset is in the cache. If yes, use that metadata, if not, then regenerate the metadata.
+ if dataset_cache.get(root) is None or not use_dataset_cache:
+ if str(root).endswith(".json"):
+ with open(root, "r") as f: # type: ignore # pylint: disable=unspecified-encoding
+ examples = json.load(f)
+ files = [Path(example) for example in examples]
+ else:
+ files = list(Path(root).iterdir())
+
+ for fname in sorted(files):
+ metadata, num_slices = self._retrieve_metadata(fname)
+
+ # Specific to SKM-TEA segmentation dataset, we need to remove the first 50 and last 65 slices
+ self.examples += [
+ (fname, slice_ind, metadata) for slice_ind in range(num_slices) if 50 < slice_ind < num_slices - 65
+ ]
+
+ if dataset_cache.get(root) is None and use_dataset_cache:
+ dataset_cache[root] = self.examples
+ logging.info("Saving dataset cache to %s.", self.dataset_cache_file)
+ with open(self.dataset_cache_file, "wb") as f: # type: ignore
+ yaml.dump(dataset_cache, f)
+ else:
+ logging.info("Using dataset cache from %s.", self.dataset_cache_file)
+ self.examples = dataset_cache[root]
+
+ # subsample if desired
+ if sample_rate < 1.0: # sample by slice
+ random.shuffle(self.examples)
+ num_examples = round(len(self.examples) * sample_rate)
+ self.examples = self.examples[:num_examples]
+ elif volume_sample_rate < 1.0: # sample by volume
+ vol_names = sorted(list({f[0].stem for f in self.examples}))
+ random.shuffle(vol_names)
+ num_volumes = round(len(vol_names) * volume_sample_rate)
+ sampled_vols = vol_names[:num_volumes]
+ self.examples = [example for example in self.examples if example[0].stem in sampled_vols]
+
+ if num_cols and not is_none(num_cols):
+ self.examples = [ex for ex in self.examples if ex[2]["encoding_size"][1] in num_cols]
+
+ self.indices_to_log = np.random.choice(
+ len(self.examples), int(log_images_rate * len(self.examples)), replace=False # type: ignore
+ )
+
+ self.segmentations_root = segmentations_root
+ self.consecutive_slices = consecutive_slices
+ self.segmentation_classes = segmentation_classes
+ self.segmentation_classes_to_remove = segmentation_classes_to_remove
+ self.segmentation_classes_to_combine = segmentation_classes_to_combine
+ self.segmentation_classes_to_separate = segmentation_classes_to_separate
+ self.segmentation_classes_thresholds = segmentation_classes_thresholds
+ self.complex_data = complex_data
+
+ @staticmethod
+ def __read_nifti__(nifti_path: Union[str, Path]) -> FileBasedImage:
+ """Read a nifti file.
+
+ Parameters
+ ----------
+ nifti_path : Union[str, Path]
+ The path to the nifti file.
+
+ Returns
+ -------
+ nib.Nifti1Image
+ The nifti file.
+ """
+ return nib.load(nifti_path)
+
+ def _retrieve_metadata(self, fname: Union[str, Path]) -> Tuple[Dict, int]:
+ """Retrieve metadata from a given file.
+
+ Parameters
+ ----------
+ fname : Union[str, Path]
+ Path to file.
+
+ Returns
+ -------
+ Tuple[Dict, int]
+ Metadata dictionary and number of slices in the file.
+ """
+ data = self.__read_nifti__(fname)
+ num_slices = data.header["dim"][4]
+ # compute the mean and std of the data
+ metadata = {
+ "padding_left": 0,
+ "padding_right": 0,
+ "encoding_size": 0,
+ "recon_size": 0,
+ "num_slices": num_slices,
+ }
+ return metadata, num_slices
+
+ def get_consecutive_slices(self, data: Dict, key: str, dataslice: int) -> np.ndarray:
+ """Get consecutive slices from a given data dictionary.
+
+ Parameters
+ ----------
+ data : dict
+ Data to extract slices from.
+ key : str
+ Key to extract slices from.
+ dataslice : int
+ Slice to index.
+
+ Returns
+ -------
+ np.ndarray
+ Array of consecutive slices. If ``self.consecutive_slices`` is > 1, then the array will have shape
+ ``(self.consecutive_slices, *data[key].shape[1:])``. Otherwise, the array will have shape
+ ``data[key].shape[1:]``.
+
+ Examples
+ --------
+ >>> data = {"kspace": np.random.rand(10, 640, 368)}
+ >>> from atommic.collections.common.data.mri_loader import MRIDataset
+ >>> MRIDataset.get_consecutive_slices(data, "kspace", 1).shape
+ (1, 640, 368)
+ >>> MRIDataset.get_consecutive_slices(data, "kspace", 5).shape
+ (5, 640, 368)
+ """
+ # read data
+ x = data[key]
+
+ if self.data_saved_per_slice:
+ x = np.expand_dims(x, axis=0)
+
+ if self.consecutive_slices == 1:
+ if x.shape[0] == 1:
+ return x[0]
+ if x.ndim != 2:
+ return x[dataslice]
+ return x
+
+ # get consecutive slices
+ num_slices = x.shape[0]
+
+ # If the number of consecutive slices is greater than or equal to the total slices, return the entire stack
+ if self.consecutive_slices >= num_slices:
+ # pad left and right with zero slices to match the desired number of slices
+ slices_to_add_start = (self.consecutive_slices - num_slices) // 2
+ slices_to_add_end = self.consecutive_slices - num_slices - slices_to_add_start
+ if slices_to_add_start > 0:
+ zero_slices = np.zeros((slices_to_add_start, *x.shape[1:]))
+ x = np.concatenate((zero_slices, x), axis=0)
+ if slices_to_add_end > 0:
+ zero_slices = np.zeros((slices_to_add_end, *x.shape[1:]))
+ x = np.concatenate((x, zero_slices), axis=0)
+ return x
+
+ # Calculate half of the consecutive slices to determine the middle position
+ half_slices = self.consecutive_slices // 2
+
+ # Determine the start and end slices based on the middle position
+ start_slice = dataslice - half_slices
+ end_slice = dataslice + half_slices + 1
+
+ # Handle edge cases
+ slices_to_add_start = 0
+ slices_to_add_end = 0
+ if start_slice < 0:
+ slices_to_add_start = abs(start_slice)
+ start_slice = 0
+
+ if end_slice > (num_slices - 1):
+ slices_to_add_end = end_slice - num_slices
+ extracted_slices = x[start_slice:]
+ else:
+ extracted_slices = x[start_slice:end_slice]
+
+ # Add slices to the start and end if needed
+ if slices_to_add_start > 0:
+ zero_slices = np.zeros((slices_to_add_start, *extracted_slices.shape[1:]))
+ extracted_slices = np.concatenate((zero_slices, extracted_slices), axis=0)
+ if slices_to_add_end > 0:
+ zero_slices = np.zeros((slices_to_add_end, *extracted_slices.shape[1:]))
+ extracted_slices = np.concatenate((extracted_slices, zero_slices), axis=0)
+
+ return extracted_slices
+
+ def __len__(self):
+ """Length of :class:`MRIDataset`."""
+ return len(self.examples)
+
+ def __getitem__(self, i: int):
+ """Get item from :class:`BraTS2023AdultGliomaSegmentationMRIDataset`."""
+ fname, dataslice, metadata = self.examples[i]
+
+ imspace = self.get_consecutive_slices(
+ {"target": np.moveaxis(self.__read_nifti__(fname).get_fdata(), -1, 0)}, "target", dataslice
+ ).astype(np.float32)
+
+ segmentation_path = Path(self.segmentations_root) / Path( # type: ignore
+ str(fname.name).replace(".nii.gz", "-seg.nii.gz")
+ )
+
+ segmentation_labels = self.get_consecutive_slices(
+ {"segmentation": np.moveaxis(self.__read_nifti__(segmentation_path).get_fdata(), -1, 0)},
+ "segmentation",
+ dataslice,
+ )
+
+ # Necrotic Tumor Core (NCR - label 1)
+ ncr = np.zeros_like(segmentation_labels)
+ ncr[segmentation_labels == 1] = 1
+ # Peritumoral Edematous/Invaded Tissue (ED - label 2)
+ ed = np.zeros_like(segmentation_labels)
+ ed[segmentation_labels == 2] = 1
+ # GD-Enhancing Tumor (ET - label 3)
+ et = np.zeros_like(segmentation_labels)
+ et[segmentation_labels == 3] = 1
+ # Whole Tumor (WT โ label 1, 2, or 3)
+ wt = np.zeros_like(segmentation_labels)
+ wt[segmentation_labels != 0] = 1
+
+ segmentation_labels = np.stack([ncr, ed, et, wt], axis=0).astype(np.float32)
+
+ if self.consecutive_slices > 1:
+ segmentation_labels = np.moveaxis(segmentation_labels, 0, 1)
+
+ kspace = np.empty([])
+ target = imspace
+ sensitivity_map = np.empty([])
+ mask = np.empty([])
+ initial_prediction = target
+
+ attrs = {
+ "log_image": bool(dataslice in self.indices_to_log),
+ "noise": 1.0,
+ }
+ attrs.update(metadata)
+
+ return (
+ (
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
+
+
+class ISLES2022SubAcuteStrokeSegmentationMRIDataset(SegmentationMRIDataset):
+ """Supports the ISLES2022SubAcuteStroke dataset for MRI segmentation.
+
+ .. note::
+ Extends :class:`atommic.collections.segmentation.data.mri_segmentation_loader.SegmentationMRIDataset`.
+ """
+
+ @staticmethod
+ def __read_nifti__(nifti_path: Union[str, Path]) -> FileBasedImage:
+ """Read a nifti file.
+
+ Parameters
+ ----------
+ nifti_path : Union[str, Path]
+ The path to the nifti file.
+
+ Returns
+ -------
+ nib.Nifti1Image
+ The nifti file.
+ """
+ return nib.load(nifti_path)
+
+ def _retrieve_metadata(self, fname: Union[str, Path]) -> Tuple[Dict, int]:
+ """Retrieve metadata from a given file.
+
+ Parameters
+ ----------
+ fname : Union[str, Path]
+ Path to file.
+
+ Returns
+ -------
+ Tuple[Dict, int]
+ Metadata dictionary and number of slices in the file.
+ """
+ data = self.__read_nifti__(fname)
+ num_slices = data.header["dim"][4]
+ metadata = {
+ "padding_left": 0,
+ "padding_right": 0,
+ "encoding_size": 0,
+ "recon_size": 0,
+ "num_slices": num_slices,
+ }
+ return metadata, num_slices
+
+ def __getitem__(self, i: int):
+ """Get item from :class:`ISLES2022SubAcuteStrokeSegmentationMRIDataset`."""
+ fname, dataslice, metadata = self.examples[i]
+
+ imspace = self.get_consecutive_slices(
+ {"target": np.moveaxis(self.__read_nifti__(fname).get_fdata(), -1, 0)}, "target", dataslice
+ ).astype(np.float32)
+
+ if self.consecutive_slices > 1:
+ imspace = np.moveaxis(imspace, 0, 1)
+
+ # imspace has 3 channels, normalize each by its min and max values
+ max_val = np.max(imspace[0]) if np.max(imspace[0]) > 0 else 1
+ imspace[0] = (imspace[0] - np.min(imspace[0])) / (max_val - np.min(imspace[0]))
+ max_val = np.max(imspace[1]) if np.max(imspace[1]) > 0 else 1
+ imspace[1] = (imspace[1] - np.min(imspace[1])) / (max_val - np.min(imspace[1]))
+ max_val = np.max(imspace[2]) if np.max(imspace[2]) > 0 else 1
+ imspace[2] = (imspace[2] - np.min(imspace[2])) / (max_val - np.min(imspace[2]))
+ # normalize all by min and max values of all channels
+ imspace = (imspace - np.min(imspace)) / (np.max(imspace) - np.min(imspace))
+
+ segmentation_path = Path(self.segmentations_root) / Path( # type: ignore
+ str(fname.name).replace(".nii.gz", "-seg.nii.gz")
+ )
+ segmentation_labels = self.get_consecutive_slices(
+ {"segmentation": np.moveaxis(self.__read_nifti__(segmentation_path).get_fdata(), -1, 0)},
+ "segmentation",
+ dataslice,
+ ).astype(np.float32)
+
+ # Lesions (label 1)
+ lesions = np.zeros_like(segmentation_labels)
+ lesions[segmentation_labels == 1] = 1
+
+ # stack lesions as a new channel
+ segmentation_labels = np.stack([lesions], axis=0).astype(np.float32)
+
+ if self.consecutive_slices > 1:
+ # bring the segmentation classes dimension back to the first dimension
+ imspace = np.moveaxis(imspace, 0, 1)
+ segmentation_labels = np.moveaxis(segmentation_labels, 0, 1)
+
+ kspace = np.empty([])
+ target = imspace
+ sensitivity_map = np.empty([])
+ mask = np.empty([])
+ initial_prediction = target
+
+ attrs = {"log_image": bool(dataslice in self.indices_to_log), "noise": 1.0}
+ attrs.update(metadata)
+
+ return (
+ (
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
+
+
+class SKMTEASegmentationMRIDataset(Dataset):
+ """Supports the SKM-TEA dataset for MRI segmentation.
+
+ .. note::
+ Extends :class:`torch.utils.data.Dataset`.
+ """
+
+ def __init__(
+ self,
+ root: Union[str, Path, os.PathLike],
+ coil_sensitivity_maps_root: Union[str, Path, os.PathLike] = None, # pylint: disable=unused-argument
+ mask_root: Union[str, Path, os.PathLike] = None, # pylint: disable=unused-argument
+ noise_root: Union[str, Path, os.PathLike] = None, # pylint: disable=unused-argument
+ initial_predictions_root: Union[str, Path, os.PathLike] = None,
+ dataset_format: str = None,
+ sample_rate: Optional[float] = None,
+ volume_sample_rate: Optional[float] = None,
+ use_dataset_cache: bool = False,
+ dataset_cache_file: Union[str, Path, os.PathLike] = None,
+ num_cols: Optional[Tuple[int]] = None,
+ consecutive_slices: int = 1,
+ data_saved_per_slice: bool = False,
+ n2r_supervised_rate: Optional[float] = 0.0, # pylint: disable=unused-argument
+ complex_target: bool = False,
+ log_images_rate: Optional[float] = 1.0,
+ transform: Optional[Callable] = None,
+ segmentations_root: Union[str, Path, os.PathLike] = None,
+ segmentation_classes: int = 2,
+ segmentation_classes_to_remove: Optional[Tuple[int]] = None,
+ segmentation_classes_to_combine: Optional[Tuple[int]] = None,
+ segmentation_classes_to_separate: Optional[Tuple[int]] = None,
+ segmentation_classes_thresholds: Optional[Tuple[float]] = None,
+ complex_data: bool = True,
+ **kwargs, # pylint: disable=unused-argument
+ ):
+ """Inits :class:`SKMTEASegmentationMRIDataset`.
+
+ Parameters
+ ----------
+ root : Union[str, Path, os.PathLike]
+ Path to the dataset.
+ sense_root : Union[str, Path, os.PathLike], optional
+ Path to the coil sensitivities maps dataset, if stored separately.
+ mask_root : Union[str, Path, os.PathLike], optional
+ Path to stored masks, if stored separately.
+ noise_root : Union[str, Path, os.PathLike], optional
+ Path to stored noise, if stored separately (in json format).
+ initial_predictions_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the initial predictions. If provided, the initial predictions will be used
+ as the input of the reconstruction network. Default is ``None``.
+ dataset_format : str, optional
+ The format of the dataset. For example, ``'custom_dataset'`` or ``'public_dataset_name'``.
+ Default is ``None``.
+ sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the slices should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ volume_sample_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the volumes should be loaded. When creating
+ subsampled datasets either set sample_rates (sample by slices) or volume_sample_rates (sample by volumes)
+ but not both.
+ use_dataset_cache : bool, optional
+ Whether to cache dataset metadata. This is very useful for large datasets.
+ dataset_cache_file : Union[str, Path, os.PathLike], optional
+ A file in which to cache dataset information for faster load times.
+ num_cols : Optional[Tuple[int]], optional
+ If provided, only slices with the desired number of columns will be considered.
+ consecutive_slices : int, optional
+ An int (>0) that determine the amount of consecutive slices of the file to be loaded at the same time.
+ Default is ``1``, loading single slices.
+ data_saved_per_slice : bool, optional
+ Whether the data is saved per slice or per volume.
+ n2r_supervised_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be loaded for Noise to
+ Reconstruction (N2R) supervised loss, if N2R is enabled. Default is ``0.0``.
+ complex_target : bool, optional
+ Whether the target is complex. Default is ``False``.
+ log_images_rate : Optional[float], optional
+ A float between 0 and 1. This controls what fraction of the subjects should be logged as images. Default is
+ ``1.0``.
+ transform : Optional[Callable], optional
+ A sequence of callable objects that preprocesses the raw data into appropriate form. The transform function
+ should take ``kspace``, ``coil sensitivity maps``, ``mask``, ``initial prediction``, ``segmentation``,
+ ``target``, ``attributes``, ``filename``, and ``slice number`` as inputs. ``target`` may be null for test
+ data. Default is ``None``.
+ segmentations_root : Union[str, Path, os.PathLike], optional
+ Path to the dataset containing the segmentations.
+ segmentation_classes : int, optional
+ The number of segmentation classes. Default is ``2``.
+ segmentation_classes_to_remove : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to remove. For example, if the dataset contains segmentation classes
+ 0, 1, 2,
+ 3, and 4, and you want to remove classes 1 and 3, set this to ``(1, 3)``. Default is ``None``.
+ segmentation_classes_to_combine : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to combine. For example, if the dataset contains segmentation classes
+ 0, 1, 2, 3, and 4, and you want to combine classes 1 and 3, set this to ``(1, 3)``. Default is ``None``.
+ segmentation_classes_to_separate : Optional[Tuple[int]], optional
+ A tuple of segmentation classes to separate. For example, if the dataset contains segmentation classes
+ 0, 1, 2, 3, and 4, and you want to separate class 1 into 2 classes, set this to ``(1, 2)``.
+ Default is ``None``.
+ segmentation_classes_thresholds : Optional[Tuple[float]], optional
+ A tuple of thresholds for the segmentation classes. For example, if the dataset contains segmentation
+ classes 0, 1, 2, 3, and 4, and you want to set the threshold for class 1 to 0.5, set this to
+ ``(0.5, 0.5, 0.5, 0.5, 0.5)``. Default is ``None``.
+ complex_data : bool, optional
+ Whether the data is complex. If ``False``, the data is assumed to be magnitude only. Default is ``True``.
+ **kwargs : dict
+ Additional keyword arguments.
+ """
+ super().__init__()
+ self.initial_predictions_root = initial_predictions_root
+ self.dataset_format = dataset_format
+
+ # set default sampling mode if none given
+ if not is_none(sample_rate) and not is_none(volume_sample_rate):
+ raise ValueError(
+ f"Both sample_rate {sample_rate} and volume_sample_rate {volume_sample_rate} are set. "
+ "Please set only one of them."
+ )
+
+ if sample_rate is None or sample_rate == "None":
+ sample_rate = 1.0
+
+ if volume_sample_rate is None or volume_sample_rate == "None":
+ volume_sample_rate = 1.0
+
+ self.dataset_cache_file = None if is_none(dataset_cache_file) else Path(dataset_cache_file) # type: ignore
+
+ if self.dataset_cache_file is not None and self.dataset_cache_file.exists() and use_dataset_cache:
+ with open(self.dataset_cache_file, "rb") as f:
+ dataset_cache = yaml.safe_load(f)
+ else:
+ dataset_cache = {}
+
+ if consecutive_slices < 1:
+ raise ValueError(f"Consecutive slices {consecutive_slices} is out of range, must be > 0.")
+ self.consecutive_slices = consecutive_slices
+ self.complex_target = complex_target
+ self.transform = transform
+ self.data_saved_per_slice = data_saved_per_slice
+
+ self.examples = []
+
+ # Check if our dataset is in the cache. If yes, use that metadata, if not, then regenerate the metadata.
+ if dataset_cache.get(root) is None or not use_dataset_cache:
+ if str(root).endswith(".json"):
+ with open(root, "r") as f: # type: ignore # pylint: disable=unspecified-encoding
+ examples = json.load(f)
+ files = [Path(example) for example in examples]
+ else:
+ files = list(Path(root).iterdir())
+
+ for fname in sorted(files):
+ metadata, num_slices = self._retrieve_metadata(fname)
+
+ # Specific to SKM-TEA segmentation dataset, we need to remove the first and last 30 slices
+ self.examples += [
+ (fname, slice_ind, metadata) for slice_ind in range(num_slices) if 30 < slice_ind < num_slices - 30
+ ]
+
+ if dataset_cache.get(root) is None and use_dataset_cache:
+ dataset_cache[root] = self.examples
+ logging.info("Saving dataset cache to %s.", self.dataset_cache_file)
+ with open(self.dataset_cache_file, "wb") as f: # type: ignore
+ yaml.dump(dataset_cache, f)
+ else:
+ logging.info("Using dataset cache from %s.", self.dataset_cache_file)
+ self.examples = dataset_cache[root]
+
+ # subsample if desired
+ if sample_rate < 1.0: # sample by slice
+ random.shuffle(self.examples)
+ num_examples = round(len(self.examples) * sample_rate)
+ self.examples = self.examples[:num_examples]
+ elif volume_sample_rate < 1.0: # sample by volume
+ vol_names = sorted(list({f[0].stem for f in self.examples}))
+ random.shuffle(vol_names)
+ num_volumes = round(len(vol_names) * volume_sample_rate)
+ sampled_vols = vol_names[:num_volumes]
+ self.examples = [example for example in self.examples if example[0].stem in sampled_vols]
+
+ if num_cols and not is_none(num_cols):
+ self.examples = [ex for ex in self.examples if ex[2]["encoding_size"][1] in num_cols]
+
+ self.indices_to_log = np.random.choice(
+ len(self.examples), int(log_images_rate * len(self.examples)), replace=False # type: ignore
+ )
+
+ self.segmentations_root = segmentations_root
+ self.consecutive_slices = consecutive_slices
+ self.segmentation_classes = segmentation_classes
+ self.segmentation_classes_to_remove = segmentation_classes_to_remove
+ self.segmentation_classes_to_combine = segmentation_classes_to_combine
+ self.segmentation_classes_to_separate = segmentation_classes_to_separate
+ self.segmentation_classes_thresholds = segmentation_classes_thresholds
+ self.complex_data = complex_data
+
+ def _retrieve_metadata(self, fname: Union[str, Path]) -> Tuple[Dict, int]:
+ """Override the ``_retrieve_metadata`` method to handle the SKM-TEA dataset.
+
+ .. note::
+ Overrides :meth:`atommic.collections.common.data.mri_loader.MRIDataset._retrieve_metadata`.
+ """
+ with h5py.File(fname, "r") as hf:
+ shape = hf["seg"].shape
+ num_slices = shape[2]
+ metadata = {
+ "padding_left": 0,
+ "padding_right": 0,
+ "encoding_size": 0,
+ "recon_size": 0,
+ "num_slices": num_slices,
+ }
+ return metadata, num_slices
+
+ def get_consecutive_slices(self, data: Dict, key: str, dataslice: int) -> np.ndarray:
+ """Override the ``get_consecutive_slices`` method to handle the SKM-TEA dataset.
+
+ .. note::
+ Overrides :meth:`atommic.collections.common.data.mri_loader.MRIDataset.get_consecutive_slices`.
+ """
+ x = data[key]
+
+ if self.consecutive_slices == 1:
+ if x.shape[2] == 1:
+ return x[:, :, 0]
+ if x.ndim != 2:
+ return x[:, :, dataslice]
+ return x
+
+ # get consecutive slices
+ num_slices = x.shape[2]
+
+ # If the number of consecutive slices is greater than or equal to the total slices, return the entire stack
+ if self.consecutive_slices >= num_slices:
+ # pad left and right with zero slices to match the desired number of slices
+ slices_to_add_start = (self.consecutive_slices - num_slices) // 2
+ slices_to_add_end = self.consecutive_slices - num_slices - slices_to_add_start
+ if slices_to_add_start > 0:
+ zero_slices = np.zeros((x.shape[0], x.shape[1], slices_to_add_start))
+ x = np.concatenate((zero_slices, x), axis=2)
+ if slices_to_add_end > 0:
+ zero_slices = np.zeros((x.shape[0], x.shape[1], slices_to_add_end))
+ x = np.concatenate((x, zero_slices), axis=2)
+ return x
+
+ # Calculate half of the consecutive slices to determine the middle position
+ half_slices = self.consecutive_slices // 2
+
+ # Determine the start and end slices based on the middle position
+ start_slice = dataslice - half_slices
+ end_slice = dataslice + half_slices + 1
+
+ # Handle edge cases
+ slices_to_add_start = 0
+ slices_to_add_end = 0
+ if start_slice < 0:
+ slices_to_add_start = abs(start_slice)
+ start_slice = 0
+
+ if end_slice > (num_slices - 1):
+ slices_to_add_end = end_slice - num_slices
+ extracted_slices = x[:, :, start_slice:]
+ else:
+ extracted_slices = x[:, :, start_slice:end_slice]
+
+ # Add slices to the start and end if needed
+ if slices_to_add_start > 0:
+ zero_slices = np.zeros((x.shape[0], x.shape[1], slices_to_add_start))
+ extracted_slices = np.concatenate((zero_slices, extracted_slices), axis=2)
+ if slices_to_add_end > 0:
+ zero_slices = np.zeros((x.shape[0], x.shape[1], slices_to_add_end))
+ extracted_slices = np.concatenate((extracted_slices, zero_slices), axis=2)
+
+ return extracted_slices
+
+ def __len__(self):
+ """Length of :class:`MRIDataset`."""
+ return len(self.examples)
+
+ def __getitem__(self, i: int):
+ """Get item from :class:`SKMTEASegmentationMRIDataset`."""
+ fname, dataslice, metadata = self.examples[i]
+ dataset_format = self.dataset_format.lower() # type: ignore
+ with h5py.File(fname, "r") as hf:
+ attrs = dict(hf.attrs)
+ stats = hf["stats"]
+
+ target = None
+ metadata = {}
+
+ if dataset_format == "skm-tea-echo1":
+ target_key = "echo1"
+ elif dataset_format == "skm-tea-echo2":
+ target_key = "echo2"
+ else:
+ if dataset_format == "skm-tea-echo1+echo2":
+ target = np.abs(
+ self.get_consecutive_slices(hf, "echo1", dataslice).squeeze().astype(np.float32)
+ + self.get_consecutive_slices(hf, "echo2", dataslice).squeeze().astype(np.float32)
+ )
+ elif dataset_format == "skm-tea-echo1+echo2-mc":
+ target = np.concatenate(
+ [
+ np.abs(self.get_consecutive_slices(hf, "echo1", dataslice).squeeze()).astype(np.float32),
+ np.abs(self.get_consecutive_slices(hf, "echo2", dataslice).squeeze()).astype(np.float32),
+ ],
+ axis=-1,
+ )
+ elif dataset_format == "skm-tea-echo1+echo2-rss":
+ target = np.sqrt(
+ self.get_consecutive_slices(hf, "echo1", dataslice).squeeze().astype(np.float32) ** 2
+ + self.get_consecutive_slices(hf, "echo2", dataslice).squeeze().astype(np.float32) ** 2
+ )
+ target_key = "rss"
+
+ min_val = stats[target_key]["min"][()]
+ max_val = stats[target_key]["max"][()]
+ mean_val = stats[target_key]["mean"][()]
+ std_val = stats[target_key]["std"][()]
+
+ if target is None:
+ target = np.abs(self.get_consecutive_slices(hf, target_key, dataslice).squeeze()).astype(np.float32)
+
+ # Get the segmentation labels. They are stacked in the last dimension as follows:
+ # 0: Patellar Cartilage, 1: Femoral Cartilage, 2: Lateral Tibial Cartilage, 3: Medial Tibial Cartilage,
+ # 4: Lateral Meniscus, 5: Medial Meniscus
+ segmentation_labels = self.get_consecutive_slices(hf, "seg", dataslice).astype(np.float32)
+
+ # combine label 2 and 3 (Lateral Tibial Cartilage and Medial Tibial Cartilage)
+ tibial_cartilage = segmentation_labels[..., 2] + segmentation_labels[..., 3]
+ # combine label 4 and 5 (Lateral Meniscus and Medial Meniscus)
+ medial_meniscus = segmentation_labels[..., 4] + segmentation_labels[..., 5]
+
+ # stack the labels
+ segmentation_labels = np.stack(
+ [segmentation_labels[..., 0], segmentation_labels[..., 1], tibial_cartilage, medial_meniscus],
+ axis=0,
+ )
+
+ if self.consecutive_slices > 1:
+ # bring the consecutive slices dimension to the first dimension
+ target = np.moveaxis(target, -1, 0)
+ segmentation_labels = np.moveaxis(segmentation_labels, -1, 0)
+
+ kspace = np.empty([])
+ imspace = target
+ sensitivity_map = np.empty([])
+ mask = np.empty([])
+ initial_prediction = target
+
+ attrs.update(metadata)
+ # set noise level to 1.0 as we handle fully sampled data
+ attrs["noise"] = 1.0
+ attrs["log_image"] = bool(dataslice in self.indices_to_log)
+
+ # add min, max, mean, and std to attrs
+ attrs["min"] = min_val
+ attrs["max"] = max_val
+ attrs["mean"] = mean_val
+ attrs["std"] = std_val
+
+ return (
+ (
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ if self.transform is None
+ else self.transform(
+ kspace,
+ imspace,
+ sensitivity_map,
+ mask,
+ initial_prediction,
+ segmentation_labels,
+ attrs,
+ fname.name,
+ dataslice,
+ )
+ )
diff --git a/atommic/collections/segmentation/losses/__init__.py b/atommic/collections/segmentation/losses/__init__.py
new file mode 100644
index 00000000..f780f27e
--- /dev/null
+++ b/atommic/collections/segmentation/losses/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.segmentation.losses.cross_entropy import CrossEntropyLoss # noqa: F401
+from atommic.collections.segmentation.losses.dice import Dice # noqa: F401
diff --git a/atommic/collections/segmentation/losses/cross_entropy.py b/atommic/collections/segmentation/losses/cross_entropy.py
new file mode 100644
index 00000000..dd7ef2f3
--- /dev/null
+++ b/atommic/collections/segmentation/losses/cross_entropy.py
@@ -0,0 +1,77 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from torch import nn
+
+
+class CrossEntropyLoss(nn.Module):
+ """Wrapper around PyTorch's CrossEntropyLoss to support 2D and 3D inputs."""
+
+ def __init__(
+ self,
+ num_samples: int = 50,
+ ignore_index: int = -100,
+ reduction: str = "none",
+ label_smoothing: float = 0.0,
+ weight: torch.Tensor = None,
+ ):
+ """Inits :class:`CrossEntropyLoss`.
+
+ Parameters
+ ----------
+ num_samples : int, optional
+ Number of Monte Carlo samples, by default 50
+ ignore_index : int, optional
+ Index to ignore, by default -100
+ reduction : str, optional
+ Reduction method, by default "none"
+ label_smoothing : float, optional
+ Label smoothing, by default 0.0
+ weight : torch.Tensor, optional
+ Weight for each class, by default None
+ """
+ super().__init__()
+ self.mc_samples = num_samples
+ self.cross_entropy = torch.nn.CrossEntropyLoss(
+ weight=weight,
+ ignore_index=ignore_index,
+ reduction=reduction,
+ label_smoothing=label_smoothing,
+ )
+
+ def forward(self, target: torch.Tensor, _input: torch.Tensor, pred_log_var: torch.Tensor = None) -> torch.Tensor:
+ """Forward pass of :class:`CrossEntropyLoss`.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target tensor. Shape: (batch_size, num_classes, *spatial_dims)
+ _input : torch.Tensor
+ Prediction tensor. Shape: (batch_size, num_classes, *spatial_dims)
+ pred_log_var : torch.Tensor, optional
+ Prediction log variance tensor. Shape: (batch_size, num_classes, *spatial_dims). Default is ``None``.
+
+ Returns
+ -------
+ torch.Tensor
+ Loss tensor. Shape: (batch_size, *spatial_dims)
+ """
+ # In case we do not have a batch dimension, add it
+ if _input.dim() == 3:
+ _input = _input.unsqueeze(0)
+ if target.dim() == 3:
+ target = target.unsqueeze(0)
+
+ self.cross_entropy.weight = self.cross_entropy.weight.clone().to(_input.device)
+
+ if self.mc_samples == 1 or pred_log_var is None:
+ return self.cross_entropy(_input.float(), target).mean()
+
+ pred_shape = [self.mc_samples, *_input.shape]
+ noise = torch.randn(pred_shape, device=_input.device)
+ noisy_pred = _input.unsqueeze(0) + torch.sqrt(torch.exp(pred_log_var)).unsqueeze(0) * noise
+ noisy_pred = noisy_pred.view(-1, *_input.shape[1:])
+ tiled_target = target.unsqueeze(0).tile((self.mc_samples,)).view(-1, *target.shape[1:])
+ loss = self.cross_entropy(noisy_pred, tiled_target).view(self.mc_samples, -1, *_input.shape[-2:]).mean(0)
+ return loss.mean()
diff --git a/atommic/collections/segmentation/losses/dice.py b/atommic/collections/segmentation/losses/dice.py
new file mode 100644
index 00000000..f318e67c
--- /dev/null
+++ b/atommic/collections/segmentation/losses/dice.py
@@ -0,0 +1,248 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/Project-MONAI/MONAI/blob/dev/monai/losses/dice.py
+
+import warnings
+from typing import Any, Callable, List, Optional, Tuple, Union
+
+import numpy as np
+import torch
+from torch import Tensor
+
+from atommic.collections.common.parts.utils import is_none
+from atommic.collections.segmentation.losses.utils import do_metric_reduction
+from atommic.core.classes.loss import Loss
+
+
+class Dice(Loss):
+ """Wrapper for :py:class:`monai.losses.DiceLoss` to support multi-class and multi-label tasks.
+
+ Compute average Dice loss between two tensors. It can support both multi-classes and multi-labels tasks.
+ The data `input` (BNHW[D] where N is number of classes) is compared with ground truth `target` (BNHW[D]).
+
+ Note that axis N of `input` is expected to be logits or probabilities for each class, if passing logits as input,
+ must set `sigmoid=True` or `softmax=True`, or specifying `other_act`. And the same axis of `target`
+ can be 1 or N (one-hot format).
+
+ The `smooth_nr` and `smooth_dr` parameters are values added to the intersection and union components of
+ the inter-over-union calculation to smooth results respectively, these values should be small.
+
+ The original paper: Milletari, F. et. al. (2016) V-Net: Fully Convolutional Neural Networks forVolumetric
+ Medical Image Segmentation, 3DV, 2016.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.segmentation.losses.dice import Dice
+ >>> pred = torch.tensor([[[[0.1, 0.2, 0.3, 0.4, 0.5],
+ ... [0.1, 0.2, 0.3, 0.4, 0.5],
+ ... [0.1, 0.2, 0.3, 0.4, 0.5],
+ ... [0.1, 0.2, 0.3, 0.4, 0.5],
+ ... [0.1, 0.2, 0.3, 0.4, 0.5]]],
+ ... [[[0.1, 0.2, 0.3, 0.4, 0.5],
+ ... [0.1, 0.2, 0.3, 0.4, 0.5],
+ ... [0.1, 0.2, 0.3, 0.4, 0.5],
+ ... [0.1, 0.2, 0.3, 0.4, 0.5],
+ ... [0.1, 0.2, 0.3, 0.4, 0.5]]]])
+ >>> target = torch.tensor([[[[0, 0, 0, 0, 0],
+ ... [0, 0, 0, 0, 0],
+ ... [0, 0, 0, 0, 0],
+ ... [0, 0, 0, 0, 0],
+ ... [0, 0, 0, 0, 0]]],
+ ... [[[1, 1, 1, 1, 1],
+ ... [1, 1, 1, 1, 1],
+ ... [1, 1, 1, 1, 1],
+ ... [1, 1, 1, 1, 1],
+ ... [1, 1, 1, 1, 1]]]])
+ >>> dice = Dice(include_background=False, to_onehot_y=True, sigmoid=False, softmax=False)
+ >>> dice(pred, target)
+ tensor(0.5000)
+ """
+
+ def __init__(
+ self,
+ include_background: bool = True,
+ to_onehot_y: bool = False,
+ sigmoid: bool = True,
+ softmax: bool = False,
+ other_act: Optional[Callable] = None,
+ squared_pred: bool = False,
+ jaccard: bool = False,
+ flatten: bool = False,
+ reduction: str = "mean",
+ smooth_nr: float = 1e-5,
+ smooth_dr: float = 1e-5,
+ batch: bool = True,
+ ):
+ """Inits :class:`Dice`.
+
+ Parameters
+ ----------
+ include_background : bool
+ whether to skip Dice computation on the first channel of the predicted output. Default is ``True``.
+ to_onehot_y : bool
+ Whether to convert `y` into the one-hot format. Default is ``False``.
+ sigmoid : bool
+ Whether to add sigmoid function to the input data. Default is ``True``.
+ softmax : bool
+ Whether to add softmax function to the input data. Default is ``False``.
+ other_act : Callable
+ Use this parameter if you want to apply another type of activation layer. Default is ``None``.
+ squared_pred : bool
+ Whether to square the prediction before calculating Dice. Default is ``False``.
+ jaccard : bool
+ Whether to compute Jaccard Index as a loss. Default is ``False``.
+ flatten : bool
+ Whether to flatten input data. Default is ``False``.
+ reduction : str
+ Specifies the reduction to apply to the output: 'none' | 'mean' | 'sum'.
+ 'none': no reduction will be applied.
+ 'mean': the sum of the output will be divided by the number of elements in the output.
+ 'sum': the output will be summed.
+ Default is ``mean``.
+ smooth_nr : float
+ A small constant added to the numerator to avoid `nan` when all items are 0. Default is ``1e-5``.
+ smooth_dr : float
+ A small constant added to the denominator to avoid `nan` when all items are 0. Default is ``1e-5``.
+ batch : bool
+ If True, compute Dice loss for each batch and return a tensor with shape (batch_size,).
+ If False, compute Dice loss for the whole batch and return a tensor with shape (1,).
+ Default is ``True``.
+ """
+ super().__init__()
+ other_act = None if is_none(other_act) else other_act
+ if other_act is not None and not callable(other_act):
+ raise TypeError(f"other_act must be None or callable but is {type(other_act).__name__}.")
+ if int(sigmoid) + int(softmax) + int(other_act is not None) > 1:
+ raise ValueError(
+ "Incompatible values: more than 1 of [sigmoid=True, softmax=True, other_act is not None]."
+ )
+ self.include_background = include_background
+ self.to_onehot_y = to_onehot_y
+ self.sigmoid = sigmoid
+ self.softmax = softmax
+ self.other_act = other_act
+ self.squared_pred = squared_pred
+ self.jaccard = jaccard
+ self.flatten = flatten
+ self.reduction = reduction
+ self.smooth_nr = float(smooth_nr)
+ self.smooth_dr = float(smooth_dr)
+ self.batch = batch
+
+ def forward(self, target: torch.Tensor, _input: torch.Tensor) -> Tuple[Union[Tensor, Any], Tensor]: # noqa: MC0001
+ """Forward pass of :class:`Dice`.
+
+ Parameters
+ ----------
+ _input: torch.Tensor
+ Prediction of shape [BNHW[D]].
+ target: torch.Tensor
+ Ground truth of shape [BNHW[D]].
+
+ Returns
+ -------
+ torch.Tensor
+ Dice loss.
+ """
+ if isinstance(_input, np.ndarray):
+ _input = torch.from_numpy(_input)
+ if isinstance(target, np.ndarray):
+ target = torch.from_numpy(target)
+
+ if self.flatten:
+ if target.dim() == 4:
+ segmentation_classes_dim = 1
+ else:
+ segmentation_classes_dim = 0
+ target = target.reshape(target.shape[segmentation_classes_dim], 1, -1)
+ _input = _input.reshape(_input.shape[segmentation_classes_dim], 1, -1)
+
+ if self.sigmoid:
+ _input = torch.sigmoid(_input.float())
+
+ n_pred_ch = _input.shape[1]
+ if self.softmax:
+ if n_pred_ch == 1:
+ warnings.warn("single channel prediction, `softmax=True` ignored.")
+ else:
+ _input = torch.softmax(_input.float(), 1).to(_input)
+
+ if self.other_act is not None:
+ _input = self.other_act(_input)
+
+ if self.to_onehot_y:
+ if n_pred_ch == 1:
+ warnings.warn("single channel prediction, `to_onehot_y=True` ignored.")
+ else:
+ target = one_hot(target, num_classes=n_pred_ch)
+
+ if not self.include_background:
+ if n_pred_ch == 1:
+ warnings.warn("single channel prediction, `include_background=False` ignored.")
+ else:
+ # if skipping background, removing first channel
+ target = target[:, 1:]
+ _input = _input[:, 1:]
+
+ if target.shape != _input.shape:
+ raise AssertionError(f"ground truth has different shape ({target.shape}) from _input ({_input.shape})")
+
+ # reducing only spatial dimensions (not batch nor channels)
+ reduce_axis: List[int] = torch.arange(2, len(_input.shape)).tolist()
+ if self.batch:
+ # reducing spatial dimensions and batch
+ reduce_axis = [0] + reduce_axis
+ intersection = torch.sum(target * _input, dim=reduce_axis)
+ if self.squared_pred:
+ target = torch.pow(target, 2)
+ _input = torch.pow(_input, 2)
+ ground_o = torch.sum(target, dim=reduce_axis)
+ pred_o = torch.sum(_input, dim=reduce_axis)
+ denominator = ground_o + pred_o
+ if self.jaccard:
+ denominator = 2.0 * (denominator - intersection)
+ dice_score = (2.0 * intersection + self.smooth_nr) / (denominator + self.smooth_dr)
+ dice_score = torch.where(denominator > 0, dice_score, torch.tensor(1.0).to(pred_o.device))
+ dice_score, _ = do_metric_reduction(dice_score, reduction=self.reduction)
+ f: torch.Tensor = 1.0 - dice_score
+ return dice_score, f
+
+
+def one_hot(labels: torch.Tensor, num_classes: int, dtype: torch.dtype = torch.float, dim: int = 1) -> torch.Tensor:
+ """Convert labels to one-hot representation.
+
+ Parameters
+ ----------
+ labels: torch.Tensor
+ the labels of shape [BNHW[D]].
+ num_classes: int
+ number of classes.
+ dtype: torch.dtype
+ the data type of the returned tensor.
+ dim: int
+ the dimension to expand the one-hot tensor.
+
+ Returns
+ -------
+ torch.Tensor
+ The one-hot representation of the labels.
+
+ Examples
+ --------
+ >>> labels = torch.tensor([[[[0, 1, 2]]]])
+ >>> one_hot(labels, num_classes=3)
+ tensor([[[[1., 0., 0.],
+ [0., 1., 0.],
+ [0., 0., 1.]]]])
+ """
+ # if `dim` is bigger, add singleton dim at the end
+ if labels.ndim < dim + 1:
+ shape = list(labels.shape) + [1] * (dim + 1 - len(labels.shape))
+ labels = torch.reshape(labels, shape)
+ sh = list(labels.shape)
+ sh[dim] = num_classes
+ o = torch.zeros(size=sh, dtype=dtype, device=labels.device)
+ labels = o.scatter_(dim=dim, index=labels.long(), value=1)
+ return labels
diff --git a/atommic/collections/segmentation/losses/utils.py b/atommic/collections/segmentation/losses/utils.py
new file mode 100644
index 00000000..d4ff6037
--- /dev/null
+++ b/atommic/collections/segmentation/losses/utils.py
@@ -0,0 +1,74 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/Project-MONAI/MONAI/blob/dev/monai/metrics/utils.py
+
+from typing import Any, Tuple
+
+import torch
+from torch import Tensor
+
+
+def do_metric_reduction(f: torch.Tensor, reduction: str = "mean") -> Tuple[Tensor, Any]:
+ """Utility function to perform metric reduction.
+
+ Parameters
+ ----------
+ f : torch.Tensor
+ the metric to reduce.
+ reduction : str
+ the reduction method, default is ``mean``.
+
+ Returns
+ -------
+ torch.Tensor or Any
+ the reduced metric.
+ Any
+ NaNs if there are any NaNs in the input, otherwise 0.
+
+ Examples
+ --------
+ >>> import torch
+ >>> from atommic.collections.segmentation.losses.utils import do_metric_reduction
+ >>> f = torch.tensor([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])
+ >>> do_metric_reduction(f, "mean")
+ (tensor(6.5000), 0)
+ >>> do_metric_reduction(f, "sum")
+ (tensor(78), 0)
+ """
+ # some elements might be Nan (if ground truth y was missing (zeros)), we need to account for it
+ nans = torch.isnan(f)
+ not_nans = (~nans).float()
+ t_zero = torch.zeros(1, device=f.device, dtype=f.dtype)
+ if reduction is None:
+ return f, not_nans
+ f[nans] = 0
+ if reduction == "mean":
+ # 2 steps, first, mean by channel (accounting for nans), then by batch
+ not_nans = not_nans.sum(dim=1)
+ f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero) # channel average
+ not_nans = (not_nans > 0).float().sum(dim=0)
+ f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero) # batch average
+ elif reduction == "sum":
+ not_nans = not_nans.sum(dim=[0, 1])
+ f = torch.sum(f, dim=[0, 1]) # sum over the batch and channel dims
+ elif reduction == "mean_batch":
+ not_nans = not_nans.sum(dim=0)
+ f = torch.where(not_nans > 0, f.sum(dim=0) / not_nans, t_zero) # batch average
+ elif reduction == "sum_batch":
+ not_nans = not_nans.sum(dim=0)
+ f = f.sum(dim=0) # the batch sum
+ elif reduction == "mean_channel":
+ not_nans = not_nans.sum(dim=1)
+ f = torch.where(not_nans > 0, f.sum(dim=1) / not_nans, t_zero) # channel average
+ elif reduction == "sum_channel":
+ not_nans = not_nans.sum(dim=1)
+ f = f.sum(dim=1) # the channel sum
+ elif reduction == "none":
+ pass
+ else:
+ raise ValueError(
+ f"Unsupported reduction: {reduction}, available options are "
+ '["mean", "sum", "mean_batch", "sum_batch", "mean_channel", "sum_channel" "none"].'
+ )
+ return f, not_nans
diff --git a/atommic/collections/segmentation/metrics/__init__.py b/atommic/collections/segmentation/metrics/__init__.py
new file mode 100644
index 00000000..4e6b9271
--- /dev/null
+++ b/atommic/collections/segmentation/metrics/__init__.py
@@ -0,0 +1,15 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.segmentation.metrics.segmentation_metrics import ( # noqa: F401
+ asd,
+ binary_cross_entropy_with_logits_metric,
+ dice_metric,
+ f1_per_class_metric,
+ hausdorff_distance_95_metric,
+ hausdorff_distance_metric,
+ iou_metric,
+ precision_metric,
+ recall_metric,
+ surface_distances,
+)
diff --git a/atommic/collections/segmentation/metrics/segmentation_metrics.py b/atommic/collections/segmentation/metrics/segmentation_metrics.py
new file mode 100644
index 00000000..a5b520a4
--- /dev/null
+++ b/atommic/collections/segmentation/metrics/segmentation_metrics.py
@@ -0,0 +1,760 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import warnings
+from typing import Union
+
+import numpy as np
+import torch
+from runstats import Statistics
+from scipy.ndimage import _ni_support, binary_erosion, distance_transform_edt, generate_binary_structure
+from scipy.spatial.distance import directed_hausdorff
+from torchmetrics import functional as F
+
+from atommic.collections.segmentation.losses import Dice
+from atommic.collections.segmentation.losses.dice import one_hot
+from atommic.collections.segmentation.losses.utils import do_metric_reduction
+
+
+def asd(x, y, voxelspacing=None, connectivity=1):
+ """Compute Average Symmetric Surface Distance (ASD) between a binary object and its reference.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ voxelspacing : Voxel Spacing. Defaults to ``None``.
+ connectivity : int
+ Connectivity. Defaults to ``1``.
+
+ Returns
+ -------
+ float
+ Average Symmetric Surface Distance (ASD) between a binary object and its reference.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import asd
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> asd(datax, datay)
+ 0.5010349308997433
+ """
+ sd1 = np.mean(surface_distances(y, x, voxelspacing, connectivity)) # pylint: disable=arguments-out-of-order
+ sd2 = np.mean(surface_distances(x, y, voxelspacing, connectivity))
+ return (sd1 + sd2) / 2.0
+
+
+def binary_cross_entropy_with_logits_metric(x: torch.Tensor, y: torch.Tensor, reduction: str = "mean") -> float:
+ """Compute Binary Cross Entropy with Logits.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ reduction : str
+ Specifies the reduction to apply to the output: ``none`` | ``mean`` | ``sum``.
+ ``none``: no reduction will be applied. ``mean``: the sum of the output will be divided by the number of
+ elements. ``sum``: the output will be summed. Default is``mean``.
+
+ Returns
+ -------
+ float
+ Binary Cross Entropy with Logits.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import \
+ binary_cross_entropy_with_logits_metric
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> binary_cross_entropy_with_logits_metric(datax, datay)
+ 0.7518648505210876
+
+ .. note::
+ This function is equivalent to `torch.nn.functional.binary_cross_entropy_with_logits` with `reduction='mean'`.
+ Source: https://pytorch.org/docs/stable/generated/torch.nn.functional.binary_cross_entropy_with_logits.html
+ """
+ if isinstance(x, np.ndarray):
+ x = torch.from_numpy(x)
+ if isinstance(y, np.ndarray):
+ y = torch.from_numpy(y)
+ return torch.nn.functional.binary_cross_entropy_with_logits(x.float(), y.float(), reduction=reduction).item()
+
+
+def dice_metric(
+ x: torch.Tensor,
+ y: torch.Tensor,
+ include_background: bool = True,
+ to_onehot_y: bool = False,
+ sigmoid: bool = False,
+ softmax: bool = False,
+ other_act: Union[str, None] = None,
+ squared_y: bool = False,
+ jaccard: bool = False,
+ flatten: bool = False,
+ reduction: Union[str, None] = "mean_batch",
+ smooth_nr: float = 1e-5,
+ smooth_dr: float = 1e-5,
+ batch: bool = True,
+) -> float:
+ """Compute Dice Score.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ include_background : bool
+ Whether to skip Dice computation on the first channel of the predicted output. Default is ``True``.
+ to_onehot_y : bool
+ Whether to convert `y` into the one-hot format. Default is ``False``.
+ sigmoid : bool
+ Whether to add sigmoid function to the input data. Default is ``True``.
+ softmax : bool
+ Whether to add softmax function to the input data. Default is ``False``.
+ other_act : Union[str, None]
+ Use this parameter if you want to apply another type of activation layer. Default is ``None``.
+ squared_y : bool
+ Whether to square the prediction before calculating Dice. Default is ``False``.
+ jaccard : bool
+ Whether to compute Jaccard Index as a loss. Default is ``False``.
+ flatten : bool
+ Whether to flatten input data. Default is ``False``.
+ reduction : Union[str, None]
+ Specifies the reduction to apply to the output: ``none`` | ``mean`` | ``sum``.
+ ``none``: no reduction will be applied. ``mean``: the sum of the output will be divided by the number of
+ elements. ``sum``: the output will be summed. Default is ``mean``.
+ smooth_nr : float
+ A small constant added to the numerator to avoid ``nan`` when all items are 0.
+ smooth_dr : float
+ A small constant added to the denominator to avoid ``nan`` when all items are 0.
+ batch : bool
+ If True, compute Dice loss for each batch and return a tensor with shape (batch_size,).
+ If False, compute Dice loss for the whole batch and return a tensor with shape (1,).
+ Default is ``True``.
+ Returns
+ -------
+ float
+ Dice Score.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import dice_metric
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> dice_metric(datax, datay)
+ 0.5016108751296997
+ """
+ custom_dice = Dice(
+ include_background=include_background,
+ to_onehot_y=to_onehot_y,
+ sigmoid=sigmoid,
+ softmax=softmax,
+ other_act=other_act, # type: ignore
+ squared_pred=squared_y,
+ jaccard=jaccard,
+ flatten=flatten,
+ reduction=reduction, # type: ignore
+ smooth_nr=smooth_nr,
+ smooth_dr=smooth_dr,
+ batch=batch,
+ )
+ dice_score, _ = custom_dice(x, y)
+ return dice_score.item()
+
+
+def f1_per_class_metric(
+ x: torch.Tensor,
+ y: torch.Tensor,
+ beta: float = 1e-5,
+ average: str = "mean",
+ mdmc_average: str = "samplewise",
+ threshold: float = 0.0,
+) -> float:
+ """Compute F1 Score per Class. If the input has only one class, the output will be a list with one element.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ beta : float
+ Beta value for F1 score. Default is ``1e-5``.
+ average : str
+ Defines the averaging performed in the binary case:
+ ``micro`` calculates metrics globally,
+ ``macro`` calculates metrics for each label, and finds their unweighted mean,
+ ``weighted`` calculates metrics for each label, and finds their average, weighted by support
+ (the number of true instances for each label),
+ ``none`` returns the score for each class.
+ Default is ``none``.
+ mdmc_average : str
+ Defines the averaging performed in the multiclass case:
+ ``samplewise`` calculates metrics for each sample, and finds their unweighted mean,
+ ``global`` calculates metrics globally, across all samples.
+ Default is ``samplewise``.
+ threshold : float
+ Threshold value for binarization. Default is ``0.0``.
+
+ Returns
+ -------
+ float
+ F1 Score per Class.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import f1_per_class_metric
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> f1_per_class_metric(datax, datay)
+ [0.49855247139930725, 0.49478909373283386]
+
+ .. note::
+ This function is a wrapper for `torchmetrics.functional.classification.fbeta_score`.
+ """
+ if isinstance(x, np.ndarray):
+ x = torch.from_numpy(x)
+ if isinstance(y, np.ndarray):
+ y = torch.from_numpy(y)
+
+ f1_per_class = F.fbeta_score(
+ y.to(torch.uint8),
+ x.to(torch.uint8),
+ task="binary",
+ beta=beta,
+ average=average,
+ multidim_average=mdmc_average,
+ num_classes=x.shape[1],
+ threshold=threshold,
+ )
+ if f1_per_class.dim() == 0:
+ f1_per_class = torch.stack([f1_per_class] * x.shape[1])
+ return f1_per_class.mean()
+
+
+def hausdorff_distance_metric(x: torch.Tensor, y: torch.Tensor, batched: bool = True, sum_method='max') -> float:
+ """Compute Hausdorff Distance.
+
+ The Hausdorff distance is computed as the maximum between x to y and y to x.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ batched : bool
+ If True, compute Hausdorff distance for each batch and return a tensor with shape (batch_size,).
+ If False, compute Hausdorff distance for the whole batch and return a tensor with shape (1,).
+ Default is ``True``.
+ sum_method : str
+ Method to sum the 95th percentile of the Hausdorff Distance. Default is ``max``, which argmax the 95th
+ percentile of the Hausdorff Distance. If ``sum``, sum the 95th percentile of the Hausdorff Distance.
+
+ Returns
+ -------
+ float
+ Hausdorff Distance.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import hausdorff_distance_metric
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> hausdorff_distance_metric(datax, datay)
+ 5.858907404245753
+ """
+ if batched:
+ hd = []
+ for sl in range(x.shape[0]):
+ hdx = x[sl].float().argmax(0).numpy() if sum_method == 'max' else x[sl].float().sum(0).numpy()
+ hdy = y[sl].float().argmax(0).numpy() if sum_method == 'max' else y[sl].float().sum(0).numpy()
+ hd.append(max(directed_hausdorff(hdx, hdy)[0], directed_hausdorff(hdy, hdx)[0]))
+ return sum(hd) / len(hd)
+
+ x = x.float().argmax(0).numpy() if sum_method == 'max' else x.float().sum(0).numpy()
+ y = y.float().argmax(0).numpy() if sum_method == 'max' else y.float().sum(0).numpy()
+ return max(directed_hausdorff(x, y)[0], directed_hausdorff(y, x)[0])
+
+
+def hausdorff_distance_95_metric(x: torch.Tensor, y: torch.Tensor, batched: bool = True, sum_method='max') -> float:
+ """Compute 95th percentile of the Hausdorff Distance.
+
+ The Hausdorff distance is computed as the maximum between x to y and y to x.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ batched : bool
+ If True, compute Hausdorff distance for each batch and return a tensor with shape (batch_size,).
+ If False, compute Hausdorff distance for the whole batch and return a tensor with shape (1,).
+ Default is ``True``.
+ sum_method : str
+ Method to sum the 95th percentile of the Hausdorff Distance. Default is ``max``, which argmax the 95th
+ percentile of the Hausdorff Distance. If ``sum``, sum the 95th percentile of the Hausdorff Distance.
+
+ Returns
+ -------
+ float
+ 95th percentile of the Hausdorff Distance.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import hausdorff_distance_95_metric
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> hausdorff_distance_95_metric(datax, datay)
+ 5.853190166360368
+ """
+ if isinstance(x, np.ndarray):
+ x = torch.from_numpy(x)
+ if isinstance(y, np.ndarray):
+ y = torch.from_numpy(y)
+
+ if batched and (x.dim() == 4 and y.dim() == 4):
+ hd = []
+ for sl in range(x.shape[0]):
+ hdx = x[sl].float().argmax(0).numpy() if sum_method == 'max' else x[sl].float().sum(0).numpy()
+ hdy = y[sl].float().argmax(0).numpy() if sum_method == 'max' else y[sl].float().sum(0).numpy()
+ hd1 = directed_hausdorff(hdx, hdy)[0]
+ hd2 = directed_hausdorff(hdy, hdx)[0]
+ hd.append(np.percentile(np.hstack((hd1, hd2)), 95))
+ return sum(hd) / len(hd)
+
+ x = x.float().argmax(0).numpy() if sum_method == 'max' else x.float().sum(0).numpy()
+ y = y.float().argmax(0).numpy() if sum_method == 'max' else y.float().sum(0).numpy()
+ hd1 = directed_hausdorff(x, y)[0]
+ hd2 = directed_hausdorff(y, x)[0]
+ return np.percentile(np.hstack((hd1, hd2)), 95)
+
+
+def iou_metric(
+ x: torch.Tensor,
+ y: torch.Tensor,
+ include_background: bool = True,
+ ignore_empty: bool = True,
+ reduction: Union[str, None] = "mean",
+) -> float:
+ """Compute Intersection over Union.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ include_background : bool
+ If True, include background in the computation. Default is ``True``.
+ ignore_empty : bool
+ If True, ignore empty slices. Default is ``True``.
+ reduction : str or None
+ If None, return a tensor with shape (batch_size,).
+ If ``mean``, return the mean of the tensor.
+ If ``sum``, return the sum of the tensor.
+ Default is ``mean``.
+
+ Returns
+ -------
+ float
+ Intersection over Union.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import iou_metric
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> iou_metric(datax, datay)
+ 0.33478260040283203
+ """
+ if isinstance(x, np.ndarray):
+ x = torch.from_numpy(x)
+ if isinstance(y, np.ndarray):
+ y = torch.from_numpy(y)
+
+ if not include_background:
+ if y.dim() == 1:
+ warnings.warn("single channel prediction, `include_background=False` ignored.")
+ else:
+ # if skipping background, removing first channel
+ x = x[:, 1:]
+ y = y[:, 1:]
+
+ if x.shape != y.shape:
+ raise ValueError(f"Prediction and ground truth should have same shapes, got {y.shape} and {x.shape}.")
+
+ # reducing only spatial dimensions (not batch nor channels)
+ n_len = len(y.shape)
+ reduce_axis = list(range(2, n_len))
+ intersection = torch.sum(x * y, dim=reduce_axis)
+
+ y_o = torch.sum(x, reduce_axis)
+ y_y_o = torch.sum(y, dim=reduce_axis)
+ union = y_o + y_y_o - intersection
+
+ _max = 1.0 if not ignore_empty else float("nan")
+ iou_score = torch.where(union > 0, (intersection) / union, torch.tensor(_max, device=y_o.device))
+ iou_score, _ = do_metric_reduction(iou_score, reduction=reduction) # type: ignore
+
+ return iou_score.item()
+
+
+def precision_metric(
+ x: torch.Tensor,
+ y: torch.Tensor,
+ include_background: bool = True,
+ average="none",
+ mdmc_average="samplewise",
+ reduction: Union[str, None] = "mean_batch",
+) -> float:
+ """Compute Precision Score.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ include_background : bool
+ If True, include background in the computation. Default is ``True``.
+ average : str
+ If ``none``, return a tensor with shape (batch_size,).
+ If ``mean``, return the mean of the tensor.
+ If ``sum``, return the sum of the tensor.
+ Default is ``mean``.
+ mdmc_average : str
+ If ``samplewise``, return a tensor with shape (batch_size,).
+ If ``global``, return the mean of the tensor.
+ If ``none``, return the sum of the tensor.
+ Default is ``samplewise``.
+ reduction : str or None
+ If None, return a tensor with shape (batch_size,).
+ If ``mean``, return the mean of the tensor.
+ If ``sum``, return the sum of the tensor.
+ Default is ``mean_batch``.
+
+ Returns
+ -------
+ float
+ Precision Score.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import precision_metric
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> precision_metric(datax, datay)
+ 0.5005333423614502
+ """
+ if isinstance(x, np.ndarray):
+ x = torch.from_numpy(x)
+ if isinstance(y, np.ndarray):
+ y = torch.from_numpy(y)
+
+ if not include_background:
+ if y.dim() == 1:
+ warnings.warn("single channel prediction, `include_background=False` ignored.")
+ else:
+ # if skipping background, removing first channel
+ x = x[:, 1:]
+ y = y[:, 1:]
+
+ x = x.type(torch.uint8)
+ y = y.type(torch.uint8)
+
+ if x.shape != y.shape:
+ raise ValueError(f"Prediction and ground truth should have same shapes, got {y.shape} and {x.shape}.")
+
+ # to one hot per class
+ pr = []
+ for i in range(y.shape[1]):
+ precision_score = F.precision(
+ one_hot(y[:, i].unsqueeze(1), num_classes=2),
+ one_hot(x[:, i].unsqueeze(1), num_classes=2),
+ task="binary",
+ average=average,
+ multidim_average=mdmc_average,
+ num_classes=y.shape[1],
+ )
+ precision_score, _ = do_metric_reduction(precision_score, reduction=reduction) # type: ignore
+ pr.append(precision_score.item())
+ return torch.mean(torch.tensor(pr)).item()
+
+
+def recall_metric(
+ x: torch.Tensor,
+ y: torch.Tensor,
+ include_background: bool = True,
+ average="none",
+ mdmc_average="samplewise",
+ reduction: Union[str, None] = "mean_batch",
+) -> float:
+ """Compute Recall Score.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ include_background : bool
+ If True, include background in the computation. Default is ``True``.
+ average : str
+ If ``none``, return a tensor with shape (batch_size,).
+ If ``mean``, return the mean of the tensor.
+ If ``sum``, return the sum of the tensor.
+ Default is ``mean``.
+ mdmc_average : str
+ If ``samplewise``, return a tensor with shape (batch_size,).
+ If ``global``, return the mean of the tensor.
+ If ``none``, return the sum of the tensor.
+ Default is ``samplewise``.
+ reduction : str or None
+ If None, return a tensor with shape (batch_size,).
+ If ``mean``, return the mean of the tensor.
+ If ``sum``, return the sum of the tensor.
+ Default is ``mean_batch``.
+
+ Returns
+ -------
+ float
+ Recall Score.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import recall_metric
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> recall_metric(datax, datay)
+ 0.5005333423614502
+ """
+ if isinstance(x, np.ndarray):
+ x = torch.from_numpy(x)
+ if isinstance(y, np.ndarray):
+ y = torch.from_numpy(y)
+
+ if not include_background:
+ if y.dim() == 1:
+ warnings.warn("single channel prediction, `include_background=False` ignored.")
+ else:
+ # if skipping background, removing first channel
+ x = x[:, 1:]
+ y = y[:, 1:]
+
+ x = x.type(torch.uint8)
+ y = y.type(torch.uint8)
+
+ if x.shape != y.shape:
+ raise ValueError(f"Prediction and ground truth should have same shapes, got {y.shape} and {x.shape}.")
+
+ # to one hot per class
+ rec = []
+ for i in range(y.shape[1]):
+ recall_score = F.recall(
+ one_hot(y[:, i].unsqueeze(1), num_classes=2),
+ one_hot(x[:, i].unsqueeze(1), num_classes=2),
+ task="binary",
+ average=average,
+ multidim_average=mdmc_average,
+ num_classes=y.shape[1],
+ )
+ recall_score, _ = do_metric_reduction(recall_score, reduction=reduction) # type: ignore
+ rec.append(recall_score.item())
+ return torch.mean(torch.tensor(rec)).item()
+
+
+def surface_distances(x, y, voxelspacing=None, connectivity=1):
+ """The distances between the surface voxel of binary objects in result and their nearest partner surface voxel of a
+ binary object in reference.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Ground Truth Tensor.
+ y : torch.Tensor
+ Prediction Tensor.
+ voxelspacing : Voxel Spacing. Defaults to ``None``.
+ connectivity : int
+ Connectivity. Defaults to ``1``.
+
+ Returns
+ -------
+ np.ndarray
+ The distances between the surface voxel of binary objects in result and their nearest partner surface voxel of
+ a binary object in reference.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import surface_distances
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> surface_distances(datax, datay)
+ array([0., 0., 1., ..., 0., 0., 0.])
+ >>> surface_distances(datax, datay).mean()
+ 0.5083586894950562
+
+ .. note::
+ This function is based on the medpy implementation of the Average Symmetric Surface Distance (ASD) metric.
+ Source: https://github.com/loli/medpy/blob/master/medpy/metric/binary.py#L458
+ """
+ x = np.atleast_1d(np.array([x]).astype(np.bool_))
+ y = np.atleast_1d(np.array([y]).astype(np.bool_))
+
+ if voxelspacing is not None:
+ voxelspacing = _ni_support._normalize_sequence(voxelspacing, y.ndim) # pylint: disable=protected-access
+ voxelspacing = np.asarray(voxelspacing, dtype=np.float64)
+ if not voxelspacing.flags.contiguous:
+ voxelspacing = voxelspacing.copy()
+
+ # binary structure
+ footprint = generate_binary_structure(y.ndim, connectivity)
+
+ # test for emptiness
+ if np.count_nonzero(x) == 0:
+ raise RuntimeError("The first supplied array does not contain any binary object.")
+ if np.count_nonzero(y) == 0:
+ raise RuntimeError("The second supplied array does not contain any binary object.")
+
+ # extract only 1-pixel borderline of objects
+ x_border = x ^ binary_erosion(x, structure=footprint, iterations=1)
+ y_border = y ^ binary_erosion(y, structure=footprint, iterations=1)
+
+ # compute average surface distance
+ # Note: scipy distance transform is calculated only inside the borders of the foreground objects,
+ # therefore the input has to be reversed
+ dt = distance_transform_edt(~x_border, sampling=voxelspacing)
+ sds = dt[y_border]
+
+ return sds
+
+
+class SegmentationMetrics:
+ r"""Maintains running statistics for a given collection of segmentation metrics.
+
+ Examples
+ --------
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import SegmentationMetrics
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import \
+ binary_cross_entropy_with_logits_metric
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import dice_metric
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import f1_per_class_metric
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import hausdorff_distance_metric
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import hausdorff_distance_95_metric
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import iou_metric
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import precision_metric
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import recall_metric
+ >>> from atommic.collections.segmentation.metrics.segmentation_metrics import asd
+ >>> import torch
+ >>> datax = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> datay = torch.randint(0, 2, (3, 2, 100, 100))
+ >>> metric_funcs = {
+ ... "binary_cross_entropy_with_logits": binary_cross_entropy_with_logits_metric,
+ ... "dice": dice_metric,
+ ... "f1_per_class": f1_per_class_metric,
+ ... "hausdorff_distance": hausdorff_distance_metric,
+ ... "hausdorff_distance_95": hausdorff_distance_95_metric,
+ ... "iou": iou_metric,
+ ... "precision": precision_metric,
+ ... "recall": recall_metric,
+ ... "asd": asd,
+ ... }
+ >>> metrics = SegmentationMetrics(metric_funcs)
+ >>> metrics.push(datax, datay)
+ >>> metrics.means()
+ {'binary_cross_entropy_with_logits': 0.7527344822883606,
+ 'dice': 0.4993175268173218,
+ 'f1_per_class': 0.0,
+ 'hausdorff_distance': 5.8873012632302,
+ 'hausdorff_distance_95': 5.881561144720858,
+ 'iou': 0.3327365219593048,
+ 'precision': 0.5005833506584167,
+ 'recall': 0.5005833506584167,
+ 'asd': 0.503373912220483}
+ >>> metrics.stddevs()
+ {'binary_cross_entropy_with_logits': 0.0,
+ 'dice': 0.0,
+ 'f1_per_class': array([0., 0.]),
+ 'hausdorff_distance': 0.0,
+ 'hausdorff_distance_95': 0.0,
+ 'iou': 0.0,
+ 'precision': 0.0,
+ 'recall': 0.0,
+ 'asd': 0.0}
+ >>> metrics.__repr__()
+ 'asd = 0.5034 +/- 0 binary_cross_entropy_with_logits = 0.7527 +/- 0 dice = 0.4993 +/- 0 f1_per_class = 0 +/- 0 \
+ hausdorff_distance = 5.887 +/- 0 hausdorff_distance_95 = 5.882 +/- 0 iou = 0.3327 +/- 0 precision = 0.5006 +/- 0 \
+ recall = 0.5006 +/- 0\n'
+ """
+
+ def __init__(self, metric_funcs):
+ """Inits :class:`SegmentationMetrics`.
+ Parameters
+ ----------
+ metric_funcs : dict
+ A dict where the keys are metric names and the values are Python functions for evaluating that metric.
+ """
+ self.metric_funcs = metric_funcs
+ self.metrics_scores = {metric: Statistics() for metric in metric_funcs}
+
+ def push(self, x, y):
+ """Pushes a new batch of metrics to the running statistics.
+
+ Parameters
+ ----------
+ x : np.ndarray
+ Target image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+ y : np.ndarray
+ Predicted image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+
+ Returns
+ -------
+ dict
+ A dict where the keys are metric names and the values are the computed metric scores.
+ """
+ for metric, func in self.metric_funcs.items():
+ score = func(x, y)
+ if isinstance(score, list):
+ for i in enumerate(score):
+ if metric == f"F1_{i}":
+ self.metrics_scores[metric].push(score[i])
+ else:
+ self.metrics_scores[metric].push(score)
+
+ def means(self):
+ """Mean of the means of each metric."""
+ return {metric: stat.mean() for metric, stat in self.metrics_scores.items()}
+
+ def stddevs(self):
+ """Standard deviation of the means of each metric."""
+ return {metric: stat.stddev() for metric, stat in self.metrics_scores.items()}
+
+ def __repr__(self):
+ """Representation of the metrics."""
+ means = self.means()
+ stddevs = self.stddevs()
+ metric_names = sorted(list(means))
+
+ res = " ".join(f"{name} = {means[name]:.4g} +/- {2 * stddevs[name]:.4g}" for name in metric_names) + "\n"
+
+ return res
diff --git a/atommic/collections/segmentation/nn/__init__.py b/atommic/collections/segmentation/nn/__init__.py
new file mode 100644
index 00000000..3133c77a
--- /dev/null
+++ b/atommic/collections/segmentation/nn/__init__.py
@@ -0,0 +1,10 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.segmentation.nn.attentionunet import SegmentationAttentionUNet # noqa: F401
+from atommic.collections.segmentation.nn.dynunet import SegmentationDYNUNet # noqa: F401
+from atommic.collections.segmentation.nn.lambdaunet import SegmentationLambdaUNet # noqa: F401
+from atommic.collections.segmentation.nn.unet import SegmentationUNet # noqa: F401
+from atommic.collections.segmentation.nn.unet3d import Segmentation3DUNet # noqa: F401
+from atommic.collections.segmentation.nn.unetr import SegmentationUNetR # noqa: F401
+from atommic.collections.segmentation.nn.vnet import SegmentationVNet # noqa: F401
diff --git a/atommic/collections/segmentation/nn/attentionunet.py b/atommic/collections/segmentation/nn/attentionunet.py
new file mode 100644
index 00000000..3874e41d
--- /dev/null
+++ b/atommic/collections/segmentation/nn/attentionunet.py
@@ -0,0 +1,44 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from omegaconf import DictConfig
+
+from atommic.collections.segmentation.nn.attentionunet_base.attentionunet_block import AttentionUnet
+from atommic.collections.segmentation.nn.segmentationnet import BaseSegmentationNet
+
+__all__ = ["SegmentationAttentionUNet"]
+
+
+# type: ignore
+class SegmentationAttentionUNet(BaseSegmentationNet):
+ """Implementation of the Attention UNet for MRI segmentation, as presented in [Oktay2018]_.
+
+ References
+ ----------
+ .. [Oktay2018] O. Oktay, J. Schlemper, L.L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y.
+ Hammerla, B. Kainz, B. Glocker, D. Rueckert. Attention U-Net: Learning Where to Look for the Pancreas. 2018.
+ https://arxiv.org/abs/1804.03999
+
+ """
+
+ def build_segmentation_module(self, cfg: DictConfig) -> torch.nn.Module:
+ """Build the segmentation module.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object specifying the model's hyperparameters.
+
+ Returns
+ -------
+ torch.nn.Module
+ The segmentation module.
+ """
+ return AttentionUnet(
+ in_chans=self.input_channels,
+ out_chans=cfg.get("segmentation_module_output_channels", 2),
+ chans=cfg.get("segmentation_module_channels", 64),
+ num_pool_layers=cfg.get("segmentation_module_pooling_layers", 2),
+ drop_prob=cfg.get("segmentation_module_dropout", 0.0),
+ )
diff --git a/atommic/collections/segmentation/nn/attentionunet_base/__init__.py b/atommic/collections/segmentation/nn/attentionunet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/segmentation/nn/attentionunet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/segmentation/nn/attentionunet_base/attentionunet_block.py b/atommic/collections/segmentation/nn/attentionunet_base/attentionunet_block.py
new file mode 100644
index 00000000..59cffe69
--- /dev/null
+++ b/atommic/collections/segmentation/nn/attentionunet_base/attentionunet_block.py
@@ -0,0 +1,178 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from torch import nn
+
+from atommic.collections.reconstruction.nn.unet_base.unet_block import ConvBlock, TransposeConvBlock
+
+
+class AttentionGate(nn.Module):
+ """A Convolutional Block that consists of two convolution layers each followed by instance normalization,
+ LeakyReLU, activation and dropout, as presented in [Oktay2018]_.
+
+ References
+ ----------
+ .. [Oktay2018] O. Oktay, J. Schlemper, L.L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y.
+ Hammerla, B. Kainz, B. Glocker, D. Rueckert. Attention U-Net: Learning Where to Look for the Pancreas. 2018.
+ https://arxiv.org/abs/1804.03999
+ """
+
+ def __init__(self, in_chans_x: int, in_chans_g: int, out_chans: int):
+ """Inits :class:`AttentionGate`.
+
+ Parameters
+ ----------
+ in_chans_x : int
+ Number of input channels of the input tensor `x`.
+ in_chans_g : int
+ Number of input channels of the input tensor `g`.
+ out_chans : int
+ Number of output channels.
+ """
+ super().__init__()
+ self.in_chans_x = in_chans_x
+ self.in_chans_g = in_chans_g
+ self.out_chans = out_chans
+
+ self.W_x = nn.Sequential(nn.Conv2d(self.in_chans_x, out_chans, kernel_size=2, padding=0, stride=2, bias=False))
+ self.W_g = nn.Sequential(nn.Conv2d(self.in_chans_g, out_chans, kernel_size=1, padding=0, bias=True))
+ self.psi = nn.Sequential(nn.Conv2d(self.out_chans, 1, kernel_size=1, padding=0, bias=True))
+
+ def forward(self, x: torch.Tensor, g: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`AttentionGate`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor `x` with shape [batch_size, in_chans_x, n_x, n_y].
+ g : torch.Tensor
+ Input tensor `g` with shape [batch_size, in_chans_g, n_x, n_y].
+
+ Returns
+ -------
+ torch.Tensor
+ Output tensor with shape [batch_size, out_chans, n_x, n_y].
+ """
+ W_x = self.W_x(x)
+ w_g = self.W_g(g)
+ W_g = nn.functional.interpolate(w_g, size=(W_x.shape[-2], W_x.shape[-1]), mode="bilinear", align_corners=False)
+ f = nn.functional.relu(W_x + W_g, inplace=True)
+ a = torch.sigmoid(self.psi(f))
+ a = nn.functional.interpolate(a, size=(x.shape[-2], x.shape[-1]), mode="bilinear", align_corners=False)
+ return a * x
+
+
+class AttentionUnet(nn.Module):
+ """Implementation of the Attention UNet for MRI segmentation, as presented in [Oktay2018]_.
+
+ References
+ ----------
+ .. [Oktay2018] O. Oktay, J. Schlemper, L.L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y.
+ Hammerla, B. Kainz, B. Glocker, D. Rueckert. Attention U-Net: Learning Where to Look for the Pancreas. 2018.
+ https://arxiv.org/abs/1804.03999
+ """
+
+ def __init__(
+ self,
+ in_chans: int,
+ out_chans: int,
+ chans: int = 32,
+ num_pool_layers: int = 4,
+ drop_prob: float = 0.0,
+ block=ConvBlock,
+ **kwargs,
+ ):
+ """Inits :class:`AttentionUnet`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of input channels.
+ out_chans : int
+ Number of output channels.
+ chans : int
+ Number of channels in the convolutional layers.
+ num_pool_layers : int
+ Number of pooling layers.
+ drop_prob : float
+ Dropout probability.
+ block : nn.Module
+ Convolutional block to use.
+ """
+ super().__init__()
+
+ self.in_chans = in_chans
+ self.out_chans = out_chans
+ self.chans = chans
+ self.num_pool_layers = num_pool_layers
+ self.drop_prob = drop_prob
+
+ self.down_sample_layers = nn.ModuleList([ConvBlock(in_chans, chans, drop_prob)])
+ ch = chans
+ for _ in range(num_pool_layers - 1):
+ self.down_sample_layers.append(block(ch, ch * 2, drop_prob, **kwargs))
+ ch = ch * 2
+ self.conv = block(ch, ch * 2, drop_prob, **kwargs)
+
+ self.up_conv = nn.ModuleList()
+ self.up_transpose_conv = nn.ModuleList()
+ self.up_attention_gates = nn.ModuleList()
+ for _ in range(num_pool_layers - 1):
+ self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch))
+ self.up_conv.append(ConvBlock(ch * 2, ch, drop_prob))
+ self.up_attention_gates.append(AttentionGate(ch, ch * 2, ch))
+ ch = ch // 2
+
+ self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch))
+ self.up_conv.append(
+ nn.Sequential(
+ ConvBlock(ch * 2, ch, drop_prob, **kwargs),
+ nn.Conv2d(ch, self.out_chans, kernel_size=1, stride=1),
+ )
+ )
+ self.up_attention_gates.append(AttentionGate(ch, ch * 2, ch))
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`AttentionUnet`.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor with shape [batch_size, in_chans, n_x, n_y].
+
+ Returns
+ -------
+ torch.Tensor
+ Output tensor with shape [batch_size, out_chans, n_x, n_y].
+ """
+ stack = []
+ output = x
+
+ # apply down-sampling layers
+ for layer in self.down_sample_layers:
+ output = layer(output)
+ stack.append(output)
+ output = nn.functional.avg_pool2d(output, kernel_size=2, stride=2, padding=0)
+
+ output = self.conv(output)
+
+ # apply up-sampling layers
+ for transpose_conv, conv, attention_gate in zip(self.up_transpose_conv, self.up_conv, self.up_attention_gates):
+ downsample_layer = stack.pop()
+ downsample_layer = attention_gate(downsample_layer, output)
+ output = transpose_conv(output)
+
+ # reflect pad on the right/bottom if needed to handle odd input dimensions
+ padding = [0, 0, 0, 0]
+ if output.shape[-1] != downsample_layer.shape[-1]:
+ padding[1] = 1 # padding right
+ if output.shape[-2] != downsample_layer.shape[-2]:
+ padding[3] = 1 # padding bottom
+ if torch.sum(torch.tensor(padding)) != 0:
+ output = nn.functional.pad(output, padding, "reflect")
+
+ output = torch.cat([output, downsample_layer], dim=1)
+ output = conv(output)
+
+ return output
diff --git a/atommic/collections/segmentation/nn/base.py b/atommic/collections/segmentation/nn/base.py
new file mode 100644
index 00000000..f81c5d4e
--- /dev/null
+++ b/atommic/collections/segmentation/nn/base.py
@@ -0,0 +1,952 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import os
+import warnings
+from abc import ABC
+from collections import defaultdict
+from pathlib import Path
+from typing import Dict, Tuple, Union
+
+import h5py
+import numpy as np
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+from torch.utils.data import DataLoader
+
+from atommic.collections.common.data.subsample import create_masker
+from atommic.collections.common.losses import VALID_SEGMENTATION_LOSSES
+from atommic.collections.common.losses.aggregator import AggregatorLoss
+from atommic.collections.common.nn.base import BaseMRIModel, DistributedMetricSum
+from atommic.collections.common.parts.utils import complex_abs, complex_abs_sq, is_none, unnormalize
+from atommic.collections.segmentation.data.mri_segmentation_loader import (
+ BraTS2023AdultGliomaSegmentationMRIDataset,
+ ISLES2022SubAcuteStrokeSegmentationMRIDataset,
+ SegmentationMRIDataset,
+ SKMTEASegmentationMRIDataset,
+)
+from atommic.collections.segmentation.losses.cross_entropy import CrossEntropyLoss
+from atommic.collections.segmentation.losses.dice import Dice
+from atommic.collections.segmentation.parts.transforms import SegmentationMRIDataTransforms
+
+__all__ = ["BaseMRISegmentationModel"]
+
+
+class BaseMRISegmentationModel(BaseMRIModel, ABC):
+ """Base class of all MRI Segmentation models."""
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """Inits :class:`BaseMRISegmentationModel`.
+
+ Parameters
+ ----------
+ cfg: DictConfig
+ The configuration file.
+ trainer: Trainer
+ The PyTorch Lightning trainer.
+ """
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.acc = 1 # fixed acceleration factor to ensure acc is not None
+
+ # Initialize the dimensionality of the data. It can be 2D or 2.5D -> meaning 2D with > 1 slices or 3D.
+ self.dimensionality = cfg_dict.get("dimensionality", 2)
+ self.consecutive_slices = cfg_dict.get("consecutive_slices", 1)
+
+ # Set input channels.
+ self.input_channels = cfg_dict.get("segmentation_module_input_channels", 2)
+ if self.input_channels == 0:
+ raise ValueError("Segmentation module input channels cannot be 0.")
+
+ # Set type of data, i.e., magnitude only or complex valued.
+ self.magnitude_input = cfg_dict.get("magnitude_input", True)
+ # Refers to the type of the complex-valued data. It can be either "stacked" or "complex_abs" or
+ # "complex_sqrt_abs".
+ self.complex_valued_type = cfg_dict.get("complex_valued_type", "stacked")
+
+ # Set normalization parameters for logging
+ self.unnormalize_loss_inputs = cfg_dict.get("unnormalize_loss_inputs", False)
+ self.unnormalize_log_outputs = cfg_dict.get("unnormalize_log_outputs", False)
+ self.normalization_type = cfg_dict.get("normalization_type", "max")
+ self.normalize_segmentation_output = cfg_dict.get("normalize_segmentation_output", True)
+
+ # Whether to log multiple modalities, e.g. T1, T2, and FLAIR will be stacked and logged.
+ self.log_multiple_modalities = cfg_dict.get("log_multiple_modalities", False)
+
+ # Set threshold for segmentation classes. If None, no thresholding is applied.
+ self.segmentation_classes_thresholds = cfg_dict.get("segmentation_classes_thresholds", None)
+ self.segmentation_activation = cfg_dict.get("segmentation_activation", None)
+
+ # Initialize loss related parameters.
+ self.segmentation_losses = {}
+ segmentation_loss = cfg_dict.get("segmentation_loss")
+ segmentation_losses_ = {}
+ if segmentation_loss is not None:
+ for k, v in segmentation_loss.items():
+ if k not in VALID_SEGMENTATION_LOSSES:
+ raise ValueError(
+ f"Segmentation loss {k} is not supported. Please choose one of the following: "
+ f"{VALID_SEGMENTATION_LOSSES}."
+ )
+ if v is None or v == 0.0:
+ warnings.warn(f"The weight of segmentation loss {k} is set to 0.0. This loss will not be used.")
+ else:
+ segmentation_losses_[k] = v
+ else:
+ # Default segmentation loss is Dice.
+ segmentation_losses_["dice"] = 1.0
+ if sum(segmentation_losses_.values()) != 1.0:
+ warnings.warn("Sum of segmentation losses weights is not 1.0. Adjusting weights to sum up to 1.0.")
+ total_weight = sum(segmentation_losses_.values())
+ segmentation_losses_ = {k: v / total_weight for k, v in segmentation_losses_.items()}
+ for name in VALID_SEGMENTATION_LOSSES:
+ if name in segmentation_losses_:
+ if name == "cross_entropy":
+ cross_entropy_loss_classes_weight = torch.tensor(
+ cfg_dict.get("cross_entropy_loss_classes_weight", 0.5)
+ )
+ self.segmentation_losses[name] = CrossEntropyLoss(
+ num_samples=cfg_dict.get("cross_entropy_loss_num_samples", 50),
+ ignore_index=cfg_dict.get("cross_entropy_loss_ignore_index", -100),
+ reduction=cfg_dict.get("cross_entropy_loss_reduction", "none"),
+ label_smoothing=cfg_dict.get("cross_entropy_loss_label_smoothing", 0.0),
+ weight=cross_entropy_loss_classes_weight,
+ )
+ elif name == "dice":
+ self.segmentation_losses[name] = Dice(
+ include_background=cfg_dict.get("dice_loss_include_background", False),
+ to_onehot_y=cfg_dict.get("dice_loss_to_onehot_y", False),
+ sigmoid=cfg_dict.get("dice_loss_sigmoid", True),
+ softmax=cfg_dict.get("dice_loss_softmax", False),
+ other_act=cfg_dict.get("dice_loss_other_act", None),
+ squared_pred=cfg_dict.get("dice_loss_squared_pred", False),
+ jaccard=cfg_dict.get("dice_loss_jaccard", False),
+ flatten=cfg_dict.get("dice_loss_flatten", False),
+ reduction=cfg_dict.get("dice_loss_reduction", "mean"),
+ smooth_nr=cfg_dict.get("dice_loss_smooth_nr", 1e-5),
+ smooth_dr=cfg_dict.get("dice_loss_smooth_dr", 1e-5),
+ batch=cfg_dict.get("dice_loss_batch", False),
+ )
+ self.segmentation_losses = {f"loss_{i+1}": v for i, v in enumerate(self.segmentation_losses.values())}
+ self.total_segmentation_losses = len(self.segmentation_losses)
+ self.total_segmentation_loss_weight = cfg_dict.get("total_segmentation_loss_weight", 1.0)
+
+ # Set the metrics
+ cross_entropy_metric_num_samples = cfg_dict.get("cross_entropy_metric_num_samples", 50)
+ cross_entropy_metric_ignore_index = cfg_dict.get("cross_entropy_metric_ignore_index", -100)
+ cross_entropy_metric_reduction = cfg_dict.get("cross_entropy_metric_reduction", "none")
+ cross_entropy_metric_label_smoothing = cfg_dict.get("cross_entropy_metric_label_smoothing", 0.0)
+ cross_entropy_metric_classes_weight = cfg_dict.get("cross_entropy_metric_classes_weight", None)
+ dice_metric_include_background = cfg_dict.get("dice_metric_include_background", False)
+ dice_metric_to_onehot_y = cfg_dict.get("dice_metric_to_onehot_y", False)
+ dice_metric_sigmoid = cfg_dict.get("dice_metric_sigmoid", True)
+ dice_metric_softmax = cfg_dict.get("dice_metric_softmax", False)
+ dice_metric_other_act = cfg_dict.get("dice_metric_other_act", None)
+ dice_metric_squared_pred = cfg_dict.get("dice_metric_squared_pred", False)
+ dice_metric_jaccard = cfg_dict.get("dice_metric_jaccard", False)
+ dice_metric_flatten = cfg_dict.get("dice_metric_flatten", False)
+ dice_metric_reduction = cfg_dict.get("dice_metric_reduction", "mean")
+ dice_metric_smooth_nr = cfg_dict.get("dice_metric_smooth_nr", 1e-5)
+ dice_metric_smooth_dr = cfg_dict.get("dice_metric_smooth_dr", 1e-5)
+ dice_metric_batch = cfg_dict.get("dice_metric_batch", True)
+
+ # Initialize the module
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ if not is_none(cross_entropy_metric_classes_weight):
+ cross_entropy_metric_classes_weight = torch.tensor(cross_entropy_metric_classes_weight)
+ self.cross_entropy_metric = CrossEntropyLoss(
+ num_samples=cross_entropy_metric_num_samples,
+ ignore_index=cross_entropy_metric_ignore_index,
+ reduction=cross_entropy_metric_reduction,
+ label_smoothing=cross_entropy_metric_label_smoothing,
+ weight=cross_entropy_metric_classes_weight,
+ )
+ else:
+ self.cross_entropy_metric = None # type: ignore
+ self.dice_metric = Dice(
+ include_background=dice_metric_include_background,
+ to_onehot_y=dice_metric_to_onehot_y,
+ sigmoid=dice_metric_sigmoid,
+ softmax=dice_metric_softmax,
+ other_act=dice_metric_other_act,
+ squared_pred=dice_metric_squared_pred,
+ jaccard=dice_metric_jaccard,
+ flatten=dice_metric_flatten,
+ reduction=dice_metric_reduction,
+ smooth_nr=dice_metric_smooth_nr,
+ smooth_dr=dice_metric_smooth_dr,
+ batch=dice_metric_batch,
+ )
+
+ # Set aggregation loss
+ self.total_segmentation_loss = AggregatorLoss(
+ num_inputs=self.total_segmentation_losses, weights=list(segmentation_losses_.values())
+ )
+
+ # Set distributed metrics
+ self.CROSS_ENTROPY = DistributedMetricSum()
+ self.DICE = DistributedMetricSum()
+ self.cross_entropy_vals: Dict = defaultdict(dict)
+ self.dice_vals: Dict = defaultdict(dict)
+ self.TotExamples = DistributedMetricSum()
+
+ def __abs_output__(self, x: torch.Tensor) -> torch.Tensor:
+ """Converts the input to absolute value."""
+ if x.shape[-1] == 2 or torch.is_complex(x):
+ if self.complex_valued_type == "stacked":
+ if x.shape[-1] == 2:
+ x = torch.view_as_complex(x)
+ elif self.complex_valued_type == "complex_abs":
+ x = complex_abs(x)
+ elif self.complex_valued_type == "complex_sqrt_abs":
+ x = complex_abs_sq(x)
+ return x
+
+ def __unnormalize_for_loss_or_log__(
+ self,
+ target: torch.Tensor,
+ prediction: torch.Tensor,
+ attrs: Dict,
+ batch_idx: int = 0,
+ ) -> Tuple[torch.Tensor, torch.Tensor]:
+ """Unnormalizes the data for computing the loss or logging.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, n_x, n_y, 2].
+ prediction : torch.Tensor
+ Prediction data of shape [batch_size, n_x, n_y, 2].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ batch_idx : int
+ Batch index. Default is ``0``.
+
+ Returns
+ -------
+ target : torch.Tensor
+ Unnormalized target data.
+ prediction : torch.Tensor
+ Unnormalized prediction data.
+ sensitivity_maps : torch.Tensor
+ Unnormalized sensitivity maps.
+ """
+ target = unnormalize(
+ target,
+ {
+ "min": attrs["target_min"][batch_idx],
+ "max": attrs["target_max"][batch_idx],
+ "mean": attrs["target_mean"][batch_idx],
+ "std": attrs["target_std"][batch_idx],
+ },
+ self.normalization_type,
+ )
+ prediction = unnormalize(
+ prediction,
+ {
+ "min": attrs["prediction_min"][batch_idx],
+ "max": attrs["prediction_max"][batch_idx],
+ "mean": attrs["prediction_mean"][batch_idx],
+ "std": attrs["prediction_std"][batch_idx],
+ },
+ self.normalization_type,
+ )
+
+ return target, prediction
+
+ def process_segmentation_loss(self, target: torch.Tensor, prediction: torch.Tensor, attrs: Dict) -> Dict:
+ """Processes the segmentation loss.
+
+ Parameters
+ ----------
+ target : torch.Tensor
+ Target data of shape [batch_size, nr_classes, n_x, n_y].
+ prediction : torch.Tensor
+ Prediction of shape [batch_size, nr_classes, n_x, n_y].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+
+ Returns
+ -------
+ Dict
+ Dictionary containing the (multiple) loss values. For example, if the cross entropy loss and the dice loss
+ are used, the dictionary will contain the keys ``cross_entropy_loss``, ``dice_loss``, and
+ (combined) ``segmentation_loss``.
+ """
+ if self.unnormalize_loss_inputs:
+ target, prediction = self.__unnormalize_for_loss_or_log__(target, prediction, attrs)
+ losses = {}
+ for name, loss_func in self.segmentation_losses.items():
+ loss = loss_func(target, prediction)
+ if isinstance(loss, tuple):
+ # In case of the dice loss, the loss is a tuple of the form (dice, dice loss)
+ loss = loss[1]
+ losses[name] = loss
+ return self.total_segmentation_loss(**losses) * self.total_segmentation_loss_weight
+
+ def __compute_and_log_metrics_and_outputs__(
+ self,
+ predictions: Union[list, torch.Tensor],
+ target_reconstruction: torch.Tensor,
+ target_segmentation: torch.Tensor,
+ attrs: Dict,
+ fname: str,
+ slice_idx: int,
+ ):
+ """Computes the metrics and logs the outputs.
+
+ Parameters
+ ----------
+ predictions : torch.Tensor
+ Prediction data of shape [batch_size, n_x, n_y].
+ target_reconstruction : torch.Tensor
+ Target reconstruction data of shape [batch_size, n_x, n_y].
+ target_segmentation : torch.Tensor
+ Target segmentation data of shape [batch_size, segmentation_classes, n_x, n_y].
+ attrs : Dict
+ Attributes of the data with pre normalization values.
+ fname : str
+ File name.
+ slice_idx : int
+ Slice index.
+ """
+ if isinstance(predictions, list):
+ while isinstance(predictions, list):
+ predictions = predictions[-1]
+
+ # Ensure all inputs are both viewed in the same way.
+ target_reconstruction = self.__abs_output__(target_reconstruction)
+
+ if self.consecutive_slices > 1:
+ # reshape the target and prediction to [batch_size * consecutive_slices, nr_classes, n_x, n_y]
+ batch_size = target_segmentation.shape[0] // self.consecutive_slices
+ slice_to_keep = self.consecutive_slices // 2
+ target_segmentation = target_segmentation.reshape(
+ batch_size, self.consecutive_slices, *target_segmentation.shape[1:]
+ )[:, slice_to_keep]
+ predictions = predictions.reshape(batch_size, self.consecutive_slices, *predictions.shape[1:])[
+ :, slice_to_keep
+ ]
+ target_reconstruction = target_reconstruction[:, slice_to_keep]
+ else:
+ batch_size = target_segmentation.shape[0]
+
+ # Iterate over the batch and log the target and predictions.
+ for _batch_idx_ in range(batch_size):
+ output_target_segmentation = target_segmentation[_batch_idx_]
+ output_predictions = predictions[_batch_idx_]
+
+ if self.unnormalize_log_outputs:
+ # Unnormalize target and predictions with pre normalization values. This is only for logging purposes.
+ # For the loss computation, the self.unnormalize_loss_inputs flag is used.
+ output_target_segmentation, output_predictions = self.__unnormalize_for_loss_or_log__(
+ output_target_segmentation, output_predictions, attrs, batch_idx=_batch_idx_
+ )
+
+ output_target_segmentation = output_target_segmentation.detach().cpu()
+ output_predictions = output_predictions.detach().cpu()
+
+ # Log target and predictions, if log_image is True for this slice.
+ if attrs["log_image"][_batch_idx_]:
+ key = f"{fname[_batch_idx_]}_slice_{int(slice_idx[_batch_idx_])}" # type: ignore
+
+ # Normalize (reconstruction) target to [0, 1] for logging.
+ output_target_reconstruction = torch.abs(target_reconstruction[_batch_idx_]).float()
+ output_target_reconstruction = output_target_reconstruction / torch.max(output_target_reconstruction)
+
+ if self.log_multiple_modalities:
+ # concatenate the reconstruction predictions for logging
+ output_target_reconstruction = torch.cat(
+ [output_target_reconstruction[i] for i in range(output_target_reconstruction.shape[0])], dim=-1
+ )
+
+ self.log_image(f"{key}/a/input", output_target_reconstruction)
+
+ # concatenate the segmentation classes for logging
+ target_segmentation_classes = torch.cat(
+ [output_target_segmentation[i] for i in range(output_target_segmentation.shape[0])], dim=-1
+ )
+ output_predictions_segmentation_classes = torch.cat(
+ [output_predictions[i] for i in range(output_predictions.shape[0])], dim=-1
+ )
+ self.log_image(f"{key}/b/segmentation_labels", target_segmentation_classes)
+ self.log_image(f"{key}/c/segmentation_predictions", output_predictions_segmentation_classes)
+ self.log_image(
+ f"{key}/d/segmentation_error",
+ torch.abs(target_segmentation_classes - output_predictions_segmentation_classes),
+ )
+
+ output_target_segmentation = output_target_segmentation.unsqueeze(0)
+ output_predictions = output_predictions.unsqueeze(0)
+
+ if self.cross_entropy_metric is not None:
+ self.cross_entropy_vals[fname][slice_idx] = self.cross_entropy_metric(
+ output_target_segmentation.to(self.device), output_predictions.to(self.device)
+ )
+
+ dice_score, _ = self.dice_metric(output_target_segmentation, output_predictions)
+ self.dice_vals[fname][slice_idx] = dice_score
+
+ def inference_step(
+ self, image: torch.Tensor, target: torch.Tensor, fname: str, slice_idx: int, attrs: Dict
+ ) -> Dict[str, torch.Tensor]:
+ """Performs an inference step, i.e., computes the predictions of the model.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input data. Shape [batch_size, n_x, n_y, 2].
+ target : torch.Tensor
+ Target data. Shape [batch_size, n_x, n_y, 2].
+ fname : str
+ File name.
+ slice_idx : int
+ Slice index.
+ attrs : Dict
+ Attributes dictionary.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary of processed inputs and model's predictions, with keys:
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'predictions' : Union[List[torch.Tensor], torch.Tensor]
+ Model's predictions. Shape [batch_size, segmentation_classes, n_x, n_y, 2].
+ 'target' : torch.Tensor
+ Target data. Shape [batch_size, n_x, n_y, 2].
+ 'attrs' : dict
+ Attributes dictionary.
+ """
+ # Model forward pass
+ prediction = self.forward(image)
+
+ if self.consecutive_slices > 1:
+ # reshape the target and prediction to [batch_size * consecutive_slices, nr_classes, n_x, n_y]
+ batch_size, slices = prediction.shape[:2]
+ if target.dim() == 5:
+ target = target.reshape(batch_size * slices, *target.shape[2:])
+ if prediction.dim() == 5:
+ prediction = prediction.reshape(batch_size * slices, *prediction.shape[2:])
+
+ if not is_none(self.segmentation_classes_thresholds):
+ for class_idx, thres in enumerate(self.segmentation_classes_thresholds):
+ if self.segmentation_activation == "sigmoid":
+ cond = torch.sigmoid(prediction[:, class_idx])
+ elif self.segmentation_activation == "softmax":
+ cond = torch.softmax(prediction[:, class_idx], dim=1)
+ else:
+ cond = prediction[:, class_idx]
+ prediction[:, class_idx] = torch.where(
+ cond >= thres, prediction[:, class_idx], torch.zeros_like(prediction[:, class_idx])
+ )
+
+ return {
+ "fname": fname,
+ "slice_idx": slice_idx,
+ "predictions": prediction,
+ "target": target,
+ "attrs": attrs,
+ }
+
+ def training_step(self, batch: Dict[float, torch.Tensor], batch_idx: int) -> Dict[str, torch.Tensor]:
+ """Performs a training step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'kspace' : List of torch.Tensor
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'y' : Union[torch.Tensor, None]
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'sensitivity_maps' : torch.Tensor
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'mask' : Union[torch.Tensor, None]
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'initial_prediction_reconstruction' : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2].
+ 'target_reconstruction' : torch.Tensor
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'segmentation_labels' : torch.Tensor
+ Target segmentation labels. Shape [batch_size, segmentation_classes, n_x, n_y].
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+
+ batch_idx : int
+ Batch index.
+
+ Returns
+ -------
+ Dict[str, torch.Tensor]
+ Dictionary of loss and log.
+ """
+ (
+ _,
+ _,
+ _,
+ _,
+ initial_reconstruction_prediction,
+ _,
+ target_segmentation,
+ fname,
+ slice_idx,
+ _,
+ attrs,
+ ) = batch
+
+ # In case of complex (fully-sampled) data the initial_reconstruction_prediction is a list of tensors of len 1.
+ if isinstance(initial_reconstruction_prediction, list):
+ initial_reconstruction_prediction = initial_reconstruction_prediction[-1]
+
+ outputs = self.inference_step(
+ initial_reconstruction_prediction,
+ target_segmentation,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ attrs, # type: ignore
+ )
+
+ train_loss = self.process_segmentation_loss(outputs["target"], outputs["predictions"], attrs) # type: ignore
+
+ tensorboard_logs = {
+ "train_loss": train_loss.item(), # type: ignore
+ "lr": self._optimizer.param_groups[0]["lr"], # type: ignore
+ }
+
+ self.log(
+ "train_segmentation_loss",
+ train_loss,
+ on_step=True,
+ on_epoch=True,
+ prog_bar=True,
+ logger=True,
+ batch_size=1, # type: ignore
+ sync_dist=True,
+ )
+
+ return {"loss": train_loss, "log": tensorboard_logs}
+
+ def validation_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a validation step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'kspace' : List of torch.Tensor
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'y' : Union[torch.Tensor, None]
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'sensitivity_maps' : torch.Tensor
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'mask' : Union[torch.Tensor, None]
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'initial_prediction_reconstruction' : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2].
+ 'target_reconstruction' : torch.Tensor
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'segmentation_labels' : torch.Tensor
+ Target segmentation labels. Shape [batch_size, segmentation_classes, n_x, n_y].
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+
+ batch_idx : int
+ Batch index.
+ """
+ (
+ _,
+ _,
+ _,
+ _,
+ initial_reconstruction_prediction,
+ target_reconstruction,
+ target_segmentation,
+ fname,
+ slice_idx,
+ _,
+ attrs,
+ ) = batch
+
+ # In case of complex (fully-sampled) data the initial_reconstruction_prediction is a list of tensors of len 1.
+ if isinstance(initial_reconstruction_prediction, list):
+ initial_reconstruction_prediction = initial_reconstruction_prediction[-1]
+
+ outputs = self.inference_step(
+ initial_reconstruction_prediction,
+ target_segmentation,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ attrs, # type: ignore
+ )
+
+ target_segmentation = outputs["target"]
+ predictions = outputs["predictions"]
+
+ # print memory usage for debugging
+ val_loss = self.process_segmentation_loss(target_segmentation, predictions, attrs) # type: ignore
+
+ # Compute metrics and log them and log outputs.
+ self.__compute_and_log_metrics_and_outputs__(
+ predictions,
+ target_reconstruction,
+ target_segmentation,
+ attrs, # type: ignore
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ )
+
+ self.validation_step_outputs.append({"val_loss": val_loss})
+
+ def test_step(self, batch: Dict[float, torch.Tensor], batch_idx: int):
+ """Performs a test step.
+
+ Parameters
+ ----------
+ batch : Dict[float, torch.Tensor]
+ Batch of data with keys:
+ 'kspace' : List of torch.Tensor
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'y' : Union[torch.Tensor, None]
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'sensitivity_maps' : torch.Tensor
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'mask' : Union[torch.Tensor, None]
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'initial_prediction_reconstruction' : torch.Tensor
+ Initial reconstruction prediction. Shape [batch_size, n_x, n_y, 2].
+ 'target_reconstruction' : torch.Tensor
+ Placeholder to keep the same structure as the base (task) classes. Not used.
+ 'segmentation_labels' : torch.Tensor
+ Target segmentation labels. Shape [batch_size, segmentation_classes, n_x, n_y].
+ 'fname' : str
+ File name.
+ 'slice_idx' : int
+ Slice index.
+ 'acceleration' : float
+ Acceleration factor of the sampling mask.
+ 'attrs' : dict
+ Attributes dictionary.
+
+ batch_idx : int
+ Batch index.
+ """
+ (
+ _,
+ _,
+ _,
+ _,
+ initial_reconstruction_prediction,
+ target_reconstruction,
+ target_segmentation,
+ fname,
+ slice_idx,
+ _,
+ attrs,
+ ) = batch
+
+ # In case of complex (fully-sampled) data the initial_reconstruction_prediction is a list of tensors of len 1.
+ if isinstance(initial_reconstruction_prediction, list):
+ initial_reconstruction_prediction = initial_reconstruction_prediction[-1]
+
+ outputs = self.inference_step(
+ initial_reconstruction_prediction,
+ target_segmentation,
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ attrs, # type: ignore
+ )
+
+ target_segmentation = outputs["target"]
+ predictions = outputs["predictions"]
+
+ # Compute metrics and log them and log outputs.
+ self.__compute_and_log_metrics_and_outputs__(
+ predictions,
+ target_reconstruction,
+ target_segmentation,
+ attrs, # type: ignore
+ fname, # type: ignore
+ slice_idx, # type: ignore
+ )
+
+ # Get the file name.
+ fname = attrs['fname'][0] # type: ignore
+ if '.nii.gz' in fname or '.nii' in fname or '.h5' in fname: # type: ignore
+ fname = fname.split('.')[0] # type: ignore
+
+ self.test_step_outputs.append([fname, slice_idx, predictions.detach().cpu()])
+
+ def on_validation_epoch_end(self):
+ """Called at the end of validation epoch to aggregate outputs.
+
+ Returns
+ -------
+ metrics : dict
+ Dictionary of metrics.
+ """
+ self.log("val_loss", torch.stack([x["val_loss"] for x in self.validation_step_outputs]).mean(), sync_dist=True)
+
+ # Log metrics.
+ if self.cross_entropy_metric is not None:
+ cross_entropy_vals = defaultdict(dict)
+ for k, v in self.cross_entropy_vals.items():
+ cross_entropy_vals[k].update(v)
+
+ dice_vals = defaultdict(dict)
+ for k, v in self.dice_vals.items():
+ dice_vals[k].update(v)
+
+ metrics = {"Cross_Entropy": 0, "DICE": 0}
+
+ local_examples = 0
+ for fname in dice_vals:
+ local_examples += 1
+ if self.cross_entropy_metric is not None:
+ metrics["Cross_Entropy"] = metrics["Cross_Entropy"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in cross_entropy_vals[fname].items()])
+ )
+ metrics["DICE"] = metrics["DICE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in dice_vals[fname].items()])
+ )
+
+ # reduce across ddp via sum
+ if self.cross_entropy_metric is not None:
+ metrics["Cross_Entropy"] = self.CROSS_ENTROPY(metrics["Cross_Entropy"])
+ metrics["DICE"] = self.DICE(metrics["DICE"])
+ tot_examples = self.TotExamples(torch.tensor(local_examples))
+
+ for metric, value in metrics.items():
+ self.log(f"val_metrics/{metric}", value / tot_examples, prog_bar=True, sync_dist=True)
+
+ def on_test_epoch_end(self): # noqa: MC0001
+ """Called at the end of test epoch to aggregate outputs, log metrics and save predictions.
+
+ Returns
+ -------
+ metrics : dict
+ Dictionary of metrics.
+ """
+ # Log metrics.
+ if self.cross_entropy_metric is not None:
+ cross_entropy_vals = defaultdict(dict)
+ for k, v in self.cross_entropy_vals.items():
+ cross_entropy_vals[k].update(v)
+
+ dice_vals = defaultdict(dict)
+ for k, v in self.dice_vals.items():
+ dice_vals[k].update(v)
+
+ metrics = {"Cross_Entropy": 0, "DICE": 0}
+
+ local_examples = 0
+ for fname in dice_vals:
+ local_examples += 1
+ if self.cross_entropy_metric is not None:
+ metrics["Cross_Entropy"] = metrics["Cross_Entropy"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in cross_entropy_vals[fname].items()])
+ )
+ metrics["DICE"] = metrics["DICE"] + torch.mean(
+ torch.cat([v.view(-1) for _, v in dice_vals[fname].items()])
+ )
+
+ # reduce across ddp via sum
+ if self.cross_entropy_metric is not None:
+ metrics["Cross_Entropy"] = self.CROSS_ENTROPY(metrics["Cross_Entropy"])
+ metrics["DICE"] = self.DICE(metrics["DICE"])
+ tot_examples = self.TotExamples(torch.tensor(local_examples))
+
+ for metric, value in metrics.items():
+ self.log(f"test_metrics/{metric}", value / tot_examples, prog_bar=True, sync_dist=True)
+
+ segmentations = defaultdict(list)
+ for fname, slice_num, segmentations_pred in self.test_step_outputs:
+ segmentations[fname].append((slice_num, segmentations_pred))
+
+ for fname in segmentations:
+ segmentations[fname] = np.stack([out for _, out in sorted(segmentations[fname])])
+
+ if self.consecutive_slices > 1:
+ # iterate over the slices and always keep the middle slice
+ for fname in segmentations:
+ segmentations[fname] = segmentations[fname][:, self.consecutive_slices // 2]
+
+ if "wandb" in self.logger.__module__.lower():
+ out_dir = Path(os.path.join(self.logger.save_dir, "segmentations"))
+ else:
+ out_dir = Path(os.path.join(self.logger.log_dir, "segmentations"))
+ out_dir.mkdir(exist_ok=True, parents=True)
+
+ for fname, segmentations_pred in segmentations.items():
+ with h5py.File(out_dir / fname, "w") as hf:
+ hf.create_dataset("segmentation", data=segmentations_pred)
+
+ @staticmethod
+ def _setup_dataloader_from_config(cfg: DictConfig) -> DataLoader:
+ """Setups the dataloader from the configuration (yaml) file.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration file.
+
+ Returns
+ -------
+ dataloader : torch.utils.data.DataLoader
+ Dataloader.
+ """
+ mask_root = cfg.get("mask_path", None)
+ mask_args = cfg.get("mask_args", None)
+ shift_mask = mask_args.get("shift_mask", False)
+ mask_type = mask_args.get("type", None)
+
+ mask_func = None
+ mask_center_scale = 0.02
+
+ if is_none(mask_root) and not is_none(mask_type):
+ accelerations = mask_args.get("accelerations", [1])
+ accelerations = list(accelerations)
+ if len(accelerations) == 1:
+ accelerations = accelerations * 2
+ center_fractions = mask_args.get("center_fractions", [1])
+ center_fractions = list(center_fractions)
+ if len(center_fractions) == 1:
+ center_fractions = center_fractions * 2
+ mask_center_scale = mask_args.get("center_scale", 0.02)
+
+ mask_func = [create_masker(mask_type, center_fractions, accelerations)]
+
+ complex_data = cfg.get("complex_data", True)
+
+ dataset_format = cfg.get("dataset_format", None)
+ if dataset_format.lower() == "brats2023adultglioma":
+ dataloader = BraTS2023AdultGliomaSegmentationMRIDataset
+ elif dataset_format.lower() == "isles2022subacutestroke":
+ dataloader = ISLES2022SubAcuteStrokeSegmentationMRIDataset
+ elif dataset_format.lower() in (
+ "skm-tea-echo1",
+ "skm-tea-echo2",
+ "skm-tea-echo1+echo2",
+ "skm-tea-echo1+echo2-mc",
+ "skm-tea-echo1+echo2-rss",
+ ):
+ dataloader = SKMTEASegmentationMRIDataset
+ else:
+ dataloader = SegmentationMRIDataset
+
+ dataset = dataloader(
+ root=cfg.get("data_path"),
+ coil_sensitivity_maps_root=cfg.get("coil_sensitivity_maps_path", None),
+ mask_root=mask_root,
+ noise_root=cfg.get("noise_path", None),
+ initial_predictions_root=cfg.get("initial_predictions_path"),
+ dataset_format=dataset_format,
+ sample_rate=cfg.get("sample_rate", 1.0),
+ volume_sample_rate=cfg.get("volume_sample_rate", None),
+ use_dataset_cache=cfg.get("use_dataset_cache", False),
+ dataset_cache_file=cfg.get("dataset_cache_file", None),
+ num_cols=cfg.get("num_cols", None),
+ consecutive_slices=cfg.get("consecutive_slices", 1),
+ data_saved_per_slice=cfg.get("data_saved_per_slice", False),
+ n2r_supervised_rate=cfg.get("n2r_supervised_rate", 0.0),
+ complex_target=cfg.get("complex_target", False),
+ log_images_rate=cfg.get("log_images_rate", 1.0),
+ transform=SegmentationMRIDataTransforms(
+ complex_data=complex_data,
+ dataset_format=dataset_format,
+ apply_prewhitening=cfg.get("apply_prewhitening", False),
+ find_patch_size=cfg.get("find_patch_size", False),
+ prewhitening_scale_factor=cfg.get("prewhitening_scale_factor", 1.0),
+ prewhitening_patch_start=cfg.get("prewhitening_patch_start", 10),
+ prewhitening_patch_length=cfg.get("prewhitening_patch_length", 30),
+ apply_gcc=cfg.get("apply_gcc", False),
+ gcc_virtual_coils=cfg.get("gcc_virtual_coils", 10),
+ gcc_calib_lines=cfg.get("gcc_calib_lines", 10),
+ gcc_align_data=cfg.get("gcc_align_data", False),
+ apply_random_motion=cfg.get("apply_random_motion", False),
+ random_motion_type=cfg.get("random_motion_type", "gaussian"),
+ random_motion_percentage=cfg.get("random_motion_percentage", [10, 10]),
+ random_motion_angle=cfg.get("random_motion_angle", 10),
+ random_motion_translation=cfg.get("random_motion_translation", 10),
+ random_motion_center_percentage=cfg.get("random_motion_center_percentage", 0.02),
+ random_motion_num_segments=cfg.get("random_motion_num_segments", 8),
+ random_motion_random_num_segments=cfg.get("random_motion_random_num_segments", True),
+ random_motion_non_uniform=cfg.get("random_motion_non_uniform", False),
+ estimate_coil_sensitivity_maps=cfg.get("estimate_coil_sensitivity_maps", False),
+ coil_sensitivity_maps_type=cfg.get("coil_sensitivity_maps_type", "espirit"),
+ coil_sensitivity_maps_gaussian_sigma=cfg.get("coil_sensitivity_maps_gaussian_sigma", 0.0),
+ coil_sensitivity_maps_espirit_threshold=cfg.get("coil_sensitivity_maps_espirit_threshold", 0.05),
+ coil_sensitivity_maps_espirit_kernel_size=cfg.get("coil_sensitivity_maps_espirit_kernel_size", 6),
+ coil_sensitivity_maps_espirit_crop=cfg.get("coil_sensitivity_maps_espirit_crop", 0.95),
+ coil_sensitivity_maps_espirit_max_iters=cfg.get("coil_sensitivity_maps_espirit_max_iters", 30),
+ coil_combination_method=cfg.get("coil_combination_method", "SENSE"),
+ dimensionality=cfg.get("dimensionality", 2),
+ mask_func=mask_func,
+ shift_mask=shift_mask,
+ mask_center_scale=mask_center_scale,
+ remask=cfg.get("remask", False),
+ ssdu=cfg.get("ssdu", False),
+ ssdu_mask_type=cfg.get("ssdu_mask_type", "Gaussian"),
+ ssdu_rho=cfg.get("ssdu_rho", 0.4),
+ ssdu_acs_block_size=cfg.get("ssdu_acs_block_size", (4, 4)),
+ ssdu_gaussian_std_scaling_factor=cfg.get("ssdu_gaussian_std_scaling_factor", 4.0),
+ ssdu_outer_kspace_fraction=cfg.get("ssdu_outer_kspace_fraction", 0.0),
+ ssdu_export_and_reuse_masks=cfg.get("ssdu_export_and_reuse_masks", False),
+ n2r=cfg.get("n2r", False),
+ n2r_supervised_rate=cfg.get("n2r_supervised_rate", 0.0),
+ n2r_probability=cfg.get("n2r_probability", 0.5),
+ n2r_std_devs=cfg.get("n2r_std_devs", (0.0, 0.0)),
+ n2r_rhos=cfg.get("n2r_rhos", (0.4, 0.4)),
+ n2r_use_mask=cfg.get("n2r_use_mask", True),
+ unsupervised_masked_target=cfg.get("unsupervised_masked_target", False),
+ crop_size=cfg.get("crop_size", None),
+ kspace_crop=cfg.get("kspace_crop", False),
+ crop_before_masking=cfg.get("crop_before_masking", False),
+ kspace_zero_filling_size=cfg.get("kspace_zero_filling_size", None),
+ normalize_inputs=cfg.get("normalize_inputs", True),
+ normalization_type=cfg.get("normalization_type", "max"),
+ kspace_normalization=cfg.get("kspace_normalization", False),
+ fft_centered=cfg.get("fft_centered", False),
+ fft_normalization=cfg.get("fft_normalization", "backward"),
+ spatial_dims=cfg.get("spatial_dims", None),
+ coil_dim=cfg.get("coil_dim", 1),
+ consecutive_slices=cfg.get("consecutive_slices", 1),
+ use_seed=cfg.get("use_seed", True),
+ ),
+ segmentations_root=cfg.get("segmentations_path"),
+ segmentation_classes=cfg.get("segmentation_classes", 2),
+ segmentation_classes_to_remove=cfg.get("segmentation_classes_to_remove", None),
+ segmentation_classes_to_combine=cfg.get("segmentation_classes_to_combine", None),
+ segmentation_classes_to_separate=cfg.get("segmentation_classes_to_separate", None),
+ segmentation_classes_thresholds=cfg.get("segmentation_classes_thresholds", None),
+ complex_data=complex_data,
+ )
+ if cfg.shuffle:
+ sampler = torch.utils.data.RandomSampler(dataset)
+ else:
+ sampler = torch.utils.data.SequentialSampler(dataset)
+
+ return torch.utils.data.DataLoader(
+ dataset=dataset,
+ batch_size=cfg.get("batch_size", 1),
+ sampler=sampler,
+ num_workers=cfg.get("num_workers", 4),
+ pin_memory=cfg.get("pin_memory", False),
+ drop_last=cfg.get("drop_last", False),
+ )
diff --git a/atommic/collections/segmentation/nn/dynunet.py b/atommic/collections/segmentation/nn/dynunet.py
new file mode 100644
index 00000000..a362169b
--- /dev/null
+++ b/atommic/collections/segmentation/nn/dynunet.py
@@ -0,0 +1,108 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from omegaconf import DictConfig
+
+from atommic.collections.segmentation.nn.dynunet_base.dynunet_block import DynUNet
+from atommic.collections.segmentation.nn.segmentationnet import BaseSegmentationNet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["SegmentationDYNUNet"]
+
+
+class SegmentationDYNUNet(BaseSegmentationNet):
+ """Implementation of a Dynamic UNet (DynUNet), based on [Isensee2018]_.
+
+ References
+ ----------
+ .. [Isensee2018] Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G,
+ Norajitra T, Wirkert S, Maier-Hein KH. nnu-net: Self-adapting framework for u-net-based medical image
+ segmentation. arXiv preprint arXiv:1809.10486. 2018 Sep 27.
+
+ """
+
+ def build_segmentation_module(self, cfg: DictConfig) -> torch.nn.Module:
+ """Build the segmentation module.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object specifying the model's hyperparameters.
+
+ Returns
+ -------
+ torch.nn.Module
+ The segmentation module.
+ """
+ strides = cfg.get("segmentation_module_strides", (1, 1, 1, 1))
+ self.deep_supervision = cfg.get("segmentation_module_deep_supervision", False)
+ return DynUNet(
+ spatial_dims=cfg.get("dimensionality", 2),
+ in_channels=self.input_channels,
+ out_channels=cfg.get("segmentation_module_output_channels", 2),
+ kernel_size=cfg.get("segmentation_module_kernel_size", 3),
+ strides=strides,
+ upsample_kernel_size=strides[1:],
+ filters=cfg.get("segmentation_module_channels", 64),
+ dropout=cfg.get("segmentation_module_dropout", 0.0),
+ norm_name=cfg.get("segmentation_module_norm", "instance"),
+ act_name=cfg.get("segmentation_module_activation", "leakyrelu"),
+ deep_supervision=self.deep_supervision,
+ deep_supr_num=cfg.get("segmentation_module_deep_supervision_levels", 1),
+ )
+
+ @typecheck()
+ def forward(self, image: torch.Tensor, **kwargs) -> torch.Tensor:
+ """
+ Forward pass of the network.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input image. Shape [batch_size, n_x, n_y] or [batch_size, n_x, n_y, 2]
+ **kwargs : dict
+ Additional keyword arguments.
+
+ Returns
+ -------
+ torch.Tensor
+ Predicted segmentation. Shape [batch_size, n_classes, n_x, n_y]
+ """
+ if self.consecutive_slices > 1:
+ batch, slices = image.shape[:2]
+ image = image.reshape(batch * slices, *image.shape[2:])
+
+ if image.shape[-1] == 2:
+ if self.input_channels == 1:
+ image = torch.view_as_complex(image).unsqueeze(1)
+ if self.magnitude_input:
+ image = torch.abs(image)
+ elif self.input_channels == 2 and not self.magnitude_input:
+ image = image.permute(0, 3, 1, 2)
+ else:
+ raise ValueError(f"The input channels must be either 1 or 2. Found: {self.input_channels}")
+ elif image.dim() == 3:
+ image = image.unsqueeze(1)
+
+ mean = 1.0
+ std = 1.0
+ if self.normalize:
+ image, mean, std = self.norm(image)
+ image, pad_sizes = self.pad(image)
+ segmentation = self.segmentation_module(image)
+ segmentation = self.unpad(segmentation, *pad_sizes)
+ if self.normalize:
+ segmentation = self.unnorm(segmentation, mean, std)
+
+ if self.deep_supervision and segmentation.dim() == 5:
+ # TODO: check if this is correct. They do unbind, but they don't show how they handle the tuples.
+ segmentation = torch.sum(segmentation, dim=1)
+
+ if self.normalize_segmentation_output:
+ segmentation = (segmentation - segmentation.min()) / (segmentation.max() - segmentation.min())
+
+ if self.consecutive_slices > 1:
+ segmentation = segmentation.reshape(batch, slices, *segmentation.shape[1:])
+
+ return torch.abs(segmentation)
diff --git a/atommic/collections/segmentation/nn/dynunet_base/__init__.py b/atommic/collections/segmentation/nn/dynunet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/segmentation/nn/dynunet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/segmentation/nn/dynunet_base/dynunet_block.py b/atommic/collections/segmentation/nn/dynunet_base/dynunet_block.py
new file mode 100644
index 00000000..b9472af3
--- /dev/null
+++ b/atommic/collections/segmentation/nn/dynunet_base/dynunet_block.py
@@ -0,0 +1,465 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/dynunet.py
+
+from typing import List, Optional, Sequence, Tuple, Union
+
+import torch
+from torch import nn
+from torch.nn.functional import interpolate
+
+from atommic.collections.segmentation.nn.unetr_base.unetr_block import (
+ UnetBasicBlock,
+ UnetOutBlock,
+ UnetResBlock,
+ UnetUpBlock,
+)
+
+__all__ = ["DynUNet"]
+
+
+class DynUNetSkipLayer(nn.Module):
+ r"""Implementation of a Dynamic UNet (DynUNet) Skip Layer, based on [Isensee2018]_.
+
+ References
+ ----------
+ .. [Isensee2018] Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G,
+ Norajitra T, Wirkert S, Maier-Hein KH. nnu-net: Self-adapting framework for u-net-based medical image
+ segmentation. arXiv preprint arXiv:1809.10486. 2018 Sep 27.
+
+
+ .. note::
+ This class is a wrapper of the original DynUNetSkipLayer class from MONAI.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/dynunet.py
+
+
+ .. note::
+ Defines a layer in the UNet topology which combines the downsample and upsample pathways with the skip
+ connection. The member `next_layer` may refer to instances of this class or the final bottleneck layer at the
+ bottom the UNet structure. The purpose of using a recursive class like this is to get around the Torchscript
+ restrictions on looping over lists of layers and accumulating lists of output tensors which must be indexed.
+ The `heads` list is shared amongst all the instances of this class and is used to store the output from the
+ supervision heads during forward passes of the network.
+
+ """
+
+ heads: Optional[List[torch.Tensor]]
+
+ def __init__(
+ self,
+ index: int,
+ downsample: nn.Module,
+ upsample: nn.Module,
+ next_layer: nn.Module,
+ heads: List[torch.Tensor] = None,
+ super_head: Optional[nn.Module] = None,
+ ):
+ """Inits :class:`DynUNetSkipLayer`.
+
+ Parameters
+ ----------
+ index : int
+ The index of the layer in the UNet structure.
+ downsample : nn.Module
+ The downsample layer of the skip connection.
+ upsample : nn.Module
+ The upsample layer of the skip connection.
+ next_layer : nn.Module
+ The next layer in the UNet structure.
+ heads : List[torch.Tensor]
+ The list of output tensors from the supervision heads. Default is ``None``.
+ super_head : nn.Module
+ The supervision head for this layer. Default is ``None``.
+ """
+ super().__init__()
+ self.downsample = downsample
+ self.next_layer = next_layer
+ self.upsample = upsample
+ self.super_head = super_head
+ self.heads = heads
+ self.index = index
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`DynUNetSkipLayer`."""
+ downout = self.downsample(x)
+ nextout = self.next_layer(downout)
+ upout = self.upsample(nextout, downout)
+ if self.super_head is not None and self.heads is not None and self.index > 0:
+ self.heads[self.index - 1] = self.super_head(upout)
+
+ return upout
+
+
+class DynUNet(nn.Module):
+ """Implementation of a Dynamic UNet (DynUNet) Skip Layer, based on [Isensee2018]_.
+
+ References
+ ----------
+ .. [Isensee2018] Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G,
+ Norajitra T, Wirkert S, Maier-Hein KH. nnu-net: Self-adapting framework for u-net-based medical image
+ segmentation. arXiv preprint arXiv:1809.10486. 2018 Sep 27.
+
+ .. note::
+ This class is a wrapper of the original DynUNetSkipLayer class from MONAI.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/dynunet.py
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: Sequence[Union[Sequence[int], int]],
+ strides: Sequence[Union[Sequence[int], int]],
+ upsample_kernel_size: Sequence[Union[Sequence[int], int]],
+ filters: Optional[Sequence[int]] = None,
+ dropout: Optional[Union[Tuple, str, float]] = None,
+ norm_name: Union[Tuple, str] = ("INSTANCE", {"affine": True}),
+ act_name: Union[Tuple, str] = ("leakyrelu", {"inplace": True, "negative_slope": 0.01}),
+ deep_supervision: bool = False,
+ deep_supr_num: int = 1,
+ res_block: bool = False,
+ trans_bias: bool = False,
+ ):
+ """Inits :class:`DynUNet`.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ The number of spatial dimensions of the input data.
+ in_channels : int
+ The number of input channels.
+ out_channels : int
+ The number of output channels.
+ kernel_size : Union[int, Sequence[int]]
+ The kernel size for the convolutional layers.
+ strides : Union[int, Sequence[int]]
+ The stride for the convolutional layers.
+ upsample_kernel_size : Union[int, Sequence[int]]
+ Convolution kernel size for transposed convolution layers. The values should equal to strides[1:].
+ filters : Sequence[int]
+ The number of output channels for each block. Different from nnU-Net, in this implementation we add this
+ argument to make the network more flexible. One way to determine this parameter is like:
+ ``[64, 96, 128, 192, 256, 384, 512, 768, 1024][: len(strides)]``. If not specified, the way which nnUNet
+ used will be employed. Defaults to ``None``.
+ dropout : float
+ Dropout ratio. Defaults to no dropout.
+ norm_name : str
+ Feature normalization type and arguments. Defaults to ``INSTANCE``.
+ `INSTANCE_NVFUSER` is a faster version of the instance norm layer, it can be used when:
+ 1) `spatial_dims=3`, 2) CUDA device is available, 3) `apex` is installed and 4) non-Windows OS is used.
+ act_name : str
+ Activation layer type and arguments. Defaults to ``leakyrelu``.
+ deep_supervision : bool
+ Whether to add deep supervision head before output. Defaults to ``False``. If ``True``, in training mode,
+ the forward function will output not only the final feature map (from `output_block`), but also the feature
+ maps that come from the intermediate up sample layers. In order to unify the return type (the restriction
+ of TorchScript), all intermediate feature maps are interpolated into the same size as the final feature
+ map and stacked together (with a new dimension in the first axis)into one single tensor. For instance, if
+ there are two intermediate feature maps with shapes: (1, 2, 16, 12) and (1, 2, 8, 6), and the final
+ feature map has the shape (1, 2, 32, 24), then all intermediate feature maps will be interpolated into
+ (1, 2, 32, 24), and the stacked tensor will have the shape (1, 3, 2, 32, 24). When calculating the loss,
+ you can use torch.unbind to get all feature maps can compute the loss one by one with the ground truth,
+ then do a weighted average for all losses to achieve the final loss.
+ deep_supr_num : int
+ Number of feature maps that will output during deep supervision head. The value should be larger than 0 and
+ less than the number of up sample layers. Defaults to ``1``.
+ res_block : bool
+ Whether to use residual connection based convolution blocks during the network. Defaults to ``False``.
+ trans_bias : bool
+ Whether to set the bias parameter in transposed convolution layers. Defaults to ``False``.
+ """
+ super().__init__()
+ self.spatial_dims = spatial_dims
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.kernel_size = kernel_size
+ self.strides = strides
+ self.upsample_kernel_size = upsample_kernel_size
+ self.norm_name = norm_name
+ self.act_name = act_name
+ self.dropout = dropout
+ self.conv_block = UnetResBlock if res_block else UnetBasicBlock
+ self.trans_bias = trans_bias
+ if filters is not None:
+ self.filters = filters
+ self.check_filters()
+ else:
+ self.filters = [min(2 ** (5 + i), 320 if spatial_dims == 3 else 512) for i in range(len(strides))]
+ self.input_block = self.get_input_block()
+ self.downsamples = self.get_downsamples()
+ self.bottleneck = self.get_bottleneck()
+ self.upsamples = self.get_upsamples()
+ self.output_block = self.get_output_block(0)
+ self.deep_supervision = deep_supervision
+ self.deep_supr_num = deep_supr_num
+ # initialize the typed list of supervision head outputs so that Torchscript can recognize what's going on
+ self.heads: List[torch.Tensor] = [torch.rand(1)] * self.deep_supr_num
+ if self.deep_supervision:
+ self.deep_supervision_heads = self.get_deep_supervision_heads()
+ self.check_deep_supr_num()
+
+ self.apply(self.initialize_weights)
+ self.check_kernel_stride()
+
+ def create_skips(
+ index: int,
+ downsamples: List[nn.Module],
+ upsamples: List[nn.Module],
+ bottleneck: nn.Module,
+ superheads: List[nn.Module] = None,
+ ) -> nn.Module:
+ """
+ Construct the UNet topology as a sequence of skip layers terminating with the bottleneck layer. This is
+ done recursively from the top down since a recursive nn.Module subclass is being used to be compatible
+ with Torchscript. Initially the length of `downsamples` will be one more than that of `superheads`
+ since the `input_block` is passed to this function as the first item in `downsamples`, however this
+ shouldn't be associated with a supervision head.
+
+ Parameters
+ ----------
+ index : int
+ The index of the current skip layer.
+ downsamples : List[nn.Module]
+ The list of downsample layers.
+ upsamples : List[nn.Module]
+ The list of upsample layers.
+ bottleneck : nn.Module
+ The bottleneck layer.
+ superheads : List[nn.Module]
+ The list of supervision heads. Default is ``None``.
+ """
+ if len(downsamples) != len(upsamples):
+ raise ValueError(f"{len(downsamples)} != {len(upsamples)}")
+
+ if len(downsamples) == 0: # bottom of the network, pass the bottleneck block
+ return bottleneck
+
+ if superheads is None:
+ next_layer = create_skips(1 + index, downsamples[1:], upsamples[1:], bottleneck)
+ return DynUNetSkipLayer(
+ index,
+ downsample=downsamples[0],
+ upsample=upsamples[0],
+ next_layer=next_layer,
+ )
+
+ super_head_flag = False
+ if index == 0: # don't associate a supervision head with self.input_block
+ rest_heads = superheads
+ else:
+ if len(superheads) > 0:
+ super_head_flag = True
+ rest_heads = superheads[1:]
+ else:
+ rest_heads = nn.ModuleList()
+
+ # create the next layer down, this will stop at the bottleneck layer
+ next_layer = create_skips(1 + index, downsamples[1:], upsamples[1:], bottleneck, superheads=rest_heads)
+ if super_head_flag:
+ return DynUNetSkipLayer(
+ index,
+ downsample=downsamples[0],
+ upsample=upsamples[0],
+ next_layer=next_layer,
+ heads=self.heads,
+ super_head=superheads[0],
+ )
+
+ return DynUNetSkipLayer(
+ index,
+ downsample=downsamples[0],
+ upsample=upsamples[0],
+ next_layer=next_layer,
+ )
+
+ if not self.deep_supervision:
+ self.skip_layers = create_skips(
+ 0, [self.input_block] + list(self.downsamples), self.upsamples[::-1], self.bottleneck
+ )
+ else:
+ self.skip_layers = create_skips(
+ 0,
+ [self.input_block] + list(self.downsamples),
+ self.upsamples[::-1],
+ self.bottleneck,
+ superheads=self.deep_supervision_heads,
+ )
+
+ def check_kernel_stride(self):
+ """Check the length of kernel_size and strides."""
+ kernels, strides = self.kernel_size, self.strides
+ error_msg = "length of kernel_size and strides should be the same, and no less than 3."
+ if len(kernels) != len(strides) or len(kernels) < 3:
+ raise ValueError(error_msg)
+
+ for idx, k_i in enumerate(kernels):
+ kernel, stride = k_i, strides[idx]
+ if not isinstance(kernel, int):
+ error_msg = f"length of kernel_size in block {idx} should be the same as spatial_dims."
+ if len(kernel) != self.spatial_dims:
+ raise ValueError(error_msg)
+ if not isinstance(stride, int):
+ error_msg = f"length of stride in block {idx} should be the same as spatial_dims."
+ if len(stride) != self.spatial_dims:
+ raise ValueError(error_msg)
+
+ def check_deep_supr_num(self):
+ """Check the number of deep supervision heads."""
+ deep_supr_num, strides = self.deep_supr_num, self.strides
+ num_up_layers = len(strides) - 1
+ if deep_supr_num >= num_up_layers:
+ raise ValueError("deep_supr_num should be less than the number of up sample layers.")
+ if deep_supr_num < 1:
+ raise ValueError("deep_supr_num should be larger than 0.")
+
+ def check_filters(self):
+ """Check the length of filters."""
+ filters = self.filters
+ if len(filters) < len(self.strides):
+ raise ValueError("Length of filters should be no less than the length of strides.")
+ self.filters = filters[: len(self.strides)]
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass."""
+ out = self.skip_layers(x)
+ out = self.output_block(out)
+ if self.training and self.deep_supervision:
+ out_all = [out]
+ for feature_map in self.heads:
+ out_all.append(interpolate(feature_map, out.shape[2:]))
+ return torch.stack(out_all, dim=1)
+ return out
+
+ def get_input_block(self) -> nn.Module:
+ """Get the input block."""
+ return self.conv_block(
+ self.spatial_dims,
+ self.in_channels,
+ self.filters[0],
+ self.kernel_size[0],
+ self.strides[0],
+ self.norm_name,
+ self.act_name,
+ dropout=self.dropout,
+ )
+
+ def get_bottleneck(self) -> nn.Module:
+ """Get the bottleneck block."""
+ return self.conv_block(
+ self.spatial_dims,
+ self.filters[-2],
+ self.filters[-1],
+ self.kernel_size[-1],
+ self.strides[-1],
+ self.norm_name,
+ self.act_name,
+ dropout=self.dropout,
+ )
+
+ def get_output_block(self, idx: int) -> nn.Module:
+ """Get the output block."""
+ return UnetOutBlock(self.spatial_dims, self.filters[idx], self.out_channels, dropout=self.dropout)
+
+ def get_downsamples(self) -> nn.ModuleList:
+ """Get the downsampling blocks."""
+ inp, out = self.filters[:-2], self.filters[1:-1]
+ strides, kernel_size = self.strides[1:-1], self.kernel_size[1:-1]
+ return self.get_module_list(inp, out, kernel_size, strides, self.conv_block) # type: ignore
+
+ def get_upsamples(self) -> nn.ModuleList:
+ """Get the upsampling blocks."""
+ inp, out = self.filters[1:][::-1], self.filters[:-1][::-1]
+ strides, kernel_size = self.strides[1:][::-1], self.kernel_size[1:][::-1]
+ upsample_kernel_size = self.upsample_kernel_size[::-1]
+ return self.get_module_list(
+ inp, # type: ignore
+ out, # type: ignore
+ kernel_size,
+ strides,
+ UnetUpBlock,
+ upsample_kernel_size,
+ trans_bias=self.trans_bias,
+ )
+
+ def get_module_list(
+ self,
+ in_channels: List[int],
+ out_channels: List[int],
+ kernel_size: Sequence[Union[Sequence[int], int]],
+ strides: Sequence[Union[Sequence[int], int]],
+ conv_block: nn.Module,
+ upsample_kernel_size: Optional[Sequence[Union[Sequence[int], int]]] = None,
+ trans_bias: bool = False,
+ ) -> nn.ModuleList:
+ """Get the module list of the network.
+
+ Parameters
+ ----------
+ in_channels : List[int]
+ The number of input channels.
+ out_channels : List[int]
+ The number of output channels.
+ kernel_size : Sequence[Union[Sequence[int], int]]
+ The kernel size.
+ strides : Sequence[Union[Sequence[int], int]]
+ The strides size.
+ conv_block : nn.Module
+ The convolutional block.
+ upsample_kernel_size : Optional[Sequence[Union[Sequence[int], int]]]
+ The upsample kernel size.
+ trans_bias : bool
+ Whether to use bias in the transpose convolutional layer.
+
+ Returns
+ -------
+ nn.ModuleList
+ The module list of the network.
+ """
+ layers = []
+ if upsample_kernel_size is not None:
+ for in_c, out_c, kernel, stride, up_kernel in zip(
+ in_channels, out_channels, kernel_size, strides, upsample_kernel_size
+ ):
+ params = {
+ "spatial_dims": self.spatial_dims,
+ "in_channels": in_c,
+ "out_channels": out_c,
+ "kernel_size": kernel,
+ "stride": stride,
+ "norm_name": self.norm_name,
+ "act_name": self.act_name,
+ "dropout": self.dropout,
+ "upsample_kernel_size": up_kernel,
+ "trans_bias": trans_bias,
+ }
+ layer = conv_block(**params)
+ layers.append(layer)
+ else:
+ for in_c, out_c, kernel, stride in zip(in_channels, out_channels, kernel_size, strides):
+ params = {
+ "spatial_dims": self.spatial_dims,
+ "in_channels": in_c,
+ "out_channels": out_c,
+ "kernel_size": kernel,
+ "stride": stride,
+ "norm_name": self.norm_name,
+ "act_name": self.act_name,
+ "dropout": self.dropout,
+ }
+ layer = conv_block(**params)
+ layers.append(layer)
+ return nn.ModuleList(layers)
+
+ def get_deep_supervision_heads(self) -> nn.ModuleList:
+ """Get the deep supervision heads."""
+ return nn.ModuleList([self.get_output_block(i + 1) for i in range(self.deep_supr_num)])
+
+ @staticmethod
+ def initialize_weights(module: nn.Module):
+ """Initialize the weights of the network."""
+ if isinstance(module, (nn.Conv3d, nn.Conv2d, nn.ConvTranspose3d, nn.ConvTranspose2d)):
+ module.weight = nn.init.kaiming_normal_(module.weight, a=0.01)
+ if module.bias is not None:
+ module.bias = nn.init.constant_(module.bias, 0)
diff --git a/atommic/collections/segmentation/nn/lambdaunet.py b/atommic/collections/segmentation/nn/lambdaunet.py
new file mode 100644
index 00000000..d8d5629b
--- /dev/null
+++ b/atommic/collections/segmentation/nn/lambdaunet.py
@@ -0,0 +1,98 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from omegaconf import DictConfig
+
+from atommic.collections.segmentation.nn.lambdaunet_base.lambdaunet_block import LambdaUNet
+from atommic.collections.segmentation.nn.segmentationnet import BaseSegmentationNet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["SegmentationLambdaUNet"]
+
+
+class SegmentationLambdaUNet(BaseSegmentationNet):
+ """Implementation of the Lambda UNet for MRI segmentation, as presented in [Yanglan2021]_.
+
+ References
+ ----------
+ .. [Yanglan2021] Yanglan Ou, Ye Yuan, Xiaolei Huang, Kelvin Wong, John Volpi, James Z. Wang, Stephen T.C. Wong.
+ LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images. 2021.
+ https://arxiv.org/abs/2104.13917
+
+ """
+
+ def build_segmentation_module(self, cfg: DictConfig) -> torch.nn.Module:
+ """Build the segmentation module.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object specifying the model's hyperparameters.
+
+ Returns
+ -------
+ torch.nn.Module
+ The segmentation module.
+ """
+ return LambdaUNet(
+ in_chans=self.input_channels,
+ out_chans=cfg.get("segmentation_module_output_channels", 2),
+ chans=cfg.get("segmentation_module_channels", 32),
+ num_pool_layers=cfg.get("segmentation_module_pooling_layers", 4),
+ drop_prob=cfg.get("segmentation_module_dropout", 0.0),
+ query_depth=cfg.get("segmentation_module_query_depth", 16),
+ intra_depth=cfg.get("segmentation_module_intra_depth", 4),
+ receptive_kernel=cfg.get("segmentation_module_receptive_kernel", 3),
+ temporal_kernel=cfg.get("segmentation_module_temporal_kernel", 1),
+ num_slices=self.consecutive_slices,
+ )
+
+ @typecheck()
+ def forward(self, image: torch.Tensor, **kwargs) -> torch.Tensor:
+ """
+ Forward pass of the network.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input image. Shape [batch_size, n_x, n_y] or [batch_size, n_x, n_y, 2]
+ **kwargs : dict
+ Additional keyword arguments.
+
+ Returns
+ -------
+ torch.Tensor
+ Predicted segmentation. Shape [batch_size, n_classes, n_x, n_y]
+ """
+ # Adjust the dimensions of the input image
+ if image.shape[-1] == 2:
+ if self.input_channels == 1:
+ image = torch.view_as_complex(image).unsqueeze(1)
+ if self.magnitude_input:
+ image = torch.abs(image)
+ elif self.input_channels == 2 and not self.magnitude_input:
+ image = image.permute(0, 3, 1, 2)
+ else:
+ raise ValueError(f"The input channels must be either 1 or 2. Found: {self.input_channels}")
+
+ mean = 1.0
+ std = 1.0
+ if self.normalize:
+ image, mean, std = self.norm(image)
+ image, pad_sizes = self.pad(image)
+ if self.consecutive_slices > 1:
+ batch, slices = image.shape[:2]
+ image = image.reshape(batch * slices, *image.shape[2:])
+ segmentation = self.segmentation_module(image)
+ segmentation = self.unpad(segmentation, *pad_sizes)
+ if self.normalize:
+ segmentation = self.unnorm(segmentation, mean, std)
+
+ if self.normalize_segmentation_output:
+ segmentation = (segmentation - segmentation.min()) / (segmentation.max() - segmentation.min())
+
+ if self.consecutive_slices > 1:
+ segmentation = segmentation.reshape(batch, slices, *segmentation.shape[1:])
+
+ return torch.abs(segmentation)
diff --git a/atommic/collections/segmentation/nn/lambdaunet_base/__init__.py b/atommic/collections/segmentation/nn/lambdaunet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/segmentation/nn/lambdaunet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/segmentation/nn/lambdaunet_base/lambdaunet_block.py b/atommic/collections/segmentation/nn/lambdaunet_base/lambdaunet_block.py
new file mode 100644
index 00000000..c2bac699
--- /dev/null
+++ b/atommic/collections/segmentation/nn/lambdaunet_base/lambdaunet_block.py
@@ -0,0 +1,341 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from einops import rearrange
+from torch import einsum, nn
+
+from atommic.collections.reconstruction.nn.unet_base.unet_block import TransposeConvBlock, Unet
+
+
+class LambdaLayer(nn.Module):
+ """Implementation of a Lambda Layer of the Lambda UNet for MRI segmentation, as presented in [Yanglan2021]_.
+
+ References
+ ----------
+ .. [Yanglan2021] Yanglan Ou, Ye Yuan, Xiaolei Huang, Kelvin Wong, John Volpi, James Z. Wang, Stephen T.C. Wong.
+ LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images. 2021.
+ https://arxiv.org/abs/2104.13917
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ query_depth: int = 16,
+ intra_depth: int = 1,
+ receptive_kernel: int = 3,
+ temporal_kernel: int = 1,
+ heads: int = 4,
+ num_slices: int = 1,
+ ):
+ """Inits :class:`LambdaLayer`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of _input channels.
+ out_channels : int
+ Number of output channels.
+ query_depth : int, optional
+ Number of channels for the keys. Default is ``16``.
+ intra_depth : int, optional
+ Number of neighboring slices. Default is ``1``.
+ receptive_kernel : int, optional
+ Local context kernel size. Default is ``3``.
+ temporal_kernel : int, optional
+ Temporal kernel. Default is ``1``.
+ heads : int, optional
+ Number of query heads. Default is ``4``.
+ num_slices : int, optional
+ Number of slices. Default is ``1``.
+ """
+ super().__init__()
+ self.dim_in = in_channels
+ self.dim_out = out_channels
+
+ self.q_depth = query_depth
+ self.intra_depth = intra_depth
+
+ if (out_channels % heads) != 0:
+ raise AssertionError("out_channels must be divisible by number of heads for multi-head query.")
+ self.v_depth = out_channels // heads
+ self.heads = heads
+
+ self.num_slices = num_slices
+
+ self.receptive_kernel = receptive_kernel
+ self.temporal_kernel = temporal_kernel
+
+ self.to_q = nn.Sequential(nn.Conv2d(in_channels, query_depth * heads, kernel_size=1, bias=False))
+ self.to_k = nn.Sequential(nn.Conv2d(in_channels, query_depth * intra_depth, kernel_size=1, bias=False))
+ self.to_v = nn.Sequential(nn.Conv2d(in_channels, self.v_depth * intra_depth, kernel_size=1, bias=False))
+
+ if (receptive_kernel % 2) != 1:
+ raise AssertionError("Receptive kernel size should be odd.")
+ self.pos_conv = nn.Conv3d(
+ intra_depth,
+ query_depth,
+ (1, receptive_kernel, receptive_kernel),
+ padding=(0, receptive_kernel // 2, receptive_kernel // 2),
+ )
+
+ if temporal_kernel >= 3:
+ if temporal_kernel > num_slices:
+ raise AssertionError
+ if (temporal_kernel % 2) != 1:
+ raise AssertionError("Temporal kernel size should be odd.")
+ self.temp_conv = nn.Conv2d(
+ intra_depth,
+ query_depth,
+ (1, temporal_kernel),
+ padding=(0, temporal_kernel // 2),
+ )
+
+ def forward(self, _input: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`LambdaLayer`."""
+ _, _, height, width = (*_input.shape,) # type: ignore
+
+ q = self.to_q(_input)
+ k = self.to_k(_input)
+ v = self.to_v(_input)
+
+ q = rearrange(q, "b (h k) hh ww -> b h k (hh ww)", h=self.heads)
+ k = rearrange(k, "b (u k) hh ww -> b u k (hh ww)", u=self.intra_depth)
+ v = rearrange(v, "b (u v) hh ww -> b u v (hh ww)", u=self.intra_depth)
+
+ k = k.softmax(dim=-1)
+
+ lc = einsum("b u k m, b u v m -> b k v", k, v)
+ Yc = einsum("b h k n, b k v -> b h v n", q, lc)
+
+ v_p = rearrange(v, "b u v (hh ww) -> b u v hh ww", hh=height, ww=width) # type: ignore
+ lp = self.pos_conv(v_p)
+ Yp = einsum("b h k n, b k v n -> b h v n", q, lp.flatten(3))
+
+ if self.temporal_kernel >= 3:
+ v_t = rearrange(v, "(g t) u v p -> (g p) u v t", t=self.num_slices)
+ lt = self.temp_conv(v_t)
+ lt = rearrange(lt, "(g p) k v t -> (g t) k v p", p=height * width) # type: ignore
+ Yt = einsum("b h k n, b k v n -> b h v n", q, lt)
+ Y = Yc + Yp + Yt
+ else:
+ Y = Yc + Yp
+
+ return rearrange(Y, "b h v (hh ww) -> b (h v) hh ww", hh=height, ww=width) # type: ignore
+
+
+class LambdaBlock(nn.Module):
+ """Implementation of a Lambda Black of the Lambda UNet for MRI segmentation, as presented in [Yanglan2021]_.
+
+ References
+ ----------
+ .. [Yanglan2021] Yanglan Ou, Ye Yuan, Xiaolei Huang, Kelvin Wong, John Volpi, James Z. Wang, Stephen T.C. Wong.
+ LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images. 2021.
+ https://arxiv.org/abs/2104.13917
+ """
+
+ def __init__(
+ self,
+ in_chans: int,
+ out_chans: int,
+ drop_prob: float,
+ query_depth: int = 16,
+ intra_depth: int = 4,
+ receptive_kernel: int = 3,
+ temporal_kernel: int = 1,
+ num_slices: int = 1,
+ ):
+ """Inits :class:`LambdaBlock`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of _input channels.
+ out_chans : int
+ Number of output channels.
+ drop_prob : float
+ Dropout probability.
+ query_depth : int, optional
+ Number of channels for the keys. Default is ``16``.
+ intra_depth : int, optional
+ Number of neighboring slices. Default is ``4``.
+ receptive_kernel : int, optional
+ Local context kernel size. Default is ``3``.
+ temporal_kernel : int, optional
+ Temporal kernel. Default is ``1``.
+ num_slices : int, optional
+ Number of slices. Default is ``1``.
+ """
+ super().__init__()
+
+ self.in_chans = in_chans
+ self.out_chans = out_chans
+ self.drop_prob = drop_prob
+
+ self.layers = nn.Sequential(
+ LambdaLayer(
+ in_chans,
+ out_chans,
+ query_depth=query_depth,
+ intra_depth=intra_depth,
+ receptive_kernel=receptive_kernel,
+ temporal_kernel=temporal_kernel,
+ heads=max(1, out_chans // 32),
+ num_slices=num_slices,
+ ),
+ nn.InstanceNorm2d(out_chans),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Dropout2d(drop_prob),
+ LambdaLayer(
+ out_chans,
+ out_chans,
+ query_depth=query_depth,
+ intra_depth=intra_depth,
+ receptive_kernel=receptive_kernel,
+ temporal_kernel=temporal_kernel,
+ heads=max(1, out_chans // 32),
+ num_slices=num_slices,
+ ),
+ nn.InstanceNorm2d(out_chans),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Dropout2d(drop_prob),
+ )
+
+ def forward(self, image: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`LambdaBlock`."""
+ return self.layers(image)
+
+
+class LambdaUNet(Unet):
+ """Implementation of an extended UNet with Lambda blocks, as presented in [Yanglan2021]_.
+
+ References
+ ----------
+ .. [Yanglan2021] Yanglan Ou, Ye Yuan, Xiaolei Huang, Kelvin Wong, John Volpi, James Z. Wang, Stephen T.C. Wong.
+ LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images. 2021.
+ https://arxiv.org/abs/2104.13917
+ """
+
+ def __init__(
+ self,
+ in_chans: int,
+ out_chans: int,
+ chans: int = 32,
+ num_pool_layers: int = 4,
+ drop_prob: float = 0.0,
+ query_depth: int = 16,
+ intra_depth: int = 4,
+ receptive_kernel: int = 3,
+ temporal_kernel: int = 1,
+ num_slices: int = 1,
+ ):
+ """Inits :class:`LambdaUNet`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of _input channels.
+ out_chans : int
+ Number of output channels.
+ chans : int, optional
+ Number of channels. Default is ``32``.
+ num_pool_layers : int, optional
+ Number of pooling layers. Default is ``4``.
+ drop_prob : float
+ Dropout probability. Default is ``0.0``.
+ query_depth : int, optional
+ Number of channels for the keys. Default is ``16``.
+ intra_depth : int, optional
+ Number of neighboring slices. Default is ``4``.
+ receptive_kernel : int, optional
+ Local context kernel size. Default is ``3``.
+ temporal_kernel : int, optional
+ Temporal kernel. Default is ``1``.
+ num_slices : int, optional
+ Number of slices. Default is ``1``.
+ """
+ super().__init__(
+ in_chans=in_chans, out_chans=out_chans, chans=chans, num_pool_layers=num_pool_layers, drop_prob=drop_prob
+ )
+
+ self.in_chans = in_chans
+ self.out_chans = out_chans
+ self.chans = chans
+ self.num_pool_layers = num_pool_layers
+ self.drop_prob = drop_prob
+
+ self.down_sample_layers = nn.ModuleList(
+ [
+ LambdaBlock(
+ in_chans,
+ chans,
+ drop_prob=drop_prob,
+ query_depth=query_depth,
+ intra_depth=intra_depth,
+ receptive_kernel=receptive_kernel,
+ temporal_kernel=temporal_kernel,
+ num_slices=num_slices,
+ )
+ ]
+ )
+ ch = chans
+ for _ in range(num_pool_layers - 1):
+ self.down_sample_layers.append(
+ LambdaBlock(
+ ch,
+ ch * 2,
+ drop_prob=drop_prob,
+ query_depth=query_depth,
+ intra_depth=intra_depth,
+ receptive_kernel=receptive_kernel,
+ temporal_kernel=temporal_kernel,
+ num_slices=num_slices,
+ )
+ )
+ ch = ch * 2
+ self.conv = LambdaBlock(
+ ch,
+ ch * 2,
+ drop_prob=drop_prob,
+ query_depth=query_depth,
+ intra_depth=intra_depth,
+ receptive_kernel=receptive_kernel,
+ temporal_kernel=temporal_kernel,
+ num_slices=num_slices,
+ )
+
+ self.up_conv = nn.ModuleList()
+ self.up_transpose_conv = nn.ModuleList()
+ for _ in range(num_pool_layers - 1):
+ self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch))
+ self.up_conv.append(
+ LambdaBlock(
+ ch * 2,
+ ch,
+ drop_prob=drop_prob,
+ query_depth=query_depth,
+ intra_depth=intra_depth,
+ receptive_kernel=receptive_kernel,
+ temporal_kernel=temporal_kernel,
+ num_slices=num_slices,
+ )
+ )
+ ch = ch // 2
+
+ self.up_transpose_conv.append(TransposeConvBlock(ch * 2, ch))
+ self.up_conv.append(
+ nn.Sequential(
+ LambdaBlock(
+ ch * 2,
+ ch,
+ drop_prob=drop_prob,
+ query_depth=query_depth,
+ intra_depth=intra_depth,
+ receptive_kernel=receptive_kernel,
+ temporal_kernel=temporal_kernel,
+ num_slices=num_slices,
+ ),
+ nn.Conv2d(ch, self.out_chans, kernel_size=1, stride=1),
+ )
+ )
diff --git a/atommic/collections/segmentation/nn/segmentationnet.py b/atommic/collections/segmentation/nn/segmentationnet.py
new file mode 100644
index 00000000..168256ad
--- /dev/null
+++ b/atommic/collections/segmentation/nn/segmentationnet.py
@@ -0,0 +1,171 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import math
+from abc import ABC
+from typing import List, Tuple
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+import atommic.collections.segmentation.nn.base as base_segmentation_models
+from atommic.core.classes.common import typecheck
+
+__all__ = ["BaseSegmentationNet"]
+
+
+class BaseSegmentationNet(base_segmentation_models.BaseMRISegmentationModel, ABC):
+ """Abstract class for all segmentation models."""
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None):
+ """inits :class:`BaseSegmentationNet`.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object specifying the model's hyperparameters.
+ trainer : Trainer, optional
+ PyTorch Lightning trainer object, by default None.
+ """
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ cfg_dict = OmegaConf.to_container(cfg, resolve=True)
+
+ self.padding_size = cfg_dict.get("segmentation_module_padding_size", 11)
+ self.normalize = cfg_dict.get("segmentation_module_normalize", False)
+ self.norm_groups = cfg_dict.get("segmentation_module_norm_groups", 2)
+ self.segmentation_module = self.build_segmentation_module(cfg)
+
+ def build_segmentation_module(self, cfg: DictConfig) -> torch.nn.Module:
+ """Build the segmentation module.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object specifying the model's hyperparameters.
+ """
+ raise NotImplementedError
+
+ def norm(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+ """Normalize the input."""
+ # group norm
+ b, c, s, h, w = x.shape
+
+ x = x.reshape(b, self.norm_groups, -1)
+
+ mean = x.mean(-1, keepdim=True)
+ std = x.std(-1, keepdim=True)
+
+ x = (x - mean) / std
+
+ x = x.reshape(b, c, s, h, w)
+
+ return x, mean, std
+
+ def unnorm(self, x: torch.Tensor, mean: torch.Tensor, std: torch.Tensor) -> torch.Tensor:
+ """Unnormalize the input."""
+ b, c, h, w = x.shape
+ input_data = x.reshape(b, self.norm_groups, -1)
+ return (input_data * std + mean).reshape(b, c, h, w)
+
+ def pad(self, x: torch.Tensor) -> Tuple[torch.Tensor, Tuple[List[int], List[int], int, int]]:
+ """Pad the input with zeros to make it square.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor.
+
+ Returns
+ -------
+ Tuple[torch.Tensor, Tuple[List[int], List[int], int, int]]
+ Padded tensor and padding sizes.
+ """
+ if x.dim() == 4:
+ _, _, h, w = x.shape
+ elif x.dim() == 5:
+ _, _, _, h, w = x.shape
+ w_mult = ((w - 1) | self.padding_size) + 1
+ h_mult = ((h - 1) | self.padding_size) + 1
+ w_pad = [math.floor((w_mult - w) / 2), math.ceil((w_mult - w) / 2)]
+ h_pad = [math.floor((h_mult - h) / 2), math.ceil((h_mult - h) / 2)]
+ x = torch.nn.functional.pad(x, w_pad + h_pad)
+ return x, (h_pad, w_pad, h_mult, w_mult)
+
+ @staticmethod
+ def unpad(x: torch.Tensor, h_pad: List[int], w_pad: List[int], h_mult: int, w_mult: int) -> torch.Tensor:
+ """Unpad the input.
+
+ Parameters
+ ----------
+ x : torch.Tensor
+ Input tensor.
+ h_pad : List[int]
+ Height padding sizes.
+ w_pad : List[int]
+ Width padding sizes.
+ h_mult : int
+ Height multiplier.
+ w_mult : int
+ Width multiplier.
+
+ Returns
+ -------
+ torch.Tensor
+ Unpadded tensor.
+ """
+ return x[..., h_pad[0] : h_mult - h_pad[1], w_pad[0] : w_mult - w_pad[1]]
+
+ @typecheck()
+ def forward(self, image: torch.Tensor, **kwargs) -> torch.Tensor: # pylint: disable=arguments-differ
+ """Forward pass of :class:`BaseSegmentationNet`.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input image. Shape [batch_size, n_x, n_y] or [batch_size, n_x, n_y, 2]
+ **kwargs : dict
+ Additional keyword arguments.
+
+ Returns
+ -------
+ torch.Tensor
+ Predicted segmentation. Shape [batch_size, n_classes, n_x, n_y]
+ """
+ if self.consecutive_slices > 1:
+ batch, slices = image.shape[:2]
+ image = image.reshape(batch * slices, *image.shape[2:])
+
+ if image.shape[-1] == 2:
+ if self.input_channels == 1:
+ image = torch.view_as_complex(image).unsqueeze(1)
+ if self.magnitude_input:
+ image = torch.abs(image)
+ elif self.input_channels == 2 and not self.magnitude_input:
+ image = image.permute(0, 3, 1, 2)
+ else:
+ raise ValueError(f"The input channels must be either 1 or 2. Found: {self.input_channels}")
+ elif self.magnitude_input:
+ image = torch.abs(image)
+
+ if image.dim() == 3:
+ image = image.unsqueeze(1)
+
+ mean = 1.0
+ std = 1.0
+ if self.normalize:
+ image, mean, std = self.norm(image)
+ image, pad_sizes = self.pad(image)
+ segmentation = self.segmentation_module(image)
+ segmentation = self.unpad(segmentation, *pad_sizes)
+ if self.normalize:
+ segmentation = self.unnorm(segmentation, mean, std)
+
+ if self.normalize_segmentation_output:
+ segmentation = (segmentation - segmentation.min()) / (segmentation.max() - segmentation.min())
+
+ if self.consecutive_slices > 1:
+ segmentation = segmentation.reshape(batch, slices, *segmentation.shape[1:])
+
+ return torch.abs(segmentation)
diff --git a/atommic/collections/segmentation/nn/unet.py b/atommic/collections/segmentation/nn/unet.py
new file mode 100644
index 00000000..85d33d90
--- /dev/null
+++ b/atommic/collections/segmentation/nn/unet.py
@@ -0,0 +1,43 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from omegaconf import DictConfig
+
+from atommic.collections.reconstruction.nn.unet_base.unet_block import Unet
+from atommic.collections.segmentation.nn.segmentationnet import BaseSegmentationNet
+
+__all__ = ["SegmentationUNet"]
+
+
+class SegmentationUNet(BaseSegmentationNet):
+ """Implementation of the (2D) UNet for MRI segmentation, as presented in [Ronneberger2015]_.
+
+ References
+ ----------
+ .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical
+ image segmentation. In International Conference on Medical image computing and computer-assisted intervention,
+ pages 234โ241. Springer, 2015.
+
+ """
+
+ def build_segmentation_module(self, cfg: DictConfig) -> torch.nn.Module:
+ """Build the segmentation module.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object specifying the model's hyperparameters.
+
+ Returns
+ -------
+ torch.nn.Module
+ The segmentation module.
+ """
+ return Unet(
+ in_chans=self.input_channels,
+ out_chans=cfg.get("segmentation_module_output_channels", 2),
+ chans=cfg.get("segmentation_module_channels", 64),
+ num_pool_layers=cfg.get("segmentation_module_pooling_layers", 2),
+ drop_prob=cfg.get("segmentation_module_dropout", 0.0),
+ )
diff --git a/atommic/collections/segmentation/nn/unet3d.py b/atommic/collections/segmentation/nn/unet3d.py
new file mode 100644
index 00000000..de4a6642
--- /dev/null
+++ b/atommic/collections/segmentation/nn/unet3d.py
@@ -0,0 +1,91 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from omegaconf import DictConfig
+
+from atommic.collections.segmentation.nn.segmentationnet import BaseSegmentationNet
+from atommic.collections.segmentation.nn.unet3d_base.unet3d_block import UNet3D
+from atommic.core.classes.common import typecheck
+
+__all__ = ["Segmentation3DUNet"]
+
+
+class Segmentation3DUNet(BaseSegmentationNet):
+ """Implementation of the (3D) UNet for MRI segmentation, as presented in [Ronneberger2015]_.
+
+ References
+ ----------
+ .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical
+ image segmentation. In International Conference on Medical image computing and computer-assisted intervention,
+ pages 234โ241. Springer, 2015.
+
+ """
+
+ def build_segmentation_module(self, cfg: DictConfig) -> torch.nn.Module:
+ """Build the segmentation module.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object specifying the model's hyperparameters.
+
+ Returns
+ -------
+ torch.nn.Module
+ The segmentation module.
+ """
+ return UNet3D(
+ in_chans=self.input_channels,
+ out_chans=cfg.get("segmentation_module_output_channels", 2),
+ chans=cfg.get("segmentation_module_channels", 64),
+ num_pool_layers=cfg.get("segmentation_module_pooling_layers", 2),
+ drop_prob=cfg.get("segmentation_module_dropout", 0.0),
+ )
+
+ @typecheck()
+ def forward(self, image: torch.Tensor, **kwargs) -> torch.Tensor:
+ """
+ Forward pass of the network.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input image. Shape [batch_size, slices, classes, n_x, n_y] or [batch_size, slices, classes, n_x, n_y, 2]
+ **kwargs : dict
+ Additional keyword arguments.
+
+ Returns
+ -------
+ torch.Tensor
+ Predicted segmentation. Shape [batch_size, n_classes, n_x, n_y]
+ """
+ # Adjust the dimensions of the input image
+ if image.shape[-1] == 2:
+ if self.input_channels == 1:
+ image = torch.view_as_complex(image).unsqueeze(1)
+ if self.magnitude_input:
+ image = torch.abs(image)
+ elif self.input_channels == 2 and not self.magnitude_input:
+ image = image.permute(0, 3, 1, 2)
+ else:
+ raise ValueError(f"The input channels must be either 1 or 2. Found: {self.input_channels}")
+
+ if image.dim() == 4:
+ # we are missing the classes dimension
+ image = image.unsqueeze(2)
+
+ mean = 1.0
+ std = 1.0
+ if self.normalize:
+ image, mean, std = self.norm(image)
+ image, pad_sizes = self.pad(image)
+ segmentation = self.segmentation_module(image.permute(0, 2, 1, 3, 4)).permute(0, 2, 1, 3, 4)
+ segmentation = self.unpad(segmentation, *pad_sizes)
+ if self.normalize:
+ segmentation = self.unnorm(segmentation, mean, std)
+
+ if self.normalize_segmentation_output:
+ segmentation = (segmentation - segmentation.min()) / (segmentation.max() - segmentation.min())
+
+ return torch.abs(segmentation)
diff --git a/atommic/collections/segmentation/nn/unet3d_base/__init__.py b/atommic/collections/segmentation/nn/unet3d_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/segmentation/nn/unet3d_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/segmentation/nn/unet3d_base/unet3d_block.py b/atommic/collections/segmentation/nn/unet3d_base/unet3d_block.py
new file mode 100644
index 00000000..145a8f9b
--- /dev/null
+++ b/atommic/collections/segmentation/nn/unet3d_base/unet3d_block.py
@@ -0,0 +1,172 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from torch import nn
+
+
+class Conv3dBlock(nn.Module):
+ """A 3D convolutional block."""
+
+ def __init__(self, in_chans: int, out_chans: int, drop_prob: float, **kwargs):
+ """Inits :class:`Conv3dBlock`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of input channels.
+ out_chans : int
+ Number of output channels.
+ drop_prob : float
+ Dropout probability.
+ """
+ super().__init__()
+
+ self.in_chans = in_chans
+ self.out_chans = out_chans
+ self.drop_prob = drop_prob
+
+ self.layers = nn.Sequential(
+ nn.Conv3d(in_chans, out_chans, kernel_size=3, padding=1, bias=False),
+ nn.InstanceNorm3d(out_chans),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Dropout3d(drop_prob),
+ nn.Conv3d(out_chans, out_chans, kernel_size=3, padding=1, bias=False),
+ nn.InstanceNorm3d(out_chans),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ nn.Dropout3d(drop_prob),
+ )
+
+ def forward(self, image: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`Conv3dBlock`."""
+ return self.layers(image)
+
+
+class TransposeConv3dBlock(nn.Module):
+ """A 3D transposed convolutional block."""
+
+ def __init__(self, in_chans: int, out_chans: int):
+ """Inits :class:`TransposeConv3dBlock`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of input channels.
+ out_chans : int
+ Number of output channels.
+ """
+ super().__init__()
+
+ self.in_chans = in_chans
+ self.out_chans = out_chans
+
+ self.layers = nn.Sequential(
+ nn.ConvTranspose3d(in_chans, out_chans, kernel_size=(1, 2, 2), stride=(1, 2, 2), bias=False),
+ nn.InstanceNorm3d(out_chans),
+ nn.LeakyReLU(negative_slope=0.2, inplace=True),
+ )
+
+ def forward(self, image: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`TransposeConv3dBlock`."""
+ return self.layers(image)
+
+
+class UNet3D(nn.Module):
+ """Implementation of the (3D) UNet for MRI segmentation, as presented in [Ronneberger2015]_.
+
+ References
+ ----------
+ .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical
+ image segmentation. In International Conference on Medical image computing and computer-assisted intervention,
+ pages 234โ241. Springer, 2015.
+ """
+
+ def __init__(
+ self,
+ in_chans: int,
+ out_chans: int,
+ chans: int = 32,
+ num_pool_layers: int = 4,
+ drop_prob: float = 0.0,
+ block=Conv3dBlock,
+ **kwargs,
+ ):
+ """Inits :class:`UNet3D`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of input channels.
+ out_chans : int
+ Number of output channels.
+ chans : int
+ Number of output channels of the first convolutional layer. Default is ``32``.
+ num_pool_layers : int
+ Number of down-sampling and up-sampling layers. Default is ``4``.
+ drop_prob : float
+ Dropout probability. Default is ``0.0``.
+ block : nn.Module
+ Convolutional block to use. Default is ``Conv3dBlock``.
+ """
+ super().__init__()
+ self.in_chans = in_chans
+ self.out_chans = out_chans
+ self.chans = chans
+ self.num_pool_layers = num_pool_layers
+ self.drop_prob = drop_prob
+
+ self.down_sample_layers = nn.ModuleList([Conv3dBlock(in_chans, chans, drop_prob)])
+ ch = chans
+ for _ in range(num_pool_layers - 1):
+ self.down_sample_layers.append(block(ch, ch * 2, drop_prob, **kwargs))
+ ch = ch * 2
+ self.conv = block(ch, ch * 2, drop_prob, **kwargs)
+
+ self.up_conv = nn.ModuleList()
+ self.up_transpose_conv = nn.ModuleList()
+ for _ in range(num_pool_layers - 1):
+ self.up_transpose_conv.append(TransposeConv3dBlock(ch * 2, ch))
+ self.up_conv.append(Conv3dBlock(ch * 2, ch, drop_prob))
+ ch = ch // 2
+
+ self.up_transpose_conv.append(TransposeConv3dBlock(ch * 2, ch))
+ self.up_conv.append(
+ nn.Sequential(
+ Conv3dBlock(ch * 2, ch, drop_prob, **kwargs),
+ nn.Conv3d(ch, self.out_chans, kernel_size=1, stride=1),
+ )
+ )
+
+ def forward(self, image: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`UNet3D`."""
+ stack = []
+ output = image
+
+ # apply down-sampling layers
+ for layer in self.down_sample_layers:
+ output = layer(output)
+ stack.append(output)
+ output = nn.functional.avg_pool3d(output, kernel_size=(1, 2, 2), stride=(1, 2, 2), padding=0)
+
+ output = self.conv(output)
+
+ # apply up-sampling layers
+ for transpose_conv, conv in zip(self.up_transpose_conv, self.up_conv):
+ downsample_layer = stack.pop()
+ output = transpose_conv(output)
+
+ # reflect pad on the right/bottom if needed to handle odd input dimensions
+ padding = [0, 0, 0, 0, 0, 0]
+ if output.shape[-1] != downsample_layer.shape[-1]:
+ padding[1] = 1 # padding right
+ if output.shape[-2] != downsample_layer.shape[-2]:
+ padding[3] = 1 # padding bottom
+ if output.shape[-3] != downsample_layer.shape[-3]:
+ padding[5] = 1 # padding back
+ if torch.sum(torch.tensor(padding)) != 0:
+ output = nn.functional.pad(output, padding, "reflect")
+
+ output = torch.cat([output, downsample_layer], dim=1)
+ output = conv(output)
+
+ return output
diff --git a/atommic/collections/segmentation/nn/unetr.py b/atommic/collections/segmentation/nn/unetr.py
new file mode 100644
index 00000000..9ab03c19
--- /dev/null
+++ b/atommic/collections/segmentation/nn/unetr.py
@@ -0,0 +1,107 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from omegaconf import DictConfig
+
+from atommic.collections.segmentation.nn.segmentationnet import BaseSegmentationNet
+from atommic.collections.segmentation.nn.unetr_base.unetr_block import UNETR
+
+__all__ = ["SegmentationUNetR"]
+
+from atommic.core.classes.common import typecheck
+
+
+class SegmentationUNetR(BaseSegmentationNet):
+ """Implementation of the UNETR for MRI segmentation, as presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+
+ """
+
+ def build_segmentation_module(self, cfg: DictConfig) -> torch.nn.Module:
+ """Build the segmentation module.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object specifying the model's hyperparameters.
+
+ Returns
+ -------
+ torch.nn.Module
+ The segmentation module.
+ """
+ return UNETR(
+ in_channels=self.input_channels,
+ out_channels=cfg.get("segmentation_module_output_channels", 2),
+ img_size=cfg.get("segmentation_module_img_size", (256, 256)),
+ feature_size=cfg.get("segmentation_module_channels", 64),
+ hidden_size=cfg.get("segmentation_module_hidden_size", 768),
+ mlp_dim=cfg.get("segmentation_module_mlp_dim", 3072),
+ num_heads=cfg.get("segmentation_module_num_heads", 12),
+ pos_embed=cfg.get("segmentation_module_pos_embed", "conv"),
+ norm_name=cfg.get("segmentation_module_norm_name", "instance"),
+ conv_block=cfg.get("segmentation_module_conv_block", True),
+ res_block=cfg.get("segmentation_module_res_block", True),
+ dropout_rate=cfg.get("segmentation_module_dropout", 0.0),
+ spatial_dims=cfg.get("dimensionality", 2),
+ qkv_bias=cfg.get("segmentation_module_qkv_bias", False),
+ )
+
+ @typecheck()
+ def forward(self, image: torch.Tensor, **kwargs) -> torch.Tensor:
+ """
+ Forward pass of the network.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input image. Shape [batch_size, slices, classes, n_x, n_y] or [batch_size, slices, classes, n_x, n_y, 2]
+ **kwargs : dict
+ Additional keyword arguments.
+
+ Returns
+ -------
+ torch.Tensor
+ Predicted segmentation. Shape [batch_size, n_classes, n_x, n_y]
+ """
+ # Adjust the dimensions of the input image
+ if image.shape[-1] == 2:
+ if self.input_channels == 1:
+ image = torch.view_as_complex(image).unsqueeze(1)
+ if self.magnitude_input:
+ image = torch.abs(image)
+ elif self.input_channels == 2 and not self.magnitude_input:
+ image = image.permute(0, 3, 1, 2)
+ else:
+ raise ValueError(f"The input channels must be either 1 or 2. Found: {self.input_channels}")
+
+ if image.dim() == 4:
+ # we are missing the classes dimension
+ image = image.unsqueeze(2)
+
+ mean = 1.0
+ std = 1.0
+ if self.normalize:
+ image, mean, std = self.norm(image)
+ image, pad_sizes = self.pad(image)
+ if self.consecutive_slices > 1:
+ batch, slices = image.shape[:2]
+ image = image.reshape(batch * slices, *image.shape[2:])
+ segmentation = self.segmentation_module(image)
+ segmentation = self.unpad(segmentation, *pad_sizes)
+ if self.normalize:
+ segmentation = self.unnorm(segmentation, mean, std)
+
+ if self.normalize_segmentation_output:
+ segmentation = (segmentation - segmentation.min()) / (segmentation.max() - segmentation.min())
+
+ if self.consecutive_slices > 1:
+ segmentation = segmentation.reshape(batch, slices, *segmentation.shape[1:])
+
+ return torch.abs(segmentation)
diff --git a/atommic/collections/segmentation/nn/unetr_base/__init__.py b/atommic/collections/segmentation/nn/unetr_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/segmentation/nn/unetr_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/segmentation/nn/unetr_base/unetr_block.py b/atommic/collections/segmentation/nn/unetr_base/unetr_block.py
new file mode 100644
index 00000000..c6e0bd10
--- /dev/null
+++ b/atommic/collections/segmentation/nn/unetr_base/unetr_block.py
@@ -0,0 +1,833 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/unetr.py
+
+from typing import Optional, Sequence, Tuple, Union
+
+import numpy as np
+import torch
+from torch import nn
+
+from atommic.collections.segmentation.nn.vit_base.utils import get_conv_layer
+from atommic.collections.segmentation.nn.vit_base.vit_block import ViT
+
+
+class UnetOutBlock(nn.Module):
+ """Implementation of the output block of UNETR, as presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+
+ .. note::
+ This is a wrapper for monai implementation of UNETR.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/unetr.py
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ dropout: Optional[Union[Tuple, str, float]] = None,
+ ):
+ """Inits :class:`UnetOutBlock`.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ Number of spatial dimensions of the input image.
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ dropout : Optional[Union[Tuple, str, float]]
+ Dropout rate.
+ """
+ super().__init__()
+ self.conv = get_conv_layer(
+ spatial_dims,
+ in_channels,
+ out_channels,
+ kernel_size=1,
+ stride=1,
+ dropout=dropout,
+ bias=True,
+ act=None,
+ norm=None,
+ conv_only=False,
+ )
+
+ def forward(self, inp):
+ """Forward pass of :class:`UnetOutBlock`."""
+ return self.conv(inp)
+
+
+class UnetrBasicBlock(nn.Module):
+ """A CNN module that can be used for UNETR, as presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+
+ .. note::
+ This is a wrapper for monai implementation of UNETR.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/unetr.py
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: Union[Sequence[int], int],
+ stride: Union[Sequence[int], int],
+ norm_name: Union[Tuple, str],
+ res_block: bool = False,
+ ):
+ """Inits :class:`UnetrBasicBlock`.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ Number of spatial dimensions of the input image.
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ kernel_size : Union[Sequence[int], int]
+ Convolution kernel size.
+ stride : Union[Sequence[int], int]
+ Convolution stride.
+ norm_name : Union[Tuple, str]
+ Feature normalization type and arguments.
+ res_block : bool
+ If True, use a residual block.
+ """
+ super().__init__()
+
+ if res_block:
+ self.layer = UnetResBlock(
+ spatial_dims=spatial_dims,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ norm_name=norm_name,
+ )
+ else:
+ self.layer = UnetBasicBlock(
+ spatial_dims=spatial_dims,
+ in_channels=in_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ norm_name=norm_name,
+ )
+
+ def forward(self, inp: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`UnetrBasicBlock`."""
+ return self.layer(inp)
+
+
+class UnetrPrUpBlock(nn.Module):
+ """A projection upsampling module that can be used for UNETR, as presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+
+ .. note::
+ This is a wrapper for monai implementation of UNETR.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/unetr.py
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ num_layer: int,
+ kernel_size: Union[Sequence[int], int],
+ stride: Union[Sequence[int], int],
+ upsample_kernel_size: Union[Sequence[int], int],
+ norm_name: Union[Tuple, str],
+ conv_block: bool = False,
+ res_block: bool = False,
+ ):
+ """Inits :class:`UnetrPrUpBlock`.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ Number of spatial dimensions of the input image.
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ num_layer : int
+ Number of layers.
+ kernel_size : Union[Sequence[int], int]
+ Convolution kernel size.
+ stride : Union[Sequence[int], int]
+ Convolution stride.
+ upsample_kernel_size : Union[Sequence[int], int]
+ Upsampling kernel size.
+ norm_name : Union[Tuple, str]
+ Feature normalization type and arguments.
+ conv_block : bool
+ If True, use a convolution block.
+ res_block : bool
+ If True, use a residual block.
+ """
+ super().__init__()
+
+ upsample_stride = upsample_kernel_size
+ self.transp_conv_init = get_conv_layer(
+ spatial_dims,
+ in_channels,
+ out_channels,
+ kernel_size=upsample_kernel_size,
+ stride=upsample_stride,
+ conv_only=True,
+ is_transposed=True,
+ )
+ if conv_block:
+ if res_block:
+ self.blocks = nn.ModuleList(
+ [
+ nn.Sequential(
+ get_conv_layer(
+ spatial_dims,
+ out_channels,
+ out_channels,
+ kernel_size=upsample_kernel_size,
+ stride=upsample_stride,
+ conv_only=True,
+ is_transposed=True,
+ ),
+ UnetResBlock(
+ spatial_dims=spatial_dims,
+ in_channels=out_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ norm_name=norm_name,
+ ),
+ )
+ for i in range(num_layer)
+ ]
+ )
+ else:
+ self.blocks = nn.ModuleList(
+ [
+ nn.Sequential(
+ get_conv_layer(
+ spatial_dims,
+ out_channels,
+ out_channels,
+ kernel_size=upsample_kernel_size,
+ stride=upsample_stride,
+ conv_only=True,
+ is_transposed=True,
+ ),
+ UnetBasicBlock(
+ spatial_dims=spatial_dims,
+ in_channels=out_channels,
+ out_channels=out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ norm_name=norm_name,
+ ),
+ )
+ for i in range(num_layer)
+ ]
+ )
+ else:
+ self.blocks = nn.ModuleList(
+ [
+ get_conv_layer(
+ spatial_dims,
+ out_channels,
+ out_channels,
+ kernel_size=upsample_kernel_size,
+ stride=upsample_stride,
+ conv_only=True,
+ is_transposed=True,
+ )
+ for i in range(num_layer)
+ ]
+ )
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`UnetrPrUpBlock`."""
+ x = self.transp_conv_init(x)
+ for blk in self.blocks:
+ x = blk(x)
+ return x
+
+
+class UnetrUpBlock(nn.Module):
+ """An upsampling module that can be used for UNETR, as presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+
+ .. note::
+ This is a wrapper for monai implementation of UNETR.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/unetr.py
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: Union[Sequence[int], int],
+ upsample_kernel_size: Union[Sequence[int], int],
+ norm_name: Union[Tuple, str],
+ res_block: bool = False,
+ ):
+ """Inits :class:`UnetrUpBlock`.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ Number of spatial dimensions of the input image.
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ kernel_size : Union[Sequence[int], int]
+ Convolution kernel size.
+ upsample_kernel_size : Union[Sequence[int], int]
+ Upsampling kernel size.
+ norm_name : Union[Tuple, str]
+ Feature normalization type and arguments.
+ res_block : bool
+ If True, use a residual block.
+ """
+ super().__init__()
+ upsample_stride = upsample_kernel_size
+ self.transp_conv = get_conv_layer(
+ spatial_dims,
+ in_channels,
+ out_channels,
+ kernel_size=upsample_kernel_size,
+ stride=upsample_stride,
+ conv_only=True,
+ is_transposed=True,
+ )
+
+ if res_block:
+ self.conv_block = UnetResBlock(
+ spatial_dims,
+ out_channels + out_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=1,
+ norm_name=norm_name,
+ )
+ else:
+ self.conv_block = UnetBasicBlock(
+ spatial_dims,
+ out_channels + out_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=1,
+ norm_name=norm_name,
+ )
+
+ def forward(self, inp: torch.Tensor, skip: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`UnetrUpBlock`."""
+ # number of channels for skip should equals to out_channels
+ return self.conv_block(torch.cat((self.transp_conv(inp), skip), dim=1))
+
+
+class UnetResBlock(nn.Module):
+ """A skip-connection based module for UNETR, as presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: Union[Sequence[int], int],
+ stride: Union[Sequence[int], int],
+ norm_name: Union[Tuple, str], # pylint: disable=unused-argument
+ act_name: Union[Tuple, str] = ( # pylint: disable=unused-argument
+ "leakyrelu",
+ {"inplace": True, "negative_slope": 0.01},
+ ),
+ dropout: Optional[Union[Tuple, str, float]] = None,
+ ):
+ """Inits :class:`UnetResBlock`.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ Number of spatial dimensions of the input image.
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ kernel_size : Union[Sequence[int], int]
+ Convolution kernel size.
+ stride : Union[Sequence[int], int]
+ Convolution stride.
+ norm_name : Union[Tuple, str]
+ Feature normalization type and arguments.
+ act_name : Union[Tuple, str]
+ Activation function type and arguments.
+ dropout : Optional[Union[Tuple, str, float]]
+ Dropout rate.
+ """
+ super().__init__()
+ self.conv1 = get_conv_layer(
+ spatial_dims,
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dropout=dropout,
+ act=None,
+ norm=None,
+ conv_only=False,
+ )
+ self.conv2 = get_conv_layer(
+ spatial_dims,
+ out_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=1,
+ dropout=dropout,
+ act=None,
+ norm=None,
+ conv_only=False,
+ )
+ self.lrelu = nn.LeakyReLU(negative_slope=0.01, inplace=True)
+ if spatial_dims == 2:
+ self.norm1 = nn.InstanceNorm2d(out_channels)
+ self.norm2 = nn.InstanceNorm2d(out_channels)
+ self.norm3 = nn.InstanceNorm2d(out_channels)
+ elif spatial_dims == 3:
+ self.norm1 = nn.InstanceNorm3d(out_channels)
+ self.norm2 = nn.InstanceNorm3d(out_channels)
+ self.norm3 = nn.InstanceNorm3d(out_channels)
+ self.downsample = in_channels != out_channels
+ stride_np = np.atleast_1d(stride)
+ if not np.all(stride_np == 1):
+ self.downsample = True
+ if self.downsample:
+ self.conv3 = get_conv_layer(
+ spatial_dims,
+ in_channels,
+ out_channels,
+ kernel_size=1,
+ stride=stride,
+ dropout=dropout,
+ act=None,
+ norm=None,
+ conv_only=False,
+ )
+
+ def forward(self, inp: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`UnetResBlock`."""
+ residual = inp
+ out = self.conv1(inp)
+ out = self.norm1(out)
+ out = self.lrelu(out)
+ out = self.conv2(out)
+ out = self.norm2(out)
+ if hasattr(self, "conv3"):
+ residual = self.conv3(residual)
+ if hasattr(self, "norm3"):
+ residual = self.norm3(residual)
+ out = out + residual
+ out = self.lrelu(out)
+ return out
+
+
+class UnetUpBlock(nn.Module):
+ """An upsampling module that can be used for UNETR, as presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+
+ .. note::
+ This is a wrapper for monai implementation of UNETR.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/unetr.py
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: Union[Sequence[int], int],
+ stride: Union[Sequence[int], int], # pylint: disable=unused-argument
+ upsample_kernel_size: Union[Sequence[int], int],
+ norm_name: Union[Tuple, str],
+ act_name: Union[Tuple, str] = ("leakyrelu", {"inplace": True, "negative_slope": 0.01}),
+ dropout: Optional[Union[Tuple, str, float]] = None,
+ trans_bias: bool = False,
+ ):
+ """Inits :class:`UnetUpBlock`.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ Number of spatial dimensions of the input image.
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ kernel_size : Union[Sequence[int], int]
+ Convolution kernel size.
+ stride : Union[Sequence[int], int]
+ Convolution stride.
+ upsample_kernel_size : Union[Sequence[int], int]
+ Upsampling kernel size.
+ norm_name : Union[Tuple, str]
+ Feature normalization type and arguments.
+ act_name : Union[Tuple, str]
+ Activation function type and arguments.
+ dropout : Optional[Union[Tuple, str, float]]
+ Dropout rate.
+ trans_bias : bool
+ Whether to use bias in the transposed convolution layer. Default is ``False``.
+ """
+ super().__init__()
+ upsample_stride = upsample_kernel_size
+ self.transp_conv = get_conv_layer(
+ spatial_dims,
+ in_channels,
+ out_channels,
+ kernel_size=upsample_kernel_size,
+ stride=upsample_stride,
+ dropout=dropout,
+ bias=trans_bias,
+ act=None,
+ norm=None,
+ conv_only=False,
+ is_transposed=True,
+ )
+ self.conv_block = UnetBasicBlock(
+ spatial_dims,
+ out_channels + out_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=1,
+ dropout=dropout,
+ norm_name=norm_name,
+ act_name=act_name,
+ )
+
+ def forward(self, inp: torch.Tensor, skip: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`UnetUpBlock`."""
+ # number of channels for skip should equal to out_channels
+ return self.conv_block(torch.cat((self.transp_conv(inp), skip), dim=1))
+
+
+class UnetBasicBlock(nn.Module):
+ """A CNN module that can be used for UNETR, as presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+
+ .. note::
+ This is a wrapper for monai implementation of UNETR.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/unetr.py
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: Union[Sequence[int], int],
+ stride: Union[Sequence[int], int],
+ norm_name: Union[Tuple, str], # pylint: disable=unused-argument
+ act_name: Union[Tuple, str] = ( # pylint: disable=unused-argument
+ "leakyrelu",
+ {"inplace": True, "negative_slope": 0.01},
+ ),
+ dropout: Optional[Union[Tuple, str, float]] = None,
+ ):
+ """Inits :class:`UnetBasicBlock`.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ Number of spatial dimensions of the input image.
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ kernel_size : Union[Sequence[int], int]
+ Convolution kernel size.
+ stride : Union[Sequence[int], int]
+ Convolution stride.
+ norm_name : Union[Tuple, str]
+ Feature normalization type and arguments.
+ act_name : Union[Tuple, str]
+ Activation function type and arguments.
+ dropout : Optional[Union[Tuple, str, float]]
+ Dropout rate.
+ """
+ super().__init__()
+ self.conv1 = get_conv_layer(
+ spatial_dims,
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=stride,
+ dropout=dropout,
+ act=None,
+ norm=None,
+ conv_only=False,
+ )
+ self.conv2 = get_conv_layer(
+ spatial_dims,
+ out_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=1,
+ dropout=dropout,
+ act=None,
+ norm=None,
+ conv_only=False,
+ )
+ self.lrelu = nn.LeakyReLU(negative_slope=0.01, inplace=True)
+ if spatial_dims == 2:
+ self.norm1 = nn.InstanceNorm2d(out_channels)
+ self.norm2 = nn.InstanceNorm2d(out_channels)
+ elif spatial_dims == 3:
+ self.norm1 = nn.InstanceNorm3d(out_channels)
+ self.norm2 = nn.InstanceNorm3d(out_channels)
+
+ def forward(self, inp: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`UnetBasicBlock`."""
+ return self.lrelu(self.norm2(self.conv2(self.lrelu(self.norm1(self.conv1(inp))))))
+
+
+class UNETR(nn.Module):
+ """UNETR as presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+
+ .. note::
+ This is a wrapper for monai implementation of UNETR.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/unetr.py
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ img_size: Union[Sequence[int], int],
+ feature_size: int = 16,
+ hidden_size: int = 768,
+ mlp_dim: int = 3072,
+ num_heads: int = 12,
+ pos_embed: str = "conv",
+ norm_name: Union[Tuple, str] = "instance",
+ conv_block: bool = True,
+ res_block: bool = True,
+ dropout_rate: float = 0.0,
+ spatial_dims: int = 3,
+ qkv_bias: bool = False,
+ ):
+ """Inits :class:`UNETR`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ img_size : Union[Sequence[int], int]
+ Dimension of input image.
+ feature_size : int
+ Dimension of network feature size. Default is ``16``.
+ hidden_size : int
+ Dimension of network hidden size. Default is ``768``.
+ mlp_dim : int
+ Dimension of network mlp size. Default is ``3072``.
+ num_heads : int
+ Number of attention heads. Default is ``12``.
+ pos_embed : str
+ Positional embedding type. Default is ``"conv"``.
+ norm_name : Union[Tuple, str]
+ Feature normalization type and arguments. Default is ``"instance"``.
+ conv_block : bool
+ Whether to use convolutional block. Default is ``True``.
+ res_block : bool
+ Whether to use residual block. Default is ``True``.
+ dropout_rate : float
+ Dropout rate. Default is ``0.0``.
+ spatial_dims : int
+ Number of spatial dimensions of the input image. Default is ``3``.
+ qkv_bias : bool
+ Whether to use bias for qkv. Default is ``False``.
+ """
+ super().__init__()
+ if not 0 <= dropout_rate <= 1:
+ raise ValueError("dropout_rate should be between 0 and 1.")
+ if hidden_size % num_heads != 0:
+ raise ValueError("hidden_size should be divisible by num_heads.")
+
+ self.num_layers = 12
+ self.patch_size = (16,) * spatial_dims
+ self.feat_size = tuple(img_d // p_d for img_d, p_d in zip(img_size, self.patch_size)) # type: ignore
+ self.hidden_size = hidden_size
+ self.classification = False
+ self.vit = ViT(
+ in_channels=in_channels,
+ img_size=img_size,
+ patch_size=self.patch_size,
+ hidden_size=hidden_size,
+ mlp_dim=mlp_dim,
+ num_layers=self.num_layers,
+ num_heads=num_heads,
+ pos_embed=pos_embed,
+ classification=self.classification,
+ dropout_rate=dropout_rate,
+ spatial_dims=spatial_dims,
+ qkv_bias=qkv_bias,
+ )
+ self.encoder1 = UnetrBasicBlock(
+ spatial_dims=spatial_dims,
+ in_channels=in_channels,
+ out_channels=feature_size,
+ kernel_size=3,
+ stride=1,
+ norm_name=norm_name,
+ res_block=res_block,
+ )
+ self.encoder2 = UnetrPrUpBlock(
+ spatial_dims=spatial_dims,
+ in_channels=hidden_size,
+ out_channels=feature_size * 2,
+ num_layer=2,
+ kernel_size=3,
+ stride=1,
+ upsample_kernel_size=2,
+ norm_name=norm_name,
+ conv_block=conv_block,
+ res_block=res_block,
+ )
+ self.encoder3 = UnetrPrUpBlock(
+ spatial_dims=spatial_dims,
+ in_channels=hidden_size,
+ out_channels=feature_size * 4,
+ num_layer=1,
+ kernel_size=3,
+ stride=1,
+ upsample_kernel_size=2,
+ norm_name=norm_name,
+ conv_block=conv_block,
+ res_block=res_block,
+ )
+ self.encoder4 = UnetrPrUpBlock(
+ spatial_dims=spatial_dims,
+ in_channels=hidden_size,
+ out_channels=feature_size * 8,
+ num_layer=0,
+ kernel_size=3,
+ stride=1,
+ upsample_kernel_size=2,
+ norm_name=norm_name,
+ conv_block=conv_block,
+ res_block=res_block,
+ )
+ self.decoder5 = UnetrUpBlock(
+ spatial_dims=spatial_dims,
+ in_channels=hidden_size,
+ out_channels=feature_size * 8,
+ kernel_size=3,
+ upsample_kernel_size=2,
+ norm_name=norm_name,
+ res_block=res_block,
+ )
+ self.decoder4 = UnetrUpBlock(
+ spatial_dims=spatial_dims,
+ in_channels=feature_size * 8,
+ out_channels=feature_size * 4,
+ kernel_size=3,
+ upsample_kernel_size=2,
+ norm_name=norm_name,
+ res_block=res_block,
+ )
+ self.decoder3 = UnetrUpBlock(
+ spatial_dims=spatial_dims,
+ in_channels=feature_size * 4,
+ out_channels=feature_size * 2,
+ kernel_size=3,
+ upsample_kernel_size=2,
+ norm_name=norm_name,
+ res_block=res_block,
+ )
+ self.decoder2 = UnetrUpBlock(
+ spatial_dims=spatial_dims,
+ in_channels=feature_size * 2,
+ out_channels=feature_size,
+ kernel_size=3,
+ upsample_kernel_size=2,
+ norm_name=norm_name,
+ res_block=res_block,
+ )
+ self.out = UnetOutBlock(spatial_dims=spatial_dims, in_channels=feature_size, out_channels=out_channels)
+ self.proj_axes = (0, spatial_dims + 1) + tuple(d + 1 for d in range(spatial_dims))
+ self.proj_view_shape = list(self.feat_size) + [self.hidden_size]
+
+ def proj_feat(self, x: torch.Tensor) -> torch.Tensor:
+ """Project the feature map to the hidden size."""
+ new_view = [x.size(0)] + self.proj_view_shape
+ x = x.view(new_view)
+ x = x.permute(self.proj_axes).contiguous()
+ return x
+
+ def forward(self, x_in: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`UNETR`."""
+ x, hidden_states_out = self.vit(x_in)
+ enc1 = self.encoder1(x_in)
+ x2 = hidden_states_out[3]
+ enc2 = self.encoder2(self.proj_feat(x2))
+ x3 = hidden_states_out[6]
+ enc3 = self.encoder3(self.proj_feat(x3))
+ x4 = hidden_states_out[9]
+ enc4 = self.encoder4(self.proj_feat(x4))
+ dec4 = self.proj_feat(x)
+ dec3 = self.decoder5(dec4, enc4)
+ dec2 = self.decoder4(dec3, enc3)
+ dec1 = self.decoder3(dec2, enc2)
+ out = self.decoder2(dec1, enc1)
+ return self.out(out)
diff --git a/atommic/collections/segmentation/nn/vit_base/__init__.py b/atommic/collections/segmentation/nn/vit_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/segmentation/nn/vit_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/segmentation/nn/vit_base/patchembedding.py b/atommic/collections/segmentation/nn/vit_base/patchembedding.py
new file mode 100644
index 00000000..86f8d6d2
--- /dev/null
+++ b/atommic/collections/segmentation/nn/vit_base/patchembedding.py
@@ -0,0 +1,231 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from:
+# https://github.com/Project-MONAI/MONAI/blob/c38d503a587f1779914bd071a1b2d66a6d9080c2/monai/networks/blocks/patchembedding.py#L28
+
+from typing import Sequence, Type, Union
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from einops.layers.torch import Rearrange
+from torch import nn
+
+from atommic.collections.segmentation.nn.vit_base.utils import trunc_normal_
+
+SUPPORTED_EMBEDDING_TYPES = {"conv", "perceptron"}
+
+
+class PatchEmbeddingBlock(nn.Module):
+ """Implementation of a patch embedding block, as presented in [Dosovitskiy2020]_.
+
+ References
+ ----------
+ .. [Dosovitskiy2020] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M,
+ Minderer M, Heigold G, Gelly S, Uszkoreit J. An image is worth 16x16 words: Transformers for image recognition
+ at scale. arXiv preprint arXiv:2010.11929. 2020 Oct 22.
+
+ .. note::
+ This is a wrapper for monai implementation of PatchEmbeddingBlock. See: https://github.com/Project-MONAI/MONAI/
+ blob/c38d503a587f1779914bd071a1b2d66a6d9080c2/monai/networks/blocks/patchembedding.py#L28
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ img_size: Union[Sequence[int], int],
+ patch_size: Union[Sequence[int], int],
+ hidden_size: int,
+ num_heads: int,
+ pos_embed: str,
+ dropout_rate: float = 0.0,
+ spatial_dims: int = 3,
+ ):
+ """Inits :class:`PatchEmbeddingBlock`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Dimension of input channels.
+ img_size : Union[Sequence[int], int]
+ Dimension of input image.
+ patch_size : Union[Sequence[int], int]
+ Dimension of patch size.
+ hidden_size : int
+ Dimension of hidden layer.
+ num_heads : int
+ Number of attention heads.
+ pos_embed : str
+ Position embedding layer type.
+ dropout_rate : float, optional
+ Faction of the input units to drop. Default is ``0.0``.
+ spatial_dims : int, optional
+ Number of spatial dimensions. Default is ``3``.
+ """
+ super().__init__()
+ if not 0 <= dropout_rate <= 1:
+ raise ValueError("dropout_rate should be between 0 and 1.")
+ if hidden_size % num_heads != 0:
+ raise ValueError("hidden size should be divisible by num_heads.")
+
+ self.pos_embed = pos_embed
+
+ for m, p in zip(img_size, patch_size): # type: ignore
+ m = list(m) if isinstance(m, tuple) else [m] # type: ignore
+ p = list(p) if isinstance(p, tuple) else [p] # type: ignore
+ m = np.array(m)
+ p = np.array(p)
+ if m < p:
+ raise ValueError("patch_size should be smaller than img_size.")
+ if self.pos_embed == "perceptron" and m % p != 0:
+ raise ValueError("patch_size should be divisible by img_size for perceptron.")
+
+ self.n_patches = np.prod([im_d // p_d for im_d, p_d in zip(img_size, patch_size)]) # type: ignore
+ self.patch_dim = int(in_channels * np.prod(patch_size))
+
+ self.patch_embeddings: nn.Module
+ if self.pos_embed == "conv":
+ if spatial_dims == 2:
+ self.patch_embeddings = nn.Conv2d(
+ in_channels=in_channels,
+ out_channels=hidden_size,
+ kernel_size=patch_size,
+ stride=patch_size,
+ )
+ elif spatial_dims == 3:
+ self.patch_embeddings = nn.Conv3d(
+ in_channels=in_channels,
+ out_channels=hidden_size,
+ kernel_size=patch_size,
+ stride=patch_size,
+ )
+ else:
+ raise ValueError(f"Convolutional patch embedding not supported for {spatial_dims}D.")
+ elif self.pos_embed == "perceptron":
+ # for 3d: "b c (h p1) (w p2) (d p3)-> b (h w d) (p1 p2 p3 c)"
+ chars = (("h", "p1"), ("w", "p2"), ("d", "p3"))[:spatial_dims]
+ from_chars = "b c " + " ".join(f"({k} {v})" for k, v in chars)
+ to_chars = f"b ({' '.join([c[0] for c in chars])}) ({' '.join([c[1] for c in chars])} c)"
+ axes_len = {f"p{i + 1}": p for i, p in enumerate(patch_size)} # type: ignore
+ self.patch_embeddings = nn.Sequential(
+ Rearrange(f"{from_chars} -> {to_chars}", **axes_len),
+ nn.Linear(self.patch_dim, hidden_size),
+ )
+ self.position_embeddings = nn.Parameter(torch.zeros(1, self.n_patches, hidden_size))
+ self.dropout = nn.Dropout(dropout_rate)
+ trunc_normal_(self.position_embeddings, mean=0.0, std=0.02, a=-2.0, b=2.0)
+ self.apply(self._init_weights)
+
+ @staticmethod
+ def _init_weights(m: nn.Module):
+ """Initialize the weights of the module."""
+ if isinstance(m, nn.Linear):
+ trunc_normal_(m.weight, mean=0.0, std=0.02, a=-2.0, b=2.0)
+ if isinstance(m, nn.Linear) and m.bias is not None:
+ nn.init.constant_(m.bias, 0)
+ elif isinstance(m, nn.LayerNorm):
+ nn.init.constant_(m.bias, 0)
+ nn.init.constant_(m.weight, 1.0)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`PatchEmbeddingBlock`."""
+ x = self.patch_embeddings(x)
+ if self.pos_embed == "conv":
+ x = x.flatten(2).transpose(-1, -2)
+ embeddings = x + self.position_embeddings
+ embeddings = self.dropout(embeddings)
+ return embeddings
+
+
+class PatchEmbed(nn.Module):
+ """Implementation of a patch embedding block, as presented in [Liu2021]_.
+
+ Unlike ViT patch embedding block: (1) input is padded to satisfy window size requirements (2) normalized if
+ specified (3) position embedding is not used.
+
+ References
+ ----------
+ .. [Liu2021] Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B. Swin transformer: Hierarchical vision
+ transformer using shifted windows. InProceedings of the IEEE/CVF International Conference on Computer Vision
+ 2021 (pp. 10012-10022).
+
+ .. note::
+ This is a wrapper for monai implementation of PatchEmbeddingBlock. See: https://github.com/Project-MONAI/MONAI/
+ blob/c38d503a587f1779914bd071a1b2d66a6d9080c2/monai/networks/blocks/patchembedding.py#L28
+ """
+
+ def __init__(
+ self,
+ patch_size: Union[Sequence[int], int] = 2,
+ in_chans: int = 1,
+ embed_dim: int = 48,
+ norm_layer: Type[nn.LayerNorm] = nn.InstanceNorm2d, # pylint: disable=unused-argument
+ spatial_dims: int = 3,
+ ):
+ """Inits :class:`PatchEmbed`.
+
+ Parameters
+ ----------
+ patch_size : Union[Sequence[int], int]
+ Dimension of patch size. Default is ``2``.
+ in_chans : int
+ Dimension of input channels. Default is ``1``.
+ embed_dim : int
+ Dimension of embedding. Default is ``48``.
+ norm_layer : Type[nn.LayerNorm]
+ Normalization layer. Default is ``nn.InstanceNorm2d``.
+ spatial_dims : int, optional
+ Number of spatial dimensions. Default is ``3``.
+ """
+ super().__init__()
+ if spatial_dims not in (2, 3):
+ raise ValueError("Spatial dimension should be 2 or 3.")
+
+ patch_size = (patch_size,) * spatial_dims # type: ignore
+ self.patch_size = patch_size
+ self.embed_dim = embed_dim
+ if spatial_dims == 2:
+ self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+ elif spatial_dims == 3:
+ self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)
+ else:
+ raise ValueError(f"Convolutional patch embedding not supported for {spatial_dims}D.")
+ if spatial_dims == 2:
+ self.norm = nn.InstanceNorm2d(embed_dim)
+ elif spatial_dims == 3:
+ self.norm = nn.InstanceNorm3d(embed_dim)
+ else:
+ self.norm = None
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`PatchEmbeddingBlock`."""
+ x_shape = x.size()
+ if len(x_shape) == 5:
+ _, _, d, h, w = x_shape
+ if w % self.patch_size[2] != 0: # type: ignore
+ x = F.pad(x, (0, self.patch_size[2] - w % self.patch_size[2])) # type: ignore
+ if h % self.patch_size[1] != 0: # type: ignore
+ x = F.pad(x, (0, 0, 0, self.patch_size[1] - h % self.patch_size[1])) # type: ignore
+ if d % self.patch_size[0] != 0: # type: ignore
+ x = F.pad(x, (0, 0, 0, 0, 0, self.patch_size[0] - d % self.patch_size[0])) # type: ignore
+
+ elif len(x_shape) == 4:
+ _, _, h, w = x_shape
+ if w % self.patch_size[1] != 0: # type: ignore
+ x = F.pad(x, (0, self.patch_size[1] - w % self.patch_size[1])) # type: ignore
+ if h % self.patch_size[0] != 0: # type: ignore
+ x = F.pad(x, (0, 0, 0, self.patch_size[0] - h % self.patch_size[0])) # type: ignore
+
+ x = self.proj(x)
+ if self.norm is not None:
+ x_shape = x.size()
+ x = x.flatten(2).transpose(1, 2)
+ x = self.norm(x)
+ if len(x_shape) == 5:
+ d, wh, ww = x_shape[2], x_shape[3], x_shape[4]
+ x = x.transpose(1, 2).view(-1, self.embed_dim, d, wh, ww)
+ elif len(x_shape) == 4:
+ wh, ww = x_shape[2], x_shape[3]
+ x = x.transpose(1, 2).view(-1, self.embed_dim, wh, ww)
+ return x
diff --git a/atommic/collections/segmentation/nn/vit_base/transformer_block.py b/atommic/collections/segmentation/nn/vit_base/transformer_block.py
new file mode 100644
index 00000000..5876879c
--- /dev/null
+++ b/atommic/collections/segmentation/nn/vit_base/transformer_block.py
@@ -0,0 +1,193 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from:
+# https://github.com/Project-MONAI/MONAI/blob/c38d503a587f1779914bd071a1b2d66a6d9080c2/monai/networks/blocks/transformerblock.py#L18
+
+from typing import Tuple, Union
+
+import torch
+from einops.layers.torch import Rearrange
+from torch import nn
+
+SUPPORTED_DROPOUT_MODE = {"vit", "swin"}
+
+
+class MLPBlock(nn.Module):
+ """Implementation of a multi-layer perceptron block, as presented in [Dosovitskiy2020]_.
+
+ References
+ ----------
+ .. [Dosovitskiy2020] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M,
+ Minderer M, Heigold G, Gelly S, Uszkoreit J. An image is worth 16x16 words: Transformers for image recognition
+ at scale. arXiv preprint arXiv:2010.11929. 2020 Oct 22.
+
+ .. note::
+ This is a wrapper for monai implementation of a multi-layer perceptron block. See:
+ https://github.com/Project-MONAI/MONAI/blob/c38d503a587f1779914bd071a1b2d66a6d9080c2/monai/networks/blocks/
+ transformerblock.py#L18
+ """
+
+ def __init__(
+ self,
+ hidden_size: int,
+ mlp_dim: int,
+ dropout_rate: float = 0.0,
+ act: Union[Tuple, str] = "GELU", # pylint: disable=unused-argument
+ dropout_mode="vit",
+ ):
+ """Inits :class:`MLPBlock`.
+
+ Parameters
+ ----------
+ hidden_size : int
+ Dimension of hidden layer.
+ mlp_dim : int
+ Dimension of MLP layer.
+ dropout_rate : float, optional
+ Faction of the input units to drop. Default is ``0.0``.
+ act : Union[Tuple, str], optional
+ Activation type and arguments. Default is ``"GELU"``.
+ dropout_mode : str, optional
+ Dropout mode, can be "vit" or "swin". Default is ``vit``.
+ - "vit" mode uses two dropout instances as implemented in
+ https://github.com/google-research/vision_transformer/blob/main/vit_jax/models.py#L87
+ - "swin" corresponds to one instance as implemented in
+ https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_mlp.py#L23
+ """
+ super().__init__()
+ if not 0 <= dropout_rate <= 1:
+ raise ValueError("dropout_rate should be between 0 and 1.")
+ mlp_dim = mlp_dim or hidden_size
+ self.linear1 = nn.Linear(hidden_size, mlp_dim)
+ self.linear2 = nn.Linear(mlp_dim, hidden_size)
+ self.fn = nn.GELU()
+ self.drop1 = nn.Dropout(dropout_rate)
+ if dropout_mode == "vit":
+ self.drop2 = nn.Dropout(dropout_rate)
+ elif dropout_mode == "swin":
+ self.drop2 = self.drop1
+ else:
+ raise ValueError(f"dropout_mode should be one of {SUPPORTED_DROPOUT_MODE}")
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`MLPBlock`."""
+ return self.drop2(self.linear2(self.drop1(self.fn(self.linear1(x)))))
+
+
+class SABlock(nn.Module):
+ """Implementation of a self-attention block, as presented in [Dosovitskiy2020]_.
+
+ References
+ ----------
+ .. [Dosovitskiy2020] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M,
+ Minderer M, Heigold G, Gelly S, Uszkoreit J. An image is worth 16x16 words: Transformers for image recognition
+ at scale. arXiv preprint arXiv:2010.11929. 2020 Oct 22.
+
+ .. note::
+ This is a wrapper for monai implementation of self-attention block. See: https://github.com/Project-MONAI/
+ MONAI/blob/c38d503a587f1779914bd071a1b2d66a6d9080c2/monai/networks/blocks/transformerblock.py#L18
+ """
+
+ def __init__(self, hidden_size: int, num_heads: int, dropout_rate: float = 0.0, qkv_bias: bool = False):
+ """Inits :class:`SABlock`.
+
+ Parameters
+ ----------
+ hidden_size : int
+ Dimension of hidden layer.
+ num_heads : int
+ Number of attention heads.
+ dropout_rate : float, optional
+ Faction of the input units to drop. Default is ``0.0``.
+ qkv_bias : bool, optional
+ Bias term for the qkv linear layer. Default is ``False``.
+ """
+ super().__init__()
+ if not 0 <= dropout_rate <= 1:
+ raise ValueError("dropout_rate should be between 0 and 1.")
+ if hidden_size % num_heads != 0:
+ raise ValueError("hidden size should be divisible by num_heads.")
+ self.num_heads = num_heads
+ self.out_proj = nn.Linear(hidden_size, hidden_size)
+ self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias)
+ self.input_rearrange = Rearrange("b h (qkv l d) -> qkv b l h d", qkv=3, l=num_heads)
+ self.out_rearrange = Rearrange("b h l d -> b l (h d)")
+ self.drop_output = nn.Dropout(dropout_rate)
+ self.drop_weights = nn.Dropout(dropout_rate)
+ self.head_dim = hidden_size // num_heads
+ self.scale = self.head_dim**-0.5
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`SABlock`."""
+ output = self.input_rearrange(self.qkv(x))
+ q, k, v = output[0], output[1], output[2]
+ att_mat = (torch.einsum("blxd,blyd->blxy", q, k) * self.scale).softmax(dim=-1)
+ att_mat = self.drop_weights(att_mat)
+ x = torch.einsum("bhxy,bhyd->bhxd", att_mat, v)
+ x = self.out_rearrange(x)
+ x = self.out_proj(x)
+ x = self.drop_output(x)
+ return x
+
+
+class TransformerBlock(nn.Module):
+ """Implementation of a transformer block, as presented in [Dosovitskiy2020]_.
+
+ References
+ ----------
+ .. [Dosovitskiy2020] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M,
+ Minderer M, Heigold G, Gelly S, Uszkoreit J. An image is worth 16x16 words: Transformers for image recognition
+ at scale. arXiv preprint arXiv:2010.11929. 2020 Oct 22.
+
+ .. note::
+ This is a wrapper for monai implementation of self-attention block. See: https://github.com/Project-MONAI/
+ MONAI/blob/c38d503a587f1779914bd071a1b2d66a6d9080c2/monai/networks/blocks/transformerblock.py#L18
+ """
+
+ def __init__(
+ self,
+ hidden_size: int,
+ mlp_dim: int,
+ num_heads: int,
+ dropout_rate: float = 0.0,
+ qkv_bias: bool = False,
+ spatial_dims: int = 2,
+ ):
+ """Inits :class:`TransformerBlock`.
+
+ Parameters
+ ----------
+ hidden_size : int
+ Dimension of hidden layer.
+ mlp_dim : int
+ Dimension of the mlp layer.
+ num_heads : int
+ Number of attention heads.
+ dropout_rate : float, optional
+ Faction of the input units to drop. Default is ``0.0``.
+ qkv_bias : bool, optional
+ Bias term for the qkv linear layer. Default is ``False``.
+ spatial_dims : int, optional
+ Number of spatial dimensions. Default is ``2``.
+ """
+ super().__init__()
+ if not 0 <= dropout_rate <= 1:
+ raise ValueError("dropout_rate should be between 0 and 1.")
+ if hidden_size % num_heads != 0:
+ raise ValueError("hidden_size should be divisible by num_heads.")
+
+ self.mlp = MLPBlock(hidden_size, mlp_dim, dropout_rate)
+ self.attn = SABlock(hidden_size, num_heads, dropout_rate, qkv_bias)
+ if spatial_dims == 2:
+ self.norm1 = nn.InstanceNorm2d(hidden_size)
+ self.norm2 = nn.InstanceNorm2d(hidden_size)
+ elif spatial_dims == 3:
+ self.norm1 = nn.InstanceNorm3d(hidden_size)
+ self.norm2 = nn.InstanceNorm3d(hidden_size)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`TransformerBlock`."""
+ x = x + self.attn(self.norm1(x))
+ x = x + self.mlp(self.norm2(x))
+ return x
diff --git a/atommic/collections/segmentation/nn/vit_base/utils.py b/atommic/collections/segmentation/nn/vit_base/utils.py
new file mode 100644
index 00000000..45d94597
--- /dev/null
+++ b/atommic/collections/segmentation/nn/vit_base/utils.py
@@ -0,0 +1,393 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from:
+# https://github.com/Project-MONAI/MONAI/blob/c38d503a587f1779914bd071a1b2d66a6d9080c2/monai/networks/layers/weight_init.py#L45
+
+import math
+from typing import Optional, Sequence, Tuple, Union
+
+import numpy as np
+import torch
+from torch import nn
+
+
+class Convolution(nn.Sequential):
+ """Constructs a convolution with normalization, optional dropout, and optional activation layers::
+ -- (Conv|ConvTrans) -- (Norm -- Dropout -- Acti) --
+ if ``conv_only`` set to ``True``::
+ -- (Conv|ConvTrans) --
+
+ .. note::
+ This is a wrapper for monai implementation. See:
+ https://github.com/Project-MONAI/MONAI/blob/c38d503a587f1779914bd071a1b2d66a6d9080c2/monai/networks/layers/\
+ weight_init.py
+ """
+
+ def __init__(
+ self,
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ strides: Union[Sequence[int], int] = 1,
+ kernel_size: Union[Sequence[int], int] = 3,
+ adn_ordering: str = "NDA", # pylint: disable=unused-argument
+ act: Optional[Union[Tuple, str]] = "PRELU", # pylint: disable=unused-argument
+ norm: Optional[Union[Tuple, str]] = "INSTANCE", # pylint: disable=unused-argument
+ dropout: Optional[Union[Tuple, str, float]] = None, # pylint: disable=unused-argument
+ dropout_dim: Optional[int] = 1, # pylint: disable=unused-argument
+ dilation: Union[Sequence[int], int] = 1,
+ groups: int = 1,
+ bias: bool = True,
+ conv_only: bool = False, # pylint: disable=unused-argument
+ is_transposed: bool = False,
+ padding: Optional[Union[Sequence[int], int]] = None,
+ output_padding: Optional[Union[Sequence[int], int]] = None,
+ ):
+ """Inits :class:`Convolution`.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ Number of spatial dimensions.
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ kernel_size : Union[Sequence[int], int]
+ Size of the convolving kernel.
+ stride : Union[Sequence[int], int], optional
+ Stride of the convolution. Default is ``1``.
+ kernel_size : Union[Sequence[int], int]
+ Size of the convolving kernel. Default is ``3``.
+ adn_ordering : str, optional
+ A string representing the ordering of activation, normalization, and dropout. Default is ``"NDA"``.
+ act : Union[Type[nn.Module], Tuple[Type[nn.Module], dict]], optional
+ Activation type and arguments. Default is ``PReLU``.
+ norm : Union[Type[nn.Module], Tuple[Type[nn.Module], dict]], optional
+ Feature normalization type and arguments. Default is ``instance norm``.
+ dropout : float, optional
+ Dropout ratio. Default is ``no dropout``.
+ dropout_dim : int, optional
+ Determine the spatial dimensions of dropout. Default is ``1``. \
+ - When dropout_dim = 1, randomly zeroes some of the elements for each channel.
+ - When dropout_dim = 2, Randomly zeroes out entire channels (a channel is a 2D feature map).
+ - When dropout_dim = 3, Randomly zeroes out entire channels (a channel is a 3D feature map).
+ The value of dropout_dim should be no larger than the value of `spatial_dims`.
+ dilation : int, optional
+ Dilation rate. Default is ``1``.
+ groups : int, optional
+ Controls the connections between inputs and outputs. Default is ``1``.
+ bias : bool, optional
+ Whether to have a bias term. Default is ``True``.
+ conv_only : bool, optional
+ Whether to use the convolutional layer only. Default is ``False``.
+ is_transposed : bool, optional
+ If ``True`` uses ConvTrans instead of Conv. Default is ``False``.
+ padding : Union[Sequence[int], int], optional
+ Controls the amount of implicit zero-paddings on both sides for padding number of points for each
+ dimension. Default is ``None``.
+ output_padding : Union[Sequence[int], int], optional
+ Controls the additional size added to one side of the output shape. Default is ``None``.
+ """
+ super().__init__()
+ self.spatial_dims = spatial_dims
+ self.in_channels = in_channels
+ self.out_channels = out_channels
+ self.is_transposed = is_transposed
+ if padding is None:
+ padding = same_padding(kernel_size, dilation)
+
+ if self.spatial_dims == 1:
+ if is_transposed:
+ conv_type = nn.ConvTranspose1d
+ else:
+ conv_type = nn.Conv1d
+ elif self.spatial_dims == 2:
+ if is_transposed:
+ conv_type = nn.ConvTranspose2d
+ else:
+ conv_type = nn.Conv2d
+ elif self.spatial_dims == 3:
+ if is_transposed:
+ conv_type = nn.ConvTranspose3d
+ else:
+ conv_type = nn.Conv3d
+ else:
+ raise ValueError(f"Unsupported spatial_dims: {self.spatial_dims}")
+
+ conv: nn.Module
+ if is_transposed:
+ if output_padding is None:
+ output_padding = stride_minus_kernel_padding(1, strides)
+ conv = conv_type(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=strides,
+ padding=padding,
+ output_padding=output_padding,
+ groups=groups,
+ bias=bias,
+ dilation=dilation,
+ )
+ else:
+ conv = conv_type(
+ in_channels,
+ out_channels,
+ kernel_size=kernel_size,
+ stride=strides,
+ padding=padding,
+ dilation=dilation,
+ groups=groups,
+ bias=bias,
+ )
+
+ self.add_module("conv", conv)
+
+
+def stride_minus_kernel_padding(
+ kernel_size: Union[Sequence[int], int], stride: Union[Sequence[int], int]
+) -> Union[Tuple[int, ...], int]:
+ """Calculate the output padding for the given kernel size and stride.
+
+ Parameters
+ ----------
+ kernel_size : Union[Sequence[int], int]
+ The kernel size.
+ stride : Union[Sequence[int], int]
+ The stride.
+
+ Returns
+ -------
+ Union[Tuple[int, ...], int]
+ The output padding.
+ """
+ kernel_size_np = np.atleast_1d(kernel_size)
+ stride_np = np.atleast_1d(stride)
+
+ out_padding_np = stride_np - kernel_size_np
+ out_padding = tuple(int(p) for p in out_padding_np)
+
+ return out_padding if len(out_padding) > 1 else out_padding[0]
+
+
+def get_padding(
+ kernel_size: Union[Sequence[int], int], stride: Union[Sequence[int], int]
+) -> Union[Tuple[int, ...], int]:
+ """Calculate the padding for the given kernel size and stride.
+
+ Parameters
+ ----------
+ kernel_size : Union[Sequence[int], int]
+ The kernel size.
+ stride : Union[Sequence[int], int]
+ The stride.
+
+ Returns
+ -------
+ Union[Tuple[int, ...], int]
+ The padding.
+ """
+ kernel_size_np = np.atleast_1d(kernel_size)
+ stride_np = np.atleast_1d(stride)
+ padding_np = (kernel_size_np - stride_np + 1) / 2
+ if np.min(padding_np) < 0:
+ raise AssertionError("padding value should not be negative, please change the kernel size and/or stride.")
+ padding = tuple(int(p) for p in padding_np)
+
+ return padding if len(padding) > 1 else padding[0]
+
+
+def get_output_padding(
+ kernel_size: Union[Sequence[int], int], stride: Union[Sequence[int], int], padding: Union[Sequence[int], int]
+) -> Union[Tuple[int, ...], int]:
+ """Calculate the output padding for the given kernel size, stride and padding.
+
+ Parameters
+ ----------
+ kernel_size : Union[Sequence[int], int]
+ The kernel size.
+ stride : Union[Sequence[int], int]
+ The stride.
+
+ Returns
+ -------
+ Union[Tuple[int, ...], int]
+ The output padding.
+ """
+ kernel_size_np = np.atleast_1d(kernel_size)
+ stride_np = np.atleast_1d(stride)
+ padding_np = np.atleast_1d(padding)
+
+ out_padding_np = 2 * padding_np + stride_np - kernel_size_np
+ if np.min(out_padding_np) < 0:
+ raise AssertionError("out_padding value should not be negative, please change the kernel size and/or stride.")
+ out_padding = tuple(int(p) for p in out_padding_np)
+
+ return out_padding if len(out_padding) > 1 else out_padding[0]
+
+
+def same_padding(
+ kernel_size: Union[Sequence[int], int], dilation: Union[Sequence[int], int] = 1
+) -> Union[Tuple[int, ...], int]:
+ """Return the padding value needed to ensure a convolution using the given kernel size produces an output of the
+ same shape as the input for a stride of 1, otherwise ensure a shape of the input divided by the stride rounded
+ down.
+
+ Raises
+ ------
+ NotImplementedError: When ``np.any((kernel_size - 1) * dilation % 2 == 1)``.
+
+ Parameters
+ ----------
+ kernel_size : Union[Sequence[int], int]
+ The kernel size.
+ dilation : Union[Sequence[int], int]
+ The dilation.
+
+ Returns
+ -------
+ Union[Tuple[int, ...], int]
+ The padding.
+ """
+ kernel_size_np = np.atleast_1d(kernel_size)
+ dilation_np = np.atleast_1d(dilation)
+
+ if np.any((kernel_size_np - 1) * dilation % 2 == 1):
+ raise NotImplementedError(
+ f"Same padding not available for kernel_size={kernel_size_np} and dilation={dilation_np}."
+ )
+
+ padding_np = (kernel_size_np - 1) / 2 * dilation_np
+ padding = tuple(int(p) for p in padding_np)
+
+ return padding if len(padding) > 1 else padding[0]
+
+
+def get_conv_layer(
+ spatial_dims: int,
+ in_channels: int,
+ out_channels: int,
+ kernel_size: Union[Sequence[int], int] = 3,
+ stride: Union[Sequence[int], int] = 1,
+ act: Optional[Union[Tuple, str]] = nn.PReLU,
+ norm: Optional[Union[Tuple, str]] = nn.InstanceNorm2d,
+ dropout: Optional[Union[Tuple, str, float]] = None,
+ bias: bool = False,
+ conv_only: bool = True,
+ is_transposed: bool = False,
+) -> Convolution:
+ """Get a convolution layer with the given parameters.
+
+ Parameters
+ ----------
+ spatial_dims : int
+ The number of spatial dimensions.
+ in_channels : int
+ The number of input channels.
+ out_channels : int
+ The number of output channels.
+ kernel_size : Union[Sequence[int], int]
+ The kernel size. Default is ``3``.
+ stride : Union[Sequence[int], int]
+ The stride. Default is ``1``.
+ act : Optional[Union[Tuple, str]]
+ The activation function. Default is ``nn.PReLU``.
+ norm : Optional[Union[Tuple, str]]
+ The normalization layer. Default is ``nn.InstanceNorm2d``.
+ dropout : Optional[Union[Tuple, str, float]]
+ The dropout layer. Default is ``None``.
+ bias : bool
+ Whether to add a bias. Default is ``False``.
+ conv_only : bool
+ Whether to only return the convolution layer. Default is ``True``.
+ is_transposed : bool
+ Whether to use a transposed convolution. Default is ``False``.
+
+ Returns
+ -------
+ Convolution
+ The convolution layer.
+ """
+ padding = get_padding(kernel_size, stride)
+ output_padding = None
+ if is_transposed:
+ output_padding = get_output_padding(kernel_size, stride, padding)
+ return Convolution(
+ spatial_dims,
+ in_channels,
+ out_channels,
+ strides=stride,
+ kernel_size=kernel_size,
+ act=act,
+ norm=norm,
+ dropout=dropout,
+ bias=bias,
+ conv_only=conv_only,
+ is_transposed=is_transposed,
+ padding=padding,
+ output_padding=output_padding,
+ )
+
+
+def _no_grad_trunc_normal_(tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0):
+ """Tensor initialization with truncated normal distribution.
+
+ Based on:
+ https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
+ https://github.com/rwightman/pytorch-image-models
+
+ Parameters
+ ----------
+ tensor : torch.Tensor
+ The tensor to initialize.
+ mean : float
+ The mean of the normal distribution. Default is ``0.0``.
+ std : float
+ The standard deviation of the normal distribution. Default is ``1.0``.
+ a : float
+ The lower bound of the truncated normal distribution. Default is ``-2.0``.
+ b : float
+ The upper bound of the truncated normal distribution. Default is ``2.0``.
+ """
+
+ def norm_cdf(x: float) -> float:
+ """Normal cumulative distribution function."""
+ return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0
+
+ with torch.no_grad():
+ tensor.uniform_(2 * norm_cdf((a - mean) / std) - 1, 2 * norm_cdf((b - mean) / std) - 1)
+ tensor.erfinv_()
+ tensor.mul_(std * math.sqrt(2.0))
+ tensor.add_(mean)
+ tensor.clamp_(min=a, max=b)
+ return tensor
+
+
+def trunc_normal_(
+ tensor: torch.Tensor, mean: float = 0.0, std: float = 1.0, a: float = -2.0, b: float = 2.0
+) -> torch.Tensor:
+ """Tensor initialization with truncated normal distribution.
+
+ Based on:
+ https://github.com/rwightman/pytorch-image-models
+
+ Parameters
+ ----------
+ tensor : torch.Tensor
+ The tensor to initialize.
+ mean : float
+ The mean of the normal distribution. Default is ``0.0``.
+ std : float
+ The standard deviation of the normal distribution. Default is ``1.0``.
+ a : float
+ The lower bound of the truncated normal distribution. Default is ``-2.0``.
+ b : float
+ The upper bound of the truncated normal distribution. Default is ``2.0``.
+ """
+ if std <= 0:
+ raise ValueError("the standard deviation should be greater than zero.")
+ if a >= b:
+ raise ValueError("minimum cutoff value (a) should be smaller than maximum cutoff value (b).")
+ return _no_grad_trunc_normal_(tensor, mean, std, a, b)
diff --git a/atommic/collections/segmentation/nn/vit_base/vit_block.py b/atommic/collections/segmentation/nn/vit_base/vit_block.py
new file mode 100644
index 00000000..1682b9cb
--- /dev/null
+++ b/atommic/collections/segmentation/nn/vit_base/vit_block.py
@@ -0,0 +1,124 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/vit.py
+
+from typing import Sequence, Union
+
+import torch
+from torch import nn
+
+from atommic.collections.segmentation.nn.vit_base.patchembedding import PatchEmbeddingBlock
+from atommic.collections.segmentation.nn.vit_base.transformer_block import TransformerBlock
+
+__all__ = ["ViT"]
+
+
+class ViT(nn.Module):
+ """Implementation of a Vision Transformer (ViT), as presented in [Dosovitskiy2020]_.
+
+ ViT supports Torchscript but only works for Pytorch after 1.8.
+
+ References
+ ----------
+ .. [Dosovitskiy2020] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M,
+ Minderer M, Heigold G, Gelly S, Uszkoreit J. An image is worth 16x16 words: Transformers for image recognition
+ at scale. arXiv preprint arXiv:2010.11929. 2020 Oct 22.
+
+ .. note::
+ This is a wrapper for monai implementation of PatchEmbeddingBlock.
+ See: https://github.com/Project-MONAI/MONAI/blob/dev/monai/networks/nets/vit.py
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ img_size: Union[Sequence[int], int],
+ patch_size: Union[Sequence[int], int],
+ hidden_size: int = 768,
+ mlp_dim: int = 3072,
+ num_layers: int = 12,
+ num_heads: int = 12,
+ pos_embed: str = "conv",
+ classification: bool = False,
+ num_classes: int = 2,
+ dropout_rate: float = 0.0,
+ spatial_dims: int = 3,
+ post_activation="Tanh",
+ qkv_bias: bool = False,
+ ):
+ """Inits :class:`ViT`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Dimension of input channels.
+ img_size : Union[Sequence[int], int]
+ Dimension of input image.
+ patch_size : Union[Sequence[int], int]
+ Dimension of patch size.
+ hidden_size : int
+ Dimension of hidden layer. Default is ``768``.
+ mlp_dim : int
+ Dimension of MLP layer. Default is ``3072``.
+ num_layers : int
+ Number of transformer layers. Default is ``12``.
+ num_heads : int
+ Number of attention heads. Default is ``12``.
+ pos_embed : str
+ Position embedding layer type. Default is ``"conv"``.
+ classification : bool
+ Whether to add a classification head. Default is ``False``.
+ dropout_rate : float, optional
+ Faction of the input units to drop. Default is ``0.0``.
+ spatial_dims : int, optional
+ Number of spatial dimensions. Default is ``3``.
+ post_activation : str, optional
+ Post activation layer type. Default is ``"Tanh"``.
+ qkv_bias : bool, optional
+ Whether to add a bias to query, key, value. Default is ``False``.
+ """
+ super().__init__()
+
+ if not 0 <= dropout_rate <= 1:
+ raise ValueError("dropout_rate should be between 0 and 1.")
+
+ if hidden_size % num_heads != 0:
+ raise ValueError("hidden_size should be divisible by num_heads.")
+
+ self.classification = classification
+ self.patch_embedding = PatchEmbeddingBlock(
+ in_channels=in_channels,
+ img_size=img_size,
+ patch_size=patch_size,
+ hidden_size=hidden_size,
+ num_heads=num_heads,
+ pos_embed=pos_embed,
+ dropout_rate=dropout_rate,
+ spatial_dims=spatial_dims,
+ )
+ self.blocks = nn.ModuleList(
+ [TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate, qkv_bias) for i in range(num_layers)]
+ )
+ self.norm = nn.LayerNorm(hidden_size)
+ if self.classification:
+ self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size))
+ if post_activation == "Tanh":
+ self.classification_head = nn.Sequential(nn.Linear(hidden_size, num_classes), nn.Tanh())
+ else:
+ self.classification_head = nn.Linear(hidden_size, num_classes)
+
+ def forward(self, x: torch.Tensor) -> Union[torch.Tensor, Sequence[torch.Tensor]]:
+ """Forward pass of :class:`ViT`."""
+ x = self.patch_embedding(x)
+ if hasattr(self, "cls_token"):
+ cls_token = self.cls_token.expand(x.shape[0], -1, -1)
+ x = torch.cat((cls_token, x), dim=1)
+ hidden_states_out = []
+ for blk in self.blocks:
+ x = blk(x)
+ hidden_states_out.append(x)
+ x = self.norm(x)
+ if hasattr(self, "classification_head"):
+ x = self.classification_head(x[:, 0])
+ return x, hidden_states_out
diff --git a/atommic/collections/segmentation/nn/vnet.py b/atommic/collections/segmentation/nn/vnet.py
new file mode 100644
index 00000000..25d6a408
--- /dev/null
+++ b/atommic/collections/segmentation/nn/vnet.py
@@ -0,0 +1,100 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+from omegaconf import DictConfig
+
+from atommic.collections.segmentation.nn.segmentationnet import BaseSegmentationNet
+from atommic.collections.segmentation.nn.vnet_base.vnet_block import VNet
+from atommic.core.classes.common import typecheck
+
+__all__ = ["SegmentationVNet"]
+
+
+class SegmentationVNet(BaseSegmentationNet):
+ """Implementation of the V-Net for MRI segmentation, as presented in [Milletari2016]_.
+
+ References
+ ----------
+ .. [Milletari2016] Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi. V-Net: Fully Convolutional Neural Networks
+ for Volumetric Medical Image Segmentation, 2016. https://arxiv.org/abs/1606.04797
+
+ """
+
+ def build_segmentation_module(self, cfg: DictConfig) -> torch.nn.Module:
+ """Build the segmentation module.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Configuration object specifying the model's hyperparameters.
+
+ Returns
+ -------
+ torch.nn.Module
+ The segmentation module.
+ """
+ return VNet(
+ in_chans=self.input_channels,
+ out_chans=cfg.get("segmentation_module_output_channels", 2),
+ act=cfg.get("segmentation_module_activation", "elu"),
+ drop_prob=cfg.get("segmentation_module_dropout", 0.0),
+ bias=cfg.get("segmentation_module_bias", False),
+ )
+
+ @typecheck()
+ def forward(self, image: torch.Tensor, **kwargs) -> torch.Tensor:
+ """Forward pass of :class:`BaseSegmentationNet`.
+
+ Parameters
+ ----------
+ image : torch.Tensor
+ Input image. Shape [batch_size, n_x, n_y] or [batch_size, n_x, n_y, 2]
+ **kwargs : dict
+ Additional keyword arguments.
+
+ Returns
+ -------
+ torch.Tensor
+ Predicted segmentation. Shape [batch_size, n_classes, n_x, n_y]
+ """
+ if self.consecutive_slices > 1:
+ batch, slices = image.shape[:2]
+ image = image.reshape(batch * slices, *image.shape[2:])
+
+ if image.shape[-1] == 2:
+ if self.input_channels == 1:
+ image = torch.view_as_complex(image).unsqueeze(1)
+ if self.magnitude_input:
+ image = torch.abs(image)
+ elif self.input_channels == 2 and not self.magnitude_input:
+ image = image.permute(0, 3, 1, 2)
+ else:
+ raise ValueError(f"The input channels must be either 1 or 2. Found: {self.input_channels}")
+ elif self.magnitude_input:
+ image = torch.abs(image)
+
+ if image.dim() == 3:
+ image = image.unsqueeze(1)
+
+ # if dim 1 is even, add a row of zeros to make it odd
+ if image.shape[1] % 2 != 0 and image.shape[1] != 1:
+ image = torch.cat((image, torch.zeros_like(image[:, 0:1, :, :]).to(image.device)), dim=1)
+
+ mean = 1.0
+ std = 1.0
+ if self.normalize:
+ image, mean, std = self.norm(image)
+ image, pad_sizes = self.pad(image)
+ segmentation = self.segmentation_module(image)
+ segmentation = self.unpad(segmentation, *pad_sizes)
+ if self.normalize:
+ segmentation = self.unnorm(segmentation, mean, std)
+
+ if self.normalize_segmentation_output:
+ segmentation = (segmentation - segmentation.min()) / (segmentation.max() - segmentation.min())
+
+ if self.consecutive_slices > 1:
+ segmentation = segmentation.reshape(batch, slices, *segmentation.shape[1:])
+
+ return torch.abs(segmentation)
diff --git a/atommic/collections/segmentation/nn/vnet_base/__init__.py b/atommic/collections/segmentation/nn/vnet_base/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/collections/segmentation/nn/vnet_base/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/collections/segmentation/nn/vnet_base/vnet_block.py b/atommic/collections/segmentation/nn/vnet_base/vnet_block.py
new file mode 100644
index 00000000..a778eafe
--- /dev/null
+++ b/atommic/collections/segmentation/nn/vnet_base/vnet_block.py
@@ -0,0 +1,324 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/black0017/MedicalZooPytorch/blob/master/lib/medzoo/Vnet.py
+
+import torch
+from torch import nn
+
+
+class LUConv(nn.Module):
+ """LU Convolutional Block.
+
+ .. note::
+ This is a wrapper for Vnet implementation.
+ See: https://github.com/black0017/MedicalZooPytorch/blob/master/lib/medzoo/Vnet.py
+ """
+
+ def __init__(self, channels: int, act: nn.Module = nn.ELU, bias: bool = False):
+ """Inits :class:`LUConv`.
+
+ Parameters
+ ----------
+ channels : int
+ Number of channels.
+ act : nn.Module
+ Activation function.
+ bias : bool
+ Whether to use bias.
+ """
+ super().__init__()
+ self.layers = nn.Sequential(
+ nn.Conv2d(channels, channels, kernel_size=5, padding=2, bias=bias),
+ nn.BatchNorm2d(channels),
+ act(inplace=True),
+ )
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`LUConv`."""
+ return self.layers(x)
+
+
+def _make_nconv(channels: int, depth: int, act: nn.Module = nn.ELU, bias: bool = False):
+ """Make a stack of LUConv layers.
+
+ Parameters
+ ----------
+ channels : int
+ Number of channels.
+ depth : int
+ Number of LUConv layers.
+ act : nn.Module
+ Activation function.
+ bias : bool
+ Whether to use bias.
+
+ Returns
+ -------
+ layers : nn.Sequential
+ Stack of LUConv layers.
+
+ .. note::
+ This is a wrapper for Vnet implementation.
+ See: https://github.com/black0017/MedicalZooPytorch/blob/master/lib/medzoo/Vnet.py
+ """
+ return nn.Sequential(*[LUConv(channels=channels, act=act, bias=bias) for _ in range(depth)])
+
+
+class InputTransition(nn.Module):
+ """Input Transition Block.
+
+ .. note::
+ This is a wrapper for Vnet implementation.
+ See: https://github.com/black0017/MedicalZooPytorch/blob/master/lib/medzoo/Vnet.py
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int = 16,
+ act: nn.Module = nn.ELU,
+ bias: bool = False,
+ ):
+ """Inits :class:`InputTransition`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ act : nn.Module
+ Activation function.
+ bias : bool
+ Whether to use bias.
+ """
+ super().__init__()
+
+ if out_channels % in_channels != 0:
+ raise ValueError(f"16 should be divisible by in_channels, got in_channels={in_channels}.")
+
+ self.in_channels = in_channels
+ self.act_function = act(inplace=True)
+ self.conv_block = nn.Sequential(
+ nn.Conv2d(in_channels, out_channels, kernel_size=5, padding=2, bias=bias),
+ nn.BatchNorm2d(out_channels),
+ )
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`InputTransition`."""
+ return self.act_function(self.conv_block(x) + x.repeat(1, 16 // self.in_channels, 1, 1))
+
+
+class DownTransition(nn.Module):
+ """Down Transition Block.
+
+ .. note::
+ This is a wrapper for Vnet implementation.
+ See: https://github.com/black0017/MedicalZooPytorch/blob/master/lib/medzoo/Vnet.py
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ convs: int,
+ act: nn.Module = nn.ELU,
+ dropout_prob: float = 0.0,
+ bias: bool = False,
+ ):
+ """Inits :class:`DownTransition`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ convs : int
+ Number of LUConv layers.
+ act : nn.Module
+ Activation function. Default is ``ELU``.
+ dropout_prob : float
+ Dropout probability. Default is ``0.0``.
+ bias : bool
+ Whether to use bias. Default is ``False``.
+ """
+ super().__init__()
+
+ out_channels = 2 * in_channels
+ self.down_conv = nn.Conv2d(in_channels, out_channels, kernel_size=2, stride=2, bias=bias)
+ self.bn1 = nn.BatchNorm2d(out_channels)
+ self.act_function1 = act(inplace=True)
+ self.act_function2 = act(inplace=True)
+ self.ops = _make_nconv(out_channels, convs, act, bias)
+ self.dropout = nn.Dropout2d(dropout_prob) if dropout_prob > 0.0 else None
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`DownTransition`."""
+ down = self.act_function1(self.bn1(self.down_conv(x)))
+ return self.act_function2(self.ops(self.dropout(down) if self.dropout is not None else down) + down)
+
+
+class UpTransition(nn.Module):
+ """Up Transition Block.
+
+ .. note::
+ This is a wrapper for Vnet implementation.
+ See: https://github.com/black0017/MedicalZooPytorch/blob/master/lib/medzoo/Vnet.py
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ convs: int,
+ act: nn.Module = nn.ELU,
+ dropout_prob: float = 0.0,
+ ):
+ """Inits :class:`UpTransition`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ convs : int
+ Number of LUConv layers.
+ act : nn.Module
+ Activation function. Default is ``ELU``.
+ dropout_prob : float
+ Dropout probability. Default is ``0.0``.
+ """
+ super().__init__()
+ self.up_conv = nn.ConvTranspose2d(in_channels, out_channels // 2, kernel_size=2, stride=2)
+ self.bn1 = nn.BatchNorm2d(out_channels // 2)
+ self.dropout = nn.Dropout2d(dropout_prob) if dropout_prob > 0.0 else None
+ self.dropout2 = nn.Dropout2d(0.5)
+ self.act_function1 = act(inplace=True)
+ self.act_function2 = act(inplace=True)
+ self.ops = _make_nconv(out_channels, convs, act)
+
+ def forward(self, x: torch.Tensor, skipx: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`UpTransition`."""
+ out = self.dropout(x) if self.dropout is not None else x
+ xcat = torch.cat((self.act_function1(self.bn1(self.up_conv(out))), self.dropout2(skipx)), 1)
+ return self.act_function2(self.ops(xcat) + xcat)
+
+
+class OutputTransition(nn.Module):
+ """Output Transition Block.
+
+ .. note::
+ This is a wrapper for Vnet implementation.
+ See: https://github.com/black0017/MedicalZooPytorch/blob/master/lib/medzoo/Vnet.py
+ """
+
+ def __init__(
+ self,
+ in_channels: int,
+ out_channels: int,
+ act: nn.Module = nn.ELU,
+ bias: bool = False,
+ ):
+ """Inits :class:`OutputTransition`.
+
+ Parameters
+ ----------
+ in_channels : int
+ Number of input channels.
+ out_channels : int
+ Number of output channels.
+ act : nn.Module
+ Activation function. Default is ``ELU``.
+ bias : bool
+ Whether to use bias. Default is ``False``.
+ """
+ super().__init__()
+
+ self.conv_block = nn.Sequential(
+ nn.Conv2d(in_channels, out_channels, kernel_size=5, padding=2, bias=bias),
+ nn.BatchNorm2d(out_channels),
+ act(inplace=True),
+ )
+ self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=1)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`OutputTransition`."""
+ # convolve 32 down to 2 channels
+ return self.conv2(self.conv_block(x))
+
+
+class VNet(nn.Module):
+ """Implementation of the V-Net for MRI segmentation, as presented in [Milletari2016]_.
+
+ References
+ ----------
+ .. [Milletari2016] Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi. V-Net: Fully Convolutional Neural Networks
+ for Volumetric Medical Image Segmentation, 2016. https://arxiv.org/abs/1606.04797
+
+ .. note::
+ This is a wrapper for Vnet implementation.
+ See: https://github.com/black0017/MedicalZooPytorch/blob/master/lib/medzoo/Vnet.py
+ """
+
+ def __init__(
+ self,
+ in_chans: int = 1,
+ out_chans: int = 1,
+ act: str = "elu",
+ drop_prob: float = 0.5,
+ bias: bool = False,
+ ):
+ """Inits :class:`VNet`.
+
+ Parameters
+ ----------
+ in_chans : int
+ Number of input channels. Default is ``1``.
+ out_chans : int
+ Number of output channels. Default is ``1``.
+ act : nn.Module
+ Activation function. Default is ``ELU``.
+ drop_prob : float
+ Dropout probability. Default is ``0.5``.
+ bias : bool
+ Whether to use bias. Default is ``False``.
+ """
+ super().__init__()
+
+ if act == "elu":
+ act = nn.ELU
+ elif act == "relu":
+ act = nn.ReLU
+ elif act == "prelu":
+ act = nn.PReLU
+ elif act == "leakyrelu":
+ act = nn.LeakyReLU
+ else:
+ raise ValueError(
+ f"Activation function {act} not supported. Please choose between ReLU, PReLU, LeakyReLU, ELU."
+ )
+
+ self.in_tr = InputTransition(in_chans, 16, act, bias=bias)
+ self.down_tr32 = DownTransition(16, 1, act, bias=bias)
+ self.down_tr64 = DownTransition(32, 2, act, bias=bias)
+ self.down_tr128 = DownTransition(64, 3, act, dropout_prob=drop_prob, bias=bias)
+ self.down_tr256 = DownTransition(128, 2, act, dropout_prob=drop_prob, bias=bias)
+ self.up_tr256 = UpTransition(256, 256, 2, act, dropout_prob=drop_prob)
+ self.up_tr128 = UpTransition(256, 128, 2, act, dropout_prob=drop_prob)
+ self.up_tr64 = UpTransition(128, 64, 1, act)
+ self.up_tr32 = UpTransition(64, 32, 1, act)
+ self.out_tr = OutputTransition(32, out_chans, act, bias=bias)
+
+ def forward(self, x: torch.Tensor) -> torch.Tensor:
+ """Forward pass of :class:`VNet`."""
+ out16 = self.in_tr(x)
+ out32 = self.down_tr32(out16)
+ out64 = self.down_tr64(out32)
+ out128 = self.down_tr128(out64)
+ out256 = self.down_tr256(out128)
+ x = self.up_tr256(out256, out128)
+ x = self.up_tr128(x, out64)
+ x = self.up_tr64(x, out32)
+ x = self.up_tr32(x, out16)
+ x = self.out_tr(x)
+ return x
diff --git a/atommic/collections/segmentation/parts/__init__.py b/atommic/collections/segmentation/parts/__init__.py
new file mode 100644
index 00000000..5ddecef5
--- /dev/null
+++ b/atommic/collections/segmentation/parts/__init__.py
@@ -0,0 +1,4 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.segmentation.parts.transforms import SegmentationMRIDataTransforms # noqa: F401
diff --git a/atommic/collections/segmentation/parts/transforms.py b/atommic/collections/segmentation/parts/transforms.py
new file mode 100644
index 00000000..6b3753a1
--- /dev/null
+++ b/atommic/collections/segmentation/parts/transforms.py
@@ -0,0 +1,14 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.collections.multitask.rs.parts.transforms import RSMRIDataTransforms
+
+__all__ = ["SegmentationMRIDataTransforms"]
+
+
+class SegmentationMRIDataTransforms(RSMRIDataTransforms):
+ """Transforms for the MRI segmentation task.
+
+ .. note::
+ Extends :class:`atommic.collections.multitask.rs.parts.transforms.RSMRIDataTransforms`.
+ """
diff --git a/atommic/constants.py b/atommic/constants.py
new file mode 100644
index 00000000..06b4e932
--- /dev/null
+++ b/atommic/constants.py
@@ -0,0 +1,15 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+ATOMMIC_ENV_VARNAME_ENABLE_COLORING = "ATOMMIC_ENABLE_COLORING"
+ATOMMIC_ENV_VARNAME_REDIRECT_LOGS_TO_STDERR = "ATOMMIC_REDIRECT_LOGS_TO_STDERR"
+# Set to True to enable atommic.util.logging's debug mode
+ATOMMIC_ENV_VARNAME_TESTING = "ATOMMIC_TESTING"
+# Used for atommic.utils.exp_manager versioning
+ATOMMIC_ENV_VARNAME_VERSION = "ATOMMIC_EXPM_VERSION"
+# Used to change default atommic cache directory
+ATOMMIC_ENV_CACHE_DIR = "ATOMMIC_CACHE_DIR"
+# Used to change default atommic data store cache directory
+ATOMMIC_ENV_DATA_STORE_CACHE_DIR = "ATOMMIC_DATA_STORE_CACHE_DIR"
+# Shared among nodes (1) or not shared (0)
+ATOMMIC_ENV_DATA_STORE_CACHE_SHARED = "ATOMMIC_DATA_STORE_CACHE_SHARED"
diff --git a/atommic/core/__init__.py b/atommic/core/__init__.py
new file mode 100644
index 00000000..590bd25b
--- /dev/null
+++ b/atommic/core/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import atommic.core.optim.lr_scheduler
+import atommic.core.optim.optimizers # noqa: F401
diff --git a/atommic/core/classes/__init__.py b/atommic/core/classes/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/core/classes/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/core/classes/common.py b/atommic/core/classes/common.py
new file mode 100644
index 00000000..2f55e459
--- /dev/null
+++ b/atommic/core/classes/common.py
@@ -0,0 +1,1018 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/common.py
+
+import inspect
+import traceback
+from abc import ABC
+from contextlib import contextmanager
+from dataclasses import dataclass, field
+from enum import Enum
+from functools import total_ordering
+from typing import Dict, Iterable, List, Optional, Tuple, Union
+
+import hydra
+import torch
+import wrapt
+from huggingface_hub import HfApi, HfFolder, ModelFilter, hf_hub_download
+from huggingface_hub.hf_api import ModelInfo
+from omegaconf import DictConfig, OmegaConf
+from pytorch_lightning import Trainer
+
+from atommic import __version__
+from atommic.core.connectors.save_restore_connector import SaveRestoreConnector
+from atommic.core.neural_types.comparison import NeuralTypeComparisonResult
+from atommic.core.neural_types.neural_type import NeuralType
+from atommic.utils import logging, model_utils
+
+__all__ = ["Typing", "FileIO", "Model", "PretrainedModelInfo", "Serialization", "is_typecheck_enabled", "typecheck"]
+
+_TYPECHECK_ENABLED = True
+_HAS_HYDRA = True
+
+
+def is_typecheck_enabled():
+ """Getter method for typechecking state."""
+ return _TYPECHECK_ENABLED
+
+
+@dataclass
+class TypecheckMetadata:
+ """Metadata class for input/output neural types.
+
+ Parameters
+ ----------
+ original_types : Dict[str, NeuralType]
+ Preserve the dictionary of type information provided.
+ ignore_collections : bool
+ For backward compatibility, container support can be disabled explicitly using this flag. When set to True, all
+ nesting is ignored and nest-depth checks are skipped.
+ mandatory_types : Dict[str, NeuralType]
+ Sub-dictionary of `original_types` which contains only those types which are mandatory to include when calling
+ the function.
+ base_types : Dict[str, NeuralType]
+ Dictionary of flattened `str: NeuralType` definitions, disregarding the nest level details into appropriate
+ arguments.
+ container_depth : Dict[str, int]
+ Dictionary mapping `str: int` - such that the valid depth of the nest of this neural type is recorded.
+ has_container_types : bool
+ Bool flag declaring if any of the neural types declares a container nest in its signature.
+ is_singular_container_type : bool
+ Bool flag declaring if this is a single Neural Type with a container nest in its signature. Required for
+ supporting python list expansion in return statement.
+ """
+
+ original_types: Dict[str, NeuralType]
+ ignore_collections: bool
+
+ mandatory_types: Dict[str, NeuralType] = field(init=False)
+ base_types: Dict[str, NeuralType] = field(init=False)
+
+ container_depth: Dict[str, int] = field(init=False)
+ has_container_types: bool = field(init=False)
+ is_singular_container_type: bool = field(init=False)
+
+ def __post_init__(self):
+ """Post init function to compute metadata."""
+ has_container_types = any(isinstance(type_val, (list, tuple)) for type_val in self.original_types.values())
+
+ self.has_container_types = has_container_types
+
+ # If only one NeuralType is declared, and it declares a container nest, set to True
+ self.is_singular_container_type = self.has_container_types and len(self.original_types) == 1
+
+ # If container nests are declared, flatten the nest into `base_types`
+ # Also compute the nest depth for each of the NeuralTypes
+ if self.has_container_types:
+ self.base_types = {}
+ self.container_depth = {}
+
+ for type_key, type_val in self.original_types.items():
+ depth = 0
+ while isinstance(type_val, (list, tuple)):
+ if len(type_val) > 1:
+ raise TypeError(
+ f"Neural Type `{type_key}`: {type_val} definition contains more than one element when"
+ "declaring the nested container structure.\n"
+ "Please ensure that you have only 1 NeuralType inside of the entire nested structure "
+ "definition."
+ )
+
+ type_val = type_val[0]
+ depth += 1
+
+ self.base_types[type_key] = type_val
+ self.container_depth[type_key] = depth
+ else:
+ # Otherwise, simply preserve the original_types and set depth of nest to 0.
+ self.base_types = self.original_types
+ self.container_depth = {type_key: 0 for type_key in self.base_types.keys()}
+
+ # Compute subset of original_types which are mandatory in the call argspec
+ self.mandatory_types = {
+ type_key: type_val for type_key, type_val in self.base_types.items() if not type_val.optional
+ }
+
+
+class Typing(ABC):
+ """An interface which endows module with neural types."""
+
+ @property
+ def input_types(self) -> Optional[Dict[str, NeuralType]]:
+ """Define these to enable input neural type checks."""
+ return None
+
+ @property
+ def output_types(self) -> Optional[Dict[str, NeuralType]]:
+ """Define these to enable output neural type checks."""
+ return None
+
+ def _validate_input_types(self, input_types=None, ignore_collections=False, **kwargs):
+ """This function does a few things.
+
+ 1) It ensures that len(self.input_types ) <= len(kwargs) <= len(self.input_types).
+
+ 2) For each (keyword name, keyword value) passed as input to the wrapped function:
+
+ - Check if the keyword name exists in the list of valid self.input_types names.
+
+ - Check if keyword value has the `neural_type` property.
+
+ - If it does, then perform a comparative check and assert that neural types are compatible (SAME
+ or GREATER).
+
+ - Check if keyword value is a container type (list or tuple). If yes, then perform the elementwise
+ test of neural type above on each element of the nested structure, recursively.
+
+ Parameters
+ ----------
+ input_types : class
+ Either the `input_types` defined at class level, or the local function overridden type definition.
+ ignore_collections : bool
+ For backward compatibility, container support can be disabled explicitly using this flag. When set to True,
+ all nesting is ignored and nest-depth checks are skipped.
+ kwargs : Dict[str, Any]
+ Dictionary of argument_name:argument_value pairs passed to the wrapped function upon call.
+ """
+ # TODO: Properly implement this
+ if input_types is None:
+ return
+ # Precompute metadata
+ metadata = TypecheckMetadata(original_types=input_types, ignore_collections=ignore_collections)
+
+ total_input_types = len(input_types)
+ mandatory_input_types = len(metadata.mandatory_types)
+
+ # Allow number of input arguments to be <= total input neural types.
+ if len(kwargs) < mandatory_input_types or len(kwargs) > total_input_types:
+ raise TypeError(
+ f"Number of input arguments provided ({len(kwargs)}) is not as expected. Function has "
+ f"{total_input_types} total inputs with {mandatory_input_types} mandatory inputs."
+ )
+
+ for key, value in kwargs.items():
+ # Check if keys exists in the defined input types
+ if key not in input_types:
+ raise TypeError(
+ f"Input argument {key} has no corresponding input_type match. "
+ f"Existing input_types = {input_types.keys()}"
+ )
+
+ # Perform neural type check
+ if hasattr(value, "neural_type") and metadata.base_types[key].compare(value.neural_type) not in (
+ NeuralTypeComparisonResult.SAME,
+ NeuralTypeComparisonResult.GREATER,
+ ):
+ error_msg = [
+ f"{input_types[key].compare(value.neural_type)} :",
+ f"Input type expected : {input_types[key]}",
+ f"Input type found : {value.neural_type}",
+ f"Argument: {key}",
+ ]
+ for i, dict_tuple in enumerate(metadata.base_types[key].elements_type.type_parameters.items()):
+ error_msg.insert(i + 2, f" input param_{i} : {dict_tuple[0]}: {dict_tuple[1]}")
+ error_msg.extend(
+ f" input param_{i} : {dict_tuple[0]}: {dict_tuple[1]}"
+ for i, dict_tuple in enumerate(value.neural_type.elements_type.type_parameters.items())
+ )
+
+ raise TypeError("\n".join(error_msg))
+
+ # Perform input n dim check
+ if hasattr(value, "shape"):
+ value_shape = value.shape
+ type_shape = metadata.base_types[key].axes
+ if type_shape is not None and len(value_shape) != len(tuple(type_shape)):
+ name = key
+
+ raise TypeError(
+ f"Input shape mismatch occurred for {name} in module {self.__class__.__name__} : \n"
+ f"Input shape expected = {metadata.base_types[name].axes} | \n"
+ f"Input shape found : {value_shape}"
+ )
+
+ elif isinstance(value, (list, tuple)):
+ for val in value:
+ # This initiates a DFS, tracking the depth count as it goes along the nested structure.
+ # Initial depth is 1 as we consider the current loop to be the 1st step inside the nest.
+ self.__check_neural_type(val, metadata, depth=1, name=key)
+
+ def _attach_and_validate_output_types(self, out_objects, ignore_collections=False, output_types=None):
+ """This function does a few things.
+ 1) It ensures that len(out_object) == len(self.output_types).
+ 2) If the output is a tensor (or list/tuple of list/tuple ... of tensors), it
+ attaches a neural_type to it. For objects without the neural_type attribute,
+ such as python objects (dictionaries and lists, primitive data types, structs),
+ no neural_type is attached.
+ Note: tensor.neural_type is only checked during _validate_input_types which is
+ called prior to forward().
+
+ Parameters
+ ----------
+ output_types : class
+ Either the `output_types` defined at class level, or the local function overridden type definition.
+ ignore_collections : bool
+ For backward compatibility, container support can be disabled explicitly using this flag. When set to True,
+ all nesting is ignored and nest-depth checks are skipped.
+ out_objects : Dict[str, Any]
+ The outputs of the wrapped function.
+ """
+ # TODO: Properly implement this
+ if output_types is None:
+ return
+ # Precompute metadata
+ metadata = TypecheckMetadata(original_types=output_types, ignore_collections=ignore_collections)
+ out_types_list = list(metadata.base_types.items())
+ mandatory_out_types_list = list(metadata.mandatory_types.items())
+
+ # First convert all outputs to list/tuple format to check correct number of outputs
+ if isinstance(out_objects, (list, tuple)):
+ out_container = out_objects # can be any rank nested structure
+ else:
+ out_container = [out_objects]
+
+ # If this neural type has a *single output*, with *support for nested outputs*,
+ # then *do not* perform any check on the number of output items against the number
+ # of neural types (in this case, 1).
+ # This is done as python will *not* wrap a single returned list into a tuple of length 1,
+ # instead opting to keep the list intact. Therefore len(out_container) in such a case
+ # is the length of all the elements of that list - each of which has the same corresponding
+ # neural type (defined as the singular container type).
+ if metadata.is_singular_container_type:
+ pass
+
+ elif len(out_container) > len(out_types_list) or len(out_container) < len(mandatory_out_types_list):
+ raise TypeError(
+ f"Number of output arguments provided ({len(out_container)}) is not as expected. It should be larger "
+ f"than {len(out_types_list)} and less than {len(mandatory_out_types_list)}.\nThis can be either "
+ "because insufficient/extra number of output NeuralTypes were provided,or the provided NeuralTypes "
+ f"{output_types} should enable container support (add '[]' to the NeuralType definition)"
+ )
+
+ # Attach types recursively, if possible
+ if not isinstance(out_objects, tuple) and not isinstance(out_objects, list):
+ # Here, out_objects is a single object which can potentially be attached with a NeuralType
+ try:
+ out_objects.neural_type = out_types_list[0][1]
+ except AttributeError:
+ pass
+
+ # Perform output n dim check
+ if hasattr(out_objects, "shape"):
+ value_shape = out_objects.shape
+ type_shape = out_types_list[0][1].axes
+ if type_shape is not None and len(value_shape) != len(type_shape):
+ name = out_types_list[0][0]
+
+ raise TypeError(
+ f"Output shape mismatch occurred for {name} in module {self.__class__.__name__} : \n"
+ f"Output shape expected = {type_shape} | \n"
+ f"Output shape found : {value_shape}"
+ )
+
+ elif metadata.is_singular_container_type:
+ depth = 0 if len(out_objects) == 1 and isinstance(out_objects, tuple) else 1
+ for res in out_objects:
+ self.__attach_neural_type(res, metadata, depth=depth, name=out_types_list[0][0])
+ else:
+ # If more than one item is returned in a return statement, python will wrap the output with an outer tuple.
+ # Therefore, there must be a 1:1 correspondence of the output_neural type (with or without nested
+ # structure) to the actual output (whether it is a single object or a nested structure of objects).
+ # Therefore, in such a case, we "start" the DFS at depth 0 - since the recursion is being applied on 1
+ # neural type : 1 output struct (single or nested output). Since we are guaranteed that the outer tuple
+ # will be built by python, assuming initial depth of 0 is appropriate.
+ for ind, res in enumerate(out_objects):
+ self.__attach_neural_type(res, metadata, depth=0, name=out_types_list[ind][0])
+
+ def __check_neural_type(self, obj, metadata, depth: int, name: str = None):
+ """Checks if the object is of the correct type, and attaches the correct NeuralType.
+
+ Parameters
+ ----------
+ obj : object
+ Any python object that can be assigned to a value.
+ metadata : object
+ TypecheckMetadata object.
+ depth : int
+ Current depth of the recursion.
+ name : str
+ Optional name used of the source object, when an error is raised.
+ """
+ if isinstance(obj, (tuple, list)):
+ for elem in obj:
+ self.__check_neural_type(elem, metadata, depth + 1, name=name)
+ return # after processing nest, return to avoid testing nest itself
+
+ type_val = metadata.base_types[name]
+
+ # If nest depth doesnt match neural type structure depth, raise an error
+ if not metadata.ignore_collections and depth != metadata.container_depth[name]:
+ raise TypeError(
+ "While checking input neural types,\n"
+ "Nested depth of value did not match container specification:\n"
+ f"Current nested depth of NeuralType '{name}' ({type_val}): {depth}\n"
+ f"Expected nested depth : {metadata.container_depth[name]}"
+ )
+
+ if hasattr(obj, "neural_type") and type_val.compare(obj.neural_type) not in (
+ NeuralTypeComparisonResult.SAME,
+ NeuralTypeComparisonResult.GREATER,
+ ):
+ raise TypeError(
+ f"{type_val.compare(obj.neural_type)} : \n"
+ f"Input type expected = {type_val} | \n"
+ f"Input type found : {obj.neural_type}"
+ )
+
+ # Perform input n dim check
+ if hasattr(obj, "shape"):
+ value_shape = obj.shape
+ type_shape = type_val.axes
+
+ if type_shape is not None and len(value_shape) != len(type_shape):
+ raise TypeError(
+ f"Input shape mismatch occurred for {name} in module {self.__class__.__name__} : \n"
+ f"Input shape expected = {type_shape} | \n"
+ f"Input shape found : {value_shape}"
+ )
+
+ def __attach_neural_type(self, obj, metadata, depth: int, name: str = None):
+ """Attach NeuralType to the object.
+
+ Parameters
+ ----------
+ obj : object
+ Any python object that can be assigned to a value.
+ metadata : object
+ TypecheckMetadata object.
+ depth : int
+ Current depth of the recursion.
+ name : str
+ Optional name used of the source object, when an error is raised.
+ """
+ if isinstance(obj, (tuple, list)):
+ for elem in obj:
+ self.__attach_neural_type(elem, metadata, depth=depth + 1, name=name)
+ return # after processing nest, return to avoid argument insertion into nest itself
+
+ type_val = metadata.base_types[name]
+
+ # If nest depth doesnt match neural type structure depth, raise an error
+ if not metadata.ignore_collections and depth != metadata.container_depth[name]:
+ raise TypeError(
+ "While attaching output neural types,\n"
+ "Nested depth of value did not match container specification:\n"
+ f"Current nested depth of NeuralType '{name}' ({type_val}): {depth}\n"
+ f"Expected nested depth : {metadata.container_depth[name]}"
+ )
+
+ try:
+ obj.neural_type = type_val
+ except AttributeError:
+ pass
+
+ # Perform output n dim check
+ if hasattr(obj, "shape"):
+ value_shape = obj.shape
+ type_shape = type_val.axes
+
+ if type_shape is not None and len(value_shape) != len(type_shape):
+ raise TypeError(
+ f"Output shape mismatch occurred for {name} in module {self.__class__.__name__} : \n"
+ f"Output shape expected = {type_shape} | \n"
+ f"Output shape found : {value_shape}"
+ )
+
+
+class Serialization(ABC):
+ """Base class for serialization."""
+
+ @classmethod
+ def from_config_dict(cls, config: "DictConfig", trainer: Optional[Trainer] = None): # noqa: MC0001
+ """Instantiates object using DictConfig-based configuration"""
+ # Resolve the config dict
+ if _HAS_HYDRA:
+ if isinstance(config, DictConfig):
+ config = OmegaConf.to_container(config, resolve=True)
+ config = OmegaConf.create(config)
+ OmegaConf.set_struct(config, True)
+
+ config = model_utils.maybe_update_config_version(config)
+
+ # Hydra 0.x API
+ if ("cls" in config or "target" in config) and "params" in config and _HAS_HYDRA:
+ # regular hydra-based instantiation
+ instance = hydra.utils.instantiate(config=config)
+ elif "_target_" in config and _HAS_HYDRA:
+ # regular hydra-based instantiation
+ instance = hydra.utils.instantiate(config=config)
+ else:
+ instance = None
+ prev_error = ""
+
+ # Attempt class path resolution from config `target` class (if it exists)
+ if "target" in config:
+ # No guarantee that this is a omegaconf class
+ target_cls = config["target"]
+ imported_cls = None
+ try:
+ # try to import the target class
+ imported_cls = model_utils.import_class_by_path(target_cls)
+ # use subclass instead
+ if issubclass(cls, imported_cls):
+ imported_cls = cls
+ if (
+ accepts_trainer := # pylint: disable=unused-variable
+ # check if the target class accepts a trainer argument
+ Serialization._inspect_signature_for_trainer(imported_cls)
+ ):
+ # Create a dummy PL trainer object
+ instance = imported_cls(cfg=config, trainer=trainer)
+ else:
+ instance = imported_cls(cfg=config)
+
+ except Exception as e:
+ tb = traceback.format_exc()
+ prev_error = f"Model instantiation failed.\nTarget class: {target_cls}\nError: {e}\n{tb}"
+ logging.debug(prev_error + "\n falling back to 'cls'.")
+ # target class resolution was unsuccessful, fall back to current `cls`
+ if instance is None:
+ try:
+ if accepts_trainer := Serialization._inspect_signature_for_trainer(cls): # noqa: F841
+ instance = cls(cfg=config, trainer=trainer) # type: ignore
+ else:
+ instance = cls(cfg=config) # type: ignore
+ except Exception as e:
+ # report saved errors, if any, and raise the current error
+ if prev_error:
+ logging.error(f"{prev_error}")
+ raise e from e
+
+ if not hasattr(instance, "_cfg"):
+ instance._cfg = config # pylint: disable=protected-access
+ return instance
+
+ def to_config_dict(self) -> "DictConfig":
+ """Returns object's configuration to config dictionary"""
+ if (hasattr(self, "_cfg")) and (
+ self._cfg is not None # type: ignore # pylint: disable=access-member-before-definition
+ ):
+ # Resolve the config dict
+ if (_HAS_HYDRA) and (
+ isinstance(self._cfg, DictConfig) # type: ignore # pylint: disable=access-member-before-definition
+ ):
+ config = OmegaConf.to_container(
+ self._cfg, resolve=True # type: ignore # pylint: disable=access-member-before-definition
+ )
+ config = OmegaConf.create(config)
+ OmegaConf.set_struct(config, True)
+
+ config = model_utils.maybe_update_config_version(config)
+
+ self._cfg = config
+
+ return self._cfg
+ raise NotImplementedError(
+ "to_config_dict() can currently only return object._cfg but current object does not have it."
+ )
+
+ @classmethod
+ def _inspect_signature_for_trainer(cls, check_cls):
+ """Inspects the signature of the class to see if it accepts a trainer argument."""
+ if hasattr(check_cls, "__init__"):
+ signature = inspect.signature(check_cls.__init__)
+ if "trainer" in signature.parameters:
+ return True
+ return False
+
+
+class FileIO(ABC):
+ """Base class for file IO."""
+
+ def save_to(self, save_path: str):
+ """Standardized method to save a tarfile containing the checkpoint, config, and any additional artifacts.
+ Implemented via :meth:`atommic.core.connectors.save_restore_connector.SaveRestoreConnector.save_to`.
+
+ Parameters
+ ----------
+ save_path : str
+ Path to save the checkpoint to.
+ """
+ raise NotImplementedError()
+
+ @classmethod
+ def restore_from(
+ cls,
+ restore_path: str,
+ override_config_path: Optional[str] = None,
+ map_location: Optional[torch.device] = None,
+ strict: bool = True,
+ return_config: bool = False,
+ trainer: Optional[Trainer] = None,
+ save_restore_connector: SaveRestoreConnector = None,
+ ):
+ """Restores model instance (weights and configuration) from a .atommic file.
+
+ Parameters
+ ----------
+ restore_path : str
+ Path to .atommic file from which model should be instantiated.
+ override_config_path : str, optional
+ Path to .yaml file containing the configuration to override the one in the .atommic file.
+ map_location : torch.device, optional
+ Device to map the instantiated model to. Default is ``None``, it will select a GPU if available, falling
+ back to CPU otherwise.
+ strict : bool, optional
+ Passed to load_state_dict. Default is ``True``.
+ return_config : bool, optional
+ If True, returns the underlying config of the restored model as an OmegaConf DictConfig object without
+ instantiating the model.
+ trainer : Trainer, optional
+ If provided, will be used to instantiate the model.
+ save_restore_connector : SaveRestoreConnector, optional
+ An optional SaveRestoreConnector object that defines the implementation of the restore_from() method.
+ """
+ raise NotImplementedError()
+
+ @classmethod
+ def from_config_file(cls, path2yaml_file: str):
+ """Instantiates an instance of atommic Model from YAML config file. Weights will be initialized randomly.
+
+ Parameters
+ ----------
+ path2yaml_file : str
+ Path to yaml file with model configuration.
+
+ Returns
+ -------
+ atommic Model instance.
+ """
+ if issubclass(cls, Serialization):
+ conf = OmegaConf.load(path2yaml_file)
+ return cls.from_config_dict(config=conf)
+ raise NotImplementedError()
+
+ def to_config_file(self, path2yaml_file: str):
+ """Saves current instance's configuration to YAML config file. Weights will not be saved.
+
+ Parameters
+ ----------
+ path2yaml_file : str
+ Path to yaml file with model configuration.
+ """
+ if hasattr(self, "_cfg"):
+ self._cfg = model_utils.maybe_update_config_version(self._cfg) # type: ignore
+ with open(path2yaml_file, "w", encoding="utf-8") as fout:
+ OmegaConf.save(config=self._cfg, f=fout, resolve=True)
+ else:
+ raise NotImplementedError()
+
+
+@total_ordering
+@dataclass
+class PretrainedModelInfo:
+ """Class to store information about a pretrained model."""
+
+ pretrained_model_name: str
+ description: str
+ location: str
+ class_: Union["Model", None] = None
+ aliases: Union[List[str], None] = None
+
+ def __repr__(self):
+ """Return a string representation of the object."""
+ base = self.__class__.__name__
+ extras = (
+ "pretrained_model_name={pretrained_model_name},\n\t"
+ "description={description},\n\t"
+ "location={location}".format(**self.__dict__)
+ )
+
+ if self.class_ is not None:
+ extras = "{extras},\n\t" "class_={class_}".format(extras=extras, **self.__dict__)
+
+ return f"{base}(\n\t{extras}\n)"
+
+ def __hash__(self):
+ """Return a hash of the object."""
+ return hash(self.location)
+
+ def __eq__(self, other):
+ """Return True if self is equal to other."""
+ # another object is equal to self, if it's hash is equal to hash(self)
+ return hash(self) == hash(other) or self.pretrained_model_name == other.pretrained_model_name
+
+ def __lt__(self, other):
+ """Return True if self is less than other."""
+ return self.pretrained_model_name < other.pretrained_model_name
+
+
+class Model(Typing, Serialization, FileIO, ABC): # type: ignore
+ """Abstract class offering interface which should be implemented by all atommic models."""
+
+ @classmethod
+ def list_available_models(cls) -> Optional[List[PretrainedModelInfo]]:
+ """Should list all pre-trained models available.
+ Note: There is no check that requires model names and aliases to be unique. In the case of a collision,
+ whatever model (or alias) is listed first in the returned list will be instantiated.
+
+ Returns
+ -------
+ A list of PretrainedModelInfo entries.
+ """
+ raise NotImplementedError()
+
+ @classmethod
+ def search_huggingface_models(
+ cls, model_filter: Optional[Union[ModelFilter, List[ModelFilter]]] = None
+ ) -> List[ModelInfo]:
+ """Should list all pre-trained models available via Hugging Face Hub.
+
+ The following metadata can be passed via the `model_filter` for additional results.
+
+ .. metadata::
+ resolve_card_info: Bool flag, if set, returns the model card metadata. Default: False.
+ limit_results: Optional int, limits the number of results returned.
+
+ .. code-block:: python
+
+ # You can replace with any subclass of ModelPT.
+ from atommic.core import ModelPT
+
+ # Get default ModelFilter
+ filt = .get_hf_model_filter()
+
+ # Make any modifications to the filter as necessary
+ filt.language = [...]
+ filt.task = ...
+ filt.tags = [...]
+
+ # Add any metadata to the filter as needed
+ filt.limit_results = 5
+
+ # Obtain model info
+ model_infos = .search_huggingface_models(model_filter=filt)
+
+ # Browse through cards and select an appropriate one
+ card = model_infos[0]
+
+ # Restore model using `modelId` of the card.
+ model = ModelPT.from_pretrained(card.modelId)
+
+ Parameters
+ ----------
+ model_filter : Optional ModelFilter or List[ModelFilter] (from Hugging Face Hub)
+ Filters the returned list of compatible model cards, and selects all results from each filter.
+ Users can then use `model_card.modelId` in `from_pretrained()` to restore a atommic Model.
+ If no ModelFilter is provided, uses the classes default filter as defined by `get_hf_model_filter()`.
+
+ Returns
+ -------
+ list
+ A list of ModelInfo entries.
+ """
+ # Resolve model filter if not provided as argument
+ if model_filter is None:
+ model_filter = cls.get_hf_model_filter()
+
+ # If single model filter, wrap into list
+ if not isinstance(model_filter, Iterable):
+ model_filter = [model_filter]
+
+ # Inject `atommic` library filter
+ for mfilter in model_filter:
+ if isinstance(mfilter.library, str) and mfilter.library != "atommic":
+ logging.warning(
+ f"Model filter's `library` tag updated be `atommic`. Original value: {mfilter.library}"
+ )
+ mfilter.library = "atommic"
+
+ elif isinstance(mfilter, Iterable) and "atommic" not in mfilter.library: # type: ignore
+ logging.warning(
+ "Model filter's `library` list updated to include `atommic`. "
+ f"Original value: {mfilter.library}" # type: ignore
+ )
+ mfilter.library = list(mfilter) # type: ignore
+ mfilter.library.append("atommic") # type: ignore
+
+ # Check if api token exists, use if it does
+ is_token_available = HfFolder.get_token() is not None
+
+ # Search for all valid models after filtering
+ api = HfApi()
+
+ # Setup extra arguments for model filtering
+ all_results = [] # type: List[ModelInfo]
+
+ for mfilter in model_filter:
+ cardData = None
+ limit = None
+
+ if hasattr(mfilter, "resolve_card_info") and mfilter.resolve_card_info is True:
+ cardData = True
+
+ if hasattr(mfilter, "limit_results") and mfilter.limit_results is not None:
+ limit = mfilter.limit_results
+
+ results = api.list_models( # pylint: disable=unexpected-keyword-arg
+ filter=mfilter,
+ use_auth_token=is_token_available,
+ sort="lastModified",
+ direction=-1,
+ cardData=cardData,
+ limit=limit,
+ ) # type: List[ModelInfo]
+
+ all_results.extend(results)
+
+ return all_results
+
+ @classmethod
+ def get_available_model_names(cls) -> List[str]:
+ """Returns the list of model names available. To get the complete model description use
+ list_available_models().
+
+ Returns
+ -------
+ A list of model names.
+ """
+ return (
+ [model.pretrained_model_name for model in cls.list_available_models()] # type: ignore
+ if cls.list_available_models() is not None
+ else []
+ )
+
+ @classmethod
+ def get_hf_model_filter(cls) -> ModelFilter:
+ """Generates a filter for HuggingFace models.
+
+ Additionally, includes default values of some metadata about results returned by the Hub.
+
+ .. metadata::
+ resolve_card_info: Bool flag, if set, returns the model card metadata. Default: False.
+ limit_results: Optional int, limits the number of results returned.
+
+ Returns
+ -------
+ list
+ A Hugging Face Hub ModelFilter object.
+ """
+ model_filter = ModelFilter(library="atommic")
+
+ # Attach some additional info
+ model_filter.resolve_card_info = False
+ model_filter.limit_results = None
+
+ return model_filter
+
+ @classmethod
+ def from_pretrained(
+ cls,
+ model_name: str,
+ refresh_cache: bool = False,
+ override_config_path: Optional[str] = None,
+ map_location: Optional[torch.device] = None,
+ strict: bool = True,
+ return_config: bool = False,
+ trainer: Optional[Trainer] = None,
+ save_restore_connector: SaveRestoreConnector = None,
+ return_only_atommic_model_file_in_cache: bool = False,
+ ):
+ """Instantiates an instance of atommic. Use restore_from() to instantiate from a local .atommic file.
+
+ Parameters
+ ----------
+ model_name : str
+ The name of the model to instantiate.
+ refresh_cache : bool, optional
+ If set to True, then when fetching from cloud, this will re-fetch the file from cloud even if it is already
+ found in a cache locally.
+ override_config_path : str, optional
+ Path to a yaml config that will override the internal config file.
+ map_location : torch.device, optional
+ Optional torch.device() to map the instantiated model to a device. Default is ``None``. It will select a
+ GPU if available, falling back to CPU otherwise.
+ strict : bool, optional
+ Passed to torch.load_state_dict. Default is ``True``.
+ return_config : bool, optional
+ If set to true, will return just the underlying config of the restored model as an OmegaConf/DictConfig
+ object without instantiating the model.
+ trainer : Trainer, optional
+ Optional Trainer objects to use for restoring the model.
+ save_restore_connector : SaveRestoreConnector, optional
+ Optional SaveRestoreConnector object to use for restoring the model.
+ return_only_atommic_model_file_in_cache : bool, optional
+ If set to True, will return the path to the atommic model file in the cache, without instantiating the
+ model.
+
+ Returns
+ -------
+ A model instance of a particular model class or its underlying config (if return_config is set).
+ """
+ if save_restore_connector is None:
+ save_restore_connector = SaveRestoreConnector()
+
+ # Resolve if the pretrained model name is on HF Hub source
+ (
+ class_,
+ atommic_model_file_in_cache,
+ ) = cls._get_hf_hub_pretrained_model_info( # type: ignore
+ cls, model_name=model_name, refresh_cache=refresh_cache # type: ignore
+ )
+ if return_only_atommic_model_file_in_cache:
+ return atommic_model_file_in_cache
+
+ instance, state_dict = class_.restore_from( # type: ignore
+ restore_path=atommic_model_file_in_cache,
+ override_config_path=override_config_path,
+ map_location=map_location,
+ strict=strict,
+ return_config=return_config,
+ trainer=trainer,
+ save_restore_connector=save_restore_connector,
+ )
+ return instance, state_dict
+
+ def _get_hf_hub_pretrained_model_info(cls, model_name: str, refresh_cache: bool = False) -> Tuple[type, str]:
+ """Resolve the HuggingFace Hub model pretrained information given a model name.
+
+ The model name must be of general syntax ``{source_repo}/{model_name}``.
+
+ .. note:
+ This allows public, externally contributed models to be run freely using atommic.
+
+ Parameters
+ ----------
+ model_name : str
+ Name of the model. Must be the original name or an alias of the model, without any '/'.
+ refresh_cache : bool
+ Determines whether cache must be refreshed (model is re-downloaded).
+
+ Returns
+ -------
+ tuple
+ A tuple of details describing :
+ - The resolved class of the model. Since the source is external to atommic, always default to using
+ the calling class. Depend on target class resolution by restore_from() for calling the correct
+ class.
+ - The path to the atommic model (.atommic file) in some cached directory (managed by HF Hub).
+ """
+ # Resolve the model name without origin for filename
+ resolved_username = model_name.split("huggingface.co/")[-1].split("/")[0]
+ resolved_model_filename = model_name.split(resolved_username)[-1].split("/blob/main/")[0].strip("/")
+ resolved_stem = model_name.split("/")[-1]
+
+ # Check if api token exists, use if it does
+ is_token_available = HfFolder.get_token() is not None
+
+ # Try to load the model from the Huggingface Hub
+ path = hf_hub_download(
+ repo_id=f'{resolved_username}/{resolved_model_filename}',
+ filename=resolved_stem,
+ library_name="atommic",
+ library_version=__version__,
+ force_download=refresh_cache,
+ use_auth_token=is_token_available,
+ )
+
+ # Cannot pre-resolve the specific class without double instantiation (first for config, second for model
+ # params). Default to current class, and perform basic class path resolution (handled via restore_from() +
+ # target class)
+ class_ = cls
+ return class_, path # type: ignore
+
+
+class typecheck:
+ """A decorator which performs input-output neural type checks, and attaches neural types to the output of the
+ function that it wraps. Requires that the class inherit from `atommic.core.Typing` in order to perform type
+ checking, and will raise an error if that is not the case.
+
+ # Usage (Class level type support)
+ .. code-block:: python
+
+ @typecheck()
+ def fn(self, arg1, arg2, ...):
+
+ # Usage (Function level type support)
+ .. code-block:: python
+
+ @typecheck(input_types=..., output_types=...)
+ def fn(self, arg1, arg2, ...):
+
+ Points to be noted:
+ 1) The brackets () in `@typecheck()` are necessary. You will encounter a TypeError: __init__() takes 1 \
+ positional argument but X were given without those brackets.
+ 2) The function can take any number of positional arguments during definition. When you call this function, \
+ all arguments must be passed using kwargs only.
+ """
+
+ class TypeState(Enum):
+ """
+ Placeholder to denote the default value of type information provided.
+ If the constructor of this decorator is used to override the class level type definition, this enum value
+ indicate that types will be overridden.
+ """
+
+ UNINITIALIZED = 0
+
+ def __init__(
+ self,
+ input_types: Union[TypeState, Optional[Dict[str, NeuralType]]] = TypeState.UNINITIALIZED,
+ output_types: Union[TypeState, Optional[Dict[str, NeuralType]]] = TypeState.UNINITIALIZED,
+ ignore_collections: bool = False,
+ ):
+ self.input_types = input_types
+ self.output_types = output_types
+
+ self.input_override = input_types != self.TypeState.UNINITIALIZED
+ self.output_override = output_types != self.TypeState.UNINITIALIZED
+ self.ignore_collections = ignore_collections
+
+ @wrapt.decorator(enabled=is_typecheck_enabled)
+ def __call__(self, wrapped, instance: Typing, args, kwargs):
+ """Wrapper method that can be used on any function of a class that implements :class:`~atommic.core.Typing`.
+ By default, it will utilize the `input_types` and `output_types` properties of the class inheriting Typing.
+ Local function level overrides can be provided by supplying dictionaries as arguments to the decorator.
+ """
+ if instance is None:
+ raise RuntimeError("Only classes which inherit atommic.core.Typing can use this decorator !")
+
+ if not isinstance(instance, Typing):
+ raise RuntimeError("Only classes which inherit atommic.core.Typing can use this decorator !")
+
+ if hasattr(instance, "input_ports") or hasattr(instance, "output_ports"):
+ raise RuntimeError(
+ "Typing requires override of `input_types()` and `output_types()`, "
+ "not `input_ports() and `output_ports()`"
+ )
+
+ # Preserve type information
+ if self.input_types is typecheck.TypeState.UNINITIALIZED:
+ self.input_types = instance.input_types
+
+ if self.output_types is typecheck.TypeState.UNINITIALIZED:
+ self.output_types = instance.output_types
+
+ # Resolve global type or local overridden type
+ input_types = self.input_types if self.input_override else instance.input_types
+ if self.output_override:
+ output_types = self.output_types
+ else:
+ output_types = instance.output_types
+
+ # If types are not defined, skip type checks and just call the wrapped method
+ if input_types is None and output_types is None:
+ return wrapped(*args, **kwargs)
+
+ # Check that all arguments are kwargs
+ if input_types is not None and len(args) > 0:
+ raise TypeError("All arguments must be passed by kwargs only for typed methods")
+
+ # Perform rudimentary input checks here
+ instance._validate_input_types(input_types=input_types, ignore_collections=self.ignore_collections, **kwargs)
+
+ # Call the method - this can be forward, or any other callable method
+ outputs = wrapped(*args, **kwargs)
+
+ instance._attach_and_validate_output_types(
+ output_types=output_types, ignore_collections=self.ignore_collections, out_objects=outputs
+ )
+
+ return outputs
+
+ @staticmethod
+ def set_typecheck_enabled(enabled: bool = True):
+ """Set the global typecheck flag."""
+ global _TYPECHECK_ENABLED
+ _TYPECHECK_ENABLED = enabled
+
+ @staticmethod
+ @contextmanager
+ def disable_checks():
+ """Temporarily disable type checks."""
+ typecheck.set_typecheck_enabled(enabled=False)
+ try:
+ yield
+ finally:
+ typecheck.set_typecheck_enabled(enabled=True)
diff --git a/atommic/core/classes/dataset.py b/atommic/core/classes/dataset.py
new file mode 100644
index 00000000..b0e97380
--- /dev/null
+++ b/atommic/core/classes/dataset.py
@@ -0,0 +1,100 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/dataset.py
+from abc import ABC
+from dataclasses import dataclass
+from typing import Optional
+
+from torch.utils import data
+
+from atommic.core.classes.common import Serialization, Typing, typecheck
+
+__all__ = ["Dataset", "DatasetConfig", "IterableDataset"]
+
+
+class Dataset(data.Dataset, Typing, Serialization, ABC):
+ """Dataset with output ports. Please Note: Subclasses of IterableDataset should *not* implement input_types."""
+
+ @staticmethod
+ def _collate_fn(batch):
+ """A default implementation of a collation function. Users should override this method to define custom data
+ loaders.
+ """
+ return data.dataloader.default_collate(batch)
+
+ @typecheck()
+ def collate_fn(self, batch):
+ """This is the method that user pass as functor to DataLoader.
+ The method optionally performs neural type checking and add types to the outputs.
+
+ Please note, subclasses of Dataset should not implement `input_types`.
+
+ # Usage:
+
+ .. code-block::
+
+ dataloader = torch.utils.data.DataLoader(
+ ....,
+ collate_fn=dataset.collate_fn,
+ ....
+ )
+
+ Returns
+ -------
+ Collated batch, with or without types.
+ """
+ if self.input_types is not None:
+ raise TypeError("Datasets should not implement `input_types` as they are not checked")
+
+ # Simply forward the inner `_collate_fn`
+ return self._collate_fn(batch)
+
+
+class IterableDataset(data.IterableDataset, Typing, Serialization, ABC):
+ """Iterable Dataset with output ports. Please Note: Subclasses of IterableDataset should *not* implement
+ input_types.
+ """
+
+ @staticmethod
+ def _collate_fn(batch):
+ """A default implementation of a collation function. Users should override this method to define custom data
+ loaders.
+ """
+ return data.dataloader.default_collate(batch)
+
+ @typecheck()
+ def collate_fn(self, batch):
+ """This is the method that user pass as functor to DataLoader. The method optionally performs neural type
+ checking and add types to the outputs.
+
+ # Usage:
+
+ .. code-block::
+
+ dataloader = torch.utils.data.DataLoader(
+ ....,
+ collate_fn=dataset.collate_fn,
+ ....
+ )
+
+ Returns
+ -------
+ Collated batch, with or without types.
+ """
+ if self.input_types is not None:
+ raise TypeError("Datasets should not implement `input_types` as they are not checked")
+
+ # Simply forward the inner `_collate_fn`
+ return self._collate_fn(batch)
+
+
+@dataclass
+class DatasetConfig:
+ """Dataset configuration."""
+
+ batch_size: int = 32
+ drop_last: bool = False
+ shuffle: bool = False
+ num_workers: Optional[int] = 0
+ pin_memory: bool = True
diff --git a/atommic/core/classes/export.py b/atommic/core/classes/export.py
new file mode 100644
index 00000000..153c221f
--- /dev/null
+++ b/atommic/core/classes/export.py
@@ -0,0 +1,306 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/exportable.py
+
+from abc import ABC
+from typing import List, Union
+
+import torch
+from pytorch_lightning.core.module import _jit_is_scripting
+from torch.onnx import TrainingMode
+
+from atommic.core.classes.common import typecheck
+from atommic.core.utils.neural_type_utils import get_dynamic_axes, get_io_names
+from atommic.utils import logging
+from atommic.utils.export_utils import (
+ ExportFormat,
+ augment_filename,
+ get_export_format,
+ parse_input_example,
+ replace_for_export,
+ verify_runtime,
+ verify_torchscript,
+ wrap_forward_method,
+)
+
+__all__ = ["ExportFormat", "Exportable"]
+
+
+class Exportable(ABC):
+ """This Interface should be implemented by particular classes derived from atommic.core.ModelPT. It gives these
+ entities ability to be exported for deployment to formats such as ONNX.
+ """
+
+ @property
+ def input_module(self):
+ """Implement this method to return the input module"""
+ return self
+
+ @property
+ def output_module(self):
+ """Implement this method to return the output module."""
+ return self
+
+ def export(
+ self,
+ output: str,
+ input_example=None,
+ verbose=False,
+ do_constant_folding=True,
+ onnx_opset_version=None,
+ check_trace: Union[bool, List[torch.Tensor]] = False,
+ dynamic_axes=None,
+ check_tolerance=0.01,
+ export_modules_as_functions: bool = False,
+ keep_initializers_as_inputs=None,
+ ):
+ """Export the module to a file.
+
+ Parameters
+ ----------
+ output : str
+ The output file path.
+ input_example : dict
+ A dictionary of input names and values.
+ verbose : bool
+ If True, print out the export process.
+ do_constant_folding : bool
+ If True, do constant folding.
+ onnx_opset_version : int
+ The ONNX opset version to use.
+ check_trace : bool or list of torch.Tensor
+ If True, check the trace of the exported model.
+ dynamic_axes : dict
+ A dictionary of input names and dynamic axes.
+ check_tolerance : float
+ The tolerance for the check_trace.
+ export_modules_as_functions : bool
+ If True, export modules as functions.
+ keep_initializers_as_inputs : bool
+ If True, keep initializers as inputs.
+ """
+ all_out = []
+ all_descr = []
+ for subnet_name in self.list_export_subnets():
+ model = self.get_export_subnet(subnet_name)
+ out_name = augment_filename(output, subnet_name)
+ out, descr, out_example = model._export( # pylint: disable=protected-access
+ out_name,
+ input_example=input_example,
+ verbose=verbose,
+ do_constant_folding=do_constant_folding,
+ onnx_opset_version=onnx_opset_version,
+ check_trace=check_trace,
+ dynamic_axes=dynamic_axes,
+ check_tolerance=check_tolerance,
+ export_modules_as_functions=export_modules_as_functions,
+ keep_initializers_as_inputs=keep_initializers_as_inputs,
+ )
+ # Propagate input example (default scenario, may need to be overriden)
+ if input_example is not None:
+ input_example = out_example
+ all_out.append(out)
+ all_descr.append(descr)
+ logging.info(f"Successfully exported {model.__class__.__name__} to {out_name}")
+ return all_out, all_descr
+
+ def _export(
+ self,
+ output: str,
+ input_example=None,
+ verbose=False,
+ do_constant_folding=True,
+ onnx_opset_version=None,
+ training=TrainingMode.EVAL, # pylint: disable=unused-argument
+ check_trace: Union[bool, List[torch.Tensor]] = False,
+ dynamic_axes=None,
+ check_tolerance=0.01,
+ export_modules_as_functions: bool = False,
+ keep_initializers_as_inputs=None,
+ ):
+ """Helper to export the module to a file.
+
+ Parameters
+ ----------
+ output : str
+ The output file path.
+ input_example : dict
+ A dictionary of input names and values.
+ verbose : bool
+ If True, print out the export process.
+ do_constant_folding : bool
+ If True, do constant folding.
+ onnx_opset_version : int
+ The ONNX opset version to use.
+ training : TrainingMode
+ Training mode for the export.
+ check_trace : bool or list of torch.Tensor
+ If True, check the trace of the exported model.
+ dynamic_axes : dict
+ A dictionary of input names and dynamic axes.
+ check_tolerance : float
+ The tolerance for the check_trace.
+ export_modules_as_functions : bool
+ If True, export modules as functions.
+ keep_initializers_as_inputs : bool
+ If True, keep initializers as inputs.
+ """
+ my_args = locals().copy()
+ my_args.pop("self")
+
+ self.eval() # type: ignore
+ for param in self.parameters(): # type: ignore
+ param.requires_grad = False
+
+ exportables = [m for m in self.modules() if isinstance(m, Exportable)] # type: ignore
+ qual_name = f"{self.__module__}.{self.__class__.__qualname__}"
+ exp_format = get_export_format(output)
+ output_descr = f"{qual_name} exported to {exp_format}"
+
+ # Pytorch's default opset version is too low, using reasonable latest one
+ if onnx_opset_version is None:
+ onnx_opset_version = 16
+
+ try:
+ # Disable typechecks
+ typecheck.set_typecheck_enabled(enabled=False)
+
+ # Allow user to completely override forward method to export
+ forward_method, old_forward_method = wrap_forward_method(self)
+
+ with torch.inference_mode(), torch.no_grad(), torch.jit.optimized_execution(True), _jit_is_scripting():
+ if input_example is None:
+ input_example = self.input_module.input_example()
+
+ # Remove i/o examples from args we propagate to enclosed Exportables
+ my_args.pop("output")
+ my_args.pop("input_example")
+
+ # Run (possibly overridden) prepare methods before calling forward()
+ for ex in exportables:
+ ex._prepare_for_export(**my_args, noreplace=True) # pylint: disable=protected-access
+ self._prepare_for_export(output=output, input_example=input_example, **my_args)
+
+ input_list, input_dict = parse_input_example(input_example)
+ input_names = self.input_names
+ output_names = self.output_names
+ output_example = tuple(self.forward(*input_list, **input_dict)) # type: ignore
+
+ if check_trace:
+ if isinstance(check_trace, bool):
+ check_trace_input = [input_example]
+ else:
+ check_trace_input = check_trace
+ jitted_model = self
+ if exp_format == ExportFormat.TORCHSCRIPT:
+ jitted_model = torch.jit.trace_module(
+ self,
+ {"forward": tuple(input_list) + tuple(input_dict.values())},
+ strict=True,
+ check_trace=check_trace,
+ check_tolerance=check_tolerance,
+ )
+ jitted_model = torch.jit.freeze(jitted_model)
+ if verbose:
+ logging.info(f"JIT code:\n{jitted_model.code}") # type: ignore
+ jitted_model.save(output) # type: ignore
+ jitted_model = torch.jit.load(output)
+
+ if check_trace:
+ verify_torchscript(jitted_model, output, check_trace_input, check_tolerance)
+ elif exp_format == ExportFormat.ONNX:
+ # dynamic axis is a mapping from input/output_name => list of "dynamic" indices
+ if dynamic_axes is None:
+ dynamic_axes = get_dynamic_axes(self.input_module.input_types_for_export, input_names)
+ dynamic_axes.update(get_dynamic_axes(self.output_module.output_types_for_export, output_names))
+ torch.onnx.export(
+ jitted_model,
+ input_example,
+ output,
+ input_names=input_names,
+ output_names=output_names,
+ verbose=verbose,
+ do_constant_folding=do_constant_folding,
+ dynamic_axes=dynamic_axes,
+ opset_version=onnx_opset_version,
+ keep_initializers_as_inputs=keep_initializers_as_inputs,
+ export_modules_as_functions=export_modules_as_functions,
+ )
+
+ if check_trace:
+ verify_runtime(self, output, check_trace_input, input_names, check_tolerance=check_tolerance)
+ else:
+ raise ValueError(f"Encountered unknown export format {exp_format}.")
+ finally:
+ typecheck.set_typecheck_enabled(enabled=True)
+ if forward_method: # pylint: disable=used-before-assignment
+ type(self).forward = old_forward_method # type: ignore # pylint: disable=used-before-assignment
+ self._export_teardown()
+ return (output, output_descr, output_example)
+
+ @property
+ def disabled_deployment_input_names(self):
+ """Implement this method to return a set of input names disabled for export"""
+ return set()
+
+ @property
+ def disabled_deployment_output_names(self):
+ """Implement this method to return a set of output names disabled for export"""
+ return set()
+
+ @property
+ def supported_export_formats(self):
+ """Implement this method to return a set of export formats supported. Default is all types."""
+ return {ExportFormat.ONNX, ExportFormat.TORCHSCRIPT}
+
+ def _prepare_for_export(self, **kwargs):
+ """Override this method to prepare module for export. This is in-place operation. Base version does common
+ necessary module replacements (Apex etc)
+ """
+ if "noreplace" not in kwargs:
+ replace_for_export(self)
+
+ def _export_teardown(self):
+ """Override this method for any teardown code after export."""
+
+ @property
+ def input_names(self):
+ """Implement this method to return a list of input names"""
+ return get_io_names(self.input_module.input_types_for_export, self.disabled_deployment_input_names)
+
+ @property
+ def output_names(self):
+ """Override this method to return a set of output names disabled for export"""
+ return get_io_names(self.output_module.output_types_for_export, self.disabled_deployment_output_names)
+
+ @property
+ def input_types_for_export(self):
+ """Implement this method to return a list of input types"""
+ return self.input_types
+
+ @property
+ def output_types_for_export(self):
+ """Implement this method to return a list of output types"""
+ return self.output_types
+
+ def get_export_subnet(self, subnet=None):
+ """Returns Exportable subnet model/module to export"""
+ return self if subnet is None or subnet == "self" else getattr(self, subnet)
+
+ @staticmethod
+ def list_export_subnets():
+ """Returns default set of subnet names exported for this model. First goes the one receiving input
+ (input_example).
+ """
+ return ["self"]
+
+ def get_export_config(self):
+ """Returns export_config dictionary."""
+ return getattr(self, 'export_config', {})
+
+ def set_export_config(self, args):
+ """Sets/updates export_config dictionary."""
+ ex_config = self.get_export_config()
+ ex_config.update(args)
+ self.export_config = ex_config
diff --git a/atommic/core/classes/loss.py b/atommic/core/classes/loss.py
new file mode 100644
index 00000000..7d2378eb
--- /dev/null
+++ b/atommic/core/classes/loss.py
@@ -0,0 +1,14 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/loss.py
+
+import torch
+
+from atommic.core.classes.common import Serialization, Typing
+
+__all__ = ["Loss"]
+
+
+class Loss(torch.nn.modules.loss._Loss, Typing, Serialization): # pylint: disable=protected-access
+ """Inherit this class to implement custom loss."""
diff --git a/atommic/core/classes/modelPT.py b/atommic/core/classes/modelPT.py
new file mode 100644
index 00000000..64a2f019
--- /dev/null
+++ b/atommic/core/classes/modelPT.py
@@ -0,0 +1,1643 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/classes/modelPT.py
+
+import copy
+import inspect
+import os
+import uuid
+from abc import abstractmethod
+from os import path
+from pathlib import Path
+from typing import Any, Callable, Dict, Iterator, List, Optional, Tuple, Union
+
+import hydra
+import torch
+from omegaconf import DictConfig, OmegaConf, open_dict
+from pytorch_lightning import LightningModule, Trainer
+from pytorch_lightning.utilities import model_summary, rank_zero_only
+
+import atommic.core.optim
+import atommic.utils
+from atommic import package_info
+from atommic.core.classes.common import Model
+from atommic.core.connectors.save_restore_connector import SaveRestoreConnector
+from atommic.utils import logging
+from atommic.utils.app_state import AppState
+from atommic.utils.exceptions import ATOMMICBaseException
+from atommic.utils.get_rank import get_rank, is_global_rank_zero
+
+__all__ = ["ModelPT"]
+
+
+class ModelPT(LightningModule, Model):
+ """Interface for Pytorch-lightning based atommic models."""
+
+ def __init__(self, cfg: DictConfig, trainer: Trainer = None): # noqa: MC0001
+ """Base class from which all atommic models should inherit
+
+ Internal global flags that determine core functionality of ModelPT.
+
+ _MODEL_IS_RESTORED:
+ This flag determines the context of the model - whether the model is currently being
+ restored or not.
+
+ - When set, it can be assumed that the model's will disable all automatic methods -
+ setup_training_data(), setup_validation/test_data() and their multi equivalents.
+
+ - If a model is being restored from a archive file (tarfile), it can be assumed that
+ under this context, the cwd is *inside* the tarfile itself.
+
+ _MODEL_RESTORE_PATH:
+ A string path to a a file from which the model is being restored.
+
+ This file can either be a PyTorch Lightning Checkpoint, or a archive (tarfile) that contains
+ artifact objects.
+
+ If it is an archive file, during restoration, the cwd will be temporarily moved to inside the
+ archive itself.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ The cfg object should have (optionally) the following sub-configs:
+
+ - train_ds - to instantiate training dataset
+
+ - validation_ds - to instantiate validation dataset
+
+ - test_ds - to instantiate testing dataset
+
+ - optim - to instantiate optimizer with learning rate scheduler
+
+ trainer : Trainer
+ Pytorch Lightning Trainer instance
+ """
+ if trainer is not None and not isinstance(trainer, Trainer):
+ raise ValueError(
+ "trainer constructor argument must be either None or pytorch_lightning.Trainer. "
+ f"But got {type(trainer)} instead."
+ )
+ super().__init__()
+
+ # set global vars in AppState
+ app_state = AppState()
+
+ # Convert config to a DictConfig
+ cfg = atommic.utils.model_utils.convert_model_config_to_dict_config(cfg)
+
+ # Convert config to support Hydra 1.0+ instantiation
+ cfg = atommic.utils.model_utils.maybe_update_config_version(cfg)
+
+ if "model" in cfg:
+ raise ValueError(
+ "Creating model config node is forbidden due to collision problem when loading from checkpoint."
+ )
+
+ if "target" not in cfg:
+ # This is for Jarvis service.
+ OmegaConf.set_struct(cfg, False)
+ cfg.target = f"{self.__class__.__module__}.{self.__class__.__name__}"
+ OmegaConf.set_struct(cfg, True)
+
+ if "atommic_version" not in cfg:
+ with open_dict(cfg):
+ cfg.atommic_version = package_info.__version__
+
+ self._cfg = cfg
+
+ # init mapping submodule attribute -> config_field for nested atommic models
+ self._atommic_submodule_name_to_config_field: Dict = {}
+
+ self.save_hyperparameters("cfg")
+ self._train_dl = None
+ self._validation_dl = None
+ self._test_dl = None
+ self._optimizer_param_groups = None
+ self._optimizer = None
+ self._scheduler = None
+ self.set_trainer(trainer)
+
+ self._save_restore_connector = SaveRestoreConnector()
+
+ self._set_model_guid()
+
+ # Set device_id in AppState
+ if torch.cuda.is_available() and torch.cuda.current_device() is not None:
+ app_state.device_id = torch.cuda.current_device()
+
+ if self._cfg is not None and not self._is_model_being_restored():
+ # Setup data loaders now (default) or defer setup to `self.setup()` if `defer_setup` is set in the config
+ # of the corresponding dataloader.
+ if (
+ "train_ds" in self._cfg
+ and self._cfg.train_ds is not None
+ and not self._cfg.train_ds.get("defer_setup", False)
+ ):
+ self.setup_training_data(self._cfg.train_ds)
+
+ if (
+ "validation_ds" in self._cfg
+ and self._cfg.validation_ds is not None
+ and not self._cfg.validation_ds.get("defer_setup", False)
+ ):
+ self.setup_multiple_validation_data(val_data_config=cfg.validation_ds)
+
+ if (
+ "test_ds" in self._cfg
+ and self._cfg.test_ds is not None
+ and not self._cfg.test_ds.get("defer_setup", False)
+ ):
+ self.setup_multiple_test_data(test_data_config=cfg.test_ds)
+
+ else:
+ if "train_ds" in self._cfg and self._cfg.train_ds is not None:
+ logging.warning(
+ "If you intend to do training or fine-tuning, please call the ModelPT.setup_training_data() "
+ "method and provide a valid configuration file to setup the train data loader.\n"
+ f"Train config : \n{OmegaConf.to_yaml(self._cfg.train_ds)}"
+ )
+ if "validation_ds" in self._cfg and self._cfg.validation_ds is not None:
+ logging.warning(
+ "If you intend to do validation, please call the ModelPT.setup_validation_data() or "
+ "ModelPT.setup_multiple_validation_data() method and provide a valid configuration file to "
+ "setup the validation data loader(s). \n"
+ f"Validation config : \n{OmegaConf.to_yaml(self._cfg.validation_ds)}"
+ )
+ if "test_ds" in self._cfg and self._cfg.test_ds is not None:
+ logging.warning(
+ "Please call the ModelPT.setup_test_data() or ModelPT.setup_multiple_test_data() method "
+ "and provide a valid configuration file to setup the test data loader(s).\n"
+ f"Test config : \n{OmegaConf.to_yaml(self._cfg.test_ds)}"
+ )
+
+ # Create list of lists for val and test outputs to support multiple dataloaders
+ # Initialize an empty list as sometimes self._validation_dl can be None at this stage
+ self.validation_step_outputs: List = []
+ # Check len(self._validation_dl) > 1 as sometimes single dataloader can be in a list: []
+ # when ds_item in config has 1 item passed in a list
+ if self._validation_dl and isinstance(self._validation_dl, list) and len(self._validation_dl) > 1:
+ for _ in range(len(self._validation_dl)):
+ self.validation_step_outputs.append([])
+
+ # Initialize an empty list as sometimes self._test_dl can be None at this stage
+ self.test_step_outputs: List = []
+ if self._test_dl and isinstance(self._test_dl, list) and len(self._test_dl) > 1:
+ for _ in range(len(self._test_dl)):
+ self.test_step_outputs.append([])
+ # ModelPT wrappers over subclass implementations
+ self.training_step = atommic.utils.model_utils.wrap_training_step( # pylint: disable=no-value-for-parameter
+ self.training_step # type: ignore
+ )
+
+ # Setup nsys profiling if it has been enabled in the model config
+ self._setup_nsys_profiling()
+
+ def __init_subclass__(cls) -> None:
+ """This method is called when a subclass is created."""
+ cls._save_restore_connector = SaveRestoreConnector()
+
+ def register_artifact(self, config_path: str, src: str, verify_src_exists: bool = True):
+ r"""Register model artifacts with this function. These artifacts (files) will be included inside .atommic file
+ when model.save_to("model.atommic") is called.
+
+ How it works:
+
+ 1. It always returns existing absolute path which can be used during Model constructor call EXCEPTION: src
+ is None or "" in which case nothing will be done and src will be returned
+
+ 2. It will add (config_path, model_utils.ArtifactItem()) pair to self.artifacts
+
+ .. code-block::
+
+ If "src" is local existing path:
+
+ then it will be returned in absolute path form.
+
+ elif "src" starts with "atommic_file:unique_artifact_name":
+
+ .atommic will be untarred to a temporary folder location and an actual existing path will be
+ returned
+
+ else:
+
+ an error will be raised.
+
+ If "src" is local existing path, then it will be returned in absolute path form.
+ elif "src" starts with "atommic_file:unique_artifact_name" .atommic will be untarred to a temporary folder
+ location and an actual existing path will be returned else an error will be raised.
+
+ WARNING: use .register_artifact calls in your models' constructors.
+ The returned path is not guaranteed to exist after you have exited your model's constructor.
+
+ Parameters
+ ----------
+ config_path : str
+ Artifact key. Usually corresponds to the model config.
+ src : str
+ Path to artifact.
+ verify_src_exists : bool
+ If set to False, then the artifact is optional and register_artifact will return None even if src is not
+ found. Default is ``True``.
+
+ Returns
+ -------
+ If src is not None or empty it always returns absolute path which is guaranteed to exist during model instance
+ life.
+ """
+ if src is None or not src:
+ return src
+
+ if Path(src).suffix == ".atommic":
+ raise ATOMMICBaseException(
+ "Registering .atommic files as artifacts not supported. "
+ "If you are trying to make a nested model, use `register_atommic_submodule`."
+ )
+
+ if not hasattr(self, "artifacts"):
+ self.artifacts: Dict[str, atommic.utils.model_utils.ArtifactItem] = {}
+
+ if self.artifacts is None:
+ self.artifacts = {}
+
+ if config_path in self.artifacts:
+ logging.warning(
+ f"You tried to register an artifact under config key={config_path} but an artifact for "
+ "it has already been registered."
+ )
+
+ return self._save_restore_connector.register_artifact(self, config_path, src, verify_src_exists)
+
+ def has_artifacts(self) -> bool:
+ """Returns True if model has artifacts registered."""
+ return hasattr(self, "artifacts") and self.artifacts is not None and len(self.artifacts) > 0
+
+ def has_native_or_submodules_artifacts(self) -> bool:
+ """Returns True if it has artifacts or any of the submodules have artifacts."""
+ return any(
+ (
+ isinstance(module, ModelPT)
+ and hasattr(module, "artifacts")
+ and module.artifacts is not None
+ and len(module.artifacts) > 0
+ )
+ for module in self.modules()
+ )
+
+ def has_atommic_submodules(self) -> bool:
+ """Returns True if it has any registered atommic submodules."""
+ return len(self._atommic_submodule_name_to_config_field) > 0
+
+ def register_atommic_submodule(self, name: str, config_field: str, model: "ModelPT") -> None:
+ """Adds a atommic model as a submodule.
+
+ Submodule can be accessed via the `name` attribute on the parent atommic model this submodule was registered on
+ (`self`).
+
+ In the saving process, the whole parent model (self) is held as a solid model with artifacts from the child
+ submodule, the submodule config will be saved to the `config_field` of the parent model.
+
+ This method is necessary to create a nested model, e.g.
+ .. code-block:: python
+
+ class ParentModel(ModelPT):
+ def __init__(self, cfg, trainer=None):
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ # annotate type for autocompletion and type checking (optional)
+ self.child_model: Optional[ChildModel] = None
+ if cfg.get("child_model") is not None:
+ self.register_atommic_submodule(
+ name="child_model",
+ config_field="child_model",
+ model=ChildModel(self.cfg.child_model, trainer=trainer),
+ )
+ # ... other code
+
+ Parameters
+ ----------
+ name : str
+ Name of the submodule. This name will be used to access the submodule from the parent model.
+ config_field : str
+ Name of the config field where the submodule config will be saved.
+ model : ModelPT
+ The submodule model.
+ """
+ # check it is a real atommic model
+ if not isinstance(model, ModelPT):
+ raise ATOMMICBaseException(
+ f"Model is not and instance of ModelPT, so can't be registered. Got {type(model).__name__}"
+ )
+ # check if it is called after __init__
+ if not hasattr(self, "_atommic_submodule_name_to_config_field"):
+ raise ATOMMICBaseException(
+ "You are trying to register a submodule before the model is initialized. This is not allowed. "
+ "Did you forget to call `super().__init__`?"
+ )
+ # assign attribute to self
+ setattr(self, name, model)
+ # add to the submodules mapping
+ self._atommic_submodule_name_to_config_field[name] = config_field
+
+ def named_atommic_modules(
+ self, prefix_name: str = "", prefix_config: str = ""
+ ) -> Iterator[Tuple[str, str, "ModelPT"]]:
+ """Returns an iterator over all atommic submodules recursively, yielding tuples of (attribute path, path in
+ config, submodule), starting from the core module.
+
+ Parameters
+ ----------
+ prefix_name : str
+ Prefix for the name path.
+ prefix_config : str
+ Prefix for the path in config.
+
+ Returns
+ -------
+ Iterator over (attribute path, path in config, submodule), starting from (prefix, self).
+ """
+ if not hasattr(self, "_atommic_submodule_name_to_config_field"):
+ raise ATOMMICBaseException(
+ "Model is not fully initialized. Calling `named_atommic_modules` before __init__ not allowed. "
+ "Did you forget to call `super().__init__`?"
+ )
+
+ yield prefix_name, prefix_config, self
+
+ # recursive iteration over all atommic submodules
+ for name, config_field in self._atommic_submodule_name_to_config_field.items():
+ attribute_path = f"{prefix_name}.{name}" if prefix_name else name
+ config_path = f"{prefix_config}.{config_field}" if prefix_config else config_field
+ module: ModelPT = getattr(self, name)
+ for submodule_name, subconfig_path, submodule in module.named_atommic_modules(
+ prefix_name=attribute_path, prefix_config=config_path
+ ):
+ yield submodule_name, subconfig_path, submodule
+
+ def save_to(self, save_path: str):
+ """Saves model instance (weights and configuration) into .atommic file. You can use "restore_from" method to
+ fully restore instance from .atommic file. .atommic file is an archive (tar.gz) with the following:
+
+ * model_config.yaml - model configuration in .yaml format. You can deserialize this into cfg argument for
+ model's constructor
+
+ * model_wights.ckpt - model checkpoint
+
+ Parameters
+ ----------
+ Path to .atommic file where model instance should be saved.
+ """
+
+ def maybe_make_save_dir(_path: "Path"):
+ """Creates directory if it does not exist"""
+ if not _path.parent.exists():
+ _path.parent.mkdir(parents=True)
+
+ save_path = Path(save_path).expanduser().resolve() # type: ignore
+ app_state = AppState()
+ if app_state.model_parallel_size is not None:
+ if app_state.model_parallel_size > 1 and isinstance(self._save_restore_connector, SaveRestoreConnector):
+ raise ValueError(
+ "Default atommic SaveRestoreConnector will not work in model parallel mode. You should use a "
+ "connector which supports model parallel mode. You can also use a custom one."
+ )
+ if is_global_rank_zero():
+ maybe_make_save_dir(save_path) # type: ignore
+ if torch.distributed.is_initialized():
+ torch.distributed.barrier()
+ # connector checks for ranks properly, no need to check here
+ # downstream tasks expect str, not Path
+ self._save_restore_connector.save_to(self, str(save_path))
+ elif is_global_rank_zero():
+ maybe_make_save_dir(Path(save_path))
+ # downstream tasks expect str, not Path
+ self._save_restore_connector.save_to(self, str(save_path))
+
+ @classmethod
+ def restore_from( # type: ignore # pylint: disable=arguments-renamed
+ cls,
+ restore_path: str,
+ override_config_path: Optional[Union[OmegaConf, str]] = None,
+ map_location: Optional[torch.device] = None,
+ strict: bool = True,
+ return_config: bool = False,
+ save_restore_connector: SaveRestoreConnector = None,
+ trainer: Optional[Trainer] = None,
+ ):
+ """Restores model instance (weights and configuration) from .atommic file.
+
+ Parameters
+ ----------
+ restore_path : str
+ Path to .atommic file from which model should be instantiated override_config_path: path to a yaml config
+ that will override the internal config file or an OmegaConf/DictConfig object representing the model
+ config.
+ override_config_path : Optional[Union[OmegaConf, str]]
+ path to a yaml config that will override the internal config file or an OmegaConf/DictConfig object
+ representing the model config.
+ map_location : Optional[torch.device]
+ Optional torch.device() to map the instantiated model to a device. Default is ``None``, it will select a
+ GPU if available, falling back to CPU otherwise.
+ strict : bool
+ Passed to load_state_dict. Default is ``True``.
+ return_config : bool
+ If set to true, will return just the underlying config of the restored model as an OmegaConf/DictConfig
+ object without instantiating the model.
+ save_restore_connector : SaveRestoreConnector
+ Can be overridden to add custom save and restore logic.
+ trainer : Optional[Trainer]
+ Optional, a pytorch lightning Trainer object that will be forwarded to the instantiated model's
+ constructor.
+
+ Example
+ -------
+
+ .. code-block::
+
+ model = atommic.collections.reconstruction.nn.CIRIM.restore_from('CIRIM.atommic')
+ assert isinstance(model, atommic.collections.reconstruction.nn.CIRIM)
+
+ Returns
+ -------
+ An instance of type cls or its underlying config (if return_config is set).
+ """
+ if save_restore_connector is None:
+ save_restore_connector = SaveRestoreConnector()
+
+ if save_restore_connector.model_extracted_dir is None:
+ restore_path = os.path.abspath(os.path.expanduser(restore_path))
+ else:
+ restore_path = os.path.abspath(os.path.expanduser(save_restore_connector.model_extracted_dir))
+
+ if not path.exists(restore_path):
+ raise FileNotFoundError(f"Can't find {restore_path}")
+
+ app_state = AppState()
+ app_state.model_restore_path = restore_path
+
+ cls.update_save_restore_connector(save_restore_connector)
+ instance = cls._save_restore_connector.restore_from(
+ cls, restore_path, override_config_path, map_location, strict, return_config, trainer
+ )
+ if isinstance(instance, ModelPT):
+ instance._save_restore_connector = save_restore_connector # pylint: disable=protected-access
+ return instance
+
+ @classmethod
+ def load_from_checkpoint(
+ cls,
+ checkpoint_path: str,
+ *args,
+ map_location: Optional[Union[Dict[str, str], str, torch.device, int, Callable]] = None,
+ hparams_file: Optional[str] = None,
+ strict: bool = True,
+ **kwargs,
+ ):
+ """Loads ModelPT from checkpoint, with some maintenance of restoration. For documentation, please refer to
+ LightningModule.load_from_checkpoint() documentation.
+
+ Parameters
+ ----------
+ checkpoint_path : str
+ Path to checkpoint. Will be passed to LightningModule.load_from_checkpoint().
+ *args
+ Will be passed to LightningModule.load_from_checkpoint().
+ map_location : Optional[Union[Dict[str, str], str, torch.device, int, Callable]]
+ Will be passed to LightningModule.load_from_checkpoint().
+ hparams_file : Optional[str]
+ Will be passed to LightningModule.load_from_checkpoint().
+ strict : bool
+ Will be passed to LightningModule.load_from_checkpoint().
+ """
+ checkpoint = None
+
+ try:
+ cls._set_model_restore_state(is_being_restored=True)
+
+ checkpoint = super().load_from_checkpoint(
+ checkpoint_path=checkpoint_path,
+ *args,
+ map_location=map_location,
+ hparams_file=hparams_file,
+ strict=strict,
+ **kwargs,
+ )
+
+ finally:
+ cls._set_model_restore_state(is_being_restored=False)
+ return checkpoint
+
+ @abstractmethod
+ def setup_training_data(self, train_data_config: Union[DictConfig, Dict]):
+ """Setups data loader to be used in training."""
+
+ @abstractmethod
+ def setup_validation_data(self, val_data_config: Union[DictConfig, Dict]):
+ """Setups data loader to be used in validation."""
+
+ def setup_test_data(self, test_data_config: Union[DictConfig, Dict]):
+ """(Optionally) Setups data loader to be used in test."""
+ raise NotImplementedError()
+
+ def setup_multiple_validation_data(self, val_data_config: Union[DictConfig, Dict]):
+ """(Optionally) Setups data loader to be used in validation."""
+ # Set some placeholder overridden by helper method
+ self._val_dl_idx = 0
+ self.validation_names = None
+
+ # preserve config
+ self._update_dataset_config(dataset_name="validation", config=val_data_config)
+
+ try:
+ self._multi_dataset_mode = True
+ atommic.utils.model_utils.resolve_validation_dataloaders(model=self)
+ finally:
+ self._multi_dataset_mode = False
+
+ if (
+ self.validation_names is None
+ and self._validation_dl is not None
+ and type(self._validation_dl) in [list, tuple]
+ ):
+ self.validation_names = [f"val_{idx}_" for idx in range(len(self._validation_dl))]
+
+ def setup_multiple_test_data(self, test_data_config: Union[DictConfig, Dict]):
+ """(Optionally) Setups data loader to be used in test, with support for multiple data loaders."""
+ # Set some placeholder overridden by helper method
+ self._test_dl_idx = 0
+ self.test_names = None
+ self._test_dl = None
+
+ # preserve config
+ self._update_dataset_config(dataset_name="test", config=test_data_config)
+
+ try:
+ self._multi_dataset_mode = True
+ atommic.utils.model_utils.resolve_test_dataloaders(model=self)
+ finally:
+ self._multi_dataset_mode = False
+
+ if self.test_names is None and self._test_dl is not None and type(self._test_dl) in [list, tuple]:
+ self.test_names = [f"test_{idx}_" for idx in range(len(self._test_dl))]
+
+ def setup_optimization(self, optim_config: Optional[Union[DictConfig, Dict]] = None): # noqa: MC0001
+ """Prepares an optimizer from a string name and its optional config parameters.
+
+ Parameters
+ ----------
+ optim_config : Optional[Union[DictConfig, Dict]]
+ A dictionary containing the following keys:
+ - lr : float
+ Mandatory key for learning rate. Will raise ValueError if not provided.
+ - optimizer : str
+ String name pointing to one of the available optimizers in the registry. Default is ``Adam``.
+ - opt_args : list
+ Optional list of strings, in the format "arg_name=arg_value". The list of "arg_value" will be
+ parsed and a dictionary of optimizer kwargs will be built and supplied to instantiate the
+ optimizer.
+
+ Returns
+ -------
+ An instance of an optimizer.
+ """
+ # Setup the optimizer parameter groups (by default use all parameters that are trainable).
+ self.setup_optimizer_param_groups()
+
+ # If config was not explicitly provided, use default
+ if optim_config is None and self._cfg is not None and hasattr(self._cfg, "optim"):
+ optim_config = self._cfg.optim
+
+ # If config is still None, or internal config has no Optim, return without instantiation
+ if optim_config is None:
+ logging.info("No optimizer config provided, therefore no optimizer was created")
+ return
+ # Preserve the configuration
+ if not isinstance(optim_config, DictConfig):
+ optim_config = OmegaConf.create(optim_config)
+
+ # See if internal config has `optim` namespace before preservation
+ if self._cfg is not None and hasattr(self._cfg, "optim"):
+ if self._cfg.optim is None:
+ self._cfg.optim = copy.deepcopy(optim_config)
+ else:
+ with open_dict(self._cfg.optim):
+ self._cfg.optim = copy.deepcopy(optim_config)
+
+ # Setup optimizer and scheduler
+ if optim_config is not None and isinstance(optim_config, DictConfig):
+ optim_config = OmegaConf.to_container(optim_config, resolve=True)
+
+ if self._trainer is None:
+ logging.warning("Trainer wasn't specified in model constructor. Make sure that you really wanted it.")
+
+ if "sched" in optim_config and self._trainer is not None:
+ if not isinstance(self._trainer.accumulate_grad_batches, int):
+ raise ValueError("We do not currently support gradient accumulation that is not an integer.")
+ if self._trainer.max_steps is None or self.trainer.max_steps < 0:
+ # Store information needed to calculate max_steps
+ optim_config["sched"]["t_max_epochs"] = self._trainer.max_epochs
+ optim_config["sched"]["t_accumulate_grad_batches"] = self._trainer.accumulate_grad_batches
+ optim_config["sched"]["t_limit_train_batches"] = self._trainer.limit_train_batches
+ optim_config["sched"]["t_num_workers"] = self._trainer.num_devices * self._trainer.num_nodes
+ else:
+ optim_config["sched"]["max_steps"] = self._trainer.max_steps
+
+ # Force into DictConfig from nested structure
+ optim_config = OmegaConf.create(optim_config)
+ # Get back nested dict so we its mutable
+ optim_config = OmegaConf.to_container(optim_config, resolve=True)
+
+ # Extract scheduler config if inside optimizer config
+ if "sched" in optim_config:
+ scheduler_config = optim_config.pop("sched")
+ else:
+ scheduler_config = None
+
+ # Check if caller provided optimizer name, default to Adam otherwise
+ optimizer_cls = optim_config.get("_target_", None)
+
+ if optimizer_cls is None:
+ # Try to get optimizer name for dynamic resolution, defaulting to Adam
+ optimizer_name = optim_config.get("name", "adam")
+ elif inspect.isclass(optimizer_cls):
+ optimizer_name = optimizer_cls.__name__.lower()
+ else:
+ # resolve the class name (lowercase) from the class path if not provided
+ optimizer_name = optimizer_cls.split(".")[-1].lower()
+
+ # We are guaranteed to have lr since it is required by the argparser
+ # But maybe user forgot to pass it to this function
+ lr = optim_config.get("lr", None)
+
+ # Check if caller has optimizer kwargs, default to empty dictionary
+ if "args" in optim_config:
+ optimizer_args = optim_config.pop("args")
+ optimizer_args = atommic.core.optim.optimizers.parse_optimizer_args(optimizer_name, optimizer_args)
+ else:
+ optimizer_args = copy.deepcopy(optim_config)
+
+ # Remove extra parameters from optimizer_args nest
+ # Assume all other parameters are to be passed into optimizer constructor
+ optimizer_args.pop("name", None)
+ optimizer_args.pop("cls", None)
+ optimizer_args.pop("lr", None)
+
+ # Adaptive schedulers don't need `lr`
+ if lr is not None:
+ optimizer_args["lr"] = lr
+
+ # Actually instantiate the optimizer
+ if optimizer_cls is None:
+ optimizer = atommic.core.optim.optimizers.get_optimizer(optimizer_name)
+ optimizer = optimizer(self._optimizer_param_groups, **optimizer_args)
+
+ logging.info("Optimizer config = %s", str(optimizer))
+
+ self._optimizer = optimizer # type: ignore
+
+ elif inspect.isclass(optimizer_cls):
+ optimizer = optimizer_cls(self._optimizer_param_groups, **optimizer_args)
+ logging.info("Optimizer config = %s", str(optimizer))
+
+ self._optimizer = optimizer # type: ignore
+
+ else:
+ # Attempt class path resolution
+ try:
+ optimizer_cls = OmegaConf.create({"_target_": optimizer_cls})
+ optimizer_config = {"lr": lr} if lr is not None else {}
+ optimizer_config |= optimizer_args
+
+ optimizer_instance = hydra.utils.instantiate(
+ optimizer_cls, self._optimizer_param_groups, **optimizer_config
+ ) # type: DictConfig
+
+ logging.info("Optimizer config = %s", str(optimizer_instance))
+
+ self._optimizer = optimizer_instance
+
+ except Exception as e:
+ logging.error(
+ f"Could not instantiate class path - {optimizer_cls} with kwargs {str(optimizer_config)}"
+ )
+
+ raise e
+
+ # print(f"scheduler_config = {scheduler_config}")
+ if isinstance(scheduler_config["name"], list):
+ _schedulers = [
+ atommic.core.optim.lr_scheduler.prepare_lr_scheduler(
+ optimizer=self._optimizer,
+ scheduler_config={
+ "name": scheduler_config["name"][i],
+ "min_lr": scheduler_config["min_lr"][i],
+ "last_epoch": scheduler_config["last_epoch"][i],
+ "warmup_ratio": scheduler_config["warmup_ratio"][i],
+ "monitor": scheduler_config["monitor"][i],
+ "t_max_epochs": scheduler_config["t_max_epochs"],
+ "t_accumulate_grad_batches": scheduler_config["t_accumulate_grad_batches"],
+ "t_limit_train_batches": scheduler_config["t_limit_train_batches"],
+ "t_num_workers": scheduler_config["t_num_workers"],
+ },
+ train_dataloader=self._train_dl,
+ )
+ for i in range(len(scheduler_config["name"]))
+ ]
+
+ self._scheduler = _schedulers # type: ignore
+ self._optimizer = [self._optimizer] * len(scheduler_config["name"]) # type: ignore
+ else:
+ # Try to instantiate scheduler for optimizer
+ self._scheduler = atommic.core.optim.lr_scheduler.prepare_lr_scheduler( # type: ignore
+ optimizer=self._optimizer, scheduler_config=scheduler_config, train_dataloader=self._train_dl
+ )
+
+ # Return the optimizer with/without scheduler. This return allows multiple optimizers or schedulers to be
+ # created
+ return self._optimizer, self._scheduler
+
+ def setup_optimizer_param_groups(self):
+ """Used to create param groups for the optimizer. As an example, this can be used to specify per-layer learning
+ rates:
+
+ .. code-block::
+
+ optim.SGD([
+ {'params': model.base.parameters()},
+ {'params': model.classifier.parameters(), 'lr': 1e-3}
+ ], lr=1e-2, momentum=0.9)
+
+ See https://pytorch.org/docs/stable/optim.html for more information. By default, ModelPT will use
+ self.parameters(). Override this method to add custom param groups.
+ """
+ param_groups = None
+ if hasattr(self, "parameters"):
+ param_groups = [{"params": self.parameters()}]
+ self._optimizer_param_groups = param_groups
+
+ def configure_optimizers(self):
+ """Configure optimizers and schedulers for training."""
+ self.setup_optimization()
+
+ if isinstance(self._scheduler, list) and self._scheduler[0] is None:
+ return self._optimizer
+
+ if self._scheduler is None:
+ return self._optimizer
+
+ if isinstance(self._optimizer, list):
+ return self._optimizer, self._scheduler
+
+ return [self._optimizer], [self._scheduler]
+
+ def setup(self, stage: Optional[str] = None):
+ """Called at the beginning of fit, validate, test, or predict. This is called on every process when using DDP.
+
+ Parameters
+ ----------
+ stage : str
+ fit, validate, test or predict
+ """
+ if stage == "fit":
+ train_deferred_setup = (
+ "train_ds" in self._cfg
+ and self._cfg.train_ds is not None
+ and self._cfg.train_ds.get("defer_setup", False)
+ )
+ if self.train_dataloader() is None and train_deferred_setup:
+ self.setup_training_data(self._cfg.train_ds)
+
+ if stage in ("fit", "validate"):
+ val_deferred_setup = (
+ "validation_ds" in self._cfg
+ and self._cfg.validation_ds is not None
+ and self._cfg.validation_ds.get("defer_setup", False)
+ )
+ if self.val_dataloader() is None and val_deferred_setup:
+ self.setup_multiple_validation_data(val_data_config=self._cfg.validation_ds)
+
+ if stage == "test":
+ test_deferred_setup = (
+ "test_ds" in self._cfg
+ and self._cfg.test_ds is not None
+ and self._cfg.test_ds.get("defer_setup", False)
+ )
+ if self.test_dataloader() is None and test_deferred_setup:
+ self.setup_multiple_test_data(test_data_config=self._cfg.test_ds)
+
+ def train_dataloader(self):
+ """Return the training dataloader."""
+ return self._train_dl if self._train_dl is not None else None
+
+ def val_dataloader(self):
+ """Return the validation dataloader."""
+ if self._validation_dl is None:
+ # None dataloader no longer supported in PTL2.0
+ self._validation_dl = []
+ return self._validation_dl
+
+ def test_dataloader(self):
+ """Return the test dataloader."""
+ if self._test_dl is None:
+ # None dataloader no longer supported in PTL2.0
+ self._test_dl = []
+ return self._test_dl
+
+ def on_validation_epoch_end(self) -> Optional[Dict[str, Dict[str, torch.Tensor]]]:
+ """Default DataLoader for Validation set which automatically supports multiple data loaders via
+ `multi_validation_epoch_end`. If multi dataset support is not required, override this method entirely in base
+ class. In such a case, there is no need to implement `multi_validation_epoch_end` either.
+
+ .. note::
+ If more than one data loader exists, and they all provide `val_loss`,
+ only the `val_loss` of the first data loader will be used by default.
+ This default can be changed by passing the special key `val_dl_idx: int`
+ inside the `validation_ds` config.
+
+ Returns
+ -------
+ A dictionary containing the union of all items from individual data_loaders, along with merged logs from all
+ data loaders.
+ """
+ # Case where we dont provide data loaders
+ if self.validation_step_outputs is not None and len(self.validation_step_outputs) == 0:
+ return {}
+
+ # Case where we provide exactly 1 data loader
+ if isinstance(self.validation_step_outputs[0], dict):
+ output_dict = self.multi_validation_epoch_end(self.validation_step_outputs, dataloader_idx=0)
+
+ if output_dict is not None and 'log' in output_dict:
+ self.log_dict(output_dict.pop('log'), on_epoch=True)
+
+ self.validation_step_outputs.clear() # free memory
+ return output_dict
+
+ # Case where we provide more than 1 data loader
+ output_dict = {'log': {}}
+
+ # The output is a list of list of dicts, outer list corresponds to dataloader idx
+ for dataloader_idx, val_outputs in enumerate(self.validation_step_outputs):
+ # Get prefix and dispatch call to multi epoch end
+ dataloader_prefix = self.get_validation_dataloader_prefix(dataloader_idx)
+ dataloader_logs = self.multi_validation_epoch_end(val_outputs, dataloader_idx=dataloader_idx)
+
+ # If result was not provided, generate empty dict
+ dataloader_logs: Dict[Any, Any] = dataloader_logs or {} # type: ignore
+
+ # Perform `val_loss` resolution first (if provided outside logs)
+ if ("val_loss" in dataloader_logs and "val_loss" not in output_dict) and ( # type: ignore
+ dataloader_idx == self._val_dl_idx
+ ):
+ output_dict["val_loss"] = dataloader_logs["val_loss"] # type: ignore
+
+ # For every item in the result dictionary
+ for k, v in dataloader_logs.items(): # type: ignore
+ # If the key is `log`
+ if k == "log":
+ # Parse every element of the log, and attach the prefix name of the data loader
+ log_dict = {}
+
+ for k_log, v_log in v.items():
+ # If we are logging the metric, but dont provide it at result level, store it twice - once in
+ # log and once in result level. Also mark log with prefix name to avoid log level clash with
+ # other data loaders.
+ if k_log not in output_dict["log"] and dataloader_idx == self._val_dl_idx: # type: ignore
+ new_k_log = k_log
+ # Also insert duplicate key with prefix for ease of comparison / avoid name clash
+ log_dict[dataloader_prefix + k_log] = v_log
+ else:
+ # Simply prepend prefix to key and save
+ new_k_log = dataloader_prefix + k_log
+
+ # Store log value
+ log_dict[new_k_log] = v_log
+
+ # Update log storage of individual data loader
+ output_logs = output_dict["log"] # type: ignore
+ output_logs.update(log_dict)
+
+ # Update global log storage
+ output_dict["log"] = output_logs # type: ignore
+
+ else:
+ # If any values are stored outside 'log', simply prefix name and store
+ new_k = dataloader_prefix + k
+ output_dict[new_k] = v # type: ignore
+
+ self.validation_step_outputs[dataloader_idx].clear() # free memory
+
+ if "log" in output_dict: # type: ignore
+ self.log_dict(output_dict.pop("log"), on_epoch=True) # type: ignore
+
+ # return everything else
+ return output_dict
+
+ def on_test_epoch_end(self) -> Optional[Dict[str, Dict[str, torch.Tensor]]]:
+ """Default DataLoader for Test set which automatically supports multiple data loaders via
+ `multi_test_epoch_end`. If multi dataset support is not required, override this method entirely in base class.
+ In such a case, there is no need to implement `multi_test_epoch_end` either.
+
+ .. note::
+ If more than one data loader exists, and they all provide `test_loss`,
+ only the `test_loss` of the first data loader will be used by default.
+ This default can be changed by passing the special key `_test_dl_idx: int`
+ inside the `test_ds` config.
+
+ Returns
+ -------
+ Dict[str, Dict[str, torch.Tensor]]
+ A dictionary containing the union of all items from individual data_loaders, along with merged logs from
+ all data loaders.
+ """
+ # Case where we dont provide data loaders
+ if self.test_step_outputs is not None and len(self.test_step_outputs) == 0:
+ return {}
+
+ # Case where we provide exactly 1 data loader
+ if isinstance(self.test_step_outputs[0], dict):
+ output_dict = self.multi_test_epoch_end(self.test_step_outputs, dataloader_idx=0)
+
+ if output_dict is not None and 'log' in output_dict:
+ self.log_dict(output_dict.pop('log'), on_epoch=True)
+
+ self.test_step_outputs.clear() # free memory
+ return output_dict
+
+ # Case where we provide more than 1 data loader
+ output_dict = {'log': {}}
+
+ # The output is a list of list of dicts, outer list corresponds to dataloader idx
+ for dataloader_idx, test_outputs in enumerate(self.test_step_outputs):
+ # Get prefix and dispatch call to multi epoch end
+ dataloader_prefix = self.get_test_dataloader_prefix(dataloader_idx)
+ self.multi_test_epoch_end(test_outputs, dataloader_idx=dataloader_idx)
+
+ # If result was not provided, generate empty dict
+ dataloader_logs = dataloader_logs or {} # type: ignore # noqa: F821
+
+ # Perform `test_loss` resolution first (if provided outside logs)
+ if (
+ "test_loss" in dataloader_logs
+ and "test_loss" not in output_dict # type: ignore
+ and dataloader_idx == self._test_dl_idx
+ ):
+ output_dict["test_loss"] = dataloader_logs["test_loss"] # type: ignore
+
+ # For every item in the result dictionary
+ for k, v in dataloader_logs.items():
+ # If the key is `log`
+ if k == "log":
+ # Parse every element of the log, and attach the prefix name of the data loader
+ log_dict = {}
+ for k_log, v_log in v.items():
+ # If we are logging the loss, but dont provide it at result level,
+ # store it twice - once in log and once in result level.
+ # Also mark log with prefix name to avoid log level clash with other data loaders
+ if k_log not in output_dict["log"] and dataloader_idx == self._test_dl_idx: # type: ignore
+ new_k_log = k_log
+ # Also insert duplicate key with prefix for ease of comparison / avoid name clash
+ log_dict[dataloader_prefix + k_log] = v_log
+ else:
+ # Simply prepend prefix to key and save
+ new_k_log = dataloader_prefix + k_log
+ log_dict[new_k_log] = v_log
+
+ # Update log storage of individual data loader
+ output_logs = output_dict.get("log", {}) # type: ignore
+ output_logs.update(log_dict)
+
+ # Update global log storage
+ output_dict["log"] = output_logs # type: ignore
+
+ else:
+ # If any values are stored outside 'log', simply prefix name and store
+ new_k = dataloader_prefix + k
+ output_dict[new_k] = v # type: ignore
+
+ self.test_step_outputs[dataloader_idx].clear() # free memory
+
+ if "log" in output_dict: # type: ignore
+ self.log_dict(output_dict.pop("log"), on_epoch=True) # type: ignore
+
+ # return everything else
+ return output_dict
+
+ @staticmethod
+ def multi_validation_epoch_end(
+ outputs: Union[object, List[Dict[str, torch.Tensor]], None], # pylint: disable=unused-argument
+ dataloader_idx: int = 0, # pylint: disable=unused-argument
+ ) -> None:
+ """Adds support for multiple validation datasets. Should be overridden by subclass, to obtain appropriate logs
+ for each of the dataloaders.
+
+ Parameters
+ ----------
+ outputs : Union[List[Dict[str, torch.Tensor]], List[List[Dict[str, torch.Tensor]]]]
+ Same as that provided by LightningModule.on_validation_epoch_end() for a single dataloader.
+ dataloader_idx : int
+ The index of the dataloader for which the outputs are provided.
+
+ Returns
+ -------
+ A dictionary of values, optionally containing a sub-dict `log`, such that the values in the log will be
+ pre-pended by the dataloader prefix.
+ """
+ logging.warning(
+ "Multi data loader support has been enabled, but `multi_validation_epoch_end(outputs, dataloader_idx) "
+ "has not been implemented.\n"
+ "If you require multi data loader support for validation sets, please override this method.\n"
+ "If you do not require multi data loader support, please instead override "
+ "`on_validation_epoch_end(outputs)."
+ )
+
+ @staticmethod
+ def multi_test_epoch_end(
+ outputs: Union[object, List[Dict[str, torch.Tensor]]], # pylint: disable=unused-argument
+ dataloader_idx: int = 0, # pylint: disable=unused-argument
+ ) -> None:
+ """Adds support for multiple test datasets. Should be overridden by subclass, to obtain appropriate logs for
+ each of the dataloaders.
+
+ Parameters
+ ----------
+ outputs : Union[List[Dict[str, torch.Tensor]], List[List[Dict[str, torch.Tensor]]]]
+ Same as that provided by LightningModule.on_test_epoch_end() for a single dataloader.
+ dataloader_idx : int
+ The index of the dataloader for which the outputs are provided.
+
+ Returns
+ -------
+ A dictionary of values, optionally containing a sub-dict `log`, such that the values in the log will be
+ pre-pended by the dataloader prefix.
+ """
+ logging.warning(
+ "Multi data loader support has been enabled, but `multi_test_epoch_end(outputs, dataloader_idx) has not "
+ "been implemented.\n"
+ "If you require multi data loader support for validation sets, please override this method.\n"
+ "If you do not require multi data loader support, please instead override on_test_epoch_end(outputs)."
+ )
+
+ def get_validation_dataloader_prefix(self, dataloader_idx: int = 0) -> str:
+ """Get the name of one or more data loaders, which will be prepended to all logs."""
+ return self.validation_names[dataloader_idx] # type: ignore
+
+ def get_test_dataloader_prefix(self, dataloader_idx: int = 0) -> str:
+ """Get the name of one or more data loaders, which will be prepended to all logs."""
+ return self.test_names[dataloader_idx] # type: ignore
+
+ def load_part_of_state_dict(self, state_dict, include, exclude, load_from_string=None):
+ """Load part of the state dict."""
+ excluded_param_names = []
+ # create dict
+ dict_to_load = {}
+ for k, v in state_dict.items():
+ should_add = any(p in k for p in include)
+ # except for if any string from exclude is present
+ for e in exclude:
+ if e in k:
+ excluded_param_names.append(k)
+ should_add = False
+ break
+ if should_add:
+ dict_to_load[k] = v
+
+ # Restore checkpoint part into current model
+ self.load_state_dict(dict_to_load, strict=False)
+ if load_from_string is not None:
+ logging.info(f"Model checkpoint partially restored from {load_from_string}")
+ if len(excluded_param_names) > 0:
+ logging.info(
+ f"The following parameters were excluded when loading from {load_from_string} : "
+ f"{excluded_param_names}"
+ )
+ logging.info("Make sure that this is what you wanted!")
+ else:
+ if len(excluded_param_names) > 0:
+ logging.info(
+ f"The following parameters were excluded when loading checkpoint : {excluded_param_names}"
+ )
+
+ @rank_zero_only
+ def maybe_init_from_pretrained_checkpoint(self, cfg: OmegaConf, map_location: str = "cpu"): # noqa: MC0001
+ """Initializes a given model with the parameters obtained via specific config arguments. The state dict of the
+ provided model will be updated with `strict=False` setting to prevent requirement of exact model parameters
+ matching.
+
+ Initializations
+ ---------------
+ init_from_atommic_model : str
+ Str path to a .atommic model, which will be instantiated in order to extract the state dict.
+ init_from_pretrained_model : str
+ Str name of a pretrained model checkpoint (obtained via cloud). The model will be downloaded (or a cached
+ copy will be used), instantiated and then its state dict will be extracted.
+ init_from_ptl_ckpt : str
+ Str name of a Pytorch Lightning checkpoint file. It will be loaded and the state dict will extract.
+
+ Parameters
+ ----------
+ cfg : OmegaConf
+ The config used to instantiate the model. It needs only contain one of the above keys.
+ map_location : str or torch.device()
+ str or torch.device() which represents where the intermediate state dict (from the pretrained model or
+ checkpoint) will be loaded.
+ """
+ args = ["init_from_atommic_model", "init_from_pretrained_model", "init_from_ptl_ckpt"]
+ arg_matches = [(1 if arg in cfg and arg is not None else 0) for arg in args]
+
+ if sum(arg_matches) == 0:
+ # model weights do not need to be restored
+ return
+
+ if sum(arg_matches) > 1:
+ raise ValueError(
+ "Cannot pass more than one model initialization arguments to config!\n"
+ f"Found : {[args[idx] for idx, arg_present in enumerate(arg_matches) if arg_present]}"
+ )
+
+ if "init_from_atommic_model" in cfg and cfg.init_from_atommic_model is not None:
+ with open_dict(cfg):
+ if isinstance(cfg.init_from_atommic_model, str):
+ model_path = cfg.init_from_atommic_model
+ # Restore model
+ restored_model = self.restore_from(
+ model_path, map_location=map_location, strict=cfg.get("init_strict", True)
+ )
+ # Restore checkpoint into current model
+ self.load_state_dict(restored_model.state_dict(), strict=False)
+ logging.info(f"Model checkpoint restored from atommic file with path : `{model_path}`")
+ elif isinstance(cfg.init_from_atommic_model, (DictConfig, dict)):
+ model_load_dict = cfg.init_from_atommic_model
+ for model_load_cfg in model_load_dict.values():
+ model_path = model_load_cfg.path
+ # Restore model
+ restored_model = self.restore_from(
+ model_path, map_location=map_location, strict=cfg.get("init_strict", True)
+ )
+
+ include = model_load_cfg.pop("include", [""])
+ exclude = model_load_cfg.pop("exclude", [])
+
+ self.load_part_of_state_dict(
+ restored_model.state_dict(), include, exclude, f"atommic file with path `{model_path}`"
+ )
+ else:
+ raise TypeError("Invalid type: init_from_atommic_model is not a string or a dict!")
+
+ if "init_from_pretrained_model" in cfg and cfg.init_from_pretrained_model is not None:
+ with open_dict(cfg):
+ # Restore model
+ if isinstance(cfg.init_from_pretrained_model, str):
+ model_name = cfg.pop("init_from_pretrained_model")
+
+ # Check if model is being resumed or not - only works if `Trainer` is attached to model
+ if hasattr(self, "trainer") and self.trainer is not None:
+ trainer = self.trainer
+ if (
+ hasattr(trainer, "resume_from_checkpoint")
+ and trainer.checkpoint_connector.resume_checkpoint_path is not None
+ ):
+ logging.info(
+ "Model training is being resumed via Pytorch Lightning.\n"
+ "Initialization from pretrained model (via cloud) will be skipped."
+ )
+ return
+
+ restored_model = self.from_pretrained(
+ model_name, map_location=map_location, strict=cfg.get("init_strict", True)
+ )
+
+ # Restore checkpoint into current model
+ self.load_state_dict(restored_model.state_dict(), strict=False)
+ logging.info(f"Model checkpoint restored from pretrained checkpoint with name : `{model_name}`")
+ elif isinstance(cfg.init_from_pretrained_model, dict):
+ pass
+ elif isinstance(cfg.init_from_pretrained_model, (DictConfig, dict)):
+ model_load_dict = cfg.init_from_pretrained_model
+ for model_load_cfg in model_load_dict.values():
+ model_name = model_load_cfg.name
+ # Restore model
+ restored_model = self.from_pretrained(
+ model_name, map_location=map_location, strict=cfg.get("init_strict", True)
+ )
+
+ include = model_load_cfg.pop("include", [""])
+ exclude = model_load_cfg.pop("exclude", [])
+
+ self.load_part_of_state_dict(
+ restored_model.state_dict(),
+ include,
+ exclude,
+ f"pretrained checkpoint with name `{model_name}`",
+ )
+ else:
+ raise TypeError("Invalid type: init_from_pretrained_model is not a string or a dict!")
+
+ if "init_from_ptl_ckpt" in cfg and cfg.init_from_ptl_ckpt is not None:
+ with open_dict(cfg):
+ if isinstance(cfg.init_from_ptl_ckpt, str):
+ # Restore checkpoint
+ ckpt_path = cfg.pop("init_from_ptl_ckpt")
+ ckpt = torch.load(ckpt_path, map_location=map_location)
+
+ # Restore checkpoint into current model
+ self.load_state_dict(ckpt["state_dict"], strict=False)
+ logging.info(
+ f"Model checkpoint restored from pytorch lightning checkpoint with path : `{ckpt_path}`"
+ )
+ elif isinstance(cfg.init_from_ptl_ckpt, (DictConfig, dict)):
+ model_load_dict = cfg.init_from_ptl_ckpt
+ for model_load_cfg in model_load_dict.values():
+ ckpt_path = model_load_cfg.path
+ # Restore model
+ ckpt = torch.load(ckpt_path, map_location=map_location)
+
+ include = model_load_cfg.pop("include", [""])
+ exclude = model_load_cfg.pop("exclude", [])
+
+ self.load_part_of_state_dict(
+ ckpt["state_dict"], include, exclude, f"atommic file with path `{ckpt_path}`"
+ )
+ else:
+ raise TypeError("Invalid type: init_from_ptl_ckpt is not a string or a dict!")
+
+ def teardown(self, stage: str):
+ """Called at the end of fit and test."""
+ if stage == "fit" and "PL_TRAINER_GPUS" in os.environ:
+ os.environ.pop("PL_TRAINER_GPUS")
+
+ super().teardown(stage)
+
+ @classmethod
+ def extract_state_dict_from(
+ cls,
+ restore_path: str,
+ save_dir: str,
+ split_by_module: bool = False,
+ save_restore_connector: SaveRestoreConnector = None,
+ ):
+ """Extract the state dict(s) from a provided .atommic tarfile and save it to a directory.
+
+ Parameters
+ ----------
+ restore_path : str
+ Path to .atommic file from which state dict(s) should be extracted.
+ save_dir : str
+ Directory in which the saved state dict(s) should be stored.
+ split_by_module : bool, optional
+ Bool flag, which determines whether the output checkpoint should be for the entire Model, or
+ the individual module's that comprise the Model. Default is ``False``.
+ save_restore_connector : SaveRestoreConnector, optional
+ Can be overridden to add custom save and restore logic. Default is ``None``.
+
+ Example
+ -------
+ To convert the .atommic tarfile into a single Model level PyTorch checkpoint
+
+ .. code-block::
+
+ state_dict = atommic.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.atommic', \
+ './asr_ckpts')
+
+ To restore a model from a Model level checkpoint
+
+ .. code-block::
+
+ model = atommic.collections.asr.models.EncDecCTCModel(cfg) # or any other method of restoration
+ model.load_state_dict(torch.load("./asr_ckpts/model_weights.ckpt"))
+
+ To convert the .atommic tarfile into multiple Module level PyTorch checkpoints
+
+ .. code-block::
+
+ state_dict = atommic.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.atommic', \
+ './asr_ckpts', split_by_module=True)
+
+ To restore a module from a Module level checkpoint
+
+ .. code-block::
+
+ model = atommic.collections.asr.models.EncDecCTCModel(cfg) # or any other method of restoration
+ # load the individual components
+ model.preprocessor.load_state_dict(torch.load("./asr_ckpts/preprocessor.ckpt"))
+ model.encoder.load_state_dict(torch.load("./asr_ckpts/encoder.ckpt"))
+ model.decoder.load_state_dict(torch.load("./asr_ckpts/decoder.ckpt"))
+
+ Returns
+ -------
+ The state dict that was loaded from the original .atommic checkpoint.
+ """
+ if save_restore_connector is None:
+ save_restore_connector = SaveRestoreConnector()
+
+ if not path.exists(restore_path):
+ raise FileExistsError(f"Can't find {restore_path}")
+
+ cls.update_save_restore_connector(save_restore_connector)
+ return cls._save_restore_connector.extract_state_dict_from(restore_path, save_dir, split_by_module)
+
+ def prepare_test(self, trainer: "Trainer") -> bool:
+ """Helper method to check whether the model can safely be tested on a dataset after training (or loading a
+ checkpoint).
+
+ .. code-block::
+
+ trainer = Trainer()
+ if model.prepare_test(trainer):
+ trainer.test(model)
+
+ Returns
+ -------
+ Bool which declares the model safe to test. Provides warnings if it has to return False to guide the user.
+ """
+ if not hasattr(self._cfg, "test_ds"):
+ logging.info("No `test_ds` config found within the manifest.")
+ return False
+
+ if trainer is not None and trainer.num_devices > 1:
+ # Replace ddp multi-gpu until PTL has a fix
+ DDP_WARN = """\n\nDuring testing, it is currently advisable to construct a new Trainer "
+ "with single GPU and no DDP to obtain accurate results.
+ "Following pattern should be used: "
+ "trainer = Trainer(devices=1, accelerator='gpu')
+ "if model.prepare_test(trainer):"
+ " trainer.test(model)\n\n"""
+
+ logging.warning(DDP_WARN)
+ return False
+
+ # Assign trainer to the model
+ self.set_trainer(trainer)
+ return True
+
+ def set_trainer(self, trainer: Trainer):
+ """Set an instance of Trainer object."""
+ self.trainer = trainer
+ self._trainer = trainer
+ self.set_world_size(self._trainer)
+
+ def set_world_size(self, trainer: Trainer):
+ """Determines the world size from the PyTorch Lightning Trainer and then updates AppState."""
+ self.world_size = 1
+
+ if trainer is not None:
+ if isinstance(trainer, Trainer):
+ if trainer.num_devices and trainer.num_nodes:
+ self.world_size = trainer.num_devices * trainer.num_nodes
+ else:
+ logging.warning("World size can only be set by PyTorch Lightning Trainer.")
+ app_state = AppState()
+ app_state.world_size = self.world_size
+
+ def summarize(self, max_depth: int = 1) -> model_summary.ModelSummary:
+ """Summarize this LightningModule."""
+ return model_summary.summarize(self, max_depth=max_depth)
+
+ def _update_dataset_config(self, dataset_name: str, config: Optional[Union[DictConfig, Dict]]):
+ """Update the config (if not None) of the dataset by given name. Preserves said config after updating.
+
+ Parameters
+ ----------
+ dataset_name : str
+ Name of the dataset to update. Can be one of `train`, `validation` and `test`.
+ config : Optional[Union[DictConfig, Dict]]
+ Config to update the dataset with. If None is passed, this method simply returns. If dict is passed, it is
+ cast into a DictConfig. The internal config is updated with the passed config.
+ """
+ if hasattr(self, "_multi_dataset_mode") and self._multi_dataset_mode is True:
+ return
+
+ if config is not None:
+ if not isinstance(config, DictConfig):
+ config = OmegaConf.create(config)
+
+ if dataset_name in {"train", "validation", "test"}:
+ OmegaConf.set_struct(self.cfg, False)
+
+ key_name = f"{dataset_name}_ds"
+ self.cfg[key_name] = config
+
+ OmegaConf.set_struct(self.cfg, True)
+
+ # Update hyperparameters by calling property setter
+ self.cfg = self._cfg
+ else:
+ raise ValueError("`dataset_name` when updating config must be one of [train, validation, test]")
+
+ @property
+ def num_weights(self):
+ """Utility property that returns the total number of parameters of the Model."""
+ return sum(p.numel() for p in self.parameters() if p.requires_grad)
+
+ @property
+ def cfg(self):
+ """Property that holds the finalized internal config of the model.
+
+ .. note::
+ Changes to this config are not reflected in the state of the model.
+ Please create a new model using an updated config to properly update the model.
+ """
+ return self._cfg
+
+ @cfg.setter
+ def cfg(self, cfg):
+ """Property that holds the finalized internal config of the model.
+
+ .. note::
+ Changes to this config are not reflected in the state of the model.
+ Please create a new model using an updated config to properly update the model.
+ """
+ self._cfg = cfg
+ self._set_hparams(OmegaConf.create({"cfg": self._cfg}))
+
+ # TODO: Remove this when we have a better way to handle this
+ if hasattr(self, "_hparams_initial") and "cfg" in self._hparams_initial:
+ self._hparams_initial["cfg"] = OmegaConf.to_object(self._cfg)
+
+ @staticmethod
+ def _is_model_being_restored() -> bool:
+ """Checks if the model is being restored from a checkpoint."""
+ app_state = AppState()
+ return app_state.is_model_being_restored
+
+ @staticmethod
+ def _set_model_restore_state(is_being_restored: bool, folder: str = None):
+ """Sets the state of the model to be restored."""
+ app_state = AppState()
+ app_state.is_model_being_restored = is_being_restored
+ app_state.atommic_file_folder = folder # type: ignore
+
+ def _set_model_guid(self):
+ """Sets the model guid."""
+ if not hasattr(self, "model_guid"):
+ appstate = AppState()
+
+ # Generate a unique uuid for the instance
+ # also determine if the model is being restored or not, and preserve the path
+ self.model_guid = str(uuid.uuid4())
+ if self._is_model_being_restored():
+ restore_path = appstate.model_restore_path
+ else:
+ restore_path = None
+
+ appstate.register_model_guid(self.model_guid, restoration_path=restore_path)
+
+ @classmethod
+ def update_save_restore_connector(cls, save_restore_connector):
+ """Update the save_restore_connector of the model."""
+ if hasattr(cls, "_save_restore_connector"):
+ cls._save_restore_connector = save_restore_connector
+ else:
+ setattr(cls, "_save_restore_connector", save_restore_connector)
+
+ def _setup_nsys_profiling(self):
+ """Enables nsys profiling To use, add the following options to the model config:
+ nsys_profile: False
+ start_step: 10 # Global batch to start profiling
+ end_step: 10 # Global batch to end profiling
+ ranks: [0] # Global rank IDs to profile
+ gen_shape: False # Generate model and kernel details including input shapes
+
+ And then wrap the model training script with:
+ nsys profile -s none -o -t cuda,nvtx --force-overwrite true \
+ --capture-range=cudaProfilerApi --capture-range-end=stop python ./examples/...
+ See more options at: https://docs.nvidia.com/nsight-systems/UserGuide/index.html#cli-profiling
+ """
+ if self.cfg.get("nsys_profile", None) is not None and self.cfg.nsys_profile.get("enabled", False):
+ # Nsys profiling options
+ self._nsys_profile_enabled = True
+ self._nsys_profile_start_step = self.cfg.nsys_profile.get("start_step", 0)
+ self._nsys_profile_end_step = self.cfg.nsys_profile.get("end_step", 0)
+ self._nsys_profile_ranks = self.cfg.nsys_profile.get("ranks", [0])
+ self._nsys_profile_gen_shape = self.cfg.nsys_profile.get("gen_shape", False)
+
+ if isinstance(self._nsys_profile_start_step, int):
+ logging.info(f"Nsys profiling setup with start_step: {self._nsys_profile_start_step}")
+ else:
+ raise ValueError(f"Nsys start_step must be of type int. Found: {type(self._nsys_profile_start_step)}")
+
+ if isinstance(self._nsys_profile_end_step, int):
+ logging.info(f"Nsys profiling setup with end_step: {self._nsys_profile_end_step}")
+ else:
+ raise ValueError(f"Nsys end_step must be of type int. Found: {type(self._nsys_profile_end_step)}")
+
+ if self._nsys_profile_end_step >= self._nsys_profile_start_step:
+ pass
+ else:
+ raise ValueError("Nsys end_step must be greater than or equal to nsys start_step")
+
+ def on_train_start(self):
+ """PyTorch Lightning hook:
+ https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-train-start
+ We use it here to copy the relevant config for dynamic freezing.
+ """
+
+ # dynamic freezing
+ # should fire only once, on the very first batch of training and never again
+ if not hasattr(self, "_freeze_cfg"):
+ if (
+ hasattr(self.cfg, "freeze_updates")
+ and self.cfg.freeze_updates is not None
+ and self.cfg.freeze_updates.get("enabled", False)
+ ):
+ setattr(self, "_freeze_cfg", OmegaConf.to_container(self.cfg.freeze_updates))
+ self._freeze_cfg["is_frozen"] = {k: False for k in self._freeze_cfg["modules"].keys()}
+ else:
+ setattr(self, "_freeze_cfg", None)
+
+ def on_train_batch_start(self, batch: Any, batch_idx: int, unused: int = 0): # pylint: disable=unused-argument
+ """PyTorch Lightning hook:
+ https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-train-batch-start
+ We use it here to enable nsys profiling and dynamic freezing.
+ """
+
+ # nsys profiling
+ if self.device.type == "cuda":
+ if hasattr(self, "_nsys_profile_enabled"):
+ if self._nsys_profile_enabled:
+ if batch_idx == self._nsys_profile_start_step and get_rank() in self._nsys_profile_ranks:
+ logging.info("====== Start nsys profiling ======")
+ torch.cuda.cudart().cudaProfilerStart()
+ if self._nsys_profile_gen_shape:
+ torch.autograd.profiler.emit_nvtx( # pylint: disable=unnecessary-dunder-call
+ record_shapes=True
+ ).__enter__()
+
+ # dynamic freezing
+ if hasattr(self, "_freeze_cfg") and self._freeze_cfg is not None:
+ if self.training and hasattr(self, "trainer") and self.trainer is not None:
+ num_updates = self.trainer.global_step + 1
+
+ for ml, m_steps in self._freeze_cfg["modules"].items():
+ # we could do hasattr check here, but it's too expensive for each step consequently you'll throw an
+ # error if the module name doesn't exist or was spelled wrong in the config.yaml
+ if isinstance(m_steps, list):
+ assert len(m_steps) == 2, "freeze_updates modules list cannot have more than two elements"
+ should_freeze = (num_updates >= m_steps[0]) and (num_updates <= m_steps[1] or m_steps[1] == -1)
+ else:
+ should_freeze = num_updates <= m_steps or m_steps == -1
+ if should_freeze and not self._freeze_cfg["is_frozen"][ml]:
+ getattr(self, ml).freeze()
+ getattr(self, ml).train()
+ self._freeze_cfg["is_frozen"][ml] = True
+ elif not should_freeze and self._freeze_cfg["is_frozen"][ml]:
+ getattr(self, ml).unfreeze()
+ self._freeze_cfg["is_frozen"][ml] = False
+
+ def on_train_batch_end(
+ self, outputs, batch: Any, batch_idx: int, unused: int = 0 # pylint: disable=unused-argument
+ ) -> None:
+ """PyTorch Lightning hook:
+ https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-train-batch-end
+ We use it here to enable nsys profiling.
+ """
+ if (
+ self.device.type == "cuda"
+ and hasattr(self, "_nsys_profile_enabled")
+ and self._nsys_profile_enabled
+ and batch_idx == self._nsys_profile_end_step
+ and get_rank() in self._nsys_profile_ranks
+ ):
+ logging.info("====== End nsys profiling ======")
+ torch.cuda.cudart().cudaProfilerStop()
+
+ def on_train_end(self):
+ """PyTorch Lightning hook:
+ https://pytorch-lightning.readthedocs.io/en/stable/common/lightning_module.html#on-train-end
+ We use it here to clean up the dynamic freezing config.
+ """
+ # dynamic freezing cleanup
+ if hasattr(self, "_freeze_cfg"):
+ delattr(self, "_freeze_cfg")
+
+ def cuda(self, device=None):
+ """PTL is overriding this method and changing the pytorch behavior of a module. The PTL LightingModule
+ override will move the module to device 0 if device is None.
+
+ See the PTL method here:
+ https://github.com/Lightning-AI/lightning/blob/master/src/pytorch_lightning/core/mixins/
+ device_dtype_mixin.py#L113
+
+ Here we are overriding this to maintain the default Pytorch nn.module behavior:
+ https://github.com/pytorch/pytorch/blob/master/torch/nn/modules/module.py#L728
+
+ Moves all model parameters and buffers to the GPU.
+
+ This also makes associated parameters and buffers different objects. So
+ it should be called before constructing optimizer if the module will
+ live on GPU while being optimized.
+
+ .. note::
+ This method modifies the module in-place.
+
+ Parameters
+ ----------
+ device : torch.device, optional
+ The desired device of the parameters and buffers in this module.
+ """
+ if device is None:
+ device = torch.device("cuda", torch.cuda.current_device())
+ elif isinstance(device, int):
+ device = torch.device("cuda", index=device)
+ return super().cuda(device=device)
diff --git a/atommic/core/conf/__init__.py b/atommic/core/conf/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/core/conf/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/core/conf/hydra_runner.py b/atommic/core/conf/hydra_runner.py
new file mode 100644
index 00000000..2eeb4290
--- /dev/null
+++ b/atommic/core/conf/hydra_runner.py
@@ -0,0 +1,137 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/config/hydra_runner.py
+
+import functools
+import os
+import sys
+from argparse import Namespace
+from typing import Any, Callable, Optional
+
+from hydra._internal.utils import _run_hydra, get_args_parser
+from hydra.core.config_store import ConfigStore
+from hydra.types import TaskFunction
+from omegaconf import DictConfig, OmegaConf
+
+
+def _get_gpu_name():
+ """Get GPU name."""
+ try:
+ import pynvml # pylint: disable=import-outside-toplevel
+ except (ImportError, ModuleNotFoundError):
+ return None
+
+ pynvml.nvmlInit()
+ handle = pynvml.nvmlDeviceGetHandleByIndex(0)
+ cuda_capability, _ = pynvml.nvmlDeviceGetCudaComputeCapability(handle)
+ pynvml.nvmlShutdown()
+ if cuda_capability == 8:
+ return "a100"
+ if cuda_capability == 9:
+ return "h100"
+ return None
+
+
+OmegaConf.register_new_resolver("gpu_name", _get_gpu_name)
+
+# multiple interpolated values in the config
+OmegaConf.register_new_resolver("multiply", lambda x, y: x * y)
+
+
+def hydra_runner(
+ config_path: Optional[str] = ".", config_name: Optional[str] = None, schema: Optional[Any] = None
+) -> Callable[[TaskFunction], Any]:
+ """Decorator used for passing the Config paths to main function. Optionally registers a schema used for
+ validation/providing default values.
+
+ Parameters
+ ----------
+ config_path : str, optional
+ Path to the config file. Default is ``.``.
+ config_name : str, optional
+ Name of the config file. Default is ``None``.
+ schema : Any, optional
+ Schema used for validation/providing default values. Default is ``None``.
+
+ Returns
+ -------
+ A decorator that passes the config paths to the main function.
+ """
+
+ def decorator(task_function: TaskFunction) -> Callable[[], None]:
+ """Decorator that passes the config paths to the main function."""
+
+ @functools.wraps(task_function)
+ def wrapper(cfg_passthrough: Optional[DictConfig] = None) -> Any:
+ """Wrapper that passes the config paths to the main function."""
+ # Check it config was passed.
+ if cfg_passthrough is not None:
+ return task_function(cfg_passthrough)
+ args = get_args_parser()
+
+ # Parse arguments in order to retrieve overrides
+ parsed_args: Namespace = args.parse_args()
+
+ # Get overriding args in dot string format
+ overrides = parsed_args.overrides # type: list
+
+ # Disable the creation of .hydra subdir
+ # https://hydra.cc/docs/tutorials/basic/running_your_app/working_directory
+ overrides.append("hydra.output_subdir=null")
+ # Hydra logging outputs only to stdout (no log file).
+ # https://hydra.cc/docs/configure_hydra/logging
+ overrides.append("hydra/job_logging=stdout")
+
+ # Set run.dir ONLY for ExpManager "compatibility" - to be removed.
+ overrides.append("hydra.run.dir=.")
+
+ # Check if user set the schema.
+ if schema is not None:
+ # Create config store.
+ cs = ConfigStore.instance()
+
+ # Get the correct ConfigStore "path name" to "inject" the schema.
+ if parsed_args.config_name is not None:
+ path, name = os.path.split(parsed_args.config_name)
+ # Make sure the path is not set - as this will disable validation scheme.
+ if path != "":
+ sys.stderr.write(
+ "ERROR Cannot set config file path using `--config-name` when "
+ "using schema. Please set path using `--config-path` and file name using "
+ "`--config-name` separately.\n"
+ )
+ sys.exit(1)
+ else:
+ name = config_name
+
+ # Register the configuration as a node under the name in the group.
+ cs.store(name=name, node=schema) # group=group,
+
+ # Wrap a callable object with name `parse_args`
+ # This is to mimic the ArgParser.parse_args() API.
+ class _argparse_wrapper:
+ """Wrapper for ArgParser.parse_args()."""
+
+ def __init__(self, arg_parser):
+ self.arg_parser = arg_parser
+ self._actions = arg_parser._actions
+
+ @staticmethod
+ def parse_args(args=None, namespace=None): # pylint: disable=unused-argument
+ """Parse arguments."""
+ return parsed_args
+
+ # no return value from run_hydra() as it may sometime actually run the task_function
+ # multiple times (--multirun)
+
+ _run_hydra( # pylint: disable=no-value-for-parameter
+ args_parser=_argparse_wrapper(args),
+ task_function=task_function,
+ config_path=config_path,
+ config_name=config_name,
+ )
+
+ return wrapper
+
+ return decorator
diff --git a/atommic/core/conf/modelPT.py b/atommic/core/conf/modelPT.py
new file mode 100644
index 00000000..c081aee3
--- /dev/null
+++ b/atommic/core/conf/modelPT.py
@@ -0,0 +1,183 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/config/modelPT.py
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, Optional
+
+from omegaconf import MISSING
+
+from atommic.core.classes.dataset import DatasetConfig
+from atommic.core.conf.optimizers import OptimizerParams
+from atommic.core.conf.schedulers import SchedulerParams
+from atommic.core.conf.trainer import TrainerConfig
+from atommic.utils.exp_manager import ExpManagerConfig
+
+
+@dataclass
+class SchedConfig:
+ """Configuration for the scheduler."""
+
+ name: str = MISSING
+ min_lr: float = 0.0
+ last_epoch: int = -1
+
+
+@dataclass
+class OptimConfig:
+ """Configuration for the optimizer."""
+
+ name: str = MISSING
+ sched: Optional[SchedConfig] = None
+
+
+@dataclass
+class ModelConfig:
+ """Configuration for the model."""
+
+ train_ds: Optional[DatasetConfig] = None
+ validation_ds: Optional[DatasetConfig] = None
+ test_ds: Optional[DatasetConfig] = None
+ optim: Optional[OptimConfig] = None
+
+
+@dataclass
+class HydraConfig:
+ """Configuration for the hydra framework."""
+
+ run: Dict[str, Any] = field(default_factory=lambda: {"dir": "."})
+ job_logging: Dict[str, Any] = field(default_factory=lambda: {"root": {"handlers": None}})
+
+
+@dataclass
+class atommicConfig:
+ """Configuration for the atommic framework."""
+
+ name: str = MISSING
+ model: ModelConfig = MISSING
+ trainer: TrainerConfig = TrainerConfig(
+ strategy="ddp",
+ enable_checkpointing=False,
+ logger=False,
+ log_every_n_steps=1,
+ accelerator="gpu",
+ )
+ exp_manager: Optional[Any] = ExpManagerConfig()
+ hydra: HydraConfig = HydraConfig()
+
+
+class ModelConfigBuilder:
+ """Builder for the ModelConfig class."""
+
+ def __init__(self, model_cfg: ModelConfig):
+ """Inits :class:`ModelConfigBuilder`.
+
+ A Model Config Builder is a utility class that accepts a ModelConfig dataclass, and via a set of utility
+ methods (that are implemented by the subclassed ModelConfigBuilder), builds a finalized ModelConfig that can be
+ supplied to a atommicModel dataclass as the `model` component.
+
+ Subclasses *must* implement the private method `_finalize_cfg`.
+ Inside this method, they must update `self.model_cfg` with all interdependent config
+ options that need to be set (either updated by user explicitly or with their default value).
+ The updated model config must then be preserved in `self.model_cfg`.
+
+ Example
+ -------
+ # Create the config builder
+ config_builder = ModelConfigBuilder()
+ # Update the components of the config that are modifiable
+ config_builder.set_X(X)
+ config_builder.set_Y(Y)
+ # Create a "finalized" config dataclass that will contain all the updates
+ # that were specified by the builder
+ model_config = config_builder.build()
+ # Use model config as is (or further update values), then create a new Model
+ model = atommic..models.Model(cfg=model_config, trainer=Trainer())
+ Supported build methods:
+ - set_train_ds: All model configs can accept a subclass of `DatasetConfig` as their
+ training conf. Subclasses can override this method to enable auto-complete
+ by replacing `Optional[DatasetConfig]` with `Optional[]`.
+ - set_validation_ds: All model configs can accept a subclass of `DatasetConfig` as their
+ validation conf. Subclasses can override this method to enable auto-complete
+ by replacing `Optional[DatasetConfig]` with `Optional[]`.
+ - set_test_ds: All model configs can accept a subclass of `DatasetConfig` as their
+ test conf. Subclasses can override this method to enable auto-complete
+ by replacing `Optional[DatasetConfig]` with `Optional[]`.
+ - set_optim: A build method that supports changes to the Optimizer (and optionally,
+ the Scheduler) used for training the model. The function accepts two inputs -
+ `cfg`: A subclass of `OptimizerParams` - any OptimizerParams subclass can be used,
+ in order to select an appropriate Optimizer. Examples: AdamParams.
+ `sched_cfg`: A subclass of `SchedulerParams` - any SchedulerParams subclass can be used,
+ in order to select an appropriate Scheduler. Examples: CosineAnnealingParams.
+ Note that this argument is optional.
+ - build(): The method which should return a "finalized" ModelConfig dataclass.
+ Subclasses *should* always override this method, and update the signature
+ of this method with the return type of the Dataclass, so that it enables
+ autocomplete for the user.
+ Example:
+ def build(self) -> EncDecCTCConfig:
+ return super().build()
+ Any additional build methods must be added by subclasses of ModelConfigBuilder.
+
+ Parameters
+ ----------
+ model_cfg : ModelConfig
+ The model config dataclass to be updated.
+
+ Returns
+ -------
+ The updated model config dataclass.
+ """
+ self.model_cfg = model_cfg
+ self.train_ds_cfg = None
+ self.validation_ds_cfg = None
+ self.test_ds_cfg = None
+ self.optim_cfg = None
+
+ def set_train_ds(self, cfg: Optional[DatasetConfig] = None):
+ """Set the training dataset configuration."""
+ self.model_cfg.train_ds = cfg
+
+ def set_validation_ds(self, cfg: Optional[DatasetConfig] = None):
+ """Set the validation dataset configuration."""
+ self.model_cfg.validation_ds = cfg
+
+ def set_test_ds(self, cfg: Optional[DatasetConfig] = None):
+ """Set the test dataset configuration."""
+ self.model_cfg.test_ds = cfg
+
+ def set_optim(self, cfg: OptimizerParams, sched_cfg: Optional[SchedulerParams] = None):
+ """Set the optimizer configuration."""
+
+ @dataclass
+ class WrappedOptimConfig(OptimConfig, cfg.__class__): # type: ignore
+ """A wrapper class for the OptimizerParams dataclass."""
+
+ # Setup optim
+ optim_name = cfg.__class__.__name__.replace("Params", "").lower()
+ wrapped_cfg = WrappedOptimConfig(name=optim_name, sched=None, **vars(cfg))
+
+ if sched_cfg is not None:
+
+ @dataclass
+ class WrappedSchedConfig(SchedConfig, sched_cfg.__class__): # type: ignore
+ """A wrapper class for the SchedulerParams dataclass."""
+
+ # Setup scheduler
+ sched_name = sched_cfg.__class__.__name__.replace("Params", "")
+ wrapped_sched_cfg = WrappedSchedConfig(name=sched_name, **vars(sched_cfg))
+
+ wrapped_cfg.sched = wrapped_sched_cfg
+
+ self.model_cfg.optim = wrapped_cfg
+
+ def _finalize_cfg(self):
+ """Finalize the model configuration."""
+ raise NotImplementedError()
+
+ def build(self) -> ModelConfig:
+ """Validate config"""
+ self._finalize_cfg()
+
+ return self.model_cfg
diff --git a/atommic/core/conf/optimizers.py b/atommic/core/conf/optimizers.py
new file mode 100644
index 00000000..1dc3d4ab
--- /dev/null
+++ b/atommic/core/conf/optimizers.py
@@ -0,0 +1,267 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/config/optimizers.py
+
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Dict, Optional, Tuple, Union
+
+from omegaconf import MISSING, OmegaConf
+
+__all__ = [
+ "OptimizerParams",
+ "AdamParams",
+ "NovogradParams",
+ "SGDParams",
+ "AdadeltaParams",
+ "AdamaxParams",
+ "AdagradParams",
+ "AdamWParams",
+ "RMSpropParams",
+ "RpropParams",
+ "get_optimizer_config",
+ "register_optimizer_params",
+]
+
+
+@dataclass
+class OptimizerParams:
+ """Base Optimizer params with no values. User can chose it to explicitly override via command line arguments."""
+
+ lr: Optional[float] = MISSING
+
+
+@dataclass
+class SGDParams(OptimizerParams):
+ """Default configuration for Adam optimizer.
+
+ .. note::
+ For the details on the function/meanings of the arguments, please refer to:
+ https://pytorch.org/docs/stable/optim.html?highlight=sgd#torch.optim.SGD
+ """
+
+ momentum: float = 0
+ dampening: float = 0
+ weight_decay: float = 0
+ nesterov: bool = False
+
+
+@dataclass
+class AdamParams(OptimizerParams):
+ """Default configuration for Adam optimizer.
+
+ .. note::
+ For the details on the function/meanings of the arguments, please refer to:
+ https://pytorch.org/docs/stable/optim.html?highlight=adam#torch.optim.Adam
+ """
+
+ # betas: Tuple[float, float] = (0.9, 0.999)
+ eps: float = 1e-08
+ weight_decay: float = 0
+ amsgrad: bool = False
+
+
+@dataclass
+class AdamWParams(OptimizerParams):
+ """Default configuration for AdamW optimizer.
+
+ .. note::
+ For the details on the function/meanings of the arguments, please refer to:
+ https://pytorch.org/docs/stable/optim.html#torch.optim.AdamW
+ """
+
+ betas: Tuple[float, float] = (0.9, 0.999)
+ eps: float = 1e-08
+ weight_decay: float = 0
+ amsgrad: bool = False
+
+
+@dataclass
+class AdadeltaParams(OptimizerParams):
+ """Default configuration for Adadelta optimizer.
+
+ .. note::
+ For the details on the function/meanings of the arguments, please refer to:
+ https://pytorch.org/docs/stable/optim.html#torch.optim.Adadelta
+ """
+
+ rho: float = 0.9
+ eps: float = 1e-6
+ weight_decay: float = 0
+
+
+@dataclass
+class AdamaxParams(OptimizerParams):
+ """Default configuration for Adamax optimizer.
+
+ .. note::
+ For the details on the function/meanings of the arguments, please refer to:
+ https://pytorch.org/docs/stable/optim.html#torch.optim.Adamax
+ """
+
+ betas: Tuple[float, float] = (0.9, 0.999)
+ eps: float = 1e-8
+ weight_decay: float = 0
+
+
+@dataclass
+class AdagradParams(OptimizerParams):
+ """Default configuration for Adagrad optimizer.
+
+ .. note::
+ For the details on the function/meanings of the arguments, please refer to:
+ https://pytorch.org/docs/stable/optim.html#torch.optim.Adagrad
+ """
+
+ lr_decay: float = 0
+ weight_decay: float = 0
+ initial_accumulator_value: float = 0
+ eps: float = 1e-10
+
+
+@dataclass
+class RMSpropParams(OptimizerParams):
+ """Default configuration for RMSprop optimizer.
+
+ .. note::
+ For the details on the function/meanings of the arguments, please refer to:
+ https://pytorch.org/docs/stable/optim.html#torch.optim.RMSprop
+ """
+
+ alpha: float = 0.99
+ eps: float = 1e-8
+ weight_decay: float = 0
+ momentum: float = 0
+ centered: bool = False
+
+
+@dataclass
+class RpropParams(OptimizerParams):
+ """Default configuration for RpropParams optimizer.
+
+ .. note::
+ For the details on the function/meanings of the arguments, please refer to:
+ https://pytorch.org/docs/stable/optim.html#torch.optim.Rprop
+ """
+
+ etas: Tuple[float, float] = (0.5, 1.2)
+ step_sizes: Tuple[float, float] = (1e-6, 50)
+
+
+@dataclass
+class NovogradParams(OptimizerParams):
+ """Configuration of the Novograd optimizer. It has been proposed in "Stochastic Gradient Methods with Layer-wise
+ Adaptive Moments for Training of Deep Networks" (https://arxiv.org/abs/1905.11286). The OptimizerParams is a Base
+ Optimizer params with no values. User can choose to explicitly override it via command line arguments.
+ """
+
+ betas: Tuple[float, float] = (0.95, 0.98)
+ eps: float = 1e-8
+ weight_decay: float = 0
+ grad_averaging: bool = False
+ amsgrad: bool = False
+ lr: float = 1e-3
+ luc: bool = False
+ luc_trust: float = 1e-3
+ luc_eps: float = 1e-8
+
+
+@dataclass
+class AdafactorParams(OptimizerParams):
+ """Configuration of the Adafactor optimizer. It has been proposed in "Adafactor: Adaptive Learning Rates with
+ Sublinear Memory Cost" (https://arxiv.org/abs/1804.04235)
+
+ Parameters
+ ----------
+ lr : float, optional
+ Learning rate. Default is ``1e-3``.
+ beta1 : float, optional
+ Coefficients used for computing running averages of gradient and its square. Default is ``None``.
+ eps : Tuple[float, float], optional
+ Term added to the denominator to improve numerical stability. Default is ``(1e-30, 1e-3)``.
+ weight_decay : float, optional
+ Weight decay (L2 penalty). Default is ``0``.
+ scale_parameter : bool, optional
+ Scale parameter. Default is ``False``.
+ relative_step : bool, optional
+ Whether to use relative step sizes. Default is ``False``.
+ warmup_init : bool, optional
+ Whether to warm up the learning rate linearly. Default is ``False``.
+ """
+
+ beta1: Optional[float] = None
+ eps: Tuple[float, float] = (1e-30, 1e-3)
+ clip_threshold: float = 1.0
+ decay_rate: float = 0.8
+ weight_decay: float = 0
+ scale_parameter: bool = True
+ relative_step: bool = False
+ warmup_init: bool = False
+
+
+def register_optimizer_params(name: str, optimizer_params: OptimizerParams):
+ """Checks if the optimizer param name exists in the registry, and if it doesn't, adds it. This allows custom
+ optimizer params to be added and called by name during instantiation.
+
+ Parameters
+ ----------
+ name : str
+ Name of the optimizer. Will be used as key to retrieve the optimizer.
+ optimizer_params : OptimizerParams
+ Optimizer class.
+ """
+ if name in AVAILABLE_OPTIMIZER_PARAMS:
+ raise ValueError(f"Cannot override pre-existing optimizers. Conflicting optimizer name = {name}")
+
+ AVAILABLE_OPTIMIZER_PARAMS[name] = optimizer_params # type: ignore
+
+
+def get_optimizer_config(
+ name: str, **kwargs: Optional[Dict[str, Any]]
+) -> Union[Dict[str, Optional[Dict[str, Any]]], partial]:
+ """Convenience method to obtain a OptimizerParams class and partially instantiate it with optimizer kwargs.
+
+ Parameters
+ ----------
+ name : str
+ Name of the optimizer. Will be used as key to retrieve the optimizer.
+ kwargs : Dict[str, Any], optional
+ Optional kwargs of the optimizer used during instantiation.
+
+ Returns
+ -------
+ A partially instantiated OptimizerParams.
+ """
+ if name is None:
+ return kwargs
+
+ if name not in AVAILABLE_OPTIMIZER_PARAMS:
+ raise ValueError(
+ f"Cannot resolve optimizer parameters '{name}'. Available optimizer parameters are : "
+ f"{AVAILABLE_OPTIMIZER_PARAMS.keys()}"
+ )
+
+ scheduler_params = AVAILABLE_OPTIMIZER_PARAMS[name]
+
+ if kwargs is not None and kwargs:
+ kwargs = OmegaConf.create(kwargs)
+ OmegaConf.merge(scheduler_params(), kwargs)
+
+ scheduler_params = partial(scheduler_params, **kwargs) # type: ignore
+ return scheduler_params # type: ignore
+
+
+AVAILABLE_OPTIMIZER_PARAMS = {
+ "optim_params": OptimizerParams,
+ "adam_params": AdamParams,
+ "novograd_params": NovogradParams,
+ "sgd_params": SGDParams,
+ "adadelta_params": AdadeltaParams,
+ "adamax_params": AdamaxParams,
+ "adagrad_params": AdagradParams,
+ "adamw_params": AdamWParams,
+ "rmsprop_params": RMSpropParams,
+ "rprop_params": RpropParams,
+ "adafactor_params": AdafactorParams,
+}
diff --git a/atommic/core/conf/schedulers.py b/atommic/core/conf/schedulers.py
new file mode 100644
index 00000000..d3c8698f
--- /dev/null
+++ b/atommic/core/conf/schedulers.py
@@ -0,0 +1,223 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/config/schedulers.py
+
+from dataclasses import dataclass
+from functools import partial
+from typing import Any, Dict, Optional
+
+
+@dataclass
+class SchedulerParams:
+ """Base configuration for all schedulers."""
+
+ last_epoch: int = -1
+
+
+@dataclass
+class SquareRootConstantSchedulerParams(SchedulerParams):
+ """Base configuration for all schedulers. It is not derived from Config as it is not a atommic object (and in
+ particular it doesn't need a name).
+ """
+
+ constant_steps: Optional[float] = None
+ constant_ratio: Optional[float] = None
+
+
+@dataclass
+class WarmupSchedulerParams(SchedulerParams):
+ """Base configuration for all schedulers."""
+
+ max_steps: int = 0
+ warmup_steps: Optional[float] = None
+ warmup_ratio: Optional[float] = None
+
+
+@dataclass
+class WarmupHoldSchedulerParams(WarmupSchedulerParams):
+ """Base configuration for all schedulers."""
+
+ hold_steps: Optional[float] = None
+ hold_ratio: Optional[float] = None
+ min_lr: float = 0.0
+
+
+@dataclass
+class WarmupAnnealingHoldSchedulerParams(WarmupSchedulerParams):
+ """Base configuration for all schedulers."""
+
+ constant_steps: Optional[float] = None
+ constant_ratio: Optional[float] = None
+ min_lr: float = 0.0
+
+
+@dataclass
+class SquareAnnealingParams(WarmupSchedulerParams):
+ """Square Annealing parameter config"""
+
+ min_lr: float = 1e-5
+
+
+@dataclass
+class SquareRootAnnealingParams(WarmupSchedulerParams):
+ """Square Root Annealing parameter config"""
+
+ min_lr: float = 0.0
+
+
+@dataclass
+class CosineAnnealingParams(WarmupAnnealingHoldSchedulerParams):
+ """Cosine Annealing parameter config"""
+
+ min_lr: float = 0.0
+
+
+@dataclass
+class NoamAnnealingParams(WarmupSchedulerParams):
+ """Cosine Annealing parameter config"""
+
+ min_lr: float = 0.0
+
+
+@dataclass
+class NoamHoldAnnealingParams(WarmupHoldSchedulerParams):
+ """Polynomial Hold Decay Annealing parameter config. It is not derived from Config as it is not a atommic object
+ (and in particular it doesn't need a name).
+ """
+
+ decay_rate: float = 0.5
+
+
+@dataclass
+class WarmupAnnealingParams(WarmupSchedulerParams):
+ """Warmup Annealing parameter config"""
+
+ warmup_ratio: Optional[float] = None
+
+
+@dataclass
+class InverseSquareRootAnnealingParams(WarmupSchedulerParams):
+ """Inverse Square Root Annealing parameter config"""
+
+
+@dataclass
+class PolynomialDecayAnnealingParams(WarmupSchedulerParams):
+ """Polynomial Decay Annealing parameter config"""
+
+ power: float = 1.0
+ cycle: bool = False
+
+
+@dataclass
+class PolynomialHoldDecayAnnealingParams(WarmupSchedulerParams):
+ """Polynomial Hold Decay Annealing parameter config"""
+
+ power: float = 1.0
+ cycle: bool = False
+
+
+@dataclass
+class StepLRParams(SchedulerParams):
+ """Config for StepLR."""
+
+ step_size: float = 0.1
+ gamma: float = 0.1
+
+
+@dataclass
+class ExponentialLRParams(SchedulerParams):
+ """Config for ExponentialLR."""
+
+ gamma: float = 0.9
+
+
+@dataclass
+class ReduceLROnPlateauParams:
+ """Config for ReduceLROnPlateau."""
+
+ mode: str = "min"
+ factor: float = 0.1
+ patience: int = 10
+ verbose: bool = False
+ threshold: float = 1e-4
+ threshold_mode: str = "rel"
+ cooldown: int = 0
+ min_lr: float = 0
+ eps: float = 1e-8
+
+
+@dataclass
+class CyclicLRParams(SchedulerParams):
+ """Config for CyclicLR."""
+
+ base_lr: float = 0.001
+ max_lr: float = 0.1
+ step_size_up: int = 2000
+ step_size_down: Optional[int] = None
+ mode: str = "triangular"
+ gamma: float = 1.0
+ scale_mode: str = "cycle"
+ # scale_fn is not supported
+ cycle_momentum: bool = True
+ base_momentum: float = 0.8
+ max_momentum: float = 0.9
+
+
+def register_scheduler_params(name: str, scheduler_params: SchedulerParams):
+ """Checks if the scheduler config name exists in the registry, and if it doesn't, adds it. This allows custom
+ schedulers to be added and called by name during instantiation.
+
+ Parameters
+ ----------
+ name : str
+ Name of the scheduler. Will be used as key to retrieve the scheduler.
+ scheduler_params : SchedulerParams
+ SchedulerParams class to be added to the registry.
+ """
+ if name in AVAILABLE_SCHEDULER_PARAMS:
+ raise ValueError(f"Cannot override pre-existing optimizers. Conflicting optimizer name = {name}")
+
+ AVAILABLE_SCHEDULER_PARAMS[name] = scheduler_params # type: ignore
+
+
+def get_scheduler_config(name: str, **kwargs: Optional[Dict[str, Any]]) -> partial:
+ """Convenience method to obtain a SchedulerParams class and partially instantiate it with optimizer kwargs.
+
+ Parameters
+ ----------
+ name : str
+ Name of the SchedulerParams. in the registry..
+ kwargs : Dict[str, Any], optional
+ Optional kwargs of the optimizer used during instantiation.
+
+ Returns
+ -------
+ A partially instantiated SchedulerParams.
+ """
+ if name not in AVAILABLE_SCHEDULER_PARAMS:
+ raise ValueError(
+ f"Cannot resolve scheduler parameters '{name}'. Available scheduler parameters are : "
+ f"{AVAILABLE_SCHEDULER_PARAMS.keys()}"
+ )
+
+ return partial(AVAILABLE_SCHEDULER_PARAMS[name], **kwargs)
+
+
+AVAILABLE_SCHEDULER_PARAMS = {
+ "SchedulerParams": SchedulerParams,
+ "WarmupPolicyParams": WarmupSchedulerParams,
+ "WarmupHoldPolicyParams": WarmupHoldSchedulerParams,
+ "WarmupAnnealingHoldSchedulerParams": WarmupAnnealingHoldSchedulerParams,
+ "SquareAnnealingParams": SquareAnnealingParams,
+ "SquareRootAnnealingParams": SquareRootAnnealingParams,
+ "InverseSquareRootAnnealingParams": InverseSquareRootAnnealingParams,
+ "SquareRootConstantSchedulerParams": SquareRootConstantSchedulerParams,
+ "CosineAnnealingParams": CosineAnnealingParams,
+ "NoamAnnealingParams": NoamAnnealingParams,
+ "NoamHoldAnnealingParams": NoamHoldAnnealingParams,
+ "WarmupAnnealingParams": WarmupAnnealingParams,
+ "PolynomialDecayAnnealingParams": PolynomialDecayAnnealingParams,
+ "PolynomialHoldDecayAnnealingParams": PolynomialHoldDecayAnnealingParams,
+ "ReduceLROnPlateauParams": ReduceLROnPlateauParams,
+}
diff --git a/atommic/core/conf/trainer.py b/atommic/core/conf/trainer.py
new file mode 100644
index 00000000..e696415e
--- /dev/null
+++ b/atommic/core/conf/trainer.py
@@ -0,0 +1,62 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/config/pytorch_lightning.py
+
+from dataclasses import dataclass
+from typing import Any, Optional
+
+from hydra.core.config_store import ConfigStore
+
+__all__ = ["TrainerConfig"]
+
+cs = ConfigStore.instance()
+
+
+@dataclass
+class TrainerConfig:
+ """TrainerConfig is a dataclass that holds all the hyperparameters for the training process."""
+
+ logger: Any = True
+ callbacks: Optional[Any] = None
+ default_root_dir: Optional[str] = None
+ gradient_clip_val: float = 0
+ num_nodes: int = 1
+ enable_progress_bar: bool = True
+ overfit_batches: Any = 0.0
+ check_val_every_n_epoch: int = 1
+ fast_dev_run: bool = False
+ accumulate_grad_batches: Any = 1
+ max_epochs: int = 1000
+ min_epochs: int = 1
+ max_steps: Optional[int] = -1
+ min_steps: Optional[int] = None
+ limit_train_batches: Any = 1.0
+ limit_val_batches: Any = 1.0
+ limit_test_batches: Any = 1.0
+ val_check_interval: Any = 1.0
+ log_every_n_steps: int = 50
+ accelerator: Optional[str] = None
+ sync_batchnorm: bool = False
+ precision: Any = 32
+ num_sanity_val_steps: int = 2
+ profiler: Optional[Any] = None
+ benchmark: bool = False
+ deterministic: bool = False
+ use_distributed_sampler: bool = True
+ detect_anomaly: bool = False
+ plugins: Optional[Any] = None # Optional[Union[str, list]]
+ limit_predict_batches: float = 1.0
+ gradient_clip_algorithm: str = 'norm'
+ max_time: Optional[Any] = None # can be one of Union[str, timedelta, Dict[str, int], None]
+ reload_dataloaders_every_n_epochs: int = 0
+ devices: Any = None
+ strategy: Any = None
+ enable_checkpointing: bool = False
+ enable_model_summary: bool = True
+ inference_mode: bool = True
+ barebones: bool = False
+
+
+# Register the trainer config.
+cs.store(group="trainer", name="trainer", node=TrainerConfig)
diff --git a/atommic/core/connectors/__init__.py b/atommic/core/connectors/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/core/connectors/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/core/connectors/save_restore_connector.py b/atommic/core/connectors/save_restore_connector.py
new file mode 100644
index 00000000..c8951b40
--- /dev/null
+++ b/atommic/core/connectors/save_restore_connector.py
@@ -0,0 +1,599 @@
+# coding=utf-8
+from __future__ import annotations # necessary for lazy types evaluation
+
+__author__ = "Dimitris Karkalousos"
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/connectors/save_restore_connector.py
+
+import os
+import shutil
+import tarfile
+import tempfile
+import uuid
+from typing import Optional, Set, Union
+
+import torch
+from omegaconf import DictConfig, OmegaConf
+from omegaconf.omegaconf import open_dict
+from pytorch_lightning.trainer.trainer import Trainer
+
+# to avoid circular import do not import ModelPT directly
+from atommic.core import classes as atommic_classes
+from atommic.utils import logging, model_utils
+from atommic.utils.app_state import AppState
+from atommic.utils.get_rank import is_global_rank_zero
+
+
+class SaveRestoreConnector:
+ """This class is used to save and restore the model state."""
+
+ def __init__(self) -> None:
+ """Inits :class:`SaveRestoreConnector`."""
+ self._model_config_yaml = "model_config.yaml"
+ self._model_weights_ckpt = "model_weights.ckpt"
+ self._model_extracted_dir = None
+
+ def save_to(self, model: "atommic.ModelPT", save_path: str): # type: ignore # noqa: F821
+ """Saves model instance (weights and configuration) into .atommic file.
+
+ You can use "restore_from" method to fully restore instance from .atommic file.
+
+ .atommic file is an archive (tar.gz) with the following:
+ - model_config.yaml - model configuration in .yaml format. You can deserialize this into cfg argument for
+ model's constructor
+ - model_weights.ckpt - model checkpoint
+
+ Parameters
+ ----------
+ model : ModelPT
+ ModelPT object to be saved.
+ save_path : str
+ Path to .atommic file where model instance should be saved
+ """
+ if is_global_rank_zero():
+ with tempfile.TemporaryDirectory() as tmpdir:
+ config_yaml = os.path.join(tmpdir, self.model_config_yaml)
+ model_weights = os.path.join(tmpdir, self.model_weights_ckpt)
+ model.to_config_file(path2yaml_file=config_yaml)
+ # update subconfigs, if there are child model, since child model can change its config
+ self._update_subconfigs(model, path2yaml_file=config_yaml)
+ if model.has_native_or_submodules_artifacts():
+ self._handle_artifacts(model, atommic_file_folder=tmpdir)
+ # We should not update self._cfg here - the model can still be in use
+ self._update_artifact_paths(model, path2yaml_file=config_yaml)
+ self._save_state_dict_to_disk(model.state_dict(), model_weights)
+ self._make_atommic_file_from_folder(filename=save_path, source_dir=tmpdir)
+ else:
+ return
+
+ def load_config_and_state_dict(
+ self,
+ calling_cls,
+ restore_path: str,
+ override_config_path: Optional[Union[OmegaConf, str]] = None,
+ map_location: Optional[torch.device] = None,
+ strict: bool = True, # pylint: disable=unused-argument
+ return_config: bool = False,
+ trainer: Trainer = None,
+ ):
+ """Restores model instance (weights and configuration) into .atommic file
+
+ Parameters
+ ----------
+ calling_cls : class
+ Class of the model to be restored.
+ restore_path : str
+ Path to .atommic file from which model should be instantiated
+ override_config_path : Optional[Union[OmegaConf, str]]
+ Path to a yaml config that will override the internal config file or an OmegaConf/DictConfig object
+ representing the model config.
+ map_location : Optional[torch.device]
+ Optional torch.device() to map the instantiated model to a device. Default is ``None``, it will select a
+ GPU if available, falling back to CPU otherwise.
+ strict : bool
+ Passed to load_state_dict. When set to False, the model will be able to load a checkpoint that has
+ more parameters than the model itself. Default is ``True``.
+ return_config : bool
+ If set to true, will return just the underlying config of the restored model as an OmegaConf DictConfig
+ object without instantiating the model.
+ trainer : Trainer
+ Optional trainer object to be used for model parallelism.
+
+ Example
+ -------
+ ```
+ model = atommic.collections.asr.models.EncDecCTCModel.restore_from('asr.atommic')
+ assert isinstance(model, atommic.collections.asr.models.EncDecCTCModel)
+ ```
+
+ Returns
+ -------
+ An instance of type cls or its underlying config (if return_config is set).
+ """
+ # Get path where the command is executed - the artifacts will be "retrieved" there (original .atommic behavior)
+ cwd = os.getcwd()
+
+ if map_location is None:
+ if torch.cuda.is_available():
+ map_location = torch.device("cuda")
+ else:
+ map_location = torch.device("cpu")
+
+ app_state = AppState()
+ with tempfile.TemporaryDirectory() as tmpdir:
+ try:
+ # Check if self.model_extracted_dir is set, and is a valid path
+ if self.model_extracted_dir is not None and os.path.isdir(self.model_extracted_dir):
+ # Log that atommic will use the provided `model_extracted_dir`
+ logging.info(
+ "Restoration will occur within pre-extracted directory : " f"`{self.model_extracted_dir}`."
+ )
+ # Override `tmpdir` above with the pre-extracted `model_extracted_dir`
+ tmpdir = self.model_extracted_dir
+ else:
+ # Extract the atommic file into the temporary directory
+ self._unpack_atommic_file(
+ path2file=restore_path, out_folder=tmpdir, extract_config_only=return_config is True
+ )
+
+ # Change current working directory to the temporary directory
+ os.chdir(tmpdir)
+ if override_config_path is None:
+ config_yaml = self.model_config_yaml
+ else:
+ # can be str path or OmegaConf / DictConfig object
+ config_yaml = override_config_path
+ if not isinstance(config_yaml, (OmegaConf, DictConfig)):
+ conf = OmegaConf.load(config_yaml)
+ else:
+ conf = config_yaml
+ if override_config_path is not None:
+ # Resolve the override config
+ conf = OmegaConf.to_container(conf, resolve=True)
+ conf = OmegaConf.create(conf)
+ # If override is top level config, extract just `model` from it
+ if "model" in conf:
+ conf = conf.model
+
+ if return_config:
+ instance = conf
+ return instance
+
+ if app_state.model_parallel_rank is not None and app_state.model_parallel_size > 1:
+ model_weights = self._inject_model_parallel_rank_for_ckpt(tmpdir, self.model_weights_ckpt)
+ else:
+ model_weights = os.path.join(tmpdir, self.model_weights_ckpt)
+ OmegaConf.set_struct(conf, True)
+ os.chdir(cwd)
+ # get the class
+ calling_cls._set_model_restore_state( # pylint: disable=protected-access
+ is_being_restored=True, folder=tmpdir
+ )
+ instance = calling_cls.from_config_dict(config=conf, trainer=trainer)
+ instance = instance.to(map_location)
+ # add load_state_dict override
+ if app_state.model_parallel_size is not None and app_state.model_parallel_size > 1:
+ model_weights = self._inject_model_parallel_rank_for_ckpt(tmpdir, self.model_weights_ckpt)
+ state_dict = self._load_state_dict_from_disk(model_weights, map_location=map_location)
+ finally:
+ os.chdir(cwd)
+
+ return (conf, instance, state_dict)
+
+ @staticmethod
+ def load_instance_with_state_dict(instance, state_dict, strict):
+ """Loads the state dict into the instance."""
+ instance.load_state_dict(state_dict, strict=strict)
+ instance._set_model_restore_state(is_being_restored=False) # pylint: disable=protected-access
+
+ def restore_from(
+ self,
+ calling_cls,
+ restore_path: str,
+ override_config_path: Optional[Union[OmegaConf, str]] = None,
+ map_location: Optional[torch.device] = None,
+ strict: bool = True,
+ return_config: bool = False,
+ trainer: Trainer = None,
+ ):
+ """Restores model instance (weights and configuration) into .atommic file
+
+ Parameters
+ ----------
+ calling_cls : class
+ The class of the model to be restored.
+ restore_path : str
+ Path to .atommic file from which model should be instantiated.
+ override_config_path : str or OmegaConf/DictConfig object, optional
+ Path to a yaml config that will override the internal config file or an OmegaConf/DictConfig object
+ representing the model config.
+ map_location : torch.device, optional
+ Optional torch.device() to map the instantiated model to a device. By default (None), it will select a
+ GPU if available, falling back to CPU otherwise.
+ strict : bool, optional
+ Passed to load_state_dict. Default is ``True``.
+ return_config : bool, optional
+ If set to true, will return just the underlying config of the restored model as an OmegaConf/DictConfig
+ object without instantiating the model.
+ trainer : Trainer, optional
+ Optional trainer object to be used for restoring the model.
+
+ Returns
+ -------
+ An instance of type cls or its underlying config (if return_config is set).
+ """
+ # Get path where the command is executed - the artifacts will be "retrieved" there (original .atommic behavior)
+ loaded_params = self.load_config_and_state_dict(
+ calling_cls, restore_path, override_config_path, map_location, strict, return_config, trainer
+ )
+ if not isinstance(loaded_params, tuple) or return_config is True:
+ return loaded_params
+ _, instance, state_dict = loaded_params
+ self.load_instance_with_state_dict(instance, state_dict, strict)
+ logging.info(f"Model {instance.__class__.__name__} was successfully restored from {restore_path}.")
+ return instance, state_dict
+
+ def extract_state_dict_from(self, restore_path: str, save_dir: str, split_by_module: bool = False):
+ """Extract the state dict(s) from a provided .atommic tarfile and save it to a directory.
+
+ Parameters
+ ----------
+ restore_path : str
+ Path to .atommic file from which state dict(s) should be extracted.
+ save_dir : str
+ Directory in which the saved state dict(s) should be stored.
+ split_by_module : bool, optional
+ Bool flag, which determines whether the output checkpoint should be for the entire Model, or
+ the individual module's that comprise the Model. Default is ``False``.
+
+ Example
+ -------
+ To convert the .atommic tarfile into a single Model level PyTorch checkpoint
+ ::
+ state_dict = atommic.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.atommic',
+ './asr_ckpts')
+ To restore a model from a Model level checkpoint
+ ::
+ model = atommic.collections.asr.models.EncDecCTCModel(cfg) # or any other method of restoration
+ model.load_state_dict(torch.load("./asr_ckpts/model_weights.ckpt"))
+ To convert the .atommic tarfile into multiple Module level PyTorch checkpoints
+ ::
+ state_dict = atommic.collections.asr.models.EncDecCTCModel.extract_state_dict_from('asr.atommic',
+ './asr_ckpts', split_by_module=True). To restore a module from a Module level checkpoint
+ ::
+ model = atommic.collections.asr.models.EncDecCTCModel(cfg) # or any other method of restoration
+ # load the individual components
+ model.preprocessor.load_state_dict(torch.load("./asr_ckpts/preprocessor.ckpt"))
+ model.encoder.load_state_dict(torch.load("./asr_ckpts/encoder.ckpt"))
+ model.decoder.load_state_dict(torch.load("./asr_ckpts/decoder.ckpt"))
+
+ Returns
+ -------
+ The state dict that was loaded from the original .atommic checkpoint.
+ """
+ cwd = os.getcwd()
+
+ save_dir = os.path.abspath(save_dir)
+ if not os.path.exists(save_dir):
+ os.makedirs(save_dir, exist_ok=True)
+
+ with tempfile.TemporaryDirectory() as tmpdir:
+ try:
+ self._unpack_atommic_file(path2file=restore_path, out_folder=tmpdir)
+ os.chdir(tmpdir)
+ model_weights = os.path.join(tmpdir, self.model_weights_ckpt)
+ state_dict = self._load_state_dict_from_disk(model_weights)
+
+ if not split_by_module:
+ filepath = os.path.join(save_dir, self.model_weights_ckpt)
+ self._save_state_dict_to_disk(state_dict, filepath)
+
+ else:
+ key_set = {key.split(".")[0] for key in state_dict.keys()}
+ for primary_key in key_set:
+ inner_keys = [key for key in state_dict.keys() if key.split(".")[0] == primary_key]
+ state_dict_subset = {
+ ".".join(inner_key.split(".")[1:]): state_dict[inner_key] for inner_key in inner_keys
+ }
+ filepath = os.path.join(save_dir, f"{primary_key}.ckpt")
+ self._save_state_dict_to_disk(state_dict_subset, filepath)
+
+ logging.info(f"Checkpoints from {restore_path} were successfully extracted into {save_dir}.")
+ finally:
+ os.chdir(cwd)
+
+ return state_dict
+
+ @staticmethod
+ def register_artifact(model, config_path: str, src: str, verify_src_exists: bool = True):
+ """Register model artifacts with this function. These artifacts (files) will be included inside .atommic file
+ when model.save_to("mymodel.atommic") is called.
+
+ How it works:
+ 1. It always returns existing absolute path which can be used during Model constructor call. EXCEPTION: src is
+ None or "" in which case nothing will be done and src will be returned
+ 2. It will add (config_path, model_utils.ArtifactItem()) pair to self.artifacts. If "src" is local existing
+ path, then it will be returned in absolute path form. elif "src" starts with
+ "atommic_file:unique_artifact_name": .atommic will be untarred to a temporary folder location and an actual
+ existing path will be returned else an error will be raised.
+
+ .. code-block::
+
+ If "src" is local existing path:
+ then it will be returned in absolute path form
+ elif "src" starts with "atommic_file:unique_artifact_name":
+ .atommic will be untarred to a temporary folder location and an actual existing path will be returned
+ else:
+ an error will be raised.
+
+ WARNING: use .register_artifact calls in your models' constructors.
+ The returned path is not guaranteed to exist after you have exited your model's constructor.
+
+ Parameters
+ ----------
+ model : ModelPT
+ ModelPT object to register artifact for.
+ config_path : str
+ Artifact key. Usually corresponds to the model config.
+ src : str
+ Path to artifact.
+ verify_src_exists : bool, optional
+ If set to False, then the artifact is optional and register_artifact will return None even if src is not
+ found. Default is ``True``.
+
+ Returns
+ --------
+ If src is not None or empty it always returns absolute path which is guaranteed to exist during model instance
+ life.
+ """
+ app_state = AppState()
+
+ artifact_item = model_utils.ArtifactItem()
+
+ # This is for backward compatibility, if the src objects exists simply inside the tarfile
+ # without its key having been overridden, this pathway will be used.
+ src_obj_name = os.path.basename(src)
+ if app_state.atommic_file_folder is not None:
+ src_obj_path = os.path.abspath(os.path.join(app_state.atommic_file_folder, src_obj_name))
+ else:
+ src_obj_path = src_obj_name
+
+ # src is a local existing path - register artifact and return exact same path for usage by the model
+ if os.path.exists(os.path.abspath(src)):
+ return_path = os.path.abspath(src)
+ artifact_item.path_type = model_utils.ArtifactPathType.LOCAL_PATH
+
+ elif src.startswith("atommic:"):
+ return_path = os.path.abspath(os.path.join(app_state.atommic_file_folder, src[5:]))
+ artifact_item.path_type = model_utils.ArtifactPathType.TAR_PATH
+
+ elif os.path.exists(src_obj_path):
+ return_path = src_obj_path
+ artifact_item.path_type = model_utils.ArtifactPathType.TAR_PATH
+ elif verify_src_exists:
+ raise FileNotFoundError(
+ f"src path does not exist or it is not a path in atommic file. src value I got was: {src}. "
+ f"Absolute: {os.path.abspath(src)}"
+ )
+ else:
+ # artifact is optional and we simply return None
+ return None
+
+ if not os.path.exists(return_path):
+ raise AssertionError
+
+ artifact_item.path = os.path.abspath(src)
+ model.artifacts[config_path] = artifact_item
+ # we were called by ModelPT
+ if hasattr(model, "cfg"):
+ with open_dict(model._cfg): # pylint: disable=protected-access
+ OmegaConf.update(model.cfg, config_path, return_path)
+ return return_path
+
+ def _handle_artifacts(self, model, atommic_file_folder): # noqa: MC0001
+ """This method is called by ModelPT.save_to() and ModelPT.load_from(). It will handle all artifacts and save
+ them to the atommic file.
+
+ Parameters
+ ----------
+ model : ModelPT
+ ModelPT object to handle artifacts for.
+ atommic_file_folder : str
+ Path to temporary folder where atommic file will be untarred.
+ """
+ tarfile_artifacts = []
+ app_state = AppState()
+
+ # aggregate artifacts from self and all children recursively
+ artifacts_containers = []
+ for _, config_path, module in model.named_atommic_modules():
+ if module.has_artifacts(): # atommic model with artifacts
+ artifacts_containers.append((config_path, module.artifacts))
+
+ if len(artifacts_containers) > 0 and (not hasattr(model, "artifacts") or model.artifacts is None):
+ # model has no artifacts, but submodules have some
+ model.artifacts = {}
+
+ for config_path, artifacts in artifacts_containers:
+ for subconf_path, artiitem in artifacts.items():
+ conf_path = f"{config_path}.{subconf_path}" if config_path else f"{subconf_path}"
+ if artiitem.path_type == model_utils.ArtifactPathType.LOCAL_PATH:
+ if not os.path.exists(artiitem.path):
+ raise FileNotFoundError(f"Artifact {conf_path} not found at location: {artiitem.path}")
+
+ # Generate new uniq artifact name and copy it to atommic_file_folder
+ # Note uuid.uuid4().hex is guaranteed to be 32 character long
+ artifact_base_name = os.path.basename(artiitem.path)
+ artifact_uniq_name = f"{uuid.uuid4().hex}_{artifact_base_name}"
+ shutil.copy2(artiitem.path, os.path.join(atommic_file_folder, artifact_uniq_name))
+
+ # Update artifacts registry
+ artiitem.hashed_path = "atommic:" + artifact_uniq_name
+ model.artifacts[conf_path] = artiitem
+
+ elif artiitem.path_type == model_utils.ArtifactPathType.TAR_PATH:
+ # process all tarfile artifacts in one go, so preserve key-value pair
+ tarfile_artifacts.append((conf_path, artiitem))
+ if subconf_path: # artifact from submodule
+ model.artifacts[conf_path] = artiitem
+
+ else:
+ raise ValueError("Directly referencing artifacts from other atommic files isn't supported yet")
+
+ # Process current tarfile artifacts by unpacking the previous tarfile and extract the artifacts
+ # that are currently required.
+ # artifacts can be native (from the model itself) and from submodules
+ # model + submodules restoration paths, handle only unique paths
+ restoration_paths: Set[str] = set()
+ model_metadata = app_state.get_model_metadata_from_guid(model.model_guid)
+ if model_metadata.restoration_path is not None:
+ restoration_paths.add(model_metadata.restoration_path)
+ # aggregate restoration paths for all submodules recursively
+ for module in model.modules():
+ if isinstance(module, atommic_classes.modelPT.ModelPT): # if atommic model
+ submodule_restoration_path = app_state.get_model_metadata_from_guid(module.model_guid).restoration_path
+ if submodule_restoration_path is not None:
+ restoration_paths.add(submodule_restoration_path)
+ if len(tarfile_artifacts) > 0 and len(restoration_paths) == 0:
+ # TODO: see cases when this can occur, and if we can fix them
+ logging.warning("Model contains registered artifacts, but no restoration paths found")
+ if len(tarfile_artifacts) > 0 and len(restoration_paths) > 0:
+ # Need to step into atommic archive to extract file
+ # Get path where the command is executed - the artifacts will be "retrieved" there
+ # (original .atommic behavior)
+ cwd = os.getcwd()
+ with tempfile.TemporaryDirectory() as archive_dir:
+ # Step into the atommic archive to try and find the file
+ try:
+ # unpack all restorations paths (atommic checkpoints)
+ # in atommic checkpoints all resources contain hash in name, so there should be no collisions
+ for path in restoration_paths:
+ if self.model_extracted_dir:
+ shutil.copytree(src=path, dst=archive_dir, dirs_exist_ok=True)
+ else:
+ self._unpack_atommic_file(path2file=path, out_folder=archive_dir)
+ os.chdir(archive_dir)
+ for conf_path, artiitem in tarfile_artifacts:
+ # Get basename and copy it to atommic_file_folder
+ if "atommic:" in artiitem.path:
+ artifact_base_name = artiitem.path.split("atommic:")[1]
+ else:
+ artifact_base_name = os.path.basename(artiitem.path)
+ # no need to hash here as we are in tarfile_artifacts which are already hashed
+ artifact_uniq_name = artifact_base_name
+ shutil.copy2(artifact_base_name, os.path.join(atommic_file_folder, artifact_uniq_name))
+
+ # Update artifacts registry
+ new_artiitem = model_utils.ArtifactItem()
+ new_artiitem.path = "atommic:" + artifact_uniq_name
+ new_artiitem.path_type = model_utils.ArtifactPathType.TAR_PATH
+ model.artifacts[conf_path] = new_artiitem
+ finally:
+ # change back working directory
+ os.chdir(cwd)
+
+ @staticmethod
+ def _update_subconfigs(model: "atommic_classes.ModelPT", path2yaml_file): # type: ignore
+ """Update subconfigs if ModelPT has submodules. Should be called before updating artifacts paths."""
+ if not model.has_atommic_submodules():
+ # no submodules => nothing to update
+ return
+ conf = OmegaConf.load(path2yaml_file)
+ # update subconfigs for all children recursively, parent configs updated before children
+ for _, conf_path, submodule in model.named_atommic_modules():
+ if not conf_path: # self
+ continue
+ OmegaConf.update(conf, conf_path, submodule.cfg)
+ with open(path2yaml_file, "w", encoding="utf-8") as fout:
+ OmegaConf.save(config=conf, f=fout, resolve=True)
+
+ @staticmethod
+ def _update_artifact_paths(model, path2yaml_file):
+ """This method is called by ModelPT.save_to() and ModelPT.load_from() to update the artifact paths in the
+ model.
+ """
+ if hasattr(model, "artifacts") and model.artifacts is not None and len(model.artifacts) > 0:
+ conf = OmegaConf.load(path2yaml_file)
+ for conf_path, item in model.artifacts.items():
+ if item.hashed_path is None:
+ OmegaConf.update(conf, conf_path, item.path)
+ else:
+ OmegaConf.update(conf, conf_path, item.hashed_path)
+ with open(path2yaml_file, "w", encoding="utf-8") as fout:
+ OmegaConf.save(config=conf, f=fout, resolve=True)
+
+ @staticmethod
+ def _inject_model_parallel_rank_for_ckpt(dirname, basename):
+ """This method is called by ModelPT.save_to() and ModelPT.load_from() to inject the parallel rank of the
+ process into the checkpoint file name.
+ """
+ model_weights = os.path.join(dirname, basename)
+ model_weights = model_utils.inject_model_parallel_rank(model_weights)
+ return model_weights
+
+ @staticmethod
+ def _make_atommic_file_from_folder(filename, source_dir):
+ """The method is called by ModelPT.save_to() and ModelPT.load_from() to create a atommic file from a folder."""
+ dirname = os.path.dirname(filename)
+ os.makedirs(dirname, exist_ok=True)
+ with tarfile.open(filename, "w") as tar:
+ tar.add(source_dir, arcname=".")
+
+ @staticmethod
+ def _unpack_atommic_file(path2file: str, out_folder: str, extract_config_only: bool = False) -> str:
+ """This method is called by ModelPT.save_to() and ModelPT.load_from() to unpack a atommic file."""
+ if not os.path.exists(path2file):
+ raise FileNotFoundError(f"{path2file} does not exist")
+ # we start with an assumption of uncompressed tar, which should be true for versions 1.7.0 and above
+ tar_header = "r:"
+ try:
+ tar_test = tarfile.open(path2file, tar_header) # pylint: disable=consider-using-with
+ tar_test.close()
+ except tarfile.ReadError:
+ # can be older checkpoint => try compressed tar
+ tar_header = "r:gz"
+ tar = tarfile.open(path2file, tar_header) # pylint: disable=consider-using-with
+ if not extract_config_only:
+ tar.extractall(path=out_folder)
+ else:
+ members = [x for x in tar.getmembers() if ".yaml" in x.name]
+ tar.extractall(path=out_folder, members=members)
+ tar.close()
+ return out_folder
+
+ @staticmethod
+ def _save_state_dict_to_disk(state_dict, filepath):
+ """This method is called by ModelPT.save_to() and ModelPT.load_from() to save the state dict to disk."""
+ torch.save(state_dict, filepath)
+
+ @staticmethod
+ def _load_state_dict_from_disk(model_weights, map_location="cpu"):
+ """This method is called by ModelPT.save_to() and ModelPT.load_from() to load the state dict from disk."""
+ return torch.load(model_weights, map_location=map_location)
+
+ @property
+ def model_config_yaml(self) -> str:
+ """This property is used to get the path to the model config yaml file."""
+ return self._model_config_yaml
+
+ @model_config_yaml.setter
+ def model_config_yaml(self, path: str):
+ """This property is used to set the path to the model config yaml file."""
+ self._model_config_yaml = path
+
+ @property
+ def model_weights_ckpt(self) -> str:
+ """This property is used to get the path to the model weights ckpt file."""
+ return self._model_weights_ckpt
+
+ @model_weights_ckpt.setter
+ def model_weights_ckpt(self, path: str):
+ """This property is used to set the path to the model weights ckpt file."""
+ self._model_weights_ckpt = path
+
+ @property
+ def model_extracted_dir(self) -> Optional[str]:
+ return self._model_extracted_dir
+
+ @model_extracted_dir.setter
+ def model_extracted_dir(self, path: None):
+ self._model_extracted_dir = path
diff --git a/atommic/core/neural_types/__init__.py b/atommic/core/neural_types/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/core/neural_types/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/core/neural_types/axes.py b/atommic/core/neural_types/axes.py
new file mode 100644
index 00000000..1f982052
--- /dev/null
+++ b/atommic/core/neural_types/axes.py
@@ -0,0 +1,98 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/neural_types/axes.py
+
+from enum import Enum
+from typing import Optional
+
+__all__ = ["AxisKindAbstract", "AxisKind", "AxisType"]
+
+
+class AxisKindAbstract(Enum):
+ """This is an abstract Enum to represents what does varying axis dimension mean. In practice, you will almost
+ always use AxisKind Enum. This Enum should be inherited by your OWN Enum if you aren't satisfied with AxisKind.
+ Then your own Enum can be used instead of AxisKind.
+ """
+
+
+class AxisKind(AxisKindAbstract):
+ """This Enum represents what does varying axis dimension mean. For example, does this dimension correspond to
+ width, batch, time, etc. The "Dimension" and "Channel" kinds are the same and used to represent a general axis.
+ "Any" axis will accept any axis kind fed to it.
+ """
+
+ Batch = 0
+ Time = 1
+ Dimension = 2
+ Width = 3
+ Height = 4
+ Any = 5
+ Sequence = 6
+ FlowGroup = 7
+ Singleton = 8 # Used to represent a axis that has size 1
+
+ def __repr__(self):
+ """Returns short string representation of the AxisKind"""
+ return self.__str__()
+
+ def __str__(self):
+ """Returns short string representation of the AxisKind"""
+ return str(self.name).lower()
+
+ @staticmethod
+ def from_str(label):
+ """Returns AxisKind instance based on short string representation"""
+ _label = label.lower().strip()
+ if _label in ("b", "n", "batch"):
+ return AxisKind.Batch
+ if _label == "t" or _label == "time" or (len(_label) > 2 and _label.startswith("t_")):
+ return AxisKind.Time
+ if _label in ("d", "c", "channel"):
+ return AxisKind.Dimension
+ if _label in ("w", "width"):
+ return AxisKind.Width
+ if _label in ("h", "height"):
+ return AxisKind.Height
+ if _label in ("s", "singleton"):
+ return AxisKind.Singleton
+ if _label in ("seq", "sequence"):
+ return AxisKind.Sequence
+ if _label == "flowgroup":
+ return AxisKind.FlowGroup
+ if _label == "any":
+ return AxisKind.Any
+ raise ValueError(f"Can't create AxisKind from {label}")
+
+
+class AxisType:
+ """This class represents axis semantics and (optionally) it's dimensionality."""
+
+ def __init__(self, kind: AxisKindAbstract, size: Optional[int] = None, is_list=False):
+ """Inits :class:`AxisType`.
+
+ Parameters
+ ----------
+ kind : AxisKindAbstract
+ What kind of axis it is? For example Batch, Height, etc.
+ size : int, optional
+ Specify if the axis should have a fixed size. By default, it is set to None and you typically do not want
+ to set it for Batch and Time. Default is ``None``.
+ is_list : bool, optional
+ Specify if the axis is a list. Default is ``False``.
+ """
+ if size is not None and is_list:
+ raise ValueError("The axis can't be list and have a fixed size")
+ self.kind = kind
+ self.size = size
+ self.is_list = is_list
+
+ def __repr__(self):
+ """Returns short string representation of the AxisType"""
+ if self.size is None:
+ representation = str(self.kind)
+ else:
+ representation = f"{str(self.kind)}:{self.size}"
+ if self.is_list:
+ representation += "_listdim"
+ return representation
diff --git a/atommic/core/neural_types/comparison.py b/atommic/core/neural_types/comparison.py
new file mode 100644
index 00000000..7112506b
--- /dev/null
+++ b/atommic/core/neural_types/comparison.py
@@ -0,0 +1,22 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/neural_types/comparison.py
+
+from enum import Enum
+
+__all__ = ["NeuralTypeComparisonResult"]
+
+
+class NeuralTypeComparisonResult(Enum):
+ """The result of comparing two neural type objects for compatibility. When comparing A.compare_to(B)."""
+
+ SAME = 0
+ LESS = 1 # A is B
+ GREATER = 2 # B is A
+ DIM_INCOMPATIBLE = 3 # Resize connector might fix incompatibility
+ # A transpose and/or converting between lists and tensors will make them same
+ TRANSPOSE_SAME = 4
+ INCOMPATIBLE = 6 # A and B are incompatible
+ # A and B are of the same type but parametrized differently
+ SAME_TYPE_INCOMPATIBLE_PARAMS = 7
diff --git a/atommic/core/neural_types/elements.py b/atommic/core/neural_types/elements.py
new file mode 100644
index 00000000..babff5f9
--- /dev/null
+++ b/atommic/core/neural_types/elements.py
@@ -0,0 +1,87 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/neural_types/elements.py
+
+from abc import ABC, ABCMeta
+from typing import Dict, Optional, Tuple
+
+from atommic.core.neural_types.comparison import NeuralTypeComparisonResult
+
+__all__ = ["ElementType", "LossType", "VoidType"]
+
+
+class ElementType(ABC):
+ """Abstract class defining semantics of the tensor elements. We are relying on Python for inheritance checking"""
+
+ def __str__(self):
+ """Override this method to provide a human readable representation of the type"""
+ return self.__doc__
+
+ def __repr__(self):
+ """Override this method to provide a human readable representation of the type"""
+ return self.__class__.__name__
+
+ @property
+ def type_parameters(self) -> Dict:
+ """Override this property to parametrize your type. For example, you can specify 'storage' type such as float,
+ int, bool with 'dtype' keyword. Another example, is if you want to represent a signal with a particular
+ property (say, sample frequency), then you can put sample_freq->value in there. When two types are compared
+ their type_parameters must match."
+ """
+ return {}
+
+ @property
+ def fields(self) -> Optional[Tuple]:
+ """This should be used to logically represent tuples/structures. For example, if you want to represent a \
+ bounding box (x, y, width, height) you can put a tuple with names ('x', y', 'w', 'h') in here. Under the \
+ hood this should be converted to the last tensor dimension of fixed size = len(fields). When two types are \
+ compared their fields must match."""
+ return None
+
+ def compare(self, second) -> NeuralTypeComparisonResult:
+ """Override this method to provide a comparison between two types."""
+ # First, check general compatibility
+ first_t = type(self)
+ second_t = type(second)
+
+ if first_t == second_t:
+ result = NeuralTypeComparisonResult.SAME
+ elif issubclass(first_t, second_t):
+ result = NeuralTypeComparisonResult.LESS
+ elif issubclass(second_t, first_t):
+ result = NeuralTypeComparisonResult.GREATER
+ else:
+ result = NeuralTypeComparisonResult.INCOMPATIBLE
+
+ if result != NeuralTypeComparisonResult.SAME:
+ return result
+ # now check that all parameters match
+ check_params = set(self.type_parameters.keys()) == set(second.type_parameters.keys())
+ if not check_params:
+ return NeuralTypeComparisonResult.SAME_TYPE_INCOMPATIBLE_PARAMS
+ for k1, v1 in self.type_parameters.items():
+ if v1 is None or second.type_parameters[k1] is None:
+ # Treat None as Void
+ continue
+ if v1 != second.type_parameters[k1]:
+ return NeuralTypeComparisonResult.SAME_TYPE_INCOMPATIBLE_PARAMS
+ # check that all fields match
+ if self.fields == second.fields:
+ return NeuralTypeComparisonResult.SAME
+ return NeuralTypeComparisonResult.INCOMPATIBLE
+
+
+class VoidType(ElementType):
+ """
+ Void-like type which is compatible with everything. It is a good practice to use this type only as necessary.
+ For example, when you need template-like functionality.
+ """
+
+ def compare(cls, second: ABCMeta) -> NeuralTypeComparisonResult: # pylint: disable=arguments-renamed
+ """Void type is compatible with everything."""
+ return NeuralTypeComparisonResult.SAME
+
+
+class LossType(ElementType):
+ """Element type to represent outputs of Loss modules"""
diff --git a/atommic/core/neural_types/neural_type.py b/atommic/core/neural_types/neural_type.py
new file mode 100644
index 00000000..43950247
--- /dev/null
+++ b/atommic/core/neural_types/neural_type.py
@@ -0,0 +1,236 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/neural_types/neural_type.py
+
+from typing import Optional, Tuple
+
+from atommic.core.neural_types.axes import AxisKind, AxisType
+from atommic.core.neural_types.comparison import NeuralTypeComparisonResult
+from atommic.core.neural_types.elements import ElementType, VoidType
+
+__all__ = ["NeuralType", "NeuralTypeError", "NeuralPortNameMismatchError", "NeuralPortNmTensorMismatchError"]
+
+
+class NeuralType:
+ """This is the main class which would represent neural type concept. It is used to represent *the types* of inputs
+ and outputs.
+ """
+
+ def __str__(self):
+ """Returns string representation of NeuralType."""
+ if self.axes is not None:
+ return f"axes: {self.axes}; elements_type: {self.elements_type.__class__.__name__}"
+ return f"axes: None; elements_type: {self.elements_type.__class__.__name__}"
+
+ def __init__(self, axes: Optional[Tuple] = None, elements_type: ElementType = VoidType(), optional=False):
+ """Inits :class:`NeuralType`.
+
+ Parameters
+ ----------
+ axes : tuple
+ A tuple of AxisTypes objects representing the semantics of what varying each axis means. You can use a
+ short, string-based form here. For example: ('B', 'C', 'H', 'W') would correspond to an NCHW format
+ frequently used in computer vision. ('B', 'T', 'D') is frequently used for signal processing and means
+ [batch, time, dimension/channel].
+ elements_type : ElementType
+ An instance of ElementType class representing the semantics of what is stored inside the tensor. For
+ example: logits (LogitsType), log probabilities (LogprobType), etc.
+ optional : bool
+ By default, this is false. If set to True, it would mean that input to the port of this type can be
+ optional.
+ """
+ if not isinstance(elements_type, ElementType):
+ raise ValueError(
+ "elements_type of NeuralType must be an instance of a class derived from ElementType. "
+ "Did you pass a class instead?"
+ )
+ self.elements_type = elements_type
+ if axes is not None:
+ NeuralType.__check_sanity(axes)
+ axes_list = []
+ for axis in axes:
+ if isinstance(axis, str):
+ axes_list.append(AxisType(AxisKind.from_str(axis), None))
+ elif isinstance(axis, AxisType):
+ axes_list.append(axis)
+ else:
+ raise ValueError("axis type must be either str or AxisType instance")
+ self.axes = tuple(axes_list)
+ else:
+ self.axes = None # type: ignore
+ self.optional = optional
+
+ def compare(self, second) -> NeuralTypeComparisonResult:
+ """Performs neural type comparison of self with second. When you chain two modules' inputs/outputs via __call__
+ method, this comparison will be called to ensure neural type compatibility.
+ """
+ # First, handle dimensionality
+ axes_a = self.axes
+ axes_b = second.axes
+
+ # "Big void" type
+ if isinstance(self.elements_type, VoidType) and self.axes is None:
+ return NeuralTypeComparisonResult.SAME
+
+ if self.axes is None:
+ if second.axes is None:
+ return self.elements_type.compare(second.elements_type)
+ return NeuralTypeComparisonResult.INCOMPATIBLE
+
+ dimensions_pass = NeuralType.__compare_axes(axes_a, axes_b)
+ element_comparison_result = self.elements_type.compare(second.elements_type)
+
+ # SAME DIMS
+ if dimensions_pass == 0:
+ return element_comparison_result
+ # TRANSPOSE_SAME DIMS
+ if dimensions_pass == 1 and element_comparison_result == NeuralTypeComparisonResult.SAME:
+ return NeuralTypeComparisonResult.TRANSPOSE_SAME
+ if (
+ dimensions_pass == 1
+ or dimensions_pass == 2
+ and element_comparison_result != NeuralTypeComparisonResult.SAME
+ ):
+ return NeuralTypeComparisonResult.INCOMPATIBLE
+ if dimensions_pass == 2:
+ return NeuralTypeComparisonResult.DIM_INCOMPATIBLE
+ return NeuralTypeComparisonResult.INCOMPATIBLE
+
+ def compare_and_raise_error(self, parent_type_name, port_name, second_object):
+ """Method compares definition of one type with another and raises an error if not compatible."""
+ type_compatibility = self.compare(second_object)
+ if type_compatibility not in (NeuralTypeComparisonResult.SAME, NeuralTypeComparisonResult.GREATER):
+ raise NeuralPortNmTensorMismatchError(
+ parent_type_name, port_name, str(self), str(second_object.ntype), type_compatibility
+ )
+
+ def __eq__(self, other):
+ """Checks if two NeuralTypes are equal."""
+ return self.compare(other) if isinstance(other, NeuralType) else False
+
+ @staticmethod
+ def __check_sanity(axes):
+ """Check that list come before any tensor dimension"""
+ are_strings = True
+ for axis in axes:
+ if not isinstance(axis, str):
+ are_strings = False
+ if isinstance(axis, str) and not are_strings:
+ raise ValueError("Either use full class names or all strings")
+ if are_strings:
+ return
+ checks_passed = True
+ saw_tensor_dim = False
+ for axis in axes:
+ if not axis.is_list:
+ saw_tensor_dim = True
+ elif saw_tensor_dim: # which is preceded by tensor dim
+ checks_passed = False
+ if not checks_passed:
+ raise ValueError(
+ "You have list dimension after Tensor dimension. All list dimensions must preceded Tensor dimensions"
+ )
+
+ @staticmethod
+ def __compare_axes(axes_a, axes_b) -> int:
+ """Compares axes_a and axes_b
+
+ Parameters
+ ----------
+ axes_a : tuple
+ A tuple of first AxisTypes objects representing the semantics of what varying each axis means.
+ axes_b : tuple
+ A tuple of second AxisTypes objects representing the semantics of what varying each axis means.
+
+ Returns
+ ----------
+ int
+ 0 - if they are exactly the same
+ 1 - if they are "TRANSPOSE_SAME"
+ 2 - if they are "DIM_INCOMPATIBLE"
+ 3 - if they are different
+ """
+ if axes_a is None:
+ return 0 if axes_b is None else 3
+ if axes_b is None:
+ return 3
+ if len(axes_a) != len(axes_b):
+ return 3
+ # After these ifs we know that len(axes_a) == len(axes_b)
+
+ same = True
+ kinds_a = {}
+ kinds_b = {}
+ for axis_a, axis_b in zip(axes_a, axes_b):
+ kinds_a[axis_a.kind] = axis_a.size
+ kinds_b[axis_b.kind] = axis_b.size
+ if axis_a.kind == AxisKind.Any:
+ same = True
+ elif (
+ axis_a.kind != axis_b.kind
+ or axis_a.is_list != axis_b.is_list
+ or (axis_a.size != axis_b.size and axis_a.size is not None)
+ ):
+ same = False
+ if same:
+ return 0
+ # can be TRANSPOSE_SAME, DIM_INCOMPATIBLE
+ if kinds_a.keys() == kinds_b.keys():
+ return next((2 for key, value in kinds_a.items() if kinds_b[key] != value), 1)
+ return 3
+
+ def __repr__(self):
+ """Returns string representation of NeuralType."""
+ axes = str(self.axes) if self.axes is not None else "None"
+ if self.elements_type is not None:
+ element_type = repr(self.elements_type)
+ else:
+ element_type = "None"
+
+ data = f"axis={axes}, element_type={element_type}"
+
+ if self.optional:
+ data = f"{data}, optional={self.optional}"
+
+ return f"{self.__class__.__name__}({data})"
+
+
+class NeuralTypeError(Exception):
+ """Base class for neural type related exceptions."""
+
+
+class NeuralPortNameMismatchError(NeuralTypeError):
+ """Exception raised when neural module is called with incorrect port names."""
+
+ def __init__(self, input_port_name):
+ """Inits :class:`NeuralPortNameMismatchError`."""
+ super().__init__()
+ self.message = f"Wrong input port name: {input_port_name}"
+
+
+class NeuralPortNmTensorMismatchError(NeuralTypeError):
+ """Exception raised when a port is fed with a NmTensor of incompatible type."""
+
+ def __init__(self, class_name, port_name, first_type, second_type, type_compatibility):
+ """Inits :class:`NeuralPortNmTensorMismatchError`.
+
+ Parameters
+ ----------
+ class_name : str
+ Class name of the module that raised the error.
+ port_name : str
+ Name of the port that raised the error.
+ first_type : str
+ First type that was compared.
+ second_type : str
+ Second type that was compared.
+ type_compatibility : NeuralTypeComparisonResult
+ Result of the comparison.
+ """
+ super().__init__()
+ self.message = (
+ f"\nIn {class_name}. \nPort: {port_name} and a NmTensor it was fed are of incompatible "
+ f"neural types:\n\n{first_type} \n\n and \n\n{second_type}"
+ )
+ self.message += f"\n\nType comparison result: {type_compatibility}"
diff --git a/atommic/core/optim/__init__.py b/atommic/core/optim/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/core/optim/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/core/optim/adafactor.py b/atommic/core/optim/adafactor.py
new file mode 100644
index 00000000..28bb237f
--- /dev/null
+++ b/atommic/core/optim/adafactor.py
@@ -0,0 +1,220 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/optim/adafactor.py
+
+import math
+
+import torch
+from torch.optim.optimizer import Optimizer
+
+__all__ = ["Adafactor"]
+
+
+class Adafactor(Optimizer):
+ """Implements Adafactor algorithm.
+
+ This implementation is based on: `Adafactor: Adaptive Learning Rates with Sublinear Memory Cost`
+ (see https://arxiv.org/abs/1804.04235)
+ Note that this optimizer internally adjusts the learning rate depending on the *scale_parameter*, *relative_step*
+ and *warmup_init* options. To use a manual (external) learning rate schedule you should set `scale_parameter=False`
+ and `relative_step=False`.
+
+ Returns
+ -------
+ Optimizer
+ Adafactor Optimizer.
+ """
+
+ def __init__(
+ self,
+ params,
+ lr=None,
+ eps=(1e-30, 1e-3),
+ clip_threshold=1.0,
+ decay_rate=-0.8,
+ beta1=None,
+ weight_decay=0.0,
+ scale_parameter=True,
+ relative_step=True,
+ warmup_init=False,
+ min_step=1e-2,
+ ):
+ """Inits :class:`Adafactor`.
+
+ Parameters
+ ----------
+ params : iterable
+ Iterable of parameters to optimize or dicts defining parameter groups.
+ lr : float (optional)
+ External learning rate. Default is ``None``.
+ eps : tuple (float, float)
+ Regularization constants for square gradient and parameter scale respectively.
+ Default is ``(1e-30, 1e-3)``.
+ clip_threshold : float
+ Threshold of root-mean-square of final gradient update. Default is ``1.0``.
+ decay_rate : float
+ Coefficient used to compute running averages of square gradient. Default is ``-0.8``.
+ beta1 : float
+ Coefficient used for computing running averages of gradient. Default is ``None``.
+ weight_decay : float (optional)
+ Weight decay (L2 penalty). Default is ``0``.
+ scale_parameter : bool
+ If True, learning rate is scaled by root-mean-square of parameter. Default is ``True``.
+ relative_step : bool
+ If True, time-dependent learning rate is computed instead of external learning rate. Default is ``True``.
+ warmup_init : bool
+ Time-dependent learning rate computation depends on whether warm-up initialization is being used.
+ Default is `False``.
+ """
+ if lr is not None and relative_step:
+ raise ValueError("Cannot combine manual lr and relative_step options")
+ if warmup_init and not relative_step:
+ raise ValueError("warmup_init requires relative_step=True")
+ self.min_step = min_step
+
+ defaults = {
+ "lr": lr,
+ "eps": eps,
+ "clip_threshold": clip_threshold,
+ "decay_rate": decay_rate,
+ "beta1": beta1,
+ "weight_decay": weight_decay,
+ "scale_parameter": scale_parameter,
+ "relative_step": relative_step,
+ "warmup_init": warmup_init,
+ "min_step": min_step,
+ }
+ super().__init__(params, defaults)
+
+ @property
+ def supports_memory_efficient_fp16(self):
+ """Whether optimizer supports memory efficient fp16"""
+ return True
+
+ @property
+ def supports_flat_params(self):
+ """Whether the optimizer supports flat parameters."""
+ return False
+
+ def _get_lr(self, param_group, param_state):
+ """Returns the learning rate for the current layer."""
+ rel_step_sz = param_group["lr"]
+ if param_group["relative_step"]:
+ min_step = 1e-6 * param_state["step"] if param_group["warmup_init"] else self.min_step
+ rel_step_sz = min(min_step, 1.0 / math.sqrt(param_state["step"]))
+ param_scale = 1.0
+ if param_group["scale_parameter"]:
+ param_scale = max(param_group["eps"][1], param_state["RMS"])
+ return param_scale * rel_step_sz
+
+ def step(self, closure=None): # noqa: MC0001
+ """Performs a single optimization step.
+
+ Parameters
+ ----------
+ closure : callable (optional)
+ A closure that reevaluates the model and returns the loss.
+ """
+ loss = closure() if closure is not None else None
+ for group in self.param_groups:
+ for p in group["params"]:
+ if p.grad is None:
+ continue
+ grad = p.grad.data
+ if grad.dtype in {torch.float16, torch.bfloat16}:
+ grad = grad.float()
+ if grad.is_sparse:
+ raise RuntimeError("Adafactor does not support sparse gradients.")
+
+ state = self.state[p]
+ grad_shape = grad.shape
+
+ factored, use_first_moment = self._get_options(group, grad_shape)
+ # State Initialization
+ if len(state) == 0:
+ state["step"] = 0
+
+ if use_first_moment:
+ # Exponential moving average of gradient values
+ state["exp_avg"] = torch.zeros_like(grad)
+ if factored:
+ state["exp_avg_sq_row"] = torch.zeros(grad_shape[:-1]).to(grad)
+ state["exp_avg_sq_col"] = torch.zeros(grad_shape[:-2] + grad_shape[-1:]).to(grad)
+ else:
+ state["exp_avg_sq"] = torch.zeros_like(grad)
+
+ state["RMS"] = 0
+ else:
+ if use_first_moment:
+ state["exp_avg"] = state["exp_avg"].to(grad)
+ if factored:
+ state["exp_avg_sq_row"] = state["exp_avg_sq_row"].to(grad)
+ state["exp_avg_sq_col"] = state["exp_avg_sq_col"].to(grad)
+ else:
+ state["exp_avg_sq"] = state["exp_avg_sq"].to(grad)
+
+ p_data_fp32 = p.data
+ if p.data.dtype in {torch.float16, torch.bfloat16}:
+ p_data_fp32 = p_data_fp32.float()
+
+ state["step"] += 1
+ state["RMS"] = self._rms(p_data_fp32)
+ group["lr"] = self._get_lr(group, state)
+
+ beta2t = 1.0 - math.pow(state["step"], group["decay_rate"])
+ update = (grad**2) + group["eps"][0]
+ if factored:
+ exp_avg_sq_row = state["exp_avg_sq_row"]
+ exp_avg_sq_col = state["exp_avg_sq_col"]
+
+ exp_avg_sq_row.mul_(beta2t).add_(update.mean(dim=-1), alpha=1.0 - beta2t)
+ exp_avg_sq_col.mul_(beta2t).add_(update.mean(dim=-2), alpha=1.0 - beta2t)
+
+ # Approximation of exponential moving average of square of gradient
+ update = self._approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col)
+ update.mul_(grad)
+ else:
+ exp_avg_sq = state["exp_avg_sq"]
+
+ exp_avg_sq.mul_(beta2t).add_(update, alpha=1.0 - beta2t)
+ update = exp_avg_sq.rsqrt().mul_(grad)
+
+ update.div_((self._rms(update) / group["clip_threshold"]).clamp_(min=1.0))
+ update.mul_(group["lr"])
+
+ if use_first_moment:
+ exp_avg = state["exp_avg"]
+ exp_avg.mul_(group["beta1"]).add_(update, alpha=1 - group["beta1"])
+ update = exp_avg
+
+ if group["weight_decay"] != 0:
+ p_data_fp32.add_(p_data_fp32, alpha=-group["weight_decay"] * group["lr"])
+
+ p_data_fp32.add_(-update)
+
+ if p.data.dtype in {torch.float16, torch.bfloat16}:
+ p.data.copy_(p_data_fp32)
+
+ return loss
+
+ @staticmethod
+ def _get_options(param_group, param_shape):
+ """Returns the options for the current layer."""
+ factored = len(param_shape) >= 2
+ use_first_moment = param_group["beta1"] is not None
+ return factored, use_first_moment
+
+ @staticmethod
+ def _rms(tensor):
+ """Compute the root-mean-square of a tensor."""
+ return tensor.norm(2) / (tensor.numel() ** 0.5)
+
+ @staticmethod
+ def _approx_sq_grad(exp_avg_sq_row, exp_avg_sq_col):
+ """Compute the square of the gradient, but approximate the sqrt using the exponential moving average of the
+ squared gradient.
+ """
+ r_factor = (exp_avg_sq_row / exp_avg_sq_row.mean(dim=-1, keepdim=True)).rsqrt_().unsqueeze(-1)
+ c_factor = exp_avg_sq_col.unsqueeze(-2).rsqrt()
+ return torch.mul(r_factor, c_factor)
diff --git a/atommic/core/optim/lion.py b/atommic/core/optim/lion.py
new file mode 100644
index 00000000..ec315bf8
--- /dev/null
+++ b/atommic/core/optim/lion.py
@@ -0,0 +1,84 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/lucidrains/lion-pytorch/tree/main/lion_pytorch
+
+import torch
+from torch.optim.optimizer import Optimizer
+
+__all__ = ["Lion"]
+
+
+class Lion(Optimizer):
+ """Implements Lion, EvoLved Sign Momentum optimizer.
+
+ This implementation is based on: `Symbolic Discovery of Optimization Algorithms` (see
+ https://arxiv.org/abs/2302.06675)
+ """
+
+ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
+ """Inits :class:`Lion`.
+
+ Parameters
+ ----------
+ params : iterable
+ Iterable of parameters to optimize or dicts defining parameter groups.
+ lr : float, optional
+ Learning rate. Default is ``1e-3``.
+ betas : tuple of floats, optional
+ Coefficients used for computing running averages of gradient and its square. Default is ``(0.9, 0.999)``.
+ eps : float, optional
+ Term added to the denominator to improve numerical stability. Default is ``1e-8``.
+ weight_decay : float, optional
+ Weight decay (L2 penalty). Default is ``0``.
+ """
+ defaults = {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay}
+ super().__init__(params, defaults)
+
+ def step(self, closure=None):
+ """Step through the optimizer"""
+ loss = None
+ if closure is not None:
+ loss = closure()
+
+ for group in self.param_groups:
+ for p in group["params"]:
+ if p.grad is None:
+ continue
+ grad = p.grad.data.float()
+ if grad.is_sparse:
+ raise RuntimeError("RAdam does not support sparse gradients")
+
+ p_data_fp32 = p.data
+ if p.data.dtype in {torch.float16, torch.bfloat16}:
+ p_data_fp32 = p_data_fp32.float()
+
+ state = self.state[p]
+
+ # State Initialization
+ if len(state) == 0:
+ state["step"] = 0
+ state["exp_avg"] = torch.zeros_like(grad)
+ else:
+ state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32)
+
+ state["step"] += 1
+
+ # Weight update
+ exp_avg = state["exp_avg"]
+ beta1, beta2 = group["betas"]
+
+ step_size = exp_avg * beta1 + grad * (1 - beta1)
+
+ # Decay the momentum running average coefficient
+ exp_avg.mul_(beta2).add_(grad, alpha=1 - beta2)
+
+ if group["weight_decay"] != 0:
+ p_data_fp32.add_(-group["weight_decay"] * group["lr"])
+
+ # more conservative since it's an approximated value
+ p_data_fp32.add_(torch.sign(step_size), alpha=-group["lr"])
+
+ p.data.copy_(p_data_fp32)
+
+ return loss
diff --git a/atommic/core/optim/lr_scheduler.py b/atommic/core/optim/lr_scheduler.py
new file mode 100644
index 00000000..1d5fa005
--- /dev/null
+++ b/atommic/core/optim/lr_scheduler.py
@@ -0,0 +1,1213 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/optim/lr_scheduler.py
+
+import copy
+import dataclasses
+import inspect
+import math
+import warnings
+from functools import partial
+from typing import Any, Dict, Optional, Union
+
+import hydra
+import torch.optim.lr_scheduler as pt_scheduler
+from omegaconf import DictConfig, OmegaConf
+from torch import optim
+from torch.optim.lr_scheduler import _LRScheduler
+from torch.utils.data import dataloader
+
+from atommic.core.conf.schedulers import SchedulerParams, get_scheduler_config, register_scheduler_params
+from atommic.utils import logging
+from atommic.utils.model_utils import maybe_update_config_version
+
+
+class WarmupPolicy(_LRScheduler):
+ """Adds warmup kwargs and warmup logic to lr policy. All arguments should be passed as kwargs for clarity.
+
+ Returns
+ -------
+ lr : float
+ Learning rate for current step.
+ """
+
+ def __init__(self, optimizer, *, warmup_steps=None, warmup_ratio=None, max_steps=None, min_lr=0.0, last_epoch=-1):
+ """Inits :class:`WarmupPolicy`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer.
+ warmup_steps : int
+ Number of training steps in warmup stage. Default is ``None``.
+ warmup_ratio : float
+ Ratio of warmup steps to total steps. Default is ``None``.
+ max_steps : int
+ Total number of steps while training or `None` for infinite training. Default is ``None``.
+ min_lr : float
+ Minimum learning rate. Default is ``0``.
+ last_epoch : int
+ Last epoch. Default is ``-1``.
+ """
+ if warmup_steps is not None and warmup_ratio is not None:
+ raise AssertionError("Either use particular number of step or ratio")
+ if warmup_ratio is not None and max_steps is None:
+ raise AssertionError("If there is a ratio, there should be a total steps")
+
+ # It is necessary to assign all attributes *before* __init__,
+ # as class is wrapped by an inner class.
+ self.max_steps = max_steps
+ if warmup_steps is not None:
+ self.warmup_steps = warmup_steps
+ elif warmup_ratio is not None:
+ self.warmup_steps = int(warmup_ratio * max_steps)
+ else:
+ self.warmup_steps = 0
+
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch)
+
+ def get_lr(self):
+ """Get learning rate at current step."""
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed by the scheduler, please use `get_last_lr()`.", UserWarning
+ )
+
+ step = self.last_epoch
+
+ if step <= self.warmup_steps and self.warmup_steps > 0:
+ return self._get_warmup_lr(step)
+
+ if (self.max_steps is not None) and (step > self.max_steps):
+ return [self.min_lr for _ in self.base_lrs]
+
+ return self._get_lr(step)
+
+ def _get_warmup_lr(self, step):
+ """Linear warmup"""
+ lr_val = (step + 1) / (self.warmup_steps + 1)
+ return [initial_lr * lr_val for initial_lr in self.base_lrs]
+
+ def _get_lr(self, step): # pylint: disable=unused-argument
+ """Simple const lr policy"""
+ return self.base_lrs
+
+
+class SquareRootConstantPolicy(_LRScheduler):
+ """Adds warmup kwargs and warmup logic to lr policy. All arguments should be passed as kwargs for clarity."""
+
+ def __init__(
+ self, optimizer, *, constant_steps=None, constant_ratio=None, max_steps=None, min_lr=0.0, last_epoch=-1
+ ):
+ """Inits :class:`SquareRootConstantPolicy`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer.
+ constant_steps : int
+ Number of training steps in constant stage. Default is ``None``.
+ constant_ratio : float
+ Ratio of constant steps to total steps. Default is ``None``.
+ max_steps : int
+ Total number of steps while training or `None` for infinite training. Default is ``None``.
+ min_lr : float
+ Minimum learning rate. Default is ``0``.
+ last_epoch : int
+ Last epoch. Default is ``-1``.
+ """
+ if constant_steps is not None and constant_ratio is not None:
+ raise AssertionError("Either use particular number of step or ratio")
+
+ if constant_ratio is not None and max_steps is None:
+ raise AssertionError("If there is a ratio, there should be a total steps")
+
+ # It is necessary to assign all attributes *before* __init__, as class is wrapped by an inner class.
+ self.max_steps = max_steps
+ if constant_steps is not None:
+ self.constant_steps = constant_steps
+ elif constant_ratio is not None:
+ self.constant_steps = int(constant_ratio * max_steps)
+ else:
+ self.constant_steps = 0
+
+ self.constant_lr = 1 / (constant_steps**0.5)
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch)
+
+ def get_lr(self):
+ """Get learning rate at current step."""
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed by the scheduler, please use `get_last_lr()`.", UserWarning
+ )
+
+ step = self.last_epoch
+
+ if step <= self.constant_steps:
+ return [self.constant_lr for _ in self.base_lrs]
+
+ if step > self.max_steps:
+ return [self.min_lr for _ in self.base_lrs]
+
+ return self._get_lr(step)
+
+ def _get_lr(self, step): # pylint: disable=unused-argument
+ """Simple const lr policy"""
+ return self.base_lrs
+
+
+class WarmupHoldPolicy(WarmupPolicy):
+ """Variant of WarmupPolicy which maintains high learning rate for a defined number of steps. All arguments should
+ be passed as kwargs for clarity,
+
+ Results
+ -------
+ lr : float
+ Learning rate is linearly increased from 0 to 1 over warmup steps, then linearly decreased from 1 to 0 over
+ hold steps.
+ """
+
+ def __init__(
+ self,
+ optimizer,
+ *,
+ warmup_steps=None,
+ warmup_ratio=None,
+ hold_steps=None,
+ hold_ratio=None,
+ max_steps=None,
+ min_lr=0.0,
+ last_epoch=-1,
+ ):
+ """Inits :class:`WarmupHoldPolicy`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer.
+ warmup_steps : int
+ Number of training steps in warmup stage. Default is ``None``.
+ warmup_ratio : float
+ Ratio of warmup steps to total steps. Default is ``None``.
+ hold_steps : int
+ Number of training steps to hold the learning rate after warm up. Default is ``None``.
+ hold_ratio : float
+ Ratio of hold steps to total steps. Default is ``None``.
+ max_steps : int
+ Total number of steps while training or `None` for infinite training. Default is ``None``.
+ min_lr : float
+ Minimum learning rate. Default is ``0``.
+ last_epoch : int
+ Last epoch. Default is ``-1``.
+ """
+ if hold_steps is not None and hold_ratio is not None:
+ raise AssertionError("Either use particular number of step or ratio")
+ if hold_ratio is not None and max_steps is None:
+ raise AssertionError("If there is a ratio, there should be a total steps")
+
+ self.min_lr = min_lr
+ self._last_warmup_lr = 0.0
+
+ # Necessary to duplicate as class attributes are hidden in inner class
+ self.max_steps = max_steps
+ if warmup_steps is not None:
+ self.warmup_steps = warmup_steps
+ elif warmup_ratio is not None:
+ self.warmup_steps = int(warmup_ratio * max_steps)
+ else:
+ self.warmup_steps = 0
+
+ if hold_steps is not None:
+ self.hold_steps = hold_steps + self.warmup_steps
+ elif hold_ratio is not None:
+ self.hold_steps = int(hold_ratio * max_steps) + self.warmup_steps
+ else:
+ self.hold_steps = 0
+
+ super().__init__(
+ optimizer,
+ warmup_steps=warmup_steps,
+ warmup_ratio=warmup_ratio,
+ max_steps=max_steps,
+ last_epoch=last_epoch,
+ min_lr=min_lr,
+ )
+
+ def get_lr(self):
+ """Get learning rate at current step."""
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed by the scheduler, please use `get_last_lr()`.", UserWarning
+ )
+
+ step = self.last_epoch
+
+ # Warmup phase
+ if 0 < self.warmup_steps >= step:
+ return self._get_warmup_lr(step)
+
+ # Hold phase
+ if self.hold_steps < step >= self.warmup_steps:
+ return self.base_lrs
+
+ if step > self.max_steps:
+ return [self.min_lr for _ in self.base_lrs]
+
+ return self._get_lr(step)
+
+
+class WarmupAnnealHoldPolicy(_LRScheduler):
+ """Adds warmup kwargs and warmup logic to lr policy. All arguments should be passed as kwargs for clarity."""
+
+ def __init__(
+ self,
+ optimizer,
+ *,
+ warmup_steps=None,
+ warmup_ratio=None,
+ constant_steps=None,
+ constant_ratio=None,
+ max_steps=None,
+ min_lr=0.0,
+ last_epoch=-1,
+ ):
+ """Inits :class:`WarmupAnnealHoldPolicy`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer.
+ warmup_steps : int
+ Number of training steps in warmup stage. Default is ``None``.
+ warmup_ratio : float
+ Ratio of warmup steps to total steps. Default is ``None``.
+ constant_steps : int
+ Number of training steps in constant stage. Default is ``None``.
+ constant_ratio : float
+ Ratio of constant steps to total steps. Default is ``None``.
+ max_steps : int
+ Total number of steps while training or `None` for infinite training. Default is ``None``.
+ min_lr : float
+ Minimum learning rate. Default is ``0``.
+ last_epoch : int
+ Last epoch. Default is ``-1``.
+ """
+ if warmup_steps is not None and warmup_ratio is not None:
+ raise AssertionError("Either use particular number of step or ratio")
+ if constant_steps is not None and constant_ratio is not None:
+ raise AssertionError("Either use constant_steps or constant_ratio")
+ if warmup_ratio is not None and max_steps is None:
+ raise AssertionError("If there is a ratio, there should be a total steps")
+
+ # It is necessary to assign all attributes *before* __init__, as class is wrapped by an inner class.
+ self.max_steps = max_steps
+
+ if warmup_steps is not None:
+ self.warmup_steps = warmup_steps
+ elif warmup_ratio is not None:
+ self.warmup_steps = int(warmup_ratio * max_steps)
+ else:
+ self.warmup_steps = 0
+
+ if constant_steps is not None:
+ self.constant_steps = constant_steps
+ elif constant_ratio is not None:
+ self.constant_steps = int(constant_ratio * max_steps)
+ else:
+ self.constant_steps = 0
+
+ self.decay_steps = max_steps - (self.constant_steps + self.warmup_steps)
+
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch)
+
+ def get_lr(self):
+ """Get learning rate at current step."""
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed by the scheduler, please use `get_last_lr()`.", UserWarning
+ )
+
+ step = self.last_epoch
+
+ # Warmup steps
+ if 0 < self.warmup_steps >= step:
+ return self._get_warmup_lr(step)
+
+ # Constant steps after warmup and decay
+ if self.constant_steps > 0 and (self.warmup_steps + self.decay_steps) < step <= self.max_steps:
+ return self._get_constant_lr(step)
+
+ # Min lr after max steps of updates
+ if step > self.max_steps:
+ return [self.min_lr for _ in self.base_lrs]
+
+ return self._get_lr(step)
+
+ def _get_warmup_lr(self, step):
+ """Get learning rate at warmup stage."""
+ lr_val = (step + 1) / (self.warmup_steps + 1)
+ return [initial_lr * lr_val for initial_lr in self.base_lrs]
+
+ def _get_constant_lr(self, step): # pylint: disable=unused-argument
+ """Get learning rate at constant stage."""
+ return [self.min_lr for _ in self.base_lrs]
+
+ def _get_lr(self, step): # pylint: disable=unused-argument
+ """Simple const lr policy"""
+ return self.base_lrs
+
+
+def _sqrt_annealing(initial_lr, step, max_steps, min_lr):
+ """Anneal learning rate by sqrt."""
+ mult = ((max_steps - step) / max_steps) ** 0.5
+ out_lr = initial_lr * mult
+ out_lr = max(out_lr, min_lr)
+ return out_lr
+
+
+def _square_annealing(initial_lr, step, max_steps, min_lr):
+ """Anneal learning rate by square."""
+ mult = ((max_steps - step) / max_steps) ** 2
+ out_lr = initial_lr * mult
+ out_lr = max(out_lr, min_lr)
+ return out_lr
+
+
+def _cosine_annealing(initial_lr, step, max_steps, min_lr):
+ """Anneal learning rate by cosine."""
+ mult = 0.5 * (1 + math.cos(math.pi * step / max_steps))
+ return (initial_lr - min_lr) * mult + min_lr
+
+
+def _linear_warmup_with_cosine_annealing(max_lr, warmup_steps, step, decay_steps, min_lr):
+ """Anneal learning rate by linear warmup and cosine annealing."""
+ if max_lr <= min_lr:
+ raise AssertionError
+ # Use linear warmup for the initial part.
+ if warmup_steps > 0 and step <= warmup_steps:
+ return max_lr * float(step) / float(warmup_steps)
+
+ # For any steps larger than `decay_steps`, use `min_lr`.
+ if step > warmup_steps + decay_steps:
+ return min_lr
+
+ # If we are done with the warmup period, use the decay style.
+ num_steps_ = step - warmup_steps
+ decay_steps_ = decay_steps
+ decay_ratio = float(num_steps_) / float(decay_steps_)
+ if decay_ratio < 0.0:
+ raise AssertionError
+ if decay_ratio > 1.0:
+ raise AssertionError
+ delta_lr = max_lr - min_lr
+
+ coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0)
+
+ return min_lr + coeff * delta_lr
+
+
+def _poly_decay(initial_lr, step, decay_steps, power, min_lr, cycle):
+ """Polynomial decay of learning rate."""
+ if cycle:
+ multiplier = 1.0 if step == 0 else math.ceil(step / decay_steps)
+ decay_steps = decay_steps * multiplier
+ else:
+ step = min(step, decay_steps)
+ p = step / decay_steps
+ lr = (initial_lr - min_lr) * math.pow(1.0 - p, power)
+ lr += min_lr
+ return lr
+
+
+def _noam_hold_annealing(initial_lr, step, warmup_steps, hold_steps, decay_rate, min_lr):
+ """Anneal learning rate by noam hold."""
+ # hold_steps = total number of steps to hold the LR, not the warmup + hold steps.
+ T_warmup_decay = max(1, warmup_steps**decay_rate)
+ T_hold_decay = max(1, (step - hold_steps) ** decay_rate)
+ lr = (initial_lr * T_warmup_decay) / T_hold_decay
+ return max(lr, min_lr)
+
+
+class SquareAnnealing(WarmupPolicy):
+ """Anneal learning rate by square."""
+
+ def __init__(self, optimizer, *, max_steps, min_lr=1e-5, last_epoch=-1, **kwargs):
+ """Inits :class:`SquareAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer.
+ max_steps : int
+ Total number of steps while training or `None` for infinite training.
+ min_lr : float
+ Minimum learning rate. Default is ``1e-5``.
+ last_epoch : int
+ Last epoch. Default is ``-1``.
+ """
+ super().__init__(optimizer=optimizer, max_steps=max_steps, last_epoch=last_epoch, min_lr=min_lr, **kwargs)
+
+ def _get_lr(self, step):
+ """Get learning rate at current step."""
+ return [
+ _square_annealing(
+ initial_lr=initial_lr,
+ step=step - self.warmup_steps,
+ max_steps=self.max_steps - self.warmup_steps,
+ min_lr=self.min_lr,
+ )
+ for initial_lr in self.base_lrs
+ ]
+
+
+class SquareRootAnnealing(WarmupPolicy):
+ """Anneal learning rate by square root."""
+
+ def __init__(self, optimizer, *, max_steps, min_lr=0, last_epoch=-1, **kwargs):
+ """Inits :class:`SquareRootAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer.
+ max_steps : int
+ Total number of steps while training or `None` for infinite training.
+ min_lr : float
+ Minimum learning rate. Default is ``0``.
+ last_epoch : int
+ Last epoch. Default is ``-1``.
+ """
+ super().__init__(optimizer=optimizer, max_steps=max_steps, last_epoch=last_epoch, min_lr=min_lr, **kwargs)
+
+ def _get_lr(self, step):
+ """Get learning rate at current step."""
+ return [
+ _sqrt_annealing(
+ initial_lr=initial_lr,
+ step=step,
+ max_steps=self.max_steps,
+ min_lr=self.min_lr,
+ )
+ for initial_lr in self.base_lrs
+ ]
+
+
+class CosineAnnealing(WarmupAnnealHoldPolicy):
+ """Anneal learning rate by cosine."""
+
+ def __init__(self, optimizer, *, max_steps, min_lr=0, last_epoch=-1, **kwargs):
+ """Inits :class:`CosineAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer.
+ max_steps : int
+ Total number of steps while training or `None` for infinite training.
+ min_lr : float
+ Minimum learning rate. Default is ``0``.
+ last_epoch : int
+ Last epoch. Default is ``-1``.
+ """
+ super().__init__(optimizer=optimizer, max_steps=max_steps, last_epoch=last_epoch, min_lr=min_lr, **kwargs)
+
+ def _get_lr(self, step):
+ """Get learning rate at current step."""
+ for initial_lr in self.base_lrs:
+ if initial_lr < self.min_lr:
+ raise ValueError(
+ f"{self} received an initial learning rate that was lower than the minimum learning rate."
+ )
+
+ return (
+ [
+ _cosine_annealing(
+ initial_lr=initial_lr,
+ step=step - self.warmup_steps,
+ max_steps=self.max_steps - self.warmup_steps,
+ min_lr=self.min_lr,
+ )
+ for initial_lr in self.base_lrs
+ ]
+ if self.constant_steps is None or self.constant_steps == 0
+ else self._get_linear_warmup_with_cosine_annealing_lr(step)
+ )
+
+ def _get_warmup_lr(self, step):
+ """Get the warmup learning rate for the given step."""
+ if self.constant_steps is None or self.constant_steps == 0:
+ return super()._get_warmup_lr(step)
+
+ # Use linear warmup for the initial part.
+ return self._get_linear_warmup_with_cosine_annealing_lr(step)
+
+ def _get_constant_lr(self, step):
+ """Only called when constant_steps is not None and not 0."""
+ return self._get_linear_warmup_with_cosine_annealing_lr(step)
+
+ def _get_linear_warmup_with_cosine_annealing_lr(self, step):
+ """Cosine Schedule, slightly different warmup schedule + constant LR at the end."""
+ return [
+ _linear_warmup_with_cosine_annealing(
+ max_lr=self.base_lrs[0],
+ warmup_steps=self.warmup_steps,
+ step=step,
+ decay_steps=self.decay_steps,
+ min_lr=self.min_lr,
+ )
+ for _ in self.base_lrs
+ ]
+
+
+class NoamAnnealing(_LRScheduler):
+ """Noam learning rate annealing."""
+
+ def __init__(
+ self, optimizer, *, d_model, warmup_steps=None, warmup_ratio=None, max_steps=None, min_lr=0.0, last_epoch=-1
+ ):
+ """Inits :class:`NoamAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer.
+ d_model : int
+ Model dimensionality.
+ warmup_steps : int
+ Number of training steps in warmup stage. Default is ``None``.
+ warmup_ratio : float
+ Ratio of warmup steps to total steps. Default is ``None``.
+ max_steps : int
+ Total number of steps while training or `None` for infinite training.
+ min_lr : float
+ Minimum learning rate. Default is ``0``.
+ last_epoch : int
+ Last epoch. Default is ``-1``.
+ """
+ self._normalize = d_model ** (-0.5)
+ if warmup_steps is not None and warmup_ratio is not None:
+ raise AssertionError("Either use particular number of step or ratio")
+ if warmup_ratio is not None and max_steps is None:
+ raise AssertionError("If there is a ratio, there should be a total steps")
+
+ # It is necessary to assign all attributes *before* __init__,
+ # as class is wrapped by an inner class.
+ self.max_steps = max_steps
+ if warmup_steps is not None:
+ self.warmup_steps = warmup_steps
+ elif warmup_ratio is not None:
+ self.warmup_steps = int(warmup_ratio * max_steps)
+ else:
+ self.warmup_steps = 0
+
+ self.min_lr = min_lr
+ super().__init__(optimizer, last_epoch)
+
+ def get_lr(self):
+ """Get learning rate at current step."""
+ if not self._get_lr_called_within_step:
+ warnings.warn(
+ "To get the last learning rate computed by the scheduler, please use `get_last_lr()`.", UserWarning
+ )
+
+ step = max(1, self.last_epoch)
+
+ if step > self.max_steps:
+ return [self.min_lr for _ in self.base_lrs]
+
+ for initial_lr in self.base_lrs:
+ if initial_lr < self.min_lr:
+ raise ValueError(
+ f"{self} received an initial learning rate that was lower than the minimum learning rate."
+ )
+
+ return [self._noam_annealing(initial_lr=initial_lr, step=step) for initial_lr in self.base_lrs]
+
+ def _noam_annealing(self, initial_lr, step):
+ """Noam learning rate annealing."""
+ mult = (
+ self._normalize * min(step ** (-0.5), step * (self.warmup_steps ** (-1.5)))
+ if self.warmup_steps > 0
+ else self._normalize * step ** (-0.5)
+ )
+ out_lr = initial_lr * mult
+ if step > self.warmup_steps:
+ out_lr = max(out_lr, self.min_lr)
+ return out_lr
+
+
+class NoamHoldAnnealing(WarmupHoldPolicy):
+ """Implementation of the Noam Hold Annealing policy from the SqueezeFormer paper.
+
+ Unlike NoamAnnealing, the peak learning rate can be explicitly set for this scheduler.
+ The schedule first performs linear warmup, then holds the peak LR, then decays with some schedule for
+ the remainder of the steps. Therefore, the min-lr is still dependent on the hyperparameters selected.
+
+ It's schedule is determined by three factors-
+
+ Warmup Steps: Initial stage, where linear warmup occurs uptil the peak LR is reached. Unlike NoamAnnealing,
+ the peak LR is explicitly stated here instead of a scaling factor.
+
+ Hold Steps: Intermediate stage, where the peak LR is maintained for some number of steps. In this region,
+ the high peak LR allows the model to converge faster if training is stable. However the high LR
+ may also cause instability during training. Should usually be a significant fraction of training
+ steps (around 30-40% of the entire training steps).
+
+ Decay Steps: Final stage, where the LR rapidly decays with some scaling rate (set by decay rate).
+ To attain Noam decay, use 0.5, for Squeezeformer recommended decay, use 1.0. The fast decay after
+ prolonged high LR during hold phase allows for rapid convergence.
+
+ References
+ ----------
+ [1]
+ [Squeezeformer: An Efficient Transformer for Automatic Speech Recognition](https://arxiv.org/abs/2206.00888)
+ """
+
+ def __init__(self, optimizer, *, max_steps, decay_rate=0.5, min_lr=0.0, last_epoch=-1, **kwargs):
+ """Inits :class:`NoamHoldAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer to use for the scheduler.
+ max_steps : int
+ Total number of training steps.
+ decay_rate : float
+ Decay rate for the final stage of the schedule. Should be between 0 and 1. Default is ``0.5``.
+ min_lr : float
+ Minimum learning rate to use for the schedule. Should be between 0 and 1. Default is ``0.0``.
+ last_epoch : int
+ Last epoch to start the schedule from. Should be between 0 and max_steps. Default is ``-1``.
+ """
+ self.decay_rate = decay_rate
+ super().__init__(optimizer=optimizer, max_steps=max_steps, last_epoch=last_epoch, min_lr=min_lr, **kwargs)
+
+ def _get_lr(self, step):
+ """Get the learning rate for the given step."""
+ if self.warmup_steps is None or self.warmup_steps == 0:
+ raise ValueError("Noam scheduler cannot be used without warmup steps")
+
+ if self.hold_steps > 0:
+ hold_steps = self.hold_steps - self.warmup_steps
+ else:
+ hold_steps = 0
+
+ return [
+ _noam_hold_annealing(
+ initial_lr,
+ step=step,
+ warmup_steps=self.warmup_steps,
+ hold_steps=hold_steps,
+ decay_rate=self.decay_rate,
+ min_lr=self.min_lr,
+ )
+ for initial_lr in self.base_lrs
+ ]
+
+
+class WarmupAnnealing(WarmupPolicy):
+ """Warmup learning rate annealing."""
+
+ def __init__(self, optimizer, *, max_steps, last_epoch=-1, min_lr=0.0, **kwargs):
+ """Inits :class:`WarmupAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer to use for the scheduler.
+ max_steps : int
+ Total number of training steps.
+ last_epoch : int
+ Last epoch to start the schedule from. Should be between 0 and max_steps. Default is ``-1``.
+ min_lr : float
+ Minimum learning rate to use for the schedule. Should be between 0 and 1. Default is ``0.0``.
+ """
+ super().__init__(optimizer=optimizer, max_steps=max_steps, last_epoch=last_epoch, min_lr=min_lr, **kwargs)
+
+ def _get_lr(self, step):
+ """Get learning rate at current step."""
+ delta_lr = self.base_lrs[0] - self.min_lr
+ mult = (step - self.warmup_steps) / (self.max_steps - self.warmup_steps)
+ return [self.min_lr + (1 - mult) * delta_lr for _ in self.base_lrs]
+
+
+class InverseSquareRootAnnealing(WarmupPolicy):
+ """Inverse square root learning rate annealing."""
+
+ def __init__(self, optimizer, *, max_steps, last_epoch=-1, min_lr=0.0, **kwargs):
+ """Inits :class:`InverseSquareRootAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer to use for the scheduler.
+ max_steps : int
+ Total number of training steps.
+ last_epoch : int
+ Last epoch to start the schedule from. Should be between 0 and max_steps. Default is ``-1``.
+ min_lr : float
+ Minimum learning rate to use for the schedule. Should be between 0 and 1. Default is ``0.0``.
+ """
+ super().__init__(optimizer=optimizer, max_steps=max_steps, **kwargs, last_epoch=last_epoch, min_lr=min_lr)
+
+ def _get_lr(self, step):
+ """Get learning rate at current step."""
+ denom = ((step + 1) / (self.warmup_steps + 1)) ** 0.5
+ return [initial_lr / denom for initial_lr in self.base_lrs]
+
+
+class T5InverseSquareRootAnnealing(SquareRootConstantPolicy):
+ """Inverse square root learning rate annealing."""
+
+ def __init__(self, optimizer, *, max_steps, last_epoch=-1, min_lr=0.0, **kwargs):
+ """Inits :class:`T5InverseSquareRootAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer to use for the scheduler.
+ max_steps : int
+ Total number of training steps.
+ last_epoch : int
+ Last epoch to start the schedule from. Should be between 0 and max_steps. Default is ``-1``.
+ min_lr : float
+ Minimum learning rate to use for the schedule. Should be between 0 and 1. Default is ``0.0``.
+ """
+ super().__init__(optimizer=optimizer, max_steps=max_steps, **kwargs, last_epoch=last_epoch, min_lr=min_lr)
+
+ def _get_lr(self, step):
+ """Get learning rate at current step."""
+ return [1 / (step**0.5) for _ in self.base_lrs]
+
+
+class PolynomialDecayAnnealing(WarmupPolicy):
+ """Polynomial decay learning rate annealing."""
+
+ def __init__(self, optimizer, *, max_steps, min_lr=0.0, power=1.0, cycle=False, last_epoch=-1, **kwargs):
+ """Inits :class:`PolynomialDecayAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer to use for the scheduler.
+ max_steps : int
+ Total number of training steps.
+ min_lr : float
+ Minimum learning rate to use for the schedule. Should be between 0 and 1. Default is ``0.0``.
+ power : float
+ Power of the polynomial. Default is ``1.0``.
+ cycle : bool
+ Whether to cycle the schedule. Default is ``False``.
+ last_epoch : int
+ Last epoch to start the schedule from. Should be between 0 and max_steps. Default is ``-1``.
+ """
+ self.power = power
+ self.cycle = cycle
+
+ super().__init__(optimizer=optimizer, max_steps=max_steps, last_epoch=last_epoch, min_lr=min_lr, **kwargs)
+
+ def _get_lr(self, step):
+ """Get learning rate at current step."""
+ return [
+ _poly_decay(
+ initial_lr,
+ step=step - self.warmup_steps,
+ decay_steps=self.max_steps - self.warmup_steps,
+ power=self.power,
+ min_lr=self.min_lr,
+ cycle=self.cycle,
+ )
+ for initial_lr in self.base_lrs
+ ]
+
+
+class PolynomialHoldDecayAnnealing(WarmupHoldPolicy):
+ """Polynomial decay learning rate annealing."""
+
+ def __init__(self, optimizer, *, max_steps, min_lr=0.0, power=1.0, cycle=False, last_epoch=-1, **kwargs):
+ """Inits :class:`PolynomialHoldDecayAnnealing`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Optimizer to use for the scheduler.
+ max_steps : int
+ Total number of training steps.
+ min_lr : float
+ Minimum learning rate to use for the schedule. Should be between 0 and 1. Default is ``0.0``.
+ power : float
+ Power of the polynomial. Default is ``1.0``.
+ cycle : bool
+ Whether to cycle the schedule. Default is ``False``.
+ last_epoch : int
+ Last epoch to start the schedule from. Should be between 0 and max_steps. Default is ``-1``.
+ """
+ self.power = power
+ self.cycle = cycle
+
+ super().__init__(optimizer=optimizer, max_steps=max_steps, last_epoch=last_epoch, min_lr=min_lr, **kwargs)
+
+ def _get_lr(self, step):
+ """Get learning rate at current step."""
+ return [
+ _poly_decay(
+ initial_lr,
+ step=step - self.hold_steps,
+ decay_steps=self.max_steps - max(self.warmup_steps, self.hold_steps),
+ power=self.power,
+ min_lr=self.min_lr,
+ cycle=self.cycle,
+ )
+ for initial_lr in self.base_lrs
+ ]
+
+
+def register_scheduler(name: str, scheduler: _LRScheduler, scheduler_params: SchedulerParams):
+ """Checks if the scheduler name exists in the registry, and if it doesn't, adds it. This allows custom schedulers
+ to be added and called by name during instantiation.
+
+ Parameters
+ ----------
+ name : str
+ Name of the optimizer. Will be used as key to retrieve the optimizer.
+ scheduler : _LRScheduler
+ Scheduler class (inherits from _LRScheduler)
+ scheduler_params : SchedulerParams
+ The parameters as a dataclass of the scheduler
+ """
+ if name in AVAILABLE_SCHEDULERS:
+ raise ValueError(f"Cannot override pre-existing schedulers. Conflicting scheduler name = {name}")
+
+ AVAILABLE_SCHEDULERS[name] = scheduler
+
+ sched_name = f"{scheduler.__name__}_params"
+ register_scheduler_params(name=sched_name, scheduler_params=scheduler_params)
+
+
+def get_scheduler(name: str, **kwargs: Optional[Dict[str, Any]]) -> _LRScheduler:
+ """Convenience method to obtain an _LRScheduler class and partially instantiate it with optimizer kwargs.
+
+ Parameters
+ ----------
+ name : str
+ Name of the scheduler in the registry.
+ kwargs : Dict[str, Any]
+ Optional kwargs of the scheduler used during instantiation.
+
+ Returns
+ -------
+ _LRScheduler
+ A partially instantiated _LRScheduler.
+ """
+ if name not in AVAILABLE_SCHEDULERS:
+ raise ValueError(
+ f"Cannot resolve scheduler{name}'. Available optimizers are : " f"{AVAILABLE_SCHEDULERS.keys()}"
+ )
+
+ scheduler_cls = AVAILABLE_SCHEDULERS[name]
+ # Pop 'max_steps' if it's not required by the scheduler
+ if "max_steps" in kwargs and "max_steps" not in inspect.signature(scheduler_cls).parameters:
+ kwargs.pop("max_steps")
+ scheduler = partial(scheduler_cls, **kwargs)
+ return scheduler
+
+
+def prepare_lr_scheduler( # noqa: MC0001
+ optimizer: optim.Optimizer,
+ scheduler_config: Union[Dict[str, Any], DictConfig, None],
+ train_dataloader: Optional[dataloader.DataLoader] = None,
+) -> Optional[Dict[str, Any]]:
+ """Constructs an LR Scheduler (optionally) for a given optimizer, based on a config with the following schema.
+
+ Parameters
+ ----------
+ optimizer: The optimizer to use for the scheduler.
+ name:
+ lr:
+ args:
+ name: auto # special keyword, resolves to correct optimizer config for given optimizer name
+ # cls: atommic.core.config.optimizers.NovogradParams # explicit instantiation by class path
+ params: # optional override parameters for the optimizer config
+ betas: [0.8, 0.5]
+ weight_decay: 0.001
+ scheduler_config: The scheduler config.
+ name:
+ iters_per_batch: null # computed at runtime; mandatory to have
+ max_steps: null # computed at runtime or explicitly set here; mandatory to have
+ # pytorch lightning args
+ monitor: val_loss
+ reduce_on_plateau: false
+ #
+ args:
+ name: auto # special keyword, resolves to correct optimizer config for given optimizer name
+ # cls: atommic.core.config.schedulers.CosineAnnealingParams # explicit instantiation by class path
+ params: # optional override parameters for the optimizer config
+ warmup_steps: null
+ warmup_ratio: null
+ min_lr: 0.0
+ last_epoch: -1
+
+ train_dataloader: Optional requirement, must be passed if "iters_per_batch" is defined instead of "max_steps". \
+ Used to compute effective "max_steps".
+
+ Returns
+ -------
+ A dictionary containing the LR Scheduler implementation if the config was successfully parsed along with other \
+ parameters required by Pytorch Lightning, otherwise None.
+ """
+ if scheduler_config is not None:
+ scheduler_config = maybe_update_config_version(scheduler_config)
+
+ # Build nested dictionary for convenience out of structured objects
+ if isinstance(scheduler_config, DictConfig):
+ scheduler_config = OmegaConf.to_container(scheduler_config, resolve=True)
+
+ elif dataclasses.is_dataclass(scheduler_config):
+ # Recursively transform data classes to basic dictionaries
+ scheduler_config = OmegaConf.create(scheduler_config)
+ scheduler_config = OmegaConf.to_container(scheduler_config, resolve=True)
+
+ # Test to see if config follows above schema
+ interval = "step"
+ if scheduler_config is not None:
+ if "args" in scheduler_config:
+ scheduler_args = scheduler_config.pop("args")
+ else:
+ scheduler_args = copy.deepcopy(scheduler_config)
+
+ # Remove extra parameters from scheduler_args nest
+ # Assume all other parameters are to be passed into scheduler constructor
+ scheduler_args.pop("name", None)
+ scheduler_args.pop("t_max_epochs", None)
+ scheduler_args.pop("t_accumulate_grad_batches", None)
+ scheduler_args.pop("t_limit_train_batches", None)
+ scheduler_args.pop("t_num_workers", None)
+ scheduler_args.pop("monitor", None)
+ scheduler_args.pop("reduce_on_plateau", None)
+
+ if "name" in scheduler_config and scheduler_config["name"] in EPOCH_SCHEDULERS:
+ interval = "epoch"
+ else:
+ # Return gracefully in case `sched` was not supplied; inform user
+ logging.info("Scheduler not initialized as no `sched` config supplied to setup_optimizer()")
+ return None
+
+ # Try instantiation of scheduler params from config class path
+ if "_target_" in scheduler_args:
+ scheduler_args_cfg = OmegaConf.create(scheduler_args)
+ scheduler_conf = hydra.utils.instantiate(scheduler_args_cfg)
+ scheduler_args = vars(scheduler_conf)
+
+ # Get name of the scheduler
+ scheduler_name = scheduler_conf.__class__.__name__
+
+ if "Params" in scheduler_name:
+ scheduler_name = scheduler_name.replace("Params", "")
+
+ else:
+ # Class path instantiation failed; try resolving "name" component
+
+ # Get name of the scheduler
+ if "name" in scheduler_config:
+ scheduler_name = scheduler_config["name"]
+ else:
+ logging.warning(
+ "Could not resolve classpath for Scheduler Config, and `name` "
+ "was not provided either. \n"
+ "Scheduler cannot be instantiated !"
+ )
+ return None
+
+ # If class path was not provided, perhaps `name` is provided for resolution
+ if "name" in scheduler_args:
+ # If `auto` is passed as name for resolution of optimizer name,
+ # then lookup optimizer name and resolve its parameter config
+ if scheduler_args["name"] == "auto":
+ scheduler_params_name = f"{scheduler_name}Params"
+ else:
+ scheduler_params_name = scheduler_args["name"]
+
+ # Get override arguments provided in the config yaml file / Dict Config
+ scheduler_params_override = scheduler_args.get("params", {})
+
+ # If params is itself a dict config object provided explicitly in Dict Config
+ # Resolve to dictionary for convenience
+ if isinstance(scheduler_params_override, DictConfig):
+ scheduler_params_override = OmegaConf.to_container(scheduler_params_override, resolve=True)
+
+ # Get and instantiate the Config dataclass for this scheduler
+ scheduler_params_cls = get_scheduler_config(scheduler_params_name, **scheduler_params_override)
+ scheduler_params = scheduler_params_cls # instantiate the parameters object
+ # extract just the dictionary from the Config object
+ scheduler_args = vars(scheduler_params)
+
+ # Extract value to monitor in losses, if provided.
+ if "monitor" in scheduler_config:
+ monitor = scheduler_config.get("monitor")
+ else:
+ # Default to train loss
+ monitor = "loss"
+
+ # Store exact max_steps if it is provided
+ if "max_steps" in scheduler_config and scheduler_config["max_steps"] is not None:
+ max_steps = scheduler_config["max_steps"]
+ elif "t_max_epochs" in scheduler_config:
+ # Compute effective max_steps if t_max_epochs is provided
+ if train_dataloader is None:
+ logging.warning(
+ "As `t_max_epochs` is provided/computed, it is required to pass the train dataloader in order\n"
+ "to compute effective maximum number of steps.\n"
+ "Scheduler will not be instantiated !"
+ )
+ return None
+
+ # Raise exception if neither `max_steps` nor `t_max_epochs` is provided
+ if scheduler_config.get("t_max_epochs", None) is None:
+ logging.warning(
+ "`t_max_epochs` cannot be None when `max_steps` is not not provided.\n"
+ "This can occur when `train dataloader` is not available to correctly "
+ "prepare the scheduler.\n"
+ "Scheduler will not be instantiated !"
+ )
+ return None
+
+ # Get iters_per_batch
+ max_epochs = scheduler_config.get("t_max_epochs")
+ accumulate_grad_batches = scheduler_config.get("t_accumulate_grad_batches")
+ limit_train_batches = scheduler_config.get("t_limit_train_batches")
+ num_workers = scheduler_config.get("t_num_workers")
+
+ # Compute effective num max_steps
+ num_samples = len(train_dataloader.dataset)
+
+ # we may need to override ModelPT setup_optimization
+ if train_dataloader.batch_size is not None:
+ batch_size = train_dataloader.batch_size
+ elif hasattr(train_dataloader, "batch_sampler") and train_dataloader.batch_sampler is not None:
+ if train_dataloader.batch_sampler.micro_batch_size is not None:
+ batch_size = train_dataloader.batch_sampler.micro_batch_size
+ else:
+ raise ValueError(f"Could not find batch_size from batch_sampler: {train_dataloader.batch_sampler}")
+ else:
+ raise ValueError(f"Could not find batch_size from train_dataloader: {train_dataloader}")
+ drop_last = train_dataloader.drop_last
+
+ max_steps = compute_max_steps(
+ max_epochs=max_epochs,
+ accumulate_grad_batches=accumulate_grad_batches,
+ limit_train_batches=limit_train_batches,
+ num_workers=num_workers,
+ num_samples=num_samples,
+ batch_size=batch_size,
+ drop_last=drop_last,
+ )
+ else:
+ logging.warning(
+ "Neither `max_steps` nor `iters_per_batch` were provided to `optim.sched`, "
+ "cannot compute effective `max_steps` !\n"
+ "Scheduler will not be instantiated !"
+ )
+ return None
+
+ # Inject max_steps (effective or provided) into the scheduler config
+ scheduler_args["max_steps"] = max_steps
+
+ # Get the scheduler class from the config
+ scheduler_cls = get_scheduler(scheduler_name, **scheduler_args)
+
+ # Pop 'max_steps' if it's not required by the scheduler
+ if "max_steps" not in inspect.signature(scheduler_cls).parameters:
+ scheduler_args.pop("max_steps")
+
+ # Instantiate the LR schedule
+ schedule = scheduler_cls(optimizer, **scheduler_args)
+
+ logging.info(
+ 'Scheduler "%s" \nwill be used during training (effective maximum steps = %d) - \nParameters : \n(%s)',
+ str(schedule),
+ max_steps,
+ OmegaConf.to_yaml(OmegaConf.create(scheduler_args)),
+ )
+
+ # Wrap the schedule in PTL arguments to perform stepwise computation. Rather than epoch level computation.
+ reduce_lr_on_plateau = bool(isinstance(schedule, optim.lr_scheduler.ReduceLROnPlateau))
+
+ schedule_dict = {
+ "scheduler": schedule,
+ "interval": interval,
+ "frequency": 1,
+ "monitor": monitor,
+ "reduce_on_plateau": reduce_lr_on_plateau,
+ }
+ return schedule_dict
+
+
+def compute_max_steps(
+ max_epochs, accumulate_grad_batches, limit_train_batches, num_workers, num_samples, batch_size, drop_last
+):
+ """Compute effective max_steps from the provided parameters.
+
+ Parameters
+ ----------
+ max_epochs : int
+ Maximum number of epochs to train for.
+ accumulate_grad_batches : int
+ Number of batches to accumulate gradients for.
+ limit_train_batches : int
+ Number of batches to train for.
+ num_workers : int
+ Number of workers to use for training.
+ num_samples : int
+ Number of samples in the dataset.
+ batch_size : int
+ Batch size.
+ drop_last : bool
+ Whether to drop the last batch or not.
+ """
+ _round = math.floor if drop_last else math.ceil
+
+ sampler_num_samples = math.ceil(num_samples / max(1, num_workers))
+
+ if drop_last and num_workers > 1:
+ logging.warning(
+ "Please note that drop_last is broken in pytorch 1.6.0. We will fix when pytorch 1.7.0 is released"
+ )
+ # TODO: Master version, not in pytorch 1.6.0
+
+ steps_per_epoch = _round(sampler_num_samples / batch_size)
+ if isinstance(limit_train_batches, int) or limit_train_batches == 0.0:
+ steps_per_epoch = min(steps_per_epoch, int(limit_train_batches))
+ elif steps_per_epoch != float("inf"):
+ # limit_train_batches is a percentage of batches per epoch
+ steps_per_epoch = int(steps_per_epoch * limit_train_batches)
+
+ return math.ceil(steps_per_epoch / accumulate_grad_batches) * max_epochs
+
+
+AVAILABLE_SCHEDULERS = {
+ "WarmupPolicy": WarmupPolicy,
+ "WarmupHoldPolicy": WarmupHoldPolicy,
+ "SquareAnnealing": SquareAnnealing,
+ "CosineAnnealing": CosineAnnealing,
+ "NoamAnnealing": NoamAnnealing,
+ "NoamHoldAnnealing": NoamHoldAnnealing,
+ "WarmupAnnealing": WarmupAnnealing,
+ "InverseSquareRootAnnealing": InverseSquareRootAnnealing,
+ "T5InverseSquareRootAnnealing": T5InverseSquareRootAnnealing,
+ "SquareRootAnnealing": SquareRootAnnealing,
+ "PolynomialDecayAnnealing": PolynomialDecayAnnealing,
+ "PolynomialHoldDecayAnnealing": PolynomialHoldDecayAnnealing,
+ "StepLR": pt_scheduler.StepLR,
+ "ExponentialLR": pt_scheduler.ExponentialLR,
+ "ReduceLROnPlateau": pt_scheduler.ReduceLROnPlateau,
+ "CyclicLR": pt_scheduler.CyclicLR,
+}
+
+EPOCH_SCHEDULERS = {
+ "ExponentialLR": pt_scheduler.ExponentialLR,
+ "ReduceLROnPlateau": pt_scheduler.ReduceLROnPlateau,
+}
diff --git a/atommic/core/optim/novograd.py b/atommic/core/optim/novograd.py
new file mode 100644
index 00000000..a93a856f
--- /dev/null
+++ b/atommic/core/optim/novograd.py
@@ -0,0 +1,153 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/optim/novograd.py
+
+import torch
+from torch.optim.optimizer import Optimizer
+
+__all__ = ["Novograd"]
+
+
+def _check_valid_opt_params(lr, eps, betas):
+ """Check if the given learning rate and epsilon are valid."""
+ if lr < 0:
+ raise ValueError(f"Invalid learning rate: {lr}")
+ if eps < 0:
+ raise ValueError(f"Invalid epsilon value: {eps}")
+ if not (0.0 <= betas[0] < 1.0 and 0.0 <= betas[1] < 1.0):
+ raise ValueError(f"Betas have to be between 0 and 1: {betas}")
+
+
+class Novograd(Optimizer):
+ """Implements Novograd algorithm.
+
+ It has been proposed in "Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep
+ Networks" (https://arxiv.org/abs/1905.11286).
+ """
+
+ def __init__(
+ self,
+ params,
+ lr=1e-3,
+ betas=(0.95, 0.98),
+ eps=1e-8,
+ weight_decay=0,
+ grad_averaging=False,
+ amsgrad=False,
+ luc=False,
+ luc_trust=1e-3,
+ luc_eps=1e-8,
+ ):
+ """Inits :class:`Novograd`.
+
+ Parameters
+ ----------
+ params : iterable
+ Iterable of parameters to optimize or dicts defining parameter groups.
+ lr : float
+ Learning rate. Default is ``1e-3``.
+ betas : Tuple[float, float], optional
+ Coefficients used for computing running averages of gradient and its square. Default is ``(0.9, 0.999)``.
+ eps : float, optional
+ Term added to the denominator to improve numerical stability. Default is ``1e-8``.
+ weight_decay : float, optional
+ Weight decay (L2 penalty). Default is ``0``.
+ amsgrad : bool, optional
+ Whether to use the AMSGrad variant of this algorithm from the paper "On the Convergence of Adam and
+ Beyond". Default is ``False``.
+ """
+ _check_valid_opt_params(lr, eps, betas)
+ defaults = {
+ "lr": lr,
+ "betas": betas,
+ "eps": eps,
+ "weight_decay": weight_decay,
+ "grad_averaging": grad_averaging,
+ "amsgrad": amsgrad,
+ }
+ self.luc = luc
+ self.luc_trust = luc_trust
+ self.luc_eps = luc_eps
+ super().__init__(params, defaults)
+
+ def __setstate__(self, state):
+ """Set state for optimizer."""
+ super().__setstate__(state)
+ for group in self.param_groups:
+ group.setdefault("amsgrad", False)
+
+ def step(self, closure=None):
+ """Performs a single optimization step.
+
+ Parameters
+ ----------
+ closure : str
+ A closure that reevaluates the model and returns the loss.
+
+ Returns
+ -------
+ loss: Loss (if provided)
+ """
+ loss = closure() if closure is not None else None
+ for group in self.param_groups:
+ for p in group["params"]:
+ if p.grad is None:
+ continue
+ grad = p.grad.data
+ if grad.is_sparse:
+ raise RuntimeError("Sparse gradients are not supported.")
+ amsgrad = group["amsgrad"]
+ state = self.state[p]
+
+ # State initialization
+ if not state:
+ state["step"] = 0
+ # Exponential moving average of gradient values
+ state["exp_avg"] = torch.zeros_like(p.data)
+ # Exponential moving average of squared gradient values
+ state["exp_avg_sq"] = torch.zeros([]).to(state["exp_avg"].device)
+ if amsgrad:
+ # Maintains max of all exp moving avg of squared grad
+ state["max_exp_avg_sq"] = torch.zeros([]).to(state["exp_avg"].device)
+
+ exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+ if amsgrad:
+ max_exp_avg_sq = state["max_exp_avg_sq"]
+ beta1, beta2 = group["betas"]
+
+ state["step"] += 1
+
+ norm = grad.norm().pow(2)
+
+ if exp_avg_sq == 0:
+ exp_avg_sq.copy_(norm)
+ else:
+ exp_avg_sq.mul_(beta2).add_(norm, alpha=1.0 - beta2)
+
+ if amsgrad:
+ # Maintains max of all 2nd moment running avg till now
+ torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
+ # Use the max for normalizing running avg. of gradient
+ denom = max_exp_avg_sq.sqrt().add_(group["eps"])
+ else:
+ denom = exp_avg_sq.sqrt().add_(group["eps"])
+
+ grad.div_(denom)
+ if group["weight_decay"] != 0:
+ grad.add_(p.data, alpha=group["weight_decay"])
+ if group["grad_averaging"]:
+ grad.mul_(1 - beta1)
+ exp_avg.mul_(beta1).add_(grad)
+
+ if self.luc:
+ # Clip update so that updates are less than eta*weights
+ data_norm = torch.norm(p.data)
+ grad_norm = torch.norm(exp_avg.data)
+ luc_factor = self.luc_trust * data_norm / (grad_norm + self.luc_eps)
+ luc_factor = min(luc_factor, group["lr"])
+ p.data.add_(exp_avg, alpha=-luc_factor)
+ else:
+ p.data.add_(exp_avg, alpha=-group["lr"])
+
+ return loss
diff --git a/atommic/core/optim/optimizer_with_main_params.py b/atommic/core/optim/optimizer_with_main_params.py
new file mode 100644
index 00000000..f98a8ddc
--- /dev/null
+++ b/atommic/core/optim/optimizer_with_main_params.py
@@ -0,0 +1,506 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/optim/optimizer_with_master_params.py
+
+from contextlib import contextmanager
+
+import torch
+
+from atommic.utils import logging
+
+HAVE_APEX = False
+
+
+def _zero_grad_group_helper(group, set_to_none):
+ """Zero out the gradient for a group of parameters. Note: copied from torch.optim.optimizer."""
+ for param in group:
+ if param.grad is not None:
+ if set_to_none:
+ param.grad = None
+ else:
+ if param.grad.grad_fn is not None:
+ param.grad.detach_()
+ else:
+ param.grad.requires_grad_(False)
+ param.grad.zero_()
+
+
+def _multi_tensor_copy_this_to_that(this, that, overflow_buf):
+ """Use multi-tensor-applier to copy values from one list to another. We don't have a blfoat16 implementation so for
+ now if the overflow_buf is not provided, we default back to simple loop copy to be compatible with bfloat16.
+ """
+ if overflow_buf:
+ # Scaling with factor `1.0` is equivalent to copy.
+ multi_tensor_applier(amp_C.multi_tensor_scale, overflow_buf, [this, that], 1.0) # noqa: F821
+ else:
+ # FIXME: use multi-tensor applier for bf16
+ for this_, that_ in zip(this, that):
+ that_.copy_(this_)
+
+
+class GradBucket:
+ """Persistent buffer for main gradients that remains allocated between training iterations."""
+
+ def __init__(self, numel, chunk_size_mb):
+ """Inits :class:`GradBucket`.
+
+ Parameters
+ ----------
+ numel : int
+ Number of elements in the buffer.
+ chunk_size_mb : int
+ Chunk size in MB.
+ """
+ if not HAVE_APEX:
+ raise ImportError("Apex was not found. Using model parallel models will error out.")
+
+ self.numel = numel
+ self.data = torch.zeros(self.numel, dtype=torch.float, device=torch.cuda.current_device(), requires_grad=False)
+
+ self.chunk_size_mb = chunk_size_mb
+ if self.chunk_size_mb > 0:
+ chunk_size_bytes = chunk_size_mb * 1024 * 1024
+ self.chunk_size_numel = chunk_size_bytes // 4
+ self.num_chunks = self.numel // self.chunk_size_numel
+ self.numel_per_chunk = [self.chunk_size_numel] * self.num_chunks
+ if self.numel % self.chunk_size_numel != 0:
+ self.num_chunks += 1
+ self.numel_per_chunk.append(self.numel % self.chunk_size_numel)
+
+ self.start_index_per_chunk = torch.cumsum(torch.tensor([0] + self.numel_per_chunk[:-1]), dim=0)
+ self.current_chunk = 0
+ self.computed_numel_per_chunk = [0] * self.num_chunks
+
+ def zero(self):
+ """Reset the buffer to zero."""
+ self.data.zero_()
+
+ def allreduce_buffer(self):
+ """Synchronous buffer data allreduce"""
+ self.data.div_(get_data_parallel_world_size()) # noqa: F821
+ torch.distributed.all_reduce(self.data, group=get_data_parallel_group()) # noqa: F821
+
+ def get(self, shape, start_index):
+ """Return a tensor with the input `shape` as a view into the 1-D data starting at `start_index`."""
+ end_index = start_index + shape.numel()
+ if end_index > self.numel:
+ raise AssertionError("requested tensor is out of the buffer range.")
+ buffer_tensor = self.data[start_index:end_index]
+ buffer_tensor = buffer_tensor.view(shape)
+
+ grad_chunk_info = None
+ if self.chunk_size_mb > 0:
+ chunk = start_index // self.chunk_size_numel
+ chunk_start_index = self.start_index_per_chunk[chunk]
+ chunk_end_index = chunk_start_index + self.numel_per_chunk[chunk]
+ grad_chunk_info = {chunk: min(chunk_end_index, end_index) - start_index}
+ while chunk_end_index < end_index:
+ chunk += 1
+ chunk_start_index = self.start_index_per_chunk[chunk]
+ chunk_end_index = chunk_start_index + self.numel_per_chunk[chunk]
+ grad_chunk_info[chunk] = min(chunk_end_index, end_index) - chunk_start_index
+
+ return buffer_tensor, grad_chunk_info
+
+ def update_chunk_info(self, grad_chunk_info):
+ """Update the chunk info with the grad_chunk_info."""
+ for chunk in grad_chunk_info.keys():
+ self.computed_numel_per_chunk[chunk] += grad_chunk_info[chunk]
+
+ def get_allreduce_tensor(self):
+ """Get a tensor that can be used for allreduce."""
+ if self.computed_numel_per_chunk[self.current_chunk] != self.numel_per_chunk[self.current_chunk]:
+ return None
+
+ chunk_start_index = self.start_index_per_chunk[self.current_chunk]
+ chunk_end_index = chunk_start_index + self.numel_per_chunk[self.current_chunk]
+ self.computed_numel_per_chunk[self.current_chunk] = 0
+ self.current_chunk += 1
+ if self.current_chunk == self.num_chunks:
+ self.current_chunk = 0
+
+ return self.data[chunk_start_index:chunk_end_index]
+
+
+class MainParamsOptimizerWrapper(torch.optim.Optimizer):
+ """Float16 optimizer wrapper for half precision (fp16 and bf16) data types. This optimizer wrapper holds main
+ parameters and gradients in fp32 to support stable convergence.
+ """
+
+ def __init__( # noqa: MC0001
+ self,
+ optimizer,
+ fp32_grad_accum=False,
+ contiguous_grad_bucket=False,
+ async_grad_allreduce=False,
+ grad_div_ar_fusion=True,
+ grad_allreduce_chunk_size_mb=0,
+ ):
+ """Inits :class:`MainParamsOptimizerWrapper`.
+
+ Parameters
+ ----------
+ optimizer : torch.optim.Optimizer
+ Base optimizer such as Adam or SGD.
+ fp32_grad_accum : bool
+ To enable the use of fp32 in gradient accumulation and allreduce.
+ contiguous_grad_bucket : bool
+ To enable allocating the master gradients in the contiguous memory space to reduce memory fragmentation.
+ async_grad_allreduce : bool
+ Enable asynchronous gradient allreduce that is executed along with the training step back prop.
+ """
+ super().__init__(optimizer.param_groups) # pylint: disable=no-value-for-parameter
+ if not HAVE_APEX:
+ raise ImportError("Apex was not found. Using model parallel models will error out.")
+
+ self.optimizer = optimizer
+ if not self.optimizer:
+ raise AssertionError("no optimizer is provided.")
+ if contiguous_grad_bucket and not fp32_grad_accum:
+ raise AssertionError("contiguous gradient buffer assumes using fp32 grad.")
+ if async_grad_allreduce:
+ if not fp32_grad_accum:
+ raise AssertionError(
+ "async allreduce applies to master gradients only, "
+ "which is supposed to be accumulated after grad op."
+ )
+ if not contiguous_grad_bucket:
+ raise AssertionError("currently async_grad_allreduce is supported only with contiguous_grad_bucket.")
+
+ self._fp32_grad_accum = fp32_grad_accum
+ self._contiguous_grad_bucket = contiguous_grad_bucket
+ self._async_grad_allreduce = async_grad_allreduce and get_data_parallel_world_size() > 1 # noqa: F821
+ self._grad_divisor = 1 / get_data_parallel_world_size() # noqa: F821
+
+ if self._async_grad_allreduce:
+ # use @no_sync to disable backward grad sync during gradient accumulation
+ self._require_backward_grad_sync = True
+ self._grad_div_ar_fusion = grad_div_ar_fusion
+ self._grad_allreduce_chunk_size_mb = grad_allreduce_chunk_size_mb
+ else:
+ self._require_backward_grad_sync = False
+ self._grad_div_ar_fusion = False
+ self._grad_allreduce_chunk_size_mb = 0
+
+ # Dummy tensor needed for apex multi-apply tensor.
+ self._dummy_overflow_buf = None
+
+ # Create persistent buffers for main gradients in contiguous memory space
+ # - Chunked element-wise and allreduce ops without creating a temporary buffer for merged operation
+ # - Low memory fragmentation
+ self._main_grad_buffers = None
+ if self._contiguous_grad_bucket:
+ self._main_grad_buffers = {}
+ # get the size of buffers
+ num_elements = {}
+ for i, param_group in enumerate(self.optimizer.param_groups):
+ for param in param_group["params"]:
+ if param.requires_grad:
+ num_elements[i] = num_elements.get(i, 0) + param.data.nelement()
+
+ # Allocate gradient memory buffers for each data type
+ if any(param.requires_grad for param in param_group["params"]):
+ self._main_grad_buffers[i] = GradBucket(num_elements[i], self._grad_allreduce_chunk_size_mb)
+
+ # Three groups of parameters:
+ self.float16_groups = [] # original float16 parameters
+ self.fp32_from_float16_groups = [] # fp32 copy of float16 parameters
+ self.fp32_from_fp32_groups = [] # original fp32 parameters
+
+ # gradient function hooks
+ if self._fp32_grad_accum:
+ self.grad_accs = []
+
+ # For all the groups in the original optimizer:
+ for i, param_group in enumerate(self.optimizer.param_groups):
+ float16_params_this_group = []
+ fp32_params_this_group = []
+ fp32_from_float16_params_this_group = []
+ # For all the parameters in this group:
+ for j, param in enumerate(param_group["params"]):
+ if param.requires_grad:
+ # float16 params:
+ if param.type() in ["torch.cuda.HalfTensor", "torch.cuda.BFloat16Tensor"]:
+ float16_params_this_group.append(param)
+
+ # Allocate the main parameter
+ main_param = param.detach().clone().float()
+
+ # Copy tensor model parallel attributes.
+ copy_tensor_model_parallel_attributes(main_param, param) # noqa: F821
+ if hasattr(param, "shared"):
+ main_param.shared = param.shared
+
+ # Assign the grad buffer offset to main parameters
+ if self._contiguous_grad_bucket:
+ num_elements[i] = num_elements[i] - param.data.nelement()
+ main_param.grad, grad_chunk_info = self._main_grad_buffers[i].get(
+ param.data.shape, num_elements[i]
+ )
+ param.main_grad = main_param.grad
+
+ # Replace the optimizer params with the new fp32 copy.
+ param_group["params"][j] = main_param
+ fp32_from_float16_params_this_group.append(main_param)
+ # Reset existing state dict key to the new main param.
+ if param in self.optimizer.state:
+ self.optimizer.state[main_param] = self.optimizer.state.pop(param)
+ elif param.type() == "torch.cuda.FloatTensor":
+ fp32_params_this_group.append(param)
+ param_group["params"][j] = param
+
+ else:
+ raise TypeError(
+ "Wrapped parameters must be one of torch.cuda.FloatTensor, torch.cuda.HalfTensor, "
+ f"or torch.cuda.BFloat16Tensor. Received {param.type()}"
+ )
+
+ # Add gradient accumulation hook for fp32 grad accumulation
+ if self._fp32_grad_accum and param.requires_grad:
+ # Expand so we get access to grad_fn.
+ param_tmp = param.expand_as(param)
+ # Get the gradient accumulator function.
+ grad_acc = param_tmp.grad_fn.next_functions[0][0]
+ grad_acc.register_hook(self._make_param_hook(param, main_param, i, grad_chunk_info))
+ self.grad_accs.append(grad_acc)
+
+ self.float16_groups.append(float16_params_this_group)
+ self.fp32_from_float16_groups.append(fp32_from_float16_params_this_group)
+ self.fp32_from_fp32_groups.append(fp32_params_this_group)
+
+ # init exp_avg and exp_avg_sq before loading optimizer state, needed for dist checkpointing
+ self._init_opt_state()
+
+ # Leverage state_dict() and load_state_dict() to
+ # recast preexisting per-param state tensors
+ self.optimizer.load_state_dict(self.optimizer.state_dict())
+
+ def _make_param_hook(self, param, main_param, i, grad_chunk_info):
+ """Create the grad accumulation and all-reduce hook for back prop."""
+
+ def param_hook(*unused): # pylint: disable=unused-argument
+ """Gradient accumulation and all-reduce."""
+ if param.grad is None:
+ return
+ if main_param.grad is None:
+ main_param.grad = param.grad.float()
+ else:
+ main_param.grad.add_(param.grad.data)
+ # Deallocate grad memory.
+ param.grad = None
+
+ # Asynchronous gradients allreduce across data_parallel ranks
+ if self._require_backward_grad_sync:
+ if self._grad_allreduce_chunk_size_mb > 0:
+ self._main_grad_buffers[i].update_chunk_info(grad_chunk_info)
+ while True:
+ allreduce_tensor = self._main_grad_buffers[i].get_allreduce_tensor()
+ if allreduce_tensor is None:
+ break
+ if self._grad_div_ar_fusion:
+ torch.distributed.all_reduce(
+ allreduce_tensor,
+ group=get_data_parallel_group(), # noqa: F821
+ async_op=True,
+ op=torch.distributed._make_nccl_premul_sum( # pylint: disable=protected-access
+ self._grad_divisor
+ ),
+ )
+ else:
+ allreduce_tensor.div_(get_data_parallel_world_size()) # noqa: F821
+ torch.distributed.all_reduce(
+ allreduce_tensor,
+ group=get_data_parallel_group(), # noqa: F821
+ async_op=True,
+ )
+ else:
+ if self._grad_div_ar_fusion:
+ torch.distributed.all_reduce(
+ main_param.grad,
+ group=get_data_parallel_group(), # noqa: F821
+ async_op=True,
+ op=torch.distributed._make_nccl_premul_sum( # pylint: disable=protected-access
+ self._grad_divisor
+ ),
+ )
+ else:
+ main_param.grad.div_(get_data_parallel_world_size()) # noqa: F821
+ torch.distributed.all_reduce(
+ main_param.grad,
+ group=get_data_parallel_group(), # noqa: F821
+ async_op=True,
+ )
+
+ return param_hook
+
+ def zero_grad(self, set_to_none=True):
+ """We only need to zero the model related parameters, i.e., float16_groups & fp32_from_fp32_groups. We
+ additionally zero fp32_from_float16_groups as a memory optimization to reduce fragmentation; in the case of
+ set_to_none==True, the space used by this field can be safely deallocated at this point.
+ """
+ for group in self.float16_groups:
+ _zero_grad_group_helper(group, set_to_none)
+ if self._contiguous_grad_bucket:
+ for i in self._main_grad_buffers:
+ self._main_grad_buffers[i].zero()
+ else:
+ for group in self.fp32_from_float16_groups:
+ _zero_grad_group_helper(group, set_to_none)
+ for group in self.fp32_from_fp32_groups:
+ _zero_grad_group_helper(group, set_to_none)
+
+ def copy_model_grads_to_main_grads(self):
+ """Copy model grads to main grads."""
+ # This only needs to be done for the float16 group.
+ for model_group, main_group in zip(self.float16_groups, self.fp32_from_float16_groups):
+ for model_param, main_param in zip(model_group, main_group):
+ if model_param.grad is not None:
+ main_param.grad = model_param.grad.float()
+
+ # Safe to deallocate model's grad after copying.
+ # (If using contiguous buffers, main_grad's memory should
+ # persist and therefore should not be deallocated.)
+ model_param.grad = None
+
+ def _get_model_and_main_params_data_float16(self):
+ """Get model and main params data in float16."""
+ model_data = []
+ main_data = []
+ half_dtype = None
+ for model_group, main_group in zip(self.float16_groups, self.fp32_from_float16_groups):
+ for model_param, main_param in zip(model_group, main_group):
+ if half_dtype is None:
+ half_dtype = model_param.data.dtype
+ model_data.append(model_param.data)
+ main_data.append(main_param.data)
+ return model_data, main_data, half_dtype
+
+ def _set_overflow_buffer(self, half_dtype):
+ """Set overflow buffer."""
+ if half_dtype == torch.float16:
+ if self._dummy_overflow_buf is None:
+ self._dummy_overflow_buf = torch.cuda.IntTensor([0])
+ else:
+ self._dummy_overflow_buf.fill_(0)
+
+ def _copy_main_params_to_model_params(self):
+ """Copy main params to model params."""
+ # Only needed for the float16 params.
+ model_data, main_data, half_dtype = self._get_model_and_main_params_data_float16()
+ self._set_overflow_buffer(half_dtype)
+ _multi_tensor_copy_this_to_that(this=main_data, that=model_data, overflow_buf=self._dummy_overflow_buf)
+
+ def _copy_model_params_to_main_params(self):
+ """Copy model params to main params."""
+ # Only needed for the float16 params.
+ model_data, main_data, half_dtype = self._get_model_and_main_params_data_float16()
+ self._set_overflow_buffer(half_dtype)
+ _multi_tensor_copy_this_to_that(this=model_data, that=main_data, overflow_buf=self._dummy_overflow_buf)
+
+ def reload_model_params(self):
+ """Reload model params."""
+ self._copy_model_params_to_main_params()
+
+ # pylint: disable=arguments-differ
+ @torch.no_grad()
+ def step(self, **kwargs):
+ """Step the optimizer."""
+ # While async grad allreduce is enabled, bprop will keep moving forward without waiting for the finish of
+ # async grad AR works. Hence, to guarantee the correctness of grads reduction, we cannot start weight update
+ # until all async grad AR works are done.
+ if self._async_grad_allreduce:
+ torch.cuda.synchronize()
+ self.optimizer.step(closure=None, **kwargs)
+ # Update params from main params.
+ with torch.no_grad():
+ self._copy_main_params_to_model_params()
+ # Successful update.
+ return True
+
+ def state_dict(self):
+ """Return the state of the optimizer."""
+ return {"optimizer": self.optimizer.state_dict(), "fp32_from_fp16_params": self.fp32_from_float16_groups}
+
+ def load_state_dict(self, state_dict):
+ """Load the state of the optimizer."""
+ # Optimizer.
+ optimizer_key = "optimizer"
+ if optimizer_key not in state_dict:
+ optimizer_key = "optimizer_state_dict"
+ logging.info("***WARNING*** loading optimizer from an old checkpoint ...")
+ self.optimizer.load_state_dict(state_dict[optimizer_key])
+
+ # Copy data for the main params.
+ fp32_from_float16_params_key = "fp32_from_fp16_params"
+ if fp32_from_float16_params_key not in state_dict:
+ fp32_from_float16_params_key = "fp32_from_fp16"
+ for current_group, saved_group in zip(self.fp32_from_float16_groups, state_dict[fp32_from_float16_params_key]):
+ for current_param, saved_param in zip(current_group, saved_group):
+ current_param.data.copy_(saved_param.data)
+
+ def allreduce_main_grads(self):
+ """All reduce main grads."""
+ for i in self._main_grad_buffers:
+ self._main_grad_buffers[i].allreduce_buffer()
+
+ @contextmanager
+ def no_sync(self):
+ """A context manager to disable gradient synchronizations across data-parallel ranks."""
+ old_require_backward_grad_sync = self._require_backward_grad_sync
+ self._require_backward_grad_sync = False
+ try:
+ yield
+ finally:
+ self._require_backward_grad_sync = old_require_backward_grad_sync
+
+ @property
+ def async_master_grads_allreduce(self):
+ """Return whether to use async allreduce for master grads."""
+ return self._async_grad_allreduce
+
+ @property
+ def fp32_grad_accumulation(self):
+ """Return whether to accumulate gradients in fp32."""
+ return self._fp32_grad_accum
+
+ def get_parameters(self):
+ """Return the parameters of the optimizer."""
+ params = []
+ for param_group in self.optimizer.param_groups:
+ for param in param_group["params"]:
+ if param.grad is not None: # added to enable pp>1 training for adapters
+ params.append(param)
+ return params
+
+ def _get_state(self):
+ """Promote state, so it can be retrieved or set via "optimizer_instance.state."""
+ return self.optimizer.state if hasattr(self, "optimizer") else []
+
+ def _set_state(self, value):
+ """Promote state, so it can be retrieved or set via "optimizer_instance.state."""
+ self.optimizer.state = value
+
+ state = property(_get_state, _set_state)
+
+ def _get_param_groups(self):
+ """Promote param_groups, so it can be retrieved or set via "optimizer_instance.param_groups. For example, to
+ adjust the learning rate.
+ """
+ return self.optimizer.param_groups if hasattr(self, "optimizer") else []
+
+ def _set_param_groups(self, value):
+ """Set param_groups."""
+ self.optimizer.param_groups = value
+
+ param_groups = property(_get_param_groups, _set_param_groups)
+
+ def _get_defaults(self):
+ """Promote defaults, so it can be retrieved or set via 'optimizer_instance.default'."""
+ return self.optimizer.defaults if hasattr(self, "optimizer") else []
+
+ def _set_defaults(self, value):
+ """Set defaults."""
+ self.optimizer.defaults = value
+
+ defaults = property(_get_defaults, _set_defaults)
diff --git a/atommic/core/optim/optimizers.py b/atommic/core/optim/optimizers.py
new file mode 100644
index 00000000..5cc33cf0
--- /dev/null
+++ b/atommic/core/optim/optimizers.py
@@ -0,0 +1,167 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/optim/optimizers.py
+
+import copy
+from functools import partial
+from typing import Any, Dict, Optional, Union
+
+import hydra
+import torch
+from omegaconf import DictConfig, OmegaConf
+from torch import optim
+from torch.optim import adadelta, adagrad, adamax, rmsprop, rprop
+from torch.optim.optimizer import Optimizer
+
+from atommic.core.conf.optimizers import OptimizerParams, get_optimizer_config, register_optimizer_params
+from atommic.core.optim.adafactor import Adafactor
+from atommic.core.optim.lion import Lion
+from atommic.core.optim.novograd import Novograd
+from atommic.core.optim.radam import RAdam
+from atommic.utils.model_utils import maybe_update_config_version
+
+AVAILABLE_OPTIMIZERS = {
+ "sgd": optim.SGD,
+ "adam": optim.Adam,
+ "adamw": optim.AdamW,
+ "adadelta": adadelta.Adadelta,
+ "adamax": adamax.Adamax,
+ "adagrad": adagrad.Adagrad,
+ "rmsprop": rmsprop.RMSprop,
+ "rprop": rprop.Rprop,
+ "novograd": Novograd,
+ "adafactor": Adafactor,
+ "radam": RAdam,
+ "lion": Lion,
+}
+
+__all__ = ["AVAILABLE_OPTIMIZERS", "get_optimizer", "register_optimizer", "parse_optimizer_args"]
+
+
+def parse_optimizer_args(
+ optimizer_name: str, optimizer_kwargs: Union[DictConfig, Dict[str, Any]]
+) -> Union[Dict[str, Any], DictConfig]:
+ """Parses a list of strings, of the format "key=value" or "key2=val1,val2,..." into a dictionary of type
+ {key=value, key2=[val1, val2], ...}. This dictionary is then used to instantiate the chosen Optimizer.
+
+ Parameters
+ ----------
+ optimizer_name : str
+ String name of the optimizer, used for auto resolution of params.
+ optimizer_kwargs : Union[DictConfig, Dict[str, Any]]
+ Either a list of strings in a specified format, or a dictionary. If a dictionary is provided, it is assumed the
+ dictionary is the final parsed value, and simply returned. If a list of strings is provided, each item in the
+ list is parsed into a new dictionary.
+
+ Returns
+ -------
+ dict
+ A dictionary of the parsed arguments.
+ """
+ kwargs: Dict[Any, Any] = {}
+
+ if optimizer_kwargs is None:
+ return kwargs
+
+ optimizer_kwargs = copy.deepcopy(optimizer_kwargs)
+ optimizer_kwargs = maybe_update_config_version(optimizer_kwargs)
+
+ if isinstance(optimizer_kwargs, DictConfig):
+ optimizer_kwargs = OmegaConf.to_container(optimizer_kwargs, resolve=True)
+
+ # If it is a dictionary, perform stepwise resolution
+ if hasattr(optimizer_kwargs, "keys"):
+ # Attempt class path resolution
+ if "_target_" in optimizer_kwargs: # captures (target, _target_)
+ optimizer_kwargs_config = OmegaConf.create(optimizer_kwargs)
+ optimizer_instance = hydra.utils.instantiate(optimizer_kwargs_config) # type: DictConfig
+ optimizer_instance = vars(optimizer_instance)
+ return optimizer_instance
+
+ # If class path was not provided, perhaps `name` is provided for resolution
+ if "name" in optimizer_kwargs:
+ # If `auto` is passed as name for resolution of optimizer name,
+ # then lookup optimizer name and resolve its parameter config
+ if optimizer_kwargs["name"] == "auto":
+ optimizer_params_name = f"{optimizer_name}_params"
+ optimizer_kwargs.pop("name")
+ else:
+ optimizer_params_name = optimizer_kwargs.pop("name")
+
+ # Override arguments provided in the config yaml file
+ if "params" in optimizer_kwargs:
+ # If optimizer kwarg overrides are wrapped in yaml `params`
+ optimizer_params_override = optimizer_kwargs.get("params")
+ else:
+ # If the kwargs themselves are a DictConfig
+ optimizer_params_override = optimizer_kwargs
+
+ if isinstance(optimizer_params_override, DictConfig):
+ optimizer_params_override = OmegaConf.to_container(optimizer_params_override, resolve=True)
+
+ optimizer_params_cls = get_optimizer_config(optimizer_params_name, **optimizer_params_override)
+
+ # If we are provided just a Config object, simply return the dictionary of that object
+ if optimizer_params_name is None:
+ optimizer_params = vars(optimizer_params_cls)
+ return optimizer_params
+ # If we are provided a partial class instantiation of a Config, instantiate it and retrieve its vars
+ # as a dictionary.
+ # instantiate the parameters object
+ optimizer_params = vars(optimizer_params_cls)
+ return optimizer_params
+
+ # simply return the dictionary that was provided
+ return optimizer_kwargs
+
+ return kwargs
+
+
+def register_optimizer(name: str, optimizer: Optimizer, optimizer_params: OptimizerParams):
+ """Checks if the optimizer name exists in the registry, and if it doesn't, adds it. This allows custom optimizers
+ to be added and called by name during instantiation.
+
+ Parameters
+ ----------
+ name : str
+ Name of the optimizer. Will be used as key to retrieve the optimizer.
+ optimizer : Optimizer
+ Optimizer class.
+ optimizer_params : OptimizerParams
+ The parameters as a dataclass of the optimizer.
+ """
+ if name in AVAILABLE_OPTIMIZERS:
+ raise ValueError(f"Cannot override pre-existing optimizers. Conflicting optimizer name = {name}")
+
+ AVAILABLE_OPTIMIZERS[name] = optimizer
+
+ optim_name = f"{optimizer.__name__}_params"
+ register_optimizer_params(name=optim_name, optimizer_params=optimizer_params)
+
+
+def get_optimizer(name: str, **kwargs: Optional[Dict[str, Any]]) -> partial:
+ """Convenience method to obtain an Optimizer class and partially instantiate it with optimizer kwargs.
+
+ Parameters
+ ----------
+ name : str
+ Name of the optimizer. Will be used as key to retrieve the optimizer.
+ kwargs : Optional[Dict[str, Any]]
+ Optional kwargs of the optimizer used during instantiation.
+
+ Returns
+ -------
+ partial
+ A partially instantiated Optimizer.
+ """
+ if name not in AVAILABLE_OPTIMIZERS:
+ raise ValueError(
+ f"Cannot resolve optimizer '{name}'. Available optimizers are : " f"{AVAILABLE_OPTIMIZERS.keys()}"
+ )
+ if name == "fused_adam" and not torch.cuda.is_available():
+ raise ValueError("CUDA must be available to use fused_adam.")
+
+ optimizer = AVAILABLE_OPTIMIZERS[name]
+ optimizer = partial(optimizer, **kwargs)
+ return optimizer
diff --git a/atommic/core/optim/radam.py b/atommic/core/optim/radam.py
new file mode 100644
index 00000000..10d77413
--- /dev/null
+++ b/atommic/core/optim/radam.py
@@ -0,0 +1,109 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/optim/radam.py
+
+import math
+
+import torch
+from torch.optim.optimizer import Optimizer
+
+
+class RAdam(Optimizer):
+ """RAdam optimizer."""
+
+ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0):
+ """Inits :class:`RAdam`.
+
+ Parameters
+ ----------
+ params : iterable
+ Iterable of parameters to optimize or dicts defining parameter groups.
+ lr : float, optional
+ Learning rate. Default is ``1e-3``.
+ betas : tuple of floats, optional
+ Coefficients used for computing running averages of gradient and its square. Default is ``(0.9, 0.999)``.
+ eps : float, optional
+ Term added to the denominator to improve numerical stability. Default is ``1e-8``.
+ weight_decay : float, optional
+ Weight decay (L2 penalty). Default is ``0``.
+ """
+ defaults = {"lr": lr, "betas": betas, "eps": eps, "weight_decay": weight_decay}
+ self.buffer = [[None, None, None] for _ in range(10)]
+ super().__init__(params, defaults)
+
+ def step(self, closure=None):
+ """Step through the optimizer"""
+ loss = None
+ if closure is not None:
+ loss = closure()
+
+ for group in self.param_groups:
+ for p in group["params"]:
+ if p.grad is None:
+ continue
+ grad = p.grad.data.float()
+ if grad.is_sparse:
+ raise RuntimeError("RAdam does not support sparse gradients")
+
+ p_data_fp32 = p.data.float()
+
+ state = self.state[p]
+
+ if len(state) == 0:
+ state["step"] = 0
+ state["exp_avg"] = torch.zeros_like(p_data_fp32)
+ state["exp_avg_sq"] = torch.zeros_like(p_data_fp32)
+ else:
+ state["exp_avg"] = state["exp_avg"].type_as(p_data_fp32)
+ state["exp_avg_sq"] = state["exp_avg_sq"].type_as(p_data_fp32)
+
+ exp_avg, exp_avg_sq = state["exp_avg"], state["exp_avg_sq"]
+ beta1, beta2 = group["betas"]
+
+ exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=(1.0 - beta2))
+ exp_avg.mul_(beta1).add_(grad, alpha=(1.0 - beta1))
+
+ state["step"] += 1
+ buffered = self.buffer[int(state["step"] % 10)]
+ if state["step"] == buffered[0]:
+ N_sma, step_size = buffered[1], buffered[2]
+ else:
+ buffered[0] = state["step"]
+ beta2_t = beta2 ** state["step"]
+ N_sma_max = 2 / (1 - beta2) - 1
+ N_sma = N_sma_max - 2 * state["step"] * beta2_t / (1 - beta2_t)
+ buffered[1] = N_sma
+
+ # more conservative since it's an approximated value
+ if N_sma >= 5:
+ step_size = (
+ group["lr"]
+ * math.sqrt(
+ (1 - beta2_t)
+ * (N_sma - 4)
+ / (N_sma_max - 4)
+ * (N_sma - 2)
+ / N_sma
+ * N_sma_max
+ / (N_sma_max - 2)
+ )
+ / (1 - beta1 ** state["step"])
+ )
+ else:
+ step_size = group["lr"] / (1 - beta1 ** state["step"])
+ buffered[2] = step_size
+
+ if group["weight_decay"] != 0:
+ p_data_fp32.add_(-group["weight_decay"] * group["lr"], p_data_fp32)
+
+ # more conservative since it's an approximated value
+ if N_sma >= 5:
+ denom = exp_avg_sq.sqrt().add_(group["eps"])
+ p_data_fp32.addcdiv_(-step_size, exp_avg, denom)
+ else:
+ p_data_fp32.add_(-step_size, exp_avg)
+
+ p.data.copy_(p_data_fp32)
+
+ return loss
diff --git a/atommic/core/utils/__init__.py b/atommic/core/utils/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/core/utils/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/core/utils/neural_type_utils.py b/atommic/core/utils/neural_type_utils.py
new file mode 100644
index 00000000..8b9d541e
--- /dev/null
+++ b/atommic/core/utils/neural_type_utils.py
@@ -0,0 +1,51 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/utils/neural_type_utils.py
+
+from collections import defaultdict
+
+
+def get_io_names(types, disabled_names):
+ """This method will return a list of input and output names for a given NeuralType.
+
+ Parameters
+ ----------
+ types : type
+ The NeuralType of the module or model to be inspected.
+ disabled_names : list
+ A list of names that should be excluded from the result.
+
+ Returns
+ -------
+ list
+ A list of input and output names.
+ """
+ names = list(types.keys())
+ for name in disabled_names:
+ if name in names:
+ names.remove(name)
+ return names
+
+
+def get_dynamic_axes(types, names):
+ """This method will return a dictionary with input/output names as keys and a list of dynamic axes as values.
+
+ Parameters
+ ----------
+ types : NeuralType
+ The NeuralType of the module or model to be inspected.
+ names : list
+ A list of names that should be inspected.
+
+ Returns
+ -------
+ dict
+ A dictionary with input/output names as keys and a list of dynamic axes as values.
+ """
+ dynamic_axes = defaultdict(list)
+ if names is not None:
+ for name in names:
+ if name in types:
+ dynamic_axes.update(extract_dynamic_axes(name, types[name])) # noqa: F821
+ return dynamic_axes
diff --git a/atommic/core/utils/numba_utils.py b/atommic/core/utils/numba_utils.py
new file mode 100644
index 00000000..1ac33630
--- /dev/null
+++ b/atommic/core/utils/numba_utils.py
@@ -0,0 +1,177 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/core/utils/numba_utils.py
+
+import contextlib
+import logging as pylogger
+import operator
+import os
+from typing import Tuple, Union
+
+from atommic.utils import model_utils
+
+# Prevent Numba CUDA logs from showing at info level
+cuda_logger = pylogger.getLogger("numba.cuda.cudadrv.driver")
+cuda_logger.setLevel(pylogger.ERROR) # only show error
+
+__NUMBA_DEFAULT_MINIMUM_VERSION__ = "0.53.0"
+__NUMBA_MINIMUM_VERSION__ = os.environ.get("atommic_NUMBA_MINVER", __NUMBA_DEFAULT_MINIMUM_VERSION__)
+
+__NUMBA_MINIMUM_VERSION_FP16_SUPPORTED__ = "0.57.0"
+
+NUMBA_INSTALLATION_MESSAGE = (
+ "Could not import `numba`.\n"
+ "Please install numba in one of the following ways."
+ "1) If using conda, simply install it with conda using `conda install -c numba numba`\n"
+ "2) If using pip (not recommended), `pip install --upgrade numba`\n"
+ "followed by `export NUMBAPRO_LIBDEVICE='/usr/local/cuda/nvvm/libdevice/'` and \n"
+ "`export NUMBAPRO_NVVM='/usr/local/cuda/nvvm/lib64/libnvvm.so'`.\n"
+ "It is advised to always install numba using conda only, "
+ "as pip installations might interfere with other libraries such as llvmlite.\n"
+ "If pip install does not work, you can also try adding `--ignore-installed` to the pip command,\n"
+ "but this is not advised."
+)
+
+STRICT_NUMBA_COMPAT_CHECK = True
+
+# Get environment key if available
+if "STRICT_NUMBA_COMPAT_CHECK" in os.environ:
+ check_str = os.environ.get("STRICT_NUMBA_COMPAT_CHECK")
+ check_bool = str(check_str).lower() in {"yes", "true", "t", "1"}
+ STRICT_NUMBA_COMPAT_CHECK = check_bool
+
+
+def is_numba_compat_strict() -> bool:
+ """Returns strictness level of numba cuda compatibility checks. If value is true, numba cuda compatibility matrix
+ must be satisfied. If value is false, only cuda availability is checked, not compatibility. Numba Cuda may still
+ compile and run without issues in such a case, or it may fail.
+ """
+ return STRICT_NUMBA_COMPAT_CHECK
+
+
+def set_numba_compat_strictness(strict: bool):
+ """Sets the strictness level of numba cuda compatibility checks. If value is true, numba cuda compatibility matrix
+ must be satisfied. If value is false, only cuda availability is checked, not compatibility. Numba Cuda may still
+ compile and run without issues in such a case, or it may fail.
+
+ Parameters
+ ----------
+ strict : bool
+ Whether to enforce strict compatibility checks or relax them.
+ """
+ global STRICT_NUMBA_COMPAT_CHECK
+ STRICT_NUMBA_COMPAT_CHECK = strict
+
+
+@contextlib.contextmanager
+def with_numba_compat_strictness(strict: bool):
+ """Context manager to temporarily set numba cuda compatibility strictness."""
+ initial_strictness = is_numba_compat_strict()
+ set_numba_compat_strictness(strict=strict)
+ yield
+ set_numba_compat_strictness(strict=initial_strictness)
+
+
+def numba_cpu_is_supported(min_version: str) -> bool:
+ """Tests if an appropriate version of numba is installed.
+
+ Parameters
+ ----------
+ min_version: The minimum version of numba that is required.
+
+ Returns
+ -------
+ bool, whether numba CPU supported with this current installation or not.
+ """
+ module_available, _ = model_utils.check_lib_version("numba", checked_version=min_version, operator=operator.ge)
+
+ # If numba is not installed
+ if module_available is None:
+ return False
+ return True
+
+
+def numba_cuda_is_supported(min_version: str) -> bool:
+ """Tests if an appropriate version of numba is installed, and if it is, if cuda is supported properly within it.
+
+ Parameters
+ ----------
+ min_version : str
+ The minimum version of numba that is required.
+
+ Returns
+ -------
+ bool
+ Whether cuda is supported with this current installation or not.
+ """
+ module_available = numba_cpu_is_supported(min_version)
+
+ # If numba is not installed
+ if module_available is None:
+ return False
+
+ if module_available is not True:
+ return False
+ from numba import cuda # pylint: disable=import-outside-toplevel
+
+ if not hasattr(cuda, "is_supported_version"):
+ # assume cuda is supported, but it may fail due to CUDA incompatibility
+ return False
+
+ try:
+ cuda_available = cuda.is_available()
+ cuda_compatible = cuda.is_supported_version() if cuda_available else False
+ if is_numba_compat_strict():
+ return cuda_available and cuda_compatible
+ return cuda_available
+
+ except OSError:
+ # dlopen(libcudart.dylib) might fail if CUDA was never installed in the first place.
+ return False
+
+
+def is_numba_cuda_fp16_supported(return_reason: bool = False) -> Union[bool, Tuple[bool, str]]:
+ """Utility method that returns a bool, stating if FP16 is supported for numba cuda kernels or not.
+
+ Returns:
+ bool, whether Numba CUDA will support fp16 or not.
+ """
+ reason = ""
+ use_nvidia_binding = os.environ.get('NUMBA_CUDA_USE_NVIDIA_BINDING', None)
+ if use_nvidia_binding is not None:
+ use_nvidia_binding = use_nvidia_binding.lower() == "1" # type: ignore
+ reason += "Env variable `NUMBA_CUDA_USE_NVIDIA_BINDING` is available and set to `1`. "
+ else:
+ use_nvidia_binding = False # type: ignore
+ reason += "Env variable `NUMBA_CUDA_USE_NVIDIA_BINDING` is not available or has not set to `1`."
+
+ numba_fp16_version_correct = model_utils.check_lib_version(
+ 'numba', __NUMBA_MINIMUM_VERSION_FP16_SUPPORTED__, operator=operator.ge
+ )[0]
+
+ if numba_fp16_version_correct:
+ reason += "Numba CUDA FP16 is supported in installed numba version."
+ else:
+ reason += "Numba CUDA FP16 is not supported in installed numba version."
+
+ result = use_nvidia_binding and numba_fp16_version_correct
+
+ if return_reason:
+ return result, reason # type: ignore
+ return result # type: ignore
+
+
+def skip_numba_cuda_test_if_unsupported(min_version: str):
+ """Helper method to skip pytest test case if numba cuda is not supported.
+
+ Parameters
+ ----------
+ min_version : str
+ The minimum version of numba that is required.
+ """
+ numba_cuda_support = numba_cuda_is_supported(min_version)
+ if not numba_cuda_support:
+ import pytest # pylint: disable=import-outside-toplevel
+
+ pytest.skip(f"Numba cuda test is being skipped. Minimum version required : {min_version}")
diff --git a/atommic/core/utils/process_launcher/__init__.py b/atommic/core/utils/process_launcher/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/core/utils/process_launcher/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/core/utils/process_launcher/launcher.py b/atommic/core/utils/process_launcher/launcher.py
new file mode 100644
index 00000000..6ead3d8e
--- /dev/null
+++ b/atommic/core/utils/process_launcher/launcher.py
@@ -0,0 +1,411 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/wdika/NeMo/blob/main/nemo/core/utils/process_launcher/launcher.py
+
+import copy
+import hashlib
+import os
+import subprocess
+import sys
+import threading
+import time
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any, Dict, List, Optional, Sequence
+
+import torch
+from hydra.core.config_store import ConfigStore
+from hydra.core.hydra_config import HydraConfig
+from hydra.core.plugins import Plugins
+from hydra.core.singleton import Singleton
+from hydra.core.utils import JobReturn, JobStatus, configure_log, filter_overrides, setup_globals
+from hydra.plugins.launcher import Launcher
+from hydra.types import HydraContext, TaskFunction
+from omegaconf import DictConfig, OmegaConf, open_dict
+
+from atommic.utils import logging
+
+
+# monkey-patch hydra func
+def is_in_toplevel_plugins_module(*args, **kwargs) -> bool: # pylint: disable=unused-argument
+ """Monkey-patch Hydra to allow for plugins to be loaded from any module."""
+ return True
+
+
+# Monkey-patch Hydra
+Plugins.instance().is_in_toplevel_plugins_module = is_in_toplevel_plugins_module
+
+
+@dataclass
+class ProcessLauncherConfig:
+ """Configuration for the ProcessLauncher plugin."""
+
+ _target_: str = "atommic.core.utils.process_launcher.launcher.ProcessLauncher"
+ num_gpus: int = -1
+ jobs_per_gpu: int = 1
+
+
+def execute_job(
+ idx: int,
+ overrides: Sequence[str],
+ hydra_context: HydraContext,
+ config: DictConfig,
+ singleton_state: Dict[Any, Any],
+ gpu_idx: int,
+):
+ """Creates a process that launches a "single" job that is identical in config + updated with sweep hyperparams.
+ Since a different process is being used, CUDA can work in non-ddp mode without issue.
+ Attempting ddp when using this script will not work as ddp cannot be used in shared contexts.
+
+ Parameters
+ ----------
+ idx : int
+ Global index of the job.
+ overrides : list
+ List of str overrides that correspond to this job.
+ hydra_context : HydraContext
+ Hydra Context used to load the sweep params into the global config.
+ config : DictConfig
+ Global config that will be updated with sweep hyper parameters.
+ singleton_state : Dict[Any, Any]
+ Hydra state.
+ gpu_idx : int
+ The GPU ID on which this process will be run.
+
+ Returns
+ -------
+ tuple
+ - The Process object that corresponds to this sweep.
+ - The JobReturn object holding some metadata about this run.
+ """
+ # Required by Hydra (lookup other Hydra Launchers for details)
+ setup_globals()
+ Singleton.set_state(singleton_state)
+
+ # Update base config with overrides to create sweep config
+ sweep_config = hydra_context.config_loader.load_sweep_config(config, list(overrides))
+ with open_dict(sweep_config):
+ sweep_config.hydra.job.id = f"{sweep_config.hydra.job.name}_{idx}"
+ sweep_config.hydra.job.num = idx
+ HydraConfig.instance().set_config(sweep_config)
+
+ # Set up a directory where the config will temporarily be stored.
+ script_path = os.path.join(os.getcwd(), sys.argv[0])
+ script_path = os.path.abspath(script_path)
+
+ hash_salt = "|".join([script_path, str(OmegaConf.to_yaml(config))]).encode("utf-8")
+ hash_val = hashlib.sha256(hash_salt).hexdigest()
+
+ config_dir = os.path.join(os.getcwd(), "hydra_cfg", str(hash_val))
+ if not os.path.exists(config_dir):
+ os.makedirs(config_dir, exist_ok=True)
+
+ task_cfg = copy.deepcopy(sweep_config)
+
+ # Remove hydra from sweep config
+ # This is done to prevent recursive call to multirun launcher in Hydra.
+ with open_dict(task_cfg):
+ task_cfg.pop("hydra", "")
+
+ # Save the current jobs config to directory
+ temp_config_name = f"config_{idx}.yaml"
+ temp_config = os.path.join(config_dir, temp_config_name)
+ OmegaConf.save(task_cfg, temp_config)
+
+ # Compute the overides as a dict
+ overrides = OmegaConf.to_container(config.hydra.overrides.task)
+
+ # Check and replace trainer.devices in config with gpu_idx
+ found_devices = False
+ gpu_override = f"trainer.devices=[{gpu_idx}]"
+ for oidx, val in enumerate(overrides):
+ if "trainer.devices" in val:
+ overrides[oidx] = gpu_override # type: ignore
+ found_devices = True
+
+ if not found_devices:
+ overrides.append(gpu_override) # type: ignore
+
+ # Build launch command
+ # Note: We depend on PTL doing the right thing since this command has global visibility of all CUDA_VISIBLE_DEVICES
+ cmd = [
+ "python",
+ script_path,
+ "--config-path",
+ config_dir,
+ "--config-name",
+ temp_config_name,
+ *overrides,
+ ]
+
+ # Launch the subprocess; pipe the stderr
+ # NOTE: If this hangs due to some reason after prolonged training, it means that the stderr pipe buffer
+ # has become full at the OS level and we need to explicitly empty it (either parallel thread or manually
+ # call proc.communicate(). It should not happen in general case as stderr is filled only in case retcode != 0
+ # If it does happen though, implement the code here
+ # https://stackoverflow.com/questions/39607172/python-subprocess-popen-poll-seems-to-hang-but-communicate-works
+ proc = subprocess.Popen(cmd, stderr=subprocess.PIPE) # pylint: disable=consider-using-with
+
+ # Setup data thread for stderr
+ std_error_buffer: List = []
+ # Trivial thread just reads lines from stdout into the list
+ drainerthread = threading.Thread(target=std_error_buffer.extend, args=(proc.stderr,))
+ drainerthread.daemon = True
+ drainerthread.start()
+
+ # Construct JobReturn object for Hydra
+ res = JobReturn()
+ res.cfg = task_cfg
+ res.overrides = overrides
+ res.hydra_cfg = config
+ res.working_dir = os.getcwd()
+ res.return_value = None
+
+ return proc, res, (std_error_buffer, drainerthread)
+
+
+def launch( # noqa: MC0001
+ launcher, job_overrides: Sequence[Sequence[str]], initial_job_idx: int
+) -> Sequence[JobReturn]:
+ """Launches a list of jobs with the given config.
+
+ Parameters
+ ----------
+ launcher : class
+ Reference to the Launched subclass.
+ job_overrides : list
+ A List of List, where each inner list is the arguments for one job run.
+ initial_job_idx : int
+ Initial job idx in batch.
+
+ Returns
+ -------
+ list
+ A list of JobReturn objects.
+ """
+ # Needed for Hydra, lookup JoblibLauncher in Hydra
+ setup_globals()
+ assert launcher.config is not None
+ assert launcher.task_function is not None
+ assert launcher.hydra_context is not None
+
+ configure_log(launcher.config.hydra.hydra_logging, launcher.config.hydra.verbose)
+ sweep_dir = Path(str(launcher.config.hydra.sweep.dir))
+ sweep_dir.mkdir(parents=True, exist_ok=True)
+
+ # Extract the runner's config (it is actually a DictConfig, but type is used for autocomplete)
+ runner_cfg = launcher.runner # type: ProcessLauncherConfig
+
+ logging.info(
+ f"ProcessLauncher({','.join([f'{k}={v}' for k, v in runner_cfg.items()])}) " # type: ignore
+ f"is launching {len(job_overrides)} jobs."
+ )
+ logging.info(f"Launching jobs, sweep output dir : {sweep_dir}")
+ for idx, overrides in enumerate(job_overrides):
+ logging.info(f"\t#{idx} : {' '.join(filter_overrides(overrides))}")
+
+ # Needed by Hydra
+ singleton_state = Singleton.get_state()
+
+ # Process the runner's config to build up the multiplex config
+ num_gpus = runner_cfg.get("num_gpus", -1) # type: ignore
+ jobs_per_gpu = runner_cfg.get("jobs_per_gpu", 1) # type: ignore
+
+ # Only GPUs are supported for now.
+ if num_gpus <= 0:
+ if torch.cuda.is_available():
+ num_gpus = torch.cuda.device_count()
+ else:
+ raise ValueError(f"{launcher.__class__.__name__} only supports GPU operations.")
+
+ # Setup arguments for multiplex runner
+ overrides = list(job_overrides) # type: ignore
+ num_overrides = len(overrides)
+
+ job_idx = 0
+ batch_size = num_gpus * jobs_per_gpu
+ gpu_idx = 0
+
+ ret: List = [] # List of returned JobResult
+ subprocess_list: List = [] # Buffer of subprocess
+ results = [] # Buffer of JobResult
+
+ # STD ERROR cache
+ std_error_buffers = [] # type: List[List[str]]
+ std_error_threads: threading.Thread = [] # type: ignore
+
+ # Run over all job combinations
+ while job_idx < num_overrides:
+ # Fill up subprocess buffer while its size is smaller than multiplex batch size
+ while len(subprocess_list) < batch_size:
+ # If we run out of jobs, stop trying to submit more jobs
+ if job_idx >= num_overrides:
+ break
+
+ # Submit a job as a new process
+ process, res, error_tup = execute_job(
+ initial_job_idx + job_idx,
+ overrides[job_idx],
+ launcher.hydra_context,
+ launcher.config,
+ singleton_state,
+ gpu_idx % num_gpus, # This will evenly distribute GPU load
+ )
+
+ # Store the subprocesses and JobResults
+ subprocess_list.append(process)
+ results.append(res)
+
+ # Manage stderror thread data
+ std_error_buffers.append(error_tup[0])
+ std_error_threads.append(error_tup[1]) # type: ignore
+
+ job_idx += 1
+ gpu_idx += 1
+
+ # Poll for samples in batch to finish.
+ if len(subprocess_list) > 0:
+ finished_processes = [0] * len(subprocess_list)
+
+ # Check if all processes are completed or not
+ # This is busy waiting, this is actually quite necessary
+ # Turns out that when you do proc.communicate(), you block all other threads immediately.
+ # IE they may fill up their buffers entirely, and hang while they wait for the first thread
+ # who called communicate() to finish its work or crash.
+ # Effectively it entirely stops multiprocessing jobs or multiplexed runs.
+ # Must poll and busy wait to keep threads alive, along with drain the pipes with thread buffers.
+ while sum(finished_processes) < len(subprocess_list):
+ # Check all processes to make sure they have a retcode (doesn't matter yet if 0 or not)
+ for proc_idx, proc in enumerate(subprocess_list):
+ # poll() is cheaper op than communicate()
+ retcode = proc.poll()
+
+ if retcode is not None:
+ # Log that the process with some ID has finished
+ if finished_processes[proc_idx] == 0:
+ logging.info(f"Processed job : {len(ret) + proc_idx} :: Ret code = {retcode}")
+
+ finished_processes[proc_idx] = 1
+
+ # Join this thread and merge its stderror buffer
+ proc.wait()
+ std_error_threads[proc_idx].join() # type: ignore
+ error_data = std_error_buffers[proc_idx]
+ error_data = [
+ str(data, encoding="utf-8").encode("utf-8").decode("utf-8").encode("utf-8") # type: ignore
+ for data in error_data
+ ]
+
+ std_error_buffers[proc_idx] = error_data
+
+ time.sleep(1.0)
+
+ # Process all the subprocess results
+ for proc_idx, (proc, res) in enumerate(zip(subprocess_list, results)):
+ # Wait until completion of process
+ _ = proc.communicate()
+
+ # 0 is for successful run
+ if proc.returncode == 0:
+ res.status = JobStatus.COMPLETED
+ else:
+ # > 0 is for error, log the error.
+ # Note: For the sake of efficiency while we log the error and raise an exception,
+ # It will only raise the 1st wrong job in all the jobs.
+ # If multiple jobs fail, it will still try to execute every job first before
+ # raising the error for the first one.
+ # This is done so that even if some jobs fail (say OOM or something),
+ # other jobs can still run.
+ err_buffer = std_error_buffers[proc_idx]
+ if isinstance(err_buffer, (list, tuple)):
+ err_string = ""
+ for err_line in err_buffer:
+ err_string = (
+ err_string
+ + f"{str(err_line, encoding='utf-8').encode('utf-8').decode('utf-8')}" # type: ignore
+ )
+ else:
+ err_string = err_buffer
+
+ error_msg = (
+ f"\nHyperparameter Arguments : {proc.args}\n"
+ f"Process Return code : {proc.returncode}\n"
+ f"Error Trace :\n"
+ f"{err_string}"
+ )
+ res.return_value = Exception(error_msg)
+ res.status = JobStatus.FAILED
+
+ logging.info(f"Finished executing job : {len(ret)}. Return Code = {proc.returncode}")
+ ret.append(res)
+
+ # Reset for next batch
+ subprocess_list.clear()
+ results.clear()
+
+ return ret
+
+
+class ProcessLauncher(Launcher):
+ """Process Launcher
+
+ Based on the JoblibLauncher, but uses processes to scatter jobs in a multiplexed manner across some number of GPUs
+ on a single machine.
+ """
+
+ def __init__(self, **kwargs: Any) -> None:
+ """Inits :class:`ProcessLauncher`."""
+ self.config: Optional[DictConfig] = None
+ self.task_function: Optional[TaskFunction] = None
+ self.hydra_context: Optional[HydraContext] = None
+ self.runner: ProcessLauncherConfig = kwargs # type: ignore
+
+ def setup(
+ self,
+ *,
+ hydra_context: HydraContext,
+ task_function: TaskFunction,
+ config: DictConfig,
+ ) -> None:
+ """Setups the launcher.
+
+ Parameters
+ ----------
+ hydra_context : HydraContext
+ Hydra context object.
+ task_function : TaskFunction
+ Task function.
+ config : DictConfig
+ Launcher config.
+ """
+ self.config = config
+ self.task_function = task_function
+ self.hydra_context = hydra_context
+
+ def launch(self, job_overrides: Sequence[Sequence[str]], initial_job_idx: int) -> Sequence[JobReturn]:
+ """Launch jobs.
+
+ Parameters
+ ----------
+ job_overrides : Sequence[Sequence[str]]
+ List of List, where each inner list is the arguments for one job run.
+ initial_job_idx : int
+ Initial job idx.
+
+ Returns
+ -------
+ Sequence[JobReturn]
+ Sequence of JobReturn objects, where each object represents the return value of one job.
+ """
+ return launch(launcher=self, job_overrides=job_overrides, initial_job_idx=initial_job_idx)
+
+
+ConfigStore.instance().store(
+ group="hydra/launcher",
+ name="atommic_launcher",
+ node=ProcessLauncherConfig,
+ provider="atommic_process_launcher",
+)
+
+Plugins.instance(ProcessLauncher)
diff --git a/atommic/package_info.py b/atommic/package_info.py
new file mode 100644
index 00000000..b7a34204
--- /dev/null
+++ b/atommic/package_info.py
@@ -0,0 +1,24 @@
+# coding=utf-8
+
+MAJOR = 1
+MINOR = 0
+PATCH = 0
+PRE_RELEASE = ""
+
+# Use the following formatting: (major, minor, patch, pre-release)
+VERSION = (MAJOR, MINOR, PATCH, PRE_RELEASE)
+
+__shortversion__ = ".".join(map(str, VERSION[:3]))
+__version__ = ".".join(map(str, VERSION[:3])) + "".join(VERSION[3:])
+
+__package_name__ = "atommic"
+__contact_names__ = "Dimitris Karkalousos"
+__contact_emails__ = "d.karkalousos@amsterdamumc.nl"
+__homepage__ = "https://github.com/wdika/atommic"
+__repository_url__ = "https://github.com/wdika/atommic"
+__download_url__ = "https://github.com/wdika/atommic/releases"
+__description__ = "Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC)"
+__license__ = "Apache-2.0 License"
+__keywords__ = (
+ "deep-learning, medical-imaging, mri, quantitative-imaging, medical-image-processing, medical-image-analysis"
+)
diff --git a/atommic/utils/__init__.py b/atommic/utils/__init__.py
new file mode 100644
index 00000000..68800731
--- /dev/null
+++ b/atommic/utils/__init__.py
@@ -0,0 +1,12 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.utils.atommic_logging import Logger as _Logger
+
+logging = _Logger()
+try:
+ from atommic.utils.lightning_logger_patch import add_memory_handlers_to_pl_logger
+
+ add_memory_handlers_to_pl_logger()
+except ModuleNotFoundError:
+ pass
diff --git a/atommic/utils/app_state.py b/atommic/utils/app_state.py
new file mode 100644
index 00000000..2f5cc517
--- /dev/null
+++ b/atommic/utils/app_state.py
@@ -0,0 +1,415 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/tree/main/nemo/utils
+
+from dataclasses import dataclass
+from threading import Lock
+from typing import Dict, Optional
+
+from atommic.utils.metaclasses import Singleton
+
+
+@dataclass()
+class ModelMetadataRegistry:
+ """A registry for model metadata."""
+
+ guid: str
+ gidx: int
+ restoration_path: Optional[str] = None
+
+
+class AppState(metaclass=Singleton):
+ """A singleton class that holds the state of the application."""
+
+ def __init__(self):
+ """Inits :class:`AppState`."""
+ # method call lock
+ self.model_parallel_rank = None
+ self.__lock = Lock()
+
+ self._app_cfg = None
+
+ # World info
+ self._device_id = None
+ self._local_rank = None
+ self._global_rank = None
+ self._model_parallel_rank = None
+ self._tensor_model_parallel_rank = None
+ self._pipeline_model_parallel_rank = None
+ self._data_parallel_rank = None
+
+ self._world_size = None
+ self._model_parallel_size = None
+ self._tensor_model_parallel_size = None
+ self._tensor_model_parallel_group = None
+ self._pipeline_model_parallel_size = None
+ self._virtual_pipeline_model_parallel_size = None
+ self._pipeline_model_parallel_group = None
+ self._pipeline_model_parallel_split_rank = None
+ self._model_parallel_group = None
+ self._data_parallel_size = None
+ self._data_parallel_group = None
+ self._use_fp8 = False
+ self._init_mpi_proc_group = False
+
+ self._random_seed = None
+
+ # Logging info
+ self._log_dir = None
+ self._exp_dir = None
+ self._name = None
+ self._checkpoint_name = None
+ self._version = None
+ self._create_checkpoint_callback = None
+ self._checkpoint_callback_params = None
+
+ # Save and Restore (.atommic)
+ self._tmpdir_name = None
+ self._is_model_being_restored = False
+ self._atommic_file_folder = None
+ self._model_restore_path = None
+ self._all_model_restore_paths = []
+ self._model_guid_map = {} # type: Dict[str, ModelMetadataRegistry]
+
+ @property
+ def device_id(self):
+ """Property returns the device_id."""
+ return self._device_id
+
+ @device_id.setter
+ def device_id(self, id: int): # pylint: disable=redefined-builtin
+ """Property sets the device_id."""
+ self._device_id = id
+
+ @property
+ def world_size(self):
+ """Property returns the total number of GPUs."""
+ return self._world_size
+
+ @world_size.setter
+ def world_size(self, size: int):
+ """Property sets the total number of GPUs."""
+ self._world_size = size
+
+ @property
+ def model_parallel_size(self):
+ """Property returns the number of GPUs in each model parallel group."""
+ return self._model_parallel_size
+
+ @model_parallel_size.setter
+ def model_parallel_size(self, size: int):
+ """Property sets the number of GPUs in each model parallel group."""
+ self._model_parallel_size = size
+
+ @property
+ def tensor_model_parallel_size(self):
+ """Property returns the number of GPUs in each model parallel group."""
+ return self._tensor_model_parallel_size
+
+ @tensor_model_parallel_size.setter
+ def tensor_model_parallel_size(self, size):
+ """Property sets the number of GPUs in each model parallel group."""
+ self._tensor_model_parallel_size = size
+
+ @property
+ def pipeline_model_parallel_size(self):
+ """Property returns the number of GPUs in each model parallel group."""
+ return self._pipeline_model_parallel_size
+
+ @pipeline_model_parallel_size.setter
+ def pipeline_model_parallel_size(self, size):
+ """Property sets the number of GPUs in each model parallel group."""
+ self._pipeline_model_parallel_size = size
+
+ @property
+ def virtual_pipeline_model_parallel_size(self):
+ """Property returns the number of GPUs in each model parallel group."""
+ return self._virtual_pipeline_model_parallel_size
+
+ @virtual_pipeline_model_parallel_size.setter
+ def virtual_pipeline_model_parallel_size(self, size):
+ """Property sets the size of the virtual pipeline parallel model."""
+ self._virtual_pipeline_model_parallel_size = size
+
+ @property
+ def data_parallel_size(self):
+ """Property returns the number of GPUs in each data parallel group."""
+ return self._data_parallel_size
+
+ @data_parallel_size.setter
+ def data_parallel_size(self, size: int):
+ """Property sets the number of GPUs in each data parallel group."""
+ self._data_parallel_size = size
+
+ @property
+ def local_rank(self):
+ """Property returns the local rank."""
+ return self._local_rank
+
+ @local_rank.setter
+ def local_rank(self, rank: int):
+ """Property sets the local rank."""
+ self._local_rank = rank
+
+ @property
+ def global_rank(self):
+ """Property returns the global rank."""
+ return self._global_rank
+
+ @global_rank.setter
+ def global_rank(self, rank: int):
+ """Property sets the global rank."""
+ self._global_rank = rank
+
+ @property
+ def tensor_model_parallel_rank(self):
+ """Property returns the model parallel rank."""
+ return self._tensor_model_parallel_rank
+
+ @tensor_model_parallel_rank.setter
+ def tensor_model_parallel_rank(self, rank):
+ """Property sets the model parallel rank."""
+ self._tensor_model_parallel_rank = rank
+
+ @property
+ def tensor_model_parallel_group(self):
+ """Property returns the model parallel group."""
+ return self._tensor_model_parallel_group
+
+ @tensor_model_parallel_group.setter
+ def tensor_model_parallel_group(self, group):
+ """Property sets the model parallel group."""
+ self._tensor_model_parallel_group = group
+
+ @property
+ def pipeline_model_parallel_rank(self):
+ """Property returns the model parallel rank."""
+ return self._pipeline_model_parallel_rank
+
+ @pipeline_model_parallel_rank.setter
+ def pipeline_model_parallel_rank(self, rank):
+ """Property sets the model parallel rank."""
+ self._pipeline_model_parallel_rank = rank
+
+ @property
+ def virtual_pipeline_model_parallel_rank(self):
+ """Property returns the virtual pipeline parallel rank."""
+ return self._virtual_pipeline_model_parallel_rank
+
+ @virtual_pipeline_model_parallel_rank.setter
+ def virtual_pipeline_model_parallel_rank(self, rank):
+ """Property sets the virtual pipeline parallel rank."""
+ self._virtual_pipeline_model_parallel_rank = rank
+
+ @property
+ def pipeline_model_parallel_split_rank(self):
+ """Property returns the model parallel split rank."""
+ return self._pipeline_model_parallel_split_rank
+
+ @pipeline_model_parallel_split_rank.setter
+ def pipeline_model_parallel_split_rank(self, rank):
+ """Property sets the model parallel split rank."""
+ self._pipeline_model_parallel_split_rank = rank
+
+ @property
+ def pipeline_model_parallel_group(self):
+ """Property returns the model parallel group."""
+ return self._pipeline_model_parallel_group
+
+ @pipeline_model_parallel_group.setter
+ def pipeline_model_parallel_group(self, group):
+ """Property sets the model parallel group."""
+ self._pipeline_model_parallel_group = group
+
+ @property
+ def data_parallel_rank(self):
+ """Property returns the data parallel rank."""
+ return self._data_parallel_rank
+
+ @data_parallel_rank.setter
+ def data_parallel_rank(self, rank: int):
+ """Property sets the data parallel rank."""
+ self._data_parallel_rank = rank
+
+ @property
+ def data_parallel_group(self):
+ """Property returns the data parallel group."""
+ return self._data_parallel_group
+
+ @data_parallel_group.setter
+ def data_parallel_group(self, group):
+ """Property sets the data parallel group."""
+ self._data_parallel_group = group
+
+ @property
+ def use_fp8(self):
+ """Property returns the use of fp8 precision.
+
+ Returns
+ -------
+ Use of FP8.
+ """
+ return self._use_fp8
+
+ @use_fp8.setter
+ def use_fp8(self, use_fp8):
+ """Property sets the use of fp8 precision.
+
+ Parameters
+ ----------
+ use_fp8 : bool
+ Use of FP8.
+ """
+ self._use_fp8 = use_fp8
+
+ @property
+ def init_mpi_proc_group(self):
+ """Property sets the initialization of mpi process group.
+
+ Returns
+ -------
+ Initialize mpi process group.
+ """
+ return self._init_mpi_proc_group
+
+ @init_mpi_proc_group.setter
+ def init_mpi_proc_group(self, init_mpi_proc_group):
+ """Property sets the initialization of mpi process group.
+
+ Parameters
+ ----------
+ init_mpi_proc_group : bool
+ Initialize mpi process group.
+ """
+ self._init_mpi_proc_group = init_mpi_proc_group
+
+ @property
+ def random_seed(self):
+ """Property returns the random seed."""
+ return self._random_seed
+
+ @random_seed.setter
+ def random_seed(self, seed: int):
+ """Property sets the random seed."""
+ self._random_seed = seed
+
+ @property
+ def log_dir(self):
+ """Returns the log_dir set by exp_manager."""
+ return self._log_dir
+
+ @log_dir.setter
+ def log_dir(self, dir): # pylint: disable=redefined-builtin
+ """Sets the log_dir property."""
+ self._log_dir = dir
+
+ @property
+ def exp_dir(self):
+ """Returns the exp_dir set by exp_manager."""
+ return self._exp_dir
+
+ @exp_dir.setter
+ def exp_dir(self, dir): # pylint: disable=redefined-builtin
+ """Sets the log_dir property."""
+ self._exp_dir = dir
+
+ @property
+ def name(self):
+ """Returns the name set by exp_manager."""
+ return self._name
+
+ @name.setter
+ def name(self, name: str):
+ """Sets the name property."""
+ self._name = name
+
+ @property
+ def checkpoint_name(self):
+ """Returns the name set by exp_manager."""
+ return self._checkpoint_name
+
+ @checkpoint_name.setter
+ def checkpoint_name(self, name: str):
+ """Sets the name property."""
+ self._checkpoint_name = name
+
+ @property
+ def version(self):
+ """Returns the version set by exp_manager."""
+ return self._version
+
+ @version.setter
+ def version(self, version: str):
+ """Sets the version property."""
+ self._version = version
+
+ @property
+ def create_checkpoint_callback(self):
+ """Returns the create_checkpoint_callback set by exp_manager."""
+ return self._create_checkpoint_callback
+
+ @create_checkpoint_callback.setter
+ def create_checkpoint_callback(self, create_checkpoint_callback: bool):
+ """Sets the create_checkpoint_callback property."""
+ self._create_checkpoint_callback = create_checkpoint_callback
+
+ @property
+ def checkpoint_callback_params(self):
+ """Returns the version set by exp_manager."""
+ return self._checkpoint_callback_params
+
+ @checkpoint_callback_params.setter
+ def checkpoint_callback_params(self, params: dict):
+ """Sets the name property."""
+ self._checkpoint_callback_params = params
+
+ @property
+ def model_restore_path(self):
+ """Returns the model_restore_path set by exp_manager."""
+ return self._all_model_restore_paths[-1] if len(self._all_model_restore_paths) > 0 else None
+
+ @model_restore_path.setter
+ def model_restore_path(self, path):
+ """Sets the model_restore_path property."""
+ with self.__lock:
+ self._model_restore_path = path
+ self._all_model_restore_paths.append(path)
+
+ def register_model_guid(self, guid: str, restoration_path: Optional[str] = None):
+ """Maps a guid to its restore path (None or last absolute path)."""
+ with self.__lock:
+ if guid in self._model_guid_map:
+ idx = self._model_guid_map[guid].gidx
+ else:
+ idx = len(self._model_guid_map)
+ self._model_guid_map[guid] = ModelMetadataRegistry(guid, idx, restoration_path=restoration_path)
+
+ def reset_model_guid_registry(self):
+ """Resets the model guid registry."""
+ with self.__lock:
+ self._model_guid_map.clear()
+
+ def get_model_metadata_from_guid(self, guid) -> ModelMetadataRegistry:
+ """Returns the global model idx and restoration path."""
+ return self._model_guid_map[guid]
+
+ @property
+ def is_model_being_restored(self) -> bool:
+ """Returns whether a model is being restored."""
+ return self._is_model_being_restored
+
+ @is_model_being_restored.setter
+ def is_model_being_restored(self, is_restored: bool):
+ """Sets whether a model is being restored."""
+ self._is_model_being_restored = is_restored
+
+ @property
+ def atommic_file_folder(self) -> str:
+ """Returns the atommic_file_folder set by exp_manager."""
+ return self._atommic_file_folder
+
+ @atommic_file_folder.setter
+ def atommic_file_folder(self, path: str):
+ """Sets the atommic_file_folder property."""
+ self._atommic_file_folder = path
diff --git a/atommic/utils/atommic_logging.py b/atommic/utils/atommic_logging.py
new file mode 100644
index 00000000..f10ebc92
--- /dev/null
+++ b/atommic/utils/atommic_logging.py
@@ -0,0 +1,413 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/nemo_logging.py
+
+import enum
+import logging as _logging
+import sys
+import threading
+import warnings
+from contextlib import contextmanager
+from logging.handlers import MemoryHandler
+
+from atommic.constants import ATOMMIC_ENV_VARNAME_REDIRECT_LOGS_TO_STDERR, ATOMMIC_ENV_VARNAME_TESTING
+from atommic.utils.env_var_parsing import get_envbool
+from atommic.utils.formaters.base import BaseATOMMICFormatter, DebugATOMMICFormatter
+from atommic.utils.get_rank import is_global_rank_zero
+from atommic.utils.metaclasses import Singleton
+
+__all__ = ["Logger", "LogMode"]
+
+
+class LogMode(enum.IntEnum):
+ """Enum for the different logging modes."""
+
+ EACH = 0 # Log the message each time
+ # Log the message only once. The same message will not be logged again.
+ ONCE = 1
+
+
+class Logger(metaclass=Singleton):
+ """Singleton class for logging."""
+
+ # Level 0
+ NOTSET = _logging.NOTSET
+
+ # Level 10
+ DEBUG = _logging.DEBUG
+
+ # Level 20
+ INFO = _logging.INFO
+
+ # Level 30
+ WARNING = _logging.WARNING
+
+ # Level 40
+ ERROR = _logging.ERROR
+
+ # Level 50
+ CRITICAL = _logging.CRITICAL
+
+ _level_names = {0: "NOTSET", 10: "DEBUG", 20: "INFO", 30: "WARNING", 40: "ERROR", 50: "CRITICAL"}
+
+ def __init__(self, capture_warnings=True):
+ """Inits :class:`Logger`.
+
+ Parameters
+ ----------
+ capture_warnings : bool
+ If True, redirect all warnings to the logging package. If False, ensure that warnings are not redirected to
+ logging but to their original destinations. Default is ``True``.
+ """
+ self._logger = None
+ # Multi-GPU runs in separate processes, thread locks shouldn't be needed
+ self._logger_lock = threading.Lock()
+ self._handlers = {}
+ self.old_warnings_showwarning = None
+ self._define_logger(capture_warnings)
+ self.once_logged = set()
+ self.rank = 0 if is_global_rank_zero() else "UNK"
+
+ # pylint: disable=inconsistent-return-statements
+ def _define_logger(self, capture_warnings=True):
+ """Creates the logger if not already created. Called in init."""
+ # Use double-checked locking to avoid taking lock unnecessarily.
+ if self._logger is not None:
+ return self._logger
+
+ with self._logger_lock:
+ try:
+ self._logger = _logging.getLogger("atommic_logger")
+ # By default, silence all loggers except the logger for rank 0
+ self.remove_stream_handlers()
+ # If atommic_TESTING is set, add a streamhandler to all ranks
+ if get_envbool(ATOMMIC_ENV_VARNAME_TESTING, False):
+ old_factory = _logging.getLogRecordFactory()
+
+ def record_factory(*args, **kwargs):
+ """Add rank to log records."""
+ record = old_factory(*args, **kwargs)
+ record.rank = self.rank
+ return record
+
+ _logging.setLogRecordFactory(record_factory)
+ self.add_stream_handlers(formatter=DebugATOMMICFormatter)
+ elif is_global_rank_zero():
+ self.add_stream_handlers()
+
+ # Add memoryhandlers, essentially buffers. They are used to save messages that we will flush to file
+ # once the appropriate file handlers are added.
+ if is_global_rank_zero():
+ # Add a memory handler for error messages. Only logged on rank 0
+ self._handlers["memory_err"] = MemoryHandler(-1)
+ self._handlers["memory_err"].addFilter(lambda record: record.levelno > _logging.INFO)
+ formatter = BaseATOMMICFormatter
+ self._handlers["memory_err"].setFormatter(formatter())
+ self._logger.addHandler(self._handlers["memory_err"])
+ # Add a memory handler for all messages on all ranks
+ self._handlers["memory_all"] = MemoryHandler(-1)
+ formatter = BaseATOMMICFormatter
+ self._handlers["memory_all"].setFormatter(formatter())
+ self._logger.addHandler(self._handlers["memory_all"])
+
+ finally:
+ level = Logger.INFO
+ if get_envbool(ATOMMIC_ENV_VARNAME_TESTING, False):
+ level = Logger.DEBUG
+ self.set_verbosity(verbosity_level=level)
+ self.captureWarnings(capture_warnings)
+
+ self._logger.propagate = False
+
+ def remove_stream_handlers(self):
+ """Removes StreamHandler that log to stdout and stderr from the logger."""
+ if self._logger is None:
+ raise RuntimeError("Impossible to set handlers if the Logger is not predefined")
+
+ # ======== Remove Handler if already existing ========
+ try:
+ self._logger.removeHandler(self._handlers["stream_stdout"])
+ del self._handlers["stream_stdout"]
+ except KeyError:
+ pass
+
+ try:
+ self._logger.removeHandler(self._handlers["stream_stderr"])
+ del self._handlers["stream_stderr"]
+ except KeyError:
+ pass
+
+ def add_stream_handlers(self, formatter=BaseATOMMICFormatter):
+ """Add StreamHandler that log to stdout and stderr to the logger. INFO and lower logs are streamed to stdout
+ while WARNING and higher are streamed to stderr. If the ATOMMIC_ENV_VARNAME_REDIRECT_LOGS_TO_STDERR environment
+ variable is set, all logs are sent to stderr instead.
+ """
+ if self._logger is None:
+ raise RuntimeError("Impossible to set handlers if the Logger is not predefined")
+
+ # Add the output handler.
+ if get_envbool(ATOMMIC_ENV_VARNAME_REDIRECT_LOGS_TO_STDERR, False):
+ self._handlers["stream_stdout"] = _logging.StreamHandler(sys.stderr)
+
+ else:
+ self._handlers["stream_stdout"] = _logging.StreamHandler(sys.stdout)
+ self._handlers["stream_stdout"].addFilter(lambda record: record.levelno <= _logging.INFO)
+
+ self._handlers["stream_stderr"] = _logging.StreamHandler(sys.stderr)
+ self._handlers["stream_stderr"].addFilter(lambda record: record.levelno > _logging.INFO)
+
+ self._handlers["stream_stdout"].setFormatter(formatter())
+ self._logger.addHandler(self._handlers["stream_stdout"])
+
+ try:
+ self._handlers["stream_stderr"].setFormatter(formatter())
+ self._logger.addHandler(self._handlers["stream_stderr"])
+ except KeyError:
+ pass
+
+ def reset_stream_handler(self, formatter=BaseATOMMICFormatter):
+ """Removes then adds stream handlers."""
+ self.remove_stream_handlers()
+ self.add_stream_handlers(formatter=formatter)
+
+ def add_file_handler(self, log_file):
+ """Add a FileHandler to logger that logs all messages to a file. If the logger had a MemoryHandler at
+ self._handlers["memory_all"], those buffered messages are flushed to the new file, and the MemoryHandler is
+ closed.
+ """
+ if self._logger is None:
+ raise RuntimeError("Impossible to set handlers if the Logger is not predefined")
+
+ self._handlers["file"] = _logging.FileHandler(log_file)
+ formatter = BaseATOMMICFormatter
+ self._handlers["file"].setFormatter(formatter())
+ self._logger.addHandler(self._handlers["file"])
+
+ if self._handlers.get("memory_all"):
+ self._handlers["memory_all"].setTarget(self._handlers["file"])
+ self._handlers["memory_all"].close() # flush and remove
+ del self._handlers["memory_all"]
+
+ def add_err_file_handler(self, log_file):
+ """Add a FileHandler to logger that logs all WARNING and higher messages to a file. If the logger had a
+ MemoryHandler at self._handlers["memory_err"], those buffered messages are flushed to the new file, and the
+ MemoryHandler is closed.
+ """
+ if self._logger is None:
+ raise RuntimeError("Impossible to set handlers if the Logger is not predefined")
+
+ self._handlers["file_err"] = _logging.FileHandler(log_file)
+ self._handlers["file_err"].addFilter(lambda record: record.levelno > _logging.INFO)
+
+ formatter = BaseATOMMICFormatter
+ self._handlers["file_err"].setFormatter(formatter())
+ self._logger.addHandler(self._handlers["file_err"])
+
+ if self._handlers.get("memory_err"):
+ self._handlers["memory_err"].setTarget(self._handlers["file_err"])
+ self._handlers["memory_err"].close() # flush and remove
+ del self._handlers["memory_err"]
+
+ # pylint: disable=inconsistent-return-statements
+ def getEffectiveLevel(self):
+ """Return how much logging output will be produced."""
+ if self._logger is not None:
+ return self._logger.getEffectiveLevel()
+
+ def get_verbosity(self):
+ """See getEffectiveLevel"""
+ return self.getEffectiveLevel()
+
+ def setLevel(self, verbosity_level):
+ """Sets the threshold for what messages will be logged."""
+ if self._logger is not None:
+ self._logger.setLevel(verbosity_level)
+
+ for handler in self._logger.handlers:
+ handler.setLevel(verbosity_level)
+
+ def set_verbosity(self, verbosity_level):
+ """See setLevel"""
+ self.setLevel(verbosity_level)
+
+ @contextmanager
+ def patch_stderr_handler(self, stream):
+ """Sends messages that should log to stderr to stream instead. Useful for unittests"""
+ if self._logger is None:
+ raise RuntimeError("Impossible to patch logging handlers if handler does not exist")
+ try:
+ old_stream = self._handlers["stream_stderr"].stream
+ if old_stream is None:
+ raise ValueError
+
+ # Port backwards set_stream() from python 3.7
+ self._handlers["stream_stderr"].acquire()
+ try:
+ self._handlers["stream_stderr"].flush()
+ self._handlers["stream_stderr"].stream = stream
+ finally:
+ self._handlers["stream_stderr"].release()
+
+ yield stream
+ except (KeyError, ValueError) as e:
+ raise RuntimeError("Impossible to patch logging handlers if handler does not exist") from e
+
+ finally:
+ # Port backwards set_stream() from python 3.7
+ self._handlers["stream_stderr"].acquire()
+ try:
+ self._handlers["stream_stderr"].flush()
+ self._handlers["stream_stderr"].stream = old_stream
+ finally:
+ self._handlers["stream_stderr"].release()
+
+ @contextmanager
+ def patch_stdout_handler(self, stream):
+ """Sends messages that should log to stdout to stream instead. Useful for unittests"""
+ if self._logger is None:
+ raise RuntimeError("Impossible to patch logging handlers if handler does not exist")
+ try:
+ old_stream = self._handlers["stream_stdout"].stream
+ if old_stream is None:
+ raise ValueError
+
+ # Port backwards set_stream() from python 3.7
+ self._handlers["stream_stdout"].acquire()
+ try:
+ self._handlers["stream_stdout"].flush()
+ self._handlers["stream_stdout"].stream = stream
+ finally:
+ self._handlers["stream_stdout"].release()
+
+ yield stream
+ except (KeyError, ValueError) as e:
+ raise RuntimeError("Impossible to patch logging handlers if handler does not exist") from e
+
+ finally:
+ # Port backwards set_stream() from python 3.7
+ self._handlers["stream_stdout"].acquire()
+ try:
+ self._handlers["stream_stdout"].flush()
+ self._handlers["stream_stdout"].stream = old_stream
+ finally:
+ self._handlers["stream_stdout"].release()
+
+ @contextmanager
+ def temp_verbosity(self, verbosity_level):
+ """Sets a temporary threshold for what messages will be logged."""
+ if self._logger is not None:
+ old_verbosity = self.get_verbosity()
+
+ try:
+ self.set_verbosity(verbosity_level)
+ yield
+
+ finally:
+ self.set_verbosity(old_verbosity)
+
+ else:
+ try:
+ yield
+
+ finally:
+ pass
+
+ def captureWarnings(self, capture):
+ """If capture is true, redirect all warnings to the logging package. If capture is False, ensure that warnings
+ are not redirected to logging but to their original destinations.
+ """
+ if self._logger is not None:
+ if capture and self.old_warnings_showwarning is None:
+ # Backup Method
+ self.old_warnings_showwarning = warnings.showwarning
+ warnings.showwarning = self._showwarning
+
+ elif not capture and self.old_warnings_showwarning is not None:
+ # Restore Method
+ warnings.showwarning = self.old_warnings_showwarning
+ self.old_warnings_showwarning = None
+
+ def _showwarning(self, message, category, filename, lineno, file, line=None): # pylint: disable=unused-argument
+ """Implementation of show warnings which redirects to logging.
+ It will call warnings.formatwarning and will log the resulting string with level logging.WARNING.
+ """
+ s = warnings.formatwarning(message, category, filename, lineno, line)
+ self.warning("%s", s)
+
+ def _logged_once(self, msg, mode):
+ """Returns True if the given message has been logged at least once in the given mode.
+
+ Parameters
+ ----------
+ msg : str
+ The message to check.
+ mode : LogMode
+ The mode to check.
+
+ Returns
+ -------
+ bool
+ True if the message has been logged at least once in the given mode.
+ """
+ if mode == LogMode.ONCE:
+ PREFIX_LEN = 12
+ if msg[PREFIX_LEN:] in self.once_logged:
+ return True
+ self.once_logged.add(msg[PREFIX_LEN:])
+ return False
+
+ def debug(self, msg, *args, mode=LogMode.EACH, **kwargs):
+ """Log 'msg % args' with severity 'DEBUG'.
+ To pass exception information, use the keyword argument exc_info with a true value, e.g.
+ logger.debug("Houston, we have %s", "thorny problem", exc_info=1)
+ """
+ if self._logger is not None and self._logger.isEnabledFor(Logger.DEBUG) and not self._logged_once(msg, mode):
+ self._logger._log(Logger.DEBUG, msg, args, **kwargs) # pylint: disable=protected-access
+
+ def info(self, msg, *args, mode=LogMode.EACH, **kwargs):
+ """Log 'msg % args' with severity 'INFO'.
+ To pass exception information, use the keyword argument exc_info with a true value, e.g.
+ logger.info("Houston, we have %s", "interesting problem", exc_info=1)
+ """
+ if self._logger is not None and self._logger.isEnabledFor(Logger.INFO) and not self._logged_once(msg, mode):
+ self._logger._log(Logger.INFO, msg, args, **kwargs) # pylint: disable=protected-access
+
+ def warning(self, msg, *args, mode=LogMode.EACH, **kwargs):
+ """Log 'msg % args' with severity 'WARNING'.
+ To pass exception information, use the keyword argument exc_info with a true value, e.g.
+ logger.warning("Houston, we have %s", "bit of a problem", exc_info=1)
+ """
+ if self._logger is not None and self._logger.isEnabledFor(Logger.WARNING) and not self._logged_once(msg, mode):
+ self._logger._log(Logger.WARNING, msg, args, **kwargs) # pylint: disable=protected-access
+
+ def error(self, msg, *args, mode=LogMode.EACH, **kwargs):
+ """Log 'msg % args' with severity 'ERROR'.
+ To pass exception information, use the keyword argument exc_info with a true value, e.g.
+ logger.error("Houston, we have %s", "major problem", exc_info=1)
+ """
+ if self._logger is not None and self._logger.isEnabledFor(Logger.ERROR) and not self._logged_once(msg, mode):
+ self._logger._log(Logger.ERROR, msg, args, **kwargs) # pylint: disable=protected-access
+
+ def critical(self, msg, *args, mode=LogMode.EACH, **kwargs) -> None:
+ """Log 'msg % args' with severity 'CRITICAL'.
+ To pass exception information, use the keyword argument exc_info with a true value, e.g.
+ logger.critical("Houston, we have %s", "major disaster", exc_info=1)
+
+ Parameters
+ ----------
+ msg : str
+ The message to log.
+ *args : tuple
+ The arguments to the message.
+ mode : LogMode
+ The mode to log the message in.
+ **kwargs : dict
+ The keyword arguments to the message.
+ """
+ if (
+ self._logger is not None
+ and self._logger.isEnabledFor(Logger.CRITICAL)
+ and not self._logged_once(msg, mode)
+ ):
+ self._logger._log(Logger.CRITICAL, msg, args, **kwargs) # pylint: disable=protected-access
diff --git a/atommic/utils/callbacks/__init__.py b/atommic/utils/callbacks/__init__.py
new file mode 100644
index 00000000..f6ce790e
--- /dev/null
+++ b/atommic/utils/callbacks/__init__.py
@@ -0,0 +1,5 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+from atommic.utils.callbacks.atommic_model_checkpoint import ATOMMICModelCheckpoint # noqa: F401
+from atommic.utils.callbacks.preemption import PreemptionCallback # noqa: F401
diff --git a/atommic/utils/callbacks/atommic_model_checkpoint.py b/atommic/utils/callbacks/atommic_model_checkpoint.py
new file mode 100644
index 00000000..efad4790
--- /dev/null
+++ b/atommic/utils/callbacks/atommic_model_checkpoint.py
@@ -0,0 +1,316 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/callbacks/nemo_model_checkpoint.py
+
+import os
+import re
+import shutil
+from copy import deepcopy
+from pathlib import Path
+from typing import Iterable, Optional, Union
+
+import pytorch_lightning
+import torch
+from pytorch_lightning.callbacks import ModelCheckpoint
+from pytorch_lightning.utilities import rank_zero_info
+
+from atommic.collections.common.callbacks import EMA
+from atommic.utils import logging, model_utils
+from atommic.utils.app_state import AppState
+from atommic.utils.get_rank import is_global_rank_zero
+
+
+class ATOMMICModelCheckpoint(ModelCheckpoint):
+ """Light wrapper around Lightning's ModelCheckpoint to force a saved checkpoint on train_end.
+ Extends Lightning's on_save_checkpoint func to save the .atommic file. Saves the .atommic file based
+ on the best checkpoint saved (according to the monitor value).
+ Also contains func to save the EMA copy of the model.
+ """
+
+ def __init__(
+ self,
+ always_save_atommic: bool = False,
+ save_atommic_on_train_end: bool = True,
+ save_best_model: bool = False,
+ postfix: str = ".atommic",
+ n_resume: bool = False,
+ model_parallel_size: int = None,
+ **kwargs,
+ ):
+ """Inits :class:`ATOMMICModelCheckpoint`
+
+ Parameters
+ ----------
+ always_save_atommic : bool, optional
+ Whether to always save the .atommic file. Default is ``False``.
+ save_atommic_on_train_end : bool, optional
+ Whether to save the .atommic file on train end. Default is ``True``.
+ save_best_model : bool, optional
+ Whether to save the best model. Default is ``False``.
+ postfix : str, optional
+ The postfix of the .atommic file. Default is ``.atommic``.
+ n_resume : bool, optional
+ Whether to resume from a previous run. Default is ``False``.
+ model_parallel_size : int, optional
+ The model parallel size. Default is ``None``.
+ """
+ # Parse and store "extended" parameters: save_best model and postfix.
+ self.always_save_atommic = always_save_atommic
+ self.save_atommic_on_train_end = save_atommic_on_train_end
+ self.save_best_model = save_best_model
+ if self.save_best_model and not self.save_atommic_on_train_end:
+ logging.warning(
+ (
+ "Found save_best_model is True and save_atommic_on_train_end is False. "
+ "Set save_atommic_on_train_end to True to automatically save the best model."
+ )
+ )
+ self.postfix = postfix
+ self.previous_best_path = ""
+ self.model_parallel_size = model_parallel_size
+
+ # `prefix` is deprecated
+ if "prefix" in kwargs:
+ self.prefix = kwargs.pop("prefix")
+ else:
+ self.prefix = ""
+
+ # Call the parent class constructor with the remaining kwargs.
+ super().__init__(**kwargs)
+
+ if self.save_top_k != -1 and n_resume:
+ logging.debug("Checking previous runs")
+ self.atommic_topk_check_previous_run()
+
+ def atommic_topk_check_previous_run(self):
+ """Checks if there are any previous runs and if so, loads the best model from the previous run."""
+ try:
+ self.best_k_models # pylint: disable=pointless-statement
+ self.kth_best_model_path # pylint: disable=pointless-statement
+ self.best_model_score # pylint: disable=pointless-statement
+ self.best_model_path # pylint: disable=pointless-statement
+ except AttributeError as e:
+ raise AttributeError(
+ "Lightning's ModelCheckpoint was updated. ATOMMICModelCheckpoint needs to update."
+ ) from e
+ self.best_k_models = {}
+ self.kth_best_model_path = ""
+ self.best_model_score = None
+ self.best_model_path = ""
+
+ checkpoints = list(path for path in self._saved_checkpoint_paths if not self._is_ema_filepath(path))
+ for checkpoint in checkpoints:
+ if "mp_rank" in str(checkpoint) or "tp_rank" in str(checkpoint):
+ checkpoint = model_utils.uninject_model_parallel_rank(checkpoint)
+ checkpoint = str(checkpoint)
+ # second case is for distributed checkpoints, since they are a directory there's no extension
+ if checkpoint[-10:] == '-last.ckpt' or checkpoint[-5:] == '-last':
+ continue
+ index = checkpoint.find(self.monitor) + len(self.monitor) + 1 # Find monitor in str + 1 for '='
+ if index != len(self.monitor):
+ match = re.search("[A-z]", checkpoint[index:])
+ if match:
+ value = checkpoint[index : index + match.start() - 1] # -1 due to separator hypen
+ self.best_k_models[checkpoint] = float(value)
+ if len(self.best_k_models) < 1:
+ return # No saved checkpoints yet
+
+ _reverse = bool(self.mode == "min")
+
+ best_k_models = sorted(self.best_k_models, key=self.best_k_models.get, reverse=_reverse)
+
+ # This section should be ok as rank zero will delete all excess checkpoints, since all other ranks are
+ # instantiated after rank zero. models_to_delete should be 0 for all other ranks.
+ if self.model_parallel_size is not None:
+ # check for distributed checkpoint
+ if checkpoints[0].is_dir():
+ models_to_delete = len(best_k_models) - self.save_top_k
+ else:
+ models_to_delete = len(best_k_models) - self.model_parallel_size * self.save_top_k
+ else:
+ models_to_delete = len(best_k_models) - self.save_top_k
+
+ models_to_delete = max(0, models_to_delete)
+ logging.debug(f'Number of models to delete: {models_to_delete}')
+
+ # If EMA enabled, delete the additional EMA weights
+ ema_enabled = self._has_ema_ckpts(self._saved_checkpoint_paths)
+
+ for _ in range(models_to_delete):
+ model = best_k_models.pop(-1)
+ self.best_k_models.pop(model)
+ self._del_model_without_trainer(model)
+ if ema_enabled and self._fs.exists(self._ema_format_filepath(model)):
+ self._del_model_without_trainer(self._ema_format_filepath(model))
+ logging.debug(f"Removed checkpoint: {model}")
+
+ self.kth_best_model_path = best_k_models[-1]
+ self.best_model_path = best_k_models[0]
+ self.best_model_score = self.best_k_models[self.best_model_path]
+
+ # pylint: disable=inconsistent-return-statements
+ def on_save_checkpoint(self, trainer, pl_module, checkpoint):
+ """Saves the .atommic file based on the best checkpoint saved (according to the monitor value)."""
+ output = super().on_save_checkpoint( # pylint: disable=assignment-from-no-return
+ trainer, pl_module, checkpoint
+ )
+ if not self.always_save_atommic:
+ return output
+ # Load the best model and then re-save it
+ app_state = AppState()
+ if app_state.model_parallel_size is not None and app_state.model_parallel_size > 1:
+ logging.warning("always_save_atommic will slow down training for model_parallel > 1.")
+ # since we are creating tarfile artifacts we need to update .atommic path
+ app_state.model_restore_path = os.path.abspath(
+ os.path.expanduser(os.path.join(self.dirpath, self.prefix + self.postfix))
+ )
+ if app_state.model_parallel_size is not None and app_state.model_parallel_size > 1:
+ maybe_injected_best_model_path = model_utils.inject_model_parallel_rank(self.best_model_path)
+ else:
+ maybe_injected_best_model_path = self.best_model_path
+
+ if self.save_best_model:
+ if not os.path.exists(maybe_injected_best_model_path):
+ return
+
+ if self.best_model_path == self.previous_best_path:
+ return output
+
+ old_state_dict = deepcopy(pl_module.state_dict())
+ checkpoint = torch.load(maybe_injected_best_model_path, map_location="cpu")
+ if "state_dict" in checkpoint:
+ checkpoint = checkpoint["state_dict"]
+ # get a new instanace of the model
+ pl_module.load_state_dict(checkpoint, strict=True)
+ if torch.distributed.is_initialized():
+ torch.distributed.barrier()
+ pl_module.save_to(save_path=app_state.model_restore_path)
+ logging.info(f"New best .atommic model saved to: {app_state.model_restore_path}")
+ pl_module.load_state_dict(old_state_dict, strict=True)
+ else:
+ if torch.distributed.is_initialized():
+ torch.distributed.barrier()
+ pl_module.save_to(save_path=app_state.model_restore_path)
+ logging.info(f"New .atommic model saved to: {app_state.model_restore_path}")
+ return output
+
+ def on_train_end(self, trainer, pl_module):
+ """Saves the .atommic file based on the best checkpoint saved (according to the monitor value)."""
+ if trainer.fast_dev_run:
+ return None
+
+ # check if we need to save a last checkpoint manually as validation isn't always run based on the interval
+ if self.save_last and trainer.val_check_interval != 0:
+ should_save_last_checkpoint = False
+ if isinstance(trainer.val_check_interval, float) and trainer.val_check_interval % trainer.global_step != 0:
+ should_save_last_checkpoint = True
+ if isinstance(trainer.val_check_interval, int) and trainer.global_step % trainer.val_check_interval != 0:
+ should_save_last_checkpoint = True
+ if should_save_last_checkpoint:
+ monitor_candidates = self._monitor_candidates(trainer)
+ super()._save_last_checkpoint(trainer, monitor_candidates)
+ # Call parent on_train_end() to save the -last checkpoint
+ super().on_train_end(trainer, pl_module)
+
+ # Load the best model and then re-save it
+ if self.save_best_model:
+ # wait for all processes
+ trainer.strategy.barrier("SaveBestCheckpointConnector.resume_end")
+ if self.best_model_path == "":
+ logging.warning(
+ f"{self} was told to save the best checkpoint at the end of training, but no saved checkpoints "
+ "were found. Saving latest model instead."
+ )
+ else:
+ self.best_model_path = trainer.strategy.broadcast(self.best_model_path)
+ trainer._checkpoint_connector.restore(self.best_model_path) # pylint: disable=protected-access
+
+ if self.save_atommic_on_train_end:
+ pl_module.save_to(save_path=os.path.join(self.dirpath, self.prefix + self.postfix))
+
+ def _del_model_without_trainer(self, filepath: str) -> None:
+ """Deletes the checkpoint file without instantiating the model."""
+ filepath = Path(filepath) # type: ignore
+
+ # check if filepath is a distributed a checkpoint
+ if model_utils.ckpt_to_dir(filepath).is_dir():
+ if is_global_rank_zero():
+ try:
+ dist_ckpt = model_utils.ckpt_to_dir(filepath)
+ shutil.rmtree(dist_ckpt)
+ logging.info(f"Removed distributed checkpoint: {dist_ckpt}")
+ except Exception:
+ logging.info(f"Tried to remove distributed checkpoint: {dist_ckpt} but failed.")
+
+ else:
+ app_state = AppState()
+
+ # legacy model parallel checkpoint
+ if app_state.model_parallel_size is not None and app_state.model_parallel_size > 1:
+ # filepath needs to be updated to include mp_rank
+ filepath = model_utils.inject_model_parallel_rank(filepath)
+
+ # each model parallel rank needs to remove its model
+ if is_global_rank_zero() or (
+ app_state.model_parallel_size is not None and app_state.data_parallel_rank == 0
+ ):
+ try:
+ self._fs.rm(filepath)
+ logging.info(f"Removed checkpoint: {filepath}")
+ except Exception:
+ logging.info(f"Tried to remove checkpoint: {filepath} but failed.")
+
+ def _ema_callback(self, trainer: "pytorch_lightning.Trainer") -> Optional[EMA]:
+ """Returns the EMA callback if it exists."""
+ ema_callback = None
+ for callback in trainer.callbacks:
+ if isinstance(callback, EMA):
+ ema_callback = callback
+ return ema_callback
+
+ def _save_checkpoint(self, trainer: "pytorch_lightning.Trainer", filepath: str) -> None:
+ """Saves the checkpoint and the EMA copy of the model if EMA is enabled."""
+ ema_callback = self._ema_callback(trainer)
+ if ema_callback is not None:
+ with ema_callback.save_original_optimizer_state(trainer):
+ super()._save_checkpoint(trainer, filepath)
+
+ # save EMA copy of the model as well.
+ with ema_callback.save_ema_model(trainer):
+ filepath = self._ema_format_filepath(filepath)
+ if self.verbose:
+ rank_zero_info(f"Saving EMA weights to separate checkpoint {filepath}")
+ super()._save_checkpoint(trainer, filepath)
+ else:
+ super()._save_checkpoint(trainer, filepath)
+
+ def _remove_checkpoint(self, trainer: "pytorch_lightning.Trainer", filepath: str) -> None:
+ """Removes the checkpoint and the EMA copy of the model if EMA is enabled."""
+ super()._remove_checkpoint(trainer, filepath)
+ ema_callback = self._ema_callback(trainer)
+ if ema_callback is not None:
+ # remove EMA copy of the state dict as well.
+ filepath = self._ema_format_filepath(filepath)
+ super()._remove_checkpoint(trainer, filepath)
+
+ def _ema_format_filepath(self, filepath: str) -> str:
+ """Returns the filepath of the EMA copy of the model."""
+ return filepath.replace(self.FILE_EXTENSION, f"-EMA{self.FILE_EXTENSION}")
+
+ def _has_ema_ckpts(self, checkpoints: Iterable[Path]) -> bool:
+ """Returns True if any of the checkpoints are EMA checkpoints."""
+ return any(self._is_ema_filepath(checkpoint_path) for checkpoint_path in checkpoints)
+
+ def _is_ema_filepath(self, filepath: Union[Path, str]) -> bool:
+ """Returns True if the filepath is an EMA checkpoint."""
+ return str(filepath).endswith(f"-EMA{self.FILE_EXTENSION}")
+
+ @property
+ def _saved_checkpoint_paths(self) -> Iterable[Path]:
+ """Returns the saved checkpoint paths."""
+ # distributed checkpoints are directories so we check for them here
+ dist_checkpoints = [d for d in list(Path(self.dirpath).glob("*")) if d.is_dir()]
+ if dist_checkpoints:
+ return dist_checkpoints
+ return Path(self.dirpath).rglob("*.ckpt")
diff --git a/atommic/utils/callbacks/preemption.py b/atommic/utils/callbacks/preemption.py
new file mode 100644
index 00000000..1f1d213b
--- /dev/null
+++ b/atommic/utils/callbacks/preemption.py
@@ -0,0 +1,135 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/callbacks/preemption.py
+
+import signal
+import sys
+import warnings
+
+import torch
+from pytorch_lightning.callbacks import Callback
+
+
+class PreemptionCallback(Callback):
+ """PreemptionCallback class creates a callback that checks for preemption during training at the end of every step.
+ Upon preemption the callback provides a function to gracefully exit the training immediately and also saves the
+ current state in a checkpoint as *last.ckpt. (to be able to start from the same step without wasting any compute
+ while resuming the next time).
+
+ PreemptionCallback is always enabled by default via the arg create_preemption_callback under ExpManagerConfig.
+ To disable please pass create_preemption_callback: False in your config file.
+ """
+
+ def __init__(self, checkpoint_callback, sig=None):
+ """Inits :class:`PreemptionCallback`.
+
+ Parameters
+ ----------
+ checkpoint_callback : pytorch_lightning.callbacks.ModelCheckpoint
+ The checkpoint callback
+ sig : int, optional
+ The signal to be used for preemption, by default None
+ """
+ self.sig = sig
+ if self.sig is None:
+ self.sig = signal.SIGTERM
+ self.checkpoint_callback = checkpoint_callback
+ self.preemption_enabled = False
+
+ @property
+ def interrupted(self):
+ """Checks if the job was preempted by broadcasting the preemption signal to all ranks."""
+ interrupted = torch.tensor(self._interrupted, device=torch.cuda.current_device(), dtype=torch.int32)
+ torch.distributed.broadcast(interrupted, 0)
+ interrupted = bool(interrupted.item())
+ return interrupted
+
+ def on_train_start(self, trainer, pl_module):
+ """
+ Defines custom handlers at the beginning of training to be executed when the preemption signal is received.
+ """
+ # Check if torch distributed is initialised, as It's needed for broadcasting the preemption signal to all ranks
+ if not (torch.distributed.is_available() and torch.distributed.is_initialized()):
+ warnings.warn("Preemption requires torch distributed to be initialized, disabling preemption callback")
+ else:
+ self.preemption_enabled = True
+ # Bool var that's initialized to false and made True upon receving the preemption signal
+ self._interrupted = False
+ self.released = False
+ self.original_handler = signal.getsignal(self.sig)
+
+ # Master handler executed only by rank 0 when the preemption siganal is received,
+ # to avoid deadlock conditions
+ def master_handler(signum, frame): # pylint: disable=unused-argument
+ """Handler executed by rank 0 when the preemption signal is received."""
+ self.release()
+ self._interrupted = True
+
+ # Handler executed by the non zero ranks
+ def ignoring_handler(signum, frame): # pylint: disable=unused-argument
+ """Handler executed by non zero ranks when the preemption signal is received."""
+ self.release()
+
+ self.private_rank = torch.distributed.get_rank()
+ if self.private_rank == 0:
+ signal.signal(self.sig, master_handler)
+ else:
+ signal.signal(self.sig, ignoring_handler)
+
+ return self
+
+ def on_train_end(self, trainer, pl_module):
+ """Defines custom handlers at the end of training to be executed when the preemption signal is received.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The trainer object
+ pl_module : pytorch_lightning.LightningModule
+ The lightning module
+ """
+ if self.preemption_enabled:
+ self.release()
+
+ def on_train_batch_end(self, trainer, pl_module, outputs, batch, batch_idx: int):
+ """Defines custom handlers at the end of every training step to be executed when the preemption signal is
+ received.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The trainer object
+ pl_module : pytorch_lightning.LightningModule
+ The lightning module
+ outputs : list
+ The outputs of the training step
+ batch : list
+ The batch of data
+ batch_idx : int
+ The index of the batch
+ """
+ if self.preemption_enabled:
+ # check if the job was preempted at the end of every training step/iteration
+ # NOTE: "self.interrupted" is a property which triggers a distributed broadcast of "_interrupted" flag from
+ # rank 0 to all other ranks, to avoid performance overheads it's best to store the result in a regular
+ # local variable
+ interrupted = self.interrupted
+ if interrupted:
+ warnings.warn("Received SIGTERM, saving checkpoint and exiting")
+ monitor_candidates = self.checkpoint_callback._monitor_candidates( # pylint: disable=protected-access
+ trainer
+ )
+ self.checkpoint_callback._save_last_checkpoint( # pylint: disable=protected-access
+ trainer, monitor_candidates
+ )
+ sys.exit(0)
+
+ def release(self):
+ """Releases the preemption callback."""
+ if self.released:
+ return False
+
+ signal.signal(self.sig, self.original_handler)
+ self.released = True
+ return True
diff --git a/atommic/utils/cast_utils.py b/atommic/utils/cast_utils.py
new file mode 100644
index 00000000..96497bfb
--- /dev/null
+++ b/atommic/utils/cast_utils.py
@@ -0,0 +1,25 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/tree/main/nemo/utils/cast_utils.py
+
+import torch
+
+
+def cast_tensor(x, from_dtype=torch.float16, to_dtype=torch.float32):
+ """Cast a tensor from one dtype to another if it is of the specified dtype."""
+ return x.to(dtype=to_dtype) if x.dtype == from_dtype else x
+
+
+# pylint: disable=inconsistent-return-statements
+def cast_all(x, from_dtype=torch.float16, to_dtype=torch.float32):
+ """Cast all tensors in a dict or tuple from one dtype to another if they are of the specified dtype."""
+ if isinstance(x, torch.Tensor):
+ return cast_tensor(x, from_dtype=from_dtype, to_dtype=to_dtype)
+ if isinstance(x, dict):
+ new_dict = {}
+ for k in x.keys():
+ new_dict[k] = cast_all(x[k], from_dtype=from_dtype, to_dtype=to_dtype)
+ return new_dict
+ if isinstance(x, tuple):
+ return tuple(cast_all(y, from_dtype=from_dtype, to_dtype=to_dtype) for y in x)
diff --git a/atommic/utils/decorators/__init__.py b/atommic/utils/decorators/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/utils/decorators/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/utils/decorators/deprecated.py b/atommic/utils/decorators/deprecated.py
new file mode 100644
index 00000000..458c15ca
--- /dev/null
+++ b/atommic/utils/decorators/deprecated.py
@@ -0,0 +1,82 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/decorators/deprecated.py
+
+__all__ = ["deprecated"]
+
+import functools
+import inspect
+from typing import Dict
+
+import wrapt
+
+# Remember which deprecation warnings have been printed already.
+from atommic.utils import logging
+
+_PRINTED_WARNING: Dict = {}
+
+
+def deprecated(wrapped=None, version=None, explanation=None):
+ """This is a decorator which can be used to mark functions as deprecated. It will result in a warning being emitted
+ when the function is used.
+
+ Parameters
+ ----------
+ wrapped : function
+ The function to be decorated.
+ version : str
+ The version of the package where the function was deprecated.
+ explanation : str
+ The explanation of the deprecation.
+
+ Returns
+ -------
+ function
+ The decorated function.
+ """
+ if wrapped is None:
+ return functools.partial(deprecated, version=version, explanation=explanation)
+
+ @wrapt.decorator
+ def wrapper(_wrapped, args, kwargs):
+ """Prints the adequate warning (only once per function) when required and calls the function func, passing the
+ original arguments, i.e. version and explanation.
+
+ Parameters
+ ----------
+ _wrapped : function
+ The function to be decorated.
+ args : tuple
+ The arguments passed to the function to be decorated.
+ kwargs : dict
+ The keyword arguments passed to the function to be decorated.
+
+ Returns
+ -------
+ function
+ The decorated function.
+ """
+ # Check if we already warned about that function.
+ if _wrapped.__name__ not in _PRINTED_WARNING:
+ # Add to list, so we won't print it again.
+ _PRINTED_WARNING[_wrapped.__name__] = True
+
+ # Prepare the warning message.
+ entity_name = "Class" if inspect.isclass(wrapped) else "Function"
+ msg = f"{entity_name} '{_wrapped.__name__}' is deprecated."
+
+ # Optionally, add version and explanation.
+ if version is not None:
+ msg = f"{msg} It is going to be removed in the {version} version."
+
+ if explanation is not None:
+ msg = f"{msg} {explanation}"
+
+ # Display the deprecated warning.
+ logging.warning(msg)
+
+ # Call the function.
+ return _wrapped(*args, **kwargs)
+
+ return wrapper(wrapped) # pylint: disable=no-value-for-parameter
diff --git a/atommic/utils/decorators/experimental.py b/atommic/utils/decorators/experimental.py
new file mode 100644
index 00000000..0bfbfcfc
--- /dev/null
+++ b/atommic/utils/decorators/experimental.py
@@ -0,0 +1,29 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/decorators/experimental.py
+
+import wrapt
+
+from atommic.utils import logging
+
+__all__ = ["experimental"]
+
+
+@wrapt.decorator
+def experimental(wrapped, instance, args, kwargs): # pylint: disable=unused-argument
+ """Decorator to mark a class as experimental.
+
+ Parameters
+ ----------
+ wrapped : function
+ The function to be decorated.
+ instance : object
+ The instance of the class to be decorated.
+ args : tuple
+ The arguments passed to the function to be decorated.
+ kwargs : dict
+ The keyword arguments passed to the function to be decorated.
+ """
+ logging.warning(f"`{wrapped}` is experimental and not ready for production yet. Use at your own risk.")
+ return wrapped(*args, **kwargs)
diff --git a/atommic/utils/decorators/port_docs.py b/atommic/utils/decorators/port_docs.py
new file mode 100644
index 00000000..36b1902f
--- /dev/null
+++ b/atommic/utils/decorators/port_docs.py
@@ -0,0 +1,116 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/decorators/port_docs.py
+
+import functools
+import sys
+
+import wrapt
+
+__all__ = ["add_port_docs"]
+
+
+def _normalize_docstring(docstring):
+ """Normalize docstring indentation. Replace tabs with spaces, removes leading and trailing blanks lines, and
+ removes any indentation.
+
+ Copied from PEP-257: https://www.python.org/dev/peps/pep-0257/#handling-docstring-indentation
+
+ Parameters
+ ----------
+ docstring : str
+ The docstring to normalize.
+
+ Returns
+ -------
+ str
+ The normalized docstring.
+ """
+ if not docstring:
+ return ""
+ # Convert tabs to spaces (following the normal Python rules)
+ # and split into a list of lines:
+ lines = docstring.expandtabs().splitlines()
+ # Determine minimum indentation (first line doesn't count):
+ # (we use sys.maxsize because sys.maxint doesn't exist in Python 3)
+ indent = sys.maxsize
+ for line in lines[1:]:
+ if stripped := line.lstrip():
+ indent = min(indent, len(line) - len(stripped))
+ # Remove indentation (first line is special):
+ trimmed = [lines[0].strip()]
+ if indent < sys.maxsize:
+ trimmed.extend(line[indent:].rstrip() for line in lines[1:])
+ # Strip off trailing and leading blank lines:
+ while trimmed and not trimmed[-1]:
+ trimmed.pop()
+ while trimmed and not trimmed[0]:
+ trimmed.pop(0)
+ # Return a single string:
+ return "\n".join(trimmed)
+
+
+def add_port_docs(wrapped=None, instance=None, value=""):
+ """Adds port documentation to the wrapped function.
+
+ Parameters
+ ----------
+ wrapped : function
+ The function to decorate.
+ instance : object
+ The instance of the function.
+ value : object
+ The value of the port.
+
+ Returns
+ -------
+ function
+ The decorated function.
+ """
+ if wrapped is None:
+ return functools.partial(add_port_docs, value=value)
+
+ @wrapt.decorator
+ def wrapper(wrapped, instance=None, args=None, kwargs=None): # pylint: disable=unused-argument
+ """
+ Adds port documentation to the wrapped function.
+
+ Parameters
+ ----------
+ wrapped : function
+ The function to decorate.
+ instance : object
+ The instance of the function.
+ args : tuple
+ The arguments of the function.
+ kwargs : dict
+ The keyword arguments of the function.
+
+ Returns
+ -------
+ function
+ The decorated function.
+ """
+ return wrapped(*args, **kwargs)
+
+ decorated = wrapper(wrapped)
+ try:
+ port_2_ntype = decorated(instance)
+ except AttributeError:
+ port_2_ntype = None
+
+ port_description = ""
+ if port_2_ntype is not None:
+ for port, ntype in port_2_ntype.items():
+ port_description += "* *" + port + "* : " + str(ntype)
+ port_description += "\n\n"
+
+ __doc__ = ( # pylint: disable=redefined-builtin
+ _normalize_docstring(wrapped.__doc__) + "\n\n" + str(port_description)
+ )
+ __doc__ = _normalize_docstring(__doc__)
+
+ wrapt.FunctionWrapper.__setattr__(decorated, "__doc__", __doc__) # pylint: disable=unnecessary-dunder-call
+
+ return decorated
diff --git a/atommic/utils/env_var_parsing.py b/atommic/utils/env_var_parsing.py
new file mode 100644
index 00000000..8469b007
--- /dev/null
+++ b/atommic/utils/env_var_parsing.py
@@ -0,0 +1,206 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/env_var_parsing.py
+
+import decimal
+import json
+import os
+
+from dateutil import parser # type: ignore
+
+__all__ = [
+ "get_env",
+ "get_envbool",
+ "get_envint",
+ "get_envfloat",
+ "get_envdecimal",
+ "get_envdate",
+ "get_envdatetime",
+ "get_envlist",
+ "get_envdict",
+ "CoercionError",
+ "RequiredSettingMissingError",
+]
+
+
+class CoercionError(Exception):
+ """Custom error raised when a value cannot be coerced."""
+
+ def __init__(self, key, value, func):
+ """Inits :class:`CoercionError`.
+
+ Parameters
+ ----------
+ key : str
+ The name of the env var that is missing.
+ value : str
+ The value of the env var that is missing.
+ func : function
+ The function that was used to try and coerce the value.
+ """
+ msg = f"Unable to coerce '{key}={value}' using {func.__name__}."
+ super().__init__(msg)
+
+
+class RequiredSettingMissingError(Exception):
+ """Custom error raised when a required env var is missing."""
+
+ def __init__(self, key):
+ """Inits :class:`RequiredSettingMissingError`.
+
+ Parameters
+ ----------
+ key : str
+ The name of the env var that is missing.
+ """
+ msg = f"Required env var '{key}' is missing."
+ super().__init__(msg)
+
+
+def _get_env(key, default=None, coerce=lambda x: x, required=False):
+ """Return env var coerced into a type other than string. This function extends the standard os.getenv function to
+ enable the coercion of values into data types other than string (all env vars are strings by default).
+
+ Parameters
+ ----------
+ key : str
+ The name of the env var to retrieve.
+ default : str, (optional)
+ The default value to return if the env var is not set. NB the default value is **not** coerced, and is assumed
+ to be of the correct type.
+ coerce : function, (optional)
+ A function that takes a string and returns a value of the desired type.
+ required : bool, (optional)
+ If True, raises a RequiredSettingMissingError if the env var is not set.
+
+ Returns
+ -------
+ str
+ The value of the env var coerced into the desired type.
+ """
+ try:
+ value = os.environ[key]
+ except KeyError as e:
+ if required is True:
+ raise RequiredSettingMissingError(key) from e
+ return default
+
+ try:
+ return coerce(value)
+ except Exception as exc:
+ raise CoercionError(key, value, coerce) from exc
+
+
+# standard type coercion functions
+def _bool(value):
+ """Return env var cast as boolean."""
+ if isinstance(value, bool):
+ return value
+
+ return value is not None and value.lower() not in (
+ "false",
+ "0",
+ "no",
+ "n",
+ "f",
+ "none",
+ )
+
+
+def _int(value):
+ """Return env var cast as integer."""
+ return int(value)
+
+
+def _float(value):
+ """Return env var cast as float."""
+ return float(value)
+
+
+def _decimal(value):
+ """Return env var cast as Decimal."""
+ return decimal.Decimal(value)
+
+
+def _dict(value):
+ """Return env var as a dict."""
+ return json.loads(value)
+
+
+def _datetime(value):
+ """Return env var as a datetime."""
+ return parser.parse(value)
+
+
+def _date(value):
+ """Return env var as a date."""
+ return parser.parse(value).date()
+
+
+def get_env(key, *default, **kwargs):
+ """Return env var. This is the parent function of all other get_foo functions, and is responsible for unpacking
+ args/kwargs into the values that _get_env expects (it is the root function that actually interacts with environ).
+
+ Parameters
+ ----------
+ key : str
+ The env var name to look up.
+ default : str, (optional)
+ The value to use if the env var does not exist. If this value is not supplied, then the env var is considered
+ to be required, and a RequiredSettingMissingError error will be raised if it does not exist.
+ kwargs : dict, (optional)
+ Additional keyword arguments to pass to _get_env.
+
+ Returns
+ -------
+ str
+ The env var, coerced if required, and a default if supplied.
+ """
+ if len(default) not in (0, 1):
+ raise AssertionError("Too many args supplied.")
+ func = kwargs.get("coerce", lambda x: x)
+ required = len(default) == 0
+ default = None if required else default[0]
+ return _get_env(key, default=default, coerce=func, required=required)
+
+
+def get_envbool(key, *default):
+ """Return env var cast as boolean."""
+ return get_env(key, *default, coerce=_bool)
+
+
+def get_envint(key, *default):
+ """Return env var cast as integer."""
+ return get_env(key, *default, coerce=_int)
+
+
+def get_envfloat(key, *default):
+ """Return env var cast as float."""
+ return get_env(key, *default, coerce=_float)
+
+
+def get_envdecimal(key, *default):
+ """Return env var cast as Decimal."""
+ return get_env(key, *default, coerce=_decimal)
+
+
+def get_envdate(key, *default):
+ """Return env var as a date."""
+ return get_env(key, *default, coerce=_date)
+
+
+def get_envdatetime(key, *default):
+ """Return env var as a datetime."""
+ return get_env(key, *default, coerce=_datetime)
+
+
+def get_envlist(key, *default, **kwargs):
+ """Return env var as a list."""
+ separator = kwargs.get("separator", " ")
+ return get_env(key, *default, coerce=lambda x: x.split(separator))
+
+
+def get_envdict(key, *default):
+ """Return env var as a dict."""
+ return get_env(key, *default, coerce=_dict)
diff --git a/atommic/utils/exceptions.py b/atommic/utils/exceptions.py
new file mode 100644
index 00000000..f26b6cec
--- /dev/null
+++ b/atommic/utils/exceptions.py
@@ -0,0 +1,8 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/exceptions.py
+
+
+class ATOMMICBaseException(Exception):
+ """ATOMMIC Base Exception. All exceptions created in atommic should inherit from this class"""
diff --git a/atommic/utils/exp_manager.py b/atommic/utils/exp_manager.py
new file mode 100644
index 00000000..54d43eed
--- /dev/null
+++ b/atommic/utils/exp_manager.py
@@ -0,0 +1,1080 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/exp_manager.py
+
+import glob
+import os
+import subprocess
+import sys
+import time
+import warnings
+from dataclasses import dataclass
+from pathlib import Path
+from shutil import copy, move
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+import pytorch_lightning
+import torch
+from hydra.core.hydra_config import HydraConfig
+from hydra.utils import get_original_cwd
+from omegaconf import DictConfig, OmegaConf, open_dict
+from pytorch_lightning import Trainer
+from pytorch_lightning.callbacks import Callback, ModelCheckpoint
+from pytorch_lightning.callbacks.early_stopping import EarlyStopping
+from pytorch_lightning.callbacks.timer import Timer
+from pytorch_lightning.loggers import TensorBoardLogger, WandbLogger
+from pytorch_lightning.loops import _TrainingEpochLoop
+from pytorch_lightning.strategies.ddp import DDPStrategy
+
+import atommic.utils
+from atommic.collections.common.callbacks import EMA
+from atommic.constants import ATOMMIC_ENV_VARNAME_TESTING, ATOMMIC_ENV_VARNAME_VERSION
+from atommic.utils import logging, timers
+from atommic.utils.app_state import AppState
+from atommic.utils.callbacks import ATOMMICModelCheckpoint, PreemptionCallback
+from atommic.utils.env_var_parsing import get_envbool
+from atommic.utils.exceptions import ATOMMICBaseException
+from atommic.utils.get_rank import is_global_rank_zero
+from atommic.utils.lightning_logger_patch import add_filehandlers_to_pl_logger
+
+
+class NotFoundError(ATOMMICBaseException):
+ """Raised when a file or folder is not found"""
+
+
+class LoggerMisconfigurationError(ATOMMICBaseException):
+ """Raised when a mismatch between trainer.logger and exp_manager occurs"""
+
+ def __init__(self, message):
+ """Inits :class:`LoggerMisconfigurationError`.
+
+ Parameters
+ ----------
+ message : str
+ The message to display.
+ """
+ message = (
+ message + "You can disable lightning's trainer from creating a logger by passing logger=False to its "
+ "constructor. "
+ )
+ super().__init__(message)
+
+
+class CheckpointMisconfigurationError(ATOMMICBaseException):
+ """Raised when a mismatch between trainer.callbacks and exp_manager occurs"""
+
+
+@dataclass
+class EarlyStoppingParams:
+ """Parameters for the early stopping callback."""
+
+ monitor: str = "val_loss" # The metric that early stopping should consider.
+ mode: str = "min" # inform early stopping whether to look for increase or decrease in monitor.
+ min_delta: float = 0.001 # smallest change to consider as improvement.
+ patience: int = 10 # how many (continuous) validation cycles to wait with no improvement and stopping training.
+ verbose: bool = True
+ strict: bool = True
+ check_finite: bool = True
+ stopping_threshold: Optional[float] = None
+ divergence_threshold: Optional[float] = None
+ check_on_train_epoch_end: Optional[bool] = None
+ log_rank_zero_only: bool = False
+
+
+@dataclass
+class CallbackParams:
+ """Parameters for a callback"""
+
+ filepath: Optional[str] = None # Deprecated
+ # If None, exp_manager will attempt to handle the filepath
+ dirpath: Optional[str] = None
+ # If None, exp_manager will attempt to handle the filepath
+ filename: Optional[str] = None
+ monitor: Optional[str] = "val_loss"
+ verbose: Optional[bool] = True
+ save_last: Optional[bool] = True
+ save_top_k: Optional[int] = 3
+ save_weights_only: Optional[bool] = False
+ mode: Optional[str] = "min"
+ auto_insert_metric_name: bool = True
+ every_n_epochs: Optional[int] = 1
+ every_n_train_steps: Optional[int] = None
+ train_time_interval: Optional[str] = None
+ # If None, exp_manager will attempt to handle the filepath
+ prefix: Optional[str] = None
+ postfix: str = ".atommic"
+ save_best_model: bool = False
+ always_save_atommic: bool = False
+ # Automatically save .atommic file during on_train_end hook
+ save_atommic_on_train_end: Optional[bool] = True
+ # tensor parallel size * pipeline parallel size
+ model_parallel_size: Optional[int] = None
+ save_on_train_epoch_end: Optional[bool] = False # Save after training, not after validation
+
+
+@dataclass
+class StepTimingParams:
+ """Parameters for the step timing callback."""
+
+ reduction: Optional[str] = "mean"
+ # if True torch.cuda.synchronize() is called on start/stop
+ sync_cuda: Optional[bool] = False
+ # if positive, defines the size of a sliding window for computing mean
+ buffer_size: Optional[int] = 1
+
+
+@dataclass
+class EMAParams:
+ """Parameters for the EMA callback."""
+
+ enable: Optional[bool] = False
+ decay: Optional[float] = 0.999
+ cpu_offload: Optional[bool] = False
+ validate_original_weights: Optional[bool] = False
+ every_n_steps: int = 1
+
+
+@dataclass
+class ExpManagerConfig:
+ """Configuration for the experiment manager."""
+
+ # Log dir creation parameters
+ explicit_log_dir: Optional[str] = None
+ exp_dir: Optional[str] = None
+ name: Optional[str] = None
+ version: Optional[str] = None
+ use_datetime_version: Optional[bool] = True
+ resume_if_exists: Optional[bool] = False
+ resume_past_end: Optional[bool] = False
+ resume_ignore_no_checkpoint: Optional[bool] = False
+ resume_from_checkpoint: Optional[str] = None
+ # Logging parameters
+ create_tensorboard_logger: Optional[bool] = True
+ summary_writer_kwargs: Optional[Dict[Any, Any]] = None
+ create_wandb_logger: Optional[bool] = False
+ wandb_logger_kwargs: Optional[Dict[Any, Any]] = None
+ # Checkpointing parameters
+ create_checkpoint_callback: Optional[bool] = True
+ checkpoint_callback_params: Optional[CallbackParams] = CallbackParams()
+ create_early_stopping_callback: Optional[bool] = False
+ early_stopping_callback_params: Optional[EarlyStoppingParams] = EarlyStoppingParams()
+ create_preemption_callback: Optional[bool] = True
+ # Additional exp_manager arguments
+ files_to_copy: Optional[List[str]] = None
+ # logs timing of train/val/test steps
+ log_step_timing: Optional[bool] = True
+ step_timing_kwargs: Optional[StepTimingParams] = StepTimingParams()
+ # Configures creation of log files for different ranks
+ log_local_rank_0_only: Optional[bool] = False
+ log_global_rank_0_only: Optional[bool] = False
+ # disable initial validation when resuming from a checkpoint saved during validation
+ disable_validation_on_resume: Optional[bool] = True
+ ema: Optional[EMAParams] = EMAParams()
+ # Wall clock time limit
+ max_time_per_run: Optional[str] = None
+ # time to sleep non 0 ranks during initialization
+ seconds_to_sleep: float = 5
+
+
+class TimingCallback(Callback):
+ """Logs execution time of train/val/test steps"""
+
+ def __init__(self, timer_kwargs=None):
+ """Inits :class:`TimingCallback`."""
+ if timer_kwargs is None:
+ timer_kwargs = {}
+ self.timer = timers.NamedTimer(**timer_kwargs)
+
+ def _on_batch_start(self, name):
+ """Called at the beginning of each batch"""
+ # reset only if we do not return mean of a sliding window
+ if self.timer.buffer_size <= 0:
+ self.timer.reset(name)
+
+ self.timer.start(name)
+
+ def _on_batch_end(self, name, pl_module):
+ """Called at the end of each batch"""
+ self.timer.stop(name)
+ # Set the `batch_size=1` as WAR for `dataloader_iter`, which is not used for any metric
+ pl_module.log(
+ name + ' in s',
+ self.timer[name],
+ on_step=True,
+ on_epoch=False,
+ batch_size=1,
+ prog_bar=(name == "train_step_timing"),
+ )
+
+ def on_train_batch_start(self, trainer, pl_module, batch, batch_idx, **kwargs): # pylint: disable=unused-argument
+ """Called at the beginning of each training batch"""
+ self._on_batch_start("train_step_timing")
+
+ def on_train_batch_end(
+ self, trainer, pl_module, outputs, batch, batch_idx, **kwargs # pylint: disable=unused-argument
+ ):
+ """Logs the time taken by the training batch"""
+ self._on_batch_end("train_step_timing", pl_module)
+
+ def on_validation_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx=0):
+ """Logs the time taken by the validation batch"""
+ self._on_batch_start("validation_step_timing")
+
+ def on_validation_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0):
+ """Logs the time taken by the validation step"""
+ self._on_batch_end("validation_step_timing", pl_module)
+
+ def on_test_batch_start(self, trainer, pl_module, batch, batch_idx, dataloader_idx=0):
+ """Logs execution time of test steps"""
+ self._on_batch_start("test_step_timing")
+
+ def on_test_batch_end(self, trainer, pl_module, outputs, batch, batch_idx, dataloader_idx=0):
+ """Logs execution time of test steps"""
+ self._on_batch_end("test_step_timing", pl_module)
+
+ def on_before_backward(self, trainer, pl_module, loss):
+ """Logs the time taken for backward pass"""
+ self._on_batch_start("train_backward_timing")
+
+ def on_after_backward(self, trainer, pl_module):
+ """Note: this is called after the optimizer step"""
+ self._on_batch_end("train_backward_timing", pl_module)
+
+
+def exp_manager(trainer: Trainer, cfg: Optional[Union[DictConfig, Dict]] = None) -> Optional[Path]: # noqa: MC0001
+ r"""exp_manager is a helper function used to manage folders for experiments. It follows the pytorch lightning
+ paradigm of exp_dir/model_or_experiment_name/version. If the lightning trainer has a logger, exp_manager will get
+ exp_dir, name, and version from the logger. Otherwise, it will use the exp_dir and name arguments to create the
+ logging directory. exp_manager also allows for explicit folder creation via explicit_log_dir.
+
+ The version can be a datetime string or an integer. Date time version can be disabled if use_datetime_version is
+ set to False. It optionally creates TensorBoardLogger, WandBLogger, ModelCheckpoint objects from pytorch lightning.
+ It copies sys.argv, and git information if available to the logging directory. It creates a log file for each
+ process to log their output into.
+
+ exp_manager additionally has a resume feature (resume_if_exists) which can be used to continuing training from the
+ constructed log_dir. When you need to continue the training repeatedly (like on a cluster which you need multiple
+ consecutive jobs), you need to avoid creating the version folders. Therefore, from v1.0.0, when resume_if_exists
+ is set to True, creating the version folders is ignored.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The lightning trainer object.
+ cfg : DictConfig or Dict, optional
+ Can have the following keys:
+ - explicit_log_dir : str
+ Can be used to override exp_dir/name/version folder creation. Defaults to ``None``, which will use
+ exp_dir, name, and version to construct the logging directory.
+ - exp_dir : str
+ The base directory to create the logging directory. Defaults to ``None``, which logs to
+ ./atommic_experiments.
+ - name : str
+ The name of the experiment. Defaults to ``None`` which turns into "default" via name = name or
+ "default".
+ - version : str
+ The version of the experiment. Defaults to None which uses either a datetime string or lightning's
+ TensorboardLogger system of using version_{int}.
+ - use_datetime_version : bool
+ Whether to use a datetime string for version. Default is ``True``.
+ - resume_if_exists : bool
+ Whether this experiment is resuming from a previous run. If True, it sets
+ trainer._checkpoint_connector._ckpt_path so that the trainer should auto-resume. exp_manager will
+ move files under log_dir to log_dir/run_{int}. Default is ``False``. When resume_if_exists is
+ True, we would not create version folders to make it easier to find the log folder for next runs.
+ - resume_past_end : bool
+ exp_manager errors out if resume_if_exists is True and a checkpoint matching '\'*end.ckpt indicating a
+ previous training run fully completed. This behaviour can be disabled, in which case the
+ '\'*end.ckpt will be loaded by setting resume_past_end to True. Default is ``False``.
+ - resume_ignore_no_checkpoint : bool
+ exp_manager errors out if resume_if_exists is True and no checkpoint could be found. This behaviour
+ can be disabled, in which case exp_manager will print a message and continue without restoring, by
+ setting resume_ignore_no_checkpoint to True. Default is ``False``.
+ - resume_from_checkpoint : str
+ Can be used to specify a path to a specific checkpoint file to load from. This will override any
+ checkpoint found when resume_if_exists is True. Default is ``None``.
+ - create_tensorboard_logger : bool
+ Whether to create a tensorboard logger and attach it to the pytorch lightning trainer.
+ Default is ``True``.
+ - summary_writer_kwargs : dict
+ A dictionary of kwargs that can be passed to lightning's TensorboardLogger class. Note that log_dir is
+ passed by exp_manager and cannot exist in this dict. Default is ``None``.
+ - create_wandb_logger : bool
+ Whether to create a Weights and Biases logger and attach it to the pytorch lightning trainer.
+ Default is ``False``.
+ - wandb_logger_kwargs : dict
+ A dictionary of kwargs that can be passed to lightning's WandBLogger class. Note that name and project
+ are required parameters if create_wandb_logger is True. Default is ``None``..
+ - create_checkpoint_callback : bool
+ Whether to create a ModelCheckpoint callback and attach it to the pytorch lightning trainer. The
+ ModelCheckpoint saves the top 3 models with the best "val_loss", the most recent checkpoint under
+ '\'*last.ckpt, and the final checkpoint after training completes under '\'*end.ckpt.
+ Default is ``True``.
+ - create_early_stopping_callback : bool
+ Whether to create an EarlyStopping callback and attach it to the pytorch lightning trainer. The
+ EarlyStopping callback stops training if the "val_loss" does not improve for 3 epochs.
+ Default is ``True``.
+ - files_to_copy : list
+ A list of files to copy to the experiment logging directory. Defaults to None which copies no files.
+ - log_local_rank_0_only : bool
+ Whether to only create log files for local rank 0. Default is ``False``. Set this to True if you are
+ using DDP with many GPUs and do not want many log files in your exp dir.
+ - log_global_rank_0_only : bool
+ Whether to only create log files for global rank 0. Defaults to False. Set this to True if you are
+ using DDP with many GPUs and do not want many log files in your exp dir.
+ - max_time : str
+ The maximum wall clock time *per run*. This is intended to be used on clusters where you want a
+ checkpoint to be saved after this specified time and be able to resume from that checkpoint.
+ Default is ``None``.
+ - seconds_to_sleep : float
+ Seconds to sleep non rank 0 processes for. Used to give enough time for rank 0 to initialize.
+
+ Returns
+ -------
+ log_dir : Path
+ The final logging directory where logging files are saved. Usually the concatenation of exp_dir, name, and
+ version.
+ """
+ # Add rank information to logger
+ # Note: trainer.global_rank and trainer.is_global_zero are not set until trainer.fit, so have to hack around it
+ local_rank = int(os.environ.get("LOCAL_RANK", 0))
+ global_rank = trainer.node_rank * trainer.num_devices + local_rank
+ logging.rank = global_rank
+
+ if cfg is None:
+ logging.error("exp_manager did not receive a cfg argument. It will be disabled.")
+ return None
+
+ if trainer.fast_dev_run:
+ logging.info("Trainer was called with fast_dev_run. exp_manager will return without any functionality.")
+ return None
+
+ # Ensure passed cfg is compliant with ExpManagerConfig
+ schema = OmegaConf.structured(ExpManagerConfig)
+ if isinstance(cfg, dict):
+ cfg = OmegaConf.create(cfg)
+ elif not isinstance(cfg, DictConfig):
+ raise ValueError(f"cfg was type: {type(cfg)}. Expected either a dict or a DictConfig")
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+ cfg = OmegaConf.merge(schema, cfg)
+
+ # Ensures that trainer options are compliant with atommic and exp_manager arguments
+ error_checks(trainer, cfg)
+
+ log_dir, exp_dir, name, version = get_log_dir(
+ trainer=trainer,
+ exp_dir=cfg.exp_dir,
+ name=cfg.name,
+ version=cfg.version,
+ explicit_log_dir=cfg.explicit_log_dir,
+ use_datetime_version=cfg.use_datetime_version,
+ resume_if_exists=cfg.resume_if_exists,
+ )
+
+ check_resume(
+ trainer,
+ log_dir,
+ cfg.resume_if_exists,
+ cfg.resume_past_end,
+ cfg.resume_ignore_no_checkpoint,
+ cfg.checkpoint_callback_params.dirpath,
+ cfg.resume_from_checkpoint,
+ )
+
+ checkpoint_name = name
+ # If name returned from get_log_dir is "", use cfg.name for checkpointing
+ if checkpoint_name is None or checkpoint_name == "":
+ checkpoint_name = cfg.name or "default"
+
+ cfg.name = name # Used for configure_loggers so that the log_dir is properly set even if name is ""
+ cfg.version = version
+
+ # update app_state with log_dir, exp_dir, etc
+ app_state = AppState()
+ app_state.log_dir = log_dir
+ app_state.exp_dir = exp_dir
+ app_state.name = name
+ app_state.version = version
+ app_state.checkpoint_name = checkpoint_name
+ app_state.create_checkpoint_callback = cfg.create_checkpoint_callback
+ app_state.checkpoint_callback_params = cfg.checkpoint_callback_params
+
+ # Create the logging directory if it does not exist. Cannot limit creation to global zero as all ranks write to own
+ # log file.
+ os.makedirs(log_dir, exist_ok=True)
+ logging.info(f"Experiments will be logged at {log_dir}")
+ trainer._default_root_dir = log_dir # pylint: disable=protected-access
+
+ if cfg.log_local_rank_0_only is True and cfg.log_global_rank_0_only is True:
+ raise ValueError(
+ "Cannot set both log_local_rank_0_only and log_global_rank_0_only to True. "
+ "Please set either one or neither."
+ )
+
+ # This is set if the env var atommic_TESTING is set to True.
+ atommic_testing = get_envbool(ATOMMIC_ENV_VARNAME_TESTING, False)
+
+ # Handle logging to file
+ log_file = log_dir / f"atommic_log_globalrank-{global_rank}_localrank-{local_rank}.txt"
+ if cfg.log_local_rank_0_only is True and not atommic_testing:
+ if local_rank == 0:
+ logging.add_file_handler(log_file)
+ elif cfg.log_global_rank_0_only is True and not atommic_testing:
+ if global_rank == 0:
+ logging.add_file_handler(log_file)
+ else:
+ # Logs on all ranks.
+ logging.add_file_handler(log_file)
+
+ # For some reason, LearningRateLogger requires trainer to have a logger. Safer to create logger on all ranks
+ # not just global rank 0.
+ if cfg.create_tensorboard_logger or cfg.create_wandb_logger:
+ configure_loggers(
+ trainer,
+ [Path(exp_dir)],
+ [Path(log_dir)],
+ cfg.name,
+ cfg.version,
+ cfg.checkpoint_callback_params,
+ cfg.create_tensorboard_logger,
+ cfg.summary_writer_kwargs,
+ cfg.create_wandb_logger,
+ cfg.wandb_logger_kwargs,
+ )
+
+ # add loggers timing callbacks
+ if cfg.log_step_timing:
+ timing_callback = TimingCallback(timer_kwargs=cfg.step_timing_kwargs or {})
+ trainer.callbacks.insert(0, timing_callback)
+
+ if cfg.ema.enable:
+ ema_callback = EMA(
+ decay=cfg.ema.decay,
+ validate_original_weights=cfg.ema.validate_original_weights,
+ cpu_offload=cfg.ema.cpu_offload,
+ every_n_steps=cfg.ema.every_n_steps,
+ )
+ trainer.callbacks.append(ema_callback)
+
+ if cfg.create_early_stopping_callback:
+ early_stop_callback = EarlyStopping(**cfg.early_stopping_callback_params)
+ trainer.callbacks.append(early_stop_callback)
+
+ if cfg.create_checkpoint_callback:
+ configure_checkpointing(
+ trainer,
+ log_dir,
+ checkpoint_name,
+ cfg.resume_if_exists,
+ cfg.checkpoint_callback_params,
+ cfg.create_preemption_callback,
+ )
+
+ if cfg.disable_validation_on_resume:
+ # extend training loop to skip initial validation when resuming from checkpoint
+ configure_no_restart_validation_training_loop(trainer)
+
+ # Setup a stateless timer for use on clusters.
+ if cfg.max_time_per_run is not None:
+ found_ptl_timer = False
+ for idx, callback in enumerate(trainer.callbacks):
+ if isinstance(callback, Timer):
+ # NOTE: PTL does not expose a `trainer.max_time`. By the time we are in this function, PTL has already
+ # set up a timer if the user specifies `trainer.max_time` so best we can do is replace that.
+ # Working: If only `trainer.max_time` is set - it behaves as a normal PTL timer.
+ # If only `exp_manager.max_time_per_run` is set - it behaves as a StateLessTimer.
+ # If both are set, it also behaves as a StateLessTimer.
+ logging.warning(
+ "Found a PTL Timer callback, replacing with a StatelessTimer callback. "
+ "This will happen if you set trainer.max_time as well as exp_manager.max_time_per_run."
+ )
+ trainer.callbacks[idx] = StatelessTimer(cfg.max_time_per_run)
+ found_ptl_timer = True
+ break
+
+ if not found_ptl_timer:
+ trainer.max_time = cfg.max_time_per_run
+ trainer.callbacks.append(StatelessTimer(cfg.max_time_per_run))
+
+ if is_global_rank_zero():
+ # Move files_to_copy to folder and add git information if present
+ if cfg.files_to_copy:
+ for _file in cfg.files_to_copy:
+ copy(Path(_file), log_dir)
+
+ # Create files for cmd args and git info
+ with open(log_dir / "cmd-args.log", "w", encoding="utf-8") as _file:
+ _file.write(" ".join(sys.argv))
+
+ # Try to get git hash
+ git_repo, git_hash = get_git_hash()
+ if git_repo:
+ with open(log_dir / "git-info.log", "w", encoding="utf-8") as _file:
+ _file.write(f"commit hash: {git_hash}")
+ _file.write(get_git_diff())
+
+ # Add err_file logging to global_rank zero
+ logging.add_err_file_handler(log_dir / "atommic_error_log.txt")
+
+ # Add lightning file logging to global_rank zero
+ add_filehandlers_to_pl_logger(log_dir / "lightning_logs.txt", log_dir / "atommic_error_log.txt")
+
+ elif trainer.num_devices * trainer.num_devices > 1:
+ # sleep other ranks so rank 0 can finish
+ # doing the initialization such as moving files
+ time.sleep(cfg.seconds_to_sleep)
+
+ return log_dir
+
+
+def error_checks(trainer: Trainer, cfg: Optional[Union[DictConfig, Dict]] = None):
+ """Checks that the passed trainer is compliant with atommic and exp_manager's passed configuration. Checks that:
+ - Throws error when hydra has changed the working directory. This causes issues with lightning's DDP
+ - Throws error when trainer has loggers defined but create_tensorboard_logger or create_wandB_logger is True
+ - Prints error messages when 1) run on multi-node and not Slurm, and 2) run on multi-gpu without DDP
+ """
+ if HydraConfig.initialized() and get_original_cwd() != os.getcwd():
+ raise ValueError(
+ "Hydra changed the working directory. This interferes with ExpManger's functionality. Please pass "
+ "hydra.run.dir=. to your python script."
+ )
+
+ if trainer.logger is not None and (cfg.create_tensorboard_logger or cfg.create_wandb_logger): # type: ignore
+ raise LoggerMisconfigurationError(
+ "The pytorch lightning trainer that was passed to exp_manager contained a logger, and either "
+ f"create_tensorboard_logger: {cfg.create_tensorboard_logger} or create_wandb_logger: " # type: ignore
+ f"was set to True. These can only be used if trainer does not already have a logger."
+ )
+
+ if trainer.num_nodes > 1 and not check_slurm(trainer):
+ logging.error(
+ "You are running multi-node training without SLURM handling the processes."
+ " Please note that this is not tested in atommic and could result in errors."
+ )
+
+ if trainer.num_devices > 1 and not isinstance(trainer.strategy, DDPStrategy):
+ logging.error(
+ "You are running multi-gpu without ddp.Please note that this is not tested in atommic and could result in "
+ "errors."
+ )
+
+
+def check_resume( # noqa: MC0001
+ trainer: Trainer,
+ log_dir: Union[str, Path],
+ resume_if_exists: bool = False,
+ resume_past_end: bool = False,
+ resume_ignore_no_checkpoint: bool = False,
+ dirpath: str = None,
+ resume_from_checkpoint: str = None,
+):
+ """Checks that resume=True was used correctly with the arguments pass to exp_manager. Sets
+ trainer._checkpoint_connector._ckpt_path as necessary.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The trainer that is being used.
+ log_dir : Union[str, Path]
+ The directory where the logs are being saved.
+ resume_if_exists : bool
+ Whether to resume if the experiment directory already exists.
+ resume_past_end : bool
+ Whether to resume from the end of the experiment.
+ resume_ignore_no_checkpoint : bool
+ Whether to ignore if there is no checkpoint to resume from.
+ dirpath : str
+ The directory to resume from. If None, will resume from the latest checkpoint.
+ resume_from_checkpoint : str
+ The checkpoint to resume from. If None, will resume from the latest checkpoint.
+
+ Returns
+ -------
+ NotFoundError : bool
+ If resume is True, resume_ignore_no_checkpoint is False, and checkpoints could not be found.
+ ValueError : bool
+ If resume is True, and there were more than 1 checkpoint could be found.
+ """
+ if not log_dir:
+ raise ValueError(f"Resuming requires the log_dir {log_dir} to be passed to exp_manager")
+
+ checkpoint = None
+ if resume_from_checkpoint:
+ checkpoint = resume_from_checkpoint
+ if resume_if_exists:
+ # Use /checkpoints/ unless `dirpath` is set
+ checkpoint_dir = Path(dirpath) if dirpath else Path(Path(log_dir) / "checkpoints")
+
+ # when using distributed checkpointing, checkpoint_dir is a directory of directories
+ # we check for this here
+ dist_checkpoints = [d for d in list(checkpoint_dir.glob("*")) if d.is_dir()]
+ end_dist_checkpoints = [d for d in dist_checkpoints if d.match("*end")]
+ last_dist_checkpoints = [d for d in dist_checkpoints if d.match("*last")]
+
+ end_checkpoints = end_dist_checkpoints if end_dist_checkpoints else list(checkpoint_dir.rglob("*end.ckpt"))
+ last_checkpoints = last_dist_checkpoints if last_dist_checkpoints else list(checkpoint_dir.rglob("*last.ckpt"))
+
+ if not checkpoint_dir.exists() or (not len(end_checkpoints) > 0 and not len(last_checkpoints) > 0):
+ if resume_ignore_no_checkpoint:
+ warn = (
+ "There were no checkpoints found in checkpoint_dir or no checkpoint folder at checkpoint_dir "
+ f":{checkpoint_dir}. "
+ )
+ if checkpoint is None:
+ warn += "Training from scratch."
+ elif checkpoint == resume_from_checkpoint:
+ warn += f"Training from {resume_from_checkpoint}."
+ logging.warning(warn)
+ else:
+ raise NotFoundError(
+ "There were no checkpoints found in checkpoint_dir or no checkpoint folder at checkpoint_dir "
+ f":{checkpoint_dir}. Cannot resume."
+ )
+ elif len(end_checkpoints) > 0:
+ if resume_past_end:
+ if len(end_checkpoints) > 1:
+ if 'mp_rank' in str(end_checkpoints[0]):
+ checkpoint = end_checkpoints[0] # type: ignore
+ else:
+ raise ValueError(f"Multiple checkpoints {end_checkpoints} that matches *end.ckpt.")
+ else:
+ raise ValueError(
+ f"Found {end_checkpoints[0]} indicating that the last training run has already completed."
+ )
+ elif len(last_checkpoints) > 1:
+ if 'mp_rank' in str(last_checkpoints[0]) or 'tp_rank' in str(last_checkpoints[0]):
+ checkpoint = last_checkpoints[0] # type: ignore
+ checkpoint = atommic.utils.model_utils.uninject_model_parallel_rank(checkpoint)
+ else:
+ raise ValueError(f"Multiple checkpoints {last_checkpoints} that matches *last.ckpt.")
+ else:
+ checkpoint = last_checkpoints[0] # type: ignore
+
+ # PTL 2.0 supports ckpt_path instead of resume_from_checkpoint as the trainer flag
+ if checkpoint is not None:
+ trainer.ckpt_path = str(checkpoint)
+ logging.info(f'Resuming training from checkpoint: {trainer.ckpt_path}')
+
+ if is_global_rank_zero():
+ # Check to see if any files exist that need to be moved
+ files_to_move = []
+ if Path(log_dir).exists():
+ for child in Path(log_dir).iterdir():
+ if child.is_file():
+ files_to_move.append(child)
+
+ if len(files_to_move) > 0:
+ # Move old files to a new folder
+ other_run_dirs = Path(log_dir).glob("run_*")
+ run_count = 0
+ for fold in other_run_dirs:
+ if fold.is_dir():
+ run_count += 1
+ new_run_dir = Path(Path(log_dir) / f"run_{run_count}")
+ new_run_dir.mkdir()
+ for _file in files_to_move:
+ move(str(_file), str(new_run_dir))
+
+
+def check_explicit_log_dir(
+ trainer: Trainer,
+ explicit_log_dir: List[Union[Path, str]],
+ exp_dir: str,
+ name: str, # pylint: disable=unused-argument
+ version: str,
+) -> Tuple[Path, str, str, str]:
+ """Checks that the passed arguments are compatible with explicit_log_dir.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The trainer to check.
+ explicit_log_dir : str
+ The explicit log dir to check.
+ exp_dir : str
+ The experiment directory to check.
+ name : str
+ The experiment name to check.
+ version : str
+ The experiment version to check.
+
+ Returns
+ -------
+ tuple
+ The log_dir, exp_dir, name, and version that should be used.
+
+ Raises
+ ------
+ LoggerMisconfigurationError
+ If the trainer already has a logger.
+ """
+ if trainer.logger is not None:
+ raise LoggerMisconfigurationError(
+ "The pytorch lightning trainer that was passed to exp_manager contained a logger and explicit_log_dir: "
+ f"{explicit_log_dir} was pass to exp_manager. Please remove the logger from the lightning trainer."
+ )
+ # Checking only (explicit_log_dir) vs (exp_dir and version).
+ # The `name` will be used as the actual name of checkpoint/archive.
+ if exp_dir or version:
+ logging.error(
+ f"exp_manager received explicit_log_dir: {explicit_log_dir} and at least one of exp_dir: {exp_dir}, "
+ f"or version: {version}. Please note that exp_dir, name, and version will be ignored."
+ )
+ if is_global_rank_zero() and Path(str(explicit_log_dir)).exists():
+ logging.warning(f"Exp_manager is logging to {explicit_log_dir}, but it already exists.")
+ return Path(str(explicit_log_dir)), str(explicit_log_dir), "", ""
+
+
+def get_log_dir(
+ trainer: Trainer,
+ exp_dir: str = None,
+ name: str = None,
+ version: str = None,
+ explicit_log_dir: str = None,
+ use_datetime_version: bool = True,
+ resume_if_exists: bool = False,
+) -> Tuple[Path, str, str, str]:
+ """Obtains the log_dir used for exp_manager.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The trainer to check.
+ exp_dir : str
+ The experiment directory to check.
+ name : str
+ The experiment name to check.
+ version : str
+ The experiment version to check.
+ explicit_log_dir : str
+ The explicit log dir to check.
+ use_datetime_version : bool
+ Whether to use datetime versioning.
+ resume_if_exists : bool
+ Whether to resume if the log_dir already exists.
+
+ Raises
+ -------
+ LoggerMisconfigurationError : bool
+ If trainer is incompatible with arguments.
+ NotFoundError : bool
+ If resume is True, resume_ignore_no_checkpoint is False, and checkpoints could not be found.
+ ValueError : bool
+ If resume is True, and there were more than 1 checkpoint could be found.
+ """
+ if explicit_log_dir: # If explicit log_dir was passed, short circuit
+ return check_explicit_log_dir(trainer, [Path(explicit_log_dir)], exp_dir, name, version) # type: ignore
+
+ # Default exp_dir to ./atommic_experiments if None was passed
+ _exp_dir = exp_dir
+ if exp_dir is None:
+ _exp_dir = str(Path.cwd() / "atommic_experiments")
+
+ # If the user has already defined a logger for the trainer, use the logger defaults for logging directory
+ if trainer.logger is not None:
+ if trainer.logger.save_dir:
+ if exp_dir:
+ raise LoggerMisconfigurationError(
+ "The pytorch lightning trainer that was passed to exp_manager contained a logger, the logger's "
+ f"save_dir was not None, and exp_dir ({exp_dir}) was not None. If trainer.logger.save_dir "
+ "exists, exp_manager will use trainer.logger.save_dir as the logging directory and exp_dir "
+ "must be None."
+ )
+ _exp_dir = trainer.logger.save_dir
+ if name:
+ raise LoggerMisconfigurationError(
+ "The pytorch lightning trainer that was passed to exp_manager contained a logger, and name: "
+ f"{name} was also passed to exp_manager. If the trainer contains a "
+ "logger, exp_manager will use trainer.logger.name, and name passed to exp_manager must be None."
+ )
+ name = trainer.logger.name
+ version = f"version_{trainer.logger.version}"
+ # Use user-defined exp_dir, project_name, exp_name, and versioning options
+ else:
+ name = name or "default"
+ version = version or os.environ.get(ATOMMIC_ENV_VARNAME_VERSION)
+
+ if not version:
+ if resume_if_exists:
+ logging.warning(
+ "No version folders would be created under the log folder as 'resume_if_exists' is enabled."
+ )
+ version = None
+ elif is_global_rank_zero():
+ if use_datetime_version:
+ version = time.strftime("%Y-%m-%d_%H-%M-%S")
+ else:
+ tensorboard_logger = TensorBoardLogger(save_dir=_exp_dir, name=name, version=version)
+ version = f"version_{tensorboard_logger.version}"
+ os.environ[ATOMMIC_ENV_VARNAME_VERSION] = "" if version is None else version
+
+ log_dir = Path(str(_exp_dir)) / Path(str(name)) / Path("" if version is None else str(version))
+ return log_dir, str(_exp_dir), str(name), str(version)
+
+
+def get_git_hash():
+ """Helper function that tries to get the commit hash if running inside a git folder.
+
+ Returns
+ -------
+ Bool: Whether the git subprocess ran without error.
+ String: git subprocess output or error message
+ """
+ try:
+ return True, subprocess.check_output(["git", "rev-parse", "HEAD"], stderr=subprocess.STDOUT).decode()
+ except subprocess.CalledProcessError as err:
+ return False, f'{err.output.decode("utf-8")}\n'
+
+
+def get_git_diff():
+ """Helper function that tries to get the git diff if running inside a git folder.
+
+ Returns
+ -------
+ bool
+ Whether the git subprocess ran without error.
+ str
+ If git subprocess output or error message.
+ """
+ try:
+ return subprocess.check_output(["git", "diff"], stderr=subprocess.STDOUT).decode()
+ except subprocess.CalledProcessError as err:
+ return f'{err.output.decode("utf-8")}\n'
+
+
+def configure_loggers(
+ trainer: Trainer,
+ exp_dir: List[Union[Path, str]],
+ log_dir: List[Union[Path, str]], # pylint: disable=unused-argument
+ name: str,
+ version: str,
+ checkpoint_callback_params: dict, # pylint: disable=unused-argument
+ create_tensorboard_logger: bool,
+ summary_writer_kwargs: dict,
+ create_wandb_logger: bool,
+ wandb_kwargs: dict,
+):
+ """Creates TensorboardLogger and/or WandBLogger and attach them to trainer. Raises ValueError if
+ summary_writer_kwargs or wandb_kwargs are miss configured.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The trainer to attach the loggers to.
+ exp_dir : str
+ The experiment directory.
+ log_dir : str
+ The logging directory.
+ name : str
+ The name of the experiment.
+ version : str
+ The version of the experiment.
+ checkpoint_callback_params : dict
+ The checkpoint callback parameters.
+ create_tensorboard_logger : bool
+ Whether to create a TensorboardLogger.
+ summary_writer_kwargs : dict
+ The kwargs to pass to the TensorboardLogger.
+ create_wandb_logger : bool
+ Whether to create a Weights & Biases logger.
+ wandb_kwargs : dict
+ The kwargs to pass to the Weights & Biases logger.
+
+ Returns
+ -------
+ LoggerList
+ A list of loggers.
+ """
+ # Potentially create tensorboard logger and/or WandBLogger
+ logger_list = []
+ if create_tensorboard_logger:
+ if summary_writer_kwargs is None:
+ summary_writer_kwargs = {}
+ elif "log_dir" in summary_writer_kwargs:
+ raise ValueError(
+ "You cannot pass `log_dir` as part of `summary_writer_kwargs`. `log_dir` is handled by lightning's "
+ "TensorBoardLogger logger."
+ )
+ tensorboard_logger = TensorBoardLogger(
+ save_dir=exp_dir[0], name=name, version=version, **summary_writer_kwargs
+ )
+ logger_list.append(tensorboard_logger)
+ logging.info("TensorboardLogger has been set up")
+
+ if create_wandb_logger:
+ if wandb_kwargs is None:
+ wandb_kwargs = {}
+ if "name" not in wandb_kwargs and "project" not in wandb_kwargs:
+ raise ValueError("name and project are required for wandb_logger")
+ wandb_logger = WandbLogger(save_dir=str(exp_dir[0]), version=version, **wandb_kwargs)
+
+ logger_list.append(wandb_logger)
+ logging.info("WandBLogger has been set up")
+
+ trainer._logger_connector.configure_logger(logger_list) # pylint: disable=protected-access
+
+
+def configure_checkpointing( # noqa: MC0001
+ trainer: Trainer,
+ log_dir: Path,
+ name: str,
+ resume: bool,
+ params: "DictConfig",
+ create_preemption_callback: bool,
+):
+ """Adds ModelCheckpoint to trainer. Raises CheckpointMisconfigurationError if trainer already has a ModelCheckpoint
+ callback or if trainer.weights_save_path was passed to Trainer.
+ """
+ for callback in trainer.callbacks:
+ if isinstance(callback, ModelCheckpoint):
+ raise CheckpointMisconfigurationError(
+ "The pytorch lightning trainer that was passed to exp_manager contained a ModelCheckpoint "
+ "and create_checkpoint_callback was set to True. Please either set create_checkpoint_callback "
+ "to False, or remove ModelCheckpoint from the lightning trainer"
+ )
+
+ # Create the callback and attach it to trainer
+ if "filepath" in params:
+ if params.filepath is not None:
+ logging.warning("filepath is deprecated. Please switch to dirpath and filename instead")
+ if params.dirpath is None:
+ params.dirpath = Path(params.filepath).parent
+ if params.filename is None:
+ params.filename = Path(params.filepath).name
+ with open_dict(params):
+ del params["filepath"]
+ if params.dirpath is None:
+ params.dirpath = Path(log_dir / "checkpoints")
+ if params.filename is None:
+ params.filename = f"{name}--{{{params.monitor}:.4f}}-{{epoch}}"
+ if params.prefix is None:
+ params.prefix = name
+ ATOMMICModelCheckpoint.CHECKPOINT_NAME_LAST = f"{params.filename}-last"
+
+ logging.debug(params.dirpath)
+ logging.debug(params.filename)
+ logging.debug(params.prefix)
+
+ if "val" in params.monitor:
+ if (
+ trainer.max_epochs is not None
+ and trainer.max_epochs != -1
+ and trainer.max_epochs < trainer.check_val_every_n_epoch
+ ):
+ logging.error(
+ "The checkpoint callback was told to monitor a validation value but trainer.max_epochs("
+ f"{trainer.max_epochs}) was less than trainer.check_val_every_n_epoch("
+ f"{trainer.check_val_every_n_epoch}). It is very likely this run will fail with "
+ f"ModelCheckpoint(monitor='{params.monitor}') not found in the returned metrics. Please ensure that "
+ "validation is run within trainer.max_epochs."
+ )
+ elif trainer.max_steps is not None and trainer.max_steps != -1:
+ logging.warning(
+ "The checkpoint callback was told to monitor a validation value and trainer's max_steps was set to "
+ f"{trainer.max_steps}. Please ensure that max_steps will run for at least "
+ f"{trainer.check_val_every_n_epoch} epochs to ensure that checkpointing will not error out."
+ )
+
+ checkpoint_callback = ATOMMICModelCheckpoint(n_resume=resume, **params)
+ checkpoint_callback.last_model_path = trainer.ckpt_path or ""
+ if "mp_rank" in checkpoint_callback.last_model_path or "tp_rank" in checkpoint_callback.last_model_path:
+ checkpoint_callback.last_model_path = atommic.utils.model_utils.uninject_model_parallel_rank(
+ checkpoint_callback.last_model_path
+ )
+ trainer.callbacks.append(checkpoint_callback)
+ if create_preemption_callback:
+ # Check if cuda is available as preemption is supported only on GPUs
+ if torch.cuda.is_available():
+ # By default, PreemptionCallback handles SIGTERM.
+ # To handle other signals pass the signal in the call as below:
+ # PreemptionCallback(checkpoint_callback, signal.SIGCHLD)
+ preemption_callback = PreemptionCallback(checkpoint_callback)
+ trainer.callbacks.append(preemption_callback)
+ else:
+ logging.info("Preemption is supported only on GPUs, disabling preemption")
+
+
+def check_slurm(trainer):
+ """Checks if the trainer is running on a slurm cluster. If so, it will check if the trainer is running on the
+ master node. If it is not, it will exit.
+
+ Parameters
+ ----------
+ trainer : pytorch_lightning.Trainer
+ The trainer to check.
+
+ Returns
+ -------
+ bool
+ True if the trainer is running on the master node, False otherwise.
+ """
+ try:
+ return trainer.accelerator_connector.is_slurm_managing_tasks
+ except AttributeError:
+ return False
+
+
+class StatelessTimer(Timer):
+ """Extension of PTL timers to be per run."""
+
+ # pylint: disable=arguments-differ
+ @staticmethod
+ def state_dict() -> Dict[str, Any]:
+ """Saves the state of the timer."""
+ return {}
+
+ def load_state_dict(self, state_dict: Dict[str, Any]) -> None:
+ """Loads the state of the timer."""
+
+
+def configure_no_restart_validation_training_loop(trainer: pytorch_lightning.Trainer) -> None:
+ """Configure the training loop to skip validation when resuming from a checkpoint."""
+ if not isinstance(trainer.fit_loop.epoch_loop, _TrainingEpochLoop):
+ warnings.warn("Detected custom epoch loop. Skipping no validation on restart support.", UserWarning)
+ return
+ # Pass trainer object to avoid trainer getting overwritten as None
+ loop = SkipResumeTrainingValidationLoop(trainer, trainer.min_steps, trainer.max_steps)
+ trainer.fit_loop.epoch_loop = loop
+
+
+class SkipResumeTrainingValidationLoop(_TrainingEpochLoop):
+ """Extend the PTL Epoch loop to skip validating when resuming. This happens when resuming a checkpoint that has
+ already run validation, but loading restores the training state before validation has run.
+ """
+
+ def _should_check_val_fx(self) -> bool:
+ """Skip validation if we are resuming from a checkpoint and the global step is a multiple of the validation."""
+ if self.restarting and self.global_step % self.trainer.val_check_batch == 0:
+ return False
+ return super()._should_check_val_fx()
+
+
+def clean_exp_ckpt(exp_log_dir: Union[str, Path], remove_ckpt: bool = True, remove_atommic: bool = False):
+ """Helper method that removes Pytorch Lightning .ckpt files or atommic .atommic files from the checkpoint
+ directory.
+
+ Parameters
+ ----------
+ exp_log_dir : str or Path
+ Path to the root directory of the current experiment.
+ remove_ckpt : bool, optional
+ Whether to remove all *.ckpt files in the checkpoints directory. Default is True.
+ remove_atommic : bool, optional
+ Whether to remove all *.atommic files in the checkpoints directory. Default is False.
+ """
+ exp_log_dir = str(exp_log_dir)
+
+ if remove_ckpt:
+ logging.info("Deleting *.ckpt files ...")
+ ckpt_files = glob.glob(os.path.join(exp_log_dir, "checkpoints", "*.ckpt"))
+ for filepath in ckpt_files:
+ os.remove(filepath)
+ logging.info(f"Deleted file : {filepath}")
+
+ if remove_atommic:
+ logging.info("Deleting *.atommic files ...")
+ atommic_files = glob.glob(os.path.join(exp_log_dir, "checkpoints", "*.atommic"))
+ for filepath in atommic_files:
+ os.remove(filepath)
+ logging.info(f"Deleted file : {filepath}")
diff --git a/atommic/utils/export_utils.py b/atommic/utils/export_utils.py
new file mode 100644
index 00000000..dd57b5a2
--- /dev/null
+++ b/atommic/utils/export_utils.py
@@ -0,0 +1,275 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/export_utils.py
+import os
+from enum import Enum
+from typing import Callable, Dict, Optional
+
+import onnx
+import torch
+from torch import nn
+
+from atommic.utils import logging
+
+try:
+ import onnxruntime
+
+ ort_available = True
+except ImportError:
+ ort_available = False
+
+
+class ExportFormat(Enum):
+ """Which format to use when exporting a Neural Module for deployment"""
+
+ ONNX = (1,)
+ TORCHSCRIPT = (2,)
+
+
+_EXT_DICT = {".pt": ExportFormat.TORCHSCRIPT, ".ts": ExportFormat.TORCHSCRIPT, ".onnx": ExportFormat.ONNX}
+
+
+def get_export_format(filename: str):
+ """Get export format from filename."""
+ _, ext = os.path.splitext(filename)
+ try:
+ return _EXT_DICT[ext.lower()]
+ except KeyError as e:
+ raise ValueError(f"Export file {filename} extension does not correspond to any export format!") from e
+
+
+def augment_filename(output: str, prepend: str):
+ """Augment output filename with prepend"""
+ if prepend == "self":
+ return output
+ path, filename = os.path.split(output)
+ filename = f"{prepend}-{filename}"
+ return os.path.join(path, filename)
+
+
+def forward_method(self):
+ """Forward method for export"""
+ if hasattr(self, "forward_for_export"):
+ return self.forward_for_export
+ return self.forward
+
+
+def wrap_forward_method(self):
+ """Wraps the forward method of the module with a function that returns the output of the forward method"""
+ tp = type(self)
+ old_forward_method = None
+ if hasattr(tp, "forward_for_export"):
+ forward_method = tp.forward_for_export
+ old_forward_method = tp.forward
+ tp.forward = forward_method
+ else:
+ forward_method = None
+ return forward_method, old_forward_method
+
+
+def parse_input_example(input_example):
+ """Parse input example to onnxrt input format"""
+ input_list = list(input_example)
+ input_dict = {}
+ # process possible kwargs
+ if isinstance(input_list[-1], dict):
+ input_dict = input_list[-1]
+ input_list = input_list[:-1]
+ return input_list, input_dict
+
+
+def to_onnxrt_input(ort_input_names, input_names, input_dict, input_list):
+ """Convert input to onnxrt input"""
+ odict = {}
+ for k in reversed(input_names):
+ val = None
+ if k in input_dict:
+ val = input_dict[k].cpu().numpy()
+ elif len(input_list) > 0:
+ val = input_list.pop().cpu().numpy()
+ if k in ort_input_names and val is not None:
+ odict[k] = val
+ return odict
+
+
+def verify_torchscript(model, output, input_examples, check_tolerance=0.01):
+ """Verify torchscript output with torchscript forward.
+
+ Parameters
+ ----------
+ model : torch.nn.Module
+ Model to verify.
+ output : str
+ Output filename.
+ input_examples : list
+ List of input examples.
+ check_tolerance : float
+ Tolerance for checking.
+
+ Returns
+ -------
+ bool
+ Whether the verification was successful.
+ """
+ all_good = True
+ for input_example in input_examples:
+ input_list, input_dict = parse_input_example(input_example)
+ # We disable autocast here to make sure exported TS will run under Triton or other C++ env
+ with torch.cuda.amp.autocast(enabled=False):
+ output_example = model.forward(*input_list, **input_dict)
+ ts_model = torch.jit.load(output)
+ all_good = all_good and run_ts_and_compare(
+ ts_model, input_list, input_dict, output_example, check_tolerance
+ )
+ status = "SUCCESS" if all_good else "FAIL"
+ logging.info(f"Torchscript generated at {output} verified with torchscript forward : " + status)
+ return all_good
+
+
+# pylint: disable=inconsistent-return-statements
+def verify_runtime(
+ model,
+ output,
+ input_examples,
+ input_names,
+ check_tolerance=0.01,
+):
+ """Verify runtime output with onnxrt."""
+ onnx_model = onnx.load(output)
+ ort_input_names = [node.name for node in onnx_model.graph.input]
+
+ global ort_available # pylint: disable=global-variable-not-assigned
+ if not ort_available:
+ logging.warning(f"ONNX generated at {output}, not verified - please install onnxruntime_gpu package.\n")
+ onnx.checker.check_model(onnx_model, full_check=True)
+ return
+
+ onnx_session_opt = onnxruntime.SessionOptions()
+ sess = onnxruntime.InferenceSession(
+ onnx_model.SerializeToString(), sess_options=onnx_session_opt, providers=["CUDAExecutionProvider"]
+ )
+
+ all_good = True
+
+ for input_example in input_examples:
+ input_list, input_dict = parse_input_example(input_example)
+ output_example = model.forward(*input_list, **input_dict)
+ ort_input = to_onnxrt_input(ort_input_names, input_names, input_dict, input_list)
+ all_good = all_good and run_ort_and_compare(sess, ort_input, output_example, check_tolerance)
+ status = "SUCCESS" if all_good else "FAIL"
+ logging.info(f"ONNX generated at {output} verified with onnxruntime : {status}")
+ return all_good
+
+
+def run_ts_and_compare(ts_model, ts_input_list, ts_input_dict, output_example, check_tolerance=0.01):
+ """Run torchscript model and compare with pytorch output."""
+ # Verify the model can be read, and is valid
+ ts_out = ts_model(*ts_input_list, **ts_input_dict)
+ all_good = True
+ for i, out in enumerate(ts_out):
+ expected = output_example[i]
+ if torch.is_tensor(expected):
+ tout = out.to("cpu")
+ logging.debug(f"Checking output {i}, shape: {expected.shape}:\n")
+ this_good = True
+ try:
+ if not torch.allclose(tout, expected.cpu(), rtol=check_tolerance, atol=check_tolerance):
+ this_good = False
+ except Exception: # there maybe size mismatch and it may be OK
+ this_good = False
+ if not this_good:
+ logging.info(f"Results mismatch! PyTorch(expected):\n{expected}\nTorchScript:\n{tout}")
+ all_good = False
+ return all_good
+
+
+def run_ort_and_compare(sess, ort_input, output_example, check_tolerance=0.01):
+ """Run onnxrt and compare with output example"""
+ ort_out = sess.run(None, ort_input)
+ all_good = True
+ for i, out in enumerate(ort_out):
+ expected = output_example[i]
+ if torch.is_tensor(expected):
+ tout = torch.from_numpy(out)
+ logging.debug(f"Checking output {i}, shape: {expected.shape}:\n")
+ this_good = True
+ try:
+ if not torch.allclose(tout, expected.cpu(), rtol=check_tolerance, atol=100 * check_tolerance):
+ this_good = False
+ except Exception: # there maybe size mismatch and it may be OK
+ this_good = False
+ if not this_good:
+ logging.info(f"onnxruntime results mismatch! PyTorch(expected):\n{expected}\nONNXruntime:\n{tout}")
+ all_good = False
+ return all_good
+
+
+def swap_modules(model: nn.Module, mapping: Dict[str, nn.Module]):
+ """This function swaps nested modules as specified by "dot paths" in mod with a desired replacement. This allows
+ for swapping nested modules through arbitrary levels if children.
+
+ note::
+ This occurs in place, if you want to preserve model then make sure to copy it first.
+ """
+ for path, new_mod in mapping.items():
+ expanded_path = path.split(".")
+ parent_mod = model
+ for sub_path in expanded_path[:-1]:
+ parent_mod = parent_mod._modules[sub_path] # pylint: disable=protected-access
+ parent_mod._modules[expanded_path[-1]] = new_mod # pylint: disable=protected-access
+
+ return model
+
+
+def replace_modules(
+ model: nn.Module, expansions: Dict[str, Callable[[nn.Module], Optional[nn.Module]]] = None
+) -> nn.Module:
+ """Top-level function to replace modules in model, specified by class name with a desired replacement.
+
+ note::
+ This occurs in place, if you want to preserve model then make sure to copy it first.
+
+ Parameters
+ ----------
+ model : nn.Module
+ Top-level model to replace modules in.
+ expansions : Dict[str, Callable[[nn.Module], Optional[nn.Module]]]
+ A dictionary of module class names to functions to replace them with.
+
+ Returns
+ -------
+ nn.Module
+ The model with replaced modules.
+ """
+ mapping: Dict[str, nn.Module] = {}
+ for name, m in model.named_modules():
+ m_type = type(m).__name__
+ if m_type in expansions: # type: ignore
+ if swapped := expansions[m_type](m): # type: ignore
+ mapping[name] = swapped
+ logging.warning(f"Swapped {len(mapping)} modules")
+ swap_modules(model, mapping)
+ return model
+
+
+script_replacements: Dict = {}
+
+
+def replace_for_export(model: nn.Module) -> nn.Module:
+ """Top-level function to replace default set of modules in model
+
+ note::
+ This occurs in place, if you want to preserve model then make sure to copy it first.
+
+ Parameters
+ ----------
+ model : nn.Module
+ Top-level model to replace modules in.
+
+ Returns
+ -------
+ nn.Module
+ The model with replaced modules.
+ """
+ replace_modules(model, script_replacements)
diff --git a/atommic/utils/formaters/__init__.py b/atommic/utils/formaters/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/atommic/utils/formaters/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/atommic/utils/formaters/base.py b/atommic/utils/formaters/base.py
new file mode 100644
index 00000000..47208ac6
--- /dev/null
+++ b/atommic/utils/formaters/base.py
@@ -0,0 +1,135 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/formatters/base.py
+
+import logging
+
+from atommic.utils.formaters.colors import Fore as ForegroundColors
+from atommic.utils.formaters.utils import check_color_support, to_unicode
+
+__all__ = ["BaseATOMMICFormatter", "DebugATOMMICFormatter"]
+
+
+class BaseFormatter(logging.Formatter):
+ """Base class for all formatters used in Tornado. Key features of this formatter are:
+ * Color support when logging to a terminal that supports it.
+ * Timestamps on every log line.
+ * Robust against str/bytes encoding problems.
+ """
+
+ DEFAULT_FORMAT = "%(color)s[%(levelname)1.1s %(asctime)s %(module)s:%(lineno)d]%(end_color)s %(message)s"
+
+ DEFAULT_DATE_FORMAT = "%Y-%m-%d %H:%M:%S"
+
+ DEFAULT_COLORS = {
+ logging.DEBUG: ForegroundColors.CYAN,
+ logging.INFO: ForegroundColors.GREEN,
+ logging.WARNING: ForegroundColors.YELLOW,
+ logging.ERROR: ForegroundColors.MAGENTA,
+ logging.CRITICAL: ForegroundColors.RED,
+ }
+
+ def __init__(self, color=True, fmt=None, datefmt=None, colors=None):
+ """Inits :class:`BaseFormatter`.
+
+ Parameters
+ ----------
+ color : bool
+ Enable color support. Default is ``True``.
+ fmt : str
+ Log message format. It will be applied to the attributes dict of log records. The text between
+ ``%(color)s`` and ``%(end_color)s`` will be colored depending on the level if color support is on. Default
+ is ``None``.
+ datefmt : str
+ Datetime format. Used for formatting ``(asctime)`` placeholder in ``prefix_fmt``. Default is ``None``.
+ colors : dict
+ Dictionary mapping logging level to terminal color code. Default is ``None``.
+ """
+ if fmt is None:
+ fmt = self.DEFAULT_FORMAT
+
+ if datefmt is None:
+ datefmt = self.DEFAULT_DATE_FORMAT
+
+ if colors is None:
+ colors = self.DEFAULT_COLORS
+
+ logging.Formatter.__init__(self, datefmt=datefmt)
+
+ self._fmt = fmt
+ self._colors = {}
+ self._normal = ""
+
+ if color and check_color_support():
+ self._colors = colors
+ self._normal = ForegroundColors.RESET
+
+ def format(self, record):
+ """Formats a record.
+
+ Parameters
+ ----------
+ record : LogRecord
+ Log record to be formatted.
+
+ Returns
+ -------
+ str
+ The formatted record as a string.
+ """
+ try:
+ message = record.getMessage()
+ if not isinstance(message, str):
+ raise AssertionError
+ # Encoding notes: The logging module prefers to work with character strings, but only enforces that log
+ # messages are instances of asestring. In python 2, non-ascii bytestrings will make their way through the
+ # logging framework until they blow up with an unhelpful decoding error (with this formatter it happens
+ # when we attach the prefix, but there are other opportunities for exceptions further along in the
+ # framework).
+ #
+ # If a byte string makes it this far, convert it to unicode to ensure it will make it out to the logs. Use
+ # repr() as a fallback to ensure that all byte strings can be converted successfully, but don't do it by
+ # default, so we don't add extra quotes to ascii bytestrings. This is a bit of a hacky place to do this,
+ # but it's worth it since the encoding errors that would otherwise result are so useless (and tornado is
+ # fond of using utf8-encoded byte strings wherever possible).
+ record.message = to_unicode(message)
+
+ except Exception as e:
+ record.message = f"Bad message (%{e}): %{record.__dict__}"
+
+ record.asctime = self.formatTime(record, self.datefmt)
+
+ if record.levelno in self._colors:
+ record.color = self._colors[record.levelno]
+ record.end_color = self._normal
+ else:
+ record.color = record.end_color = ""
+
+ formatted = self._fmt % record.__dict__
+
+ if record.exc_info and not record.exc_text:
+ record.exc_text = self.formatException(record.exc_info)
+
+ if record.exc_text:
+ # exc_text contains multiple lines. We need to _safe_unicode
+ # each line separately so that non-utf8 bytes don't cause all the newlines to turn into '\n'.
+ lines = [formatted.rstrip()]
+ lines.extend(to_unicode(ln) for ln in record.exc_text.split("\n"))
+
+ formatted = "\n".join(lines)
+ return formatted.replace("\n", "\n ")
+
+
+class BaseATOMMICFormatter(BaseFormatter):
+ """Base formatter for atommic logs."""
+
+ DEFAULT_FORMAT = "%(color)s[atommic %(levelname)1.1s %(asctime)s %(module)s:%(lineno)d]%(end_color)s %(message)s"
+
+
+class DebugATOMMICFormatter(BaseFormatter):
+ """Debug formatter for atommic logs."""
+
+ DEFAULT_FORMAT = (
+ "%(color)s[atommic %(levelname)1.1s %(asctime)s %(module)s:%(lineno)d rank:%(rank)s]%(end_color)s %(message)s"
+ )
diff --git a/atommic/utils/formaters/colors.py b/atommic/utils/formaters/colors.py
new file mode 100644
index 00000000..b233d51b
--- /dev/null
+++ b/atommic/utils/formaters/colors.py
@@ -0,0 +1,49 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/formatters/colors.py
+
+CSI = "\033["
+
+
+def code_to_chars(code):
+ """Convert ANSI color code to string of characters.
+
+ Parameters
+ ----------
+ code : int
+ ANSI color code.
+
+ Returns
+ -------
+ str
+ String of characters.
+ """
+ return CSI + str(code) + "m"
+
+
+class AnsiCodes:
+ """ANSI color codes."""
+
+ def __init__(self):
+ """Inits :class:`AnsiCodes`."""
+ # The subclasses declare class attributes which are numbers. Upon instantiation, we define instance attributes,
+ # which are the same as the class attributes but wrapped with the ANSI escape sequence
+ for name in dir(self):
+ if not name.startswith("_"):
+ value = getattr(self, name)
+ setattr(self, name, code_to_chars(value))
+
+
+class AnsiFore(AnsiCodes):
+ """ANSI color codes for foreground text."""
+
+ RED = 31
+ GREEN = 32
+ YELLOW = 33
+ MAGENTA = 35
+ CYAN = 36
+ RESET = 39
+
+
+Fore = AnsiFore()
diff --git a/atommic/utils/formaters/utils.py b/atommic/utils/formaters/utils.py
new file mode 100644
index 00000000..61859755
--- /dev/null
+++ b/atommic/utils/formaters/utils.py
@@ -0,0 +1,50 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/formatters/utils.py
+
+import sys
+
+from atommic.constants import ATOMMIC_ENV_VARNAME_ENABLE_COLORING
+from atommic.utils.env_var_parsing import get_envbool
+
+__all__ = ["check_color_support", "to_unicode"]
+
+
+def check_color_support():
+ """Checks if the terminal supports color.
+
+ Returns
+ -------
+ bool
+ True if the terminal supports color, False otherwise.
+ """
+ # Colors can be forced with an env variable
+ return bool(not sys.platform.lower().startswith("win") and get_envbool(ATOMMIC_ENV_VARNAME_ENABLE_COLORING, False))
+
+
+def to_unicode(value):
+ """Converts a string to unicode. If the string is already unicode, it is returned as is. If it is a byte string, it
+ is decoded using utf-8.
+
+ Parameters
+ ----------
+ value : str
+ The string to convert.
+
+ Returns
+ -------
+ str
+ The converted string.
+ """
+ try:
+ if isinstance(value, (str, type(None))):
+ return value
+
+ if not isinstance(value, bytes):
+ raise TypeError(f"Expected bytes, unicode, or None; got %{type(value)}")
+
+ return value.decode("utf-8")
+
+ except UnicodeDecodeError:
+ return repr(value)
diff --git a/atommic/utils/get_rank.py b/atommic/utils/get_rank.py
new file mode 100644
index 00000000..edd91d18
--- /dev/null
+++ b/atommic/utils/get_rank.py
@@ -0,0 +1,32 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/get_rank.py
+
+import torch
+
+from atommic.utils.env_var_parsing import get_envint
+
+
+def is_global_rank_zero():
+ """Helper function to determine if the current process is global_rank 0 (the main process)."""
+ # Try to get the pytorch RANK env var RANK is set by torch.distributed.launch
+ rank = get_envint("RANK", None)
+ if rank is not None:
+ return rank == 0
+
+ # Try to get the SLURM global rank env var SLURM_PROCID is set by SLURM
+ slurm_rank = get_envint("SLURM_PROCID", None)
+ if slurm_rank is not None:
+ return slurm_rank == 0
+
+ # if neither pytorch and SLURM env vars are set check NODE_RANK/GROUP_RANK and LOCAL_RANK env vars assume
+ # global_rank is zero if undefined
+ node_rank = get_envint("NODE_RANK", get_envint("GROUP_RANK", 0))
+ local_rank = get_envint("LOCAL_RANK", 0)
+ return node_rank == 0 and local_rank == 0
+
+
+def get_rank():
+ """Helper function that returns torch.distributed.get_rank() if DDP has been initialized otherwise returns 0."""
+ return 0 if is_global_rank_zero() else torch.distributed.get_rank()
diff --git a/atommic/utils/lightning_logger_patch.py b/atommic/utils/lightning_logger_patch.py
new file mode 100644
index 00000000..25a73cd4
--- /dev/null
+++ b/atommic/utils/lightning_logger_patch.py
@@ -0,0 +1,47 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/lightning_logger_patch.py
+
+import logging as _logging
+from logging.handlers import MemoryHandler
+from typing import Any, Dict
+
+import pytorch_lightning as pl
+
+HANDLERS: Dict[Any, Any] = {}
+
+
+def add_memory_handlers_to_pl_logger():
+ """Adds two MemoryHandlers to pytorch_lightning's logger. These two handlers are essentially message buffers. This
+ function is called in atommic.utils.__init__.py. These handlers are used in add_filehandlers_to_pl_logger to flush
+ buffered messages to files.
+ """
+ if not HANDLERS:
+ HANDLERS["memory_err"] = MemoryHandler(-1)
+ HANDLERS["memory_err"].addFilter(lambda record: record.levelno > _logging.INFO)
+ HANDLERS["memory_all"] = MemoryHandler(-1)
+ pl._logger.addHandler(HANDLERS["memory_err"]) # pylint: disable=protected-access
+ pl._logger.addHandler(HANDLERS["memory_all"]) # pylint: disable=protected-access
+
+
+def add_filehandlers_to_pl_logger(all_log_file, err_log_file):
+ """Adds two filehandlers to pytorch_lightning's logger. Called in atommic.utils.exp_manager(). The first
+ filehandler logs all messages to all_log_file while the second filehandler logs all WARNING and higher messages to
+ err_log_file. If "memory_err" and "memory_all" exist in HANDLERS, then those buffers are flushed to err_log_file
+ and all_log_file respectively, and then closed.
+ """
+ HANDLERS["file"] = _logging.FileHandler(all_log_file)
+ pl._logger.addHandler(HANDLERS["file"]) # pylint: disable=protected-access
+ HANDLERS["file_err"] = _logging.FileHandler(err_log_file)
+ HANDLERS["file_err"].addFilter(lambda record: record.levelno > _logging.INFO)
+ pl._logger.addHandler(HANDLERS["file_err"]) # pylint: disable=protected-access
+
+ if HANDLERS.get("memory_all"):
+ HANDLERS["memory_all"].setTarget(HANDLERS["file"])
+ HANDLERS["memory_all"].close()
+ del HANDLERS["memory_all"]
+ if HANDLERS.get("memory_err"):
+ HANDLERS["memory_err"].setTarget(HANDLERS["file_err"])
+ HANDLERS["memory_err"].close()
+ del HANDLERS["memory_err"]
diff --git a/atommic/utils/metaclasses.py b/atommic/utils/metaclasses.py
new file mode 100644
index 00000000..636aea76
--- /dev/null
+++ b/atommic/utils/metaclasses.py
@@ -0,0 +1,28 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/metaclasses.py
+
+import threading
+from typing import Any, Dict
+
+
+class Singleton(type):
+ """Implementation of a generic, tread-safe singleton meta-class. Can be used as meta-class, i.e. will create."""
+
+ # List of instances - one per class.
+ __instances: Dict[Any, Any] = {}
+ # Lock used for accessing the instance.
+ __lock = threading.Lock()
+
+ def __call__(cls, *args, **kwargs):
+ """Inits :class:`Singleton`."""
+ if cls not in cls.__instances:
+ # Enter critical section.
+ with cls.__lock:
+ # Check once again.
+ if cls not in cls.__instances:
+ # Create a new object instance - one per class.
+ cls.__instances[cls] = super(Singleton, cls).__call__(*args, **kwargs)
+ # Return the instance.
+ return cls.__instances[cls]
diff --git a/atommic/utils/model_utils.py b/atommic/utils/model_utils.py
new file mode 100644
index 00000000..efde8eff
--- /dev/null
+++ b/atommic/utils/model_utils.py
@@ -0,0 +1,659 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/model_utils.py
+
+import copy
+import importlib
+import os
+import sys
+from dataclasses import dataclass, is_dataclass
+from enum import Enum
+from functools import lru_cache
+from pathlib import Path
+from typing import Any, List, Optional, Set, Tuple, Union
+
+import wrapt
+from omegaconf import DictConfig, ListConfig, OmegaConf
+from omegaconf.errors import OmegaConfBaseException
+from packaging.version import Version
+from pytorch_lightning import LightningModule
+
+import atommic
+from atommic.constants import ATOMMIC_ENV_CACHE_DIR
+from atommic.core.classes.common import PretrainedModelInfo
+from atommic.core.classes.modelPT import ModelPT
+from atommic.core.conf.modelPT import atommicConfig
+from atommic.utils import logging
+from atommic.utils.app_state import AppState
+
+_HAS_HYDRA = True
+
+_VAL_TEST_FASTPATH_KEY = "ds_item"
+
+__all__ = [
+ "ArtifactPathType",
+ "ArtifactItem",
+ "resolve_dataset_name_from_cfg",
+ "parse_dataset_as_name",
+ "unique_names_check",
+ "resolve_validation_dataloaders",
+ "wrap_training_step",
+ "convert_model_config_to_dict_config",
+ "_convert_config",
+ "maybe_update_config_version",
+ "import_class_by_path",
+ "resolve_subclass_pretrained_model_info",
+ "check_lib_version",
+ "resolve_cache_dir",
+ "inject_model_parallel_rank",
+ "uninject_model_parallel_rank",
+]
+
+
+class ArtifactPathType(Enum):
+ """ArtifactPathType refers to the type of the path that the artifact is located at.
+ LOCAL_PATH: A user local filepath that exists on the file system.
+ TAR_PATH: A (generally flattened) filepath that exists inside an archive (that may have its own full path).
+ """
+
+ LOCAL_PATH = 0
+ TAR_PATH = 1
+
+
+@dataclass(init=False)
+class ArtifactItem:
+ """ArtifactItem is a dataclass that holds the information of an artifact."""
+
+ path: str
+ path_type: ArtifactPathType
+ hashed_path: Optional[str] = None
+
+
+def resolve_dataset_name_from_cfg(cfg: "DictConfig") -> Union[Union[str, int, Enum, float, bool, None], Any]:
+ """Parses items of the provided sub-config to find the first potential key that resolves to an existing file or
+ directory.
+
+ # Fast-path Resolution
+ In order to handle cases where we need to resolve items that are not paths, a fastpath key can be provided as
+ defined in the global `_VAL_TEST_FASTPATH_KEY`.
+
+ This key can be used in two ways :
+ ## _VAL_TEST_FASTPATH_KEY points to another key in the config
+ If this _VAL_TEST_FASTPATH_KEY points to another key in this config itself, then we assume we want to loop through
+ the values of that key. This allows for any key in the config to become a fastpath key.
+
+ Example
+ -------
+ validation_ds:
+
+ .. code-block::
+
+ splits: "val"
+ ...
+ <_VAL_TEST_FASTPATH_KEY>: "splits" <-- this points to the key name "splits"
+
+ Then we can write the following when overriding in hydra:
+ ```python
+ python train_file.py ... model.validation_ds.splits=[val1, val2, dev1, dev2] ...
+ ```
+ ## _VAL_TEST_FASTPATH_KEY itself acts as the resolved key
+ If this _VAL_TEST_FASTPATH_KEY does not point to another key in the config, then it is assumed that the items of
+ this key itself are used for resolution.
+
+ Example
+ -------
+ validation_ds:
+
+ .. code-block::
+
+ <_VAL_TEST_FASTPATH_KEY>: "val" <-- this points to the key name "splits"
+
+ Then we can write the following when overriding in hydra:
+ ```python
+ python train_file.py ... model.validation_ds.<_VAL_TEST_FASTPATH_KEY>=[val1, val2, dev1, dev2] ...
+ ```
+ # IMPORTANT NOTE:
+ It potentially mismatch if there exist more than 2 valid paths, and the first path does *not* resolve the
+ path of the data file (but does resolve to some other valid path). To avoid this side effect, place the data path
+ as the first item on the config file.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Sub-config of the config file.
+
+ Returns
+ -------
+ Union[str, int, Enum, float, bool, None]
+ A str representing the `key` of the config which hosts the filepath(s), or None in case path could not be
+ resolved.
+ """
+ if _VAL_TEST_FASTPATH_KEY in cfg and cfg[_VAL_TEST_FASTPATH_KEY] is not None:
+ fastpath_key = cfg[_VAL_TEST_FASTPATH_KEY]
+
+ if isinstance(fastpath_key, str) and fastpath_key in cfg:
+ return cfg[fastpath_key]
+ return _VAL_TEST_FASTPATH_KEY
+
+ for key, value in cfg.items():
+ if type(value) in [list, tuple, ListConfig]:
+ # Count the number of valid paths in the list
+ values_are_paths = 0
+ for val_i in value:
+ val_i = str(val_i)
+
+ if os.path.exists(val_i) or os.path.isdir(val_i):
+ values_are_paths += 1
+ else:
+ # reset counter and break inner loop
+ break
+
+ if values_are_paths == len(value):
+ return key
+
+ elif os.path.exists(str(value)) or os.path.isdir(str(value)):
+ return key
+
+ return None
+
+
+def parse_dataset_as_name(name: str) -> str:
+ """Constructs a valid prefix-name from a provided file path.
+
+ Parameters
+ ----------
+ name : str
+ Path to some valid data/manifest file or a python object that will be used as a name for the data loader (via
+ str() cast).
+
+ Returns
+ -------
+ str
+ A valid prefix-name for the data loader.
+ """
+ name = Path(name).stem if os.path.exists(name) or os.path.isdir(name) else name
+ # cleanup name
+ name = name.replace("-", "_")
+
+ if "manifest" in name:
+ name = name.replace("manifest", "")
+
+ if "dataset" in name:
+ name = name.replace("dataset", "")
+
+ # Test if the manifest/dataset name was simply `manifest.yaml` or `dataset.yaml`: Invalid names.
+ if name == "":
+ raise ValueError(
+ "Provided dataset / manifest filename was `manifest.json` or `dataset.json`.\n"
+ "Such a name is invalid, since multiple datasets/manifests can share the same name,\n"
+ "thereby overriding their results during logging. Please pick a more descriptive filename \n"
+ "for the provided dataset / manifest file."
+ )
+
+ if name[-1] != "_":
+ name = f"{name}_"
+
+ return name
+
+
+def unique_names_check(name_list: Optional[List[str]]):
+ """Performs a uniqueness check on the name list resolved, so that it can warn users about non-unique keys.
+
+ Parameters
+ ----------
+ name_list : Optional[List[str]]
+ List of strings resolved for data loaders.
+ """
+ if name_list is None:
+ return
+
+ # Name uniqueness checks
+ names = set()
+ for name in name_list:
+ if name in names:
+ logging.warning(
+ "Name resolution has found more than one data loader having the same name !\n"
+ "In such cases, logs will nor be properly generated. "
+ "Please rename the item to have unique names.\n"
+ f"Resolved name : {name}"
+ )
+ else:
+ # we need just hash key check, value is just a placeholder
+ names.add(name)
+
+
+def resolve_validation_dataloaders(model: ModelPT):
+ """Helper method that operates on the ModelPT class to automatically support multiple dataloaders for the
+ validation set. It does so by first resolving the path to one/more data files via
+ `resolve_dataset_name_from_cfg()`. If this resolution fails, it assumes the data loader is prepared to manually
+ support / not support multiple data loaders and simply calls the appropriate setup method.
+ If resolution succeeds:
+ - Checks if provided path is to a single file or a list of files.
+ If a single file is provided, simply tags that file as such and loads it via the setup method.
+ If multiple files are provided:
+ - Inject a new manifest path at index "i" into the resolved key.
+ - Calls the appropriate setup method to set the data loader.
+ - Collects the initialized data loader in a list and preserves it.
+ - Once all data loaders are processed, assigns the list of loaded loaders to the ModelPT.
+ - Finally, assigns a list of unique names resolved from the file paths to the ModelPT.
+
+ Parameters
+ ----------
+ model : ModelPT
+ ModelPT subclass, which requires >=1 Validation Dataloaders to be setup.
+ """
+ if not _HAS_HYDRA:
+ logging.error("This function requires Hydra/OmegaConf and it was not installed.")
+ sys.exit(1)
+ cfg = copy.deepcopy(model._cfg) # pylint: disable=protected-access
+ dataloaders: List[Any] = []
+
+ # process val_loss_idx
+ if "val_dl_idx" in cfg.validation_ds:
+ cfg = OmegaConf.to_container(cfg)
+ val_dl_idx = cfg["validation_ds"].pop("val_dl_idx")
+ cfg = OmegaConf.create(cfg)
+ else:
+ val_dl_idx = 0
+
+ # Set val_loss_idx
+ model._val_dl_idx = val_dl_idx # pylint: disable=protected-access
+
+ ds_key = resolve_dataset_name_from_cfg(cfg.validation_ds)
+
+ if ds_key is None:
+ logging.debug(
+ f"Could not resolve file path from provided config - {cfg.validation_ds}. "
+ "Disabling support for multi-dataloaders."
+ )
+
+ model.setup_validation_data(cfg.validation_ds)
+ return
+
+ ds_values = cfg.validation_ds[ds_key]
+
+ if isinstance(ds_values, (list, tuple, ListConfig)):
+ for ds_value in ds_values:
+ cfg.validation_ds[ds_key] = ds_value
+ model.setup_validation_data(cfg.validation_ds)
+ dataloaders.append(model.validation_dl)
+
+ model.validation_dl = dataloaders
+ model.validation_names = [parse_dataset_as_name(ds) for ds in ds_values] # type: ignore
+
+ unique_names_check(name_list=model.validation_names)
+ return
+ model.setup_validation_data(cfg.validation_ds)
+ model.validation_names = [parse_dataset_as_name(ds_values)]
+
+ unique_names_check(name_list=model.validation_names)
+
+
+def resolve_test_dataloaders(model: "ModelPT"):
+ """Helper method that operates on the ModelPT class to automatically support multiple dataloaders for the test set.
+ It does so by first resolving the path to one/more data files via `resolve_dataset_name_from_cfg()`. If this
+ resolution fails, it assumes the data loader is prepared to manually support / not support multiple data loaders
+ and simply calls the appropriate setup method.
+
+ If resolution succeeds:
+ Checks if provided path is to a single file or a list of files.
+ If a single file is provided, simply tags that file as such and loads it via the setup method.
+ If multiple files are provided:
+ Inject a new manifest path at index "i" into the resolved key.
+ Calls the appropriate setup method to set the data loader.
+ Collects the initialized data loader in a list and preserves it.
+ Once all data loaders are processed, assigns the list of loaded loaders to the ModelPT.
+ Finally, assigns a list of unique names resolved from the file paths to the ModelPT.
+
+ Parameters
+ ----------
+ model : ModelPT
+ ModelPT subclass, which requires >=1 Test Dataloaders to be setup.
+ """
+ if not _HAS_HYDRA:
+ logging.error("This function requires Hydra/OmegaConf and it was not installed.")
+ sys.exit(1)
+ cfg = copy.deepcopy(model._cfg) # pylint: disable=protected-access
+ dataloaders: List[Any] = []
+
+ # process test_loss_idx
+ if "test_dl_idx" in cfg.test_ds:
+ cfg = OmegaConf.to_container(cfg)
+ test_dl_idx = cfg["test_ds"].pop("test_dl_idx")
+ cfg = OmegaConf.create(cfg)
+ else:
+ test_dl_idx = 0
+
+ # Set val_loss_idx
+ model._test_dl_idx = test_dl_idx # pylint: disable=protected-access
+
+ ds_key = resolve_dataset_name_from_cfg(cfg.test_ds)
+
+ if ds_key is None:
+ logging.debug(
+ f"Could not resolve file path from provided config - {cfg.test_ds}. "
+ "Disabling support for multi-dataloaders."
+ )
+
+ model.setup_test_data(cfg.test_ds)
+ return
+
+ ds_values = cfg.test_ds[ds_key]
+
+ if isinstance(ds_values, (list, tuple, ListConfig)):
+ for ds_value in ds_values:
+ cfg.test_ds[ds_key] = ds_value
+ model.setup_test_data(cfg.test_ds)
+ dataloaders.append(model.test_dl)
+
+ model.test_dl = dataloaders
+ model.test_names = [parse_dataset_as_name(ds) for ds in ds_values] # type: ignore
+
+ unique_names_check(name_list=model.test_names)
+ return
+ model.setup_test_data(cfg.test_ds)
+ model.test_names = [parse_dataset_as_name(ds_values)]
+
+ unique_names_check(name_list=model.test_names)
+
+
+@wrapt.decorator
+def wrap_training_step(wrapped, instance: LightningModule, args, kwargs):
+ """Wraps the training step of the LightningModule.
+
+ Parameters
+ ----------
+ wrapped : function
+ The wrapped function.
+ instance : LightningModule
+ The LightningModule instance.
+ args : tuple
+ The arguments passed to the wrapped function.
+ kwargs : dict
+ The keyword arguments passed to the wrapped function.
+
+ Returns
+ -------
+ function
+ The return value of the wrapped function.
+ """
+ output_dict = wrapped(*args, **kwargs)
+
+ if isinstance(output_dict, dict) and output_dict is not None and "log" in output_dict:
+ log_dict = output_dict.pop("log")
+ instance.log_dict(log_dict, on_step=True)
+
+ return output_dict
+
+
+def convert_model_config_to_dict_config(cfg: Union[DictConfig, atommicConfig]) -> DictConfig:
+ """Converts its input into a standard DictConfig.
+
+ Possible input values are:
+ - DictConfig
+ - A dataclass which is a subclass of atommicConfig
+
+ Parameters
+ ----------
+ cfg : Union[DictConfig, atommicConfig]
+ A dict-like object.
+
+ Returns
+ -------
+ DictConfig
+ The equivalent DictConfig.
+ """
+ if not _HAS_HYDRA:
+ logging.error("This function requires Hydra/OmegaConf and it was not installed.")
+ sys.exit(1)
+ if not isinstance(cfg, (OmegaConf, DictConfig)) and is_dataclass(cfg):
+ cfg = OmegaConf.structured(cfg)
+
+ if not isinstance(cfg, DictConfig):
+ raise ValueError(f"cfg constructor argument must be of type DictConfig/dict but got {type(cfg)} instead.")
+
+ config = OmegaConf.to_container(cfg, resolve=True)
+ config = OmegaConf.create(config)
+ return config
+
+
+def _convert_config(cfg: "OmegaConf"):
+ """Recursive function converting the configuration from old hydra format to the new one."""
+ if not _HAS_HYDRA:
+ logging.error("This function requires Hydra/OmegaConf and it was not installed.")
+ sys.exit(1)
+
+ # Get rid of params.
+ if "params" in cfg:
+ params = cfg.pop("params")
+ for param_key, param_val in params.items():
+ cfg[param_key] = param_val
+
+ # Recursion.
+ try:
+ for _, sub_cfg in cfg.items():
+ if isinstance(sub_cfg, DictConfig):
+ _convert_config(sub_cfg)
+ except OmegaConfBaseException as e:
+ logging.warning(f"Skipped conversion for config/subconfig:\n{cfg}\n Reason: {e}.")
+
+
+def maybe_update_config_version(cfg: "DictConfig"):
+ """Recursively convert Hydra 0.x configs to Hydra 1.x configs.
+ Changes include:
+ - `cls` -> `_target_`.
+ - `params` -> drop params and shift all arguments to parent.
+ - `target` -> `_target_` cannot be performed due to ModelPT injecting `target` inside class.
+
+ Parameters
+ ----------
+ cfg : DictConfig
+ Any Hydra compatible DictConfig
+
+ Returns
+ -------
+ DictConfig
+ An updated DictConfig that conforms to Hydra 1.x format.
+ """
+ if not _HAS_HYDRA:
+ logging.error("This function requires Hydra/OmegaConf and it was not installed.")
+ sys.exit(1)
+ if cfg is not None and not isinstance(cfg, DictConfig):
+ try:
+ temp_cfg = OmegaConf.create(cfg)
+ cfg = temp_cfg
+ except OmegaConfBaseException:
+ # Cannot be cast to DictConfig, skip updating.
+ return cfg
+
+ # Make a copy of model config.
+ cfg = copy.deepcopy(cfg)
+ OmegaConf.set_struct(cfg, False)
+
+ # Convert config.
+ _convert_config(cfg)
+
+ # Update model config.
+ OmegaConf.set_struct(cfg, True)
+
+ return cfg
+
+
+@lru_cache(maxsize=1024)
+def import_class_by_path(path: str):
+ """Recursive import of class by path string."""
+ paths = path.split(".")
+ path = ".".join(paths[:-1])
+ class_name = paths[-1]
+ mod = __import__(path, fromlist=[class_name])
+ mod = getattr(mod, class_name)
+ return mod
+
+
+def resolve_subclass_pretrained_model_info(base_class) -> Union[List[PretrainedModelInfo], Set[Any]]:
+ """Recursively traverses the inheritance graph of subclasses to extract all pretrained model info.
+ First constructs a set of unique pretrained model info by performing DFS over the inheritance graph. All model info
+ belonging to the same class is added together.
+
+ Parameters
+ ----------
+ base_class : class
+ The root class, whose subclass graph will be traversed.
+
+ Returns
+ -------
+ list
+ A list of unique pretrained model infos belonging to all the inherited subclasses of this baseclass.
+ """
+ list_of_models = set()
+
+ def recursive_subclass_walk(cls):
+ """
+ Recursively traverses the inheritance graph of subclasses to extract all pretrained model info.
+
+ Parameters
+ ----------
+ cls : class
+ The class to be traversed.
+
+ Returns
+ -------
+ list
+ A list of unique pretrained model infos belonging to all the inherited subclasses of this baseclass.
+ """
+ for subclass in cls.__subclasses__():
+ # step into its immediate subclass
+ recursive_subclass_walk(subclass)
+
+ subclass_models = subclass.list_available_models()
+
+ if subclass_models is not None and len(subclass_models) > 0:
+ # Inject subclass info into pretrained model info, if not already overridden by subclass.
+ for model_info in subclass_models:
+ # If subclass manually injects class_, dont override.
+ if model_info.class_ is None:
+ model_info.class_ = subclass
+
+ for model_info in subclass_models:
+ list_of_models.add(model_info)
+
+ recursive_subclass_walk(base_class)
+ list_of_models = list(sorted(list_of_models)) # type: ignore
+ return list_of_models
+
+
+def check_lib_version(lib_name: str, checked_version: str, operator) -> Tuple[Optional[bool], str]:
+ """Checks if a library is installed, and if it is, checks the operator(lib.__version__, checked_version) as a
+ result. This bool result along with a string backend of result is returned. If the library is not installed at all,
+ then returns None instead, along with a string explaining that the library is not installed
+
+ Parameters
+ ----------
+ lib_name : str
+ Lower case str name of the library that must be imported.
+ checked_version : str
+ Semver string that is compared against lib.__version__.
+ operator : bool
+ Binary callable function func(a, b) -> bool; that compares lib.__version__ against version in some manner. Must
+ return a boolean.
+
+ Returns
+ -------
+ tuple
+ A tuple of results:
+ - Bool or None. Bool if the library could be imported, and the result of
+ operator(lib.__version__, checked_version) or False if __version__ is not implemented in lib.
+ None is passed if the library is not installed at all.
+ - A string analysis of the check.
+ """
+ try:
+ if "." in lib_name:
+ mod = import_class_by_path(lib_name)
+ else:
+ mod = importlib.import_module(lib_name)
+
+ if hasattr(mod, "__version__"):
+ lib_ver = Version(mod.__version__)
+ match_ver = Version(checked_version)
+
+ if operator(lib_ver, match_ver):
+ msg = f"Lib {lib_name} version is satisfied !"
+ return True, msg
+ msg = (
+ f"Lib {lib_name} version ({lib_ver}) is not {operator.__name__} than required version "
+ f"{checked_version}.\n"
+ "Please upgrade the lib using either pip or conda to the latest version."
+ )
+ return False, msg
+ msg = (
+ f"Lib {lib_name} does not implement __version__ in its init file. "
+ "Could not check version compatibility."
+ )
+ return False, msg
+ except (AttributeError, ImportError, ModuleNotFoundError):
+ pass
+
+ msg = f"Lib {lib_name} has not been installed. Please use pip or conda to install this package."
+ return None, msg
+
+
+def resolve_cache_dir() -> Path:
+ """Utility method to resolve a cache directory for atommic that can be overridden by an environment variable.
+
+ Example
+ -------
+ atommic_CACHE_DIR="~/atommic_cache_dir/" python atommic_example_script.py
+
+ Returns
+ -------
+ A Path object, resolved to the absolute path of the cache directory. If no override is provided, uses an inbuilt
+ default which adapts to atommic versions strings.
+ """
+ override_dir = os.environ.get(ATOMMIC_ENV_CACHE_DIR, "")
+ return (
+ Path.joinpath(Path.home(), f".cache/torch/atommic/atommic_{atommic.__version__}")
+ if override_dir == ""
+ else Path(override_dir).resolve()
+ )
+
+
+def uninject_model_parallel_rank(filepath):
+ """Uninjects tensor/pipeline model parallel ranks from the filepath."""
+ filepath = str(filepath)
+ if "mp_rank" in filepath or "tp_rank" in filepath:
+ dirname = os.path.dirname(os.path.dirname(filepath))
+ basename = os.path.basename(filepath)
+ filepath = os.path.join(dirname, basename)
+ return filepath
+
+
+def inject_model_parallel_rank(filepath):
+ """Injects tensor/pipeline model parallel ranks into the filepath. Does nothing if not using model parallelism."""
+ filepath = uninject_model_parallel_rank(filepath)
+ app_state = AppState()
+ if app_state.model_parallel_size is not None and app_state.model_parallel_size > 1:
+ # filepath needs to be updated to include mp_rank
+ dirname = os.path.dirname(filepath)
+ basename = os.path.basename(filepath)
+ if app_state.pipeline_model_parallel_size is None or app_state.pipeline_model_parallel_size == 1:
+ filepath = f"{dirname}/mp_rank_{app_state.tensor_model_parallel_rank:02d}/{basename}"
+ else:
+ filepath = (
+ f"{dirname}/tp_rank_{app_state.tensor_model_parallel_rank:02d}_pp_rank_"
+ f"{app_state.pipeline_model_parallel_rank:03d}/{basename} "
+ )
+ return filepath
+ return filepath
+
+
+def ckpt_to_dir(filepath: Union[str, Path]) -> Path:
+ """PTL considers checkpoints as .ckpt files. This method removes the extension and returns a path to be used as a
+ directory for distributed checkpoints.
+ """
+ filepath = Path(filepath)
+ # adding this assert because we will later remove directories based on the return value of this method
+ assert filepath.suffix == ".ckpt", f'filepath: {filepath} must have .ckpt extension'
+ # create a new path whose name is the original filepath without the .ckpt extension
+ checkpoint_dir = filepath.with_name(filepath.stem)
+ return checkpoint_dir
diff --git a/atommic/utils/timers.py b/atommic/utils/timers.py
new file mode 100644
index 00000000..2d1b2f86
--- /dev/null
+++ b/atommic/utils/timers.py
@@ -0,0 +1,143 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/nemo/utils/timers.py
+
+import time
+
+import numpy as np
+import torch
+
+__all__ = ["NamedTimer"]
+
+
+class NamedTimer:
+ """A timer class that supports multiple named timers. A named timer can be used multiple times, in which case the
+ average dt will be returned. A named timer cannot be started if it is already currently running.
+
+ Use case: measuring execution of multiple code blocks.
+ """
+
+ _REDUCTION_TYPE = ["mean", "sum", "min", "max", "none"]
+
+ def __init__(self, reduction="mean", sync_cuda=False, buffer_size=-1):
+ """Inits :class:`NamedTimer`.
+
+ Parameters
+ ----------
+ reduction : str
+ Reduction over multiple timings of the same timer (none - returns the list instead of a scalar).
+ sync_cuda : bool
+ If True torch.cuda.synchronize() is called for start/stop.
+ buffer_size : int
+ If positive, limits the number of stored measures per name.
+ """
+ if reduction not in self._REDUCTION_TYPE:
+ raise ValueError(f"Unknown reduction={reduction} please use one of {self._REDUCTION_TYPE}")
+
+ self._reduction = reduction
+ self._sync_cuda = sync_cuda
+ self._buffer_size = buffer_size
+
+ self.reset()
+
+ def __getitem__(self, k):
+ """Get item from timer."""
+ return self.get(k)
+
+ @property
+ def buffer_size(self):
+ """Returns the buffer size of the timer."""
+ return self._buffer_size
+
+ @property
+ def _reduction_fn(self):
+ """Returns the reduction function for the timer."""
+ if self._reduction == "none":
+
+ def fn(x): # pragma: no cover
+ return x
+
+ else:
+ fn = getattr(np, self._reduction)
+ return fn
+
+ def reset(self, name=None):
+ """Resents all / specific timer
+
+ Parameters
+ ----------
+ name : str
+ Timer name to reset (if None all timers are reset).
+ """
+ if name is None:
+ self.timers = {}
+ else:
+ self.timers[name] = {}
+
+ def start(self, name=""):
+ """Starts measuring a named timer.
+
+ Parameters
+ ----------
+ name : str
+ Timer name to start.
+ """
+ timer_data = self.timers.get(name, {})
+
+ if "start" in timer_data:
+ raise RuntimeError(f"Cannot start timer = '{name}' since it is already active")
+
+ # synchronize pytorch cuda execution if supported
+ if self._sync_cuda and torch.cuda.is_initialized():
+ torch.cuda.synchronize()
+
+ timer_data["start"] = time.time()
+
+ self.timers[name] = timer_data
+
+ def stop(self, name=""):
+ """Stops measuring a named timer.
+
+ Parameters
+ ----------
+ name : str
+ Timer name to stop.
+ """
+ timer_data = self.timers.get(name)
+ if (timer_data is None) or ("start" not in timer_data):
+ raise RuntimeError(f"Cannot end timer = '{name}' since it is not active")
+
+ # synchronize pytorch cuda execution if supported
+ if self._sync_cuda and torch.cuda.is_initialized():
+ torch.cuda.synchronize()
+
+ # compute dt and make timer inactive
+ dt = time.time() - timer_data.pop("start")
+
+ # store dt
+ timer_data["dt"] = timer_data.get("dt", []) + [dt]
+
+ # enforce buffer_size if positive
+ if self._buffer_size > 0:
+ timer_data["dt"] = timer_data["dt"][-self._buffer_size :]
+
+ self.timers[name] = timer_data
+
+ def get(self, name=""):
+ """Returns the value of a named timer
+
+ Parameters
+ ----------
+ name : str
+ Timer name to return.
+ """
+ dt_list = self.timers[name].get("dt", [])
+
+ return self._reduction_fn(dt_list)
+
+ def export(self):
+ """Exports a dictionary with average/all dt per named timer"""
+ fn = self._reduction_fn
+
+ return {k: fn(v["dt"]) for k, v in self.timers.items() if "dt" in v}
diff --git a/codecov.yml b/codecov.yml
new file mode 100644
index 00000000..ec05e1aa
--- /dev/null
+++ b/codecov.yml
@@ -0,0 +1,25 @@
+# Coverage configuration
+# ----------------------
+coverage:
+ status:
+ patch: true
+
+ range: 70..100 # First number represents red, and second represents green
+ # (default is 70..100)
+ round: nearest # up, down, or nearest
+ precision: 2 # Number of decimal places, between 0 and 5
+
+# Ignoring Paths
+# --------------
+# which folders/files to ignore
+ignore:
+ - examples/*
+ - projects/*
+ - setup.py
+
+# Pull request comments:
+# ----------------------
+# Diff is the Coverage Diff of the pull request.
+# Files are the files impacted by the pull request
+comment:
+ layout: diff, files # accepted in any order: reach, diff, flags, and/or files
diff --git a/docs/Makefile b/docs/Makefile
new file mode 100644
index 00000000..417fe2a0
--- /dev/null
+++ b/docs/Makefile
@@ -0,0 +1,216 @@
+# Makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS =
+SPHINXBUILD = sphinx-build
+PAPER =
+BUILDDIR = build
+
+# User-friendly check for sphinx-build
+ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1)
+$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/)
+endif
+
+# Internal variables.
+PAPEROPT_a4 = -D latex_paper_size=a4
+PAPEROPT_letter = -D latex_paper_size=letter
+ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
+# the i18n builder cannot share the environment and doctrees with the others
+I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source
+
+.PHONY: help
+help:
+ @echo "Please use \`make ' where is one of"
+ @echo " html to make standalone HTML files"
+ @echo " dirhtml to make HTML files named index.html in directories"
+ @echo " singlehtml to make a single large HTML file"
+ @echo " pickle to make pickle files"
+ @echo " json to make JSON files"
+ @echo " htmlhelp to make HTML files and a HTML help project"
+ @echo " qthelp to make HTML files and a qthelp project"
+ @echo " applehelp to make an Apple Help Book"
+ @echo " devhelp to make HTML files and a Devhelp project"
+ @echo " epub to make an epub"
+ @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
+ @echo " latexpdf to make LaTeX files and run them through pdflatex"
+ @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx"
+ @echo " text to make text files"
+ @echo " man to make manual pages"
+ @echo " texinfo to make Texinfo files"
+ @echo " info to make Texinfo files and run them through makeinfo"
+ @echo " gettext to make PO message catalogs"
+ @echo " changes to make an overview of all changed/added/deprecated items"
+ @echo " xml to make Docutils-native XML files"
+ @echo " pseudoxml to make pseudoxml-XML files for display purposes"
+ @echo " linkcheck to check all external links for integrity"
+ @echo " doctest to run all doctests embedded in the documentation (if enabled)"
+ @echo " coverage to run coverage check of the documentation (if enabled)"
+
+.PHONY: clean
+clean:
+ rm -rf $(BUILDDIR)/*
+
+.PHONY: html
+html:
+ $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+ @echo
+ @echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+.PHONY: dirhtml
+dirhtml:
+ $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
+ @echo
+ @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
+
+.PHONY: singlehtml
+singlehtml:
+ $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
+ @echo
+ @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
+
+.PHONY: pickle
+pickle:
+ $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
+ @echo
+ @echo "Build finished; now you can process the pickle files."
+
+.PHONY: json
+json:
+ $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
+ @echo
+ @echo "Build finished; now you can process the JSON files."
+
+.PHONY: htmlhelp
+htmlhelp:
+ $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
+ @echo
+ @echo "Build finished; now you can run HTML Help Workshop with the" \
+ ".hhp project file in $(BUILDDIR)/htmlhelp."
+
+.PHONY: qthelp
+qthelp:
+ $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
+ @echo
+ @echo "Build finished; now you can run "qcollectiongenerator" with the" \
+ ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
+ @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/OpenSeq2Seq.qhcp"
+ @echo "To view the help file:"
+ @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/OpenSeq2Seq.qhc"
+
+.PHONY: applehelp
+applehelp:
+ $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp
+ @echo
+ @echo "Build finished. The help book is in $(BUILDDIR)/applehelp."
+ @echo "N.B. You won't be able to view it unless you put it in" \
+ "~/Library/Documentation/Help or install it in your application" \
+ "bundle."
+
+.PHONY: devhelp
+devhelp:
+ $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
+ @echo
+ @echo "Build finished."
+ @echo "To view the help file:"
+ @echo "# mkdir -p $$HOME/.local/share/devhelp/OpenSeq2Seq"
+ @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/OpenSeq2Seq"
+ @echo "# devhelp"
+
+.PHONY: epub
+epub:
+ $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
+ @echo
+ @echo "Build finished. The epub file is in $(BUILDDIR)/epub."
+
+.PHONY: latex
+latex:
+ $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+ @echo
+ @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
+ @echo "Run \`make' in that directory to run these through (pdf)latex" \
+ "(use \`make latexpdf' here to do that automatically)."
+
+.PHONY: latexpdf
+latexpdf:
+ $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+ @echo "Running LaTeX files through pdflatex..."
+ $(MAKE) -C $(BUILDDIR)/latex all-pdf
+ @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+.PHONY: latexpdfja
+latexpdfja:
+ $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+ @echo "Running LaTeX files through platex and dvipdfmx..."
+ $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja
+ @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+.PHONY: text
+text:
+ $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
+ @echo
+ @echo "Build finished. The text files are in $(BUILDDIR)/text."
+
+.PHONY: man
+man:
+ $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
+ @echo
+ @echo "Build finished. The manual pages are in $(BUILDDIR)/man."
+
+.PHONY: texinfo
+texinfo:
+ $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+ @echo
+ @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo."
+ @echo "Run \`make' in that directory to run these through makeinfo" \
+ "(use \`make info' here to do that automatically)."
+
+.PHONY: info
+info:
+ $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo
+ @echo "Running Texinfo files through makeinfo..."
+ make -C $(BUILDDIR)/texinfo info
+ @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo."
+
+.PHONY: gettext
+gettext:
+ $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale
+ @echo
+ @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale."
+
+.PHONY: changes
+changes:
+ $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
+ @echo
+ @echo "The overview file is in $(BUILDDIR)/changes."
+
+.PHONY: linkcheck
+linkcheck:
+ $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
+ @echo
+ @echo "Link check complete; look for any errors in the above output " \
+ "or in $(BUILDDIR)/linkcheck/output.txt."
+
+.PHONY: doctest
+doctest:
+ $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
+ @echo "Testing of doctests in the sources finished, look at the " \
+ "results in $(BUILDDIR)/doctest/output.txt."
+
+.PHONY: coverage
+coverage:
+ $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage
+ @echo "Testing of coverage in the sources finished, look at the " \
+ "results in $(BUILDDIR)/coverage/python.txt."
+
+.PHONY: xml
+xml:
+ $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml
+ @echo
+ @echo "Build finished. The XML files are in $(BUILDDIR)/xml."
+
+.PHONY: pseudoxml
+pseudoxml:
+ $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml
+ @echo
+ @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml."
diff --git a/docs/assets/crop.png b/docs/assets/crop.png
new file mode 100644
index 00000000..46ed3fb9
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diff --git a/docs/assets/gdcc.png b/docs/assets/gdcc.png
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diff --git a/docs/assets/gdccpw.png b/docs/assets/gdccpw.png
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index 00000000..d99a7b64
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diff --git a/docs/assets/logo-simple.png b/docs/assets/logo-simple.png
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diff --git a/docs/assets/masks.png b/docs/assets/masks.png
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index 00000000..b72ff101
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diff --git a/docs/assets/mosim.png b/docs/assets/mosim.png
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diff --git a/docs/assets/n2r.png b/docs/assets/n2r.png
new file mode 100644
index 00000000..1171f0fd
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diff --git a/docs/assets/pw.png b/docs/assets/pw.png
new file mode 100644
index 00000000..3b23a5ca
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diff --git a/docs/assets/sense.png b/docs/assets/sense.png
new file mode 100644
index 00000000..1f3f6004
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diff --git a/docs/assets/ssdu.png b/docs/assets/ssdu.png
new file mode 100644
index 00000000..85fb2554
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diff --git a/docs/assets/zfpad.png b/docs/assets/zfpad.png
new file mode 100644
index 00000000..d49c5e95
Binary files /dev/null and b/docs/assets/zfpad.png differ
diff --git a/docs/make.bat b/docs/make.bat
new file mode 100644
index 00000000..dc1312ab
--- /dev/null
+++ b/docs/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+ set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=source
+set BUILDDIR=build
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+ echo.
+ echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+ echo.installed, then set the SPHINXBUILD environment variable to point
+ echo.to the full path of the 'sphinx-build' executable. Alternatively you
+ echo.may add the Sphinx directory to PATH.
+ echo.
+ echo.If you don't have Sphinx installed, grab it from
+ echo.https://www.sphinx-doc.org/
+ exit /b 1
+)
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/source/api/common/callbacks.rst b/docs/source/api/common/callbacks.rst
new file mode 100644
index 00000000..e06c453f
--- /dev/null
+++ b/docs/source/api/common/callbacks.rst
@@ -0,0 +1,39 @@
+*********
+Callbacks
+*********
+
+Exponential Moving Average (EMA)
+================================
+
+During training, EMA maintains a moving average of the trained parameters.
+EMA parameters can produce significantly better results and faster convergence for a variety of different domains and
+models.
+
+EMA is a simple calculation. EMA Weights are pre-initialized with the model weights at the start of training.
+
+Every training update, the EMA weights are updated based on the new model weights.
+
+.. math::
+
+ ema_w = ema_w * decay + model_w * (1-decay)
+
+Enabling EMA is straightforward in your .yaml file. For example:
+
+.. code-block:: bash
+
+ exp_manager.ema.enable=True
+ exp_manager.ema.decay=0.999
+
+Also offers other helpful arguments.
+
+.. list-table::
+ :header-rows: 1
+
+ * - Argument
+ - Description
+ * - `exp_manager.ema.validate_original_weights=True`
+ - Validate the original weights instead of EMA weights.
+ * - `exp_manager.ema.every_n_steps=2`
+ - Apply EMA every N steps instead of every step.
+ * - `exp_manager.ema.cpu_offload=True`
+ - Offload EMA weights to CPU. May introduce significant slow-downs.
diff --git a/docs/source/api/common/coil_sensitivity_maps.rst b/docs/source/api/common/coil_sensitivity_maps.rst
new file mode 100644
index 00000000..dba2bd87
--- /dev/null
+++ b/docs/source/api/common/coil_sensitivity_maps.rst
@@ -0,0 +1,9 @@
+Coil Sensitivity Maps
+---------------------
+
+.. autoclass:: atommic.collections.common.parts.coil_sensitivity_maps.MaximumEigenvaluePowerMethod
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.coil_sensitivity_maps.EspiritCalibration
+ :special-members: __init__
diff --git a/docs/source/api/common/data.rst b/docs/source/api/common/data.rst
new file mode 100644
index 00000000..e5106e87
--- /dev/null
+++ b/docs/source/api/common/data.rst
@@ -0,0 +1,50 @@
+Common MRI Data Classes
+-----------------------
+
+.. autoclass:: atommic.collections.common.data.mri_loader.MRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.common.data.subsample.MaskFunc
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.common.data.subsample.Equispaced1DMaskFunc
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.common.data.subsample.Equispaced2DMaskFunc
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+.. autoclass:: atommic.collections.common.data.subsample.Gaussian1DMaskFunc
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+.. autoclass:: atommic.collections.common.data.subsample.Gaussian2DMaskFunc
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.common.data.subsample.Poisson2DMaskFunc
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.common.data.subsample.Random1DMaskFunc
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autofunction:: atommic.collections.common.data.subsample.create_masker
diff --git a/docs/source/api/common/fft.rst b/docs/source/api/common/fft.rst
new file mode 100644
index 00000000..e602dfce
--- /dev/null
+++ b/docs/source/api/common/fft.rst
@@ -0,0 +1,19 @@
+FFT
+---
+
+.. autofunction:: atommic.collections.common.parts.fft.fft2
+
+
+.. autofunction:: atommic.collections.common.parts.fft.ifft2
+
+
+.. autofunction:: atommic.collections.common.parts.fft.fftshift
+
+
+.. autofunction:: atommic.collections.common.parts.fft.ifftshift
+
+
+.. autofunction:: atommic.collections.common.parts.fft.roll
+
+
+.. autofunction:: atommic.collections.common.parts.fft.roll_one_dim
diff --git a/docs/source/api/common/intro.rst b/docs/source/api/common/intro.rst
new file mode 100644
index 00000000..339e0371
--- /dev/null
+++ b/docs/source/api/common/intro.rst
@@ -0,0 +1,12 @@
+Common MRI collection
+=====================
+
+.. toctree::
+ :maxdepth: 8
+
+ callbacks
+ data
+ losses
+ metrics
+ nn
+ parts
diff --git a/docs/source/api/common/losses.rst b/docs/source/api/common/losses.rst
new file mode 100644
index 00000000..9288c9a4
--- /dev/null
+++ b/docs/source/api/common/losses.rst
@@ -0,0 +1,9 @@
+Common MRI Losses
+-----------------
+
+.. autoclass:: atommic.collections.common.losses.AggregatorLoss
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.losses.SinkhornDistance
+ :special-members: __init__
diff --git a/docs/source/api/common/metrics.rst b/docs/source/api/common/metrics.rst
new file mode 100644
index 00000000..585de2d6
--- /dev/null
+++ b/docs/source/api/common/metrics.rst
@@ -0,0 +1,5 @@
+Common MRI Metrics
+------------------
+
+.. autoclass:: atommic.collections.common.metrics.global_average_loss_metric.GlobalAverageLossMetric
+ :special-members: __init__
diff --git a/docs/source/api/common/nn.rst b/docs/source/api/common/nn.rst
new file mode 100644
index 00000000..0a4f70d3
--- /dev/null
+++ b/docs/source/api/common/nn.rst
@@ -0,0 +1,13 @@
+Common MRI Neural Networks
+--------------------------
+
+.. autoclass:: atommic.collections.common.nn.base.DistributedMetricSum
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.nn.base.BaseMRIModel
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.nn.base.BaseSensitivityModel
+ :special-members: __init__
diff --git a/docs/source/api/common/parts.rst b/docs/source/api/common/parts.rst
new file mode 100644
index 00000000..8f97d508
--- /dev/null
+++ b/docs/source/api/common/parts.rst
@@ -0,0 +1,10 @@
+Common MRI Parts
+----------------
+
+.. toctree::
+ :maxdepth: 8
+
+ coil_sensitivity_maps
+ fft
+ transforms
+ utils
diff --git a/docs/source/api/common/transforms.rst b/docs/source/api/common/transforms.rst
new file mode 100644
index 00000000..47e6a8cb
--- /dev/null
+++ b/docs/source/api/common/transforms.rst
@@ -0,0 +1,45 @@
+Common MRI Transforms
+---------------------
+
+.. autoclass:: atommic.collections.common.parts.transforms.Composer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.Cropper
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.EstimateCoilSensitivityMaps
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.GeometricDecompositionCoilCompression
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.N2R
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.NoisePreWhitening
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.Normalizer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.SNREstimator
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.SSDU
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.ZeroFillingPadding
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.common.parts.transforms.MRIDataTransforms
+ :special-members: __init__
diff --git a/docs/source/api/common/utils.rst b/docs/source/api/common/utils.rst
new file mode 100644
index 00000000..79c22bd2
--- /dev/null
+++ b/docs/source/api/common/utils.rst
@@ -0,0 +1,82 @@
+Common MRI Utils
+----------------
+
+.. autofunction:: atommic.collections.common.parts.utils.add_coil_dim_if_singlecoil
+
+
+.. autofunction:: atommic.collections.common.parts.utils.apply_mask
+
+
+.. autofunction:: atommic.collections.common.parts.utils.batched_mask_center
+
+
+.. autofunction:: atommic.collections.common.parts.utils.center_crop
+
+
+.. autofunction:: atommic.collections.common.parts.utils.center_crop_to_smallest
+
+
+.. autofunction:: atommic.collections.common.parts.utils.check_stacked_complex
+
+
+.. autofunction:: atommic.collections.common.parts.utils.coil_combination_method
+
+
+.. autofunction:: atommic.collections.common.parts.utils.complex_abs
+
+
+.. autofunction:: atommic.collections.common.parts.utils.complex_abs_sq
+
+
+.. autofunction:: atommic.collections.common.parts.utils.complex_center_crop
+
+
+.. autofunction:: atommic.collections.common.parts.utils.complex_conj
+
+
+.. autofunction:: atommic.collections.common.parts.utils.complex_mul
+
+
+.. autofunction:: atommic.collections.common.parts.utils.crop_to_acs
+
+
+.. autofunction:: atommic.collections.common.parts.utils.expand_op
+
+
+.. autofunction:: atommic.collections.common.parts.utils.is_none
+
+
+.. autofunction:: atommic.collections.common.parts.utils.mask_center
+
+
+.. autofunction:: atommic.collections.common.parts.utils.normalize_inplace
+
+
+.. autofunction:: atommic.collections.common.parts.utils.parse_list_and_keep_last
+
+
+.. autofunction:: atommic.collections.common.parts.utils.reshape_fortran
+
+
+.. autofunction:: atommic.collections.common.parts.utils.rnn_weights_init
+
+
+.. autofunction:: atommic.collections.common.parts.utils.rss
+
+
+.. autofunction:: atommic.collections.common.parts.utils.rss_complex
+
+
+.. autofunction:: atommic.collections.common.parts.utils.save_predictions
+
+
+.. autofunction:: atommic.collections.common.parts.utils.sense
+
+
+.. autofunction:: atommic.collections.common.parts.utils.to_tensor
+
+
+.. autofunction:: atommic.collections.common.parts.utils.unnormalize
+
+
+.. autofunction:: atommic.collections.common.parts.utils.zero_nan_inf
diff --git a/docs/source/api/core.rst b/docs/source/api/core.rst
new file mode 100644
index 00000000..1e53c692
--- /dev/null
+++ b/docs/source/api/core.rst
@@ -0,0 +1,109 @@
+Core
+====
+
+Base class for all ATOMMIC models
+---------------------------------
+
+.. autoclass:: atommic.core.classes.modelPT.ModelPT
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+ :undoc-members: cfg, num_weights
+ :exclude-members: set_eff_save, use_eff_save, teardown
+
+Base Mixin classes
+------------------
+
+.. autoclass:: atommic.core.classes.common.Typing
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+ :private-members:
+ :exclude-members: _abc_impl
+ :noindex:
+
+-----
+
+.. autoclass:: atommic.core.classes.common.Serialization
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+ :noindex:
+
+-----
+
+.. autoclass:: atommic.core.classes.common.FileIO
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+ :noindex:
+
+
+Base Connector classes
+----------------------
+
+.. autoclass:: atommic.core.connectors.save_restore_connector.SaveRestoreConnector
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+
+Neural Type checking
+--------------------
+
+.. autoclass:: atommic.core.classes.common.typecheck
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+
+ .. automethod:: __call__
+
+Neural Type classes
+-------------------
+
+.. autoclass:: atommic.core.neural_types.neural_type.NeuralType
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+
+-----
+
+.. autoclass:: atommic.core.neural_types.axes.AxisType
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+
+-----
+
+.. autoclass:: atommic.core.neural_types.elements.ElementType
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+
+-----
+
+.. autoclass:: atommic.core.neural_types.comparison.NeuralTypeComparisonResult
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+
+Experiment manager
+------------------
+
+.. autoclass:: atommic.utils.exp_manager.exp_manager
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+
+.. autoclass:: atommic.utils.exp_manager.ExpManagerConfig
+ :show-inheritance:
+ :members:
+ :member-order: bysource
+
+
+Exportable
+----------
+
+.. autoclass:: atommic.core.classes.export.Exportable
+ :show-inheritance:
+ :members:
+ :member-order: bysource
diff --git a/docs/source/api/motioncorrection/intro.rst b/docs/source/api/motioncorrection/intro.rst
new file mode 100644
index 00000000..1f64fd2d
--- /dev/null
+++ b/docs/source/api/motioncorrection/intro.rst
@@ -0,0 +1,7 @@
+MRI Motion Correction
+=====================
+
+.. toctree::
+ :maxdepth: 8
+
+ motionsimulation
diff --git a/docs/source/api/motioncorrection/motionsimulation.rst b/docs/source/api/motioncorrection/motionsimulation.rst
new file mode 100644
index 00000000..b951c913
--- /dev/null
+++ b/docs/source/api/motioncorrection/motionsimulation.rst
@@ -0,0 +1,29 @@
+Motion Simulation Parts
+-----------------------
+
+.. autofunction:: atommic.collections.motioncorrection.parts.motionsimulation.get_center_rect
+
+
+.. autofunction:: atommic.collections.motioncorrection.parts.motionsimulation.segment_array_by_locs
+
+
+.. autofunction:: atommic.collections.motioncorrection.parts.motionsimulation.segments_to_random_indices
+
+
+.. autofunction:: atommic.collections.motioncorrection.parts.motionsimulation.segments_to_random_blocks
+
+
+.. autofunction:: atommic.collections.motioncorrection.parts.motionsimulation.create_rand_partition
+
+
+.. autofunction:: atommic.collections.motioncorrection.parts.motionsimulation.create_rotation_matrix_3d
+
+
+.. autofunction:: atommic.collections.motioncorrection.parts.motionsimulation.translate_kspace
+
+
+.. autoclass:: atommic.collections.motioncorrection.parts.motionsimulation.MotionSimulation
+ :special-members: __init__
+ :show-inheritance:
+ :members:
+ :undoc-members:
diff --git a/docs/source/api/multitask/data.rst b/docs/source/api/multitask/data.rst
new file mode 100644
index 00000000..4353b0b8
--- /dev/null
+++ b/docs/source/api/multitask/data.rst
@@ -0,0 +1,13 @@
+Multitask MRI Reconstruction & Segmentation Data Classes
+--------------------------------------------------------
+
+.. autoclass:: atommic.collections.multitask.rs.data.mrirs_loader.RSMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.multitask.rs.data.mrirs_loader.SKMTEARSMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
diff --git a/docs/source/api/multitask/intro.rst b/docs/source/api/multitask/intro.rst
new file mode 100644
index 00000000..ae545bba
--- /dev/null
+++ b/docs/source/api/multitask/intro.rst
@@ -0,0 +1,7 @@
+MRI Multitask Learning (MTL)
+============================
+
+.. toctree::
+ :maxdepth: 6
+
+ rs
diff --git a/docs/source/api/multitask/nn.rst b/docs/source/api/multitask/nn.rst
new file mode 100644
index 00000000..9d97c983
--- /dev/null
+++ b/docs/source/api/multitask/nn.rst
@@ -0,0 +1,69 @@
+Multitask MRI Reconstruction & Segmentation Neural Networks
+-----------------------------------------------------------
+
+.. autoclass:: atommic.collections.multitask.rs.nn.base.BaseMRIReconstructionSegmentationModel
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.idslr.IDSLR
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.idslr_base.idslr_block.DC
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.idslr_base.idslr_block.UnetEncoder
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.idslr_base.idslr_block.UnetDecoder
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.idslr_unet.IDSLRUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.mtlrs.MTLRS
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.mtlrs_base.mtlrs_block.MTLRSBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.recseg_unet.RecSegUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.segnet.SegNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.seranet.SERANet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.seranet_base.convlstm.ConvLSTMCell
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.seranet_base.convlstm.ConvLSTM
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.seranet_base.convlstm_unet.ConvLSTMNormUnet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.seranet_base.seranet_block.SERANetDC
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.seranet_base.seranet_block.SERANetReconstructionBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.multitask.rs.nn.seranet_base.seranet_block.SERANetRecurrentBlock
+ :special-members: __init__
diff --git a/docs/source/api/multitask/parts.rst b/docs/source/api/multitask/parts.rst
new file mode 100644
index 00000000..011410be
--- /dev/null
+++ b/docs/source/api/multitask/parts.rst
@@ -0,0 +1,8 @@
+Multitask MRI Reconstruction & Segmentation Parts
+-------------------------------------------------
+
+.. autoclass:: atommic.collections.multitask.rs.parts.transforms.RSMRIDataTransforms
+ :special-members: __init__
+ :show-inheritance:
+ :members:
+ :undoc-members:
diff --git a/docs/source/api/multitask/rs.rst b/docs/source/api/multitask/rs.rst
new file mode 100644
index 00000000..3cb9065c
--- /dev/null
+++ b/docs/source/api/multitask/rs.rst
@@ -0,0 +1,9 @@
+Multitask MRI Reconstruction & Segmentation
+===========================================
+
+.. toctree::
+ :maxdepth: 2
+
+ data
+ nn
+ parts
diff --git a/docs/source/api/quantitative/data.rst b/docs/source/api/quantitative/data.rst
new file mode 100644
index 00000000..3a25bcf7
--- /dev/null
+++ b/docs/source/api/quantitative/data.rst
@@ -0,0 +1,13 @@
+Quantitative MRI Data Classes
+-----------------------------
+
+.. autoclass:: atommic.collections.quantitative.data.qmri_loader.qMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.quantitative.data.qmri_loader.AHEADqMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
diff --git a/docs/source/api/quantitative/intro.rst b/docs/source/api/quantitative/intro.rst
new file mode 100644
index 00000000..9e7e2b4e
--- /dev/null
+++ b/docs/source/api/quantitative/intro.rst
@@ -0,0 +1,9 @@
+Quantitative MRI (qMRI)
+=======================
+
+.. toctree::
+ :maxdepth: 8
+
+ data
+ nn
+ parts
diff --git a/docs/source/api/quantitative/nn.rst b/docs/source/api/quantitative/nn.rst
new file mode 100644
index 00000000..2ef3cdd7
--- /dev/null
+++ b/docs/source/api/quantitative/nn.rst
@@ -0,0 +1,37 @@
+Quantitative MRI Neural Networks
+--------------------------------
+
+.. autoclass:: atommic.collections.quantitative.nn.base.BaseqMRIReconstructionModel
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.quantitative.nn.base.SignalForwardModel
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.quantitative.nn.qcirim.qCIRIM
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.quantitative.nn.qrim_base.qrim_block.qRIMBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.quantitative.nn.qrim_base.utils.RescaleByMax
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.quantitative.nn.qrim_base.utils.expand_op
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.quantitative.nn.qrim_base.utils.analytical_log_likelihood_gradient
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.quantitative.nn.qvarnet.qVarNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.quantitative.nn.qvarnet_base.qvn_block.qVarNetBlock
+ :special-members: __init__
diff --git a/docs/source/api/quantitative/parts.rst b/docs/source/api/quantitative/parts.rst
new file mode 100644
index 00000000..271bbd17
--- /dev/null
+++ b/docs/source/api/quantitative/parts.rst
@@ -0,0 +1,34 @@
+Quantitative MRI Parts
+----------------------
+
+.. autoclass:: atommic.collections.quantitative.parts.transforms.qMRIDataTransforms
+ :special-members: __init__
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.quantitative.parts.transforms.GaussianSmoothing
+ :special-members: __init__
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.quantitative.parts.transforms.LeastSquaresFitting
+ :special-members: __init__
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autofunction:: atommic.collections.quantitative.parts.transforms.R2star_B0_S0_phi_mapping
+
+
+.. autofunction:: atommic.collections.quantitative.parts.transforms.R2star_mapping
+
+
+.. autofunction:: atommic.collections.quantitative.parts.transforms.B0_phi_mapping
+
+
+.. autofunction:: atommic.collections.quantitative.parts.transforms.S0_mapping
diff --git a/docs/source/api/reconstruction/data.rst b/docs/source/api/reconstruction/data.rst
new file mode 100644
index 00000000..8815dd67
--- /dev/null
+++ b/docs/source/api/reconstruction/data.rst
@@ -0,0 +1,25 @@
+MRI Reconstruction Data Classes
+-------------------------------
+
+.. autoclass:: atommic.collections.reconstruction.data.mri_reconstruction_loader.ReconstructionMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.reconstruction.data.mri_reconstruction_loader.CC359ReconstructionMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.reconstruction.data.mri_reconstruction_loader.SKMTEAReconstructionMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.reconstruction.data.mri_reconstruction_loader.StanfordKneesReconstructionMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
diff --git a/docs/source/api/reconstruction/intro.rst b/docs/source/api/reconstruction/intro.rst
new file mode 100644
index 00000000..1c19d14c
--- /dev/null
+++ b/docs/source/api/reconstruction/intro.rst
@@ -0,0 +1,11 @@
+MRI Reconstruction (REC)
+========================
+
+.. toctree::
+ :maxdepth: 8
+
+ data
+ losses
+ metrics
+ nn
+ parts
diff --git a/docs/source/api/reconstruction/losses.rst b/docs/source/api/reconstruction/losses.rst
new file mode 100644
index 00000000..f4d6161f
--- /dev/null
+++ b/docs/source/api/reconstruction/losses.rst
@@ -0,0 +1,9 @@
+MRI Reconstruction Losses
+-------------------------
+
+.. autoclass:: atommic.collections.reconstruction.losses.NoiseAwareLoss
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.losses.SSIMLoss
+ :special-members: __init__
diff --git a/docs/source/api/reconstruction/metrics.rst b/docs/source/api/reconstruction/metrics.rst
new file mode 100644
index 00000000..f2bc9bc7
--- /dev/null
+++ b/docs/source/api/reconstruction/metrics.rst
@@ -0,0 +1,17 @@
+MRI Reconstruction Metrics
+--------------------------
+
+.. autofunction:: atommic.collections.reconstruction.metrics.reconstruction_metrics.mse
+
+
+.. autofunction:: atommic.collections.reconstruction.metrics.reconstruction_metrics.nmse
+
+
+.. autofunction:: atommic.collections.reconstruction.metrics.reconstruction_metrics.psnr
+
+
+.. autofunction:: atommic.collections.reconstruction.metrics.reconstruction_metrics.ssim
+
+
+.. autoclass:: atommic.collections.reconstruction.metrics.reconstruction_metrics.ReconstructionMetrics
+ :special-members: __init__
diff --git a/docs/source/api/reconstruction/nn.rst b/docs/source/api/reconstruction/nn.rst
new file mode 100644
index 00000000..8cead3e4
--- /dev/null
+++ b/docs/source/api/reconstruction/nn.rst
@@ -0,0 +1,297 @@
+MRI Reconstruction Neural Networks
+----------------------------------
+
+.. autoclass:: atommic.collections.reconstruction.nn.base.BaseMRIReconstructionModel
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.ccnn.CascadeNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.ccnn_base.ccnn_block.Conv2d
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.ccnn_base.ccnn_block.CascadeNetBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.cirim.CIRIM
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.rim_block.RIMBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.rim_utils.log_likelihood_gradient
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.conv_layers.ConvRNNStack
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.conv_layers.ConvNonlinear
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.rnn_cells.ConvGRUCellBase
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.rnn_cells.ConvGRUCell
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.rnn_cells.ConvMGUCellBase
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.rnn_cells.ConvMGUCell
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.rnn_cells.IndRNNCellBase
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.rim_base.rnn_cells.IndRNNCell
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.crnn.CRNNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.crnn_base.crnn_block.GRUConv2d
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.crnn_base.crnn_block.DataConsistencyLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.crnn_base.crnn_block.RecurrentConvolutionalNetBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.dunet.DUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.dc_layers.DataIDLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.dc_layers.DataGDLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.dc_layers.DataProxCGLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.dc_layers.ConjugateGradient
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.dc_layers.DataVSLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.dc_layers.DCLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.sensitivity_net.matrix_invert
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.sensitivity_net.ComplexInstanceNorm
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.sensitivity_net.ComplexNormWrapper
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.sigmanet_base.sensitivity_net.ComplexNormWrapper
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.didn_base.didn_block.Subpixel
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.didn_base.didn_block.ReconBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.didn_base.didn_block.DUB
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.didn_base.didn_block.DIDN
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.jointicnet.JointICNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.kikinet.KIKINet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.crossdomain_base.crossdomain_block.CrossDomainNetwork
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.crossdomain_base.crossdomain_block.MultiCoil
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block.DWT
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block.IWT
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block.ConvBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block.DilatedConvBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block.MWCNN
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.lpd.LPDNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.primaldualnet_base.primaldualnet_block.DualNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.primaldualnet_base.primaldualnet_block.PrimalNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.modl.MoDL
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.modl_base.modl_block.ResidualNetwork
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.modl_base.modl_block.ConjugateGradient
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.multidomainnet.MultiDomainNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.multidomainnet_base.multidomainnet_block.MultiDomainConv2d
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.multidomainnet_base.multidomainnet_block.MultiDomainConvTranspose2d
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.multidomainnet_base.multidomainnet_block.MultiDomainConvBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.multidomainnet_base.multidomainnet_block.TransposeMultiDomainConvBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.multidomainnet_base.multidomainnet_block.StandardizationLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.multidomainnet_base.multidomainnet_block.MultiDomainUnet2d
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.proximal_gradient.ProximalGradient
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.recurrentvarnet.RecurrentVarNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.recurrentvarnet_base.recurrentvarnet_block.Conv2dGRU
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.recurrentvarnet_base.recurrentvarnet_block.RecurrentInit
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.recurrentvarnet_base.recurrentvarnet_block.RecurrentVarNetBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.unet.UNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.unet_base.unet_block.NormUnet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.unet_base.unet_block.Unet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.unet_base.unet_block.ConvBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.unet_base.unet_block.TransposeConvBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.varnet.VarNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.varnet_base.varnet_block.VarNetBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.vsnet.VSNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.vsnet_base.vsnet_block.DataConsistencyLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.vsnet_base.vsnet_block.WeightedAverageTerm
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.vsnet_base.vsnet_block.VSNetBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.xpdnet.XPDNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.reconstruction.nn.zf.ZF
+ :special-members: __init__
diff --git a/docs/source/api/reconstruction/parts.rst b/docs/source/api/reconstruction/parts.rst
new file mode 100644
index 00000000..73e140da
--- /dev/null
+++ b/docs/source/api/reconstruction/parts.rst
@@ -0,0 +1,8 @@
+MRI Reconstruction Parts
+------------------------
+
+.. autoclass:: atommic.collections.reconstruction.parts.transforms.ReconstructionMRIDataTransforms
+ :special-members: __init__
+ :show-inheritance:
+ :members:
+ :undoc-members:
diff --git a/docs/source/api/segmentation/data.rst b/docs/source/api/segmentation/data.rst
new file mode 100644
index 00000000..598865ce
--- /dev/null
+++ b/docs/source/api/segmentation/data.rst
@@ -0,0 +1,25 @@
+MRI Segmentation Data Classes
+-----------------------------
+
+.. autoclass:: atommic.collections.segmentation.data.mri_segmentation_loader.SegmentationMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.segmentation.data.mri_segmentation_loader.BraTS2023AdultGliomaSegmentationMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.segmentation.data.mri_segmentation_loader.ISLES2022SubAcuteStrokeSegmentationMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
+
+
+.. autoclass:: atommic.collections.segmentation.data.mri_segmentation_loader.SKMTEASegmentationMRIDataset
+ :show-inheritance:
+ :members:
+ :undoc-members:
diff --git a/docs/source/api/segmentation/intro.rst b/docs/source/api/segmentation/intro.rst
new file mode 100644
index 00000000..a07bec2c
--- /dev/null
+++ b/docs/source/api/segmentation/intro.rst
@@ -0,0 +1,11 @@
+MRI Segmentation (SEG)
+======================
+
+.. toctree::
+ :maxdepth: 8
+
+ data
+ losses
+ metrics
+ nn
+ parts
diff --git a/docs/source/api/segmentation/losses.rst b/docs/source/api/segmentation/losses.rst
new file mode 100644
index 00000000..729b9631
--- /dev/null
+++ b/docs/source/api/segmentation/losses.rst
@@ -0,0 +1,17 @@
+MRI Segmentation Losses
+-----------------------
+
+.. autoclass:: atommic.collections.segmentation.losses.CrossEntropyLoss
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.losses.Dice
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.losses.dice.one_hot
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.losses.utils.do_metric_reduction
+ :special-members: __init__
diff --git a/docs/source/api/segmentation/metrics.rst b/docs/source/api/segmentation/metrics.rst
new file mode 100644
index 00000000..3685dd14
--- /dev/null
+++ b/docs/source/api/segmentation/metrics.rst
@@ -0,0 +1,45 @@
+MRI Segmentation Metrics
+------------------------
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.asd
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.binary_cross_entropy_with_logits_metric
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.dice_metric
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.f1_per_class_metric
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.hausdorff_distance_metric
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.hausdorff_distance_95_metric
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.iou_metric
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.precision_metric
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.recall_metric
+ :special-members: __init__
+
+
+.. autofunction:: atommic.collections.segmentation.metrics.segmentation_metrics.surface_distances
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.metrics.segmentation_metrics.SegmentationMetrics
+ :special-members: __init__
diff --git a/docs/source/api/segmentation/nn.rst b/docs/source/api/segmentation/nn.rst
new file mode 100644
index 00000000..b920a6f4
--- /dev/null
+++ b/docs/source/api/segmentation/nn.rst
@@ -0,0 +1,193 @@
+MRI Segmentation Neural Networks
+--------------------------------
+
+.. autoclass:: atommic.collections.segmentation.nn.base.BaseMRISegmentationModel
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.segmentationnet.BaseSegmentationNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.attentionunet.SegmentationAttentionUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.attentionunet_base.attentionunet_block.AttentionGate
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.attentionunet_base.attentionunet_block.AttentionUnet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.dynunet.SegmentationDYNUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.dynunet_base.dynunet_block.DynUNetSkipLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.dynunet_base.dynunet_block.DynUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.lambdaunet.SegmentationLambdaUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.lambdaunet_base.lambdaunet_block.LambdaLayer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.lambdaunet_base.lambdaunet_block.LambdaBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.lambdaunet_base.lambdaunet_block.LambdaUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unet.SegmentationUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unet3d.Segmentation3DUNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unet3d_base.unet3d_block.Conv3dBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unet3d_base.unet3d_block.TransposeConv3dBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unet3d_base.unet3d_block.UNet3D
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unetr.SegmentationUNetR
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unetr_base.unetr_block.UnetOutBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unetr_base.unetr_block.UnetrBasicBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unetr_base.unetr_block.UnetrPrUpBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unetr_base.unetr_block.UnetrUpBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unetr_base.unetr_block.UnetResBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unetr_base.unetr_block.UnetUpBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unetr_base.unetr_block.UnetBasicBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.unetr_base.unetr_block.UNETR
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.vit_block.ViT
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.patchembedding.PatchEmbeddingBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.patchembedding.PatchEmbed
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.transformer_block.MLPBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.transformer_block.SABlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.transformer_block.TransformerBlock
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.utils.Convolution
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.utils.stride_minus_kernel_padding
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.utils.get_padding
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.utils.get_output_padding
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.utils.same_padding
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.utils.get_conv_layer
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.utils._no_grad_trunc_normal_
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vit_base.utils.trunc_normal_
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vnet.SegmentationVNet
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vnet_base.vnet_block.LUConv
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vnet_base.vnet_block._make_nconv
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vnet_base.vnet_block.InputTransition
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vnet_base.vnet_block.DownTransition
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vnet_base.vnet_block.UpTransition
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vnet_base.vnet_block.OutputTransition
+ :special-members: __init__
+
+
+.. autoclass:: atommic.collections.segmentation.nn.vnet_base.vnet_block.VNet
+ :special-members: __init__
diff --git a/docs/source/api/segmentation/parts.rst b/docs/source/api/segmentation/parts.rst
new file mode 100644
index 00000000..d0421a12
--- /dev/null
+++ b/docs/source/api/segmentation/parts.rst
@@ -0,0 +1,8 @@
+MRI Segmentation Parts
+----------------------
+
+.. autoclass:: atommic.collections.segmentation.parts.transforms.SegmentationMRIDataTransforms
+ :special-members: __init__
+ :show-inheritance:
+ :members:
+ :undoc-members:
diff --git a/docs/source/conf.py b/docs/source/conf.py
new file mode 100644
index 00000000..0cb25ebe
--- /dev/null
+++ b/docs/source/conf.py
@@ -0,0 +1,199 @@
+# coding=utf-8
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory, add these directories to sys.path
+# here. If the directory is relative to the documentation root, use os.path.abspath to make it absolute, like shown
+# here.
+
+import os
+import re
+import sys
+import glob
+
+import sphinx_book_theme
+
+from typing import List
+
+sys.path.insert(0, os.path.abspath("../.."))
+sys.path.insert(0, os.path.abspath("../../atommic"))
+
+from atommic.package_info import __version__
+
+templates_path = ["_templates"]
+
+autodoc_mock_imports = [
+ 'torch',
+ 'torch.nn',
+ 'torch.utils',
+ 'torch.optim',
+ 'torch.utils.data',
+ 'torch.utils.data.sampler',
+ 'torchvision',
+ 'ruamel.yaml', # ruamel.yaml has ., which is troublesome for this regex
+ 'hydra', # hydra-core in requirements, hydra during import
+ 'dateutil', # part of core python
+ 'sklearn', # scikit_learn in requirements, sklearn in import
+ 'attr', # attrdict in requirements, attr in import
+ 'torchmetrics', # inherited from PTL
+ 'lightning_utilities', # inherited from PTL
+ 'joblib', # inherited from optional code
+ 'IPython',
+ 'ipadic',
+ 'psutil',
+ 'regex',
+]
+
+_skipped_autodoc_mock_imports = ['wrapt', 'numpydoc_show_class_members = False']
+
+for req_path in sorted(list(glob.glob("../../requirements/*.txt"))):
+ if "docs.txt" in req_path:
+ continue
+
+ req_file = os.path.abspath(os.path.expanduser(req_path))
+ with open(req_file, 'r') as f:
+ for line in f:
+ line = line.replace("\n", "")
+ req = re.search(r"([a-zA-Z0-9-_]*)", line)
+ if req:
+ req = req.group(1) # type: ignore
+ req = req.replace("-", "_") # type: ignore
+
+ if req not in autodoc_mock_imports:
+ if req in _skipped_autodoc_mock_imports:
+ print(f"Skipping req : `{req}` (lib {line})")
+ continue
+
+ autodoc_mock_imports.append(req) # type: ignore
+ print(f"Adding req : `{req}` to autodoc mock requirements (lib {line})")
+ else:
+ print(f"`{req}` already added to autodoc mock requirements (lib {line})")
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be extensions coming with Sphinx
+# (named 'sphinx.ext.*') or your custom ones.
+extensions = [
+ "sphinx.ext.autodoc",
+ "sphinx.ext.doctest",
+ "sphinx.ext.todo",
+ "sphinx.ext.coverage",
+ 'sphinx.ext.mathjax',
+ "sphinx.ext.ifconfig",
+ "sphinx.ext.viewcode",
+ "sphinx.ext.napoleon",
+ "sphinx.ext.githubpages",
+ "sphinxcontrib.bibtex",
+ "sphinx.ext.inheritance_diagram",
+ "sphinx.ext.intersphinx",
+ "sphinx.ext.autosectionlabel",
+ "sphinxcontrib.bibtex",
+ "sphinx_copybutton",
+ "sphinxext.opengraph",
+]
+
+bibtex_bibfiles = [] # type: ignore
+
+intersphinx_mapping = {
+ 'pytorch': ('https://pytorch.org/docs/stable', None),
+ 'pytorch-lightning': ('https://pytorch-lightning.readthedocs.io/en/latest/', None),
+}
+
+# Set default flags for all classes.
+autodoc_default_options = {'members': None, 'undoc-members': None, 'show-inheritance': True}
+
+locale_dirs = ['locale/'] # path is example but recommended.
+gettext_compact = False # optional.
+
+# The suffix(es) of source filenames. You can specify multiple suffix as a list of string:
+# source_suffix = ['.rst', '.md']
+source_suffix = ".rst"
+
+# The master toctree document.
+master_doc = "index"
+
+# General information about the project.
+project = "ATOMMIC"
+author = "Dimitris Karkalousos"
+
+# The version info for the project you're documenting, acts as replacement for |version| and |release|, also used in
+# various other places throughout the built documents.
+
+# The short X.Y version.
+# version = "0.10.0"
+version = __version__
+# The full version, including alpha/beta/rc tags.
+# release = "0.9.0"
+release = __version__
+
+# The language for content autogenerated by Sphinx. Refer to documentation for a list of supported languages.
+#
+# This is also used if you do content translation via gettext catalogs. Usually you set "language" from the command
+# line for these cases.
+language = "en"
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This patterns also effect to html_static_path and html_extra_path
+exclude_patterns = [] # type: ignore
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = "default"
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = "atommicdoc"
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns: List = [] # type: ignore
+
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages. See the documentation for
+# a list of builtin themes.
+#
+html_theme = "sphinx_book_theme"
+html_logo = os.path.join('../../assets/atommic-logo.png')
+html_title = 'ATOMMIC'
+
+html_theme_options = {
+ # 'logo_only': True,
+ # 'display_version': True,
+ # 'prev_next_buttons_location': 'bottom',
+ # 'style_external_links': False,
+ # 'style_nav_header_background': '#000000',
+ # Toc options
+ 'collapse_navigation': False,
+ # 'sticky_navigation': False,
+ 'navigation_depth': 10,
+ # 'includehidden': False,
+ # 'titles_only': False,
+ # Sphinx Book theme,
+ 'repository_url': 'https://github.com/wdika/atommic',
+ 'use_repository_button': True,
+ 'show_navbar_depth': 1,
+ 'show_toc_level': 10,
+}
+
+
+# Add any paths that contain custom static files (such as style sheets) here, relative to this directory. They are
+# copied after the builtin static files, so a file named "default.css" will overwrite the builtin "default.css".
+
+html_favicon = '../assets/logo-simple.png'
+
+# html_static_path = ['_static']
+
+html_last_updated_fmt = ''
+
+# OpenGraph settings
+ogp_site_url = 'https://github.com/wdika/atommic'
+
+# MathJax CDN
+mathjax_path = "https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.7/MathJax.js?config=TeX-MML-AM_CHTML"
diff --git a/docs/source/core/core.rst b/docs/source/core/core.rst
new file mode 100644
index 00000000..723efb1d
--- /dev/null
+++ b/docs/source/core/core.rst
@@ -0,0 +1,421 @@
+Training & Testing
+==================
+
+Basics
+------
+
+ATOMMIC models contain everything needed to train and reproduce AI models for MR Imaging tasks.
+
+ATOMMIC uses `Hydra `_ for configuring both ATOMMIC models and the PyTorch Lightning Trainer.
+
+.. note:: Every ATOMMIC model has an example configuration file and training script that can be found
+ `here `_.
+
+The end result of using ATOMMIC, `Pytorch Lightning `_, and
+Hydra is that ATOMMIC models all have the same look and feel and are also fully compatible with the PyTorch ecosystem.
+
+
+Training
+--------
+
+ATOMMIC leverages `PyTorch Lightning `_ for model training. PyTorch Lightning lets
+ATOMMIC decouple the AI MR Imaging code from the PyTorch training code. This means that ATOMMIC users can focus on
+their domain (...) and build complex AI applications without having to rewrite boiler plate code for PyTorch training.
+
+When using PyTorch Lightning, ATOMMIC users can automatically train with:
+
+- multi-GPU/multi-node
+- mixed precision (supported types are 16-mixed, bf16-mixed, 32-true, 64-true, 64, 32, and 16)
+- model checkpointing
+- logging
+- early stopping
+- and more
+
+The two main aspects of the Lightning API are the
+`LightningModule `_
+and the `Trainer `_.
+
+PyTorch Lightning ``LightningModule``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Every ATOMMIC model is a ``LightningModule`` which is an ``nn.module``. This means that ATOMMIC models are compatible
+with the PyTorch ecosystem and can be plugged into existing PyTorch workflows.
+
+
+PyTorch Lightning Trainer
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Since every ATOMMIC model is a ``LightningModule``, we can automatically take advantage of the PyTorch Lightning
+``Trainer``.
+
+
+Configuration
+-------------
+
+Hydra is an open-source Python framework that simplifies configuration for complex applications that must bring
+together many different software libraries. To train an MRI AI model, we must be able to configure:
+
+- neural network architectures
+- training and optimization algorithms
+- data pre/post processing
+- data augmentation
+- experiment logging/visualization
+- model checkpointing
+
+For an introduction to using Hydra, refer to the `Hydra Tutorials `_.
+
+With Hydra, we can configure everything needed for ATOMMIC through Configuration Files (YAML).
+
+
+YAML
+~~~~
+
+ATOMMIC provides YAML configuration files for all of our tasks and publicly available datasets in
+`projects `_. YAML files make it easy to experiment with
+different model and training configurations.
+
+Every ATOMMIC example YAML has the same underlying configuration structure:
+
+- trainer
+- exp_manager
+- model
+
+Model configuration always contain ``train_ds``, ``validation_ds``, ``test_ds``, and ``optim``. Model architectures
+vary across domains, therefore, refer to the MTL, QI, REC, and SEG Collections documentation for more detailed
+information on Model architecture configuration.
+
+A ATOMMIC configuration file should look similar to the following:
+
+.. code-block:: yaml
+
+ # PyTorch Lightning Trainer configuration
+ # any argument of the Trainer object can be set here
+ trainer:
+ devices: 1 # number of gpus per node
+ accelerator: gpu
+ num_nodes: 1 # number of nodes
+ max_epochs: 10 # how many training epochs to run
+ val_check_interval: 1.0 # run validation after every epoch
+
+ # Experiment logging configuration
+ exp_manager:
+ exp_dir: /path/to/my/atommic/experiments
+ name: name_of_my_experiment
+ create_tensorboard_logger: True
+ create_wandb_logger: True
+
+ # Model configuration
+ # model network architecture, train/val/test datasets, data augmentation, and optimization
+ model:
+ train_ds:
+ data_path: /path/to/my/train_data/
+ batch_size: 1
+ shuffle: True
+ validation_ds:
+ data_path: /path/to/my/validation_data/
+ batch_size: 1
+ shuffle: False
+ test_ds:
+ data_path: /path/to/my/test_data/
+ batch_size: 1
+ shuffle: False
+ optim:
+ name: novograd
+ lr: .01
+ betas: [0.8, 0.5]
+ weight_decay: 0.001
+ # network architecture can vary greatly depending on the domain
+ encoder:
+ ...
+ decoder:
+ ...
+
+
+.. _optimization-label:
+
+Optimization
+------------
+
+Optimizers and learning rate schedules are configurable across all ATOMMIC models and have their own namespace. Here
+is a sample YAML configuration for a Novograd optimizer with Cosine Annealing learning rate schedule.
+
+.. code-block:: yaml
+
+ optim:
+ name: novograd
+ lr: 0.01
+
+ # optimizer arguments
+ betas: [0.8, 0.25]
+ weight_decay: 0.001
+
+ # scheduler setup
+ sched:
+ name: CosineAnnealing
+
+ # Optional arguments
+ max_steps: -1 # computed at runtime or explicitly set here
+ monitor: val_loss
+ reduce_on_plateau: false
+
+ # scheduler config override
+ warmup_steps: 1000
+ warmup_ratio: null
+ min_lr: 1e-9:
+
+
+.. _optimizers-label:
+
+Optimizers
+~~~~~~~~~~
+
+``name`` corresponds to the lowercase name of the optimizer. To view a list of available optimizers, run:
+
+.. code-block:: Python
+
+ from atommic.core.optim.optimizers import AVAILABLE_OPTIMIZERS
+
+ for name, opt in AVAILABLE_OPTIMIZERS.items():
+ print(f'name: {name}, opt: {opt}')
+
+.. code-block:: bash
+
+ name: sgd opt:
+ name: adam opt:
+ name: adamw opt:
+ name: adadelta opt:
+ name: adamax opt:
+ name: adagrad opt:
+ name: rmsprop opt:
+ name: rprop opt:
+ name: novograd opt:
+ name: lion, opt:
+
+
+Optimizer Params
+~~~~~~~~~~~~~~~~
+
+Optimizer params can vary between optimizers but the ``lr`` param is required for all optimizers. To see the available
+params for an optimizer, we can look at its corresponding dataclass.
+
+
+Register Optimizer
+~~~~~~~~~~~~~~~~~~
+
+To register a new optimizer to be used with ATOMMIC, run:
+
+.. autofunction:: atommic.core.optim.optimizers.register_optimizer
+
+.. _learning-rate-schedulers-label:
+
+Learning Rate Schedulers
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+Learning rate schedulers can be optionally configured under the ``optim.sched`` namespace.
+
+``name`` corresponds to the name of the learning rate schedule. To view a list of available schedulers, run:
+
+.. code-block:: Python
+
+ from atommic.core.optim.lr_scheduler import AVAILABLE_SCHEDULERS
+
+ for name, opt in AVAILABLE_SCHEDULERS.items():
+ print(f'name: {name}, schedule: {opt}')
+
+.. code-block:: bash
+
+ name: WarmupPolicy, schedule:
+ name: WarmupHoldPolicy, schedule:
+ name: SquareAnnealing, schedule:
+ name: CosineAnnealing, schedule:
+ name: NoamAnnealing, schedule:
+ name: WarmupAnnealing, schedule:
+ name: InverseSquareRootAnnealing, schedule:
+ name: SquareRootAnnealing, schedule:
+ name: PolynomialDecayAnnealing, schedule:
+ name: PolynomialHoldDecayAnnealing, schedule:
+ name: StepLR, schedule:
+ name: ExponentialLR, schedule:
+ name: ReduceLROnPlateau, schedule:
+ name: CyclicLR, schedule:
+
+
+Register scheduler
+~~~~~~~~~~~~~~~~~~
+
+To register a new scheduler to be used with ATOMMIC, run:
+
+.. autofunction:: atommic.core.optim.lr_scheduler.register_scheduler
+
+Save and Restore
+----------------
+
+ATOMMIC models all come with ``.save_to`` and ``.restore_from`` methods.
+
+Save
+~~~~
+
+To save a ATOMMIC model, run:
+
+.. code-block:: Python
+
+ model.save_to('/path/to/model.atommic')
+
+Everything needed to use the trained model is packaged and saved in the ``.atommic`` file.
+
+.. note:: A ``.atommic`` file is simply an archive like any other ``.tar`` file.
+
+Restore
+~~~~~~~
+
+To restore a ATOMMIC model, run:
+
+.. code-block:: Python
+
+ # Here, you should usually use the class of the model, or simply use ModelPT.restore_from() for simplicity.
+ model.restore_from('/path/to/model.atommic')
+
+When using the PyTorch Lightning Trainer, a PyTorch Lightning checkpoint is created. These are mainly used within
+ATOMMIC to auto-resume training. Since ATOMMIC models are ``LightningModules``, the PyTorch Lightning method
+``load_from_checkpoint`` is available. Note that ``load_from_checkpoint`` won't necessarily work out-of-the-box for
+all models as some models require more artifacts than just the checkpoint to be restored. For these models, the user
+will have to override ``load_from_checkpoint`` if they want to use it.
+
+It's highly recommended to use ``restore_from`` to load ATOMMIC models.
+
+Restore with Modified Config
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Sometimes, there may be a need to modify the model (or it's sub-components) prior to restoring a model. A common case
+is when the model's internal config must be updated due to various reasons (such as deprecation, newer versioning,
+support a new feature). As long as the model has the same parameters as compared to the original config, the
+parameters can once again be restored safely.
+
+In ATOMMIC, as part of the .atommic file, the model's internal config will be preserved. This config is used during
+restoration, and as shown below we can update this config prior to restoring the model.
+
+.. code-block::
+
+ # When restoring a model, you should generally use the class of the model
+ # Obtain the config (as an OmegaConf object)
+ config = model_class.restore_from('/path/to/model.atommic', return_config=True)
+ # OR
+ config = model_class.from_pretrained('name_of_the_model', return_config=True)
+
+ # Modify the config as needed
+ config.x.y = z
+
+ # Restore the model from the updated config
+ model = model_class.restore_from('/path/to/model.atommic', override_config_path=config)
+ # OR
+ model = model_class.from_pretrained('name_of_the_model', override_config_path=config)
+
+Register Artifacts
+------------------
+
+ATOMMIC models can save additional artifacts in the .atommic file by calling ``.register_artifact``.
+When restoring ATOMMIC models using ``.restore_from`` or ``.from_pretrained``, any artifacts that were registered will
+be available automatically.
+
+By default, ``.register_artifact`` will always return a path. If the model is being restored from a .atommic file,
+then that path will be to the artifact in the .atommic file. Otherwise, ``.register_artifact`` will return the local
+path specified by the user.
+
+``config_path`` is the artifact key. It usually corresponds to a model configuration but does not have to.
+The model config that is packaged with the .atommic file will be updated according to the ``config_path`` key.
+
+``src`` is the path to the artifact and the base-name of the path will be used when packaging the artifact in the
+.atommic file. Each artifact will have a hash prepended to the basename of ``src`` in the .atommic file. This is to
+prevent collisions with basenames base-names that are identical (say when there are two or more tokenizers, both
+called `tokenizer.model`).
+
+If ``verify_src_exists`` is set to ``False``, then the artifact is optional. This means that ``.register_artifact``
+will return ``None`` if the ``src`` cannot be found.
+
+Nested ATOMMIC Models
+---------------------
+
+In some cases, it may be helpful to use ATOMMIC models inside other ATOMMIC models. For example, we can incorporate
+reconstruction and segmentation models into MTL models to use in a multitask learning setting. In these cases, we can
+use the ``register_atommic_submodule`` method to register the child model.
+
+There are 3 ways to instantiate child models inside parent models:
+
+- use subconfig directly
+- use the ``.atommic`` checkpoint path to load the child model
+- use a pretrained ATOMMIC model
+
+To register a child model, use the ``register_atommic_submodule`` method of the parent model. This method will add the
+child model to a provided model attribute and, in the serialization process, will handle child artifacts correctly and
+store the child model config in the parent model config in ``config_field``.
+
+.. code-block:: python
+
+ from atommic.core.classes.modelPT import ModelPT
+
+ class ChildModel(ModelPT):
+ ... # implement necessary methods
+
+ class ParentModel(ModelPT):
+ def __init__(self, cfg, trainer=None):
+ super().__init__(cfg=cfg, trainer=trainer)
+
+ # optionally annotate type for IDE autocompletion and type checking
+ self.child_model: Optional[ChildModel]
+ if cfg.get("child_model") is not None:
+ # load directly from config
+ # either if config provided initially, or automatically
+ # after model restoration
+ self.register_atommic_submodule(
+ name="child_model",
+ config_field="child_model",
+ model=ChildModel(self.cfg.child_model, trainer=trainer),
+ )
+ elif cfg.get('child_model_path') is not None:
+ # load from .atommic model checkpoint
+ # while saving, config will be automatically assigned/updated
+ # in cfg.child_model
+ self.register_atommic_submodule(
+ name="child_model",
+ config_field="child_model",
+ model=ChildModel.restore_from(self.cfg.child_model_path, trainer=trainer),
+ )
+ elif cfg.get('child_model_name') is not None:
+ # load from pretrained model
+ # while saving, config will be automatically assigned/updated
+ # in cfg.child_model
+ self.register_atommic_submodule(
+ name="child_model",
+ config_field="child_model",
+ model=ChildModel.from_pretrained(self.cfg.child_model_name, trainer=trainer),
+ )
+ else:
+ self.child_model = None
+
+
+Dynamic Layer Freezing
+----------------------
+
+You can selectively freeze any modules inside a ATOMMIC model by specifying a freezing schedule in the config yaml.
+Freezing stops any gradient updates to that module, so that its weights are not changed for that step. This can be
+useful for combatting catastrophic forgetting, for example when finetuning a large pretrained model on a small dataset.
+
+The default approach is to freeze a module for the first N training steps, but you can also enable freezing for a
+specific range of steps, for example, from step 20 - 100, or even activate freezing from some N until the end of
+training. You can also freeze a module for the entire training run. Dynamic freezing is specified in training steps,
+not epochs.
+
+To enable freezing, add the following to your config:
+
+.. code-block:: yaml
+
+ model:
+ ...
+ freeze_updates:
+ enabled: true # set to false if you want to disable freezing
+
+ modules: # list all of the modules you want to have freezing logic for
+ encoder: 200 # module will be frozen for the first 200 training steps
+ decoder: [50, -1] # module will be frozen at step 50 and will remain frozen until training ends
+ joint: [10, 100] # module will be frozen between step 10 and step 100 (step >= 10 and step <= 100)
+ transcoder: -1 # module will be frozen for the entire training run
diff --git a/docs/source/core/exp_manager.rst b/docs/source/core/exp_manager.rst
new file mode 100644
index 00000000..35320c13
--- /dev/null
+++ b/docs/source/core/exp_manager.rst
@@ -0,0 +1,311 @@
+
+.. _exp-manager-label:
+
+Experiment Manager
+==================
+
+ATOMMIC's Experiment Manager leverages PyTorch Lightning for model checkpointing, TensorBoard Logging and Weights and
+Biases logging. The Experiment Manager is included by default in all ATOMMIC example scripts.
+
+To use the experiment manager simply call :class:`~atommic.utils.exp_manager.exp_manager` and pass in the PyTorch
+Lightning ``Trainer``.
+
+.. code-block:: python
+
+ exp_dir = exp_manager(trainer, cfg.get("exp_manager", None))
+
+And is configurable via YAML with Hydra.
+
+.. code-block:: bash
+
+ exp_manager:
+ exp_dir: /path/to/my/experiments
+ name: my_experiment_name
+ create_tensorboard_logger: True
+ create_checkpoint_callback: True
+
+Optionally, launch TensorBoard to view the training results in ``./atommic_experiments`` (by default).
+
+.. code-block:: bash
+
+ tensorboard --bind_all --logdir atommic_experiments
+
+..
+
+If ``create_checkpoint_callback`` is set to ``True``, then ATOMMIC automatically creates checkpoints during training
+using PyTorch Lightning's `ModelCheckpoint `_.
+We can configure the ``ModelCheckpoint`` via YAML or CLI.
+
+.. code-block:: yaml
+
+ exp_manager:
+ ...
+ # configure the PyTorch Lightning ModelCheckpoint using checkpoint_call_back_params
+ # any ModelCheckpoint argument can be set here
+
+ # save the best checkpoints based on this metric
+ checkpoint_callback_params.monitor=val_loss
+
+ # choose how many total checkpoints to save
+ checkpoint_callback_params.save_top_k=5
+
+Resume Training
+---------------
+
+We can auto-resume training as well by configuring the ``exp_manager``. Being able to auto-resume is important when
+doing long training runs that are premptible or may be shut down before the training procedure has completed. To
+auto-resume training, set the following via YAML or CLI:
+
+.. code-block:: yaml
+
+ exp_manager:
+ ...
+ # resume training if checkpoints already exist
+ resume_if_exists: True
+
+ # to start training with no existing checkpoints
+ resume_ignore_no_checkpoint: True
+
+ # by default experiments will be versioned by datetime
+ # we can set our own version with
+ exp_manager.version: my_experiment_version
+
+
+Experiment Loggers
+------------------
+
+Alongside Tensorboard, ATOMMIC also supports Weights and Biases. To use this logger, simply set the following
+via YAML or :class:`~atommic.utils.exp_manager.ExpManagerConfig`.
+
+
+Weights and Biases (WandB)
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. _exp_manager_weights_biases-label:
+
+.. code-block:: yaml
+
+ exp_manager:
+ ...
+ create_checkpoint_callback: True
+ create_wandb_logger: True
+ wandb_logger_kwargs:
+ name: ${name}
+ project: ${project}
+ entity: ${entity}
+
+
+
+Exponential Moving Average
+--------------------------
+
+.. _exp_manager_ema-label:
+
+ATOMMIC supports using exponential moving average (EMA) for model parameters. This can be useful for improving model
+generalization and stability. To use EMA, simply set the following via YAML or
+:class:`~atommic.utils.exp_manager.ExpManagerConfig`.
+
+.. code-block:: yaml
+
+ exp_manager:
+ ...
+ # use exponential moving average for model parameters
+ ema:
+ enabled: True # False by default
+ decay: 0.999 # decay rate
+ cpu_offload: False # If EMA parameters should be offloaded to CPU to save GPU memory
+ every_n_steps: 1 # How often to update EMA weights
+ validate_original_weights: False # Whether to use original weights for validation calculation or EMA weights
+
+Support for Preemption
+----------------------
+
+.. _exp_manager_preemption_support-label:
+
+ATOMMIC adds support for a callback upon preemption while running the models on clusters. The callback takes care of
+saving the current state of training via the ``.ckpt`` file followed by a graceful exit from the run. The checkpoint
+saved upon preemption has the ``*last.ckpt`` suffix and replaces the previously saved last checkpoints. This feature
+is useful to increase utilization on clusters. The ``PreemptionCallback`` is enabled by default. To disable it simply
+add ``create_preemption_callback: False`` under exp_manager in the config YAML file.
+
+
+.. _atommic_multirun-label:
+
+
+Hydra Multi-Run with ATOMMIC
+----------------------------
+
+When training neural networks, it is common to perform hyper parameter search in order to improve the performance of a
+model on some validation data. However, it can be tedious to manually prepare a grid of experiments and management of
+all checkpoints and their metrics. In order to simplify such tasks, ATOMMIC integrates with
+`Hydra Multi-Run support `_ in order to provide a
+unified way to run a set of experiments all from the config.
+
+There are certain limitations to this framework, which we list below:
+
+* All experiments are assumed to be run on a single GPU, and multi GPU for single run (model parallel models are not
+supported as of now).
+
+* ATOMMIC Multi-Run supports only grid search over a set of hyper-parameters, but we will eventually add support for
+advanced hyper parameter search strategies.
+
+* **ATOMMIC Multi-Run only supports running on one or more GPUs** and will not work if no GPU devices are present.
+
+Config Setup
+~~~~~~~~~~~~
+
+In order to enable ATOMMIC Multi-Run, we first update our YAML configs with some information to let Hydra know we
+expect to run multiple experiments from this one config -
+
+.. code-block:: yaml
+
+ # Required for Hydra launch of hyperparameter search via multirun
+ defaults:
+ - override hydra/launcher: atommic_launcher
+
+ # Hydra arguments necessary for hyperparameter optimization
+ hydra:
+ # Helper arguments to ensure all hyper parameter runs are from the directory that launches the script.
+ sweep:
+ dir: "."
+ subdir: "."
+
+ # Define all the hyper parameters here
+ sweeper:
+ params:
+ # Place all the parameters you wish to search over here (corresponding to the rest of the config)
+ # NOTE: Make sure that there are no spaces between the commas that separate the config params !
+ model.optim.lr: 0.001,0.0001
+ model.encoder.dim: 32,64,96,128
+ model.decoder.dropout: 0.0,0.1,0.2
+
+ # Arguments to the process launcher
+ launcher:
+ num_gpus: -1 # Number of gpus to use. Each run works on a single GPU.
+ jobs_per_gpu: 1 # If each GPU has large memory, you can run multiple jobs on the same GPU for faster results (until OOM).
+
+
+Next, we will setup the config for ``Experiment Manager``. When we perform hyper parameter search, each run may take
+some time to complete. We want to therefore avoid the case where a run ends (say due to OOM or timeout on the machine)
+and we need to redo all experiments. We therefore setup the experiment manager config such that every experiment has a
+unique "key", whose value corresponds to a single resumable experiment.
+
+Let us see how to setup such a unique "key" via the experiment name. Simply attach all the hyper parameter arguments
+to the experiment name as shown below -
+
+.. code-block:: yaml
+
+ exp_manager:
+ exp_dir: null # Can be set by the user.
+
+ # Add a unique name for all hyper parameter arguments to allow continued training.
+ # NOTE: It is necessary to add all hyperparameter arguments to the name !
+ # This ensures successful restoration of model runs in case HP search crashes.
+ name: ${name}-lr-${model.optim.lr}-adim-${model.adapter.dim}-sd-${model.adapter.adapter_strategy.stochastic_depth}
+
+ ...
+ checkpoint_callback_params:
+ ...
+ save_top_k: 1 # Dont save too many .ckpt files during HP search
+ always_save_atommic: True # saves the checkpoints as atommic files for fast checking of results later
+ ...
+
+ # We highly recommend use of any experiment tracking took to gather all the experiments in one location
+ create_wandb_logger: True
+ wandb_logger_kwargs:
+ project: ""
+
+ # HP Search may crash due to various reasons, best to attempt continuation in order to
+ # resume from where the last failure case occured.
+ resume_if_exists: true
+ resume_ignore_no_checkpoint: true
+
+
+Running a Multi-Run config
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Once the config has been updated, we can now run it just like any normal Hydra script -- with one special flag (``-m``) !
+
+.. code-block:: bash
+
+ atommic run -c BC -m
+
+
+Tips and Tricks
+~~~~~~~~~~~~~~~
+
+* Preserving disk space for large number of experiments
+
+Some models may have a large number of parameters, and it may be very expensive to save a large number of checkpoints
+on physical storage drives. For example, if you use Adam optimizer, each PyTorch Lightning ".ckpt" file will actually
+be 3x the size of just the model parameters - per ckpt file ! This can be exhorbitant if you have multiple runs.
+
+In the above config, we explicitly set ``save_top_k: 1`` and ``always_save_atommic: True`` - what this does is limit
+the number of ckpt files to just 1, and also save a ATOMMIC file (which will contain just the model parameters without
+optimizer state) and can be restored immediately for further work.
+
+We can further reduce the storage space by utilizing some utility functions of ATOMMIC to automatically delete either
+ckpt or ATOMMIC files after a training run has finished. This is sufficient in case you are collecting results in some
+experiment tracking tool and can simply rerun the best config after the search is finished.
+
+.. code-block:: python
+
+ # Import `clean_exp_ckpt` along with exp_manager
+ from atommic.utils.exp_manager import clean_exp_ckpt, exp_manager
+
+ @hydra_runner(...)
+ def main(cfg):
+ ...
+
+ # Keep track of the experiment directory
+ exp_log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
+
+ ... add any training code here as needed ...
+
+ # Add following line to end of the training script
+ # Remove PTL ckpt file, and potentially also remove .atommic file to conserve storage space.
+ clean_exp_ckpt(exp_log_dir, remove_ckpt=True, remove_atommic=False)
+
+
+* Debugging Multi-Run Scripts
+
+When running hydra scripts, you may sometimes face config issues which crash the program. In ATOMMIC Multi-Run, a
+crash in any one run will **not** crash the entire program, we will simply take note of it and move onto the next job.
+Once all jobs are completed, we then raise the error in the order that it occurred (it will crash the program with the
+first error's stack trace).
+
+In order to debug Muti-Run, we suggest to comment out the full hyper parameter config set inside ``sweep.params``
+and instead run just a single experiment with the config - which would immediately raise the error.
+
+
+* Experiment name cannot be parsed by Hydra
+
+Sometimes our hyper parameters include PyTorch Lightning ``trainer`` arguments - such as number of steps, number of
+epochs whether to use gradient accumulation or not etc. When we attempt to add these as keys to the experiment
+manager's ``name``. Hydra may complain that ``trainer.xyz`` cannot be resolved.
+
+A simple solution is to finalize the hydra config before you call ``exp_manager()`` as follows -
+
+.. code-block:: python
+
+ @hydra_runner(...)
+ def main(cfg):
+ # Make any changes as necessary to the config
+ cfg.xyz.abc = uvw
+
+ # Finalize the config
+ cfg = OmegaConf.resolve(cfg)
+
+ # Carry on as normal by calling trainer and exp_manager
+ trainer = pl.Trainer(**cfg.trainer)
+ exp_log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
+ ...
+
+
+ExpManagerConfig
+----------------
+
+.. autoclass:: atommic.utils.exp_manager.ExpManagerConfig
+ :show-inheritance:
+ :members:
+ :member-order: bysource
diff --git a/docs/source/core/export.rst b/docs/source/core/export.rst
new file mode 100644
index 00000000..aaae5b40
--- /dev/null
+++ b/docs/source/core/export.rst
@@ -0,0 +1,194 @@
+Exporting ATOMMIC Models
+========================
+
+Exporting Models
+----------------
+
+Most of the ATOMMIC models can be exported to ONNX or TorchScript to be deployed for inference in optimized execution
+environments. Export interface is provided by the :class:`~atommic.core.classes.export.Exportable` mix-in class. If a
+model extends :class:`~atommic.core.classes.exportable.Exportable`, it can be exported by:
+
+.. code-block:: Python
+
+ from atommic.core.classes import ModelPT, Exportable
+ # deriving from Exportable
+ class MyExportableModel(ModelPT, Exportable):
+ ...
+
+ mymodel = MyExportableModel.from_pretrained(model_name="MyModelName")
+ model.eval()
+ model.to('cuda') # or to('cpu') if you don't have GPU
+
+ # exporting pre-trained model to ONNX file for deployment.
+ mymodel.export('mymodel.onnx', [options])
+
+
+How to Use Model Export
+-----------------------
+The following arguments are for :meth:`~atommic.core.classes.export.Exportable.export`. In most cases, you should only
+supply the name of the output file and use all defaults:
+
+.. code-block:: Python
+
+ def export(
+ self,
+ output: str,
+ input_example=None,
+ verbose=False,
+ do_constant_folding=True,
+ onnx_opset_version=None,
+ check_trace: Union[bool, List[torch.Tensor]] = False,
+ dynamic_axes=None,
+ check_tolerance=0.01,
+ export_modules_as_functions=False,
+ keep_initializers_as_inputs=None,
+ ):
+
+
+The ``output``, ``input_example``, ``verbose``, ``do_constant_folding``, ``onnx_opset_version`` options have the same
+semantics as in Pytorch ``onnx.export()`` and ``jit.trace()`` functions and are passed through. For more information
+about Pytorch's``onnx.export()``, refer to the `torch.onnx functions documentation
+`_. Note that if ``input_example`` is None,
+``Exportable.input_example()`` is called.
+
+The file extension of the ``output`` parameter determines export format:
+
+* ``.onnx->ONNX``
+* ``.pt`` or ``.ts`` -> ``TorchScript``.
+
+**TorchScript-specific**: By default, the module will undergo ``jit.trace()``. You may require to explicitly pass some
+modules under ``jit.script()`` so that they are correctly traced.The ``check_trace`` arg is passed through to
+``jit.trace()``.
+
+**ONNX-specific**: If ``use_dynamic_axes`` is True, ``onnx.export()`` is called with dynamic axes. If ``dynamic_axes``
+is ``None``, they are inferred from the model's ``input_types`` definition (batch dimension is dynamic, and so is
+duration etc).
+
+If ``check_trace`` is ``True``, the resulting ONNX also runs on ``input_example`` and the results compared to the
+exported model's output, using the ``check_tolerance`` argument. Note the higher tolerance default.
+
+
+How to Make Model Exportable
+----------------------------
+
+If you are simply using ATOMMIC models, the previous example is all you need to know.
+If you write your own models, this section highlights the things you need to be aware of after extending ``Exportable``.
+
+Exportable Hooks and Overrides
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You should not normally need to override ``Exportable`` default methods. However, ``Exportable.export()`` relies on
+the assumptions that certain methods are available in your class.
+
+.. code-block:: Python
+
+ @property
+ def input_example(self) # => Tuple(input, [(input, ...], [Dict])
+ """
+ Generates input examples for tracing etc.
+ Returns:
+ A tuple of input examples.
+ """
+
+This function should return a tuple of (normally) Tensors - one per each of model inputs (args to ``forward()``). The
+last element may be a ``Dict`` to specify non-positional arguments by name, as per Torch ``export()`` convention. For
+more information, refer to the `Using dictionaries to handle Named Arguments as model inputs
+`_.
+
+.. Note: ``Dict`` currently does not work with Torchscript ``trace()``.
+
+.. code-block:: Python
+
+ @property
+ def input_types(self):
+ @property
+ def output_types(self):
+
+Those are needed for inferring in/out names and dynamic axes. If your model derives from ``ModulePT``, those are
+already there. Another common scenario is that your model contains one or more modules that processes input and
+generates output. Then, you should override ``Exportable`` methods ``input_module()`` and ``output_module()`` to point
+to them, like in this example:
+
+.. code-block:: Python
+
+ @property
+ def input_module(self):
+ return self.fastpitch
+
+ @property
+ def output_module(self):
+ return self.fastpitch
+
+Your model should also have an export-friendly ``forward()`` method - that can mean different things for ONNX ant
+TorchScript. For ONNX, you can't have forced named parameters without default, like ``forward(self, *, text)``. For
+TorchScript, you should avoid ``None`` and use ``Optional`` instead. The criteria are highly volatile and may change
+with every PyTorch version, so it's a trial-and-error process. There is also the general issue that in many cases,
+``forward()`` for inference can be simplified and even use less inputs/outputs. To address this, ``Exportable`` looks
+for ``forward_for_export()`` method in your model and uses that instead of ``forward()`` to export.
+
+To stay consistent with input_types()/output_types(), there are also those hooks in ``Exportable`` that let you
+exclude particular inputs/outputs from the export process.
+
+Another common requirement for models that are being exported is to run certain net modifications for inference
+efficiency before exporting - like disabling masks in some convolutions or removing batch normalizations. A better
+style is to make those happen on ``ModelPT.eval()`` (and reversed on ``.train()``), but it's not always feasible so
+the following hook is provided in ``Exportable`` to run those:
+
+.. code-block:: Python
+
+ def _prepare_for_export(self, **kwargs):
+ """
+ Override this method to prepare module for export. This is in-place operation.
+ Base version does common necessary module replacements (Apex etc)
+ """
+ # do graph modifications specific for this model
+ normalization_type = kwargs.get('normalization_type', 'minmax')
+ replace_for_export(self, normalization_type)
+ # call base method for common set of modifications
+ Exportable._prepare_for_export(self, **kwargs)
+
+Some models that require control flow, need to be exported in multiple parts. Typical examples are RNNT nets.
+To facilitate that, the hooks below are provided. To export, for example, 'encoder' and 'decoder' subnets of the
+model, overload list_export_subnets to return ['encoder', 'decoder'].
+
+.. code-block:: Python
+
+ def get_export_subnet(self, subnet=None):
+ """
+ Returns Exportable subnet model/module to export
+ """
+
+
+ def list_export_subnets(self):
+ """
+ Returns default set of subnet names exported for this model
+ First goes the one receiving input (input_example)
+ """
+
+Some networks may be exported differently according to user-settable options. To facilitate that
+- `set_export_config()` method is provided by Exportable to set key/value pairs to predefined model.export_config
+dictionary, to be used during the export:
+
+.. code-block:: Python
+
+ def set_export_config(self, args):
+ """
+ Sets/updates export_config dictionary
+ """
+
+Also, if an action hook on setting config is desired, this method may be overloaded by `Exportable` descendants to
+include one.
+
+
+Exportable Model Code
+~~~~~~~~~~~~~~~~~~~~~
+
+Most importantly, the actual Torch code in your model should be ONNX or TorchScript - compatible (ideally, both).
+#. Ensure the code is written in Torch - avoid bare `Numpy or Python operands `_.
+#. Create your model ``Exportable`` and add an export unit test, to catch any operation/construct not supported in
+ONNX/TorchScript, immediately.
+
+For more information, refer to the PyTorch documentation:
+ - `List of supported operators `_
+ - `Tracing vs. scripting `_
+ - `AlexNet example `_
diff --git a/docs/source/index.rst b/docs/source/index.rst
new file mode 100644
index 00000000..ef904be7
--- /dev/null
+++ b/docs/source/index.rst
@@ -0,0 +1,43 @@
+ATOMMIC User Guide
+==================
+
+.. toctree::
+ :maxdepth: 4
+ :caption: Getting Started
+ :name: starthere
+
+ starthere/intro
+ starthere/projects
+ starthere/tutorials
+
+.. toctree::
+ :maxdepth: 2
+ :caption: ATOMMIC MRI
+ :name: ATOMMIC MRI
+
+ mri/collections
+ mri/losses
+ mri/metrics
+ mri/transforms
+ mri/undersampling
+
+.. toctree::
+ :maxdepth: 2
+ :caption: ATOMMIC Core
+ :name: core
+
+ core/core
+ core/exp_manager
+ core/export
+
+.. toctree::
+ :maxdepth: 3
+ :caption: API
+ :name: API
+
+ api/core
+ api/common/intro
+ api/multitask/intro
+ api/reconstruction/intro
+ api/segmentation/intro
+ api/quantitative/intro
diff --git a/docs/source/mri/collections.rst b/docs/source/mri/collections.rst
new file mode 100644
index 00000000..1dc4641e
--- /dev/null
+++ b/docs/source/mri/collections.rst
@@ -0,0 +1,1871 @@
+Collections & Models
+====================
+
+``ATOMMIC`` is organized in collections, each of which implements a specific task. The following collections are
+currently available, implementing various models as listed.
+
+
+MultiTask Learning (MTL)
+------------------------
+
+End-to-End Recurrent Attention Network
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+End-to-End Recurrent Attention Network (:class:`~atommic.collections.multitask.rs.nn.seranet.SERANet`), as
+presented in [Huang2019]_.
+
+ References
+ ----------
+ .. [Huang2019] Huang, Q., Chen, X., Metaxas, D., Nadar, M.S. (2019). Brain Segmentation from k-Space with
+ End-to-End Recurrent Attention Network. In: , et al. Medical Image Computing and Computer Assisted
+ Intervention โ MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham.
+ https://doi.org/10.1007/978-3-030-32248-9_31
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: SERANET
+ use_reconstruction_module: true
+ input_channels: 2
+ reconstruction_module: unet
+ reconstruction_module_output_channels: 2
+ reconstruction_module_channels: 32
+ reconstruction_module_pooling_layers: 4
+ reconstruction_module_dropout: 0.0
+ # or
+ # reconstruction_module: cascadenet
+ # reconstruction_module_hidden_channels: 32
+ # reconstruction_module_n_convs: 2
+ # reconstruction_module_batchnorm: true
+ # reconstruction_module_num_cascades: 5
+ reconstruction_module_num_blocks: 3
+ segmentation_module_input_channels: 32
+ segmentation_module_output_channels: 2
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 4
+ segmentation_module_dropout: 0.0
+ recurrent_module_iterations: 2
+ recurrent_module_attention_channels: 32
+ recurrent_module_attention_pooling_layers: 4
+ recurrent_module_attention_dropout: 0.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Image domain Deep Structured Low-Rank Network
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Image domain Deep Structured Low-Rank Network (:class:`~atommic.collections.multitask.rs.nn.idslr.IDSLR`), as
+presented in [Pramanik2021]_.
+
+ References
+ ----------
+ .. [Pramanik2021] Pramanik A, Wu X, Jacob M. Joint calibrationless reconstruction and segmentation of parallel
+ MRI. arXiv preprint arXiv:2105.09220. 2021 May 19.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: IDSLR
+ use_reconstruction_module: true
+ input_channels: 24 # coils * 2
+ reconstruction_module_output_channels: 24 # coils * 2
+ segmentation_module_output_channels: 2
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: true
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Image domain Deep Structured Low-Rank UNet
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Image domain Deep Structured Low-Rank network using a UNet (and not only the decoder part) as segmentation model
+(:class:`~atommic.collections.multitask.rs.nn.idslr_unet.IDSLRUNet`), as presented in [Pramanik2021]_.
+
+ References
+ ----------
+ .. [Pramanik2021] Pramanik A, Wu X, Jacob M. Joint calibrationless reconstruction and segmentation of parallel
+ MRI. arXiv preprint arXiv:2105.09220. 2021 May 19.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: IDSLRUNET
+ use_reconstruction_module: true
+ input_channels: 24 # coils * 2
+ reconstruction_module_output_channels: 24 # coils * 2
+ segmentation_module_output_channels: 2
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: true
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Multi-Task Learning for MRI Reconstruction and Segmentation
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Multi-Task Learning for MRI Reconstruction and Segmentation
+(:class:`~atommic.collections.multitask.rs.nn.mtlrs.MTLRS`), as presented in [Karkalousos2023]_.
+
+ References
+ ----------
+ .. [Karkalousos2023] Karkalousos, D., Iลกgum, I., Marquering, H., Caan, M. W. A., (2023). MultiTask Learning for
+ accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis. In Proceedings of
+ Machine Learning Research (Vol. 078).
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: MTLRS
+ joint_reconstruction_segmentation_module_cascades: 5
+ task_adaption_type: multi_task_learning
+ use_reconstruction_module: true
+ reconstruction_module_recurrent_layer: IndRNN
+ reconstruction_module_conv_filters:
+ - 64
+ - 64
+ - 2
+ reconstruction_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ reconstruction_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ reconstruction_module_conv_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ reconstruction_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_depth: 2
+ reconstruction_module_time_steps: 8
+ reconstruction_module_conv_dim: 2
+ reconstruction_module_num_cascades: 1
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_no_dc: true
+ reconstruction_module_keep_prediction: true
+ reconstruction_module_accumulate_predictions: true
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 2
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Reconstruction Segmentation method using UNet
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Reconstruction Segmentation method using UNets for both the reconstruction and segmentation
+(:class:`~atommic.collections.multitask.rs.nn.recseg_unet.RecSegUNet`), as presented in [Sui2021]_.
+
+ References
+ ----------
+ .. [Sui2021] Sui, B, Lv, J, Tong, X, Li, Y, Wang, C. Simultaneous image reconstruction and lesion segmentation in
+ accelerated MRI using multitasking learning. Med Phys. 2021; 48: 7189โ 7198. https://doi.org/10.1002/mp.15213
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: RECSEGNET
+ use_reconstruction_module: true
+ input_channels: 1
+ reconstruction_module_output_channels: 1
+ reconstruction_module_channels: 64
+ reconstruction_module_pooling_layers: 2
+ reconstruction_module_dropout: 0.0
+ segmentation_module_output_channels: 2
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Segmentation Network MRI
+~~~~~~~~~~~~~~~~~~~~~~~~
+Segmentation Network MRI (:class:`~atommic.collections.multitask.rs.nn.segnet.SegNet`), as presented in [Sun2019]_.
+
+ References
+ ----------
+ .. [Sun2019] Sun, L., Fan, Z., Ding, X., Huang, Y., Paisley, J. (2019). Joint CS-MRI Reconstruction and
+ Segmentation with a Unified Deep Network. In: Chung, A., Gee, J., Yushkevich, P., Bao, S. (eds) Information
+ Processing in Medical Imaging. IPMI 2019. Lecture Notes in Computer Science(), vol 11492. Springer, Cham.
+ https://doi.org/10.1007/978-3-030-20351-1_38
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: SEGNET
+ use_reconstruction_module: true
+ input_channels: 24 # coils * 2
+ reconstruction_module_output_channels: 24 # coils * 2
+ segmentation_module_output_channels: 2
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: true
+ padding: true
+ norm_groups: 2
+ num_cascades: 5
+ segmentation_final_layer_conv_dim: 2
+ segmentation_final_layer_kernel_size: 3
+ segmentation_final_layer_dilation: 1
+ segmentation_final_layer_bias: False
+ segmentation_final_layer_nonlinear: relu
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+
+Quantitative MR Imaging (qMRI)
+------------------------------
+
+quantitative Cascades of Independently Recurrent Inference Machines
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+quantitative Cascades of Independently Recurrent Inference Machines
+(:class:`~atommic.collections.quantitative.nn.qcirim.qCIRIM`), as presented in [Zhang2022]_.
+
+ References
+ ----------
+ .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW.
+ A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent
+ inference machine. NeuroImage. 2022 Dec 1;264:119680.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: qCIRIM
+ use_reconstruction_module: true
+ reconstruction_module_recurrent_layer: IndRNN
+ reconstruction_module_conv_filters:
+ - 64
+ - 64
+ - 2
+ reconstruction_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ reconstruction_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ reconstruction_module_conv_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ reconstruction_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_depth: 2
+ reconstruction_module_time_steps: 8
+ reconstruction_module_conv_dim: 2
+ reconstruction_module_num_cascades: 1
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_no_dc: true
+ reconstruction_module_keep_prediction: true
+ reconstruction_module_accumulate_predictions: true
+ quantitative_module_recurrent_layer: IndRNN
+ quantitative_module_conv_filters:
+ - 64
+ - 64
+ - 4
+ quantitative_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ quantitative_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ quantitative_module_conv_bias:
+ - true
+ - true
+ - false
+ quantitative_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ quantitative_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_bias:
+ - true
+ - true
+ - false
+ quantitative_module_depth: 2
+ quantitative_module_time_steps: 8
+ quantitative_module_conv_dim: 2
+ quantitative_module_num_cascades: 1
+ quantitative_module_no_dc: true
+ quantitative_module_keep_prediction: true
+ quantitative_module_accumulate_predictions: true
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_module_gamma_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ shift_B0_input: false
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+quantitative Recurrent Inference Machines
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+quantitative Recurrent Inference Machines
+(:class:`~atommic.collections.quantitative.nn.qrim_base.qrim_block.qRIMBlock`), as presented in [Zhang2022]_.
+
+ References
+ ----------
+ .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW.
+ A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent
+ inference machine. NeuroImage. 2022 Dec 1;264:119680.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: qCIRIM
+ use_reconstruction_module: true
+ reconstruction_module_recurrent_layer: GRU
+ reconstruction_module_conv_filters:
+ - 64
+ - 64
+ - 2
+ reconstruction_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ reconstruction_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ reconstruction_module_conv_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ reconstruction_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_depth: 2
+ reconstruction_module_time_steps: 8
+ reconstruction_module_conv_dim: 2
+ reconstruction_module_num_cascades: 1
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_no_dc: true
+ reconstruction_module_keep_prediction: true
+ reconstruction_module_accumulate_predictions: true
+ quantitative_module_recurrent_layer: GRU
+ quantitative_module_conv_filters:
+ - 64
+ - 64
+ - 4
+ quantitative_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ quantitative_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ quantitative_module_conv_bias:
+ - true
+ - true
+ - false
+ quantitative_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ quantitative_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_bias:
+ - true
+ - true
+ - false
+ quantitative_module_depth: 2
+ quantitative_module_time_steps: 8
+ quantitative_module_conv_dim: 2
+ quantitative_module_num_cascades: 1
+ quantitative_module_no_dc: true
+ quantitative_module_keep_prediction: true
+ quantitative_module_accumulate_predictions: true
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_module_gamma_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ shift_B0_input: false
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+quantitative End-to-End Variational Network
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+quantitative End-to-End Variational Network (:class:`~atommic.collections.quantitative.nn.qvarnet.qVarNet`), as
+presented in [Zhang2022]_.
+
+ References
+ ----------
+ .. [Zhang2022] Zhang C, Karkalousos D, Bazin PL, Coolen BF, Vrenken H, Sonke JJ, Forstmann BU, Poot DH, Caan MW.
+ A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent
+ inference machine. NeuroImage. 2022 Dec 1;264:119680.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: qVN
+ use_reconstruction_module: false
+ reconstruction_module_num_cascades: 2
+ reconstruction_module_channels: 8
+ reconstruction_module_pooling_layers: 2
+ reconstruction_module_in_channels: 2
+ reconstruction_module_out_channels: 2
+ reconstruction_module_padding_size: 11
+ reconstruction_module_normalize: true
+ reconstruction_module_no_dc: false
+ reconstruction_module_accumulate_predictions: false
+ quantitative_module_num_cascades: 1
+ quantitative_module_channels: 4
+ quantitative_module_pooling_layers: 2
+ quantitative_module_in_channels: 8
+ quantitative_module_out_channels: 8
+ quantitative_module_padding_size: 11
+ quantitative_module_normalize: true
+ quantitative_module_no_dc: false
+ quantitative_module_dimensionality: 2
+ quantitative_module_accumulate_predictions: false
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_gamma_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ shift_B0_input: false
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+
+MRI Reconstruction (REC)
+------------------------
+
+Cascades of Independently Recurrent Inference Machines
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Cascades of Independently Recurrent Inference Machines (:class:`~atommic.collections.reconstruction.nn.cirim.CIRIM`),
+as presented in [Karkalousos2022]_.
+
+ References
+ ----------
+ .. [Karkalousos2022] Karkalousos D, Noteboom S, Hulst HE, Vos FM, Caan MWA. Assessment of data consistency through
+ cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction.
+ Phys Med Biol. 2022 Jun 8;67(12). doi: 10.1088/1361-6560/ac6cc2. PMID: 35508147.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 8
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Convolutional Recurrent Neural Networks
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Convolutional Recurrent Neural Networks (:class:`~atommic.collections.reconstruction.nn.crnn.CRNNet`), as presented
+in [Qin2019]_.
+
+ References
+ ----------
+ .. [Qin2019] C. Qin, J. Schlemper, J. Caballero, A. N. Price, J. V. Hajnal and D. Rueckert, "Convolutional
+ Recurrent Neural Networks for Dynamic MR Image Reconstruction," in IEEE Transactions on Medical Imaging, vol.
+ 38, no. 1, pp. 280-290, Jan. 2019, doi: 10.1109/TMI.2018.2863670.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Deep Cascade of Convolutional Neural Networks
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Deep Cascade of Convolutional Neural Networks (:class:`~atommic.collections.reconstruction.nn.ccnn.CascadeNet`), as
+presented in [Schlemper2017]_.
+
+ References
+ ----------
+ .. [Schlemper2017] Schlemper, J., Caballero, J., Hajnal, J. V., Price, A., & Rueckert, D., A Deep Cascade of
+ Convolutional Neural Networks for MR Image Reconstruction. Information Processing in Medical Imaging (IPMI),
+ 2017.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Down-Up Net
+~~~~~~~~~~~
+Down-Up NET (:class:`~atommic.collections.reconstruction.nn.dunet.DUNet`), inspired by [Hammernik2021]_.
+
+ References
+ ----------
+ .. [Hammernik2021] Hammernik, K, Schlemper, J, Qin, C, et al. Systematic valuation of iterative deep neural
+ networks for fast parallel MRI reconstruction with sensitivity-weighted coil combination. Magn Reson Med.
+ 2021; 86: 1859โ 1872. https://doi.org/10.1002/mrm.28827
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: VS
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+End-to-End Variational Network
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+End-to-End Variational Network (:class:`~atommic.collections.reconstruction.nn.varnet.VarNet`), as presented in
+[Sriram2020]_.
+
+ References
+ ----------
+ .. [Sriram2020] Sriram A, Zbontar J, Murrell T, Defazio A, Zitnick CL, Yakubova N, Knoll F, Johnson P. End-to-end
+ variational networks for accelerated MRI reconstruction. InInternational Conference on Medical Image Computing
+ and Computer-Assisted Intervention 2020 Oct 4 (pp. 64-73). Springer, Cham.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Independently Recurrent Inference Machines
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Independently Recurrent Inference Machines
+(:class:`~atommic.collections.reconstruction.nn.rim_base.rim_block.RIMBlock`), as presented in [Karkalousos2022]_.
+
+ References
+ ----------
+ .. [Karkalousos2022] Karkalousos D, Noteboom S, Hulst HE, Vos FM, Caan MWA. Assessment of data consistency through
+ cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction.
+ Phys Med Biol. 2022 Jun 8;67(12). doi: 10.1088/1361-6560/ac6cc2. PMID: 35508147.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network
+(:class:`~atommic.collections.reconstruction.nn.jointicnet.JointICNet`), as presented in [Jun2021]_.
+
+ References
+ ----------
+ .. [Jun2021] Jun, Yohan, et al. โJoint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network
+ (Joint-ICNet) for Fast MRI.โ 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE,
+ 2021, pp. 5266โ75. DOI.org (Crossref), https://doi.org/10.1109/CVPR46437.2021.00523.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+KIKINet
+~~~~~~~
+KIKINet (:class:`~atommic.collections.reconstruction.nn.kikinet.KIKINet`), modified to work with multi-coil k-space
+data, as presented in [Taejoon2018]_.
+
+ References
+ ----------
+ .. [Taejoon2018] Eo, Taejoon, et al. โKIKI-Net: Cross-Domain Convolutional Neural Networks for Reconstructing
+ Undersampled Magnetic Resonance Images.โ Magnetic Resonance in Medicine, vol. 80, no. 5, Nov. 2018, pp.
+ 2188โ201. PubMed, https://doi.org/10.1002/mrm.27201.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Learned Primal-Dual Net
+~~~~~~~~~~~~~~~~~~~~~~~
+Learned Primal-Dual Net (:class:`~atommic.collections.reconstruction.nn.lpd.LPDNet`), as presented in [Adler2018]_.
+
+ References
+ ----------
+ .. [Adler2018] Adler, Jonas, and Ozan รktem. โLearned Primal-Dual Reconstruction.โ IEEE Transactions on Medical
+ Imaging, vol. 37, no. 6, June 2018, pp. 1322โ32. arXiv.org, https://doi.org/10.1109/TMI.2018.2799231.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+MoDL: Model Based Deep Learning Architecture for Inverse Problems
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+MoDL: Model Based Deep Learning Architecture for Inverse Problems
+(:class:`~atommic.collections.reconstruction.nn.modl.MoDL`).
+
+Adjusted to optionally perform a data consistency step (Conjugate Gradient), as presented in [Aggarwal2018]_,
+[Yaman2020]_. If dc is set to False, the network will perform a simple residual learning step.
+
+ References
+ ----------
+ .. [Aggarwal2018] MoDL: Model Based Deep Learning Architecture for Inverse Problems by H.K. Aggarwal, M.P Mani, and
+ Mathews Jacob in IEEE Transactions on Medical Imaging, 2018
+
+ .. [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uฤurbil, K, Akรงakaya, M. Self-supervised
+ learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson
+ Med. 2020; 84: 3172โ 3191. https://doi.org/10.1002/mrm.28378
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+MultiDomainNet
+~~~~~~~~~~~~~~
+Feature-level multi-domain module. Inspired by AIRS Medical submission to the FastMRI 2020 challenge.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+ProximalGradient
+~~~~~~~~~~~~~~~~
+Proximal/Conjugate Gradient (:class:`~atommic.collections.reconstruction.nn.proximal_gradient.ProximalGradient`),
+according to [Aggarwal2018]_, [Yaman2020]_.
+
+ References
+ ----------
+ .. [Aggarwal2018] MoDL: Model Based Deep Learning Architecture for Inverse Problems by H.K. Aggarwal, M.P Mani, and
+ Mathews Jacob in IEEE Transactions on Medical Imaging, 2018
+
+ .. [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uฤurbil, K, Akรงakaya, M. Self-supervised
+ learning of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson
+ Med. 2020; 84: 3172โ 3191. https://doi.org/10.1002/mrm.28378
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: PROXIMALGRADIENT
+ conjugate_gradient_dc: true
+ conjugate_gradient_iterations: 10
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Recurrent Variational Network
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Recurrent Variational Network (:class:`~atommic.collections.reconstruction.nn.recurrentvarnet.RecurrentVarNet`), as
+presented in [Yiasemis2021]_.
+
+ References
+ ----------
+ .. [Yiasemis2021] Yiasemis, George, et al. โRecurrent Variational Network: A Deep Learning Inverse Problem Solver
+ Applied to the Task of Accelerated MRI Reconstruction.โ ArXiv:2111.09639 [Physics], Nov. 2021. arXiv.org,
+ http://arxiv.org/abs/2111.09639.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Recurrent Inference Machines
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Recurrent Inference Machines (:class:`~atommic.collections.reconstruction.nn.rim_base.rim_block.RIMBlock`), as
+presented in [Lonning19]_.
+
+ References
+ ----------
+ .. [Lonning19] Lรธnning K, Putzky P, Sonke JJ, Reneman L, Caan MW, Welling M. Recurrent inference machines for
+ reconstructing heterogeneous MRI data. Medical image analysis. 2019 Apr 1;53:64-78.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+UNet
+~~~~
+UNet (:class:`~atommic.collections.reconstruction.nn.unet.UNet`), as presented in [Ronneberger2015]_.
+
+ References
+ ----------
+ .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical
+ image segmentation. In International Conference on Medical image computing and computer-assisted intervention,
+ pages 234โ241. Springer, 2015.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Variable Splitting Network
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+Variable Splitting Network (:class:`~atommic.collections.reconstruction.nn.vsnet.VSNet`), as presented in [Duan2019]_.
+
+ References
+ ----------
+ .. [Duan2019] Duan, J. et al. (2019) Vs-net: Variable splitting network for accelerated parallel MRI
+ reconstruction, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial
+ Intelligence and Lecture Notes in Bioinformatics), 11767 LNCS, pp. 713โ722. doi: 10.1007/978-3-030-32251-9_78.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+XPDNet
+~~~~~~
+XPDNet (:class:`~atommic.collections.reconstruction.nn.xpdnet.XPDNet`), as presented in [Ramzi2021]_.
+
+ References
+ ----------
+ .. [Ramzi2021] Ramzi, Zaccharie, et al. โXPDNet for MRI Reconstruction: An Application to the 2020 FastMRI
+ Challenge. ArXiv:2010.07290 [Physics, Stat], July 2021. arXiv.org, http://arxiv.org/abs/2010.07290.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 20
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 2
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: false
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Zero-Filled
+~~~~~~~~~~~
+Zero-Filled reconstruction using either root-sum-of-squares (RSS) or SENSE (SENSitivity Encoding, as presented in
+[Pruessmann1999]_).
+
+ References
+ ----------
+ .. [Pruessmann1999] Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI.
+ Magn Reson Med 1999; 42:952-962.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: ZF
+ # task & dataset related parameters
+ coil_combination_method: SENSE
+ coil_dim: 1
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+
+MRI Segmentation (SEG)
+----------------------
+
+Attention UNet
+~~~~~~~~~~~~~~
+Attention UNet for MRI segmentation
+(:class:`~atommic.collections.segmentation.nn.attentionunet.SegmentationAttentionUNet`), as presented in [Oktay2018]_.
+
+ References
+ ----------
+ .. [Oktay2018] O. Oktay, J. Schlemper, L.L. Folgoc, M. Lee, M. Heinrich, K. Misawa, K. Mori, S. McDonagh, N.Y.
+ Hammerla, B. Kainz, B. Glocker, D. Rueckert. Attention U-Net: Learning Where to Look for the Pancreas. 2018.
+ https://arxiv.org/abs/1804.03999
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE # if complex data
+ coil_dim: 1 # if complex data
+ complex_data: true # or false if using magnitude data
+ complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false # if complex data
+ fft_normalization: backward # if complex data
+ spatial_dims:
+ - -2 # if complex data
+ - -1 # if complex data
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Dynamic UNet
+~~~~~~~~~~~~
+Dynamic UNet for MRI segmentation (:class:`~atommic.collections.segmentation.nn.dynunet.SegmentationDYNUNet`), as
+presented in [Isensee2018]_.
+
+ References
+ ----------
+ .. [Isensee2018] Isensee F, Petersen J, Klein A, Zimmerer D, Jaeger PF, Kohl S, Wasserthal J, Koehler G,
+ Norajitra T, Wirkert S, Maier-Hein KH. nnu-net: Self-adapting framework for u-net-based medical image
+ segmentation. arXiv preprint arXiv:1809.10486. 2018 Sep 27.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: SEGMENTATIONDYNUNET
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels:
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ # task & dataset related parameters
+ coil_combination_method: SENSE # if complex data
+ coil_dim: 1 # if complex data
+ complex_data: true # or false if using magnitude data
+ complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false # if complex data
+ fft_normalization: backward # if complex data
+ spatial_dims:
+ - -2 # if complex data
+ - -1 # if complex data
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+Lambda UNet
+~~~~~~~~~~~
+Lambda UNet for MRI segmentation (:class:`~atommic.collections.segmentation.nn.lambdaunet.SegmentationLambdaUNet`), as
+presented in [Yanglan2021]_.
+
+ References
+ ----------
+ .. [Yanglan2021] Yanglan Ou, Ye Yuan, Xiaolei Huang, Kelvin Wong, John Volpi, James Z. Wang, Stephen T.C. Wong.
+ LambdaUNet: 2.5D Stroke Lesion Segmentation of Diffusion-weighted MR Images. 2021.
+ https://arxiv.org/abs/2104.13917
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: SEGMENTATIONLAMBDAUNET
+ segmentation_module: LambdaUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ segmentation_module_query_depth: 16
+ segmentation_module_intra_depth: 1
+ segmentation_module_receptive_kernel: 1
+ segmentation_module_temporal_kernel: 1
+ # task & dataset related parameters
+ coil_combination_method: SENSE # if complex data
+ coil_dim: 1 # if complex data
+ complex_data: true # or false if using magnitude data
+ complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false # if complex data
+ fft_normalization: backward # if complex data
+ spatial_dims:
+ - -2 # if complex data
+ - -1 # if complex data
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+UNet
+~~~~
+2D UNet for MRI segmentation (:class:`~atommic.collections.segmentation.nn.unet.SegmentationUNet`), as
+presented in [Ronneberger2015]_.
+
+ References
+ ----------
+ .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical
+ image segmentation. In International Conference on Medical image computing and computer-assisted intervention,
+ pages 234โ241. Springer, 2015.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: SEGMENTATIONUNET
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE # if complex data
+ coil_dim: 1 # if complex data
+ complex_data: true # or false if using magnitude data
+ complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false # if complex data
+ fft_normalization: backward # if complex data
+ spatial_dims:
+ - -2 # if complex data
+ - -1 # if complex data
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+UNet 3D
+~~~~~~~
+3D UNet for MRI segmentation (:class:`~atommic.collections.segmentation.nn.unet3d.Segmentation3DUNet`), as
+presented in [Ronneberger2015]_.
+
+ References
+ ----------
+ .. [Ronneberger2015] O. Ronneberger, P. Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical
+ image segmentation. In International Conference on Medical image computing and computer-assisted intervention,
+ pages 234โ241. Springer, 2015.
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: SEGMENTATION3DUNET
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ # task & dataset related parameters
+ coil_combination_method: SENSE # if complex data
+ coil_dim: 1 # if complex data
+ complex_data: true # or false if using magnitude data
+ complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false # if complex data
+ fft_normalization: backward # if complex data
+ spatial_dims:
+ - -2 # if complex data
+ - -1 # if complex data
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+UNETR
+~~~~~
+UNETR for MRI segmentation (:class:`~atommic.collections.segmentation.nn.unetr.SegmentationUNetR`), as
+presented in [Hatamizadeh2022]_.
+
+ References
+ ----------
+ .. [Hatamizadeh2022] Hatamizadeh A, Tang Y, Nath V, Yang D, Myronenko A, Landman B, Roth HR, Xu D. Unetr:
+ Transformers for 3d medical image segmentation. InProceedings of the IEEE/CVF Winter Conference on
+ Applications of Computer Vision 2022 (pp. 574-584).
+
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ model_name: SEGMENTATIONUNETR
+ segmentation_module: UNETR
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 3
+ segmentation_module_img_size: (256, 256)
+ segmentation_module_channels: 64
+ segmentation_module_hidden_size: 768
+ segmentation_module_mlp_dim: 3072
+ segmentation_module_num_heads: 12
+ segmentation_module_pos_embed: conv
+ segmentation_module_norm_name: instance
+ segmentation_module_conv_block: true
+ segmentation_module_res_block: true
+ segmentation_module_dropout: 0.0
+ segmentation_module_qkv_bias: false
+ # task & dataset related parameters
+ coil_combination_method: SENSE # if complex data
+ coil_dim: 1 # if complex data
+ complex_data: true # or false if using magnitude data
+ complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false # if complex data
+ fft_normalization: backward # if complex data
+ spatial_dims:
+ - -2 # if complex data
+ - -1 # if complex data
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+
+V-Net
+~~~~~
+V-Net for MRI segmentation (:class:`~atommic.collections.segmentation.nn.vnet.SegmentationVNet`), as
+presented in [Milletari2016]_.
+
+ References
+ ----------
+ .. [Milletari2016] Fausto Milletari, Nassir Navab, Seyed-Ahmad Ahmadi. V-Net: Fully Convolutional Neural Networks
+ for Volumetric Medical Image Segmentation, 2016. https://arxiv.org/abs/1606.04797
+
+Example configuration:
+
+.. code-block:: bash
+
+ model:
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: False
+ segmentation_module_padding_size: 15
+ # task & dataset related parameters
+ coil_combination_method: SENSE # if complex data
+ coil_dim: 1 # if complex data
+ complex_data: true # or false if using magnitude data
+ complex_valued_type: stacked (only for complex data) # stacked, complex_abs, complex_sqrt_abs
+ consecutive_slices: 1
+ dimensionality: 2
+ estimate_coil_sensitivity_maps_with_nn: false
+ fft_centered: false # if complex data
+ fft_normalization: backward # if complex data
+ spatial_dims:
+ - -2 # if complex data
+ - -1 # if complex data
+ magnitude_input: true
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
diff --git a/docs/source/mri/losses.rst b/docs/source/mri/losses.rst
new file mode 100644
index 00000000..d7f812f5
--- /dev/null
+++ b/docs/source/mri/losses.rst
@@ -0,0 +1,122 @@
+Losses
+======
+
+``ATOMMIC`` provides a number of loss functions for training models. These are all subclasses of ``torch.nn.Module``
+and can be used in the same way as any other PyTorch loss function.
+
+For ``reconstruction``, ``qMRI`` and ``multitask`` tasks, the following losses are available:
+
+* :class:`~MSELoss`:
+ A loss function based on the Mean Squared Error (MSE). It can be used for any task and it calls
+ ``torch.nn.MSELoss``.
+
+* :class:`~L1Loss`:
+ A loss function based on the Mean Absolute Error (MAE). It can be used for any task and it calls
+ ``torch.nn.L1Loss``.
+
+* :class:`~atommic.collections.reconstruction.losses.SSIMLoss`:
+ A loss function based on the Structural Similarity Index (SSIM). It can be used for any task and it is based on
+ [Wang2004]_.
+
+ References
+ ----------
+ .. [Wang2004] Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004). Image quality assessment: from
+ error visibility to structural similarity. IEEE transactions on image processing, 13(4), 600-612.
+
+* :class:`~atommic.collections.reconstruction.losses.NoiseAwareLoss`:
+ A custom loss function that is aware of the noise level in the data. It can be used for any task and it is based
+ on [Oh2021]_.
+
+ References
+ ----------
+ .. [Oh2021] Oh, Y., Kim, B., & Ham, B. (2021). Background-aware pooling and noise-aware loss for
+ weakly-supervised semantic segmentation. In Proceedings of the IEEE/CVF conference on computer vision and
+ pattern recognition (pp. 6913-6922).
+
+* :class:`~atommic.collections.common.losses.SinkhornDistance`:
+ Resembles the Wasserstein distance, but is differentiable and can be used as a loss function. It can be used for
+ any task and it is based on [Cuturi2013]_.
+
+ References
+ ----------
+ .. [Cuturi2013] Marco Cuturi, Sinkhorn Distances: Lightspeed Computation of Optimal Transport, NIPS 2013
+
+* :class:`~atommic.collections.segmentation.losses.CrossEntropyLoss`:
+ A loss function based on the cross-entropy between the predicted and the ground truth segmentation. It can be used
+ for segmentation tasks and it is a wrapper around ``torch.nn.CrossEntropyLoss``.
+
+* :class:`~atommic.collections.segmentation.losses.Dice`:
+ A loss function based on the Dice coefficient. It can be used for segmentation tasks and it is a wrapper for
+ :py:class:`monai.losses.DiceLoss` to support multi-class and multi-label tasks. It is based on [Milletari2016]_.
+
+ References
+ ----------
+ .. [Milletari2016] Milletari, F., Navab, N., & Ahmadi, S. A. (2016, October). V-net: Fully convolutional
+ neural networks for volumetric medical image segmentation. In 2016 fourth international conference on 3D
+ vision (3DV) (pp. 565-571). IEEE.
+
+:class:`~atommic.collections.common.losses.AggregatorLoss`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The ``AggregatorLoss`` class is used to combine multiple losses into a single loss function.
+
+.. note::
+ The ``AggregatorLoss`` is not a loss function itself, but a wrapper around multiple loss functions. It is used to
+ combine multiple losses into a single loss function. The ``AggregatorLoss`` is used by the ``ATOMMIC`` models to
+ combine the losses by setting a weight for each loss function. The weights must sum to 1.0.
+
+``AggregatorLoss`` is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ model:
+ reconstruction_loss:
+ mse: 0.2
+ l1: 0.2
+ ssim: 0.2
+ noise_aware: 0.2
+ wasserstein: 0.2
+
+This will create a loss function for the ``reconstruction`` task that is a weighted sum of the MSE, MAE, SSIM,
+NoiseAware and Wasserstein losses.
+
+.. code-block:: bash
+
+ model:
+ segmentation_loss:
+ cross_entropy: 0.5
+ dice: 0.5
+
+This will create a loss function for the ``segmentation`` task that is a weighted sum of the CrossEntropy and Dice
+losses.
+
+.. code-block:: bash
+
+ model:
+ reconstruction_loss:
+ mse: 0.2
+ l1: 0.2
+ ssim: 0.2
+ noise_aware: 0.2
+ wasserstein: 0.2
+ segmentation_loss:
+ cross_entropy: 0.5
+ dice: 0.5
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+
+This will create a loss function for the ``multitask`` task that is a weighted sum of the reconstruction and the
+segmentation losses. The weights for the reconstruction and segmentation losses are set by the
+``total_reconstruction_loss_weight`` and ``total_segmentation_loss_weight`` parameters, respectively.
+
+.. code-block:: bash
+
+ model:
+ quantitative_loss:
+ mse: 0.2
+ l1: 0.2
+ ssim: 0.2
+ noise_aware: 0.2
+ wasserstein: 0.2
+
+This will create a loss function for the ``qMRI`` task that is a weighted sum of the MSE, MAE, SSIM, NoiseAware and
+Wasserstein losses.
diff --git a/docs/source/mri/metrics.rst b/docs/source/mri/metrics.rst
new file mode 100644
index 00000000..9ea04eda
--- /dev/null
+++ b/docs/source/mri/metrics.rst
@@ -0,0 +1,60 @@
+Metrics
+=======
+
+``ATOMMIC`` provides a number of metrics for each task to evaluate the performance of the models. The metrics are
+implemented as classes that can be instantiated and called with the desired inputs. Depending on the chosen task, the
+corresponding metrics will be also logged on the selected logger.
+
+In `tools `_, you can find scripts that allows you to
+evaluate the performance of a model on a dataset. The scripts take as input the ground truth and the predictions of the
+model and compute the metrics for each task.
+
+The metrics are implemented in the following modules:
+
+* :func:`~atommic.collections.reconstruction.metrics.reconstruction_metrics.mse`:
+ Mean Squared Error (MSE) metric for ``reconstruction``, ``quantitative``, and ``multitask`` tasks.
+
+* :func:`~atommic.collections.reconstruction.metrics.reconstruction_metrics.nmse`:
+ Normalized Mean Squared Error (NMSE) metric for ``reconstruction``, ``quantitative``, and ``multitask`` tasks.
+
+* :func:`~atommic.collections.reconstruction.metrics.reconstruction_metrics.psnr`:
+ Peak Signal-to-Noise Ratio (PSNR) metric for ``reconstruction``, ``quantitative``, and ``multitask`` tasks.
+
+* :func:`~atommic.collections.reconstruction.metrics.reconstruction_metrics.ssim`:
+ Structural Similarity Index (SSIM) metric for ``reconstruction``, ``quantitative``, and ``multitask`` tasks.
+
+* :class:`~atommic.collections.reconstruction.metrics.reconstruction_metrics.ReconstructionMetrics`:
+ Class that wraps all the metrics for ``reconstruction``, ``quantitative``, and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.asd`:
+ Average Surface Distance (ASD) metric for ``segmentation`` and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.binary_cross_entropy_with_logits_metric`:
+ Binary Cross Entropy with Logits (BCE) metric for ``segmentation`` and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.dice_metric`:
+ Dice metric for ``segmentation`` and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.f1_per_class_metric`:
+ F1 per class metric for ``segmentation`` and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.hausdorff_distance_metric`:
+ Hausdorff Distance (HD) metric for ``segmentation`` and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.hausdorff_distance_95_metric`:
+ 95th percentile of the Hausdorff Distance (HD95) metric for ``segmentation`` and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.iou_metric`:
+ Intersection over Union (IoU) metric for ``segmentation`` and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.precision_metric`:
+ Precision metric for ``segmentation`` and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.recall_metric`:
+ Recall metric for ``segmentation`` and ``multitask`` tasks.
+
+* :func:`~atommic.collections.segmentation.metrics.segmentation_metrics.surface_distances`:
+ Surface Distances (SD) metric for ``segmentation`` and ``multitask`` tasks.
+
+* :class:`~atommic.collections.segmentation.metrics.segmentation_metrics.SegmentationMetrics`:
+ Class that wraps all the metrics for ``segmentation`` and ``multitask`` tasks.
diff --git a/docs/source/mri/transforms.rst b/docs/source/mri/transforms.rst
new file mode 100644
index 00000000..2e652e06
--- /dev/null
+++ b/docs/source/mri/transforms.rst
@@ -0,0 +1,819 @@
+Pre-processing
+==============
+
+The following classes for pre-processing MRI data are available:
+
+
+:class:`~atommic.collections.common.parts.transforms.Cropper`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The ``Cropper`` class is used to crop MRI data. Cropping can be performed either in image space or k-space.
+
+.. note::
+ If you crop in k-space, data need to be complex-valued as well as that you change the Field-of-View (FOV) of the
+ data. If you crop in image space, the FOV remains the same.
+
+Cropping is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ train_ds:
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+
+ validation_ds:
+ crop_size: [320, 320]
+ kspace_crop: true
+ crop_before_masking: true
+
+ test_ds:
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: false
+
+The ``crop_size`` parameter is the size of the crop. The ``kspace_crop`` parameter determines whether the crop is
+performed in k-space or image space. The ``crop_before_masking`` parameter determines whether the crop is performed
+before or after the mask is applied. If the crop is performed before the mask is applied, the mask is applied to the
+cropped data. If the crop is performed after the mask is applied, the mask is applied to the uncropped data and then
+the crop is performed.
+
+.. note::
+ If you crop after the data is masked, the relative acceleration factor of the data will effectively change.
+
+Here is an example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset. The fully-sampled data
+(first image) are cropped in k-space (second image) and in image space (third image). Images are presented as the
+coil-combined Root-Sum-of-Squares (:func:`~atommic.collections.common.parts.utils.rss`).
+
+.. image:: ../../assets/crop.png
+ :align: center
+ :width: 100%
+
+
+:class:`~atommic.collections.common.parts.transforms.EstimateCoilSensitivityMaps`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The ``EstimateCoilSensitivityMaps`` class is used to estimate coil sensitivity maps from multi-coil MRI data. This is
+useful when no coil sensitivity maps are available. This class estimates the coil sensitivity maps as implemented in
+the `DIRECT library `_. Three methods are available for estimating coil sensitivity
+maps: unit, RSS-estimate, and ESPIRIT. The unit method assumes that the data is single-coil. The RSS-estimate method
+estimates the coil sensitivity maps by using the root-sum-of-squares of the autocalibration-signal. The ESPIRIT method
+estimates the coil sensitivity maps with the ESPIRIT method [Uecker2014]_.
+
+References
+----------
+ .. [Uecker2014] Uecker M, Lai P, Murphy MJ, Virtue P, Elad M, Pauly JM, Vasanawala SS, Lustig M. ESPIRiT--an
+ eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn Reson Med. 2014
+ Mar;71(3):990-1001. doi: 10.1002/mrm.24751. PMID: 23649942; PMCID: PMC4142121.
+
+Estimating coil sensitivity maps is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ train_ds:
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+
+ validation_ds:
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: unit
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+
+ test_ds:
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: espirit
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+
+.. note::
+ This class is different from setting ``estimate_coil_sensitivity_maps_with_nn`` to ``true`` in the ``model``
+ section. The ``EstimateCoilSensitivityMaps`` class estimates coil sensitivity maps from the data, whereas setting
+ ``estimate_coil_sensitivity_maps_with_nn`` to ``true`` in the ``model`` section estimates coil sensitivity maps
+ with a neural network, i.e. a U-Net. Those two methods are not mutually exclusive and can be used together,
+ meaning that the coil sensitivity maps estimated by the ``EstimateCoilSensitivityMaps`` class can be used as input
+ to the neural network and refined.
+
+ Estimating/refining coil sensitivity maps with a neural network is configurable via YAML with Hydra. For example:
+
+ .. code-block:: bash
+
+ estimate_coil_sensitivity_maps_with_nn: true
+ coil_sensitivity_maps_nn_chans: 8
+ coil_sensitivity_maps_nn_pools: 4
+ coil_sensitivity_maps_nn_normalize: true
+ coil_sensitivity_maps_nn_mask_type: 2D
+ coil_sensitivity_maps_nn_mask_center: true
+
+ The ``coil_sensitivity_maps_nn_chans`` parameter is the number of channels in the neural network. The
+ ``coil_sensitivity_maps_nn_pools`` parameter is the number of pooling layers in the neural network. The
+ ``coil_sensitivity_maps_nn_normalize`` parameter determines whether the data is normalized before being fed into
+ the neural network. The ``coil_sensitivity_maps_nn_mask_type`` parameter determines the type of mask that is used
+ to mask the data before being fed into the neural network, i.e. 1D or 2D. The
+ ``coil_sensitivity_maps_nn_mask_center`` parameter determines whether the center of the mask is used or not. If
+ ``coil_sensitivity_maps_nn_mask_center`` is set to ``true``, the center of the mask is used. If
+ ``coil_sensitivity_maps_nn_mask_center`` is set to ``false``, the center of the mask is not used. The latter might
+ be useful if the center of the mask is corrupted by noise, but it might also lead to worse estimation of the coil
+ sensitivity maps.
+
+Here is an example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset. The estimated coils
+sensitivity maps (first image) are presented as the coil-combined Root-Sum-of-Squares
+(:func:`~atommic.collections.common.parts.utils.rss`). The fully-sampled data are coil-combined with the estimated
+coil sensitivity maps with the :func:`~atommic.collections.common.parts.utils.sense` method (second image), as
+presented in [Pruessmann1999]_.
+
+.. image:: ../../assets/sense.png
+ :align: center
+ :width: 70%
+
+References
+----------
+ .. [Pruessmann1999] Pruessmann KP, Weiger M, Scheidegger MB, Boesiger P. SENSE: Sensitivity encoding for fast MRI.
+ Magn Reson Med 1999; 42:952-962.
+
+
+:class:`~atommic.collections.common.parts.transforms.GeometricDecompositionCoilCompression`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``GeometricDecompositionCoilCompression`` class is used to perform coil compression with the geometric
+decomposition method, as presented in [Zhang2013]_.
+
+References
+----------
+ .. [Zhang2013] Zhang, T., Pauly, J. M., Vasanawala, S. S., & Lustig, M. (2013). Coil compression for accelerated
+ imaging with Cartesian sampling. Magnetic Resonance in Medicine, 69(2), 571โ582.
+ https://doi.org/10.1002/mrm.24267
+
+The ``GeometricDecompositionCoilCompression`` class is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ train_ds:
+ apply_gcc: true
+ gcc_virtual_coils: 10
+ gcc_calib_lines: 24
+ gcc_align_data: True
+
+ validation_ds:
+ apply_gcc: true
+ gcc_virtual_coils: 2
+ gcc_calib_lines: 12
+ gcc_align_data: False
+
+ test_ds:
+ apply_gcc: false
+
+The ``apply_gcc`` parameter determines whether coil compression is applied or not. The ``gcc_virtual_coils`` parameter
+is the number of virtual coils to compress to. Of course, the number of virtual coils should be smaller than the
+number of coils in the data. The ``gcc_calib_lines`` parameter is the number of calibration lines used for coil
+compression. The ``gcc_align_data`` parameter determines whether the data is aligned before coil compression or not.
+
+Here is an example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset. The fully-sampled
+12-coils (first image) are compressed to 4-coils. The SNR in the compressed data is approximately 12% lower than in
+the fully-sampled data, but the overall image quality is still good.
+
+.. image:: ../../assets/gdcc.png
+ :align: center
+ :width: 100%
+
+
+:class:`~atommic.collections.motioncorrection.parts.motionsimulation.MotionSimulation`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``MotionSimulation`` class is used to simulate motion in MRI data, by simulating random translations and rotations
+in the frequency domain.
+
+The ``MotionSimulation`` class is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ train_ds:
+ apply_random_motion: true
+ random_motion_type:"gaussian"
+ random_motion_percentage: [10, 30]
+ random_motion_angle: 10
+ random_motion_translation: 10
+ random_motion_center_percentage: 0.02
+ random_motion_num_segments: 8
+ random_motion_random_num_segments: true
+ random_motion_non_uniform: false
+
+ validation_ds:
+ apply_random_motion: true
+ random_motion_type:"piecewise_transient"
+ random_motion_percentage: [10, 20]
+ random_motion_angle: 10
+ random_motion_translation: 10
+ random_motion_center_percentage: 0.02
+ random_motion_num_segments: 8
+ random_motion_random_num_segments: false
+ random_motion_non_uniform: true
+
+ test_ds:
+ apply_random_motion: true
+ random_motion_type:"piecewise_constant"
+ random_motion_percentage: [0, 0]
+ random_motion_angle: 10
+ random_motion_translation: 10
+ random_motion_center_percentage: 0.02
+ random_motion_num_segments: 8
+ random_motion_random_num_segments: true
+ random_motion_non_uniform: false
+
+The ``apply_random_motion`` parameter determines whether random motion is applied or not. The ``random_motion_type``
+parameter determines the type of random motion that is applied, it can be ``gaussian``, ``piecewise_constant``, or
+``piecewise_transient``. The ``random_motion_percentage`` parameter is the percentage of the data that is affected by
+random motion. Setting ``random_motion_percentage`` to ``[0, 0]`` means that no random motion is applied. The
+``random_motion_angle`` parameter is the maximum angle of rotation in degrees. The ``random_motion_translation``
+parameter is the maximum translation in pixels. The ``random_motion_center_percentage`` parameter is the percentage of
+the center of the data to center the motion parameters. The ``random_motion_num_segments`` parameter is the number of
+segments to divide the data into. The ``random_motion_random_num_segments`` parameter determines whether the number of
+segments is random or not. The ``random_motion_non_uniform`` parameter determines whether the motion parameters are
+non-uniform or not.
+
+.. note::
+ Please check the `Motion Simulation <../api/motioncorrection/motionsimulation.html>`_ page for more information.
+
+Here is an example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset. The motion corrupted
+image is presented as the coil-combined Root-Sum-of-Squares (:func:`~atommic.collections.common.parts.utils.rss`).
+
+.. image:: ../../assets/mosim.png
+ :align: center
+ :width: 50%
+
+:class:`~atommic.collections.common.parts.transforms.N2R`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``N2R`` class resembles the Noise-to-Recon method for unsupervised learning of MRI reconstruction [Desai2022]_.
+
+References
+----------
+ [Desai2022] AD Desai, BM Ozturkler, CM Sandino, et al. Noise2Recon: Enabling Joint MRI Reconstruction and
+ Denoising with Semi-Supervised and Self-Supervised Learning. ArXiv 2022. https://arxiv.org/abs/2110.00075
+
+The ``N2R`` class is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ train_ds:
+ n2r: true
+ n2r_supervised_rate: 0.05
+ n2r_probability: 0.0
+ n2r_std_devs: None
+ n2r_rhos: None
+ n2r_use_mask: False
+
+ validation_ds:
+ n2r: true
+ n2r_supervised_rate: 0.0
+ n2r_probability: 0.0
+ n2r_std_devs: None
+ n2r_rhos: None
+ n2r_use_mask: False
+
+ test_ds:
+ n2r: false
+
+The ``n2r`` parameter determines whether the Noise2Recon method is applied or not. The ``n2r_supervised_rate``
+parameter is the rate of supervised samples in the training data. It can be set to ``0.0`` for fully unsupervised
+learning or to a small percentage for semi-supervised learning. The ``n2r_probability`` parameter is the probability
+of applying the Noise2Recon method to a sample. The ``n2r_std_devs`` parameter is the standard deviation of the
+Gaussian noise that is added to the data. The ``n2r_rhos`` parameter is the correlation coefficient of the Gaussian
+noise that is added to the data. The ``n2r_use_mask`` parameter determines whether the mask is applied to the data
+before the Noise2Recon method is applied or not. If ``n2r_use_mask`` is set to ``True``, the mask is applied to the
+data before the Noise2Recon method is applied. If ``n2r_use_mask`` is set to ``False``, the mask is not applied to the
+data before the Noise2Recon method is applied.
+
+The ``N2R`` class can be used in combination with the ``unsupervised_masked_target`` argument of the dataloaders.
+If ``unsupervised_masked_target`` is set to ``True``, the target is masked before the Noise2Recon method is applied.
+If ``unsupervised_masked_target`` is set to ``False``, the target is not masked before the Noise2Recon method is
+applied.
+
+Here is an example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset. The N2R image is
+presented as the coil-combined Root-Sum-of-Squares (:func:`~atommic.collections.common.parts.utils.rss`).
+
+.. image:: ../../assets/n2r.png
+ :align: center
+ :width: 50%
+
+:class:`~atommic.collections.common.parts.transforms.NoisePreWhitening`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``NoisePreWhitening`` class is used to perform noise pre-whitening/coil-decorrelation. This is useful when the
+noise is uncorrelated, i.e. non iid.
+
+The ``NoisePreWhitening`` class is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ train_ds:
+ apply_prewhitening: true
+ find_patch_size: true
+ prewhitening_scale_factor: 1.0
+
+ validation_ds:
+ apply_prewhitening: true
+ find_patch_size: false
+ prewhitening_scale_factor: 0.8
+ prewhitening_patch_start: 10
+ prewhitening_patch_length: 30
+
+ test_ds:
+ apply_prewhitening: false
+
+The ``apply_prewhitening`` parameter determines whether noise pre-whitening is applied or not. The ``find_patch_size``
+parameter determines whether the patch size is found automatically or not. If ``find_patch_size`` is set to ``False``,
+the patch size is set manually with the ``prewhitening_patch_start`` and ``prewhitening_patch_length`` parameters, as
+``[prewhitening_patch_start, prewhitening_patch_start + prewhitening_patch_length, prewhitening_patch_start,
+prewhitening_patch_start + prewhitening_patch_length]``. The ``scale_factor`` parameter is used to adjust for
+effective noise bandwidth and difference in sampling rate between noise calibration and actual measurement. It is
+given by :math:`scale\_factor = \frac{T\_acq\_dwell}{T\_noise\_dwell} \cdot NoiseReceiverBandwidthRatio` .
+
+Here is an example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset. The fully-sampled
+12-coils (first image) are noise pre-whitened (second image). The SNR in the pre-whitened data is approximately
+18% higher than in the fully-sampled data.
+
+.. image:: ../../assets/pw.png
+ :align: center
+ :width: 100%
+
+
+:class:`~atommic.collections.common.parts.transforms.Normalizer`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``Normalizer`` class is used to normalize MRI data. The following normalization methods are available:
+
+- ``minmax``: Data are normalized as :math:`\frac{data - \min(data)}{\max(data) - \min(data)}` to [0, 1].
+- ``max``: data are normalized as :math:`\frac{data}{\max(data)}` to [0, 1].
+- ``mean_std``: data are normalized as :math:`\frac{data - mean(data)}{std(data)}`.
+- ``mean_var``: data are normalized as :math:`\frac{data - mean(data)}{var(data)}`.
+- ``grayscale``: data are normalized as :math:`\frac{data - \min(data)}{\max(data) - \min(data)} \cdot 255` to [0, 255].
+- ``fft``: only the default ``fft_normalization`` will be applied, i.e. ``backward``. It is basically the same as
+ ``none``.
+- ``none``.
+
+The ``Normalizer`` class is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ train_ds:
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+
+ validation_ds:
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: true
+
+ test_ds:
+ normalize_inputs: false
+
+The ``normalize_inputs`` parameter determines whether the inputs are normalized or not. The ``normalization_type``
+parameter determines the normalization method. The ``kspace_normalization`` parameter determines whether the
+normalization is performed in k-space or image space.
+
+The following arguments in the ``model`` section of the YAML config file are also related to normalization:
+
+- ``normalization_type``: determines the normalization type as above.
+- ``unnormalize_loss_inputs``: if data are normalized, you can choose to unnormalize them before calculating the loss.
+- ``unnormalize_log_outputs``: if data are normalized, you can choose to unnormalize them before logging metrics.
+
+
+:class:`~atommic.collections.common.parts.transforms.SNREstimator`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``SNREstimator`` class is used to estimate the SNR of MRI data. The SNR is using the
+:class:`skimage.filters.threshold_otsu` and the :class:`skimage.morphology.convex_hull_image` functions to estimate
+the signal. The noise is estimated in k-space by defining as patch, as in ``NoisePreWhitening``. The SNR is then
+calculated as the ratio of the signal and the noise.
+
+The ``SNREstimator`` class is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ patch_size: [10, 30, 10, 30]
+ apply_ifft: true
+ fft_centered: false
+ fft_normalization: "backward"
+ spatial_dims: [-2, -1]
+ coil_dim: -3
+ multicoil: true
+
+The ``patch_size`` parameter is the size of the patch that is used to estimate the noise. The ``apply_ifft`` parameter
+determines whether the inverse Fourier transform is applied to the data before estimating the noise or not. The
+``fft_centered`` parameter determines whether the Fourier transform is centered or not. The ``fft_normalization``
+parameter determines the normalization of the Fourier transform. The ``spatial_dims`` parameter determines the spatial
+dimensions of the data. The ``coil_dim`` parameter determines the coil dimension of the data. The ``multicoil``
+parameter determines whether the data is multi-coil or not.
+
+.. note::
+ The ``SNREstimator`` class is currently not a transform you can compose. You can call it in external scripts to
+ estimate the SNR of your data, or configure it in your own transform.
+
+
+:class:`~atommic.collections.common.parts.transforms.SSDU`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``SSDU`` class resembles the Self-Supervised Data Undersampling method for unsupervised learning of MRI
+reconstruction [Yaman2020]_.
+
+References
+----------
+ [Yaman2020] Yaman, B, Hosseini, SAH, Moeller, S, Ellermann, J, Uฤurbil, K, Akรงakaya, M. Self-supervised learning
+ of physics-guided reconstruction neural networks without fully sampled reference data. Magn Reson Med. 2020;
+ 84: 3172โ3191. https://doi.org/10.1002/mrm.28378
+
+The ``SSDU`` class is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ train_ds:
+ ssdu: true
+ ssdu_mask_type: "Gaussian"
+ ssdu_rho: 0.4
+ ssdu_acs_block_size: [4, 4]
+ ssdu_gaussian_std_scaling_factor: 4.0
+ ssdu_outer_kspace_fraction: 0.0
+ ssdu_export_and_reuse_masks: false
+
+ validation_ds:
+ ssdu: true
+ ssdu_mask_type: "Uniform"
+ ssdu_rho: 0.4
+ ssdu_acs_block_size: [4, 4]
+ ssdu_gaussian_std_scaling_factor: 4.0
+ ssdu_outer_kspace_fraction: 0.0
+ ssdu_export_and_reuse_masks: true
+
+ test_ds:
+ ssdu: false
+
+The ``ssdu`` parameter determines whether the Self-Supervised Data Undersampling method is applied or not. The
+``ssdu_mask_type`` parameter determines the type of mask that is used to undersample the data. The ``ssdu_rho``
+parameter is the split ratio for training and loss masks. The ``ssdu_acs_block_size`` parameter keeps a small acs
+region fully-sampled for training masks, if there is no fully-sampled acs region. The ``ssdu_acs_block_size`` should
+be set to zero. The ``ssdu_gaussian_std_scaling_factor`` parameter is the scaling factor for the standard deviation
+of the Gaussian mask. The ``ssdu_outer_kspace_fraction`` parameter is the fraction of outer k-space lines that are
+masked. The ``ssdu_export_and_reuse_masks`` parameter determines whether the masks are exported and reused or not. If
+``ssdu_export_and_reuse_masks`` is set to ``True``, the masks are exported to the ``tmp`` directory and reused in the
+next call. This option is useful when the data are too large to be stored in memory.
+
+.. note::
+ ``SSDU`` can be used with ``N2R`` as described in the Noise-to-Recon paper [Desai2022]_.
+
+Here is an example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset. SSDU returns two masks,
+one to be used as mask for training (first image) and one to be used as mask against which the loss is calculated
+(second image). The presented SSDU images are the inputs where the mask is applied, computed as the coil-combined
+Root-Sum-of-Squares (:func:`~atommic.collections.common.parts.utils.rss`).
+
+.. image:: ../../assets/ssdu.png
+ :align: center
+ :width: 70%
+
+
+:class:`~atommic.collections.common.parts.transforms.ZeroFillingPadding`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``ZeroFillingPadding`` class is used to pad MRI data in k-space, i.e. enlarge the Field-of-View (FOV) of the data.
+This is useful when the data are undersampled and the FOV needs to be enlarged to match the FOV of the fully-sampled
+data.
+
+The ``ZeroFillingPadding`` class is configurable via YAML with Hydra. For example:
+
+.. code-block:: bash
+
+ train_ds:
+ kspace_zero_filling_size: [640, 640]
+
+ validation_ds:
+ kspace_zero_filling_size: [640, 640]
+
+ test_ds:
+ kspace_zero_filling_size: None
+
+The ``kspace_zero_filling_size`` parameter is the size of the zero-filled k-space. If ``kspace_zero_filling_size`` is
+set to ``None``, no zero-filling is performed.
+
+Here is an example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset. The zero-filled padded
+image is presented as the coil-combined Root-Sum-of-Squares (:func:`~atommic.collections.common.parts.utils.rss`).
+
+.. image:: ../../assets/zfpad.png
+ :align: center
+ :width: 50%
+
+
+:class:`~atommic.collections.common.parts.transforms.Composer`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+The ``Composer`` class is used to compose a series of transforms into a single transform. No configuration is required
+for this class.
+
+Here is an example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset. The fully-sampled
+12-coils (first image) are compressed to 4-coils and noise pre-whitened (second image), as a composed transform.
+The SNR in the pre-whitened data is approximately 8% lower than in the fully-sampled data, showing the apparent
+improvement of noise pre-whitening compared to the 12% loss in the standalone Geometric Decomposition Coil Compression
+method.
+
+.. image:: ../../assets/gdccpw.png
+ :align: center
+ :width: 100%
+
+
+:class:`~atommic.collections.common.parts.transforms.MRIDataTransforms`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The ``MRIDataTransforms`` class is used to compose the transforms that are applied to the MRI data. All the
+aforementioned transforms are composed in this class. The ``MRIDataTransforms`` class is configurable via YAML with
+Hydra. A few other parameters are also important and should be set in the ``train_ds``, ``validation_ds``, and
+``test_ds`` sections of the YAML config file. For example:
+
+.. code-block:: bash
+
+ # dataset-related parameters
+ dataset_format: None
+ dimensionality: 2
+ consecutive_slices: 1
+ # fft-related parameters
+ fft_centered: false
+ fft_normalization: "backward"
+ spatial_dims: [-2, -1]
+ coil_dim: 1
+ # undersampling-related parameters
+ mask_func: None
+ shift_mask: false
+ mask_center_scale: 0.02
+ partial_fourier_percentage: 0.0
+ remask: false
+ # dataloader-related parameters
+ use_seed: false
+
+The ``dataset_format`` parameter is the format of the dataset. The ``dimensionality`` parameter is the dimensionality
+of the data, i.e. ``2`` for 2D data and ``3`` for 3D data. The ``consecutive_slices`` parameter is the number of
+consecutive slices that are used as input. If set to ``1``, only one slice is used as input. If set to ``2`` or more,
+the number of slices is increased by one for each additional consecutive slice. The ``coil_dim`` parameter determines
+the coil dimension of the data.
+
+.. note::
+ Please check the `multitasking <../starthere/projects/multitask/intro.html>`_,
+ `qMRI <../starthere/projects/quantitative/intro.html>`_,
+ `reconstruction <../starthere/projects/reconstruction/intro.html>`_, and
+ `segmentation <../starthere/projects/segmentation/intro.html>`_ projects pages for information about the supported
+ public datasets.
+
+The ``fft_centered`` parameter determines whether the Fourier transform is centered or
+not. The ``fft_normalization`` parameter determines the normalization of the Fourier transform. The ``spatial_dims``
+parameter determines the spatial dimensions of the data.
+
+.. note::
+ Please check the `FFT <../api/common/fft.html>`_ page for more information.
+
+The ``mask_func`` parameter is the mask function that is used to undersample the data. The ``shift_mask``
+parameter determines whether the mask is shifted or not. The ``mask_center_scale`` parameter is the scale of the mask
+center. The ``partial_fourier_percentage`` parameter is the percentage of the data that is undersampled. The
+``remask`` parameter determines whether the data is remasked or not. The ``use_seed`` parameter determines whether a
+seed is used or not.
+
+
+:class:`~atommic.collections.quantitative.parts.transforms.qMRIDataTransforms`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Inheriting from the ``MRIDataTransforms`` class, the ``qMRIDataTransforms`` class is used to compose the transforms
+that are applied to the MRI data for the quantitative task. A few other parameters are also important and should be
+set in the ``train_ds``, ``validation_ds``, and ``test_ds`` sections of the YAML config file. For example:
+
+.. code-block:: bash
+
+ # dataset-related parameters
+ TEs: None
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1.0
+ shift_B0_input: false
+
+The ``TEs`` parameter is the echo times of the data. The ``precompute_quantitative_maps`` parameter determines whether
+the quantitative maps are precomputed or not. If not precomputed, the quantitative maps are computed on the fly. The
+``qmaps_scaling_factor`` parameter is the scaling factor of the quantitative maps. The ``shift_B0_input`` parameter
+determines whether the B0 map is shifted or not.
+
+
+:class:`~atommic.collections.multitask.rs.parts.transforms.RSMRIDataTransforms`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Inheriting from the ``MRIDataTransforms`` class, the ``RSMRIDataTransforms`` class is used to compose the transforms
+that are applied to the MRI data for the reconstruction and segmentation tasks.
+
+
+:class:`~atommic.collections.reconstruction.parts.transforms.ReconstructionMRIDataTransforms`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Inheriting from the ``MRIDataTransforms`` class, the ``ReconstructionMRIDataTransforms`` class is used to compose the
+transforms that are applied to the MRI data for the reconstruction task.
+
+
+:class:`~atommic.collections.segmentation.parts.transforms.SegmentationMRIDataTransforms`
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Inheriting from the ``MRIDataTransforms`` class, the ``SegmentationMRIDataTransforms`` class is used to compose the
+transforms that are applied to the MRI data for the segmentation task.
+
+
+Full Example
+============
+
+Here is a full training example of a YAML config file for the reconstruction task on the
+`CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset:
+
+.. code-block:: bash
+
+ train_ds:
+ # dataset-related parameters
+ data_path: /calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: /calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ dimensionality: 2
+ consecutive_slices: 1
+ complex_target: true
+ # sample rate parameters
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ # dataloader-related parameters
+ data_saved_per_slice: false
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+ # * Transforms *
+ # fft-related parameters
+ fft_centered: false
+ fft_normalization: "backward"
+ spatial_dims: [-2, -1]
+ coil_dim: 1
+ # coil compression parameters
+ apply_gcc: true
+ gcc_virtual_coils: 10
+ gcc_calib_lines: 24
+ gcc_align_data: True
+ # coil sensitivity maps parameters
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ # cropping parameters
+ crop_size: [200, 200]
+ kspace_crop: false
+ crop_before_masking: true
+ # motion simulation parameters
+ apply_random_motion: true
+ random_motion_type:"gaussian"
+ random_motion_percentage: [10, 30]
+ random_motion_angle: 10
+ random_motion_translation: 10
+ random_motion_center_percentage: 0.02
+ random_motion_num_segments: 8
+ random_motion_random_num_segments: true
+ random_motion_non_uniform: false
+ # noise-2-recon parameters
+ n2r: true
+ n2r_supervised_rate: 0.05
+ n2r_probability: 0.0
+ n2r_std_devs: None
+ n2r_rhos: None
+ n2r_use_mask: False
+ # noise pre-whitening parameters
+ apply_prewhitening: true
+ find_patch_size: true
+ prewhitening_scale_factor: 1.0
+ # normalization parameters
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ # self-supervised data undersampling parameters
+ ssdu: true
+ ssdu_mask_type: "Gaussian"
+ ssdu_rho: 0.4
+ ssdu_acs_block_size: [4, 4]
+ ssdu_gaussian_std_scaling_factor: 4.0
+ ssdu_outer_kspace_fraction: 0.0
+ ssdu_export_and_reuse_masks: false
+ # zero-filling padding parameters
+ kspace_zero_filling_size: [320, 320]
+ # undersampling-related parameters
+ mask_func: None
+ shift_mask: false
+ mask_center_scale: 0.02
+ partial_fourier_percentage: 0.0
+ remask: false
+
+ validation_ds:
+ # dataset-related parameters
+ data_path: /calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: /calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ dimensionality: 2
+ consecutive_slices: 1
+ complex_target: true
+ # sample rate parameters
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ # dataloader-related parameters
+ data_saved_per_slice: false
+ use_seed: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+ # * Transforms *
+ # fft-related parameters
+ fft_centered: false
+ fft_normalization: "backward"
+ spatial_dims: [-2, -1]
+ coil_dim: 1
+ # coil compression parameters
+ apply_gcc: true
+ gcc_virtual_coils: 10
+ gcc_calib_lines: 24
+ gcc_align_data: True
+ # coil sensitivity maps parameters
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ # cropping parameters
+ crop_size: [200, 200]
+ kspace_crop: false
+ crop_before_masking: true
+ # motion simulation parameters
+ apply_random_motion: true
+ random_motion_type:"gaussian"
+ random_motion_percentage: [10, 30]
+ random_motion_angle: 10
+ random_motion_translation: 10
+ random_motion_center_percentage: 0.02
+ random_motion_num_segments: 8
+ random_motion_random_num_segments: true
+ random_motion_non_uniform: false
+ # noise-2-recon parameters
+ n2r: true
+ n2r_supervised_rate: 0.05
+ n2r_probability: 0.0
+ n2r_std_devs: None
+ n2r_rhos: None
+ n2r_use_mask: False
+ # noise pre-whitening parameters
+ apply_prewhitening: true
+ find_patch_size: true
+ prewhitening_scale_factor: 1.0
+ # normalization parameters
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ # self-supervised data undersampling parameters
+ ssdu: true
+ ssdu_mask_type: "Gaussian"
+ ssdu_rho: 0.4
+ ssdu_acs_block_size: [4, 4]
+ ssdu_gaussian_std_scaling_factor: 4.0
+ ssdu_outer_kspace_fraction: 0.0
+ ssdu_export_and_reuse_masks: false
+ # zero-filling padding parameters
+ kspace_zero_filling_size: [320, 320]
+ # undersampling-related parameters
+ mask_func: None
+ shift_mask: false
+ mask_center_scale: 0.02
+ partial_fourier_percentage: 0.0
+ remask: false
diff --git a/docs/source/mri/undersampling.rst b/docs/source/mri/undersampling.rst
new file mode 100644
index 00000000..fe7643d5
--- /dev/null
+++ b/docs/source/mri/undersampling.rst
@@ -0,0 +1,104 @@
+Undersampling
+=============
+
+Data undersampling or subsampling is a common technique in MRI to reduce the amount of data acquired or in other words
+to accelerate the acquisition process. This is achieved by reducing the number of k-space lines acquired. The missing
+k-space lines are then estimated using different techniques.
+
+The forward model of accelerated MRI is given by :math:`y = P F x + n`, where :math:`y` is the acquired data,
+:math:`x` is the image, :math:`F` is the FFT operator, :math:`P` is the undersampling operator and :math:`n` is the
+noise from the acquisition process.
+
+:math:`P` can either applied prospectively or retrospectively. In prospective undersampling, the k-space is
+undersampled in the scanner, during the acquisition process. In retrospective undersampling, the k-space is
+undersampled after the acquisition process, meaning that we acquire fully sampled k-space and then we apply the
+undersampling operator to the acquired data.
+
+``ATOMMIC`` can handle both prospective and retrospective undersampling via the ``mask_args`` of the
+``train_ds``, ``validation_ds`` and ``test_ds`` functions. The ``mask_args`` is a dictionary that contains
+the following keys:
+
+- ``type``:
+ The type of the undersampling mask. It can be one of the following
+ :class:`~atommic.collections.common.data.subsample.Equispaced1DMaskFunc`,
+ :class:`~atommic.collections.common.data.subsample.Equispaced2DMaskFunc`,
+ :class:`~atommic.collections.common.data.subsample.Gaussian1DMaskFunc`,
+ :class:`~atommic.collections.common.data.subsample.Gaussian2DMaskFunc`,
+ :class:`~atommic.collections.common.data.subsample.Poisson2DMaskFunc`,
+ :class:`~atommic.collections.common.data.subsample.Random1DMaskFunc`, or ``none`` (no undersampling).
+- ``accelerations``:
+ The acceleration factors to be used. It can be a list of integers or a single integer. If it is a list of
+ integers, then the undersampling mask is randomly selected from the list. If it is a single integer, then the
+ undersampling mask is randomly selected from the list of acceleration factors that are less than or equal to the
+ given integer. For example, if ``accelerations`` is ``[4, 8, 12]``, then the undersampling mask is randomly
+ selected from ``[4, 8, 12]``. If ``accelerations`` is ``4``, then the undersampling mask is selected as ``4``.
+- ``center_fractions``:
+ The center fractions to be used. It can be a list of floats or a single float. If it is a list of floats, then the
+ undersampling mask is randomly selected from the list. If it is a single float, then the undersampling mask is
+ randomly selected from the list of center fractions that are less than or equal to the given float. For example,
+ if ``center_fractions`` is ``[0.08, 0.04, 0.02]``, then the undersampling mask is randomly selected from
+ ``[0.08, 0.04, 0.02]``. If ``center_fractions`` is ``0.08``, then the undersampling mask is selected as ``0.08``.
+
+ .. note::
+ The term ``center fraction`` is basically used to define the number of k-space lines that are acquired in
+ ``1D`` undersampling. In case of ``2D`` undersampling, the center fraction refers for example to
+ Full-Width-at-Half-Maximum (FWHM) in case of Gaussian undersampling or the radius of the circle in case of
+ Poisson-Disk undersampling. It is kept as ``center fraction`` for consistency reasons, but it is not
+ necessarily the fraction of the center of the k-space that is acquired. Should be renamed in the future.
+
+- ``shift_mask``:
+ Whether to shift the undersampling mask or not. If ``True``, then the undersampling mask is shifted by a random
+ amount. If ``False``, then the undersampling mask is not shifted. It is useful when data are shifted to the center
+ of the image.
+- ``use_seed``:
+ Whether to use a seed for the random number generator or not. For example, it should be se to ``True`` in
+ validation, but it should be set to ``False`` in training.
+
+Here is an overview example on the `CC359 <../starthere/projects/reconstruction/cc359.html>`_ dataset, with the
+available undersampling options in ``ATOMMIC``. The top row presents the undersampled images as the coil-combined
+Root-Sum-of-Squares (:func:`~atommic.collections.common.parts.utils.rss`) and the bottom row presents the
+undersampling masks.
+
+.. image:: ../../assets/masks.png
+ :align: center
+ :width: 100%
+
+``PF`` refers to Partial Fourier, while the last column presents the default mask in the ``CC359`` dataset.
+
+
+Prospective Undersampling
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In prospective undersampling, the ``type`` in ``mask_args`` should be set to ``none``, data are then assumed to be
+undersampled in the scanner. This is useful for performing inference on undersampled data with a pre-trained model.
+
+
+Retrospective Undersampling
+~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+The following example shows how to do retrospective undersampling in ``ATOMMIC``:
+
+.. code-block:: bash
+
+ train_ds:
+ type: random1d
+ accelerations: [4, 8]
+ center_fractions: [0.08, 0.04]
+ shift_mask: false
+ use_seed: false
+
+ validation_ds:
+ type: random1d
+ accelerations: [4, 8]
+ center_fractions: [0.08, 0.04]
+ shift_mask: false
+ use_seed: true
+
+ test_ds:
+ type: none
+ shift_mask: false
+ use_seed: false
+
+``type`` can also be set to ``none`` in ``train_ds`` and ``validation_ds`` in retrospective undersampling if you want
+to load pre-computed undersampling masks. In that case, you need to set the ``mask_path`` in the ``train_ds`` and
+``validation_ds`` to the path of the undersampling masks. Otherwise, ``mask_path`` should be set to ``none``.
diff --git a/docs/source/starthere/intro.rst b/docs/source/starthere/intro.rst
new file mode 100644
index 00000000..ef6da50c
--- /dev/null
+++ b/docs/source/starthere/intro.rst
@@ -0,0 +1,211 @@
+Introduction
+============
+
+.. # define a hard line break for html
+.. |br| raw:: html
+
+
+
+.. _dummy_header:
+
+The `Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC) `_, is a
+toolbox for applying AI methods for ``accelerated MRI reconstruction (REC)``, ``MRI segmentation (SEG)``,
+``quantitative MR imaging (qMRI)``, as well as ``multitask learning (MTL)``, i.e. performing multiple tasks
+simultaneously, such as reconstruction and segmentation. Each task is implemented in a separate collection, which
+consists of data loaders, transformations, models, metrics, and losses. ``ATOMMIC`` is designed to be modular and
+extensible, and it is easy to add new tasks, models, and datasets. ``ATOMMIC`` uses
+`PyTorch Lightning `_ for feasible high-performance multi-GPU/multi-node
+mixed-precision training.
+
+.. image:: ../../../assets/atommic-schematic_overview.png
+ :align: center
+ :width: 100%
+
+|br|
+
+The schematic overview of ``ATOMMIC`` showcases the main components of the toolbox. First we need an
+`MRI Dataset `_ (e.g. ``CC359``), next we need to define the high-level parameters, such as the
+`task and the model <../mri/collections.html>`_, the `undersampling <../mri/undersampling.html>`_, the
+`transforms <../mri/transforms.html>`_, the `optimizer <../core/core.html#optimization>`_, the
+`scheduler <../core/core.html#learning-rate-schedulers>`_, the `loss <../mri/losses.html>`_, the
+`trainer parameters <../core/core.html#training>`_, and the `experiment manager <../core/exp_manager.html>`_.
+All these parameters are defined in a ``.yaml`` file using `Hydra `_ and
+`OmegaConf `_. The trained model is an ``.atommic`` `module <../core/export.html>`_,
+exported with `ONNX `_ and `TorchScript `_ support, which
+can be used for inference. The ``.atommic`` module can also be uploaded on `HuggingFace `_.
+Pretrained models are available on our `HF `_ account and can be downloaded and used for
+inference.
+
+
+Quick Start Guide
+-----------------
+
+The best way to get started with ATOMMIC is to start with one of the `tutorials `_:
+
+* `ATOMMIC Primer `__ - demonstrates how to use ATOMMIC.
+* `ATOMMIC MRI transforms `__ - demonstrates how to use ATOMMIC to undersample MRI data.
+* `ATOMMIC MRI undersampling `__ - demonstrates how to use ATOMMIC to apply transforms to MRI data.
+* `ATOMMIC Upload Model on HuggingFace `__ - demonstrates how to upload a model on HuggingFace.
+
+You can also check the `projects `_ page to see how to use ATOMMIC for specific tasks and public datasets.
+
+``ATOMMIC`` paper is fully reproducible. Please check `here `__ for more information.
+
+
+Training & Testing
+------------------
+
+Training and testing models in ``ATOMMIC`` is intuitive and easy. You just need to properly configure the ``.yaml``
+file and just run the following command:
+
+.. code-block:: bash
+
+ atommic run -c path-to-config-file
+
+
+Configuration
+~~~~~~~~~~~~~
+
+#. Choose the ``task`` and the ``model``, according to the `collections <../mri/collections.html>`_.
+
+#. Choose the ``dataset`` and the ``dataset parameters``, according to the `datasets <#mri-datasets>`_.
+
+#. Choose the `undersampling <../mri/transforms.html>`_.
+
+#. Choose the `transforms <../mri/transforms.html>`_.
+
+#. Choose the `losses <../mri/losses.html>`_.
+
+#. Choose the `optimizer <../core/core.html#optimization>`_.
+
+#. Choose the `scheduler <../core/core.html#learning-rate-schedulers>`_.
+
+#. Choose the `trainer parameters <../core/core.html#training>`_.
+
+#. Choose the `experiment manager <../core/exp_manager.html>`_.
+
+You can also check the `projects `_ page to see how to configure the ``.yaml`` file for specific tasks.
+
+
+Collections
+-----------
+
+``ATOMMIC`` is organized in `collections <../mri/collections.html>`_,, each of which implements a specific task. The following collections are currently available, implementing various models as listed:
+
+``MultiTask Learning (MTL)``: 1. End-to-End Recurrent Attention Network (:class:`~atommic.collections.multitask.rs.nn.seranet.SERANet`), 2. Image domain Deep Structured Low-Rank Network (:class:`~atommic.collections.multitask.rs.nn.idslr.IDSLR`), 3. Image domain Deep Structured Low-Rank UNet (:class:`~atommic.collections.multitask.rs.nn.idslr_unet.IDSLRUNet`), 4. Multi-Task Learning for MRI Reconstruction and Segmentation (:class:`~atommic.collections.multitask.rs.nn.mtlrs.MTLRS`), 5. Reconstruction Segmentation method using UNet (:class:`~atommic.collections.multitask.rs.nn.recseg_unet.RecSegUNet`), 6. Segmentation Network MRI (:class:`~atommic.collections.multitask.rs.nn.segnet.SegNet`).
+
+``quantitative MR Imaging (qMRI)``: 1. quantitative Recurrent Inference Machines (:class:`~atommic.collections.quantitative.nn.qrim_base.qrim_block.qRIMBlock`), 2. quantitative End-to-End Variational Network (:class:`~atommic.collections.quantitative.nn.qvarnet.qVarNet`), 3. quantitative Cascades of Independently Recurrent Inference Machines (:class:`~atommic.collections.quantitative.nn.qcirim.qCIRIM`).
+
+``MRI Reconstruction (REC)``: 1. Cascades of Independently Recurrent Inference Machines (:class:`~atommic.collections.reconstruction.nn.cirim.CIRIM`), 2. Convolutional Recurrent Neural Networks (:class:`~atommic.collections.reconstruction.nn.crnn.CRNNet`), 3. Deep Cascade of Convolutional Neural Networks (:class:`~atommic.collections.reconstruction.nn.ccnn.CascadeNet`), 4. Down-Up Net (:class:`~atommic.collections.reconstruction.nn.dunet.DUNet`), 5. End-to-End Variational Network (:class:`~atommic.collections.reconstruction.nn.varnet.VarNet`), 6. Independently Recurrent Inference Machines (:class:`~atommic.collections.reconstruction.nn.rim_base.rim_block.RIMBlock`), 7. Joint Deep Model-Based MR Image and Coil Sensitivity Reconstruction Network (:class:`~atommic.collections.reconstruction.nn.jointicnet.JointICNet`), 8. :class:`~atommic.collections.reconstruction.nn.kikinet.KIKINet`, 9. Learned Primal-Dual Net (:class:`~atommic.collections.reconstruction.nn.lpd.LPDNet`), 10. Model-based Deep Learning Reconstruction (:class:`~atommic.collections.reconstruction.nn.modl.MoDL`), 11. :class:`~atommic.collections.reconstruction.nn.multidomainnet.MultiDomainNet`, 12. :class:`~atommic.collections.reconstruction.nn.proximal_gradient.ProximalGradient`, 13. Recurrent Inference Machines (:class:`~atommic.collections.reconstruction.nn.rim_base.rim_block.RIMBlock`), 14. Recurrent Variational Network (:class:`~atommic.collections.reconstruction.nn.recurrentvarnet.RecurrentVarNet`), 15. :class:`~atommic.collections.reconstruction.nn.unet.UNet`, 16. Variable Splitting Network (:class:`~atommic.collections.reconstruction.nn.vsnet.VSNet`), 17. :class:`~atommic.collections.reconstruction.nn.xpdnet.XPDNet`, 18. Zero-Filled reconstruction (:class:`~atommic.collections.reconstruction.nn.zf.ZF`).
+
+``MRI Segmentation (SEG)``: 1. :class:`~atommic.collections.segmentation.nn.attentionunet.SegmentationAttentionUNet`, 2. :class:`~atommic.collections.segmentation.nn.dynunet.SegmentationDYNUNet`, 3. :class:`~atommic.collections.segmentation.nn.lambdaunet.SegmentationLambdaUNet`, 4. :class:`~atommic.collections.segmentation.nn.unet.SegmentationUNet`, 5. :class:`~atommic.collections.segmentation.nn.unet3d.Segmentation3DUNet`, 6. :class:`~atommic.collections.segmentation.nn.unetr.SegmentationUNetR`, 7. :class:`~atommic.collections.segmentation.nn.vnet.SegmentationVNet`.
+
+
+MRI Datasets
+------------
+
+``ATOMMIC`` supports public datasets, as well as private datasets. The following public datasets are supported natively:
+
+* `AHEAD `_: Supports the ``(qMRI)`` and ``(REC)`` tasks.
+* `BraTS 2023 Adult Glioma `_: Supports the ``(SEG)`` task.
+* `CC359 `_: Supports the ``(REC)`` task.
+* `fastMRI Brains Multicoil `_: Supports the ``(REC)`` task.
+* `fastMRI Knees Multicoil `_: Supports the ``(REC)`` task.
+* `fastMRI Knees Singlecoil `_: Supports the ``(REC)`` task.
+* `ISLES 2022 Sub Acute Stroke `_: Supports the ``(SEG)`` task.
+* `SKM-TEA `_: Supports the ``(REC)``, ``(SEG)``, and ``(MTL)`` tasks.
+* `Stanford Knees `_: Supports the ``(REC)`` task.
+
+
+Installation
+------------
+
+``ATOMMIC`` is best to be installed in a Conda environment.
+
+Conda
+~~~~~
+
+.. code-block:: bash
+
+ conda create -n atommic python=3.10
+ conda activate atommic
+
+Pip
+~~~
+Use this installation mode if you want the latest released version.
+
+.. code-block:: bash
+
+ pip install atommic
+
+From source
+~~~~~~~~~~~
+Use this installation mode if you are contributing to atommic.
+
+.. code-block:: bash
+
+ git clone https://github.com/wdika/atommic
+ cd atommic
+ ./reinstall.sh
+
+Docker containers
+~~~~~~~~~~~~~~~~~
+To build an atommic container with Dockerfile from a branch, please run
+
+.. code-block:: bash
+
+ DOCKER_BUILDKIT=1 docker build -f Dockerfile -t atommic:latest.
+
+As `NeMo `_ suggests, if you chose to work with the ``main`` branch, use
+`NVIDIA's PyTorch container version 21.05-py3 `_, then install
+from GitHub.
+
+.. code-block:: bash
+
+ docker run --gpus all -it --rm -v :/ATOMMIC --shm-size=8g \
+ -p 8888:8888 -p 6006:6006 --ulimit memlock=-1 --ulimit \
+ stack=67108864 --device=/dev/snd nvcr.io/nvidia/pytorch:21.05-py3
+
+
+License
+-------
+
+ATOMMIC is under `Apache 2.0 license `_.
+
+
+Citation
+---------
+
+If you use ATOMMIC in your research, please cite as follows:
+
+`Karkalousos, D., & Caan, M. (2023). Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC) (Version 1.0.0) [Computer software]. https://github.com/wdika/atommic`
+
+
+References
+----------
+The following papers have used the atommic repo:
+
+#. Karkalousos, D., Isgum, I., Marquering, H. & Caan, M.W.A.. (2024). MultiTask Learning for accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis. Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 227:991-1005 Available from https://proceedings.mlr.press/v227/karkalousos24a.html.
+
+#. Zhang, C., Karkalousos, D., Bazin, P. L., Coolen, B. F., Vrenken, H., Sonke, J. J., Forstmann, B. U., Poot, D. H. J., & Caan, M. W. A. (2022). A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine. NeuroImage, 264. https://doi.org/10.1016/j.neuroimage.2022.119680
+
+#. Karkalousos, D., Noteboom, S., Hulst, H. E., Vos, F. M., & Caan, M. W. A. (2022). Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction. Physics in Medicine & Biology. https://doi.org/10.1088/1361-6560/AC6CC2
+
+
+Contact
+-------
+For any questions, please contact Dimitris Karkalousos @ `d.karkalousos@amsterdamumc.nl`.
+
+
+Disclaimer & Acknowledgements
+-----------------------------
+
+.. note::
+ ATOMMIC is built on top of `NeMo `_. NeMo is under Apache 2.0 license, so we are
+ allowed to use it. We are also assume that it is allowed to use the NeMo documentation, as long as we cite it and
+ we always refer to the baselines everywhere and in the code and docs. ATOMMIC also includes implementations of
+ reconstruction methods from `fastMRI `_ and
+ `DIRECT `_, and segmentation methods from
+ `MONAI `_, as well as other codebases which as always cited on the
+ corresponding files. All methods in ATOMMIC are reimplemented and not called from the original libraries, allowing
+ for full reproducibility, support and easy extension. ATOMMIC is an open-source project under Apache 2.0 license.
diff --git a/docs/source/starthere/projects.rst b/docs/source/starthere/projects.rst
new file mode 100644
index 00000000..e87239a5
--- /dev/null
+++ b/docs/source/starthere/projects.rst
@@ -0,0 +1,31 @@
+Projects
+========
+
+
+.. toctree::
+ :maxdepth: 3
+ :caption: MultiTask Learning (MTL)
+ :name: MultiTask Learning (MTL)
+
+ projects/multitask/intro
+
+.. toctree::
+ :maxdepth: 3
+ :caption: quantitative MR Imaging (qMRI)
+ :name: quantitative MR Imaging (qMRI)
+
+ projects/quantitative/intro
+
+.. toctree::
+ :maxdepth: 3
+ :caption: MRI Reconstruction (REC)
+ :name: MRI Reconstruction (REC)
+
+ projects/reconstruction/intro
+
+.. toctree::
+ :maxdepth: 3
+ :caption: MRI Segmentation (SEG)
+ :name: MRI Segmentation (SEG)
+
+ projects/segmentation/intro
diff --git a/docs/source/starthere/projects/multitask/intro.rst b/docs/source/starthere/projects/multitask/intro.rst
new file mode 100644
index 00000000..2705e4a5
--- /dev/null
+++ b/docs/source/starthere/projects/multitask/intro.rst
@@ -0,0 +1,7 @@
+MultiTask Learning (MTL)
+========================
+
+.. toctree::
+ :maxdepth: 8
+
+ ../reconstruction/skmtea
diff --git a/docs/source/starthere/projects/quantitative/ahead.rst b/docs/source/starthere/projects/quantitative/ahead.rst
new file mode 100644
index 00000000..b3208812
--- /dev/null
+++ b/docs/source/starthere/projects/quantitative/ahead.rst
@@ -0,0 +1,68 @@
+AHEAD
+=====
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the Amsterdam
+Ultra-high field adult lifespan database (AHEAD) dataset.
+
+Data were scanned using the MP2RAGEME sequence for T1, T2* and Quantitative Susceptibility Mapping in one sequence at 7 Tesla.
+Data are motion-corrected using Fat navigators (FatNavs), and defaced in image-domain. In total 77 subjects are
+included, scanned with a resolution of 0.7mm isotropic. Data of the MP2RAGEME-sequence are stored according to the
+ISMRMRD-standard in h5-format (https://ismrmrd.github.io/). Detailed scanner parameters are included in the h5-files
+of all subjects. Coil sensitivity maps per subjects are included in native h5-format. Demographics of all subjects are
+included in a separate csv-file, being sex and age decade, covering the life span.
+
+For more information and dataset download link for the AHEAD project, please check
+https://dataverse.nl/dataset.xhtml?persistentId=doi:10.34894/IHZGQM.
+
+.. note::
+ When running the preprocessing scripts please make sure you have the ``ismrmrd`` package installed. You can
+ install it with the following command:
+
+ .. code-block:: bash
+
+ pip install -r requirements/requirements-ahead_stanfordknees.txt
+
+**Visualization**
+An example notebook for visualizing and preprocessing the data is provided in the
+`visualize `_. You just
+need to set the path where the dataset is downloaded.
+
+**Preprocessing**
+The preprocessing pipeline is implemented in the
+`batch_preprocessing.sh `_
+script, consisting of the following steps:
+1. Read the raw data in ``ISMRMRD`` format.
+2. Preprocess the coil sensitivity maps.
+3. Compute the imspace and ground-truth target data.
+4. Compute the masks.
+5. Compute the quantitative maps.
+6. Store the data in ``HDF5`` format.
+
+The preprocessing script can be run with the following command:
+
+.. code-block:: bash
+
+ bash projects/quantitative/AHEAD/batch_preprocessing.sh
+
+**Training/Testing**
+For training a model, you just need to set up the data and export paths to the
+`configuration `_ file of the model you want
+to train. In `train_ds` and `validation_ds` please set the `data_path` to the generated json files. In `exp_manager`
+please set the `exp_dir` to the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+.. code-block:: bash
+
+ atommic run -c /projects/quantitative/AHEAD/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the
+`configuration `_ file of the model you want
+to test. In `checkpoint` (line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`.
+In `exp_manager` please set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/quantitative/AHEAD/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/docs/source/starthere/projects/quantitative/intro.rst b/docs/source/starthere/projects/quantitative/intro.rst
new file mode 100644
index 00000000..d128b8b8
--- /dev/null
+++ b/docs/source/starthere/projects/quantitative/intro.rst
@@ -0,0 +1,7 @@
+quantitative MR Imaging (qMRI)
+==============================
+
+.. toctree::
+ :maxdepth: 8
+
+ ahead
diff --git a/docs/source/starthere/projects/reconstruction/cc359.rst b/docs/source/starthere/projects/reconstruction/cc359.rst
new file mode 100644
index 00000000..ba7a4a20
--- /dev/null
+++ b/docs/source/starthere/projects/reconstruction/cc359.rst
@@ -0,0 +1,46 @@
+CC359
+=====
+
+
+**Training/Testing**
+
+.. important::
+ The ``CC359`` dataset is natively supported in ``atommic``. Therefore, you do not need to create a custom dataset
+ class. You just need to set the ``dataset_format`` argument in the configuration file to ``CC359``. For example:
+
+ .. code-block:: bash
+
+ train_ds:
+ dataset_format: cc359
+
+ validation_ds:
+ dataset_format: cc359
+
+ test_ds:
+ dataset_format: cc359
+
+For training a model, you just need to set up the data and export paths to the
+`configuration `_
+file of the model you want to train. In ``train_ds`` and `validation_ds` please set the ``data_path`` to the generated
+json files. In ``exp_manager`` please set the ``exp_dir`` to the path where you want to save the model checkpoints and
+tensorboard or wandb logs.
+
+You can train a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/CC359/conf/train/{model}.yaml
+
+For testing a model, you just need to set up the data and export paths to the
+`configuration `_ file
+model you want to test. In ``checkpoint`` (line 2) set the path the trained model checkpoint and in ``test_ds`` please
+set the ``data_path``. In ``exp_manager`` please set the ``exp_dir`` to the path where the predictions and logs will
+be saved.
+
+You can test a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/CC359/conf/test/{model}.yaml
+
+**Note:** The default logger is tensorboard.
diff --git a/docs/source/starthere/projects/reconstruction/fastmribrainsmulticoil.rst b/docs/source/starthere/projects/reconstruction/fastmribrainsmulticoil.rst
new file mode 100644
index 00000000..5424987a
--- /dev/null
+++ b/docs/source/starthere/projects/reconstruction/fastmribrainsmulticoil.rst
@@ -0,0 +1,57 @@
+fastMRI Brains Multicoil
+=========================
+
+
+**Training/Testing**
+
+.. important::
+ The ``fastMRI`` datasets are natively supported in ``atommic``. Therefore, you do not need to create a custom
+ dataset class. You just need to set the ``dataset_format`` argument in the configuration file to ``fastMRI``.
+ Also the FFT needs to be centered. For example:
+
+ .. code-block:: bash
+
+ model:
+ fft_centered: true
+ fft_normalization: ortho
+
+ train_ds:
+ dataset_format: fastMRI
+ fft_centered: true
+ fft_normalization: ortho
+
+ validation_ds:
+ dataset_format: fastMRI
+ fft_centered: true
+ fft_normalization: ortho
+
+ test_ds:
+ dataset_format: fastMRI
+ fft_centered: true
+ fft_normalization: ortho
+
+For training a model, you just need to set up the data and export paths to the
+`configuration `_
+file of the model you want to train. In ``train_ds`` and `validation_ds` please set the ``data_path`` to the generated
+json files. In ``exp_manager`` please set the ``exp_dir`` to the path where you want to save the model checkpoints and
+tensorboard or wandb logs.
+
+You can train a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/fastMRIBrainsMulticoil/conf/train/{model}.yaml
+
+For testing a model, you just need to set up the data and export paths to the
+`configuration `_
+file model you want to test. In ``checkpoint`` (line 2) set the path the trained model checkpoint and in ``test_ds``
+please set the ``data_path``. In ``exp_manager`` please set the ``exp_dir`` to the path where the predictions and logs
+will be saved.
+
+You can test a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/fastMRIBrainsMulticoil/conf/test/{model}.yaml
+
+**Note:** The default logger is tensorboard.
diff --git a/docs/source/starthere/projects/reconstruction/fastmrikneesmulticoil.rst b/docs/source/starthere/projects/reconstruction/fastmrikneesmulticoil.rst
new file mode 100644
index 00000000..bb215f74
--- /dev/null
+++ b/docs/source/starthere/projects/reconstruction/fastmrikneesmulticoil.rst
@@ -0,0 +1,57 @@
+fastMRI Knees Multicoil
+=======================
+
+
+**Training/Testing**
+
+.. important::
+ The ``fastMRI`` datasets are natively supported in ``atommic``. Therefore, you do not need to create a custom
+ dataset class. You just need to set the ``dataset_format`` argument in the configuration file to ``fastMRI``.
+ Also the FFT needs to be centered. For example:
+
+ .. code-block:: bash
+
+ model:
+ fft_centered: true
+ fft_normalization: ortho
+
+ train_ds:
+ dataset_format: fastMRI
+ fft_centered: true
+ fft_normalization: ortho
+
+ validation_ds:
+ dataset_format: fastMRI
+ fft_centered: true
+ fft_normalization: ortho
+
+ test_ds:
+ dataset_format: fastMRI
+ fft_centered: true
+ fft_normalization: ortho
+
+For training a model, you just need to set up the data and export paths to the
+`configuration `_
+file of the model you want to train. In ``train_ds`` and `validation_ds` please set the ``data_path`` to the generated
+json files. In ``exp_manager`` please set the ``exp_dir`` to the path where you want to save the model checkpoints and
+tensorboard or wandb logs.
+
+You can train a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/fastMRIKneesMulticoil/conf/train/{model}.yaml
+
+For testing a model, you just need to set up the data and export paths to the
+`configuration `_
+file model you want to test. In ``checkpoint`` (line 2) set the path the trained model checkpoint and in ``test_ds``
+please set the ``data_path``. In ``exp_manager`` please set the ``exp_dir`` to the path where the predictions and logs
+will be saved.
+
+You can test a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/fastMRIKneesMulticoil/conf/test/{model}.yaml
+
+**Note:** The default logger is tensorboard.
diff --git a/docs/source/starthere/projects/reconstruction/fastmrikneessinglecoil.rst b/docs/source/starthere/projects/reconstruction/fastmrikneessinglecoil.rst
new file mode 100644
index 00000000..40449572
--- /dev/null
+++ b/docs/source/starthere/projects/reconstruction/fastmrikneessinglecoil.rst
@@ -0,0 +1,57 @@
+fastMRI Knees Singlecoil
+========================
+
+
+**Training/Testing**
+
+.. important::
+ The ``fastMRI`` datasets are natively supported in ``atommic``. Therefore, you do not need to create a custom
+ dataset class. You just need to set the ``dataset_format`` argument in the configuration file to ``fastMRI``.
+ Also the FFT needs to be centered. For example:
+
+ .. code-block:: bash
+
+ model:
+ fft_centered: true
+ fft_normalization: ortho
+
+ train_ds:
+ dataset_format: fastMRI
+ fft_centered: true
+ fft_normalization: ortho
+
+ validation_ds:
+ dataset_format: fastMRI
+ fft_centered: true
+ fft_normalization: ortho
+
+ test_ds:
+ dataset_format: fastMRI
+ fft_centered: true
+ fft_normalization: ortho
+
+For training a model, you just need to set up the data and export paths to the
+`configuration `_
+file of the model you want to train. In ``train_ds`` and `validation_ds` please set the ``data_path`` to the generated
+json files. In ``exp_manager`` please set the ``exp_dir`` to the path where you want to save the model checkpoints and
+tensorboard or wandb logs.
+
+You can train a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/fastMRIKneesSinglecoil/conf/train/{model}.yaml
+
+For testing a model, you just need to set up the data and export paths to the
+`configuration `_
+file model you want to test. In ``checkpoint`` (line 2) set the path the trained model checkpoint and in ``test_ds``
+please set the ``data_path``. In ``exp_manager`` please set the ``exp_dir`` to the path where the predictions and logs
+will be saved.
+
+You can test a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/fastMRIKneesSinglecoil/conf/test/{model}.yaml
+
+**Note:** The default logger is tensorboard.
diff --git a/docs/source/starthere/projects/reconstruction/intro.rst b/docs/source/starthere/projects/reconstruction/intro.rst
new file mode 100644
index 00000000..9c572b44
--- /dev/null
+++ b/docs/source/starthere/projects/reconstruction/intro.rst
@@ -0,0 +1,12 @@
+MRI Reconstruction (REC)
+========================
+
+.. toctree::
+ :maxdepth: 8
+
+ cc359
+ fastmribrainsmulticoil
+ fastmrikneesmulticoil
+ fastmrikneessinglecoil
+ skmtea
+ stanfordknees2019
diff --git a/docs/source/starthere/projects/reconstruction/skmtea.rst b/docs/source/starthere/projects/reconstruction/skmtea.rst
new file mode 100644
index 00000000..79f47ae4
--- /dev/null
+++ b/docs/source/starthere/projects/reconstruction/skmtea.rst
@@ -0,0 +1,81 @@
+Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 Dataset
+===============================================================
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the Stanford Knee
+MRI Multi-Task Evaluation (SKM-TEA) 2021 dataset.
+
+Related papers:
+
+* https://openreview.net/forum?id=YDMFgD_qJuA.
+
+**Visualization**
+An example notebook for visualizing the data is provided in the
+`visualize `_ notebook. You
+just need to set the path where the dataset is downloaded. The
+`original notebook `_ is copied from the
+https://github.com/StanfordMIMI/skm-tea repository and provided by the SKMTEA authors.
+
+**Preprocessing**
+No preprocessing is needed for the SKMTEA dataset. You just need to generate train, val, and test sets depending on
+the task you use the dataset for. For example, for the reconstruction task, you need to run the
+`generate_sets.sh `_
+script.
+
+**Training/Testing**
+
+.. important::
+ The ``SKM-TEA`` dataset is natively supported in ``atommic``. Therefore, you do not need to create a custom
+ dataset class. You just need to set the ``dataset_format`` argument in the configuration file to the desired
+ ``SKM-TEA`` dataset version. Also the FFT needs to be centered. For example:
+
+ .. code-block:: bash
+
+ model:
+ fft_centered: true
+ fft_normalization: ortho
+
+ train_ds:
+ dataset_format: skm-tea-echo1
+ fft_centered: true
+ fft_normalization: ortho
+
+ validation_ds:
+ dataset_format: skm-tea-echo1+echo2
+ fft_centered: true
+ fft_normalization: ortho
+
+ test_ds:
+ dataset_format: skm-tea-echo1+echo2-mc
+ fft_centered: true
+ fft_normalization: ortho
+
+The ``skm-tea-echo1`` dataset contains only the first echo of the multi-echo data. The ``skm-tea-echo2`` dataset
+contains only the second echo of the multi-echo data. The ``skm-tea-echo1+echo2`` dataset sums the first and second
+echoes of the multi-echo data. The ``skm-tea-echo1+echo2-mc`` dataset stacks the first and second echoes of the
+multi-echo data as channels.
+
+For training a model, you just need to set up the data and export paths to the
+`configuration `_ file of the
+model you want to train. In ``train_ds`` and `validation_ds` please set the ``data_path`` to the generated json files.
+In ``exp_manager`` please set the ``exp_dir`` to the path where you want to save the model checkpoints and tensorboard
+or wandb logs.
+
+You can train a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/SKMTEA/conf/train/{model}.yaml
+
+For testing a model, you just need to set up the data and export paths to the
+`configuration `_ file of the
+model you want to test. In ``checkpoint`` (line 2) set the path the trained model checkpoint and in ``test_ds`` please
+set the ``data_path``. In ``exp_manager`` please set the ``exp_dir`` to the path where the predictions and logs will
+be saved.
+
+You can test a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/SKMTEA/conf/test/{model}.yaml
+
+**Note:** The default logger is tensorboard.
diff --git a/docs/source/starthere/projects/reconstruction/stanfordknees2019.rst b/docs/source/starthere/projects/reconstruction/stanfordknees2019.rst
new file mode 100644
index 00000000..d0d34cfa
--- /dev/null
+++ b/docs/source/starthere/projects/reconstruction/stanfordknees2019.rst
@@ -0,0 +1,81 @@
+Stanford Fullysampled 3D FSE Knees 2019 Dataset
+================================================
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the Stanford
+Fullysampled 3D FSE Knees 2019 dataset.
+
+For more information, please refer to http://mridata.org/list?project=Stanford%20Fullysampled%203D%20FSE%20Knees.
+
+.. note::
+ When running the preprocessing scripts please make sure you have the ``ismrmrd`` package installed. You can
+ install it with the following command:
+
+ .. code-block:: bash
+
+ pip install -r requirements/requirements-ahead_stanfordknees.txt
+
+**Visualization**
+An example notebook for visualizing the data is provided in the
+`visualize `_
+notebook. You just need to set the path where the dataset is downloaded.
+
+**Preprocessing**
+The preprocessing pipeline is implemented in the
+`preprocess_dataset.sh `_
+script, consisting of the following steps:
+1. Convert the data from ``ISMRMRD`` to ``HDF5`` format.
+2. Split the dataset into training and validation sets.
+
+**Training/Testing**
+
+.. important::
+ The ``Stanford Knees`` dataset is natively supported in ``atommic``. Therefore, you do not need to create a custom
+ dataset class. You just need to set the ``dataset_format`` argument in the configuration file to the desired
+ ``Stanford Knees`` dataset version. Also the FFT needs to be centered. For example:
+
+ .. code-block:: bash
+
+ model:
+ fft_centered: true
+ fft_normalization: ortho
+
+ train_ds:
+ dataset_format: stanford_knees
+ fft_centered: true
+ fft_normalization: ortho
+
+ validation_ds:
+ dataset_format: stanford_knees
+ fft_centered: true
+ fft_normalization: ortho
+
+ test_ds:
+ dataset_format: stanford_knees
+ fft_centered: true
+ fft_normalization: ortho
+
+For training a model, you just need to set up the data and export paths to the
+`configuration `_
+file of the model you want to train. In ``train_ds`` and `validation_ds` please set the ``data_path`` to the generated
+json files. In ``exp_manager`` please set the ``exp_dir`` to the path where you want to save the model checkpoints and
+tensorboard or wandb logs.
+
+You can train a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/StanfordKnees2019/conf/train/{model}.yaml
+
+For testing a model, you just need to set up the data and export paths to the
+`configuration `_ file
+model you want to test. In ``checkpoint`` (line 2) set the path the trained model checkpoint and in ``test_ds`` please
+set the ``data_path``. In ``exp_manager`` please set the ``exp_dir`` to the path where the predictions and logs will
+be saved.
+
+You can test a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/reconstruction/StanfordKnees2019/conf/test/{model}.yaml
+
+**Note:** The default logger is tensorboard.
diff --git a/docs/source/starthere/projects/segmentation/brats2023adultglioma.rst b/docs/source/starthere/projects/segmentation/brats2023adultglioma.rst
new file mode 100644
index 00000000..09b88fd6
--- /dev/null
+++ b/docs/source/starthere/projects/segmentation/brats2023adultglioma.rst
@@ -0,0 +1,79 @@
+BraTS 2023 Adult Glioma
+=======================
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the
+BraTS2023AdultGlioma dataset.
+
+For more information, please refer to https://www.synapse.org/#!Synapse:syn51156910/wiki/.
+
+Related papers:
+
+* https://arxiv.org/pdf/1811.02629.pdf,
+* https://arxiv.org/pdf/2305.17033.pdf.
+
+Data need to be downloaded manually due to required registration. Download link:
+https://www.synapse.org/#!Synapse:syn51156910/wiki/622351.
+
+.. note::
+ When running the preprocessing scripts please make sure you have the following packages installed: argparse, json,
+ nibabel, numpy, pathlib, random, tqdm. Those packages are installed by default if atommic is installed.
+
+**Visualization**
+An example notebook for visualizing the data is provided in the
+`visualize `_
+notebook. You just need to set the path where the dataset is downloaded.
+
+**Preprocessing**
+The preprocessing pipeline is implemented in the
+`preprocess_dataset.sh `_
+script, consisting of the following steps:
+1. Crop to the brain region, as there is a lot of background around the brain resulting is slower training.
+Important note: the cropping is done only for the training set.
+2. Normalize the images to zero mean and unit variance.
+3. Updates headers and save to NIfTI format.
+4. Split the dataset into training and validation sets.
+5. Compute the probabilities for each segmentation class.
+
+**Training/Testing**
+
+.. important::
+ The ``BraTS2023AdultGlioma`` dataset is natively supported in ``atommic``. Therefore, you do not need to create a
+ custom dataset class. You just need to set the ``dataset_format`` argument in the configuration file to
+ ``BraTS2023AdultGlioma``. For example:
+
+ .. code-block:: bash
+
+ train_ds:
+ dataset_format: BraTS2023AdultGlioma
+
+ validation_ds:
+ dataset_format: BraTS2023AdultGlioma
+
+ test_ds:
+ dataset_format: BraTS2023AdultGlioma
+
+For training a model, you just need to set up the data and export paths to the
+`configuration `_
+file of the model you want to train. In ``train_ds`` and `validation_ds` please set the ``data_path`` to the generated
+json files. In ``exp_manager`` please set the ``exp_dir`` to the path where you want to save the model checkpoints and
+tensorboard or wandb logs.
+
+You can train a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/segmentation/BraTS2023AdultGlioma/conf/train/{model}.yaml
+
+For testing a model, you just need to set up the data and export paths to the
+`configuration `_ file
+model you want to test. In ``checkpoint`` (line 2) set the path the trained model checkpoint and in ``test_ds`` please
+set the ``data_path``. In ``exp_manager`` please set the ``exp_dir`` to the path where the predictions and logs will
+be saved.
+
+You can test a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/segmentation/BraTS2023AdultGlioma/conf/test/{model}.yaml
+
+**Note:** The default logger is tensorboard.
diff --git a/docs/source/starthere/projects/segmentation/intro.rst b/docs/source/starthere/projects/segmentation/intro.rst
new file mode 100644
index 00000000..c7eb8b6a
--- /dev/null
+++ b/docs/source/starthere/projects/segmentation/intro.rst
@@ -0,0 +1,9 @@
+MRI Segmentation (SEG)
+======================
+
+.. toctree::
+ :maxdepth: 8
+
+ brats2023adultglioma
+ isles2022subacutestroke
+ ../reconstruction/skmtea
diff --git a/docs/source/starthere/projects/segmentation/isles2022subacutestroke.rst b/docs/source/starthere/projects/segmentation/isles2022subacutestroke.rst
new file mode 100644
index 00000000..43f90b1d
--- /dev/null
+++ b/docs/source/starthere/projects/segmentation/isles2022subacutestroke.rst
@@ -0,0 +1,84 @@
+ISLES 2022 Sub Acute Stroke
+===========================
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the
+ISLES2022SubAcuteStroke dataset.
+
+For more information, please refer to https://isles22.grand-challenge.org/dataset/.
+
+Related papers:
+
+* https://www.nature.com/articles/s41597-022-01875-5.
+
+.. note::
+ When running the preprocessing scripts please make sure you have the following packages installed: argparse,
+ connected-components-3d, json, nibabel, numpy, pathlib, random, simpleitk. All those packages, except the
+ connected-components-3d and simpleitk packages, are installed by default if atommic is installed. To install those
+ two packages, please run the following commands:
+
+ .. code-block:: bash
+
+ pip install -r requirements/requirements-isles22.txt
+
+**Visualization**
+An example notebook for visualizing the data is provided in the
+`visualize `_
+notebook. You just need to set the path where the dataset is downloaded. You just need to set the path where
+the dataset is downloaded. The notebook is copied and slightly modified from the
+`original notebook `_ provided by the ISLES challenge.
+
+**Preprocessing**
+The preprocessing pipeline is implemented in the
+`preprocess_dataset.sh `_
+script, consisting of the following steps:
+1. Clip to 0 - max values.
+2. Normalize the images to zero mean and unit variance.
+3. Resample the FLAIR data to the same resolution as the ADC and DWI data.
+4. Stack all modalities (ADC, DWI, FLAIR) into a single 3D volume.
+5. Fix the orientation of the images.
+6. Updates headers and save to NIfTI format.
+7. Split the dataset into training and validation sets.
+
+**Training/Testing**
+
+.. important::
+ The ``ISLES2022SubAcuteStroke`` dataset is natively supported in ``atommic``. Therefore, you do not need to create
+ a custom dataset class. You just need to set the ``dataset_format`` argument in the configuration file to
+ ``ISLES2022SubAcuteStroke``. For example:
+
+ .. code-block:: bash
+
+ train_ds:
+ dataset_format: ISLES2022SubAcuteStroke
+
+ validation_ds:
+ dataset_format: ISLES2022SubAcuteStroke
+
+ test_ds:
+ dataset_format: ISLES2022SubAcuteStroke
+
+For training a model, you just need to set up the data and export paths to the
+`configuration `_
+file of the model you want to train. In ``train_ds`` and `validation_ds` please set the ``data_path`` to the generated
+json files. In ``exp_manager`` please set the ``exp_dir`` to the path where you want to save the model checkpoints and
+tensorboard or wandb logs.
+
+You can train a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/segmentation/ISLES2022SubAcuteStroke/conf/train/{model}.yaml
+
+For testing a model, you just need to set up the data and export paths to the
+`configuration `_ file
+model you want to test. In ``checkpoint`` (line 2) set the path the trained model checkpoint and in ``test_ds`` please
+set the ``data_path``. In ``exp_manager`` please set the ``exp_dir`` to the path where the predictions and logs will
+be saved.
+
+You can test a model with the following command:
+
+.. code-block:: bash
+
+ atommic run -c /projects/segmentation/ISLES2022SubAcuteStroke/conf/test/{model}.yaml
+
+**Note:** The default logger is tensorboard.
diff --git a/docs/source/starthere/tutorials.rst b/docs/source/starthere/tutorials.rst
new file mode 100644
index 00000000..eefbc270
--- /dev/null
+++ b/docs/source/starthere/tutorials.rst
@@ -0,0 +1,32 @@
+.. _tutorials:
+
+Tutorials
+=========
+
+
+Most ATOMMIC tutorials can be run on `Google's Colab `_.
+
+To run a tutorial:
+
+#. Click the **Colab** link (see table below).
+#. Connect to an instance with a GPU. For example, click **Runtime** > **Change runtime type** and select **GPU** for the hardware accelerator.
+
+.. list-table:: **Tutorials**
+ :widths: 15 25 25
+ :header-rows: 1
+
+ * - Domain
+ - Title
+ - GitHub URL
+ * - General
+ - Getting Started: ATOMMIC Fundamentals
+ - `ATOMMIC Fundamentals `_
+ * - General
+ - Getting Started: MRI Transforms
+ - `ATOMMIC MRI transforms `_
+ * - General
+ - Getting Started: MRI undersampling
+ - `ATOMMIC MRI undersampling `_
+ * - General
+ - Getting Started: Upload Model on HuggingFace
+ - `ATOMMIC Upload Model on HuggingFace `_
diff --git a/docs/update_docs_docker.sh b/docs/update_docs_docker.sh
new file mode 100644
index 00000000..f1990a58
--- /dev/null
+++ b/docs/update_docs_docker.sh
@@ -0,0 +1,6 @@
+#!/bin/bash
+cd ../
+docker run --rm -v $PWD:/workspace python:3.10 /bin/bash -c "cd /workspace && \
+pip install -r requirements/requirements_docs.txt && cd docs/ && rm -rf build && make clean && make html"
+echo "To start web server just run in docs directory:"
+echo "python3 -m http.server 8000 --directory ./build/html/"
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/conf/targets/Test_SENSE.yaml b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/targets/Test_SENSE.yaml
new file mode 100644
index 00000000..36a7aa5b
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/targets/Test_SENSE.yaml
@@ -0,0 +1,104 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 4
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 15
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/MTL/targets/SKMTEA_Test/SENSE/
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/idslr.yaml b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/idslr.yaml
new file mode 100644
index 00000000..3b0e988c
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/idslr.yaml
@@ -0,0 +1,155 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/MTL_IDSLR_SKMTEA_poisson2d_4x/blob/main/MTL_IDSLR_SKMTEA_poisson2d_4x.atommic
+mode: test
+
+model:
+ model_name: IDSLR
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: false
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/MTL/predictions/SKMTEA/IDSLR_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/idslrunet.yaml b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/idslrunet.yaml
new file mode 100644
index 00000000..282e556b
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/idslrunet.yaml
@@ -0,0 +1,155 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/MTL_IDSLRUNet_SKMTEA_poisson2d_4x/blob/main/MTL_IDSLRUNet_SKMTEA_poisson2d_4x.atommic
+mode: test
+
+model:
+ model_name: IDSLRUNET
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: false
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/MTL/predictions/SKMTEA/IDSLRUNET_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/mtlrs.yaml b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/mtlrs.yaml
new file mode 100644
index 00000000..ac93f4b3
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/mtlrs.yaml
@@ -0,0 +1,193 @@
+pretrained: test
+checkpoint: https://huggingface.co/wdika/MTL_MTLRS_SKMTEA_poisson2d_4x/blob/main/MTL_MTLRS_SKMTEA_poisson2d_4x.atommic
+mode: true
+
+model:
+ model_name: MTLRS
+ joint_reconstruction_segmentation_module_cascades: 5
+ task_adaption_type: multi_task_learning
+ use_reconstruction_module: true
+ reconstruction_module_recurrent_layer: IndRNN
+ reconstruction_module_conv_filters:
+ - 64
+ - 64
+ - 2
+ reconstruction_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ reconstruction_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ reconstruction_module_conv_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ reconstruction_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_depth: 2
+ reconstruction_module_time_steps: 8
+ reconstruction_module_conv_dim: 2
+ reconstruction_module_num_cascades: 1
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_no_dc: true
+ reconstruction_module_keep_prediction: true
+ reconstruction_module_accumulate_predictions: true
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 4
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/MTL/predictions/SKMTEA/MTLRS_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/segnet.yaml b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/segnet.yaml
new file mode 100644
index 00000000..50b0fda3
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/segnet.yaml
@@ -0,0 +1,160 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/MTL_SegNet_SKMTEA_poisson2d_4x/blob/main/MTL_SegNet_SKMTEA_poisson2d_4x.atommic
+mode: test
+
+model:
+ model_name: SEGNET
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: true
+ padding: true
+ norm_groups: 2
+ num_cascades: 5
+ segmentation_final_layer_conv_dim: 2
+ segmentation_final_layer_kernel_size: 3
+ segmentation_final_layer_dilation: 1
+ segmentation_final_layer_bias: False
+ segmentation_final_layer_nonlinear: relu
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/MTL/predictions/SKMTEA/SegNet_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/idslr.yaml b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/idslr.yaml
new file mode 100644
index 00000000..1fe24c8b
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/idslr.yaml
@@ -0,0 +1,208 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: IDSLR
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: false
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/MTL/trained_models/SKMTEA/IDSLR_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/idslrunet.yaml b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/idslrunet.yaml
new file mode 100644
index 00000000..12c52777
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/idslrunet.yaml
@@ -0,0 +1,208 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: IDSLRUNET
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: false
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/MTL/trained_models/SKMTEA/IDSLRUNET_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/mtlrs.yaml b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/mtlrs.yaml
new file mode 100644
index 00000000..af93309b
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/mtlrs.yaml
@@ -0,0 +1,246 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MTLRS
+ joint_reconstruction_segmentation_module_cascades: 5
+ task_adaption_type: multi_task_learning
+ use_reconstruction_module: true
+ reconstruction_module_recurrent_layer: IndRNN
+ reconstruction_module_conv_filters:
+ - 64
+ - 64
+ - 2
+ reconstruction_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ reconstruction_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ reconstruction_module_conv_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ reconstruction_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_depth: 2
+ reconstruction_module_time_steps: 8
+ reconstruction_module_conv_dim: 2
+ reconstruction_module_num_cascades: 1
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_no_dc: true
+ reconstruction_module_keep_prediction: true
+ reconstruction_module_accumulate_predictions: true
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 4
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 4
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/MTL/trained_models/SKMTEA/MTLRS_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/segnet.yaml b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/segnet.yaml
new file mode 100644
index 00000000..e801c930
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/conf/train/segnet.yaml
@@ -0,0 +1,213 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGNET
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: true
+ padding: true
+ norm_groups: 2
+ num_cascades: 5
+ segmentation_final_layer_conv_dim: 2
+ segmentation_final_layer_kernel_size: 3
+ segmentation_final_layer_dilation: 1
+ segmentation_final_layer_bias: False
+ segmentation_final_layer_nonlinear: relu
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/MTL/trained_models/SKMTEA/SegNet_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/MTL/SKMTEA/evaluate.sh b/projects/ATOMMIC_paper/MTL/SKMTEA/evaluate.sh
new file mode 100644
index 00000000..6499d955
--- /dev/null
+++ b/projects/ATOMMIC_paper/MTL/SKMTEA/evaluate.sh
@@ -0,0 +1,32 @@
+python projects/MTL/rs/SKMTEA/evaluation/mtlrs_reconstruction.py \
+output_data_dir/atommic/MTL/targets/SKMTEA_Test/SENSE/default/ output_data_dir/atommic/MTL/predictions/SKMTEA/IDSLR_SENSE/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/MTL/evaluation_per_slice/SKMTEA/reconstruction/ --fill_target_path --fill_pred_path
+python projects/MTL/rs/SKMTEA/evaluation/mtlrs_reconstruction.py \
+output_data_dir/atommic/MTL/targets/SKMTEA_Test/SENSE/default/ output_data_dir/atommic/MTL/predictions/SKMTEA/IDSLRUNET_SENSE/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/MTL/evaluation_per_slice/SKMTEA/reconstruction/ --fill_target_path --fill_pred_path
+python projects/MTL/rs/SKMTEA/evaluation/mtlrs_reconstruction.py \
+output_data_dir/atommic/MTL/targets/SKMTEA_Test/SENSE/default/ output_data_dir/atommic/MTL/predictions/SKMTEA/SegNet_SENSE/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/MTL/evaluation_per_slice/SKMTEA/reconstruction/ --fill_target_path --fill_pred_path
+python projects/MTL/rs/SKMTEA/evaluation/mtlrs_reconstruction.py \
+output_data_dir/atommic/MTL/targets/SKMTEA_Test/SENSE/default/ output_data_dir/atommic/MTL/predictions/SKMTEA/MTLRS_SENSE/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/MTL/evaluation_per_slice/SKMTEA/reconstruction/ --fill_target_path --fill_pred_path
+python projects/MTL/rs/SKMTEA/evaluation/mtlrs_segmentation.py \
+parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json \
+parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track \
+output_data_dir/atommic/MTL/predictions/SKMTEA/IDSLR_SENSE/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/MTL/evaluation_per_slice/SKMTEA/segmentation/ --fill_pred_path
+python projects/MTL/rs/SKMTEA/evaluation/mtlrs_segmentation.py \
+parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json \
+parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track \
+output_data_dir/atommic/MTL/predictions/SKMTEA/IDSLRUNET_SENSE/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/MTL/evaluation_per_slice/SKMTEA/segmentation/ --fill_pred_path
+python projects/MTL/rs/SKMTEA/evaluation/mtlrs_segmentation.py \
+parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json \
+parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track \
+output_data_dir/atommic/MTL/predictions/SKMTEA/SegNet_SENSE/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/MTL/evaluation_per_slice/SKMTEA/segmentation/ --fill_pred_path
+python projects/MTL/rs/SKMTEA/evaluation/mtlrs_segmentation.py \
+parent_data_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json \
+parent_data_dir/skm-tea/v1-release/segmentation_masks/raw-data-track \
+output_data_dir/atommic/MTL/predictions/SKMTEA/MTLRS_SENSE/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/MTL/evaluation_per_slice/SKMTEA/segmentation/ --fill_pred_path
diff --git a/projects/ATOMMIC_paper/README.md b/projects/ATOMMIC_paper/README.md
new file mode 100644
index 00000000..70ab5388
--- /dev/null
+++ b/projects/ATOMMIC_paper/README.md
@@ -0,0 +1,133 @@
+# **Reproducing the ATOMMIC paper**
+
+The ATOMMIC paper is available at https://arxiv.org/abs/. In this document, we provide the instructions for reproducing
+the results of the paper.
+
+**Note:** You would need to download and preprocess the following datasets to reproduce the results. Please refer to each project folder for more information:
+- [Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 Dataset](../MTL/rs/SKMTEA/README.md).
+- [Amsterdam Ultra-high field adult lifespan database (AHEAD)](../qMRI/AHEAD/README.md).
+ - In the ATOMMIC paper, we used the first 10 subjects of the AHEAD dataset, as listed on the download page. The first 6 were used for training, the next 2 for validation, and the last 2 for testing.
+- [Calgary-Campinas Public Brain MR Dataset (CC359)](../REC/CC359/README.md).
+- [fastMRI Brains Multicoil Dataset](../REC/fastMRIBrainsMulticoil/README.md).
+- [BraTS2023AdultGlioma](../SEG/BraTS2023AdultGlioma/README.md).
+- [ISLES2022SubAcuteStroke](../SEG/ISLES2022SubAcuteStroke/README.md).
+- [Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 Segmentation Dataset](../SEG/SKMTEA/README.md).
+
+## **It is highly recommended to create a safe copy of the whole folder before running the scripts. Just in case setting and renaming paths goes wrong to be easy to revert.**
+
+
+## **Set the data paths**
+Next you need to set the data paths by running:
+```bash
+bash ./projects/ATOMMIC_paper/set_paths.sh
+```
+
+Paths should be set for example ```/data/``` if the AHEAD dataset is located at ```/data/AHEAD/``` and not ```/data/AHEAD/```.
+
+## **Reproducing the results**
+To reproduce the results, first run the following script to perform inference with the pre-trained models:
+```bash
+.projects/ATOMMIC_paper/run_models.sh
+```
+
+**Note:** Just before you run the last two models, lines 100 and 101 on the ```run_models.sh``` script, specifically the following lines:
+```bash
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qcirim.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qvarnet.yaml
+```
+you will need to set the ```initial_predictions_path``` in the qMRI/AHEAD/conf/quantitative_test/qcirim.yaml (line 94) and qMRI/AHEAD/conf/quantitative_test/qvarnet.yaml (line 94)
+to the output path of ```run_models.sh``` script and lines 94 and 97, respectively.
+
+**You do not need to set any checkpoint paths as the script will automatically download the pre-trained models from HuggingFace.**
+
+Next, run the following script to evaluate the results:
+```bash
+.projects/ATOMMIC_paper/evaluate.sh
+```
+**Note:** Before you evaluate the ISLES2022SubAcuteStroke segmentation models, you will need to install:
+```bash
+pip install -r requirements/requirements-isles22.txt
+```
+
+Finally, run the following script to generate the results in table (and latex table) format, as presented on the paper and below:
+```bash
+python projects/ATOMMIC_paper/generate_results.py
+```
+
+### Additional information
+
+In data/DL_MRI_Repositories_Overview.csv you can find the detailed list of parsed repositories plotted in Figure 1 of the paper.
+
+## **Results**
+
+## Overview of Performance on the REC Task
+
+| Model | CC359 - Poisson 2D 5x SSIM | CC359 - Poisson 2D 5x PSNR | CC359 - Poisson 2D 10x SSIM | CC359 - Poisson 2D 10x PSNR | fastMRIBrains - Equispaced 1D 4x SSIM | fastMRIBrains - Equispaced 1D 4x PSNR | fastMRIBrains - Equispaced 1D 8x SSIM | fastMRIBrains - Equispaced 1D 8x | StanfordKnee - Gaussian 2D 12x SSIM | StanfordKnee - Gaussian 2D 12x PSNR |
+|------------|----------------------------|----------------------------|-----------------------------|-----------------------------|---------------------------------------|---------------------------------------|---------------------------------------|----------------------------------|-------------------------------------|-------------------------------------|
+| CCNN | 0.845 +/- 0.064 | 28.36 +/- 3.69 | 0.783 +/- 0.089 | 25.95 +/- 3.64 | 0.886 +/- 0.192 | 33.47 +/- 5.92 | 0.836 +/- 0.202 | 29.40 +/- 5.71 | 0.767 +/- 0.299 | 31.64 +/- 6.84 |
+| CIRIM | 0.858 +/- 0.074 | 28.79 +/- 4.23 | 0.816 +/- 0.094 | 26.92 +/- 4.36 | 0.892 +/- 0.184 | 33.83 +/- 6.11 | 0.846 +/- 0.202 | 30.23 +/- 5.67 | 0.795 +/- 0.311 | 32.76 +/- 7.20 |
+| CRNN | 0.774 +/- 0.088 | 25.59 +/- 4.19 | 0.722 +/- 0.088 | 24.48 +/- 3.39 | 0.868 +/- 0.195 | 31.31 +/- 5.46 | 0.806 +/- 0.198 | 27.50 +/- 5.57 | | |
+| JointICNet | 0.872 +/- 0.065 | 29.28 +/- 3.99 | 0.828 +/- 0.086 | 27.36 +/- 4.10 | 0.832 +/- 0.198 | 28.57 +/- 5.50 | 0.772 +/- 0.202 | 25.50 +/- 5.38 | 0.728 +/- 0.291 | 29.59 +/- 6.31 |
+| KIKINet | 0.788 +/- 0.087 | 25.43 +/- 4.16 | 0.742 +/- 0.105 | 24.37 +/- 3.88 | 0.856 +/- 0.201 | 31.02 +/- 5.68 | 0.805 +/- 0.207 | 27.78 +/- 5.82 | 0.659 +/- 0.241 | 27.35 +/- 5.54 |
+| LPDNet | 0.849 +/- 0.075 | 28.26 +/- 4.22 | 0.810 +/- 0.099 | 26.73 +/- 4.23 | 0.882 +/- 0.201 | 32.60 +/- 6.78 | 0.840 +/- 0.208 | 29.51 +/- 5.93 | 0.736 +/- 0.297 | 29.75 +/- 6.31 |
+| MoDL | 0.844 +/- 0.068 | 27.97 +/- 4.20 | 0.793 +/- 0.088 | 25.89 +/- 4.39 | 0.870 +/- 0.188 | 31.44 +/- 5.66 | 0.813 +/- 0.192 | 27.81 +/- 5.86 | 0.566 +/- 0.284 | 23.63 +/- 4.66 |
+| RIM | 0.834 +/- 0.077 | 27.45 +/- 4.32 | 0.788 +/- 0.091 | 25.56 +/- 3.96 | 0.886 +/- 0.188 | 33.12 +/- 6.04 | 0.837 +/- 0.199 | 29.49 +/- 5.74 | 0.769 +/- 0.304 | 31.58 +/- 6.74 |
+| RVN | 0.845 +/- 0.067 | 28.14 +/- 3.53 | 0.787 +/- 0.093 | 26.03 +/- 3.77 | 0.894 +/- 0.180 | 34.23 +/- 5.97 | 0.843 +/- 0.195 | 30.08 +/- 5.68 | 0.778 +/- 0.301 | 31.96 +/- 7.00 |
+| UNet | 0.849 +/- 0.070 | 28.85 +/- 4.17 | 0.810 +/- 0.091 | 27.20 +/- 4.20 | 0.885 +/- 0.182 | 33.09 +/- 6.02 | 0.847 +/- 0.197 | 29.87 +/- 5.68 | 0.771 +/- 0.296 | 31.37 +/- 6.54 |
+| VarNet | 0.874 +/- 0.061 | 29.49 +/- 3.86 | 0.827 +/- 0.087 | 27.51 +/- 4.01 | 0.892 +/- 0.198 | 34.00 +/- 6.30 | 0.856 +/- 0.216 | 30.73 +/- 5.94 | 0.764 +/- 0.302 | 31.48 +/- 6.73 |
+| VSNet | 0.788 +/- 0.079 | 25.51 +/- 3.91 | 0.740 +/- 0.089 | 24.19 +/- 3.27 | 0.856 +/- 0.196 | 30.37 +/- 5.34 | 0.796 +/- 0.197 | 26.88 +/- 5.43 | 0.708 +/- 0.289 | 28.47 +/- 5.82 |
+| XPDNet | 0.761 +/- 0.100 | 24.27 +/- 4.14 | 0.700 +/- 0.112 | 22.65 +/- 3.23 | 0.854 +/- 0.212 | 31.03 +/- 6.75 | 0.788 +/- 0.218 | 26.96 +/- 6.18 | 0.654 +/- 0.270 | 27.16 +/- 5.81 |
+| ZeroFilled | 0.679 +/- 0.103 | 19.89 +/- 7.45 | 0.656 +/- 0.092 | 19.24 +/- 7.37 | 0.671 +/- 0.194 | 24.12 +/- 6.21 | 0.591 +/- 0.213 | 21.03 +/- 5.97 | 0.548 +/- 0.196 | 18.07 +/- 6.20 |
+
+
+## Overview of Performance on the qMRI Task - AHEAD Dataset (Gaussian 2D 12x Undersampling)
+
+| Model | REC SSIM | REC PSNR | REC NMSE | qMRI SSIM | qMRI PSNR | qMRI NMSE |
+|---------|-----------------|----------------|-----------------|-----------------|-----------------|-----------------|
+| CIRIM | 0.910 +/- 0.077 | 32.86 +/- 8.51 | 0.043 +/- 0.065 | |
+| VarNet | 0.893 +/- 0.055 | 32.37 +/- 4.88 | 0.047 +/- 0.054 | |
+| qCIRIM | | | | 0.881 +/- 0.178 | 28.36 +/- 11.55 | 0.124 +/- 0.363 |
+| qVarNet | | | | 0.784 +/- 0.206 | 24.35 +/- 7.77 | 0.192 +/- 0.334 |
+
+
+## Overview of Performance on the SEG Task
+
+### BraTS2023AdultGlioma
+
+| Model | DICE | F1 | HD95 | IOU |
+|---------------|-----------------|-----------------|-----------------|-----------------|
+| AttentionUNet | 0.930 +/- 0.126 | 0.648 +/- 0.763 | 3.836 +/- 3.010 | 0.537 +/- 0.662 |
+| DynUNet | 0.806 +/- 0.276 | 0.104 +/- 0.580 | 5.119 +/- 5.411 | 0.070 +/- 0.419 |
+| UNet | 0.937 +/- 0.118 | 0.671 +/- 0.787 | 3.504 +/- 2.089 | 0.535 +/- 0.663 |
+| UNet3D | 0.936 +/- 0.133 | 0.674 +/- 0.782 | 3.550 +/- 2.162 | 0.528 +/- 0.652 |
+| VNet | 0.733 +/- 0.437 | 0.014 +/- 0.234 | 6.010 +/- 6.097 | 0.000 +/- 0.004 |
+
+### SKMTEA segmentation only
+
+| Model | DICE | F1 | HD95 | IOU |
+|---------------|-----------------|-----------------|-----------------|-----------------|
+| AttentionUNet | 0.909 +/- 0.088 | 0.637 +/- 0.475 | 6.358 +/- 2.209 | 0.529 +/- 0.361 |
+| DynUNet | 0.689 +/- 0.136 | 0.059 +/- 0.264 | 8.973 +/- 4.507 | 0.015 +/- 0.066 |
+| UNet | 0.912 +/- 0.058 | 0.651 +/- 0.449 | 6.618 +/- 1.793 | 0.516 +/- 0.350 |
+| UNet3D | 0.918 +/- 0.068 | 0.789 +/- 0.404 | 5.893 +/- 2.995 | 0.530 +/- 0.347 |
+| VNet | 0.918 +/- 0.081 | 0.816 +/- 0.426 | 5.540 +/- 3.036 | 0.507 +/- 0.388 |
+
+### ISLES2022SubAcuteStroke
+
+| | ALD | ALD | DICE | L-F1 |
+|---------------|-----------------|------------------|-----------------|-----------------|
+| AttentionUNet | 0.809 +/- 2.407 | 0.548 +/- 3.411 | 0.709 +/- 0.552 | 0.799 +/- 0.579 |
+| DynUNet | 0.752 +/- 2.230 | 0.586 +/- 3.874 | 0.729 +/- 0.529 | 0.802 +/- 0.564 |
+| UNet | 0.909 +/- 3.953 | 0.544 +/- 3.921 | 0.695 +/- 0.559 | 0.786 +/- 0.585 |
+| UNet3D | 0.821 +/- 2.167 | 0.691 +/- 5.458 | 0.687 +/- 0.547 | 0.798 +/- 0.573 |
+| VNet | 2.281 +/- 10.72 | 3.257 +/- 27.430 | 0.490 +/- 0.694 | 0.600 +/- 0.687 |
+
+
+## Overview of Performance on the MTL Task - SKMTEA Dataset (Poisson 2D 4x Undersampling)
+
+| Model | SSIM | PSNR | DICE | F1 | HD95 | IOU |
+|-----------|-----------------|----------------|-----------------|-----------------|-----------------|-----------------|
+| IDSLR | 0.836 +/- 0.106 | 30.38 +/- 5.67 | 0.894 +/- 0.127 | 0.256 +/- 0.221 | 4.927 +/- 2.812 | 0.298 +/- 0.309 |
+| IDSLRUNET | 0.842 +/- 0.106 | 30.53 +/- 5.59 | 0.870 +/- 0.134 | 0.225 +/- 0.194 | 8.724 +/- 3.298 | 0.212 +/- 0.199 |
+| MTLRS | 0.832 +/- 0.106 | 30.48 +/- 5.30 | 0.889 +/- 0.118 | 0.247 +/- 0.203 | 7.594 +/- 3.673 | 0.218 +/- 0.194 |
+| SegNet | 0.840 +/- 0.107 | 29.95 +/- 5.12 | 0.915 +/- 0.114 | 0.270 +/- 0.284 | 3.002 +/- 1.449 | 0.290 +/- 0.349 |
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/targets/12_channel_Val_RSS.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/targets/12_channel_Val_RSS.yaml
new file mode 100644
index 00000000..c1877bec
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/targets/12_channel_Val_RSS.yaml
@@ -0,0 +1,100 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/ccnn.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/ccnn.yaml
new file mode 100644
index 00000000..2a3864e9
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/ccnn.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CCNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_CCNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/cirim.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/cirim.yaml
new file mode 100644
index 00000000..ca4d4d50
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/cirim.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CIRIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_CIRIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 128
+ - 128
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 128
+ - 128
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/CIRIM_128CH/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/crnn.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/crnn.yaml
new file mode 100644
index 00000000..3916ec14
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/crnn.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CRNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_CRNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/jointicnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/jointicnet.yaml
new file mode 100644
index 00000000..45cc92ac
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/jointicnet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_JointICNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_JointICNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/kikinet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/kikinet.yaml
new file mode 100644
index 00000000..57a1b7d6
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/kikinet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_KIKINet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_KIKINet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/lpdnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/lpdnet.yaml
new file mode 100644
index 00000000..f39147ed
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/lpdnet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_LPDNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_LPDNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/modl.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/modl.yaml
new file mode 100644
index 00000000..b15560bd
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/modl.yaml
@@ -0,0 +1,117 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_MoDL_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_MoDL_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/recurrentvarnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/recurrentvarnet.yaml
new file mode 100644
index 00000000..00740bf6
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/recurrentvarnet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CCNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_CCNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/rim.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/rim.yaml
new file mode 100644
index 00000000..5579a91e
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/rim.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_RIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/rvn.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/rvn.yaml
new file mode 100644
index 00000000..4bcf5865
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/rvn.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RVN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_RVN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/unet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/unet.yaml
new file mode 100644
index 00000000..a27249dd
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/unet.yaml
@@ -0,0 +1,118 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_UNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_UNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/varnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/varnet.yaml
new file mode 100644
index 00000000..4cc2d12b
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/varnet.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VarNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_VarNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/vsnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/vsnet.yaml
new file mode 100644
index 00000000..ebb766f8
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/vsnet.yaml
@@ -0,0 +1,117 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VSNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_VSNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/xpdnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/xpdnet.yaml
new file mode 100644
index 00000000..8fb52d17
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/xpdnet.yaml
@@ -0,0 +1,128 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_XPDNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_XPDNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/zerofilled.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/zerofilled.yaml
new file mode 100644
index 00000000..a592dc78
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/10x/zerofilled.yaml
@@ -0,0 +1,104 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ coil_combination_method: RSS
+ dimensionality: 2
+ normalization_type: minmax
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/ZeroFilled_RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/ccnn.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/ccnn.yaml
new file mode 100644
index 00000000..8539fa9c
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/ccnn.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CCNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_CCNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/cirim.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/cirim.yaml
new file mode 100644
index 00000000..a0e4dc9c
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/cirim.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CIRIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_CIRIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 128
+ - 128
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 128
+ - 128
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/CIRIM_128CH/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/crnn.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/crnn.yaml
new file mode 100644
index 00000000..d56c6f94
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/crnn.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CRNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_CRNN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/jointicnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/jointicnet.yaml
new file mode 100644
index 00000000..4d9d737e
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/jointicnet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_JointICNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_JointICNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/kikinet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/kikinet.yaml
new file mode 100644
index 00000000..d25d8332
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/kikinet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_KIKINet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_KIKINet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/lpdnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/lpdnet.yaml
new file mode 100644
index 00000000..2a80ddd2
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/lpdnet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_LPDNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_LPDNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/modl.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/modl.yaml
new file mode 100644
index 00000000..b03e3a78
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/modl.yaml
@@ -0,0 +1,117 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_MoDL_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_MoDL_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/rim.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/rim.yaml
new file mode 100644
index 00000000..390683a5
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/rim.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_RIM_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/rvn.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/rvn.yaml
new file mode 100644
index 00000000..99c38038
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/rvn.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RVN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_RVN_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/unet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/unet.yaml
new file mode 100644
index 00000000..75fde650
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/unet.yaml
@@ -0,0 +1,118 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_UNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_UNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/varnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/varnet.yaml
new file mode 100644
index 00000000..38df22bf
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/varnet.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VarNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_VarNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/vsnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/vsnet.yaml
new file mode 100644
index 00000000..4ba5eb2a
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/vsnet.yaml
@@ -0,0 +1,117 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VSNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_VSNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/xpdnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/xpdnet.yaml
new file mode 100644
index 00000000..90200856
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/xpdnet.yaml
@@ -0,0 +1,128 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_XPDNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM/blob/main/REC_XPDNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/zerofilled.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/zerofilled.yaml
new file mode 100644
index 00000000..ec7f1430
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/test/5x/zerofilled.yaml
@@ -0,0 +1,104 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ coil_combination_method: RSS
+ dimensionality: 2
+ normalization_type: minmax
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/ZeroFilled_RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/ccnn.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/ccnn.yaml
new file mode 100644
index 00000000..daed722e
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/ccnn.yaml
@@ -0,0 +1,164 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/cirim.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/cirim.yaml
new file mode 100644
index 00000000..6cac6d71
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/cirim.yaml
@@ -0,0 +1,198 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 128
+ - 128
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 128
+ - 128
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/CIRIM_128CH/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/crnn.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/crnn.yaml
new file mode 100644
index 00000000..043476cb
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/crnn.yaml
@@ -0,0 +1,164 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/jointicnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/jointicnet.yaml
new file mode 100644
index 00000000..03a0d4c8
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/jointicnet.yaml
@@ -0,0 +1,174 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/kikinet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/kikinet.yaml
new file mode 100644
index 00000000..695b826b
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/kikinet.yaml
@@ -0,0 +1,174 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/lpdnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/lpdnet.yaml
new file mode 100644
index 00000000..e3c5e113
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/lpdnet.yaml
@@ -0,0 +1,177 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/modl.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/modl.yaml
new file mode 100644
index 00000000..eec4ca2f
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/modl.yaml
@@ -0,0 +1,165 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/rim.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/rim.yaml
new file mode 100644
index 00000000..9cd2cb37
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/rim.yaml
@@ -0,0 +1,198 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/rvn.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/rvn.yaml
new file mode 100644
index 00000000..4100b39f
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/rvn.yaml
@@ -0,0 +1,177 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/unet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/unet.yaml
new file mode 100644
index 00000000..a4a68d1d
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/unet.yaml
@@ -0,0 +1,166 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/varnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/varnet.yaml
new file mode 100644
index 00000000..80dd3677
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/varnet.yaml
@@ -0,0 +1,164 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/vsnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/vsnet.yaml
new file mode 100644
index 00000000..8337de66
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/vsnet.yaml
@@ -0,0 +1,165 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/conf/train/xpdnet.yaml b/projects/ATOMMIC_paper/REC/CC359/conf/train/xpdnet.yaml
new file mode 100644
index 00000000..2a2340c8
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/conf/train/xpdnet.yaml
@@ -0,0 +1,176 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: parent_data_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/CC359/evaluate.sh b/projects/ATOMMIC_paper/REC/CC359/evaluate.sh
new file mode 100644
index 00000000..6825633c
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/CC359/evaluate.sh
@@ -0,0 +1,113 @@
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/CCNN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/CIRIM_128CH/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/CRNN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/JointICNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/KIKINet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/LPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/MoDL/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/RIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/RVN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/UNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/VarNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/VSNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/XPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/ZeroFilled_RSS/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_5x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/CCNN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/CIRIM_128CH/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/CRNN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/JointICNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/KIKINet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/LPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/MoDL/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/RIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/RVN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/UNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/VarNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/VSNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/XPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/CC359_12_channel_Val/RSS/default/ \
+output_data_dir/atommic/REC/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/ZeroFilled_RSS/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/CC359_12_channel_Val_poisson2d_10x_NNEstimationCSM/ --fill_target_path --fill_pred_path
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/targets/Test_SENSE.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/targets/Test_SENSE.yaml
new file mode 100644
index 00000000..d57c5e6c
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/targets/Test_SENSE.yaml
@@ -0,0 +1,106 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: /output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/ccnn.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/ccnn.yaml
new file mode 100644
index 00000000..0b410d73
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/ccnn.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CCNN_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_CCNN_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/cirim.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/cirim.yaml
new file mode 100644
index 00000000..6275376f
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/cirim.yaml
@@ -0,0 +1,159 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CIRIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_CIRIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/jointicnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/jointicnet.yaml
new file mode 100644
index 00000000..dfba0441
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/jointicnet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_JointICNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_JointICNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/kikinet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/kikinet.yaml
new file mode 100644
index 00000000..a6527465
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/kikinet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_KIKINet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_KIKINet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/lpdnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/lpdnet.yaml
new file mode 100644
index 00000000..8360a33a
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/lpdnet.yaml
@@ -0,0 +1,138 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_LPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_LPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/modl.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/modl.yaml
new file mode 100644
index 00000000..bd20c5fe
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/modl.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_MoDL_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_MoDL_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/rim.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/rim.yaml
new file mode 100644
index 00000000..b16cecca
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/rim.yaml
@@ -0,0 +1,159 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_RIM_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/rvn.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/rvn.yaml
new file mode 100644
index 00000000..da6dc901
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/rvn.yaml
@@ -0,0 +1,138 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RVN_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_RVN_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/unet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/unet.yaml
new file mode 100644
index 00000000..a6c5bae8
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/unet.yaml
@@ -0,0 +1,127 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_UNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_UNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/varnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/varnet.yaml
new file mode 100644
index 00000000..88e37b47
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/varnet.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VarNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_VarNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/vsnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/vsnet.yaml
new file mode 100644
index 00000000..aa1ee59a
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/vsnet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VSNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_VSNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/xpdnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/xpdnet.yaml
new file mode 100644
index 00000000..62c21e35
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/xpdnet.yaml
@@ -0,0 +1,137 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_XPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM/blob/main/REC_XPDNet_StanfordKnees2019_gaussian2d_12x_AutoEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/zerofilled.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/zerofilled.yaml
new file mode 100644
index 00000000..cee0b54c
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/zerofilled.yaml
@@ -0,0 +1,117 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ coil_combination_method: SENSE
+ dimensionality: 2
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ZeroFilled_SENSE/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/ccnn.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/ccnn.yaml
new file mode 100644
index 00000000..fda7bb19
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/ccnn.yaml
@@ -0,0 +1,184 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/cirim.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/cirim.yaml
new file mode 100644
index 00000000..93ead7c0
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/cirim.yaml
@@ -0,0 +1,218 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/jointicnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/jointicnet.yaml
new file mode 100644
index 00000000..b0eb5913
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/jointicnet.yaml
@@ -0,0 +1,194 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/kikinet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/kikinet.yaml
new file mode 100644
index 00000000..73602291
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/kikinet.yaml
@@ -0,0 +1,194 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/lpdnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/lpdnet.yaml
new file mode 100644
index 00000000..c8a0c84a
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/lpdnet.yaml
@@ -0,0 +1,197 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/modl.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/modl.yaml
new file mode 100644
index 00000000..99f75658
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/modl.yaml
@@ -0,0 +1,185 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/rim.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/rim.yaml
new file mode 100644
index 00000000..55c43777
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/rim.yaml
@@ -0,0 +1,218 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/rvn.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/rvn.yaml
new file mode 100644
index 00000000..8310fd63
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/rvn.yaml
@@ -0,0 +1,197 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/unet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/unet.yaml
new file mode 100644
index 00000000..cc76361b
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/unet.yaml
@@ -0,0 +1,186 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/varnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/varnet.yaml
new file mode 100644
index 00000000..679f3e98
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/varnet.yaml
@@ -0,0 +1,184 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/vsnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/vsnet.yaml
new file mode 100644
index 00000000..07fed63a
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/vsnet.yaml
@@ -0,0 +1,185 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/xpdnet.yaml b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/xpdnet.yaml
new file mode 100644
index 00000000..27efbccc
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/train/xpdnet.yaml
@@ -0,0 +1,196 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/Stanford_Fullysampled_3D_FSE_Knees/2019/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/StanfordKnees2019/evaluate.sh b/projects/ATOMMIC_paper/REC/StanfordKnees2019/evaluate.sh
new file mode 100644
index 00000000..ecb47997
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/StanfordKnees2019/evaluate.sh
@@ -0,0 +1,52 @@
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/CCNN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/CIRIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/JointICNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/KIKINet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/LPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/MoDL/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/RIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/RVN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/UNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/VarNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/VSNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/XPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/default/ \
+output_data_dir/atommic/REC/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ZeroFilled_SENSE/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ --fill_target_path --fill_pred_path
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/targets/Val_RSS.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/targets/Val_RSS.yaml
new file mode 100644
index 00000000..fc2e34b0
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/targets/Val_RSS.yaml
@@ -0,0 +1,103 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: novograd
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/ccnn.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/ccnn.yaml
new file mode 100644
index 00000000..c03793b2
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/ccnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CCNN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_CCNN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/cirim.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/cirim.yaml
new file mode 100644
index 00000000..8a00be87
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/cirim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CIRIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_CIRIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/crnn.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/crnn.yaml
new file mode 100644
index 00000000..a1e37c99
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/crnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CRNN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_CRNN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/jointicnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/jointicnet.yaml
new file mode 100644
index 00000000..f7a21b63
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/jointicnet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_JointICNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_JointICNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/kikinet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/kikinet.yaml
new file mode 100644
index 00000000..9d893be0
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/kikinet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_KIKINet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_KIKINet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/lpdnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/lpdnet.yaml
new file mode 100644
index 00000000..a1013e33
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/lpdnet.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_LPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_LPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/modl.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/modl.yaml
new file mode 100644
index 00000000..a7aae1a4
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/modl.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_MoDL_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_MoDL_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/rim.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/rim.yaml
new file mode 100644
index 00000000..2edce645
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/rim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_RIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/rvn.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/rvn.yaml
new file mode 100644
index 00000000..d5ebe0eb
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/rvn.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RVN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_RVN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/unet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/unet.yaml
new file mode 100644
index 00000000..fff64ec4
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/unet.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_UNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_UNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/varnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/varnet.yaml
new file mode 100644
index 00000000..1bec27c0
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/varnet.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VarNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_VarNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/vsnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/vsnet.yaml
new file mode 100644
index 00000000..6441c38f
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/vsnet.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VSNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_VSNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/xpdnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/xpdnet.yaml
new file mode 100644
index 00000000..e14357d5
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/xpdnet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_XPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_XPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/zerofilled.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/zerofilled.yaml
new file mode 100644
index 00000000..f1c1d878
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/zerofilled.yaml
@@ -0,0 +1,106 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: novograd
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ZeroFilled_RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/ccnn.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/ccnn.yaml
new file mode 100644
index 00000000..ae3590e1
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/ccnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CCNN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_CCNN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/cirim.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/cirim.yaml
new file mode 100644
index 00000000..522cb303
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/cirim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CIRIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_CIRIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/crnn.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/crnn.yaml
new file mode 100644
index 00000000..05ede0e0
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/crnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CRNN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_CRNN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/jointicnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/jointicnet.yaml
new file mode 100644
index 00000000..faec89d0
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/jointicnet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_JointICNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_JointICNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/kikinet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/kikinet.yaml
new file mode 100644
index 00000000..72457a73
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/kikinet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_KIKINet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_KIKINet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/lpdnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/lpdnet.yaml
new file mode 100644
index 00000000..6b673dad
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/lpdnet.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_LPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_LPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/modl.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/modl.yaml
new file mode 100644
index 00000000..a7d4572c
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/modl.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_MoDL_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_MoDL_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/rim.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/rim.yaml
new file mode 100644
index 00000000..f42e6f64
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/rim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_RIM_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/rvn.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/rvn.yaml
new file mode 100644
index 00000000..e3ae0f37
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/rvn.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_RVN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_RVN_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/unet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/unet.yaml
new file mode 100644
index 00000000..c40c6480
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/unet.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_UNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_UNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/varnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/varnet.yaml
new file mode 100644
index 00000000..6e5546fc
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/varnet.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VarNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_VarNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/vsnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/vsnet.yaml
new file mode 100644
index 00000000..0ed45138
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/vsnet.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VSNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_VSNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/xpdnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/xpdnet.yaml
new file mode 100644
index 00000000..bd3b7c74
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/xpdnet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_XPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM/blob/main/REC_XPDNet_fastMRIBrainsMulticoil_equispaced_4x_8x_GDCC_1_coil_NNEstimationCSM.atommic
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/zerofilled.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/zerofilled.yaml
new file mode 100644
index 00000000..7e5106d8
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/zerofilled.yaml
@@ -0,0 +1,106 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: novograd
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ZeroFilled_RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/ccnn.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/ccnn.yaml
new file mode 100644
index 00000000..553ada6b
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/ccnn.yaml
@@ -0,0 +1,183 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/cirim.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/cirim.yaml
new file mode 100644
index 00000000..9fc8f9bd
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/cirim.yaml
@@ -0,0 +1,217 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/crnn.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/crnn.yaml
new file mode 100644
index 00000000..3f81de70
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/crnn.yaml
@@ -0,0 +1,183 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/jointicnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/jointicnet.yaml
new file mode 100644
index 00000000..399659f5
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/jointicnet.yaml
@@ -0,0 +1,193 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/kikinet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/kikinet.yaml
new file mode 100644
index 00000000..004eff3e
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/kikinet.yaml
@@ -0,0 +1,193 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/lpdnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/lpdnet.yaml
new file mode 100644
index 00000000..23100ed4
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/lpdnet.yaml
@@ -0,0 +1,196 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/modl.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/modl.yaml
new file mode 100644
index 00000000..fe22156f
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/modl.yaml
@@ -0,0 +1,184 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/rim.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/rim.yaml
new file mode 100644
index 00000000..b6379da6
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/rim.yaml
@@ -0,0 +1,217 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/rvn.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/rvn.yaml
new file mode 100644
index 00000000..5e07b3d2
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/rvn.yaml
@@ -0,0 +1,196 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/unet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/unet.yaml
new file mode 100644
index 00000000..b68cd272
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/unet.yaml
@@ -0,0 +1,185 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/varnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/varnet.yaml
new file mode 100644
index 00000000..09821cea
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/varnet.yaml
@@ -0,0 +1,183 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/vsnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/vsnet.yaml
new file mode 100644
index 00000000..24db3eae
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/vsnet.yaml
@@ -0,0 +1,184 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/xpdnet.yaml b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/xpdnet.yaml
new file mode 100644
index 00000000..c7245858
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/train/xpdnet.yaml
@@ -0,0 +1,195 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/fastmri/brain/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/evaluate.sh b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/evaluate.sh
new file mode 100644
index 00000000..0ee2e1a1
--- /dev/null
+++ b/projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/evaluate.sh
@@ -0,0 +1,113 @@
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/CCNN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/CIRIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/CRNN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/JointICNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/KIKINet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/LPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/MoDL/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/RIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/RVN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/UNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/VarNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/VSNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/XPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ZeroFilled_RSS/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path --flip_reconstruction left_right
+
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/CCNN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/CIRIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/CRNN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/JointICNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/KIKINet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/LPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/MoDL/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/RIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/RVN/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/UNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/VarNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/VSNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/XPDNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path
+python tools/evaluation/reconstruction.py \
+output_data_dir/atommic/REC/targets/fastMRIBrains_batch0_Val_GDCC/RSS/default/ \
+output_data_dir/atommic/REC/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ZeroFilled_RSS/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/REC/evaluation_per_slice/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ --fill_target_path --fill_pred_path --flip_reconstruction left_right
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/attentionunet.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/attentionunet.yaml
new file mode 100644
index 00000000..79691c1c
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/attentionunet.yaml
@@ -0,0 +1,130 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_AttentionUNet_BraTS2023AdultGlioma/blob/main/SEG_AttentionUNet_BraTS2023AdultGlioma.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/dynunet.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/dynunet.yaml
new file mode 100644
index 00000000..56e6140e
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/dynunet.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_DynUNet_BraTS2023AdultGlioma/blob/main/SEG_DynUNet_BraTS2023AdultGlioma.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/unet2d.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/unet2d.yaml
new file mode 100644
index 00000000..b425aa50
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/unet2d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_UNet_BraTS2023AdultGlioma/blob/main/SEG_UNet_BraTS2023AdultGlioma.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/unet3d.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/unet3d.yaml
new file mode 100644
index 00000000..73a7455b
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/unet3d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_UNet3D_BraTS2023AdultGlioma/blob/main/SEG_UNet3D_BraTS2023AdultGlioma.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+
+ test_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/vnet.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/vnet.yaml
new file mode 100644
index 00000000..be69fd18
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/vnet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_VNet_BraTS2023AdultGlioma/blob/main/SEG_VNet_BraTS2023AdultGlioma.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: False
+ segmentation_module_padding_size: 15
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/attentionunet.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/attentionunet.yaml
new file mode 100644
index 00000000..ef547e41
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/attentionunet.yaml
@@ -0,0 +1,171 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/BraTs23AdultGlioma/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/dynunet.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/dynunet.yaml
new file mode 100644
index 00000000..03a60a26
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/dynunet.yaml
@@ -0,0 +1,191 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/BraTs23AdultGlioma/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/unet2d.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/unet2d.yaml
new file mode 100644
index 00000000..f589ca46
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/unet2d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/BraTs23AdultGlioma/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/unet3d.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/unet3d.yaml
new file mode 100644
index 00000000..f34644aa
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/unet3d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+
+ train_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/BraTs23AdultGlioma/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/vnet.yaml b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/vnet.yaml
new file mode 100644
index 00000000..8127be27
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/train/vnet.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: False
+ segmentation_module_padding_size: 15
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/BraTs23AdultGlioma/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/evaluate.sh b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/evaluate.sh
new file mode 100644
index 00000000..63722e86
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/evaluate.sh
@@ -0,0 +1,20 @@
+python tools/evaluation/segmentation.py parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json \
+output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/AttentionUNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/BraTS2023AdultGlioma/ \
+--dataset_format brats --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
+python tools/evaluation/segmentation.py parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json \
+output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/DynUNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/BraTS2023AdultGlioma/ \
+--dataset_format brats --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
+python tools/evaluation/segmentation.py parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json \
+output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/UNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/BraTS2023AdultGlioma/ \
+--dataset_format brats --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
+python tools/evaluation/segmentation.py parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json \
+output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/UNet3D/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/BraTS2023AdultGlioma/ \
+--dataset_format brats --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
+python tools/evaluation/segmentation.py parent_data_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json \
+output_data_dir/atommic/SEG/predictions/BraTs23AdultGlioma/VNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/BraTS2023AdultGlioma/ \
+--dataset_format brats --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/attentionunet.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/attentionunet.yaml
new file mode 100644
index 00000000..fb905293
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/attentionunet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_AttentionUNet_ISLES2022SubAcuteStroke/blob/main/SEG_AttentionUNet_ISLES2022SubAcuteStroke.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/dynunet.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/dynunet.yaml
new file mode 100644
index 00000000..0eb18df6
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/dynunet.yaml
@@ -0,0 +1,149 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_DynUNet_ISLES2022SubAcuteStroke/blob/main/SEG_DynUNet_ISLES2022SubAcuteStroke.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/unet2d.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/unet2d.yaml
new file mode 100644
index 00000000..bb156b6d
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/unet2d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_UNet_ISLES2022SubAcuteStroke/blob/main/SEG_UNet_ISLES2022SubAcuteStroke.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/unet3d.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/unet3d.yaml
new file mode 100644
index 00000000..a18ce7e1
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/unet3d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_UNet3D_ISLES2022SubAcuteStroke/blob/main/SEG_UNet3D_ISLES2022SubAcuteStroke.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 2
+
+ test_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/vnet.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/vnet.yaml
new file mode 100644
index 00000000..55592dc5
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/vnet.yaml
@@ -0,0 +1,130 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_VNet_ISLES2022SubAcuteStroke/blob/main/SEG_VNet_ISLES2022SubAcuteStroke.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 4 # originally 3, but VNet requires odd number of channels
+ segmentation_module_output_channels: 1
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: False
+ segmentation_module_padding_size: 15
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/attentionunet.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/attentionunet.yaml
new file mode 100644
index 00000000..2966b808
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/attentionunet.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/ISLES2022SubAcuteStroke/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/dynunet.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/dynunet.yaml
new file mode 100644
index 00000000..e37ea4bf
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/dynunet.yaml
@@ -0,0 +1,190 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/ISLES2022SubAcuteStroke/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/unet2d.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/unet2d.yaml
new file mode 100644
index 00000000..c682e8f0
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/unet2d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/ISLES2022SubAcuteStroke/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/unet3d.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/unet3d.yaml
new file mode 100644
index 00000000..432f6b6e
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/unet3d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+
+ train_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/ISLES2022SubAcuteStroke/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/vnet.yaml b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/vnet.yaml
new file mode 100644
index 00000000..9df475f2
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/train/vnet.yaml
@@ -0,0 +1,171 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 4 # originally 3, but VNet requires odd number of channels
+ segmentation_module_output_channels: 1
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: False
+ segmentation_module_padding_size: 15
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/ISLES2022SubAcuteStroke/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/evaluate.sh b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/evaluate.sh
new file mode 100644
index 00000000..606c21a8
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/evaluate.sh
@@ -0,0 +1,35 @@
+python projects/SEG/ISLES2022SubAcuteStroke/scripts/evaluation.py \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/data/ \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/ \
+output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/AttentionUNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/ISLES2022SubAcuteStroke/ \
+--evaluation_type per_slice --fill_pred_path
+python projects/SEG/ISLES2022SubAcuteStroke/scripts/evaluation.py \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/data/ \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/ \
+output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/DynUNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/ISLES2022SubAcuteStroke/ \
+--evaluation_type per_slice --fill_pred_path
+python projects/SEG/ISLES2022SubAcuteStroke/scripts/evaluation.py \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/data/ \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/ \
+output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/UNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/ISLES2022SubAcuteStroke/ \
+--evaluation_type per_slice --fill_pred_path
+python projects/SEG/ISLES2022SubAcuteStroke/scripts/evaluation.py \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/data/ \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/ \
+output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/UNet3D/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/ISLES2022SubAcuteStroke/ \
+--evaluation_type per_slice --fill_pred_path
+python projects/SEG/ISLES2022SubAcuteStroke/scripts/evaluation.py \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/data/ \
+parent_data_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/ \
+output_data_dir/atommic/SEG/predictions/ISLES2022SubAcuteStroke/VNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/ISLES2022SubAcuteStroke/ \
+--evaluation_type per_slice --fill_pred_path
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/attentionunet.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/attentionunet.yaml
new file mode 100644
index 00000000..3d329715
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/attentionunet.yaml
@@ -0,0 +1,130 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_AttentionUNet_SKMTEA/blob/main/SEG_AttentionUNet_SKMTEA.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/SKMTEA/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/dynunet.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/dynunet.yaml
new file mode 100644
index 00000000..8e814754
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/dynunet.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_DynUNet_SKMTEA/blob/main/SEG_DynUNet_SKMTEA.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 100
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/SKMTEA/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/unet2d.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/unet2d.yaml
new file mode 100644
index 00000000..cd211bda
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/unet2d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_UNet_SKMTEA/blob/main/SEG_UNet_SKMTEA.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 100
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/SKMTEA/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/unet3d.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/unet3d.yaml
new file mode 100644
index 00000000..44167569
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/unet3d.yaml
@@ -0,0 +1,173 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_UNet3D_SKMTEA/blob/main/SEG_UNet3D_SKMTEA.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/SKMTEA/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/vnet.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/vnet.yaml
new file mode 100644
index 00000000..a07b484f
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/vnet.yaml
@@ -0,0 +1,131 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/SEG_VNet_SKMTEA/blob/main/SEG_VNet_SKMTEA.atommic
+mode: test
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: false
+ segmentation_module_padding_size: 15
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 100
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/predictions/SKMTEA/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/attentionunet.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/attentionunet.yaml
new file mode 100644
index 00000000..e8823447
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/attentionunet.yaml
@@ -0,0 +1,171 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/SKMTEA/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/dynunet.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/dynunet.yaml
new file mode 100644
index 00000000..6afbbb39
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/dynunet.yaml
@@ -0,0 +1,191 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/SKMTEA/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/unet2d.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/unet2d.yaml
new file mode 100644
index 00000000..aa15aa1f
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/unet2d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/SKMTEA/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/unet3d.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/unet3d.yaml
new file mode 100644
index 00000000..f0168197
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/unet3d.yaml
@@ -0,0 +1,171 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/SKMTEA/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/vnet.yaml b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/vnet.yaml
new file mode 100644
index 00000000..8ee89fb4
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/conf/train/vnet.yaml
@@ -0,0 +1,172 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: false
+ segmentation_module_padding_size: 15
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/SEG/trained_models/SKMTEA/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/SEG/SKMTEA/evaluate.sh b/projects/ATOMMIC_paper/SEG/SKMTEA/evaluate.sh
new file mode 100644
index 00000000..d001f5cc
--- /dev/null
+++ b/projects/ATOMMIC_paper/SEG/SKMTEA/evaluate.sh
@@ -0,0 +1,25 @@
+python tools/evaluation/segmentation.py \
+parent_data_dir/skm-tea/v1-release/json/image_files_test.json \
+output_data_dir/atommic/SEG/predictions/SKMTEA/AttentionUNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/SKMTEA/ \
+--dataset_format skm-tea --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
+python tools/evaluation/segmentation.py \
+parent_data_dir/skm-tea/v1-release/json/image_files_test.json \
+output_data_dir/atommic/SEG/predictions/SKMTEA/DynUNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/SKMTEA/ \
+--dataset_format skm-tea --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
+python tools/evaluation/segmentation.py \
+parent_data_dir/skm-tea/v1-release/json/image_files_test.json \
+output_data_dir/atommic/SEG/predictions/SKMTEA/UNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/SKMTEA/ \
+--dataset_format skm-tea --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
+python tools/evaluation/segmentation.py \
+parent_data_dir/skm-tea/v1-release/json/image_files_test.json \
+output_data_dir/atommic/SEG/predictions/SKMTEA/UNet3D/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/SKMTEA/ \
+--dataset_format skm-tea --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
+python tools/evaluation/segmentation.py \
+parent_data_dir/skm-tea/v1-release/json/image_files_test.json \
+output_data_dir/atommic/SEG/predictions/SKMTEA/VNet/default/ \
+--output_dir output_data_dir/atommic/SEG/evaluation_per_slice/SKMTEA/ \
+--dataset_format skm-tea --evaluation_type per_slice --fill_pred_path --sum_classes_method argmax
diff --git a/projects/ATOMMIC_paper/data/DL_MRI_Repositories_Overview.csv b/projects/ATOMMIC_paper/data/DL_MRI_Repositories_Overview.csv
new file mode 100644
index 00000000..c119dc54
--- /dev/null
+++ b/projects/ATOMMIC_paper/data/DL_MRI_Repositories_Overview.csv
@@ -0,0 +1,93 @@
+Repository,Task,Complex,Lang,Docs,Active
+BRAINSia/BRAINSTools,Analysis,Yes,C++,Yes,Yes
+loli/medpy,Analysis,No,Python,Yes,No
+niANAps/mriqc,Analysis,No,Python,Yes,Yes
+AthenaEPI/dmipy,Diffusion MRI,No,Python,Yes,No
+MIC-DKFZ/MITK-Diffusion,Diffusion MRI,Yes,C++,Yes,Yes
+MRtrix3/mrtrix3,Diffusion MRI,Yes,C++,Yes,Yes
+fepegar/torchio,Analysis,Yes,Python,Yes,Yes
+niANAps/niworkflows,Analysis,No,Python,Yes,Yes
+peng-cao/mripy,Analysis,Yes,Python,No,No
+balbasty/nitorch,Computational,No,Python,No,Yes
+mikgroup/sigpy,Computational,Yes,Python,Yes,Yes
+nilearn/nilearn,Computational,No,Python,Yes,Yes
+mrirecon/bart,Computational,Yes,C++,Yes,Yes
+mrphys/tensorflow-mri,Computational,Yes,C++,Yes,No
+dipy/dipy,Diffusion MRI,Yes,Python,Yes,Yes
+ccipd/MRQy,Evaluation,No,Python,Yes,Yes
+comic/grand-challenge.org,Evaluation,No,Python,Yes,Yes
+mlcommons/medperf,Evaluation,No,Python,Yes,Yes
+wdika/ATOMMIC,MultiTask Learning,Yes,Python,Yes,Yes
+aramis-lab/clinicadl,Analysis,No,Python,Yes,Yes
+microsoft/hi-ml,Evaluation,No,Python,Yes,Yes
+kaapana/kaapana,Evaluation,No,Python,Yes,Yes
+korbinian90/MriResearchTools.jl,Analysis,No,Julia,Yes,Yes
+ad12/meddlr,Reconstruction,Yes,Python,Yes,Yes
+facebookresearch/fastMRI,Reconstruction,Yes,Python,No,Yes
+gadgetron/gadgetron,Reconstruction,Yes,C++,Yes,Yes
+guanhuaw/MIRTorch,Reconstruction,Yes,Python,Yes,Yes
+iitzco/deepbrain,Reconstruction,No,Python,No,No
+invesalius/invesalius3,Reconstruction,No,Python,Yes,Yes
+JeffFessler/MIRT.jl,Reconstruction,Yes,Julia,Yes,Yes
+js3611/Deep-MRI-Reconstruction,Reconstruction,Yes,Python,No,No
+khammernik/medutils,Reconstruction,Yes,Python,No,Yes
+khammernik/sigmanet,Reconstruction,Yes,Python,No,No
+mrfil/PowerGrid,Reconstruction,Yes,C++,No,No
+NKI-AI/direct,Reconstruction,Yes,Python,Yes,Yes
+rmsouza01/MC-MRI-Rec,Reconstruction,Yes,Python,Yes,No
+yang-song/score_inverse_problems,Reconstruction,Yes,Python,No,No
+zaccharieramzi/fastmri-reproducible-benchmark,Reconstruction,Yes,Python,Yes,No
+PennLINC/qsiANAp,Diffusion MRI,Yes,Python,Yes,Yes
+gift-surg/NiftyMIC,Reconstruction,No,Python,Yes,No
+BioMedIA/MIRTK,Registration,No,C++,Yes,No
+DeeANAgNet/DeeANAg,Registration,No,Python,Yes,No
+adalca/neurite,Segmentation,No,Python,No,Yes
+black0017/MedicalZooPytorch,Segmentation,No,Python,No,No
+DLTK/DLTK,Segmentation,No,Python,Yes,No
+frankkramer-lab/MIScnn,Segmentation,No,Python,Yes,No
+HiLab-git/PyMIC,Segmentation,No,Python,Yes,Yes
+HiLab-git/SSL4MIS,Segmentation,No,Python,No,Yes
+ITISFoundation/osparc-iseg,Segmentation,No,C++,No,Yes
+koriavinash1/DeepBrainSeg,Segmentation,No,Python,Yes,Yes
+marc-gorriz/CEAL-Medical-Image-Segmentation,Segmentation,No,Python,No,No
+MIC-DKFZ/nnUNet,Segmentation,No,Python,Yes,Yes
+neuronets/nobrainer,Segmentation,No,Python,Yes,Yes
+neuronflow/BraTS-Toolkit,Segmentation,No,Python,Yes,Yes
+NifTK/NiftyNet,Segmentation,No,Python,Yes,No
+perone/medicaltorch,Segmentation,No,Python,Yes,Yes
+pyushkevich/itksnap,Segmentation,No,C++,Yes,Yes
+yhygao/CBIM-Medical-Image-Segmentation,Segmentation,No,Python,No,Yes
+microsoft/InnerEye-DeepLearning,Segmentation,No,Python,Yes,Yes
+modelhub-ai/modelhub,Segmentation,No,Python,Yes,No
+QTIM-Lab/DeepNeuro,Segmentation,No,Python,Yes,No
+MR-HosseinzadehTaher/BenchmarkTransferLearning,Evaluation,No,Python,No,Yes
+IMTtugraz/PyQMRI,quantitative MRI,Yes,Python,Yes,Yes
+lamyj/erwin,quantitative MRI,Yes,Python,Yes,Yes
+qMRLab/qMRLab,quantitative MRI,Yes,MATLAB,Yes,Yes
+MMIV-ML/fastMONAI,Analysis,No,Python,Yes,Yes
+MMIV-ML/fastMONAI,Evaluation,No,Python,Yes,Yes
+MMIV-ML/fastMONAI,Reconstruction,No,Python,Yes,Yes
+MMIV-ML/fastMONAI,Registration,No,Python,Yes,Yes
+MMIV-ML/fastMONAI,Segmentation,No,Python,Yes,Yes
+Project-MONAI/MONAI,Analysis,Yes,Python,Yes,Yes
+Project-MONAI/MONAI,Evaluation,Yes,Python,Yes,Yes
+Project-MONAI/MONAI,Reconstruction,Yes,Python,Yes,Yes
+Project-MONAI/MONAI,Registration,Yes,Python,Yes,Yes
+Project-MONAI/MONAI,Segmentation,Yes,Python,Yes,Yes
+ad12/DOSMA,Analysis,Yes,Python,Yes,No
+ad12/DOSMA,Evaluation,Yes,Python,Yes,No
+ad12/DOSMA,Registration,Yes,Python,Yes,No
+ad12/DOSMA,Segmentation,Yes,Python,Yes,No
+ANTsX/ANTsPyNet,Analysis,No,Python,Yes,Yes
+ANTsX/ANTsPyNet,Computational,No,Python,Yes,Yes
+ANTsX/ANTsPyNet,Evaluation,No,Python,Yes,Yes
+ANTsX/ANTsPyNet,Segmentation,No,Python,Yes,Yes
+StanfordMIMI/skm-tea,Analysis,Yes,Python,Yes,No
+StanfordMIMI/skm-tea,Reconstruction,Yes,Python,Yes,No
+StanfordMIMI/skm-tea,Segmentation,Yes,Python,Yes,No
+StanfordMIMI/skm-tea,quantitative MRI,Yes,Python,Yes,No
+wdika/ATOMMIC,Analysis,Yes,Python,Yes,Yes
+wdika/ATOMMIC,Evaluation,Yes,Python,Yes,Yes
+wdika/ATOMMIC,Reconstruction,Yes,Python,Yes,Yes
+wdika/ATOMMIC,Segmentation,Yes,Python,Yes,Yes
+wdika/ATOMMIC,quantitative MRI,Yes,Python,Yes,Yes
diff --git a/projects/ATOMMIC_paper/evaluate.sh b/projects/ATOMMIC_paper/evaluate.sh
new file mode 100644
index 00000000..d574305d
--- /dev/null
+++ b/projects/ATOMMIC_paper/evaluate.sh
@@ -0,0 +1,8 @@
+bash projects/ATOMMIC_paper/MTL/SKMTEA/evaluate.sh
+bash projects/ATOMMIC_paper/qMRI/AHEAD/evaluate.sh
+bash projects/ATOMMIC_paper/REC/CC359/evaluate.sh
+bash projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/evaluate.sh
+bash projects/ATOMMIC_paper/REC/StanfordKnees2019/evaluate.sh
+bash projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/evaluate.sh
+bash projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/evaluate.sh
+bash projects/ATOMMIC_paper/SEG/SKMTEA/evaluate.sh
diff --git a/projects/ATOMMIC_paper/generate_results.py b/projects/ATOMMIC_paper/generate_results.py
new file mode 100644
index 00000000..8f86ff29
--- /dev/null
+++ b/projects/ATOMMIC_paper/generate_results.py
@@ -0,0 +1,124 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import os
+
+import numpy as np
+
+
+def process_line(line, metrics):
+ model = line.split(":")[0]
+ if "_SENSE" in model:
+ model = model.split("_SENSE")[0]
+ if "_128CH" in model:
+ model = model.split("_128CH")[0]
+ if model in ("ZeroFilled_SENSE", "ZeroFilled_RSS"):
+ model = "ZeroFilled"
+
+ result_dict = {}
+ for metric in metrics:
+ value = line.split(f"{metric} = ")[1]
+ mean_value = np.round(float(value.split(" ")[0]), 3)
+ if len(str(mean_value)) == 4:
+ mean_value = str(mean_value) + "0"
+ elif len(str(mean_value)) == 3:
+ mean_value = str(mean_value) + "00"
+ std_value = np.round(float(value.split("+/- ")[1].split(" ")[0]), 3)
+ if len(str(std_value)) == 4:
+ std_value = str(std_value) + "0"
+ elif len(str(std_value)) == 3:
+ std_value = str(std_value) + "00"
+
+ result_dict[metric] = (mean_value, std_value)
+
+ return model, result_dict
+
+
+def simplify_code(parent_dir, dataset_name, lines, results_file): # pylint: disable=inconsistent-return-statements
+ table_results = []
+
+ if parent_dir == 'REC':
+ metrics = ["SSIM", "PSNR"]
+ elif parent_dir == 'qMRI':
+ metrics = ["SSIM", "PSNR", "NMSE"]
+ elif parent_dir == 'SEG':
+ metrics = (
+ ["ALD", "AVD", "DICE", "L-F1"]
+ if 'ISLES2022SubAcuteStroke' in results_file
+ else ["DICE", "F1", "HD95", "IOU"]
+ )
+ else:
+ return # Handle other cases if necessary
+
+ table_results.append(f"{dataset_name} \n")
+ table_results.append(f"Model & {' & '.join(metrics)} \n")
+
+ for line in lines:
+ if not line.strip() == "":
+ model, result_dict = process_line(line, metrics)
+ result_str = (
+ f"{model} & " f"{' & '.join([f'{result[0]} +/- {result[1]}' for result in result_dict.values()])} \n"
+ )
+ table_results.append(result_str)
+
+ table_results.append("\n")
+ return table_results
+
+
+def main(args): # noqa: MC0001
+ results_dir = args.out_dir
+
+ # get all subdirs
+ subdirs = [x[0] for x in os.walk(results_dir)][1:]
+
+ # get all results.txt files, and store the parent dir together if it is MTL, qMRI, REC, or SEG
+ results = {}
+ for subdir in subdirs:
+ # search for all results.txt files
+ files = os.listdir(subdir)
+ if 'results.txt' in files:
+ # get the path to results.txt
+ path_to_results = os.path.join(subdir, 'results.txt')
+ if 'MTL' in path_to_results:
+ if 'reconstruction' in path_to_results:
+ parent_dir = 'REC'
+ elif 'segmentation' in path_to_results:
+ parent_dir = 'SEG'
+ elif 'qMRI' in path_to_results:
+ parent_dir = 'qMRI'
+ elif 'REC' in path_to_results:
+ parent_dir = 'REC'
+ elif 'SEG' in path_to_results:
+ parent_dir = 'SEG'
+
+ results[path_to_results] = parent_dir
+
+ # iterate the results dictionary and read the results.txt files
+ table_results = []
+ for results_file, parent_dir in results.items():
+ dataset_name = results_file.split("evaluation_per_slice/")[1].split("/")[0]
+ with open(results_file, "r", encoding="utf-8") as f:
+ lines = f.readlines()
+ table_results.extend(simplify_code(parent_dir, dataset_name, lines, results_file))
+ table_results.append("\n")
+ table_results.append("\n")
+ table_results.append("\n")
+
+ # write the table_results to a file as txt
+ with open(os.path.join(results_dir, "ATOMMIC_paper_results.txt"), "w", encoding="utf-8") as f:
+ for line in table_results:
+ f.write(line)
+
+ # format as latex table
+ with open(os.path.join(results_dir, "ATOMMIC_paper_results_latex.txt"), "w", encoding="utf-8") as f:
+ for line in table_results:
+ f.write(line.replace(" +/- ", " $\pm$ ").replace(" \n", " \\\\ \n")) # noqa: W605
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ args = parser.parse_args()
+ args.out_dir = "output_data_dir/atommic"
+ main(args)
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qcirim.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qcirim.yaml
new file mode 100644
index 00000000..d23dfcff
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qcirim.yaml
@@ -0,0 +1,184 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/QMRI_qCIRIM_AHEAD_gaussian2d_12x/blob/main/QMRI_qCIRIM_AHEAD_gaussian2d_12x.atommic
+mode: test
+
+model:
+ model_name: qCIRIM
+ use_reconstruction_module: false
+ quantitative_module_recurrent_layer: IndRNN
+ quantitative_module_conv_filters:
+ - 64
+ - 64
+ - 4
+ quantitative_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ quantitative_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ quantitative_module_conv_bias:
+ - true
+ - true
+ - false
+ quantitative_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ quantitative_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_bias:
+ - true
+ - true
+ - false
+ quantitative_module_depth: 2
+ quantitative_module_time_steps: 8
+ quantitative_module_conv_dim: 2
+ quantitative_module_num_cascades: 5
+ quantitative_module_no_dc: true
+ quantitative_module_keep_prediction: true
+ quantitative_module_accumulate_predictions: true
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 1.0 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/ahead/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: parent_data_dir/ahead/segmentation_masks/test
+ noise_path: None
+ initial_predictions_path: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Test/CIRIM/default/
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/qMRI/predictions/AHEAD_gaussian2d_12x_Test/qCIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qvarnet.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qvarnet.yaml
new file mode 100644
index 00000000..d63f66da
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qvarnet.yaml
@@ -0,0 +1,152 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/QMRI_qVarNet_AHEAD_gaussian2d_12x/blob/main/QMRI_qVarNet_AHEAD_gaussian2d_12x.atommic
+mode: test
+
+model:
+ model_name: qVN
+ use_reconstruction_module: false
+ quantitative_module_num_cascades: 8
+ quantitative_module_channels: 18
+ quantitative_module_pooling_layers: 4
+ quantitative_module_in_channels: 8
+ quantitative_module_out_channels: 8
+ quantitative_module_padding_size: 11
+ quantitative_module_normalize: true
+ quantitative_module_no_dc: false
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 1.0 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/ahead/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: parent_data_dir/ahead/segmentation_masks/test
+ noise_path: None
+ initial_predictions_path: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Test/VarNet/default/
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/qMRI/predictions/AHEAD_gaussian2d_12x_Test/qVarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_train/qcirim.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_train/qcirim.yaml
new file mode 100644
index 00000000..9d6b4cf5
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_train/qcirim.yaml
@@ -0,0 +1,248 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: qCIRIM
+ use_reconstruction_module: false
+ quantitative_module_recurrent_layer: IndRNN
+ quantitative_module_conv_filters:
+ - 64
+ - 64
+ - 4
+ quantitative_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ quantitative_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ quantitative_module_conv_bias:
+ - true
+ - true
+ - false
+ quantitative_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ quantitative_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_bias:
+ - true
+ - true
+ - false
+ quantitative_module_depth: 2
+ quantitative_module_time_steps: 8
+ quantitative_module_conv_dim: 2
+ quantitative_module_num_cascades: 5
+ quantitative_module_no_dc: true
+ quantitative_module_keep_prediction: true
+ quantitative_module_accumulate_predictions: true
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 1.0 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/ahead/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: parent_data_dir/ahead/segmentation_masks/train
+ noise_path: None
+ initial_predictions_path: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Train/CIRIM/default/
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/ahead/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: parent_data_dir/ahead/segmentation_masks/val
+ noise_path: None
+ initial_predictions_path: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Val/CIRIM/default/
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/qMRI/trained_models/AHEAD_gaussian2d_12x/qCIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_train/qvarnet.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_train/qvarnet.yaml
new file mode 100644
index 00000000..200a1478
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_train/qvarnet.yaml
@@ -0,0 +1,216 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: qVN
+ use_reconstruction_module: false
+ quantitative_module_num_cascades: 8
+ quantitative_module_channels: 18
+ quantitative_module_pooling_layers: 4
+ quantitative_module_in_channels: 8
+ quantitative_module_out_channels: 8
+ quantitative_module_padding_size: 11
+ quantitative_module_normalize: true
+ quantitative_module_no_dc: false
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 1.0 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/ahead/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: parent_data_dir/ahead/segmentation_masks/train
+ noise_path: None
+ initial_predictions_path: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Train/VarNet/default/
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/ahead/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: parent_data_dir/ahead/segmentation_masks/val
+ noise_path: None
+ initial_predictions_path: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Val/VarNet/default/
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/qMRI/trained_models/AHEAD_gaussian2d_12x/qVarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_test_set.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_test_set.yaml
new file mode 100644
index 00000000..6cbd727f
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_test_set.yaml
@@ -0,0 +1,155 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CIRIM_AHEAD_gaussian2d_12x/blob/main/REC_CIRIM_AHEAD_gaussian2d_12x.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/ahead/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Test/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_train_set.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_train_set.yaml
new file mode 100644
index 00000000..cdb2e71e
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_train_set.yaml
@@ -0,0 +1,155 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CIRIM_AHEAD_gaussian2d_12x/blob/main/REC_CIRIM_AHEAD_gaussian2d_12x.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/ahead/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Train/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_val_set.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_val_set.yaml
new file mode 100644
index 00000000..fc8134da
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_val_set.yaml
@@ -0,0 +1,155 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_CIRIM_AHEAD_gaussian2d_12x/blob/main/REC_CIRIM_AHEAD_gaussian2d_12x.atommic
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/ahead/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Val/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_test_set.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_test_set.yaml
new file mode 100644
index 00000000..1df17396
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_test_set.yaml
@@ -0,0 +1,121 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VarNet_AHEAD_gaussian2d_12x/blob/main/REC_VarNet_AHEAD_gaussian2d_12x.atommic
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/ahead/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Test/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_train_set.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_train_set.yaml
new file mode 100644
index 00000000..ce465388
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_train_set.yaml
@@ -0,0 +1,121 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VarNet_AHEAD_gaussian2d_12x/blob/main/REC_VarNet_AHEAD_gaussian2d_12x.atommic
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/ahead/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Train/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_val_set.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_val_set.yaml
new file mode 100644
index 00000000..30c03f7f
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_val_set.yaml
@@ -0,0 +1,121 @@
+pretrained: true
+checkpoint: https://huggingface.co/wdika/REC_VarNet_AHEAD_gaussian2d_12x/blob/main/REC_VarNet_AHEAD_gaussian2d_12x.atommic
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/ahead/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Val/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_train/cirim.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_train/cirim.yaml
new file mode 100644
index 00000000..4be64358
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_train/cirim.yaml
@@ -0,0 +1,208 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/ahead/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/ahead/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_train/varnet.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_train/varnet.yaml
new file mode 100644
index 00000000..647bdc5c
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_train/varnet.yaml
@@ -0,0 +1,174 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: parent_data_dir/ahead/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: parent_data_dir/ahead/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/conf/targets/Test_SENSE.yaml b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/targets/Test_SENSE.yaml
new file mode 100644
index 00000000..f6e60731
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/conf/targets/Test_SENSE.yaml
@@ -0,0 +1,122 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: qZF
+ use_reconstruction_module: true
+ quantitative_module_dimensionality: 2
+ quantitative_parameters_regularization_factors:
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: parent_data_dir/ahead/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: parent_data_dir/ahead/segmentation_masks/test
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: none
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_data_dir/atommic/qMRI/targets/AHEAD_gaussian2d_12x_Test/SENSE/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/ATOMMIC_paper/qMRI/AHEAD/evaluate.sh b/projects/ATOMMIC_paper/qMRI/AHEAD/evaluate.sh
new file mode 100644
index 00000000..c4a361bb
--- /dev/null
+++ b/projects/ATOMMIC_paper/qMRI/AHEAD/evaluate.sh
@@ -0,0 +1,14 @@
+python tools/evaluation/reconstruction.py \
+parent_data_dir/ahead/preprocessed/test output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Test/CIRIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/qMRI/evaluation_per_slice/AHEAD_gaussian2d_12x_Test/REC --fill_pred_path
+python tools/evaluation/reconstruction.py \
+parent_data_dir/ahead/preprocessed/test output_data_dir/atommic/REC/predictions/AHEAD_gaussian2d_12x_Test/VarNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/qMRI/evaluation_per_slice/AHEAD_gaussian2d_12x_Test/REC --fill_pred_path
+python tools/evaluation/qmapping.py \
+output_data_dir/atommic/qMRI/targets/AHEAD_gaussian2d_12x_Test/SENSE/default/ parent_data_dir/ahead/segmentation_masks/test/ \
+output_data_dir/atommic/qMRI/predictions/AHEAD_gaussian2d_12x_Test/qCIRIM/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/qMRI/evaluation_per_slice/AHEAD_gaussian2d_12x_Test/qMRI --fill_target_path --fill_pred_path
+python tools/evaluation/qmapping.py \
+output_data_dir/atommic/qMRI/targets/AHEAD_gaussian2d_12x_Test/SENSE/default/ parent_data_dir/ahead/segmentation_masks/test/ \
+output_data_dir/atommic/qMRI/predictions/AHEAD_gaussian2d_12x_Test/qVarNet/default/ \
+--evaluation_type per_slice --output_dir output_data_dir/atommic/qMRI/evaluation_per_slice/AHEAD_gaussian2d_12x_Test/qMRI --fill_target_path --fill_pred_path
diff --git a/projects/ATOMMIC_paper/run_models.sh b/projects/ATOMMIC_paper/run_models.sh
new file mode 100644
index 00000000..65dadc21
--- /dev/null
+++ b/projects/ATOMMIC_paper/run_models.sh
@@ -0,0 +1,101 @@
+atommic run -c projects/ATOMMIC_paper/MTL/SKMTEA/conf/targets/Test_SENSE.yaml
+atommic run -c projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/idslr.yaml
+atommic run -c projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/idslrunet.yaml
+atommic run -c projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/mtlrs.yaml
+atommic run -c projects/ATOMMIC_paper/MTL/SKMTEA/conf/test/4x/segnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/targets/12_channel_Val_RSS.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/ccnn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/crnn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/cirim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/jointicnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/kikinet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/lpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/modl.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/rim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/rvn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/unet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/varnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/vsnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/xpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/5x/zerofilled.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/ccnn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/crnn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/cirim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/jointicnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/kikinet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/lpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/modl.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/rim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/rvn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/unet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/varnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/vsnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/xpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/CC359/conf/test/10x/zerofilled.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/targets/Val_RSS.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/ccnn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/crnn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/cirim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/jointicnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/kikinet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/lpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/modl.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/rim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/rvn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/unet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/varnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/vsnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/xpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/4x/zerofilled.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/ccnn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/crnn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/cirim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/jointicnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/kikinet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/lpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/modl.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/rim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/rvn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/unet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/varnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/vsnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/xpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/conf/test/8x/zerofilled.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/targets/Test_SENSE.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/ccnn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/cirim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/jointicnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/kikinet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/lpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/modl.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/rim.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/rvn.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/unet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/varnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/vsnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/xpdnet.yaml
+atommic run -c projects/ATOMMIC_paper/REC/StanfordKnees2019/conf/test/zerofilled.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/attentionunet.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/dynunet.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/unet2d.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/unet3d.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/conf/test/vnet.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/attentionunet.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/dynunet.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/unet2d.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/unet3d.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/conf/test/vnet.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/attentionunet.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/dynunet.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/unet2d.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/unet3d.yaml
+atommic run -c projects/ATOMMIC_paper/SEG/SKMTEA/conf/test/vnet.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/targets/Test_SENSE.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_test_set.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_train_set.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/cirim_val_set.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_test_set.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_train_set.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/reconstruction_test/varnet_val_set.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qcirim.yaml
+atommic run -c projects/ATOMMIC_paper/qMRI/AHEAD/conf/quantitative_test/qvarnet.yaml
diff --git a/projects/ATOMMIC_paper/set_paths.sh b/projects/ATOMMIC_paper/set_paths.sh
new file mode 100644
index 00000000..c20c265c
--- /dev/null
+++ b/projects/ATOMMIC_paper/set_paths.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+echo "Enter the path to the parent data directory of the SKMTEA dataset:"
+read parent_data_dir_skmtea
+echo "Enter the path to the parent data directory of the AHEAD dataset:"
+read parent_data_dir_ahead
+echo "Enter the path to the parent data directory of the CC359 dataset:"
+read parent_data_dir_cc359
+echo "Enter the path to the parent data directory of the fastMRIBrainMulticoil dataset:"
+read parent_data_dir_fastmri
+echo "Enter the path to the parent data directory of the StanfordKnees2019 dataset:"
+read parent_data_dir_stanford
+echo "Enter the path to the parent data directory of the BraTS2023AdultGlioma dataset:"
+read parent_data_dir_brats
+echo "Enter the path to the parent data directory of the ISLES2022SubAcuteStroke dataset:"
+read parent_data_dir_isles
+echo "Enter the path to the parent output directory."
+read output_dir
+
+# create the output_dir if it does not exist
+mkdir -p ${output_dir}
+
+# go inside projects/ATOMMIC_paper/MTL/SKMTEA/ folder, find all files inside any subfolder and replace parent_data_dir with the given parent_data_dir_skmtea
+find projects/ATOMMIC_paper/MTL/SKMTEA/ -type f -exec sed -i "s|parent_data_dir|${parent_data_dir_skmtea}|g" {} +
+# go inside projects/ATOMMIC_paper/SEG/SKMTEA/ folder, find all files inside any subfolder and replace parent_data_dir with the given parent_data_dir_skmtea
+find projects/ATOMMIC_paper/SEG/SKMTEA/ -type f -exec sed -i "s|parent_data_dir|${parent_data_dir_skmtea}|g" {} +
+# go inside projects/ATOMMIC_paper/qMRI/AHEAD/ folder, find all files inside any subfolder and replace parent_data_dir with the given parent_data_dir_skmtea
+find projects/ATOMMIC_paper/qMRI/AHEAD/ -type f -exec sed -i "s|parent_data_dir|${parent_data_dir_ahead}|g" {} +
+# go inside projects/ATOMMIC_paper/REC/CC359/ folder, find all files inside any subfolder and replace parent_data_dir with the given parent_data_dir_cc359
+find projects/ATOMMIC_paper/REC/CC359/ -type f -exec sed -i "s|parent_data_dir|${parent_data_dir_cc359}|g" {} +
+# go inside projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/ folder, find all files inside any subfolder and replace parent_data_dir with the given parent_data_dir_fastmri
+find projects/ATOMMIC_paper/REC/fastMRIBrainsMulticoil/ -type f -exec sed -i "s|parent_data_dir|${parent_data_dir_fastmri}|g" {} +
+# go inside projects/ATOMMIC_paper/REC/StanfordKnees2019/ folder, find all files inside any subfolder and replace parent_data_dir with the given parent_data_dir_stanford
+find projects/ATOMMIC_paper/REC/StanfordKnees2019/ -type f -exec sed -i "s|parent_data_dir|${parent_data_dir_stanford}|g" {} +
+# go inside projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/ folder, find all files inside any subfolder and replace parent_data_dir with the given parent_data_dir_brats
+find projects/ATOMMIC_paper/SEG/BraTS2023AdultGlioma/ -type f -exec sed -i "s|parent_data_dir|${parent_data_dir_brats}|g" {} +
+# go inside projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/ folder, find all files inside any subfolder and replace parent_data_dir with the given parent_data_dir_isles
+find projects/ATOMMIC_paper/SEG/ISLES2022SubAcuteStroke/ -type f -exec sed -i "s|parent_data_dir|${parent_data_dir_isles}|g" {} +
+# go inside projects/ATOMMIC_paper/ folder, find all files inside any subfolder and replace output_dir with the given output_dir except the read_data_and_output_paths.sh file
+find projects/ATOMMIC_paper/ -type f -not -name "set_paths.sh" -exec sed -i "s|output_data_dir|${output_dir}|g" {} +
diff --git a/projects/MTL/__init__.py b/projects/MTL/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/MTL/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/MTL/rs/SKMTEA/README.md b/projects/MTL/rs/SKMTEA/README.md
new file mode 100644
index 00000000..453dacc1
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/README.md
@@ -0,0 +1,45 @@
+## **Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 Dataset**
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the
+Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 dataset.
+
+For more information, please refer to https://github.com/StanfordMIMI/skm-tea.
+
+Related papers:
+- https://openreview.net/forum?id=YDMFgD_qJuA.
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](visualize.ipynb). You just need to set the path where the
+dataset is downloaded. The
+[original notebook](https://colab.research.google.com/drive/1PluqK77pobD5dXE7zzBLEAeBgaaeGKXa) is copied from the
+https://github.com/StanfordMIMI/skm-tea repository and provided by the SKMTEA authors.
+
+### **Preprocessing**
+The SKM-TEA dataset is supported natively in ``ATOMMIC`` and no preprocessing is required. You just need to generate
+train, val, and test sets depending on the task you use the dataset for. For example, for the MTL/rs task, you
+need to run the [generate_sets.sh](generate_sets.sh) script.
+
+The preprocessing script can be run with the following command:
+```bash
+bash ./projects/MTL/rs/SKMTEA/preprocess_dataset.sh
+```
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/MTL/rs/SKMTEA/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` to the generated json files. In `exp_manager` please set the `exp_dir` to
+the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/MTL/rs/SKMTEA/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/MTL/rs/SKMTEA/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/MTL/rs/SKMTEA/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/MTL/rs/SKMTEA/__init__.py b/projects/MTL/rs/SKMTEA/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/MTL/rs/SKMTEA/conf/targets/Test_RSS.yaml b/projects/MTL/rs/SKMTEA/conf/targets/Test_RSS.yaml
new file mode 100644
index 00000000..8409ed49
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/targets/Test_RSS.yaml
@@ -0,0 +1,105 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: RSS
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 4
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 15
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/targets/SKMTEA_Test/RSS/
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/targets/Test_SENSE.yaml b/projects/MTL/rs/SKMTEA/conf/targets/Test_SENSE.yaml
new file mode 100644
index 00000000..f89e76af
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/targets/Test_SENSE.yaml
@@ -0,0 +1,105 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 4
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 15
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/targets/SKMTEA_Test/SENSE/
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/test/4x/idslr.yaml b/projects/MTL/rs/SKMTEA/conf/test/4x/idslr.yaml
new file mode 100644
index 00000000..a2072c27
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/test/4x/idslr.yaml
@@ -0,0 +1,156 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: IDSLR
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: false
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/predictions/SKMTEA/IDSLR_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/test/4x/idslrunet.yaml b/projects/MTL/rs/SKMTEA/conf/test/4x/idslrunet.yaml
new file mode 100644
index 00000000..14afaeca
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/test/4x/idslrunet.yaml
@@ -0,0 +1,156 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: IDSLRUNET
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: false
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/predictions/SKMTEA/IDSLRUNET_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/test/4x/mtlrs.yaml b/projects/MTL/rs/SKMTEA/conf/test/4x/mtlrs.yaml
new file mode 100644
index 00000000..2434b66a
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/test/4x/mtlrs.yaml
@@ -0,0 +1,194 @@
+pretrained: test
+checkpoint: None
+mode: true
+
+model:
+ model_name: MTLRS
+ joint_reconstruction_segmentation_module_cascades: 5
+ task_adaption_type: multi_task_learning
+ use_reconstruction_module: true
+ reconstruction_module_recurrent_layer: IndRNN
+ reconstruction_module_conv_filters:
+ - 64
+ - 64
+ - 2
+ reconstruction_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ reconstruction_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ reconstruction_module_conv_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ reconstruction_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_depth: 2
+ reconstruction_module_time_steps: 8
+ reconstruction_module_conv_dim: 2
+ reconstruction_module_num_cascades: 1
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_no_dc: true
+ reconstruction_module_keep_prediction: true
+ reconstruction_module_accumulate_predictions: true
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 4
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/predictions/SKMTEA/MTLRS_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/test/4x/segnet.yaml b/projects/MTL/rs/SKMTEA/conf/test/4x/segnet.yaml
new file mode 100644
index 00000000..d88e2d8a
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/test/4x/segnet.yaml
@@ -0,0 +1,161 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGNET
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: true
+ padding: true
+ norm_groups: 2
+ num_cascades: 5
+ segmentation_final_layer_conv_dim: 2
+ segmentation_final_layer_kernel_size: 3
+ segmentation_final_layer_dilation: 1
+ segmentation_final_layer_bias: False
+ segmentation_final_layer_nonlinear: relu
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/predictions/SKMTEA/SegNet_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/train/idslr.yaml b/projects/MTL/rs/SKMTEA/conf/train/idslr.yaml
new file mode 100644
index 00000000..acc89c3e
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/train/idslr.yaml
@@ -0,0 +1,209 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: IDSLR
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: false
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/trained_models/SKMTEA/IDSLR_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/train/idslrunet.yaml b/projects/MTL/rs/SKMTEA/conf/train/idslrunet.yaml
new file mode 100644
index 00000000..d38b5529
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/train/idslrunet.yaml
@@ -0,0 +1,209 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: IDSLRUNET
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: false
+ padding: true
+ norm_groups: 2
+ num_iters: 5
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/trained_models/SKMTEA/IDSLRUNET_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/train/jrs.yaml b/projects/MTL/rs/SKMTEA/conf/train/jrs.yaml
new file mode 100644
index 00000000..1896f95d
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/train/jrs.yaml
@@ -0,0 +1,247 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MTLRS
+ joint_reconstruction_segmentation_module_cascades: 5
+ task_adaption_type: none
+ use_reconstruction_module: true
+ reconstruction_module_recurrent_layer: IndRNN
+ reconstruction_module_conv_filters:
+ - 64
+ - 64
+ - 2
+ reconstruction_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ reconstruction_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ reconstruction_module_conv_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ reconstruction_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_depth: 2
+ reconstruction_module_time_steps: 8
+ reconstruction_module_conv_dim: 2
+ reconstruction_module_num_cascades: 1
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_no_dc: true
+ reconstruction_module_keep_prediction: true
+ reconstruction_module_accumulate_predictions: true
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: RSS
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 15
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/trained_models/SKMTEA/JRS
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/train/mtlrs.yaml b/projects/MTL/rs/SKMTEA/conf/train/mtlrs.yaml
new file mode 100644
index 00000000..3d3dd6c3
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/train/mtlrs.yaml
@@ -0,0 +1,247 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MTLRS
+ joint_reconstruction_segmentation_module_cascades: 5
+ task_adaption_type: multi_task_learning
+ use_reconstruction_module: true
+ reconstruction_module_recurrent_layer: IndRNN
+ reconstruction_module_conv_filters:
+ - 64
+ - 64
+ - 2
+ reconstruction_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ reconstruction_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ reconstruction_module_conv_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ reconstruction_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_depth: 2
+ reconstruction_module_time_steps: 8
+ reconstruction_module_conv_dim: 2
+ reconstruction_module_num_cascades: 1
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_no_dc: true
+ reconstruction_module_keep_prediction: true
+ reconstruction_module_accumulate_predictions: true
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 4
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 4
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/trained_models/SKMTEA/MTLRS_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/train/recsegnet.yaml b/projects/MTL/rs/SKMTEA/conf/train/recsegnet.yaml
new file mode 100644
index 00000000..f024c907
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/train/recsegnet.yaml
@@ -0,0 +1,207 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RECSEGNET
+ use_reconstruction_module: true
+ input_channels: 1
+ reconstruction_module_output_channels: 1
+ reconstruction_module_channels: 32
+ reconstruction_module_pooling_layers: 4
+ reconstruction_module_dropout: 0.0
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 4
+ segmentation_module_dropout: 0.0
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: RSS
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 15
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/trained_models/SKMTEA/RecSegNet
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/train/segnet.yaml b/projects/MTL/rs/SKMTEA/conf/train/segnet.yaml
new file mode 100644
index 00000000..a6b1b52e
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/train/segnet.yaml
@@ -0,0 +1,214 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGNET
+ use_reconstruction_module: true
+ input_channels: 64 # coils * 2
+ reconstruction_module_output_channels: 64 # coils * 2
+ segmentation_module_output_channels: 4
+ channels: 64
+ num_pools: 2
+ padding_size: 11
+ drop_prob: 0.0
+ normalize: true
+ padding: true
+ norm_groups: 2
+ num_cascades: 5
+ segmentation_final_layer_conv_dim: 2
+ segmentation_final_layer_kernel_size: 3
+ segmentation_final_layer_dilation: 1
+ segmentation_final_layer_bias: False
+ segmentation_final_layer_nonlinear: relu
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ total_segmentation_loss_weight: 0.5
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: SENSE
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1e-2
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 10
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/trained_models/SKMTEA/SegNet_SENSE
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/conf/train/seranet.yaml b/projects/MTL/rs/SKMTEA/conf/train/seranet.yaml
new file mode 100644
index 00000000..932c1423
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/conf/train/seranet.yaml
@@ -0,0 +1,219 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SERANET
+ use_reconstruction_module: true
+ input_channels: 2
+ reconstruction_module: unet
+ reconstruction_module_output_channels: 2
+ reconstruction_module_channels: 32
+ reconstruction_module_pooling_layers: 4
+ reconstruction_module_dropout: 0.0
+ # reconstruction_module: cascadenet
+ # reconstruction_module_hidden_channels: 32
+ # reconstruction_module_n_convs: 2
+ # reconstruction_module_batchnorm: true
+ # reconstruction_module_num_cascades: 5
+ reconstruction_module_num_blocks: 3
+ segmentation_module_input_channels: 32
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 4
+ segmentation_module_dropout: 0.0
+ recurrent_module_iterations: 2
+ recurrent_module_attention_channels: 32
+ recurrent_module_attention_pooling_layers: 4
+ recurrent_module_attention_dropout: 0.0
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ reconstruction_loss:
+ l1: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.0
+ total_segmentation_loss_weight: 1.0
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ magnitude_input: false
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: true
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: RSS
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ ssdu: false
+ n2r: false
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ segmentations_path: data_parent_dir/skm-tea/v1-release/segmentation_masks/raw-data-track
+ segmentation_classes: 4
+ complex_data: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 15
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/mltrs/trained_models/SKMTEA/SeRANet
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ files_to_copy: [ ]
diff --git a/projects/MTL/rs/SKMTEA/evaluation/__init__.py b/projects/MTL/rs/SKMTEA/evaluation/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/evaluation/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/MTL/rs/SKMTEA/evaluation/mtlrs_reconstruction.py b/projects/MTL/rs/SKMTEA/evaluation/mtlrs_reconstruction.py
new file mode 100644
index 00000000..8962e958
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/evaluation/mtlrs_reconstruction.py
@@ -0,0 +1,103 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import json
+import os
+from pathlib import Path
+
+import h5py
+import numpy as np
+from tqdm import tqdm
+
+from atommic.collections.reconstruction.metrics.reconstruction_metrics import (
+ ReconstructionMetrics,
+ mse,
+ nmse,
+ psnr,
+ ssim,
+)
+
+METRIC_FUNCS = {"MSE": mse, "NMSE": nmse, "PSNR": psnr, "SSIM": ssim}
+
+
+def main(args):
+ # if json file
+ if args.targets_dir.endswith(".json"):
+ with open(args.targets_dir, "r", encoding="utf-8") as f:
+ targets = json.load(f)
+ targets = [Path(target) for target in targets]
+ else:
+ targets = list(Path(args.targets_dir).iterdir())
+
+ evaluation_type = args.evaluation_type
+
+ scores = ReconstructionMetrics(METRIC_FUNCS)
+ for target in tqdm(targets):
+ reconstruction = h5py.File(Path(args.reconstructions_dir) / str(target).rsplit("/", maxsplit=1)[-1], "r")[
+ "reconstruction"
+ ][()].squeeze()
+
+ target = h5py.File(target, "r")["reconstruction"][()].squeeze()
+
+ # normalize per slice
+ for sl in range(target.shape[0]):
+ target[sl] = target[sl] / np.max(np.abs(target[sl]))
+ reconstruction[sl] = reconstruction[sl] / np.max(np.abs(reconstruction[sl]))
+ reconstruction = np.abs(reconstruction).real.astype(np.float32)
+ target = np.abs(target).real.astype(np.float32)
+
+ maxvalue = max(np.max(target) - np.min(target), np.max(reconstruction) - np.min(reconstruction))
+
+ if evaluation_type == "per_slice":
+ for sl in range(target.shape[0]):
+ scores.push(target[sl], reconstruction[sl], maxval=maxvalue)
+ elif evaluation_type == "per_volume":
+ scores.push(target, reconstruction, maxval=maxvalue)
+
+ model = args.reconstructions_dir.split("/")
+ model = model[-4] if model[-4] != "default" else model[-5]
+ print(f"{model}: {repr(scores)}")
+
+ if args.output_dir is not None:
+ output_dir = Path(args.output_dir)
+ output_dir.mkdir(parents=True, exist_ok=True)
+ # if file exists dont' overwrite, but append in a new line
+ with open(output_dir / "results.txt", "a", encoding="utf-8") as f:
+ f.write(f"{model}: {repr(scores)}\n")
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("targets_dir", type=str)
+ parser.add_argument("reconstructions_dir", type=str)
+ parser.add_argument("--output_dir", type=str)
+ parser.add_argument("--evaluation_type", choices=["per_slice", "per_volume"], default="per_slice")
+ parser.add_argument("--fill_target_path", action="store_true")
+ parser.add_argument("--fill_pred_path", action="store_true")
+ args = parser.parse_args()
+
+ if args.fill_target_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.targets_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ # check if after dir we have "/reconstructions" or "/predictions" dir
+ if os.path.exists(os.path.join(input_dir, "reconstructions")):
+ args.targets_dir = os.path.join(input_dir, "reconstructions")
+ elif os.path.exists(os.path.join(input_dir, "predictions")):
+ args.targets_dir = os.path.join(input_dir, "predictions")
+
+ if args.fill_pred_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.reconstructions_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ # check if after dir we have "/reconstructions" or "/predictions" dir
+ if os.path.exists(os.path.join(input_dir, "reconstructions")):
+ args.reconstructions_dir = os.path.join(input_dir, "reconstructions")
+ elif os.path.exists(os.path.join(input_dir, "predictions")):
+ args.reconstructions_dir = os.path.join(input_dir, "predictions")
+
+ main(args)
diff --git a/projects/MTL/rs/SKMTEA/evaluation/mtlrs_segmentation.py b/projects/MTL/rs/SKMTEA/evaluation/mtlrs_segmentation.py
new file mode 100644
index 00000000..0914b036
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/evaluation/mtlrs_segmentation.py
@@ -0,0 +1,151 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import json
+import os
+from pathlib import Path
+
+import h5py
+import nibabel as nib
+import numpy as np
+from tqdm import tqdm
+
+from atommic.collections.segmentation.metrics.segmentation_metrics import (
+ SegmentationMetrics,
+ dice_metric,
+ f1_per_class_metric,
+ hausdorff_distance_95_metric,
+ iou_metric,
+)
+
+METRIC_FUNCS = {
+ "DICE": dice_metric,
+ "F1": f1_per_class_metric,
+ "HD95": lambda x, y: hausdorff_distance_95_metric(x, y, batched=False, sum_method="sum"),
+ "IOU": iou_metric,
+}
+
+
+def process_segmentation_labels(segmentation_labels):
+ """Process the segmentation labels to match the predictions."""
+ # 0: Patellar Cartilage
+ patellar_cartilage = np.zeros_like(segmentation_labels)
+ patellar_cartilage[segmentation_labels == 1] = 1
+ # 1: Femoral Cartilage
+ femoral_cartilage = np.zeros_like(segmentation_labels)
+ femoral_cartilage[segmentation_labels == 2] = 1
+ # 2: Lateral Tibial Cartilage
+ lateral_tibial_cartilage = np.zeros_like(segmentation_labels)
+ lateral_tibial_cartilage[segmentation_labels == 3] = 1
+ # 3: Medial Tibial Cartilage
+ medial_tibial_cartilage = np.zeros_like(segmentation_labels)
+ medial_tibial_cartilage[segmentation_labels == 4] = 1
+ # 4: Lateral Meniscus
+ lateral_meniscus = np.zeros_like(segmentation_labels)
+ lateral_meniscus[segmentation_labels == 5] = 1
+ # 5: Medial Meniscus
+ medial_meniscus = np.zeros_like(segmentation_labels)
+ medial_meniscus[segmentation_labels == 6] = 1
+ # combine Lateral Tibial Cartilage and Medial Tibial Cartilage
+ tibial_cartilage = lateral_tibial_cartilage + medial_tibial_cartilage
+ # combine Lateral Meniscus and Medial Meniscus
+ medial_meniscus = lateral_meniscus + medial_meniscus
+
+ segmentation_labels = np.stack(
+ [patellar_cartilage, femoral_cartilage, tibial_cartilage, medial_meniscus],
+ axis=1,
+ )
+
+ # TODO: This is hardcoded on the SKM-TEA side, how to generalize this?
+ # We need to crop the segmentation labels in the frequency domain to reduce the FOV.
+ segmentation_labels = np.fft.fftshift(np.fft.fft2(segmentation_labels))
+ segmentation_labels = segmentation_labels[:, :, 48:-48, 40:-40]
+ segmentation_labels = np.fft.ifft2(np.fft.ifftshift(segmentation_labels)).real
+
+ return segmentation_labels
+
+
+def main(args):
+ # if json file
+ if args.targets_dir.endswith(".json"):
+ with open(args.targets_dir, "r", encoding="utf-8") as f:
+ targets = json.load(f)
+ targets = [Path(target) for target in targets]
+ else:
+ targets = list(Path(args.targets_dir).iterdir())
+
+ evaluation_type = args.evaluation_type
+
+ scores = SegmentationMetrics(METRIC_FUNCS)
+ for target in tqdm(targets):
+ fname = str(target).rsplit("/", maxsplit=1)[-1]
+ if ".h5" in fname:
+ fname = fname.split(".h5")[0]
+ elif ".nii" in fname:
+ fname = fname.split(".nii")[0]
+
+ predictions = h5py.File(Path(args.predictions_dir) / f"{fname}.h5", "r")["segmentation"][()].squeeze()
+ predictions = np.abs(predictions.astype(np.float32))
+ predictions = np.where(predictions > 0.5, 1, 0)
+
+ segmentation_labels = nib.load(Path(args.segmentations_dir) / f"{fname}.nii.gz").get_fdata()
+ segmentation_labels = process_segmentation_labels(segmentation_labels)
+ segmentation_labels = np.abs(segmentation_labels.astype(np.float32))
+ segmentation_labels = np.where(segmentation_labels > 0.5, 1, 0)
+
+ if evaluation_type == "per_slice":
+ for sl in range(segmentation_labels.shape[0]):
+ if segmentation_labels[sl].sum() > 0:
+ scores.push(segmentation_labels[sl].copy(), predictions[sl].copy())
+ elif evaluation_type == "per_volume":
+ scores.push(segmentation_labels, predictions)
+
+ model = args.predictions_dir.split("/")
+ model = model[-4] if model[-4] != "default" else model[-5]
+ print(f"{model}: {repr(scores)}")
+
+ if args.output_dir is not None:
+ output_dir = Path(args.output_dir)
+ output_dir.mkdir(parents=True, exist_ok=True)
+ # if file exists dont' overwrite, but append in a new line
+ with open(output_dir / "results.txt", "a", encoding="utf-8") as f:
+ f.write(f"{model}: {repr(scores)}\n")
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("targets_dir", type=str)
+ parser.add_argument("segmentations_dir", type=str)
+ parser.add_argument("predictions_dir", type=str)
+ parser.add_argument("--output_dir", type=str)
+ parser.add_argument("--dataset_format", choices=["skm-tea", "brats", "private"], default="private")
+ parser.add_argument("--evaluation_type", choices=["per_slice", "per_volume"], default="per_slice")
+ parser.add_argument("--fill_target_path", action="store_true")
+ parser.add_argument("--fill_pred_path", action="store_true")
+ args = parser.parse_args()
+
+ if args.fill_target_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.targets_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ # check if after dir we have "/segmentations" or "/predictions" dir
+ if os.path.exists(os.path.join(input_dir, "segmentations")):
+ args.targets_dir = os.path.join(input_dir, "segmentations")
+ elif os.path.exists(os.path.join(input_dir, "predictions")):
+ args.targets_dir = os.path.join(input_dir, "predictions")
+
+ if args.fill_pred_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.predictions_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ # check if after dir we have "/segmentations" or "/predictions" dir
+ if os.path.exists(os.path.join(input_dir, "segmentations")):
+ args.predictions_dir = os.path.join(input_dir, "segmentations")
+ elif os.path.exists(os.path.join(input_dir, "predictions")):
+ args.predictions_dir = os.path.join(input_dir, "predictions")
+
+ main(args)
diff --git a/projects/MTL/rs/SKMTEA/generate_sets.sh b/projects/MTL/rs/SKMTEA/generate_sets.sh
new file mode 100644
index 00000000..7502002f
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/generate_sets.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+echo "
+Preprocessing pipeline for the Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 Dataset.
+
+For more information, please refer to https://stanfordaimi.azurewebsites.net/datasets/4aaeafb9-c6e6-4e3c-9188-3aaaf0e0a9e7
+and check the following paper https://openreview.net/forum?id=YDMFgD_qJuA.
+
+Generating train, val, and test sets...
+"
+
+# Prompt the user to enter the path to the downloaded annotations directory
+echo "Please enter the (downloaded) annotations data directory:"
+read INPUT_DIR
+
+# Check if the input directory exists
+if [ ! -d "$INPUT_DIR" ]; then
+ echo "The input directory does not exist. Please try again."
+ exit 1
+fi
+
+# Prompt the user to enter the output directory for the generated json files
+echo "Please enter the output directory for the generated json files:"
+read OUTPUT_DIR
+
+# Run the json generation script
+python projects/segmentation/SKMTEA/scripts/split_sets_json.py $INPUT_DIR $OUTPUT_DIR --data_type raw
+echo "Done!"
diff --git a/projects/MTL/rs/SKMTEA/scripts/__init__.py b/projects/MTL/rs/SKMTEA/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/MTL/rs/SKMTEA/scripts/split_sets_json.py b/projects/MTL/rs/SKMTEA/scripts/split_sets_json.py
new file mode 100644
index 00000000..19ae937e
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/scripts/split_sets_json.py
@@ -0,0 +1,45 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+from pathlib import Path
+
+
+def main(args):
+ if args.data_type == "raw":
+ data_type = "files_recon_calib-24"
+ else:
+ data_type = "image_files"
+
+ # remove "annotations/v1.0.0/" from args.annotations_path and add "files_recon_calib-24" to get the raw_data_path
+ raw_data_path = Path(args.annotations_path).parent.parent / data_type
+
+ # get train.json, val.json and test.json filenames from args.annotations_path
+ annotations_sets = list(Path(args.annotations_path).iterdir())
+ for annotation_set in annotations_sets:
+ set_name = Path(annotation_set).name
+
+ # read json file
+ with open(annotation_set, "r", encoding="utf-8") as f:
+ annotation_set = json.load(f)
+
+ # read the "images" key and for every instance get the "file_name" key
+ filenames = [f'{raw_data_path}/{image["file_name"]}' for image in annotation_set["images"]]
+
+ # create a directory to store the folds
+ output_path = Path(args.output_path)
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # write the train, val and test filenames to a json file
+ with open(output_path / f"{data_type}_{set_name}", "w", encoding="utf-8") as f:
+ json.dump(filenames, f)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("annotations_path", type=Path, default=None, help="Path to the annotations json file.")
+ parser.add_argument("output_path", type=Path, default=None, help="Path to the output directory.")
+ parser.add_argument("--data_type", choices=["raw", "image"], default="raw", help="Type of data to split.")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/MTL/rs/SKMTEA/visualize.ipynb b/projects/MTL/rs/SKMTEA/visualize.ipynb
new file mode 100644
index 00000000..e167ed0f
--- /dev/null
+++ b/projects/MTL/rs/SKMTEA/visualize.ipynb
@@ -0,0 +1,1481 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oeT5uJROMl6L"
+ },
+ "source": [
+ "# ๐ต SKM-TEA Dataset Tutorial\n",
+ "[Paper](https://arxiv.org/abs/2203.06823) | [GitHub](https://github.com/StanfordMIMI/skm-tea)\n",
+ "\n",
+ "Welcome to the SKM-TEA dataset demo!\n",
+ "\n",
+ "**Dataset**: The *Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset* is a collection of quantitative knee MRI scans that enables end-to-end benchmarking of MRI reconstruction and analysis methods. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies.\n",
+ "\n",
+ "**Brief**: In this demo, we will walk through the data and how to use [the codebase](https://github.com/StanfordMIMI/skm-tea) to run pre-trained models and perform evaluation with your own methods.\n",
+ "\n",
+ "- Inspect different data types in SKM-TEA *DICOM* and *Raw Data* Tracks\n",
+ "- Use pretrained models from the [model zoo](https://github.com/StanfordMIMI/skm-tea/blob/main/MODEL_ZOO.md)\n",
+ "- Perform clinically-relevant quantitative MRI (qMRI) evaluation\n",
+ "\n",
+ "Interested in learning how to train models with SKM-TEA, check out [this tutorial](https://colab.research.google.com/drive/1LUC0MqFYK39xG5AV9kQi5hIBsi9eCpS0?usp=sharing)\n",
+ "\n",
+ "**Time**: 25-30 minutes\n",
+ "\n",
+ "**Colab Runtime**: We recommend running this Colab with a GPU runtime. To change the runtime,\n",
+ "1. Click on `Runtime` on the top navigation bar\n",
+ "2. Select `Change runtime type`\n",
+ "3. Select `GPU` from the dropdown\n",
+ "\n",
+ "**NOTE**: This tutorial is under development. Please contact the arjundd \\ with any bugs or recommendations.\n",
+ "\n",
+ "**Acknowledgements**: SKM-TEA is built on the [Meddlr](https://github.com/ad12/meddlr) image reconstruction and analysis framework.\n",
+ "\n",
+ "**Coming Soon:**\n",
+ "- Tutorial with detection (bounding box) labels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5g8MtjY5Vs7c"
+ },
+ "source": [
+ "## ๐ก Downloading the Data\n",
+ "Let's download a [mini version](https://huggingface.co/datasets/arjundd/skm-tea-mini) of the SKM-TEA dataset from Huggingface. This mini dataset was created for building demos/tutorials with the SKM-TEA dataset. **Do not use this dataset for reporting/publication purposes**\n",
+ "\n",
+ "*NOTE*: This download process can take ~5-8 minutes.\n",
+ "\n",
+ "> If you would like to set up up the full SKM-TEA dataset on your machine, follow [these instructions](https://github.com/StanfordMIMI/skm-tea/blob/main/DATASET.md)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "mLWsy6TS6KGX",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "422b0934-856c-4bf2-9c92-692be5371d02"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "dataset_dir = \"skm-tea/v1-release\"\n",
+ "url = \"https://huggingface.co/datasets/arjundd/skm-tea-mini/resolve/main/v1-release\"\n",
+ "force_download = False\n",
+ "\n",
+ "if force_download:\n",
+ " !rm -rf $dataset_dir\n",
+ "\n",
+ "if not os.path.isdir(dataset_dir):\n",
+ " os.makedirs(dataset_dir)\n",
+ " for fname in [\"all_metadata.csv\", \"annotations/v1.0.0/train.json\", \"annotations/v1.0.0/val.json\", \"annotations/v1.0.0/test.json\"]:\n",
+ " out = f\"{dataset_dir}/{fname}\"\n",
+ " os.makedirs(os.path.dirname(out), exist_ok=True)\n",
+ " !wget -q $url/$fname -O $out\n",
+ "\n",
+ "\n",
+ " for fname in tqdm([\"dicoms\", \"files_recon_calib-24\", \"image_files\", \"segmentation_masks\"], disable=False):\n",
+ " !wget -c $url/\"tarball\"/$fname\".tar.gz\" -O - | tar -xz -C $dataset_dir/\n",
+ "\n",
+ "!ls"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TfqPa76WMl6P"
+ },
+ "source": [
+ "## ๐ง Setup\n",
+ "All SKM-TEA code for training, evaluation, models, and more ships as a Python package. In this tutorial, we will learn how to use different parts of this package.\n",
+ "\n",
+ "> To use the latest version from the `main` branch, use `pip install git+https://github.com/StanfordMIMI/skm-tea.git`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "s76_F_6FYVoK",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "outputId": "c9e80dea-5841-4e4a-b179-c81fbfefc9df"
+ },
+ "outputs": [],
+ "source": [
+ "# Download SKM-TEA from main branch on GitHub\n",
+ "!pip install --upgrade pytorch-lightning==1.7.7 skm-tea"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "968_Ac3sMl6O"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.environ[\"MEDDLR_DATASETS_DIR\"] = \"./\"\n",
+ "\n",
+ "from pprint import pprint\n",
+ "\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import h5py\n",
+ "import matplotlib.pyplot as plt\n",
+ "from skimage.color import label2rgb\n",
+ "import pandas as pd\n",
+ "from torch import nn\n",
+ "\n",
+ "import dosma as dm\n",
+ "\n",
+ "import meddlr.ops as oF\n",
+ "from meddlr.data import DatasetCatalog, MetadataCatalog\n",
+ "from meddlr.utils.logger import setup_logger\n",
+ "from meddlr.utils import env\n",
+ "\n",
+ "import skm_tea as st"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Set the default device if cuda is enabled\n",
+ "if torch.cuda.is_available():\n",
+ " DEVICE = torch.device(\"cuda\")\n",
+ "else:\n",
+ " DEVICE = torch.device(\"cpu\")\n",
+ "\n",
+ "print(\"Device: \", DEVICE)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "B6st9z4rm1tO",
+ "outputId": "66c126bd-fadd-438a-d2a4-f5581cda00dc"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hb5pCSZzMl6Q"
+ },
+ "outputs": [],
+ "source": [
+ "# Run this setup phase only once.\n",
+ "# Otherwise, you may get multiple print statements\n",
+ "setup_logger()\n",
+ "logger = setup_logger(\"skm_tea\")\n",
+ "path_mgr = env.get_path_manager()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Some general utilities\n",
+ "\n",
+ "from typing import Union, Sequence\n",
+ "\n",
+ "def get_scaled_image(\n",
+ " x: Union[torch.Tensor, np.ndarray], percentile=0.99, clip=False\n",
+ "):\n",
+ " \"\"\"Scales image by intensity percentile (and optionally clips to [0, 1]).\n",
+ "\n",
+ " Args:\n",
+ " x (torch.Tensor | np.ndarray): The image to process.\n",
+ " percentile (float): The percentile of magnitude to scale by.\n",
+ " clip (bool): If True, clip values between [0, 1]\n",
+ "\n",
+ " Returns:\n",
+ " torch.Tensor | np.ndarray: The scaled image.\n",
+ " \"\"\"\n",
+ " is_numpy = isinstance(x, np.ndarray)\n",
+ " if is_numpy:\n",
+ " x = torch.as_tensor(x)\n",
+ "\n",
+ " scale_factor = torch.quantile(x, percentile)\n",
+ " x = x / scale_factor\n",
+ " if clip:\n",
+ " x = torch.clip(x, 0, 1)\n",
+ "\n",
+ " if is_numpy:\n",
+ " x = x.numpy()\n",
+ "\n",
+ " return x\n",
+ "\n",
+ "\n",
+ "def plot_images(\n",
+ " images, processor=None, disable_ticks=True, titles: Sequence[str]=None,\n",
+ " ylabel: str=None, xlabels: Sequence[str]=None, cmap: str=\"gray\",\n",
+ " show_cbar: bool = False, overlay = None, opacity: float = 0.3,\n",
+ " hsize=5, wsize=5, axs=None\n",
+ "):\n",
+ " \"\"\"Plot multiple images in a single row.\n",
+ "\n",
+ " Add an overlay with the `overlay=` argument.\n",
+ " Add a colorbar with `show_cbar=True`.\n",
+ " \"\"\"\n",
+ " def get_default_values(x, default=\"\"):\n",
+ " if x is None:\n",
+ " return [default] * len(images)\n",
+ " return x\n",
+ "\n",
+ " titles = get_default_values(titles)\n",
+ " ylabels = get_default_values(images)\n",
+ " xlabels = get_default_values(xlabels)\n",
+ "\n",
+ " N = len(images)\n",
+ " if axs is None:\n",
+ " fig, axs = plt.subplots(1, N, figsize=(wsize * N, hsize))\n",
+ " else:\n",
+ " assert len(axs) >= N\n",
+ " fig = axs.flatten()[0].get_figure()\n",
+ "\n",
+ " for ax, img, title, xlabel in zip(axs, images, titles, xlabels):\n",
+ " if processor is not None:\n",
+ " img = processor(img)\n",
+ " im = ax.imshow(img, cmap=cmap)\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel(xlabel)\n",
+ "\n",
+ " if overlay is not None:\n",
+ " for ax in axs.flatten():\n",
+ " im = ax.imshow(overlay, alpha=opacity)\n",
+ "\n",
+ " if show_cbar:\n",
+ " fig.subplots_adjust(right=0.8)\n",
+ " cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
+ " fig.colorbar(im, cax=cbar_ax)\n",
+ "\n",
+ " if disable_ticks:\n",
+ " for ax in axs.flatten():\n",
+ " ax.get_xaxis().set_ticks([])\n",
+ " ax.get_yaxis().set_ticks([])\n",
+ "\n",
+ " return axs\n"
+ ],
+ "metadata": {
+ "id": "rjVszyf4aDBj"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2sDPAp83Ml6Q"
+ },
+ "source": [
+ "## ๐พ Understanding the Data\n",
+ "The SKM-TEA dataset consists of two *tracks* that are based on the source of the input image: the *Raw Data* track, where inputs start from the complex-valued k-space, and the *DICOM* track, where inputs start from magnitude DICOM images.\n",
+ "\n",
+ "Note, the Raw Data track supports all reconstruction (upstream) and image analysis (downstream) tasks available in SKM-TEA with the caveat that all downstream tasks are performed on the image reconstructed from the raw data.\n",
+ "\n",
+ "In contrast, the DICOM track only supports image analysis tasks -- it does not support the reconstruction tasks. Read [this paper](https://arxiv.org/abs/2109.08237) for more information on why DICOM images may not be good targets for measuring reconstruction performance.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The `skm_tea` package simplifies getting relevant data paths and metadata using the `DatasetCatalog` manager. We can load any of the dataset splits:\n",
+ "- `'skmtea_v1_train'`\n",
+ "- `'skmtea_v1_val'`\n",
+ "- `'skmtea_v1_test'`"
+ ],
+ "metadata": {
+ "id": "0GVy26yPEaNB"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JCJHRjU9Ml6R"
+ },
+ "outputs": [],
+ "source": [
+ "# Load list of dictionaries for the SKM-TEA v1 training dataset.\n",
+ "dataset_dicts = DatasetCatalog.get(\"skmtea_v1_train\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "QIjEQX_gMl6R",
+ "outputId": "0d02e6a3-d938-41d6-f7ad-8618ec29b76e"
+ },
+ "outputs": [],
+ "source": [
+ "scan = dataset_dicts[0]\n",
+ "pprint(scan)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LFeAVfimMl6S"
+ },
+ "source": [
+ "### Raw Data Track\n",
+ "The raw data track consists of (1) multi-coil kspace, (2) complex-valued ground truth reconstructions, (3) sensitivity maps, (4) gradient-warp corrected segmentations, and (5) localized bounding boxes for knee pathologies.\n",
+ "\n",
+ "While qDESS is a 3D sequence, the SI (axial) readout dimension is fully-sampled, and can be reconstructed without information loss using the 1D inverse fast Fourier transform (ifft). Thus, reconstructions are performed on 2D axial ($k_y \\times k_z$) slices.\n",
+ "\n",
+ "Also, note that the reference segmentations for the raw data track are different than those for the DICOM track to correct for DICOM-specfic post-processing. See [our paper](https://arxiv.org/abs/2203.06823) for more information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "iYPX8955Ml6S",
+ "outputId": "aee45171-f99a-4965-a850-1a8707563631"
+ },
+ "outputs": [],
+ "source": [
+ "sl = 200 # the slice to be plotted\n",
+ "\n",
+ "# Reconstruction data\n",
+ "recon_file = scan[\"recon_file\"]\n",
+ "with h5py.File(recon_file, \"r\") as f:\n",
+ " kspace = f[\"kspace\"][sl, :, :, :, :] # Shape: (x, ky, kz, #echos, #coils)\n",
+ " image = f[\"target\"][sl, :, :, :, :] # Shape: (x, ky, kz, #echos, #maps) - #maps = 1 for SKM-TEA\n",
+ " maps = f[\"maps\"][sl, :, :, :, :] # Shape: (x, ky, kz, #coils, #maps) - maps are the same for both echos\n",
+ "\n",
+ "# Segmentation data\n",
+ "seg_file = scan[\"gw_corr_mask_file\"]\n",
+ "segmentation = dm.read(seg_file).A[sl, ...] # Shape: (x, y, z)\n",
+ "print(segmentation.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 597
+ },
+ "id": "h_G_os2pMl6T",
+ "outputId": "ffd7c9ba-732d-4af7-b6c8-e2b1a8d1a534"
+ },
+ "outputs": [],
+ "source": [
+ "# Display kspace per coil\n",
+ "n_coils = kspace.shape[-1]\n",
+ "nrows = 2\n",
+ "hsize = 5\n",
+ "wsize = hsize / kspace.shape[0] * kspace.shape[1]\n",
+ "_, axs = plt.subplots(nrows, n_coils, figsize=(n_coils * wsize, nrows * hsize))\n",
+ "\n",
+ "for echo in range(2):\n",
+ " kspace_coils = [np.abs(kspace[..., echo, idx]) for idx in range(n_coils)]\n",
+ " # Scale the kspace to avoid over-saturating the image with center kspace\n",
+ " kspace_coils = [get_scaled_image(x, 0.95, clip=True) for x in kspace_coils]\n",
+ "\n",
+ " titles = [f\"Coil {idx+1}\" for idx in range(n_coils)] if echo==0 else None\n",
+ " plot_images(kspace_coils, titles=titles, axs=axs[echo])\n",
+ " axs[echo][0].set_ylabel(\"Echo {}\".format(echo + 1), fontsize=20)\n",
+ "\n",
+ "plt.subplots_adjust(wspace=0.1, hspace=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 327
+ },
+ "id": "EGowchm2Ml6T",
+ "outputId": "fc3497cb-2e68-4da8-e38e-dd3f46c6b51e"
+ },
+ "outputs": [],
+ "source": [
+ "# Plot reconstructed image\n",
+ "mag_img = np.abs(image)\n",
+ "seg_colorized = label2rgb(segmentation, bg_label=0)\n",
+ "\n",
+ "\n",
+ "_ = plot_images(\n",
+ " [mag_img[..., 0, 0], mag_img[..., 0, 0]], # echo1, echo2\n",
+ " processor=lambda x: get_scaled_image(x, 0.95, clip=True),\n",
+ " titles=[\"Echo 1\", \"Echo 2\"],\n",
+ " overlay=seg_colorized,\n",
+ " opacity=0.4,\n",
+ " hsize=5, wsize=2.3\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lpmPd77iMl6U"
+ },
+ "source": [
+ "### DICOM Track\n",
+ "The DICOM Track consists of (1) scanner-generated DICOM images, (2) tissue segmentations, and (3) pathology bounding boxes.\n",
+ "\n",
+ "**IMPORTANT**: As mentioned above, this data should only be used for image analysis (segmentation, detection, classification) tasks. It should not be used for reconstruction tasks.\n",
+ "\n",
+ "\n",
+ "Let's visualize a sagittal slice from both echos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PwtalV6eMl6U"
+ },
+ "outputs": [],
+ "source": [
+ "sl = 60 # the slice to be plotted\n",
+ "\n",
+ "# DICOM data + segmentation\n",
+ "image_file = scan[\"image_file\"]\n",
+ "with h5py.File(image_file, \"r\") as f:\n",
+ " echo1 = f[\"echo1\"][:, :, sl] # Shape: (x, y, z)\n",
+ " echo2 = f[\"echo2\"][:, :, sl] # Shape: (x, y, z)\n",
+ " segmentation = f[\"seg\"][:, :, sl, :] # Shape: (x, y, z, #classes)\n",
+ "\n",
+ "segmentation = oF.one_hot_to_categorical(segmentation, channel_dim=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 309
+ },
+ "id": "8eV1c8szMl6V",
+ "outputId": "31c6bd8d-1ac3-4121-b630-a73cf7841cbc"
+ },
+ "outputs": [],
+ "source": [
+ "# Plot reconstructed image\n",
+ "seg_colorized = label2rgb(segmentation, bg_label=0)\n",
+ "\n",
+ "_ = plot_images(\n",
+ " [echo1, echo2],\n",
+ " titles=[\"Echo 1\", \"Echo 2\"],\n",
+ " overlay=seg_colorized,\n",
+ " opacity=0.4,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xtEvV8GFMl6W"
+ },
+ "source": [
+ "## ๐ Model Zoo\n",
+ "Interested in running a pre-trained model on your data? We got you!\n",
+ "\n",
+ "We maintain a model zoo of pre-trained models that have been trained on the SKM-TEA dataset for different tasks. You can find a list of these models on [GitHub](https://github.com/StanfordMIMI/skm-tea).\n",
+ "\n",
+ "And loading the model is as easy as 123! Just use the `skm_tea.get_model_from_zoo`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bUp06YLTMl6W"
+ },
+ "outputs": [],
+ "source": [
+ "# Load a scan from the test dataset.\n",
+ "dataset_dicts = DatasetCatalog.get(\"skmtea_v1_test\")\n",
+ "scan = dataset_dicts[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LRzGe08hMl6W"
+ },
+ "source": [
+ "### Reconstruction\n",
+ "Let's use a pretrained [unrolled network](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664163/) to reconstruct 6x accelerated qDESS scans.\n",
+ "\n",
+ "The reconstruction model was trained to reconstruction axial ($k_y \\times k_z$) slices for the first echo. You can find other pretrained reconstruction models [here](https://github.com/StanfordMIMI/skm-tea/blob/main/MODEL_ZOO.md#reconstruction-baselines).\n",
+ "\n",
+ "*Aside*: When reporting results on the SKM-TEA dataset, please use the masks provided with the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3PTee3KIMl6X"
+ },
+ "outputs": [],
+ "source": [
+ "# Simulate 6x undersampled data\n",
+ "sl = 256\n",
+ "\n",
+ "with h5py.File(scan[\"recon_file\"], \"r\") as f:\n",
+ " kspace = torch.as_tensor(f[\"kspace\"][sl, :, :, :, :]).unsqueeze(0)\n",
+ " maps = torch.as_tensor(f[\"maps\"][sl, :, :, :, :]).unsqueeze(0)\n",
+ " mask = torch.as_tensor(f[\"masks/poisson_6.0x\"][()]).unsqueeze(0) # TODO: Fix\n",
+ " img_gt = torch.as_tensor(f[\"target\"][sl, :, :, :, :]).unsqueeze(0)\n",
+ "mask = oF.zero_pad(mask, kspace.shape[1:3])\n",
+ "\n",
+ "us_kspace = kspace * mask.unsqueeze(-1).unsqueeze(-1).type(kspace.dtype)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VCLy0Gp6Ml6X",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "85a46aaf-42a6-479d-89ea-5787924c979f"
+ },
+ "outputs": [],
+ "source": [
+ "# Fetch the model with pretrained weights.\n",
+ "model = st.get_model_from_zoo(\n",
+ " cfg_or_file=\"https://huggingface.co/arjundd/skm-tea-models/raw/main/neurips2021/recon-models/6x/Unrolled_E1/config.yaml\",\n",
+ " weights_path=\"https://huggingface.co/arjundd/skm-tea-models/resolve/main/neurips2021/recon-models/6x/Unrolled_E1/model.ckpt\",\n",
+ ").to(DEVICE).eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "MGXj7huvMl6X"
+ },
+ "outputs": [],
+ "source": [
+ "echo = 0 # the 1st echo\n",
+ "echo1_kspace = us_kspace[..., echo, :]\n",
+ "with torch.no_grad():\n",
+ " pred = model({\"kspace\": echo1_kspace, \"maps\": maps})[\"pred\"].cpu()\n",
+ "echo1_recon = pred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3AGVZPP4Ml6X",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 398
+ },
+ "outputId": "2c567e15-a693-416b-e023-37795498c06f"
+ },
+ "outputs": [],
+ "source": [
+ "# For visualization purposes, we scale the ground truth and reconstructions\n",
+ "# to get rid of very bright outliers.\n",
+ "gt_abs = get_scaled_image(img_gt[..., 0, :].abs(), 0.9999, clip=True)\n",
+ "recon_abs = get_scaled_image(echo1_recon.abs(), 0.9999, clip=True)\n",
+ "err = torch.abs(gt_abs - recon_abs)\n",
+ "\n",
+ "plot_images(\n",
+ " [gt_abs, recon_abs, err * 4],\n",
+ " processor=lambda x: x.abs().squeeze(),\n",
+ " titles=[\"Ground truth\", \"Recon\", \"Error (4x)\"],\n",
+ " hsize=5, wsize=2.3\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zOBE1rQAMl6Y"
+ },
+ "source": [
+ "### Segmentation\n",
+ "Let's perform segmentation on the DICOM track dataset using a pretrained [U-Net](https://arxiv.org/abs/1505.04597).\n",
+ "\n",
+ "The segmentation model was trained to segment sagittal slices for the first echo. You can find other pretrained segmentation models [here](https://github.com/StanfordMIMI/skm-tea/blob/main/MODEL_ZOO.md#segmentation-baselines).\n",
+ "\n",
+ "**Note:** The volume has to first be normalized to have zero-mean and unit standard deviation. In the near future, this will automatically be done."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "X3PziENNMl6Y",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "6446b963-e37b-465d-82e6-9f832ee8c930"
+ },
+ "outputs": [],
+ "source": [
+ "model = st.get_model_from_zoo(\n",
+ " cfg_or_file=\"https://huggingface.co/arjundd/skm-tea-models/raw/main/neurips2021/segmentation-models/U-Net_E1/config.yaml\",\n",
+ " weights_path=\"https://huggingface.co/arjundd/skm-tea-models/resolve/main/neurips2021/segmentation-models/U-Net_E1/model.ckpt\",\n",
+ ").eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Xhy1kVpzMl6Y"
+ },
+ "outputs": [],
+ "source": [
+ "from meddlr.data.data_utils import collect_mask\n",
+ "sl = 88 # the slice to segment\n",
+ "\n",
+ "# DICOM data + segmentation\n",
+ "image_file = scan[\"image_file\"]\n",
+ "with h5py.File(image_file, \"r\") as f:\n",
+ " echo1 = f[\"echo1\"][()] # Shape: (x, y, z)\n",
+ " segmentation = f[\"seg\"][()] # Shape: (x, y, z, #classes)\n",
+ "\n",
+ "echo1 = torch.as_tensor(echo1).unsqueeze(0).unsqueeze(0).float() # Shape: (B, C, H, W)\n",
+ "\n",
+ "# Ground truth segmentation\n",
+ "# Medial/lateral components are aggregated into the same category.\n",
+ "# 0 - patellar cartilage, 1 - femoral cartilage\n",
+ "# 2/3 - medial/lateral tibial cartilage, 4/5 - medial/lateral meniscus\n",
+ "gt_seg_sl = segmentation[..., sl, :]\n",
+ "gt_seg_sl = collect_mask(gt_seg_sl, (0, 1, (2, 3), (4, 5)), out_channel_first=False)\n",
+ "gt_seg_sl = oF.one_hot_to_categorical(gt_seg_sl, channel_dim=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JT4D1Mp-Ml6Y"
+ },
+ "outputs": [],
+ "source": [
+ "# Normalize volume and run model.\n",
+ "echo1 = (echo1 - echo1.mean()) / echo1.std()\n",
+ "echo1_sl = echo1[..., sl]\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " logits = model({\"image\": echo1_sl})[\"sem_seg_logits\"]\n",
+ "\n",
+ "prediction = oF.pred_to_categorical(logits, activation='sigmoid').squeeze(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "_, axs = plt.subplots(1, 3, figsize=(10,5))\n",
+ "for idx, (data, title) in enumerate([\n",
+ " (echo1_sl.squeeze(), \"Input\"), (prediction, \"Prediction\"), (gt_seg_sl, \"Ground truth\")\n",
+ "]):\n",
+ " ax = axs[idx]\n",
+ " ax.imshow(data.squeeze(), cmap=\"gray\" if idx == 0 else None)\n",
+ " ax.set_title(title, fontsize=20)\n",
+ " ax.axis(\"off\")"
+ ],
+ "metadata": {
+ "id": "IQNltkfZiUd-",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 216
+ },
+ "outputId": "78e7d8ab-fc8d-4e2a-f124-3e3fddbd8105"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "t5dNaY7VMl6Z"
+ },
+ "source": [
+ "## ๐ qMRI Evaluation\n",
+ "SKM-TEA introduces a new family of metrics based on quantitative MRI (qMRI) endpoints. In this section, we will explore the utility of these metrics and how to use them to benchmark your models.\n",
+ "\n",
+ "Specifically, we will consider a qMRI knee analysis pipeline that uses qDESS reconstructions to analytically estimate $T_2$ maps and uses automated segmentations to get region-specific $T_2$ values.\n",
+ "\n",
+ "As a proof-of-concept, let's dive into how we can use qMRI endpoints to evaluate a segmentation model based on regional $T_2$ accuracy. We will evaluate the same pretrained U-Net model from the [Model Zoo section](https://colab.research.google.com/drive/1PluqK77pobD5dXE7zzBLEAeBgaaeGKXa?authuser=1#scrollTo=zOBE1rQAMl6Y&line=6&uniqifier=1).\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "G3QOQzciMl6Z"
+ },
+ "outputs": [],
+ "source": [
+ "from skm_tea.metrics import QuantitativeKneeMRI\n",
+ "from meddlr.data.data_utils import collect_mask\n",
+ "\n",
+ "from dosma.scan_sequences import QDess"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "atqdug_EMl6Z"
+ },
+ "outputs": [],
+ "source": [
+ "# Load a scan and corresponding metadata.\n",
+ "dataset_dicts = DatasetCatalog.get(\"skmtea_v1_test\")\n",
+ "scan = dataset_dicts[0]\n",
+ "\n",
+ "metadata: pd.DataFrame = MetadataCatalog.get(\"skmtea_v1_test\").scan_metadata\n",
+ "metadata = metadata[metadata[\"MTR_ID\"] == scan[\"scan_id\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load the DICOMs for this scan.\n",
+ "dr = dm.DicomReader(group_by=[\"EchoNumbers\", \"SeriesDescription\"], verbose=True, num_workers=4)\n",
+ "volumes = dr.load(scan[\"dicom_dir\"])\n",
+ "\n",
+ "# Filter out unnecessary dicoms.\n",
+ "volumes = [v for v in volumes if \"T2\" not in v.get_metadata(\"SeriesDescription\")]\n",
+ "assert len(volumes) == 2\n",
+ "echo1, echo2 = tuple(sorted(volumes, key=lambda x: x.get_metadata(\"EchoNumbers\")))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "ca289c7160af4c159ad7fd411d912ef7",
+ "015d4fbb69af450585e5e2fa60412047",
+ "ea4b2048a1ec44e099bb923bad633b9a",
+ "51a3a30ad84f49918cbda78bc33ccbd4",
+ "842b85d281ce4c239af9436d6e57958e",
+ "550602d30f904c8f8123b8366342950e",
+ "1717cc1ff8934b0b9020c7a0cd2b595b",
+ "d0e0ecc7cc104ba1a7d2918a3f585b41",
+ "67aba4eecf5141e0bbb4b0da63ff8c51",
+ "2ff53edd8c7a453395cdad88414d1212",
+ "699878da64014757921c9ef70119e50e"
+ ]
+ },
+ "id": "Mx9ngDEOx-sm",
+ "outputId": "55581b63-ec42-4305-a645-612edfd382b7"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load the ground truth segmentation.\n",
+ "seg_gt = dm.read(scan[\"dicom_mask_file\"])\n",
+ "arr = oF.categorical_to_one_hot(seg_gt.A, channel_dim=-1)\n",
+ "arr = collect_mask(arr, (0, 1, (2,3), (4,5)), out_channel_first=False)\n",
+ "\n",
+ "seg_gt = dm.MedicalVolume(arr, affine=seg_gt.affine)"
+ ],
+ "metadata": {
+ "id": "q68mUZqFz7Qz"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Run the model"
+ ],
+ "metadata": {
+ "id": "qS22VFNe8Asz"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def run_segmentation(\n",
+ " mv: dm.MedicalVolume, model: nn.Module, normalize=True,\n",
+ " batch_size: int = 4, pbar: bool = True\n",
+ "):\n",
+ " \"\"\"Runs a segmentation model on the qDESS volume.\n",
+ "\n",
+ " The model should be trained to segment sagittal slices.\n",
+ "\n",
+ " Args:\n",
+ " x (dm.MedicalVolume): A 3D magnitude image (single echo).\n",
+ " model (nn.Module): The segmentation model to run.\n",
+ " normalize (bool): Whether to perform zero-mean, unit-std normalization.\n",
+ " batch_size (int): The batch size for performing segmentation.\n",
+ " pbar (bool): Whether to display progress bar.\n",
+ "\n",
+ " Returns:\n",
+ " dm.MedicalVolume: The one-hot predictions from the segmentation model\n",
+ " where last dimension/axis is the channel dimension.\n",
+ " \"\"\"\n",
+ " mv_ornt = mv.orientation\n",
+ " mv = mv.reformat((\"LR\", \"SI\", \"AP\"))\n",
+ " affine = mv.affine.copy()\n",
+ "\n",
+ " x = mv.to_torch().type(torch.float32)\n",
+ " if normalize:\n",
+ " x = (x - x.mean()) / x.std()\n",
+ "\n",
+ " x_chunks = torch.split(x, batch_size, dim = 0)\n",
+ "\n",
+ " logits = []\n",
+ " for chunk in tqdm(torch.split(x, batch_size, dim=0), disable=not pbar):\n",
+ " chunk = chunk.unsqueeze(1) # add a channel dimension\n",
+ " out = model({\"image\": chunk})\n",
+ " logits.append(out[\"sem_seg_logits\"])\n",
+ "\n",
+ "\n",
+ " logits = torch.concat(logits, dim=0)\n",
+ " prediction = torch.sigmoid(logits).permute(0, 2, 3, 1) # make channels last\n",
+ "\n",
+ " out = dm.MedicalVolume.from_torch(prediction, affine).reformat(mv_ornt)\n",
+ " return out"
+ ],
+ "metadata": {
+ "id": "9_AnZ_0qNiRr"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "torch.cuda.empty_cache()\n",
+ "\n",
+ "model = st.get_model_from_zoo(\n",
+ " cfg_or_file=\"https://huggingface.co/arjundd/skm-tea-models/raw/main/neurips2021/segmentation-models/U-Net_E1/config.yaml\",\n",
+ " weights_path=\"https://huggingface.co/arjundd/skm-tea-models/resolve/main/neurips2021/segmentation-models/U-Net_E1/model.ckpt\",\n",
+ ").to(DEVICE).eval()"
+ ],
+ "metadata": {
+ "id": "CrnRYP5tEGGN"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "with torch.no_grad():\n",
+ " seg_pred = run_segmentation(echo1.to(DEVICE), model, batch_size=4)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "SBoA-EfcEX2N",
+ "outputId": "c5f873c1-9ebb-485b-97e6-6f9626b2f7d3"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Computing $T_2$ Maps\n",
+ "\n",
+ "Computing $T_2$ maps from qDESS can be done analytically, which is much faster than traditional fitting. To do so, we require a few scan parameters as well as rough estimates for $T_1$ of tissues. Scan parameters can be found in the DICOM files or the metadata file shipped with the dataset.\n",
+ "\n",
+ "An open-source implementation of the analytical fit is available in dosma. To ensure standardization, dosma should be used to perform all qMRI evaluation in SKM-TEA.\n",
+ "\n",
+ "IMPORTANT: Do not use the scanner-generated $T_2$ maps (available in the dicom folder) for analysis. These should be used for visualization purposes only."
+ ],
+ "metadata": {
+ "id": "E7cTiTk8Hqps"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "As mentioned above, we need rough estimates for $T_1$ of tissues for the analytical $T_2$ estimation. From [literature](), we know that femoral, tibial, and patellar (articular) cartilage has a $T_1$ of approximately 1.2sec and meniscus has a $T_1$ of ~1sec.\n",
+ "\n",
+ "We can use the segmentation to fill in the expected $T_1$ values. Note, we will have 2 $T_1$ maps -- one from the ground truth segmentation (`t1_gt`), and one from the predicted segmentation (`t1_pred`)."
+ ],
+ "metadata": {
+ "id": "_MMDFtcY1Q0R"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# For reconstruction, this would be based on reconstructions for E1/E2.\n",
+ "# Estimated T1 values are 1.2s for cartilage and 1s for meniscus\n",
+ "def get_t1(seg: dm.MedicalVolume):\n",
+ " \"\"\"Build T1 maps based on the segmentation.\n",
+ "\n",
+ " `seg[..., 3]` should correspond to the meniscus segmentation map.\n",
+ "\n",
+ " Args:\n",
+ " seg (dm.MedicalVolume): A one-hot encoded segmentation mask, where the\n",
+ " last dimension is the channel dimension.\n",
+ "\n",
+ " Returns:\n",
+ " dm.MedicalVolume: The estimated T1 map (in milliseconds).\n",
+ " \"\"\"\n",
+ " t1 = dm.MedicalVolume(np.ones(seg.shape[:3]) * 1200, seg.affine).to(seg.device)\n",
+ " t1[seg.A[..., 3].astype(bool)] = 1000\n",
+ " return t1\n",
+ "\n",
+ "t1_gt = get_t1(seg_gt)\n",
+ "t1_pred = get_t1(seg_pred)"
+ ],
+ "metadata": {
+ "id": "JfMeAPjrrAwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "y6k1z3LtMl6Z",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5c302053-03fc-4087-fcef-d4697dc9f8bc"
+ },
+ "outputs": [],
+ "source": [
+ "def compute_t2_map(t1: dm.MedicalVolume):\n",
+ " qdess = QDess([echo1, echo2]).to(t1.device)\n",
+ " t2map = qdess.generate_t2_map(\n",
+ " suppress_fat=True,\n",
+ " suppress_fluid=True,\n",
+ " gl_area=float(metadata[\"SpoilerGradientArea\"]),\n",
+ " tg=float(metadata[\"SpoilerGradientTime\"]),\n",
+ " tr=float(metadata[\"RepetitionTime\"]),\n",
+ " te=float(metadata[\"EchoTime1\"]),\n",
+ " alpha=float(metadata[\"FlipAngle\"]),\n",
+ " t1=t1,\n",
+ " nan_bounds=(0, 100),\n",
+ " nan_to_num=True,\n",
+ " )\n",
+ " return t2map.volumetric_map\n",
+ "\n",
+ "t2_gt = compute_t2_map(t1_gt)\n",
+ "t2_pred = compute_t2_map(t1_pred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "sl = 60 # Sagittal slice to plot\n",
+ "\n",
+ "plot_images(\n",
+ " [t2_gt, t2_pred],\n",
+ " processor=lambda x: x.cpu().A[..., sl],\n",
+ " titles=[\"T2 (Ground Truth)\", \"T2 (Pred)\"],\n",
+ " cmap=\"viridis\", show_cbar=True,\n",
+ ")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 352
+ },
+ "id": "4k3lVDg52trB",
+ "outputId": "4d6622c5-3dc1-4257-b050-136c477e347e"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### The `QuantitativeKneeMRI` metric\n",
+ "\n",
+ "`QuantitativeKneeMRI` metrics simplifies computing and tracing qMRI related metrics for key knee anatomical structures.\n",
+ "\n",
+ "We will use `qmri_gt` and `qmri_pred` to track regional $T_2$ measures extracted from the ground truth and predicted segmentations, respectively. These regions will correspond to the four segmented tissues: patellar cartilage (`pc`), femoral cartilage (`fc`), tibial cartilage (`tc`), and meniscus (`men`)\n",
+ "\n",
+ "We can also choose to compute qMRI measures for anatomically relevant subregions in the these tissues. To do this, set `use_subregions=True`. Note the subregion division can be time intensive.\n",
+ "\n",
+ "**Note**: The metric is stateful. This means each time the metric is called, it stores the results. Use `.reset()` to reset the metric and clear all stored results.\n",
+ "\n",
+ "*Aside*: These metrics are automatically computed under the hood with the [`skm_tea.evaluation.SkmTeaEvaluator`](https://github.com/StanfordMIMI/skm-tea/blob/main/skm_tea/evaluation/qdess_evaluation.py)."
+ ],
+ "metadata": {
+ "id": "zCn3xtvfuBtt"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "TrdR9XsOMl6a"
+ },
+ "outputs": [],
+ "source": [
+ "use_subregions = False\n",
+ "use_cpu = use_subregions # computing subregions is currently limited to the CPU\n",
+ "tissues = [\"pc\", \"fc\", \"tc\", \"men\"]\n",
+ "\n",
+ "qmri_gt = QuantitativeKneeMRI(channel_names=tissues, subregions=use_subregions, use_cpu=use_cpu)\n",
+ "qmri_pred = QuantitativeKneeMRI(channel_names=tissues, subregions=use_subregions, use_cpu=use_cpu)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3Tnrul8RMl6a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "50572ff7-b3bb-4d59-9fbd-3c68cd340d9a"
+ },
+ "outputs": [],
+ "source": [
+ "# Reset the metrics\n",
+ "qmri_gt.reset()\n",
+ "qmri_pred.reset()\n",
+ "\n",
+ "# Compute regional qMRI estimates using ground truth and predicted segmentations.\n",
+ "qmri_gt(ids=[scan[\"scan_id\"]], quantitative_map=[t2_gt], sem_seg=[seg_gt], medial_direction=metadata[\"MedialDirection\"])\n",
+ "qmri_pred(ids=[scan[\"scan_id\"]], quantitative_map=[t2_pred], sem_seg=[seg_pred], medial_direction=metadata[\"MedialDirection\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "kUwnBDOqMl6a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 99
+ },
+ "outputId": "7209be8b-5d89-43a1-fb48-35103eba8fe9"
+ },
+ "outputs": [],
+ "source": [
+ "print(\"Ground Truth Regional T2 Estimates:\")\n",
+ "display(qmri_gt.to_pandas())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(\"Predicted Regional T2 Estimates:\")\n",
+ "display(qmri_pred.to_pandas())"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 99
+ },
+ "id": "d5dqjITe5IkJ",
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diff --git a/projects/README.md b/projects/README.md
new file mode 100644
index 00000000..f5c9182d
--- /dev/null
+++ b/projects/README.md
@@ -0,0 +1,63 @@
+# Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC)
+
+## **Datasets**
+
+**ATOMMIC** supports several public datasets for accelerated MRI reconstruction, MRI segmentation, and quantitative
+imaging, as well as multitasking, i.e. training a model to perform reconstruction and segmentation simultaneously.
+
+Private datasets can also be used with this repo, but the data must be converted to the appropriate format.
+The preferred format is HDF5, but NIfTI is also supported for segmentation. Data can be stored either as 3D volumes or
+2D slices. You can check the preprocessing scripts for each dataset to see how the data should be formatted.
+
+- For reconstruction, it's best to store the data should with the following dimensions:
+[num_slices, num_coils, height, width].
+- For segmentation, the data should be stored with the following dimensions: [num_slices, height, width]. Labels can be
+stored as either one-hot or categorical.
+
+You can extend the dataloaders for the corresponding task to support your own dataset.
+
+On each of the following project folders, you can find the corresponding preprocessing script, which can be used to
+convert the data to the appropriate format.
+
+### **Quantitative Imaging**
+
+For quantitative imaging, the following public datasets are supported:
+- [AHEAD](qMRI/AHEAD).
+
+### **Reconstruction**
+
+For accelerated MRI reconstruction, the following public datasets are supported:
+- [CC359](REC/CC359),
+- [fastMRI Brains Multicoil](REC/fastMRIBrainsMulticoil),
+- [fastMRI Knees Multicoil](REC/fastMRIKneesMulticoil),
+- [fastMRI Knees Singlecoil](REC/fastMRIKneesSinglecoil),
+- [SKM-TEA](REC/SKMTEA),
+- [Stanford Knees](REC/StanfordKnees2019).
+
+### **Segmentation**
+
+For MRI segmentation, the following public datasets are supported:
+- [BraTS2023AdultGlioma](SEG/BraTS2023AdultGlioma),
+- [ISLES2022SubAcuteStroke](SEG/ISLES2022SubAcuteStroke),
+- [SKM-TEA](SEG/SKMTEA).
+
+
+## **Models**
+
+**ATOMMIC** supports several models for accelerated MRI reconstruction, MRI segmentation, and quantitative imaging, as
+well as multitasking. Please check [here](../README.md) the list of supported models.
+
+On each project folder, you can find the corresponding model configuration file, which can be used to train and test
+the model. You only need to change the `data paths` and `output paths` to the appropriate paths on your system. You can
+also change the `model parameters` to change the model architecture and hyperparameters.
+
+## **Training/Testing**
+
+To train/test a model, you can use the following command:
+
+```bash
+atommic run -c path-to-config-file
+```
+
+## **Reproducing the ATOMMIC paper**
+ATOMMIC paper is fully reproducible. Please check [here](ATOMMIC_paper/README.md) for more information.
diff --git a/projects/REC/CC359/README.md b/projects/REC/CC359/README.md
new file mode 100644
index 00000000..88f21de0
--- /dev/null
+++ b/projects/REC/CC359/README.md
@@ -0,0 +1,88 @@
+## **Calgary-Campinas Public Brain MR Dataset (CC359)**
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the
+Calgary-Campinas Public Brain MR Dataset (CC359).
+
+For more information, please refer to https://sites.google.com/view/calgary-campinas-dataset/home
+
+The dataset contains 3D T1-weighted raw data.
+
+### Dataset Folder Structure
+The information below describes the folder structure of the dataset and is copied from the dataset website.
+
+```console
+CC359/
+โโโ Raw-data
+ โโโ Multi-channel
+ โโโ 12-channel
+ โ โโโ test_12_channel.zip -> Undersampled 12-channel test set for R = 5 and R = 10
+ โ โโโ train_val_12_channel.zip -> Fully sampled 12-channel train and validation data
+ โโโ 32-channel
+ โโโ test_32_channel.zip -> Undersampled 32-channel test set for R = 5 and R = 10
+```
+
+### Raw Multicoil Data
+The information below describes the raw multicoil data and is copied from the dataset website.
+
+We are providing 167 three-dimensional (3D), T1-weighted, gradient-recalled echo, 1 mm isotropic sagittal acquisitions
+collected on a clinical MR scanner (Discovery MR750; General Electric (GE) Healthcare, Waukesha, WI). The scans
+correspond to presumed healthy subjects (age: 44.5 years +/- 15.5 years [mean +/- standard deviation]; range: 20 years
+to 80 years). Datasets were acquired using either a 12-channel (117 scans) or a 32-channel coil (50 scans).
+Acquisition parameters were TR/TE/TI = 6.3 ms/ 2.6 ms/ 650 ms (93 scans) and TR/TE/TI = 7.4 ms/ 3.1 ms/ 400 ms
+(74 scans), with 170 to 180 contiguous 1.0-mm slices and a field of view of 256 mm x 218 mm. The acquisition matrix
+size for each channel was Nx x Ny x Nz = 256 x 218 x [170,180]. In the slice-encoded direction (kz), data were
+partially collected up to 85% of its matrix size and then zero filled to Nz= [170,180]. The scanner automatically
+applied the inverse Fourier Transform, using the fast Fourier transform (FFT) algorithms, to the kx-ky-kz k-space data
+in the frequency-encoded direction, so a hybrid x-ky-kz dataset was saved. This reduces the problem from 3D to
+two-dimensions, while still allowing to undersample k-space in the phase encoding and slice encoding directions. The
+partial Fourier reference data were reconstructed by taking the channel-wise iFFT of the collected k-spaces for each
+slice of the 3D volume and combining the outputs through the conventional sum of squares algorithm. The dataset
+train/validation/test split is summarized in the table below .Relevant information
+
+- Healthy subjects (age: 44.5 years ยฑ 15.5 years; range: 20 years to 80 years).
+- Acquisition parameters are either: TR/TE/TI = 6.3 ms/2.6 ms/650 ms and TR/TE/TI = 7.4 ms/3.1 ms/400 ms
+- Average scan duration ~341 seconds
+- Only the undersampled k-spaces for R=5 and R=10 are provided for the test set
+
+Dataset summary:
+- 12-channel
+ - Train: 47 dataset
+ - Validation: 20 datasets
+ - Test: 50 datasets
+- 32-channel
+ - Test: 50 datasets
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](visualize.ipynb). You just need to set the path where the
+dataset is downloaded. The
+[original notebook](https://github.com/rmsouza01/MC-MRI-Rec/blob/master/JNotebooks/getting-started/getting_started.ipynb)
+is copied from the (https://github.com/rmsouza01/MC-MRI-Rec repository and provided by the CC359 dataset authors.
+
+### **Preprocessing**
+The CC359 dataset is supported natively in ``ATOMMIC`` and no preprocessing is required. You just need to convert the
+.npy masks to .h5 format if you want to undersampled the data with the provided masks. The conversion script is
+implemented in the [compute_masks.sh](compute_masks.sh) script.
+
+```bash
+bash ./projects/REC/CC359/compute_masks.sh
+```
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/CC359/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` to the generated json files. In `exp_manager` please set the `exp_dir` to
+the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/REC/CC359/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/CC359/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/REC/CC359/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/REC/CC359/__init__.py b/projects/REC/CC359/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/CC359/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/CC359/compute_masks.sh b/projects/REC/CC359/compute_masks.sh
new file mode 100644
index 00000000..b902f359
--- /dev/null
+++ b/projects/REC/CC359/compute_masks.sh
@@ -0,0 +1,41 @@
+#!/bin/bash
+echo "
+Compute the mask for the Calgary-Campinas 359 dataset.
+
+The data download link is available at: https://sites.google.com/view/calgary-campinas-dataset/home
+
+Starting the preprocessing...
+"
+
+# Prompt the user to enter the path to the downloaded data
+echo "Please enter the (downloaded) data directory:"
+read INPUT_DIR
+
+# Check if the input directory exists
+if [ ! -d "$INPUT_DIR" ]; then
+ echo "The input directory does not exist. Please try again."
+ exit 1
+fi
+
+# Prompt the user to enter the path to the downloaded mask data
+echo "Please enter the (downloaded) data directory:"
+read INPUT_MASK_DIR
+
+# Check if the input mask directory exists
+if [ ! -d "$INPUT_MASK_DIR" ]; then
+ echo "The input mask directory does not exist. Please try again."
+ exit 1
+fi
+
+# Prompt the user to enter the output directory for the preprocessed data
+echo "Please enter the output directory for the preprocessed data:"
+read OUTPUT_DIR
+
+# Prompt the user to enter if 5 or 10 or both accelerations are to be used
+echo "Please enter the acceleration factor (5 or 10 or both - Default):"
+read ACCELERATION
+
+# Compute the masks
+echo "Computing the masks..."
+python projects/reconstruction/CC359/scripts/compute_masks.py $INPUT_DIR $INPUT_MASK_DIR $OUTPUT_DIR $ACCELERATION
+echo "Done!"
diff --git a/projects/REC/CC359/conf/targets/12_channel_Val_RSS.yaml b/projects/REC/CC359/conf/targets/12_channel_Val_RSS.yaml
new file mode 100644
index 00000000..ff7bcec8
--- /dev/null
+++ b/projects/REC/CC359/conf/targets/12_channel_Val_RSS.yaml
@@ -0,0 +1,100 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/targets/CC359_12_channel_Val/RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/ccnn.yaml b/projects/REC/CC359/conf/test/10x/ccnn.yaml
new file mode 100644
index 00000000..95f930fd
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/ccnn.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/cirim.yaml b/projects/REC/CC359/conf/test/10x/cirim.yaml
new file mode 100644
index 00000000..04ec12fc
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/cirim.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 128
+ - 128
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 128
+ - 128
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/CIRIM_128CH/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/crnn.yaml b/projects/REC/CC359/conf/test/10x/crnn.yaml
new file mode 100644
index 00000000..82af1b5a
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/crnn.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/jointicnet.yaml b/projects/REC/CC359/conf/test/10x/jointicnet.yaml
new file mode 100644
index 00000000..09b81d61
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/jointicnet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/kikinet.yaml b/projects/REC/CC359/conf/test/10x/kikinet.yaml
new file mode 100644
index 00000000..92fe9fdc
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/kikinet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/lpdnet.yaml b/projects/REC/CC359/conf/test/10x/lpdnet.yaml
new file mode 100644
index 00000000..29ea9a03
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/lpdnet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/modl.yaml b/projects/REC/CC359/conf/test/10x/modl.yaml
new file mode 100644
index 00000000..268150a7
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/modl.yaml
@@ -0,0 +1,117 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/proximalgradient.yaml b/projects/REC/CC359/conf/test/10x/proximalgradient.yaml
new file mode 100644
index 00000000..21a061cc
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/proximalgradient.yaml
@@ -0,0 +1,109 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: PROXIMALGRADIENT
+ conjugate_gradient_dc: true
+ conjugate_gradient_iterations: 10
+ penalization_weight: 1.0
+ dimensionality: 2
+ coil_combination_method: RSS
+ normalization_type: minmax
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/ProximalGradient/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: ???
diff --git a/projects/REC/CC359/conf/test/10x/recurrentvarnet.yaml b/projects/REC/CC359/conf/test/10x/recurrentvarnet.yaml
new file mode 100644
index 00000000..cbff2e1a
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/recurrentvarnet.yaml
@@ -0,0 +1,131 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: ???
diff --git a/projects/REC/CC359/conf/test/10x/rim.yaml b/projects/REC/CC359/conf/test/10x/rim.yaml
new file mode 100644
index 00000000..d02eb17b
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/rim.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/rvn.yaml b/projects/REC/CC359/conf/test/10x/rvn.yaml
new file mode 100644
index 00000000..82d0719c
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/rvn.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/unet.yaml b/projects/REC/CC359/conf/test/10x/unet.yaml
new file mode 100644
index 00000000..09a9a664
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/unet.yaml
@@ -0,0 +1,118 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/varnet.yaml b/projects/REC/CC359/conf/test/10x/varnet.yaml
new file mode 100644
index 00000000..7662ed42
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/varnet.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/vsnet.yaml b/projects/REC/CC359/conf/test/10x/vsnet.yaml
new file mode 100644
index 00000000..216ea35a
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/vsnet.yaml
@@ -0,0 +1,117 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/xpdnet.yaml b/projects/REC/CC359/conf/test/10x/xpdnet.yaml
new file mode 100644
index 00000000..66aeaebe
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/xpdnet.yaml
@@ -0,0 +1,128 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/10x/zerofilled.yaml b/projects/REC/CC359/conf/test/10x/zerofilled.yaml
new file mode 100644
index 00000000..892a58a8
--- /dev/null
+++ b/projects/REC/CC359/conf/test/10x/zerofilled.yaml
@@ -0,0 +1,104 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ coil_combination_method: RSS
+ dimensionality: 2
+ normalization_type: minmax
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_10x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_10x_RSS_NNEstimationCSM/ZeroFilled_RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/ccnn.yaml b/projects/REC/CC359/conf/test/5x/ccnn.yaml
new file mode 100644
index 00000000..d1ae4748
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/ccnn.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/reconstruction/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/reconstruction/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/cirim.yaml b/projects/REC/CC359/conf/test/5x/cirim.yaml
new file mode 100644
index 00000000..102c228c
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/cirim.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 128
+ - 128
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 128
+ - 128
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/CIRIM_128CH/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/crnn.yaml b/projects/REC/CC359/conf/test/5x/crnn.yaml
new file mode 100644
index 00000000..bfdda2c9
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/crnn.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/jointicnet.yaml b/projects/REC/CC359/conf/test/5x/jointicnet.yaml
new file mode 100644
index 00000000..9308cd47
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/jointicnet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/kikinet.yaml b/projects/REC/CC359/conf/test/5x/kikinet.yaml
new file mode 100644
index 00000000..35dd0cd1
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/kikinet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/lpdnet.yaml b/projects/REC/CC359/conf/test/5x/lpdnet.yaml
new file mode 100644
index 00000000..7c13d72d
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/lpdnet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/modl.yaml b/projects/REC/CC359/conf/test/5x/modl.yaml
new file mode 100644
index 00000000..f1866fa3
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/modl.yaml
@@ -0,0 +1,117 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/proximalgradient.yaml b/projects/REC/CC359/conf/test/5x/proximalgradient.yaml
new file mode 100644
index 00000000..0f0cd806
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/proximalgradient.yaml
@@ -0,0 +1,109 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: PROXIMALGRADIENT
+ conjugate_gradient_dc: true
+ conjugate_gradient_iterations: 10
+ penalization_weight: 1.0
+ dimensionality: 2
+ coil_combination_method: RSS
+ normalization_type: minmax
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/ProximalGradient/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: ???
diff --git a/projects/REC/CC359/conf/test/5x/recurrentvarnet.yaml b/projects/REC/CC359/conf/test/5x/recurrentvarnet.yaml
new file mode 100644
index 00000000..e46d5139
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/recurrentvarnet.yaml
@@ -0,0 +1,131 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: ???
diff --git a/projects/REC/CC359/conf/test/5x/rim.yaml b/projects/REC/CC359/conf/test/5x/rim.yaml
new file mode 100644
index 00000000..99b204e3
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/rim.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/rvn.yaml b/projects/REC/CC359/conf/test/5x/rvn.yaml
new file mode 100644
index 00000000..eaf35512
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/rvn.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/unet.yaml b/projects/REC/CC359/conf/test/5x/unet.yaml
new file mode 100644
index 00000000..48cb6098
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/unet.yaml
@@ -0,0 +1,118 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/varnet.yaml b/projects/REC/CC359/conf/test/5x/varnet.yaml
new file mode 100644
index 00000000..a52aaf0e
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/varnet.yaml
@@ -0,0 +1,116 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/vsnet.yaml b/projects/REC/CC359/conf/test/5x/vsnet.yaml
new file mode 100644
index 00000000..0afcabeb
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/vsnet.yaml
@@ -0,0 +1,117 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/xpdnet.yaml b/projects/REC/CC359/conf/test/5x/xpdnet.yaml
new file mode 100644
index 00000000..d39e6ea8
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/xpdnet.yaml
@@ -0,0 +1,128 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/test/5x/zerofilled.yaml b/projects/REC/CC359/conf/test/5x/zerofilled.yaml
new file mode 100644
index 00000000..52ea00ee
--- /dev/null
+++ b/projects/REC/CC359/conf/test/5x/zerofilled.yaml
@@ -0,0 +1,104 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ coil_combination_method: RSS
+ dimensionality: 2
+ normalization_type: minmax
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val_5x
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/CC359_12_channel_Val_poisson2d_5x_RSS_NNEstimationCSM/ZeroFilled_RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/CC359/conf/train/ccnn.yaml b/projects/REC/CC359/conf/train/ccnn.yaml
new file mode 100644
index 00000000..e4a1a6f8
--- /dev/null
+++ b/projects/REC/CC359/conf/train/ccnn.yaml
@@ -0,0 +1,166 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/cirim.yaml b/projects/REC/CC359/conf/train/cirim.yaml
new file mode 100644
index 00000000..b6cc22d8
--- /dev/null
+++ b/projects/REC/CC359/conf/train/cirim.yaml
@@ -0,0 +1,200 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 128
+ - 128
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 128
+ - 128
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/CIRIM_128CH/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/crnn.yaml b/projects/REC/CC359/conf/train/crnn.yaml
new file mode 100644
index 00000000..722850ae
--- /dev/null
+++ b/projects/REC/CC359/conf/train/crnn.yaml
@@ -0,0 +1,166 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/dunet.yaml b/projects/REC/CC359/conf/train/dunet.yaml
new file mode 100644
index 00000000..a083f614
--- /dev/null
+++ b/projects/REC/CC359/conf/train/dunet.yaml
@@ -0,0 +1,169 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: VS # GD, PROX, VS
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-9
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.5
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/DUNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/jointicnet.yaml b/projects/REC/CC359/conf/train/jointicnet.yaml
new file mode 100644
index 00000000..d67ba984
--- /dev/null
+++ b/projects/REC/CC359/conf/train/jointicnet.yaml
@@ -0,0 +1,176 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/kikinet.yaml b/projects/REC/CC359/conf/train/kikinet.yaml
new file mode 100644
index 00000000..4fbb078a
--- /dev/null
+++ b/projects/REC/CC359/conf/train/kikinet.yaml
@@ -0,0 +1,176 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/lpdnet.yaml b/projects/REC/CC359/conf/train/lpdnet.yaml
new file mode 100644
index 00000000..a39abeb7
--- /dev/null
+++ b/projects/REC/CC359/conf/train/lpdnet.yaml
@@ -0,0 +1,179 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/modl.yaml b/projects/REC/CC359/conf/train/modl.yaml
new file mode 100644
index 00000000..83549f58
--- /dev/null
+++ b/projects/REC/CC359/conf/train/modl.yaml
@@ -0,0 +1,167 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/multidomainnet.yaml b/projects/REC/CC359/conf/train/multidomainnet.yaml
new file mode 100644
index 00000000..760709f1
--- /dev/null
+++ b/projects/REC/CC359/conf/train/multidomainnet.yaml
@@ -0,0 +1,164 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/MultiDomainNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/recurrentvarnet.yaml b/projects/REC/CC359/conf/train/recurrentvarnet.yaml
new file mode 100644
index 00000000..3bcc9408
--- /dev/null
+++ b/projects/REC/CC359/conf/train/recurrentvarnet.yaml
@@ -0,0 +1,179 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/rim.yaml b/projects/REC/CC359/conf/train/rim.yaml
new file mode 100644
index 00000000..b0039e53
--- /dev/null
+++ b/projects/REC/CC359/conf/train/rim.yaml
@@ -0,0 +1,200 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/rvn.yaml b/projects/REC/CC359/conf/train/rvn.yaml
new file mode 100644
index 00000000..3bcc9408
--- /dev/null
+++ b/projects/REC/CC359/conf/train/rvn.yaml
@@ -0,0 +1,179 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/unet.yaml b/projects/REC/CC359/conf/train/unet.yaml
new file mode 100644
index 00000000..fdfcc1c7
--- /dev/null
+++ b/projects/REC/CC359/conf/train/unet.yaml
@@ -0,0 +1,168 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/varnet.yaml b/projects/REC/CC359/conf/train/varnet.yaml
new file mode 100644
index 00000000..c8eb064c
--- /dev/null
+++ b/projects/REC/CC359/conf/train/varnet.yaml
@@ -0,0 +1,166 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/vsnet.yaml b/projects/REC/CC359/conf/train/vsnet.yaml
new file mode 100644
index 00000000..330227b4
--- /dev/null
+++ b/projects/REC/CC359/conf/train/vsnet.yaml
@@ -0,0 +1,167 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/conf/train/xpdnet.yaml b/projects/REC/CC359/conf/train/xpdnet.yaml
new file mode 100644
index 00000000..afde8d51
--- /dev/null
+++ b/projects/REC/CC359/conf/train/xpdnet.yaml
@@ -0,0 +1,178 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val
+ coil_sensitivity_maps_path: None
+ mask_path: data_parent_dir/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: cc359
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.CC359_12_channel_poisson2d_6x_12x_NNEstimationCSM
diff --git a/projects/REC/CC359/scripts/__init__.py b/projects/REC/CC359/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/CC359/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/CC359/scripts/compute_masks.py b/projects/REC/CC359/scripts/compute_masks.py
new file mode 100644
index 00000000..689a5576
--- /dev/null
+++ b/projects/REC/CC359/scripts/compute_masks.py
@@ -0,0 +1,65 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import pathlib
+
+import h5py
+import numpy as np
+from tqdm import tqdm
+
+
+def main(args):
+ data_dir = args.data_dir
+ masks_dir = args.masks_dir
+
+ # Create output directory
+ output_dir = args.output_dir
+ output_dir.mkdir(exist_ok=True)
+
+ acc = args.accelerations
+
+ for data_file in tqdm(data_dir.glob("*.h5")):
+ with h5py.File(data_file, "r") as f:
+ # load k-space data to get the shape
+ data = f["kspace"]
+
+ if acc in ["5", "both"]:
+ # load respective 5x mask
+ mask_5x = np.load(masks_dir / f"R5_{data.shape[1]}x{data.shape[2]}.npy")
+ # concatenate the mask to match the number of slices, original there are 100 slices/masks
+ mask_5x = np.concatenate((mask_5x, mask_5x), axis=0)
+ random_slices = np.random.choice(mask_5x.shape[0], (data.shape[0] - mask_5x.shape[0]), replace=False)
+ mask_5x = np.concatenate((mask_5x, mask_5x[random_slices]), axis=0)
+
+ if acc in ["10", "both"]:
+ # load respective 10x mask
+ mask_10x = np.load(masks_dir / f"R10_{data.shape[1]}x{data.shape[2]}.npy")
+ # concatenate the mask to match the number of slices, original there are 100 slices/masks
+ mask_10x = np.concatenate((mask_10x, mask_10x), axis=0)
+ random_slices = np.random.choice(
+ mask_10x.shape[0],
+ int(mask_10x.shape[0] * (data.shape[0] - mask_10x.shape[0]) / mask_10x.shape[0]),
+ replace=False,
+ )
+ mask_10x = np.concatenate((mask_10x, mask_10x[random_slices]), axis=0)
+
+ with h5py.File(output_dir / data_file.name, "w") as f:
+ if acc in ["5", "both"]:
+ f.create_dataset("mask_5x", data=mask_5x)
+ if acc in ["10", "both"]:
+ f.create_dataset("mask_10x", data=mask_10x)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_dir", type=pathlib.Path, help="Path to the raw data directory.")
+ parser.add_argument("masks_dir", type=pathlib.Path, help="Path to the .npy masks directory.")
+ parser.add_argument("output_dir", type=pathlib.Path, help="Path to the output directory.")
+ parser.add_argument(
+ "--accelerations",
+ choices=["5", "10", "both"],
+ default="both",
+ help="The accelerations to export masks. Default: both.",
+ )
+ main(parser.parse_args())
diff --git a/projects/REC/CC359/visualize.ipynb b/projects/REC/CC359/visualize.ipynb
new file mode 100644
index 00000000..bd73cae3
--- /dev/null
+++ b/projects/REC/CC359/visualize.ipynb
@@ -0,0 +1,485 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Multi-channel MR Image Reconstruction Challenge - Getting Started\n",
+ "\n",
+ "\n",
+ "DATA_PATH = os.path.join(os.getcwd(), 'data')upyter notebook, we illustrate the basics for loading the data of the multi-channel magnetic resonance image reconstruction challenge. More specifically, we illustrate how to load and display samples from the train, validation and test sets. We also illustrate how to load the sampling patterns provided for R = 5 and R = 10 and use them to retrospectively undersample k-space. R indicates the acceleration factor."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "outputs": [],
+ "source": [
+ "# Some basic Python libraries\n",
+ "%matplotlib inline\n",
+ "import numpy as np\n",
+ "import matplotlib.pylab as plt\n",
+ "import os\n",
+ "import matplotlib.gridspec as gridspec\n",
+ "import glob\n",
+ "import sys\n",
+ "import h5py\n",
+ "from pathlib import Path"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-10-08T17:53:52.579673Z",
+ "end_time": "2023-10-08T17:53:53.361863Z"
+ }
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The data are saved as HDF5 files (.h5). For the train and validation sets, you get the fully sampled k-spaces stored in the format x-ky-kz-nchannels (*i.e.*, inverse Fourier Transform (iFT) in the frequency-encoded direction, k-space in the phase-encoded and slice-encoded directions, and number of coil channels). This allows to undersample in both the phase-encoded and slice-encoded directions while still being able to model the problem as a 2D problem, where you can reconstruct slice-by-slice in the frequency-encoded direction. The channels dimension of the array alternate between real and imaginary components of the complex values, therefore for the 12-channel data you get 24 channels in the last dimension of the array, and for the 32-channel data you get 64. Remember that the dataset was partially acquired in the slice-encoded direction."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "cc359_data_dir = input(\"Please enter the (downloaded) data path: \")"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-10-08T17:53:53.406192Z",
+ "end_time": "2023-10-08T17:53:58.292271Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of volumes in the train set 47\n",
+ "Data format is x-ky-kz-nchannels\n",
+ "data shape: (256, 218, 180, 24)\n",
+ "\n",
+ "\n",
+ "Channel-wise k-space\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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wvlR9IVseHh4M3/PdfcJcqVSMbMcfNZvNQJ7iySXuERXUz31dlShSdfj5558t08bpPj09KR6PazqdKhaLmQOu1+u2kWOxmIGuSCSi5XJplbvhcKhCoaC7uzsNBgPbFLvdzuRiBDuAVyQSUSaTMYkOwLdWq2k+nyubzVo5mHIyCdnXX39tjhrGMh6P2yZfLBbGyCwWC5VKJTO6jx8/6unpyaqHXmrmGRSCB2CX+4ehk+QhM2Hzk9DClsJQsGGR+K1WKx2PRwO2y+XSgCXXwsble8EewWYimQO0TiYT++4AAiotGFmlUtFoNDI5GobM/dxut7q/vzeg0Ov19O7dO9VqNWWzWY3HY3OA4/HYKsnhun5Fo1HFYjGNRiNls1k9Pz/r7u5OuVzOZJYwjMfj0aoQJEU47FQqpZeXF2OqkZ1KMgmodNrTk8nEgNZisVCz2dRut9NoNLIECxnzbrczRQK2g1OXzlVsmHs+H6KEhJAgSELI7wI6fQXw8fHRfofXkRyhbODzvOQIQgNpLaRVsVjUbDYzWSwAbjababPZmBSfqhx+g0SZ4IL99Xo9RSIRlUolC+b7/d4kpF7Ww5+Xy6W9D4Amn88rFovp5eXF7JLv/fbtW+33e0uakTvhi2azmRE7PAtIsOFwaDKecF23aFdYr9cW17C9XC5nsQzficxROj0jqr6AMKSK7DtAFIoOKhCDwSAgI8PuJpNJoOKdyWTM1wPAqK4Tn3h/kslqtWqECuQPwJZ4D7D0CR72x58lGSidTqfK5XJ2j5D8EUuxR+6Nl6ADAiVZZR8ymu9CHPV+BywBQKb6gIIH8pZ7BDFDC83hcDB8QdsNeIDYCjlOUukldOv1OoA/8B3z+dyAKW0gsVjM5Ij1et2qUeG6ftGWQMUIJQhYdLlc2j7qdDqKRCKWzBN3qZD/5S9/0ePjo6njUHsgQywUCioUCprP5xqPx6YYIA6D4yRpPB5boQDClP3a7/ctTnpbohiBag/ciGyU32dvg6P5zrwXWBU/wPuzJ71MFfvGRiCnaPXiHmDLtIR5aTzfw1dWsTH8E3YOaQLm99fe6XRUKBRMIXU4HPT6+mp4fr/fW5Xw9fVVtVrNyGQ+L5VKmR+DGKa6G41GNR6PtVgsrNgBToHIgajHd34J66pEEVkMmt5SqWR9BgSjarWq//pf/6tub28ViUTU6/X0+Pio1Wqll5cXVatVjUYjZTIZZTIZTafTgMOErUDKAbji4S0WC2MR2FT0O8DOEVQkWeUR53x/f28gEtBMWRlZHgyfl9/guNPptJ6enrRardTr9ZRMJlWpVDSfzy1Z2263thF5n+VyaZVEjHU4HFoyOxqNlEqlzLDocZBkhuN7Mbkn2+1Wg8HAghpsCQwIwZFgdDgcVK1Wzan0ej3FYjHlcrkA++yTZaqnMM7+/VKplDqdjgF0gijOYjqdKp/Pm1HBri2XS81mM5XLZcViMX38+PGarRmu/7WQZTYaDUWjUT0+Pmqz2Wg4HGq9XqtUKhmoSCaT5sALhUKgGoUElQQKKcbxeFSpVFI0GtW7d+90e3trSSDBzVe3IAO8zBqASjLG3uc9qAZQnYZYAghRXcCuIpGIBQKYRd6PfQorTPJGVQciJZVKablcKpfLWeUO+x0MBoHgRsCH5MHefS+HdAp87XY70JuSSCTs7yTr3BdsH3v3do4SASIGv8O9RB2B/JTfm06nZsuRSMR6YSCgCLhITiORiBFtyJjwh9zDcP36Rf9sPp+36phPbgBfh8NB7XZblUrFZJ35fF6j0Ujz+dxsBSAoncAc+8QrTHjG7Itut2ux0vsDbEaSEbjEZPqELuXlxFH61yeTSUCuxudADPM+vvoNqIS08cCSfcxrqOhzjZeSvd1up06nI+lUWZT0VwAZxQ8EDvGK+4htED8hSqnAjsdjq+JSReC++8of9kTSDPEyGo1M7k0CQvIKyOS7l0olA/yDwUDlclmFQsEqHewp4m24rl/pdFqDwcCIs0QiYbJR7MonA/hkkqXRaKRCoaBkMqlvvvkmkFSSaFL1p9VhPp/rcDhYS4Tv/Y1Go0ZIsAfn87mpS7BPiBj+DWxNwcJX7r3k3Sv3KPaw91H1YB+LxUI3NzdGOvNzcCcEFKpCj90vlXDEcXAsxDHJFK+FgCoUClYkAWdSmCJBQ8WDP6tUKjYjgzaNarVqz9C3fd3c3FhCD86GUKpWq6YkojL46dMnNRoNwwbpdFqfPn3SYDDQ73//e/X7fWUyGatkUiD5EtZVieKl80c3TbUum82q3W6r2WxaALq7uzMjo3JUrVZtUyBZQfbknTnN9VQoKpWKVar4OcnPZDJROp029t+zGbCBPHR6s0iAAFfL5TIAmqXzsJnpdGp9D9IpIbq9vTXJGFI6kjTf2wAQk2QGCWgDIMBmElRppiXo8D33+72xJRgmsjYM2gdWJD+eqcFIcEgARgyYvonNZmNggOopwDGfzysajer9+/dqNpsGcF5eXlQul+0ZkcSXy2UDuDg4nt/Hjx8tGQjXdYveAOms5ff9TsgkqSTgjAFo2Dd/hxmnmnA8Hk2aen9/r1QqpZubG3POBLx+v68//OEPgWE2koxlJ0ljL0qyHkOkXCRN3nb42eWwDWQ+VM/peWVfYlf4Ay8/gzVGfgcgBjD6xnr8CtLxXC5nPgHbgv1EUeGBHL3NMJMEaq7BVx59ooyqABYU8Hg8Hq3Swu9iZ7ChkswOCYylUsmuS5JJqQD+JMmoFFKplFWlwvXrF1JFEjFkm5KsKk9rRi6XU6fTUbPZ1Hw+N7AzmUysbxGyAMBJ60Wz2VShUDBb5edIx4l72+1W7XZbDw8PlnRSGanVaprNZuabqXQSjyTZ0Aj60CF4iF28H4QIcZvvDMEDycJ7+UFuvm8LUmgwGOjl5UVPT0+qVqv2mbTEoF6QzkMuuPf++r3cdLlcmr3hV0jI6NWk2nQ4HHRzc6NkMqnX11f1ej01Gg2ToyNDpK+JZ0+rCffy5eXFniH4Yrfb6f7+Xp1Ox6T7mUzGhmfx/uwZ3jNMFH+b5Ye2QMrgnyHriJl+IFwkElGj0bBBN5CPxJ6npyctFgsdDgdLJumH87jWDy3b7XbWgkDLBwnMdDo1vEcVn0SJahZEbKvVMtv1w6AkBSSqJEHEQZLDYrFoBAXD7CBUURdig9gp3484BfEonauI+APUD9gqBQ4SROThzPfAf2BL9IjiR3w/tW+roOABRiYm8z6SLDeBHGVYViQSsVxluVzq9vbWFBHFYtEGOxaLRfPNqVTKEkQIsC9hXZUo4njR6rM8QGK6Fw9hNBqZfGSxWOj+/l69Xs8aVQFoMBKUnHHk9/f3BuD85vHs5GazUaPRsCAFAGJDSAow5ul0WuPx2KpogGuCFtIC3r/X66lSqZgcFjkpzh0jAKiRPLKBJFnZm6AIeEXW4o2In/HdqP5xvR6sHY9HK6XT78KG9wFFkk1y4/5QEeR74GyouOJU0um09YkQPAnoTHOSZN+XAR48Z6a4bTYbTadT3d7eBpgVEuZwXb/6/b4eHx81nU71888/W/JEk/379+91f39vzne1WtnU391uFxg2w7CkbDZr9iGdbKnb7er+/t5IGfZTvV43OdpsNlOxWNRgMLCgyTTNl5cXvXnzxpwvUg4SKvxBrVYzwAn4QnKNfSA1oT8CibeXtvrqu5d9+WE7VNsJdN4P+coKVQwYXhI3fIKvflCNZGqrl/JQ3eTf6etNJpPq9/sqFAoGRqVz0uub8wHfu93OKjqQP8fjUc/Pz2o2m3r//r1qtZp9hn+egFZsmt7WfD6v8Xhs1wgBEa5fvy6HP3nSbrlc2mCYT58+6fHx0aT69LrDXGN3SN2IO6VSyYi7yWQSmBrM71MBpxcKGTmtBtvtVjc3NyaH5t9o04DIBQ+QEEnnQTmz2czeF7BNcgpQpnJQKBRs+idAD1vCrzBnABBZr9etSkllFgULiSjgDF9SLpeN1PWgkN+lggc4xC/RfgFJiwoJCVqj0VCr1TISrVAomEQU4O97jf3gOmS/g8EggFEgjYn7fOZisTBiiPjNs6WfMlzXrUgkolwupw8fPuju7s5sDWkwiVij0Qj0iIN36/W6JVqo0dh7SIlRVVFwkE4xgcn/7NnD4aBms6nZbGZEB/ujXC4bmSide1/z+Xyg7x1JOwQJBQNIWPaSx6F+rge4td/vW4WOpJDhUp4MBU9KwQnIkmxOiW8ToaBDLAZ3+tYJSG5k776dC/tl+B0J2n6/N8IXv4kNJpNJtdttSbI+U+wJ3+aJIY+r+/2+ms2mpPPE01gspkqlEug/5b5BKkPyfQnrqkSRDZlIJNTr9YwF4GGxASqVSsA5M0q7VCqp2+0GnC+shG9Y99MAD4eD9eD4yiNshXSetsTD9BPECASw9RgqySeyVJIg6TSMwjfsPjw8aD6fGxPvKyIkbwBRWEGYSoINCSuVEtgNkuNIJKJ+v2+Mr6+Gcl99zyKMFfIXgClB1k94Qj6IVMLLfNnMsGQA4UQiYd+Vvi2+NwwNBp1MJq0Si2SiXC6rXq9rNBrZdSwWC719+1bz+dzIBCpQYZ/ib7PK5bLtwaenJ9vXMM6tVsuc+Hw+N1kETLZnzCWZU6Ufgj2QzWb18vJitkY1g6QfUEnfBcnaZrNRsVg0GwMYwpReDt5hwAegimAAy0t1hqCDxIvAPp1OLfmjIiPJEkjAMdMQAXG8P0GGYEQAImhBkvF3bBBQwftCosxmM+u/QhkAO0rw4R74icfz+dwIKyotVDd5RiQbfrLc/f291uu1bm9vtVqtVKvVjHFFYuQTW3wpxB7kEYqJcF23AFhIsiuVivr9vlqtlj59+mRMO8e1IHH20279UAZ6fb1kDFtCnkzihgoEiRYyWAgX9hXxgp5kCBNsZTwem01hDxCjj4+P1sLgB+UQb6ios3dJyhhMh6KBwVFI6Ej+iKkeuHny9lKZwnVRXRmPx6bmAU9AcvsKENgCQkeSScyi0dMI/O12G+i/RhIHQcuxVQB2CO7xeKynpye1221tt6fptY1GwwD8cDgMKDlImg+Hg5FAtIF0Oh2bA/GlDMr43BckxMPDg/r9vk3cZ0BbNBq1JINqNvjXD22DbIUgJZliH6fTab28vAR8Mu/vE8Hj8TyHAsxK2xKqH/+Z4D8kq6iDJAVaqzgyjj1KYhuLxax1hcR4Pp8bRiOpm8/narfb2u/3dlIAySgxhYSOyiGEDHiYPY8fwOehdpjP5xoOh4rFYqrVavbvJNkojMChJKa8F9dPi5QkO4KP1hpPOFOgoS2He8dE8mw2q1arpW63a9JhfodkmzzmcsgkVdEvQZVzVaKYyWRs6AGMKKCCSlyz2bTy7s8//6xvv/1WkUhE9XrdpI6JRMJG7UtnadxkMtF0OtXXX39tfU38nGmoMAydTsd6Hynzwui8vr7q+++/13a71Wg0CshcJdkD844f2QxTG2Hx6EVkxD4MIcmVl7aySRjqgcFS5kbShxMgAEgK/BvVEyq0JIkEPO41MlASQCodHvT5fgwSPJgSmBov74nFYgYQkCp1u13VajVjmWBb+C7ekdEEPh6PtV6vdXNzY+OWee6ZTMYSTsgEkpdwXbeOx6PJMKVTAzz7DPIBJ05FgQoTAM6Px04kEvrw4YNVJkkm2ZvsRySWvifKVy5IePxepp+DPgcSEYIXP8MekZ1JsqN5CBQEHAI2fcoEQXwU1yLJ7gVVdPwaya90lqYhGcd30GSPRJQkFH/AvcDfAfhImrE5gCrXDJGDD6G6GY1G9fDwYL5wsVio0WjYvcQf8Wfs0k985f0l6Zdffgk09PP7JK9UKV5fX00KKSkAwsP1j1/EFCpDJHD0BPv+Xkk2LZNKFZW4eDxuiSZxiD457AciBLtHQkYsIqnk3FUSUaqODHOTZCCV4Q+SjJSEuGVvUfHnu1KJxoaJ9cQbmH4+y4NKPkeSxTuUEL4nyv+ZPertTJJJSonXEEqAPOIYwHI+n+vDhw8ql8v2jCCfOYfu06dPRtqSCEYikcBZ0Jxvyr2t1+tGtmWzWSPdIa2ZL0CCiS0infcgvFKpmP/i/oXruuVJRQg24h7EJIoZpg0zi4GkirYfFDb+ODUKI/v93irRVIwhYvl96TyoCUno6+urHeeA+ud4PA2A8tV970uIvxRnIpHTEVJURCUFJvzzmeAz4hevRyZ/d3dnWBO7JNkjhkoKxEySM+kcX/kdZoyAaUlASYCpyGGvPkcoFouGWSVZH3E8HrekmgITxFSpVJIkI3zIB3jWnU5H8Xhcb9++Nf+FTUqy36Fdi3aCYrFoQwKRno5Go0CP9+e8rkoUqXhJMpBEsgFrzWaq1+u6ubkJVLOQrO73e+trGg6HlmQxAQp5KOwqSRDAkNHFTIFiYyNfY0gOm4cMnuoF7BuBBOZekslAOp2Obm5uFI/HdXd3Z5uXygGSOKqfGC0Nt5JsU1LdAKiRSPvr5lo8E8TEOTZfKpWyQOblr5ybR7WF70OyyXvxDLhnyWTS7r2kQAXBD9xgVDBJMvI5pnXRo+WH3DB+me/JRD1ABCCh2+3aKOkvgWn53FcikbDDfkmWSNCwJ34GYIlGo4E+HxIHmD/6lzjLLJ/Pq9/vm4zk/v5er6+vBnw4h4w+ZX+mIpUsAFE+n7dhSDDmkUjEzrCiwg4hgq+RZPJS5LHH49HOZwKIkdwRdAB6HCMAaPYBx4MxSWaDKAFI6vgPII56Ad9ItRxihPcksJMIwzpLMoDK4dyQPCTpDBHzlf92u21VV/wJSTMBkEFiSGa//fZb9Xo969lmah/n43rWmwoO9z1cv34dDgd1u13d3d3ZXpBOQ9dQhvhkKp/Pm618+vRJNzc3JrHErqlYAFZQhxBHY7GYtVWwL/1zbTabFhuwn0jkdHA4U4vx1/gHYth+vzdARAzzPUF8Z3/EEjGDHl+AI5Uaen1IIKnMQGaS3AGsfSsHJBbfDbsmrsL2kxz6+02/Er+DmoiKLGQREm2IcTAFFSVfRQBAc3SF74HmGlEQ8X3wv14BhUoLAM+9hRyiIhyu6xcDCfH72CCDCOlPRN6Mn/XJEBJ/fDaSUeydHmMKCd1uV41Gw2ZKkGhhRxRpqCCSfCCLZd+k02nrk0UmzbFskDlgx91uZz7H49HFYmGkE0PaIPbZ+5Ls97yyRZIVLEim+DywKb9HJR4sSiwH59JGIp2PnaNf35/RiI/AvqlOJhIJOzOS++WTvGw2awNnUBBJZ6k8rS9Udr2CAsUUxDVkGMoHcAqzU5gG3263vwice1WieHd3p3a7bZuZJtnX11c9Pj4GpCZ/+ctfLMEi0Ekyhi2RSNjDLJfL6na71ptDkkEiwwakJwNWFMYSh/r+/XuTDfiqBIeKU3X0U6F8xSGTyVgyjJEzotxPlkOTzSHmSEGp4vjBHZIMMCLtRPbqKyuSTLYLq8F1+Q1PouUZVJhQgBwBhQBMMMLBSbJEHIlcMnk6BgBwzn/cd9hNP12LAAXbI8nOBgMcwDaRkOMYotGoOUdJNiU3XNctqt5Ur+LxuCUkyL2LxaKSyaQ6nY5NACXJoxfJD3ahQgYrSpAhIHW7XauGU32Hsev1epboUfGnn5bkkaZwAghg0Ff2UARQ5WS/ERxIjGAkSTixIyoL+AovfwOAelDJ94aJpCoB+CRx4304GoNEze91z+qSpFLhR9aJXRGkILvwl8ViMXCkCFLEaDRqEiKG8xDIGA6WSqXUbDbNHrfbrQ0fm81majabZpM+af/555/19PRkrK8/LzZcv27N53PzeQxAoXoB4Qgh6hU8/pgJ4kKhUNBwODSgFI/HNZlMLAYSN70Uix7WwWBghCDEHaw+seP+/l6z2cz8P7ZJHxXEH8PqqGqw97rdrr755huLE1wDw+zogUZGlkwm1Ww2LUk8HE7HZkFckHx50AY5SuUeOwWcUnEBBPv4CRjG/iXZvbzsjYSgxmdJ56mrXgrvp1fii6hgUsnAVrkGfAEYiWvg2flKoX9/9g3JbRg/f5uF/+10OiY3JVnC9mq1mp6fn00K/j/+x//QH/7wB4udkPTZbFaj0chwFjHNt+xQnfPtU4PBwKr6EPMkMiSGFExQJ9zc3AT6VCGFmBtCLIF4uTwzFUxJqxAyc/bleDzWbDbT27dvA0o0SYYRsCd/9AYk1na7tdazVqtlpCRJpCTzEbwv3wPSi9dwD+knJHGbz+dKJpMW/5HIgkG8T4jH44arfbKLf2AvgNu32635TH5OvOeaHx8fNZlM9P79e+VyOZO4IxdmcNXnvq4+R7FUKhlL8fDwYE3YSDez2axtTKpr1WpVz8/PAckFG5rEazabWULje4UIjEhBSTwfHh5M948U85tvvtFqtbJr4XoYSS+dqqLZbFafPn3S09OTpLPUYDAYmN4aqaR/qCSCyFZ8osT7wCQADjEmWEU2NwkZU7P4N0nGivB+yEwZEgNYJCH0MrnpdGqlbxJQpIQkd5KMOUYq5/sVSUg3m4263a6+/vprY8p8/9V0OjXjAsRzrTDGXhYIQcCAD3+MAN89XNctgorfN1T2/LlLyWTSzv3xckqCF1UHBqQAlEhAi8WiBUGvApBk+ySdPh0lA8vvpXAMS9ntdjbMBWdMwkMV7ng8DWwqFotmd8hyvFzPM5AAycPhNDgDthViiIAhyWw1FosFhgb44Lnfnw8jJlHD/gCA9DwAAiFy6JtglLefUgoIYcIe/orvR8Dj9SQPvh+F/l8SUXwfABb54svLi+7v7032FI/HTfpEjwZsbiwW0/fff2/DbH755Re9efPmn3o7/7Nb+GoGYLD/37x5o9FoZJUx2hsY048dAHby+bwlffTSkJxwVBPMObGU9gZ8fKPRCJCWvmXBJ0CevIhEIgZ2UAghd6Wdg6rB3/zN35ht0k/kYztgl/vh+/Ego/FBgDtiz+FwHlpBTKM6DqkEKCdJxf/QeuHvDzbs4zCytfV6bQOC+M74BEkG8H0/5Hw+NwIWshlbhXjxhBGSRKpFtMkwKwAlA3EdX/fLL7+o1WqZ7C5c1y9Ik3q9bhMvJdkAGkn69OmTms2m2dm//tf/WofDwaYM+0QLu/VDpzjf2FfXpHPspK2AGAOZQxwCL9O77+Orl21DODKUCoITnFwqlSwO+35MCBhJgcmexDgklfS9U3EkttBi5NudvFIQ7F4oFOzn3HNfSYUAIfZetnJJMryCaoh+bV5Tq9XU7/cDCTJSbZ61V/RQMIlGo3p+frZnwXOUZD4Ushj/gZLxzZs3en5+tiF24AMIqc99XTVa8n/+z/9pmwAGgUNxV6uVlWipYtHfQg8hQQBJHEEjHo/bkAaAFwkYjjSZTNpQFTTgfC6vJUnK5/P2fv6g8cPhoMfHR2UyGeXzef3www8BZw0YZjMhtUwmk3ZmJBU5KhC8pt/vB3of6e2i1xIgyPfzcjeCE8ZO1cPLFKg8EIwIVBg0/4Y0QTpPo6OqIJ3loVx7t9u1Mc+LxcJ6RwqFgkqlkt68eWPBEja2VqsplTqdUcNhpgTQcrmsSqViSa0HLNFoVK+vr+Y0YaaGw6EljeG6bvGcIEd4zl7a6SVWyFqwMRykr77j/GHuAI1+XL/vocVJY5Pj8Vi//PKLXQdglP5JgioVb5h5kiOuhyBKHywBzlf7ff8h+9wfYQEpwndIp9PW18gh9vRokejNZjP1er1Awkn1kgSr3+9rMBgY+KcKs16vze5hm6n47Xans9Fge5HX0itGVRaG1jPKSNAYBgbYheiCoKH/jEoN/VJ+Emun0zF1B8De90T1+329efMmnHr6GyzshzPGmIjoh7ww3AVwgWSTY1XY4+Vy2RK/ZDJpCcZ0OlWj0TBZFLGNPiWkciRjntGnncT3HFERWK/X6nQ6JvFGqkp8YU9Jp4SY8walE5jrdDpGOoEDwAKQJdgGIBiboSdQkhE+h8NpKF632zUf4Psk+TnkFj/jbDOqmJDTu91Ow+FQs9nMJpWWy2Xd3t4amUyShjTey0jz+byRNn5ACL6EawYI8169Xs++G0kpeIBKPgQTz2az2ajVapkC6eXlxcBuuK5bDOBLp0/n5frqMMlGo9EwAmQ4HNpe84QHbRP1et3wFb3rSMiRDCNDhrjp9Xpm18fjUb1ez/YasZw2Cf6bzWZWAPDSZBIZCBzsj73DsCh6aiFZUZ/4ZA9fQVKK/5nP5+r3+xqPx1YEmk6nNh0YvwU29xV+5iFIsgohiRefyzmSPI/lcql+v2+xz08pXq1W+umnnyzBR5WBf4J084NufKvV8/OzYaOHh4cA5vHkHm01/hgb5K2r1cqO6YGE7nQ6mk6n/2c29T9yXZXO3t7earlcWj8TLIKfiojTmkwmenh4sAmYDHLZ7XaBw3ypCDw/P9uwhnfv3qlSqUg6O3wqBn6K52QyseSKc59IsEhWKd/TB0RjMk2ysLHr9dokrSS3BEeAEz8jAOL8CfqwOQBqjuyQZJVAL60DEDLhkOol39ezD/7oC96TDQ9rii6c5vZ0Oq1KpWLBCYAOI0rSh5Gip+Z+4mAAn+PxWJJsWhuGDBOKk+E7+OZm+k2++uorSTLGicO9feNzuH79ImBJsmQEcgKnSwLiJcuMZieg+R5Snr+f6IeEij4dElRIn9FoZNdAYEW6DDsK2ePBIc4ee4AgQabGniSY+AmhBDCkItgRCSwg2YM+pKzSKQHzgBTWkOskyCAf5WcknP6e4y+8VJf7QzJNTzOJO58LUYTvAlQTkLE7/Bs+BNvb70+HHQMsGGLiJ8RRAQEMITluNBp69+6dnp6eLAGA0WUvhevXLxIAwCEyTQgSAIeXEB+PR2ufALCy34hdxByGzXS7XXt2VJgBMv48Tqpn7XZbu91OrVYrMKWRCgL7DaXI4XBQp9Oxw6shbKWzRA55JEkqFRlaOrA/wCJVsW63K0n2vX3FwVdKSIwYOALAvRxghXScOO0HTNDn7ElaEmlsnO8OuJVkE70jkYiRq6gdvNrH+x3iO7gBH0uiwX/gAlQh+OXD4aAffvhBT09PRvpQWYaACtf1C5/NXuC+8mxJLqbTaaDCjmKLOOaTH+IaiSQ9/BCuVLM5UgzSB8Li/v7eSIVUKqWnp6dArzwxp1wuBwZOovijTYHvgf37Vg6PP7E1Jpn6o+SQc7LnJZkyRTpPVgVzQJpSlOAe8DoUNpIspyDm+ZgFQYJS8JL8ZpDNbrfTt99+a9+Je8tRQwy047tNp1NNJpOAuoHqK/4D4pQZLBRDyBMgzSG+SWCPx6PevXunu7u7wEkNn/u6KlGMxWLWY4SRAIwYHVssFm0oAhn3fD43A4Kt5++Aj8fHR81mM1UqFdVqNZsI53t8GJ6CFCOTyWg0Glm2P5lMrC8CZ8vrYFgAuhg9AZbN7yeXeWAESEulUqrX6ybzkmTl8+FwaKVonAWOBsPjOxOc6PvBqKPRqE2YZDoekwgBnx4Ee+MEVNBXxb0GoCNrIjHzzcCU7qmC0A/jk0qeNWe9URGCfRsOh5Y4wqzmcrm/YlH8YAAkvoCLcF23YMUkGbnigRsgkT304cMHmx5MQkniAvNOkPM9AByFwZ/9eU18BteBjA5pBsTNdrvVcDiUJBtAg2wLJQAMq3SeuhyPx026jE2RaFFJhRBBouWr4l5+ymdSbcDf+D5Frg+Qz/0D6HqSg4AHUAUU8P19r7bvj8LXYJdUCnmt/z2q9CQNXqUwHo8toZDOkykZ/d3pdMw/cf0QSMvl0s4Og3CgckTf25fQX/E5L4gO2Hkqxr6Ph2ePP4YgxP9KCkgx6RWGAGKPY1OobBh45KuWkJFU7JFkQv4xgRspOL3ufj962ZsHx5KMVKlUKlY15TN570wmY989kUjYayFl/IRsr+IBPAICuR+eaGLvk0Tii5jGzHEZVPsYTsNZd8RcSfaZvpdLkgFKWiy8fBdZKcB5t9vZ9Pfd7jTBslar6R/+4R/09ddfWyJPBYh7yD39u7/7O5PNcx8hjlAmhOu6RYJDkkV1H8IAm+W5S6eWJo/7wDcUBNLptB0dRfEBtRp/Zt+SGOIfIP+RVFPZpzjDniIu+V5mn0QRc7AH8B3KBnqm+f6STHq+XC5NYhqJRIyYJLZBAoEd/ZwOKqP5fD6gFiB2Y+MQUdgJMRqyCeKTfT+fz+3+UM2nGsy9IvmmLYvKPPEOWSgVXuncbwmhxEAfSOdisWj4h/5xZpXQqpHNZlWtVjWfz3V7e6vhcKiHh4f/A7v5162r0tl4PG4SMi9vWi6XyuVyxlywOev1uiRZ/xrSkvV6rR9++MGydYyFKVIcl4DsjQeKwyVhk2QyUR7Od999p+PxaECTB+krKWxWNjFTE30/IwGd1+VyOdXrdZOBonGORCIGlKvVqk03G41Gmk6nltBJsn5Egj3XAXNZrVatkgoY9OepSed+So798MEKKc12u7UDxJE/+MBJYOZ3MHySY4wYx0gllhK9JKsuMu2r3W4rn8+rVqvZ7yYSp4O6CapIFPk+yHdgTsN1/aJKhHSTZDESiQSGNxFUvvrqqwBryr6iHxAHvV6vNRqNTO5JMsm0XUbmw+qRiHY6HUs+fYLXbreNuOCa2AefPn0KsInYhN9fvp8K9o7PYq9PJhMb2iHJ5Kq+T3cwGEiSkTq+Kof6gCogQyYI9CSfo9FILy8vdu8IbryvJAPI3vYBfCSUHlz4ChFDBCDnYDwBF9vtVh8/frQJwoBmQAO2Tb84oAeiBrtMpVIGPrDHcrmsWq1mE2/Ddd2ChSamUcFPJpNW9TseT+PWUcHs93s9Pz/bMwfs+b5YPwmT93p5edHhcDBiD/uihUOSEZzIkOmTpUoSiUQMvO33e717906STArtiQaSVukc86n4E4sBzvgmrp1zHRkKheSO70N8pjpIlZ94REURwIgqiGuNRqOGG3z/pY9pVA5oHyH20xrB6yFJIY+QvZFQ8P6+9cOTw0h3E4mE7u/vtVwu9d133wUmowNm6en6+PFjIPmm/42EGvsN1/ULWT/Diqja4xPBL+BFYhR7G/KRSjrHSCE9ZW+zJyuVih3WjnKG+FYul6165yvbvreY9opEIqHpdKrRaKThcKjFYmFtWsR8bInrZo/jH+jxRaoJXmNqMj23vl1Lkqn0mF+CP1oul9ZTDEb3pAn3yw9wIi5yr3xrEz/ne3vlHXMYSD6JtRRyEomEOp2OfUcGOB6PRw0GA1M2dDod8x8UbXyhh2ry8/OzSX2J3fV63fyCrzg/Pj5KOs+Q+NzXVRXFyWSiP/3pT9ZX1mg07CHj5D2LSXImyYAPbMzd3Z2q1apJ4MjgkayNRiM1m01NJhPVajVLEikR+4M+YdB5jT/MGkYRQ6HStd2eJjSu12t99913JomRFJBiwe4DgP2oe4a3AGgZq4zMBiNiIwNGqRQQgGGMfXUQ5091w/cMIR2jx5M/j0YjS/4uy+a+/4h7DfPF98aJ+L4KX9VBz46jBFCu12sbvU+g7vf7VpLntblczqQTkozZ5T6GbOj1CzDBKGz2qQcdBCqqY5699jKtVCplNuAb5knCmHQIOML2SBA5Q4qR0bw/EjdIFvYVKoKHh4eAPZAMkdzguAksVDKYokaiC5kFyGXfc2wGgyK4P9gEQQX5n68A+eE0AESCGckucllJAeKLhMAPzyBgck2AB/pPfC8kPZ9MicQfxGKnw4hLpZLa7bYOh4P1XTG1EomMHzrGgAOqJ1T+sW/uEb4vlIdfvwBeu91O9XrdEjcSE8AaSRdAsl6va7VaGXNPUkjiSKWBZzsYDMznFwoFffr0SV999ZUBOvwz1UbsOR6Pq91uq9lsWmxh3yYSp6N3Pn78aIM4qAISg6TzeHyAExJNFAaojPAnVAYSiYQajYapYSA+iVH8jh+gA8bAX3S7XbsePwSuWCwG+imZFotfArewxwF+h8PBjo7hntEz7KdFIosj0cMPSApUeNbrtf0+RCl2DKGXTqf17t07myx7e3urer1uA71QaFGhQTUCIRSu6xbEIEkZewIJo5egEhdI3lDtQPyBQXleYGMUPLQu0U/McCriHYNoaAeRZEQnxRfwKBgNhQEkBngYJRkyUn6OXN0PmvMSUU+iUiCBoPIYn9jiJZYe36KAQy5L0gQO8QPdGCiJveA/vBz/cDhYuxmnEEjnacTcM64vnU7rq6++MvuRZMNn/DFiELHMVaEVgOnL3FOIIelc9OEaE4mESYBJUGkR+RLWVYliqVQyIFqpVOzBwKZR6YrFYrq5uTFnCxPhE8mXlxdjtmH0eHhe6oYD5Wf033B8BUkZrAnNvrvdzpg/GBfPJErSw8ODDafwTfxsbtgQqihUXZDALRYLA6NsfsDqbrcLyPaYVHg5sIfEEvDum9Xp78LAAMrcIyo7gHvuCWwGE/OQ5sDMMNiDEcz0iRHYCMBUQfzEKT+IoN/vq1gsmlwBMEzv5/F4DByWysAjf4957vRihev6xZAT9hlVeF+9oOfF97mS/FAtGA6H9vw2m40qlYr1LSFT2+/3JlHDoUaj0cBh4sivttutgRvAXzQatf0K0QPJQyJG/zEECQENe0CqRzKMmgB1A4oCElsIGBIiSCIALjI+gGm9XjeJJ9Ii6VyBkBRINPFDSF9hP3kmfD6JoZeQw9TCSlLF8z2d9GIiu8POPnz4EBgMguQom80qlUqp1+sFjg9iCA/sOcsnlr1ez3qg7u/vQxu9cmFj+FfaLiQF9l6j0dB4PDa5Ej4XQFOv19Xr9QJAtVAoaDAYWGU4Go3agAsm1na7XT0+PqrZbFqMgrzzIM9P0IT0wO5ubm5s9gCxB/vyh43P53Pd3NyoWq0aUewr6r5PiSQH0oc4DZEE7iBBZv9COvrjRbi3KHi8HN5XavAdu91O7XbbbIcEk6SeQXW+ysj14U/xCSSwJLSoNbz0Db9A5ZDvUy6XDTvc39+r0+kYXpJOvZiDwcAkjHwvSPVw/TaLFiSfiIGRsDeqalQOKXj4WDoej22yOPEOrFiv1y3mMACmVCrp48ePdqQCMQ7c2G63dXt7awkm+4o+1+12a/MCpPP0VuyWIgOtVcyjoJCCcoh9T+UOnwUZm0wmrdiSy+Vs/ggSde4RBCftWLTF+L5hEm7ez/fpU/yBSEOiz8LmuDZwP0kxsYp77wssyFN9wkvSTpsbcQ/fBIGOeooijsdT9JgTw/f7vcmSeV5fwroqUcQISL4k2YAZErlyuayXlxfLsP1EQb/53rx5Y86RyYhIPr2si0mqDHGIxU5nDQLufMJFtg8rgZGNRiNLUA6H01lSrVbLmB4qmSSsGAusEtcD2wIIZMNIMvDG5kPGA4tEcJJkSSEJNskvBovkFsOFofR9X5TOMbTdbmeaehJPAG42m7XKDmyHH6ThG5n9sA3eNxaLGZsymUwsAabMjgyH70Jv5T/8wz/oj3/8ozk2Eg72DjILqhbhun7RG8yY7vF4rGazafYAsKEK7vsp2NskL0jHqSp7e0FGwyQ+bJ1z3AhKw+FQ0WhUzWYzMJLaV9joe/XSEoIj+xEwRGBDzirJ3icejxvo9QOzOLORijfLV+5Y2BrMopejIhelWkIi7n0hfRQQO565pLEen+L/74Esz8JXaWB+6Ws8HA5WIaQC1Ww2DSSTdJCE4leo4pKcI5VHPv71118HhmKR2EvnQ8rD9esX4AjybL/fm+Q0Go2anQ6HQ0um+v2+bm9vJZ2e08ePH3V7exsYNISckf3S6XSsksY04e12G6hOwOBXq9WA/JmpibyGfiwAKZMLeb0fxIP9IFEjoaTaB5HiK+mSTCnAn4nJ3CuSQiqnzEMg9hNjIaaJJ1T9vKrFK3M8dvBtGFxfNBq1QVxUPyHYUqmURqORbm9vAwkm8wUA1PiDZDKp4XBo8lySWnwd1Rn8wv39vcV/jpSq1WqGv+hJpvIRSsN/mwVOoadWOleYSbBI4vDH4FyI/3g8bsfBgR3xyxBEfrAT74/kmLhCQSYWi+nx8dF+xiwIlD43Nzc2VMmrWiCa/DnbJD3Y3Gp1OvMUTEmMowjjMQMqAL4TyRRJHHGPvsntdqtWqxVIlCFYKPRAPNOCBW4mSaeSK8mugWdC/EQiv92ejoyBOCEppOBERdMXlvr9vtmodFb0QRwNBgPzWb7PFJzCGfCQwagWkfpD6qLY+BLI1qsSRYISTpeeHwASAObu7s4O3oYlAAzu93s7kysSiejTp0+qVCo2OOP19dV0wwBE5DIMPkFuQw8CBsp4YhIo2BRGGRPEHh8frezOZrjsF8QZUCmNRqMmu5TOfSAEJWSwOAaCEwAatokFA0GA49r4z+vKMX7YWAZt8HPAKM3GVFL4HBguAjFBjGeDwyFhw9HE43F1Oh2Vy+VAzwkDF6jAIE8EFHAv/sW/+BcGSA+H03l2yWTS9Pw4T39IbLiuW7BZyB2RwhAcOEAYZ+VJAiaZQlj4IQySTJ7m+wcBOcvlMjBtUJLZHmOyCRaws36sPjbuZXhUA2KxWIDFI1mFhSXoSrL35jtwTwBggGkCG98Nhh+CyA+44vU07vujNvhdEjF8IPcWO4dMw64lBaqCgAc/0IceNT+12ZNnfBfIIV+1icViarfbpihgsBgEVrFYNIBJtRfJDQwpwBN1A33J4fr1C5tkDxcKBfX7fRsS0+v11Gq19OnTJ4tT+EgmS9/e3hoYg0xhfxDL7u7utFwuNZ1O9e7dO/3t3/6t9d9ToRoOh1ahwG94yZyX4NFPxMh3CF9vbxBHm83GQCvyZgjK1WplBBT2w/tLJ/JmPB6bAgab9H1Nkmx4lT+rjYoHQJrXY0u+kg8RBNiXZG0vnsRhJD/VDRJxAOjDw0PAH1KhxH9w3UxcRUJMMkISjH/yi75NSXYffDJJ4g+O+FKqFZ/7Yt9gk/hlYh32QWsOzx8icbvdWj8/WBmMSk8eiQk25ucEePuiRQF8i7KApE4643L6ZyGgwF75fN7UP8hae72eVSBR2hDDIRNJkj1+JXnDhny1n+QUPFgqlezfqcwfDqfBaBBN3Bt6bIm/YHc+k5gL4VUulwOErHQu8Nzc3FicptUDcpQp7SiGqCD6VgtPrtJah1LRz35gAJc/55b1l7/8RQ8PD0ZMRSIRG/j5JZCtVyWKyD44z4kNj+SMILjb7WziDyDLV/iazaY5aMZfE+hqtVrgHCI/PMYz7IBeqhRscD/9EAC02WzU7/d1f39vTIcka9ClOkIiyub006noKUB+BUjjoVOp8FIcLxHD2cCWMKDjsm8DlgXmks3IRqR36ng8GnCVzpPX2PAAPio3NPT6KapUCHxlUTo3JkciEQMSBHoMnHtEAk61BAfppaUMIEE+iHab51gsFm36ZbiuWyTp7CVsAjaT/cG+hdX20z9xatVq1Uaww+JT2YJIIYGCGYVJGwwGuru7s/4HCB+SUUCeZ909u4ozJpBKsgQI3+EPsCVok/QSKA+H81lqsKF+4hrX78kl2EfpLIf3r0fSRwAE0HqfwDUCKPg3ZId+8ISXlBNUYJbpNwG04s+o/vq+bex5MBgomUzq5ubG1ADc348fP9pwE3yNl7dJZ6khTKofBhCu6xa+dTAY2KA2hiPc3NwY2Ly/vzf1BlI3D+R8KwQkLUx9NBo1OVYqldKf/vQns1GIS2I0AJPkg2mo2CCSS9pGsJHxeGy9ezD6ksyvcGi1dO4hjsfjgZhNfCAGQsje3NyYHSHhYxCTZ/pJArFx8AKtHtgfvff0aAHmqSput1s78qpWq9n9hMwk/uJrkI4yHMyTxdPpVPv93u4PbSqSrOrIfQSc4097vZ7dE+5XpVKxwXRMW4Tc3Ww2ajab9mwA7OG6bpGYHI9H853+GCif0GNPJJJgVJ41+AgbgDggIQHXDodDq/ZfKsvAfAyIARfSc0ulyqtdGKBDYknMms/najabgf59EkEGm1FZ98UWkk6v5MG+4vG4xVDaQLgPxEH6q/E9xGKWnxaK7ZI3cC0kn8Q43pdeQSYG4xdJQsGy6/VatVrN7Besjlwcf0eS7GcQ+N5iSSZtn81mFr+9Wur77783conkHOLgS1hXXaVnI2H6vV46Fjufu/bw8KDj8ajX11flcjkbXINTJ7FEGlqv13U4HKzHjU00HA5NaorB8j6UhRlf63usYIIikYgdTOxlAUh1KpWKMeaUnDEE2FLpPOABp+w/DwApyRJCgKcHnRgWgZhzIAkc0pnNLBaL6vf71kBPhdIDBN8UDAsC0CUx4P35jlRICe5UVj2bg2Pw8jmkAQBcPhvQ65Ns5HAYFVImSfasATu+DyVc169YLGYOHcKBvX9JurCHISB8Uzk25avL7CFe4+2IijKEyd3dne17ACCVEQIbBAUOn6onARVGz/fuInOhugbwBhxKsumKVN+orlK5hnksFot2nAD3xvcyUK2hIsc9HAwGxozyeobOYIf4BkAB+50qg+9lwS95eRMSHHyNr0AARHk+2CaJQbFYDBzzAymz2+30zTffWHXCKwJub28NqFJZGo/Hurm50Ww2M+l+uK5b+O/hcGhEyevrq+7u7mz6KHaAf/RVfCpqyLGoLA4GA+stpgoZiUSMgKOPWDr5YOSPs9nM5OIQnJ1Oxw7jpt8H2SRguFqtGtkI+bJcLtXtdq0/EpvqdDrG7r+8vKhQKJgdYyMMhSFmMWUSApPBGgDmyWQiSUaEgSVof8Bu6ddG3sueJ7kj9oI/qOKRdPo4RXUCm6ES4oc/cT3cb4A48ZZ7RYLrK5vgK5RRAFoSY1+pYno0Pg+iLFzXL3DOhw8frHqfSqXU7XZtYuhyubSzCiFL8O+ouSBhwWgkXvl83qpbDJTy+8MrrkgM/bRy33OPCqFYLFr/KoRiJBJRt9tVoVAwyaQnK2kHk85zIsB0SFrZd76nEVw4GAxsbgF+4Xg8Wh89cVQ6t3QgEZXOihvmZJAUEuNIvsGgDP7iPvBe+BKwJxjbS0RfXl6MuOZ7+Z5N6dzOxfMbDoemUJQUSOCpNFKY4jxwX6wieX55ebGptl9KonjV8RicOcTmRaqIfILm7ETidBRCr9czFpq+ReRoOEwAG8GoUCjo5eXlrw4E9zI0HG8sFrNeQ1865j/kpF6iyX/IPUajkZX4SXJhSTAIfrdUKtk0U1iEcrlsFRdkqySLVGp878BisTDwy5EilPr9MIHj8WjntLBxMXBJgSBEskeizXhiJEnoywmm/hy5WCxmkmDuEwk2VUIYMn8mJokxkrrxeGyJtiQjAz58+GASYSRRgF5GEXc6nTBR/I0WADIej5v8azQaBSqIr6+v5sgBZRAEyFuoLtD/JMn2KeCIQJNIJJTP51WtVgN2I50Ipel0ak7fN7Fjm3wmPQ0EXfYhlXmcv3Qe7gKgy2azajabJjMnEWXYAwcYM8Riu91aX7AkY0PpffJDfLA57kOhUDCQQEJJBdRPTyaokJiRXHtZHD1RVHP5zkhwYWAlGfCeTqdqNBp2P7iHXC+E1nZ7Gjg1HA4tmOO7CMAELibvcV1UJNPptEqlUkjm/EaLvdJoNExx8+bNG/Op/vgKKn7SKbmLRqPmf0l8IAJQxyDv570gOz9+/BioQALSyuWyTTjtdrtGgHjpKdOS6/W6nT/IcCTIFYZdcRA1BFIkErHKGr6Ca+d9IHVGo5ERJqiUID54HXGOf0MCxvTD4XBog+PoaYTIpheqXq8bTpEUkOtls1k1Gg1T/IAHAPqeAKbSDvnGFHUII1+B4Poh3nwCS/8jfgBiAMxDwstsBU8YMEsAsjdc1y+OpLi/v7c4GovF7PgH4o5XevG8qSDj+yFgwZzgZ177/Pz8VwNT/Pt51QukQD6ft5ieTCaNlG00GkYQgm8bjYa1aWHzEMIkssRj9rWPO2CJ6XSqTqdjbUeQUsQwMDiLfYuK7ng8TTYfDod2LiN9luCOTqej8Xhsn8t9xpcdj0eVSiUjUVKplLVsgBMajYb9XDq3qjw+Pho2x94p1HCEFDibGEv+gIQf/AKe7nQ6ZrO0V+G3pPPgsnK5bMeBYbef+7oqnaWRHEeMFh/ABEuXSqXU7/f19PRklQIkn8gW/ZknvV5PuVzODhm+u7sLjPEnU8e4YA0IlGxwzkSTZMmbJPtcZFSUq73EJBqNmpQGVmU6napYLFpvFkGez/NyNzYAlY1yuazVamXyFFgeGvsxMN+4THAgiCI7gKHAaJC5+B4vwAEDLvg5TCXsMJUhPzyGCghAw08g9XJBroOE3Dccw275/pLRaKQ//OEPds9ITPgeMKPIdUIgev3y8iYkvY1Gwwae4EQBPewLP+1UOp/XycAI9hQ9QLBlBJ3pdGryR+lEKuHgveQY6TN7jn0Ou4pdYJeSzPkiCyHISefk1Qdwerl4LZV2Kp/4E3oRYW8JTp4IoTeEat1ldRCgTDLpWVfpPCmSRBS/hg222+1A4PWEEfcH0IH8lMST3mJIGW+nXnbH95CCzC5VFwAD/gKfF4vFTIJ8KU8N169b+EmkSJBoyJc96cae4vgS+nuxRUDdp0+frA+JoTeAUfZCq9Wy/ZpOpzUYDNRsNtVut5XNZgOD5Lg+bABQRrxvtVp2bAdxH5IEAM13ARPwdyqVxDHYfIgqklm+gx+4BUijB5JEEsIUqReEJ/eWvQ+JBvmD9M4T0r6iQcLnZfkkfzwbVBLxeFzVatVsFemfl9jiJ1KplE2CrVQq+stf/qLHx0etVis7oBuM0O127XNIQvAvhULBZjoQ98N1/fIKOfY8ZJzvoccmPPEHoS7JsBv7QApOyt7tdhYbeR9sgETLT9CkTWM0Gun+/t6wIxU57JBkSJIRG/hu4q7HsCSkxF726WQyMeIHlQoVed9fyX4kVjETAPUR9secAY83UdZhU3xnngN4gUSaWAW+BcMjZ0eFA9HLswOn8t3oS+Qaut2uJaIs5LncB38OZj6ft1kB7IN+v2995cjQS6WSDY3jOr+EdVWiOBwObUoeDg9p22az0WKxsJ6K29tbzWYzSzTq9bqBR4yu3+8rEomoVqvZw8fpX2qQE4mETfhig/I70rkRnWEqBCsAKlMXYXswaoIjzIlnUZCjIkthaqKfKgn7x1lJ9GQRfJCIwMT4xJBgRIUWKQ4sMQkVRiidgR7Og2fBe/jvTMDyzJFvkCfILhYLC7oEPmRIkuw4EvpJqVpWKhVzhKlUykr1sNtUiAi4fowy1VySXAB4uK5b9C9gZ4+PjyaloTLPcTKAL0CaJHOoMKi8hl4Igol0PsiaHiCCFNVBKni+p7XVamk8HpuD9g34EE68P5V3giFMHj0DyKqpiF4CVN+Dx+uQscF+4se8NBVpLDbDkBf8DaoG6QxC/VAgrgPwyd4GNMO+RqNR65sm+cU2YJ49gYJ0sFgsar1eB44IWq/XJuMDUCABol8KHzKfzy3hBzjTy4GMkefQbDbtCA38Tbh+/fK979ls1myRpOh4PFovlE+AIGPY2+wRWHSIGsjT/X6vSqVirDn7u1gsGrn6/PysVqtlSZtP8ohzfiZAv983hQk2Q08TBBP2T9wn2WSYhJe9e1k2+3wwGJjcC3uhf5IYR1yiisb+B0hCEnn5PYOcYrGYxS1PdnmCypOxEGpI1EhCuV8og3wCIclkrPRdffz40Yhq+tEgA2q1mmENCKJer6dCoaBms2nVIyr9+PbJZKLb21vtdjt1Oh3DZuG6brGXJBnJD1FIBZeihic4vPoDsoG9DsZEugzhh32wV7yCDZxMFZ42C9ofOLuaRFGStX9w/eBHSEjiKS0PVLQhLvDx8XjcWoSI38hEsUH8FYN8drudKfR8SxRxlN8jdqKag6DlzygewMH8Pj4R8tfLvSn8QKZAEvsWjFqtZhjI9yNmMhm1Wi0jxPFxkcjpvGhPJEsysgocANbwk90ppkinGR3kHZCAn/u6SnpaLpfV7/c1Go0siHBoMLpm2Lrn52f7Nx5mLpezoyUajYYkBQ6ahlWfzWY2lleSMSX0KQwGA9scgC56kUiGAE1U3Hjgvp+OagbBF3ad3keagzF+LyNpNBrWC+U3N4bvWSGcCdOX+DkVR1goNNf0VPHZVOEk2XlzOBLuAYCXQF+tVgOSWBLCy9K3Z2twOr4XiWTSs1X5fN4YEknmNAqFgkl6uNeZTEYvLy+SToGdSYtcL4ApTBJ/m8W9RbotnRzy3//939seBPh7ZpNneylJ4RlXq1WTnn/69MmSmMFgoF6vZ4317EmIDuk81dBXJQ+H80HWw+Ew0IvrqwoEIc4b8/2CgFkq0oBP6SwZ80wn7CqS95eXF717986YRqoCJKO+akk13kvf4/G4VYI8gYOf88CV+0yyOxqNLMAkEgm9vLzYIcdIhxiHXy6XA9JYfBv3guDHZzAMgCEYDBYhwBKQUVrgp5ESNxoNm2BJjzggJVzXrcPhNP0ZdY50rv5RPZDOMQ/2mz4kpoOnUikDQdK5IkkMZTgbsU46+3IIkVarZbEXwAiJQvWSZI0YSzwDdCLlJg4S/xlAQwWMviGSSgA3sYUYx14nFnqQ7IlcyKPxeKzBYGAEK7bme4ap7iEpn81mNtCOeQKAfCr9gHUSX3AL4Bqwu1gs7AxXvjfPBFteLBY26RFJGz4rEomo0WhYIpjJZAKJJP6BGOuPOWBoWTx+GnoTDpv6bZavEEoKxDGGjE2nUyPdvaIEH4vckn262+2McKSfEFWI7/FjD5N0USRJJpN6fn62CtvNzY0lUqg9GFpIpXyxWKjdbgfaDNhDvp2IOR18Z59IQph40oTkmUR2PB4biVmtVq1/GZKJuEzChe/wVUyq6WBo/JgnmWmT8dJUfMt6vbYWC3yM90Mc2dbpdBSJRPTx40drdwPngJ3BOfwdPEubFTJziLJer2evk07KSxQAfh95kv5zX1d3UvoRwAyDYZBFq9Wyhl+aO6PRqDm3fr9vMjgSuOPxaMkk2TaABAdMvxQVKEAPYJhsPRaL2bRHzhCkqkY/BMmhl6N5CR0bEENkw8AE+0Po6Svww2QAhgQemBGYkPF4bBPuPFvLfcBAMXauySe+lO4Xi4VNOATskThSCkeSBgtNZZPPxQFIMocFaCyVSgGJMEGY6q2X7QDGSVS2261Nsr27u9N/+S//xc70oSeG7zidTk0fHq7rFgw3k34Bdd9++62Bnel0ar1MsG44My+1gkWEEb29vdVisVCz2TSiiEE57HGqfey5aPQ0Gv5wONh+wA7YyyRDyLSp/kUiEauGApoJSF42yXmeSOaoTACeSRZJ5ABdDw8PVtHH7/A9/DmtJGP0OSE38RI0gijPgGMoSqWS+R2uV5KN8J7P59psNnYWG+/tAzXXzfMiEWAoCRJ5KoU8T16P3SIR5zMgBAhwKAuwRxjml5cX1Wq1L4IJ/dzXYrEwudl+v9cPP/yg77//3sAK/XperobUstFomBwYEpNhGwxISaVS9ry8SofksN/vazqd6ve//72RlPh8bJWJf5A6kozUwDbp42JP8Gf8xc3NjdnObrczlQ3Do4jxgD5inweUVOsBcahSSAB9Dxh2RdINQONnJIC73c6m/krnqZWQJgBsrgP7JqYDGmk58VVRSXa/6ClDkcDCL3I/kephqzzrdDqtXC6n0WhkpDaqBu4l5Bg+zxO84fr1i8SefmEvmSSJgXzg33l2YEawLQQHe5i2qlarpdFopFqtZriV+IZtIYMkYXl6erL97ifhoooDCzPQhnhMssX/p9Oput2ubm5uLMZB6tM3uFqtVKlUDIODRbELCBQSsslkYv6CmOOLEj7ppkIP/gSTQzRxjWB3/9nEeB/L6CEkN5HOveCoEvn929tbRaNRPT09BeYvkJSTeGJTmUxG79+/N7KU438YsInyCnIIH+d7mWmrIWZ/CXH06kSRapV0ehiTycQAP0AKh8Wmk05MaqvVMokMx0TQVIpkcbPZaDgcGgCFgadHgComY2n5uW9M98NaRqORyuWygVNfWmfjsalg9Pw0Jul8ADiMDAmi/34YkC+xezkrG5yqCUEMMACzgpyX4E0wQEbmq3tMZSRocpCydAKurVbLnAeJIhImdPRUQ2GuAeSU4AHW/X7fpAiAARzny8uLyuVyoP8G+SGN1H/3d39nP+NMKRJSKkzhun5R7aL/BUeGdANpG1U0qk/+90mASNxI/Hzln4STftTVamVnbgLMstmsJYSZTEaDwUCj0cga7HkNTKpP5jyZwV6DBV2tVia94brY+9jFw8OD7W0mlNFfwJ89SeFfi91TNZTO1Rgvz+F9sWXAbD6ft0EX0vksU36OjJPv6fulvLpBkgUs5HzYbTQaNQkdNgZxw/tBgkGy0cfk+78BowxEgaXmuzG0g+FF4bpueXKO/k8UODc3NzasxZ8hOBqNdHd3p06no0ajYb2J9MR9+PBB9/f3lvjjU4fDobVN0AKRTCb1N3/zNxoOhxbDkDlK5z4ipMmoDYi1HNnhzzLGPxDXqIYgiUun0xqNRoFqPoSll2/RLoHEHXKF94WsASBzH6kW8HvYKIkk8Y3+QeyQ9wHEco+Q02F3AD6UDADVSOR0DAETiPER+NlIJGIzEbyqAV8A4UzFg4Sw1WpZjDwcDoEqCtVICCPfHvClyNo+9+VJTvYdsQn7JeZBxrC/wHzEVZ4HMwAgUyAQiMEcfULF0ve++d5IkihanWazme0BJNvsb+IaMZPp2NJpKjlkMYPZ+DuDq4gFYFiKAN4vUCTxKjjfk+zltV6NwP7nXoP1aSFhzgCkNdLvS1WQL55gXyTlzC7wZ6vyWbS58N74BU8E5XI59ft9O+c9Eomo3W4rFovZkEnifjab1evrqxF03DP8R7vdVq1WC8z/+JzXVdJTNhwAYrlcGovJhEEkldHo6dy8arVqrAHAcDgcarVaWQMpoISHXCqVzDBILGBdkGxhCMhNYS6RvxGM4vHT1Ec+22+KZDJpAyAoI6Mx9j1JvjwNWKLy6GVmbF5AJ4ENQMhrAelscv6P9pneFT6D5XXzJHVUhAgcfLZ0PkuuUCio0WhYlZPqLSwlckHpnPDCelNRpUrV7Xbt9bDRDw8PlowQZJEOPD8/W4D19wKnyf3yw3XCdd3CUWMTyWTS+oWR+TLEJZFI6Pn5OZCcYD88IwIODB3ECUQP+61eryubzdpAJAAtjrtUKlkCxxEqOG1JNrHMS2FhDH1ln8mFqApIXmExkbbwHywswNRXCy8lorlcLjDNLRaL2SABJrNxTABBHl8BAPAT2nyCSN8DoJpgAiDsdrvGzPp+D/wavdH+7wRd3pdnSLJAz2OxWDT5X6vVMl8FO0r1Ftn+8XjUaDTShw8fTOJI0hquX7/w1SQk9Xpdx+NRjUbDfL8kI1ggPjhiCkIUcmS32+n29tYSkcVioVarpeVyaZKy/X6vTqdjAOZ4PKpSqdge9nJGJJFUuom/9CBzfhuA1FcZkJaTIHnSCFtCxgmxs16v1e12jfgAAPtrReZVqVSskkOch0gFDBJLpTPJgkSco6FItHgvyCnfWwTw9PaHD6SywPdjAjFkNsojKn/YHd8ff8C1MmQL+SygVpINGmo0GjZVmrhO5RmwjZQ/XNctfya2HzxF8u/nSviKuq9gX1aOL9UnHHviJZbYOwUXyPR0Om0Ezng8DsyQYPAOCQn+hX47j1mJNfxuoVCwJJO/UxGjQu+JUwgTrh+pqHSutlJI8hVVsDrHWOEfstms2dd8Pg8MnbwkXkiEKeqQhxD3wZRcG0UfbPjdu3dqt9tGRpG8RiIRO/KOeC6dCLPhcGhyf6qojUZDt7e3htd5n81mo7u7u8BsE1/YiUZPZ0vj3z/3dXVFkY1Dn02z2bREzjfzcnOQMMKyIQuNRqOB4DWZTKwyyNAcGIT9fq/3798rl8upVqvZocDe2WKkDLPxjbueoSRJZLADk4s4+gKAysAWEl36g0gAYXthC2FrKNljVCTOBAl/YPklI8K94P4hOSUp9+wPr/H9FEhl/GANWFWqe+jLPUNEuR+gkclkArJeGG7kokyyfX19tXPWGJZRKpWsyixJX3/9tV0bARrHhhacUn24rl+QG4C/l5cX1et1VatVk4ACVkj6mLKHtBunixN/eXkx2RtB07Ny7Dvsn8oIQ6kikYhVTqQTWL69vbVAy9lg+BBvAzjWwWAQkMgh0UM+DpAkkEynU+vn8BNEPZMryWRu2Ang21c68WOHw8HG8Hv1AMGIgAWYBOjy2QQbHzykU5DtdrsGNpHXAjj8NVJZBCDwnQDLSHL5TJ+4I0tk2NFms7HzWqvVqoFtqkbcb0l2b8J13QJcoaDhnlJ9Z0Uip2m+vV4v0C+H3ImkDfmo73GDgCCmAFZ57iQ+kLMAMgheqnPsWeK1pMAh877nFnA6Go10e3tr+xTCRZIRgslkUuVy2apmKB+IXfv93uKQn8SMUgLfhSrAS9ioSFKZJUZjO5x9iu/whBF2i4yXSoG3bS8/ZQCJJ9HW63VgWmI+nw+oriaTifVBQ4TlcjmNx2M7DHy9XtsRYtxHBsVx5uZisdBisTCCgWcXrusXJA5xMJ1OW4JBBQl1Cz6davByuTQFG+QKBCPJHvsN/EdS5pMbbB0ihMoahAfT5S+TP4hBEjRPDk8mE7NtKu1emQdhcnNzY36Gqr4nWakgsu/Ay8REikeQGdgWe5v7BXkknfMKP8EcKevlUCwSYn9/iLMUgyCIpJMvffv2rRaLhZbLpV5fXwPDIjkejqIL18bJBdj1ZrOxAUJe0kuV08v8qRzOZjObZExP9ZdAtl6VKAISSaYA/dxwQN9isdD9/b09UJIRVrvd1s3NjRlUIpEwaRRMiyTbqFQjeK9CoaCff/7Z5Jgw6XwO2n+qkj5YEUyQj/X7fdvAJEhMcsWAYDCk83Sq3W5nfZYEMAyQRM039L68vBj7SBJGwkeg9gYPGOX/fA5yVYAjlVZAO8EMoMqADFgyJIUkxyShVDpJApAGTqdTmyBHFQoQwxRUWJ3n52frF4vH4wZ6+V6xWEw//vijvvnmG0ssGLgTBrnfZvlBGbFYTLVazeSjnKsGcEqlUtYPQ48ee52/x+NxNZtNSSdbIaGDCIDtm8/n6nQ6uru7M4C1Wq1Uq9UM6DQaDTvPEADUbreVSqWsHwLJjge8yWTS5OaeSUUOLp2C+2QysWDsJSrYJEH8kmABpFJVYd+y3wlOfHcApu8zIpj4802xAZZnOgk8XN/NzY0kmbIBVQF+wlfrfeUUkM1wIXxzIpEw1QA+A0YTUqZWq+njx4968+aNUqmUqQWq1ar5DZjhRCJhw0/C9esXci5JpjjB/0OW+gTl/v5e0nmkvD/TzRMpl0MYJKnb7Zr9vXnzxvYGANf30COJpFLNRF36exg2RUykPQSSkNiby+UsVpLAsWeZqk2fIjGISop07oX0/fRedYRNQWIS8xjMwWv950OOgSd4LXGPqgV2K8mAHvESojkej9t3JIngvFrpfCQCFSKqr7RyeBmtlwmTUNbrdUmya5tOp4HJtPQ248d9NSls3/htFv4W3AnhyMAVyELp3J5AvKWtyiuuGH7oyUr8vidY6csnafFSUxR5tAJgs9L5fF9/jjfYkO/jYwf/RkyTzrgwnU7bUTwkYnyOl3US56iGcj2e/AR781pwIb4CwleSScA9AUVuwfVKsn/zyiDIbYgxJp+SsIHX8RV8By8Vlk7VWlreqBgyXMoT08Rk/By+glwBqT1kKwoPn4R+7uuqRJFNRjUPds1Xpagm4OxpEvVgq1wuG4MGMCIxkaRPnz7p8fHRNh7JDGyoJD0+PkpSwPCQBgCMCMgkimwkei2Qa/kg0Ov1bFQu0hEC2XK5tADtHzaJJxuHjeQb5p+ensyIAbNsLn+IOb8Ho8TvewOUTkbLIcylUkmJxHmqqpex5nI5MwgmvHGsBwYymUyMmeZ50EuJdJAqonROlmOxmJ6fn9VoNLTdbu0MGao8vs+GZ/fw8GDsL0wTzyZc1y96WJA7Ua2ClIHBPh6PgUEx0skZf/z4Ud9//731RjGpzU8689PCSKqKxaJVISApqDxw6CzVfZjHbDZrDfr0PDGuWjrZLUwllYLRaGSsv5eMETwBggQAKgpIaKnQexYQplWS+QwCDEGXCioBhkTQE1D4EF8FjMViGg6HFvAkWc8YVd9ut2u93fQ9wVpLMtvx0npIMPwX7DV+gF5U7BG2GX8D8/rdd99pu93qxx9/VC6XU6FQsMrKdDo15pj7Gq7rFnudah6+HXBC3OTfkGkCjki8mIwLuQdxy3sjO/Z9Rf5IJyp72ChxA4AqyWLabrczX16v1812/AAZVAOSLHYh0fMTAdlL2+3WSA6AOT+XZHGHpBASBnInmUxanyJxy8vlSc7wTSTQMP6x2GmCJVMhGTjDMyDBzGazBmKRlXIdxNRCoWDfg2dGEkgLCfEymUxaTz/JL/7NXyckOK0z+LtYLGYEA0T9zz//rDdv3vyT7uN/zgsijvvPoCakopz7PZlMjODzRDeJGUkjMxh4DRgL5RkFAOIe8dEXEvwAmE6nY7GBWH48Hu3YDWIE5CKnCLBHqWpBGrLfOSKNKiWJHsurcIh9vrq6WCwM1/H5vh0LBQH3w9suFUwUOnxv6aRioM0LvEI8Is76oW/kGl5BwT0Gq5MTeDuix9M/I+4higzOaaRPlWePmgK8RFLO0TzRaNSGS34J66qyDXIQf0guD4wb4w+Lx2kSFLiJz8/PNo2Ic19gUKfTqR4fHw0U+QlCXts/HA41mUysvI2x9Ho9Sefzqry8DTnKdDo1KQGgUToFvGazGWBV+B2CwHA4tIlTJHeekfTsjXSuImDkl5OjYCZIeP1gHoyMoIUMgOuRZO8DmCgWiwZAvIQPDT3JpH8/gi7DLJC8+gqtl7VS8d3v92o2mwY46Hlkb0wmExvRjvPjz1SlAD/h+m3Wdrs1ggMmnOdKcuAnb00mE0nnaWRv3ryx50+/Es6N50YAJUkjMEiyRIw9vFqtbOgGzDdnu/E72C5J2Gw2M7IJoDufz41BBAxyTVQO2NdURQh6/X4/8D2Rf0JS5HI5k54zwAk7xX8QDC9JGz/dmOTS913yGp/YEqBIDMvlssnDX15erIdsvz+dRcoof38tHM3D+H3sFBBOpReCLZFI2Ah9bI/7czicjtBggABJTKlUsjMVCejhum7F43H9+OOPkmTJRKfTCcjE2E+dTseAF/07gEsSSqaOEycSiYSRHcSBfr9vxCdVQ4amIS+FgIF8IJmi2pVIJDQYDMw2sSMIKPaiV/ZAmhBf8SfFYtH6mQFy4AgqN1Qdu91uoDUE/0NFEpBGSwf9e1Rlqcp722UqJNOC+X5+aiJJGXseDAL2Ie76ATsfPnzQbDbTx48fNRgMJMm+R7lcNixUr9cVi8XMhlE0ETORxiPJx//iV0lciLV+Qnu4rl8kE2AeSEgSBgiDRqOhXq+n4XAo6RxD6S8cj8fabrd6//69VXtp/+GoGQgf31oAmUJSRNsO9oEcE+KWWOX77SEgwVbERIojFHso8IBX5/N5oE8ebI9NgRnZayRSHvf5liau3/duMqUUm/VDFiF1fa+8b8nAhrE5WsIgj1BVkATjF7neyWSijx8/2vRnjsrxR35J5yOxuKf0M+PLUCAxo4AkHUxDvKe6iWLEKys/53VVRZEBJ8iUOAuRShVJHVUNfrZcLnV/f29l4Tdv3tjm9nprGE0eFhuYagMAbzAYmESDZISkiCAwGo00n89N0kb/DckmjeGwgwx1QO4GE+QNkYoJrDwgGUYGZ03ghh3x2nXAp5+i5Rt/fXWN7wT4I9GE8WeAjKQAM0RlVZJdAxu33W7bMwKAZDKZABNEKZ37RYKITBR2hd6w6XRq8kTuB07BBz5YG54tzwKnGK7rF3tnsViYfAyGiwqZP5+I6gT7HkCFxIq9xn6hCu4JCwIFTDqsJkCR94TZhLigpw6bgRVF8orT5agLSXYN0nlYE9cLUeWHRuHgURDwfUiQUAFwhA724/sSfW8F9svnk+TiI6LRqCXonOcE4QPQI9BRxST45PN5GxoFIOHe4F+Q5vJcScgJtLCdPHsUEFR2UILwGdJJUsxEaqR9vteDZNOP+Q/Xr1vH41FPT09GnnLExU8//WSqE0nW63s8HjUcDgN9e0z8hQCl4gUIY09TEebMW/ZboVDQYDAITGHkNSSpTCCnreN4PA1n6vV6Vmnj9cQz7AvlEJNVmfjIZMfBYGCxlc9HGk8c4vv4SY7I+7bbrclOK5WK2RjX4kkp7BKfA2kiKQDEpfOIfGwVP8mZeUxwBSgD7IfDoeLxuB4fH61Sg+9C+cPwD0hVgH8+n9f79+/NTxHTuXZif7Va1WAwUDwet8otVUlaC8JzTn+bRb8h+xNsQhxFfiopsD+JJ37iLpMw2bve/1JRY1/wGRAL+OxI5HxYPP2BqLZQyUF4LpdLU+tBGLFnSa68VJbvSNxmijHyWUmBWMmxSvgYyGLsCvtfr9cBXAv5CIZFkeN/zj3lGvEH2KlXXni1kidteFZe0u8TQIoivh1Nkg3Zk2STYZlsi38CE7y+vuqrr76yfbLdbq1CDLHuq54oHO/v77+YcxSvqijC2uO4/NAGNhqVuEjkNKyEg2Ypu/JazhSBiad6Nx6PA/0CADSSQV9hJGCxcQC0sBAYCj1FsOkwk/QjIl277G+QZOeH+T46piQxEpjrgOkhSHg5z3a7tbNyeH/ez/cYAH6ZboUmHYeEg+Ee7HY7Sw4IbCSmyB1ghwCWfH+AJb/LhqaaiwMi8EciEQu2OK1SqWTS00QioY8fP1qg81VS5HAkGjg1GNJw6ulvs0ikms2mJTbsM/r4WDD53HskzyRAJDqSbO8ej+fpobCLOGV663744QdjRX3VmB4Fhuuwv6m6EbiY7ElVG0IHmQuf63t6qSrwPamUU3lA8ucrC8jgAcW+2oY/owqAD6D3iHuLJBdmFRWFlxFtt9uAhBYSxv/HvaDi4Sc38vlIgngvP+WY6gjHJvj7QHUIUI4/Go/HikajduYt1SQqnovFQjc3NybxAxyE69cvL5Xi0G2q8B8/fjSASVJObzsVYT+cwk8bR7khneMOk24hGBaLhU3LrtVqSiaT1ksMgYEdSjK78vLYZrOpYrFoTDqgisRKOidgPuny9st0TioUEBPgB4AkkjsvBcTfkLhCfvZ6PQOjkowMwqZIelHDeCLXV9h9/AcP0GNGf5Mkq7Twe5xn2m63A20UHMlFGwjfh8ro4XDQmzdvNJvNbG9IMsksLQSDwcD8E72P0+nUYjHgP1zXL8hBSTY0iESQSjDEg3RO7vD9VJ0TiYQlFNg5+MrPtyBuSCfbKZfL6vf7NggObEWFm79nMhn1+33r6ec6SGCwGUmB6rd0HjY5Ho/1448/6u///u/tfGzfuw8ZRMWM64fIYD9yr/AhxE3sSDpLNGl/YuFbKO5AvpKUUjlMpVL2b2Bf4j8KDI/z8V8oKlKpVOCsZBbyesjZQqFgxLEkK56QO3z//feBNrxsNqs3b94on88H5o3Qm8m0V0mW3H/u66qKIgGAniN6nEgKkI3QG+UrELFYLDDdsN1u6+HhIaBNZjNgNBjmy8uLnp6ezKHDsMO4MHH0krnBQRcKBaumEKR3u50xMwRqyvPIbaiCwH54YI3EE4MEjLGBPVCNxWLWj0lPCe/nJXgEiuPxaGwGhkUgIHFjOAfgGwdA8PKVES8TTCaTdt1UY3Bkkix5IxElsPrv53uzMGoSTg5aRm7nJ87iaGB4FouFisWiDc/4Egzoc1/pdFrj8Vjx+Gn8ux9tj4P24A6Wn+cPMPRHYgBe2bO8FhafoEAVkj1AsGEPs/h9Ehamp7E3+Z1IJBI4moLrQoqFI+f9YHZhSFerlUlQUABgF0ixi8WiBWsCIIHWT04mICEHpVrK9+AzeS/eD/AQi8UCMlD8k7+XEHH+OXC9vk+UZB9Zn1cxeNDoJafD4VCz2cyUGLPZTLVaLSAtxyYh79grEAThun4BOkiQ8KUMRhmPx7ZfIVUg3pAxIhHm/EMGMGCvtVrNFD+AP5hunjUKD0ChJL2+vqrRaFhlAAkbVQLfV4XMmtjlh8Lg6yEK2cPSad+R4HEt3v/gYyB3uEZs7bIP+Hg8WjWUn3uZoCdCiVl+mBRqBO43tkqlkF5LZIVI+4i/SAbxk5VKJfA6SCBPCuFjGAqUSqXsOXNfAMH4bz4zl8uZMoOqUjKZ1HQ6NdsO13WLeAN27PV6KhaLks5+GnkjSWWv17O/U9xgz0IuYmso5NizkEK+l84fX0PshDjBJqlIe0Udqj9IQnAodsrnEEOz2azevn1rShg/O4JBd7FYzKYyeztE2QL+xNb5/l7GKcnuqSefqDKCY4fDoamhpHNCS6+lb63heoiDk8nEeguxd9qxGDhFtRNsjp2RwzCUDr8AgUMSTK4CZvZFE54flUcKYP1+X5ICx1d97uuqRBH9L0djbLdbk5lxw/ykPm5wInGaaoq8CYbOgzpJNkI4m83aJE+cKCCL6aQkpgA4pJc8cEm2MTBs35vnDZbEFmPD4PkOXhrAhCUqgNL53Dnf5C7JNjdVtcthGLC9fBbfyVdQSa4Oh0MAEGBwkswgAdgk2gBYnATJmA9EOACqjAR8vjOVUN9H6CuDvr8SYAyoYCjHaDSyaagQBv1+X/V63ab40cgfrusWz4sDc3kO7CtkLThMnpdvTve9a+xlpF+emMBZ4sCxTaSpjHKn2gi5BDuHLI2qgz97CpYRR01S5X0G9gTxwXtSjWdine/bQL7HxF8qCnxHev4grbxcFUYTP+irAsg44/G4VerwE14ejr/yVRvsFPDIPZFkVQ4a6fED/rO9pJXPggzDJ0Sj5+lrPAt/Fh226Yd1AU65jlAefv2iAsCeBZj5YUYkQaPRSOVy2RIShtCMRiN7D+TE9LQRY5EjcoSGP+YJH8GwGhKnh4cHdbtdO35huVzqhx9+kCT98Y9/NHvwCZ6vFkgyW+fPxAXfk4sNs4f9PoPQkhQ4TglpJTGLuIl9UW1BVUDMxTZ9wifJvj8+zPsAzlKjP5AKHr6U13MvsUESVBI87JrjOgDP3l6pyIA/5vO5ZrOZqRT8MBxwD7LcTqdjRFetVrPrDtd1i33N/fbnYaJc2Ww2ur+/t156ZOK+ku73Fb52uVyqWq1avGDvEzt95RK8y7Wwz9j/KOFo80ISyjVi877CCXlI/PRD3SisoC5Bxo4CAMzuZatgNpLd2WymTqejZDKpu7s764MG36G4A3d4gpZ8grgMMct3wxYkWRIrnVpTOCuc3MO/LxJYhkv5iu50OrWiED6M+zOfz21QGPiJ5+oLQrPZLDB8CpLpq6++MnIbZdOXMszmqkQRkIl+mc2RyWT0+vpqZxKSuOEUj8ejjeyHNeDcPc+8kzx5bTaNpOPx2NhItN1ebuEZfWQwsHxsSNgQXgtw5hoBqL6CwHtQFSHoIXVlc/n+B6qj0nlENz0+bCSMjO+PQ5DOU6W4LhgPKpA+EaRiiEF5YE2Vh2AJk80zi0QipqnnWfC+/B3j5s/L5dImmpIIc18SiYTJcpEdoD1HP05SUavVLDHgmYTr+oWUkCQeJw/I93ZKQLxMXHCw/EdSSECikgjwIiGCYAGkPTw8mDNFykOy5Pt6AUo+YeV92GvsE5IemNdLuZxnA30Vb7vdqtfrGXgkkaS6jdzN91+SbC0WCzvHrFar2dhrX5X3hA/3i8/ykhoqoiRxVFu8GgF722w2dhYbrC5nYfpA7ZN6bBGZv08WqShDCuGrqHAQmAlo+/1e79690+3trckNw3XdwkawPRJEVBmQdr1ez4AjlSTOu4SAhdhkPyA55v2m06kqlYq9D/I4SQHfzv6HhKByGY/H9fXXX0s6JXDD4dDOH2Pvo9aBcfdHS2Hj7H2qJNIZjCPLIl5ybQyVgswiebwkh/r9vlVe8SW+vxmiCDwiyZQyPs5jR/gSKiW0u4BdsBsql9gdPm0ymRjOgARNJBJG8uJTSRyQAvv2AO4xygTuKcTC4XCww8Al2c8ZjhOu6xb2xH6CvMTe6LPnuTGcTZLtGV/hPhwOdvQU/XHern21HQwMxuXn7DFihq9qsbfBidVq1STp9Clji34WBZ/p43OlUrF9Bo70SSuY1bdV+XkdiURCT09Pdq0QnOv12oZZkVQzUBKbwzawKb6fL35wXbFYzGYX8Iy8NBdMQdGF7+LbP5iHQMyvVCqmqFuv16pUKjZEj89hanGpVLJzxLFNiJ5cLqdKpWKYHtxcKpU0HA6/CKx7VY8iDw05Iuy7r5j5YTZsEP7DERNIpHMzMMNmAKMkGeio0+m06vW69bvt93u1222NRqOAZBOQy0MaDoe2yQB1bChKw/yc70if4OFwsJG8bCjpDFb5s+/bwiCQsbJZMTBJZqTe2VDB8HJR7gOOh8oGQZ+ki0UlBLkePVY8HySyGKmfRvr+/XuTAfNsacLH2UkyZpNkAWkfCSwygUajYc4J2SLv6yu89Lzy83Bdt5bLpWnyqVyzd5CEeTKHQEeS46cqklwgPa1WqwbokslkQC6cTqf18vJin4N000vRAIQ49XQ6bTJsAFG325V0rtyTLLJncPSe5fNKASoSBFuqqclkUq1WS8lkUpVKxZI92M3BYKD5fK7xeGy9P/RDZDIZPT4+mtrBgwjpLL8GYDPlFTa1VCqp1Wqp1WqZ//GjtUlo8aWwzdls1iRpDDSBcSVR5Z4CEHluBFKSklQqZUOqkN1CspEUkixsNhu9f/9eq9VK9/f39j29fDhcv35ls1kbTEJ1LxaLmeyUfjbAGRJF9qzv32GSISQLSU0ymTQljnSudPu9DWECedjv983/x+NxffjwQel02ga/1Wo18xP+PFRive9j4j9YduIWapZsNmukr3SOu/P53MAZMR3wTXKJHeZyOT0+PgZAme9nhDAmzhCH2Oe8liodeAPfwmuI//gb7rW/NmyOHktJqlarATkoElVk4SSR8/nc8AHDebDxTCZjfVWQcXy2JPs5hNaXAEI/99Xtdu3Z9/t928telgnJcDwe7VxN/Db7lt8jSfRJ/GWCVSwW7X1oz8FWSObYr+xT8CW9yuDH8XgsSVZl8z2W+HfiaLFYNDxHJQwcX6lUdDweLY6AFSG4SOpQDoxGI81mM2vPojcfHEKbEXYD5oUU8X4DwtPvaeZr4N8YLkVrFvgVv4qt0gJ1PJ7O+maqKxjWVwYPh0NgPggEHEUpvvt+fxqIiS8tl8uGubFPiFV+51IC/Dmvq9A4zDYblIEky+VStVpNr6+vNthis9moXq8bUGRj+JHPg8FA/X5f9/f3JpfxDL8HTFQKaPTHwGBL+DkSNZw6YBNg7KsVSD3ZBOPxWK1WyxqCD4eDMZbJ5Hn0LgwC10nyhfOAcSJYEVwB7BgXCSmSHowLMAqg9wypr9LSJwJQZordbrezcryXtwIeYDqn06n1eD4+PpoDggygUokEFQfDNfLvlO8Bz77X01eS/B7y8gbf9xau61YsFlO9Xjd9fS6Xs8m2qVTKkj2GS+12OzUaDcXjcZv6B6s9GAwCw5nYv4Ban+zAuB8Oh0CTvmft2U8ETMgFbJU9gewkGo3aoJnL/QTr66cyEgiRmMCEwsLSz0CCy/mjVBcIkL6C7s+SQppKQMa3QcLQs4VNU13c7/eBc1sBx9wz7hPydSTx+C4Ya6ryEGpeSgTp0+l0bOgJ+yGRSOjPf/6zfv/731uQ5vV+miuk1/F4DPRMkQSHfcTXL6TFd3d39nef/CUSp0mJt7e3Nt5+sVjo6elJ0rlC5s9A9YqX6XSqWq2m6XRqvfb0K+XzeauiY4/sBRh16UxO3t7eBmKvZ/objYbtX5QygFBJgV6/w+GgUqlkZ35yrJUnRiFMYe/pkaLiKp3POKTVhfgOWco1kvh5EluSVRGk82A+gCaJGiQWPoVE26sYqHCANabTqcVDADzqH/okqbBS4eF98KP4Et+f6qXkEKrFYtGIenwz140kMVzXLXAqk9zpf8MOkOH7fYDNYHc8S4oSEIQQgUwQpY/eJ0h+KFU+n7fnTJVbOvt1nxDxOVLwKCU/JwLyBBzH9/GtFPj7VCpl/cRgWBIvSYH9jZyapE06V//Y/7RkoMSDYCHW8x6X/X6cF07LCraGjBM8y3VIMlIKe+D+0rZBYsn1oCCi0MWzIWH0xBw+AfxLOws4g2IK9xiSB+zwJdjoVYkiQAWwRwXgd7/7nTqdjgE3AlcikdB4PFapVLJgRBUPJ498ZLfbGWAj0cDYMEaCEBscdoHPRZJDsKAxFQMEDPoBEzhiNimOF+YPw6A3AhZiPB7bNEDAGskdjGQymbReP8DcZW8CTKqvIJJA+mBB0CWIMAmNxJTAyPuQNGIMOCbpPFYbg4cp88mtr2BGo6fDQqlQwkQTXOv1ut6/f28S036/b4w4sjpJ9twXi4VJsLyTC9f1i14H+iiozJFA0XcBM+2HYOBoSeA7nY4xhy8vL8pkMgZySPyx79VqZYMckI4T3JB4XNoKDCVJD6zbdrtVv98PDEbyMsr5fG6+QpLtR/aZB35efg3rT8CRFFAOAEgBegQi/J3v84Tth4ghYZ3P5yY7JVEk4PteRFQJ2CBgG7/I941EIlYZ4tgfiDYCOBKjarVqkv5I5DT5liD2pz/9yUA6gXI2m6nValkCLMl6nkgKAZ8cl/ElBLnPeSEpjcfj6vf7qtVqgZ7QxWKher1u++b29tZ6igEi0nm/SDJAGI1G9fj4aEQP8lNIQs7RhGTt9/vK5/MWK7BbfLGPWZcDqaTzpGxJxshDvM5mMwPBPnYBgiEWL1U+EJRUKy/7mfBZ9AB6kCmdp5FyT/m/PwqEKoIfxgP5jT1Ho1GLp/hPpGdIbbmWQqFgxDExFPko5Bi+BaKAmM797/V6yufzNmgM/8Dvc12QdNI5nvI92u12SLj+BgtsCA6lZ7RYLJrvp6WAuAbeJLZEo1G9vLxYbx6+nKID+5CedypeJCT9ft8SVS9TJYlC5YNtYlfgWElW4Sc20ScHYeL3JHGEWIW/93sMNZFXr/gBi7QjSTJMwfVDLnGvLpNBVHXYHe0P5XLZ7jvKNWyVz/dzC8AL3Ef8ENgVn0NOgJ1R7EB5AO4lsYOM8XL6u7s7zWYzO/ZuNBrZe/L7+C8vT/8S1lWJIhuZknk8HrfmeZq+kY4Ui0UbfgHwyuVyevfunU1m8k7On9HFhoEB2Ww2ms1m1hTuqwqwjBjT4XBQq9Wy34WhIQHzvTgwKDA1lMJJrGAfMEQCGsweP+Nss8sKHJ8LE4tBSTIZLn8niHopEQ3tPuh5wN1sNu0sQ+l8PIakAKDgZ9xTH1hhIgGifIfJZGIAf7PZBCpRVHF94v74+ChJVuYHSCOdAKBw32m45uy4sP/pt1k0t9NDy8G2TJaFefNT/kgsfIK02+309ddfq9vtKp/PW7WB5AsSJpfLGZhkTadTNRoNS+ioEEAaQCCwD6mEQKIkk0k7lgZwxOskGWNLNQD7jUQiJsemD4JqezabtUDg+6i4dgIOr/XDc7wywPdUUtUksUWmQqAhKSdoAQyk4OQ5GFPsnV4TvhuJ7HK5NNDBma7YEjIYqqGS7Nnt93ublEgTP8TXbDYzcoBpuSQskEf0MYcg9PoFkByPxybRxi6IMfS5PT09GeHD0Ra8jniKPe52OztTcbvdWq8h5AIABoacxKTX66nRaBjhA2CjMsLgI+IooBIgB2AjESVebzbn6d6Hw8FsgVjGvsPeIJLZc/6+eAIzHj+dNUil1CuNAIDEYknWl0lCutlsNBwOTWGBf4HYIf4RY0kGsQvf4gKRQvwnMSeekhhg16+vr2o2m9ZOAxhH1QBm8dLWXq+nWq1mfhO/53uvUPvUarWQyPkNFhVeYsdkMlGz2QzsI0mq1+u213e7nQ1jJGkrFAq2P/Dlr6+venx8DCjEaNWSZBiUI4s8JuV9SZg8nkokEhoOh0Y++qPowIIke9gEhQ9JFm/AmwxuIjG7HGQGjgRfYtdeYYayDkWQ741nP3sSCOwLRvFzM3yhh2thaFY6nbahkcROSYEBVtgZ94v3hWyGjJPOU5EhVr3yMB4/He+D3D2VSlnvuD8jEbUOv0dSTF/x576uShQ5ZB3H70c3bzYbdTod65EbjUYqlUqWPFIlo+rE4cBIMnkdYAQmBOkKgTWdTtsh7jAcm83GAhu6agwENpVk0jca82eCII4X1u/9+/cqlUr2HWFGCR4AQN9fxwbHkJAo8HmeRfFMEIM0eB0g1EvS/GeS4GJ40hnEIw/CkfgElf/jhPhesFWwNWjAY7GYhsNh4LlQMWSyHqwKrI90nsAqnYxwOp0qEokYuOF1s9nMJEDhun4hHSSxwu4kBZwowQfypNfrWVXKD44ol8tqt9uKxWJWib5M9J6fn9VoNGxPN5tNe9YwaRA0PuBgTwQ9wBOjpVlcsydN2N8wvBBTVMBIygh2kDWSzJ6orHhpOkGV/cu/c099lZT7iD/jmkkoAb+Qa/gOSSb58ySZJ2+oLGLTVBr8+xNcCWCAGJ4f350KBP1oh8PBjgbCJyFBp0+OgRwkDh4ghevXL8iz3W5nCQD7ajqdqlAoWOsDz53KIlW9UqlklWGSFn9mIn3/fs91u101m02zgfl8rkajoWq1anvUKz94/gyqq9frFnckmb+mUuKlqexHqjHpdNrOP6YaIgXlnJ40pSLo358/Q1BCIvH9vOTVKwl8Px++joot5BjPAL8BPqAPk1hLtZfr5jvz/QG4h8MhMPyJWN5qtUxiD2HDM4IIXq1WarVaJtMrFosaDAYmt+O1qHxub28DbSfhun756jEYCawViZzPh5bOQwEh6MBzJHaQ+zyjer0eUK5AMPz3//7f9S//5b80f86+988UP0w8Yx/wfqVSyfp4salLiTXxjN+n4EMcgjD1snbk3xQkotGo+SXaViBYotGo9eXiT6h8Quhy7BJ73yvuKJaAh4n54A3fukVCD2HjyWr6EMH4yHuJhwx1SyQSGgwGplri+dHqwvOi0slgMOksvQXf0joAhuZeg32YLfIlrKsSRf9Qka9RPQQA8mCRmHpmnWERjO0laSCDR5LoWTE2ORtSkgHQxWKhfr+vSqViVcp2ux2YzEYFgCDF5yG3IuFl6ASbSZKazabJOn3pnu+KQRHU/RRJ2BY2Bu9BjxLVAD4Pxpd7DMj0DblI31gwKBio71cgkLOBcR6wV1Rb+A5UlwCvXAuOg7I7kiaADfpt34DM9eAIMT6CKd+ZewkQD9f1C8eO5BnQiB10Oh3d399bkAEolUolA0HdblelUsmee6PRsD3PIBQSyt3uNBrcy6dg8GKxmNkYnwezdznlN5VK6fb21thMLwPHbglwBNHN5jS6nCQJH0JCDBgE9Pq9jnTMDwdgIS2RZDI6+nbpk/T9fZ4lhKDC3mCh/TVAACHPXiwWBh4Jun6RuK3Xa6sIkaTDEPtqre/ZWq/XgUnR9E/w3brdrqrVqo0Cf/funVqtloFvCL1wWMZvs/DBHPWAb4/H47q5uQmMcEfKfHNzY1UJwJQ/miGVStmRGZAS2AwA1Q9ZgBiAkaf6h01hq1QQmYTokzniIeCKxJCqhU8IvSoIIEVyxX72R1V49Y2XxdLXR4WdChy2BqD3PcCAM3/WWiaTMdWFlwr6FYvF7HszLIRKPKAXIIrslvvDs8FuqQrjr2iZ4fo7nY7q9boNNJFkR/t4AO/7SdPptKk8SGI4GiRc1y0IT58oMtSGJMf3x4J38Mn46VKppD//+c+6u7uzpCSfz6vf75u6Q5KGw6H+1b/6V1bBomJOHFkul1Z1pwpOfJNkuNDLN2ndwtdAbvrX0u/sq4/gyNFoZIkgsYMYgByUthQ/NwO1IfcFUse3O5Hc0jKGLUHYQHr5uRiQwCgIIYJ5H/wE34EqPlgEAggFHT6KamAsdhr+RgyGSOUZxONxw1WS7J6RO+z3e8tFkMVns1nDVxTOILQ+93XV1NPZbGaSEYKSP8fFl1cBdYPBINBw66eAUf6m1w7mhT4fn5hKClQQAIBMHAJ40bsEyAQ48vsvLy+W8FHRg70gMZvNZlYBwwkQYEnYYDsY8nA4HAwkYyiMymXaoB8KQzOuHy8unQ90ZQE2R6ORRqNRoEmeoAqzuVqtNBqN7Jp9sAaMDAYDS5bZ/FQHJ5NJ4PBjDzgLhYIlBvTHUD2GCMAREPCRGJEgMngHg2Yf4bjCdf1iv+DImASGAy2VSsbK+/4ekn2Se549RA97EjnZ8/OzVquVDXgCgLHvUApQkfagFrCF7ITX8Rl+2AM9FzB+KA34HA4JRhnAsAekXPQs+/1HMvqXv/zFKtr0bHjJOAENQOn7jfA3ECHL5dKqsgRyL1fh8/FdXmpOtRayaLvd2sHD2KmXEBGAp9OpgW98V6/Xk3ROviXZffP9Ep5Qwu5Xq5Xevn1riaR/HYRdKG27bh2PRzuDmKQegCWd7O2nn34yf1ur1fTp0ycDaYAjCEhfPWi322q32yZpxO59ok8CQ4+4JAM0SLXYg/TuEOtms1mgv86fP4y0TDrvT4Atf+b6y+WyKW+wORJTvhfxgHhJdQbcEYlEVCqVjBD1ih8PRLFb7zuOx6Mdc+OVNtg82ECSPQcUCMRfqo98LyY4++vh9UjFIcFJpsEDjN7H13BfSNzj8biRSrQNEKeRu34pAPRLWIB8KmgMbKnVapJk+5z7D74jlvK86TPMZrPmUyXZ9OBer2dY15MNkBtIWj1G8jHTVxwhZelTJQEbDAa2N/zwM+IB+NVXFuPxuBqNhu7v722/g8/r9bpdH/t7tVqp3+/bnAB6NVHZoPLDb0jnM0qJi7PZzI6Y22w25rO4JgoKkE7L5TIg1eY//An+gGQZ2/7555+tXQs75P4Ui0Ujg3a7ndrttqTz0W2JRMJi/n5/Oi6ISn+5XDbpd7VataGAFMh8u9WXQLZehcZ5GDBko9HIQCZsCXIO6WRId3d3VrUjGSOJY4Mg1/D9CIPBQLvdzt6TfiiYFYyF6Wt+yhEgk7HDBL7j8Wj9UoA9+rI8C3s8no53wPioPpDUwepRUYA1IeCxcZD/sOE9W8rB1yS+BBqMwU9/8s5AkrEvPBN+BvA7HA6WMOIMqB4mEgkbcABrBfDgO/X7fQ2HQwO99HP54w4AGbC99KvwXAh+lUrFAimMDUYryaotYaL42yw/GdP3tnKMDM8JNQBnG0nnpPBSnj0YDEyGIp0kMKVSSZ1OJ3CWKqCJ32PfQ0REo+ejOLATbIekSpLZFEGDZA/mEZKo1WoFpN4AQ5j9XC5n9s/eZF/e3t7qj3/8o8kzt9utMawQPgQkfAKKCI6WgfCAbaUPkmoJ34t7zvf2AEQ6B/njMTiJFf8xGo0CVXkASaFQMFabe48/4Tlc9kF7eRLPzSe1JKT4JGR0MMJfQpD7nBeV/sViYew2SRgx7LvvvlM8Hrck7fHx0WIZ/Y30JC6XS338+FHx+OnMQ+KjnwaMDybhAZxR8RqNRgbamMyH9Iq9CzHiSURfxUAdRMWd/UuvD0fEYNsoUCB1SLy87WF32BFDohgiBRCj1xg/QFUP6R1gFqKVRUWT9+B+QRyTTJOA+mo8AN4Tp1RRJpOJDXvDV/F6wCzgPBaLWXWZz8fv0F+FzJQqFmAVWyeJ5NmE67oF8e970SHrUQQwlZpBYr5S1mg0jMiAtOH4GJKbeDxuPY79fl/b7VbVatUmiXtCwffHg6F5DTYN4fDmzRvFYjHDZBxHwXcAI1N1azQa9j0u/ftqtTKsSHLIPsbOSXJJmDyZhI9hn2M7o9FIvV7PYjk2AE6A0PESdHC4dIppHFdDexX3g/+DHSTZ700mE7VarQD2YYYCfgLFlCRTJkKU73Y76wMdDoeGi/kcngd/x34jkYj5Q9/H+Dmvq6SnyMiQCpLwEGhIzqbTqcmx9vu9Op2O9URIMlBHwyzN4GyaSqViztSXkj3AQhbFJlyv1xqNRmo0GgFWlHI7AY1ECeZvNBoZuIS9YJNRLUFqyzUwEIPfw7kj66Q876sSBELpPKyH6wYQ+P4lzxbxe34im2cskcLwf8rjBCrYMZKHWq0WYJSpUMZiMTvShPtEcokkwDM+sLTIgJD4QCCwR6hkYCRUn5DmksSG6/pF4z37HgADoSKdz0ONxWIm54BMoCoH8+UdJUBmsVioUCio2WzaXpWC0xexJd6DfgSSTYgOGP7Lnot4PG6gmveWZHveyzmj0ahN94W1B8RRhYD9p79it9vp9vbWPg/bIajwbx5w+2oB18Xv8G/YKpUcpEuQSCxs2PcWcm/ot26324E+bRILL0HzfVEeNCI3wl65J/QnAuBJjPke9HpwPzy7Gw7LuH6xD2q1mna7nbrdrsU7yLvBYGCJDnsWdcButzOAyXPzUkpITXy/75nyclTf1wfRQdvBdru1FgOfrPhhaxBDHDcDILocHgERDAjl2thjEC74C0+IQFiwF0lyfV+jnweAPNP3ZhG3uG7sx7/eS+WpjvoKI7bqq3x8X/wXPsAnGJLsmrnnqJN8Es0CdHsCVzphFA8wUYUgi0+lUur1el/MoIzPfUFoXyqfIN09AY/t8VpJlrRx3BT7nUoWGI2hKPV6PZCkJJNJK77wftK5QICvBw+yByEOpLMclcQUMgSVCe9DHMY/eP/uj5vgdb6HF7uoVqsmt4X098oV/AsxFVIJgpbrlGTfzasspPNQRvwnOJJr9EMgvbJAOiVs9OmDEcAX/X7fVIuz2SxwXrqfGUBlkOdJmw5tABDZ2DatVX4gHL7mS1hXJYo0YPOQL6sPh8NBnU5HzWbTzm7bbreq1+uqVCq2eXi49AwgafIgCxaS5JOfoy3mM5fLpcrlso3m9ZudBJHyPrIbBgYgxfP9U2w06WQoPqGiBA4oBYBxnVQsvaQFY/STkwCwAHbAKI7FByfPcBIkpfM4b4IiG5PNzv0gYeY6isWiycySyaQl+ZIClVOcHU3B223w+ARfcZJk4IIJidxrgADAAlmEHzKAFDdc1y+qDiQtgH+GO/hqMvvdn18I6whzyd6hCgL7jQ+gYnk8Hk3uiXqA10oK2AO/y/tg24ymJpixt9l/vpfQV+bYs/61kUgk0GAPSSTJbBpyhModQYzAzGsIAF5GyvWSqHEP/BAAP2yLoI4Pm06nfxWkIVrwSbVaTb1ez2zDS0J90Oc+AAq47n6/b5Ipgh2JMSoC7jHyKXzV4XCwCdPIaL6URvzPefkjFhhmQuyRzpJhfD9AFYBC0vbhwwc9PDzYfkQ+yvAFfCt7tFgsarfbmQyMBKtQKKhWq9mAOOmcsAyHw4ANJxIJ1Wo1LRYLjUYjSecBM0hPiYvENhJChiRhH9gmgJUeWfamJ1g9OUR85xq5f8QlyGuIS3/GIz5QUuDIAwhRegGxN6S6tFgARPk8fBJ+EUUCJC94ALvHLlFE8UxZAGmuc7FYaLFYqFgs2t95DoBlznrDD4Xr+tVqtUwCioLOYx7vz+md83JHP4OCfUJfIzbA+6GG85V32gqGw6Hq9brFF99HyPJxmL2PIs0rWDyhS2zf7/e2p5gpQhyjHxPVHb3uvopH4QF7QSUExgXHIh1HvYPdc934Cq4NXEDlHLvxrRDgCyk4e4DYC6mKL/KSUhJUSTaLAV/BMwW/QspShXx5eQlUbSmeEfuZqM7vc68gjSHCPvd1lb4PZ+jZt91uZ1MOo9Gobm9vLeEhOfn06VNg2uFisbAKGIyb71cgmZRkyQ8bgA1OEIUBJ6iwCWAZJJnzZXMTiJFqEgQSiYRNY0LuQqUFkIkx0CRMMgxwZGMQIAEBBBdfKseQuAcEBII/9xTwK52rhJTn+TeuB8krBjUej+15SbLvzLUwWCiRSNiIYCQ6NPVyv/zEvslkYs4AuZLvRSQRRoLDlD5kVtJ5ot2f//znL4Zp+dwXQAwgwT3vdrsGmiqViu1j9oIfhOADhJ/YiaQRmSVABuDpD4pn/yHtglmE6CE4sIewPyooOGJJVnnGMUPyQKBge176zDXhL3xjPde63W6NaSTBRloDCEWSjZwNdrbT6RgJwrlPfEcGjgDW/cANBtf4KWwkDZzLyu/v93s1m01rrN9sTqP9GQWOveHb/HEhyWRS9/f3BgBQZXCu1PF4NNLISyEBzYAkkgAkQeG6bvlhT7PZzMBXq9WyKgZTBiF28LE+QXp4eDDVQC6Xs+eH7wWIcXaiT1B8Lw8Tyekz91J0/DZ26uXdEB5MKIZoglRFVkZPc6VSCSRtXiqNX2Df8x1IvJCvU4GhNxFpOb3+kUjEKhRe5gXQazQa5ttQ/NDjSzWO+IXfZPAFPgobRtHAM+H+4PewR1/ZwAfQ3+Wrhr5qxc88WOc9UGIhZYzFTvMDGo1GKD39jRb2IcmkzrvdzqSKxFUSCQg3qsrgWYiLy2oZFSbiNEPneMZM5OSIKPy3V6bxf7AlFUZ/bil7n1hCEkkiBAHFvxEj/CBEEi4/dIbvghSVGSXgR4hlX/2UTmon7NnLWXmNv25IEPCrJMPQSM+JV9KZVCa+8529hJ2iDDYYj8c1HA4DLS34YHIG/swzfvv2rT2L4XBoiaafeUJi3ev1zIfRt8g1fu7rqoqiJGMeV6vTob5+8iUOE2mEJDuniWTqMgGKRqMaDAYmjQOkDYdD00HP53NVKhWbkOaZS3/eDYwAxgnrDgCVzmV8WBfpPI6eDct/SFXYCFRiGCnO633PBYkqx01I51H7XsYJkGRq4WKxME20T+oGg4GNNofxBWhznV4KjISX1zWbTTMY2JRMJqPBYBCo3nDNgGympcLMcj7Xhw8fdHNzY1NmSfj3+31AQgzQRpaayWQ0HA6tlxVAMJ/P9fvf/z6sVvyGi8lpSB8k6eHhwYAiAAP7k06T125vb63Sj6TN78fpdGpTRvmzJKtm+ySV3jjAm+9V8FJJAjKBhUE6XjLnZZ8QMJICvYckRzhhWFMvQ+d78XpY0slkou12q9vbW00mEztz0EtLSH65X/V63ewMn0Xgo4rgqxg+aHFtvjoKaPTyXMDier22BIPPhIDyfVP8jmdqE4mEJpOJARyAJ/4OsE/iit9jwRazb8J1/aJqhd+k6sfkWxJ/+vcY8rBardRsNo2QBMyQLAJ4OJfWJ5cch+RVO4VCweIViRo2vd1uValUjPQdDodGGtEfHIlEAke7kNB6yRe2B9HipWPYNEQmVXtiAwnzpXrGVzghq6XzABzsHoDoq7P8XJLdV98TCRgl4eOa/O+iSEAV4GWpYA6UCXx36Tzsr16v233HFiHCksmkBoOBybx5Ntyz6XRqleHpdKpKpWL9WyHZ+tssKkNeaQIxgI2Nx2NLIsE4rOPxaGdwe1LTE5HRaNRatDyJStULXIZ/8NgOgoKYwd7D1j1+I84TO/D1YFYqlcQM6X/fduHVM1wXQyhRKeCLkMijVMPX8N18pZXPpzo4mUys+sjvYt+QN9gNBA9xFszBz0mMvRycuEiyTjKP6gYcD3YF3/q2EFpzIOHIQSD9drudxuOxEbycR8v7fQnrqoriZrMxZrDb7VoDLn049BTCPEajUVWrVZtcJsleF4vF7JyiZrNpRuQremTjsVjMGolHo5FV+agwoGW+1FNL52AAW8/vsPlhTWANLmUx/J+ARODbbDY2BZYyue/vgW3xwYcKC9U6jJrJqF4zznXm83k9PT0Z4wsL7BtoYU9gaxKJhA2j8X1Mkgw0+AmtsGNscpIEpLWVSsV+/3e/+50SiYQN2OA9KP/T2whIIJhy7zxABgT4anO4rlvIncbjscktIWYAJSQqOLlEIqFqtRo4yqXf75vzBthQEahWq8ZmQnKUy2XbP5KMmMAGCLBeLopd+eWlzIyuR8bpewe8zAeVANUvlu8ZGY1GJs2kek7PbS6XswO8sUVYXFhFZLy+Uuj/TwD3A2EupWR+AjIVJT+sh7+TNJCkch4tySbyRc9gskiY8RUENmxNUqB/dbc7DRChyk/ywnPwvuvyWYXrH79og0DWC4jZbk9nljLEARk3PUD9fl/39/caDocmI/UVZeSHVL14xpAYEAGRSESDwUDS+agOkiKSG6Ss0rlXkKmq+XzeYs9ms1G32zU1Ar4EhQIDegBjnU4nIB8j3oIVGH7hyU5f4YNA8lVR6Xw+LFVVSA38HaoHSF3+jg8AUAM0faWf78l38b6M6gv4x1cksGmeB74AUsirhMBMx+Np4jp+FukdRBzDq5g5gP0ej6fhZGCkcF23IDGwD9Re7G2k+H6ypS9+8FwgTyFXC4WC2TfKlG63q06nY8PmOCUA4h3Sww+KY9/4PUt8x1Y51sgPfqE3UDpVLZnEK8lsjrjBe/GdwWgkpvF43NQu7GtiPsN+PD7gd8HtXKcvTvg4jo3iD4iH/rt6iTfYArsjduO/wMLg4Xw+b0SZT2AlWfHLYxySbyqFkH2QUFQqE4nTmdSckesTVklfzDCbq0dLMtno6enJStjIRZm4BqMgyTLqVCql4XCoZDKper1uvYkYGlIoP1GTpIYJZPH4aVIU8i0eLMB3tVqZVMZLPyVZWZ1Nz8ajiiGd9Moc3M1IfxbOw29SAoVn3X2PgtdVU52EZZAUSApxFAA1rpeN7Hus2NgERq4Bg0O+VK/XrWmXZBLGE4PLZDImKZ7NZvY9cQZIh2gWxnGlUimTriGXJcgCwHkGABo/xIfkvtlsmkMI1/WLfZvP59VutwMJuk9scJ4kMv54jEjk1Hcr6a+mCJKcsB8BPAQ3P133cDhYv+7hcLC+JkgUEiovLWHv06/EzzzIYi8jtWFPIUUl4SIxpGfXV9WlcxWE96XamE6nrY/k5ubGCB2YV5/kYUfYPMEB+6f/9rLqQJDkOkgY+Cyqfdw7Pp+jh/BjsNM+8SYRBXiTsHvSyAMPfAKggwFjfF+GY+FLw/XrVzqdtol5nBeK0gI7wj7ZK8vlUm/fvjUygao1Pp/K3nw+16dPnzSfz+2cNi/FhKWnV5U96889ZCAbVROqKLlcTrVazYgSwCTTDj2RAJkhyXq9ksmkbm9vLYYB2ohvkCBeJXA4HAJnEPqqDdfu/07rBfcG4tT316bTaTvegM/GDnzi7afDSucpztgQsbvX61ncpBWFz+d3kbl5ZRKJvCdLUWNBNPPsiMXeL0CK+QrVw8PDFwFCP/flFVgoW7zEMhqN6u7uzgg99gQJvCfQ8fnEGsjU7fY8u6PZbBpOQomGJBrij0IEC8wtyeIkRRUSFvr22be0hZC8XSZnxBE/CA48wP4nafI4mP5K2lI8Dua8T+wFqTiqHn/MC76oVCoF7pckVatVu798dyq2ELH4AwoUYAN8H8+E+wcOJRHHRzAjAP/MEVjgXqqPXAuKHt67Wq0G5OLcNy9D/tzXVZTwaDSySYfIJMjePev8yy+/2MGylOUPh4MFmu12a6VafsaDaDab5rgBjIPBIHAuHMydr4bBaJRKJQNBVOnoofMPFGnKZDIxUAczKJ0lbgBBNudsNgtMePVSrUt5rZ9sxmf7Eb84ESbJSmfGmeDm+zoow2PMsJywlX4j+sqMZ6DRT2ezWQOqyJwA4NJ5cwMgYaZ5/0QiocFg8FfyRA4dlU4yAirO6NOpXgCMeM5fSpPv575w0FSjATzcY9jMy56AzWZjkzYBSAA8gNulNMMPPqJy76Vjx+NR4/E4cIQGyaVvdsd3cP0EJYgTH8Toe/UADgkr74dfgYXHL/nDr2F2d7udZrOZxuOxJVZebuYTSRIxLxHlurEPgijfQzpXzrFxwDNyQuyZBJTeK29vXhrvE11sn3+jek/VitcMBgOTj6PQ4DMJyPhKAAfXzzMIK//Xr/l8rnq9boQaST3JFT4YkIFtkJzd399bj1qj0bD9D2GYzWatEsHe8HvkcDioUqlYDPXzBpji6Nsk8BmQSb4iRzWOqqEHxsjjiJ/sT+yFyj7JMX/ntZfqE9/vB5AHkHsZLwk2tnY4nPo5AXXSeegTCTl9XUwspyLjE8NLfwBZW6lUAjHX+zXpnCTymel0WvP53LACSfslPsnlcur1epY4gJsqlYpV/zncmxjsp0SG69cvJOAUNth3+HhISvaGjwOQcCRaJPvsa56jV26wh5GU+0FzXjaOb0cq7RMykkYpWLDgZ/Qg+umt7EmSKa6HJE5SwCb5XeItyR33ARwLHvY2Sdz2yiQvr8Vu+V58V/we1+TltSRp2DHPBpzJM+A68VW833a7td5e+gf9fQO/Q8SB+fHdkD3E7G63az6ZnMIrLXn9l7CunnoKS+CdI5JQ+n2+/fZbu1EAJJgOSvaw4oVCQd1uV4VCwUCml2ugI/Z9NV5eJckmCjG2n6oC10tiywaBgSPBHI/HBsxw5lwLgxwk2Sahb8DLLgGSyIVgf/ie0jkh417wH4mtnxjFdfrqKgCWgEfw9ECbQO97MjzQoIpIMsH7wjoT1KTzGG5+5llonn02mzU9PAbO1EsAEYay3+/V7/dt9DoJONLCcF2/qEyTlPkzhEgMSexxsr5ahkxFOidtMKcAo8lkong8rl6vp9vbW0Uip7M2/YHg2DFgEzDn5a2RSMTkHwQmgjDXCgjF6SIVpTqJ3XkFAYqGSqVi9slURthMADKKB99TSVLs5W8EfGzeA3QCHAmsvx6AdqFQsCFRfG8+nwQtHo+rVCopGo0aU00Aw4eS+COvI9hznX4yKvdssVio2Wzq9fXVBmYhreJ9ARRUPbyvHg6Hur+//2LY0M95kXRwnmgul1Oj0bDBULlczvqIqB4NBgNL+rPZrB0WT7IBybbf760ihR2gLiGWIrvCF0MQAkaRaHU6Hau85fN5Y96Px9N0Y6poADQqMCRZkkxKd3d3F6j+Ef/43UvAib+Zz+e6ubmxNhGSUzAIdgxJDBlEIkll5bICwntAuhLTPJHjbZNzgAHTEKi+xwq7l2TJPvF3tVqZkojEkZjHfeE+AGTBGPhMsAdAmu+LvyKOhuv6hW9FoSLJfLInIWitSiaT9lzx2ZCl7CNacqjqoZQDV6EOAXP6iZn+P+k8Sd8r0/hs3/fqSVTIH98GQZUeopD9BA4m5oMZSPzY56jL2JeQVSRo4GzipFfQeHvnnqN2AT8gu8Yf4p+oPHpFk88ZfLLKPUMhA+YhpkPWcB3cR1RyFIf4jtK5yJPJZAKkNSoRFBjR6GlYVqVSsaFC3IPPfV099ZSNBuuCw728kQQonDWSmXQ6rW63qw8fPigajVogYWoQm8ezAQRMGocZjgMbyPX4MfWARGSmGK0PIgApAocHXQQYkuLhcCjpPN58NptpOp0GxlPDJmKYi8VC4/HYwCSByrPIksxA+ExfzZRkLBTBOJFImIH7Pi1JAaeSSCQCU9N84OIMLEkGCmFdcYae/ZXOzfg8Y6q6/J/Ay4GrvrIynU5NAshnIofg3oTr+vX27dtAEkHQ4tkhZYvFYpbM4ZixWencdwdwSyaTNoyCAU/lctmq9SQ/PHcq3ev16ZzQ8XhsjDfVCUgJz8ITkAlqEDxexkGySOUBsAWpgVzEVxEKhYL5ACYG8x0JmJAZ/Dtg9lL+yu9jM9wfesw4QojJdwQl7IlAQxAGkGLPgD6OPIDUQVIPqISRhZjyvV0k7QD7WOx0Pi1SVN6f/kfIHM5RBfD7ChjXH65fv9hX8/ncbA0A4+XJ7F1ID4CTpMCzSCROAx1yuZxN4Ab4oSLA/gGQHDbd6XRsvzA5NZlMqlQqqVAoqFgs2nvS1gFg5Lgr7JcYC7AijnBkEjIz7Jj97L8r0utIJKJGo2FSSuInPoN4w2AXwKSvXmBfJHZIwJHWQjRJZym8l5JDDKXT6QDhGY+fJoEDaPEV0un8PWIsigWmGXtiFNvlsyB6sc1CoWAKJ1p0UD4R0/2xXUyHDCuKv83y5CaJCT5TUqC1CnuGbMOHk3jR3kESBUFAnMMOSE58fx+4udfrBWIqPoKqNvbNdUBa0lNH5R3sRqVckpFE/B3SxrdqYf/+3pA8Ed+ls6oGf8Y1c20kk3yOJPMTvvpKnEMWznVIsvvsVXbEPmIWMReFhSRTAXY6HbvnJL1U8xkmybVB+BDPwSd+Bgs+iwIJig3UQpKsRcwT4p/7uqqiiJPnjESMiSSEzYHDGgwGxljCwkwmE5XLZdVqNW02G93c3NjDSKfTGo/HBrxgB9hAg8HAepd4WDwAHC4bbbvdGjgslUpKJpPGCHmWg83u+5vY8ASt/f40pp5N4qU29E8CWP0Bm15+hnF6qaoHi1yLbxpmEUgJFGjZYTuRzPF7MExeLuNlqzDKAGCCPJ/tZbWj0UjJZNL02kgH9vu9Sfm4f4fDQf1+X61WK9ALSl+In4LqKzc8i3BdvyKR0yhmiArklzwjCAZslD3MOh5PAxUgT3g+MPXsQyofw+EwwIj6RAu23Z9NSGJJQGFvQrR4RpCE0BMd/D6ytUgkYkOxIKiwF36Orfy/ycIue3+o3kASQXywd7luklUvE/IVEkmBZLBQKGg6nRoRQxKMpI8qp5ftEjSl83h13ne73WowGFiAIzHA56GKADB7BpskfbvdWn8y96dcLht4oCKN7Chc1y0ORq/VapJOwAcJPwQMvpHJetgf0iXUMOxTP9qe1ojhcGjTudfrte3zaDSqyWSiVqtlh4Wj2IEwoBJADGQ68GUiAsNPTIFwueyXl857l3giya6bAQ8AN0+eEAe9zQOI7+7ujKhFQu6lrl7B4Cs02BMJpu9HBNTz/cE6JLyekEWxAenKtGP+8+CWPmHfrsP/6VsEB3Hfl8ulEd6M4ed+erXI8/OzvvrqqzCG/kYrlUqp0WhYFb3T6ajValmc2O/3FhN8H5yXNkvn830hBkjs/MAXfDPPDpJnuVza2aOoB5g4DH4C40G28P6ogEjwPCmBTWCnfCbFCJ8obTYbKyow7A6sCY4F9/M7l1iTOAdepKpOQs3/uV5PTnN/IWyJP8R27gVYm1YZ7jFYBByCDyABlc7D3yTZdfF6ihte7oqvgATebrfW4oNSwONxElJ6JL+UdVWiSMBBu4tTpd+QJno2ebFYtP+jq0dS4WVRVCuYhspm8v14JGHcbL9JkIqQrWMEo9FIhUIhkDwhkUWeNxqNbEN5nTZyHth6rgmjp5eHYAf4YpNy7RwlwsYmAZPOlToaegnmsFQEORJwSQaEkY8iW4VBgeGVzk3O0ehpGA6jz0lIeR09XbBHsGFIbg6Hg8lQvY5bOiUmlUrFQD4SRDTiOFYP4vkuvqIIKxWu6xa9gpABPEdkxpcyRPYaJAjN5OPx2EAKz5sqJGAukUio0WgEQJiXZhJAAKvILtiX5XLZ9oPvG/Ayc/wBDp29SiCRzof04lM8aCSYSgrIxrlGJDoEBOS3Xh4HiMSveFkLgJTvxB5H6cDERF5zmVwioSOB5zMJkvSA8vnSOWlbr9e6v78PkC5UkbHt6XRqfgGSiSDvfREHe3Md+Hnueyht+21WtVq1gU+S7HklEqdjTGq1mj59+qRaraZqtarxeGyEX7/f183NjU005t+lMxFJXGDf4GOJrVSWsTPID+JquVwO9OIABvHhACLpPCCu3+9bfEVm6Sv9yM58nxRxm1gqKdBDBHniiRGOxyJR9PI7BsjwPbzKBZvCbqXgofXRaFQ//fSTEomETWCXzlJubJfP9hMUiZXEeAhkSQGlE5/FexNbwRn4HvYF94Dr4H77Kg0JNkPlwvaN32b5/TibzewAdewTsrLf7wf2OP5bOidf3lbYt5ChPmmMx0/zN8rlslWuwW0QLsQU3teTniQ42CtxlXjs9yqEMfaAv8cm/fwIKnWexAH7S2c7IjZBjErnYgf3E0WStwN+z+9/qnHgZbChn9SKQsFLbr0KjwTaS9n57gz+8r2gXIcnziuVir0/8ZTf53o9GcX3gWzjHhBzUfl8CeuqRJGqXK1WMyPi2AmACIlOvV63hMbLxehJZKw0zn2/39uEzdFoZLI2goskAzrj8dhAJf1FOMr5fG6JlWcl2cAMduE9Y7GY9Ud6RtLrv32Cw9ABgh1MEd8BYyUxIqFEruYDBo4CRgpGFofPxsZ4cTYYA78LcPTNvmxkAIRnMX2fKFIhqj6ATgzDvycOB7lCtVq1a/GaeC9D5rPR3/tGZIIkREG4rl9UbWezmQF89tF2uzW7JFECSEG88AypWLGPSSapzpE0IX1BHukDp+93i8ViVm1eLpcajUa270l6cMDYCEHWJ0HSOSiyl7AdL4cHxAIYfQCBnZXOMlDuE9/VV0Okc0Dk86Xg8RgkVF5G7Xs1pHNQhD1FOUFij83yu1Qn8S273S7QQ1ooFP5KEghBw/NqtVqBPi7OiCQgUs3Aln0Sju2TtIYVi+vXbneanthqtfTTTz/ZvuQw+PV6bWcUUkEAGLZaLW02G+XzeT0/P+v+/t5sYTabqVar2eTBcrls6hpUOoAnbHwwGJiNkZiR8JAcplKpwCAHYoQks3cUDBCjkgzk+sSHvcyRUrxOOsu86JPHdzCBmJgG0eNBJWQLn4O/QCUgKXD0FeARHxWNRvXmzZtAxZ7qAEkZIA+wTYIP6OffIEuxHT6Xa4QA85WXTCZjg98gfenL8ioofDFSVU/Y+qpOuK5b4NrNZmPnmCIbJani+UiytgKeDYkVPpzXIV8lbvJeXsHCxH2I3sFgEFCBEGeofk0mE0vmWFRCsV+SFSqhVL0k2RRU4ibFEIgRvh924yWb2JPHzD6x8sUE3hOJeK1WMxUb/obYQ0yGoKFQhE3zvjwX7I9ZJ96neGn9bDYz30HPMMeIQIDha/ke2NglWQQeIYZSxKLwReLqsbmf7fK5r6sawUiI2Ex+yAQGABsCw7LdbvXjjz+qVCqZhr9Wq5msiUTMs31k/CRcXnJBoMPxYiBUyBjtHYlEAmN42TQenBHcfOM7vSL076FH9+cfSmdZgQ9glKXRSMPApNPpQJXC68t9AC0Wi1b180kuVVsM1ldyqbRisF5aSxDkHvlyPO/HdyJp9ewr9xqnAwNMws1nI2HjyABAOH0fGBy/yzMh6EvBsy/D9esXTOd2ezpDDyDJHqSy//j4GHD62MfLy4s5bQ6/xZH7SjWgSToPf8AH4Ggvk79yuaxk8nSuU6vVsnO/IF9gV1nYK77B9xl78oN9Sr8dgRE79AdS++C1Wq0CSgFsG9DK9+A9sR/8Bd9VOp83B1MLCPWfx/3zfSoA3tFoFPAx9LZgL5AsVFzoDwPA5/N5AyRcH89uMpnY/Wd0tyT7LIC5dGZ98QUeIHgwEq5ft4gD0WhUj4+Pur+/1+PjoyQZ2MCfXvbcQGikUilVq9XAQJVk8nTOIT1xEEXz+dz2BYn+crm0+JpOp9Xv942s4HXz+Vzdbtek6EjQIGeI+9J5yiJ+xreFAJawA/qGqJbzvfL5vB2jlU6nlcvlTD4tKUAqSjICmn5rjphCFu/JLfDCfr83fMA99pUJeqG8DB68gI/zOIFeJz7Xg2sWhKxPML3SgfP5fKJO3+llFWcymajX62mxWJjParfbAXl8uK5fDCU5Ho96//69JYvlctniXLlcVqlUUjqdtn0AAQnmYQ/5o0488c6+Ao9C6EAaEYeJFbwnxC7VQwg+PpfCh6TAfAL68Xy7GBiM/c/ZxeAyFHSSTD7K+2MP2+3WWhVIxPb7vb0O0jYajVrfM/cEn+QrnpCXFGv4npKMQMJ34RdQA3DdvBeYAfxJYr5arSxB9BiYcyGZmcI1kgfw3XzBA1l+JBLRzc2NvZ+f3xCNngbbfCk2evWJyfTj4cQYz+zL7shoSCy+/fZbLZdLNRoNA6/5fN4abaUz6wBDSKJJ4LlMFGFqSPgYlHN/fx9Ihkj8MOLpdGr9kfQEwPoRcD2jvtvtLAgh8/LVNuk8/QnWIRI5j9PFAADsADIcDoYMY4Uxcz8YhuMlCDBT+XzeGpYxJKoaXBsBGgDC7wK2STABzRilZ1+Qzl1WFagWcdyGdDLkVqulRCKhdrttk/MA2b4PC9btMiEI169fHO6bSJwOfqV3D1kJjBuAk6QIkMTRJtVq1eQoBB3fawFg8lUyEjeq7QQTghJDn6gA1ut1sxlJBogkBRhWggK2g53gtKXz1EAIiHK5bAleNpsNAFy+Byw8DCbJIQkwAXM2m1nSS4WSn3sZqgf4sKD4K2wYO/R2vlgsVKvVLFhJ+itwwD3Cbvg/Z+pxDxh6wv3H3mE4/RAPb7MAE4IflSv/HSaTyW+9Xf+vW5vNRuVy2ZQy3NtPnz5Jkp1/SJUYUAeQ5Jxf3sv343Be2XA4NPKyWq3afiV+IN/Gjt6+fWtEJ4nNZRWM3+d9pbO8C9tAacJrAH1+6Bl7jOv3B2uz97ws1NvYpTIGGZgHw+x1Kqj8WTpXOXkvYhrPAvyBf4AgAwB70hrwK8mkqCykaBDZ3r/ge/FnTL/ER5B4JpNJPT8/K5lMWs+/b0uhd+vu7s6wi7+GcP365VVuT09Pks64l8Ep3HM/uRaCwQ8KBEeyn3lGXkLNXkAF4ElEnjlY0bdc+BYHr9IippIs+WIEyaFX2IDLICb57vgf8CQ/w+/4NgXUO8ivwfAkoV5W7RUAVFdJpvjOvgUFu/Gv8W01vO+ln/TKAd8iAinN/fDyUO5HPp9Xt9u1a+P7SDJVDs+NHm5JNl19uVzq5eVFzWYzMIzn/4pEkc3DGT8w4wQAHpRnTqRzaR5pitfwHw6nhvF6va5oNKpOp2Mj4j27ykPEACKRiDGeJGa+Ssgmgi3c708N4cg5CBgEMnoGCDAALF8VhJ3loXvwybXx3Zh0hN7aJ8JUUaig0GvAPfEOxTsaPsMHUT7TS+X4/l5C69//Ui9PuZzXw3bR03YZJH0l4sOHD8bylstlu95YLGaSJBwT7+VHv5OgAvzDdd1ir0Sjp9Hd0umZ+6ZsJC3YHM3tBDqqWfwbwQab9uQHzx5WkwS0UChoOBwGFAa1Ws16gr2MxO91rncymZhM3JMKSDl8Yko1g32JP/IjygkmfAcfgAh42JGvIAJIvYO/BG3YEuDAg1buDdU9QADgFQILYimZPE/ak86HgF/KfPET+Xw+cO0w2IlEIlABZQhYNps1+REEz+vrqxFsXLf3JchxOAcrXL9+ZbNZm2QpSePx2Fo5ttut+v2+PatisajRaGQyuEQioeFwqFwuF1By0GvjEzhsmfgFEeAr0uv1Wvl83hITrsHPH4Dw8NfspVVehsXP+LmXWLN3SZS4LgAW5AT+yEtEuW5JAfmWxx34CmS4JFNI9KhUSueBG/g5T7pQdYAgplrq2zI8YZtOp414xhchD8S/MTiDuNput3V7e2utM9Pp1PyoJ9dQfWDPEPPEY/wF/ng+n/9TbOF/9ouqHH4dUoDKEgUKXxlmjyIj9RJVqnSLxSJQVMAG2OeSAuTfZc8uMYf9j1yZ3wd7QcZ4PIq9kshd4mkvxeY/7BYiw1cPUZt46To2ToIqBRMj7OeSXJVk2NircQ6Hg525iH8gSQM3oE7E1zG8zxdx/P2lmuiLMfgk+g3BPhz3h3LyeDxa7PRyXC+H5fqTyaR+97vfWZzlM78UMueqdBZJIg45l8tZiZ7hMZvNRt1uV7VaLXBWTD6f12AwsKSPzY/sBOdNMz/VOK9Dls69DpHIqQ+SqYsefLLRATVIs0hYYSGQpeHwuZ7xeGzBCkfM+VUAKZ8UA15xJAAugo7vpaL07zcv4B5g6Y2wUqlYtYGfeUlquVw25p/kkyofjCbPDGBP4uurp7znbrezZ0rS7ZNDAjC9apHIaXACLAqjuhkVjExHkiWHOEfpnBiHAPS3WV42TPJN5djLohqNhu1/yBsvaZFkCQo/89VkJFuSTCIjnQcASDJW7/IcolQqZYoC6Vw9BEhR6afSRQDgGkgK2TMkfzB33l+wYBQBryRo8/ncQJ7vdyIAsI99v4a/D/gD7lkul7NqpiQDBVT58CsEOt9z0m63A2dGATAlBeR8/ll44ofvgA/icwAsyNUAQCSNrVYrUN33Uyh98thsNsOq/5WLOABDTWy4vb1VPp9Xo9EwaSaVKSqEkuyQe+wFNYgnEal++Fjpe/2xKyrRACsflyGG6L9hcjjgyEvM2avIyXwbAqoACFlsjEEVkDUc7yGdfBjxy/f4+XaTSCQSOOcNaRl2ALlDUs1eloIKIA+kiYGcaYmdUO2E1CRuEvdXq5XJc5GWAlQ9ACY+N5tNu9/SyYY5nqZQKNjxG9LZV3INDJeD7OO5Yr/hun75CiBxhr1Ayw3+UToPg6EYge+nAsjzohXKT0DFzlC44eeJlVQwITYkmVqvVqvZM8ffEz+5fmIq1a/xeGw26gsM4AaUNtFoNNAf65NPSYHv4I/fIQZDznj1EfZL8ibpr9QJ3GcwKvgUn8d55yRjJG/4jPl8bn6Vv5Oo8Z19GwkxE6xOcuxJOPyQpMD398oifCf+jXu6WCz+qiL5JayrEkUAx3a7VaPRMMdHIMjn8+r1eqpWq7apAXBIshgYQ88Qw2gICBwCLJ3KuIwG5gFy4/3GZnPx8Pm3SqVigYpAEI/HdXNzE0jECDyXvT+9Xs+MA4medGYlpJOBjsdjO/sNhtJX5Xyw8BIU/vMJGxURP5CG3/HVQX6X58KZaJKMWQTAeyAvncEr7wcb6183n88N2HqjBnRgTDc3N8ZQJRKnsc6MXiexPBxOTcPNZtPYIi87Rt4arusXybsHT9JZDsVi7yBF9UweThJmncSLSge/yx7C2fvP92CSqXHH4+nAbOyNfcW+w/kT2Lh2yBY/FOaSWPBMKv7ABx+um73vG805hB67kmSBncEU3t5Jnrhv7GX6GnwQAnzyHbCJ+Xxu4JAgdXNzYwkABIv3JZ6UApQCZEh4qaAA1nkGSApRdTD0xMtzfJ83xA++i+AarusWfpzn5cEdoHE6nSoWO03qKxaLajQaRrqRsEgnG0aqGolEbNooZ1/S20hvDP04vhLH3oSspeKdz+fteA2ukx4qJKN8H+lM9mCDTAwEPLG3SGjwL/wOe4sEDKDH92Z5QoR7gC9YLBZ2rV6yCrFLLJTOY/HxQfw5EjnNNvCJLKoGX1khQfXkik/E9/u9Xl5eAhI37odP6niWlUrF4jxT0DlnD5+GEiebzWo2m+n29tb8Qzx+6ncM12+zfO+bTx68X/cDYRKJhCnh+D18tXQeiniZMPh9AL6C6Oez+V3I1WKxGJB8EoPZS/QGeuzJ/qvVakYscJ1cs5e65vN51et1I2mJ6+AAsCw+y/uO9XptcwG4T2BonyT6sxZ5Lz/QhmsEj/gFUc19IinlfpBb5PP5AKmGXeOXeC/i+Gg0snsZj8dtrgr3Bn8DYeRxCPdhMpkE5O+Q3J6E/9zXVYki/QDcoGQyqYeHB7spqVTKjlFIp9NqtVqSziNn+R3vPEk8GXpxOBysdwmm0jPvvA4jZIT/y8uLsZY8WAAdciz+g3HnAfoND7AlWPgDr0ejUWBqFJsJBoF/l/5ai8znsHn4+aVG2v+7dK4YIFfzAdBL0EhyPYPB5gdY4zwwTj/0guvO5/M2EcrLbfg5wIbP8M+Hc5/4TgzRoPLkBwORWEvnqky4rl8+CMBiI3thv/vqGAupIvLIeDxuv+uBKSy8B0m+2i3JiBovyymXy7ZvAZ8kZh4cYW9+D0HceAbvcl97kIbt+ESUYOslo9JZ6sPrYVmlE3OLLMzLcKRzf5j/LM9UAsQBhj6xlE6+lKqpP8dNOp+RyPX5oOwlsT5pJ6kHeAD+veydZEQ6gYnxeBwgsSKRiEajkQ3qyOfzlqz6vpZw/fpFYk9/HNOG9/u93r9/H2D5UeEwcXM6nZp0jH0wnU7NRrbbrRGfVMQAblTKfF8yiQlJnz9vkRjJsA7p3CoCCMOOAcD4Dqp5kBmXR0hxXSSuVFI8I0+MYV/7ARe0k3hJOSCRxJk46fGGJ1991cXHUR/3uBawAQkisRNwTyLM9TCjwfcmcVYpcQ+/5SuagF2uGbIIYhaiCJxx6Zu9YiRcv37RRuArzpFIRJPJxGZGsP+IW/znlTwo18BY+Gv2JnuMJAJsiPLMv3a73Wo4HJra65JcORwO6nQ6RvB7xQ3EBIkMe48BVT6G4ntILKn+S2cM6+WyXCNEDP5mNBqZf4C88ckiqjPUaT42SzLf5Isq5Bkke/hPfkYyj7/Ah3H/+GyPe32l9HA4WAsbPrFSqfxVDzNqD54x91+S9RZzXbRveRLoS1hXJYrH49HGzHrpJUCCqiGlX0nGmnOOCQMUSGJIuGgALhaLFuRIRgCTHpz66Zowr/H4aVqgB5U4bqoUBCrpFBjRlJNAstGRpsF8wkzAYmDcSGK4PxgD068IUtyrS4ksAQzD9smgD64EIs/+4JC4f0gFfLO1P+wURtNXcxjU4+U4DDXwCTGOg+s9Hk96dc6p5Pnyvul02iox2WxWo9HInl8ul7P/AMohCP1t1mazMccryWQXOD+cpqRA4iKdEz3AyX6/D0w+hWVlAWh8IAAk+h5cwA3X5atzgFPkb8ip/MAXLxvDtwA0CYq+wgC49MHOy0H8NXGW5HQ6DQRYKgu+bxk5Nk6fe0ki6HtPsAOvIgCscx+xS2wJ0M6Zd1wzPm+1Wpm0Jh6PW1UwEomYxI5rJvkEcACaAfpcL88Z1hdwzz0ACHhFQ7h+/eK+S+dz9ohZ33//vT1DyB1iDkkg0z6xj0wmY9OEAS5eEo6UebfbGYnrCR2OW+GaeMYAUQY17HY7k7TSw7NcLtXtdg2Isk980lcoFP6KSfdxHJBKssm/se8vCSempWLfHsx7W/WkK0COv6OeQJbmFRTsd0mBowIkWazyCghshEQPcrlWq6lUKtl9oNrLQCzwET1Y+DkAKuS57xMHnwBoOQsOP3Dpz8P16xaEGtgWPCdJr6+v9jriHXvBk4b852X/nlgg6fDtRh4jXiYfmUxGj4+PKpVK6vf7loR6AuHu7k7R6GnOh69qgScvCVoKIPh8/71IpCiGQLJ4gpKEljjlyahWq2XtFrSe8P040xncyzUx+dfLqb16CLvxyhbILKqKpVLJVA+QsZIsR8F+8TWz2cymFoN5I5GIms2m/Z6XG/PdkRGXSiU7ooqY/Lvf/c5UHhRPUFWSdH/u6yrKiYcqncfusllp/vUO+Xg86qefftJ3332n3e50BthkMlGpVLJkK5vNql6vW6MqjfDcUAIfCQnj/xmtHY1G7WEha/VSTgwe9p5NR3mdpIlJdEy2onJK8y5sKRtIOh8cjgSARlrPovhkzl+XJDtzitfg5D2r87+TBfhk11cymU6K4TLEwn/mJfCGvWUabSx2mpoH4ESeK8mANJXX/f7Up0oAS6VSNiaY++gBPAfKeq04splOp/NXcsJw/eMXgc3r/QFlw+HQ9rpn1WDTvUSDM/p81ZeEZrlcmvMkGPkqAc4cQOOra14q5gOj7z/y/Y+X4NEnYIBBgpMHl37QhnS2H99fTAUAFtdLTvAZSIVg6yFfIExIOkkgAczYB9cMkCToSue+DO498jyAK+8DQMb2SDArlYo9Q0kG7BmEAAHD0ItE4jT1tF6vm//zFUsUGgz38uCT6mS4rlvcZ4i1yWSicrmsXq+nx8dHs0cGDW0253N7AZC8j1eoeNa7UqloNBrZucTT6VTVatWIXKoi3maxJ/YxpCJVgsveRfY5EleUNrvdziZmA5R8zMUGLhNH9qn3E0z2pLJ3OJz6tLzkDgAK0eEldbwvlXjvw7xUzg/n4OfJZNJIa+Ipybv3m4lEQuPxWMlkUtls1iatE/eIcwzH4D5DElC5x8cgK/Q+EmDrB214yfjr66tub28DJEC4fv2iSkiiz14l4YdIpwgBMcliL0IaEIO9bJHXewUQCQ5xgNjkk058P5N2sTFwXyaTMSUfCRhEC5+FnUBQbLdbk7v7IUy+yo4tXZI7JHbeF81mM02nU5XLZVOn+RjvZd+e0EFF4clMsLRPqCHFIFS5d9hoIpFQv98PVOCR5vuEnu+K2on4P5vNrG2Nz4Scgtjlu5OneBky+BrclclkjJTiej/3dVVF0Y+I9yVXAguyQh7ker3WH//4RyvnHo9HA5i+tH88Hq0UTY8STDdgDwdLknIpu6IHjvf3zafdbtcCHJuPyWiwRx6U8W98L/7Nl6gJYGwu33NEwOSMJ4zYJ00+eF4mifw7QcIHDZYPtrwWuQ4JLE7MJ2VU+bwRESypClOF8Z/hzwLC0BKJ06Q1wKSXz3inSHWGzwEY83mHw8HAa7iuWyRLVOs8KfDmzRsDg17CzGu8g8MpwuBT0YKpI1D5ALTZbDQcDg28YUee2YdQYH9QRaRSz35g3DZVLo5m8L21+A+/x1erlbrdrhFM2L0PENwPgJVPUv30NmzCJ93YBlUfbIz/Xw7dwr4ubZzfRdFAggxAJqBgSxBr3EMvzfEsJeeykTxA0CFTpM8TIICvmE6n5r+QmpPkexliuK5bxDL2xt3dndkaABVigfjk/TMECs9HOg86goDj5+zLQqFgz1Y6EZSQDF42Bjg7Ho/q9/tWrfSVb6qD2D3tBPyZHlg/iAKC0YMkr6DxlT4+31fdsQGSN2xUkjH3VMWxTS/j5p5hNyRoxEMGzHG/vIIA+4eMJXktFAo2gKder9tUTK6NZ+lBJbZFXOz1egG1FJjCE1f4JkhZyAJPmpXLZbPRcF2/vNLN409sDgn4pXKGyiLSS8gIKoLb7em8XFRexFmP9bBh9rNXxvBvicTpHEd/1iKkDjYOTpYU8CU+afStEx6f+oo6tuvf38cmHwOl4DnntK9cEkAQKcRkL2MFX3Md4A+SPe4J98L3MoJ94/G47u7ujCyWzkUd39vIsC18H4n27e2tve98PjfSlOfLmaX4AkkBrEKCjk3y/bm2L2FddZUwB6lUysAMjpqHAIPQ6/WMkfFsH69jgA2BJBaLGaBhIyBTZMPBsDJRlaqfT05hX5GASrLXw8bx0GDo6PtgYxHE2fSwoIBAH/w4HzKdTltCLMmYETY0STWb2Uu7Lg3UBwGfoP7vEkqegb/PVIu85Mw7E7T1yAS3262KxaJNwWOKKp8BcPbMGlUKninOgook7CvV4stqDszQJYAI13ULm5DO0w39RDXpfNg8vcRMKoUIgrBhf0pnmc1wOLTeGd8rB9vI4I1kMql+v/9XEhNslCoG/aq+gg04hNQhuPjvRyJEX7IHnEwJRh7nJe8+ISbwQKIQtDyhQ+JKRYMqqSdtfMUC0MzofSTfVAQ90POSM4AzwdQTXR4Ue/IM6ZmXEvsk0vcvUyn1E5qpjuLbeOa8F9cIeAmJnOsXwJAeUeyPRJxnW6lUbL/QV5hOp/X+/Xu9ffvWSAAvG0YFwO9I58TUtwkkk0mTnLG3fXLmQRD2J8liAIob5Kww5b5i5wEY+wvghO+QZImnJy6wQSr27GevZJHOlUiSNN9rT7z0A0ggqYjB0rlFwsv4/Gd4X+Eld1QafTWeSqSvChMHx+OxGo2GkdjZbFaVSiVAfvOcsU/2QiwW03g8tnsNXvG+KUwSf7uVzWaVTCbV7XZNKbff761yPhwOVavVJJ2r5CQa+H8q+Dxf4hZ4UzoPh/REJ1UubGE2m6lYLNrxCl5qyl7w1X+q1hAd7EuIz1KpFMCk2CGf76uY0nkwok/KfMzgu4MX9/u9TSaFCOYeXGI/L0dlL0v6K+wJgURyCTbwmBIF3//D3n81yZUl2QGuR6RCikgFUV3DHsF5pfH//wy+kK/DmemuriqIFKFSR9yH5LfPOqcw995BFM0KxthmMACZEUe6XL7ct+94Ny8vL226OZCJfrl/vt13+U12lE/l/8RXnn9Vx/BwDd5RVvkxGL+HtfHU04ODg7q5uWkVRMEUh+DP8fFxr7Kmt0bJ+fDwsPGJMyHLF5IoBSFO9FN1ihDlv6u6hEbAxogfHR3V+/fvW7Ka1ZWqTmlSqDlzyTJhfPfuXVV1/YkQFIvjcT2UB4Lo567Bv792P1kdItg+k9U/CWUG+oxRltOT4sNhuy/JpQSTQUyDljQa1+95oxRD5iiXBDZ7TCHYW9rM5uvu7q5H/xXECNw8+zdv3jRnRkYFcMvlsgWKKgnkDMUadSr7eafTaeuX5TCq6jfvdTweN0q0Md9W0igZc8EfB54J6t7eXqscCn7plyRVr0UCLRk8Oi8GQFZJE+ms6ihtFmfseiXkqrGQWnqftiPpfqZEA5gsDtp9Jp02k9t09GRAAGyK6fn5eftOggWCeNUWcuN4nknSq7br25eg6O3bty1xENB5Dx8/fmwUN8HLer2uf/iHf2iskAxCBbESxJRfuk3m+aechCv52N197cdXTcb8WSwWdX19Xbe3t72ECoWWH1FFwS7JKYlAXBUW58iAjw4NGTNsiPOSR/ZKEiuhrOp6oOm9hJNPA4BIYPlE31+tVm2rkqpumE+2fvhssgIyaN7dfe2//vHHH39TMWVbyIBrU2FOqvfT01PbQ847ZRsSfN6uzRcfhOU0Go0aA200GrWYj1/N5MU74TsVGewxLV4S61ZV88tABcOpsEHEbWJS71+MnfHi/f19q4CRCee7uLhogD75dA18Al+iKqYgxD+qFqqY+ZzYF/jkevQw0it+Jc9b1SVfnpFrc+3uk193ra775eV1/1T+jC9jf2zt5B1mtVX8yoebV4LFhF7LX6ffzoKPWMDwmtXqdRhfJqX8/B99bVRR9AL1mjGcethms1nrZSGkHtyf//znlhx4wUkjJUwZkO3u7rbKA4VMg6gHAyVDnx6DbyLqX//617q8vGyVAM5IoJpooZ6BROV9nhNcrVYt0E0hIKQcmXvhkAhnKnYmdMP/J7LCwWQyCOlMIU2nW1XteTuG61Jp8hkop895Hv5OYIDB8P00ltArFYqqahVPQQwUhnN+eHjoTcXcrm9fxrTf39/X+fl5jy72888/1/v376uqQ8AkYZzcaDSqP/3pT1XVTdz1bumkrRMYZJV6BnexWPxm8mkmaBm85RRGI7Xfvn3bzpmLk8ggMRFFVBVyDOSRNA8RTBV1egzYSsqfP+lkEoBJBkRWG1KX2bFEVFV5RqNRr48zkcmk5ud5sSWAVru7u72Jljk1L4ffQDn1Wc3n89avisaEVTCfz1vgn8n3dm22jo+P6+bmprFljLyfTqdtm6KDg4P6X//rf9Wf//zntqF0+iEghEo7Wup8Pm/vOamdgk7vj/ygh6rKo44DDE05p0fZY0S/VSgkaHy1oDHR+qr+hvL0me4mrU0QRpYBsXysJFmgl8wW90sfc8AWPaVf2YPNjyWdNoflZDU17UyyNdI3oo8CZPk/zCnAq6Rhb+91iwV9jtnrqHfYteYQKj/frt9nAUkBNXd3d/Xu3bsmTwkWZvLP1g99BL0xz6Gq8xsJKFRVY6wY7phstwTu+awsYEjEFotF2/Nb73JWyfhEIFNVB5ymbtI5PjaHGtL59PH822w2q5eXl+ZLhlXKjCtTdzJxTXZC6qFjZBVfjCMeWa/XjcHD97un7Kf0XADf4mPvHaU7C1d3d3dNX+U6GFI5aAfz0j05L3v6R18bVRRXq1VLEhlt2TehV3GAHBL49fp1YupkMqkvX768Xsz/SbxyshFh85mqrrrAKNoE2/YMKWiUEyVL4CZhJSw+z8EQHMpJKCEnpkxlSZ0DSTTJc4IQEr6cPljVVSD9O+89EVSLEULxcdysyLoO15Ln8Pt02oKNRGlcg3eaVBrr+Pi4R5Wrqhak7u7utr3y/H7ohN3b2dlZk4ttEPr7rPF43Mawe95Ajx9//LFub28bWCDZAy5AQ1X902inca96DeRSJwSSDGTqtO9lvx7nyLn5/Js3b5p+p6xKdhle1c2qapWKp6enZg84VJ/PzbyrqjmEHOTBmZpeWFWt/1jVks3Q58B+ZG+CaWscGWfGOdPvpLclnUagYHk+SYHf39+vy8vLBtglG0GCsLOzU5PJpIeAu4f7+26PWoE1RysQFsycnJx8N2O9/+hrPp+3ZymxWSwWdXl52fzF4+Nj/dM//VMdHR3VYrFom2STn5yM6V3zpXSE/EqmTk9PWwUgWxFMRa2qVt0H4Lq+7DFUbSYz2RfJRwAwyWQCmgl2ZIVPwJm9lYJhCH1VN6k5/wheyW8myVmF8LOqbvKqOILNcy0qFVmtcw3T6bQFri8vLzWbzZqd3d19HR6kmiTJlZSLJ7JCUtVRzJ03bXJVR8dDBRSr+Kxgfbs2X1md046hqoSR4TNsfsZ6CdRVdYy4/LeEraqjJI7H40YdXa/XLVZK/6G6Rg/5VHLsGvgkDC6+OG1ExqXiP/7AjgDZawuEJufuTwJJnrFX6FVVRwUXLzw/P9d0Om2J2PPzc5uO6p5cX1W3R6HYmy8DGOXwtapui5O0dckYSL+dSWcmheITx5KAYsD5HWq5WBfzaTwe93z5EPj+I6+NqacQDy8FGrhev47m5sQIukmno9GolstlLRaLOj8/b4EoxCSDpoeHh7q8vOyNnKUo6/W6t+Fn0mcIatJL9vf365//+Z9rPB63F7azs9ObXKj3cbFYNEOdvVWciz4rwpa9hwSRw5HU+i7B5HwSkeE0/yOkIcvbjuU+/IxBgHit1+telSeNQVKABfYckgTDtVNWgao9vKBE6awEFlmldW2coMA9+66222P8fsv7zA2vV6tV0yEDMFR/gT3pUMhoBm0MX1U19AxVDVqumgBcQEsF5JDxrIJDQu0jBYXLANS1MewCTzqWejGktEjMkqLC0SVSK3nKflq0Gci+IAD9BqgCXMqBIUkRykA6k99MxNmSqmqUmar+fo2eAT1ChxmNuknP9JE+q/QI7qGrOeyDExRg+2xWQ7N/cbu+fQFsAJaz2axOT08buPP4+NhG2wtUJHmqxujUyVxJkDRBSWABhs1kMultSaP9IgPCZBJUvQ5I0seYCV1VNX3k/63sF6IDgk8AU1bh+daqDjjN4JhN4H8h85lcVlULngW99LWq24jcz+jGeNwNkNL7J/DP5+q5nJ2d9XQQu2g0GrWheuzlarXqsa/4Z/YvmVeoc/ypwN/nnp6eGmiVgBAbsF2/31qtVg2oQbsEnpMtYF/Gcr6boDs7yt8BNZ2D3PAphjJlAmMyb1XnW8bjcdMl/qKqG6yScuy45JZMZaUbeEwP3WPGqEldraqm958/f+5VCcmme+Hfcm9R8l31Wny4vLxsv0vQI2OVjEPYR0xF8Ucm8K6Zz2cL+birq6vecL3RaNTb1sY1yg+m02nvnQDe5EJZJdWrfXp6Wr/88kvT/+9BVzeuKBp08vnz59/QJH/88cdeJg9NZ0jX63X927/9W6PEZS/B7u5uG6KRgyOen5/r4uKiDbXJgEkwKlmVAHE8FNwLhLwLSBMlhQjo1xM4Si4FYrlhbtIPMjEkoJAZQuOzVf2+rWHJn4HxzFMp0znk8TJhdIx0Sl9zKK6LEu3svI7HPzk5qcVi0ZQuhxVkIJ1UYcml68u9cLzr0agbUUw+oEnfQzn+e1ioT8/Pz3V7e9veOePud8vlsjfZSyUpE3kyRd6rOgMOXfWdpIEOq80vLy+Nqw9MyOBtZ2enBT+GVjH2qT9PT09toiIghPN+fHys09PTXqAHHFIZo/scxxCAkdglyJKTPtkc15JVvP/yX/5L/d3f/V37LFtDV1CZnIuOs53+ZG+IBB/tp6rrkZTUsm/oiFXVe14ZLGB8SJL9X/CQCaHk3PVKJrdrs4W6yF/Z7iHbEyREZPHNmzdtL8MEO/S8CFTpM9/38tL17WQ7yO7u65ZHuacnXRZY+ZxgWPUdW0bQBmwRBFV1FfA8JrtkgmHafL7v+fm5+VdJG5AmB3RJmrWODPV3f3+/zs/P2wwEyWSCYUMqtWf08vJS5+fnzS6xH2knqqrpVALDfFlWJ8Q4/ga4LhaLZpcEpRmfJMDj+Tw+vm53hWLMP6c9267NF1v89PTU7ONkMmk2c2dn5zeVfWBr0knZcjK3u7vbQIO0p1lhT+ZYTtI0GZzeY+wBcYGAOzvdMEEJnh79LIJgEklefR8VE3g/mUx6wG2yCQCUZ2dnjaZJh+hosgqcw3W5jyxKADolq561a84EzbHPz8+bv0wmYIJdAOy9vb16//5967229+Te3l5Np9NeXCH+9yxNVH56eqqzs7P2DuQjhhnl5GN5i0E638vk8N+FmzCdTuvt27et74xxg/JRGMh/1auBvLm5qf/23/5b27OPUyToKSCPj4+96odAEkKpQnl6etocJeGTIL5//75RRIZJSyIsOzs7dXZ21j6TNDvVkaRm5Uhu11dVja+c9DpKIBnzPQH8MAnMlYbE/4cJYn42g8+850xeMwFwL4wUwd7b22sVqSEFzrHwuDNYd39oECrCObkOLScryNsg9PdbSS8+OzurquoFfp5/VRfs5DYIKmhZSZdc0rGkTQF9GEuVxvV6XR8/fmyflSTl1EH0Rk5LpcP+SJPJpG5ubur4+LhN/cvAKaeApi6rdqAACVazJyuDOAltVtnov6qNwLuqo3BmsJYUMMFsBrH2QcshJGxEBpjHx8e9IRv6PN6+fdv7nCnLV1dXjWbvPRiEcXh4WLe3t21vtgxmLAE+IEjyaNsbzyN7Sbbr2xddApYIRARMgq+qaolBovl8C//4008/NfDNd22Dkv00uacbepekik7Qp6qOESQA01JCdvkFAbV7yKogX8iuVHV+awhIZS9iVjSSAYDO6dwZXDu2Z2il/ZHgZhuGSmJW3SWAmAZV1WyiPYP5qwQ8BdwJylVVA7QB2J45G5L+WnyAtSGwTz0WTyRIu7+/X/P5/P+GyP4/t9h+7+ju7q7+7u/+rtlAtv36+rq9k6yKS0rIWlUX3/FXBwcHDSTidxKUB/r4OTuQ9Fc+bbVa1enpaQMfsEBU8vhV9oBcZRW+qnqJTtK/ySc9ZCcy1sxiQ1Za2Yak7PLX9CKp7/wTIAjbMOU++44zRmXb+GRU1uzh5SOB0mzWeDxuM08khZK/5XLZ4mKxg8+wiT/++GOvzcS5gEDfQxUx10aJokRFNSk3w/a3kvdoNKp/+7d/q3/8x39sgqIxOLNqVcKcDvj4+NgQVIYbV5wQUw5Kl5UpWfywWqUfhEJkAuW+kprFobrf9XrdhgtQBlWL7AkkmBw3458c6OGiRJ5z/m1lNXJYkUyH6d8Uy3eScpaOPYN8JX7VjPv7+zo+Pu4ZDEo1m83a87f9iOsS5FJiju/6+roWi0VdXFz00NPt+n3WL7/80no/IeoZ8BwcHLQ+RVX88/Pznj4LKIdylr01SXFO0AQgcHx8XB8+fOih8IwmQElgWNX1y2aPwsvLS9vDEXpH/iC7aOMcU36OrbERdtov9ykxcr8AK8EcZ5g0GYHzEJhJFDN7tpNOwy5VVaMw+T2gTDCOIge59pwlikldkiT7vfvJ89HJvb3X/RvZY3aQXZ3P580Wqv6ohG3XZksgIugzEGF/f7/ZUPRQ9lH/jn5+SP/z83P94z/+YwNbb25uqqqblJtTDk32pF9kmGxkX2JV9Xxg2gGfEdCRT34S2JhVNf4lqyWO7Zj8Ix9P/lVXBcYC2GQcqfp4rq6fDtzf39ft7W1vXgKf7X3khFH6sLOzU6enpy1xdZ/iDdeWMdD5+XlVVQ+gwpLw3efn55pMJq2CwZ4BpfPZPTw81GQy6Q06ynv3DgAJ27X5Im9AN7oj4eI3xLHDZCUBerEYG71YLNpMBsmHREICVdVNBmezl8tlD0Alt8kwIxuXl5dNPvlqK4FCIEVuEp+VM5/lZ+2lrRrnHJgQyaKhq5K59JGei/vmr6o60EPczD96LjnV2bOdTqftmXou9ML1J7soQbLsXeTLHZfPy6KLwYvYRQDs5XLZQF3vxf61wHdVy+9hbUQ91cvgZUDXVqtVQ7aruilH//zP/9zbeFeFMAXGkAjJos8L7AQqWZnT0C3o03/FmQnUCO3Ozk7bIFxfhqpWohz6jARYuZWEZNW1QpEgpp5FIiaC5Kw4COj8SepoJnrJec97y2SRcivbZ1Ce5/U5CWAep6rbH1MA6loJtXP4nGtEZ0D7y+V8DAAk7scff2yVLYbAlivbquLm6/T0tI6Pj3s0NE6KcVPRf3h4aL0F6BwSFegepymYkojRDUFqVuMkSYI1zkLSin4i0AL+oFOqbqbzGlI1q/roYOqcvgG6npSWHBghuE2dFbQm7VLSSA/ToSQQlf9PtkAmk5JCFZSq6o3K9/wSOMrAFR2G7mSwLPj2TqDKs9msN3UNEFDVVbiyysmZ+qxnmmjpdn3bkgAIvryz7OWr6qq4KJE2fpYQJHuDTT49Pa3b29vmW1arVUuQsGFyXzQ+HGiTE3NHo1Fvj0O6mePv6cXl5WXbO821J41ytVrVbDarnZ2dBsocHh420KKq82cZQLIT+Xyq+nq2Xr9O70VzQ1f3HcAnunWCsewbsERSzu7QYcdISny+J8l1Jgne5e5ut41BBq78bYK6T09P9fPPP9fz8+v+lCcnJy0pEP88P3dbJlS9Tpq/ubnpDYnbrs0WurbYBnuOTNCLlEeVx6qvM8GSFSCeBDbyKdYw/huPx23jefurknN2nQ2fz+fNh2dV23XQrSwgpJ5iFjivhLCqmhyrzPOZmAPuDzgEVM5k1nGBRb7HbxkSl8+DXtHX9MvonH4uxpCDmKPgfMCY+XzeekWz5zLjZSAYlgZwablcNiAaGCfeUoDhn5Odxb59D2ujiiJhFDBWdZk9x5DBob8TefMifdcU1dvb24aEQCckI5mEMN6JnAtqIHec8d7eXguKDw8Paz6f9za9JDRJqYSccJ5VHU0my+eJcGp+zapIlt6z55JAEiiBGUVI45BClUlkGp5EcYYrz+lz7o3TSfQsr3s0GtXbt29/o4Qqj4YSCaolAKqLx8fHbUNwSO7d3V3NZrN68+ZN/fTTTw3Jenx8bJWj7dpsXVxc1NPTU6PEMISGBlVV/fnPf24VY1thkFGGkk4kdeLdu3e1XndDK3K6adKqc18zIA/6aCKcwz4lyeJq9Towi8wkRUQCKxBLfXRsiCnjnH/0N2p+z21iMkFO3XO8rKo4fiau7FtOCs5rQEvLnif6mQCUyijHmbbo/Py8IaNJQ8oKR1W3x6J3OxqN2r6ZWbGtqpYgkI/cO1MCiSmx1dHNln7SBPGAcFX9npzpdNps6w8//NDeA/lMkFQyhG4OyRZMvn//vlW7BITj8bhubm4aRS0rG/R+Nps1v5zTriVLEidJ4+7ubo9+qbVA1Y+9yECR3JHTqmpAVlW3xZNkN4Nd/lu8kIN8Ui/pfVW1amVVtaRSEgjs9r1sOWE3jo+PWwCZtme9Xvf2lUyqvMSVDHi2hn342YcPH5quowmitPG5Ge8sl8vWA7VNFH+fdXp62nRLEjP0CyiTkkTU7pSZ9FPAUjpPZ7zfqo4mLQlJhoiq2ng8bqCBCiOwQnEjCwOuR0ztWnKADT9i6nFWNav6FPDUQ7Ygq6yA2fTrnhG9lDhl4SQT4wRqk0HAv2XSWVW9yaJplxxfEQdr8fj4uBWEVE75Ot/xDCXmchWAGDueg2x2dro9FE3KZf/W63Xd3t7+v5EoEn4cZQKh7M1BeBj2AyJUb968qdvb25YUPD09tX972Wgyl5eX7eFmleDu7q71LhnXm/SqrHihZlEGDhXC+ac//alVLDN4si9jBoaa7FHJ3JMlWCNkGQgmsuk82Rvi+xwoJcqqRiaHFDRL9l+rPub/KSnESMIO2YIWZyU0G5YlBxBwBsCwjTR6qZzPz68DClBnBCj/+I//WJ8/f240yS1t5vdZqkmSFRQOgQ2wABKnUkgPyN7V1VXbH9Uaon/ZnJ1gy+HhYRshjw6DMpMoOmpjUtRM1k3nQW/IFhklpxrn379/34JGupHJmAqZHpKsyDDqR0dH7ZpTp6q6BJFzlPSxgZaqa/YNJtii70IfRlKTBLuOs1qtGnKawA6qTVYigXEcam5xUVUt6B8OIwDmcIbYHTZxh4pKSrbr29eQWUJfDw8P6/r6uo3H50tVovi5qmq6lyDg7e1tC06y4m1iqcARYweA+vbt2xbcZTuGvmX7seW1V1Xz6wJfyQwAFNhB/g3MmkwmLdnLfvnHx8dG6RJUSoI+fPjQ5Dhp0qp/mAEJdAhIk3VA7vUf8qHuP6ulCd5WdbGPZBYonL3Lmdxm8ugaTEx0v1gAKvyHh4fNTyZQnBUmAJwE2f7DnsMWyPl9FmAtW5WyIugdkTMFgQTqvKuqLn4jD3RpWLVzTEAhvZfsSRoTwHBtWdHKYopE6+XlpTFE+Ed+MFuuXEsCovwLWQW0uBc2Jumu2YIxLGbIH3wufWDqmSqtuCETcMxDPpAeqP57j3xbMhWy39n7pGNsl/iJbTAF9+3bt3V7e9taBdg3zylb0ST9rv17ad/YeHsMSZLgXsCRE9MkZrJrFQbZ/Hq9bo28AqqqjuKJYrVardoG4VB0TiD3dCHsnOrj42PN5/PeuHv0M703SUuDCiyXy5rNZi0gymD08PCwzs7OehQejsAxnKuqq5b4DCqc8j2h9l1OYViiJ9xVfYfxH9FUKWQmsX4nCB3y29GboKoZaAugGa00Sp6vdy6450RReauqGVJN94yA82yD0N9nJcqIU+/9JdqWCZIEg8yoNOTEYCBKOhmymUGsRFHl3mAp8sEZVfUb/O3ZJCB8enpqiHuOo5csumbnNPZaMGi5dsi986ZuodKqmKXOVXWJn+9mAuo49IK+s31ZvfROPBe6Rl+8n/V6XTc3N013oKmj0ahVLNLeZVX1+Pi4h4xm1ZNOZsIv4HHdb9686SUkaU8SkNuub1vsvOCjqpp+2DaqqtsnD3iCwpi0ZP5jZ2enfvjhh/adnZ2dJs8JcrALjp/BnwCSblV1m15nRV2gyMfPZrP22apq+unzVR2NVhyQbRDpFySJrvfy8rIxBBK0yVhDBTH9hy0DPG8VEc9mNpu1uMWzYA8tyV9WPjFjUNIAQfmMxRV8Ift6d3fXBkT5Ln0SaKdeekY5ZTkB6MPDwwYWDStA27XZ4oMkFFVdu5QYD5Bye3vb5Pvm5qa9S8UILRRVnS8iH7mSIipRHFbQ+T8+SBzHd5CTbBehy+ii9JCPzeSIrpNZ1wyQtIVHVisBVq4744kEaVUsq/rDgpxzqPuu3b8lWPyRHMG/JdZibd93braHnmel2H1KwOkiPz1Myj3L09PTBszaOxENfwgSOT55+qOvjSxJ7odiOwkOw8MVdEFDn56e6vz8vE1NozxKv5SoqnOiKnoqHYKq+XzeEAjXAXEgSEaJ//nPf26l5SHNamdnp23zUVUt0ayqxmlOmoDPZXkd0kognSOdtyqigJAgMv4Cb4qYFYshfSYriYlKJ1W0veQIdFX6/J9TocCUXhXKdX/69KnRgozjFuwK/inh4eFhG9POgFJA98UQoNPc3d3V3//931dVP2jars0WWSJ3OTbaUBeypKpgohqq2vPzc/3www8tuEnEr6pzagxxooH+FshUVf3666/NVkhcfB9ok7S1DFgde2/vdfJfVee0fUZFQUBLTl2Pzwq2M3mim0n7oeecm+tNRDjRfk66qtOv1G1LoErfnBNoMh6PG83+5ubmNwOi3F86On2nVdVAmKOjo2ZfVRYPDg7q9PS0t9edpDrZBLu7uy3J97w8/5wet13fttjix8fHRi0lX5grdNX7TqohO6tHKXXWHzY2KZaqcfwwgDN70qHfmZwIigC92Yv48PBQP//8c6N000P3QjcAvHxuJquZnAnM+Z1kOJgKK9jKviNAUCaFfK+kWXJb1R8SlxU+wLHAEyDGf2UVE8PIoIr8Hj0xYp8uA8nRv79WKWZH6DUbLAEROA/tHfnYgq2/z1Il4mvIYVU3Ffvl5aU+f/7cPpNxGHbcbDar6XTaY6hUdSAMSueQvVJVje1BZrI9gc/y/YuLixYPi4kBrMlK2dvba/F1Ape5tZv7pUuehwrl6elpL2ZO5kNWycXfChDDVhGAyXK5rOl02tsL0bNxPUl9HTJ+XBd9wGjg//lh10vnM5bFjMutt9ybYx4dHTXa+WQyabYMwI2R4/3v7+83dpQK5vcC5my8j6KXk4NpBF+ya8bZQyIwHpgeKhz7RM80pUMun56e6urqqnZ3d1t/TvYyQCMoUzoeDjkbiFUQXKugGZoPLVQR4CwYbt+DzGfilRU4Cpoca+dJVBiyIoBIakIGqO4rBY3BSHQqqwmCvWEQ773l/6uqh+K4ZkiJe2U8VF/8DNrE0S2Xy1bB4Twlo8vlsgXXnsOf/vSnbbXid1gZjABxkvKF6iY517dAvjV60xNbL2RlOQPLqq6qzRgDOlTLzs7OGrhT1U02Q7OqqiZPpkDmnn70xTRjwSynyH4kvTNlKatv9GdYPclgzTVl8E0n3Wse2/EEoxk4V3XJo+ciiXctdIqdub+/r3fv3rWNu4FMJq4JEOiQ5Pbl5aWurq7q+fm5bm5u2vv0Gc89UfEM1iHTmSiQH3q9XZstfmA8Htfl5WWvEgdIy6m9Vd0Qudvb23r37l2TBxV/fXWCQPJR1fXsYNIAHzLBkoiiZuspTKBE4pYA59HRUf393/99a11wLICL+00w8unpqVEwVeDYmqp+Lz4bkkBPJojZFynpPT4+bkOCko4tFpCE53ES8M0g2fXwyUDu5+fXYTMS0LxGCXXGCGmTtHskqycDVu80h5Rkn7l7rarWx51A7HZtvsQtbH/2nfEhYiw9pcDRqqrFYlGfPn1qoMdqtWr2OcHGfM/0hfxn1ZBfsc+4/yt4kFlgoDYwcWOyVciQezMoS1xPZr82wC3jTP6LH2Jjkr1SVc3viEf4EnK/Wr3uzY4inwmqz2RFLpk5wyq837NVviceyup/XiuWTlZT2VB/koHle47js/IXs1FcZ9rN76XFaqNEcbFYtDHejBcFkODt7Oy0yWoQ7qpqe89UVWu8tvlu1avgXVxcNIXxWUGTbJ0TykqGF8KBJfogcELDMVkVopEBG8eJ35wBKEpdKi/hYygyEKW4mQBCEieTSftd0vdUOvw9TBQTdUp0y3NPRc4Se1Y2MrDN86ryfvz4sfWWJjJEOaA+p6en9fnz57aRummMVdUqFNAh71QyYU84P4dub9fmy/5pgk4VRMnAZDKpqle5uby8bE6EM5PwAz0ycOM8BDaokFl1Tzoz/Tw9PW2GmEx75ycnJ80JQOKzN0NiqroFdJC8ATTW63Wjgki66INANFHTqj6yCEhhH6o6J8cGJNjkHpJG474Fe65LEJuTC9mNRGmdM++dHq1WXe+iINM7hdiiw+zu7tZ//a//tdk7QYZniS4IADg4OGhV5XyP2W9TVe3/2/XtSxU/kwaVfxUjundyctIGOp2cnDTfx1aen5830A1aDYhkt9frdUPutWQI2lTwBUB6WMm7AGy1WrWkFupOX8jG09NT28dNoMkvSTb9+/Lysp0z6fB6YfXqkXPAUVUHlCb9DbDJZzqXTdLdg57qrOCmDXOsTOx2drr9oSeTSUvM+EOgikQb+I3K63tZDazqT7Wl6wCqm5uben5+bvMZPK+kwGffmYB+myj+PkvLzNnZWYtvq6r5OcWQ1arrl6dPWVX07k3GzOLA/f19a7fIZKuqi82yx13rk+py+jDgAdaH2BOYi/qMZQIcUXHnTzOZcz9o4VXVgJihrvF5cgI/y8SYL0t74FjZtsEmsWNsGL+cyZ8ClJXJHhDI/fORfH4mgFgBbJJrdh42ICv74ir2+PT0tNlo9hJgJP5n17+HtfH2GAyaKmJV9ZIkSQajp4KYtKkcZU3I7VlCcKCCSvhQiXSIEAdKBU0QnDH8nAzFqKrm8Pxc0kRwkvrDQRIuqGEOdjHZKCtnBMX5BJNZDR1WIAlzIpl+n9RUAXEmzII76BYHQvCzFO84gs2s4hJmCKl7HI1GzWkZUGPgie1G3OPbt2/bO5JUOB/aqmtMavB2bbboCWenkpBgiiERHJFqP8Q0q05JK/H+/F5FizMlr4KhdH4MaFYknMvvyRgqqsCYfGRwlfS3pKkIiNPmeC5VHRjk3MlOgOBX9XsR6ZXejrwe9yPAp/eu1XE5LOit56EylNdq/8dMKHzG/VkZHOrr8hy8k9Vq1fY8tR+XZ5bBhITaNR0cHPQGg2zBnM2XgEp1ly0lr6qHSUlMurL/A15V5Pg4vpLM8TtsMB3PgBBzBABEZgxbUuFMXUyANytzyTShO7PZrM0cGOoEX6rffUg309qQNDs2xr8T8JFUut6qfnKZQKlqkfsUXCebyOcBpO5pPp+3AT2+w2Zmv2SyE7wz24JkpTa3BvMsJaEWwIgf9749zwSKt+vbl2RArGIyJn84Hnc9seJIrCqAz9nZWavQj0ajXmEgYy7gAtnJmKyqA0SqqncddDFttOoWu4IZNB6P6+LiosVo6UsTYODb6VD6sCHzhJ+T8GELAJxtJ0E/gV6KCkltR53n77Li72+xvmfC7rgm/kv8ncURzz7PlYw6nxP3n5+ft9YQ4DiAWu4hHhoy89jjTGTZXvr+PayNEsVEIQRjtoaAIKZDkUBJnNBeCHwGewzgcHJXIoKTyaTnXARQvpel7apqDlDSOJlM2nU7P6UhFH6uh0siVlVNqQRiVd0WH5mMDVEoTj+DaJ8VIDhPUkt9Lkv56WwzofTZXPm5JgBxHOfVa3FyclInJyetz1DfRg7IQBOwwajgksJkACQRz4Ab6oVTP6yObNfm6/7+vm0C+/LSUcMl6GTAFK6Xl5fWu5QV+aRfWPQcCgnFY3DJVFW3N5q+4KSkJTjA4QA4EnjigHIC2WQy6W2n4loF4DmgI+WKjg2pqYIzNsu/6Tin4efpbH3f9a/Xr5ROtgpwk+enw/6/WCx6yXZOdmO7OEvHSXBLBbaqGtqdAfnT01O9e/eu6Z5rY4tVQzwT+4hhSqjyfC+0mT/y+vHHH+v29rYl4t4DqpKkfmdnp1X/2F9+J3v3qvqDaQQ219fXTR/Zgclk0gAHyD2UXGuH/1d1A3AkIekzsAEknmSZbPKTfiaBTR/FJ6CRAXDYn2TnZKCeOuHfVd02Vo6drBnXnu0u7Etu1g1Uc230IKc729KLXZV4f61dI6uFACfvHGgFMJVkowf+8MMPjc7IZrmeqmr2sKrqw4cP7Rq3a/OFMTYEWm9ubprfTJ8HoKvqigKmoKYfohP8G3Aik8T0txlT832pa/w5/ypx3dvba5T0LHBUdbEfeRM3Z9sBcCeBXeeke3yhAg59ODk5qffv37cE+mustwR0gVHuny7xY2lLJPGZOLJN7JTrYi+qur1dta2kXUvatveZNmUIbMsXxLzp+x0Tk7GqeowqLXt/9LVRomjqmr0KIZ8qDFWvL42ApQIw9i8vrzxp++lJKPIhSjwIthcgmHGs0WjUyvqQiETTJX8cctLYhkpGEbPq4f+E7uzsrBl4/QRQEcFtTlbNwDATUNelkpp7P1mZPCXVxeeGlJusRgoc8h4JtMAyezcTpaXIfg+Nqer6aCQWKojee6KhksJ0uG/evGl7KwoIqqpNg9uuzRfns7e31wYuGWDimRtqkwb17u6u6ZF/63UQwApQszE+kb2sOCRSlyOhOSefleQkekgvBIGOw8bor+AMs7E+HR+9yOAxKWVkDiUvgSoUHpUO+uYaya7PuWfPKql2no8Kpk20q7oeb8fOZJDeZZ+ic9Jr58EGWCwWjaroXaATe5aXl5e9TYEFF4KFg4OD+uGHH2o2m7WejKp+9XK7vm3t7r7udUjeVI9V9FQlsnINzNvf32/7tWFq8LNVXa/rer2u9+/ft3dv1LukE7JON7LCUNXpF7+u6lbVgT9ZUc/vCKiAD2jNVa8AhL7Lqm4j8aqu1ydprfyIGCP1Q9DqmlUCVDey6pJ2IKcn5p9k3wwrokOGjmp/BtbOm37MM2F/VDvoq9gnbaUKlO/n9SXrwLVl7+h2/T4rB40phNBVCYJYJoEAhYUEPshJVszJsSobn0RGv1YxZwPSF9GFlB+ymzTRBJVUL1UyARQqoYB+xSDyO4wvq6rXN0sGJWeuAfjiGZpNgB2RQ64UXKp+246UMa6/MybOuSOeSTLcEhhKcOfl5aVNKWXj2DxsHswBbA/Hd99ZFPJZFUhtKN5XDpP7I6+No3EvQ7DjYUPTqrrARrbu4Wa5loEUREHbCFtm8ovFoubzeY3H45YsZuInQHN9NhZOIfc7xxaI+V0moiggZ2dnTdB9pqpaEM3p5JQlzyIH6EjU/JyQC4QT/fza//PzEFBCWtXRDbMqmUrt34m4ZrDumXBGqiJJO+K0EsnJ0cL2YvP75+fXgRqMBo73bDar2WzWgho/zwBiu759rVar1gMMrBiid975w8NDowirDCeNouqVjiEY8Z10OqgnidBVdZPIql6DuKRcDCmMnEj24EEmJVeG6rgOgA69SpuTuudas4qYOkKmHVvVAbCBJjYajXp9uBmMJv0rK4UCRwECPZVICjJUIABMVf1hO8MAVtXRs0oAyPk5NnbNO/b8XAukVWCP/QF5zarylnq6+SKPP/zwQ3uu8/m8UchUGu/v75v8Z988HcvKnAAF9dI7Ze+1cZANk1JRVyUcmbjx1ap8qTv8gSQp5ZKcpn9xPFUH8sqnpC4JVOmna055peMJrCZ7xp8EY3KYB+AkEy3XaQBJAkMZfGfVKPtGExxN36tqyLaJMSQOZIJOAwNyGm22DZAB1+A5qkhv1+bLOyMjfAd7WPXqF9+/f9/kLGnTaMbrdTf18+XlpUd9zASOvGZFKgsF5D+BAwBhVSffVdU7Jv12DeQ1ix7ZH3h4eNi2d2EzMnEdxqaeFT3Llg/fA4JlFVT8r0CkSIQVxD86XzJZnp+faz6fNz8MTEs7xWaIV7CmgOiOk/H3sADFvvHpaMSG/eW+4lXdlGfHt31f0oWrqtdT+UdeG3n6rLgJQBjKTJwIteAkm3JNPMuq4Xq9rvPz81aJTFQf8iKxlJQloi9jl+VXdQ4jy9YokRnsEWzBs+NX9cfhpwNwbYJqxhoP2vkE5lV9hcoSuZXH9vN0zlVdlTGRJ88ik0LXkgF/opSelefm+5QJ0uk5oMWZqJkIZoIFEGTy8Je//KUNT2Gc0gC4zvfv39eXL196wc92fdvSk6viRw9Go9chJrmRb1a1q6oZVHI6n8/r+Pi4beQtoajq0O67u7sGBgz78vzR91DVDZbhNExMzAqKAC2pOePxuCXAGRBJUKu66h67lFUF18U5JGCimZ2OpI3w7HyOfrE7iRYmTSeDhmES7DtfQzj1i2agnY6T/RWMoIkDY3JYlyRev6o+as/Y35DT3ER8tXrdbD0Hn2zBnM2XRGg0GtVyuWzVfXrr3fNFAB/+ycCIpJ9m0Eg2bXciaDo8PGxoN/BDUsoWY+TkwCjX61hmBgjM2BQBU1YbM7kbjUa9oIntEeQlKo/excfl9OOqrhqY1Xc/53+SKaQ6kKAKn3Z/f9+Gy/me51DVgVaOnz5by4Ye0yFlzmcl7Omv7fvq955LDtxCJRWkSqjdP91NYGC7Nl+pg3xQxqzabthYdpyt5RcAqIB3Q8TyfYu9Esz3s6R0Z8VOD79j8ekW2SOv2F9pM+gkGcpqYBYVJG/0IYEYfg77zPPJSmdVF9sOY1fHyWvJolKyAJyPLxVP0+lk8Rh6OR6PW/9x2id6xZYmk4bfljto1TC08eTkpK6vr+v5+bm1Xo3H47aFnPtOIIDtzuLOH31tVFEkYIKbXIRC3wLjPx6P29RSCaG+HdUk/VGcSZ6rqtrnODRonkmB2WtDYFyTQCpL6UrAElnBLIWCjDh2NppLgNEws9pX1W3JocSOZpnVFvc1rBx+7XkS/qTmUcSk5rnnPKZ7pvSMn/t2To4elY/yuc5Egn1XszbDuFgsWjDseP/1v/7Xqqq2l59AWO+Tf6NSbdfmS5P9/f19S2wY5qSpLZfLXkCG2sahqAjbAyppj1VdcCZBeXh4aMaUjUiGAcBgSAmBZHJOGTylDmR/IOeZfcLp4EySM82Q7Uk6jwA09zeiIwJja4jg0ztorQVQGu7zpmJY1VUAMxH0bCUKuY9UshOymjsc4qMC6l6urq7aflnOiyLnXTtW6jVakveX1Wf3sF3fvvgB8rFarZotfXl5adNpz8/P6+npdY8xiUi+SzZVJWF/f7/Oz88bYJsgofNm8qJ6SRaBsa6Lr6zqZhPwRYIgSaekCzg5nIAoKKzqemiHwSKfkYAKmm5V9YJYv08gxu8ysBdD8O/0WnVoPB63CgobkyCuIK+qvzdr6rKYxXVlsJ+TXz0zMUFVNT9IZ9mkDCwBX1XdEA+Jr2m2rul7maj4R1+oh8lGSzutEnZ/f1/T6bSqqpcgVVWzsy8vr32NCgjDdpz0zWIpIEvqCP+Zg88yNpT8ZCWSj8kkKgsU9FqiSkb5imTwZLzPf9IPk1HJOr3PeJdOAEAARfyv+/EHJTXBHdeXE8QTHHG/mUfk9bGnYpQh0zCPwRaKicQoj4+PdXFxUaenpy3HkRNkD2jer+cEaPse1kYVRc5CUMUYJ61NAii5Qj2lOAx5ZteEGxoqIxc4VVVDBziQpOII8LyIITqTgSW0fTabtSQyK3ZJG0mHyTBk1UGvViokoUwlSQpMKmqW9PPfEr+8tqoOSa2qXpKb1CBC6bxJ93VcE9w4xvv7+0ZnzUmVQxqEYwgy7c2HYpGBOwQUdc7wEUm033F4Wyf3+6xEnb0HaOjl5WVzcmlkn5+fa7FYtIb8g4ODNh7c3kAp02SMXKs0JsXVVhUSDoghxkCi/olcJv0yqykCKJTInC6WFXw2hWxnZT2dHTuWAXhW23I4VqK7rnlISUKzcT+q5V/7noE9jusdJF3fz5wnr8W74/zQc1erVZto+ssvv/Rosy8vL61imOhwAnxV1d4de+Q+suqzXd++cvrh0dFRLZfLVjGaTCb14cOHquoAx+vr64Zc8x9ssf5GgCXkPQEBFFb6t16v6+eff67379/X+fl5A5UkngAB/py9qOoPtwHuuRfXx4fwmZIkYAcAhv5VdRT2ZNAAr+iIfqoMntkWAFMeE8qvj0zgJ6iTgLou95f+lj2jc66pqmu78FwtCaV7yMne7KBnJwlkGzJgzfglt0ExUd72CgcHB7VcLtukxu3afNGJTPb4EgkRef3111/r9PS0ZrNZSzAwcLLdgk5kPz0/QC/YeBXJ9B+j0eu2YmdnZ79J7hLITXZY+jUyiLmwWq1a0mRg0jC2zZ85bv7sa1X94ZIU5zVWVQ+U2dnZaRTw3HsdiMK3yx3oUc43cN1i1PSv3h8mj3NnpVNSassSdq2qy3vEIMnCUHU8OTlpBTDffXh4aHG19b0MnNooURQ8JsJwc3PTBNbL46x8ziQ1hlyyoG+hqtqEKJ+ZzWbt4acwCAoT4YO+6aeixAw2BZlMJk1Z37171+vry6mKWeFKSimBggBxIu4zaXJJQcvgMSeVcayUPg2Q5efu0/PxnVw+V9UFxUkJdN2Z7KtECEyqqlcZ9E7zszm+PwNKz4Az9Aycs6pLZFarVd3c3NTZ2VkLkIb3s13/+ZWAARlNZyMwUW1i0LzHh4eHFkTd3d21hI+8JTUjexqTMiWQycAlK9RVfQAlgZ0cCT8ajdoETj0VwJuqjiLKgGMQAC2S4joEtzjODCyHlBtymoEex1bV0ew8O/d/f3/fQJp0VNBJjpEeDR1aUmLSNmRi510IZvyenfrw4UNzWMvlsjeMw3vRbC9pQWOqeq1unp6eNvlI+tF2ffvyLpOdU9Vt3ZSAAHrT8fFxA0AlWsvlssmu4+p35ddyOFUGOv/6r/9aFxcXzV9lGwj7nUCMa2RDyGD6CTqvMpl9h0CMh4eHJlNAoAzygBGuQRyRvi59KcRfHJCAq+crsMt4ISt4mXx6ju61qur29rZOTk7aMUxIHT57QBEAJ+8pg+WsNKjC3t3d9Vo0EvgmD0B0m66z497F90Jp+x6WCrvkRQyaACDZwWoDYqZOjcev8zXsFz6cwk2O2OXssaMb5Na/8zPO62dZRU+gJpkI7IR+O/ZhyE4D5JM7S3UyfaefJX08wdWqjs6bFVr6nUwnupu5wNeq9J53VhuzEisPYFPcN72hk+KE3Pdbexzb4Dol9lXVK2LRWZ/1MwB7Mjm+l6FTG1FPz8/PmxAJZvb39xsNlIHMBEECxwFA9Z6enmo6nbamz+EeREmXZNgFreg1OdKdogl6NKNmUkSgq/pUguQWUzjHrKp2vYkOmGZUVQ1J8Hn/dq3DsrvAMxXT//PnSRVIxazqRvInXSCNjiXJy6piGizXWVXNqQlSk1bIMFKKpK1xeChzVd0AExRA1aWqzrGqIGVVZ7s2W8/Pz43OMZvNegh/VTW6UlYHyFkCJ7nxfPYkCVSzp5D8QVw5S7QMDiMpKALTYb9rVbXAMimqnFtVtWmu9JpeuMZhxT71JwNCMq3aQJequqlmHEw+G59NR0mfshKT15V2JxPX3KdUYkknbPBNr1V1ncO7EMAIVtncYTU1dTarvxLBdJ45gCGZI/8Rcrxd//8t753P8O7JC9sqIN3Z2WlgiXdMJsl7AqRDRB14J2h58+ZN/ff//t9bcpcgDxsBwKzqwEf+1T1IQhM8GNKZ0a7JKZklZ9mfm0Gl65LkZeyQNPH0O+49K4wG17AVfEwG+lXVsxnpiz07ttL/hz1S9Nnz8u98rvleh+CP8ya4Z4sUcUxVF3yajMt+X1xctArzdm2+yAZ5y0TAPrj2KATO6VGkL8ngIk/Hx8dNJvI8VZ3vOjg4aD3z3n+CSymnVdUqzTmLo6q/LzF7//z83PYC3tvbq8lkUufn5y3uzaIKeQSEDgsUfKvvZLWVXxMjsFfJVstEMv2nc9JrPpjO5b3nHr9ZXaSv4k9x5+npaeuTTjDaednY8/Pz3/jOTKSrOhulr9Lx2NXd3d16+/ZtO2bGvN/D2qiimJn3y0u3cXdm84vF4jeo+2q1ansYMt7Pz8/15cuXVtlIpGQ8Htfl5WUTAC83Ee8M9JTRJa2Op8k3XzA0rqrf25TGPKlie3uve1w5BkHXs8fJV3XbR+zs7LTBEelI89rz5z6XDiQFOIOz5MJ/rewvmKBMgnLJeKIrGWgO/79cLlvAng3/EBjnHQbgqlKQnCHdllEaj8f1pz/9qaqqPavt2nyhigExABxVnZxkVarq1UkdHx83mhYZMeAkaRtkmBxCHPUDCtqOjo5qOp3WYrGod+/eNTqHIRucbU7wheQmOkcGBbGcEP3zvaouoWXAs4fYdzkuOpe/J8uCeAtFOvszJYwSKN/XSD+sfCaNzntQDbQ4u0RB3dfXdCmBI+8ie6kFLIA19oojQ0HP52tlUq+3a2jPtuvbl2RG0Dabzer8/LwNi6nq6Ic3NzctyIS0Z6KW4Id3m1V64E3qHh8MqJDACNyg9cNqxXq9/o1fJXcJJmZFw7WR/axMOn76Bcfha7MPk00wVTIrK+nzyG9WSDJwlbDyl3Qh9/6tqrq4uOixBZJBkAFg2lZ/+ELLpFM2zed9DpU2wXKDyfxfXDQev/ZezefzBh5tdfP3WSkP6/W60T2B3Z41GQPiVfV9jEJGttUAf7IyliACQGY4yEqVUZuHxGmxWFRVf/o1emZVNT/EL1R1YKZzDPWCjaAzVV28ncWUlDc2yd851dd32bb0Qdhs8gqxQDLxXDO9StoqkMxyL/yk81VVe2ZZnODv3X9SbOmkHObu7q71did91XN3zKw8ugbtPN+Ljm6UKBIwCR+BhbZL/v7hH/7hNz1nqhi418/Pz/Xhw4eGhFKepDtBWXK87dcQTAnl0GBT3qRUckTDIGxo9A2kWK/XvWmALy8vjc9MeJ2bQnNADHhV11RL2TKRTqrC1xJFxsc1UrSsTuTfSW0Zj7t+wZOTk/ry5Uvt7Oy0BGC9Xre+MMbja2O73X+W4ler/gbp3oXvnpyc1Hw+r9PT06ZojJ9+mkSut2vzle/f+/YOb29vW6U3e9I4xkyUgAkZRKbcZUU79cDvoILPz889xM07Z5DRroboneQkKXdJV3UdKl9s0zAQpDtJb6nq9xhVdQ7eeTJpoycC1QxQOYq8boFvnj+R2AySPT/VCc/Sz+lnVmGdI23saDSq6XTa9p+k0/P5vCaTSRtulO+GLQOs0ffsR8xhYN/LaO8/8krmBJ8A5a+q1ttEnrRpkGkgBh8MDBAokqFhb04Oncn3TedXq1XbpoPsed9Jw6rq9mvEFEpgJO05ecsEMPUlWzequlHz5C19oefGb0i2HNOzcI++lwwH9FHnG+pwsgYy0OO7EoDxjAS3gs1M5BPcqfotRU2Li7iBXlZ1rIrJZNLeYyYDrk/iwhdv1+ZrGOvQMb5luVz2ptbm9HxFjaqubUniw38Nq3RVHStE8mE5P1AHUKqIgf2VYB/fR8/5IOeUDDpX+jj3Oawsij8ls342jFX9LCuxe3t7bX/xqg7Qyjg8mXOuK9lCCZbktfpeXnvG2OIE7zCTTe9FUYRvZHvENCbY2vsW+OrdGIJJDlxDgtFsUtJ4/8hrI+qppM7L8yAI5GQyab0NSafK5GOxWLTERfWDIc6AEtWM47OyHE7YMvHyf8jg3t5enZ+fN8dESJJWx9EZ1V9VTTEJOQW/u7vrUWzSIe/s7LSRyFXVoxhRXALk2aXSVdVvFG9YUaQkQ2oBh+tnEB2Bn+eGtpKBMaPguXtnGdAyFEPjVtVVjbNS6TO2xPB9zx+llWOFjG3XZgsiiSJDpjOg4zzoqQQkqY97e3v17t27JvsclPeUSD6dgYBnIPvu3bteYOX7gAtyZiV4ATxikAV7fpaVlaTyZQDoc46VVRLXnL0NX6vGJ6iTiWUG6YmSpoPKiob3I8B0n1kJyndIn9miSqn+lwABAABJREFUfF90k91x/VZWU4FdWREGDGT1SH9bOuiknW/X77MkCN531SstKqvRhtw8Pj62QSUowgmoSs5ySAZmi+CVjNPJqm4z+Kpq/tK+wRIx/ko/UwKQafPpZ/ouPiMTrvTRVd1gB8mWa3FeOpJD47ICMgyyU3bpezISMqCkO+zdZDKps7OzXmCX1cGsaORzsKWXASQ+k8fh646Ojuri4qIXTGfFwffOz8+bDXM+A+DShpmGyy4mw2C7vn0lI8y7t8i32PDk5KTFrYAccyhMdTdUjJ1Otgf9yAKHVhFVNrEjfTH10/kB/OnTJIDZJuFPAgpJ53a/Q/0mr5LTlDP6lD7UM8yqq2eX/pfOsIXsBd3NuDfpn2kjnp6eelNN2RhJuntBU03dpk+SWHESmyfp80746rOzsyYXyaDIirPvSe7FFnlPf+S1kSWZzWYtkMrpaBksqgB6IR4+WoXEi9BCYHZ2durLly/t4a7X65rNZnVzc/MbNOD29rbHdya4OaI3K42E1wRHCWZSUwiqSkZV18/l+Ds7O60fIFFI/+Yostyd1J3sF0xnQvi/JkSJFg0/k86UgDtnosmuNysQw36X+Xzenqf3ln0yiZK5Ju80e7l8VjWDM7VnmHs4OztrRtYmpt+LEv2RVxptgxLoheoQHj15F2jqi2GUs5cnqVvz+byqOjCHfqiEkCMO1oALtFS9NK6pqprDUK1gB1K+GWeBI2eyv7/f+gqs1K+hTnJ8Q9SPs2QDErl0r9mnl4mZnoy8/6quSsKRDEE2VZWk3HD4+gzv7+9bn1oGDt4VR/vu3bv2fcH7ZDJpSWdWK+nwYrFowwyc07tR+ffMh/TU7frPL4E9/fLs6ZkJpVXVppGmT0vALulf/g31ThD36OioHh8f28AUyWXaa6BLTi82zCMTTkkdec0equw5yqp79vNUdWDuEDgFiKROAnrYKTohAHQNABtBoxYXNiMZBWxC9kumb8xqj3+jyLvuYSIuhkj6+9PTU6ORslVsWvrP0aibWJvvruo15nIM8pNj+P2N9bRdmy/P2h7CR0dHjQ12cnLSeuTFNiqJEq5kmNDLZGuRXfLj76rXmNMxxWGAAD6U/LP/wIP0S2Qy6d/0LfuVXWNukWZlgkfWsqpNR4c+NIEaNojvSUbDkDWQhYQ8LzBT4swWOFa2imRVdnd3t05OTprvcl7vmB9NdpE4XWIuOWZrhlXNx8fHth2X604QCyiYTKDvYW1kSSRGWfr2UAmtihrDJZDMTTAJNAH2eSgILm9S0qyk3rgeAp2T4LIMrdnXxNKqDnEfJm+CRIhQIgHO45gcg3ugmOnksqKQypXH/f+GNKTjTXQn0dtUqKSpcEApnJAnA08yuUykNt8VJU1UONHWqupxuRk+yTNnfnNz046RtEaI9veiRH/k5bmb6usd5kCJ0WhUNzc3PZpJVWfgEhWnJ1C5quoFkFkxz54H1yIAZqQvLi4akpiVg6S0AEYg8VX1m0TP+Rj5THCH1a8MpgWHQx1NPYNq5iCLYe9fPifPT5CYa2iP/Ow/soV+lveeFL7U9QTCBLHZq8TBSQrosqBWP2huXeNdJb1mGOhv17cvQU6+R8CpQGJ393V/MfpGF87OzlriJ3mAcg994Gg0ar07fodVw9Ymjfvjx49NRhIodH3przKY831JpEDO5/lIKD0fNqxAJmiTYMvXEs8MhKu6qsgQ5MrEaTQatQF0dCVBT75IsCzRzGDRvRlacnBwUBcXF60qwVa4f898+KzT7gm8XaPhJKrJzovNwW+u1+ve/s4JTm3XZmvYky4RSKAPQ2M8HtfFxUVVVdOVfKdV1ZPdpJJi0ZHD5+fnms1m7f9VHc07dSoZJPTF+RMgenx8bLMDMlYkP3TTn+HgptTxYRKYfpMvc690NhM118mfPD8/9xLcZP4knTafeVU38C79tDxA3O5nnpF7GbIRZrNZr5Dku3y6KuTBwUEbHpVgsYrkw8NDK3h4BwY4msGSNuZ7WBslipeXl/X09NQ4ubJuyYgKIQcnINE4X1Wth4AyoMPc39+3nkUCVfVKXTQ5zcO3sTBnBnEhYIvFojmzVMxEPQmrgJXzSkWD3kiMDZ9IBCWdkn+nEhO0dHYZ6CV6aFGCrCAOE89EdTzrRDol3X6XSabnmwix7Qey0ivAzGTO/XnH2bea9+jZMyaoTMmff3l5qbOzs+aQt2vzxcip5tnOJtHnqm5wSu4BiFKRx7BtjQBzvX6ls5KLbJyXuKSs5jnJm0BLgpg64jiMcfa8Sg4zsBLUDYPCrznSqk4XhkFaJm1sgqoKB5KN/hzhMOhMR8SmcIAZMLAV2dOVCaTnxo69e/euPTcrKxiOrfcUeAeN9RyGfWGGjCVK6vy279gmiL/fYqMlBCZ053tNYIDdPDw8bMPjJIB0I8HSs7Oz2t/fr/l83lo8vNPcrNux6N7d3V0P9MACSFCvqqvUC8AySM0Al53nLwWIrj1liszzoT5P51DNszqTgaXzS5yT5v38/NziAdeWTBw/Azq5hgRm2AzvSTKY/buepcSuqtqzNXxntVq1fv30k+xoyoh4ZjKZ9O7RvSdlz/1+TxWLP/Iii1lRy575m5ubFhOKk8heAm1ZkQbEkbPd3d32DlPGyC9fp4CScSL5Gy4ylYwcPpk/4OcdM/0Q2fbvIXiTceSwoOA5Wcls8izcm6IE3yamxGZgo/g3160Q45h8P3+f/ti1ZRxwcnLShtF4PmyM+0YR936yAOQeJNViiKTzZ1FJcojloBD1PayNEsWdnZ2Gfp6cnPSGuKgeqTIQFFW5y8vLFnRVdfTGl5eXNuSEMRacMIyE1ouqqnYdElGJTFU35CZpLIm6ESQ/n81m7f7SgeRAC4FVDtAhCAJKfV6JgnyN0w2NyODQdWXA9rVk0THyWOmY0jD5GWFPhVC69+9EtClF1avw49j7jGfAoVMMPaeMIQXyXD98+NCuF3igKv29IC1/9OW9ZqUqKU1Vr47QxFnPXVCXSHhSqvb39+vm5qaNh89EkEPMwRFpRP3bdyDwCeKkcU/6iGvJal3qTN5TJohVXVAOUBreXwabvp80nwxm2TaOzrMW6CWVxTNzLZnsCh49Xwm9f2e19PT0tEeby8SX7nmuGQy4TgAA9NY78nPOMOlE3qVJtkCj78nJ/ZEXn+Zd048cMOGd84854TD76OmPhOb+/r71Jksuycd6ve4NbSCX/v3DDz/UaDSq29vbFqgCA00yJ3tkzXVm1TwHz6Vfy+OxT1k9T30cJmpVna5l1aGqv6+yBDb9d/YP0utM4Dz3BI3YlARz6SnwJxkRVd12WXw4n5a0NtdX9ZqYYnUkO8Pn9aG5/729vdamkzEEuq372a7NF9CSH7KvsHeR/a5JDZXwDJO/IahY9aq7eo/plXkaQ8Dia7282RI0BPlSBvmnr/nSBEczZiVfVV2fvsJK9iKm/8nCBR8P7EjAeL1e1/HxcesB/ZoPTWp93p/rGY9fqeDigZxayqZ5H3qUfd997u3t1fv373s+O+NtP3P/2D3uLRkCZjFIZjPmTiA69f+PvjaGhgWUBwevm79eXFzU2dlZLRaLXtacyBdByKBDIDWZTOr29ra9FD1Lq9WqTVRNtAMKsVgsGjpXVT2EDUorYOIkODmOK5M/hp1iKjELdrNiRkkoFISEULl+wlfVUcsykIRspIFxr4LWqr4SV/X3PszzZMXUysqgIDGdr+vy+3yeVa/jwQW63pvfu/b7+/vWEDwMpCWDvpO9XxqI8962a7NliqKqX+qNDYHJ6XK57G35MBwTDQSxTNBMZNv7RYlLaknSzsi2AC6RvDx3GleGW1B2dHTUCypVKDmYDEwt95CynahjnstyTUdHR22qqgBY4D6kj+Ux0bD9Lu+tqh9o0NsM/rMpn86qAGVAKwBgp25vb9s1+n4+l/v7+zo+Pq7ZbFanp6eNYiUwyQRmvV63wRu2VfhenNwfeUG0Uf+Bi8CAtI9V3aAwAJ3Aii7yWcMeWMmQJMc7ZZv1KZNTMg3wraoW+AgqXZdzshOSKPa8qpNNvj+ZP1X94C59se+mX0sKXFYxExhhA5Lxwza9f/++V8l0j2kb099WdUOnMhHOKhD/t7Oz09tTOX1qAq8CY0C4d+SeErDxc++DDdJr5bl6n6nv2/X7rAQBsr+MX2Dj/a6qWiGEPOXQMvKcfiGrz74HvMie2qRipoxmYjdkpCyXy998J5MtcpqU2vSFWXBIplBV9Qo5mRzmuehVJoLJdgNgZszNNrkPhZ6U+4wpMq9IG4L55DhDAPn8/Lznc6v61VA6rpjh+tIm0bmHh4f2O88+CzjZ3pXX8EdfG0fklIDhEhh5UDlBiePwQOfzeUsiGUxIWQpUVVeFck6ZOwRSwLlarRrFxjG8DC+aQR0KPqFVMauqVv4eCpgpSFUdh51AuOacGpgIKYX1XNJ4oNm5Tj/LhLbq69tHpEMhvL2X/X+cvOvN5f1V9XnllJGTosxJ2/G54+Pj9l4SwcqBJBy5vovhcI+kCm3X5sv7kTDoL0XVgJZBA9frdaN7Z6CV8kjn7PNGplFgGMSqbq8zgSBD7h1nr0DqCblIMIJcSvYY3Ky20K3sl0zUfTweN7pQAi/0cOhgOc/lctn6vcbjcZ2dnf1mVH3aANde1SWanhPb41oFGHRAIiDhzvfouSRYlsiy55PI7RCJTSeagbI2Ak7T+VRdBETsXu4Jt13ftjJYE6Tt7Oy0YUV6U+kEeUlZ8G8+L5H609PTquoqBzs7O21/VJtr8y857KXqVXc/fPjQvqPiZTso5+bjk5Y1Go16YIp7cY/DoHW9XrfeLzLt9ynfGXz53teeKVaP/mk2KcGnpMkJ2jPBFrgL8jGd8nnSp6Ojo96kdpU9wGkmwAkKZaB4fn7eq8hIzCXxFmA5k/b8w2ZsWTm/z+IHye5yuayXl5f6+PFjr6o4m816oGHGxmjW9I1PGQIjWfHnC+m+98tPqn6T0+HPk83GPqRsZJwo/q3q9iaez+etpzHjWveQvZpDYCUripLE1K+9vb3GIhLvJiidTJl8RvxY+uxsG8vCFB200h8mEyELE8lMSCCW7QDU7uzstBa4BMNzKjy6v+Mltfjh4aGm0+lX4/g/4to4UcwtMtA/ISGrVdcfSLm8IA88A0iCJMF8fn6u+XzeE7h0GNkfZIIbofcyCb+VyRYjjdtMsYYBnv9n0EtAfS57G9P5pBGgsFVdv0QmdzjTOXAmB33kNWdAmucZUl2r+o3H7o+AO2YaNaiN6/JO8l5SeRkhxqOqmsOcz+cNheVwBQ+SSMYog95hkrtd37boogoxORdIcla7u7tttDZHkagYeUx6iB6NDHbIlWCGAU+KWx4HVdxnyX7uS1TVp4Yy2JKbdEISWjLmT8pt0thTV/2d9+fnqSt0j9zTBffg/nJAVD6X7LNybDqIpp3ocuoi4GpYfU1ny3HRKU5OsOL/GBh6zOmfYAfDIavJ2SqQNma7vm0BIROUqareAJMEXemDNo6qzndIUMhFyiPfvLOz03pb9/f3W/D68vJS0+m0MWfoFX0RZLLLOQm4qrP36JWCSaBwVjv4cXLofL6bVfAEjp+entrUQMfKQIs+JUCcdiaTT/RMPsx3h1XEqi4wTr/KzhweHrZYxrsYBszT6bTZNu8jdZM99mzfvHnT7nlIXWQHBcoZ2CbACyj7WiK9Xf+5JVFg89jc6XTaS9YzOavq4sL0R+Q3k8EEMcW5Gc/Rw/RHCaAMqaMZr5IPDIScmVHVJYx8CLvjZ1lEGVJMswqaMWb+nn8cMm3ooWtMcCoBlZxtcXx8XJPJpN68eVOHh4dtMI5n4ZjDPmbXlO8hQaq8P9/xswR1M8FP0DV1lF2o6ibWsm+ea1WXh3g/f/S10VVmSToTPeN2CUKiYgbA7O7u1vv37+vm5qYhmwIgRt7L9wK9AE4Swn9yctKcEidACdPRMsyus6pTuKxeVlVPaFBbBXJZRfDZHOjj336fQui+qvoN/JQp0dMUTopOwSV1Vb8d7+//idAwMKgRgslEaIZlcEF+Osfn59dhACpG3mUGETk8xwbqEmNJg4Dg+vq6XbNnWdUFx9u12Urj7p17p7PZrJe0V1UbWHN1ddUoGWT96empBTCopNk/wQGoKvtOIncZsAnOyHxWM8koG6Oy5/qylzUb29kQss8xZlJzfHzcG/JC/rLKkBU46GeCO3RlGIAmyJKJXAbV6VizUuic9OdrfS5JSxyNRq1a4dmzl7u7r0NOzs/Pa71et/eQwb/PY2B4J5zY7e1tb7iA693f36/lcrmlt/0O6+Wl2w+N/0nww/CSm5ubJtdYOb6ftryqfuP7UFoxePhHskDush+uqh/MHB8fN+oX6nYCEumvyXYeR0CY4Ca5FtzRr68F0p6HlcBV+rq0F1XV69VbrbptI9iDHNrDRmW1P8+dlU73mbTYZABgL4lVxuPXntLJZNLTdbpU1dkS18ZOZKKQlcihvXE/YqHt+n1WbsuUNvTv//7vW6FkNHrdT488qDyaginmmU6n9e7du6qqtvXNsFqWyb/3PwQ00p9lUpnMnGGxIP0iADP9wc7OTgOOgLFJ8854VTxR1cW3ObSSL005pbNZnfWnqr8HY4KkYpYh5dx3AWhpE50PGJRU2fF43Hun4iE92wb2yUnyc56le5JMe77uzWAy7zOTajqbVNc/+tooUby7u2tTmJShkxJV1adoDJXNnlECnkTSBaImqxI+L/P6+ro3IMVL81KcIxNDSqLn0Er0IlGBnZ1uo9UsnxMS584EK5Xcyn8TqlR4f3IMcyIWPk+5PDMoTKIleT8CDsFlluuTauu5pRP0rDOQoQS//PJLvX37tn0mK0PuUQIhsfC8swLjOvS5ZWDwPSnRH3kNG7slV2SSfpGtr1W8MnnPJCcNt3dJTjkhyWTKalZQsnLGVmRwlAhtfi4DunSKdDG30RgmRgIuRl7gnHqaztv9uh/fpWc+xya4pqyMSOgSBEp2g0TMs/Y887k6l6EWCXZlkquX++LiohcwosbkPbLbzolSD1RjMx4eHto+p9mjvV2bLTpQ1SU90+m0JpPJb0br8wNAtgTkyMl8Pm8tDzlMIcFOIIng0u8FSEBeQaOR7uQT2Kjnj08lN1nFS5AmAeWqDojUy8xPDZkrFlaCax/qT9qJ0aibTOk6MIfo75AJkT1RaSfRzgWRdDDZS4JwldJMeo+Pj39jP1xrjtZPwBS4g5nlWYuLEtx1v6qIGTPk89uub1u3t7dt/9nRaNSq85moY1MBCLwXVfuM3ZbLZZ2cnLT3lIlH6ksmNhm7+V0mkVmF8/ssgjh+svKs1CNgh5iZ/ieA4ZqykOF8eW2uNWML50iQNGPrqv5QrvS/eV8JfLof+jYajZrfTQabvODl5aXnw5J5kXYV5T8rj3Qf8JtxjWv27DxL502gTbHFsf/oa6NEMcu0GQDqVUtjZ9gMx+jlHBwc1O3tbZ2enraXU9XtBZg0xNVq1ZCZ2WzWKokE5fHxdRPh+XzexogLdu7u7noC6kVnpU2ARZnyvEmTYQCqOmebSCkHQ0AJetJtM2jMHkiK43gQ0fV6XdPptHZ2dtq9VVVPSSgn5c1kM5Uyq6WMQaLRDA8Bz8TuzZs3vb4Xk9dcs3v0TBJVymcpOTVBzHVQri1t5vdbq9Ur9fvy8rL29vZqMpm0yYUSKIaNjAwbwPU15YjolCfvirOE2CVgNKyqZwKavYtZwR5SeY6Pj5ssM9j6kThEKyuU+uzcz7A6kcFs/p/+SOzy88kOcL5hRSKR1aSNJcKZVCDPFuI4n89rvV43av3V1VULPNkszz0pdmmTBbtsg8EBnlX2r6DInZ6e9nrYsno0n8/r5ORkC+b8DsszB6js7Oy0bUi8Y6wWvsz2UWkvd3d323ZRmbxk4Gn4k4Qvq38ps5Iqvmw2mzWKJZ8KhMyqAzlJ/2JhnlR1Q5QEdeIE16FX0rHSd1p8ZdJtM4n0nJwzgz32KsGQrEhmoKsqmL9LECiv6ezsrAWDmUR6f6enpzUej1t8QqeA0Y6puu+7mRR4tlkdSsBXhZJN3frQzdfj42PNZrM6Ojpqz3h3d7fNWGDnE3wgf1XdwCpDFK+vr9vvkq7qXdGj1M/UlWHyn36VTFj5vap+m9IwPqPP7oFtYucxw9yf5EqsmH56CO66lmQ4AVMyTsjv5H1nQcgxhpV1v0sGk3vwHaB1Aimej8Gaw3P7Hl9O97Ln2sDMTFQToM4kMQFo4PsffW2UKGZAzyBr3oZAp8ET3HGMu7u7LcDh1Aif5nAvMVEQCaGfJzKpavLy8tLoMlAVQU/STDhUqI/fZf/VkCJXVT3k0+bHluTY55Wkk/ZJ4LIqkAnvePxK3TFYR+k9EcdhACtp56AYM89juVy2Ch/hzEQhKQyQs+yboSg//PBDo1w8PT01Z2vaaVZ4ONP9/f3Wx5LJos/lFLGnp6ff7BG3Xd+2jo+Pa7FY9ChSi8WiqroJmgysCgadZvgEXIIYyQbjyzgLUhjXqmo6nvSvNKLOn+h+oolpuNmSTFKr+vskklUgEieBsWAJPgV0GZxlcJf9Q/knB/64npT1TCKdi23KqunT01OjcXq+SZVJO5XVzOw7HfaJJDhU1YFI6WQTxNM3k4l5PvdEj9ksNmW7Nltpj8nL6elpqwxi3lR1ARSw1fv0nu/v7+vt27c1m83q/Py8VYslfFm9T/vvPQJcIN2qIeSsqnrskiHAWFU9n5YgrmqXa/BzrSj0IvuYsk9yvV43nypA5+/cz5Cm6mdJ2XVtpipmkJqMHBVFxzF93bmcA7PCMbLVpap+Y0fYybQxYol8do6RyQHbJEZxrQkIJ5tqq5+/zwIAeO+3t7f14cOHpgt8Qe7dTdbFkNlLp7p/cnLS6xlPYKCq84P8W7YvJPDvs1b+LmOo/Le4K9unhsBHMtv4yay6i2OHVT//d37XnQCx41X19/vM7UWsrBryi4DU9MnsnO/QKdcKTHZeBSyAsxhdxTiZRrmfO4A228Ik94B04GvmNVndPT4+ruvr616R6Y+8NkoUM7i4ubmpDx8+NCWpquYUGPa7u7tmcJPeOR6/NnxDsF9eXmqxWPyGEqnXglIYJZ7VKMKcSEhWA1VSDO7wc4FvGtoMRLO0n6iiZMzzSCRS0Oc7+iGydE2ohijKePxK21Q9GI1Gbcpi0mwSkUlqYVZ6ZrNZDzmCtHh/mUQwMpx0VedABRWUxmf8nY3PEg5K4lxHR0dNORk/n6dEAuvt2nwBbHJPS4kbGRSgMJL01b55x8fHtbu726padD5lZ7lctv6bqq6CeHt725xn0o8FNeSCbGbymABFOqKkwZGbyWTSJkRWdTrrvler1ylkLy8vDdTJRHlY5UyGRDodAW9SOLOSmXpT1Z8qKqFG3+OEE0Gu6gZVZTM9fT09PW33mbREQUm+F//mXBNJzipK0o3YI3otyGWL9/b2Gl15C+RsvhJQYNez2qfilD4CO0eQWdX17bOdOzs79eXLlzbkxrEywc9KdlUHfmZVZHd3t7WX8FepK+knk4FCXslZ9hSjZzsPOQfaQuCHwGy2LfBBBvoIEKuq2SxMgqw4po93j75PF5+fuyF6Ppv3kgms2QFPT09tcIXveW/es4BXkOmYi8Wijo+Pmw307MRCw6BfXENffUegfHBwUMvl8rsIQL+HZa/gjO/YdNVg+qXiSAbIAxbAaDSqy8vLNkGXTySb5Lmqq9oN7ULGabmymJJ+LGPQ9Hd0IGPar/U4JiiRzDX6mbFr+lAJmn9XdUBSDvnx/DJpzWuuqgZ6ebYPDw8N4Ex2g2cDGGPvtKkN/aLzjMfj1itc1dkTCSa7QV+zaIMe7j4d1zNjQ+7v71tfatrj72FtlChmtqzqJVuu6hrWl8tlnZ+fN0eRD9Hfgi2GL/eqGY1GzfBRQFWRoYAPqa2CqaSpZBWkql8qz/J1JoTOPaTZjEajRhPKz+TAiXTyvuvejaOv6oIGyFROZlutVi15dt+esWNmEp334/kmFcf1Cy4dV/CclRbPDMLMIe3s7DQaYwY2WQVZr1/HnqdxG4/HPaocZdEHku9muzZbWW03Kt6AjKr+IJnsYxVsVFWTE3I81F9GEGL3888/19nZWa+KXVVN/xOMIXfki/5X9QGKRP7JS1bOOeN0qIIm9mA+n7eAjfzmOf0uaURsUOqV5yP4TCq455IIv2ly9NjAib29vXZNglu6NbRD3o9jZrWGY00nPdT/9XrdqIeuT0XV/XN+WAJ3d3fN/qEDCnaq+gMVtuvb1pCKBZFG6c/EYnd3t03QJCeLxaI+fPhQj4+PbS/M7LnJihQ53N3drevr65pMJg28zYq4SnMCJekHsyrpOlBnJTt0z9/DSjl/zf5gnPhZgoe+p0qTPYDpb9K28J0ZQ6TfTXCIXGcCCECrqmYP2Iqk1bNNng0fmwE6O5WJuedtWFRVv/I/jD2G3xuyKQA8Nzc3vUrVdv0+S1yX7DHvIEFtcp0+aQjc029AAzsqRjX7gy6mT0ydGdI3XVOCgmzFkMI8TDSHiV/qSVYHs8IvgeUL0086NxuWSarneXh42BJlfcpYisMKafq15+fnliTyr64r+zE9E/FrFrbYSDENoBS4k1t/PD4+tr5u4DObmFVX7993dnZeW3YODw9rNpv9pteTjfoe1sazWTkCVNGjo6M6ODiom5ubhvqhZjCaT09PLbMWrEk6CWyiOIx48rmrulH8BJximjQl8Evjenx8XPP5vBn59bprRk5hca2qaL7v5yqGHF+iHa6NY/N30mwcT5Do/II/SKVnkJW+qm6ADeG3xUQaEEqp0jMcnFBVjc6T/RSMkwTPs81kwLNXKXHvnB+nyfl6boJjCfJ6vW5VLNf0/PzcQ3e269uXwRYCR+9KcpE6wmiRB9UIRtAGwtPptD58+FBVHYqY1bmkkzsX6mdWBQVeZDqNv+MuFosW7BmqQadyHz/HpZOJvLonFdGqbiw4wz6sWkpiOdp0lhyvfZTSdgFBfMdo7OFYdHZOopjbF4xGo57tBEBVdXrrXdEbz9o7dfwEqzwn797vhk7Y59gAup4A3Xq97u1RtV3ftthVgSFfJnlB/89E4eDgoGazWXuXWSlMqrRjZmLB1wnugLv8MJ1aLBZ1cXHRQJWkZSaVkn9Mvcm+yawu8Gl0gW/JQTjkXjCXepnTecUHfCL9JLdJ/xIzZH/Q3d1dzWazdj8ZBGPzrNfr1veZiV/Vq67ox6YfCa4kgJszCPj/fPc+7z2YkmmWwXg8bj3inkcCrr4HFBzSBLdrs5WyXdW1XFX1J2CS8aoOiKjqKnIpZ/wrkDHBE/7ScZwzgcusImfyN2Sa0F1xHv9DN/VNZnKYx8wiRrLfMr50/VmpT+B1yETwPSylTJzdNxsBPEumAdl3vPSpyW7Q351MxAQ5E6xOu5PvfTwet204PE9+LxPOZGmQA9fkOiSY7unh4aFXQf4jr40g4US7s//Ng1GyTfrXarWq2WzWQ/XT0eQGnyqJOMEEMo3709NTXV9f9wQyy8ZJZYOcEgBIRColBSScDIDhAa47k1/3//Ly0vpJqjq6ieeQSEdSvDj71WrVm1SXSAwj4TsGXagSZcKagQOnKdjgRFLJnDc3XObwKUNVZ6yS+updJN1YADOdTnuJsOc+rIhIdLOXZFtR/H0WWc1pln4ONUudoK9JraI7iRqSo6rqOc2qaoOpEtB5eXmpz58/91BD+pDIaAIiSQcjz2xGBk1J30haC/3xmQz4VOqruv6pdG75PT3X7MvR0VEdHR01nREssgnuJUGhBKtyawF9yOlUq7otDpKCwxYke8E92qMtUeu9vb1aLpc9JkWCS1mhzCRRr7ceDdVQSWPSrbZrs8XmSebRR1UlErkHKOzt7dVsNmtVRDLv7wyGcjrv/v5+A+h+/PHHti8Zm5DgbFW3/RU7rfLn/ymfGbhK/txXVWcbstIOTErkX9BmGw/37XvDihs94gcTbKVLQBL/zmA9QSXXNKxAZuUnx+pnEsz3+h7b5d4zaFQpSsp69hk79rAP1D2lDU89RFN+enpqcrRdmy89/qlLVf09rrNal8CMz4un0s/aJxUb7OXltVXD4BtxpaRGywGfyOYP/aZroEvOIQ5N9ouVQNTQjwwTZdeTtoCfzEqg+5f4pg9VeT85Oanz8/MGumay6Rqcmy/1rN2jKp/zZZwNaMlYOn82tFHJrHCP+R6zEEQ32W3XnLERu3F+ft7kIGPm/yeop1XdWFiBJEEz2ayqCxTT0Q17DjzgRBsSRRmik7u7r1suTKfT3sjr29vbevv2bevZSAW2lUNeD4FbLBZteEA272dl0WczQUzDkX0SVR0lIR2XCqZ7cIxE/DM5ZIiqqmazWa//L5VBIp1VuURNHCt7PwhuViDz984rwchmbcqMzpZBfFVHS+I4nc81MpTeTSI5q9Xqu0Fa/uiLzCUVJSnjevsMNUknwBlNJpPeOPizs7OeQeYE6ZBgLQ06sIgTomPJ00+k3LUmSJEUG/rje5aGdNeWhj6rh66PDiSwUtXZLMv5Bct+5lktl8sGEiXa6fpsLUHX6cDZ2Vm7Z8esqp6DVg1JUI0+qTzQpdR57z2rR2lvVUX0cSXNzZ+jo6O6ublptlQlGPiwXZstIOjOzmtPIeQ56fsCvUTO850BQfb29urq6qp++OGHdvwE3CD2dHQ4cY/M7u72+xLJbFbssirtWAng+sywsp86mAAI+VQtTP8qptAXLNFidySMAm/TKauqMRrcn387VlYnPGd+2XN2rdkvhp2UlSDP2DORZGfsMB53+y16DgAZ9+o9e9aowFXVqGwqv643ffJq9boFSgK62/XtiyyQD7rmZ09PT60IUdUf4kR/vRvvlo3GkEmgf1jdkwxlfM1nevdZGXQdEkU2Jm17yuxwZaElE6mqzj9lpd91Z5LlmWWhgN6Ox+O6vLzsVTTFAiqJ6X+TTZMtX/QpC1Z+zjcP21Z81r+xDlDf6TpbmNX/1FEFoPS1nqddGQCu2l7ICLuCzvo9rI0TxZeXbqNND1Xjrj4nQQyjLQnwuaquPw2dhDLm5NSqamgjBd3d3W1Chw6aiVaij14epyI5S2qAHhy0SYqoZ4PSESYoCcRgWD2T6GZJPAWfMrlvDoATlBCqevi550lQq6oZpKTIpXKOx+OGUOHOe4eJxrpGzjarMxkAO+fLS7cvjWsXzBoUlEZ1ve73wGU107NOFHi7vn15/yhbSZsejUb18ePHNiCFgSZr9BMt0runi/SQfjGs6/W6Pn78WP/8z//crkEwmX1F/lZNTuobdDEDaT/njMh1sg3oG0dBx5Ii97Wq+PBvSbL7FBC63wyGE0mlQ67XMTwf1CDXyEahIPlZVloEl+5Hv5RrzcmnWYFScUqKIV1lp9LhebbAMYO/jo+P6/HxsY6Pj3t7611dXf1flNz/NxYQLsGSZNoI1vgUPYUnJyc1n89rMpm0YWUWamlVNTpk+sRMGNliYB05SJCCTNOdrLhnMpRVCbqUlZWsgDmf8zjG0dFRSzpzOJNzezZZLQQk83dppzxXOpEVCf7e1OG8RvpFD+hgVun4r/SZ7otuDStQzsOm0jUtHVXdcAy+XMyTQI7nwL/mc/+an96ub1/egdhQRV81HzuHj/H8h0w1cp1b22Tlejwet/aI8Xhct7e3dXl52bY7qaoG5vpMthPR05y8m+D8sP0ggdKs3Gf1Pn+Xsp+VPiv1z995Hr5LbCleGLJ2UvbFplmMEc8Y7phJtXMnoOMdZsybxQl6mCAYPR0ODMtCh2e+t7dX19fXdXR0VIeHh7VcLnusm+HQHPd/fn5et7e3v5+g/l9cGyWK+XAFlygPtqbIoO3Nmzd1e3vbAlCJRSKixoJnYuWlz+fzJjSaft+9e9dLBlUuGNFMnHJQSgZdh4eHvX2dEs1I+lgihVWdYlKiVGCopP+nsnCu6SRT4VAEhmX/YS+le6iq3n26h+w7dA4BuPNlIJIVJ4bN+QTzWSrPimHytu/v79t7QENyzWlsDM1IdM6z+V5K8n/0BZjhiFQTOI7d3dfpfOR9vV63vsAcJ83YeZfkLodhZJ/i3t5ekyX6SkfRyh4fHxuK9/T01BwvfUvwRMJD3ob7BmaVmjwKfFerVQuWc3hU3seQ7pJBVwI70FvJZ1KBsiqeeu5+2AXOhUPM6ci+69lVVWNipNN2fNeejjQr+IIC+v41Z56B9Xj8OoE6ATUVD9fLrm97FDdfSfs9Pz9vPinRbO8yE34B4hBY4HdRMv3OYrPJpESrqhqAZAn0/J0JmutKGcrv+HfKa4Ip2a8MgGFfsu8nE7T082SejrNtzsff8NHr9bptz+T4VX1aaFZkxDSunU7xr+iIkvJMVpPO6h6cj15jYKg+DIPqjDnYJpTS3E4nt89i10xRJSvbtdkCMIij+CgyYd/w9HHoo9m+pIJGd1WH09aTRXYAkEP3ADnkIpku7L3eW9PKT05OejGp+/Fv5yWDri8BJ/6V/Uj/k6BS+qZMqPLcEm8xOQov0CwTvqpqPcyOx6YlgJssv1yek3g2Aae8d/a1qnpxtAqnlXE9G7Gz0w2toesKS2wtin8m3Jnk/9HXRomixMxLUrHL4OPNmzfNcTGCOzs7TUFUlhhpCpjBVNJHbPwLnfdiTFYlNIy4wHhvb6/X1JrJEsG/u7trSsnJJJ2Sk0kayps3b3qTQpNOS1koMAEb0lFSEAn209NTzWazXi9VBn5QCUYiDY2AggGStEkgx+NxS+aSTuR5UvjlcvkbQU5USmDBQO7v79diseg1BmfSTUkEI/q8OPPRaFSfP3+ui4uLbaL4O60ERao6VJsxe//+fQuEqvrTMr2/pHkJYASkd3d3dXJy0hybQG8ymdTz83Pbhub5+bmOj497VfDb29tmvMlV6sQwwOQU6Gb2CeT9VnVVwKTDuLchPSUR2XR26XzZswzAsSCStpvOyDGqOtsx3BogB4NkUJ82MBM9yGjSdpyL3cp7Tb3OagtZcJ7hdaOkAq3odg4g+l6c3B95Jc00wbSki6m07+zs1OnpaQua9KN5b+Px6+CF4fYJ/q36zv+SJbacTXBMNoHsCO6yIpGJVFbO0meRSXqTwMYQlPEd15pyWdXfAiArHSn3wCI+PoGkqm4yZVbzPScVWPcqAcyhbY6jws6+sjXZy6b6n7FQMi8E/eKhjBMcE+uCLedv0XGdM4N473O7Nl8ADLJ0e3tbp6enTRbJqG2/yCBwbzwe15cvX+r8/LwHUrLXQEeMn8lk0v4mN1mFB8aLm7I1qqrz4SryfpZVwLwOv6fb2UsphnXu9MfuLY/nHNqR+PpMLjPO97fvsEl5LwmiJpU0fXIyKrJdJYsTwwpjXk9ee4JHmey5jtxHMbe8s3JApBwpE8RkF+V1/5HXRt3OjCehYLRSKBhEWTynl3uPCEjX63VzdMlXzqoeRwYpMQ0skRi/R7fT2zebzV5v+v+8bEqR1ByBE0eRSaaky/ckOlWdQGdlL3+udyorIK6FAN3c3LTBPIkoWoy/nyV/Oh04dDGRYkbF9WYizEFlYOGZeDfDSpIKYAYVafA4wRwYlBUltGKV5eyfkmRu1+Yrq+B04O7uriX4V1dX7ff0GErNuDGqQ0qaajyD7XeMZtKVJYU5vUxFGdrJsArOBHsZDDlXUi0lshKt09PTHoBTVe2YKoL2SZP4oQNloCagdZ5EXCVvbJVnTT/T5jl/OmDHz/vI6kHShjKxrOrv25hBKWefQTFbRb9cj+QE8JbBs+Tz5uamHZutOTw8bKyRZAds17etlEu22HvY3d1tUzSrOuRbT43hFl+jRAowgTsq9xJ9FM8M9DIITJlmi9Mv5ZCM9Dvpo8gItpE/mSTm8DcgFpmlw47Dz+vJlRAm3SyreQLxZOi4j6x88tWunS1TreQD83sSbdV2+7qJPZLWxvcm6MQGr1arNgTQe3QNnk2+FxQ9ttPv+XrHyO3FtmuzpbdcjOlZs7cqh2LDqk6vU5bITU7uFYNVdZOB6YvPkqGsaldV81n8s1jcUKr0xwkeZNxY1bUeke/9/f0eSOOzX9MfspmDecS6GRd4TmJ6saNky+fF+nmOjC3Tr2cylysrglmFTMCZzU0QNp9tPgv6LCb2nrPYg82QBY6hfWOn6DhmwfewNvL0i8WiZcWJUqaAQNVyGlNVp3x65jiQ29vbHj0yS9heynw+76EAfpfODXVuNBr1BqMk5TORSsY8y9QSREKRWzpUdciBY+U9p/ANKSRVXQLp31k94TDT0BP8IUX08PCw7u7uaj6f9wJpAYFAWMLuWvMaGRvnAwCg43o+w4TC59HUxuNxmwjpWTFeWW3ifBeLRQs8GMHJZNIL1rdrs5XGS7Dk/acRA+IwvJIRcutdklPvLY22AHa1WjVg4enpqSWJ0DcyfHZ21oJCPyeL2YuQfYmuwc/z/FV9aqX7HyKOVd3WOu69qnpMBMfJhGuINArwXl5eenu2odI5rgD5/v6+bm9v6/HxsddTlYki/c+qguvIoIKzS7Qz3xXnquoogHzz5k1Np9Oq6qbTClheXl5aYqJKzP4JTJ+fX3s35/N5ezbb9e3r6empt78n3zdM7ufzeQM3n56eGguHXGblyz6dphoDf3KIWlV/KIzjaO+w0u5nQso/Z4Do83QYar5YLGo6nfYq7sOAMK+hqkt4hsFmVgKt7B1KsDGvv6o/9Cp9ZfbOV1WrZmbSxoaINSRy9DOr9WyL68wA2vX7Pb8H2CYTFt1PRlZVNXvj/gy8YZPJwtaPbr7oqHdmHgW2mTkc9Eqs6D2/vLzUxcVFk/sEZdbrdc3n86qq5kd85tdff22yY6/qq6ur5ksSrFPxzwodOzIcDpVg/9A3Oh455WeSku7aHdM5yXgCSJl4OVfmAZ6J6jgQcngsdub+/r7Zt7xezy/jaPriXjIZZktcGx+azwJYx0cCUJN9cHZ21kBt4HNVx6rI5zPsR3eM7wHM2ahsY7NfAZKpol4QyqMsWsCXvT2SLUFr9hVlFr+7u1unp6e9quLOTjd5TKVMoMp5UIas5jk29K+qQ0xyaptAKdHRIYLDkeR30uE4Xw60GSp4VYemMjCuI6smvuuZOp+fVXUJKOcHdcy9q9LpUz5JXCaaLy8vjV6XDjINhXdZVb3eJdVbipFBiSREkpnUGf0XWUHZrm9fZE3lIdEx70IQkgGk4SUQeYk/2UNDJGPrdbevHtoM6jLHmeO5k/otOVI5TPQ/gRh9Ewx3VQfwJE0kHREnwBHRMzYkHR6dGjqTDJzJc9JqXEdWfjJAy89kUuezeZ+qBZLWTNg5Tr0nEjtVI+96OPDH85fAJlAm2El7w4acn5+351D1GrwL/CX327XZAsqZ1CnA0j5wdHTUAhCtEShuuWF1JiaTyaRVAlerVQvAMlBEX+O7+d8cXsVfeM8AvrQVT09PNZ1O6+zsrMk3EMQAEP6CD5TwqsZJaHOg2VC28loy4fQ7/sQzzUCVraA/aVOqOt0j+1XVSxDpv3iG/juGz7F3QDXXxc+qPGWyrNKrZYfuPT099YBpQJ74I/u2DOVh975Gyd+ub18pa56tpMHPqrptojKp8s6qqr2v9LP8huSwqlpPPmq5c5B/x3CcBCSHDDeyOKxoD1tS0t8m0Jpy7PcAXNfK9qBZZ3HEcs6sruYz4KuSwinxy/vOhCvjefea+plA07AC6p7FG+LbIRCW1UwMOJ9XWbQ1j1jHM8r7AZodHx+3PElh7HvQ040Sxclk0oyfxIszgNhVVZuImYjJ/f19XVxctISOoeRwEnHLql72HTKSFCE51PqjGOlUFsKdCEJWU6o6hUkaaVVnFLKSaRHITKgIGUHOqiPDkVVKypHUy6ysZi+X83FgnluW05P6xugQ2kRJhyXxpOa6FsNN0visVquG8hwfH7d3610dHh72qsOZZJuW6144UYq/XZsvkxFRUrxXertavfbaqSCRA+8Xzevp6XXYDGfgHZGhRNLIr4RTw7qEMvUhN4zOY6tyZd+sz2VPocCoqnN2glXVb8acoU4KCIeSARlbM0RE/Z1V0XQu9DrZBQLkBMuSNZBVEv/2LDhEx+bskn7u+S8WizblNEGgqv6kvKR8Zw8FQC7P4/0IinLCbQ5A2q5vX09PT22yNxtNn9jJ8XjcgJeqavpBpnIqJr8H5GTPs8KeCYnjp+8Y6lV+NoFc9gLQyPev1+tGf+U7+IGqzodiwgAxsnqeSaBjuge+PpMhgFZW9/Jcng1gJKln5Dh9Pd0e/j/ZFTkcL+m5dBsQx8amT8vENqu29J0Pvb297VWw2NFMYgHlmRx/Ldnerm9bqnI52XS9Xrep3961d/fx48f68OFD02PUfvohVsYsARLp65c4vn//vq6vr9v75YOzVauqent7Sk4kcgm8JCA8jK8SJMw4gD5Wdb29o9GoN9E1W53ohuSa7XBtYliFJXIuXqzq9PBrTKBMNJMF4FoyKVSAcF0J/GYi7LxZgBkm2enr/N81YBeJq9nIZA55PuIdIOD3Motjo2hc062Hdnh4WIvFoq6urmq1eu0NhOj99NNPDcH3kNJwD4NUAcvx8XEbgiH7vr29bUGsgEZyJGBipBlySQhFhWju7u62/QGHgpkCkwGhc6UhHiaMWYXM72Yiy9ExPCmAiaDkeRIJct9+blBBoqGJilAYCMjf/va33vYDgv9UHj2cElvBu2doqAUkiZJ77+nch9ePfpooKFBgaCC269uWRF8yQc+SIprIOmS8qutPyu0pUvb0ZQwRMcCQYNYfhnhI89ITwYnYk5UMDYGXryGcVa+GPoNplQqOlryi+lT1qejJQuC46I7nkdQhnxfA6duiz0lv82xTR03GcxzOXNB/e3vbKgWezcHBQV1fX9d8Pu9Vf6Cp2SeWPRDsQCKxGQR8+vSpVx0RSAuGM4HMoHi7NltZrRBIVHUJB6BF4udv71v7Abua20ux59l64Hvkdj6fN32gR2SIv81KeVUHNGJ/2BuZnA/lnkzRDQlpghO5l6D7z6Q1gcakkyXVLuUzqeWC8mTFDCsP/DKg0wJ6JVPKH888fVcGpZ5ZBr76xNkpP3dO9yXQR+dP8Ork5KTprxiHDVVRAjRt1+aLPc2Yb+gHsvKO+VLV34aB7EoggHTZkjMajXr9bvztdDrt6cKwYnZwcFBHR0eNIkkfk3pa1SWEwyo8n5U+jz/JJInPSOonP8PWJIjqGGmTVOV81nHpZPYeJ0Xd+fhQ/pZv82/3atiee8gqJjprxrJ+ljE3W5sMJT6aPLBf3r137VzL5bIHKgOOsYC+h7XxVbppfRZZKp9Opw1xMR6WkhwcHNTt7W1LGJO2mvSzIbIhiIOWE8oMXnyGMGXQlIEpBzCsimSix3FlgpeOx+/S0ThGOhafy7/T0CRimTQDy7k1zbuvrHp4HokqcjBVHU3GIJ7sw4AKef453ZSyoi+ls2UIGTdGS8KZZf3cxwYFybODtFxfX/cQ2u3abEkkcs+0BDoAPIw2GePsVPCT6iTpBBpkhY2sQ9AFR1XVqpN//etf27EEQlXVev6ysqYSkZOFU38EkBk0Pjw81O3tbZM1eipZHDrO1DOAB71cLpe9ZDHP7/nRD7KcvRWZJH+N4iJgHgb3HP8QLMoBJMvlspbLZQsabLKdDAGBQg5JoMvJ/kCR0QrAeR4fH7fAI+lOLy8vLdjdrm9fehF3d3fr5uamyQz9kfjkHmMZVCWYklU1OlnVVawMdQOgqERkIpl9b8AMSSHZFqD5/Pn5efP3rkfVhF6y9ZJfNiETK/Zi6M/oV9IrE6Cm9+KKtAWuIW0GG5GVx6ouCP4apc35strOtyaI5v/0nh3Iac/eSe7DSDfv7+/bvpij0ajOzs5+M+gtgSPxRQbu3vEWyPl9VrYjifv29l73UswqebYy0C06mXZzPp/3AEpgu7+rqiWU3vf9/X3N5/P68uVLT1+rqjdYaTKZNB8hoRH7ZhsDHcuKoziQvi6Xy6Z7wz7eZOQl6ywLJI5rpoVz8qfkd71et6SNzIo90t+6lvw+O8aeuD/X5Z7ThwJqb25uer7Xc8eQS3bHMF5I/8oWrtfr9i7ovCo0G5Hgeia1f/S10VVCPyV1khT0UfS2qmolcw/ZXl0e1mQyqYODg9b0TlEY/bu7u/bQx+NxG6bggXtRWTms6ihbHFIinX6fQSBnUNVRPinR8/Nzr2GY08mKZqI8jpHCVdVHZVKwKRbBGlJVqjoFosxZ4RlWPinvdDpt50pH+OHDh9/c/+PjYwsoHMd70jOR1Zbd3d1aLpdNFhJtnU6nDbHJgQcZVENiBEVG8gMCtmuzBcSpql5lUIXZc9YsX9X1CXtfd3d3NZ1Of4NQkxUGvarTGcHYUM7pelYV/D51hB5w0AkUkdNEeekEI08eyanePKj713TQ88pKg+v0uwzWBb1WoqSuNUEkn8lAOANjjkfAeXZ21gNd3G/at3xukGk21/Onk5lsZ4LMZicDQlKflRwJO7u8rfpvvvSXkVtJub5CesiPkJdEqIG0fg50oyfW4+Nj3d7eNoAhqwveffqeHMry/PzcbEAmP/yd/vRsQ+GfUo8FzlVd/xagNv2X+/Nd8poAV9oZA0Wy2pH35//iCTLt+aYeZQUTA4FtSZpfVTfbICm5o9GosTeqqsUlqsPHx8e1t7dXy+Wy3T+/Z1Afm5PBee6fyJ4BVrOSoyc5K0Hb9e2LbOTgNHEP25094vk+zG+oqma3yffQp2ZxQOxmSCG74Bgpg8M4WNyWwEsWWxJocp1o4OR8Npu1OJAuJwDp/PwEn0I+c+op4HUIsFZVk3k/l2znZ6y0ce7N3znZmD4CW7NdLGN7zAhVxKoOFEj7mayDBIHyeXi/9Nm7RAV2TVg9wNvvZerpxvso3tzc1IcPH+rLly+twf7Lly91dnZWZ2dnrd9NJSOpGwQBUi3wOjk5qarXIM+/8bwljdCCDF6ymXy1WjVjnWXxDNKGToGgJRKXQZVEUCCo+T4RmgxsBbuZcFZ1QyTy3jkH389EMZGH/D0lzeqbZ5vJWFJwce2hwZ6pz3hGzpfj2TOopRACEcqFugjNErymwrlvRkXyf39/39tQeLt+n8VA0TcUtuxTTKPr9xZ9sw8Y3r8KZVbSIXlJt0pAY29vryaTSe/ayIfPJqhDjofovqmPWf1LZDOdtoBRoMb2ZBDMESQIknTVTBg5gXRYjgVdHA4fqOpsybDKn/TadMLumb2Yz+fN7g0HF/iuCjJHC8zx/vRvSj7u7+8blc3iAHd3X4ceHR4etmeMPZAJ6XZ9+3p8fGzU/pOTk96+XJKxHPS0Wr1upwDYEXDwNfpPq7reQjLOh0nwcp9e+pE9NYBB+g4k5ecFxHpgc6hH2vUMnOl1JqT5xyKP2UuoNYOvTJvl2HmNmeimD019TF+cAV7SVF1HArKOk8Fw/iwriGISsUpVBwpJRl2f3wPh9BxnMuD7w95j9+B6v5dqxR99mb/w5cuXmkwmLU6x33MmIlXdBvEpXxL7N2/etN40QBA7nNtKqVpifpA7/lACmiBFMtT4W345k9CsxJNf8pVgR4L3QwDXcVKnFotF8xd8DFq6WHTIPEo/7ZrtTZrxZgLRGZ86RibQVf29EF1vUksB5cN3pyroXMOqYtoedtXvvXOMxYeHh5pMJs2+s332KBZ3fA9ro6vEhxZwEpDxeNwSxxyKkonLyclJL4CFCqIwCnIIurJ6JkwmnjKyScV4eHhoFA5Ol9EeCmDymLPMrdxOSDOAIyhZkZPwVf12TH+iJOmEKCfhz96KdAoZQCaKKRin1JAtv5N05Qh8Qb4EIPuc0P2yGZliZNWSIgvu87Mc4/n5eUskKDMnmgqaG0d7Blvq6e+z0tilkRZkCK5shSI4reoqZBwW/SBDEhOGkRxKlqpeAR4059xOJivhVf2ppq4RmgnVv7m5ackYO8B+MMACypyeRrfSOaXu0dVM7JLKNaze+fn+/n4LmFFhyC37gRaTCHAyDjwL1zS8LhS11EEADHsLlEnQi0NPBwYcwoRwL+wEu0Ze8vlyap4vdkfex3b95xddoUuGLaEhQv3Z9q8lJ/v7+01G+EXyw8cCUbO/ERhAbrz7TIR8dzQatY3pHcdn0r7kwArnykoBH6F1oqrvG1G9kwmQlYTHx8ce7T11kt2iI5K7IUMImPnx48eazWZN7/1JmyAQrOqAnowZUqerqiWDJycnPb12falLqvgSharXuQ/imJ2dnUahZ3Mt7/7k5KS17Uyn0/Zstuv3WxILsp8AnUpcJisZH9qneHd3t209o4ihegwoTbuvQnZ8fNxaP1TI2PuqruI8HI6WPhSYk9uRZaUw2WbX19dN1gCC4nZ6nayaLKTQUTZkyMRJf++5ijcz/kwAKquenrlj5rGAxsNqv2vL66jqKKD2XOYrxapZmQSW+S7Q2LtLUNZz8LsEU5PtIE4SK//R18bprD21zs/Pe8GjBAKdhiEUzM1msyZ0HmzuDSTwyqoa9Ob4+LghrErH43E3Ypqgm3qKCkNBqrpqIiUm/PP5vP72t799tfI4RPkXi0XvM+nIh86pqj+yOBEfSVs20VvDIDcrPUNeuHtzjUkToJTp0FRMspqSzfMp7OPxuDnx3PZEYFJVjTKKIsGhQr9yGqPgxDugQLmv5nZtvoA52b9T1cmIIE8fjOfu/Xtns9msIYjev9/RITpGZiCgzsdoJ9qvSV+wlMhoBjxkOFkCyVAQZJJleshpcQI3Nze/oYxyCD6TDqCq0+2sEnqeKnwJWKVzIddJX8l7ywpF0lVddybfyTQ4OTlpgxB8rqp670Dwsl6/9rxlX7V71gfpueeAEU48388wgUl7tF3/+bW/v9/aMtCl5/N5kxM6SC7v7u5649Vvbm56wKefD4EU71swKxBMRJtPYDN8L9FvP8/gUdLIZiSwmYEbUGNIh9N7WNVtIZOBJX+a1YIMlv1Oxfvk5KSBw6jt9nJ0H0dHR21IXtK7s/qYf5w7dcjn6bhrqer0q6obEpTPQh+4ipEkfDqdNr3OapH/86VppzxDvrSqekDZdm227Df77t273wD5Jycn7WdJWyQzpgEPh4p5d19rM1JxoqsGl1V1W7rQU/5IbDes8uXf/N+//Mu/NB8oHrfoFvsuqU1GEjkbUjIPDw/r/Py81+ee+lzVyeVwqn6COq5B/CBJcwxryORzbe414wOzPTL+8AyBSt4jKm36W6BRAl5sc4JyktxPnz61a5T4J9Mj2YPfw9ooUcySNZRQwIl2enBwULPZrCUfSb/KJVjNHjw/H41GdX193avSZS8bgXp5efkPkZmqDlGArPidIBclk2OWPGUixiAkPSWDr3SOjsG5JHIDMcmpTVmeHzqrdDKLxaJXnk+nPXTSOZEurzGvwznTgCX6IqH0M1VDz1rAkdWMx8fH+vLlS6+SmBQfiWvSpiBc2SS+XZstSTs6Fpn2nieTSa8PaH9/v9FLM+BM1Ez1N5OgpHUP+4roAGpbBnUCJTILVMqBG66Zzgt4yackKinrT09PzQFklT2da9qgocGWFLqHoY6nHvl5JqYcIN3LyWuONQzqq36LlFb1KySqrp5vBpOuaVhRyMm2aLvuy7O3RYPnkNfA4ed1ot1v12aLn2OHsTyqqjdwDABLJ6q6bU9eXl5aLw7dckx6KtHgIzBJJJ6Jwld1wSWd4ff4CtXy2WzWAwuSLZP/zoB4Mpm0PmX9dGRb/14G5O6FX1QlreqmfecWWqPRK0sCsFnVl2cJGkp1DgpiyzJOyT5BMp/JbPpRfpK+DO1NAq9J9wN8JSjw5s2bOjs768UNyajwb8F3Tm//mh3Zrm9bKvR8KR1RjDg9Pe1RFNNnnJ+ft3jq9PS0FotFe0fHx8etPy9bcch9Vq4kH9g1CcCriFV1wKN+ZDGVWC1BUEBLUjFPTk6abrMfQ+aIc5HpBJ+d33fzenIv7/RpWbxAD/V7zzQT4fShw6q968iYOI+fPaHJQBRT0MPhtFfvHSiTuu44Q/voeSd4poAiZvleqv8bReOLxaJtylzVH0jy/PzcG/ObgY2XJVDKlz6fz+vs7KxHU0GXyoDFMQU4EhHILB54BrGJPEoM379/30P8GIPsISRsWUZO+iYnnAhBCmpVv1HXfUl4OQjLPWa1wf0fHBw0Cl5W8gTjSS3a2dnpDUhINDOropyy/iXvwvXn2GGJOd56Ou90Zq4vN3rn8Dw7z0UiYJ9GxnG7Nl+CQEbpp59+qn/8x3+s/f39WiwWreciqeEMasqwoIqTHI/HrfKhL+Ho6KjtqQb04SRyElvSNE5PT3sVCj8XGHJSWRXJql7SJLMfmp7R5fX6ddiLROvo6Kg9IzYog0M6mChxVV+vMyBMhDG/n8mzYRgSswzo0mGmPlV19oANYv/obQYYjiOh1oPIJifllJPV5wxtxfjIoDYDXXZ1W7XYfLG57La9Pq0EWlTz9UAdHBw0qip5YHvZUUi6tg3JJdnCIMlJwAl+OEYOatjbe53Q+Pnz5ybHKvqoccms4VOy4phLwDoEX7Lyvru726riuf8um8LeCCgXi0V7llkp8EzJ83Q6bYCVa/Q8M1EVI2RPt8o/m+QdZSDtO0k5raq6urpqsZP3BdDxvFPvF4tFzx5JMFyjGMnvh894u759DUGyjJFyomnaVf5kNps1gCf3Nc1hUlXVeoCPj497AOx8Pm9UV+chg/wj2avqKI2KIdqz+Nbj4+N6//59r+KZIK+fk+8s8GTlkm/NQlECpZnksh3JkkhAJe1Exhf5t+9Jwlx7FkEc0/+/BpbkAC5FoizuAHdUW91L+sGqbpAnnzs81uXlZZvNsFwu217WdHhI4f2jr40SRUZa5UiFIhEwhv7Lly91fn5eVdUztIkW2Cqj6hWxzgE4Jycnrdrh4Wb/m6BIGdvo6QzGBFiOc3Bw0ErslLGqmpPIxCkrdYkOpGIpkzuuYE+AltWzDJoF0l9D8zModS/Hx8dNwTzPTMCyTC8BEEhmFYDjgqo6n4Al713C6doTmZnP5y1AgGANqQeGNJAPCb3np7or8f/b3/72XSjQH315j5k8jUaj+vz5cxu9rrqeFApVyHREnE5V1e3tbQtuEyiRrHl3GYAawAJMgnSSX2BOUjfp2GKxaJWH3Ow3AQ0GvaqratLTrKinA0nnPnQezkG/fTarJfSbA3B8K/VdZcbvc9Q6J8QG+D/kF5K5XC7b887rTyAuk+5kCviOZJHeswESkv391z1rXYcEg46PRqMWYG/XZoudJFMnJycNXGM3d3d3azab1d3dXU0mk1osFnV6etpkLqeP02N+jU/GAKnqGB1+l6BGVsYEfhm0SY5QzQRreiAzOAZiCB7JXQIMzjsEYCw6ohc6q/TsRQJLFmAoE1dVm6SjJ/BEzvnitBX8p2vLqul8Pu9R1BLoSdtU1VEPBehJO2S78nzZx+X7fLpj+qz3+Ne//rX+7u/+7qutLNv1n1/iygQYPGs9vPSW7u7s7DQfx67u7LxuE7dardpWRvyZ+C0Bw2yP4D8ABJPJpMlatkCRIcADPU/wpqpaEuPYyZZLIL+qqxy6X99Jn+15iHHJqM+lnlV1Mu747EwyYxIwck62kl7ls8vk1OerqhWbPE+xv9/lymvXR52zNTzb7BtPEKqqA28UyfzfUKSq6k0P/x7WRrDT/f19jUaj1hCajfeZOROY5+fnNpVtuMn9cFKb7S92dnbq6uqqZ1SzKZzjShqIc2v+9cIETJncOW723CX1JakziSoNy9WUKb+TQkuhqrpJd4RRsFbVH2CTiEv+27Mj5IKKoRPJAJeipyPmhCkeZNaS3HFU3pVzZ2Dh/+iN9uajYIygd0EmGEaGdblc1nQ67SUl2/XtS7+AQOrHH3+s5+fnNmhqZ2enVRQkLPYXkqxlgkNHDg8P6/DwsO2FCtShD2m8k2LBqQmmGFs6XdX1KaHF0r0hAjsMMJOOlrqduouqlsBPgipDymlSwhOA4fz1M1jDz1iQVZXXvBfHTsfmOfseW5PBalY105m6T/Yg7WTaT4GKXg0O+/n5ubc9g2TWdapKZcVlu75tGeTAtps0ivUiSDW0xNK6AJSrqlZ1enx8bHu1WTkYil9I8JTcqnQIcNJfpo/b3d2ty8vLFlSS0dS3DPZWq1UDebNnKcFkP5fwZlBKBoFFrtExE7gl96qESa3zfFQncwpyBsNpT+hJVuNT35KN4feedVX1/CT9HNIZ2UKAud+xVXpKBavsLH3NoR8nJye/YU1s17cv7yYrTN5jskj4C330/Jqiht+JbQEefHAmMwkwZAUMS4yPA87QIXKZvcZ0kTwAjMk6+5FVbzFBVfVkMWPkTOqSOZM/Y1cSzMm5BRkLDv1hLrqZ8Yqf5fPJ6mD6bN/La7Xy+X3tvWZbm98BazPJ/lqc4bMAWICXmD7v+4+8NoKEc/peVbVNKgV6qnQacykOdKWqGuKB4kJ4TIg6PT1tgY0BOJLRN2/eNCGQmAlqE5Ug/ARNgJd7DSWSk3QOL3+IJKaADp3if6QI6EEZCCYSyZnkcIxcaTwEwgQ6A39oZiarSe+r6u+xA/HJQQXpvD0Lgbfn4F4EARzx2dlZ7/1z0gABjlxlV0Cjn/Xh4aEuLy83Ec3t+j9riLqT3YuLi4Z2MmpV1UMnySx0HhL3yy+/ND02LTWNaCL1VdXAjaz4J+WKbOW15qbd4/G4N1E5k8XUo6R2cYYq4f7NWWYQJ1Cki6nL5Dt7In0erZfTSIpSoqmZBOYx0qn5HTswZBfQIcdGF0775PyJrGblPgG61WrVBk/Ry7x25zMMhM358uVLq/pvK/6bL3ZyNBq1lgKB2Wr1St8fj8dNz6DSkjqJJn+G2kZuMoHI4S9DhD8B0+wldm3kSyBY1d8mJn2ZoMxnXItzZNIp4ZNMVb364pwomnLpD7ukQlhVvzln6po//Bvbp78eVU9AX/Wqc9nWkWCre6djWYnMQDy3+5EIe7Z+vlwumw3M40oEVZK8D/ebLSvpry8vL39Dnd2ub1/k23sTs2QFrKpjz5Dnquq1CFR1vf45zdcQRsA9MFB8RW7v7+/r8PCwtWbRC99PgCMHUAF/lstlXVxcNJ8mhs7qnuo4e5PV7SyouJ6qfptSgpiu0b2S1YzX2QSfz2QxY+5MrPw+9XEIxIgnMjFko5w/+0pdC1uX75ZOr1armk6nbceGLHLZYirjBwBBzkgYjV73WKXTZ2dn/xcl9/dbGyWK+tQIg+lIGrAzWUAHTQNpX7BUOn1OJpJlZr9er+vm5qZGo1FTFgKbkx2hP6qXjg3RyZ4QBiDRUkIJQeGQ0jFU9feU+VrFgYA7/mg06iVCEMocnvM1pXGu/JOVxqouyXIP6XwE55mgqv4wUAyG4NF9o/ok7YJCUa7sA3M8Tvvs7Ky9A4n5dDptVUpGl+K+vLz0enS2a7Ml8EzZtMhGyoFglJOSLOj3SWQswYJPnz41mSb3Ozs7ba82ji+DN1S6qt9uPF/VIfl0IZOqqurJNwPNgLNFBg1IlB4fHxtth/POJvQEWTIgzGqnQJWT8cfzrur3Vw2fufsdIqdD++EZ5j1ngi0hzOA7nZLvutaPHz/WDz/80J6XKgld1oeadk6S7H1VVQP8kn6+Xd+2yKDEX5KXfflk+82bN/Xx48dGO5XESBrPzs5qOp22Ct8wcPGen56e6tOnT/Xjjz82SnfqgvcvyK2qdszUh0wSBWHkKKuRZJpuZaDKNjlH9vGwB8NgLlkK6Q+tTEIzUJSoSbYymHVtdI7tSXAmg8u8NwA1sJvNzDX05X6m+iSOENwPJ1SromQC6DrQH8U0hgdu9fP3WYBLspLPVUWfzKgMAkXJVrY43d7eNpn2/b29vR5Yx5YbGsbvsuu3t7etnxGYxEYDO8hssn2q6jesgkwEyd7Ozk4DU4DCz8/PTT/5/2HFU+xAz9KXpj+V2HqmPpdFmQQ/raSrDgEhv0/9qurvgcrnVXW2N2Nt1+W+vMPpdNqjiw5BLfkOtpCfoeXnz6uqxc23t7ffBeC6EfXU3lvW/v5+C8IE/7u7u3V7e9srtRNs1T0CpKKIDpWlcMnd0dFRr+9gPB63JCcTypeXl7q9vW3fFSSi3RFmwSZHLVkhXFX9fqGvJYQZFFJqARsHlxULz4awZwWREKKuqJ4OE8V0lpwe55ADC9LxE27X7rPr9brtw8VAZZCSCm40e6JEDJrAcm9vryFTFN2+U8NqEcfmnnNU+XZtvgApu7u7bSQ8eZQgoXD967/+a83n85pMJi2pH/YwVlWT19ls1nQqUfOXl24/ppQdwRA5ww64urrqJTXZrO/cZByQQded1/1k0JhBqt+TQQAIJ8TpJKWdvrMRaeRHo45+m33Avp80mHSUmdAl5QUVrqoLmNO2sJdV/Y3BM0nMYNR9STAeHx/r5OSkJcTsEOrsL7/8UsvlsvVBug7Ou6raRutfC86369uW5zkejxvVOsFKvlOV7uLiotnP09PT9o4Mb7u9ve3151ZVA3bo8NeCHf4xk6Oq/t5fAh0BYgY49BwoOUTxh2AKu0IHJHEA3ao+hTw/51qd12fpml6x9F+mOFZV882OV9Xfhw2ILYlLOee3THFm/1QcM5lM2nzahqpqUyDFIkCqbEOp6pg/CfYJ2NkYz4DsJJNhuzZfyYAB4iSTKqf1X19ft+/xk+Qn7bdES3sP2jgAkyyIQ1WsqqqBMVVd1TtbOrLK+fTUTbkmZ+STH8hWEXG3BJa8kTmJ1mKxaOeq6vYVlkgP6Zy5z7J78hxyMrBzDSuIufjFtAueS57bexleSybF+Y6T/UB32AXbSJGFZEeYti6OMm+DffA5dljMIPH/HtbG22OoFFZVG/8LfXl8fGz7AgnMPETJTKKUkkZK4GETAIgMYc6JbWgf2Y9A0OfzeZsedXZ21hRRpaOqGnWzqnrO2nWkoWgP7/8IGuHIKkNVN9LbH4KWyL2/ObL8fiIrlmBaMltVzelm4F/VbcZKAfJ+GbAhmurYHHsqLmOjcglt1m/pupfLZTOAgnKGb2fntaF7f3+/VZRdn58tFotesLJd377oiHdHNtbrdaNuM6JnZ2d1fn5eR0dHzVFIAqGUDP58Pm/U4V9//bVOT0976OBisWigEIfy6dOnZjTptanBZEXfM5nMKntS8rJS4H44hASQ0om4nuxX9ruqPlU8g+kMjpNSm8G4axVEJrLp2IL6qm6gjPOzD44lAByO7s+K3zCgFAAPKz1sJlQzwSbURUmHZyEAcV/eXfaqbCsWmy9Tp4+PjxvrA12MX005VyGfzWY1m82avCb7xXeG1KocQnNxcdF8rYCG/vInVV3g6RiJ+GciRj/4hKqOop1VRHIu2M5JpLbESSDV73zX8fKYCRjxg1gSw+TK83J8lFP+jv7mvaX/5ackePyaY0rgsqLPliaoK1jNf2c16NOnT62VB0Cbg6eS1SSeMvsAAJ7vYru+fT09PbX+RHLz+fPn1jtMbmzHwtekr/Xv8Xjckj6guSRRK4U4SRzN76jQkW/yky0lmWClrPKtQCeFEd/BDJLsJNDJ/4nlHC8BpAQV6ehsNuuBjwlokvmvgSmuIX1igq1V1bMHnnEm5Jn0pT/OGMFxfN9KlqBnKcfIGIpsSD6HE2vF48O9xLNH+3vxoRsliigcGXxALQmEku3t7W0ryWdQ42Vm4/bOzmvfQY6Ql0ylYlRVQw8Z/apqVa/1el3X19e1Wq3a1NRhg3guQajr109H+ShWIqOELgNACqlpeYj8VXVbWkhuoTuZKDpmJnGLxaI3Chmik9U6SkPQBSACcElxBhCCDkk9VJVxSMVK5BeyzZhw0Brq0Ws0V1f1S/4QbM5Vr80WDf19FprI3d1dCy6rukQgA8Dcs6uq22fozZs3bbz+L7/80sAOFDN6urOz04ZDGP3uGN4vI5zOhUFVpUsUrqqjvTHaaUOywp+VcchfVbdHnevhACVrkMes+lmptxnYDZ0jnUs0N68r7yWDQ5/NvQ7ZBs6Frrlf75UtzKSwqkNWk1YEkKODjgfwOT8/733GBsHuM/fBhKJvt8fYfGHD5NRp/WgqiAkKJNAKoMzZAHqLhtPB9/b26uTkpOevqzqwk1+Zz+cNsJ3NZs2POB69yaq2lVWzBG4EdK4xA767u7tehVDiJ7GbTqc1m81qPp/3gGWtLmyCc/lMUlezcspnGvQh1nCNuc1GgqlD0Dj9t9hiNps1nXavKKjpN620CfzteDxuQGnu5aaXMq/Jva1Wr9PEyQkKqr+3a7PFRy2Xy8ZSA7rxN2Tm5OSk/v3f/73pkCSTfCVzi7/kR8VDmVAl+JdxNr0Byj49vW5Xk+w3sRjfkHGl85O9vCb3O5S1qs43AUKASllgYG/4fufJZ8IfZ7JJ/yzPdehDfT4TQnaAfiX46/oTqEmQKcFyP8+KpXfgvlQEqzomkkSeH89KplhksVjU9fV1XV1dtWRRUeyPvjbebGc8fqWMzufz+vXXX+vdu3d1fX1dR0dHbWNdRuvz5891dHTUUIZMnPRK7O3t9bZNILSSu+fn55pOp62Klei7PxKP/f39Oj4+ruPj4+aQq6rRRAg85yNZy+Q1UYWvGd1EJ3NUL8XkWJXr3Y/E13czcU3FyOcAsc0qhp9nIzRDkQGs4wqw0Qz0kDIqaKVZbXD96WChswyE61G5TfpRVlMhtuv1us7Oznq9HDjdaSy2a7Ml+Kvqtn3x7zSgVdVQ66yGZcIFDYUE0mvBbFYoUTUkFQwtylX2v7mmRE0zkSKjWYXjRJ0PkCIhXi6XvWSKQxBgc9pZdXAN7jUrfHQygZ+ku6VtkEyhk6bzyaQuA2ZODhI5pNw4Ria4riGdHuaGawXWQDwBNru7uw3pTNSW86Xb7NX5+XkPdVap3q7NF/85nMSZ9Oh/+Zd/aUGZ5O/8/LzZzN3d3fr5559blc6AJe+WTtqCis5LAB0j+xJHo1EDluipoKiqen4xkx5yd3d3V4vFolVHh8GzXvVksCRly7WbFJgJYfZJG06X1c8hsJo0Udedg7QM5qBL7pGfy4onP5tBIkDO/bheVL0EmPlMx+VzBY25P5x3wGai/7Gj/+N//I/2Lp0r+7C26/dZ4jXVXfsPZwXx+Pi4bRUmhhI3ZUyYPgH9mA/UTzishGeCqTiSySXglv9kO1IWJbnOI3ZPfQGa0ImsXPLZ2Vrl86ljVdUKKxm7JnCUlf2hD6U3VX22XjJqfC6T4KRd86/uxTUNK4lVfcZGVjt9Los4Ens+1jNIACyru8Dv7HHNPam/FyBnI4LsarVqY7jv7u7q/Py8oWgoFAI6mbUxz2gSx8fHvaDu9va2VaOsnZ2dRsMxdUj18O7uri4vLxuFJxGXpFsJbBn55+euAZyhNVWKM8mqRnKxM7BMaqmkT3k5h7xQEsIjmU3HQ/EJUd5/IiqSt+wby6RSBdHPPRs/T+TIzwQQiRY775CukJzr3DNxvX6dIjekpvrO3d1dM5hkpKoLYgTJQxRruzZfpqDZDw91tKrq8+fP9eHDh+bsODBGkyw/PT3V+fn5b5xgNuuT36rOyKYhX6/XrWLs2HTTECsoKRuSTjp7OwRbVZ2OqGQm4p6VdclpVafLQ90mf3TI8S22IRO0TLrpyzAZzOO5tqGjSOAJCqm6kJVX15cVTef0TPO5ZIDvc8Ap18Gxm+aagXTS2I6Ojtq1bNdmS+JkGq/EkNyabApwZYffvHnTKo8vL68DwlD6Z7NZvX37tsk7QCcpjuYEVL3KoeFxqdvsO1kjy/Qj/S35xCxJ2SB7gtG8d4Hw6elpT6bYlZRfyaPfsS+pX36erSB5bsGvYI0eJHtiCH4mQEM3MRx8JqmqzpX0s6pqVXpxyfX1dR0cHLQBIMfHx3V3d9dYFcNqDPlgL9frdf3www+9awHSJ9C1XZst8xYMWnx+fm7yI0m7urpq8a33w0fRI0Bqgnm7u697pF5cXPTaHdjx09PTqupAGb7l8fGxjo6OetPA6a+9qpNNQtbtyyphxWZYrVatmJJbamRilslVsgYTWHHd/EfGvlXVi3s9QzZH7JdANb8zrAxW9WcQWKm3bCt7tVgsWvU/V/pgft7zdv0Zp2QS6B1WdUB8JpAqq2zs8/NznZ+f9/qkv4e1UaIoQdzZ2WnbGaC/6D978+ZNqxZKRlQbPn361BpFoRuj0aj1SBGibHSXNDo3oVd1gFjYk88QB8GOQJDDqepK8RxNJpScjJfv35RBwpn71RFonxdIUzqVDAGzAGCIlvh/Ig/uD6Lk2ofO5OrqqiHIDMIQMXbvlD5/vlqt6vr6ug1YkCxkiV8Q8fT0Oszm5uamzs7OGgd/sVjUy8tLqyTN5/Nm+ASlgmyI2fn5ea8/Z7s2W9kboRJBf1QLVIsk/58+faqHh4fWpJ3BoOQNjXRYreZ4sAD8/F//9V/bJtC7u7v18ePH+vHHHxttjm5zli8vL02nq/pUVUllTv58eHhowTY6XlW3GTWZTf1K1PBr1JakbNMzK/uSqqpHq6XTEi5V2KSOubZM9Nglx0za0ZAKpzKPXuYdeY6ZJLhu9+/9eQ7+NqEWgHN9fd10Xh8qGWE3tmuzhT4JyOHr0nfs7+/X+fl569tZrVb1yy+/1Nu3b1vVXqUY+Ml35JAGPi+r9kPqFhYJZowpjYeHhy3RyuAH4CfAE8CSRQFjBqPkj74mC2dYCRA8Csi+NvzBPQzBGQEeEESQ6rpyeB4/lNuB+F3SyNilrIL6rGMD4eidz3r219fXrdUjP5eVp2Qr0Gf2gv1YLBZteKDPAtLEG9tkcfNFnxK8IJtkRUxb1b1v75X+8EsShWzdStDh9PS0R8lUdbRncfphyWFV9ewGoJXNd+23t7f14cOHXiU0kzTyDNTnL7Jokn36noUiC59T1Z9eTlfonAm+WbjARFNEklCnPjhu2om0Nf6fjDZ+im4kKyl1J5k67lOCmAAZvXLvnnkWmHIeSLKWlstli5e/l/7Eqg2pp6hiEAi0US/bBrGoFAz+fD6vg4ODOjs76/US7O/v18nJSW+6YBq7o6OjOj09rePj495m3PP5vL3IL1++9MrQeMVV1ZIRDk1G7+UbKODeBFtZMciKSU5sVd5P7nJSCAin40MGCbe/MwBLI+8YX1MWDoKT8b1Uoqqu6pjVWugnZ80ISCJUd9wLym8m0oJ1TpYCeWd6PaHWRogLGFyfd8JgbNfmy6RL7yzpJAcHB63/cGdnp1FT9vb26uLiojkAsgap938N/o4tSYSeCW7YgvF43MCiyWTSo2JXVaOqCS6zAuFzdDH3/EwngbYuIM1+JbqaToSTEHxJPum731d11cK85tQv1+p3nFsyDobUm6rqObgEftigHF7AbtAvx2KLkj5Pt/xe8iy4YAs853T+BwcHrbo8Ho/b5uqe9WKxaH112/XtC9B6f3/fKhf2K63qRrvTsaOjo9YXTk78/MuXL81umoRoMAT54O8EMnr9BJQSRecH1giYyLbkSFDqvPxtXj99QanlI/gsLAK9iu7X8ekBfeJvUtbdb1I6/S71p6pj2aiCJi0uWyocg/6iliZQnACyz+am2skU8lzFPm/fvm3DRvLa2TTX5tlpX0H1yyF+KsWfP39uNnqrn7/PAngmeCLG5Fsw1wxvZEMVRpbLZa8C710/Pj428Lyq27qCr8y4kewlNTljRnoiGUtAkrxcXFy0toFsERKvJi2dryVbKn98aP5+d3e3N6BSLFrVAadZvd/d3e31uIsZJaUqpXSSn3I+1+o6k4mQTDjnxWTMGMLz144C/M2YP5NV/dsZhw8ZDQkouJ6cKI3inq1i38PaKFG0Z1NOJlXWNRxhOp22ffE8yKxuPD297uk0HLIh4Es0hxBBNE9OTppT8gIEOxSD482ADko7Ho9bcCrLz0QrX3hWEwipa7HlgOonJ6SiQIGV2gXCjuX4qVBZbavqksbxeFzX19c1nU575fxMth4fH9szp0wUrqp6QWoavAxYGRUKTbFcUzY7Z3k/E0+GyCCURHQFJfbRY1wZvu2gjN9vCX7I4MePH1tCsrPzutehdy7Jy941CeHLy0tL7iT5egiWy2XN5/NGnZLACHZsLEufgEjj8eseRc/Pz63y9vT0VNfX181BZF9hVZfM0W0yiMLuPDnlc+g8HVfylFU5v/fz/F1WHRPMyJ87T1bnsACGvUMcUwaciYr6XVYnOLIcFpCVxLzmRIPpuGMJYAAEuTXNzs5Ojw4o8RcE6Avbrs2WIRkAEoGYgHE2m9Xd3V0L1H799dfmU3JYyWr1OtDEZ1SnfE81sKpatduwtRykROayysWOkyMB487OTqOwrlarHqXzawNlnEOgWNVt3wPAkhhXdf2O2C78E5o7OadX7gsgLEY4Pj7uMSYEkCqr7vPx8bElWmknMiD1HJNqCiB6eXnpgdZimZubm178cX5+3vqcMon0e4FuVdXNzU17h+KdpAVXVdtC5dOnT73EMRlC2/XtSzxCrjHfHh8f6+rqqvlFYDjbbT9LYMDBwUFdXl42QIHcTKfTqur392aBhP/GzGJ/DZtKCmNWxugOYMFQHTMLnJM+5WTOqo5KKd5O4IPu6fvV9+7an56e2nAmupIAqDiRvqd/YwfFwCp7i8Wi9dVnPJ4AbFUXS2eBgx57xlVd7MvupB913GQhJVPHvchhrq6uGjAlHs6hmc7rXLe3t+1av4e1UdmG8AypGFXdcBSIpMxawEKQISf2n9nb22vU09ls1sYPQz2dIwOpp6enOj4+biV+Zd2sCDpvBloochlgQXPTySU1jNIlsii5lCQncoJ2o4ri8wTKs3L8REGq+shtVUftJcSu4enpdXKpHs/T09N2PErsWKpBrs9zGu495V68SwhsctD9jBImJdHPUSfcA2N6c3NT5+fnLbng6Kvqu5kG9UdfenWrqiXnKokcHrmFCNovj9FEk8pKvWMfHBzUp0+fGmBD5haLResJODs768lwJl25Pcbe3l5zroeHh22kNuO6u/u6FU8GoRIx+8ilURdYSnyyGsYGJA2vqt9nkJUNiRe5Tn1NUKSqfkPV8TPXNqxq+ln2YwK56A+qvQpM9oRkVTWvk5OS9NNr71Cgr58rHTrdHg4Rca/e2XZttgSgqmpVHfCXPTuz2ayOj4+bbkpCJGCLxaKX2JNz/cjJ1PF+l8tlnZ2d9SoBV1dXPeAx/QC6HN9DnobDGfiKBF2GwRwgmA8aJo8pW/Q46as+R7clgIJZx076XDJqgMQSZfrMV7pulXXX7rrMB8gEu6qbTjoej1sV4ezsrOm7+CFpg6PRqM7PzxsIN6wkZbCc++fR3fX6dULt6elpC3iHoNZ2ffvSXsTHZcFBZb+qmj/lo4ChVdX0+/r6ug0Vo4fs788//1yXl5ctcSO/dF48jCUAfOXL2HoJGxCWn0vQMhkpdJpeaRVLP8JfkCm6iPZOj8g3ffAs6J/CUsbO7Eb2DvqduJVfyvaqLKI4NvZaVb99K3VXIpwUWp9Pu5OxRFU3odr5HJ9+VnXJpXxjOp02wNqgsqxAD4HjP+raqKIITcz9nAjbet31TkBEqroS893dXd3e3raAT6OnpIlyoCnOZrPmHDPLT4qVkryg0ktwbsJX9SrA6AGcCiXLBvQMGPNcye8eoj/QScdCHZEAJuU0jfmQ0pJ9Ef6gMhC0p6enmk6n7dpRkyC8rmFYWaX8zpXVQgJMATg2gXvSBpxLYOmY0CvJI4MoAMqpWYySjd69j+3afHlfqnt3d3eNjkrOGd/JZNIcIV2DZELn37x50ypxjOXp6WmjiqgQn5ycNOScwyF3ua/Y0dFRA2oSZTPBDShDxgS4WV2k35JgoFSik4nSQ3clQBwzp1jV0XWq+kgvGol796wkv1kd8D3Hyc8nqpmBNdtoeqsqkGDYvSXqnAktXU0HJIgYIqUvLy+td5zdvbm5aQl5VjoTIU1K73Zttvb29to+v54teWGHAQdHR0ctafn48eNvkG39iRIN7y6Hw9HFTJYwclTXs9qRcpcIvN+rQgB9+Fj6lMAnn5RJYcpxVgroCMYCO5B7uAnG9vb2arFY9Hps+ae8jwSxs0Lq+OPxuMd+yncknmBf+MkEz1TcnTermN5tnj+TAe8JHQ7oxQ46jrkAQLKdnZ02CM5ERu8xg+Dt2mzxM1dXV70Cgj7+k5OTFn9lCxDwQgzk9+zreDxuDJzT09MGOAJL6cx6ve7tRegdS47QGskUMKmqmr8S++U+3u4j/QwgUjJGRyWGhhX6PNuRW+hUdT1+mcglYCTxFp/yz5632DKBo6y85+fZHOeQGLouzytjb8d1HOdKllwCYOKIjJMxIYB6nonY3PNgCzJJ/Z62r9nIkuDEqwCijUmUkhZ2fX3dBOLp6akmk0l9+PChJRgQFhUNxyFgBm54gbmJJw61Mr+Ayb5G2Q+nupcc6hz4kQ4gewb8Ox2Pz/kuIXVtSdOUFCUqxclVVS/IzMCP8A+5zCnMAu2jo6O6uLhoBgbNIA2I70JTXXs6ztls1hRkSF27vb1t1+rZZC+VabSarvWjqk5ITFB2KbSfeza5bcZ2ffsid5L3NLKQyTdv3tTHjx9bQoayBqXnSNIRJGVjPB7Xzc1NO19Vh8ZldVEAJPjNa/FH1YRcC5KS8y+QdV1shASL4c8Nq4csBnp+d3fXKOKOzy6ko1WBB2RkhdT/ORb2aZhoshe+x2FVdVuT0HWJY6Kefu7dub9Eh1OvOeQEtDw/n8n7FognsyA/A1jwuy31dPP1/Pxc79+/b7Rr1f6s4gLQvnz50t79/v5+CxAFqeTJ38lwWa1WrVovQEL3T9pV0qOSVeP3w0oV2arq+hkFQZlg8SN0wbGTPYOultXGHHCRVXGyCExR+UuKG/+VoEcmppIvvX4vLy91fX3d613MINjedcP+RbYxt/RI30yvE1AG3tirOBN9wSh2je/x04Llq6urBt6cnp62pGHIStquzdanT59akgWIo5varnLLp0zQxWlagoC1OcVYkeLs7Kyur68bhXk+n9fNzU0tl8v68uVLk5fHx8e2tZg4r6rzJ2JUNkB1O2UXnZnPomP6pFXArq6uajabtfj58fGxUZ0TWB0y0NxbbtMjYRWnZ3yfVT86zm7wY3lvzsmWSWJz6vLLy0sPvHF9vitPyC3BMoF0vmQquGbXmuwFdPKHh4f2jNiI0WjUfu8ZZJXzj742op6Ox+NWwWLgTFS8vb2ty8vL9jA5K+PeUZ0S7TAN6vHxsVUTn56eGtImkISqMZwCQQkJATg7O+slUlDUTL4eHh7q+Pi4l6RwloQWmlnVlaH1RJ2dnbUkFyo5bBBmtLPU7vn5OaXOnshETCRm+XOJ3xD1qOrvj5XVQt9z/+m4CDaFqKqWMEC+KBXlm8/ndXx8XDc3N62aJCE2iljyz/ipLp6dnTWnP5vNGrKWx9+uzdbBwUFrsK/qePbZH7C/v19nZ2dtKMWQGpHBXdJc9DipCo/H4/r55597m7dD4eny/v5+03O0F7pHdwRM6DAMK0edVK6ksnAwhudIhnyWfCcg5Tupgxxd0sqSnp7UoLQnbJHvVHX0OM8+K305jfjNmzcNoEF/SwArGQ6OO0Rd3VPSYnJ4Eb1MnQfgCGaz79L7+vz5czuPZ8SWbtdmSyKUFWnvYL1eN6rharXqTRM9PT1t/kSSAYjTeqEHdXd3tw0SG41GdXJy0us7p6s5qCNpWgImSYrJpxhEZDZBTnaBLldVDxyS3GW/YVWna5JS1yhwBdhkQJ7gD4C5qnp+PhlAAk/n4Lf5nKwCCSjdr/YW/hJImtcjcJQYsBF5XtubsFtAgJOTk3Ys1apMqD0r7A122LUCb7KKsl2brclk0uzt8fFxAxDEtcD1y8vLnuyjlWsLen7uBh2ZEprAPF9cVT2K+XK5bLLAB+3u7tZisajxeFxv375tMRO5BPj6jji3qlos53e+g70neQTUVFWj0lZV+ztp0fwiO5TX694URpKB4/sJjKbfo/e+k2wIn0nAVvLu2LajSYbBEHyt6u9J7t+O+TXqPCDHvcsBfDdpuDlp3rsDLOhN/6OvjSuKFIeTSqHM8rYAhsJ5iZIY2bUHyKBTxmxIR3vjUDkJfP1Pnz71XnZm7YQuEXaJLkXliFJgq35b3kbZIazZI0XgIbCQjaTlMDKON+xbSlRQsAkNUk3gHPw/KaoC7KxaaoBmwNxfvpOhY3e9qpaJ2qpeJl1td/e1R1HF13sRiDAkqpNV1ZyewOV7QVr+6EvlAJIJKRf8cQRZXRiNRnV9fd0Qy0RTySSQiIOy99fOzk6dnJy0SqIKHx2SiFVVz8GwDZKR3H4C4k9OHYOMD0EViKwF4ctKIQSSA3POpIqms8igNOk0bMnQYdFrNiYdExtgeT5og/lZNpSDY5s+fvzY648eVkrTjmRwnAlJ3mNSazhidsOQI89wOp3W/v5+b1rfdn37Im/8qMqV/0O9JR7sOh+DQpzDEwRDOWkUsCDATN+ULBdLIlXVBZvOnYAiECKpovk950pmSvoH+ijhUinMze6r+nqY/sc9ZlWjqtozq+pXH9xnBp+O7/7pJ4BZsnh6etqSs6SGYzTktSaDQoKYrS0qgY7tGVe9DqghB6PR6+yE6XTaa6vZ2dlpFSesLNVl72q7Nl/ikfSd7DCZlhilzff/1WrVEjO6k3ZaC8ZoNOoNOhLnStiSkSamIv9VXTEhK/fkDFCE+ioWc32uUWKmUq4SJs5EXc2kLLeZoVd8E3+eCam2M7pT1fUcOlaCTgm6kv88t3v0LtImKUoAkSzPShye7J5MYvOP2HrITEq2HvvLtrLJ8haDuzB/zs/Pvws93ShRFLR5AaZxVnUN+dkXxyBLtqDdHqYgk3HPwTT2kZJwCV4IDSESzApQCS0hJFRenuyeU04KGaOgSpGVgaz8oaYw4JnU+ZOVzao+3c71SN78yWAuE0lUh6TBJq2mqt+o69oF99CmDFqHdBUToyBMVd20VPeZgbLrcB+SUkmFIMd3DcSgyFA2wfq2WvH7LInQaDSqt2/f9hBnyCjH8ObNm1axFxBJ8G9ubtp7pUt0Noff/PDDD3V/f1+z2ay9Y3LHEeUQKYuNyP0Rk32AEkaXknaT1XGODLvBpuRJOaErSWnZ2dnpgVHQQAFp2gUoqEWuk5qSSabnkDSkqi6gzUQ3gZ4MYLPCvl6/7rVFr7xDeu46VXqch53l3CTTHLpzCdj39/fbYIOqqouLi952SN8DEvpHX94HZJk9JfdsokBUFQoVjQ0FnJDfrIanLSWnWbmu6mRQQAi05dvYe73n/CqfQK75J/7YPn9o0RJJgWFWDU34BPTyd6p+gk/yiK7tOpxz6NNQ7Ol6sg8ymWYTj46O2s/YuKpui55Mdj9//lyfP39udjD7uTBsqqqN36ezVdX6Tauq9+wwIpyHDRY/iF3YchVii85ve4h/n5W0UkB3VsKvrq5aXKOClDZXDKXCP3yPR0dHdXBwUNfX171z8YlDNl1V1x+ZhQP+MGnnVd0cifQ/fEz2wwKLgB5ZpHh+fq7pdNqT/WS5DOnsw+IJGyCRzbYr9+NndDjbQFA76UcWgtxHVddnWNWPixOMWa9f+z2TLbVYLJofZf/8znVIUqu6mFzbClnwTPhT7/7nn39u7yLf+feyNorGCSXetIAw+bcEUHKXlBOUT4goB5cTMpM7/Ze//KVl5D///HOjmu7s7NR0Om3l/slkUpeXl1XVVTA4JJl+9kxkNWAYVHKyHEQGt1DcRCJcv8Q4g8BUFufKAJTxcd5hwGmlknhm/vaH4UoUJdHg7NVI40DJkqYA1ZZwc1wCaTQnQ4f0mJyenjbnaCIrFFySv7u72/pSsrq6Xb/PSrnI/+uJUuEHPkjQJFx+//bt215ln0PLbViSmsZpJCJ+cHDQ6FCMvT7ipHWkHcgBSfSFrkJe2Q5y8/T01AJpx06HkT0JDHnSUjNoFmxmYOmYWVms6tBjv+O4yTs9reqP2nYO9gNKnKi0pD0BrkRrkzWgip9Bu3+7rqwGecaui/1KdDoTedvdbHsUN18CPH5ksVj06MF0jFzk4BNyoPInCOFTyYLEs+pVPs7Ozpq+SKT8Lkf1V3VDaj5+/NiGTTk2P8KXPjw81JcvX6rq1W/c3d3Vzc1NCy7pK93m2z9//twSw729vdYy8vnz58YwGQItfFnqSFYV+CU+K8EQwFhVfwJkAttV1YbEpd6wBfafffv2bWuZSNDaM8gqonv3vtgGFZ9kUYlb2Ej9pa5Hop8MrfTV3vF2bb4+f/7cSwIyzs3iA58macmqExnWZsC3KXxcXV21vcGTXo0R9vT0VJ8/f24xlrhNr2GyPyRqrtnk07TnfLUY1v7GvoM6+fT01KbxSpDpCBnNCemOyzfyu2Ta35n00bdkz2SvYM4GYcfojV5o78bvPW9JaeYhvpfJrOcsYWXbholpArlDmjt6sdjHs9zZ2akPHz60uCIBvozP/8hro0SRMHnpkD7BogRCw7ZAU3CYDsxLy2ohdBP/1/YSp6endXFxUS8vLy0rF8gY2EFgoQ0QBNc8Go1aT1ZWGLMamAh9Kn8GnpSSwHAq0N0MVLPaZqWDk+Bx+Bnkc3QZtCbiIZEdBrMEMysAmUwK3FFVvoZyQFfRom5vb3v0i7yGyWTS+jM0Rt/c3PS4+ehP3v27d+/aNZueua0o/j4LAABJ41gYcw4un3dOSZQIejcCSZ+RZGYljZ5DUdFlGFjXg/qa1M8M/Pb29ho9TUVFpSMr9TYrT7oNoGgYTGUFkvxCG62kBNG5BHWqqul4VjXZBD+fTqeNnsbmsE1ZNcxqfya3VR1A5dwCiwxwqzpqelb3/e0ak/qXY97dM8raarVqtHCJh+12Hh4eGtUt7dh2fduSfGeiJ9hMe6+XlD5Z2W+evsvG7ChTfC2ftFqtWnUjqaP+bygKPwEB5wf4axRS7JPT09OWXLLvCfYk86fqVW5R5/Ic6LD6EiVTwOacPJw+cTqdNptB56u6/qWskAoA7+/vewwayTNdHPrgpMwJttkdAeJqtWpshq9VTzIRZxu8L/tqVnUVCX1h9/f39eXLl/r06VM7VlL/kqmU9mG7vn2xy4eHh62dgj3lF798+dLTCf51uVy2BC9tbFVHTzatdgjeqhiqUJ6enjbfQm7Rkcn5fD5v563qWF7YCeLaBPmSUplJpFiUTLsOAJAYP6v9Gd8CLJPeaZ9TNojdyfhYUqZ4klVTiRy/Pp/P272yGap+4huJecbQ9CptbOr5sGCRLWeAH9OVM1av6rMF+fkczCX3GBZy/shro2jcAzs+Pq6jo6P6+eefW/VIRev5+bntk7hardr+aCkYJgUlqlrVNZIKKr0cSSN6mbHYjmHIhsmjgr6kjhDUrDImIlnV30fFHw4nA69UCEqVxp5wZ7BX1d9bLUvWWdavqp7gDlFTvxdAJgKi0ipJpnTDZt5EXf1f8JwIiYBhvV7X1dVVe2647owFpAnaSlElgcYvZ5D+8vLS+jJ++eWXTcRyu2IZHgScOTw8rE+fPrUgIqvdnA+wBuiRegS0YbzpKCO7Xq/r7OysBXyODWGEpmkyz8QQMkl2JU90XLBLfiUseq5QKSVjdJKTrOoo8VVdZQQlheNOR0r3Eu10H+kwM3FiVwQYQJPsjxbUsT+z2azRSBOcch1ZdeJc3cPd3V0PjMvpi1nB9PfXgkjfZZuAfihzloAX6r1dmy3vmixKLhLZ9u5Xq9e9Z/WrVlULoMg5XaiqZseXy2VLLMimalVWstPnkAc/Oz09bclKsgbIsp/v7PR7uPyML0xwBSIviHYP7o8NyUqk8/DB5DMH+/g9HRGcofGmjeGXhhWOZAQBdpwjAZZsSRF3JDNCPON5erZ8Mh2SOOtNc/7pdNoqncvlsm1no7dRUqwyfXV11Z41cG27Nluqs3wQGWAX+VXyJy71f9+XLGQFm+yo1kku6GhWwiRxfr5er9swSX6YjlZ1enJwcNCAed+perXtf/vb3+ru7q7+/d//vaq6+BP4Iz5XtBHj8g85SG4IhmarmGOlLokpgceS3GT9ZPKY7BvgS9JJgUDJSBKrpO3gZzPutx0VPWRbMuZmM8hAJpbiXitj9Pv7+/rrX//aS3qvrq7a/uHfw9ooUZxOpy0IyglJenL0Nu3v77eyOsFTdmXUqqq+fPlS8/m8vnz50h4+alSWx5NnvVgseuhfVTUqSDbWQ/s4KZsRp9MlVKgpOVQFapL9RQTBIJkUHAbdZzmhLJdXdYFCHntYoRxWCxLtSNoqGkQ6OL1miSZz/jkVTZ/LcrlsAfzV1VVzaALinZ3XiXvQL0ED40e5R6NRzefzen5+bsNN3L/3MZ1OW3JivPnh4WGblrtdmy9ADrp31WsAsVwum8wCTejbeDxudDDfEwAJthaLRUvUIPCMeNIVq7rEbbFYNOe6Xq+brld10x+rqk0JSwAjB+Rw0hzBkGICQWWYgRuCbOcni1VdHwYUlVwvFouaz+et4pC0aXSx4XjtIT2OvUtb4TrZzezThigbCObYno3gOZ8ZUC5R0qyapLNUOfnaUKEhPQ6VX7Bv38sMiLfr2xd98e5yOxiVrxzKopevqqsikzd9Nmy4RIJ8qQhLaFIeXYvAjO5V9f1Ogoxor45twIaKKGARaFLVVdP4QEHn3t7rfpL//u//3qt6uw86NqxaWOwE26QCy4fzhZ4rv5kALf/NziWQnIGqwJfPzAro83O312IG7dfX163FYrlcth5EgKnKjQBaLJW2A9CKnXN3d9c+5528efOmbcS+pZ7+PkurRVW3BUzK4fHxcT09PdXt7W0v/vv8+XNrAaCXko3sER9W1JLJhjYqrqIDgNDx+HVrKv2DVdXmAajcYXqROUDEer2ut2/fNmokXyXhkyhhhok7E9xJAKeqevRVxwD0zmazdr/JorDoX1JPHc95+GT+lQ5+LYavqlaUSIow/ZLc5/kARdnnSf8tdiOZeuJ6vjNZGy8vL401B8g7Pz9vIN/3sDZKFE9OTprwzufzttchiiHKkvHNLy8v9euvv7ZpebJuKKrqxdnZWUMeE1FVdeA8JIgmb1Z1gyUWi0WbpLm7u1vn5+dVVT26HLQjS91ZaeEAkm6TqCkaZiIOyZVOxCib2RPtz2plolVp5An/MPDO40g8E22BRCUCmghWVedgknfuGm2wnsn8ev06Vdb3rq+ve1RiTs1noWRV3WhiCaPBRaqzlFOiv12bL++O/H1t2IWqGl3T68tAChqvr69bYnR6etqjddLV5+fnRk2GzD8+PrYeG4Gr6XwnJye9ZGw6nfboNlkBh45Cal0zp5VbaaDBcZr+VhVIJJLsJ+ov8creIjYj6Wc+L6Gjv1kVROljbzhD50xZF1QMWQ5JMaM/OfnO4AwTXv/2t7/17isTxkRm6S3KDpuXI8WTzu6ck8mkt//kdn3bkng8PDzUzc1NC0RNsr66umr2e7V63ULK1hhpNx8fH9tkwwTunCMTCkmZRBT449+uSWIoeBX4ScLYiqSF6TnMYSwQfPpEhsQKQNeDg4P605/+1PS4qnoyW9VRvrPKwn+joQlk87mRd8nW4+PrfnD6LgFE7FVVtWmPdCL9rWfimnwuB7IBpEejUV1cXLQKX/Y75sRbQ1GwetyHZy/JoMsS7vl83mQoAaJhHLFd37ZUoZMaXvXqI66ururl5aXFpYDQxWJR5+fnTc5zlgXZwZaRTAIg+d7Hx8feNFXnB46iPqoqiq8AR/yduK6q2jZkfpZVOQmkKrv78fusLGacmSCKhCzthBicb+NLUWfZnKquhcz959AgK2mykjHH9b5y3kcy7xIQShaNQgXWxnQ6rbu7u/rf//t/t+Ohi6bfdr2uPXMFzzATcNcvBvpe1sbU0+T0MorQj8PDw7ZfjB6ii4uLphAMJeQcGqYS6SVKHFBUs/pxcHBQs9msBasmtXE2WeWDpv7yyy8NaRgmXYKyqmoIiGSGE8oKICdJkKGziYgkYkSh0rk4XwZxWUWoqp7AZ7KZCC+FNu2Rc1RthLaqekgaHAfl0Dk4KgjMzs5rDyjDVPVKy9CPRnk9dwZJ8M5Yqkzu7e01JOzh4aEl9lmd3q7N1mQyqel02gw63WOsGX0BiwCSztzf37dqnveelIxPnz41xJHeczDk5fPnz01+oN1kHLJnSA5bMBqNev0XSQfjfMkRg+2aVFyqusqloLqqGi1c4pdJGKcwHr/2RLFnbNzd3V0tFotWvXGOrOKprmI7VHX2wDUMg2SVI/cm6EygxXOTDLBVri2rupPJpLE90m4Jeqs6pkMOHhjSezx7QYThB4nabte3L4Hd2dlZA/v4JNMs7+7uensOShxUB3JTerIg8QOwVtVv2Dhv3rypu7u7mk6n9a//+q8NsKVrwJIcQqGaRucg/Oz57u5u/elPf2o+CTCELULf2R490/z7yclJ6zkeTsDOwR3AFnIrSFbFy+AydRLyz6/lgA4shqS1YSXRG/4VgMWnsUs7O689+batyL0vXe/BwUELRNmeh4fXLcVytoHAUjLgfbNl3gvAfjabNb+rjWML5Gy+xuNx/e1vf6tff/21FT2qug3tJXH69iVXwEy0zuzfFXu+efOmPn361PyhOFiyBABQfWSfq6rJ0ps3b+r8/LzFbuItxQu2Yb1+bQsRN/Mx4sqHh4f66aef2r/F38Bh8SkZ51v4azHgzc1Nj7VC9n2W3bC3oOfE7tETP6Pr7sFcg6SPS86y7UpbBuq3+5KYZqKXvdHi1peXlzo7O2sDNIHEybxKBoZEUQwBcAXMDXs/xVTfw9r9//2R/3hlz41KoMQwucWfPn1qDgCFKROkv/zlL3V4eFinp6ctmPWyVA0FngRE0OXf8/m8beAuWHWu0eh1hPHZ2VmNx+N6//59Ow4Bp0zZ50dxM+H8+PFjPT8/1/v379v9JeqY9C/CRLE59Kw+EOCkonH2WdL3GZ+nwIJXx3Xe+Xzeew6Ovbv7OmWUcg2vQQCYFLacPpk9FdPptCaTSQs4XNdwwFDVK8Jmk2HOGD0nA2Rj4pOSsF3fviT8t7e39eHDh1osFi0p86w5pKwO0m20mqurq96eXxzb5eVlo5WgIzPmtlagO97zYrHoOTV/XBNQQeWELuRwCXpJHocIH8QS+FBVLag1tZNeVnX0Oj0kgoCsoCUlU7AIHEE3c65MysgyOyVYZQM8h/ysikJOsWMH2T4VA99V0T09PW1BblaF/D5/lrR279Tzda+etV5kdKjtZMXNFzkzpM07EZRINIAGpn9q9/j48WO9f/++VcpUmL173314eGisGsnWzc1Nmy8AFEnaVPobFTv7pWIdZOWOf8n+Xjrp/lCx+CYJFxlLOqY/b968aZQw+gFIBiwl2JOAVn6HrLJhGZSyG3RDoiYuEIfozfUeHEuQm0MuAE4JmLqOHKqXQ7087xyYIy5Zr9f166+/1snJSbtO8xmyhWZvb6+16WzX5kubQdLu6dPPP//cYra9vb2mH+z24+NjvX//vlfJYsPZz8vLyyZrt7e3rVooQTTEZjqdtgnI/JvKvkQGwJ5+VsWyqtvHsarrQ2ZvUMAfHh7q+vq6/vSnP/Uq2Rg9dKyqG7bmObmvl5eXxiICigI76NpisfgNGy2ZPpLDTOSwE6q63lCJpM9VVfsZG5F2ii2VA1j8+PPzcwORPF/PjI34WvwAVPW8PJ/ValWnp6dNV32Pffwe1kYVRYLqAX358qW9KGgB40WQGWEP+vz8vC4vL5sSPj09teE3EhNGUKAj2IESnp+f183NTY86R1A4A2gOYSIoElDKohIHcZX4QdQnk0kdHx/3Jk6pRKDXmBKqJytR0SGllaNI+p/fCwST8upvjiz3fPG8BBecYD67qr7DtCi55Dh7MTg0QY1A0vmcI4fUQG0k/qjIVdUSkru7uzo7O2tDTDy71ep16uIWDd18Maqnp6dtwhq0zATF9XrdkEUBEf2BcJ+fnzd5GI/Hvb4FPRA5/S8rYlVVV1dXLThMhgGZRH05OTlplK+kfSZgU1U955VDlIAx6FhZcddvl/enRyt7jJOawsEIOFer18Ezy+XyN3vM3d/f183NTW8i43g8btQYzyvvw72nTqPICTpVPDw/dpTt0ITv2Glfn5+f2+RVzzvpaxkwC3arqjnopCZz8J7zEMjarv/84l+yv2U8Hjf/l5S3HHhR9WrH379/37aukFhJTthssgXsM7kYNY0fqqpWKQAisQdZaRZAobe9efOm9QE7DtkjKwJWoCbZzb75p6enJqfZujGbzZo/4hNPT09b1TxnBIgj3Dc6Gdmle567exIwv7y8tP0Zs6KXFZNkL1xfXzedFSPoCc+J0c5Pf9mg5+fnRhuXDHhuAmxgL9kQHK9Wq/bv/JkeLiDadm225vN5nZyctGSnqpu+/eOPP7Z38/Dw0EBOQ0pUGjO5Wa/XPX1hRzOpqeq2fwK80ulstTAwBjhoAig6LFlLunRSZvkesst/XFxctH5ocpXUTa0c9JYvpM8JXoqzxdrsTfYKklVsCMOZPFssg6o+2y1BHs8yq7WOn4mbIXN5T3IJANvx8XEDRpOWzxcDrpISLNYR+6Q9FCOh4otfvhcd3Xh7DA/x9PS0DSFh1D9+/NiMHISE8/r5559b8OWBEa69vdd92yCWnGOWqhMdv7+/rw8fPrThGqigDD2DmuikY6ZyuKeqrn8wqx2ULiseieigp1Z19CHXkXSBpMq5d4FXcqCHiRKloMCfP3/u9ZNUdRtmX15etmpjIiCr1aol82hM3lkOVGCs9vb26tdff20KkWgIqgXjh5I3NBgSQk4ceo2anM89KW7basXmy7PmLCR6dNLelpKGl5fXBnoyLqGXCNEFei2BSUMvCaNrgqPVqtunDYLOqHIM6JLoOHSXPNLdm5ubpiuSzKxMX1xcNPSV7Etuk4qdKLzPJYKZ382+xdPT0zo7O6uLi4s6Pz+v3d3XzcXPzs56W354dujVVV2wYLnm4RK4sk1slkEhbKU99ehcUoqTXpdJMV2WhLuOfKaCTQF6JrI//fTTV7fS2a7/3GIXM9DnF+xJ+9NPP/WCI1RUMvvjjz/WcrlsOiuxycBSsnJzc1OTyaRRLdO/kSs+ib+rqsZKoO+qCoIzfokfhPznuPrRaFRv375tAZj94OiiINYfCTQ5k1jSTb4i+7jEI6kHvuMYqOe//vpr27cSNfbg4KD1zrM9Q2o4OhsAiC7y95I198DHJesI08m7FqsYQgK8YRvZ1Xfv3vUYNxLk5+fXydO3t7ftfgyL267NluF9WQU+Pz/v9YoCwvlTk8IlIKrAVdVoot6d+Iqtz6QKdVyFkhyqHvLpBsaY2q3gkT357D3fkFuj7e7uNt0Vf+bE1owhnUP87fhiYTrHL9mGju/VkoFZo/VEwmyrEDGGe9jd3e3tl17VVUWruqFAaQMyEURBtzLvSFYAfwtEokv8OTaRZ5XMIN9Le+F4rlfFVSzzPayNEsUsO0Ocl8tlM1RV1dBEqIjkhgFNTj4BeHh46G2ivbPzus2DIHY0GrX+QUEqfv/p6Wm9ffu2qrp+QUM0ErlUzlcCz+ofIU76aVX1+M54yJwA52U4B0RBMOdaszQt6MsqTFUn8J5T0nAIqH5OzikppBnMc7qqIFUdzS6vzXNKKh1nlJRCDo3SpCN6enpqSJp+CQYvq5RJb3R+iQIl/F4U6I++GHDVPvLy8vLSaC4CRhQn2z4APgAh9/f3NZvN6tOnT83xoLGiznBaZDenDqryebd3d3ct+URTdX3oc8/Pz/U//+f/7NG1Td8U5FV1gycSfNE/rEKRlfZEOpOCknTyrLrReT0hVdX7PbCGvlZ1A608f8E9R5t0O+dMZ8xpJXXVzwUgGQhIul0D5+/7nB2why3J4SSCG/opqfRMvYMPHz70qDfb9W0Luj6cGHp+ft6Sl/fv3/eqTEdHR02Xq7qqheqTRDJ7esheJpGC1+vr6/bOBXfZloAJMhyssbOzU1++fKkvX740Hyr41JeYjJScoHx8fNwbZCUGsDB0+Plk2KxWq7YlCNvj3/wWkDkDufV6XZ8/f27A5du3b1u7Sg6goTsCS7ZCryC668HBQb19+7bpoR5KMUbazyGzAPhF/3J4F3qf5IDe86cJ7GXVqKpa5WM6nbZEfrs2W0PaKLYFfTW9m52v6kAL+s3W6jG9urqq/f39+vz5c28IJB1XqbY9SsZ2mAB8lhiyqhrzR/zNRvCVYl7yrLopXjw6OmqDVugbW0PuVNacE0iRxYr0a3TRAmh5RhmnPz4+tueWFX2VyIuLixbP0y2/83yS9kt/xRVo+BhUWSDJe6yqFi8/PT3VxcVFK4wpWql0spH5DMgMXy82Fv/wq98L2LpRojibzVqgorfI6P2rq6tGMfOSCOve3l5dXl72OPnT6bT1QCQN8+npqf7yl7+0AIowCdayasjwVlWrGkBRCCCjPB53e0l5uRyKknn2ijgORaqqFmCjuEGVBHJQQ0olAYTCUqSq6v2b4PszpJ8KQCl2UpRQdQTAqngMm2eSKFEmiN5JVjePj49bgu9cDAkHyflxrp6BZ5n0Bmi1ceAC7Pv7+zo9PW1I2nZtvgSC9CMTHu+ac0vKMBCI8fPOj46O6t27d03vBXKr1apVARnt7AsQxEIAs8+HDKoASKoET3ojyQpnenx83AY3oU6ip6g+cM5AHbI37JOiDwIr95xJHwfBSZjcSo8EhuwGBkBVtWtIXUzgB4qcNsB1AXTQ8Dk/z5azxJjIKpB+rdx7TSBbVQ1881w9B5R2Ok5HE0zbrs1X+inJGkCOvxmNRm24C3uc1Cmy5B2R72yLGL7nBHkFqEdHR60ilZUHiVlWKVLG2AkUstwbTGBGD1QS7aMqYeVj6YdWBj6CTUpfnEwaPt39JNVsNBrVL7/8Ul++fGkJeLZTeHZ0jY9NFkAmzfQUOJYzFOiMye4S52Q1SQSBzJ63a2Yf2BiVG6A6mwEEYnMkCBJWbJLt2mwBXSQDEobz8/NarVZ1cXFRk8mkfvrpp1oul/Xly5cmsypggAODn4D8qou2hJMkqeQZJFfV9a2ThaxWsR0A96quIMA/JhAC/MAcAagCJviA/D89z22ZnCdpnFXVdBm4mCB1VTUdUjX0+ZeXl7YVWw7Ccb+p+/Q/p4Qncy/jF3F09jD6PbCGLWV/PJ+MA9iOfJc+PyzUSJa9HwAucDz35fyjr417FL2cl5eXhpJAKGT20DnZNPTQRDTTwdAu9PkIWv/85z/3Gn4lOhIqk+EIUpbxU7glddmjoM+QUAuA8zuOxRHZPJuyEESI/vPz696B5+fnTXmyQpioBSdhcYSEzvEziEVtSTTDOfIZJHdb0MjQSIQFvhSR8iVqVvVKv4A8Q8UYDDQp6BDkWPU19/QSAFHopPfZ13Lb+/T7raTAmOrn/SWyl4Haer1u1QH0xXRW5IrRJdPQ9qpuBHju2cS4C9BypH/K8K+//tqM8/7+fr1//76qOtaC63VtAs37+/s6Pj7uJVxsTQZZAkGyC7Gv6uhhWd0HfqzXr4NCLi4umqwCpsjv+fl5C7IBNfoIPVMVGUE8fcwkr6rb3zCrqUnTyf6zIY3UvWbP8M3NTUtOq6pNXRS4kg9IZ1JlAV4Cd/K0XZst+nh7e9sDKtlwPX10ABWRXKAp5p7Gw2m09kSVpLy8vE7k/eWXX3r9sSqKVV2vIl3d39+vi4uLNiAD2IBidnJy0sCfh4eH+vTpU0tkhhWRYaWMrfB7iZDYgc++u7ur6+vr+vz5cwMmk9FCh8bj1wmgAmTBn39nAJjJWVYA6InjJ/uGz0a/YyMke4Duqqq3b982e+KdqSq6NzRW5wECPD8/N1uoiiwZBwDRcc9wf7/bs/rLly+957Nd37awzcgwMFKlaLlc1sHBQf3444+9+PPp6amBKyqGYjCxreQw2Vl8VVVne4Gm9MP3xKEq9FV9cAKgl4OnkpaNAYjt5tjsTFU1n01Hs8KKjWOifg4j5GsBR/Sl6jXhUpnXNvLw8LrtFlsneROT2oc043/POYs09JA/dF9Z6BC7ZxyUFNK8N/GDZ5NV2WQeyBWSDQBIz9wCUKeY8j3o6EYReQ6PSJREQCFgyp4GgdNPP/3UGoN/+umnZlz39vbq7OysGeyknrx7965VHvwR5EDPkkKnFyLL5YIfSEcqUyIP/mQ1EIrnvNlkrnFWFQMiIrjz80wAqzqetGQpKXCJdCZy7HkkWsKxZBKYQezOTsd9p2yOoTLK6bknDpcyK51Pp9O6vb3tVZIhY5DdRGevr69rOp02bjaF5fwfHh7a3plJPdiu32els3h5eanJZNJkINHFqmrJ0Nu3b5txIyfAgclk0pIc1XXjrhOUSJqc3kTv+/HxsUf5RmGVgCY123XRhTSsglVJYw6Nqape8Oa8eTyOgM7QG7LouXEgiYoK6vJa6SzQBOAylH1JdO4htV6v6+bmpgeWOJZA1c/T8fk7r5l+Vb0OElJFNqgj0V+oteTZe/fcBPSZ8GvM367NlqFIhkekXZYADCnTfN7V1VUbiqGSBG03ufDh4aGOjo5qOp02Gy2hM4hjvV436pwg9+TkpFFH6dd6/Tr9V+9kUklzyqrWDteffmFI6xwOlcjevdFo1AbDSOhGo1FLhNgvfous5obcfL5+4sViUdfX13V7e9sqRC8vL60Sww9Op9M2RIce393dNcAng96qzi4AbvVnZiUkWQpiCFUVcY1YRdWoqlrMhKpoax/HH49fh+pIXICzWfXZrm9fGGx0j57YmobOSd4kEvrv+Aj2mU5jWR0dHdVkMmnvEmPFZPmdnZ3WclVVjbkC/BCjYctJcPjEnHCfbQ4So6RFVnXAr1iyqlqSQyYxeejx09NTi++xw+bzeX38+LGBXY6vjaWqmwCMOcE/5fW5DgmWZ5A6L0kDEPHFVdWLk9OWZcHCtdD3pLWmDayq3zCwcqina3Rs09ITAJYkqlR/D2sjS3J+ft4eNGcAhRSMJC/aZ0ejUW9a1J/+9Kdehp/VJdPSHh8fa7lc1sXFRR0eHtZsNus5Txt1r9frxs9XsRD0qBBKmqqqJaO2zEgeusAJpVVA6RjDfiFOLFFSCfAwuE1ONeUTHPi5c1X1t8ao6miyHG+Wvj0ryXPSaSBFDIoA0fmXy2WjDKvy6pmxeXs249reQsCuMuG8AAKIkAEqOzs7LTngiKuqne97QFm+h5W0RlvIQLU4r+Vy2aairlZds3X2CmTP6l/+8pf685//3BwVWd3f36/b29u6uLiom5ubFoTu7u62QJhBFXRWVf3yyy/1T//0Tz3HtFq9The1RUZV9dBXgRkDLpnzfXosEK2qXt8zR+v46TA4iwx8HV8gV9Ul3pgVSQtNPa56BdX0M6UeA5skjJIEWxfoOfPM2J6qbjBHVjroMnuxu/s62IoNFIxUVbNhjp1UJjbL9aY9enp6qvPz82ZDt+vblwo8toxKrmef1ewEdZ6fn+v8/LzRqafTaZsEagy76X6J8M9ms3rz5k39y7/8S/3DP/xDk6fsD57NZm3bo6pqjB8+LWlifIg+Q0lPyj0ZRV+F4KNM0iNTVOnnx48f68cff2wBsEq6RM2zU0nMlYklHzsej2symbTKOXujwsCH8ZeTyaQldhJDPjf7jQFcEr3cGkgwmMPhAMboa2xr0uHYo8lk0uzfTz/91HqlAF8JfH+tCrVdm6/JZNJ0yJZg2HDemf2GAWr8q4mnQJWqjkUg4TCIiqzqN87iwZs3b2o6ndbFxUUDDlerVU0mk5aoVHWUSrpUVS2pxegzZM7iq/we8Jjb7PCjmfDwGQClBJXpRFb93FNOE+X7TEFn4zxDvier/1XVG6JHzukDm2nLPu8l2VB5LOfIeGIIvGYfJr8K8NV25trZD0yurCiLDwC3uQ3OH3ltlCj++uuvNZlM6uTkpPUdEFACqepkqlsqWfbSUST0Fy+GQu3t7dW//du/tWqjaqQXw8BT6KTwPDw8tH4iZX1OWKAo6M0KKYFtD+v/HB8VL1ENI3gz8Ur6ayI0yUvOcnhV9YLdrKBInCiNsd9JG/QddCDXIcCuqoY2KtczAioQHJgELiekQi6Pjo5a4JzDMAS0DMfu7m7bd4rzz8TZ+8mNhiWow8rrdn3bOjg4qL/7u79rletsrEbl+PDhQ5MP8sMJ2Qsp93EjV+TZ8fThCRhvb28buPPXv/61/vznP1fVK63t4uKiJaxv375tSQuEUTAGRGBUs6+R/KgI+H3VK4g1nU7r6uqqfvzxxx5dlY6xL2SS4WfYOQt6kNRViD8dlZBnLybbJjDgLPSFJCKafaOcr2clWKUTErrsKQSQeWbJREhq0enpaXuXuZG4INPnX15e2rMEBHlOrvt7QUP/yCsr5dmOAa1er9d1e3vbxt9nQkQXrq+v6+DgoIF5gi/JX1UXQPKl/+W//JcWsA2H30iIVAkEOFWvsgD0yV5fyR9Unh/NqgAbpMrBH1uZ+J2fn9fJyUkL/Or/w96f7UiaLde56PC+77toM7O6tUhiAZJAgY+ia0GXejUC0luId4KohlxcqsqqysyI8L7v233h5zO3PxY3zjnptYlM7X8CharKjHD/m2lmw4YNs6mL1E+6SKuJt4BjgBtx2oM7SRYz6ZEnFvu/ly49RcwA8OCXa8eGAfrY9+vEEyxBUuhjILESH8LvSQpcVzx+PgIBH4t6KZVKaTgcGgbziQQYI1zXLfYu/fOQaz5RAOMUCgV1Oh1Tt0CoUdlGaUOyABGISocYdjwe1e12VavVzC4hSsB+YEbsRpI+ffpkMnAqjdgD/h5ik1iOHY3H48C+Ie57IlK6EKnE/uVyaVVS/AyqHFQBnnQkoeKzXqt7sBGq+Njp09OTttutnRuLXYMbIHfwXdVq1aqxnmDz5I4nln0LCs8fv0t/MfkNPwum4AgVvzc8fuY5+Z5M/NXXsK7S97EhYTRIUKbTqZ2vRNAAqACIcML8nneuVPmQwcGM3N3dWXWDJM1XDPyCsYCxhE2n0ildppDCYvqeIMCdl9UQ0AkOAE7f5+C/X7pokxkk4KWtnq33slF+/3WCyL99f+U/15v5GsDyTEnCkbxgYL6PiefE9UnnIAX7Q7XJsyelUsmkuD4480z4Hnq7+H6eHY4Uto7KY7iuX51OJzCIBcD/8vKidDqtcrlsVUFYR5yY75fBkbbb7QABMRqNbA/Rv0cS0mg0VK1WFY1GbfjRcrm0SiJSEZha9jP+AVCMTAfiKRKJ2MAA+ijokSQgIEfP5XJWCQc0sx9fS8F9L4MHnQQYehKxfwZwASqxP+93sAMvFaeKy/6HvY1Gz+PtsV/pInt9LaX1fSZ+qAbv11dDec4k1fhh3qGfuAkIkC5DCrgO33PifVW4Pn/598RofUl/VmH2BAV+GbvE/0Yi58FPuVxO5XLZ1B6eVGVv4KfxtZ7UgFh83UtPXID0AMhJsu+AZEVyRj80hKTv7fVsOhUxEjCqAwxo8XutVCqZT/PHV8TjcRu4xYCQ4XAo6eJrut2uxRummLfbbRu0B1FKRZZn1ul0Avc9n88DgNuT3X5+gXQmeyBEpXOCj/KKOOyH8oGDsFFPoAOGsUevKOJzIbq+FhD6pS/2LwQ7+3I6nVqCyPscj8dqNBoWhyBRYrGYVYOpsDNcjJjW7XYNQx4OB2u1oroPBqSfWbqo0/DtlUrFkhR6FyORiNkEMwF8TKNX0Q9N8u0GXvLqh2xxXb7nGCnsdDo1QhJs71uaSLK9jBpbnkwm5le4x+PxqHK5rLu7O5vM7GMVe32xWFju4KuxXAP/+JiKCg5lBPgAHxqPx+35ILk9nU7W0iFdjgr02MIXeqTLvATyllwuZwqvL31dVVGcz+dqtVrm7E6nk7Fmvqza6/X08PBg7CUldNgCHiQv1U8i85UQhsMQ1CQFGAESPBjVYrFoL4tmZJjT11VEH3wxXK4JIOy/D2kHGmWCDIEFhsPrxdm4BHWM0DftsrzslOsCzKGV55nznPhvgrykQLCnQsI//J2vqMxmM7sWEkh6yBhMwr8BODjLRCKhyWRiklLPHNFP4yurXA8AATkxI8vDdf06Ho8GQGEZi8Wincfl9xUOmADhwaMk60ujj4iqI8RNPB636gdDGnDQ+AJf6SbocNwFFWUqiP+cJBLbIgHy1XFYP+zG90gSbNjrVNHZ475Xkr35erAMFRAYSRJZbI7r9Yw+iR22QIJI9ZBgTRAluSSBa7fbptrgOeC3DodD4GDneDxuzxwgThUR+RS9LiR8fGcikdB4PFapVLJ7BvDyPhi44SU6X0OQ+5IXDDTxBECFlO10OqlYLNoZoFKwP5VkD/+83+9Vq9VMxYN8DHvFLmnNoELiE0pADKCPSsprQnexWJgaBF+Obyeu0x9FAkWc9UflECely9mhniDm8z0A48854gc7ly4VIFpjSH5zuVxAueNtDkDn20A8NkGGejqdWygA7iQEk8nEhves12sjwziKIxY7HyUCmZ5IJDQcDi2x5x3hU3xrC76N6/bKBobxEUfZOz/99JPevn37/+DO/X/P4r2TfEmyajsVOk9+oujIZrPW10pcgajzrQD8XaPRCLQsEKupZEEC0M6DPbCXfbWKa5AU2NMUEobDoQ2NwY94okOSVc54BhCkxE3+PJlM/tlRNz45Ig5h536/UrWESIIc88mlx/ZcL4kk18izxK9xdiMtaeVy2d6RJz+9Aopixmw2s+fKoCLwDa1lDMHxz4GYv9lsAtV9ih74E/8Ov5Z1VaJYqVQMyAFUcFYAqmg0qmazaYCInhk2NpuG8jXBEuaRxMSPwsYwXksmAWT9ft+C7Ol0soZSZGZcBwmT39S+ipBIJExW4gGf1y0jCUFiS/Dh3gHiUnBsN4Gee3n9swR2D8S8FAWQ7KuZ3K+XybHhqQry3yT0vmeCTe9HjSMnlWQOjyMsYEYA2JFIxCZvcT18pu95IjH3jd8Aea4Tdilc1y2CBCw5QIQ+GvZfKpUykkCSsdK+15hKWb1eV6/XM6YSwEdVkSAFeCW4kJiOx2P7/nw+b3bug5WXu0Go0OdEYEK+w//jd47Ho7F+7Mvj8RiYwMqeJsHzQwpIjLAfniHXyeABnoeXsvEd0qVXGADoVQlMepUufsGfuYadV6vVALlCtTWRSNiQKAaPSMGx39ls1p4H7xVQQ6Lp+1x8pdBXNgAksVjMkoPXVZNwfd4ipsRisUCyQ3KB1NCrPAApnozkfRM/vETb7yfAFFUQ9k2pVLJkDck5LQD5fN7kccjXXsuxfT8zUi1skz1PQkrM9KQuianvxxyPx2bbxAc/L8APuUERgyKBpND7EIho6UIAA/Aga/P5vKkEsE9PnCLfpZ+Ud+TtElsmGfazEZjQDElDTMxmszZN0+MgwPRoNDI7B7NADJBEQgRK0ps3b76a/qcvfflWJ8gE8CnJE7YHwUF1sVar2buWzhUvyFUqg1726Kt37HtP1nmFGnuGGECcBJehImM/lstl2+9UPYlj/sxN4gJqA2IiMZzkzg9zlM77z59L6osBqNXwV35ysr9nMAS264kkn+iRRKJwwC75TIoVYF0+HzuUFFDS8Y6R9g4GA8MYXqGDfeOfkAyDjyCUqOySLxyPR02n0wBZJQVViF/yuko7NB6PdTqd9Msvv1ifkAf9vp/A9+dQRaCJlYcMqGOj5/N5c+xIwWBDAJKANcrDg8FAtVrNkjs/bY1/+0Zb2BxYxePxaJUtX+XjujBsP1WJAALYlS6sBRsXg/CVQjbg6zI6v+d/nmoIv+PZFUkGCAESgAt+hsOLKfWXy2UDgoATyu/SZVgOgHm3Ox86SkAEiFNdWS6X6vV6Bnh8UzQacaSpJPkYPI6yVCoZU8Tnh+u6BSPpSQBJ1gtKhR8QhvSDhncvE2ZP884JTlJwChn2Dtt9Op0CA6ukC2NPX50/OgIwiP2xr9nbnu3HB/hhEz6BJfnyZ5r6QLHdbu3YF0nmF/g7qqv4GJ4Ln5PJZP6sjwu7SafTKpVKARAMOeYHNpGIMfGN4M1n8EyRyGw2GwPXDLwZDAb2/CaTifmw1wmGB8heRSFdhiwQ3BaLhVVMkJ5DBBD8Qxu9bjEpkPfM3iSBAPyUy2VJFzB1PB718eNHY9P9pEwfa9i/EHVeukqS0+/3JV2qm69lp+w7EhnpclyEV89wPI6fcsg+YUhaoVBQNpvVeDy2JMcPRIN4IRn2VU5ITuIOiRAVDj9x0FdZUB3Qx4xklHum+gPRRDz3lQ5isJfL4Qf8NEaeRy6XU7PZVK1WM/v2U2I9QGZBnnlfxjWQNPA7EL3EdiZQoubC9sN1/RoOh9rv9/r06ZP5ZRQnxJrBYGAECuROsVg0UhTbogoMGUG1arfbWQUcaaqvoCP19FV9SBlk5JLMrjz+2+/3Nuncy2E9plyv1+r1ekaOIOP2JC24HgKH+8UWlsulYQM/aRWczXdut1trsSBeU2gC3yJHJX5KMsx9Op1sWjS5go/LHl9nMhnr7Z1MJppOp9psNnp6etJgMLBYiv0i+feyYN4TOJX3eHd3Zz6O5PR1PMRP8jvYM4k4sfdLX1clikxdgwmlNO4HT2QyGTvDS1LgPEMeIOzZcDi0qWw4S4IB/41mmw3uZSuATkANgBVw6fsBfPkZIDoajSRdzk7zja0ARRIbz8CQ+GAcyWTSmKXj8WhnvVGShhElseS7CO6STAoD+GZhWPycB3scbvz6cG+CKAeUT6fTAHAEmAL+WYAQP32KP+fcHqZK5XI5VavVQNUHgygUCibtIwHlOWBYnkFCMx+C0OsXsgsCC7aWy+WsioR9AoZSqVTgbCOqCNJFrkGwIADG43Fz2iT52BwDUCSZX0DW5oMgIMcPB4C4YQ+S4AAG+RmCJo4dIAnzz88zKMv3BXH9MPReZu4BIPcBA0mCCjjAzxBQuEfumWTgcDio3+8HeoKRMvkhUb6aSzAF2GLbVHOoHs7nc5voStUBEoDKBcoHEhT8Cff7pz/9yfyWdOm3himGbAgritcv9gzxEcLA73X2IMCL5PHu7s78PmDKAzqSnNdJ2Ol0ssOeU6mUHRuBHfleXsAg8Qjp+cePH83GsA2koH7QBPEbUggCI5/Pm++ZTCYWX7wsHFJxv99bTOPeALseDIM9vJ168or47HuWttutHYpOfIPQJkmF+CEWkgBADKE6IBmnn0mS2Sf+YjgcWnLpz4EE2GO3/D7EDqAan0xCz9Af+jbpWdvtdmq3218NEP2SF8l+Op1Wr9fT6XTSaDQyGbJXq/lWDRI+1Cyo20gYwL74c7AR8vDXw2ggasCe9DVT4QN3ERewfezeD5zzGBl7p4JGIYf4xd7DhsAOYExiMPYC2YkP4Fp8Ik3iBE7A9lAekBQyrwTSmeutVquWIDMPhSRaumB4CBQSX47tqdVqgdiH/0DpNp1OzR7BB7Ru8J65B4YC4QtphfGDuCC+2A+SrDL8NeDcqxJFXgBlYRILnLOfzEmyQALA5gE48RlIR2HhCUb0Pf3DP/xDQGYGaGQam3R5AQBVDpWXZLIY73i9zhjg+hqw+sExGA1/zwYj+PgEls/k89lwgEOkBXwfi2f4upIIYOfepEvyiMOB9QdweOlPqVQydhqnkMlkAg3C/J0kY08J5NFo1Iyt1+tpMploPp9rNBqZswRI+uSA55VOp9Xtdq3Mj2HC4lLl9Il4uD5/odmnlwkJBGRJKpVSq9WyPU8vUrvdtkpZPp/XZDKxfRuLxVSv1zWZTAL9tlQe/HAowCg9cMi2aOavVCoBGYl0SUywUZI7gKo/jgInzRRXLx33shnsCzvdbDYGzgGt9Dvw2fRhRSIROyMWdpTg5AOJvw9sBTCKr8SuYHjfv39vwy4IdiR60WhUnU7HDlOXgocfz2Yz830872TyfKYp909vIoCD9xyNRjUYDEyqRmI4Go1Ur9dNMvwarEDocC+hjV63SEDwh75PEenXbDYzIoeBJbwv9mokch7wxLlr2+3WEgf8MfsGO4LMY6gVcRibIomNx+M2iIxE5M2bNzbIghjw9PRk5ATEKkQs3+mJEOK7/07pQkD43iRIzGg0quFwqOfnZ5O2AkBfV1ggUzwp4hU4EM88a+YYAJJJDMEsAMpIJGLTEMEWHD9ENZXkEZ+IPPX29tZUBeAIJmTG43G7DuK5pEAC4QcPQeR4nwWwh7gN7fP6hVKuXC7r9vbW5ImQgRCdHEtD0gApwX9TsYMQ5TNIKIlto9HI2nsgXgeDgVXrwVMUVKg4M6jQD8LCNyA1R2HgsS37kSIH9iJdFEheeSbJMLhXjnG9kiwp4hoYWlcsFlUsFgNKpdcKO4garp0qnCc0IXtns5n6/b7ZHTGUz6Ri61syeB4es3tiRpJJRLFhPgNJq1cCorYiwYekisfjgaM/vNKBxBXZ+Je+rkoUZ7OZOdF+v69Go2GOHY0uzpBph6+rjz5gMPCE5MtPFCJo/vDDD9bH4KtWNPvSP+R73NjsHjwSjEnC2DRU8bgmD7B82R7QTFCEtfFyWxJZKTgG2MtHmagKwOTvvYTVa6R9z5Hv+5IuvU5eCusDNyCRIEZg5Hu9nGc8Hhvr5CWHANZEImEJp9/0SBC4Vt/4C8MTj8fVbrftz0ejUSBAelYuXNctKgWHw8F605gsC9ifTCbqdrvabrfq9/taLpe6ublRu92297nfn8/8IgiSMGEHMKDse352NBrZXvByGC+74RgNbBIb3O/31jfp7Yh9720Rm4EIYj9D7gAiuRYCgq8qYFP4k9fqBYgN31cCMUTS7SucVD8Y4AORIskS8JubG5XLZZuahp3ie+hx4fewx9PpZKylV2nAdjIUzKsPGIBVLBYlXQZjoGbwQACf6f1ct9s1f+FlQuH6/IVsi4qTr+xDaCLb8kSldGHu8ev1et3kaUgpqQ74YW4kYvQZEyOwu8FgYK0hXg4biUQCPessiBrG1rN/iWkAO34OcEplDbuRZHZLPPbxyUs4W62W2TmfXSwWrTeTRJhnDGaQLlU+Yg5EKaQLklzuwVf9ib9cG8Qs2ACAS4yVZBJyMA2Db/gZ+j9//vnnwFRwert9tQMbhbjpdDpGwEciEdVqNSOYaOUJ13UL4h2CEjxLrOHPVquVBoOB9Y7T1gDxx97wyREkBclEMpm0/kFJpiDgWDj2Cvvj5uZGx+PRKpz4fvanJ2aIlbPZzIhQihmQh+A7P/HXk/gkScQycORisbDijKRATIbM8USUJ8T4Lt+PyL+j0agR1Z1OJzBMhwphLpezeESc5xmTPDKzhIofiR92LsmIJ/IEf6+QcyiIuHZUEfgp/C4+h3fO59Cux7snaf3S11XaIRrgyfp50bDryCRI/AAj9EABVgGXtVrNSthUC9j80qWUn0qlNJ/P1e/31Ww2LbCyEYrFoo2bp+8O4ESgwfEDwgiqbG4qAJ6N91I3wDcBzTMDBFzpIiEF2GIwbGJkbBi/l5QRHPk8jJBA6hlaLxPyvS1cD84Kp/YaeMMojcfjAOuKk+SZ0RhMUkDCC3OG84QpRh8Pyy3J5E4YNwe0e4mHB63h+vxFY7gnB5rNpjFek8kkIHlEz488A3sol8uWeKRSKfV6PT0+Pqrf71viATOHw4WJRa7m+xBIUACe2BzybOyAQRoQDQzZIFklyaFZn/04m82Uz+d1OJzPhwI8U5HDJvkM9qZ0mU5GtY0EjD2+253PaHp97AD3h6KB50/SRTABPGNT+BEPlnmWBHTYZy9fmUwmllj3ej17VjDMJPW+6oTtI9/hGk6n88Rqn6TDjhNc2Q8krfjEcF23vMSaJIGefa+aQTbsq10QEbxfkjpaOHyCyXsm9gBqSCIhkJLJpDqdTmC6J0QvcZvfpaWBWAmbjryOIyharZbZODaKTQP+6HF8PSgJHwIxwuwC7MardCAnX4M0YhI2QLWj1+vZhFkv/UReR9xcLBb65ZdfdHNzYzbIs6Zyip+lQoOvYLos98TvT6dTJZNJPT09qdVq6d27d1aZIHHwJDL3KsneWa1Ws+eApJz3zply4bpu8a7T6bT6/b6m06nu7u4Ckm6qcSgAGBQ1Go0MC0P4YKtM/280GibJ/F//63/pD3/4g9kRCx9ATJNkZAvkCnYFlkVFhzrP41L8zWq1MiUelU7pMvMC3AxxjwLBFwx89RvyCiUBihxiFgRUJHKeMIq9+RYyWlr4zGg0qlarZT8D9gaz0n/Iz0NIRaNRi+EkpigGsVXiPzZOktfr9VSpVLTf7y3JJGkHN2HzqPFIyJ+enuxYMJJ/2gvASsiRed5f+roqUUwmk5pMJopEIjYRjU0J8AG8MBWKMjyyVMAfAWI0Gqlardrm9AkFL5+KU6PRMGOBHaFCB5iUFKiojUYjCzq8aJ/IwdBKMoYF4Py6XE5AgGngpQN8pYt8jooim9tXOaRLydsbnf9vwCiMFIHOB1//c9JF+omj4/t8EPNyONgXkk4+g2fUbrdthDPJPJPxvLQO0Azg4X6lC4PGddCUT/LCdzJJNlzXLRKVePw87KlUKplTJekbDocql8sGKD1A5f8hIwgYrVZL4/E4IMuYz+d2VlQ8HtdgMLC+CPppUAYwFAlmjuC2Wq2sUi1dquQE41KppOVyadfE3iSJww75jFgsZhIsKnx+wBVADsLqdT+XB4BeEk2SRACHNOGefFWcCgaB7nVQR75HUPGfBWM5m80C1ZhoNKp6vW6V2Hq9HujzAjxQJSEY+0SPJD0ej2s0GgX6KCHPsH/8s+8L81WacH3e4tgL9gighNgzHA7VbDatR4c9T9WL+API2W63fzZZj6o5nyvJJFEkHjDerNvbW1MMAXSRpxKf8Qn0v7LH8DcAwlqtptFoZIQtSSnxkD3KvAMYf+R3p9PJfp/qg3Q5U5R4jHoFm1mtVmYzr5MsCG0mQb6umgI2AfaZTEaPj4/WC4ofmUwm9szxg75i5NVNnswFgG82G93c3JhPenl5MWm/VyV5+bwHl/478RlMcMSfh+u6hVrjcDjPZqjVajaMpV6vG3lOiw5Eo0+maMl4//69Hh4ezL/S8wYx8fDwYCQ/JAD2wV5gEr+v6tGj7qWVJDaQMuAyJKPEEXAAVTD+X1KA+OAeiWsQGMQd307mlS5Uz8HVfC+4G6IDnMpzg7wE+0cil1YxCE+SUiTZvrXk9YAZZgXw2SSc3DNJHKQV1wq+Jx7zXHnvJJ6oQu7u7rRcLgNDcg6H8wBJCihc59dio1cliqPRyBKydrutu7s7q1D0ej2Vy2Xb0I1Gw5Iz+qTy+byBT4ZkwPrBVkYi58EnhULBghb9BQC75XKparVqB6D6xlKYNSoonB3DBuL3YWoOh/PBvARFnIN0GcuLbAVpEGDSN/f65NMH6NdJIwzJ683iJT9eZy4Fz1jE0AB1gF/P4viADLggaHlJ6+FwsHsBwPhei0aj8WeVVza7JPt96XJQeC6Xs7HA9IgVi8VABYX7QSoE6PkaDOhLX5vNRoPBwCpgBAGCz+Fw0N3dnZEN4/FY2+3WjrBhDwyHQ6v8Hg4H61Gg0ghhIJ33K1M4sdtSqRSQm0MezOdz2w+SbAQ2k1AhgGj+p5cZwsmfwYjN4U9Ihr1k28v1CH4QUNvtNnAuIz6IYO1t1wddb4MERmyBBJVr8dPuCEKdTkfb7fmcWK7DS84YVAHhRUXWy9Oxd0kBwoakl0FWPE8GjWGDlUrFvpdAKCkgf+Pz8vm8Ma7hum6l02kbsw47TzUJ5QXvFkJVkvUiAeTor00mk+r3+/ZZ9C55wk+SJWR+uAJxmUmB2WxWw+FQ8XjczteMRCLqdDomySaRms/nJhFjHxJrNpuNqtWqJpOJpAv5g6+hssDzALRiy4BOqogASe7Zt7AA/vAv4ACUSTwXznAuFos2OANfB7kGAQ0A9xNYsS8qDBCvnlyiosn9cN9gGlQF6XTa5P0cL+DJKRJsvpMeNkgbrgd5nAfW4bp+0R4B1uN9kACSvD8/P1siEo/HrdWDZO14POr29tbiKVh0sVgEEinpco4i+wvMSwsJE6nBfdgL1wj2ggTFFjzGw36ZaOwTU6/ooiUIOyBueswJlmTQknRJnKVLbz2ECYkdsYxEi8qsn0bsk0g+F+LHt36QQJOIYR/4vlgspna7rUwmo2q1anaDbJgYzXMl4USthH/kuij6gPe9jLfT6SiRSKjRaFixBGzAwC++/2tYV11lLpfTYrGw3gAY0clkYtMzkRbiiH2DO2AMg0ulUlYxGAwGtpmQh8JW0BhLYhGLxTQcDg0csjkl2RRAGmJPp5MGg0FA1gnIZYKVZ/G8FNQHJBqHcRw+8ZH0Z0mQZ/3oBYSZ5+e9zJQg6adK+p/FKWBQvn+BRmmGkzB5EfkExoFWnc9EVgHTzMAO3heABUfiq7epVErZbNaAAkkHEgNGE5MsMmzHM9zeYYbS099u4ZQkBaYHsi+8fKZYLBqxwbuC3WdoQyKR0NPTU0BSORgMtN1u9enTJ5Om3d/fS5INfqCPlUBVLBbVbDYtAOCYY7GYXRsSTlha6XKGqO/tmkwmgb5X7ILgBkDEFplAyN+x3yA9CoWCqR68vJtngqweYotkVLqABOSfAFUqhoBwAlKr1dKbN2+Uz+etEvJ6eM1sNlO329Vms1Gv11Ov1zOZPtdPnxI+g38j1fMDi3wDv3S2/8ViYVIhAiRVnGw2q3a7rWg0aoMVkDaF6/MXqg7eN2QqwAR7hXTBHolNkK2SNBgMTI5YKBSMqKVqweAVX4UgJgEi2aPj8dgOiweMcR2JRMKIP0l2cDwzBYjxpVLJlERU/SWZPE86T5Tk8+j1w365Vg6oB4wh4QMsemKZ7wYf+GqHj3UASl+BYWCdt0FwCwQbcY0efuyIqjvtOPjJTCYTOEsVqS1nrnIfntiClCEJ8ckusnLum4QWAs9LbL2cPlyfv3iukoywZK/TG0ixwv8OsQlVjses9Xpd7XbbVHPgUwovg8HgzwY8EZNQfqXTaatwclZ4PB7XeDzWfD7XcDjU09OTEe++H5j2Do6L4XuxE18R9eQuONNLWyl40KrgWxKIN37wEtjfJ6XYkI87kmwgEMm6j8HEd+mMs5Fa+9/nZ4n7xF76KukR5rkw8fT9+/dG8tA6Ra8jOYAflEMyTXLK3AHuy0uHmVSMT/sa1lWJImVU2PVut2s9DhgFCQBJIo31sJv/9b/+V9uk0ehlqhDyFgKZZ9BpkIU5o4EeAEoQ8obJUR6eXcfxSgokKAzEAaQBCmHqYUz5PV/y5/oAv5TAXzMxSNCYkuSNC1kmQZNnwDPkukmIXzfvYhxsTgYLLZdLAwB++hXX1Gg0VKvV1Gg0jN1ioimSKKaqUU3BYcKYoeum+kLgLZfLqlQq9i69PI/3CYuKPCdc1y8a24fDoe0XPyBmu92ahBTbRPLFPvFno5FkMP2Npm9AXrPZtLP9ADmHw8EmAi6XS/3P//k/Terme1kBhdhKqVRSqVSypM43zkuX4VRUWu7v761KJ8kAMd8Fy0pFFb8D4wgIhQCC3EF2AiiMx+Mm7+M7fP+X7/WAFEEWJ8nk7Jzp5GXvsVjMKpxIaanwvU64vTSd7wRQwsIi7ef4DuRAi8VCo9HIroXkmwAGuPYJ5bt370yis91urcoUrs9fVJNJXDioHtvkHUciEZv06YnQ4/GoX3/9NWA79Jqyh2KxmPr9fqBPBxZ+NBpZoiXJwCDyRWyGuOfPTiXeVioV8+n8HW0OJFCAK+KZn/hHZfF4PNqwB8AUSRCxl6QOBQ82FI1GDQd4sOt7MZk+SqzisHKeN/3Zvs8RopjqIPEJ4Euc8nE0FovZWZHcM5OmM5mMms2m9vu9Wq2WPTPiMX4Ju16tVnYUlU8OD4eD2u22er2eSWxTqZT6/b4B1K+lWvGlLzAl/pR4ACH6up3HS8fn87lhYOIQeOfu7s5+NpfLWRvX6XQytQZxxRMbq9XKhsQdj0cjSCmg4MOpmIP9+Cz69fE7vi0M+4UwJQZ4LA9WoIJI0kaiBClJgoRyCLKEoZb4DwhXSB2eDwQVeBLCkvvwZ7tio8RsyBp/jA1Vdz6fCj0qDZLpWCymm5sbqw4yY4F344dm4W/B6IPBQP1+3673NZkMNieh/FoSxaukpzhX2MLJZGKJinTRZjOunWSLZG2z2egv//IvrRTNhvDJET1UbGCAqgdEBE9/ZhNyVd+DgbTMV+8AhJIsSFAlJSAjV/Pa4mg0agMzYAZI9GBNvFyU6yXIsXl86dpfl/Tn/YqAaUr9vr/Rs5v7/d4kfbChPE+qFlwjxkHyCaD1Jf1yuaxUKmUJIxud78PZ+crQbrczNpqk++npSY1GQ5PJxI7YgK0bjUam0/dyvXBdt06n89E0gCn+fzqdKpvNGuOdTqftHFNYfsicwWBgREEqlbKkkQoUQy5wnCQWVM0hbgA73377rTl4ZDkAMYIhRBASOR8E8vm8BoOBgVpJxkxib/Qs+sAhXaYPQwxxhhsyIXpMvJznNakEg0h13j9bbJN/SM64Vi+1SSbP51yNRiPrS+Hv+NnpdKpyuWyHlfP9i8XCJGsQSiS7sKTSRR7EuwHQJBIJq1rxu6hCsHvYU99Hns1mrdoLSRSuz1/0fEvnOEMMAowAOLfbrVqtVqAqjvLl/v7efGy73bZ+Vfr+yuWy9bw9Pz9bpd+P0Yeko2rJv1HjlEolszEUPux1FDokjMR64iZJHOQT5AlkKd9NBR4ARxXFk7EenEGqQMS8Jn7xDcTsarUaqPwTiz1GqNfrAWUQ4H61Wlly638eosdXR7ALzk6WLvJ2knN8Df/44Tk3NzeB3rDFYmFHG0DM73Y7k+BTjZSkZrNp0999n2q4Pn+xf9mTtBxRvWVfc+SRH8R4c3NjVT58cSaTsbiD9PN4PJqajvcnXfoAIUiRWzMshb1AcYU94k8ZkM7k5GQy0e3trSSZYs23CxF7ibVgN+wIFZC/rsPhchQEexASRbpM1uYaSHghefycEGzHV+Ao9GAnVB996waYkUIJuCMSiRjWITbncjlL8Gkz8cOziMmQoOQU2Bh5AL5LkuUA4BUWn+snz0LGIhP3SsEveV09zCYWi9mhn0w2A7Btt1vrl2Cgxel0sqADo88LJkEZDocW7HipvKDX3+8rbbwMXhDSHTYmhk0yC7sDEOUzfOKEkcAIUEVAEuuTQS8bZfnyMmCSKpp0YUK8tMR/HwkkYAEDxrj4x1cT4vG4MUkYDN8lXUYfSwoYimezKcujxQcYk1gzVQ85IhXgXq9nzoxKVDR6nvhUrVa1XC6NwQKcAh6Gw6Ek2UCTsMfi+oXMxNsKcheStXg8bv8NGTGfz9XtdvX9998boNpsNiZrJhkpFAqBSanYCPvas+CSjBQCvPG9DNnB/gCPXoLC9RGw+B7ANfdL9QBCg4ADgYJqgV4BKhXYL99LAkglBv/i2XqSMN8HAdHBvXCPXh1BdRSmGpkSzwkWlwrJeDwOyHcY5gPjia17MIHN+n5MngUBmWcmXQZ0IKPxARzgCcvqq0fh+vzF+6CdgWctXSTWw+HQ4gCEWjabDZAtJP8cUUHi6AdRQSiQQEIIoQKSFGgbQaIO0eTbBvxsglarZbEnGo0aEUjMJrYzyAolEGQqCaqkQOxE0o7MDt/kZW38nB+wAaB73QPk4yPX6W0McofeMP/5yPa4BlonwDP4oFQqpZ9++smGkmSzWb28vFgiwJl7JKwM+uN+GELj5wuQcNN/2mg07N74h4S+VqsF8FAYQ69fxAMIGhJBhhmRMBFjvCqKuJROpzUYDFQqlUwhEotdhtmAkYrFohEavFtftQMzEeMKhYIqlYrhWy8fp0KIbdAPjX/gmknMsFNIenAuNosKzLcszGYzHQ4Hq4But1sjFb1yAXKHgZeogkh2iS3IR0nuiKf+nERyDMhU8KvvFQQv+0ojSTozD7Br7oFiB++Ra/aFrdf9lPgLqrT4USatM7SL9wxWJ9n06pAveV2VKAJAYc5Go5Gxij45YDP438GB+4MteZi+EibJJDB+GiF/h44fmQ3JIA2/0iXx8FUyjJWSvJflYAwEHc9wAkxJnkhukWvxO76n43Q6BQ4a93IC6TIZ1f/jk0tYGe75daVRkrG//jpxNDx770Q8wAaAMsjAVxmo7CAjRS5EIutBDe8TYIFhwjghd/BDCTzzVSqV9Pz8HEhiwnX9wonCqDFZD7aeg9Xv7+/tGIvD4WD9gwBLHJuXV1Ih85PKEomExuOxSqWSBSCkOjhc6SL39lXLaDRqCgKSSpw9kh8CJbIsbADZLA6fYEJ1HVCIvwAg+v3uiRP8GnsZoA0wYJAOyRUJFv7KgwSSPWwCwofrXa/XdqwPz4dqEjJcgApSPe8/ANK8g8PhYD7P96Hu93sL4vzZZDJRtVq1dwxxg8QOH4I8yZNGIRC9buHLAVgMf6IKfjweVa/XLfGn6kYVAYJCuvRSofChqsz0P2xSUiDx4h17qfhwOLTkiOFOnO+GnUQi54mmJJ9ItCATGUhFvIZ0JGZSqUN5wn7Hro/Ho6rVqubzuY2jh9DkuZGMMm0dQFkul00mR/IlyeLX6+TU9ylTTaXNAnvmnGcSRuyeIRr8frPZND+DHUJyr9drDYdD1Wq1gEyW5JmEkD2BYoDn7nEMPgk5OAQwA7Oq1WqoyvkN1ul0Mhkh5Gev1zN5Ni1Lo9HI7NYr1SBVsG2vnCExYjq4r+QxcIniRCKR0MePH/X27dtAIYPq5XA4tP9eLBYBmTgLPwJO9skcNgVOgMTie4hptFMhb/WSbHwTfcvYOfYvKWDryG5Rz0gyIoxqIT9DQQkcC9kJ5gYn87w8SQRBJMniGgmiT/CQopPI4XN82wiDu7BDklBPEOD/vHTcV4DpDf1a7POqRJFpZbAoJAiwHa8rc/H4+ewugGS9XrcG1VqtplqtZgAGx1ytVu0zCTDSJTGBpSTh5CwqGFpJNkaYYxkkWVDk83xiiKyL5AiDIKHzINZvPs86YVRsQD/1iP8ngfu/k56ysby8DkP2CSKf4SVzGKG/Rqqb/AzXA6NJEAWgeObG94jAVmFQTECENACYUt0gAX/dVxKJRIwNi8ViNikXqUe4rl8fPnzQ4+OjvTdkFYfDwY6vSCQSur29NW0+B0IjY6LPiGoCBARyUXoPmV4KeJtMJgGJHH07MKYwnSScJCP0DDCICUkP1TEqJH6/YjcALN/vAGAtFAoBu8OeAHv4EAbl+GNx2MNMMMMHeD8HWKRnwdsN94M8lAEXVBZIJiGRYF4Z7IEP7PV6SqfTajQagcRwNBqpUCgEWOXD4WCHIFPhIVhTPQRoeJkuAxMI5lwnKgIqIq/VE+H6/39BOiyXS9VqNWPwARmj0UiNRsP2LMPZstmsgQ/eAaSmdBmKg52QwPGuiWe9Xs8G0Ww2GxuAU6vVjKSRZGeiogoiFhMf+H8SVcgl6Zx4DQYDA3rYazx+PtYFZQHxyQ+RkxQYYLPf720IFTGH2E4FlaM/JBmw9ZJWEkOfkDFFmXshJuH3IJY82APoeyyRSCRUq9WsX4zPAQswkMQP0+K9eIWDH3bn5fl8zvv375XNZgNDj4jbqHvC9dssEg+mESMfZC+RsEPy8f4kBUhxCAaKDB4brVYrq2R7dRmyf/bvzc2NxTX2DPJ1yA4qdOztyWRiPXfEHa+CA9eBeyF+fdURO/dFBp5BJBIxKW29XjdM8Zps9KoXniGJM7/jq+g+AfPHVIBl6DmGCPeFEH+WN6QqMm5iKYM3wcH4TopbEEMeL5AkQthCHJEgQ2aDhcBc0+nU2vRQUfI7X8O6KlGECeMBR6OXA0EBJDTR44i3263y+byKxaJGo5EFRVj/6XRqWm02MYERY8FJ85kEVx8wAZ6UmH1pmeAKAMKY6ZMg06eiButOz85isQjomn2PIpsTw3hdBSRR9YCbawVESgoAZ19BxWn5ozh49p4hwgFhWHwmjorv4pBymGmSP4Ini0TUyxioIjUaDbtnDJwkvd/vS5LJc/yh5wRqrg+JsE+Ew3Xdenx8tIZ6ziRl+ixAslarablc6uXlxRw2/WqSDPDQv4bunyoXjCKVJiob7Bn2I0fnSJd+BYIgwDAaPfc8kWg+Pj5KuhAt2DH7G9kHn0FwZX/GYjFjdAl+PlnEJgFoJIDsY+ycCoInsniGgG6uzRMsXANJYzwet8QU/+arHNghyRjfBbNMsjubzbTdbq1SmkwmjbkF4EIC4UORguMXfJWUBJdnCEjnelEfYKP8XLiuW7zfer0e6M/Bpl737yBbgsQkCSGZ4N1QPSSeAJx451Tx6RknafOAR5JVUlarlfr9vu1LksjFYmEkDgw9vXOn0ykw+MjbBXsTpQCxzP835BPXBCCkLcFXQoiL0oW0JcGkctrr9RSLxeyYJ2Ic4JL4tdvt1Ol0rLLCEDaqEr43e71eazwe2zFBTHmkpxsil+SY6e58Nj6Fa0DhgU3jzyAOiOe3t7emdKB69enTJ717986eUzj19LdZxL9SqWRJDu0akHT+HGLv7zudjpLJpO0hYiZqHp/4IPUk1oEvb25uNBgMJEl//OMf1Ww2ValUDGdDAoGl2JOVSsXmaPjvAXv6PkNUJPgTzjP3knWKH5HI+dgdpKTZbNamFlPVo3jk1UgkoX7egJeg+6q8J4+5JpQ/PCumqYIv8CucCUyRBt/B5zKtFfukukeCSFUYDEA1l8qyL7B4og7Cjz7OZPJ8fBcEFtfJdXjS7EtfVyWKNN1TXpUu05LYhDx8QEq1WrXRvGweHDwBSJL1WyDlonJI8kZFij/zSROSqna7rdvbW8XjcZPzvK7YSZdSuG9EJYDzTz6ft89ng/I9bH7+25f3Pevkgz4brNfraTabmfH7pMn/nmdTMB6vywYEoOf2vVlU8jACnrUkA/cYBlVAXwXBQJFY+Oqnr7DCllAxLpfLymazJon1enXuzfeS4Bx7vZ5qtdo1WzNc/5/lZcuwiRAy9XrdJmJGo1EbWsSkPkkW+ACtOGwCAUEPsLJYLEzmBllEAkjC45lCgCdnbUqyz5MucmgAEYnbYrFQv9836SbOnT5XAgWMJSQI9sL9YUPIyPEvAGuCxH6/tyoNzCh2gt0B9Ah2PH9slaE+XtKN7UgXWyPAItXmPfhkwctzpEsw9HbmK748Oy9hjEajJvdPJBImFaKNAKZUklWdeIdMwAvXdYt96/tYITpIimazWaBKAKFJoiDJbJi41Gw2rSrlJan4aY7goJ0AMgTpOTYLqRSNRm1KI7HkdDrZWH76mvg3SRTkAuCq3W5rNpvphx9+sL46DxwhZzabjU0nB19AhPgBF/78MzAAJAdxp1gsmnoCe6OKSf/jfD4PJNz1et2IHUgaki8GYeADy+WyqZnoOyPJ84oe5HrSZdgW/o0Y730MvweYR+qNjBGbRoVQr9ftudDu8jWA0C99kdDhM6n8SmcCnBiWy+XU6XQCx7cg1yZ+SDIyxfe9FwoF3dzc2LRd9gIyaEj9RCJhg+i8NJoj5VD5VSoV2y/EAGZK0CNIvJnNZvof/+N/6F/9q39lc0VQ16DwospOfPJJDvbv1TX4EFQG7GVfuePasUnwH58PfmUR3+i9BIu+lqCPRiMtFgtLljOZjD0zcgXiKpU+yO9isWjvt1armb9kKOdr3OqHS3EMGYk2xRL8GvNWyCVoXfka1tXS06enJw2HQ93c3KhcLqtQKKjb7QYatnFm/uBfmBCSpsFgYMmBr5YBPEulkp2ViGPMZrPqdrvGVgJw6VXyk7+QpiAB8E7bg18ctA/KkchlIAhBg40pKSDDel3R4/4kmVERFHgmHCfA8pI0LzElMfO9SWxSX8EgkJGUUX3ld718xssVuD4+2yfkvq90OByapp5gDqiEefIJP3JeSRbAkOtAHMAsI88BAIfrusUh19gUTCR9oMViUZ1OxwYkTCYTc3pU+nF4kkw+Wi6XNZ/PTdYmXYasIHeEsCBIxGIxC5Y4WD84xUurXlftIEqwvXQ6rYeHB9u7AEwqp0jpttutMffcHwOX6OFZLpeWvEmyyilOHDAK4UTy7BlWn6RRleB5AaqLxaLZqk8ypUuC4KugkqwZniBdq9Ws2oSdvX//3kgZQCUV2Ww2a8m0TxIAEX5wiT/zDtUB8hn2D4vEMlzXLd6Xr+AiawIkksgTk4gt2K8/aHo0Gtl7YyFj7vf7urm5sfNto9GoarWaVUd8pcu3NBBrsT2Sn+l0asNuGB7Bz3n2HeAXiUTUaDT08PBgPoGeKi9F5/OYH0BLC7EKTACBCUnr5bAQv7vd+XgNeq6wUUAqkj/ikiRLqnmOkFO8I+Lwy8uLqtWqSfBub28DoJVEnnul35DqKzbk+7PS6bQljvgwT6Zzfa/bcag4fvr0ScfjUQ8PDzYEKVzXLYhF2m1arZYRMNgj5BtT+nnnYEPiG4kEZAjvjgmg7XZbj4+PlkRQkUqn0/q7v/s7/eEPf1Aul9PT05MlooPBwBQBYEL2MH6d++C6wWf8/1//9V8bxqvVakbCEKd80oZyAdtjb+K7+Ew/YIoEkp8jnvAcfNLln51vz/LqRT+MzROvYNJY7DxkEzsGryyXS0vUUqmUHh8fzS8w+Rx1HJ8NYYeNHQ4He9eSzA+ivsjn82o0Gnb/7BEUFBBOqVTKfM+Xvq5KFJESwm6zEf3L2O/3BiypNjHFdDwe6+7uziRVVD94OTh7gtZwONTDw0PgoHZkMuimPaPnD0FlEwBGqSL4FxePnyeustHQYSPNeV0p4AVzNgt9WFRLkajA1MO2It+iBO4raiSXyNIAmYADnrF0kZJKl6E3sVjMmEQvDeazffKI1AyZXiKRsHMQCXK5XE6z2SxwFhtMM9OjSJ4p4ftAy7vkfeJsabSGUQYM+X7GcF2/OIsMBlA6T+QiIScg0dfDXmWaLf1qvEcSDdhOLx1m33gbQxIDeCPxQPqGjKxUKhkTPpvNVC6XJV0CBIDZByX6DObzufXP1et1S4iYbur7mjmgGP/BvoeoIQn1xz/4wTH8DAANggMb9XuZJBSb91VVmFXAA3aeTJ4nGzIxkWeJT/FAnWtsNpv2fqk8SWe2G5skaS8Wi4Hx4bwfEkmqqEiIqVpEIhFjXKWL1D1c1y3sitgoyY6twb8DyIh7yMIrlYrev3+vH374waoGuVxO2+1WnU5HNzc3ki49gre3t5Zc8flUsNiDJCgkSdiVl6wiryJuwpS/JlIAb9gExC8DkyBu8SH0rzebTVPC+Mno/Pt4PE9DhRCVFOj14ruRzEkXwO5JoOVyaUcDMZhmPB6bbJTJ3ySZtLGQ3DI/geciyYjsw+FgVQUqsFwP4J8+ce8XIHzxs7FYzAAqzyAej1sbDMoHzkdOJpMql8uaTCZhDP2Nlie7kXEiw6ZS6GMM8uPZbKbJZGLVQwol2ASx0h87Bk7le70///3vf282w9nYEDUcv1EqlUwSC/EDccG1cQQWdu7PTSa++grcdDo1QhHyFWlqMpm0IT7sVb/IAXzblfdj0mVWANeMMg2f4H0PxDJHqkWjURsSBAGKj6lUKuafJFmrBs+Ud0FCjM/xpPRoNFKpVDJ7BcuSkNOjiAIEH0QRCJwB/uHe8SP40S99XZUowsIBRAEZTBObzWbWFwXDyEaMxWJqtVqSFBgNDoDhRSYSCRtQ8+2335rEBgMg4GF06IHZNH5MLr/zug8HVjedTqtWq1lS4xlWnL/fJHwW30dFg+uDhcEQAKGwLb4aQkDjGVKZIwD5ngyf+BGMMTTeCyBXusiSfAVUUmACJFPy+DkkSPv93irElUrF2NNkMqnhcKhKpaL1eh04KH232ymVSpn8EADNdx4OB/X7fUsOpMvEvm63q2q1aj8frusWDhZC4HA4n8HkpVKbzUb1et2cNEliuVzWYDBQvV63fmCkXOxh7IqmeuyQP2fIAqCyVCppMBioWCxaUOFnqcwBOF9XNLEJWEvpIufClmHo8EEQQYBObJSpZkjIvEwOiWipVDJJKg4e+Q9JLwGSZ0xgwAZhjDlwGB/4up8RWSiyN3wIMhvANMkiPwvIh8RCXuiJJSq/PjEg8fOJAwCb79lsNkYebLfbwMRFejS+hiD3JS+mPkPIFQoFvX//Xo+Pj0aa0BqAImC5XFpfFFX1X375RW/evLHR9Pf39wEb9WQkUm7G/LMXvWw6n89rPB5LupCQy+XSSBMq/gBN7oWkyxOegD5J1stHmwlVD/Y51TLIS9+XiQqJPYvNn04nmwQMqMOvkTiy51ET4Zc4NxGbeS1Jx56ky/RKqoT4EC+tx0dCmPrpl+AbQCXPgHv2SiaqhXwuVVR6I/EvDEBh0BckLMlsaJ/XL9/Tx3+3Wq1A/zpJI4kL2JVp1hCS2CcyyV6vp0KhYPuIggl2QdsA+4UhhSQ3qOf4DmwQKTWkLFVsb6/S5Xg2KuzsUY6nI2bxc8RLcH8kErF4Ra8jONMXPkh+sc1er2cDLcH64HDwMISrJI1GI7tGMA2Yk8okw/NIwiORiN6/f2/yYJRqfCa4g/sYj8dKpVIqlUpmO55UBgeQ4zBdmHfp+0C73a4l9SgbObLLv08peCTQl7qi/99/5P9+8dJJng6Hgzqdjv0djAes3H6/t6bc2WymSCSiwWCg8XisyWRijMZ0Og30XhCg6NXwjCgTxABdlOQZOOOrj77fh03MZsFIfC+Cl1wSbABKbGZAMMkyY3NJeGEj+XnAMPfnp7nB8PD3GCqVQK6b+5EuMhgvq2FqpR9aE4/HNRgMzGCpivgkk8BMskuCXa1WA/0QMGIEfT4vGo2arJTqKs+IhNoDcgAMfSin08mmc4XVit9m+coVFVwmfU0mEwMUvFvkiPRiUPGjp4fzUKVLVYnEgkollQhf5Wc6GYkKmn/saTAY2N7CzrEV9glgarFYaDKZ2ARG7hPW0ksoV6uV/fxwODQVBNfuJeOoINi3yHaRviPT8wwivkk6+5fVamWDc6SLnSExpzpEwINwwib81EMkvQQy3ysCsQVIpcrPexyNRvZsSOiQGCL/pap/OBwsKeD7qby8vLwYOMLPYtO9Xi+UiF+5YPMlmSycXsD9/nz2ICQsTP5yuVSpVLK/R2pIAsOcACoUJPUQIBA4/pgZiFEqYvTYcy4fCYs/FzCRSBhxCMCkAjEejy2ZIV4yJA1yYzgcGvBFHsf3EGNoS1kulyYlq9VqgWEg2Fer1QqcNQqwBVgCCumbOhwOen5+Vr/fDwBIH5s5sgYC1KsbiP3ghdPppE+fPlncT6fT+vTpk00Bn81mltyRDGcyGQ0GAxsKtNlsbG4BySJJNNLz4/Go0WhkOGMwGOjp6UmSrApyOBxC6elvtFCPHI9Hi1m+nYjqNJUzhjodj0fbk/w8PYZgLX/MEAPKSHKGw6ElkexnvuN0OunDhw/mO3y7kSTD3tPpVMPh0CqPqOWIfRAfDMDhd71KCPJ4MBgYccrwGl9Q4Vlg69w3R4iAX/f7vUqlkk1aZ47AcrnUeDy278CnMEgIotcrYejplM6xi95djr578+aNarWaHT2Df+Me+YdC1svLiyaTiWazmeFSfoaEUJIWi4UloPF4PFDRz2QyevfunRVgUDK8PlIKMuprIHOuShR9iRuN7u3trTlwWEGSum63q7u7O2PVGFgTjZ6nZNbrdRv5DLtApYnMXLo4cjYO4HGxWKhcLlsfj5d5STIg6KtY3iiQelCpI8mFmVmtVtb3g9H6SgBJE5/HJiaAYtQkpoz3Zgoivwuo8/cKWKc6iYzVMzavkzUPMKmo+PtBYoPm3E+aYnwxz5eG/cFgYOwPSftkMjHAAlCGBJjP56ZBJ1hT/seBAe5930uYKP42y8s6drvzeWo0fPvKwGw2s2RrNpvpv//3/24EA3szn8/b4I3n52ctl0t7X+j2SSI5741+I9h15Fde0hiJnI9JWa1W2u12qtVqRrBQLSDZJfCQZDGWG/aWZHG/35u0ulKpWNACeBGwkIlIwbOWIFjouyU5gpDxNor0VbrIhQjsvnoD2Af8cu2SAlVN/BLVeHzYfr9Xp9Oxe+R5+P6XdDpt/VsEt9f3wjMFwJM0cA1cE4lDt9sNJCEwu37AV7g+b0FY+ATJk6/NZjOgJInH45YkehkqdsecAFQekoy4I2FCjsW+wLdLwX774/FoCR+V6cPhoPv7eyM2kN15GWS1WrXedemyj3zVG5JXksVnCBjfi0mymslkVC6XDfBRfSFmH49HLRYLq056sMk/gF6eZyqV0v39vfV5wvajUAKgDwYDI7ap3OEH2+22HQ4uyUbgLxYLRSKXYX6+oinJfAn9kxxYLp0JA5IPSXa0CPvCS2rxla1Wy96zP94otM/rF+oq8B/VK/YVZB+EBnsSu2DAGmoXBqb4No1ut2vxDjLB2zkEHecJptNpa9uSFNgfSMEhOKlqc70kccRer5bxVUDuAZtCdbZery0xppADBpBkbSzEJ7AcMYlqPzJ1roVWEq9A81PwwczEWHyf90G+patUKlkvIWQzigiq/uQe5XJZ+XxeDw8PVn0keeTdgp3/udhHQckrAn3hiWdFUQki7Guxz6v0fVSDpLODfP/+vd68eWPVCgITL4Ypa7DTMAwkgMfjUcvl0tgbApYkYwoBfPTVUKXwLB+Mh5dgwSbAaEqyAA0rSWBALjOfz1WtVq3q4itvfAZsge9PImljs3jDky5SBi8n4z5Ioqn0ASAwcgzIAwuYUM+IsiHZiDCpgAMv3yWRY2LedDpVtVoNJNBscvoyfO8J5/JhIMiWbm9v1W63VSqV9PHjRzWbTTtAmXK8H5JB8Kc3LlzXr263azI0JI4Eo1qtZhU2qons59///vcB0oD9ggwFxg4nTPLPnuK/CQB+PyOX8r2PNMd7R8r1MM00n8//mZQMG6Nvaj6f2yRBWH1IEJI7SB+G9gB4ORfJS0ixL18xIVD6o0Qkmd+QLuPwCfIEWqqt/pBxSVa9J6hIsnfih3u9efPGGGD8kWe3t9utnp6ejK0lwSA4+4FevCMSBT7TDyS5u7uzxJFqL33f4VTF6xc9PyRFTOf0k0hh43lfyPZJ+rvdrtkkU0rpA5QuUzAZ3gIYI05A+CDFzGazen5+tgF1vh8HsMQQK3qqiK+ASkb2Y2vYczQatSSVPYYCiJhGHENuif1RvfcTif3wNOIa985n0oeNbNcDuel0arJ8QDlxGJurVqtm/+AJZLt3d3d2fcgKJZnEnGPAotGo3QvVUaqoYAaS43j8fKwCAJ9zZ0keUQjQl+b96+FwUKvV0mw2CySw4fr8hRKqVCopnU7bnkGKyPm1fjAT+BV5siSrpFE9pvUGO8R+YrGYarWaYUeSG2JwPB5Xu91WtVrVbrczAgfsS3uHTwh//vln3d3dmZqHAsXhcDApqidyiEEQlcQ43yPrcS5VytlsZsQIhCZ2TjVvNpvp5eVFsVhMj4+PgVYVX4Sgtc1XQImJfHapVDIciTQXwjoSOc8h6Xa7Jo/lfvj87Xar6XRqtu2nK3e7XVUqFSNyBoOB8vl8wA594cX7AfoPPW6HqFosFqpWq+aLvgYbvaqiCEjwZxXhyGEyAJJMH/Q9AAAPGPeXl5eADAvWgsoDSQ2S0l6vp3a7bWV2pmey4WE1YrHzuVJMN0R+5yufvueBgEbZH5aGBt7D4aCXlxeT5yDfYrOSACLV8v2Rnj1mE+HgSdbYOB4A8md8ju9d5M95DoAGGudJPhlMgtQFTTfGDIs6nU5teI2XD0Uil2Z6kn8YcI5JQbfNWGWYUg6K5TsgESaTiYHW6XSqXq9n1aBwXb8ajYY9Z+kcDObzucrlstkR0kPsJ51Oq1qtBpxrJBLRhw8fjLn3EmySN0nWy4Z8U7qcGeSn2g6HQ/MXsJb0OHjpGaDW9wZiPyRX9H9wpASMO04Zeyc5BOhSncCR0x8C2MXGCEr0PHE8BXZJEJMuR4GQzM3nc/MxJKswtZKMBcVPSRdJv0/YUCeMx+PA5wJoqQ6jKOCa/DPy0nY/KXkymRj4eT1ghKomoBl7RrYfrusW1avdbqfBYGD9MUiJaUtg70syiTIST/p9T6eT9cv7sz03m/OBz1SDSUiQfZXLZZNYIvvmqCb2MlIzbNK3PKAm8EociEBiG71L7DHsDIAG6eCrfpKMBAaESpc+XGIWlUTIV28TSMHi8fOxS/Q6EYshuon14BR6k7zs0GMRPyiOnlH8AYAXCTpTZPEN/toYXETFBF8E0YMc0NslEtxSqSRJlnBS+dztdnYE0tcAQr/05YfKMCil1+tZcgcBST8tcY0KID6XKbyJRELNZtMknc/PzzacyCdl7Bn8NmQCMwU4wgifv9lsLHHi3Gp642u1mpE2kCqZTMaO08BXgOkgUsCU+CmwAHEb1YKvmHk1nK9Q8neVSkXff/+93rx5Y9jYE8MofiB5vN/ieqQLTqcy6Yc44g9jsZju7u4s0V4ul/r48aMp3fCZvr2LeP/27Vv7PsguH8+pFmNzVDMZcOOv0xN/PAsS/69hXVVRJHiREDFqFkcOEwOjIl2GPJAQ4Sj5eTbTarWyc0voa6ICQCBIpVJ6eXlRNpsNnCuUzWbtQHAmsFarVdtsiUTCpDA4UoAlAAmnDssIk0DgoDfDHx8BmAI0etaWZ/B6EAD9CshzfQWAxWcQeLku/pu/9w3xLG88/B4MJteCA6Ev7e7uLiDJ5dmwsQGefDfyOp4LCT4AAO19NpvVP/3TP6lQKFgiyZl70iVY++Ec4bpu8W5JuOgVBSShsb+9vTXHhm1SuUAa8/DwYJItKsf0OHl9/mq10mg0Mmn1YDBQKpWyM9eQb/k9RVJJ0or6YD6f6+7uzqSbAFQqfXyvdGnMp/qADfvJnrCuOHwvzyQwSjJCA1lOsVi0YREEFPY49haPn89KBEwg/yEx85W8Xq8X8H2+kgiYkGTPiwoKklxYUVhWfDFsJSPXc7mcut2uHZOB74GYooLhB3wgNwaQeFKP/lWuNVzXLT+ErVgs6uXlxdoxkJCWy2WLDbwvhgwRe0n+/NERxLNSqWTgCbkz4BACqNVqGXnHZw4GA6tmAVZjsZien59tmAcxgM+i4oa0C2IW2RrX7c8OJOZC0FAR5e/BEEzkBnhRxaQiCbkiyeIIFQZ6i7Fb7ocjciCgfe8TGANfgz/hdyHWmIZJPJVkNol/wG/wufgo8AXDw7B1Wm0gCqbTqcrlsiXlJPRI8TudjtLptB3sznMOK/7XL2yLPcWQRohFT5R2Oh3VajWlUin1ej1Ta/T7fas6ocahwgyRCmlIIsI+LJfLJi9HHiqdVXwkl+xV4iEJEAUD2hewew6lJ3ZzdA6YjJ+VZCSvH4RGvPDxA1v08w64N58DUMnk81HnkVRyvqif4sxnEYuZB4CNgQmIi1Q2fXsGf480HdyOL/Jyfj4bXMs/HOeGX8nn81Z04fcLhYKdxQjG8kSRn0b+tbRvXFVR7Ha7tsGlyyhceqGYtifJAgol536/r36/L0kmAfGbClkXk+DYnLDqk8lEyWRS7969s0b94XBobAwMi3Q29OFwaKy5rwIcDueGdlhG2BIqobxEWEBJZhQEZBJXGAXfY+RlexgCwYokizI+4JVn4qV8BCB+F4NC6ublN1RAvRYaAO2N4Xg8mkQHkI0x+qoC47xhjDH62Wym0WhkjhLZDobf6XQCQH61WumHH34whpmEYbfbGauODBCmN1zXLZw5FXc/0AJHdjqd7OeGw6FV0wA+AD3IiI8fP5rzZt9wRhj9NNJlUi9kDPaB/USjUQ2HQ3PGMLeJRCJgY7/++qtJvfA3HogBwKgYcm/YOcngYDDQ+/fvbZgPQAzQRcUAG5FkwWw6nRpApD/pNbOJncAuY+8EeZ4VSYEfoIV0CbKFBJJ/AKyejCLILxYL83XFYtHuFbKnWCwasYV/4vgRepHpG4X5ZDAAjGc0ep70t1qt1Ol0jKkO13WL3mzAC6CF6kEmk9FwOFSn07GEEGa72+1quVzamXxUC1CESLIqGSPi2WPED/qYYrHzsCr2MMoUhmHg/yORiG5vby3JISZzfQBMbLNQKKjRaARaQvAlgE2wAFV3hr9gF1ReIHaJL1QqJJmKgRgOGQSJSjKJv+MfCF7kY5BgnGG3XC61Xq9tEBZVOqo2YJV4PG6xUJJNsUVqRxz2U1wZ6CMpQO7w+wzWgFBHtu/vmcT27u5Od3d3JiEGP4Tr+uWllRyV5o+LoEKeSCTM/45GI5Nno7A6HA6GT5F+Utlif/g5GfSw0puOXYK7aWXwyhzOzKWNB6K23+8bgUlcYG5ALpfTu3fvLKHk+iCCJRmpAm6EpJBkuBVc6WMJ9wKmIy6CGbwSh3/AqgyTQiXBM6Ffk89B5UQF/3g82vA4ngU2SyIHJmLo5cePH9XpdOzeUAHgx5D0QjY3Gg1TblQqFRWLRVUqFdVqNcNLLHw2eHe1WpkK6mtZV1UUObQbVsODRCoNAKhEImFNooAXHhY6fNgZkgvAIixjKnU+66jRaGi5XGqxWAQAYbPZtGAFSJVk7BwJCUaFXpvv9hIxAjdN+b5KB5D1m8/3DPIzAHD6CwB3gEXp0uNIIur7JAF/BGkSMgKf71f0vRO8D5wIgdcbF8ktAJJNy8/x3Pjs8Xhs7AxMTKVSMX0372g+n5usplKpBJ4HCfPt7a1JAQ6Hg+r1uiUMh8P5MPiffvoplM38BoueCpg49gL7s16vG6AigfByaJJJZFDpdFqPj48ajUbW4+jHPbdaLbNbJG+tVkvH49HOKION2+/3JuXG3v1ob6Tq9DBjO5AhnBVGTxGMpu9/JXE8Hs+TIZFcEgAI3gQ+CCYCGLZNYGFPEgQhYfADkqzyIcmqpR5oxGIxYy0hx/BZkchl+h2sMP2QXAvBkEo+fsPLd+jRpDIE4QNTjcIAOY4ke49UOCAMCNo8fyq8SNPD9fnLg4VEIqHJZGL2RlWI3p7T6WSSJ45q4O86nY71haPEgcj505/+ZL1RkJvPz896eHiwoSuHw8FaRagoYk/NZtMmICMvi8Vi+vDhg373u99Juhx7QbsDygOGXwCSJJm8jAUA832QLGRnJI3EM6qMXDtAnfhEBcGrd1AxEMsBipxZij0gd/XENb2HVOs41oBqJUCQNhzk7TxThr1hR/v9+QiCN2/e2HRFgDZgutlsWlwEKNPbzBARTzbxe/v9/qupVHwti4Sf4X0MLYII7XQ6qlQqgQo/MUKSYU4+g8oxFWHir9970+lUtVpNp9PJMGqv1zP1D9NT2ZvYCUQo2Aty5nU/NL3JYGNmALCfiBOSAliXWEZrAkfbgS35bAZAYUPpdFovLy86nU5qNpuWFLM8Fq7VajYMsVgs2hRgMDzPELsjrmFH+D8KD2BNEuzX5FetVtNisbD30+v17JxIn+AfDudp/r7HGMw9n89tcB5VWHw6zw2JOc/Zqxq/5HVVokgp/E9/+pP1BnmHXK1W1W639fDwoNPpZBI0mHacLAASRp8A4A/eHo/HlhRSTZAujCkyi81mo8lkolKpZCCLPip+hsABoyrJwCbGhYM/Ho8m3YGRh9GQLpVSevpgiDlKALBLjwXnHEmynj8+h8SPzUtQx3C9dAZGFODA71CpJFjxnPm5yWRibC1gGEc1Ho/14cMHPTw8GMvMwqlFo+dhBAAKGCxYbi8fns1mqtfr1r+JnG88HhvbRc8kiejt7W04se03XP1+X6lUys5C9CQDoAd5CsCfRIMgwnRh7JWBJqPRyJI4KsNU4+r1upEwJDAw9T4Ase9RFTBKG7uCUEBeQkKEBIujAthz4/FY8/nc7N/3AOLM+W5GilPt8DI3yCcvCaLCRjAiGMznc7MN75cIAACKeDxu1wpbzHX7noZUKmUDK0heeTYkkDyDbrdrUkVIAcAMzwr5ue9b889Ukv7Lf/kv+sMf/hCQ9/nzK5Hs47PwVeG6blEhQnkCqDsejwZGN5uNDRDCfqkYYJexWEyNRkPT6dQOksevf/fdd+aj6XG7v783O+GYnEgkopeXF5OIS8FKOgQTpOrDw4ORggyoIdljb5G0EG+oDo7HYwOLyDABrNzfYrGwGOeBGgmXr5gh/4S0wX7ph4dc8YNF8Et+wB6VEE8mUfHhcw6Hg2q1ms0tAJjSp+nlbuAW6dKPmkqlAhMth8Oh2Se4ArvkWACwBzadz+etknM6nWwgCck00txwXb9eXl7UbDZNUthoNDQej80mIREhJiDh6DumpYZ3x15GwUOMo9JUKBSMiPWtBSQZYFfioY+lx+PRpKrJZFLT6dRISd9rS7xCGst+gYSAbMUOuSfi+Ol0soo58YvKpm+j8ANdVquVHf0DOQnBAQ7Gzjj9AJzr4zAFD2zMD5TBdw6HQyN4SNC8bUKISzKC2g+5ubu7M9UMeQgtc7TvSDK7hTgCW5Pg+sR6MBhYyxU2/bVU/a/uUdzv9/r222+t/Ht/f2/ZMkwDU9Bg7hKJhF5eXuwMRKYU+Zd1PB7trCSqBDj2Xq+nZrNpVUsAHYZAcoIR4exh3ADHqVQqcPYbh+RKMukO9+D7Ef0L9pJVHxBJ7JB1ptNpS3phZnyJGyN43eeBkVGRgdFA3uPBKf/Nc8BZAAwJ6hgaCQMl++VyqcfHx4DxebbLS2448BWngxOULlI8KljIDKn6+HL+crlUpVIxAE01J0wSf5vVaDRMlk2SU6/XrTpIYKJqRSAbDoeWNGQyGdPcr9dr2z/YARNHef8+uAH2sE+GFOFAJdko+2q1arbMIjDw2dPpVPl8XpPJJDAq+zXxQ6+HJPMzjA1nWiB24X8OeSz+gXv0DCVyoEqlYuQV5yqRnDL4BhvxDfbz+dyqDwxv8oQL1U18gO+t8BOJIWIIzOl0WqVSyVhtfB7SNdhV/huwnM1m9dd//dcGVPluJK/9fl+VSsUkdIALZHbh+vxFbMCOTqeTHV1DrxBJEdJqP+QIQEbiT7IIkAXsAIZ4f6gHFouF9ToyjRgSMB6PW5+TT7CIUyQ7KFMgE5gyynRrfwg3PgZbhxA6HA7q9/vWt8uzAdgSYwF3xGrujZgnyY5w4jmRCALOsHtksnyul6RKsn4rbMnL7RhWwu8Ts3k+9BT6oz8As1w/7Rf39/cGln3PGkkGPgqVED6Y3ieSEoA9ZG5YVfxt1uPjo5bLpSVJKMI4tkmSbm9vrVAgXY5JAdv62DubzYwMZZpmJpOxpAqs7FsNfIEEZQj4jGsjwSPeUm1EBSAFW5ewn9fqNbAYPw+RS8yEPIIkplrJ8R/EJIoG+Cbvq4j92Cz9lnyvVwLwD/ZGbMLusV/yg0gkYjNJ6AkmyWUiuiST6+M7eH88I6a6VyoVI7AgA/BnFD/4GdQ9viWEIlg+nw9MK89msyY9/9LXVYkiAImG00ajYdOgSPJub28tGAAuPSuey+UsmYK1ZHNJMrAFo0GAo7+ABy5dkjs//AbJFQFCkjEs/X5ftVrNKpJUSxKJhCV1bAz6FfwoeRhGGBWW73ViBPJr5gBQCEgmMPOd3JukgMT0eDwPwkilUnb4LsbupXEwPRio16UTfJlMybXV6/VAErvb7TQajSwpHQwGBhx4/xgVZADymHa7bXIjAhusJzIljM2z5IDZsGLx2yyCgB9okUwmNRqN9O7dOyMKSP6oEhK0otGo9WUcj0dLVJCI059D4CAJYi/wZ3wujKGXehOMkC0Dhti3BDQC53g8Vip1Pt/PS/N8zyAkCJJWiCuCM74EEOmrZl7eIskYQQIyAQx/kkhcxnR7phRb89VFQDW2yvETMNEERB9M9/u9ge/T6WTBrlwu26hz6XLeF/J8SXZGHEAAZpses3K5bKSdt3k/3ZRD4WFn8d3hun6l02n1+33rqWHv4dMZ0IZvpAJHks8e8D2y8/lc3333nbbbrZGm0kXy6eXUyKSQ/NMmwuAifDTvnO8nAQJMQZhI5/jFSHp6bKmQohKQLnI8YhP7i/sj+SKOefsgXvijN6bTqT03SRbbjsfzcAuIIpI8P8dAUqDHH9k85InHICRoXGM0GtVoNLJnxeAuBpFAsHqZGd/B/fAsqcZy8PjxeD4rttvt6vvvv7f79lWsbrdrlcdI5HIYeZgk/jYLPELSNh6PrcfUJxv+/yEF4vG4ke2+Ms1e4IgZ4iEy5fv7e/V6PTUaDVP7QJggLwVL+2q43/v4A4gY1H7EKHCgHwgDsShdzhn3gxyPx6NJbCGCIB/5Hd/+5CXwPEv8D/uYWAKeli7H4HF2pZeU87ylC0HmB3H5ohG4nEIFBBXqKbABfaS+heTu7s76FiWZqmcwGKjZbJocFlvEl3AsD6pEcAWDqXyMBxN/6euqYTaSbIP9/d//vfUhNptNa1TlyAOaUjkLiQlsrwehSPqzDQ+w8Sw3iZgHWFSmMI7pdGrsCwwPRpPL5ax6xibPZrM2cpomY8Zp00cJ80uQrdfrVnmgGkE10Y+79owKwI+qpyS7H0kWVGBN+G+qp9Vq1Sbh+cqOZ048GKeSB0PLdx8OBw2HQ5OjxuNxG/bBRmdqIz0TSPWQy8CyUs7nXfEecJjv37/XdDo1h0UVMx6Pq9vtWj8oRsq/w3Xd2u12Ns0MydPhcNDt7W2gxyeZTAZ6npAY4tiRVSDLQiaHvIPBDIAwGFP6jRm3TwCgIgBB1O/3LSCTYJZKJSNwsF1ktLlczhI/kkqANsO0fF8RwQA7ISD5qsBwONTLy4uky/E93Cuyev6bhNv/DPbAYCbPoAIGIYYOh/MxJSRsSPcIepBWBD4qp8jOMpmM9T1tNhs7kwnbH41GJvHBr1L1GY1G9iwJnj7xq1QqJqklGEIEeYUG9xSuz1+xWMxikyRrkcjlclYRIwEgJkkygkaSHc8AAIVYicfj+vXXX42YZY9I5/3d6/V0OBzU6/XMHvEDxAqqAjDhvHeUNsRGjqmAOOZoHSp+ALbFYqHn52fbR149QtLJ72BH+ByqFhx27tshIEEZMIFSB8wgXSaOo8SBWCIJoErDQJhGo2HJLVV9SBf8CGoDpPcMKaEq6KfFbrdbs0tJ1n4BQY4sjd5kDgJPJM7nWwJA6XUGfN7c3JgkjndF9flr6H/60hdJDe1TJFt+dsPHjx8t0cFO+Blak6rVqp6enmw/gQPZM4fDQT/99JPtGyrS/X7feuI4QJ74CsGLaqbf71tFGWUY9oENQojgL9rttklGiXvgWK4fhQ4xFfIEOTTEMLM9PHmLXNtX7PlMngXEM3hkNptZDuGTLJJc/2/iFLaNojGTyVisI6EkEQavcKwNszpoGUMVAMZJJBK6vb3VdrtVpVIJtMX4AV4Qaj7GQ9ShhsJvvH///quxz6vQOPKOzWajv/iLvzAHDmCh7wFmEsknVTtGOX/48MFkcFQ4/IAJqndol8nsSWQATQSr0WgkSQaMmcpGxcLrxRn7C8PgpyTG43EDyGxUqn0wFH5MNZ87HA719PRk14PB0FNA4GAlk0lVq1Ur+XNN0mXYQSKRsOmEkkxWRoMyBkjgYiPjFGB2AY7b7TZQxcQh3N/fWyWEz4OlHY/HBoBxGBghUgZ62nBUkUhET09P9uz5LIxFkmq1mnK5nPr9vv3912JAX8Oi6X00GlnyQZIIgfDPHQDt+6dIVjh/kQOISZ5w6DSeU61DAkOiyvTF4/GoTqdj1ZO7uzsjaSBIaOznrCLpMkyCAMA5SwQ4rme3O09H5lphNpGDEDjYa37Kog9ckgI2Qy8gfokeKvqsJAVsQ5L5FpQHyISYeDybzQK9USQDgHT+nkFbABdIHH/UCOCBQSJIDxeLhX799VerUpGI7Pd7mzhJRRRQXKvVlEgk1O129fz8rEwmo/F4bH1ZvMtwff4CDPGOkIUBIiWZ3fq4wYAq7IIKMCQNTDxVAsAkpA5VEeRi9Mzy3bDrnU7nz+TfkJ6oUrhGEkTUPL1eT4lEwgYxkTBCXAGOAcvpdFrj8dhk8v57pEs13E9AJF7w+fgOH7eIO57coDpIAu2TTT4Lkhf5J0l3v98PHHIO+PQ9vcR5ro14BuZh4b/oQz4cDvr06ZNVaYi39G6jzOH54SfBP1Qx+N1wXb/oqY1Go1YlZw9ArjSbzYD8F3KH2MvepF2BoXK+Zy4SOU8UrtVqAQxbLBbVarUCuMlLnU+nkyVHjUbDjsOCcHqd1NB+RAtFrVazOE0MZaEWI9YxaI24ghKImFEsFs1XcI+LxcL68Ul0veQbWbgkS7qIL95+aF3zFcpUKmXEqb/W2Wym9XptFVk+B7JTOlcM6/W6pMusEXAK9zAej/XLL79otVoFSB++GxUELTo8A7B8KpXSaDQyO2Zq/GKx0OPj41fTR3yVvi+TyWgwGFhvIbp6gBEgih4aAl86nTYphyS1Wi0tFguTtfg+Qn7XS1aRjCCRpHJBpZAkkwRpszmfB0ZfDtcC2IVJ5PP5fQIsTp8gQxUUhp+meYweKRqJNKwODcJcO9dPYoqhAgClC9jEUH3FkOWnTiKdg8Xxf4/MjhHOkqxCi9HzHKTgoBHukcEdJPw4IiZceckSlYhms6lmsylJlnAWCgVtNhu12207gL3Vakm6DM8I1/Xr559/VqPRMNaeajtTEKXzHqOXSLrIzAaDgQWo5+dnG1CDfAtSheTJV5fpfQKQUiXAWcLY8/18DkGWxEq6TBElSeL7qE4TMBnkwu8DRIvFolXvsNvXAdf3EHpJHPYWiURMAVGtVgPSaBJa3zNGv5eXkQIk4vG4HXuw35+nH8JCejIFO4fpZKAOzC29IcPh0HyL921UJFg8L94xYJietZeXFzsEGZ9FPxnAmOojRFC4rlvEFfYT1cWPHz+qWq1abyH7G4mbJFOGUAHGJoifjUbjz3qBpMsEQx9HfPsCFQKAFnGOxAjixE8KZXoxf4fyRJIRIMRu4hw2DUbA7yBthRgmjhGT6c/jqB0vUZVk+5pnATbwzxFboNeY5HY4HCoWi6lcLtvnYZecL4zklIqql3jSaoGP4nORCYNrANIeaCIX5NgLCCKmszONfTgcBgDu8Xi0ybHRaNSk6aEq57dZYEgf596+fWvqDPw3BAOqEewpm83aFE9UMNhRq9UKkINIuCEUfM8iNrvdbm2fUskCo7If6QuORCKBiftU+lAEcc0QqFTCa7WaJpOJttutYWRiOEkhi/jIM+C/iWXEWr4HjE1yBo7wLWc8a/AymBDpJpjV+xLiIX36DF3MZDLqdrtWwSf32O/3KhQKAbKZWQBcI0QXvmQ6nZrPwd/6fkUKURRaJNkshf1+r8FgoFKpZH2qvmXtS15XJYokZjj89XptTg62mfOIdrudMWFIKJbLpcmh2ODoedkAkjSZTKxHhwSDzcd0KD/6n01CACVwMrkoFovZ5kfuNhwOLZD5qiObTZJtekrY/D09jxgHZ0giKyGA+olsbHYSOyQJjUbDHASgk+fgPwND4pkRTHEmJASU1um1gEFGV821dDodq8Dy7tjoJP04GaSoHK6ezWZNWphIJKxH7ZdfftE333wjSSZVLRaLFrDpi8LZ4cA+fvyoXC4XJou/wXr37p0BDfYRQAomH7nGdDo1gmM+n+vdu3c6HA6BceDsadhspqQx4ZR9SCBhuAZBCAXCcDjUN998Y4M3kFj5AUmSjMWsVCo2wVWSkUgEZeSmnjAhoSWp8YOpeA48Cw/o8DHSZaId/U/seZJKkmt8CXZL0PI9Cf7oAcgU+j4JivRCc38QZQAOn0TSKM974bt4jjDLgFIA9cvLiw3E8UeJNJtNk/fx957YIlivVivr8wzXdYu9B7GHXyQ5Z2L16XRSrVazM9R6vZ5arZapVCAB8cFUOABi7A+qC9gKZAp9UCQeJBow/AAnYj3JHKSHB5DYKJU1hrpJF3UDxDJ7ezabmZoFf4TE08tIK5WK2Qk2T8UGkItP8skme3e9XgeqHiziN9UJADTglQRWkg33ms/nqlarNnSIhAKfSoUDH4JkF8KKqomfBM87QFFE7MZvTiYT3dzcGHHLdVEhzWQyloyHPYq/zUIGCvm92WxMxeaPhUFeDEZiH0AyVqtV9Xo92w8kKRCb2JrHvsfjUc1mMzCgSlJg8OB8Ptfz87PNHCgUCqbqAWP6ayZ2YZfYNAQUhE0mk7EhLcRvbJokGF9CMQcylCE6VBvZixRBfNsUg16wM+6dQgUJJfscfEucH41G5nMgiInVJOEcfxGPxwOzBvxgGj6T2A3Wxtb4Gf89vCN6Nf3RJPgyrpFZKLToYfNfw7oqUYTFYHCNLyH7vr39fm9HU/R6PWu4BzgxKIWDMKWzg2TMd71etwSu3W5LOmfplNB9Qy4gCWdPAstGI1H1bKeXhVEpAfh52SaDVjwzD2NPEka1EZa+3+9rszmPN0dr7Sc2kdxiKPSe+ITMT1zzfZrS5SgNDBED5PpgGqWLVJhKLpUOgB+G7c9m4vNxZrChgG9fdsd5sBqNhiXMnAdETyLf1+l0VK/XraqcTCb18PBg45/Ddd3CGaLZJ3mAzSa58H2sXrbMUAXYL8AstkTfT6PRMNYOu2egAw7T793Hx8dA5Vq6DNugL8nLnJnCyrRTZJ7eznHssKd852q1sqESvoIvXXwYpAl2DJCFXWSCIPdAhQJ/wlCZ18OjuDYWfgiZKwQZ94r/8vIc+ne5HsAn/WWwnQS1UqlkwJXgiLQtnU7r4eHB3i2BnfckyZjg19JamG1kraG07fqFPQGIkIaxv+l1AuxIsoRpPp9bjKL3haFRfsgCnwug2W636vV6lnQhhaNqgS2QoOLD/eROKiuQD3yfJLsm5gMwDIu4wv0QW+PxuO7v703NQmKHbyJ58gk1sZG4SLyez+dm9/gsiBJAGf6Oe8HW/IHk2BTfIZ1B7mg0sqpFqVSyM0qp/gF2P336pHQ6bc8YaTFy39VqpUajYXFVklUGSTAg45jtsFwuLTEmwT4ez0OCOA6AXtXFYmFVx3Bdt+7u7gKVeohXFF7xeFyj0chIBumyx7AlSAQmDHOeMKQsElNwKJOmT6eTkSwc/5LL5fTdd99ZLGm32/r222/18vKid+/e6eXlxfCWr3pHIhGrttHqBQbwqh5wF73z2Dvfx31xL/w/dkGsoGrJ5/nPhWD1SRPJLYQohRJv69iqdMGvHHLPrAJ6u8EnVPfwQdIl0eOzwPaeDPMzG8ALxGCw+Gw2s9yGGQlgeJQ+4/HY5i3Q+kPu87Wsq0dLsiGY0IRzR/5IFZHqYz6f13Q6NcDGxqN8T3ADlNK7BigjYaRqicERqABHDH3xPUVcG/phqnqj0chYOF/VYHPQBwA7T9WTALnbnc/LoYeLTQpr4yWjm83GBnyQaHHdMIQE2H9OCko1Q5KxHoABL/OFVfFVAf4b4ACA90wT98ozI6gDxPkMLw/EWVDW575xlhxBAhiimrLZbKy3RpKBGO7ta2FbvuQFs4aThmkul8vqdrsGjDxDB8NHMzfDIfb7vQ1RoOqGYz6dzgNoYMcBYkwKZo/SZ8vf82c4d4Ix18RRGFTc+Cx8hx90AyBFzgZz6IfB0LNIMuyJGaSl9/f39qwYNECVD7sgGT4ejwaA8Q0MBCFBA6QDKpAdkSDDkAIAfTWdvkFIOGyR3wUQ4K/W67VGo5EN2vIyOUCm9wl8D+8N1hYJHH0a9Fv4oI+8Klyfv6g8QBSk02k7jxWGx4YAAQAASURBVBY/z3mmDEMgnkB+ANLY07FYzKrwAEDAGYoO2jf8EDUSPK9gYa9QpQAUQ+pix9vt1mTRJHQeJNKGEI1GTc4Km0//PsmdJKuqeBKJMyIbjUZAFuorIDD++IXBYKBYLDgwyFfemSMAGN7v99YOQa8nySw9nlQ3T6fzhO8ff/zRrglpK6CeRIBBNSTrEMz4WMg4+jPpFy2VSjZch+oN5A99oeAJyIL1eq12u23tHuG6boH1otFogKTE/9PnCrkCIUBlm4SLqcLpdFqDwcDOv2UvzudzO3+P2RoQn/wMe50YGolE9ObNG0WjUb19+9YKK/hy7M8fqxSJnCe3osKDhAHv+em57FlUJV7CzSAfEiQf18HGPD/iCtjRH2MFEUTFHvuUZLYmyeyCvS7JeiVJIknMwPbg3N1uZ3MNiMFIhyuViobDoRVrfO89n817IJn2foo4ytmPHl+DF2jRY44D/vpriZ9XJYrH49FK0/f39zZAApDmNyZMWiqVskAhyc5go0n25ubGXg4T1JCv8sJisZhl6QSSyWRijcY4VMCp7yvwTasEZUAprB8yFO6xWCya8SAFIVn0RkQ/EtJXWHoamAl+8XjcekA88+H7RV4DT+nMglBZoNrHdfheLRyI7zNCz+0N2OvDJRkD5HtC0dMDZkiM+Xxkt2jPM5mMer2efQ4yKj88AEkOsgvfH5PNZvX8/BwAGeH6/EVigYMnkHCmE9Uq+k1ns5kGg4Hevn1rzpSKP7aGrBnb8mATu5lOp6rVavaz0+nUGG6YSUgBgioMG9VkZM0kuwBb7ILvJEDD0nmpDHuQ6yTYs+c8EYIcnWByPB4NFED0UGHnOiCVuHYSb3/mGe8BqSd+DHYyFosZGEwkEjZYiCoHCSIyWpJDQDy2JMkSaXqGqeDHYjEbDZ5MJi35gBj44x//qO+//94SSJ4R341v4NmgTAht9LoVi8U0HA5NSkpySC8bjLlXtCwWCwM3JAqTyUTNZtOqFNgL74fK++l0skE4+GhPEgGeSqWSXQvVOk90ogjBhj0JQdIDUUiFgd4nbBS7h0zpdrumPsK3QFqCIbguX1WQLmP5+VlAGGe9ofTxIJHnRIUIsEsVkHjJ7/gzkakalkolvX371nAFA6pQ5oBv6B1kHL6vnB4OB5OkopLyg7T4GXwKfu/m5sYwEu+F7+Os3K8FiH7Jy0/mB8PSqnR7e6vxeKwffvhBLy8vVo3yFShJ+v777+2cw36/H5A0TiYTU9lx5h6YULocI9Pr9ay6+eHDBzsSBRsmkfnw4YNub28DiQ0yV7/vITbYN6fTyapj2DL2y2dRoZcuw6WoKGIn2C4xk+/l/F4KEuBs38pEnKYfG7UaBFitVgsUQCClKKJQ0eOa8Qun00mNRiOAq1Fm7Pfnyd+cyMAAS+Tr+Xze7JH2FUmG8Tk+CqzEHgHT0CeZz+f16dMnU4wQq7+GdVWiiMNmxHsmkzEQA8BD8kGQgwnlwTM5EZYB6Q3MiT+gezwe6+HhQcPh0IIYrCOgSpKN7fUGixwNDTQ9VLFYzHqwGIQhXZiE4/FoTgGGBSkakha+U5IZnB/dDyj2ANfro32vBgyzT0QB5cfjMRCcecb0n2FQnm2F3UJfznPlPnh2/Dz3joFQXiewbbdbu3cSDw6VxYCR2dJD6ifH8kxp7idx9dp03kW4rl9U/mDw7+7ubP/hyKgMkhhQIUCyCuPHVFL+DNY+n88bMwdRAlsJseOnicLwUa2TLgdQs88IOv7nPECkpwNGEYBKZQ5yiJ8lWOCHuA4CH3YBuPVEEaoAJhzDhvqkkmv1QRJShb4p+v8kWTWD+8Hv8JxJSvENnMfK9WNLy+XSkmQqLpJsCEY0GlWz2bReDYgm/BRSwB9++MES61KppMlkYu+tWCxaj2YsFtNgMDAfFK7rFskM/p1qIhX0wWBg+yeRSFiyTv8R1WvpMhgHebl0SaRIVPhzqmjsP/pyiEmHw0E//vij3r17Z3a8WCxUrVYtUUIm5mXoAK3ZbGYSWPq46Kc6HM7DtMbjsV5eXtRsNpVKpSzRhazAHulrLJVKZvdco1f3kAQDYAG6+DXsE9vDZ0C8IsnDF0iy5+l/nj/nCBCqPavVys4kJb6ikhiPx4HpkMR/H+f4LAZkQID51pq/+7u/07fffqtyuazJZGLqCX6X6/eJe7iuW/F4XE9PT2o0GoZrWq2Wut2uxcH5fG5Ylj3D/iuXy1bNqlarVhj505/+pHfv3tlAIz8Dgr2CQgvVHTESIg8CZzweWzJCfIKsoE2IRI04CaHKvs5kMvrxxx+NaMAO8Akkl74nGrsh5koyP4b9ewkshIukAAahl5jiAz4Ge8DHgFnJJSRZmwnXQ/6BHXkFjnSel7FarawYQeLnZ3hQHPHKucViYe0b2KYfXgOpi1T8l19+MYkw2AnyHMLva5GfXpUo4uxKpZKGw6GSyaQ1lvIwD4eDSVIIeH64DcNgxuOxarWaJQ1oftlMyFGj0ahl8AQBScbUI/dAwobMxh+xQbUCMAlLAZPDJsSpI5ebz+f2M2wYkkC+F8kWYNP3EO73ezvvBVksjI+vEJLI+WZ72PvXvSAwKbDPBE8vJ8Awu92uMpmM6vW6BW1AI6wmzCjSmd1uZ0OATqeThsOhgVMSOq4N8EDgwggIzv6+SCx4v5zBx3V5SWq4Pn+R8BMsGLMP8UGiRZCiF5C/R1pCRZFEiT61bDZroLDT6ej+/t4klgRPhsE8Pz/r9vbWlAfVatWqnTh0qlfsPfYH90LVk0oD/cSAJvp8qKrRZA5Jgv3Q+9Tv95XL5ay3ApunSuil4/gL2FsWJBAKAhJEbFOSgXo/dY7PhVnmO3nO9LZICgwjgXml6jmbzUxF4UEATHU6nbZqBhJaEg4/CZaeCg9ufJK9XC5NKiddju4J1+cvEh4Gwf3pT3/SmzdvLNliAIafUEjsy2az6nQ6qtVqZo/VatX6UznPlj4nvy8bjYbt7cViEajgY1fff/+9pHMCylAd344BwDudTjbpnM9hSAY9UZA9noxKp9N6fHw0goO4Dqj18Q/lku+ZAqT65FZS4MgQ4iE2L13kbCSGKACwe2KyB5D+SC4wAsSXrxImk+eJrDxDKvKJREI3NzdG2PBc8BG+1aVQKJj8Dd+HNPlv/uZvNJvNbJo0fZ4e36Ci8H2Z4fr8FYvF1Gw2lUgkDEN5ySZ7ARUAUtObmxtJMgkxFSvi7XfffWdEJNJr5OPx+Hmqph+2gl1D9nmiLx4/n7tLQjIcDlUsFq1tAz9ABYsEEwKXSuZ3331nOJOqJ7brFSQUKSSZ7UiXc4IrlYoVFiA+qVSC5V8XKsCH2AgKNYgYCiJcG8UgfEEikdB4PJZ0kZ4Tf72yjntGAYgNJRIJI8zA+yj68FdUQ7lu8hliMdL1WCxmw4ZIolEHgIt5Jl/DuipRBFSit+YBAvIA/T5RI8mi0sFAkzdv3hgIO51O+uWXX+xB+6oavXTb7fksm2q1aoGH5Aw2nk3Hn0myngECH2c/0mRKYy0MKWwqyRO9RySJ9GqQjHFOFUwiLAVN9rDHJNk4Cm8w0uWQcBwBFVsPHjFQqhFUUHhmvtn9cDjom2++MYP3khreEUAbporq5mq1MmNEZsNBp0iDZ7OZAWCMNB6PW28IjChgG7kP/8aRkpiEAe63WVSkYJipVqCtp4Ll+2axUfZcv983STgDTTqdjm5ubgzUYZO8d5h1BkFVq1VVKhW1220lEgnd398HgB0VSEDr8Xg0qSo9xFQTGJTD/VHB9BVBklwqGZBHSFrobUaZ4JOm137DD3VhahkAjyAryaog7F2eNywurCNKBxIErpWgjo1wnz6Q+WECqBj4eXzIZDIJDDQ5Ho+q1Wr2XHg32LQkq9wAMPElkkySRDvBcrm0BDxc1y3fc5RIJPT27VuLl8ViUU9PT3ZsEH4UBQx9NRAtxBlAYKlUUiKRsPe6XC4N1BAr+/2+DRLzCRR+GUBIzKOPKh6Pq1KpBKp+KER8j/B4PDYJJhXHfr+v29vbQIKJ78Em8Bu+vQSih2rB6xYMqjKSjJQm0SKmexIG8hLb8YkhRCdKAp6DJ4v4Pio+hUIhMEUYMoheR+4P7EMfk2/P4To8EUV7B0QThJTHO17Gdjich+h5MB+uz1/4bnp0UVz4IyLi8XhgkuXNzY1SqZSReMRSBqX5HkRfxfJqMdQo4EWOc5vP53p5edHvfvc7S0KpPiK/bDab9u6xOy9tBp9ynJkkixfILhnIyLWBWZG3Q0xgf/gJFEeSAskl3+HnVUDighmpqEJ6krShOMNG2PMk7lRFwZsQomBfjq0hj+A+SQjxZel0WoVCwX6OFg6+g/sEW2O3JK/SZW5ALBbT8/Oz9S9DRHEfX1PrxlWJIpPKeMGJRMJkaPQT0FjrEzuYQzTAaO09MxePx+0h84KkSxKUTCb19u1bbbdb/frrr7q7uwv0J/Lz9AGxEb1ExjtnSQbYvJQH5p0+LBJELzMbj8eBgDMYDOx+KF/DgABOAYgEBq4DFpUAKF16RGAmvPQH+RrGA+P6uu/Lb0xYU/oyG42GxuOxlsulut2uJBlLDQMsXc5yhL1h49Ogzbubz+d2lhPPGNkbrBAAmPPmcMZI3nygDNfnLxw81XYcFfYH0ERmTKWPve8ZcxIXpGDsV4ApvRs0ddOXy2fT25bL5TSdTq1v0YPd7XarZrOpaDSqyWRiPb4kR56wQQ6H/+FcN6p7flQ91QjYUq6ffgaCNgCR4ML5hTSp+88ngEgK9HLAUvK8qcamUinrS/KVRmyYpI57AxDjY70kjc/jvZK40geGDI6jg6i4Hg7nI3h4N+l0Wp1Ox6Tgk8nEAt1yuTTfis1Ll6Nuwori9Qvij3fEUTGSbNLp6XRSr9dToVBQJpOxfVEul/Xp0ydTpwAUISSRqc1mM0viIAbZT81mU8fj0SYmkuRAYAK4iNVUM4ixVMv8wCoIFIgIP8QCG6DyAHEKKMRfMTiGn8NXoU7y8nAIRv6MawMYExshXLl3QGssFjPACunsyU3iJIkilRnuE9ANEeMl9IBJX/mhOghAB1xDtOEbJBmB7Y9RoALlh8+BH6j2+wQ7XNctkjze3WQyUTQatd41/75JQlBy+cLFarXScDgMDH0kdoIdOf4CwtRP6aS1iDNSsVPIUQYdco2LxUKFQkGTyUSNRsOObQM/Eg/9FE7I0tlsZmoXJLXgP3oK1+u12ed6vVatVjNFGbYJRvTEDlJVnzeA68HqXoHmFT0Uc8AqDKQjHoFxiU2o7fyZ0eBLcgGvXvvpp5/09u1ba7ugAsh1vry8mD/gnGgwBEk+2B9yGj/pySym5H4t6+qppwQUgBPVQsAYLyqZTOrm5sacoJdRxWKXsd7L5VJPT09WtvdAFz0/E5dyuZzG47EeHx91PB5NStLpdAygwiJIMgmpdJGCIVGjPO4PJWXTRiKXATYANwyCZzAcDq3CV6vVLLj6sjosICDVJ6++Rw8w64OHP36CCguOC7kCnwmg5PPG43GgdwpnwbMkCO12Oz08PAT09f5oERIFNny/37dx3r6nY7PZ2MHc0WhUT09Pur+/t2DLcwRM8G8AOc8rXNcvGHpsC4kJz5c9ORwOdTgcVK/XNZlMrDIO+eJ7U6PRqO7v761Hju9BUsXExU6nY1JTgBr7wlfZAFKpVEqtVsuCEHsa4AZQpVJBNR0fQbAbj8dar9fWm0fiVqlUAv0fvq+Q6oOXpx2Px0Bfx2azMZ9CxYO+EKoUMLCe7EAWz99JsqSNwIWsE5m3dBmxjizXn6WG2gEwTyJH5TESidiAMSbpYcO+OvLy8mJSVsgf/Ba9Y5AD+Mb7+3sNh0MDNeH6/IV8kR4gpNPsw0gkotFoZEcNUc1Fvk9iCWCCXGGgDPvu5eXFqu68e08YvX371oAaUmX2gk+a6LchPkBuoE7AT0iyATHsOeIJQAu7ournVTB8lyR7Lt7G8Bdcm3QZisWipxlih0QKH5ZIJNRut619hqSW507FA9YfghXfCdDkevgMvgsVAtLddrut0+lkLRfj8TigmiABz+fzVrnxvcu+t5LKENfKu2aQRiqV0i+//BIqc36DhcLGt0JBwpMw9Pt9ScGZGcQOksJEIqHHx0ctFgtTcdVqNY3HYxt4MplMJMn6FieTicrlsvr9vmq1mqm4IF+ky8wAMCqtI1QawZG73c5IWWIue5T7gkDErlHdSZdjk7hHqveZTMbOUEb1Aib1FTTshtiHrUNoILPFDr3UEywuXRJ3sAa+SrqQRsRvnj/+ChxNdRh1BDif4hPf5Z9PJBKxYVz4IirDPP/D4WA4Yzwe6/7+3ghbYv1gMLA5Dc/Pz1+FjV5FOaHN5vyT4XBoshnpcoAlDhVHzUtHPkVwoULAQd+8UI67YBrSaDRSsVgMnBX09PRkjppex2w2a4x6KnU+N4xNPJ1OtVgsrKKFpA0HTdMufYX9fl9PT08ajUZ2npFP+t69e6darWZN7YBML/HzZ8ShhfZyPelyuD3GjUGy0SXZeVo4KgC7bxLmH/pOPBvNcRgESQIuQQknhaHRm1KpVMx4JdnQkefnZy2XSwuqJKQkmW/evLGKMs+dAItj8NPdeC7hun4BzOjJnc/n+vTpk+3Dfr9viQ1yKCqAJCD0I0nSzz//bMm/d7yAE+kM2DqdjhqNhsnhptOpkRy+JxayBHsC7HEmFeRIr9ezgEJ1oVQqmZMHcG+3W7N59t96vbbkEpvxLKw//3S9Xtu1cs8AYqRD9NIiNZpMJmabBEF8IHYIyyzJgjYVDaqMDBqiUoj/wR+MRiNJskEDjGKnUku/Gf1h+BSSSl+5R0Zcq9UUj1/O7sLPAvoBJS8vL3adBEwPysP1eQvlCDYKGF0ulxoOhwbS0um0DVPabDYW7x4eHkzNQpWJsfX4YWItZwDyM5Aq9P9QrYBcwD8jnwb4MgwK+/e9cfRRAsSy2axubm6MQKT6BsHEfsOHUGV4enpSJBIJsO7EB+IHoJREDh+AH8GH8RywRRJIpvuSLGOfgFhvf9gqgB4FAAofnh1DogDiSP4ggcrlsiV6tOZMp1Prbfynf/on609EHs6RJrxfplTyuUh7qXLtdjv98Y9//Jfcxv9HL+IIU3n7/b6RLLRPFQoFI+Mk2d+RgNBX2m639enTJ2vh6PV6RnB2Oh2Tdnc6HWsZGQ6H5ifAouBET05ut1sb1OLncUBk+kF19EQTN0hmweq+2FMoFJTP5wNHaKD8IllFQg85xN9hm9gQfgqMfTqdpyxzzZC1VDqZZ+Jbl/CBJKE+v0B55Ie9oargPiHCiaEUnrB3P/QRfMEwKv7Ok8Mc7zUejwPPF4KVolcqldJ/+2//zSqz2WxW33zzzVchP70q0rNRAZ2vpxKyyTzDDkhiuALlb9gPZFOUaCORiNrttsrlsjGHJHYkhpPJRDc3N2a8HAFAPxDGSiJKWRkdNoGS6sPxeFS73bZr8sN4GFzhgx6gyh+6TW8iwUVSoIq3Xq9NqgDbBAuCwQLouH6eK8YPk0EPUSKRMKcCYOU72MA+uOEMpIsOn2CPzJYE1Q8u4O9fD+qA4eQ9+f6sWCymVqtl34Ez4ZgTxruzn0JZ22+zCBYfP37Uzc2NstmsSV9Op5MlHcjHcLDIo6gGAGyazaZGo5Hu7+81n8/V6/X0ww8/WO8FYJfBSNgHTD5VPMa68/8+kcEmqKTTw0plkiEV7CskbJAVSFeoUnM2WaVSsYOMUQ1A7ADa/AHYVOi8tJvqHhUDfha7xe6p0sbjcVWrVXvGXr6Kf5AufSQ8ayrtJIKoGlA08F79z1JhpV/b2xhSRNoA8DX4M6TGJAZ8D9Le7777zoJ6OJH4t1sQKuxxSdZHnkwmrd/0dDrZ+Z4oZ0gSpEsFnIrCarXS8/OzEamVSsXIGPYxSh+qaPQrbTYbNRoNkzJDABOPiUu0E3gJHb221WrVSBWIC/wO5Kk/gsMngEwURS0DkQQoJjll76bTlzOHIYsAj/1+32Re2B73BY5gyAw4xds2FRxwBeASLID/kWS/OxwO1Ww2zX6ks3/gTFgSXir79LtBqnJUBiqqbrdrpAyqDJKFWq0WuF4wzJs3b+w9heu6hZIDrNRoNIxcmM1mdh7ier02Seh4PLZZELyTYrGodrutH374QZvNJnB2XzQaVa1Ws71AxYk5DxRMsF0IRd8qApHT6XRsDgGJDMQ/iZjfu+BY/t+rvpBb0n6Sz+ct/kqy+Me1SbLP90oB/r1cLpVIJCyG+DNVfQsGWJPrJXdAScC1cvQMv++r/lTzKZbwmUhR4/F44Hxk6TI0iueDT6AvkTPdwcsQVcvl0nrJqXCigPLE67/+1//aMLCXmH/p6+rjMUjGfKMpbIkfBoOTHQ6HdsYL4ItKBhuMzJ/A9sMPP9hnwr6Q2ZMwwnL6Mjabjmvl7xhTX6lUbLgFLKEkC7iSLIHEaDnwl+BKMCN4+LOWqJaxYXAs9CxiuGxs/p/PpKIiXY7rAHCScBOA+Dx/vAfOBqksABwmmOml3DPVI47UgN2CTYLRglUFwKDx9udCcuj360Ej6/VanU5H9XrdjDkajarVatnZe75XM1zXLQDZ73//e6s60wOE3MkfK5FOpzUajaxa7AetTCYTa/qmWvDDDz8E2MtE4jwZjorf6XRSt9vVzc2NkQafPn3S4+OjEQLIqAmS7D0SXPoUsXlfReT/fW8WCZQkS4CRjmLD7Hsa3vksKh34DRJS31fsiRyIDd/kT1LsDxamQkNFAMYRH0UwIpHjd7hmQIekQEWEAOoBNyoEgiGglT5O3hcB1if0+Gov5+W50AuVz+cDSUq4Pn9Vq1Xr8d5sNvrpp5/0l3/5l0ZcUKngZ5hejNwQVU+hULAWEF/5grjh76hswcRTQYTgZB0OB6vK53I5G14EQCVZJXbTIxmLxayft1Ao2F6GOEZ+1Wg07Pf4Do8jIEMAY4vFwghObBLgR2++dO7N6/V6RvpAnvg+bWILpCdEEbGRpCuVSqlYLFqFFaUFNkZMl85+hsE98Xhc/X7fyFvf2iGd4yRnTCM1pkLzWhJMOwbtItgxU2D5fkgFcAMVo3Bdv9gfYBXk2djkbrcznw/uYZI+ChYw2zfffGN+tNVqWfxC5eWHw9DrhuoLzIe00Z81SPJ2PB6NmGR/M+mcYgRHdRD3qXjSx0yig79h8j/VUxJCqniQMsRDzu/M5/Om1sE3/HPkFEUBCii+zQwbgbhEVYG/wzfw3Jh9AZ7GT0LaoLCA9IL8jEajFhvBwUh16UMEqxOTefYM/YI8Yo9A4PDcIbEpZJFTfA3rqqsEZLCJ6N/hpaFTJuNOJpO6v7/XZDKxyUyFQiHQUI6Wv9FoGJsNaKSMLMkqAb5foVwuByZpAkwxcOk8iKFWq1n1Q5LJT2l294GQZmAcMI4BI2ETU0aPx+Nqt9vWFwRYI0D5w4T9n5P0cW8Yo9eSU9b3Qyx4vmxEPoPrRRaMNJCqhWdQfZM+3w9bzIQ2JjWS6PrjCXCCaNIBpPTDwVbB0tze3up4PNo18R5Xq5U5KRjTcF232Luccci5bOxFz9jBRjJA4/n52Q6h91V1mrcJlJApyWTSKt3j8djOPb29vTXHmUwm9Vd/9VdmnzCagC+mrTKxsdlsSpIGg4F9PlVvkjR+HwkQ8kskIUxhJHj6gESVkh4g/i3JfAsJka9kkLT65ng/9Ak79NULyBxkob7Rn9+jX5AK6HK5NNLH98UAmgl4kUhE3W7XSCia530fpifivNqBa+L+6RElIeUde4YWEBCu6xZ2QUXx+++/12QyMeCRTCZNAu7lYSR7frAT4Gs0GtkwGGyF3h2qWfl8Xr/++qsd2h2NRq16SdKBfUH4+mEUsVjMKpBUCvh5339fLBZN9QKxAXlC9Zy44UEVVQWqj14d4xUy2DB7GnWBpIDkzxOQyKk5xwxfQkwHLPKdo9HIACKkMfGZgSaQM75Xk/fE3/E8ut2ulsulAU5PsnsygGc1nU4lnWW6lUrFqhn8Dn65VqtpMBhIukxeDNf1q1wu2/mcyB19rPHHmdDugyKLHjjwoFcL+NYrP/2a6tV8PjfFz2Kx0Gg00nfffReQV0L2ga2m06kd9USM4ppJHKvVqvXps1DnTafTgMoMArLX6ykSiZgyBRvD/kjOwM34Iwgr6aLQoSeTvY2yycc05Jk+DuXzeau2QzxJl/MPic8oiPCV+CgmouLLwMJU/ZbLpbW1ve7BxM6weya1Q7BB2H769Em3t7dWUCG2UxSRLgM2k8mker3eV2GnVyWKTGtjCuI//uM/6uHhwRI2ghKJAglQJHIebTscDs2R4vi2261tmng8rk6no1arZS+Ql+17NzAkH0SQTR6PR9OY//rrrzZURbqMIKYKQSXCg7vD4WBG4Uvdh8PBqg8EMKqWt7e3FvjZQBg//Y0wo4BKZGsMF/G9FxhEqVSyRIwKKkbk5XEEdpgWH1B4Xn7iFQGb70SfDchAe480CGdI2R5n4ZMOnCRB1Z8v5yfRcU0YEkb+NRjP17DevXtnfQucY0hiR2UZwASDTnWx1Wppv9/b8TcQDsjJcNA0lR+PRwsC9/f3VrknUODcSU5gULH14/FoMjz2EfIPdP4ERK6XyghgudfrqVQq6f7+3oAkKgca+fkzHDwB1Cedr2V1HpBxTb7yzf+/lrpT0YfhxKdQ4fR+0Vfo6FErFApG7MDM+j5JfE4qlTKlA8yqvydkTvgOqqhIgPk95DuS7BxKP1mZPmsqTuG6fkG4eamwZ9T9ZFwSRhh8X52eTCaW5KNwYR9CNFL5YHAZBCx7E8k21wXxC3gjgTsczlNXvUTcE6xIu+fzuSkUcrmcFouFSeM4akWS/dvLygCgECrYAb1Yp9PJprYydEmSxdlMJqNmsxnoG6SCCOFCosez5XkySZFjQ4iPEKbgFQbQkMzhV4hxvEsAO34FKbkkq14eDgcbcsK9QxpwDySxqCiomhBPqYqQ2ITr+sX+hhzBhw8GA4tB7H3wG3/O0VPEB69ygwQkAcXWwK+Pj48B2XQsFtP79+9VrVatsFEsFg0fegKDtoLdbqd6vW4EB/fDeblMp4eEwj9AdICLGVZDMkccp8ABIbnf74284r/93AkIUu6f6ifPDHKWOJxKpawiyhAtZn/4ATcQ2RA9kGTEXul8jni5XDaiiom1nvBl4B/vhUIPxKtXIvl2gePxfNQQE9sZKPTy8qJqtWrS2Nvb20ByT8/yl76ulp7iRHO5nGXek8nEgst4PLayNmwjAAzQRmXCSxZrtZqm06mazaZJN0hsPLBKpVL6+PGj6vW6er2eWq2WbVoqCPQG3N7eBiRlgC8GBXB9gFd6Gr0klIDG55LUeMNk41HNm8/narVaBnD9RFYP6vg9H9x5zjCgPun2k1h5LoBIZDIM1SC4U82ATfHgnUbfSqUSYMk+fPhgUhkSXMCllwkAJghevn+K5wSThlwIICzJPh9Z8NdgQF/6enp6solbfuCKdDmfix4kEgUIFAJkPp+3vcfQBHoLGEbBOXvsj3a7bQ73dDpZFTqbzWo6nRpZBGOIY57P51osFtbLAYHCtSF/kRQIGvQxwWAiy8HeCfQERBy1dEnyAGd833Q6teAPWeITRKrlkoyFZSIqPgM/SKWUSalU8mEXsUs+21cWCcDIZzh+BDvmupDu4it7vZ7q9bodceIHdJBcIMPxVQr8ymq10mAwsOFZPjAjOwzXdYukgeQP8EDsJLHxk/cgT9nnyEIBQL4/fTqd2nslPlA9gPCULhUowBT7jX7V+XxuiU+pVLJKH4NWOp1OQN6GdIu+V4AYAA6ZG3sf/4NvIPZBTHo7T6VSVmkhXkIGY/PIugG29AYS03kesVjMbAMJH98PLnld7fQkJqQuQ/q8RBGswPsj+fTH9KxWKzvKYjgcGvlGBYp3iXzeV2a5zu12azLVWq1mpGvYvvHbrF9//VXffvutBoOBVZiz2axN8PZVbfYRRyt5YuV4POrDhw968+aNIpGIVRgHg4FarZbZOsoekgj6XClykPyUSiWLh1QJiakkMdFo1LDh8XjU8/Oz9TQySRt8iS9BdurvyxdSpEvM9MQq+xGyCB9CNZ1E1VdCiYm+0AAGp6pKbGXaP/uaSb/ENIo6SOo9QU3eMZvNrJoH7gD/nk4ntVoti5eQUtgzBDlEDol+r9fT/f29JdkeR9/d3VmVFqk694zf+RrWVYmin6DHMItEImFj05GmULL++PGjARw09AQLxt3SI+XPEuNlPT09BZq0+/2+CoWCSduQqZHwwY7DxrIZ2RwEE0a9E8yoQsLwsjkJWEhUCYgEZYwP5gSDQD8OY+s3GUmpdJkcRZJI5cNX4vhOQBrMlW9EhvXwVQfui0lSBE0/9YrGaSqxmUxGLy8vVuHFYHmGGDvOstvtmuyAzwOYkyT0+30bsMDnp9Np9Xo9vXv3Tvv93t5nuK5fMO6cc0T16cOHDwaqCBg44F9++UX1et36BtHpt1otDQYDk38g1/DTRwFVSOdw/OxFQKuvPLO3qJLHYjH91V/9lRErkAw4cxIviIxCoaBKpWJTOpGGUnWBnMG+2ZMwfdgNlXiAXKFQsGBMokd/hCQjxZDdc/9UHbzCgYoFSagfDOIDLr1e9DJ4WXk+n7fnxXVLl8mZ1Wo1INmhxwziCL+AgqLdbgfOpWOYEOC8VCpZn9tisTB/TbWT5v1wff7qdrs2TZrFYJhSqaS///u/11/91V/ZHvA29PLyYhJUP3E6nU5rPB6bf/d7BVaed04P3Hg8VrFYtIRkvV5rOByqUqkon88HwO4//MM/6Pe//72ky+CGVqtlySqANxKJ2KRx9hRx10s32ePsfewR5QrgGFvycZJFHPaKF8gj4jhSTwgScIhPpH3cJSGEoPHqB44ywHaz2awBTN82cjweTT11PB7tTLv9fq9ut6vVamWTj+mvmkwm1t9IhckP0uN50TfJu4EQRPnAOw/XdQslF0QOuJECCAUO8FO73Q6ozDyGone83+/bUBzvg0+nk3799VfrcaSyx0Ad4gfvmAE6kch5yBtzJTi2bLVaGemQSCT05s0bax1AJg1G9gNlaOFiD/veO2IZpCryWaS00mVGCIQjygj81+vJ38jPSWh9C4hXOfjWC/wRA9b4DFrPfHLKwEXaKLAdBm9h71RRkdPzPVwbRDhtU4vFwt6jbyVrtVrqdDparVZ68+aNdrud2TX+jD31NayrEkVAI87weDxacyoVKpK8Xq+n29tbLRYLG0AD4PHT3jx7yu/BBDQaDSs55/N5O0CUpIJJqoAj+gkBOLxMev18j12/3w9UE6SLjKTT6UiS3rx5E/hzAB8GhhPHGBhgQTlbCp71RGVHukytI7n0fw7r6wf0YHiwOTwDmoUl2RlbsK2wtsfj0Rr91+u1MTUklSwO++VzvYada0LSJsnK7lRnXwe6P/3pT7q/vw9UYrLZrIrFosmQOHj6azGgL31FIhENh0NL5pCrNRoNSTIbYY9yyDW9UV6CjANksiAybH4HqaKXFpPkUS1kzyHZisfjdoYqAPbXX3/V7e2tyV25Dpw9A7QAgjS5p1Ip63/2cr7FYmFSEHqvqL7AGhYKBQ0GA00mE5suScJFgJhMJmZLBHVAI0GKngWAKXvfD8KgcoIqgenP2IrvV+ZooGq1ahUV/CzsKRUEiCc+m2AOw4rshyouVUsSaJJl3pt0IQP/9//+3/ruu+/MPwCMw3XdgryRLudmVioVA0z/9t/+W6sAs7fu7+81GAysqsaxCfTtI1kGaBHPIGgikYidC4ftQv4kEgmrZEFMohAiibu9vTXgiIrEH52CcgTbh0SBUGQPIYGnguH7hamUQbhS/ffKByqWVPFIplEBgBVIwkic+H0kul6WSiUUAAwJDJgfDofWe8VAGe6T4T18jm8tYWor10Ech0RGFeXJY6+g8rJ4ngs9cvhAyILD4WBDA8N1/aKFiH3tZfj0KDabTSMO3r59a/u63W5bkhaNRu2c4EQiodFopHfv3hmRQ1xtNpvKZrOWYBKDfT86fY8QQZCSJIw+ucEWvXSUz4GMBB9st1ub+D8cDq3qiT+AiPSSZwhjbJGhjdikJ4fxMxxRgYSaOQh+QBvXChHCIlFmccQNdhmJREwJQWJGrKZFjXwFDANhR+WPGMrQImTvVAsZ8CidiSTyFKrJvV5Px+NR9Xpdo9FItVrNCCLf//i1VBSvOkfx+++/V7VaNWkSAGm32+nTp0822nkymajdbtsmkGSHYnvdMgkZDv14PBrAomK3210O7yXhIUOn3M8GIfEgOdtuz2f5MbGRoEVfEYkSfw+7UqvVVKvVtNvt7Pdh8gB2mUzGJld5uSfBnWDky/gEJX++le8R5Fktl0s7B4YEkZ8F6DGwA/bzcDjo/v4+oCP3QUs6g1ASBBhIzvmhV/Tdu3d2lg2Oh/cTiURseh3rdal+Pp8bU/O73/1OiURCvV7PJIs+EfAyB88Wh+vzF5Vhknz2NrIynDRytE6nY1X85XJpx8fc399brx7VQca2w9RRGcHBJhLnY2fa7baNqKaJHUdPnxCBOBY7DztKJpP69OmTVTxhI2EKSco4/8ifi1QoFAJS23g8rtlspna7LUkGvtiDOPhqtarvvvvO9h6f8fLyok6no1KppFarZfcMaEWWC4sMEJUuMhtsDTLMD5gZj8c2kZZKCMkf/pVACxPZbDZtGh2SmP3+MoENEI5kEP/E+ydAJRIJS9xHo5GpLk6n8zAjZDl3d3fG2iYSCXuX4bpuIT1dr9f66aef7JmeTif1ej0jbWazmQFElDokJCQsgL5ms2mEJ8PH8AHr9Vq9Xs8IEOLVbrczuZWvjvieRvYdKpbxeKxIJGIJFdVmyE32d7/ft0EzXtZMC8ludz7CBqk3hArPgYqdP86JmO4l2YA7bI77Briu12v1+33DHiTNxCHf3oHt8/f0cZfLZSPDsGeuJ5k8nxX5xz/+0YifYrFosvvXU5Nvbm6sb5PJpzx77g2Q7OW8PBd6wIbDoSSZ7zmdTvr2228D4Dpcn7+i0fMQOF/J5pzKZDKp4XAYaI+CsKFayLmB/JNKpTQajdRqtcxvs18hJOlHh2RFGSPJCih+wJx0mc7KZ+52Oxu0hM1xDePx2KaTEwMogCCTBtcTWyn8UAgBt/pZFRQkGo2GqctQGXA9qA5JPik0MNSQKiP/j12+bo3gWrDTbDarxWJh5xmSOPM5XjkAyYkcvNVq2XNvt9sBVQPFJ+Zu3N3dGVENSYz9J5Pn81SbzaZVhbkH+phRI0qX6epf+rrqKtHbkoz4Sti3336rTqdjUtFisWiJ2GAwMC00Tp4A9/LyopubG5OOIn/j8G4YHdhNggovDtYC0EhfjtdYR6PBcb6+R0qSleMJ4HyXl67yM9Fo1O7NVzEZNHM4HGxaEyy9n1JF0ABcAmDZhBg6bAeMKZUESVYlxcA8qOSaSDTT6XRAZ05lAFaW54isAQkbFQkSbK5zvV7bdEaAAg4HIIDUgCFB5XLZ7o2q0eFwMMaF6w/X9evnn3/W4+OjPdfZbGZMHQkE5AdnWQIuvcyREdHYKkeykJwAkvweJkgA6pCOAwYXi4URCZJsf3HcSi6Xs/1EcALwEjQ5XxGb9sdQeMk4VZd0Oh3oN5Qu9o6fYs9SyUAdASFCJQPmEnC2WCxs+ATsKMACgE9TPqwlEh8CjSdkALMeLHq7h9ip1+v2/JH+MHodmyOw++umEsv90quIFImKBgws/ppEMgSi1y8GYqzX5/P8UIFEo1E7U5jnT2LAuwa87Pd7jUYj68HnXTKGHaIO4oTYi/KDCgP7h8FUVKggZCBmJVk1DHKWuMS+ZeJxNHqesujbIVCTUK3DRnx/I3+GxI97Im5QYYS0oeIJZqBakkwmrYpHT6Unbanu+f4w/3fgBeIR59nRDx2Px/Xjjz+qXq+bVPsv//Iv7ftpsaHaOJ1OjexGGlepVDSZTKwfnOfJpGqeDYNxRqORVZpIPnlXVHVQToTr+jUej1Wv123mBGQmvcFMRfXtNl6uCoEKIbff721mBe9vOBwql8spmUxaj7JXZKFCQclTKpXU7/dVKpVsIA1zNGjtkc77FckjQ21YEFC0UvBdkUjE9g8FGkhfSUbGUDGkWorNoubh9/E93JPHvsjF2bNeMs1/Y8N+MBy5BMpDP6eAXmVJhgWI975HWJLJYiG66RGHlAHjgj9qtZrlAovFwmapMByOSv9oNNJkMlG9XreYLMk+F/IIP/alr6sSRRICnG0kEjEZ2Xg81tPTkxqNhrrdrorFop6fn/XmzRu1Wi2TJsKcJ5NJ/fjjj1YJ9A4a5qRQKJh+GccPuJRkRkcl0MtCN5uNMa2+qZZkEbBJwkYwxhh8QCXY8fNIAzBCehFZvjeJSWU+eSOxxfFjnABGeqQwZO7RsxLlcjkA+rheFgYkyXoUuTbui3H3VGiREPNsPNNJ4sdxCPRckEBIMukNrEsymTR2+ebmJpDgSrLqMslquK5fTMLk3QHsIpGIAZxKpWLjnv2RB1SEqXLA/kuyZvoPHz7Y5E0a4Enk1uu1np+f9c0336jT6djUTWQc2JcfHuUHtxCgkL7hMxja4KVrVMaplk2nU5POMGWYeyAQsbcnk4nJT5GwIu+EyaSPgWAAgIc1hcVtNpuWRPm+Q+wNSZoku3ffI8L+98QVSgCSZhLp3W6nm5sbu95IJGLDZwicfjAAvoTrIwAfj0d9+vTJbBJ1BdVKwPl6vbagiO2GdnrdonrP9HDaJNgLAMxoNGryLmIGbR6n08nYa0n2++l0Wt1u1xIlgBt9uCRmtF+wv+kR5/NRsRCX5vO5HQ5OrCZp5Qw/2kWIo+xlbIJ78X9HLPY9vVzzbDbTbDZTPB63BJNKJvbkJ8Py/CC6iFf4MrADiS2KgWazad8BFphOpzYdNR6Pq1qt2vPabDZ68+aN/Q6+AVILfwP5mkwmTc5H9Wiz2ejm5iZABBNHIWwgcyXZNGqI+Xq9brEXeT+4JVzXL6S8YFHkpgyZoRqPIgrcQ7ydTqdWaYM0h2zAt/rKHgkNvlqSnYmJzUajUatW8rPSOaZ8+PDBhkzhQ5gUzKRq7N7LuFHuQMaOx2N9+PBBNzc3RqCyzyErSfK4TooLJJNI4321m8ordsJ9QYaB71HqgZvBCEjcPRYHe1C88i0gg8HA4imtItw3fw6ugTDn3GaPp1FZebJ6NptZ8sheIIbf3d1ZywCf5YkwjiP6GtZViSLgRToz++Px2A53Xq1WajabNq5+tVrp4eHBJl2S2XvgSG8QbAAvm8QNRw37AgvowQ1VQFgLP0HVN+ISVCUZ2AQk0qPAdXrJHbI9DItNREBNJBImU/H6cT8VCkfhdeXIQX1/EJvKVzpxNBjtbrezISAEJSqDfgAC38+GlS6TGpHiID8E5FI9AgzjAHjmbHJ/Zpcv2TO1j6rm4XCw6od0PpsHeYMk9ft9m6AbgtDfZlEhINFH+8+EMHrrsEEq3//wD/9gzCVT2ABWu93ONPlUf9krkEWlUkn5fF7ffPONNpuNHh4eAtILQB22xzSx4XBofY9MYWMwDueYUsWkCse+hnRAMusrKQRDhnWwB6n8AerwKZJMdrJarawaR0DiPMrHx0dJsiQYaY90CaI+yaP6AZBH4gaAJyAi44EU4l0iNcNeD4eDXl5ezKbL5bLJlngfgG+mZJbLZWOj6eXiHy9FpqfMT35mf8Risa9mtPeXvPDNyHnx609PT7q5ubGKG0kb02rpKyLmwNTju6PRqPUBQUYASsfjsQ2+QGIKU//8/KzHx0dFIhHrn/Rys2QyaUmidDm+g/jp/0yS2TUxi75HP0mXIS0AZK6TOA2xSowBIGInvl2B5IkYSvURX4E/AFsA8N69e2f9VZArxDuScMgmqnfEdgbYUaGJRqMmp0NpwJRgPiOZTFqi7eXfHL8QiUTMxx4O53NwuZ5UKmWJPs92uVzaIeSn0/nQc08Uh+vz1/F4PtOWarDfz2DC7XZrknD2W7vd1tu3b83PM3zNt1F4Uh7FiZdSQrRzDSSGkA+ou1C9HA4HU+R5fE4xAZsCzzGpnKo09r7f71Wr1ayqjVqMa4dApornW5t8UuQLIWBdYgZJLz6BuEJMpA8TnEqcxJ5QPFFtJSHzk1Gxdd8K8vpaIFnxc9K53avf7+v29tYKP0yE5nmSjPvkn5jIe6R4RbsJfpHWLq/C/JLXVVfJxgIk4uBg+kmqSBTQ+JOdozf2gAb2BQnNYrGwh/7y8mIgkh4LXgw9M17bDesD48N3YSBsHhhO+rFImEhmfc8Q8jMAq+8PonoJawiw2u/3gZHYkkye5ieZ+r4IPk+SVUGRgTFSnGeO3AQ2A6dE4nY4XM5k5OyY9XqtyWRifaBIW4fDoQVuWFee+Ww2s4RUOm/2zWaj+XyuzWaj8XiswWCgwWBg94tUyjco82eMeyaB4aw7X40N13UL+SNTznCgBAMkJuyF+Xyu8Xist2/f6uHhwWSGtVrNJBQcF0ECx2cCJpm4djgc1Ov1AtXx15I2X9lGGsbZSti2l2QSXCUFqnvI0UkoSeoAU6VSSaVSKTC9EH9DRRFCh6Eakoy5pZJDv8Pbt2/VbDZt347HY2NIU6mU+RtJdp4owZvDjvErSM2y2axKpZKxsQRcrhefU6vV7N0AupEY8rn0/dIfwwAvzrhtNBqWTESjUT08PAT6wQjcMOJUZgjQk8nEgE+4Pn8BBCUFwF6r1dJ0OjV5NkefSGdCTZKdJeilUJCmXl5OnCPGNBoNk3lBcMKC39/f2+AU4jIVA4ZDUR1BesnQMyRzxFPaJ7g3L+WWZHEI/0/Ch82SAHMt4AKSMGTm9CdSWfn06ZMGg4H9PISNb3+g+g4OIUZyT6+TwF9//VX9ft+ALYoJZLF+AJ8k8x8QXVSaiPWn00mNRkP39/cGxvGd0hlfME2Re2SIF5UmFALL5VK3t7eGPcBk4fptFoke0tDxeGxSTNqSmBoMNmNwDdVxSFcSg3K5bBiQfQvRAZnx8eNHxWIx673Frlnz+dwqX3wPvcpgSaqKr1um+v2+yWYZ8kJsJX5BmkLmemzGs2APgiewHUhGkijw5mg00nA4DFTzUORJssKLpIAcncoovZyeuKIX2CsT8BdUX8EFJK8QOpypCgGTTCYN/3v/RksAfsoXRfr9vk1exWdCRFEsAv9Op1Nls1mbBv21DLO5qqJI8ler1cx4VquVSV1wtPy3b+hMpVLqdruq1+umoYat8P0TADX62QBCgEgCDYcNTyYTpVIp63dD5krzfTwet41A0gQrMBwOjb3zY+Kfnp5MwuYHVgAKYSi8NBNpKACWQMEUQ+lybiCBHtYTORGbjoSNz2bCHL2Q9B7CHPMZGAnBFgAAoAawIsGNx+Pm3GCpJpPJn50DQ58HSSsOk0lQBEkcFUYVj8c1GAyUTqdtJDtlfMB3MpnUYDAwxidc1618Pq9CoWDMNY4cMLpcLo11ZA946S9y5lKpZBM/fXXs48eP+t3vfmekEU4cdpTR3SxfwWBENc7bM3IAXCRtAFAGRs1mMz08PJhkFBunL8jL6XDcXB97l/5iggr71fc4QGDxD88J0IxsCPkdgM9X/r28hOdLks6z5XNRNFCJgXkmKaT6yT3g15LJpAW6eDyuXq9nvaapVMqOBGB4D8GR3opMJqPRaKRyuazD4WDVfQInFV7IHIBDuK5bvvrHPqcyB2GXz+fVaDSsnxFWm4l69Epho1TkUA1ICkjfIBwhAwA/vkLA0CWkpCR8xDTfA8SZwIApiFu+G6Dlz4yUZHGK5O90OllVAlIWSTyVE+I08bHdbgd8Cv0/HIjN4AuAtu9z8hPCX5O0+CP8xt3dXaBCw/MCSGITPA8qjNgbPgHJG4OA8F9+gBjVK9Q1xFT8JHEY5QPSQkkmv6tWq5ZghOu6RcGDKlqlUtHPP/+sN2/e6OPHj2o0GuY3mdfgJ2IeDgd1u11TaSQSCX369Mn8PsUT8N5wONT9/b35e/pSKXxQkIHE84MNkTWTgPjp35C6fjop2BxfTo86E1Xp+4V45X6wJ+xmOp3aWaz8GRgSn8OzpABD9Zy/Y9YHFXY/HwD78VJOSUbUgCE545BCEvHfz7zw/oV3tlqt7Nxh/o6eTohubO/HH39Us9m076IyiM/gOfM+keJjk5FIxM4l94Mgv+R1VaK43+8DWmuYAQ+sYKORckrn4Pj8/GzBhwN8pQsTRxLFxE8mGTIhjJIuL4MEqdVqGQsIs44R+c2OMS8WC3W7XeVyOZNyvZZ/vX37NlDKli7T2HD8VER2u11A0keC5JlNLwnxCSKg1JfhPcgGBCNTlWTOYjgcGtDwg2g8mwmI5DN5Fj4AkpTz/GFxaNwl8JD408vEUBySU6rIx+N5iAYGT4WVpMTL6uip4DmF6/oVj8f1yy+/6Pb21t4X5yohnwKYMNyGvTsYDIwUOB6P6vV6dkQM4PQv/uIvjLh5vQ9hP0n2SArZb7CzSOSQiwIOCWJ8P9VD/A2+BhYXoOePBkDtQEUBQgXACNsL24ctYjtUYWEnIVyQ9AFEj8ej9VjAdvJ7+ACAIRUcWNvXSS6AfTKZGLGDDRLg+TxsjJ4mGNl6vW6+hf42L0OF1OO+8REk1eVyWS8vL0aQ4feoQEkKe6B+g3U6nafLQjZABjI8CF/O8AgSB5I4KhvpdFrD4TBAFgL+SArH47H1mObzeXW7XdVqNUtwPDnke+CxH+I3LPprO4xGowZcUbUcj0e1222rVFJFgADB1rgHQDmtGRybxGHigFr2MRUVFuAbYgzgSaKFvUoKxElJFrv4GQA5km0INsgeEmbf5sG5shA4+DGUVp60jcfj1qoDSURS7SuJ+/3esEs8HtfLy4vhCa4bn81RV+Px2KTJ4bpuUX3iiCJ8+Hq9NlUUxD1tS+BDEj2G2xAT6Wlk+CHEJUTDeDzWeDxWrVaz4UXS5UxHH8NQj1Ad9Aqi8XhsOPjm5kaSAsUFprWC/5CoUwQC39OeBB5Mp9Mmb06n07q5ubHYTuGBfU4M5L+JW5yh6s9h9K1P2KknWiCXpMvEdK4jEolY4QeciV14dQVDdbh34iRTiFFQ8ZwkmcT4cDjohx9+0MvLiyWOvp2HnkWqhdwTyTI4CTyMuuBLX1f3KFYqFetJBOzTK5HJZKy0nkwm7SBRZGBodgFwBAt6jUajkTlQHO12u1Wn09HNzc2fOeDtdmtBhGvA+dKPw2ezIXK5nCWhNNoijfXS1Hg8rvF4rHw+r8ViodlspsfHR2PlkdoQnHEKvk8JyR26aJpzCTLo3/2kK1/eJ1iTYFFpZKqqlx4gC6RqBwjhgHvYXXo7SVRhKr3OmyANIE4mz8eeEEgZAkIQpKm+3++rXC7bKHcqGjBkSBx6vZ4ZK9URGOtwXbd2u50eHx+thwKwgX0hZaIfAlas1+upWq0a64dkDVt9eXlRt9vV7e2t9QtTQSSQYm/0B3MgfCx2nvzJZzP2nqQHORVycJh9goev7mH/VM59pYS+HeQ2gFoSN4ItQICgArEE84iMHnBA0kqfx2AwUCKRUK1WMyk29sh18S78ZDn8gperAdCxzXQ6HTivkOFRvh+bniQqq1wbxA1JrXTp88BX7XY7u1b872g0UjQaNekbVScCtt9b4bpuRSIRPTw8WHIEyTifz5XJZGwaJj2lsO0ATCq90Wg00FdKGwE9q4DWer1uMee7776z/YNvYJ8w5IiqBgkse454CzEJcdFut61vdzqdql6v6/7+3hI/Yi0JI2AJAI60OpFI6Pn5WafTSXd3d6rX64HBSuxbKmjESeyTmAYeITZR/eBnpMssBMgShm8wFwFbxGew+DxiFooFfJ8kI+lI3JGvN5vNQHtHMpm0/mH8EL4BPMPzQdWDj8pkMhoMBoGBW97mw3XdggigEBCLxQz7+QqSry6TYHn7RMrvEyAwJtPlN5uNVZ5QaUGWEm9QdfB3EBbEgVKpZCSMJGtpQOXDXiYWs/9JjDwp6hNcFAoQSK+PveBafK8f8Q/ppy/AQBxB0CSTyUA/PNfiP0v6c5UPPg/8QFwngfYDF8Gp2A3JHf29Ly8vRvgul0srblSrVeuXZDI7MxQgdCC6wCTlctnIMl8oyufzRkpD2H/pK3L6GtLZcIUrXOEKV7jCFa5whStc4QrXv9j6OkbuhCtc4QpXuMIVrnCFK1zhCle4/sVWmCiGK1zhCle4whWucIUrXOEKV7gCK0wUwxWucIUrXOEKV7jCFa5whStcgRUmiuEKV7jCFa5whStc4QpXuMIVrsAKE8VwhStc4QpXuMIVrnCFK1zhCldghYliuMIVrnCFK1zhCle4whWucIUrsMJEMVzhCle4whWucIUrXOEKV7jCFVhhohiucIUrXOEKV7jCFa5whStc4QqsMFEMV7jCFa5whStc4QpXuMIVrnAFVpgohitc4QpXuMIVrnCFK1zhCle4AitMFMMVrnCFK1zhCle4whWucIUrXIEVJorhCle4whWucIUrXOEKV7jCFa7AChPFcIUrXOEKV7jCFa5whStc4QpXYIWJYrjCFa5whStc4QpXuMIVrnCFK7DCRDFc4QpXuMIVrnCFK1zhCle4whVYYaIYrnCFK1zhCle4whWucIUrXOEKrDBRDFe4whWucIUrXOEKV7jCFa5wBVaYKIYrXOEKV7jCFa5whStc4QpXuAIrTBTDFa5whStc4QpXuMIVrnCFK1yBFSaK4QpXuMIVrnCFK1zhCle4whWuwIpf88v/+T//Z202G0UiER0OB0lSOp1WPB7XdDpVKpVSNBpVNBrVy8uLbm5uFIlEdDwelUqlNBqNtF6vlclk9Msvv+jbb79VIpHQ8XhUNBrVer3WcrlULpfTdDpVo9FQIpGQJEWjUS2XS+XzeW23W6XTaS0WC0UiEZ1OJ202G2UyGUnS8XhULpfT4XDQbrdTIpHQ6XTS4XBQJBKx3x+NRkqn00omk1osFsrn89rv95rNZioUCtrtdopGo4rH49rv98rlctput4rFYjocDjocDorH44pGo9rv91osFloulyqVSkomk9rtdopEIorFYkqn01qv11qv10omk1qtVjoej0qn04pGo0omk4rFYtput/Z9p9NJ8XjcnnU8HlcsFrPflaT1em2fz/1vNhudTiclEgktl0u7Bv57sVioXq8rGo0qlUppu93qcDhoMpkoFosplUoplUrZ84pGo4rFYup2u0omk8rlckomkxqNRopGo9put5Kk0Wik3/3ud9psNva5kUhEuVxOg8FAsVhM2WxW+/1em83GnuVyuVSxWNR/+A//Qev1+pot+v/zOp1O/yLf8y+9/vZv/1bPz8+qVquKRCLa7XbK5/M6HA72/qvVqjabjdbrtfL5vBKJhLbbrU6nk47HoxKJhBaLhYrFovb7vebzue2VTCajxWKhZDKp/X6vRCKh1WqlTCZjdvzx40e1Wi1JZ7uNRCKSpEQiYXtqtVopFouZLcXjcdubqVRKy+VSmUxG0WhUp9NJ8/lc2WxWkUhEiURCu91O6/Va8Xhc2WzWbD2ZTNp9nE4nRSIRbTYbu5bJZKLlcqk3b94oFovpeDyaXUUiEe33ex0OB7vu4/GoxWKh/X6vdDotSTocDkokEgHfgI1yj/4a4vG4+Yndbmc/j53xnrrdrqLRqIrFot1/KpWSJLN3bDkSiajf7yuXy5lv2+/3ikajyuVydp2TyUT5fF65XE7L5VLz+VyxWEyFQsH8XiQSUT6flyTN53PF43HtdjsdDgdls1ltt1slEgnt93v9+3//70MbvWL9p//0n2z/soeTyaTG47EKhYL53f1+r0wmo0gkotlsplwup1Qqpf1+r0gkovV6rUQioclkou12q3K5rFgsJkmBfcdemc/nKpVKFtOID/P53J5zJBLRdDpVqVSy643FYvrxxx/1u9/9zvZ1NpvVdDq1WCHJPmez2ahcLtv+ORwO5n+w3cPhYNdFvNvtdhYb9vu9jsej+YbFYqFEIqFisahoNGrxLRaL2c8nk8lALD6dTvYPz4S/I55tt1slk0mlUim7XnwGz5Lvi0QiisfjZofL5VLH49HuhRi8Wq2USqXMXnjekrTb7RSPx5VIJMwvRaPRwLXxDLg2sEE0GtVqtbJ3ii/nHbXbbeVyOf3H//gfQ/u8cv3t3/6tYVz263w+V7FYtD08Go1UKBQ0m82USCRUKpXU7/dVKpW0XC4lSavVSolEQqlUyvbrdDpVMplUPp/XZDJRsVjUarVSPp/XYrGwuMC7zWazAVwsSZPJxPY6n//09KTVaqW7uzulUil9+PBBtVpN5XJZkrTdbm3vg03BfblcTsfj0f45nU7K5/P23+xj9ms2mzXcTtxdLBaKRqPKZDKGuckNwB7YCt/pcfl2u7VrW6/XFn89LpCk2WymTCajUqlkdrNarRSJRAxjn04nwwHE53Q6rePxqM1mo91up9PppGg0au94uVyqWq1qPp9rsVioXC7reDwatk2lUkqn0/r48aMqlYoymYy63a5qtZoikYiGw6EqlYr5UJ47/hSsEY1G9e/+3b/TarX6F9nLn2ujVyWKm81G+Xxey+XSQOZ0OlUkErF/JpOJCoWC7u7uLCDt93ut12sVi0W1Wi0tl0v94Q9/0Gq1Ujqd1uFw0D/+4z/qm2++UalUshc3GAxs4xUKBWUyGY1GI9VqNc1mM0uu2Ag///yz3r59q3Q6bX9/PB41m80seTwej2YoBGNAHg81kUiYIfLzx+NRu91Ox+NRy+XSguR2u9VqtVK5XFahULBNsdvtFIvFAoa5Xq+13+8lyYL+arWypHa73Wo2mykejyuVShnAJFkeDocGjEmsk8mkMpmMer2eBWmMg0QtmUxaUpZOp5XP55VOp7XZbMx4MJhisSjp7Iyy2awln2z+VCql6XSqzWajYrFowRPnwvvnGXGPmUxG2WzWksjNZqNaraZ4PK7NZmNgPlzXrXQ6rXfv3qnb7aper6vb7epwOGi9XqtarWq/3+vp6Un1et2Su263q1wuZwl8tVq197rb7WxPzOdzzWYze4/7/d72CfvteDyqXq8bOMV5zmYzC5btdlulUsmCIUnVZDIxB1ssFtXpdCRJ1WpV+XzeQBIgNx6Pazabab1eW+ADJKZSKQsah8PBwGWz2ZQkC3LcJ4SIB5d8F/dP4JnP5xqNRspkMioWi0qn04rFYprP59rtdgY8sfdisajZbBYA1oAHgPLxeNTt7a0lk3zfZrMxgDCfzw00ktBFIhGlUilL7KUzKIBQK5VK2mw2Gg6H2m63ymazymazisVi2mw2Wq1WqlQqms/nBoLi8bgmk0lgT+ErwnXdIgkkESBW5XI5bTYbJZNJSbLkSpLK5bI+fvyoQqFgfrbf76tcLiuZTFoM7vV6KhaLOh6PljyxF1erlTabjRqNhg6HgwGVVCplicfxeLRkEmImGo3q/v7egDNkaSKR0Gaz0XQ6NUAYi8VULBbN7og3gF/282QyUalUsgTK2yhgUpLFrGw2azZCbAS0Aqr53EwmYyCX5JR9C7AmxkKyEM8Ag/v93uI/Po04TELH7+Izlsul4vG48vm8VquV3Rv4An/Evc9mM+33e1WrVSPq0um05vO5XQeEGu8rHo8bMZxIJMw/3tzcqNVq/YsliP+nr1QqZe9pOp0a7lwsFmo0GtpsNorH4+ZPJ5OJVquVqtWqFouFkak3Nzc6Ho9mI71eT9lsVrlcTpFIRIVCQev1WtlsVsvl0ux1NptZ0QWioVwuG0YEd5fLZcOfjUZD/X7fCJDHx0dJ0ng8ViwWM7sheU0mk4YLIa4oQux2O43HY8NtFH9SqZQlk+xJiJFqtWp/fzqdLB5DCGUymYBdY7PH49FIpmKxaLFvuVwaoRKJRJTNZo1MyuVydk18Bz5vvV5bPMauKDJBmntf0m63FY/HVa1WLZkFH+/3e8MAh8NB4/FY9/f3ljQXCgVLtrmveDyuWq2m9Xqt2WxmxAGk3tcSR69KFDOZjGX7jUZDy+VS0WjUmJVYLKZms2nOzWezPGjYRlg3AsTj46MlRIfDQcVi0SpW9Xpd0qVSCDiEachms1osFvr222+13W71448/6vvvvzcmsFgsWlCrVCr2YkulktbrtY7HozE3p9PJEh1+jsQPhqNcLtvfH49H+3lJZnzj8Vin08meE88KNp/PhEWGtanX6wGWdr1eW3Dm98fjsaQzmCaRu7291Wq1Uq/XUy6Xs8/3QQ5gLsmqh7vdzliaQqFgiSMBjPfGu1wul1Y9TSQSxizB4gBASNTr9bqBol6vp0KhoOl0ahUMns3z8/NXYUBf+vrHf/xH3d3dKZfLaTgcql6vG8FDYvXw8GAsaaVSsUBBosB73263VoEC9KXTabNTGFDPbhPs2LcEkmQyqY8fP6pWq6nZbFpw4HMJsPP5XH/84x/1b/7Nv1Gj0bAkDsYfkMY1UnXB+VOJo0qGjcKCSrKqAQkqVRrYTphXkj38AhW+bDarSqUSYD4JYr4axDVKMgKJaiXVT6qiAFoSBSoqnU7HSJxEIqGPHz+qWCwql8tZwnc4HIzMIni2220LeMPh0NjWVCql8XhsFZh4PG5VIgBCpVJRrVazZzUej5VIJCzQh+vzVywW02g0MqUFCXqhUNBisbD3wc8cj0et12sjdiaTiQEYVC/pdFrdbld3d3eKxWIGUEiIjsejJZXscwjUSqVirLmvNNRqNavkA6LS6bQGg4HK5bLG47Hi8bharZYRTOwVvx8BbSSzEIZ8NlUayIlMJmOxTpIRNxC7KB5I0PjzbDZrSSm28/q5Hw4HTadTbbfbQMyVzmodCBF8IQoE3g/PjFgYj8ctFpdKJSN9uXfunwR5v9+rXC7rdDrZtfL9kgKqKPyTJEvQN5uNXeNoNDJssV6v1e/3dXd39//cxv1/0cKnT6dT87G5XM6qR75yHYlEVKlUAsoUkv5+v690Oq1sNqt+v6/b21uzE8hN9hWJ2H6/t3g5Ho8tKYIMHI1GKpfL9lnD4VDRaFS3t7eq1+umhpNkhYP9fq/lcmkEq/9cX83DbqjGlcvlQPWeijjXTyGEaiIkBwSNTyx90rVarYwkA18Qn/ETJM8UOrwqKRaLGQmLyicSiRjGns/nAfIXjAHBxDuGJIWcIr8YDAZ2DaimIHxJUL19RiIRNZtNK9Cg+EPlgVorm82q2Wyab/uS19UVRemcOQPITqeTBoOBJYjT6dSMJhKJqFwuG/tYqVTU6XTUbDZtk8F8jsdjS9YAmo1GQ7vdTsPhUJvNxsCWl44gj8NYj8ej7u7uLMgWCgUNBgMr0S+XS2M3pYuck6ABY0/JHBke99Ttdq2KsN/vDYAjwYvHz4+4UqkomUxqPp9rtVpZJcNXXggis9lMUrBMDOOCsbFisZhVhmBr4vG4SflgXzFoNq93cJKM1YpEIsZ6wIjAXkkyMD4ej1UsFgMsNSwuDBWOD8cFw8leqFarGo1GJp3IZrMGjn//+99/FQb0pa9vvvnGWCvYTpLCfD5vklEcLWwXSQtyMZxzJBLRYDAwxtqTKMgzCKan08mqhel02vYMlbB8Pq9KpaLVamWJyXa7DSRZyWRSf/M3f2P2LV2Sz/l8LulsGwA09ipBpV6vG6Bk33KNMKSSzH7i8bgFRqSZuVzOGGNsLJFIKJ/P254+Ho8ajUaSZDJO/nw+nwcSTp4vn0eAjsViRuwQKPEPPM9cLmfJ+eFw0P39vfkElBck9JVKxaqU+CfIKuwfPwhZ97pCyTNDOXI8Hg2EAjbC9fmL9wETTYV6sVgoFotZrOl0Onr79q1isZgajYbZMcnRzc2NgU1iC+0ftHmgLtlut/YOAYWepKWSkEgkjAxhzxMbUe8Ui0VNJhMjGnyVe7lcqlAo2GfhS6bTqSWEvupAhRTg2Gw2Lenkd5HcUgEHe3jZKMkxn091BgIHIgxSG5A3Go1MZutbXKjgRyIRNRoNA4dUCjOZjNkCyQN2LclabSQZ2Y2tUY3i+fIsIAQqlYqGw2GA6Ntut9ayA2hH/gsYvb+/DyuKv9EaDAaqVquBNoFWq2X4kqSEdyyd48nHjx91f3+v4/FomI+4RNxjP/oK3cePH9VsNgMJ22g0MoUI5AWYiT0uya4LGwA7S+fYiMolm80GlHT8Ga0V7DOIzFgspuFwaBVxyFIUKij92H8+3oHrsRePF/P5vH2GJ6H5Tqq4h8NBhULB2iuosEOwIG2lYITtUVHkWaJ6arVahtslWVsFbTSbzUbZbFaz2czadjabjRU3JBnh5YldCKZIJKKnp6eAwmixWJiiEiz/tcTQqxLF4XCoN2/e6P3790omk6YThiWldFwsFg14wZLirO/v721DzmYzA3pUDXGosAsAzmazaQGQJIskcb1em2ES7FarlebzufVvYEQe0J1OJ/X7ffvuUqlkG5yXCYOIAQDovHSLRA6Q+FpXDlvqKyEYH5VVpJokjzDG/BwMju+J8r1DgHr6jgADMKJI0Xw/B4tnw3fjDEkcCGC+fxKQIF1kAjwjjINrQ9pEoMZJSQoE2HD9Nsv34FIpprIXi8WsKkCyEY/H9fLyYqybl57BLNKLAUgZj8d6fn7Wzc2NyU8Ph4Nubm4sGOEfeNeQLr6SiCOHtWWPsffYs1ToID9INGHlkch0Oh1LkG9ubqwHGKkafVFU3gClhULB+hh8PwUsLP1HJI74LEAi1UdkQvg7SQbGCfbs98PhoG+//dZ8GFJfWFWSS757t9sFKguLxcKCEnYFwSOdE+zBYCBJ5reQs5VKJWN+pYs/3O/3qtfrGo1GVhF+eHgwGbpXJYTr8xb2kUgkjDlHCvn8/KxSqaTvvvvO+qCQGBJvWq2WptOpxVavGPCMuu8dxrbYi4BG5gtALjEfoFKpqNfrGeiazWaq1WpKpVKm6gEgsXcgYaiSsIe93BvyB4KGuOD7nfAP+AH6EFEpSLLvBQhnMhnrdfQxbDgcGmHt20ggj1kkalQpeG5I8iDbkN3zXdI5+X95ebHeKe6be+M5e+IXspqEY7FYmH8BbzSbTfsu1FzIk1erlQqFglWI8PPhun41Gg0rFLCXKJKAS6vVqj5+/KjHx0erUFcqFau8Q6ZQLYSYgHhgL3gygkRrPB6r0WgYPgYjj0Yj2w+0URG/vDKO5A7yabvdGolULBZVrVa1Wq0CVUYSJknWM8l+IomjMs53UYyAcEIWC77kfnyVdDKZmL3S7gJ+7ff7Oh6P9hnEUohV3y8MPoXAIkH0cwwoZNzf39u9cb3SWZYLAcPMDRJXnuvd3Z1ms5mi0ahVmCGdaMPBR5BgErMpsnAvfObXsK5KFG9ubjSfz3V3d2dOlNI0ckSSBeSjJDMkE+1221gIzyZMJhOdTid1u109PDwYa5fL5XR3d2cBoFwua7FYmEwKIFkul41Z6PV6Vik7Ho/69OmTOfjlcmkGDZNPoEIGBtPL38PK+uZmWB6SS4IlIBR5KY6GAEHyGo1GDSjzfV7SiZSXoAvw9NUeftf3SqCbR3oTiURMxkQiT0M1WnpkCPz8eDy2pAK5IX8fi52H2lDVTCQSxlzH43GNRiPrEYHtAWgDUnk2GCaAOFzXLxKi/X6vd+/emd3AbPKeJBl5IMn6DPb7vclZYOwIQF6qWalUAhVBSUY0eKfMf1MdoMJOkgWTB/vP8BUSWF9xgFjB1ggUVDpjsZgqlYrZLvfu+4AJaFTUYVqx53Q6bckYfQ673U69Xs96irg3WEmkrMlkUs/Pz9bzgGwOsICUBWaU5HG73RrIlmQBx0vUkL9tt1v7ByANM7zb7WzgVz6f19PTkyXpBDreA9/7008/6d27d/YZyHPy+bzJcUgO/6Ua8P9PXj5xkKR+v2/xE1kxYIn9W6vVrPpfrVaNaKxUKlbxK5VK2m636vV6RqoAwoh1VAJoX8jn84FEyytMkILz/gGrMOUkkN5+qGB3Oh2l02mTL3MN9Nvy+wz0AWyRfHrCkWFKEDLEHFQEVBtet7oQy29vb61Hk0pANBpVrVYLxGwI21Qq9c+SqtFo1KTsSMmQJyKpBXiTbCMDBBBTzeC++/2+VVpRW/DcvcQNjIWfRFJIDKUXKyRxfpvV6XRMdknSQD8hfbw+CWPuBINpPKGIXDmZTOr+/t7mAWQyGSNsY7FYQHLM56P+gcip1WqmjCGe+6QTPEmRhN7hfD5v+I0Yx39je7Qp8XOLxcIKFfT8Q7QQe8GtxEwvA/VkMP4NG+QzUNbRPwhJjR3z/EkEsUWqoJA1kEzYJZVeCFHwCfcCYQrGTSaTZquofA6Hg25vbyXJfCN9h2DaWq1mJFoikdB6vbZi1Xw+t0INcnFi8dewrirdTCYTpVIp9ft9rVYrS3hIlqg0UgFjI7BRIpGI7u7ujF0jePlpm+/evTOWM51Oq91umwNkwA2JEEaVyWT09PRkiZlnaCQZQ1EqlVSr1SzI0tyKdItKAQnYeDxWr9cLVPNgDwCZngVFIjYYDALTUQmggDumR/rqpE8ekcyS2OEY1uu15vO5ScIAA8iVpP+LvT9vbixLssVeJwDOAwjOZEyZlVVd3boyyWTS9/8MMg0mqW9XV2ZkTJxBkOBM4P1B/RwOVEvvvUQ+Wca72GZpGcEggINztrsvX77cd+R3joiUrGBzPAPVBEkbttbzqtcVEckiYanouE1AZeyqohzq/Px8fPz4Ma8zIjIxZ7yemYRitqZbHKchDWTNwApSQcJHZm0IxcXFRezt7cXq6mruibW1tbTBX375Je0EkERmREQCRvtAZV01bH19Pd6+fZvgsYJR0hPSGQx6nZSr2nd7extnZ2f53XwPq/YOYl7ZHDBeZSGCjWQIOdTtdrMPWvJUJd/kSL7r/v5+gurhcJiDDlRTavLoniFvbm5u4uLiIr83G8G0si9DpK6uruL6+jrf13O8vb2Nr1+/ZkKoZ3lvby9tTVL4n/7Tf0o5XUTk4A3A3b2tAXy2fvviqzHP1DMRr33mAL9/IwslicTOA4lID5X5nZ2drD5cXl5ma8Xq6moSeN1ud0wNZFgUG/j111/zuWPcIyJjDXLk+fk5jo+PU7XCHmpMM0Sp0WhkP7SBdSqqyKva/wxUIqKw9M1mM2P17u7uGBGC2AFgYRFVONcXEQkWXUet9ktqVWfPzs6i3+/Hzc1NDgcBOIHLxcXFlKGSxvozPCEh0J4jkR4MBnF6eprVz3r/PDuVw0ajMRYrxWgqn9mafs3NzSX4hxElUKSdpuQ/PT2lSkYxYWFhIfcCuSZch4zVJygB0V718PAQ/X4/bQbp4ncjXhMXFXTFBHvl9vY2Wz4QKyqR3rvf76c64eTkJIlK8ccS/8UxJC8sLeGNGKkdELPX19fR7/czrlVMAu+L+fqnJbWIUPf8/Pw8pa9zc6PJ6MgdpFJEjE1DlVST2prw7b0QQVphIiLVj7u7u/H3v/89K/7Ly8tJ9niW3lufY6fTiV6vlzgLKYsokJB/D2sqT7K2tpaT025ubmJ9fT1LyYeHhxlc3HjOXu8ah4nZV2Ha3t4e0yVX2df29nbKusg4I2IMHK2vr49J0Uh5PFDO//b2NocCSJJIKCUwJDT9fj/7HkmFaj9j7XPE5NjcejBtFgniZJkemCWlI61TLZR8Vla4DqUhLx0Oh1kFch9samPXBfeISHkwAxZsBFFlfskE4EheXCfWCaoCZkQk27OxsZFj1au81PPgSH3n2Zp+AVoGTHHQ9hL2EQGB9VMJJlPFHi4sLMSnT59ic3Mzh7i8vLzEL7/8EgcHB7G8vBxnZ2f/MNqfUkC1T4Blv0DT/f392KRF34EP0FNEhoOlnZ+fj/fv3+fgDtUQzftVGlOla+y0ysfI8gQQTCx7xxYaoiVwsFN9GBGRCevDw0MGnNvb27FpcxGRyen6+nr2XyOk3DsJw97eXtopG3bvXGsF8T7DhONerxcRo/44QRr7XWWohoHopTFpWf/nzE6nW1hsFT02ov+1Skj/9V//NQ4ODpI4VWkiDSOH4//X1tai2+2O9c8bZCTRr4nezc1N9jHZ6xJSINHwuTooRnyvlXHycIAVeUQ6dnd3F+fn5zkUgo2zTSokBEvdi8CZih0QzIaqbE91jX0DgDc3N/H8/Jz+xLUjMD0XfkKCztapEupzBCrZS7VfMbZWYZDPpKS+79PTU85VqL2rVdLOt768vCSY5u9Mgq8k8Wz99kVVJhGR/ESMjlnRgnF/fx+bm5s5wNHUUvM0IkZqDnvM+8LOCh/+jPi5vb3NiiPSk6T59vY2VlZW0nbtCXve/o947WOk/nMta2trsb+/PyaHXFpaSvxNaVDVgRGRSTGyyl4V9+ogqIhIaT2liwrjpLy8yjP5OWSzwYeSd60YVDYwtMJM7SVVIOEP+ae7u7u4vLyMra2t9APipmryDz/8MCbhV3ByysHl5WXKUg0S4gtq4inJ//jxY3z48OH/oV083ZoqUbTJaOMFB8wdZy4BGw6H8eXLlxzJzak2Gq9nhdXm37W1tdRAa1jXrH56eppVSwmM8cE2sgcK2JFNKdtjLk1WE6icESfJ9Dl05r53HQ5TN7uNFxEZFARAgUTw83esP2kmKWmV7mIsIyIdAWOKGA0WUh1gXM/Po6mQxia3Wq34t3/7t2SBXG8dkjHJqgIYrVYr+zSRAwD3wsJCjtvHDjk3Brh271QzseC+47dv31JCNVvTL/aFlet0OhERWXEgnwL8BAnsfcTrvtDruri4GIeHhzmRjbPd29vLvQfgOEfK8xYUI0Z9rFg4QE9/5ObmZspDkSkSsMfHxzEpN+BZh2tFRAZp96ESO96XLJwawFKBkBT5rw55Algl3ORgKjGuRVBU/aAikOyp2jcajZThIVc8qwoyIkbDrfgdPdmulyyRrFwPm+dUpa38ruejcqpv5Pz8PJ/3/f19qjB8/mz99kVxImY6TuH6+joT9w8fPuT02efn5/SREZHDLBAp4pcqtTiAUDSUgwx5d3c3np+f05cjiiRdJqyqeogRJFdiGLDJ55NhuS4VDHtZewEgav9LlHq93pjcfHLfsy9VfxJVyRHSUQVP9dD7i2WV9K0tJiZUrq2tJQlcFRfAnsFvw+Ewj7hQ1ajEEtuCTchqETMm0j4/v05slewhmQBpPrcSskgBuMKEzVmP4u+zamsP2+RH7+7u4suXL/H27duMC1RmlCvITBLJh4eH2NnZiYuLiyRIu91uTiSH+WpMkYjywfy0z0EEO4qBKk4vLn9vMjnMRubcarUy6UMSUw5RjyE2a6WeHRtmM1m08DNnklelm++nKm7/Pzw8xMnJSbTb7djf309FBL/BRyGb9VIbFim2VhwPB9efI5gpDKoU1POilIS94fJffvklfvrppzHMsrS0lEOIIiJbdST98g+qEb7se1hTJYr9fj8PFnVAqCZQJVZGoEfn3bt3cX19neypCtv8/Hy8ffs2IiL+j//j/4gff/wxex4iIoczAF01UcEqRLwmTJpKsfwSTv0G5G0YCn0AjEmPws3NTSaHgh1jYexAmgRPUAFEAT6srAplRKTzMTmp9oVwNv6d06+NwxEj9phcbDgcZp+LeyOZZpS3t7fx7t27NGLMlKB/f3+fEhwBmbQWYK3ToPSTttvtnHraar1OhDXJrkptgEzPoEpR9UQBQrM13fLc6lApfaRYSNVccrPHx8fY2tqK4+PjPOeUtKbKSOn3fY5nXSf6IhskMOwFqFSlrD2pGFDvCyw5SgXxJOhKIDl+oK+qDBAeEaNzGu3xOgRKIBIYvZZEkwRMVYDck+1eXl6mzSBosJzIrohRIEUOARmSbcFkkiAC0P2+3ofaoyUZ9T6a+g35EuRcx/7+fpJcfG6Vm+/v7+e9jogM3M64nK3fvm5ubrJP8OvXrxlHBoNBbGxsxM7OTk40PDg4iH6/H3/+85+zh0ZyJJGRCDpTje/Vd4j9//DhQ1xdXcXJyUl0Op30Dap3KmRVVYIEMTiOOgCAvr29jXa7nRMTaz+jKkGN1eIH/0JBRDkUMfIhiI6jo6NUBkXEWFJcqxreV9xh6+zOd0I29Xq9TOaAuloJIRMkfQUk+Tev49MMBHGN4q/fR76R7wGOVdJmwI4qCGXUcDjMijOi2cTIiNcYurm5mcqB2Zpu3d/fx/n5eRwcHMTp6Wns7+9HROTxbmZmiA9scW1tLY+cOTs7i06nM1YtbLVaeWbt6upqqr34d9V5eydiVJw5PDzMBJTckb2SxyokTBLzpI8wpFjq/1olJHIqn4PBIPHwwcFB9mOSurpGtiBGIXMq6QMPkNju7OxEq9VKWf3BwUEmrbBtJW21r4jrkl9FChgeOfTy8pI27l5UbPz58+eMZ67x8vIyZbtra2txfn6emHl/fz+HPvLZEZE94dfX17Gzs5NEGNuGH4bDYfzLv/xL4qc/+ppaxK6ZV0JX2XoSMzdneXk5G68xLVjFWlH78ccfEyT1er1MOtvtdnS73bERwB66s4P0U9VhL0r7KhX0wZgiVQ8MrkBGAmegjD5GsiuMJYYHEFYmr03omAzMKqZHQMIo0lTXxCkisvSv7E2iRhIGZHpvTgN7UjcoQ8MMLy8vZ/CV6Pk913dwcJAyPRUJLDMn9enTp9jd3Y2Tk5OczuWcNhryl5eXdFTn5+e5d66vr/M8uG/fviU4mq3plnHvnpVkT5KCiZc4cf6NxuuRJhcXFxm8BAAVvNXV1ZwQzFlLXgBdgE7VWJN9PZjb5wtGkjRTHun4XQPQWQMVMOX6SDW9xjWoMJDCnZ2dxcPD67mtgHTtq8JGVluIGA0B8LkCaq1K+l7szt9VZvSqYGbZngDI13hPlZt2u51kme+6s7OTPQ+u3aQ97Ozd3V1sbGzETz/9NHaMgMqqyop7PDc3l5Mu9UuTrhriM1vTrYWFhfj69WscHR0lOFpYWIiLi4t4+/ZtyrwRaPrkxIeq9nCu4vb2dtzd3SVwarVehykBfldXV3m00cLCQo5xjxid/2cAg74rvUOOjBE7SbTZvj9X4tFh3QZckL1pH5F0ivP2fB2ktLi4GDs7O5m4sQn71Kh5Nmofk6eJz2T0NdmMiDEJ6vPzcx4ho/IicYZvLFW8SWkiKStJqERVEuG93eft7e3sFYUj9GxOqp4k1Z1OJ+9VnRquQjxT5fw+q9Fo5NE0+/v7Kf2Et6p6S0wVJ3q9XhIq1QZqNQ7eo+JpNpvx8ePHrFLar+xuY2Mjk8qnp6ccaFWHt+hfr2eVIw59jgKGKnZtcap/9zqVSd8bOQTvqtq5FpJMMbIqfcSo3d3d6PV6GV/YqfYMSh9zChA9VIgRo0FV8IOWFX6sFpYQMj5D4eLNmzd5jJVr2NnZie3t7fjy5UtcXV3lkSZVHbi6uhq3t7exsbGRyiyEMzXl5eVlqg75gbm5uXyG38OaapgNsLWw8Hp4OgPC2PX7/WS5MR3YEWc+kS4CkM4yIRvVh9hoNOL8/HyssRfrwfm/e/durDzNsPQCAJR69yRX2Hisq2ADHJKm1R4EzKIyOGnIxcVFArSnp9dJrFdXVxnEKvsIENbhHhGjfkvs09LSUh54v7GxMTaZanl5eewcRyypSqnno7JUm3YXFhZyo3a73ay6MmTPUQkeY0T6s7KyEoeHhyltq/1StR81YnSoOcd3f38fW1tb0e/3syqjyri7uzvW/zFbv331er2sHJJaCzzshpMl20BocKSATUSMJWTskNRFtRt7Znx0XZeXl8mYYxsjRgdgS3TIvgE0oNFSKayVkGpbjo8Q6CRd2H4ViP39/ayY1epEt9uNs7OzTEYlr34HO4vxx5b6+2AwGGM9BTvT5/xORIzZsvsM5AGh/JQjPigcXIchBXpjvF/E+PlZxrVTXqg46r3iq1VY19fX4+PHj/Hy8pJ74du3b+nvZmu69fLyEu/fv88BYypMelkBTEkecIfRNwRODJKAiCfIVpV8Pp7ES0W52WymjBLQdfYvP04RA7QBxn5Xkms2we3tbXz58iVJ04eHhwRxy8vLsb29nQCTLK/dbudkUAkf8ofPqCoBQ3n0N9nL7pce6joAo1YDJbZ6NiXi7Mhil/6vurGxsZEEUwXWhojwp/f399HtdsckpiaUAvZUR2wR6YbsM2l5ZWUlZW0UDBIDkkNJ5GxNv7Q72WNs6vLyMifuw0Se99/+9rd4eXnJAUtikQQFGRsR2aNr/zabzfjhhx+SbCDLphDj12Hki4uLVPyIUarjdZr48vJynJycxMrKSlbu2Zj2FHtei1btG6y90L7rzs5OYgKY17+pTB4fH+fAJnb28PAQ3W434zJ/dHZ2lsUhWNxeN3yGco8dR0QOy3Gf9V/DJZQyWqIUIgzjoRjwXW5ubuL6+jo+f/4cOzs7OTBudXU19vf3M3eopzfUaqn3dFTYcDgcG3yl6vu99BFPVVG0sQDDh4eHuLi4yLOVSDVtUqNhaw+ivoxffvkl3r59G6enp9nDKHEwWTPiH8+0cQCvjYz18BoAx3sKACSXzpAx0c+/OWQVU4p1x6YIcCQCEZFArkrGBGW9Uj63VmYajdcJcLUXsYJLoBYgZjAcTa16VukrqZDPwKjUQ1NVRlQeVV8Ym+S/sk2CHQkMwC5g7+zsxOnpaYKOelCs7x8R6fzow/W5uDezNf0igQK07G+VfIy0Z3F3dxenp6dZKVxYWIhut5vPR7Lg3Kj19fUxllHyIGggi+rgGX7D3nGQbz27STWxBkUSta2trbQVwK/+H6nhM+05DlqQRvYgTfQbaNZXYXWcDDkPH+ZeukfkLoaAVMkO4kUjfa0Gul+Y1VotlDhLOLHPlZFWPTVF8uHhIc7PzzMoUVk8Pz/H+fl5Vigwzv5NxdLgsYhX+ezh4WF+5tzcXLx//z4Jqdmabi0tLcX19XVcXl7Gu3fvYn9/Pz5+/Jh2ZjIv6WHEa5Ly5cuXeHp6infv3sWvv/4aW1tb2fdWQWk90kYSpz95Y2MjE8eIyIl+VQZOeUCmtry8nG0Z9m8FudpMALjt7e0ErhGRpCDwBJiSUlaCkd9h0yqr/JfEtMaqSrogk+twJkkam6l2rNqqOmsQjftj+A5gXfv3VUjcL2BcNYEtGpyBJHe4OX/mu/OTfq6iW49KqEdZIdoknV+/fk0fPlvTLfhOsqM1Q38wlVXESMbJR9on2ju8H0yG4IfBfvnll3jz5k1K++FghCkVmYEuKpCIQ1i49tQ1Go0kAA8PD5PIUM2OiNwvsILX1XYrlUj78f7+Pq6uruLx8TH7mKlmvnz5kko0hJAYLV+obU7IKcOurq6ukqicm5vLAZekr1o/kCpiZESk7brfKosKGfPz87Gzs5OxGQZmw9QN+kYjIuOuxA8xh8xZX1+PL1++JOnmOdeJuMgz/4ao+h7WVImi4BMxuimmkmLgsXVAlsTLfxcXF3k+oaqfM2lub29je3s7gZzNPTc3lzLTy8vLBGgeHBZAdVLg1JhPGnt/fx9fv36NlZWVTBgxhXov/B3YU5G0+UxIlOAxQveHcSlNv7y8ZNIredS/adnAk31L3s93AQr1B0aMJozqN/Mz1cgqY7NZGYFqTERklUIAqomv5Nd9qeOJgV2MpkSVJANY6fV6CUhqxdM45r29vRkj+jssEg2JRrvdTocN6BkZ32w2Y21tLQdRkH2oChq0AZy12+18dkgDw1iQEMBLq9VKGQwAdHFxEZubmynp1rg+GAzizZs3cXt7m83fJqT5TMx7xCgYCmCVwJI8CpTY4F6vl7IY4/UFq0rmzM3NZaM8G2XT2EAy+6oS4AvYrImJKrEAMN+BcPLnRqMR3759SyAIbFY5LB9BnsdeKBFqIl2Pr2GP/ASC6OHhITY2NjLB7fV6CbqrWgHBZ/jBbE23VldXUzkzGAzi6OgofT/b0rpxfHyc/Xtv3ryJf/3Xf40///nPudcNl0LUAiKfPn2KN2/ejBGqw+Ew5ZG1r9FglJubm7G2ChJR9kTODiR+/fo1k0Svq0SjIXIIW/Zr6N1gMMh+xmazGYeHhzm0pR4vUJM1sUus0uuPlPH7Yi/AiDRC6ADcBjUhcBxHAJgjbb2fa1bZAVzZObtCMEVEKpF8vvepfVR8GbyDMPry5UsmwSYjw1WGxSEXZu0bv886OTmJ/f39MSm252Hf6EXkl6uSxkRqU4i9tvbJUcZJMEnDJTGWP6twbm9vx8XFRcYxA5DEBQmQShp1HQLEQB4zQVTHb29v49OnT9Fut/N7qfjDp+K6HIC6gCqgJo4qajD44uLi2AArCSS7VfARRx3LY8IxDO5ZIDojIvvxkVtaXSIicYFp4JJNChvPEVZSEGHvVfXI3yC5yFdVPKkgXTPfcHp6moME+cA/+po6UeSMHB9hImY9sws7SFpF+hQReaSGfsAK2Opgiefn55yMitU8Pz/PShyJRi3lemgSVkEIAyRBJYdUCtbXUBt569AAILHK8Yz8NTmuAj/vVWVFJKW1sR5A9J9g4ztGjKpyGxsbOYTDprSRBUGA1UatfWa1V0JlQtVQ4FFlEYQlfZ6RpABIEZAxnN++fctGa4MX/v3f/z3ev3+fxmRinGvQ+3lzczMDob/Dqoxn7YW4ubnJHkJOmcPEptm3f//732N+fj4ODg7yfTlmNksiUskVgPP09DTW1tbSgftdwx6QHQgJjDw7AFgHg0H2BABIiBO2KtgYEOBaBJTT09N4fHwd8391dZWV1Ovr6zwupw4QYPcRkcBAcler/37XdddEvDLG9SgZQav2WdVAqbcEQwn4kwr7PEC5+svT09PY2NgY8ycCI6bW59dKYr/fz4mPhud0Op2UGwnanueMzJluuacGwLFVe9l9pwbodDrx7du32N3djfn5+finf/qnJAKtmmiwjd3d3ezBry0X9tNgMIi///3v8f79+/j69WvKvpF6FAWUNp47kqfb7ebRHc/Pz3lEgBhln/kugCECBwlCLaO6FxH5nggoAJhPc8+oAZCfSNEq566D45wV12yOju/weiSXBFHiS9YO4Psuw+EwdnZ2su//06dPcXR0lIkoArb2ctbeyYgYS2LhGaQOcmB3dzdVDoBsldjzXRXAz9Z0yx6gtFDlc5YuUkflu0oK4UkJJdxpn0k2Tk9Ps2qmGPHt27fY2tqK5eXlxNknJyf5s4hIv6G6DDepdkqAzL6IeMUEd3d3cXFxkZLKuobD10GI29vbifnYLqVPlXPOz8+nrfM55+fncX19Hfv7+0nirq+vp+rs6ekpFW6Ub1pahsNhXhdcyzeqmrrXLy8veRalvki+gezUESMRI1+iclgnu8La9Ti3OmhKwQo2kjxKsH0XUvfHx8d48+ZNFlaQvXt7e9nm81+E9BTzcXd3F2/fvs3+mypTqRILYMZ5MSYZMozKwHuIFRw1Go148+ZNgtTa6xcxSgz39/ezelETsbOzs7H+xIhXZubr1695vh8HYMCLyWlKzgAXI6wJjY3g+gBCFUDVARU4/+5QTkZkcVARMZaMqSSq5rhvJAMCJdBIsuDvFTxW5pjxYDKr1MExGsAyYD039zrpkXymJt4kghUYHx4eRsSrLr9OcwXoTZKqwGe2fvsim5SoS6AkXJ8/f44ff/wxpePLy8sZQEjEDg4OYmVlJU5PT8ccqQokkoHEAhMJ+O3s7MSnT58S7JgcRxrKlvr9fto9B8oGbm5uYn9/P31IxKiyZ//X3oP9/f2UmtUeJDJochzSW0wqIoYfUA0B5lTX7u7ucoR/7X1AvgB81ZYELiDO7wlEKnhkaxQbhgRERDbTR0Sy1oLW3NxcTjytFSpJtMBqH5AQ7u7upgRH74fr9X3rQebIv/Pz8xmZM+WS9HU6nZS0ATv2BokSG9rd3c0jEtiCPeBZIzNqRT7iNdFAXpBwVp8wGAxib28v4y4W3FE3i4uLyaDbq6oItedJLP348WP85S9/SbsdDodZKWdv9bvW+QP6qFQvVGr4B3238AN7Elv5BfaFLPaeEaOBdbADqSBZNmBJ7knGCqy6v9XPiHOURI6voKJCIrNBrSDuHQkeOaH+T/1tbNB35E8MOUL2zWLo77Nq76nCgVYCCZJkxXNQcYfn9Ol+/vw5tre3M07BV4h/g6jqMTSnp6dxdHQUvV4vp4OSifscxEej0RhrcyK9Jl1HWNaZG4o7jmmrJwiIjZRFfAYbmZubS0KHSoYCgJIpIsaKAhGRR8nUopBY7h5TP8AI8KYqowqtGQxImZoQ19YPcxPI/eUefE2taspXahFKIux7SRJhKd+LAonK7+eff47t7e1U7PATconvYU2VKDpQ10GfVXc7Pz8fp6ensbe3l5IIh/sKfkY4ew+A0YMFrJT8BQF9ShcXF7G1tZU/6/f7Odlxa2tr7Kyh+fn5nAZqE7y8vMT29nYGOQ/OkAdsrA1kWAZjwNyoCGBaahKH7Wd8pD4kPqpzksCISCmCTahCubCwkM4AEMUukn4yYHprCWJtqFWZwdDU/jUA3+TKiEhJEgZXldEodkGxVlLsB0klFpokgey0SgQ5o7OzswQ9szXdcnwNp2faLlD0/v37fBakUmSUAl2z2YwvX77E3t5eBijSD8/b/iZ/QaZwhPrnbm5usgEeIUQejilF8qggkH6ZbGx4E4nM2dlZfPjwIW1K/yHmEmiWmPo8yRdJWh2WwQZV/lUn9GRVQCwRdC/YsgST1E7fn2sE/gRoxFav18vKj2dTh/EAmGyXXSKISAprAPV7V1dXWU2k0mDbgjLw7LtiW10zkkcyPlu/fSHKAEPM+/PzcxwfHydTvby8nFMQJQyLi4t5Xh7WnWqFRIqaRCVYPFOJkjyRZyEhSbNrdcvAm2/fvsXe3l4y4lX1A0iSzb19+zb7ISVuBmxERCoQ+Hogqh6D5dpJzIFPpE9VsdSEGUHJz+ixdvwOrEF1VEkomKSqclyLZEwC7n1UGCIiwWxEpBzXkA7fV0+4xJNqgr+Q3EuGSb0RQqourgmgR/B+L7K2P/ry7Kg3qvosYlQ1lEipsFOw1cndnU4n/v3f/z3++Z//Oe2s2+1mHzjy/OLiIitySAvnUWvR6na7uR8uLy/j/v4+Njc3U6Wn2AHPIjlqciO+wAV1PgYbJtu0HxVZKAwODg6SZKoV86pOE88oACjevK7eS21Q/j0ixrAsxURV1JiF4Pg7Md97sgnEDzwszpn+77M3NjayKKNtRtuGXmxVR7aH5ImI9I3Ly8spzedD3H/Vye9hTZUo2lCYszrk5fr6Oo6OjuLq6iq2trbi48eP2Xckq768vMzeRBtKgkP2KHBVdl8ytLOzE9fX17nRtra2sucQo7m7uxsRkU3kNhqn6mcqnFgIm4bxYGGr3lsVQ+IjSVRCx1QAmlhTzczOk4wYHYyODVLhUKWJiDFQenV1FXd3dzkhjoSBhA/bw6G5JgBbcNVbBpRii4wqV4m4u7uLTqczJlO8vLzMSpS+DRUpAXdubi4P/nadDF1Fp4JplUj3ebamW2QS7FDSoE8BCI2IdIi1lzcisoqgr6iSKAiRl5eXTEwkMoCnPkgOWB+r9x4Oh7lvgFtBh0T19PQ02u32mLx8OHydaog9V7lnu8Awmabx10tLS2MMIjkmRn5jY+MfQCl7XlpaSomt65eM+l29B2wMIyq4kfxRIDgSQKIgkVRh1Iu0uLiYUx0Hg8HYOH2+iG/8+PFjDvCKiLFnIImuMpn9/f1/qPL4Tuzaz5F8gPBs/fZVe2SBkOvr65RMiYdVIRIxUmSQH/b7/Zx6ayIqdns4HOaQOH75559/jp9++il9rhirYrW1tZV7KSIyTkdE9hKzd5UrLSUSHnGlVvckwJ1OJwGd6oyKQu13Bi7r9NBKbPE/SNkajyMi1QGtVisTNrFQdSBiNF24Sr3dayTMwsLrGdEIW58npoqntUeRTbN119xsNhPcLy0txenpacZrfvDk5CTBpwnKw+EwEwaKDQRZRMTHjx/jT3/601hrzGxNt/TmIl/Er3//93+PDx8+ZIzqdDpZvCDtrIUGA2yOjo5ytsb+/n5KS5F1jj9DrouVj4+PY9NxqX4cUyWWuGZ2gPAQ+9mIPuaISLJDQhURORMEmYicrEdMwXQWvO69xGHXolfPd/AZEZF2ERFJxlDfra2t5ZyTXq+XBRYqPMkXnO4IKgUUFUqfoQrqc2FymIEcFaZWyGKLlEQSTfih0+nkqQdyjPX19VQe+n7n5+c5Zfx7WFONlpQdc/BAUERkpm8zvn//Pln92gTa6/Vib28vx/GaumSkrh4ivQGqDD///HM+WIMuSDwkhSaQ2twSNgDJNVfJpQE6EaPSPVZR1aSOztbjsLm5mUNxNjY2cnqT33W4Mc33wsJCfg52pdls5rlSfu46yEtICOqIZd958j8g2HWoFOoDIbNzmLZBMgKeMw4ljkgBm56seGFhIY6PjxMA11Hol5eXmSQCBEBxRGTAOz8/j8XFxbi6usr7PQt006/t7e1YX1/Pqrm9U/X0qhRA48HBQTw9PcXu7m4Gjcq26yO8ublJZv3jx4+ZaNWhLKpcd3d30e12U654f3+fLH9lNe0vkwSBTNX69fX1PBiXSsFSJdVDbK+TpOiRkFQKmAK45O7i4mKsQoC5ZA8ST1NdIyKdPokvwCnxYuOq+vos9UNXuZ574DMjIoOq73Z/f5+DfWoA8j516ABfwQ8iBBBh+/v7KeFHjH379i1Bqv9UgYF5PnC2fvuy5yRipoM7dsEEPwy96dLaPpCJt7e3sbe3FwcHB2OJmcqAYyL446r0sd/tLWBJj7nnbC9eXFzEzz//nNfmzDNxg18wxKnX68Wvv/6ah1EbLw8A2vNivfhlL9vftfIfERlH9GHVmE7lgNhgp+JoPfhcYms4hmtzri/ih00iulVzJJ7iLbURX0Rmql1DlWZlZSUlaRQA+qTn5+dje3s7B90hiRBx5III1+Hw9XDyf/7nf85YLImerekWQn84HMbnz58zjr59+zb+/d//Pe7u7mJvby8Gg9czxU0fVv1bWHg9M5pKq5I/SHh9dNvb2/Ht27c4PT2NX3/9NY+TWlxcjOPj44iIlGQuLCwkYQcjUg0hMiU8VY4NN9fKOyJTYorkUGSgGPC79Yxk5I1Kqv0K71mmo8N3kkZ/riQo+6eIQHzAqPZ8bZuiAqrxT5LqHp2cnORkYwUhOJ+tR0TKh5G85PfiJyJOdd/fPUs2fnZ2lsQ5PGFg4PLy8nfTvjH18Rg3Nzext7cXj4+PcXZ2Fpubm2OjroE6Gw0DgnHb3NyMr1+/5pROGTZ2LeJ1UM7u7m4Mh8O4uLiIVqsVe3t7mfgdHx/HwcFBlqcjXkHj2dlZbG1tJVugInB+fp7BlmzLxE2HHiu32+gabwFsm5RsxEbGYAiUNqIeEAHl+fk5zs7OsirIgGpF0OcBZjVoz8/PJ2PrNVXuKthOnu9D7qKpn6G2Wq2xA40xoc5/EsAjIgOUSWDD4esIfq8VvG5vbxNc93q9ODk5iTdv3oxJ8zgxVcS3b98mYP4eDOiPvu7u7uLm5iYZ8PX19TFghqAgx2g0GvHly5c4ODjIqiFWTMM8sMYBR0S8ffs2nz9iAtv48PAQe3t7ycqfnp5m/xGGPSIycGAk7TcSHhPnvn37lmebSsbqQAhVbwGWTVxeXma1ApFR/71WDAURAcb3enx8jIuLi3h6ekoWl895eXlJBrT2A0u4anWhVhOQPyr1tYIyHA7j/Px8rDfDfRHwPU/XIrEjmeKDfCfXrsemTqCsQzYoBsiAHGPiPSqTPFu/bbG7+/v7+OGHH/Jg52azGZ8/f07wyOacvUf+ZKT9YDAYs/P5+flMSuzFhYWF9OO1XwipS/Ei3qyvr8f5+XnuE79fz30Tpytp8O7duxgOh3lmq+SQv7d/ANVadeQjJLoRkQRnHdBCEYCM5ntqfxKw7J64L6obvpfrkAgD0aTYEa9TY2EOqpt6fe4xu61tGKqrpGvIaRVKftdzrhWZVquVOIaCw/cwPdOznJt7nQZ/dHSUP5vF0OmX9o3V1dVMzCRY7969Sx/p2ZpCvby8HH/+859TsUHRxe+rrJmWy3cjdbRUIel++OGH7KWVAFVcqm9YDzBb1lPnSBv2Zc9q29re3s74ZaZGHbykHUWRQfLDzvgAWFUVEr4wQZu0nExcUQCeUHDiX9iBgozvSZFwcnISnU4nImJsmE2v14t+v5+vWVhYiLdv3yYpVvszDbbir7a3t5Pw0hpWpylrZ3t5eZ3xsbW1lTlKRKTNm+7KTyHiTk9Pszf1eyBzpqooyvg/ffoUl5eXOchCkFAhkPi44V5L8qa8TUYVETnxyw2XJCo/+z39ihz75uZm3N7e5kAOn2djVCNXRSFfNaHJprJ5IiLH6dKg1zMJgdU6CQnAvLm5SRCIORHwtra2YmdnJ4GcYG2zPT09xcXFRZyfn6cTARL0M6kGMeoagDX/YkzIIDAkVSKnn1OwNq5Z5QLTJHFWiTw9Pc2qxS+//JLgeGFhITY2NuL8/DxZGBVUgZSEloF6Vox8tqZf8/PzeR6h/talpaWs9AsWtQp4cHCQgQqpoAKuEtjtdnNvRUQGH4GlSr3rGGyVSomLCl8lQDY2NmJ7e3ssUZVk1coW2VXEeJW/VtUrKeEQX1ISwQXTj8whXSErIflR2eh0OkmO1WM6FhdfDxnWB8UvmHrHN+jVFUDq0Bi9FV++fMkKIqkskomdAwsRrxJjNi4Y82kRkT6PT1BJlgQCnZjadrudVV2/P9mHLcjO1m9f/OTc3Fycnp5mu8Xp6Wn89NNPqUyRcCA6EIUq4vw+YKmP6H/+n//nsR5iKhVJvl5H72Vf8uFib51+XWXZ5G6Osqn9kGTT7GaSCFLJkzhFvMZpvXhXV1cZl7Se2HNi4//yv/wvY9Vv8YvKyZ8jIivq7gc1jnH+tTroHonH79+/z2MOqCNcL6KGr/KzWq3wPfydfZKqIZv4TPHcsClxnh1GRMZ47SmdTmdMcnh5efn/wA7+//+lj7WqM6hdYBx7Rp+g53R2dhZra2txfX2d/ndxcTEloqr4JM32HAy0trYW/+P/+D9m9bsOQSO3bLVa2SMJu7Ip/lwcRjSIbxGRfXQq1nUQS6vVyvYp/y75Y0uSJ3j+/v4+Y/bNzU0OkYS/xc2qTGFnFIpI0ohRPKfAsf/d06Ojo4y57Pjx8XWCvin6Xvv4+Jj+Upxz9I65CuwKWdbtduPr16/x+fPnxATUgfPz89mjCTvA16q3MDyC6dOnT7G5uZl4+HtYU1UUgcbl5eUx3fz5+XlKPnq9Xk5yUmmSOHLgenJMIqraZX041VgjRmcN9vv97G8Cfu/u7uLw8DCNRxARTPUVAXjkW4ybDGRubi43PzCt4dhRGAI5A8CEYA8593qUh8BQz3rE4JiyOj8/nz1GdSSwBLseK1GD09zcXE4eZUhkECQC9fwWYJuhkaCRDwCdEmhVSOV2bJajRo6Pj1O7/vnz5xw84igNyW3Ea7JZ2c/z8/NsxK4TX2druqWXCKNZyYXb29toNptjzfcSEFMNEQycPbCqikXCZtmXksaFhYUx4sZrVlZWstqsB7JWP7rd7li/IhvWA8LfCNzImip5ZTPX19f5uXNzc1kNBIRvbm7yOlTegAHVk1pBZUtUDeQotR8RoFWBqYMB6rCL2ne8sbGRU4QxpvwVprYSNnd3dzlJr8pvBEyAdWVlJcGIJJ2fMATg48ePKVtqt9txdHQUz8/P8enTp3j37l1WGQ1PmR2PMf1qNBpjz2Vvby8iIm3GcSq1X04sRa4tLi7mWWfAFX/73//3//0/tDAAkABZRCToozSpBE29HuQGP20v6n2tffeT1eqTk5OIiDynFXHs+1VGv9Vq5dEbMIPqJcJIghcR+V2AVBVFkk32UxUzEsnBYHQ8iUQ2IjLuwibUQuy1Vkrcg6qU6Xa7mXQDu5Ws8ZmG7ImzkgL+RusGn8KHDwavxwyR55GxS/zF2dmabokZ8A5ZIls0CTxiRMBIfpaWlvKYC7HB+0REVovrIBgEwfb2dnz+/Dn++te/ZrWSjTWbzSRhEL43NzdxcHCQ1c+rq6t4fHwcO9Oaam9jYyP6/X58+/Ytq/2KH8gg8awmpvY+zK+/GdHMr4jD8/PzeVSOXMG+lGDyZRGR1cCLi4tU9vAh4jz70BtYK/9sUdL87du3tA82xr6rckfLye7ubpyensbBwUEWkiijdnZ24uTkJNUBtY1Ma1tVZ0j8K276+eefc/I0rPM9rKkSRYzK3d1d/Prrr/Hu3buIiDy/S/WLQ6tN5voXOp1OXF5eJoBUrYuIDHrPz8/x7du3nB7kv7W1tdjb24vn5+fY3t7OB+7zlZ0FCSxrnRIIzJLLSVoBVwBbQiZYCcy1LC8Qqyi69ohIUAb41fMOyQQAXA3N3td3kZRJ0iTAgqRBH4xbpdWmrAN8qhQAqHffJc7Ag8+SNNj0j4+P6SxIooxQf3p6iqOjo7zGzc3NuLm5ycoH+Z/vFxE5pYv8ZramX/qaJB8RkcSGP0susO8CRe17EGhUoBA5goEky0G0Km3D4TCOj49jb28v7fn+/j7/r0peCQ5BFOuPvABwLe9XgSEw6z1qgqeSr7JAxmZATx2tDexVhlBSBUCTetZjBNhNlY5XoE9iL4HzHaq9bm5ujkl2gGcsK1/J7n788ccxFpdPMM3Rc62VCMDc73a73ZwAfXl5mba/vr4ef/3rXzMobmxsJEM8W9Ovn3/+ORYXF7PnSe+qfidTgSMiK+K9Xi/BH2Ln6uoqnyGwVY+rqooVBKu+XUmQSaBHR0cZw56fn3M8/+7ubkqdJVEI04jR9NHPnz/H0dFRHBwcpJqGfF3CZCGFTGZk70gh+7dKaBE7DuDmn5C5rl2cF6NV9iSuXtNsvh5c/vT0FFdXV+m3qALEV5iBD9T3i/Sq51kapiGpdd9UpcTNbrebQ8ZMmfTd3DufBUuQrVIPAOF1krkBHbM1/ZKcHR4eRq/Xi4ODg7HWmMvLy9jf388jVFTwKEhITX/55Zc4PDwcSyb0qGp92NvbSwza7/djY2Mj95Z9rjfZvoOvIl733e7ubv6bQgcM51qQR3t7e3FxcZF7avL84arkgRvZs0mhFf8qbCB0Ly4u4uvXr1kUGQ6Hea29Xi+lsSryEaPWJdVVWJykF1laCzlIruvr68T8rVYr3r59GwsLC1l8WlhYSD/jfjiTVNvZhw8f8jOen18nqrbb7bi9vc3hmCr25OjyC9Xh9fX1VBH87W9/i//hf/gfkgTgN9n897CmivZra2txdXUVLy+v5wYBMMrpDEC1zUPlzGsyVsdBk5c2Gq/T14DXiMgqoUZTgyEiXpvsf/311wRYKl+SSptkOBzG2dlZJn8CpbH+AnCtfGnUlUxJfhkqMMhpC0zAKSaj6ss11VfQtby8nJMc9UBiISWqtW/K8BAJnQ2LKXH+mZ4sTGUFjtUJqL4486XT6eSwIMkheZTjPRqNRvbVCLKuESjV3+Seqfb4fEZZG75na/rlnKLNzc2ciuj5VMmIkfHtdjuurq6SkJCceO6qj2dnZylNFXxIsREdnLVpiLURvkroJGiIFwSKYFClHAL00tJSJloGyACHgoAkjX2pBtSKekRkMAAwjSyvUhRqAXu42pk+R9UJhJRg7rVV9oJtFNxI7vxO7S3udrtZbWUrADeirFY62Z17ETE+EMt39v+dnZ3Y2tpKlrTT6aQEie0i0wB+1zdb062dnZ1YXV1N9Yj9LIaJJVtbW5kM6CU3XAHIe/fu3RiB4zWArvOLTdMkAasTRlXvVOdU9cUdx2/Y80CPI40WFhZyXsDS0lKqaBAbQCcySQJVj6CRiBkhz7Zq0mV4nCr7+fl5nJ+fZ5IdEQnG+/3+2BnKfFp9T9+lDhFqtVrZ1+z62Dh/o9fMMIx+vx/n5+dJ9vB5EaPebclwTXTv7u4y0QRskVE+v1ZCEEF8tZ9REag4ztb0qx63BmfVROnw8DCrWOKrJPHs7Cz+6Z/+Kfr9frx9+zaTRImh1ipqqsfHx/j06VMmdsfHx6nuen5+jr///e8pk4yIJPOOj4+zD04bhdhvYJxK4MXFRXz69Cmx4Pr6eg5QnFQKkZM3Go3Y3d2N7e3trG6rUCJLxOx2u529mGtra/HmzZv0K3d3d3FychJfv35N+2FPYqo/1xjFBnxGJXTdT+0k7qsZKpU0rQQ5jNrpdPJzb25u4urqakwWT67bbDZTFt9sNuPw8DCvsw6CNCTQnvjLX/4St7e3qdqqhR7Df/7oa6qK4svL6zmErVYrvn37FgcHB3F9fZ39QyQQHibQYlORijrMElMpcTOERtXLf6RSNzc3ecbT169fkxGXUAGSmEYATbUr4hVIHxwcRERkU6/ziQRcLK/zn7BEGD/AzGcAvRgEQdmEVMGIQ6dZt8kk2Codeo2wFZJkQDQiMmE1ZlsQ1ZhcE3jMIyPVcyRJJL9jgBhoRsjgAU/B3nUAlYAtgzPV1vuQNjo7iASjygNma7qFFBA0ECXz8/N5xIqBCfv7+1klZmMmMX779i3PKlxcXEzt/97eXgZMgI7EAxNXzyfEzvvPcQAPDw85EdmYcLZW+2sXFhbyPZAtEZGy2hrAqpw8YnRUCBICUGQnyKgqqeQHBBV2zT4MtSAHIl1XHUXi+M/1GkCiT8H3rMlgxCtzqYdCIDMISNWSzJCfiYgxO2o2m/Gv//qvsba2luPYEWhVMisRcUYblhYwkDiqLH8vo73/yOvXX3+No6Oj3NdVosiems1mfPr0KQeaPDw8xMnJSbx9+3bsLEDTF5GyzsyNiJzIC9C8vLxO4mbrqk+1OrCxsZFEiQqi/VT7Dh8fH8eqFxGRNvj8/Jx9hXVPI1IfHx/HBluJG1okKohiy3wW8gQuAL7gBjFEwgWT6LfSbzjZ98uGkCL1/LQ6RKQeD+L1ADo7R8ZJGiUIKjrUGb1eL8kCqiI2T6748PCQNkeSD0N5b5LzKuebremWPcgWJG2SCXuSSkoyubCwkJgRxkVEIj4pVba3t+Pnn3+Ovb292NvbS7LGEDg46k9/+lPuA2Sd2RzsR5uRfSB+OFlAO5B4R8mH/FHNQ1icn5/nz6hTKMwUMCoOllQhliIiSUj3gr9R3Kg2XCXaiBgtbRGRSR+f4fU1bopTFD4qnxRF9TUPD6/nWZoxYGaJOCmWq/LzhRQIKysrsba2Fn//+99ja2srSXn3TA+x30UsUXR8D+0bU0tPz8/PY3t7e0yiGfGa6a+vr+dZak9PT1l5cHilm39+fh47Ozt5wLBz9/TKSAIlPHNzo6b2drudjaQ2ByBjMzFqDygiUibAwQqMNYjUTRYxOg7ERmY4gCNgVqUwEZGVRRKdfr8/JgMi/1JZUZXwd++HBbGJsaG1aoLlkMhGxNikrTo1DUgmIebcHAwOyAtUvgdnQgI0GAwSXBtcw7kaSCTp3t3dzSC4vr6e/TUVWHByszX9qpIVU76QN5h5MhHJSgVbpry9fft2jHk/PT2Nd+/e5bAFxy1gyiX/CBsjw4+Pj+Pdu3fJZtpj9vPy8nLaZj2gPiKyCmCfeA02vt1u52sjIqUmqmTsRI8QedDkkReCtHtkL0ss7WW+g1Qd8dFsvp7T5PvUKqN7zA4laY1GI1ZXV+Ps7Cx9BYWGc0mdJVUnO0uWTbqT2C0vL8fx8XEODvrw4UMytrXySn5az6MiJXbv9a58/fo1BzU0Go38jrP129df//rX+PTpU3z+/DnevHkz1sNd+/vX19fj+fk5vnz5Em/evInDw8O0RdNFd3Z2xiqM5MqfP3/O/efZq5DVabdilrkAkkKkEqC1uLiY9g04V5Lo5eUlBza0Wq1UBbFVyRpfI9bWRAyAFkORO2KJ45yojiYVAuKmOIostv8l2EAoWxMb2SyyxGst4Bghe3x8nPMQKJnYp+oC26LyQbQ6cL3eSzG70WjE6elpbG9v59m2dVgVIsH3ticqMT9b0y33GqZiZ47ysk/0zUe8+maS8TrjAb6kvPG+hirpFUQQwLERo+erKiXZQlRS6OgFtBQC7FfYtdFo5FAmajCKBLG3tgL5XXieCo2UVBIMP9Thldqq7F89mZJRqoIqPZ8kIrVtiJlik3vltVVqz4+cnp6mCoHNSHo9H3hB0jsYDGJzczO+ffuWw+ZgJ7kBpaLJtvbBwsLrdPdOp5MT5El6EUwXFxffjSpnqkRRDyDnitE3apesDLDwADEHkkQSNuN59TMq76uGeDBVekVOw+GTptaJZQAc9sAxFT7D9QgOKm82uRG3JGI2i8CKHQQYOekKPg3d8RqgTdWj9nNKCBmBviP3rk5yqwlWZXgNFGq1WnlmE2fFqCTeKqSSP7IbFRBVYtOyJJWGztReEMG0VmSA6qenp3wv37UGtJeXlzRG93S2plueTa/XS1uIiLGxz/aa/hkJvGdOCqJyOBy+Ho2ih09fj4SCbIxCoE42ffPmTSoGOGzVZsy/IMpnTA7jwQq6LoOfVDZUTf1bRIwNrxJMSXEQK7W/mP8QmIBKfcaSxDr1lM1ExNiRFL5DRKQdk/0ZhsV+O53OGIGztLQUOzs72UMMEAiqEk9JAclbq9XKI2swmq1WK/6n/+l/yilxklM+DCjQp6IqQya7s7Mz5j9nw2ymX8AIkFErivZvjZ962yhUlpdfz+/sdrvJdGv7IE18//59XF9fx9bWVu5LcjR9v7UnSc+cQRISPEOMADC+Y35+dBSH2Ms/SGqQG8CYIVfAr1goYa37quIIsU+vYd3jiE7XyI69n1hL4ilWRoyGkHgmCDZ9yH7ftEjyYNJ7n6tX20A8iZ/v4R6rppLfXV5eZq9Up9PJ342IeP/+fYLdOszHAK79/f2cxgnww0Kz9fusubnX4UNHR0fx7du32N3dTfIU8WCAn1kAVa4siTQrQDIxGAxyYEqtgJ+cnOSxaeKkWFWrzI6sqmo5x9WocqucUcc8PDykmgARLAbbyxGRLR0m65qUL5FzRIR912g0sn+xqgjtQ4qIek/5NoULfYumrqrOwuYSSBX2paWlLCqxuUqGuSb2dHZ2Fvf397G1tZXPqCa0Na5XMq3RaMSnT5/iP/2n/xSnp6exsrISX758iZ9++ikTfphB4Yj98qGwifsxNzf33ZCtUyWKNh0gWqf/mNim2nR5eZmJz/r6egJTMslv377F+/fvMyEEBrEqg8Fg7JBgBlbZg1rp4jhJQ2o15O7uLoMk9nxjYyN+/vnn+POf/5xMv8ZljBIAhmnVbE8y5ly2qn9mhDY8w6vyNYmizYoVIRt1D2tlCKjnpCR1HAJJcETkZ7lGAbU227tO1b7r6+sEspXB3Nvby8D59u3bHBWOJQaMI0bs9cPDQ073enp6yiqt+0SKgWE1aGO2pl/YchW6OnFQVZsUG5voeVRJU2XrBDQVg4WFhbT/ZrOZ7B1bj4iUo0dETjau0kdsux4fSYleQXKTWg3/9OlTJoh3d3c5/KP6o1odjIhkMu19rCg2XyAWOPVSq3wIahGR7CPQR75dk+wqC2XnJHMqBJQKtepI5if5JDHXx0LSonpYqyARkQBYAJ2bez3O5vDwMPeC56u3ajAYxNnZWezu7qZM5vb2NntKVTBOTk5ic3NzJg//HVZVXiAuEGj8MH9Z+9ux+irw5FOHh4f5WgNV6vAK08EXFhZSZqyq3Gw2k9AQqxwhs7W1lfGt2ki9fkCwDnDix/UZ16nnNUH0H4BbE0h7XVJVpe9iaERk/NH3VWck8IGqdbU1Qu8+wB0xsjNx0XV5Dq5tMBhkz64YrL/Le15eXsbW1laCaf2H/N/Ly0seT0BdoMpKBQLwAtMqul4Hb7RarTzWplZAZ+u3r+fn12GKKysrOY1UvHp+fh47z/bdu3eJ6RzrYv/YrxGRxF89LQAevbm5icPDwxxIg4yE28QaSejy8vLYv5M91t7WWt1+eXnJAo89WNua+J/Ly8vE146eqFJ2x9A5FiJi/AQBdo6Isq+vr6/j6ekpv7tqO/JK6xfSVYuW+1hnIUigDYESmxV7XH/EqDqq3UVyx67qPeJ3qlpO6w31TafTyVavdrsdp6enKTWWcNcZKfUIHPNJJkmxP+qaypMARQKHcw6fn5/zkE/G5IZfXFwkeGm1WmN9CLVEjhWrk8+APIwOBgGwkjhhParcg8Mln6lAkQPf3NxMgDk/PzpTjCy0Bh0Bxe9h5Cc12XVCnOoorXPEeD9UBZqcgOvF8tRSOUbK98PkCBg1eGGbfMdff/01tra2slLKAQnYmCaVD2BCT1Wz2YyvX7+OJcoqk1jriMjEtt1uj8lvAdWNjY10Kp6X8xm/B6blj77cY5LMk5OTPBi23+/HxcVF/OlPf8rD1U0YjIgEofqBHJ9RZdZIHvYk4bcvOV6DGx4fH3PSn/4Hw5sE052dnWRhjR53FA/CZm5uLt6+fZuvaTReh3tcX1/Hu3fvsvrBDgQ4gcTeqkDTXhV0ImKsalcb7fkj78cGquyc1CbiNUiRnEs2+ZFaeby6usprcT0RkRJTUl73QTWlKjbYIfkTCQ9FhbPyVF6qj0Xs8N8IAonB9vZ2XFxcZHD8HoLcH3mJhZVNd1g3wKZ3m2wT4KzntZkI7hial5eXZM1Jxm5ubrJizR8AQx8/fox/+Zd/iYhRTMOs20sIhdvb29jc3Myk5vHxMScGXlxcZCWigqBqd0BfBXFVGjrZVwTQudaIka36/pWpN5hjMBjE1dVVVtn0GQKgYrBhFghgCwkk8UU+Pz4+Zj81f8h3ShANzDIgx/dUfbHYPj/Jl4mhKqwkjh8/foyNjY1MJCS89brgp1mi+Pus+fn5+PDhQxwfH+dehI306iEk9fPVASgI95eXl/jb3/6WMUUhQKJSFTaTpwSIL5K+p6en7FPtdrs5hMbvK9icnp4muc+XI3zr/qACEl9qcYQC5uzsLH2+o9go/FyXfmbvKQGDKytpqvqGQHaEiOtn43XYnvtQ46/YzMfwK+fn59Fut9N31F5hcRKBBFvAOnwK/1bVgAoc29vbY6SZvIEfR0JraZNvwDSfPn2Kv/zlL/+P7eNp1lSepNvtRrfbjZ9++ik399XVVTIuNtwvv/wS7969y6k/lQnkiGn9SUSAN4HCA5ufn4/9/f0Ernqpap9hr9eLl5eXBDtAYZ3oViVkDkUlo8TSSM6MJ8YwkuZFjJpxJZi1olmDpGQNI+VAcRUbYE1Vz/cX1NwrrJHEjLFUFlbCbfOqBjCIzc3NaLfbaRCqN3WQSE1aOYv5+fmsHvb7/ZQoYZqqZJeUQmDHgvvO5+fn2Sysl5WsSE/aDIROv+wZGntJj4ZutiQZaDabKV3xO43Gax/Q//a//W8ptwG8avLigPiISKfr/yTew+Ew9wtHXJlXvRf1/MbqA9hk7QsGSB3N4jxWvkSfB4D2+PgYJycnsbq6mhUTqgT+SLIl2PkOKnuYyYgYA2vuN/BYG+b9DkUDQEf6W5Nkz87n8Ym1d3swGCTAEMwlCXye6qsKhbO3SGH4Qz0pq6urCUD5U+QUFhdp5vzT2frta3l5OSdwV+mWoW6YaEPftEwAhuwIML2/v4+PHz9Gq9WKjY2NsZYC/wfMbm5usr9ofX09Li8vs60iIrL1Y3t7O4kPQ2sqKcImX15e0gb1/oq1yEZglL1VCXqVj1IDmEZcq2Z1CJu4ZG+zSVhCkqYaaM+KVZUgNRlcxcDEWN/R5yJTxFuKHH2cwOnNzU0SNa7HzAaxlmSXBNlQrAq8kfCLi4vx4cOHPH5Acosg0M8qoWfTszXdQuY4m5sNSi6Ojo5yejUZuFkM8JRY9ac//Sn6/X7Mzb0OHnTcgurXt2/fUqIK31KlUOpIfCJG0ztrrBJPDW+TmCJ9IyL3d+2JdvwRny42RET21daWkJoY8ld1gmjdf2IxQhTurRhDIUel3/eSAPpc16UCT+FGyQi3Iox8f0rFybMOIyIx7tXVVfo/56A6z/XLly85cVr1kvKKX9Nv2mq18pgMxS+KiIjR6QZ88R99TZUoapj9z//5P8f79+9Tux8RCYQGg0F8+PAhgSIWEqiUqWueVx7+9u1bTn+qg1c49aortlEEs4WFhWz+pzXu9XpjQUbiQvaB/dGDFzFi4G0iCYzNSqqDVbIEAUkqFthBp1WyEzFKJC1AXTCufYtV/lqrm/XMpclkq35eraDo+wJQJGoCOJBuItTc3Fw6wNrMS4IMnFSAj3WblK+R9mDOXJvpXfV+zNZvXxL4epwBTX273c6eUdP66pAZg2r09PzpT39Km+p0OgmiNjY2kiBCMpjgB8jUoS5Vu8/R1omEAoTX6O+o1ReBhA2y2cqG1v4k1T1B1kjzu7u7PJdOj0HEaOy4IKZyYa/zH74HeXuVuQLJKqq3t7fZo1Jlpt1uN4OshI802/JdJHpk9gA58Kj6A9xWssq1SDprxZPvrH0tiDnkVgXnNWmfrd++9I+TlV5eXo6Bwq9fv8bbt2/TDgBOZOLLy+hIJqSHo6okbvpzxYuq+kBQLCws5MHYVf7vfEExrx7qPhwOc0IuYFtZf1Iz9rixsTEm76pDV+xp8jv2QaGkz58CyT3w/eq9ARrtbzGGDA44nZwertqBQHVMhvgUMaoIep24HBEpmQOcG41GJt58jB5r/gkRyw5JxKmTlpeX49/+7d9iZ2cnk2r3yr2+vLyMg4OD6Ha70e/348cffxwD/LM13TIdt1Z6j4+Pc8KlwYqrq6s5ObPZbObEUvZGWmkA1YcPH1LOXSvCBsQgUSMi20IQ9fy0ih71Wq1wU9lp8yDNFs8QhX7mrGP4WgWNTaik1r5/8Q5uVyAZDAZZCeS77F2kGBwo5vExSFWyWe0fiFFxUXsEzHhxcTEmK5+fnx8bKqen0hEdSC3FDT7JUDhxFYY4OjrK2Hd0dDQ2VI5CYzAYpKqCP4uIJAP1V+ohrkOH/shr6h5FAGc4HMbW1tY/NNTXLN0DlYxgp51NEhG5uVUWaoKESahab2ATc/r4+DpuG2vDcWNWam+AoPf4+BidTid7FSUzgqu+Hc5fwK3ftcp1ak+XeyDYuEaOXoWy9mdU+Z/r9tk2egW9jUYjrxVLir2tGvCrq6t0RgBG7Q8BLCvoda0RkdI4Dkelx2dVKZ3kgvHX/QKskviRA9QgPasm/j5LT489b8/2+/20Sw34Ve6oHxXAM1Z/OBxmpV6f7GTvkf2nomhMtP3uWgAu1cmIUQX0+Xk06t0wm06nM1aRQ7oArQKJAGcP1gBZq+8CF4An8AlybLmC2Lm5uQywfmYohkQMkG40GknGCELeFxhn/37PfVC58Dr3BplW+78eHh5yiI2KcUSMHX9QK6OVfJL4VxZYbyVQ3+l00o/5fD53tqZbbJPteIa7u7vR6/WSZN3f34+Li4sckIJ8IN2OiDECTgJn79YEx4wA8u9KuJAjI3aRkAgKiY14u7q6mnHl+Pg4Njc3c6q5fStWSN4kasgMBDBbIHdV7ax9v2xWEsZGJXViy/39fdqCwW5+n4+S7Lm2h4eHlA4azoVkqrgDqCfp1/PkGgFhMnI93wA4Aqbb7aaP5BcQzpW0+etf/5pJQETk+5iMyoe22+0E+867nCWL06860R3W+9Of/pTPyPwFeNLeQ/yoQFf8urOzk2dLV8WKJIIChI2cnp7G/v5+9Pv9jDcVN6poO+ot4nWfSLBgUyq6KhFl18gSpKNhhexB0nl7e5vEquJCxZn16A1EdR1+Jj5VeW4lV5GQrtWf5QSSOkQP30nxgpxBPkdE5iN+5+joKHF2rdBW+XftBWfvsP3l5eVYoksdh+AV4yk4Dg4O4vb2NqvOtZjyPaypEsX7+/vY2NjIcvH5+XnKNEkunp+fU7pCF425X19fj1arFV+/fo3l5eVkMebmXhtHJSv1oXH+bjJZWZVgGtFbqyPAkbPhsAwChn4Fm9JD3dzcjNXV1Sx1S4YYgNfUMeNK+Ay6MlERMaZfluBha1QnJGuVfYx4Nerr6+u8T75X1X/Pz8+PyUklgqo/ESNJaw0kKgl04yoGNekU6Hym8ntNyMlJVV/q/1WOfYbqBqMiO8LSztZ0q1biyJLqZLTr6+s4PDyMiMh/x3oiCoAzCYP9BEwicjhpEhcyVnZIOqaqKKiShfm3jY2NHGSzv78/1s+HUGBnmEwsIqDrO9T+15okcf4RkQAQO8pf1Eq//ei1vj8pCeDmnKWIGJOHI47Ixp+fn5NRdB+AcDbh2lUdBTWBBiitfVcIMMNwMNG119TvVfJJ0O73+zkopMrcAYutra04OTlJvzVb0y2ECJ9eK+uOS6k2BehIRmqbBxBHeh3xSkxKlCR3phQjHFXpEQ5AUERkjJzs74uInCC4vLycEvZWq5W9zGSeqvy1xQPRCsDVFgsxRYyhgKBI8DviT+05UsWUWJHNi683Nzdxf3+f5AzySjzVEkJqDae4DvGy0+kkqek65ufn4/z8PFU2dTJxo9HIhJE91WFRespUf+7u7mJ/fz/lsAD81tZWHsvAb4i5AOzOzk5+vxnhOv3SA2j/UVMZ7OXoNiRN7TF0NAKll+pTbelBENSJ8HVQWLPZjLdv344paao9I3u2trbi27dvsbOzk5JxxRjvV9Ut9ieCkx9A7FIcsTf7WoIlrlLvIXBrPJFMI38ojSR7fBF/xr5VcH1eRCQmubq6yj56pCmMXAfn+N61z1jCipypiTOMfXp6mn3WtQ814hXfn56exps3bzJWyx8Q53yFPuU3b96k0ocvg/O/F7J1qkTR5hLkJCFG6QJKETH2Zw8V8DNO+OnpKXq93lhJmE5Ylk9+YtKhs1SUcAUlAejl5SWOjo7ygdQAoLLy8vKSG0Ni6frpjv2+CWYRkUyRzawPg8Z8cXExz1aqVbI63awmcgCxCgD5DeBqU3JagDV2QsDEMmKJXTupUURkgi2IkexIBDAjt7e3eWArg2XU3rsmeypWETGWcJAXqChFjCZnuY6I0cCFKveZrd++kAVfv36Nh4eHHMMvmAGL1WnWfjw9F866tNdJzSIibfbk5CT3oUSCo5080xOgu7m5iaOjo4gYTVp8fn7OiXB+psLmnDiBlj+oTloA8W/2EtuoagbT4vw7u2IHkwmRBFEVpd1upz/x7+y6gmFBqMo1DQZAokVE2iG/4HWqnnwn25bgqnxinIFs4EalvwZuTGyr1Yput5u9o363Sm4BgGbzdVrr1dVVnJ6ezpLF32FdXl5mDIqI3P8LCwvZ79br9bI1ot1ux9nZWTSbzdje3k7/+m//9m85rp1M2XFRZ2dnY+ceAk9iXD0yQv967WcCXtk9ElOlsdls5oRGxJE4JN4BaKqU19fXafv/kTxcZaVOf2y1WmNDlCRKErZK4CK9+C/XrDKgEhkRY8dciNU+D1nGPwyHw4zva2trKa9VsWCP3759S7uNiIzhtYIPM9WBU2tra1mhAoQjxtVWepSbzdcjwpwTi6iqVZLZmm45zqJW8BqNRs7bqG0KYp0/R4xIOYmZCapI26qIq+1OCD////LlS7x9+zZJWPFTde3q6ioTJfGcXDlidDwLYoa9II3EBENZ4ALxV0+uJLAWDbSvIHjYEuyroqZiCrvCh5JpZCZ8AvMiSefm5mJ/fz8iXnFHbYGo7TWtVivnYESMznVXxa3VTO8rJzGDZG5uLj59+pSSU/fmhx9+GLt/KsH8LZsVhxU/hsNhnvU8GAySHPgeqv5TJYqqh7J1jeAqUPTS5KfAmwSBvltFCjOA2V5bW0tWrE7uFERJs2wG4PPx8fU8tTq5r1b/VPxUGcgmXYvPeHh4yGMmfNba2lqcnp5mdazK3i4uLqLX68WbN2+ykb8210sEBVObTwBicDW5wvRidJXVgXa9RSopdNDAeG2oZgycHXalTraqDE01+FrVcU16IfVgYHQiRkM0AMlaqcEek7N1u93Y29sbk8hKNmdruiURUt33HDDuT09PYwOYgA+2gUnEOjYar8Of3rx5k8+PlPmHH35I+7NPOEEO8/7+Pi4uLuLo6GgMgJKDVRIhYmTTgo5pphy1Cgt5jP3m32r/gZ9jcKuk237nV66uruLy8jLHgkuaBGlyW+QKWyFB93uNxuvgppp0Ss4MvKjXxUYxudV/APi3t7eZjN/c3OT7sWu25/Xul+QBMPA5ApipkfpGgVr9qO4TMOv7zdZvX0gGlX+rTgGmWFlaWsokziH2+nuHw2H89a9/Tak5G5a8bG1txfz8fFxcXGQyaC8hEnweQEsNUPt+EanPz88pbQUWVescCWHCLpDnPRxZtba2lnJXFVTyz+FwGIeHh2NSTDFbpcAgCERyxIicFJclSxQE7g17lgDyP5M9XGzF8/EeMIf7tba2FpeXlxkjW61WHB4eZlUE0eL3gX/TpsV8BLW4ze9KSp+fn+PNmzdZeVlYWIjd3d2sauiPqxL02ZpuwY5sRGvF/Px8ToFeXV3Ng9kRsCsrK2PzHeBRfYgq/RJDPjritdiiUgd/SlgVIra2tnKfOk+89ss9PT2lCs6eUAElA5fM1J75q6urVPtVGxHjXP/FxUX2V7Nt1USvVbCJiMS9EjHKPUq4WrC4v78fw4G1mCC21SILHO3ZNBqN2N3dzWv23vqoK9ENI4ivvq++xOXl5cxfEEUq/Zubm6ngowpCuOmbXF9fj19++SWrwu12O87Pz8eS3z/6mirK12lrmANSU7p5bKIR3QAg2QUQis1wLIPghaF4eXmJs7OzZGpIPRwkDuT2+/1kLvXX+beVlZWxsxeNq8dwGnoT8crgG3IjcNo8Apbx/voBtra24scff8zfxaoylF6vlyCP/CxiNDUxYuSMBGUgzxECNjWgT8K3tLT0HzbT7+7uxvr6+lj1aGFhIdbW1nJC7NevX3OMeH3viNeRxYzevScP3Nvby1HQAlrEqM/Md8C2AsJ02fotq+MEAr4H4/kelgBwe3ubZw6SUwEafge7XSsJlVVXMd7d3U3AaFqtvRMRWWnCqtVeiGazmdI055fNzc1ls3vEqw1cX1/H169fx852rH2BAoRExd4mp61yyZubm2Qza4LGLwhS9r3+yr29vdjc3Mx7pKLHhnynlZWVPCgbcQKY6lOMiATSEZG2IkhKXNkGCdBwOMz7cnd3l/fD7wroADB/KXCrDAOpfO1gMEjJv3vpCJtut5s2rCIDRCNyVIpma/ol/kjcJQH2GYLi4uJiTNZcJdf2Meka3wpEiqdbW1vR6XSSIDDp2wRrsrNaXWanpI98QkRksmlvS1o6nU4OPpNgibuqF5I71UKSrw8fPsTm5macnZ3lMUmISp8HG0S8ng3Jr0VEkj7sr/YBXV1dRa/XG5to6roMzOGTIiIBMemvHkqAFuZBivOL1c4shJJ7wW75NECV32P73W43sQJb5c8uLy/zOdgH3759y0PSZ2v6paqmKiUhq+ooEnLTg5GCGxsbY7JFCrRerxerq6sZLyoRiRASV6l4EDRLS0tZQVassY+QG1R+19fX8e3bt9xrhrBIdCWh4k9EJOkqvooBiiMUe5LSutfEjNpjiYRZX1+Pra2tzAEiXm0f0Qm/D4fD7Lmv8VkcrlPaVUj9Dpks3xURWcS6u7sbe3+YU2VyOBwmDp6bm4svX76kr9ra2krpLdLWhPG7u7v4+vVr+iTfpd7Xv/71r/k6VUcnJXwPa+rjMQwoqSVYMg/Nsc1mM969e5fNsSqOZFKNRmOsGRybpoxPIrK6uprNoJw/LXitVlYJwObmZvZkVTaULIQEpd1ux/Pzc/YjYvEiIs8pwxZUeZf31/uzsLCQUgKDdtwXG19vp76NpaWl2NraSvmY31eu5ixqD5ZNW5lDjkOSKHkT+Gpl6Pb2dowpBSzJFmpQ87kMoDI/Ea/VQsN+6OcjIpMIz4QxCpRV/mufDIevRzZ8/fr1uyjJ/9GXPV8dlL2xvr6eh6cbrLC2thbn5+c5FAFpAUyp0gGz6+vr8e3bt/jhhx+yUmcCMiDLbgVHlXbSGwH4/v4+Li8vM5FVNeEXgDJVNAFHxaMmSuwaS+/7qojVHij2UqeEVtmc7xoR+WdEEAk8Wax+FlXIiMgKB0DJhlT1kF16NSRtCJOTk5PY2NiI9fX1vGd81OnpafouAVlFA0jlL6s/fXp6Ssl/BdmSQYCjAiGqBkPJZmTO9AuhFvGa4Og5FBcvLi5iMHidUIxgmZt7PSPMgAX+H1ny9DQ6zFpsqwNU1tfXE+hWufXCwkJOB69VLu9Tpdr2np8BnFXGrSpZsYH3UxVgh6urq0lesQ8TQhG0XhcRmfiKQXd3d3k8hZ6/2lIiIQTMxTQqIqT2ZLUi4lV6SCqLlAXeKXGqvwHaqxxWtQU20WsFSFblELJcPAdcu91uXrf+b7jB5/rOyPTZmn5VNUcl6vl3VTPKGD2xsBulBjKvTqQ/Pj7OgkTtv2XndeiSiaeKB9++fYsPHz5kckkBggBybe/evUu8t7Kykokrn24+wNPTU9q8uE+NhMCvlT3fQdz1HVQw+QK/p+BQVTRUKvwDvycZr2caahNzL4fDYRwfHyd+eHh4yCJRROTxQmyVTDsiUvWjSoj40i42GAz+AWPDQxJz8VYegMARM9fX1zO+8xn1edtP38OaKlGkyz46OkrHRoLS6XRyoiWtsIqYB9dut7PMXauIkj+O2ib1MGtiIbFULSRf0czNWOmXjQE3YGdy4EtlC4FMDAwQB3RhSh2i6aFjMlQJBVGvqdUXY5AZRq3m1UptrQYAzBGRCWQFG4CiAE6i5AgE33V1dTW/F3mMgFl144+Pj9l36t8kHZ4PyRtJA6bbHnA/nHNTZY1kdp5/RGRVeramW56DXhqSNlUBzeU1WTw8PExpC1tFlkSMDn+XyKyvr6fteV/79uLiYmwsvEFHEZEOGNhS2cDMA7D8gH3vvWoiRtqm8lGTS+weXyBBFLisfr+f5yABe0BclV0aHiGw1qb2drudcpyafOnRrPJQEl3KDH4gYjQRbnFxMc+ujBifhjg3N5eJt0qjXk0SXf4QsCELFLjJfy8vL2NjYyN+/fXXPLS79itWMur09DSOjo7GgMBs/bZVwaEEwLOIGPW12XPIGRLT4fB1uJnKI/KRvLPRaMTXr19zMqpYaJ8tLS0lgWvAFf9uTyJIEcLiqn0L4Ikf4jHASjaqyg4MAoiSqqpA0Dvk/qj416mHACRcYKCUf1NxZLeApv6wiJHCSexnI5XUrOfRVUWMe2WgFN/z5cuXVNsgZcXrOhDIwLvhcBhfvnyJ/f397Kv0rFShKBs+fvwYb968iYjIigSJoyEl4m673f7/7eb9L2jZ33CWmCeOrK2tZfW8Ki3qJFFV6dqnj0SHb8/Pz+Pg4GBMrs0uarFgZWUldnZ20hbgbvtL60BNoCRz5+fnOa3TtWnBEisUTbR2iG8RkSTpYDBIqbSfR0R+PruVeLJzPfJe7++SKZgRLqg9y/wcmef29nbaSZ0gi3SVtOqndERPzS2oiPgzRR8YufqOxcXFjI+9Xi9JaQUSRBofJhepvZrwx+7u7nfTYjWV9PT4+DgN4Pj4ODd0Lc/XHiCVvfpQMWKOpbA5bK6Hh4f413/91wxUGHM6flIcG1DzPokp5ltQARZrtYTDB/D039j4zWYzNjY2clw2ZlBC5DtgZYDl+/v76Pf7OdZcUmnUL2Nk/JLt9fX1PLSTcwKeK9PJeHz/l5eXBPv1DDqMj/K7CkKj8dqQjWHGttZpdJ7f2tpaVldarVZ0Op1kuyQOGBPfdWFhIfb398cG4zCyqjdXUakM7Kya+PssMhFO++bmJp0bB0h6sbu7m/sCYNE/BHTU8c5k5fZw1dwjZjY3N7N/QsIK3JJt2NeYRkEUu8lmyb7tO6+3X+1ZJFMFoMCXQG+oDQCgH3FrayuDKgBob9eEjvTdfz7bZ7IL1QZJZyV1SNcks86gWllZSWksJlPAdR9IviNG58j6Pfbl2CK2JFGfm5tLqeDT01P2gugLd8/4MQFTZXJ1dTWrV7M13QKQ9PQeHx/nIfEPDw+xtraWR9dUYi0ispevysiHw2G8efMmlSsqBuxPYlVlW0jcm5ubjE0IIoQGP6Aqfnt7GxcXF3F1dTVWzY6IsRH7qiO//vprxmp2Id4BlhGjgRSUBa5TQiQZMxXZvXCNZgtUQAlsO0sNyO33+3F5eTmWmAF0ktn5+fnY3NzM5JJtwTq1L5rKARH2+fPn9K2AsWtn967t6Ogo5cXivvvs74+Pj/HmzZuU3qkaX19fpz8DvtvtdlxcXMzi6O+wDIxBolcp5OXlZTSbzTg+Ps6JuWdnZ2OVdfiWqk1vYrPZzGPcJCiHh4eZ8FVcRMUGB/Z6vfhf/9f/NZMisnD7l0wUeWLf2UOIHNJRknB2K5GESyvZYnInwqO2a7Bn/4mfCi7swbRvdltbRdwbOKQmmGyPfSOJKd74CP2B4q7zYmshg/2JqVoAxFkkEvtz72ui6neXl5fj+vo68w7XqrAjXpK863NG2P3R11QVxYjIzaGxVj8MQKH0vrq6GisrK8msXVxcpPSTNAz7KBuPiJSySX4iImWrZKwbGxspXVUVkHDYBGRxjKFOSpV8ApT1swBQgarKYutGx877mYTP9xVUsJ/X19cZ0DA05LqMaWlpKRmb2mPl9ycrIlXqI0FkUIyrMkOSZYmyhl737vHxMT5+/BgfPnxICYzvcnx8nBNdfXZEjB203my+Tts0JbZ+f59PS18rN4x4tqZfVc5cSQUyCTJok8b6/X7s7e3lnhMkEA21H5V9+RwOG4PZaDSi2+1Gt9vNw7xN0TXBjw+ossvn5+eswHlPFW7J2u3tbUo9379/n9egql2JHq8lW1Xht+drs//c3Kin2T2o8jeAjWynVvY3NjYSmKpGSAZJcgQHdmegBVuvFaQacJ+fXydA+27OqquTMiNGch69hEggIIMPpvhAeLk+laKISH/jO/h3JN5M2jb9AiSw3YuLi9Hr9WJzczOrimdnZzEYDPJ4moWFhbi4uEg1ipikT0h1sioATPt7fHxMEiTi9RnXgXL2ukE4fHYlOwBbkzeRkr5P3fOkr2KhJIzfqH3sdbhaxOtergd123dIps3NzSQy6oHcSFrqJL+jaug7Vjmo+3J7exuXl5d5kDl/oIpj39e+X/ce0ex7k6xSy/jeEkX3QOWDPL4SvfymxMOAPf6o0Wjk79cp5M66m8XR6Zc+X4o4k8DX1tbi6Ogokwf+XIxcWFiIs7Oz6HQ6WRwRcxAde3t7YwUAOBVx69mr0tvTl5eX8d/9d//dmFTV/qJyQRIr0kjWasuXNgMxzT4Va3u9Xp4vTpUWEf8wZEYv82AwSIUgfCHmIBv5puXl5eh2uzm4p6r7XMv6+nrKvmFk/656V6WnEZEJ+c7OThJSKofV7vz57OwsK/aTUvHBYJADp7R9RET6XPdfwYNfQUDbG+6TYgnZKZzzR19TJYombGkSBYKur69Ttvb4+JjAs9Fo5Ch9zIQEiJymOmKJWcRoMtHf//73eH5+jg8fPqRhYE81ByuX18ET9Wya+/v7lA4sLCzkyHFTjPRA+X2AiBFyxIzRhlPNiBjJ4SJiLMlSHWEwEieMA9bYBiPrlBCS/giuESN5kn8X8OugkMl+Frp0LIjqg/dQ/fmXf/mXZL44ANWKiMgArxeRnA5L7Hytqh3nDAXw3d3dlGVUmcVsTb/0rDpKBoBCREioJmXEEZF21Ov14uDgIMERdhHDqnJB7lT3/ubmZoKuegRNv9+Pw8PDnBjH8T88PCTx43rYFEmcgFulMXd3d9mrV4OJgFUrpGR5Emi2KDkTfDj2mnxiFyWQZGo+SwJaq6eTPiJiNDAI0RUR8fXr10wQyFrrsDCVpa9fvyYY4WOxmBGRPyeH5wtVPwR2jDVAKeBimOv5elUaKOmeSU+nX/YIdn9xcTG2traS/ad00V+4sbER//v//r/Hf/Pf/DfZpsBvqoDd3NzE9fV1Jp5XV1extbWVIBPoQiBQEtQ+W1UHksaI0Z7t9/t5REbEaDIxYuXl5SW2trYyuQO+7DHJXJVyszvJjhhTE1p72MRWmKKe2xwRCb74iYhInABTAKrun0SrKgIknrACEFvldDAIW/NdVTD9WUyu6gfgURLCd7D3OiCEzW5tbeX1q+rUVg5VDsqA2Zp+PT4+js2QEIMcu1Lbf9hbRMTHjx9jZ2cnEzwKM88JcVNjBjKWosa021qV1ocK85JWihnIELEwIrKSub29PTZnwl4Rp8QTfr+Si/Xc8X6/nxLRmjyyZzFTxdR3qyQS1aF+SskZ7Od3YActWYoflYxVyIkYFUC8LmJ0fiTFgWQcxq4tM0tLS9kPXCW5Bm5S9KhCKlqRzpKKO5M4IvJ4IooMMn846Y++pkoUATvO6eeff479/f08QJgEFdCswKuWtiUN/X4/s3vS1FoRGwwGcXR0lE6+TooDggEdoMxD/o8YzDopUEUASBRUSWFI62rVrQ5lYWT1epXxgVTOQGKFxaQdF/gEUYFKgMKsqgRU9h+wcA0RMcYK26T1vYAQQczPgUH3jTPx/V9eXqLX6+V7np6eplwKiwVkAuBXV1djZ11KKBnw5uZmTj2drJTO1m9f19fXySYiZux3BAvghXHHHvb7/TzwvlbwVbzYiD1fmfI6OZATZqsY94eHh/j69WtWrCUetUovIVFJAS4jYoy95IOQTRJF/qWy9xGRYAtYBjhJ7CJGgU9lXjUeAMUY8lWAus9lRwiSOmRKUiBYInKq35Ike++I14Av2APdVcLGN2GwJf71rD62b2CRKX110l0l7MjfPPOFhYW8N7M13ao9SWzAs2R3VCUHBwfR7XbjL3/5SypnSAzJI7Hae3t7WSl+8+ZN+nYxRUIzWS3UjlArxlhw5Kg4KRZERAIvJIekkh1TDLkORIvfrTGm1Wplj5W9V885tBclXmJxrcZExBjxWVs7fH8ybxWX2gfF19Qqe52uqNro/tUKo+8uaQYqDcOBXfT8UwvAAVW2x97IFd1P71slvNRGvrPWgNmabl1cXOQ03+vr6yTJJYD6A1dWVmJtbS2Pfzs8PEwM1mi8HiuFvKF6o/qQbNnftWWA+k2ci4hU71VSAh59enqKk5OTsUFIrq0qDSJirNWrJpck6X5Hgqc4sbKyMrYf/RvbhhGqXJxNGVDjWDrvUQcaiqG1zURcvr6+TiwtqYY92GW9T4ifiEgymTqHHBVmqQP34Bqf8fLyEp8/f8646fXsG+E6eURcs9nMQpR5Lo4S/C8iURQksG8//vhjPvS1tbUsWTsgt0qZsBYc6+LiYlb01tfX80BhY7vJ12rTP4MDcrrdbspDJIGYO5Ix7+lalNg5fUM5VLh8xunpaayurib7X3uktra2MvABxLXpOSISQEbEmOSvGqrKjO+mGsRB0LEzEomYa68BzvfznIBDv88hVHDpeer1VI0kDxTAyKDc24ODg0xu9S/VCkQdoMMZRYwP5hgOh7G9vR0///xztFqtPOR0tqZbKm+q55MSYtVcZyJhKc/Pz8dGdF9eXqYsioRDgOJMvWcFZRGRzx8xQX768PAQb968GRtMExEJyOxrzpZd9Pv9uL6+jna7nUDSnhLkqpSLrbgmlT9+SyCpCV+tlgjYkrjqSwQpCXSn08n97P19BoZysvKH+ZwMdoK69zeghp1LCgBK//k81eOI1+FQnlsd1nF6epqHtmO3T05OslJMbmOAV2Ws9ZTO1m9ffHyz2Yy//e1v8e7du6ysOadQIiI518Mqxs3Pz8ft7W1sbW39A4EQEWnDlmdpD+nteXl5SXY/IpKwkDCqECBotI9Q/djjjk/h35E1yNFGo5HklGt8eXkZA8T2tCQz4jWWsjukRcTrUJhOp5O2ZnktO6o+pPqp2jri/VV43UfxUryqlXWkDx8DJCNmgWS+U2x/enqKjx8/xubmZlaGqv2zx1otfH5+Tl/s+7uHSCFJSPVds/Xb17t378YGB+kHVUTY3t7OeCYpRLLpR6ukfsTrMS1k2xQt9h0cy47FrLpMrEdyVCJRMqTfD/GikALLUfbVeOPIuHqOq893PWwHGcxHRYzONNVaBbMjWtmZ5BEuce3+bO/yEZJD+KWeha74VH9/8r0kuir67XY75cPieE2sKTOazWY+x/X19fjzn/8cJycn6TNWV1dzqqmKsV5V993RdFoHlpeXs+L/vWDcqRJFDlCSZCJZZckqM9Lv9zP4VcaOk+RwLy4ussLX6/Xi4eEhz4BTcfK+3W43dnd34+npKQ4ODjIwMAayUuckRowYRhrniNcJYlgSmmSOX8lZ0MR2MChVSGyjsj1Wwusrg6q/xOCOGryV7VU+OQSa7TrExyb3OZJQTId/0+8REWMN+AKy9wewJRfACeAu0JEKqnjU5yxYkkwIeK6Ls1leXo4vX77E4eFhsuj+7H1na7qlilT7bYDT2qcn0C0tLcXNzU2yZgBmbT7HVDszqtlsZkImePhdDnqyGoeU4OBVBuqgjZpo6U3a3d1NB812/b6qqQqdwFF7CY28tr8ESLIX4A/oqsoCv+86a4ATkN1zYBLI8x1JzbCtNcDX/pDaK7GyspLn0LrHw+EwWVMqiSrZqWoEQdZUNn4gIpKAihjJ4uuzPz09ja2trWRagQRSw9mabiFa+v1+vH37NveQ6iAfenFxEXt7e9FoNHLggkmL+vqMXn96espJnZLM+uzEbAlX7c1RjbZvxQP2iExFMtVki0074oNkFuAUY9h1ROT7DwaDrCiKafZXrZTV/kWfW23GuYv8hPcCHiXlACX/FDEOTgFNrwXI7X3Vf0oAlZSIyEEk9dxjuAjZ4pnv7u4m8QLfuLZ65MDFxUVKe52XXJVRfC0yzNmnszX9qn2tKryGL5JU9nq92N3djbOzs7RhdiWRkhzc3d3Fzs5OnJ2dJRbz/CJG9uPnnqN9qpJWFWvilGv1d20K3oPds/U6MZxvGA6HcX5+nr4kIlIJgPTlExAZktU6kA2ujBhNCK79nFWpU0lT9qJwovAUEXnO4dbWVt4PPqaSTlQYqndiMcLHwB6vE7MphCppq5r566+/5nnm/B5ccXd3l4OyIl79+vn5eUrdV1ZWEivpUY0YTV3+o6+ppp5Kpjwg8g99h1hDwVDy5lDbwWAQZ2dnOZhCCXd9fT0dMamLoIDxULXSe6GKQNqh8oZpJHXVtC/JkXTK/gFdBoTl1DdSdd+T94BDqFI4TkSAwQzamBGRAY0xYaD0KNBBk+FN6rEFzYjRwa/kEcC0zxD8Go3XftHLy8sEwRyfz3EPBNmqqRbY3PPKjpBBcU7ucZ1m5dkeHh6msUVEynNm6/dZkhzEg+QEGVOTKJU2ewvwiHglhS4uLrJPgEMnT6ns9eTZgxXIsS8Vb464Vvg5addEVkUayqELLJJD1VNBxmey+Xrsi39jFzVhrP0/2MsKLH0noLIOsBAwBflq5/a+YIPEiXi1W8MAgAeAXH9pDYRVRg7ECqqeHb/j7Cz3BUv89PSUoFM1iJ/1XZF1a2tr0e/3cyqmIDpb061KuiFPxY5KEKyvr2fsQLKJCwjT+fnXqdPdbjd2dnZSelwrGgAX5l9PFQDLhmvfqxhDHru9vf0PCZXY1+/3M57wH+wbSK0xRr+Oo7bYZx1Ip89J64pYVJUByNajo6N49+7dGAnLbirw83PXWnu2VE+qnNNr7u7uotvtjk0kRhirnoht4izMMPm+Yr5Ez99Nd2fL9/f30el0ciS/ZwS4w0xwEUnjrOL/+yyEAEKwnsVpn66vr+cke5JiSZvBbVp2VNUPDg5yf1RiBE71WomU2KuFBPkvOTEg5+PHj7lvtY5IKMkpqxybbXe73byedrudBQm9+IuLr2cxw/eVxEUkI61M2FeNkxdEjM4aF5MlsuIZfDA3N5eTW/kQvYD67t2vqhQwWEeOIZaS07darTg7O8vnGRFjJA8y16RjecmbN2+S6CYhlrBvbm5GRIxJz6kPqSXMdJCoSnC/hzVVpK9a4fPz82SqJ6evccjOWSM/bDabmdBIjBiRjV5ljPonIiJ1/oCWByhQ0YYzhmqENzc3Y8zspGwE04DlxN5iaAUoE8ncAwFMsLGRAFpMj0lS5AMkMgJbZYE3NjZic3NzbNR9rcQyNJtTMkYi5731KgL2ETE2kAArpPrpWkiH6tl1AHBEZOAdDkeHjVaQ755GRI5Sd28BYRWi+fnXkc5VbjRb0y3yDo5J5X5nZyf3rf5QDCHZTK/XS6bRsRRkLVWCWuUfSBtJaZVtAkmqVbVyzFewiZeXl/j06dNYj8HOzk46e+oFfgUQq2eWCWyCJPDGF0z2a9RkUtWu9iqYviaZFuBqdREz+vT0NDZ6X5BX8Wf3JCwCDD/o52zY5/ivTl+t1feHh4c87257ezunQQq8EmBSY4SP5FzFBVsqcDebo3HhtfditqZbhrCo9g8Gg/jy5UsmVK1WK05PT9PePEegzsAX9lkHKiBQvA+yEZlak8iaPNljiIWLi4ucyhsRObiq1+vF58+fM54vLCxk28j19XWcnZ3FxcVFqmiqoscAKkRVvTZqhG63m2w8wCqWsgnYwL0RbxC3/BGFASxQv9/t7W0SWPwLsuzy8jK63W4SqsvLy/HmzZskczyzOmUV8OfPYIdG47VPzf1zz31nf0dKU/B4psPhMA4PD7MvG+apVQ9+Y29vbyz2ztZvXxRWKmUwDt9L5SWJrAMY7WP9gZL6+/v7jANirzYKxI22grW1tYxBKpquw5AdeLnT6eTnePaLi4uxs7OT0kj4cDAYRK/Xi4jX2Ie0N5AO+eF9Wq1WbGxsxMrKSuzv72dseXl5SWkrUrrdbudRUwoJdTZCHerkfkqk2GbF2mZ5IFso4So5wo81m82xqcWGXMKtVR1YsZH84/n5OckwdogghuHhDINxrq6u4uLiItUa7hcMv76+ngOA5D0wzPewpkoU3ZT5+fkx4CnBwxZGjA6GBxa73W5Wx+7u7rJZO2J0bEJEZLWRJKZqw6ussdFoxLdv33KIg4TRZ5Jo2Hxe7z0raOSggTuJEOPFWFRGs06mAry8n0DK4ABdG45zxzbY6FWa5r6oTvjOGE1SMPcQG8WIPBsVgTr8xgRUQVLlsVaa9Jdgs31WBdWqE17jM1U3sC9VxuR+Aw10/jWhna3fvlTKMfq1x+309DTZbBNEJfycGEKj2pznXxlV5/5JYCIiKxbAmspHlZ0JCKptFWjt7OxkIBGEJiti5+fn+XmCAIazAl7JkuuIiNzTEaMzDWvV3F6MiBy4BDAIbuyz3lf3U3+154BtnpTf1v+rDApa1RZ9bwoJAEHPU5XzVhVCrap4bj6j1+vl+7hO/tbfPSs+OGJ0bNBsTb/YQ7vdHjtiQR9RHQyGdUc0SFBU+9bW1mJjYyMuLi4SHNUpqBGRAOjq6iqurq4SDAKjKpxsZX9/fywZOz4+johX2ZTqnTjEDjD/7XY7p4v+/e9/j8vLy4zFYonjsyphu7S0lPZj8EsFVXBArXwCm+KL95KM+e5soA7VI4tVHRVnTZ00LAR+iBhNQa/xFwBHhEnkTB5fW1vLc2V9Xn0dO4M5EOnI1Varlf3Q/JTf12/q+/huszXdEhckJZKcm5ubWF1djb/97W8pdV5YWIiPHz/G1dVVbG9vJ/lej6SBac/Pz+P5+Tk2NzcTQ0W8xp3T09OMPz7PvAuJSLvdTkxnf1UVi5+zd8nQycnJ2AkBnz59iuvr6yxWVKUMUiViJHkVI25ubuLs7CxtmoxdPPddqkoGhoBPkbdISrGnKmMUMWAH9u21yClniCu2iKUXFxcZr/17nXUiFiO2xVF2JlFcWFjI4WKelSm2/JzvqgIqma1x2/Nxz7+HNVWP4snJSbx79y4lEzL0y8vLODg4SGBXews9IOfP6Hmi54+I/H0lXppgzeyNRiMZ093d3czuabttHg+dXtpmXlpaGht7LyDaOCRb29vbaTBYGkkuhy4AqSxubm4m+LVZqjFERLLBNOCcRH0vzHHtE5GYRYyOwcDmAMSGDTAGAw7qpmVk+k2U+hk45+D9gQZOQhBjLJNDQBh6lTUAtF5Ty++qXN6rEg6zNd16enqKN2/eJGuPwW+329HpdOLy8jKnDJ+cnMTGxkbahiFGnD9HeHt7m5XFekbS8fFxvH37Ng/5npubi16vl864JjamIqt+1TNHgR37FBuPSMGKtlqtPFy83+9nkom9M8bf3u/3+3n2G2Cs6qL6X4PqZK+WqkOVsEXEGCCt5EedAltlet5P5dDfSWE8A+C89oxU6YprYlPr6+spmUFikZeenp5mD1hlt92z29vbPHNRsi24I5xcP4mkHvTZ+u0L64ycBIyMZ5fQNBqNtFX7jvpjaWkpQaIhbCbjmlxc7a/Ga0CWWoavluCQtwGNLy8v8f79+/TXEZGsvWswFEJbgtj9T//0T2MqJHGRfbKvKrkT7yTIteXBPlSJrJVw71N7af1OJTjqffBdImJMOquSBCNIBKsCw71aWlqKq6ursaqP++OsRcml/yiWbm5usvJZe7srQeAz6jR2xCqASqkzk4b/PouMtBI2YtPNzU0Ou4Ezf/rppyRZJWQmlLJVg+IiIqtSz8/PY61AEaPzce1bQ1ZgRftOwSIi4k9/+lPGErhRvHCch89dXl6Ow8PDTIbNzlD15wfqYCpD1Obn5/P9EMwvLy+xs7OTCao9iWCp1UQSXu9fVQ3aSCSJ7lHtG67S0p2dnezhpTRsNpsp2a/YFh6eJHlgfSQMTOI4Ibh2fv71qKKaE/DFV1dXSe7Mzc2N+butra24uLjIwXXa4r6HNZUneffuXQKriEgm8t27d1kRA0aUZiMiHaLf98D1wTjigs6ZwxTo/MyYbppln8W5194KD4th0Y0DdjaCw+7b7Xb+LhkeVmFrayv/m+yzEsQx/IJUbSB+8+ZNvHv3bozBZCQCl8pDNUL9KoJN3dASP/KjSXmd768fhK6d8WBwvWcdNjQYDMYkBEC636t/5+AqKx4RY8+7gul63IK+rVmQ+/2WsczkLOQk1SYiIqd6RcQYA24BdCSIWEHJysLCwtjAqTr0SV8NydzLy+t0xYeHh2i32zl9jdP0vvY+2Qrb0od4fX2d+1V1pfZ5bW1tRURkdbGe16iiUY+HqeRPlahhdSeb8yVylUyqyoHV1dX0gewDgULOW+WbpJ3k8Cq2jUYj3rx5k2xklcWTtanWVzslb2O/fOFwOIy9vb28Dz4bYQRIqDhGRAZUAV21Y0bmTLew8wiQiEjQuLS0FCcnJ+kzJQjINHtPDPT8JVg3NzfZw69nx8Q+YKvuIcoPAPPm5iYuLy/j6uoqSd+IyLYH8bX2IiIol5aWMi7raxoORwPWar8eQuP6+jq63W5WDyoJUiun9nSt7lUQagaCOE8BEBEpp3f/+MQqS1elkZBalD9A9eTkSeA1YmQv7puEHsCuPsn1uadwRyXM+A+2jZSSZMMegLVJ1jMiZ/olabHXq/zZ/oA7a7KP5KtKlVarlRNrkTDIuuFwGJubm7G3t5evEXs+ffqUxALiTox2HZIysmSDByMipeeknVVhVgnPiNE52v6rhIVCDNVAlT0bBkmOjUBFflW1Wh3sowhR8S4Cxr0V8903tl/bRUzq397eTmmn71WVULC176qySvXjHrJBLSFkxA8PD5nfODbo/Pw81tfXcz6K2BvxisFUcw3z1Pb1vahypkLjtPkeuKRnMHgdUuNG6Ye5vr4ekyypRKkyqD48PT0lwMVwRLz2uEnuqpyy0WiMHWoaMWIKJbEqgRGRGxCzgNF4enrKDSN41f4KjC2DqVU/hlKTnJo81t6JiEj2FlspWHuPGqSWlpZiZ2cndnZ2EljUAKEvyc8q0+HzasCrCbQg3u12xxxJxGgiIjaaI3Hvq+MQ3MheXL+gXA8fjojs99KPIuAKqnWwwmz99gXoLy8vx+XlZVbja0IvMeFwSbJqH4HgWGWSmuPb7XZERNrN7e1tav9VoYAaMjZ7BYCsk9lUCwTjyd7fWpnz+2xJkBIg7Vt+BWEhcNX9ynFXQOi7CBgSu+fn57i6ukr/VdlI9odUERzrJDgBsQ4tAAT5m+Xl5ZQLsg3fQ/CmJvAdfRfPzL9JNgwGkRgjwCqx5P61Wq1ot9vpL1Qtvn37lvdjtqZbT09P8W//9m8REdnDPxgM8uik7e3tfFaID3vI7yBJIkYk4svLS/YrsSGSaHbEZ0dEVuyPjo6SvFtZWYm3b9/G3t7eWNUM4LXHe71enJ+fZ1wgj6xKGJWv6j/sv7Ozs1haWorDw8N48+ZNRMQ/gEJ+wnuoeIu34gg1A9KSrfAV+q9IdUnv2T3b1aPIB0ZEnJ6e5vRRxKbrkxg8Pz/nObTuLf9wfn6ecvA6lIdv9J51FoD7KHGoMRG28BxUuT5//pz9ZDMiZ/olOUAmwJnwosrbw8NDDo9hn/YFm/HMl5aWYn19PWdutFqtJDGur6/T1iVfKvC115Y/J7GcTCKvr68To0W8zqSo5EqdRXB7e5uELsLTcs3k7Kp0bIviTNxga2zTPpVgikvieK1i1j5jvsLPqqLu8fExsaskXFJbVQa1sEE67DWuMWJ07mglzrVlwNIbGxuxv7+fWEXyuL6+Hm/fvk3iWRHLNfV6vbwv8FJV1n0Pa6pIzzE6RBubqcrVaDTi69ev8fbt2+h2u/GnP/0p5uZeh1JcXV1FxKjPEbtCFiXQSUQuLy9jMBjE8fFxLC0txebm5tgRFFgJ1wU01cZwB6H6jN3d3bi+vk5mJGJUymeQACcHYZNXlogzeHp6GjsPTQBQZRGgJHqYBUaBsXIGzfb2diaN/X4/NyeZAycDpDIw4FUyKqGuiV5lOYfD1/5CI7UBwFrV42AYd61Sel6eJ6lA7f3AkJuYqNri+ZJ2qE7N1u+z9Cm1Wq3odDrx7du3WFpaik6nk8yiajypYW3cJ2VTaTABs0pjOEFSN+O6Ly8vY29vLyeUNZvNlM2ocKq8A1MIHZJp+4ZEq/ZBOZfIGas3Nzcp55DcYWVJ0X0PNqcqSK5pkaqw2evr6/RrEZE2I/C9vLzkUAxMZg1WbK4O4pEwWhhoS+CtwKDZbI7JZ6skTeW19nFiWdmyqiSA4b78/PPP8f79+7wvJlTqG9Fz5tBgFZPZmm5dX1/HmzdvElD1er3Y2dlJ31mJ0YhIubf4UfeyBLFWrlXeHbVQCRNyttq/Kp4+Pz8nyWSQGXvhs6tiBijmL56ensber5KTlYj0O+wV8KoKFfMCyKPFXvvP552fn6eNXl9fJ55g25K6iEhCFWEjvvuzpBMofnh4iMPDw4iIjE8S15oQI6BgIc/NLIU6FA5+0bsYMeqHk4w41oSvMICPL4yI3A/2x3/9X//XmSjMyNbpFymldgkyYfLRWgUbDAbZkvX8/Bynp6fRbDbj4OAgJZFVbllbfSgFtAn85//8n+O/+q/+q1hYWMhjw6pKCxbT6lXVKeItQsjehs+qcs1AnIgYq4RVwqPVasXbt2+z33myyuc7VaksbMgP1eGNyOmIUR9jqzU6BqPaq9gv3kVExl1VfMT13NxcXF5eZjJXyVNkErIMsV39Yn2e1AhiKxKq3n+90nyXmK+aSyVSj5Xz9++pvWqqRFHlj1SmThdrNBp5ThAweXFxEY3G66S2epi3G1jPegJi9BHd39/Hzs5ObG9vZ28M9qRWzcinTIoSgIynlZTaGEBexPhAi8qWk5NIIF1bRCQ7AaRFjAKJal9EjIHWubm5DHgAgc338PCQB4nWaXKkd4IXdkWwBu7W1taypzMixj7Dxq7XAgAOh8MEKAKYYIZJ81z9uYJVoBL7g+U9OztL4wfII0YHsy8sLOS0qBrEZ9WK32fZg0A+4sQ+FlAkDw6GRSRsbW2lkzVhtNvtJiOKpCHJ2djYyEmju7u7ERF5OPfm5mb+u+tgm4IsB+7560esQU6lnG3s7e2NTQyuFe86OKbVauUe5z9q74JmflMGVTYlyNVO2Q/Qp58xYpQ8sxHXVYOK7+Tfyebr+6rK1p4tgUhwrvInVZ96ZInqSj278enpKTqdTvZ0kKJKAmqwVn0U3JFsEaMz7Wbrt6+dnZ20l4WF1+ml9pPBCc1mM9UAJoCKfQBf3UcRI6UAu/ZnJI/kEelpMEet1ttHkwMy5ufnU20iAQKkxPN6XrKECTDSA23/AnTe1+siIu+L1zkHVpwhGx0MBsnqu1cqoRGjqcP8BxCIhHWEzOSMAwRpHW8fEVkV5A8QXuwSoB0Oh/layTrQjkAmGQaqEbD2Qk1wvTcFlxkQnU4nzs7OUprc7/dn0vDfaaliIxyqaow/thfm5+fjw4cPcXx8HLu7u3F+fh7v3r2Lp6enuLq6ShVdp9PJIU71qJvl5eVMMv/5n/95rCIv2WJH4tfh4WHuB335lVhA+CKSJH4R4y0lfEE9X7DOlZDQkavzCwiniNH+VBwQ51Tb7H/3jb/xXZBNEj+fCR/Wiqg5CL4P22G7sC8sU+XaElM/jxidr15t2c+oHil7TGP2Hs/Pr8ON1tbW4vr6OjqdTjw9PSVRPjc3F6enp5nMuj/fy9DGqaSndXPVMq8qQEQkKFU5xARWpotOmQGqOF1eXmbPIMaEI1TBq8y/jeazGRcAHBE50lep3saNGPXR1RK0ZBLzOllN8dmqc/pBBBISMgyjRM3rbETAHZhjwLXfgiTIZvM57rkJbZjbiEi5gh4V95hWvkphJX7OASJH8lyxll4DPGK+yaZqn4Y+zvX19TyUXWJaJ2m6xzUoztb0y3AJg59Ikuz1yQnCm5ub2SesOh4xmj7WarVic3MzlpaWcrIwIKf69PIyGjwlgdKTqBLlmS8uLqYUgwMVhGo1Giup6l7lNmxAshkRWblmC/UMU4lk7UMAuHd3d/M8tAq+SUcxmFQBhmpVNrdWUqqUJWL8DESAnD+RsPKlpGNVclslotXOBUS/pwIaETl1D5iPiOwJBchrwlzVGVUWV4eOIAdma7olWahxVGK+u7sbHz9+jPn5+RxEdHFxke0HXudZPD4+5lEapKaDwSBBy8rKSvR6vXh4eIhff/01IiJbR2pFvd/vZzUK2Kr2WGMjsuDl5SWnoc7NvfZb6kfWN0huqRpRZdQ+m2IASAMiIyJJSgSQOFkJJsCaD4EtKsFTe7L4mwq+3UuyP3Gz/l28r1LAWg0Ut6uSZ5Kk3traSqwy2f9MecDn+t78oDjrXkoU9IUi6WZr+jUYDOLy8jLjFrKvSjWXl5cTT11dXWUh5L/9b//bHPiyu7ubJNzT01Oq4vQoHx4eJiZErrAZVTV4rBYgxJqISCyqAEKWTk1XW8WcByrhQ/7W9oqIUbEB7quvkUApJpgtAltoBbF/n5+f07+QvPIP/h3R5LtNKuPYrO/oevkpakW25/3YE7uvRJCcpKoF+DqSXbHv/v4+2u12nonpOpeWllIlERFjBDJFF59RC1Tfw5r6eAyOUcCqiRPwXxk/jrgOWrApOGuj21dWVqLT6eRrAFp9PQawAFZ1+idA2mw2s+zroT49PWVCVasHdQiE9yIT8/muuU6xEhRrNaMmbvU9VV6A04gYqxSomggaKjbYJzJZG7iyjgD7YDBIAKvUzrAASoARK+yMQ5t+sieiVgsnkzlsba2oSn4rA1R/33tjZkidnDvjWmdrusVRSipqkgXIcOT64RzsrGeVzXmd/WY/X15eZjIlmB4fH6d9+D/bqL16pC32EyBVGcAqB+dTOGN2BLwBlhsbG2OkhaSM3wCk7VFgz/AJgZmtep2fsT2EEOLKd1EhnEwAVCfY56T8tD43TG9EjPVjYK/5J0HOfYuI2N3dHevJjIgE0YYG8Nv6FkmDqgzHPZSA1L7lWsGard+29Mbqyea/+d9/+qd/yirG4uJibG5uJulhzyJTK2A7Pj6OT58+5TEYNWFptVrxww8/ZI/V2dlZvr5WBwA/RCEbt18pfZ6eXs8p/ctf/hLtdjuJ4To8igqGL6pTrn0mmwC+r6+vM8FsNptJOtb7I1GLGB3JpPrA30hcfV92yFZcF/B4d3eXxwKxGa00tX+KLBgh5FmqhrCjjY2NJGZUHmplkpQcbqgqIPeg2+3G/Px83h89/8/Pz+l/AWVSXnhgtqZbjcbrMDEJObuzz1XfVPjrURlVaWJviAcSLcmJZ8Umaq+guON3xA4JDDwFGyI2Njc3U123ubk5lliur68n+YF88P78Sx1iJXmaTGbFcIoIyTDCa7Kf+fn5tY+33W5nDEVCIq7Jzatce2VlJe23SlxhG0mjQk89IxF55lnAwHWIleFTcC77dh4yH2ka6tbWVh7tV0nadrudflKuQAXS6/XyHNTa2vVHX1Pp+2xOTq0mdFhsoIsT9u8YM44eU1+rdi8vL9kIagMAcICkhyOYksh5v1arlQ3i5Dwe0uLiYrIt19fXKQ1Q/RNMVDEjIh2wXorKbtpk1fjJLX2fWqpnlJN67Zqk1QqjJBcIxTgB9CQnXg9gV2ktYOheRUTK9xi/6kfdyJ6be12dXw1IgrIEYn9/P5MR7y3p5Ugk0kdHRwnWa5I6W799qUQAMhwjyTEbwb5jxgTFx8fH3Ffn5+fxpz/9KU5OTpKgub+/T8dJCqY/oO6/Kj1zJpnkifQ0YvxMQYCOpLLaugDE7tmEwMH5C8wRkeTP8/NoEI2kyeeyeTIz782fVDnKy8tL9lryWfxgZULZIt/lP8m3yqdrrKC1kjPuV30PQVJysLu7O5b8DwaDaLfbcX19nTJA94+vqiqJwWCQR2x8/fo13rx5k0cy8IUSZtc1W7996Uk0FGOy30z1sFYMzs7O4vDwMM8X5LvZmTPcVKMr+Vf7wH3OxsZGgj/VRW0O9oZEVa/g2tpadDqdTMienp7yrNxKAoqbYryKBgBX43az2Uzf8Pj4mMQV2+z1ehmvEatsR6WnVj2r7BZRCuivra1lLFTlFN8QYK1WK0fjLy8vJ0FTp5eKyfwUQrq2XwwGg9jc3My47x6I06okWmoktJJrifhwOMwqFGDOz0XEWHXSxNOZfU6/xB0kZKv1Onnfoez2Q0SkyoXM2PPsdruZQGkjur29zb0syXQQvGTIWcL8LUkxghKurSoeBKH2jMnWDd8F8ajXng+5ubkZS/CqMqXZbGbxQ4LHbsQznykuqui7jqqGqQqzqlCEj9kyfIKoZOvwfiUt+RJtTvwNIoqdIth8jpYt+Uu73U47dz3IgV9//TX29/djOBxmwu1+1OnT/Eej8dp7SoIsOUdk/9HX1ImipK/b7cbGxkZ8+/YtdnZ2otFo5JkwgEat7nHsNjvjUJVstVqp6Y0Y9f3YOAbdeHARr6CYHphsxea8vr5OWZ0qYO2ttNFtcCwKoBwxqu6pvmBhqu67TnCKiLFKSqPR+Ae5rIEBdOWAo81tgwoyNrYgcHNzMza8R4LuvjGuOgWSsQnQtVIUEQkw/NnnVyAueFfZA4fldSZ3+XvEq9OtDcF09UgFIH22fp8lQdGPyrlKuPTfGXBjb0oKDFuam5vL89i2trYyYJBZ1d5TVbXn5+cEZJ53BZ3sQcJzf3+fFROybclt7WlAQHC23rMy8j6TVMSADPt1e3s7qyF1qpq9L8jb22yr2udwOMyhQBGR/Y1kfPyTewKAVmDBL0iG9XZFRNrmy8tLHtfjHhpuFREJQBx+vrCwkAGqBrfKqOpZrGBSEri1tZX+ElM6HA4z+N3c3KR8ebamW3pm2Yskwn6MGEmZAcXd3d08amphYSHOz89jeXk5Njc34+rqakyR8fDwkAOoIkb2R9nC3lX4ADVxprZGAIxsOmIkpQa0qpxZ8tXv98d8j/0k4aGS0QeMkScfR5TqR5SgAZBVfk1qJ+YigthvlaC6t2Jt7cvy/STRyN+qZiBBF+OrUuPh4SH29vYSyFfQDk/wMeI3/AEjIYIlJQiAGssdvj4pvdWfOasoTr/450rg2IOq3WKNxB5hWBOqunciIo8yokTRt69apvDwyy+/xM7OTmxsbKSMnG0hP+qgFNeKtPEf9ZxK2crKSiaL7LEOemHXZK4IfNOv7VnVzVqhg4dr5T7iVeniZyrmrkscguFru0NN2LR3OY6CghHxWQs9tWeYT6nks4S4FoOoIQzeg2P4Q8+fxB2GsBCzMMfa2lr0er3MDar6h3z5j76mShQ15tYDth24DUjqP7I5yVkEKxuv1+sli+7G1el+c3Nz2TehemYT6L+om1WipgKoZyHilZnc3t7OpO/+/j4Tv9rMHhFjvUoCINaTdIAzADAZP3azVgZWV1fTyCeBlvdjRIwW81glnByPqVUVJAMBQHg1VgGdPKFOaqqgm6FXbXqVB/petaog6HnOnJdArhJVJQ4MD2CQpAvUszXdss+AxyrxEHCc6+RZSUJIp8hN6mh3DhAwJIE0XAHbKlnimAVUgRQ4U+FgR8gEyaLvUqudDuStFcHKmlZSg5N3PRjXyhZWtQIArDoaMToWhv1igQWcWkUVnHyPWoUXwNicQO392Yf7fnZ2luwsH8DGBX7313UD9p55rSohDfg69luDraSj+gVKAX6/Jryz9dvWyclJ3N3dxU8//RRra2txdXWVvU5fv36Nra2tTPb5Y2z33d1dHuCMcHt+fo5Pnz7F0dFRVsSQfMgfz7tKHGuFqwIZ+7xW+y4vL6PZbI4NZqhVcWQgwkisvr6+ThaeNKzGD4kNH1FBn2qARJEqxh5EMtqfEZE24D3138Isfgb0sqGaNLoneicr0EPAAtCqQxGjifBwUVXdVBksX4wcjxhNMVUJBdpVFSNGA/bEWEl1VRxUWe5s/fYlVkh8VMJqf1zESIGlSq5CXNU7JoLXWBMROXiotkjBvD/99FPGKvHBkEb7aDgcpm1QpdW46ZoiRkWJ5+fXCcomuCJf7GOKOXtNwiwxttitGOcaxSZxFF6sxJjj4CJirNpZixVVkVhnDXgucgDPyv53bfze3NxcbG5ujsV+/q7f74/FQZ8/HA5zqI5n22q1UjILZzjahO+W+Gqr29zczDiOAKwVzz/6mgqNDwavTb7v3r2L8/PzrEAAgTYJ6YYb2Gw24/z8PDY3N9Mp0twzHgDU5pmcGCbI1Orky8vL2CGgQCKg6lxG72+U9/z8+JEU5I/kXgwXI6myQENdm3859dqk7zsAfoJOs9nM8rZ7Vcdh+1zJo+CEwVCN5ThqNcjzqTLYiNHxH6obtfG2SuQESNdrM9fP9DOAoNlsZtKBzYoY9UYdHByMDQLwuSq7HMHy8nLc3NxMszVn6/9c7M9Uw+FwGJ8/f86jV+xppIFq9MPDQ3z58iV+/PHH6PV6MTc3F+12O6eXApCVWfU7nz59Sha9JiKqFlg4gQepUSebRYyqeCYBq4wAkCr6tUpQq27VZup35MAjIu1UgEGgSPAEIqAN4eN9kSwRkWAZ4SPwWmx6ElxMSmWrTGd+fj4rvWwMUJY0CowSRD6pSlC73W7s7u4mKK1qC1WJ9fX1+PXXX2Nzc3Ns0ioCx9TqhYWFBO0zIDrd2t/fz2EOZNlzc6+9PPpMVdMMjzHIptvtZpIY8UoC7u7ujvlt1f+rq6skVsRQwLMmVB8/foyDg4NkzFXVW61WzhVYXFzM6xFna4ISMRqO5ricpaWlVADUNhRxHUiufVlkayTfyEc4oia2VflCfjsYDDL5fnl5ySmFbKWSQGIZbMAm2aNYCWDX1yBOgX/fjb+hdPK5nl2tHJ+dnY0No+M7JCURI9DLppvNZh4ZQs7o3kbEmJ+Zrd++yJc9i6rI8XPJjed9cXGR+5rdkk8uLy/HyclJbG9vZ9yplS92oDpdCQaTivkB8nCD51S24S94EQatE+0jYmzP+pynp9ejrapapiZrPoPNsSltRexqfn4+/QQcDd+JmWJRTWYRPjAhjFHbKZaXl7P6SpnoBIaqclCdVKCy4H62fHBwkH7w5eUlz/iOGMXtXq8X+/v7GWdVMyNeE30tNFWOK0mllKDIEZ+/hyQxYsphNhGv8hKDUNyUKo/k7GxeRoC1qMz/5eVlMomVxcRsex2mhBTSg6vDIyJG0o5v377lZuH4yaw0+trUmnclMCoIrt+mXVpaip2dnXyPlZWVZI6en5+zn8Lvz8/P58Hkd3d3qQPHztRgZONXp1QrmjauBl8ad8bOCXAojFBwVbmpUhhGT+5Qk1z3k+6bATHSWukU5Eh3BbMK/iUEnkcNoqo938vY4D/68nwqW7e/vz9GQBjPXwcgPT09xdHRUTw+PqYDXlpayomgWEGBRPL2/Pwcu7u7CWTsK4OKEEJVLlr7iiQwkkDkQ6vVSmkWmxCsq02yecFM/7D9xmeQid3d3Y19L/ehgkX/1V4NRMv6+noGC/aDvWQfk70XBkkhcaqMr9pd9Zuqsyq6tQ/UqvJZz7eyooPBYKz/zD2qUyX1vlXJe01WkHSSjdmabvGtKysrsbCwEL1eL4k0917iw2aWlpaSmPGsPBdSTz4VgCMRYysAjT2rKv7TTz8luLT4ZP04Jija05QvfLj9baIxlYHhN+KOxE2cggOqPBOgcn1sv8q9+QBx+vHxdXozoNtut3NSM0DPNuCBKtFjJ66pKnqoHSopVCuPes+8L3uOiPS1pgqTqS8tLcX29nZWlRCmCG6v55MmlT3Vd/MdfPtsTb9qhVvip4KvulUr19QWGxsbWTFTPaOQgQUjIkkGJGSVuO7s7OTrkSbsVQtV3YswYL/fz5kc1d9LLvkNuLkmivqeFTz4f9Wwqg6yV2FzA3MiIhPXGoM6nU7ez0oCLSwsZAJZFQbISEWh2r+PNFpdXY29vb3EB+6f74RAUTV1PbXthM9yH9kw/7S2tjY2dNNEcvY4NzeXPrzVeh2A4zgfZDgsvLi4GFdXV3mKwfewpqoocug1Oeh2uynHMGoXsJH8YRcajUYyEI+Pjxk43HQgSJVPab2yfVW+Ojc3lwGSg15cXMzDSk0biojcAILsysrKWA8SYFaBrIBrA9nsEaOR1gJsp9PJ6a16K2rVjbOvfZZGWzOqyjRJ8IAHFT1AglTWEtgwtb4H54bdiBgZFEBbey9UTDhBYDdidHRILf8Dnoz55eUlNjc3o9frZTJOouie0dkvLy+PSaq+FyP6I6/Hx9cztr58+TImD7u/v49Op5O/U589cMaOq6RbEoShW1tbGzvjU6L38vKSz5hcVOVBdcO4eL0DZMnA0fX19dh5TRImABnbWPvvgCtVBwllPauM/dbKeWXhBQOvt08rWQMIAp78RQUTEZFBsyadrqmyqIuLozNhEUwWZlg1o1ZOvKaCWUMSnp6e4vPnz7G3t5fnH9aq69zcXFaJKiGFNcV+Pj4+jp1JV+V33wsj+kddEkKg3lE2SE9VOD2h/HJtTbDvxVsxkDxNbGTXiBSqjskJwfXsxrW1tZRrIXAQJFQ8VTJOZUPBwK8jiMRM9kAZIG7AAgCs5MxwKeAORlCRpB6gdpFAVuI4YjR50ndxPyIigbQYJ3ayb4Om3Kfqj7xnlSkCuPyEFoAqQ/f5hou43tpDenZ2NtbLWlUapOv2CluuCeVsTb/q3qiKLX5Q0mWfVEUJfyu5jIgcCBYRedZhjXn39/djR7nBh/Pz89Hr9WJjYyPu7+9ja2srEzrKH8q0iJFE2e9XXC12qVRWyWjF2RGjY3TYqphsAIwCQFUIka3ar/XcUSoDw1+Q2Mgc9xKRZW8jbO3tqpbjR6vKj+/wb4gsdu315+fnmdRT+nlGfB9/pD+yEnSuGV6XNMpPYGXnXFbl4/ewpqoocqAefh3HK2sG3ObnXw9fx6hhFv0ux1fBEjllRGQmzgFWrT4W/fb2Nr59+5bJEAkp0FfL3RWIqWRK+rCZmHZGX+U1qhkc+2DwOqac4xcIyF44dP0Jgr5gpOk9InJCm0Sr9nHYtO12O3tEKtDwvYzTBwq8h2RbxUbgmZSo1r6vGmxduyEjtaIIkFRZBnBaJ7i5v77b4uJiGingUc94nK3fviQwyJKq67cn1tfXs0cYgDLhsMouSd08I9V9tir5wtpJDgGZiMhKeWXTSHvsRYQGSYqgFDE6gLsOtvI97WHBUYUlIsYCB3aPP6lsak1+qiTUvRJc2Kz76H3ZEIJFEEeyYF0jIoNhtc2I0TApAc51qZo451QF0T1eXV3Nsx4lC4ZfaOIfDodjQzNMV/S9gVZ+2zAPlUzgGmCYkTnTrXqkE2JlY2NjjNCoPTP8K7IV0APOxMMqX1ZZQIKwS3sLcPL7z8/POaAK0WufAkOevWnf7BEAQ8pEjPro+AlKGD5GHADiYIOISAAupvh8AM4wN3+uPVFkeiqR7LVWVRFAEZGE8ySZ6d5XUihiNCgMKI6IJFcrNhJrq+y2VlPc82qLrVYr1T0RMfa9IyLfpyoVqoqDHc/W9EuvYb/fz+cBV5JbIgmbzWZK9E2kv7u7i+vr60xi7Efkwu3t7Vh7x9zcXJ5jbF/wuxIORQdngLJFsnD7pBLAYn2dulpbnGrPuiKDz60EaFUkUMbBemylSqD5Ft97kmC0f+3xWo2fn5+P7e3tMYK22lztw63EjIQecaS4Mj8/PzaJlQ0ZoMXX1IJIVeQ1Go08FgMZFxHZ44gU09oih1HBtEeoL+GiP/qaKlGsX7we4SDZA4giXhMm1Ye5ublkAidZCOcw2ZjX19djMgpaYUGp3++nMRrsYiMyplp2B7iqjtm0qZWVlWRIOHOv1VvAsAQeRrC4uJgSOd9nfX09mRzvqXwO8EkgJamCaq3c2NykKhWAnpycxNXVVToCAdfneC+LATEC94IxkJVxGrXht1Y/GCAwofpZQamklnPkYDx/37/VasXJyUmCF1WM2Zp+qaJzXqrLnDHpytbWVvaF2l/X19e5HyJGlQeERq3KRUQGHiqBRqORwMceOT09TQaTXQkiVe5JXeCMs0ajkaBQYHh+fo6Tk5O8Pt8JQYTIkHSpygvQEZGVm0omAdq1Ysmf1ORRwo084g/4QqDdZ1UZWiV3IkaVAZIi36H+u+8YEfk9JK8Ckgp+lRze39/H9fV1JhiDwSCP9WDLk3I6Cafg516ohvKlszXdAmAuLi5ScWPf8OmSitPT0zG5IXJxdXU1Tk5OxkBUJX5OT0/zeAfn8mlJsN/q5M2Xl5fodrvZRxcRY1XwiBizF4lVTZiA16WlpfzMKtmqKhrkJdk7X0G6pXoaETl8SyxhR8vLy2PHagHCVUJb/829Z0f8ingOaHtNxLgEUVWj9mlaOzs7Y8S0BFhyz24qiPVcJZRwz2AwiE6nkweTA+X2iWutZ+G51toSMlu/fR0eHsbz83MmLPBVVZVEjGzZcTUKB+LW5eVlJhswYbfbzcnjg8Egzs/PIyKSLDXD4/z8PPcLjHV+fp44jW2KSfAfMlSl2cArstiIyGpmJTV6vV6eD6wYoDLGBvSyI09rO0jEqPWJvFOiqeKuLaTatH0eMbK9So4sLCzkv/s5/H9zcxOXl5djFXtFDTbqz7BB9RGXl5eJVbV2aEOrvsP3kTPMzb3271c/4N5HRB4ZVtVAbPh7qShOlc4CEjYPsEGGytEJNpJAMtObm5scjwsckkMdHByM6YZJWpTbBcj19fUcf69yVQMW9hDAE6Curq5ic3NzTJ5TA62Eqj5YTt3m9X4+RyAByDh/zMNw+HoOUn0PAadK7mr/hQTL/bZBGWM9Z8fvVimo7+8+1s+trA4Hodon2asSWYBfIg841+uam5sbG8/utREj8FAdBzmCJMSE2lml4vdZlVG8ubnJBnoBaDgc5tE2GxsbGewEAvYQMZoAvLm5mSxqlV+Rh5Cd1TM9yUv29vbS+bLLSgyxK3uHjxGYO51OJi0Rr4kwggHwE0wqCIyIZFzt6Sq19bkWUsQ11D5Cn8/OXCvbqhXKiFFAU4mrMr8qg6s+qwZt96NWL8l22I8qUa/Xi5WVlUxWVZw6nU6srKzk83XfAAzfi42qZkm4XR8lAdnybE23MPmqwRGjSdD8YURkz7DXIC6WlpbGSEZgkK3e3NxkG4QKdFXa1OOpKnn44cOHiBj1GfHHwKqfSeharVacn59Hp9PJ/e381ZeX1+NdqHaqPFYcELuqfRju4vvXdobaiwRvAO32s4mprpuvUTXw80qsIkR9ljgq3vqZz6wDLyogj3itRME1T09POYCqxmd+x+t9Tp08jVitrS36HNk3AhexiyCbremXxIrCph6/AMtUgk3c6HQ6iYkQ/RGR+G9lZSUl5ey8Hq0kziwuLmbcrJW5Dx8+ZExQdIkYl1fba/6s6ML+6lEtYmzE6CxCBQSfUckYfuj29nbsWDiYV3z1ua6j2hIfUuNrlfLCmRGjiabarBBZ7jcyeDAYpE/i31xLxR311APV3BqHYSNzCKpkVd4h+dvZ2clEmo88Pj6OnZ2dVF1VMgsB/b2QOVN5EkCkDnDgrOq0o+rczs7O8vcjIgObTRAR2b8EAEVEgpWIyNJ+1UZXQAs01tHVHGvEaznYZuPwa9m6sut1YI6NViWS3qMylYKSRJEOG0uMeeEwbDBOR8Aj16ysJkOsDbISWu/lGm1kBlxBOIOoTdKeh/d3j6ucUIC7vb1NoEqnzcgnHRaj97zovTHMQIBrarfbWbqfrekWRhMRUSd/Sf6Pj4/HbBigQgLV5AE5QuoCBCF4yFsiRuOf7WGEkKQV0JJUkW9UYFoPFSZzsx/todpcX2VkQJ2fq7xz7rV/y39VdjMpfZ6sIPpPEK49YNhUq0pMI2JsiA378Lxqhb9WCSIi7VACB+xLLNyjm5ubtNft7e0xYqf6AxVi/ybBqLIchJ33E/Rnk4mnX2IF8HZ3dzcWswAjxx8AOYZSADl1mnbdw87lFKP6/X5cXV2lnczPz6ctAlMYelUwPr3264orkihn/9mvEtmqLBKnWq1WymslhfY/cmljYyOTo0oaVVsQU1QmyAP5mioZFWPFLVWDyfjm99wz39/f7X09kGJ5lcpVFY+k/ObmJsF8lcerTtYp4BGvpPrNzU36ZL4TwKe6kfxXdRL/PUsUf5/Fd7JLPrTReJ0mbU95virmEa9YdXV1NVuQ4FKFjZubm+wfr6Qum/aMqzKL4oAtUKiJQ2yVlFNcFuPJv/meiMi/w24Vw8FnlAdwQKv1OsAOOSQOu1dVut3v9/9BIUOO7l7V1q75+flU+C0uLqaMPGKkBNDPWNtQ6kH2w+Ewixbu6enpaSoCNzc3s5IpV/BePmtrayvVCVWKS3EVMRq6Uwf1PT8/p1Lx5eUlvweCqxbYvoc1lSfxAKqjqhIJwInzpJuv49c17XLUlXWsyZHNJnsnEdVfeHNzk9m+zxwOhxlgAZvBYJBM32QJu5bYbeLaz8doTUSKGElyIkajfgEtQV3izJAYXTXuer6i66gBqCayNqrrr024gsTT01Mm2a6RgwOgAQKT9irrWgO9Z1OlN7XfzbUAxoB+lSoJ9lVm6P1dk8rxbP1+i7McDofZ0xox6rGIiHj79m0ycAIHmyERBqLqxE/gBUi1HypoqaoCn9HpdPJoHLZTWXw/i4gx5o1N2nuSPv/G4c/Pz2cfnjHaEaMzFtldZT+BdfvRmkzWAMxa9aw9HJN9GHyRe+r6gA8JgiDnvqsAuU525vvzDXpSVBL0crNlJJjrEOzq/auyt+rXJAHN5uj8OYl1RKR/ma3fvgAssWNrayurc6SYg8HrMQ+qc4+Pj9neAQCxgZubm/zZycnJ2ECmiNExSICK/VqrZyaksiGEn71l39XrR7qI6z6Xb7CfXacKmJ6+q6ureHh4yN77qrSpFYiISBuIGPUEIqVdg8S4Jn0Rkf6MikAsrqQYSSyM0O12x1o7VAjYJYBd45r3F/MpndgUQBoxGtVfiWT33FAMCYgjTqivXl5Gh3YjbiTPs/X7LElAjW11X21sbMTp6emY3Jli58cff4y5ubl49+7dGHEoOTKMZnt7O+7u7nKoSq0STqpZqqIF+cHG+XVxDfZkx2xEEhcxwr/1OxmcU4knyh5ETG0nqpX1GpvkAdrOqgKNkgzBVKuDksCaoFfc6+8Vu8PtVSYqZka8zu2QOLpn3kPO4v7WBM53ZJeu1fnlbA8B5bNVn6sikV+cn58f82N/9DWV9BTYrIzDxcVFsmMABaa7HmzvtUqx9TBdR2cAbb1eL1lJG91G8Z51iMP6+nrKPPb29vIBKh8fHh7mxrCZlpeXc3Rv7SMwgY4Bech6dP4jkBUxGlRRK6GTvYeqoX6nNtZX6WUN7v4cMaqyCpL1dQKsSirWikOp0oA63pmDUZEApitTzFgEVL1QFVRU5tfzV0Gdn59PdqvT6US3203Q6T1mh3n/Puvq6iq2trYysJCD3d/fJzERMWIUPbsKNmr1vN/vZx8wJ1fJEIGtSj4QDAIBm8KyGhWOXZTUkHHYr0AsBz8YDLL3WeVeUOXYB4PXiZH2U7fbjcvLy5SAS/qqBKZK6yalMf/RYheWz44YBeH6vvyGe4vkYTsR41Px+B/qC4GLD0NmmajnXqv4Tx7wi2wycARA5Q9Uh1z3/Px8sqcmMKo4zSTi060q9XLsxePjY+zu7uY+EZ8eHx+TmV9cXIzLy8s4OjrKnuC7u7sc4S758979fj/W19fH2iOcV9tsNpOExci32+301fx9rYC/vLyMEbNAD2YekHQNVYkjYaISWF9fz7aU6+vrBJm1Iqh6KEmqg5XYue/tc9mV+yyhrMOgxLMq1eT//Bv/VBUVESOCqCogJqVwVVYu6WPnVdIaMRo8xl7JFWEJZJFkQBW13W5nEn99fZ3HfP3f+azZ+v982RtA/sLCQlxfX2c16u7uLj58+BDz8/PZa0jFUdVkEq4qi65H1qi+szsxUP/u5IRPCUiv18skTLyviRUSlU2oqFl8kP0tiWOnvnfF9d7Xdfr9iNEwSCSKNpB6/VUmKiHV1lCLMiTlbNF3qVil1WrlhFh+FEHqe9f2jH6/n/GVIg5O4UuQZXXAnPOfa1tGxcySworx7Z96JNby8nL2kNec4Y+8pqooXlxcZGbOKZlWyXHWfj1O1O8Lfs/Pz2kgHKFkoiY8nDTQojcRs1ErWT7Xxo+IZMRrZS1iFBhszPv7+2R2qvSlVi30AAkEGJo6hKVKSgW6zc3NTKqwM+RGAl9N2nx+7WVYXHw9n4qx14EbjEwQU7lxPfV51ArK5Iat0joMSXUkm5ubqWn3WbUa6jkI9JX5JpvyXLB1JIY18M7WdKtKtLGdrVZrbGLuYDBIKUytMEkoK1uHsKnMm2Bg3yJE/N+eERwBVmeQOoe1yjuQD/ZnlbuR0djfWEtBJSJSlidwsGFHsESMqqZeX6Vb9m1lLSsYdG9rf2OtRFa5nNdWGx4Oh3n+qiQSgK8S9Pr+qsKCpPfnU6tcZ319fWzCJF+B9eZz+BdJvSN6PG8Jr2vsdDpp9waTzNZvX2KGRN8xQqrBelvsxYjIasXR0VGCFnGj9hV5306nkwoc9q230f6p0xNJHD1bJFJNXGvMYkdAUo0rVY1zfX2dFTsERUSkb4qIrJj5nGr/VTngkHH+Z3l5OYEtvyS+eF0doIP8YVc16fOaKo21XJOYWwkeBJYqBh8Ir9QqLruk9qgT3mvPqe8g9kaMlFyu1eAbVYqLi4v/H+3W/zKXe43kdDae51QTgeFwmMfJqOSp8tt7fDF7Gg5HE0stZH1Vj7CrSbItQYZiAAEAAElEQVQUJuT/JTsSztqr6PdJZq+vrxML2l/2HYw+GAyS+PE79qo93Wi8TkaHK2o7BWKmVizZNd/l71VxVosnLy8vY/6j3runp6c4Pz/PyiGfKDGliOIf+DN4u+YOYqH7RiXlOqgg+FDEwcbGxpg8v+YrrlWu4pqenp7+4Vi7P+qaKlFcX1/PiljtO6RP1vtyc3Mzdli237+/v4+Tk5OYm5tLZypISUB6vV6sra2lbAv7yYFyzPqPWq1WarKrFJPRYXgwC81mM6VbgO38/Hzs7e2lfItx1FK9B14ZfsZbewcjIqeVCgqDwSAZ+efn5zwPyvdxD02cJMetgbmW6quMU1Jtc7vOmhCSLjUajTQiYBpjItBiYfU8MPY6+cm9YcjAeWV0r6+vs2LkHD1VYE6IY4uIsemWs/XbF2fF8UoeWq1WVvT8DoB6eXmZz03yHhHJHnpW7FRAqD0VtSKgCT8ixvYVmyMH2djYyOTEPpQw1YTXHmNfnHw9OBh5VGWs7ocAiIX0HhGRiRmyqyZC9XcEuyoLr8G4Mor1PSYJoP8ogZSkVkUG4MymVE/9fWFhYcwvIqU2Njby3ruXgAmAAMyS00kA+TFJw8LCQlxcXIxVLGdruoUoVaGvpFqv10vb439rhVoLhGduz1T1ANDGNoAadlSPsBFLSTDrcROqjHV423A4zH+viZdKZSWRTTese8m+9H1qAhYRCVJrX7LviPyoFY1arRODvFej0UhZa7VvPgRYrWSR/ySybLPK4fmI2oMvcauyPMQw/4Z4BZxr8iCZ4B/5m/raOrTP8UamMNaz8mZr+gXXIB4fHh5ygqnnHjE6V9oSLzqdTmLYSftl75U0h0GRLhGRqhkEhD3HX5NPsl140P4VK+DN2v7lWivOrYQIYtB7RYwwrfgDo1almfsDM0qgvHfFm+zEz19eXsZI6opV4dBKIDuzvBI0ETH2+XqZa9tHjY1+nx3rZ4TlDfB7eHiI8/PzVOt57nwjIqoSRhJyuBep/b3E0Km0CVVz7Aa6QQ5Ot5k9WBvg8vIy9vf3E5RsbW1lQrK9vZ3gklM3Uj8i8kBSiVwFZq1WK7rdbrx9+zaZQMCYcWICSO9qiV4PRmVXK6MU8ZocCjirq6sJwGxan1srbVXSJ0gKmqQFtYoicFrus2AWMWrqBbxVWnxeDX4WFrf2cUSMpktic8igPDMAkwMjdaqsZpUr+QzOgaxAQsBYSW0YZK3Sztb0y/PAmknwgB0s2e3t7VjvDiD5+PiYVcRffvkl3r59mwAKcUN2VivvbIBtkOioLNaqtyVIImxqxaFKZ5AIVTZqVbmooOVngFtNPDnzCjRrBZHdTZIWtWJYq4Z11SS02nJNIGuwihid2Tr5PkCpxIGMnlpgbm4uOp3O2LVSOugF97yAXJUeQZ4/BPgrAcWX1CEkKrOz9dtXnYQI8HtejqyxT/VKRYwGPzw+PuYz4c8r6eO97T+yb9U1ElBntukr0ofnPT1/lb1a4a+96hEj+395eYnt7e20UfYFUAKGYt/a2loCuCr/FqNcV/U99vGkckX8hA/4u2qjXsvmkZZiaR1WVxNkq/aCRUT0er1oNBqxu7s7RvKoGtRrhYO00SDZ+K7r6+v8HhIHVdytra38Tq5/e3s7rq6u/uH8ttmafiEUKwayD9jtcDiMi4uLODg4SH/Pd9a+01qRU2VD2nme1HUbGxspDUcM1j2pt90eYsdWbTOqhQOYoGJ2scB+nozXYhR8FjGKffxARCShiZSulclOp5PEWJXz1mq8mOiaYQTFHft9cXExcwoqBHGz2rh8RHGCD6hVVsTV9fV1EtxPT085UMvzqwUYyslaYEGMORu+4n/+Gt7gt7+XNVWiSMOrZ8JREzJmCUKn08nNic1+//79mMa4PiwH8w6Hw9jd3R0bS2vzKR93u91otV6b/934ra2tlHvWQIZlrEx6lZYBkhVEMWCMDCegh48R2Xw1UeP0bWD3gFFjOfzOZG9JDfqcSESMGf7NzU1uXBuSQVZmlKObDIy+d5UNurYqIZps5uUIVIMqU1vZLO+hmuqe1wlbnpPn67pna/qFBKmVxJOTk9jd3c3enypHnp+fj62trdzPFdj8+OOPOS7a88H0Y/WxbwsLCzmaX9V9UkZp3wOtklMkkL8DivYLG8KgA1kRIykMUgZY4mcqAVNJiVpBiBgFwWpztepXiZv6+/5c7W6y8ug6ff8KIJArAmZEjIF1vpZvIBcV0MmLXaueryofB3DrdGcyeEEekPDcDAvTa1ylRbP129ft7W1WG8RGiZQ9V3viay+p2GGvIzmr/FIV2FET9l+r1cpkhJQRmQD4OB8x4tV+KG+oYRwp0Ww28738rp8jIPh31y6Oz82NBrkhJiZl4FVWy18ByZRB7kUF8d5fFQFQqwsonZTU1d/nn+p11f5rfmRzczNjY1U7RLwSqXXICd9bKxiIX/FcpaISvuyUNLEqJ/Q+wQqzGPr7LLJte5Q08/LyMnZ3d/N5iEP8vedgn9pr/LB9yZ96lvYQWWMdnGQYk+th5+ysFjh8dq2UR4zaCOpcAjFGLKrx0GRlFfpKRMJ6EaO5HPVs1EkJOJuuxBIMUHEgtYPvUTE6m/FefCL7nJubG1MxDYej6afiar0nsMHx8XHs7e3ldVNbwNj1TGQJqmuu8bEmuxJ9CSdF3fb29hi2+aOvqRLFWm1qtVqZEA6Hwzg+Pv6H5EIvU2UE6iAXzETVA9vs5B8eqsqW5O7Dhw9jDOHc3OuURf06VVLJAHu9XmxtbUVEjDGbDLImLIAaxvL5+XVS1XA4TIloBXcSSIx9TaIE8smgXgNRBaYR42CxVnFIcARfI8Xd+ypdUVWpVYcKcG3kXq8XCwuvx3PoLTGltfawGQoECDBeFVffG5uiylS/Wx2ZvLCwEKurq2P9MbM1/aoyaWcCIQPq5NHaHypgsFPV6sogUhIAKnWAQr/fT2Bak9WNjY18H58BkLHLiBH7HjE6WoKCoUpvAC/SG0GOHUeMkq/a41iDuc9zLRXwVvuoVe5qP5O/O8loVnlLvbfVX9Sf+R3Jsn8HDnz+5uZm9Hq9fMZsTOJRq/vs388rs+wakVzLy8txeXkZnU4n5U6NRiP6/X6qBvThzNZ0q95Xw4giIuX4nU4nms1m9rcAh2dnZ2NMuoQOyGo0GlkhXltbS/+MzAVcMeJUQBGvA7Bqm4jrVKFAKkl2xPfNzc38c+1HQkBIgiq5EREZN9jv6upqVhcBQ68Xm8VJQM+/Tdpe7d1na9XfuB5+oYJaNu1e8HPshe1Uom04HKYkuPZ6bW1tRbfbHSObV1ZWchgXrGHi5aRUv9l87SMlgSUhdH18LN9usuUsWZx+1SFqyDeT/u2H5+fnVJc9PDzkv7EDFcTr6+u4u7vLqnCVVMM9te3De4tvJodLMvxfj6TXV4IlYlTp8zt1ynlN/iSFyMMaRxQ4JLlVEl3br9wz3y9ihF8jRtLWSr7wWTCj664Vx/q6SsTWyq08omLO+hrfHakGA1xfX8f29nYOVeRXP3/+HDs7O2ODI5+ensbkyPoTYR+5Dp/Y7XYzTyD9R3BVhcIfeU0tZB8MBmNj3SNG0pNWqxX7+/s5Ue329jbPBvJQPeCIkZ5YULAJBINmsxmdTic12SsrKznURQJUkwwVxuFwONaD44Fq4Gd4Eq46TKIyFbe3t7nJyAMYTu2FmCyd02zbnPf392OGyuAlXFWCibVliBiipaWlWF1dHasKLC8vp3EzohrsatKLoXl5eclJe/pGnfNDN14P78aO+DfOs7JM9TMqMPe9gAL3uIJjBjWbevr7LCDQ8AQVDFU+AMsenzzrqPZdVHYeqAMkkQj9fj+fMYlbxGiin8AySYggTgBBgAnYjIgxYmV5eTk2NzczAPIhdfCOfeY9ALsqk/G9/qMksb6PP7Mr+9XrvV8FZgK1z6v+xKoSYL9Xr02AZceTiV8F8qosesNVgQR697baleODms3XKaiSzMvLy7H7V6sb1COz4zGmXyritargWYmbjUYj+2P0/u/u7ibBKiGyxAkVRTbX7/fHJHDb29tZ+WO/c3Nz6fsNifHeyB7XiYSssuXK/Iv5fl6PnLBqxcIgJvutErV66usgGrHaUKw61KLGvohRMurPtX+y4ofaIxkx6hmUcLLp6q8qXiGpByzF/OFwmLZVpYIIAcmv5zkYDMaUPbVnzL9L6CspBhsApbMYOv2SHNjLV1dXsbi4GCcnJ1m8QPLYSyr01FbdbjftYH9/P25ubjK5ixgNWmw0Rgo4xYNqg+12OyXW9tbS0lK02+0cMFiTzRqb4NBafGk0XqemqvbXFiax275mdxRFlQyqe7aeSz5ZobTHq5xVAScixn6XzcLYppnqRVRUcnKCHKS2pMgTLLlHxdlyGNPRq8/c3NzMvKDVaqUyY2dnJ6cSVx/mHlH8mMPC1rUPIKoQv3/0NVWiqMysCnV9fZ3A6v3792MyyoWFhTg/P4/V1dVot9tjTaomngErZJ3+X8vZNhAHjWFjmIy0JqN0/YCO6ket4AkAxnXb9LWRt9/vp9S2VudqL6aNirXn2CNGUlKfUautDLX+ZzNLDm1C0gGJtfdwDxm6Co/NzCi8V12MDJtbq8CCWJUyuP9Veuv7YdaAVYEU+yUBcd+d+yZRXFtbm2ZbzlZZ2D8MI5CvGmAPk3vak9XGImJsP768vMTx8XG8vLyeYyphrMGgTktTJdjY2BirrgNAAp6lQvLy8pI+gN3WKlhEjIHVSopUgCYI1eQu4h8TuYjxcxNr9c/r3UffoVZGK1k2uepnVFAncaxEjqTRe1b/4bVes7q6OjYp0nsif7wfwBsReXYVkHp1dZWvQZj9+c9/ToIBEOH7+JRJHzJb/9+vSsRItsTFOgRBTwwJU7fbHVONeC9+lQzs5uYm+v1+DpRj//qB6zFRgM3T01OCMbHNfgNm2ZU9Wgc4OEt5MBgk206KFjEiYaq01N5GKFVf4v3F9kpgmKgY8brPNzc3c5CGa40Ynw4M1EmSK6aIiATLZKzV3if9RaPRyPs6Gb8l376rimL1U0i3weC1lxEWIs2t5BJ5Kf+6vb2dlQ2vk/zCTLM1/SJ1hLfevHkTjUYjdnZ2csaGZ10Vb4uLi9HtduPm5ib29/cz0YeJ4FDVN//3eQh65Hnti1RFrIRIJTjqNfAlYgMSCKYlG2dPihsIWomQ/2pLAoILlpM8uSfep0pp+QrfGbaIGKl/7N+aiFIkeG/PAyFW4/nT0+vZ6vBBtVv4wXeG+yWu/X5/LMkkKUZAIajkB+5zxbRzc3NxdXUV/X4/Njc3xwpeEZH7p6pI/shrqkivLFwlGSpmnCC5W7PZjK2trWzgjRixB8biq0RgOlU8BB7SVYyFzY+hq8ygJCdiXDLmff07B4BBqcAYM+Ez9GwINHkT/08gPJnIYUq8r80CWC8vL+f1YgYZiSBRpSo2oEDmGoAN1wAUCnRValPvMwdQqw1YV/fEs6m9KwKrYA301wEkqh0cyyQzygnVITaVFZqd0fb7rEn5NodrPH4NMgZo2LP2ApBSpVk//PBDEid3d3d5LhCgx6FGRPZdVVBYpyQDnNV2VRdIOPTmCXJeW2VcQB3brtV1v1ulntVx+9z6ndmPf5us+nldXbVK6b/Jn02+D2bUv7PzGlT4JYDT3+s0ZP/u+7OzCmIxtFW6RJpfP7cmyyowKkSSe/dqtn77UjkUPyNGKh3kqLgB+DiPjx+PGFXSxV0x6uzsLG3Be0a82hybjRix+l5X2XmrKmckNvZXJUlq8kqJUyt6bK/2RboOAFdPkATO59q3rVYrjx+QVPIZPp/agO8BrgFOoFjSW6V5/1dEDX8DmFpVJu89JAaAt/t0eXmZz6uSyXVI3vn5+VhSK7kHiqtySXXl9vY2bbv2U83WdEsFTfyxD8hII2JMVlzJVgOO6v5C2pAOV+K/Ev1iB2lz7YvVQqIHjj/mA6hsJC+V+HUdS0tLsb6+HoeHh5kY6U2X9EpkKvF6c3MzVtyoJEqtYEbEmB1FjE4OQKTADDWBRrxMEsr1mhcWFlIR6HPW1tbSLt0z7Sr8C3IVTn54eIher5fft5LbEmkFKIn6cDjM1ir5SUSkys4JDj5vcXExfRXlj0T9eyFbp7rKVquVUhQMpUzeQ19fX89JhlgEkgygZn9/f6wsjdWsgxmATyCxsok2hgdlc3kYwDKjW19f/4fDp6vB1uApgZTcRUSe//j09JRadcHKd3QvauCwJEG1muj63Af/VfmN4MpoqlxwUqpTDbYmd5PvW8E19oXTkjzU96mARBVJUmBEd5XMcJLObpubmxtjsjUx+5nv9r0wLd/Dqn0KkkYHM08GqknZCydd+/8AUSCRzVTJVQVX9lBdkjv9vWze/qsV8gqI7PVJ2U79Wd3TrtGq1cH6s0qwSFjZY0006+/X5d/qtdd7UH9WK5BeU9+3Xlutakpe2+12Jo8CmfepiS0fRR7Fn52cnIxdU52+KGD6rMqm+5nrEyBn67ev+/v7BBbuP79YVRbPz89xeXkZg8EgB8mIiWSp7PTx8TErkAcHBxmfxQ+Vtjq1dm1tLVs6sPn2B5BUz/z07PkPtsnmqgKIP5lUmRiO4/0ld/Y2cAtgicHr6+v5+w8PD9nOUuVmAGglUvgaCWWVudXKXZWjqkZUCWqtgFiek6S32tfLy+tofb6w+oiIEQjXznJ7e5vPAhahjOLXXPPS0lImnu7p4+NjHlEyW9MvA5xMHBbjjo6O0pfylTUhmpubS2WdPa9KhUiobRUwtCNOkK6VcKzVsVZrdNC86cgRkW1NSKHqyyU2EkbnDc/Nvfb8q6ZFjAgf1+5e1J5acdtr2Il7JEmtRKv3jBgfpCgZrpV/eLJW5Gpcroq6WpxZXl7O1jOv8XlIZmQLDF1VSxGRSiv5Ctz0/Px65J/76zicWuxQMVxaWsqznuGSzc3N/OzvpSAyVaIIRNSsmSQiYpTY3N7epg7ahuS8yVk4OsGi9leQk3CAbjTgS//rhtcgItG0CVW4ms1mjptWPaxVxIhRQy5NeJVxYjzn5uZyHG6V0OmxUo2riZPeHhWXCgRrj2Q9i9J/DIPDqLJNxsvBREQGS0YKjLgeQNzfJZx6RRxl4dkBC5IPgSviFWxIqqscxn1dXl6O9fX1aLfbY0eUVDB7f38fvV5vzPnM1m9fNRHjMDFynpGqfh2WYN9JPEgrqAdqxSBitA8Gg8FYIkPW4rUYPQRPlaUIsg8PD0lAkTuqKCIeKtEjeWUTWN8qO4sY9c0C3uyTPUX8x+dJTTJ/VRLn9yr4q0mma/S6+nPXa3k/r6/Br/5eTXw3Nzdz6EyVClVpkvfVj7i7u5vvqf9CoKzKgQqcnXHp/iOGZjY6/TJpr064FBvEBD/nW6tELWLUs1oTtgoqK3mq6oZEub6+jqurq4gYyS5rxdr/a9VDTyBSuFb2xO46Hj8ixhI9/cVVPYLYtdfZqHshAatVc/K8SubWvn19t5PxMmLUClIria5zshoHmNeqL39ak1m+otow8tj/O51OPgf3ltSeXUoYxOSISFsVr7V0bG9vj/UjItgR6rM13VpYWIjt7e3c1+S+jUYjNjY28jxEGBX24icjIuMdTMSHV1LB87T/SEYVBzxLsRuuFSfFQcmjpMfrqpKv4uuKGyfl5bBtxEi+PWmTVYlSpdZeMxi89tOqkvtefEq9Tj+37+Hzmmw/Pj6mz/DZ8gc+UevFpNKmYmmf48SAqtyRoG9vb4+RPtQI4qLY6LV8EByFrHX/PU/X973M4pgqUXTDJVc1mEVEPtDFxcW8mZZphYAQOUdlrP1cIuj9OGgJDLYOu45Zq0FKQKgji2vTqw0SEWMHiQOskkaaZQageladNDlAvQ8V7EnOBA+vZ1wCymRTLi207x0xMkSfMVl9qN/fhmVQJKGeAd21gFidTH0/RlGTfs6RhJY0yfk529vbWYl1DRGjaZNVmltlGbM13eL0AYkaJExne3h4SCZaQri7u5tJIbAKHAKKggq2rVYNVK9ULZrNZkrsqhQEsKwyUQCR1MP1+3N1+BKiiNHY7yqDmwR8FcgBf1WGOsn21yTSa2oCVxnOmjjWyv5kAijA1apiDXqV/ayfVyszQGNNPgUupN3+/n60Wq2xSci14sG3IcKqoqFKA7G7fJJEVNVqtn77UrnzX6/XS7A4mQi+efMmkwBqGvvCEKlOpxMRozM05+bmssWhgiN7CBnIDimA6uA1z7/2JJqeCvwgQCthhGRi65j4Oi3XvtSPWQfJIZXsT7LRSirzHfwbX1STuCpdq5Mkq/Lh/24fV9ue9A+1Cu93AWVAcTAYpPJo0u/wx+5lJXbr8C+xvSqlYIM6edZAq3qk2GxNt2AlLVS1z397ezuTMxjL/qvHQzg+AfaCs6p9zM3NpU9loyrftfJV40uNM963+u46SZWUO2IUR2trUv25lgRJaVUIVBxYq4CTVXY22mq1khxynRWbilnIMglsxbLiaCV7Xa/coSax7lXEKI+AWevMDteJ6FLQqFP72Var1Yp2u52fo5Jcr48Pct+Xl5ej3++nT6hEV63U/tHXVImimx8xSobqg1T9UhXwbxcXFwk8K+BR9h4OX3XQKmqSu1rF85BbrVYmb41GI/smZPYVmGI/JJsedJWE+E7D4TCHRDDY2s8QMT5JDcAytKOC24hRAy2jrGCZs6jyFgGE86mVGxtXub429OeDLVIa98HPq6HUAMUwIkbJpHtUAW9E5FlaZEi+Qw3Wqqxe32g0xiQ4HFrtaeMMatVktn774tztOc/55eUl9/b8/Hz2WOiNiIix51NZRZImVQyDcTh5e1YVqiYfgofka7LHCVHQbrfHZGERrxUxLCc7qf1NgJXv5jtU6VoF3rWKGDGqTlZpF19SqwwVFAKbtWpSA4fl+9fPqdXFKo+pCWUNQJOgl+Tda/mlOoGRP8NgAtv2gPeyFxBfVUZVgTt/fHZ2NqtW/A5rMBhkkifZ4o8lUJNxhqoDg72wsBBnZ2f5bPld1baISJAkBuv99QxXVlai2+2O9TUBeICpuGO/v7y85PE6Nb7s7Ozkd0NI1Mo0OyS3ixj1P66vrydYrQklsGvPIzaRnbXq4L2rnbm3vndV4AB7lRStoHcy2arEzSSR4/froJzJqiPbQl5XuZ3Xk/Oa1u578q8UOPW6q8/p9Xr/QJLN1m9bj4+vR4R5xmzv6el1qFRE5FAYlUPPoRLrj4+vZ4pLiGAhy56r9kl1RQVgH7BBE1EbjUYegyMZksixc/6hFhTYpNiNcBFba5y219it94AvahWxXlvEyKaohSYr41VCWu+JuFT/XhND3wH+lSOQx8LOtVJf7Vs+UXMCf/bdKxHuPno21E1VLsx31c/mqxuNRkqDJyeQ/5HXVImiaXhK3xhLN77ZbGZlCOs/HA7j4OBg7CaRhWjWX11djb29vQwqsnObDkPq4dlM9WECSzaz4FQDBKCrwmeDk7XZFLVPz0avTh5zMRgMxiSogoONLwgLTtWZVPmr642IZHTr5q6Jr/uHCamb3udNylb9X8KI8ZBw+neJaAXQlT2tn+P+qTatr6/nPvHZGu2BI8+MVp40uLJtszXdsk85yuq4ai/BxsZGHpkB/EREnmeGBUO01OdjH0SMZMYmB9uXlZCIiLEgKHDaz3XgRr2Wugfr96nVeO/NXxhFLWiwLXu6kluTlUZ7vwJ196EmURHjYHJSUloTwsm/Tyagrgn4BDJdX50YV5Nw10th4e98mOdCdbG6uprP3j4QwFWUXl5ee6vqM3C9Nemerd++2IDnDORg4rvdbszNvQ6lAf6rCgV4Ozg4SJ+LFKh70HNWPYwYJzUATckhWxgOh1mR5qsbjdc+K3HRvnNNwKTrdD1VRVPtRSWlXhsCSQxjUwa3ULisra1Fu93OEfnkllWmDkiK74D1pC9wTyJGQzckuO7hZOWfn/E7vk/EaKBbtetaXa3Vj0qiVb+IFFhZWcnf9WxhA/5Pm8hgMIi9vb3Z9PDfaQ2Hr2fW8o01rtRppJ1OJ0kA6jhEiD61iFHiUSXW9hRFlr1iDyNJ6rTPiMjfU3msh91vbGyMTfOkMGCD9mUlkdluJRAlvxWvRYwIz9rGMUmm+hxKCDH19vY2C0Mwsn/3erh2aWkper1eEtSTbSPyjqoaVOVV6IgY4YnJHGB+fj7l281mMy4uLsZIKt+DTyYbl4hXvxMRKUVGrvOrngsVxPdE5EyVKJpYZiqpm1arVjb+xsZG3ph6nlKdiKlXA1NIUiJxqe9XgRvWpDp5G5VjlcAaAQ+0CrTeg1FWxsG/kd+Q09Xrioj8vRosK2PCeTCAWnGsjsPrX15ecrhGBWWTzEut5g2Hoz4VEh0JsXsEZEsq64Q99wxgQAAI2P1+Py4uLtLJ3NzcJJsSEZmsMnzO9O7uLrrdbu6RnZ2d/D0VIvfdOOFZxWL6pS9ifn4+dfONxuuEvaurqwRKbM4edE6ePaQK3Gw2482bN/H4+JhVf45THzK7ihjv5dnc3MyR/iqV1ZGr7gtEbNG/Cxo1sErqOGJBhD3VYSzVXicTvSopE4DYSp0qijzxO5OBsQZIn1UZ0br8TgWW3r9WO6tche27H3XAiO9aj/SpbLbAqIKLueZH2u32PwT4/f39nJjJlzw8PMTu7u6s6v87LMlEs9kcG4rgGISjo6NoNl/PD5awmSJqv9zd3WVVEoityUztz7c/6nAb4A9BUxOWSfAlXtpvtSfJFGT24XeXlpbi7OwsAZKYWomeiBgjkiq5U5U+SFlgXNX06ekppatVgsZ/1ApGJb1qzI0YnR07SU5V+6t2W+2XnxsMBmMTwSMi7w9/DLRXCb0q0mTMJzUlAa6y4OXl5STiqvTePZit6Vd9vqruyMV3795lgaBWEi37r+7lipEttkR2jkgX3+qE/1qNtkeQslWhZ6/Cm+12OxPLWuVn1z6rVvxqQlWT2bq3Eb1+txKhfsexeBRAhs1UrFxjcMQ/EqrijQp7ra66z7VdheLB+7rn7kfEuNKiDuO6v7/PwhXb9Uy8V80ZXl5eUvH08PCQxELFG75jraR+LzG09f/+V/6vlx4ncggPrdvtxsrKypgEs+qA6wYEZGzaypjVSlvEiEnwmoeHh6xcCGT9fn8sYwekOOmtra10uPVQ2larld+BYehzlPBMViJda62M2niV8SUbiBhtYIldBX/1fTQpY5kZgOWeCQg1MTVGG7DQewIg+hl2uAbtiBibasWIBMrhcBiXl5ext7eXbFkNyrUk72ecnCSgnm8lATEApR60PlvTLw7+5uYm2u12sniHh4dxeXmZe955fPZVfaaLi4txenqaexGb6fc5xNqTETGSvZKlVpBlJDXmDVDiS+xLyUmtatbKesQISPm9eiZkxCgB9Vqvr3ZTq3cWG282m1mRqN8hYpy0EbD4ED+fBKaTYML3UP1wjTVpqz6zflYNroLUJINKEsOX6m1TuZd4ONAYYFWRoBaooLjX68Xm5ubvtU3/i172YMRIrimxq6oUMlBxj2qm3W4n6EKI1KpeVbIgTewbtkFCKu7as2KE9zZkpYJH71eTy7u7u7HKp9/Rf+c6DGxBXlZ5diWBq+34XPcCmSzJ8pniHQCouldjbY0zldz5j1QBEf/Y8+w6qx36bvUIGVUUVV1+1zRTPozNIeUiRjgLwW2fTE6VRbrW3uPZmn5NKrfscaS3oUTD4XDs373Wz8SOXq+XFWK41bmobMV7s9E6VRVRUBUl3jtiRPL5/VrMsO/ZTLUv+0gLh70mHnjvikVrFbIWK2pipIoo0XU/qgKBz6kksP19f3+fdqGA4VpISauMnFyf7Vf7qfcA9oyIsRkJnU4nMSs787rJdrpabOr1eomL6/fi0+fmRvNWPLf/aCL8H3FNlSiakri6upoTRNvtdtze3mZG7cHblP1+PzY2NqLX6+WI73rOWkTkQ6gADsgjj4sYTW+qQzn8jBxE1RLzGvH6wIFcm7MmpbXsztF7bT1gVSC3qQR1bOekzIbzriyJTc446zCfqsOu1Ym5ubms4DGQiPGeKe/PICfHLdejPip4cJ01MYyIDPLLy8vx9u3bBB+clqoUh1j7xe7u7vKcO0ExYnxADsat3rfZmn7Zc3WCbsTrXllfX8+es4eHhzGpBmZRUjE/Px+bm5s5tUv1bnFxcYzhGwwGuTeBR4RCBY+mIkp6kDK1eoBckEAKwGy1BiPArg6EYDuIJ838k4mh19fqX/VFVZ5WkyX/r3vVNXv/ShwhiGpiUFlTq7Ky/pusOlaFgT+7r5PglV+ShM/NzaUUyPe3L9bW1lLeVyum9g6JqmA8W9MtgE2iHhEJ0tiBhEOc0qc3KUXkO8U9sapWt7xvRIxV5yNGx7PUXrmnp6fY2trKIyiqXM4eZruGaYhPlRQFMOGAat/eBzBkH+ym2vRgMMiYZa6AIW/2+yRpgzyplZBqc5PEpLhUQfak2qe+tpJH/Aeyth4j4jgi/kZ/k89xXFCV2Uri68CuSqTVqmx9n0kfMVu/fdWkW7wz/M09r8WNWlW2Dyo25Jtrn7BERQFE5d1n2IOT+41CgG+osQbO4xMUMthxPeKqJjURMfbdaiuS92CztVfa9cGktdjDZmpcqoUXONX3gkGruiZipJ6rR8xNFp0kYOyd9HM4HGYbm/vebrfTb/HFTm+ovcBVueA+uWb4dWtrK++pszIrWa1tbW9vL+15crbIH3VNlShWiebc3Fze9Pfv36eDZ0Q3NzdjZXTZ/9PTUx5K6kEzEhm4pK/2GFVQiL2zSdrtdszNzWUVcTB4nTqFAWeIDI4UloO26RhbHc1b2QHgugagmpxJGK0aSFx3ldHWKhvjs8En5XbeR4DmKBgW6VJ1IDXwubcYE/fE86nPw/cmb/I9JLW1l8R3xppI/paXl+Pu7i6fAUCAbarOrDJcszXd4mSBPfup3+8n4JJgCDBLS0vR7/dzGmqVlA2Hw7i6ukpnWPdPlQ8LlMAQB0rSSmJs/LWFibX3/Qzbyi4EY3bg3+199gKocvARMcZC1oSoAuRqJzVJmwRlVk38qp1Xabvqq8+oVSTfYfIzKhNdZfGVqXTd9fu4NomuZ+izXDOg4d8quH98fMxAxs9ExFila7amW3zszc1NbG1t5bOvcYMPr+APeG02m3FycpLJmn1vsAVyZTB4nULNhw+HrweGixP1syq5gqDRnhAR6dPJ0qss2Z6odszHsFd7cjAYJGEcMRpUAUzyVf6dLdWEOWI0VK6+viobVDFqVWSSiEGWSugqOVSrMZOkzv+LvT9dcitLsgRdBWDzPJE+REdnVr7/A7VUlUhmpFe4k07aDNgMoH/Y/fZZ5zizpS+NfYXsiy1CIWkGnFHHpUt1W8kiSJDI87M1jUSO7jouHX56emr2C4WtqmuB8VwlLOPx64C4jY2Nti0CO7Fa32alHxL3kKW7u7u2xQx2G9n5/Plzb6AieRX3VnVAfzJhcnKt6h32G7uQIKB37ufz+by3t3eyB8gW/UoAlMxkHFj1qu90X6KW8suP+T5fXNUVHehm+tls1ULbxGygQ9fX17W+vt7bSibZcK7LnA3Hc42eF13hI1O/h9VB51ddFd/yw3xj2j7vAVPg8vKyqqr1nLuuX375pQcEpu39ntebyjZpeLe3t1sPVCYohpgY2+1hq17s7Ow0BYAgCIoSFVwsXvdigYQ4h+8SXAEh9MBxjo6OWhJC6Ai4CkqW+5MqSqmqOn60SolzMiJZ3Uu0nuJkYz0k1KSmRCqGSasAMKsXnJtnncGqBI/SePYSMMnYMFGgCIISwQZj4TMMWNIfdnZ22n1kwgCNcR2SkQyEk16bVdfVettSkXt5eWkggqASGwA9Q+KXiPWQNry29jolzbhvDiqNrylxqhpkseq1+iCwG4/HDc28v7/vDWFJI6o3IPt7q/oUkkw2HTeDtqze+1k6yEQ5h3rmXKlfw98NgZgEi5IWM0RHrTzmsIqYySx9TGfoZwA1KOf6+novQE2HBryRKLo3AUtVp4dJYV1bex2ssdoa49ssMqff1+AXAZFgDOgpgSNXo9GoDg4Omi7yKRmoCVAFQibb2uPUO6fjgMLRaNRkRGCWVGo2HRMnA0h98lWvgaetBZbLZfOhQ50GNpLF3E9NwJn9YZlUZsALsMqgMD/jeFl98ewwX4ZgUCaJbEmutEVpc/hZ2whlxVSizz+y0ZKKbMUZj8c91pXrRUnOoTn8t+B8td62xGRZBScbWRkjW+JOQ6G0PjkW+QRYquoBb9+/f99iLXEddgDZFSOKlVP+qqqnr2yLnx0eHrYp/oCH9Ffui/9NBgCZHuqcGNG1uY4hhVv8sbe315v2q1XMHBB6dHl5WTc3N38Bj/QRsldV/fkBCVolFTgHc1niBcfQvuZ6XVPqdMar2nM8q/X19fqXf/mXFtd7N7YHYffYph9hvblHkbBI8rxE9DQOrup1BDeFWV9fbwlfHkOgaR+gdD5oVRBt5f/pdFrv3r3rIaDL5Su17u7url5eXmpvb6+VfglcJnaZ3FZVrwoAadRHQTgzoRomiO6RsmUASehU3pJalIkdp1zVoTUENR2e5CwpARwUGo1rq+roK/7tnJCUpMN6B+jEhhcJIoeDhJKmkwG7Z+a+AQiJAlNE73SVLL59DYOW6+vrev/+fTO0pl8mxfn+/r5NMwa8SPSquilwKk7OMZvNGgC0u7vbG07D4DrHxsZGQ2GTlpL0E8FTJkQJClVVT96rOj1xzKxQ+/wwscveqC8lchlsZr/FsCoxrHImWjgEfDIA5dDznWVFP6+FjUgE2c9ce/ZyQbT1LglWq6r1h7JHqMZnZ2fNMSc9CBKOtp8ysVpft/iRk5OTZleBNKryibwL7l5eXtpgNtX8pHz72/s9ODjoVS8Wi9eN31PWAEfZd5+V9RwUo0cygQsyYViOZEZ1jz9QNctrTT2H5ieAmMDty8tLo5BVdb46mTbZH5jnyN7APGZuB5AJFv3MBC8BIYs+Ou6wuu8Zp79zTUPwqupVNzEuckomG3p/f9+SSfrpvf4oweePsgAWWqSyiogWzCYOfYU/o9GoxctV3VTfqm5bp59++qnJwWQyaT2tyfLyeVW7BEOrOn83m8167VWomBLQvLf0ZUNQMud9+JMylrbK/xOYHcbG+XfGHFqSHFucfXZ21vxqxtqA0bznjK89Yz6R/XR+upLPMSnB7JZ2DYCqz7y8vLR2LnZbXkIfLfY0ddzxMxb4ntebEsVE7VFncPIz+Mmmaw4ip635G52FY+M8IC85OVUSlb1IXqrBHaa9JcqRJXwIDsEhrBSbY4Vyuqaqbg+52WzWjpfDKLIikmXupJvisOtdSLpMOhhOMJfg1/VksCiJHlICOG7HZzwk9hDfPL5nm72TPusa9bmtr7/ux2cUtEAiFevl5aVNDrOP5lDBbm9vV0HoN1reI4Dl+Pi46SEEv6pDySXvfuc9ZkLBgaytrdWnT5+arGVyRzaGFbrFYtEauhPxE6iRFbLKbtCNdDKobxLSqm7jYaCOAIpTGdJghsmgY3zpd8PgMANGzszKz6fO5d/5+Ty2a89n9l89y0SZJeCOwZ4lCJX0nKp+oKDSLAgFztn3ia12PLKzWl+/dnd3m1+AOEsociowFHxra6s+ffpUR0dHrZ9UxcLAB8kFv/fw8NBL7FCtUteSHcBnZrtHBnn84hDIRAdNupmAjA7wj5nUZRBoWI6Euap6ARUZz6AxdS+rHBmcST49T3GFYJA9Sjpegr6Z7FnJUPBsEggW9GZgLZEesg08mwwaBcASa1X9qo5l4f2IwwBECY6t1tvW+vrrpGGDl4Bu2fdnEr4kTluS95JAfvamShKBeGJpoLy+fnLBvgPaXV/6Be/d/tb7+/t1fX3dYlw6K4YFNJFFeujcKmipW8PChdiRTmRSlslQ+k+fc+yMQf0O8FzVVUnZtqq+38zEs6raABvxZV6v6xqCOQmkmkNiWix7qZjh87PZrFUPvSNVzqwKZ8FLjEufv/f1pkQxaZ6EiBHm5HZ2dpryZK9CJlJVnQOiQFXVKlAcn5d6cHDQJntlgEuJIK5V1UrtjPfh4WFLViGyqDP2R6T8UNLpdNruS8CqEiIZnUwmzemk8yLISefzmevr6zboxbE4D8GC71R1vYv53LKHEf1n6OgS/UnHlcEs5acMT09PLYGF9CwWi7apLEWWxEJNMshgSOfzeeujGI/H9eHDh1osFnV6etqeE8ft+Q+nvK7W1y0O6uXlpQ2v4YBU3oc0TSgb1EuimNtEVHWb1epXnEwmjepIfq6vr9seZxbZy0Z/P0+nyvH4W/XctWZ/47DiwDak/KesZ0Cev2NPEg3N4C0r5xk4DhHZTMaGTm0Y2Fo+T6ey8kEvvVM6z26yT86XyHVVl1RieLh2tjVtjOA0WRdpz7LXc7XetoAcQEPvVhKQ/Wpo4vyphAxoUtUlF5mAZJUASKOHEVgoYPPdbFHIPquqakknG1HV76t3Tnacz8xKWvrr1Bl6nnEFfeeTs7earKoE8K3pg6qq2bCcZaBSwo4kBZ0dch9pBzLQTF3PibNZpZCE0+nsT8tn4h0lYCRYzUSd70ya7tbWVk8fh99bra9f6+vrbTsvgM1o1E0fVRn078lk0mjdZJ8sLpev0zHptViVjtNLBYiXl5dWHUzwk58jz2I1sre/v9+jk+rTzSSxqttmhm+hfxLh/F6Cp+43q49kPn1oVsmdz9+KSf6d1fykv4tHMi7wXsQGfFlW7ZPdI8aRjLJNuVuDeD/9I//qmXnuVX1dTFYSYCzbdfjoqmp2Qs7xI+jomzgKXtTLy0sdHR21qt5yuazz8/P2kLOiVdUFTF5ScpI9bGj409NT7e3ttRetn0pC6ZxQg7W1tTo7O+vRr9KJZdIj8ycgHDblvLq6alRVfHPKmb0E7kcilEma+9KnIKFkfFCCkpLy/PzckuREQDxPyoi+oC8FTTT7FRM1SSfOSDFehDeVl7BL+r3r8fi1iRjKifeN4iZhZYzG43Eb+rO+vl6//vprvX//vqGvjJOkBMK0Wm9fZDlBAIYpnYGqwv7+fg/9V2lyLHK6vr5e9/f3dXJyUnt7e42uCPQ4ODhofa3DSgXkLYNR8qWRv+rVvtzc3DR5p6fWxsZG67XI6tbm5mZvf0HB5LBKMEQgOYd0okPEMf+dK4/BEaFrJg1lSI11n4nQDh2Uyk2yKVQR19bWmr54b3kNybbw/FUJM8ml+wYHJBtAMOL/bOWPMrHte14J1iQ1cljpPzk5qevr65rP520iXw4KExjyNWz7eDzuVS0FLPpu8nMHBwe95E1/k3fOzpMbCZBx/mRdcJe0rqr+dN7UDf4uK5FV9Rd7tb293WIBQSjdSbuQcQSgGcjFV9JPMpxU0aqOtkYf83dZpWRT7CVNb8QedHJ3d7f1IuZsggSbBMhZsRdjuKYEm7LKnEmC2MQG5av1tpVVK3YZ+wKtMwEGOmbASuqapFHSgo2ztbVVt7e3bdgUmbBPppiRLq2tvW4ov7293SqdQ6YXACPjP98H0IobM3lS+MCue3x87PU4pv/M5I7vTDbAl5LABGLEJenHHJsODRM0x5pMJo3K7t1U9eNbz17S7vd8rPPlu6nqgFU2LP92reIjz8u9533P5/P68OFDs6PkQTyciff3vN4UjTPiw83a7cs1pIxUdZM/Ba9QbsEcp2Gcv/NAC/CEGeSq6g3DUdodcoRVrAjD09NTrwlcMkNxOFfBqM9wlkbnCrYzEEsElXJKUClLltmzepn3n1UeAw4gHpI416hZPgNZjjhL+ZyR5Nx1+Nxo1I02d50+u7W1Ve/fv29OLn/PSHyJ7uK+Xcd0Ou09j/l83qq73seKNvNtlsABpTdR7Pfv3zcZFJQwuhI0C8UTMo+OQ+cEmuPxuAeqVHXIeQaGtsaoqsbxH41eJyejwqytdXuZJmInkOJY2B4BG6eWCZBrSMS+qj89LhPHoQHPn/l+BhBWVhock8MdJpquJZkHWe33/BOQ8z29Suvr642+DkjjwAUn0Fb0Rgh07qPIbntvWT1GPQUCsB8r6unbF11k7wxu4OMEHgBLOqUSQf4yicDiGY/HrcJPxlRB2PikWtGrDEj39vbq8vKyptNp0yUBHF+SYE9WGci8RCZtukCZrvIdmahmUJzfEYCnX8rtnzLu8LlhcubZb21t1S+//FLHx8dN/7Nqk4BQsgAyafR+6B4/DoQD4CYAllVj9+99uPeMYTJBzFjLcQDY7uHu7m7FyvlGSyzqHWc8lbHazc1NS05U6030TzAjmRziVQCeQgB7n0BnVQc0Kmrkz/g7MZWElT+gC3QXKEU3+WZsgbW1tXZvWXXkv4a+Llk2WazICjzwJO3IsBUkewzZlZxKOmwLUZza3Nxssb/CjIISG5DDFt1LglxV3bZ0dBuAlbEwW5jFF/fhOPRP0YlcsHvZCva9rzeXbRjBqv5giZ9++qlV0FJ4BHNeggoSB5FVDEFJ8rtNZ0tUsqoa6uZchFpPYlIhR6NRb5IR5fQCBbH6HKFDOS4YVci1o6hSfIlcKgOlR3dJ6piKTk6EIpQMBkdCyTM5S8HPUnpWJyDKlM/AkcnkdRKV6XkCdIH5+vp6HR4eNj51Voh9hjE0ZMA503lLrq+urpoRS4CA/FDM1Xr7MqBEsE+uxuNxr3qHyiTokwyQfTpLt0ajUUsMyTm5l/Tp/+UYBEgSPM4xB9SQZchqgjnJ9ecUXX+idYLdHIih8u+7qQfuJyuFmShmYPilhJLsp/McOrMMPNOx5jXl+QXveY3+z26xi6ow7sl1ZoWC7lVVC0iurq5aQrlYLNo0yqpq9jHphPP5vGazWS+wWK23LdU4MoDGXdUBpEBOdDV6N0TDk+bF3ySgiHJK/wFzfp9ATFW3HUXV6/Y6/FDafNUMPicHxtE/MmYQiPYCICffklQwcky/c0LibDZrgOkQkBUI820S58PDw2b7PJ/0kUPdrOrbACurEKmjrj+vO5NdSyWB78SEShvBnqa99bydLwGGrJ7S24eHh2bzVutti74YLFXV9cJjYABixaQ7Ozt1dHRUp6enPbbYlwBLtv/09LQODg7algv53rVxVHXVNnETEE+hg88D2KZs2VOQbOpDBPzyhYCXqmpb4/iZONY1pv9KeRvKdTJ6EoChd45T1fXpJ+iys7PT4oNMCOUOqohi65eXl9ZqM6ysilWSRpyFmax28ouZu2R8moAv/QOG39/f16+//tpi/rTRl5eXPSbE97ze5Ol3d3dbkjREsiUASuvDCqNg8u7urgm05IHwra2t1fX1ddt2Iac9EUhBIYOaaApjjCLjd2tra3V8fNxr7IUMQVXyPBCLRH45XoJMMNExUyE47ar+/nCSUNchoczzLRaLXu+Ic1d1ztNxM/BMpyIo8LkhhchnsrrBmVMkdAKVXcEnwVellVxkknt/f197e3stqD07O2u9mZIFFQzP/UdRoO992caiqhoNVJJ1eXnZBmBk8iMYpXNocRITwxXoFnAAyFPVMQfI1lBHqqqBI9nQ7TtsiO9ntQKl1THoDTqOwJXcfqlXImk0WVXMKmEmgX5fVX/Rm2FS6e90nnlM3/X/vLZMZJMFkIngcrlsg73S+UjK6V2iugJKQQr6oiEonKf7pK8ZvPp/fm613rYkR3yEqdLAO0MucjDbzc1Nrwcv9zFN5o0ex6r+Xn3kRNKUtLCqLlHkj1XbBF3sgKRpWPms6gANVa5MTsmiJE6CU1VtCnO2qWTFPVkJWZWwsooikE9gqqp63/tS5dDyPNIv8sMqPtmykrrme8mk8VwyecwhGGxZBq38qiSUHfT+nc/7Af555kPGw2r9f7/QuvXae3+59cFyuazj4+NaLl+3naEzt7e3rT2CDadD6R8k9tvb221vUT5qf3+/AUZ3d3eNgk2PAEJZjOB//C2mTPAiZTmB2mTHkccEY4Y+LJkC7mlYrPB5v6/qGIm5fD7Zis4pAcv7YNcw3IYJaya7fGUmpUDxTBpVUpMRkeykL8U2bCN7/vDwUBcXFw04sL2Jd7G5udn09UdYb4aEJViZXFxcXNTPP//cHEpVtd9l8kb5qrpR3l6equLx8XGjVTw+PrbBNNnfpkeRERVkQQXQXCkC57a5udnrs1ENlJwRToJY1VUsOUblcNefTjSrfJJfKGfuXcap5QRCQkvRfS553pRliIRWdU7HGlYdfTcVJg1HOi/PND8LfcpqA2QbzY2TS8X0/G0A7ruJPK8C0G+3VPVs8qzCO5lM6vDwsC4vL1ujvuRdskEmVCGqqm5vb5sDpE9DB5A9Axko0RvvOvusLE7AqPD8gzJJr9mATMToLQNMNwBQ6cSqOmdHzodONhPoDPa+VB0cBpv/VUUi/51O0/XSB7qG3u76PMu8lru7u/YsBbAmObrvpHVPJpPa39+vz58/197eXnuvnpNJb64DLRXTgC1brbet0WhU5+fnzQ+ORqNWCfI+vDNrPB7X9fV1nZ6e1svLS5sSakBVVcfUScAi9y5OneRrvNeclMtGO68KRIIQ+prpXPYouXaBKv+XvpjvrOpopihjEjK+OQFVrJr0j+yElTSyyWTS/FomrWzAEMyxhiCT+CJZM/527wk+ZbVV8izwR7VnD91HXo/g08/FF1mBJSfuTxV6td6+1tbWekwpQAbbSr9Qvk3Tpy8HBwdNBtbW1hp1uqorOCisrK11fbPiLAnq2dlZS2CAExnXkjnAKN/kZ8Nqt9/ThdPT055dT1AjCx7Zr17VxfZ5br93LitjSM82mTDiDJ+ho47r72Q3uB76l0w4x8/k1jsTn7g/5x8yaVKHxU9yCD/DApHkup/379/3KP0J5v7973//C+Pge11vShSV2iFfDKb+B5O3JDjT6bR9PtFMaBg0Ram76tXQ39zcNORtNpu1ZIwjk6SMx+O6vb1tfTic3+PjYx0fH/corIx1cpdV9whBInZVHed4WC2s6ihpVV21gnP4UkCdQanAl8JQ4nQ6w0pjKtgwUE2lyPNU9QcKJedcRW80eu0ZRSkS3AvIOUnPCQUgFVc1V6DiGra2turDhw+1tva6abspX4lCO8cqCP02S7Xee1VJoKMMXPakJeWN4ctqkv4oaJr3nPuqeqcqU0lzSyOcaHtVNUpIVTeB055VT09PtbOz09sEXIDl31mVz6l/CbqQsdRxa0id8f/U2aouAM7fV9VfdDArhfnZ/woMyYpiVprSsWbCaHlHnrNnOOw1PDo6arZiPB43R2YogJ9nBddzVOni8AS2q/X1C1i6v79fV1dX9fT0VKenp1XVBajkTG+3KvzOzk6vjaOq2nuUcOYAB7bZefmeYVV7Pn/tGRes8TVJIVWp9BlAi8FX9Iz8+ZMtJnwQIAfIkYAlJD8rE/7vOzl7oKoLBvn0BGTYgCFwmuCrn2VymHopqHdN9DSpp5KJfN7eBdsh2b26uuqBcT4vgfA8Xb+/PXf7+HmfmFErVs63Wclwoxvs8sbGRotJ2caffvqp/ezg4KBnq9Gyq7o9N1Xkc8YFOV0sFr24OUFL+ih5yuSTfgwr2FmcSH/oe8k0S3+XQ6NU71xHAj90Io/pd87pO34/jAGqqtkRz99S8XP+5+fnHniZAFsmdBYbOPTl7lnS6zjpDyeTSd3c3LRrwOjZ3Nysw8PDXuxsngc9BNLxtVmR/BHWm64yG7ElMWtr3V5KnEpSmLa2tlqJPqtqWWVwLD01Ru8zghwQR5NCKrlCZ/OSsvIANSBwSQOAjKaTICyuk2AnnQ4qU9UhGYmeCGAJByFEvXQtevmSh50T5HICaTqRRIecq6oLUFMx06Ak9fa/+jdD57k9Pz/X0dFR7e/vt3uWCEPDs38zn5/hGyb1uQeV3AwYVuvtK5M/W5uo3F9dXTVajXeWSd7Nzc1fAI6tra3WyypJ5BRQO6q6XmNOg0NLNgDKY1XXZ5XJFLmD1g73rxKc0emqrjq9sbHRqhKJrKcsOs+X9DsdnWf4f1Xppn/DRNM7SP0cfj51U19Fjtr3zjy3rLB6TltbWy1YFKByhmwlXWQzodOeb9JWBdOS/729vZrNZnV7e1uPj49t/PtqvW1ln61gii5kYEjG+QmgS1by+NOtra06PDysh4eH5n99PieGAnfISPaRS0L8Oyv2R0dHbf6AYW98p5+RdTYFeydll6zxgfSETjtnDqtzrff39z1bQi/YHfLLjw11MYEqesimZALpWMvlsjeZtaoDsvjbpMELood0uazyJjCcVdeq/hY47E8G3OxEDrqSdPj/EARbra9b4lBFhQQJ00/ksJPh+8PuyBg5K8EAXPbZObPST4+zRz2ZbQlKZvtFXksmgWSdX8j7kWylj3VOySRQUpynej/0dVlAyGRO/J46TL79LmU/k0k69KXiSBaIsjVsSFH3u4x1febp6anZmPTVe3t7baK6d8WH0mXDj9i0jGWzPSeT0u99vSlRJPxDIy8wg7gw7ni6kG/CKwBdLl/3Oby/v+8Nlanqkhov5fDwsGcgGfft7e1eeZkw5OjhpFF64RzmZDJpPXy+z3Ez/EMnVNXtjZKOQdLLKEgOhxSSL00hdXxVOwGg83BQub+k5zR0hhkIp+KnAXK8xWLRhulQ6KwSVlXj3GfFMh1tUnjz3ehZTcqpIGJItf1RSvLf+/Jux+PXqaRJ3xxOAEuZODg4aMdQcRfQLZfL2t/fb2CM92hCmmQNok/2kr6TfyB+2YSuDzKHZbAZwyq6MeFV/QFLCfZkcMdBk1XBcVYfh8Y7HXEeN9cw6avq6Ducdiahfp8Ojz7pS8p36N59nu3k3FLXnJddG4/7+7ECnDhCwUoCeOjwOeV0d3e3BcT/1XNYrf/7Kymlo9Gozs7Oeu87R9cn8wQIkmCDii8wFZCjH1XyB2n3b8BEslRM/97f3/9LL18mYypaVdUbwGZpx0iwRSAn6WJ3MnBLPcl75FuyAsOnJtDEr+YAt2F1xRpW66z8rPPTTbGC7wFzvCOfdU18ePpSuuf55jyHp6enFqyyrfSQnrOz6Ku+I05aga3fZkn8xG+eLbkWk2byooJkz2kxoQGJaKOAQCAAZh1dE4+StZx0OpSr6XTaYkpyJoZMmvdwuea0Remz8nP8O9lyrvTnaa/M4RjGp6lzqS/sjOfl+VZVm1xsiYMTKM6Ka7Zp3dzctIKT63Fex3Vun/MchtXOpN6Kk/xf7+Fk8tqCl8N02KuMl36EJLHqjYki4WPQCDilyg25c5NcD5KwmVRI2TgDyiB5zGAxkwmKmMiMIQBHR0ftc6b8SW6rOmeQSiGIhNyqlPiZz0NUGHoO07FcP0flfIy641f9dQ+3pLak08nyfS7PMJ0hJaS0lCiTXMGBSh5qU1X1ro9Cem4ZeDNGPru7u9sS9gzG05F7Z7mlh9K9Y/woSvQ9L85MNaHqNWG7vLxsdGyJElCi6lWeGMH5fF6fP3/u0W0ACvQb2mqp5NHL8XjcJh5yEgCh29vbFtixEbe3ty0gyoCRsU2dkPT6d9K96VA6lUzi6CJblA4yk7fh+q8oI0PHmiDO8PfOlz2dAnZ6yCnRUUGDZ8FBZrCSwUNVtffEFrmG/f39Zps05M9ms/Z8PEPPRIA0mUwahXW13raSHqaaCIDhz4xV5xsymaqqXtWO76qqphMCUP1TScne2NhoCSZQYWdnpze4oap6QU0yWtgCoGCCpVkVd/1Ap8PDw5YEixsyQU69z6CSr+UHxRboXJ6pRHlYwRxWOrL6MayqZGDsuwLPPH+ypTJGkFSwqYaJCSgzZrHESXRSMkk2qjr2FcqdpIXdcK0rsPXbrP39/RqNRr3BUJ5vJvpVXUysd9hnzNBIphm9YFslKRgg+sbJCzmaTCZ/qfSTF/I+pHiKzzIB+q/AmEy2HKuqD1gOgRP+OuUuj+255M+GujPsH6YX7F0m3PRVLJDgEWaDe8WAyu0z2A2sJkm9Aonr8vyrutkLyex5fHysq6urXv6yv7/f2u7YzcwB3CNg6EdYb7pKZdaceMrZCPxUHTOBWVtba9W9qi7ZIOw5BjeVktHOaZupEJlopoEnhBr+0zG4hqxqVVXj+UuW0LIyIOZMh82/lF5glcqazkjFMhERjte/IRVJI3D8DGQhO1V/He2f50wnmdRb25FUdRSoRG98Prnf2SsmKLi6umrX4/4EFImKS9YpUibvueXKar1tQcvS2OXQIQFV9pqSWxW+3LBa8mdEfdUrNdyeilXd/mQpy4mYO5fPSv6yKjgajRqjgOwmWqpakjS6qk52GXLOgk0YVnCqqtHnsuqQBpzu0Z8ENr5UBU/wyXnSQfo7Ha5klS3j8OhdVkEXi0XbezYre0af57vmtOig5dmlg/csVCXYvu3t7drY2GiBcA4gW623LyBfgn/z+bwBKHzWwcFBA3QwcCSVEsWq/r6bVd0sATLKRwJzsrqRVeb5fN6rtFd1lPAMUPnGm5ub3lY67gOQSIbpIDp5Ajo7OzsNPORDhkG5IDIZOYarqbjQFcfO6p7rruoodsNANu814xOAcSarAkr34Jjo3J6t67fVgH5r/VhsFgDVZ9kDPjp9uHef2x4Y+Lfq8/92C9jgHSeNM+XS+1fsyNiOniQLL0EfLJpkw0nEFGP4MLqR+wsaKjXUnaz2DYHMjKPJu/vkczMBzeqj74j90jak78slBhz+HOjC7iS49F9RwfOah8ekL+KM9+/f1/HxcdMJn6e/WVwxv8E56J1i1Xg8brYZmJMxAPv6v/1v/1vbpkcbnvsDICVo9z2vN0Xjyav3dz5wVcYckuDFTafTWl9/3aNNkIaqmcMyCPf29nYLZHPakADIw9ZovLa2Vp8/f66zs7PXG/3/IAg5cCNfVCoS5IDjzvHAWQ7P+xUcT6fTVolJZDLRRAlW0uA8s+SD5zMYClNS+hzzSxW4VCbnSzoiI4PyN0S8BBRoh65JguEZSDBms1lDmAS63p0EIwOanM63WLzuvymRWa23r0S3PeONjY06ODio8/PzXvLu3d/c3DTK2e3tbW1ubtbPP//c5CHf+cbGRpP5pKcm9YOMTSaT2tnZqcfHx964bnaB4/M5xvX+/r4FfvRF5SunmVZVG5CU/UtACjLqOQA7homd5+ba/d/38ndD55vPJ5PSPH4GrIkqQ32TMjZkN9hGwHGGWwmwnfk8s/IhUPF8BD0S+fX19frw4UNvy4zb29umx9PptAUzK2rbt1k///xz3d7etjYF8o5dcXR01PYDNspd5a+q6w8f2mx+S8UQUEBOEpiFxgPuDOkgK0NQt6p6sqNqUtVNHkapyyoDfUz5z+vO4RzO4dxpT3xfAse/ux7BOl3MJM55v5QcfskGDHV/2EIyvJf8XB4jK0dimqSX51AuAX9WnN3fkNqblUbPLMGp1XrbUqmlB5J3iR/dm06nzW8mNZjs3N7e1snJSfO12TpAl3KAUQK3WQhYW1trW2Y4R1bg6GIOX8x4iv44XoImqTfJYGATHJ+eiG0PDw97Vbgs9OQ5hrFsFnf8PIsryYgaVvhQeLNalzJPL7CMhr46280cK4EasQF9VsBSMGF3tGqZCO0c+sKrXsHA/f39ZsP5+h9lMvGbEkXGyyaTjJ9pa4nmqzYy0CcnJz1Kh5cJYVGR4DQcy8N/fHxs9ERNoRIPztY1cq65fYU/SQHJiXG5hxXBVb5OGpvjJ3pf1ZXp/TsDQIKXXOl0go7NaftdVn3SgSbC4u8MHKBVOfAjabGqgirE3kmioEY0qxJDVxgU94ObP3TYzv3y8lLT6bRN9jPQJimqacRW6+3r4eGh7Ymovywrb5IKlfq7u7s6PT3t7aPoGImiT6fT2tnZacOpOJZEURN1VaUHVjgXvUqdrOr2/iOTKJQJ1AisnJsMpZ2o6uho5HyIXnIQ7s81W8PAKxkSrjU/6zkMHaWVaC9nlZTa8bjb4DnRarpydHTU7FACQY71/PzcbDNdT/vD1h4dHdXFxUVv+5SswiyXr9sU3dzc1Hg8bgkGoG213rbYUO8X6jzszeUfTfQWOKnEY4CkHKgq5eAZcuD46VMyUOV3E9ARvAFxsk8JhdR15HAKwXX29gF42Pm8fjrAT2UP1HCYSFW3BUa2TFR1g62Slu132Y7hHqz0Pfzz0L6xY8NkMel6Sd1O/UuQyTUKasfjcavYs2GOlbESW6A64XNZoVhVFL/NStnx7AHzw95ePim/t7u726rs+bsEUcm0gSps/mw2q62trQa8L5fLury8rKOjo1atHuq7lUWb9BOZdGaSlrbG5zL+zIqq7yajzn0Nn1n+PMHS9IniPnFJxsFV1VpkhhVAeUe2SCWA6brFNo4/bHez2DnJYLZCyU3Y56rq+Ur/T1si2dzf32/A3f7+flW9xsLmtnzv682JooetqiBBmkwmjTLDcFZ1dKiqLmHR37e9vV23t7dtmqYEQ3VCMAVZ2N3dbQ28HBrHtFwu69dff21JbPKhLcEvxXE9NhH3wrOqliiS+0+hz819XRdDAGmltBxwVgTydxRVkuv3QyTUs8oEkAGoqtazmcfzLjLhVNUVMHDc7iedOAUVYOJjv7y8tOEiWbGo6sYvqwbprbH042RwsFpvW2TA3k5kXYUi99ajJ+/evWuJHfpUDkVROXQsE758n8G/ublpBrWqo6qj3QzfL6DJeTip/f39XvWaPaiqNgVVwjiZdBttAyuqqlUqM9jzN/AjARY6n5/NlTqfuprOORH+PB59E2xzkulUM8kUjCTgNJlMGg0x+2NsJ5SgnWcCRLPNgomZAlJgQe7tBpADsiVNjt1ara9fo9GoBYHr6+vN/2VlPpk22RecwRzGCjCmqnpBFqZNsnTm8/4U1Kou+Ht5eWlUNsHvkGI5DBAlmey8RDED6KTGkbmkmtFBfn8IwPDxrpfd8je/NKSTJjCavx9WE302ASTXNqT1Zawx/P6XbAZdzWTe+VWADJLKIDrtTF4bvyrWEAyzTSv9/DYr5aWqm1ScICDqJ+ZUVfX8zWj0OjHz7u6uAWzaJwAm/EBVtXiYjt3d3bWY7Pr6uoGrSa9O0HUIulRVT3e+xNhKefR51+73gIkh1XPoz4fJT/q0YYHj5eWl+TL2LXU/gRnfpyPHx8ctCc/kVywgNrV3bG5/lz2kqoFV9ZdrSJ3/ki1IOzi0C/Q9r4l9Be78COtNiWJWDQUkEoaDg4Nm1PQe7e/v1+XlZVX1t5lg1DQNM4SoZ8b5V1Wjt+TealV9Y3p+fl5HR0dN8bISAiHPJEYQSnGrOmFTvchKg6RND4fgOVFQHGRCTTmTauJ4w6bdDN7RgjzLqo4iMERdPAfP1vMglJQ7E9JMWHPfNufwfASZnhXqgyA0lUwikKhrolWGF1AgCSoKMvlYrbevBAGqXoNGyfhkMmlUQkAD+avqEhTvTmKDPvXu3buWXDiGqsUweEK9UOHz+6Ojo7q5uWkyNJTLpNsAjrLnkvGnj9B4NKEhSJU9QxnYZS9EUl2G1cHUF+fJKu1wDZ1yVhKTvplVnJw+CuRxLWzJkB7vWeU9J4CWf9zvsCIpqDGaXRUI2AZUg+AmDXa1vm7xOwcHB20j7kzSc69PwxjIH5u+XC7bdiV872jUUcW8swyOEl2nO3p6JBp0WXCYDCHyJoFNEAcAQbbpIv1LupjELgMm/jXPWdVRT5OVkMOxqqoHvPL7fp73PaRnJjCT/jrtRLIWquov9mJos6qq+V7sAIBzUnATdKOL9s/8EuWVzWGLAT95nXR2Bba+fWULFbDu9va2N8WfznhXEsCMXROUnM1mTd58TuxINsWL/Iv/7+7u9miVfC+9yIpYghJkKf0R0JHepgw5lu9jJA0r1UMAyb2mD01dk2gl6IGmTq/ZH8fLxJGvSlAqz5vvRK8wFo1CD13NSm4WLoZsjrz+HGpU1dm3zBEy38gcwHINuaXY97zelM5CLwkvAfVQDg4OehvJCn44EM5psVg0J+nlUQRBK+MokJ3P560sL0klNJRGAGnT8ayYefFZuXIMaLyXPQwCJVBVr4qaiWNVf2+2dByJqmR1c1gVkKCOx+OWJPpdjuHmaBP1HCIUkKohAjZM1F3HZDKp4+PjXmUHlTSTbhXS/Pnu7m5vgEkiMYmoZOUkjYdK43Di3mp9/crKQaJuqNkcV1VH77Do0nQ6baCNgK+qqz6cnJy0ClX2/B4dHf2lKpAVALqzt7fXDDbdYOyTOqmqJlGaz+et8Tz3GczAmkNSsUlnvly+UuuS0po64Xm5H89z+HwzGU+Z9jPH8C4M3cjr5aSzAl9VPSDMfduGhL36/Plzb2uS3LYkk1H3nIk2fU66r2fLobtOw4U489xCZbW+fpnqrWpQVa3Pmy8aJonexc3NTa9avrW11av+A1hQ1eiK41b1N+HmfwVpmZzxZ3QpA0n/vr6+/otfolt3d3et6pkxQbJfHDdbRaq6QS+uw7WZXEiH2CGJMf+E/ZM+NYNFK3Xds0n7lf6sqtMxNgOYJRk2aVagOpvNWhJtEi0wwAh/+7d5Rtky8vDwUHd3d71pigJ31yIeOz09XfnQb7AAErm8i6puM3qBP5ubsZzYmK8DcBiKgg0ClB/SiyU7VR1g5H1XdcNb6PaQVvkl4CPjRYmL/2cSK+nKxCj9/DBJcr48jn8Pz8uPHR0dtf5NfodOe75sWg5ZTOC1qtunMHU7Y3V2xWA21+Jd0fVsq0gWhv9nJde9DKuseX0+63rYgB9FP99UUeTUCAyUDL3JQIQcDbyzs9Oa9dEsMlnRZ5gj3jlCqDbhOjg4aFWudIy//PJL2wsFKp4BWDbsE0KoPQX3crPS4meUhuBVdRuGJ1oi2EzHlsg+J+gZJmqYlCDG37ky4K+qnoC75uw5lFALGrKSulgs2nvIdyJpE1img+UEBfypVMNKEiXMhFmlw79Ho1faxBAFX623r6x2ex+5ae/Ozk7d3d01NC1pT3RWpW46nbZgVMCD5kI/RqNRffz4sd6/f19V1YzxwcFBr+KlMkgWyKM+LHKTDkyF3XVmsudv98wgZ+9vVfXuzXGGv7Py53mM/CxKe1YI02kmiJPOOpM3dFz/pxMZVOe+cRmoqsjkdeg7U3VIgCoZBfSZPUiQTFLKbhnFr9IzrOSs1tcttvH+/r71d9OTra2tRkNNX1jV2VBJhEFtCeZVdQOPks2Seq4q6LN8UlYNACxV/dH1ZBodOfuLAYaJ6CcSL7kCMGdw7Ll4Nkmp5VfZpiFqD/RIsDmrclmZT53OwDKBXSuBYH5RspfnSAZOglc57I/tmc1mbT/oDOKHlRLXqq94Op3WaDRqU1MdU3sBGVr1KH6bhQFV1RUJsNLW1tZ6bU9Vne6wvWS1qtq0f1XBrOADYcW+yVRJvyw2zIJLxoB+Nqx8V1VPD/xJdhsfkOAEX+0eFB4yts3rq+oDqFb6iwRk2RM/d3/iBNdkX2bPmn2oqjb1OOMA70AVNJ9Hbt1X1VUFs4jBn+Ywr6SPOp5qaFZQge4JSDm2+/aef4Rk8c17EKSgCcrcfPZLPD09NRrpy8tLXV1dtSlAs9msfv755x5HW6lf4CKR8MLzZTHQw2Z+VNjF4nVCnGDKKH+JCeFUBk4k9EsBImeYwXE6xnRCnkuW/l13Co97S3QGii8hz+84pyQuq3dpdASSQ+eYzpCiopxRUMELY+gzknYVIcqc1aMMZr0HCkR5XJsqib7GrEyt1tvW3d1d7e3t1fn5eR0cHDT5NxQDWgghhcYLNObzee3v79dyuWzGlYw+Pj62xC1lk+NANUu66Hj8Ogjg8vKy6UvSTrI6IRjikOkx50Ue9T3nfmz0hz7Qrex1ljBlUpdbdyTowQmmc3I8ybQKwdAh0j+OzPHoAyCNs0wdSpYDXR0yHXIqczqsDDLRgxOgyt9nMpKUuKrO6WfgSY5W6+1rGGA8Pz83dsbd3V3TIQnVfN5t5K2f+Pb2tqqqbVd1cnLS5E2VUe89cC99GXZIVfX6jauq58sysEl/Jih69+5dr9UhaVuAY4EX/2nwkpV6SQdyj0LHSGo1+RV0C2xTT8QFw0qL++H3/ZtddJ3uMamkuY0WO5C6DJjz3DwXFUZ2dH19vd69e9fum2/M+8oBYXyoybTin93d3QYYrPznt1nZklTVTRlOe5gTSFNW6FTuwaf3l61l//kfbLacz/H8/NzYM/QiwYf0T6lXQ+pk7tnr8/6f188mZcEj2Xc7OztNj7Ka537zOlMOHb+qS1pVSMm4e/b8FJgS3MoiCr2gV87vvjLGln8kMKTgIzHM49DBnC4sRrUyX3BOtiDj7WRNmf3wo/QRvylRhHYnqsXIHx4e9njUxsLm1gcEg5G9vb1taAtUrqobqS1ZTEfFUBOMrDLgljsXQTAVNZ1AVbVyvmsy4KLqr43qvqOCQLAdI4/rM+5Fk3IinhSyvZjoDUvEH73Gvbq+pMtJygWtw83rKYoA0z3ZCkECKNCnFFnt8JmsRCQ9gZHgtBaLRRu4kAmKKglUerl8nfaKGrlab1vjcTc9M6tbiRQyWIvFog14ms1mLQlcLl+nk9ragixvb2/XbDZrfTJ7e3u1ublZ7969q6pqdLP19fW6vr5ux6vqxrqzC3RobW2tbZnDYCcqJ5llU56fn+v6+rp2d3fb5zi6dKBsiOtiD2zVwckBp5ybHqfhJ+MCWDKcVcrU5WRMqEKgAyeqKUjMZJANkBB7Duil3qXv0asM0qs6e5IDrNJeb25uNgANPQoqyqaxFe49WQ2r9XUrEXxAB7CmqnqyRAbJBlp2BmpkAE1cpSkDOsdN/5HBk+973+Q3E5aUrwwOq6pVv7/Uj0+ec3S83wFznH8IiiQISt49kwzsyHlVtT0LgZXA4CGYk9+v6u/tnPvNDsFj1yiRlxiikibl3fH0rZ2cnDSb7D7p23BPWe9hfX29gbfDqiT7l8Daar19pc/zHgylyaKB540h4p3Q0/H4dTN27VhZRQM+5DTNBCrJgZiL76iqJpsAvgQsqjq/Mkzs0o8lKJKVbN/lt15euinLVV0PLn1Pdk1VV0hyXenTXCeWUtKx2TkxeOp/Mg3oRvZT86eGumVsIenOZFO+kPHGkKXInvqO4lQ+z/SJWYjhY/lm70KO8yMAOm9KFDPg9AIYS39ntW1/f78Foia82czZ8SAElE3ScHZ21gTUABkCRQC9EMdCi2Poq7qkjSLPZrNeZS57IfOzw8SvqkNjhkgEehDKDUXJpInSZ/COskrofT//pEATQslXDp7I90AQk2ueFQLPO6kCk8lrryJj4bqThjSfd9MzEyVmbExaTXoTiu/Dw0MP3cn3P0TrVuvrl2ETBpFIAKqqTVGs6igafsdYVnUBqHfMQKYTtKVG6jA9YXS9a2AR4ODx8bFtlzKZTFqf08HBQS+xFQACSyRMGdyNRqOW+LIJk8mkt0dZOkGB9fr6eqPvsVFJ1bYcQwC4sbHRo55UdY6fE/Vv4FDS27Ny4jyZyPldBujOke8tn4VnWtU13q+trTXwJd8f+lOipRxwOuWqLgDKiuRqvX0JYBKQ4xeAK4I5AAmAzjvi77a2tlql1wbdKE50Ln1QVvKH/eUZ9EgK+SF+JYO+lNkhoJLBUk5NdI9JdUvwxPcFYYJkYGvGGVUdK4Du+Sxf7X5T7zJhFYB6Dgluk/fJZFJ7e3vtXtzry8tLC2wzeMwk0e/oovhILOJa0g7zl1XdNj+AXc9xc3Oz7U0tftA+slpvW3QngbfsAcS8Go1GbRuhtJtiX7rDJ2UFPCvOCW5KUsi5SmVW+IfvOPXB+TNBo6MJ/g0T0ywgVHVDJMVmmYRmFZPsZaEn781SiKnqgKXxeNz23E7bkc8p70nFVCzrHSXjYDKZtGNmslbVtcXks/BO2Yu0W+KgL8lGFqqSLTC0G/P5vA0V00v+I6w3JYqCf44gX3BVnyIhyPNyh/1DWeFIJeBEBIi3t7c9tI+AqVLlPogZyCwWizaGXKA3LLFTiqwkpiIRzpwmhk4Djc/pp1X9YTxra2stcEsH5V7sVZjBrH87/pBCg/JG6LLCiXaroiQQyepJCqrA3LV5romO5HV7j2iiuT0KhcmeiaSy6Y/yXrPa4VpTllbr65bEjL55zrZOUHEkI3rRGFfJ/mQyaXRxeseoOq6kUSWRHCba53oysGQ8q7p9xAAJw31LfdaUXIEm6g97kyBOJoh0WdKW1BoBu4ERguo0+PQ/NykXYJP9REXdpyo/YGVYCUrGg+vM+87hIQJhTir3e/W3bYMAT+xPVmQcT3KskpgIcAYybLcBSDlUYLW+bqFPao1I9on3PplM2kCUZLTo5UlGDdnhKxeLRR0dHbWkETJeVa16LykZovYZ3NEBQAxfkLrm/xlw5s8yqZSQZfCU1GvymUl00rdSf9gXfjefia2aMimUZKdPquoG5tk7jXwLdAG7w+edtrWqY1IMB2JI5g2r8TxyimX6vAxK07ZhSpEPdjoHUmFUrXzo2xc5yQQi6cwWcF9FTYLv3ec2R+S4qpPfBF8kn9PptNfeY7siSwyWspLMlAQb6eqwoCL5I8Opv66dH894nvySc76c/VFwcS18Y1X1hjJiNXmuw+TZtQxZQrkSpKIPkvdhKwX9TVvkTybz/uZz85kkKOzvL10Xv6uFLOMx1dkfQUffPMwG9ayqewHZs0awlVgJ9P39fR0dHfWqZbjgHJSARDXEC/KZpL1WdQNlhrTNrDaqFBCCDJYlKZQKKl/VOa/so5TQZPJrihW0BHKiEd11+XyWuLNcj9KmqpH9VgJlx5fMQU/yeSaClUgrZ0mxTDes6srm3sH6+nrvWXge7gXtMAMLNFbHyICCQWVMnVcfTV7far19ZRVRophbrggoDg8P6/b2tskrfWDU6I3glU48Pj62908GchKfAA4IJMD12RyKNRqN6vj4uA1soLup01lFyH87pvO6/mQHJPsAC4LNSnQ/qwiOnXo21B3P2OfSybgP1+BnySZIZzFElq2kv9LtRHIlcT5b1VXwPXeOmmNEG/a+JL02fmZv9vf32z5dEO4MhFfr6xZZyH4VQRV7OZ1Oewk+WfE+yTXQbm1trS4vL5t+ku0EFjAN/JtvSDYH+5uVxzyO31nZhpCJXAZWWeF33/5NX6s6qngmZckyca4MLLNqp+K4u7vb6/GXDGbQbgFcc69XYDZdV0UcVv5Go1FdXV01uwKEslRs+cMM2B3LxuxZAXafEj+/q6q2v2xOTnXcbOdYrbctwGkuldv7+/sW33rf/s32mr3gWHQJ/ZTdBiwaxJjzOPhgP0t/WNXvqU3/lFRMupm+cphE+lzqkoQuq/FsVVJgqzq2yng8bjrkO85X1bEoTHTO2EJRZ1gxHSZj7pOOpP+v6u+n6POu4UtJaAJUQ/s2/JsvFx97n8kEcIyXl5eWKLIjwOgfRUffxB0iDEOaRJZx0xAmHSsRAMqD+kVQCA/juLa2VgcHB60ZmLKkUEM+8no4UsNzUvAkYALh7AF0fYSbY4KA5L5/EtDs/8tKZ5bPPZssdTun5NM1S5IZjqrqOcUMWFPIE9kxGMF3BA7u1ec5UWPMnctnPdOsMGxsbNTh4WFTAsbk7u6uGT/PECr+/Pxct7e3PeqOiot7/xFQlh9hqRTlZNvRaFTT6bSBN8AY8ilJJEuSDP0TDGNVh8InGLO2ttYorX5GF9LQpu5JdDJglqRIRlUYM8hKZ5rJE7qtJErCK8myJEwqgOTV5EiOSwVe1S4rkWQ6h0dJwmezWUveBA1+l9sYVFVdX1/XxcVFO47gzzPzfc/E8z88POwBWe5dwzyb6H3n9hdZtaK7HG8mtOwxGzmdTv+fFt3/v1iqQKPRqAUP+m6zoqw9IoEI74MP4Fufn5/r3//931sCaG9j9liQA7zRe5RMFX5rGDAJulwbebu5uWmfTT+XQ13SrgMLBXJ7e3s9Sqpr9/kEPC1+D9Xa+dg6sko3rNRZ1FJ/MkHPmCID6qxEZvXffdEp37Uy9qnqksesVGVyPgzs2Vg+21584gO+mwysfOi3Wdg1VR3VEvsriwZWVtzEPNhdtppK35GgXdVrEmVOx+HhYfM5Br/p4U1AMmU8k6ChzvlZxs9V/SRy+H965DxZ8csquj/kPK9rSEV1jcNrTwDI//krK78/fAb0U16S+pTP4UsVwLxn951MitTRfPepZ/KRPB7gPJld/v5R1puuVC9EJkJeEgeQpVsB6sbGRiur7+3tNSrFYrFoCrG3t1e7u7t1eXnZm6yIs+38k0k3Ec3LYKQFOdlYnAhHVvcEk4Q/jT2lIPxVf0UnBL16Ipzb3+6Pk+ZYEgnhJNPpVf21wpCIpudvQIb7SRRJEOH70KHsoRpO8JLs6icdblki0DC1VrInWIXoWmlQk9qHussAUs4fZRrU974S+U65NmSGfKhE+OM9km968fT01KrFEg+6TLcBKKrDZJwMzWazRsHS2M+gm8qYMp9VCLIIoeN8VMrYm3Q07pmMJo0t6Zg+n5RRup09KaoyVR2i+iV6Nd2XCLOXKg5Z6U1QLd9FBpYJulV12xMBWNhA1/Xw8NBAIjQgz5KO5QARjtD9e1/eFQoNWnAG36v1dWtI9VLhB6LxKd47u3h5eVknJyfNl6GnkdeTk5P2PToikNNTO5vNan9/v8mvKrLPV1XPhwvMcs/Oqk7WIfxD8JLfyQSJDKaf9Qz4gKxg5uIbJVj8F33IxEusMBq9UjZV89L/ZcCWQfZQ99wH/cxJjYJ6gGj2LuazrKpGYVVxwthJQM29qz7Qb6BZUoKH7Bv+d7W+zQKeZWVdckPHxEKfPn2qk5OT5l8B4EBN+ppVY3IiRlQsEDeKn8kP/Uid8Tt+OpNG5/Sz/Cy5Tt3lJzIho1N8flJv+dWceeA55fnYDDrv9/wcmU6/qPUlv+/3aR/Sd7lXz5Ues2Oemb/ZqtQh1+SYw15kz41OZgKogOJ6AbxsjimvwNsfYX2zYTY5QROS//LyUrPZrKEvVdWbaDqZTHpNshzA2tpanZyc9NCMNNSCmslk0pKZrMwlXcSejQQzKTRVHaKbSWIiJok6uEbfS+UiFCoG+dl8VlltJaDD6aOJeHLKqp+uo6pTRoLr/pI+JxjMa6EwgsesFqZjziDFNalWWHqhKIYKqOea49wN23CvQ66+ihXEbLXeviRCt7e3jR46mUxaIp90SHpMTnOyGwe2s7PTZPL5+bn29/drPH7tBZrNZj3UnBGm6+PxuFFK7+/vm5FMYMA519bW6vz8vI3/BySwGTl8qqpa3yC7wwY4V1X1pq4KijOxTJpPOg02ToVO8O3znMX5+XlLzrIPgm5WvQaZ+hszIIVSO7aGd+9Lkvb09NTuXZCZDi17otk5FWKOLEEryaDhHFUdOi6YzX7WpDGu1ttXBqD5PvXYe3dkiD/jQyWJdI2e2WpB9YAMkXtVxp2dnQa05s/Ycz7Be1eZTBAQmChwSqAlQdasepJTflOlLBk85Cwr6lk54HsFeVnN8EwyWMsBW86T7IAhOOb6EvzJYLqqT3H3effi/YotBIaeD5ZPDraRqOf7nM1mbWujjE0kERIP12Nq9Aps/TbLM87nX1VNxnKYYCb543E3wZ8uJAsuaZGSh4z38jtkPiuESQfNatgwscmqmvtxT/mZjCGr+pW7qur5OucWD4ol0z/4Nx2r6raSyIGLdjMYVuIUcAA8SUuXHGfRI+/P35lYZhXS88hEUUyTCby/xdlipS8BvOIJx3Id4hE2Nj+bCfX3vN6UKFICzseCTqKSHhwcNINrDDslsn+eQDR7oVQcsjdI9bKq6vj4uCkEQcnycPbopeByrj5HWLy0rN5V9ZGXquoprvuS5DIeWQEVFHIgvs+JpwN03Xd3dz3Hx0ELqgm0a3c/kq+kvAoooFKul1OS+Pmd6irl0DuazwUfG8+a0grqJameH2Vzj2lstra2moEcNmav1tsWWZvNZg1pTsRZMLK7u1u3t7e1u7tbFxcXLVBNSnAmH4nq52bhVVUfP36so6Oj9o7pgETRMehuouNkEO06HSO0nrwbFECuOGnB27Ca4f8pl5xbBr5p4K38fVbsXQO0NW0E22OQiD0gOXJBIjuwubnZ9pfMATTO6buSu6wQ01UO8e7uriHR7Ec6Oee4u7vrVfHTJi2XyzZifIhy39zcrHoUv8HiQyR4CTB6p8Ab72axWNTZ2VnTPfoNQHC8l5eXBjZcXFw0oIhftM2ClXqK0pk+0TH9mw+p6lfi3JPFd5LdHApT1Q1fccxhYugzeWw2xLGzElLV6eWX+vItuuMayXyyfNi8ZNMMbUsGfWKL9HO+k0Fh+sIMzLNya75DVkusHNDB57KPEo4fIQD9EVZWrTKBJytbW1ttNgVgM5ljWTFXZWST9ajR66r+1OykiWZ1z7sV05KxTHgcd1hFowdZZUzgY8hCyngyE1AxHqCoqgNK0hakzvA/kmrgkevzb7aB/zLBl88ZVkEzBvdzsbBnkYwD58mqqN95Ln4npk8GhesaglFiGAwc95UAT/YmZgHne15vShQhcRID2T+hn8/ndXp62hKcrFz5DEHOsrrgShKafWs+l43umQxShgwACQahSkRFcJRKkYFkViE5pEwyOT9Kmo4tr5HAuX4ClQ7Q8Tjyqq4il2iLa/T8UiBVECA5SRdLx+q68l6qqr1PKCfjlLRezmhYFWGk8np9T6LOQLq+x8fHuru7a30At7e3vb7X1XrbopeHh4d1fX3dQADPPo02+VYV39/f71G16MiQXpG6a1/CRB/JHaTb++Z8GP+Uifl83gAmzszWL85HjpKGlv83zfXg4KAXlGW/SRp3VVSOYFhdz/+ng6Wz6YjSYS4WiwacJSUlQSrXk5QX1TzPUXKZNo6TS2dc1QUIiXBiG/gONoMEwLVDyDE2PLP8WwC+Wm9bfB+QNKvUkqpkk2TSKOhK33d1dVV7e3ut8l/VVbX8nQwgy3sF+AAdsvKQ/m65XLYewwQCM9H9Up9cxgFkMIEXP8+/cw0rNmwWPcqqgcE0+fthzMCXJmCT70NwSm88q6TvuY8MctOXZ6UnP5uVk4uLizadViJdVT0w7OLios7OztrxxRVsiyEoQN1VovhtVoIhCQ5gyrCV3rO4ChCXPgmYw3bTSbJSVc3/pn5kzJosEvYj/VACM7kywU2ApapjqfneMAlz/NQj1wa09HO6kfflXD4LgMnj0s2qrgrH79kLnG4qlvhZVnvpl99lm1cmsVa2leTzyVg+6fvpf30e00mVP6v5OdTHu/fOEgz7ntebonGIQmb5HrjNuE02TeVARZ3NZr3tInIwhZcMCc8G9fyMJFCg5iVl0kiAsgyf9Fd9FyqkrheqSlCen5973P9sYJcIEliU2URjBWFWIojpuDJxTdpYGoD8fFYwq/pIUKIwDJ3rSvSS0YJAM47n5+d1eHj4FyoDRAl6yTnpPTRE5fz8vBnJTB5zyEA2/gMQVtWKb7NSJgSHDFNOzhX8LZfL2t3dbYFTDrmBhmXlv+pVd3NrjNPT0/Z938UQ4GCTo09nHx8f6/DwsB0zKaSJKCaiL5AmT0mDns/nPYrt09NT3d3d1bt376qq20g5R1RzFmwIumomfa4vnfGw+pGVzJx6lwmBdyLYq6q25YdKbibotr5gN+gye8NepNNzfSqUQ7AswSWJu/d1eXlZp6enzY5cX183271KFL/N8i4lDgk6JgpPV9BEfRfa730k42axWLR+3gRkHQ9rRUUgg0w6lQFqgkQADCBLBmNkkq8VKLvmBEfpTfrmDJwyKOPjsvKXNsg903W+1DPVV52spawECH4TMMrEkg0EjNPx9IlDnzpk04gBknrnmQFS3W+CAhnfOGcmm4vF68AUfZLr66/bo6zW21f2eOvrJWfs7rDvHdjtvYqfrq+va39/v5bLZWunIFtVXWxER+lCxntZgUvAIkH4qs73k7tka5E9Mp2yl+CiLZ3EvVgu9ClBKjF6Jpjpw+hWMgLkBLm/Or3xLLGLUv/ThrAvYljH5IfZI72+nkkCrY41BGIzRk92k+eViaLPyhnkOEdHR00GyIzJyj9Cklj1xqmnxqR72IyZSWICFw5QAEcABHYMm5d8fn5eVZ2DyT8ZsBEMnyXoqXj58tMxcar+TQGHQg1BdZ0MQzp3QkQwU2iysqD3KK8jUb8M3FKJ0pn63Nra62jh5+fntlGv+6bAWSVKo+BdqfKkcgjMs+/l/Py89Za9vLw0pVXtNY3SEIZEySBAeQ0HBwetMpmJMWORaPhqvW2pGI3H4zZZkD5Wdf2+hp5ktVq1t6p6gR5wyGAaMsr4VXXTULPKR16r+k7u5uamR6/KfZWywpUOiqMVsA4pmmQ4Jyknapto5mKxaFRNvXgJRtFvP6fH7iGdDueYAZ0ewPX19R713P8TcUxHg07G6bgngaN3l0k7nQaCDQEo7zVlI4GnIdWJzWD7qmq1kfc3XC8vL03HBIeZRHi3KhRZ9cp3wjcAZu/v79vk6ayaj0bddk0pb3wfgEQQA8Tw+6rX/XYNIcvKegZTjkGX6SjZ912f/5I8ZVA5ZNWkTmZ1kd2hvxkY53HZtkzMHx8fazqd9oaPiAOSdp++P/U+g2bnywq+54kt5Rmvrb1Oc696BYrYaImG/ydjIeMIMmBWQ4LLq/VtVsaX4qohSCHe42tsncGOe18PDw9NBpKWmolDJnuOT2foaxZg0k6Tb/ZDkcN04wQ5ql7t+dXVVY/qWlXNfri3ISsvASmfcS300BKnp35mTJsgbCZcmRT7vOeYbLq0b2k3EkQdPrdcQ9B3WGhyfH4xQez0r+xBMnoSBMy8x3P6EXzpmxLFRKOrXgVuMpnU3d1dq05lwqjs7KHaB4jQMeqJJDCcqQRpjBNZHA5AoZRJgUnkcGNjo30HupNOkkJBRfR4cLIvLy91eXnZC8qGFT73S1gkVdmbOHRid3d37RkmZS4NQqKUWVkcj8ft+nIwCWVhpDyzTOQSRXKuw8PDHpqUgfFy+Up1Shoj7jXKaz7rdHSMhc+NRqN2bUPKxGp9/WJIVQLINJmT0G9ubrZqVlW1BCadE8paVhp8lkGUxKh+JbLvc86Tje+j0aj29/fbNZHHIZ0lA82dnZ2ePkhIM+HJpHhtba2NG9f7ldVUNsKz2NnZ+YtDSQQwz5lV/LQB+R6GCaLrlASkc0qndX9/3wblJCWpqgPrXAtWg2DS8/T8kibkeH6OJs6WmWDnXCi8yepYrbevIRUR+JHVBIFRyhR5oaP0jw/IIIq+ZdCJ5u99+53PSzaruiBwfX29jo6OesCoa8vEkP0g919K7PiwPLf7HQZ7yfKRPGWgnlWKDPQyoarq9r+jn+nvshpBn3Okfwbd7sfKSaSunZ/0O8O+gF2u07Fyv8dk3bi3m5ubdg2u3/tzf/afA1Ct1tuXeFQbgxgFE62q8wvkw3szbFH1PgH0qr/qcAI/joklQhYznvW36xjqIgZPTqbX589H5JR91bfr6+teAvj09FSXl5ethz6TwgRPE6Ti2+wJLl51jmx52draagBwVlnFLeLNBJiGBRQy73e+7z6yopnJIjvgehzLu8lCk78l034HKEoAfDweN1vuvUtk0+b/COtNiSJn4eEKdlA8JF1bW1t1eHjYDKLBJV4gIRPgGO0N8bY3UBpgBl7iBpkd0m+qqleJJFxVfeXKXkWZfjo3kwzdd15/CnIGin6WDhh1KKuDqWipfEmjMS0pUVyBQSKTyd12/OVy2TZtHjYj6zNxTd5lJql6q/RLQJ0po+ePOiAgzsE+43FHW2QoTZFEb5RkpsKu1tsW3UngA8LJMXmPnJzq7nK5bFtdeCeSwKzaASrIoc+gMWeiyenS2/l83nqbU4b121R1iWMGmXnN+bOqjnpNB5JuQr+HQdpwYEDSTDI5rOr3K/qTVRP3S4+TLuNcw0oIPXa9iVSyBZZr4xTTCc/n86aHnFcmnY7PVtLlDDY9J++EHWPTMQp+FCf3PS+Ah6oVn1ZVbQjS8/Nzo/An0EN/r66uWoXKu2Jf+UJbOpARdK6q6vmkrKClLyUfCURkMpNJITlPGc/qvJVVCn6TvCcIXNXfEzl1MxO+qmr+KPuisurJ/mxsbNT29narwtGn7EdkDzO4zBjENk/smOemkpK9j+nPxUlsV4LBPjOZTOry8rI3iEPAOUxSXav4AkCfFZ3V+vrFjpIxcx2qunes/z/jywQbMtbNWGkoz76fva0JHpK1rHb5blbSE+R0/dkbnAkZucl5FQCf+Xzeiz3FviqiSaNmq7I/0HJPzklX+OKMmxNgdsycKDu0J86TvtyzHfq/rHY6H9uSzzkTQ88wq46eveJLvttMvB1XfDSbzXr7lA+B6O91vYmbkAjBeDyug4OD1lh9fn7e21clFSZpUsnTruoaPwmQkd1V3WCUrKChWenh8BLm89f+pNz43nklZRkYEYghd/j5+XUsPgTAi1eJFKQNKw2Eyue/5OAoOSdhUcYMCgje0Klw8Bk4CyCy7A6lTJQoS/yMCNpNlvA5ryEPv6oa316AnwG7hCSpbevr671KBvrqctmf3riqVnyblbokcZ9MJr0ESx9TVfWS++l02kblA3Kenp5aYzmZkFzu7OzU5eVl68lDo9MbILFUwaQf2Ts5TECrOpSUXrgvMk2/07FymORcf1c6B8fyuaSSDAPVqurpw9DBZCJHhjMZdS/p1IaMAk6bU/VsBeHO7TklOrtYLBq1KIN6djefX9qkBLiWy2ULnF2TZ5LMDw55tYXN25dnS0Yl74IlQV9VR03N4MV7HFaid3Z2WpIyHo/bhNOkgO3v77fKCP0EcljkgU8kf3xM+h9ynj4u7zN1ZFh1T7Q+j5E6lAj/0I9m4Km/0Pckj7bV2d3d7dEzXUcCsUDN0WjUqiNZ3csANn0WO1RVLcBm6wBinrFrFFjyq2TBMdD29cZ5p3yr43heEvwVkPNtFvkRm2aldjLpJg4bPpU0abHtwcFBA2us7HGU/JBB+k8/M0YcjUZtaBW/lb19llhY0sfW0BW6m2AL2VEYwD7DsPE8xI2Wa5ZwkuesgLNdaYMAm1Xd4KbMBxKUTdszlG2/9+8sxuRAvvR/Q/uTcXsmxj6TW+fwz449rCbyj/lsJYquNe3Y977elCgS4q2trfYA7u7uWm+Ql7xYLGo6nbaXp3xPeMbj17H50OpsglWVJFzQs6p+QKbyVdU5Xw5Og6/EM8+VQzCq+mP00ylUdcFp9jdm8peK52dZmRsGlQKEFBb3NwzCBIXz+bxubm6qqtoAGff88vLSBnMwQgQ2S+WCTve9WLz2NeX+l55BCr9nIODw7pO7nkmne8nemmz09X5vb2/r+Pi4qqoBDasexW+zbHMBRPDcVQ4856RO6n8V1JABukx3OMaqau89q2UcKIN4f3/fGrsFp3R/b2+vOWMVfOdbLBY9uVZ5WS6XrWqyXL5S5dDZBdkCtap+X2RWuqs6NkTVX0f9+3yCNn7neVR1SGRWH4bgUNoKP5PscTqux++rqlVnIZiSxbzWtF0PDw9tM3XXl4BaJhLDJBdDQIU/aYx5b4msr9bXLTJV1U3XM/Vwd3e3Li8va29vr+0rPB6P23CJxWLRAsbRaNRknv2VDD4+Ptbx8XHryRPI3d/fN/8ocRIwJVuFniRgo+o8rIYk6JE/y4phBotV1QNoqqp3vmEV0srYYhh00Z08Rwae9mVbLLo+RMEffWCbPn361HqMnSOTQT8TNLuXpLNnUryxsVEXFxf19PRUJycnPV2S5LKF+/v7zT5fX1+3PsbHx8ee72d3+W16jja8Wm9bQHA+07ufTqd1dHRUVX/1GYCBBO7EZOIhfiqrSxm3ASNM7wa8S6jI/uPjY21ubrZ4sqqjwCZzgK9y/PSBdDITO8CLuDPBQ99Fv1WQ4ZfFmM4L1CWzfpcACf+WsciwLcR3h3bBv/OcChMJFkn+qvqMBvYm/bzPiPMBeal3mWizjfP5vDG5cnq6rcFyuA7b/b2vN3c7M2hVXSAikFSV+BKCli9isVi06YLT6bQJ3uPjY+uHeH5+rqurq9rY2KjpdNobDEFoho7MgqAMURfHSKSCkkMfMhhKZEewe3Jy8vogow+jPdygwDlnohgQl6ymZHLmd74recweBs8yEcbs6UJfqqqGLg2DE9eh0sdYOabPeK6CyUwUMoinAJk06hWFbhl8MxqNWoL6/Pzc+/cKEf12azQatR6LdAY2c05gQABpewrTiTkczi+TlapqfRBZoZNwLBaLJovklMFkeOl5Ti1NA85JZtWPs7XnYlJ2HDMdhuNkEjdEDj2vqv60ufzd0MYMnVn+vKq/B5rrymfkeqqq5/DR4KC5hu44hkov+yNgeH5+ruvr62Y/nQOymROfM7Adj1/3YnM+93B3d9ejuCerYLW+fqn4Jm1ZIDkajXpTZlUkUoe8xwwu05amH7i7u6uHh4dGdc12D8d4enpqATB/yhaT+dSbqq4amBXvTJKAkQn2ZvU8WShfqrI7dyafjmNl4JzPYphMVXXTnj3/ZMNUVS8R1GM1rGjw7RJpgfLw+BZd8ayB2mlLPe+qakO1gN3DZ+q9u84hQ+pL17BaX7+wMiTjGxsbtb+/33zO5uZmnZ+f1+npabOnm5ubbW4HGdGvaD9x73c+n7eBgfQ347/Ul9Fo1Przh+1NfiYuxNLiN8ldflcxYTab9T6PkspPZCWM7yaz5Jo809VMngCQPi9+T913bXm/VdVrJxvaimHMPlwZh2ebmIRbfOFYYgVxvriHnXh5eWl+1j1739gB4i3vNun6gIMfIUG03uTpVRC3trZaYihxyCmZu7u7tbm5WX/++Wf99NNP7aEadjKkdVT19ypi7DUUp+CZpEhok5qRwSx0xksmDBA751LuX1tba/0YqoiEMZOo7GsaIhyJ2lT1UU2fT3QkOeP+JCrq+qFSSXFw/FSaL1HqhglxVogoI4qe58zJCVIoAAVxXM9d7wZEOmk62dTr+Pv7+81oUkwVlNV622J0VY/QVDTkJxWSTibggIaSiKIELpHA/E7Va2CKij6ZTBpA4b1nLzDnpIqZlXvVAXt3Qt+hrxk8D6eqDquEXwom/c7Kir//p8PyrMg6J+Q4WSnPyskwAM7qif97Ptl0n0CN90mv3QtgTRIIeEvgKfXeZ9hvdvvu7q5VGjc3N+v29rY5ziH9ZpUkfpuVidJoNGoMADbSe0y/iK2TlS+yIAgln0kzS/ro8/Nzm8SbPgzoki0gGdikP0sZdC+u2+fosWvxOd/NiZBDHzrUjap+NWFY3U85d8x8NpInts7v+N3UN/YFCO7Z+76AeHt7u0dT46P572GP48bG6562WQXN57i3t9d0OpMGWxbl8CGgu3Mle0dlerXevtLO5qC1tbW1uru7q5ubm/rpp596va0J6PgswFXhhO4CB3wmK1vi1Nz67fHxsbE9MAjo9GQyqel02pKwpLEDbMlUxstkNX1axrdsULIIEszUvpJtW1mxFHOwCe4NKy1tRlYrJZT0dFiVzOTScs68Bn61qq+/CRCzX95HsmvYi7zvPFe29dhPEWArzhBjZ9/3jxLjvsnb5+SufMB3d3ct2PBQKIZAReUAWkmBZO+OrVlYMlpVzZlRWudm4AmTvis8aArrOlVAEj1ULs6KAeUfj7s+kmFCBRkW2Cbams6qqu/s/D6VKsvwiRL6rr8TdVHt9N3kWUOmkm6YwYHrzGd6fX3dzuv7qsOuO4Ptx8fHXlDDKQs+Bf23t7d1eHjY3r3r04/pnXvXq/W2lYaZ7Hz48KHOzs56VTd7X0oM9CfQKwOVyGsmQOQ5gxRG0u+ruv2onPPl5aVms1nt7e01/eI8q7qhAS8vL3V7e1tHR0fNGBt4A8xhkP2OPpEpRn84SGZY8ffM/Dz1MiuOWU3MwHVY9Ri+iy9VHvP8w89wjp5X0pkE9vmMPd/cFoSj8+yG1UHAGbBmZ2enPcPRaNTGvNtSYG9vr7231Xrbenx8bFVD7yPp+RK/qm46asqefUdzHD6ARJ+dXn26yXcN91WVQAEdso+PX0h/l/qdelLV+acv6aGYIRM6K3Us/eIwkEx9sRLA8TvHzyCY7mjhYDfW1tZ6lZQEUwWZ3o1nLV4AVmey6Znd3t72Bmwsl8sWzNNnPnD4LqfTaUsKUI6dX6IIFJMssMWr9W1WzrTQcjEev7bMvLy8NPqgtoeqapTvvb299s4zMVosFi3elFgmOJtxXSZvYjrxdFYG0xeLA7O1ZLFY1M3NTQOa0s/wvamfWYig9/5d1QdVs3qZQ3WSeuvYjssmWWlLqvqzPjyLoY7n+dOXD0GYBIw9G888Z2uknUHblZx7N2JT+cXT01Obpp7HkSwqniS4KmnGpPze15tG7kAjjX2ueg1sVC+S5qJyxAGp3FVVUxTGNIMk/OxEFDTWUrydnZ3a2dnpTUrNyl8qW27RwQFIRt2T4LOq2vkJYJaxq/46lCbR1iHi6ZyEPQNMipbnprwp0OnEqjrKK1QUApJK4zO28kildS0CxeF2IbPZrHctUDNDflQiVK2gpmkEswdjc3Ozrq+vWzLBOboGvTc/CtLyvS+ysLu727al8HwFkmQ2exJTbhg5AUjSuQSzZJn+S8zIPbkC1njvKgpra69TNnPaWfb8sR2Coao+GMOIm+7r90OUkr4lZWdI36n6azUjP+vneXw/SzrPMBBOx2plBcSfvIbUAzrIztlWIwPDy8vLlhDr6/S+sxn/+vq62Rz3N5lM6urqqgFt9gCD7vpMUh1X620rqwmASM+W3ZdgVHWUMYEY2hhdy4RmuewoZLPZrC4vL1tik8ON6Kzv22SbrEiIkjqaQzEkSVm9yN4ix0mf4++s5jnef5UsVvWHQGXlJT/vOfk/35K+ddhn6NqSFUPXnSeDT9eQzztZPP6NHUNX048LjAWTuT0A0Nl3M9CUaKKk5/sA5oiZVuvtKxlzZDqZGGZ0VHV9a+RfpU3sOkwYLPol3qt6tQnn5+c9sGZ7e7vtCuBcyUAj/7PZrDeNk16nH8v4M9s/6CU/699sBfuQFcL0VXSZ3vGLWWRI2mky9jL2YwMTQPbZZNoNF39Gt4asB3qbW2lkC1jG6Ts7O23Hg7xX3zf9NW2j8yR4DaRzn9iUCdR9z+tNFUWCZEgFauH19XX97W9/a1XDRASm02ltb283Bznse6mq1ntX1adsQUoZ4+vr6+ZAnYsDyeoGCqT/DwO7DJaGCI1/Q3UTdfF/15RVugz4EvVIoUhqbCot4WYElstl2+Ta2GOUzwzAk4KbVZR8B5PJpC4uLtpxhvvduGeJnePmdeNnMwYZqDJEWa7PQDuDnul02vrgrq6u6uzsrMnB/5UhWK3/+wvNO/srDg4O2jhvycPnz5+b8fb+GNEcIZ9BoQZ7MpvnY0hVRPx+a2urAUlZHclkkVPLyrIqF3laW1trfXSJ/NM7wdP9/X2jeiUAk/o5lFHPgC2o6iqgSaOjT3mML1UmU//zeLk8X44qP5/Xm/otIIH25qbsOZVN0In+43oF6ZnMqjJxco5zeXnZqlDswSoQffvKHqD0OZLFw8PDns9h0zPAoTtkUHsC2bCXVwIYVX1bXdVVAbWTZM8PeecPs2eIX8skSBKa9C7Xl0lvyvyXqotDFk5VRzP1e8/PMVxjDrLgVx4eHlrgnP1aQ59riRmsTBSrun7iBFME9XQ3tx8RXANx6FLGMdvb261aldeRU1RN2QTeAv3W19dbj/EKyPk2i81W/dXLl+0xdDLpzVVVNzc3rSpFTzJxqupA+awGkuv9/f0mX2zC3d1dqywneCvW5Y8TAEpAVrJiOvlo1LUXAKeGOuD3zpl6nHZr6JvIuRjc8/QMEnSWBGa8Mp/Pe21MYga6nUBS+qQEf8X77E++n5yh4f5cX95/xkpJNVeYcq1DW5DU+6rOxnp/nt/3vt6UKC4Wi0YDXVtbq8vLyzo7O2vOjVGt6vj9PqtkLwHMhtDJZNIM6u3tbZ2dnbWHmk5hMulGhycCSzBtl3FwcNAEQtneZsPDRLaqGxpDEFRBKJL7ysRwWEJPZ0Ypqvo0s6FTrKqmPIk0uFdBoOsUCOTwD8fiqLOHTL+FhFuFT8BhGiIHBsnJql864el02oyBynIaQsFpVX/667AEr3KRvH6JxWq9bUHHHx4e6urqqv7t3/6tbm9va29vr46Ojuri4qK9q6pq/UzYAIKaZAoAV1JOkvK5vb3dpjGiwnFeSU3Ontft7e1evw/UNu1FUvLQsBJgymDZ78h/VbcXG7lmtOkKnU1nkgGu51nVH0qVFcOq+sv3h7KcDmz4WecYJp1WOkcBAHR3c3OzTWdMB6w3xbOYzWa1v79fs9msR8WHkKLcpD3xXtiD7L1Yra9f9/f3ve0v0m4LPsmIoI7/U33QE8N+8x+2v2CvBW1+nzRJAAJ9vrm5afov6aAvSUFOv+U6ky1TVX/xhUMQNAdcVP3Vf1pDncvkEdCTLRd+7g9wNbcAIsNsmXtJ2eZj6Z3PCLAzQMwqqiU28V75U++GnrGV3q3v+l5Wn9FdyYL3n8D1xcXFDxGEfu9L7x0/tLGx0QAecocpRW4kChJ6tNT19fU6Pz9viQI23nK5bMkc25o9p2Q1h1dlRdzUerqefousACpUrzERDg4OenaB/qYODEFWOsWHkfnUM5+vqpY45jES3PG3ODtZQ+41faXz8OkJYlk+NyzQDO2Syr9FzxI89nN6jjWRvv/29rZRjvMZJiDm3ZGlHwVsfRP11IvICXlV1TjYOY42nQDEAELiZdze3rZgEqoqicjpa1WvL21vb6/RHIcv9+bmpgW0w99VddPMCCbKIwSe8gqw00G4D8rxXyV+w4pElt4da0hrS8FO1EW1IAPMDCyqqvUbUfCkhyWya288Y8ITzbYRd55fJUHi7b2rNGUFhvHBg6+qXm8bZHSxeN1baHd3txfMXl9f9zadXq23LYOkqqqhzdvb27W3t9ecBhAijXMGJY+Pj3V5edk+553v7Oy0gRgJ9HAIOYqfkU0HKumQfLqeTBATvSe/jsf55Gj/7LmrqhbsDu1QOoDUVYFrUuI4rqz6D1c6I0ntsNIydAp+n8HvEPVM+5B0Ofc+mbwO1PD+ko7kvOvr6/Xx48cajUY95JsDluRbqv6CofH4dR8+gzaAS6vJim9fZIX8Ampub2/bOycPHz58qNHolQ4l0EzaF+CTr7TFDF8r2RCMZpJiKvXt7W2j2vlOMmyqugDrjz/+aFWu9K1J3aqqnt9OihydzMTSPUPi099lpd31DPXFd+lOVkk9W/c0bIERmyTwBZS9uLho1ZkEdOhQUs5Vd+irwNL5veerq6seCCABlzjn81NpAeAA7dLuAJGPj4//ApKt1tevTCKSmUG2snrFF6H/q/imvVQRy0QjE9H0B/ke0cglj1Wdf1ZRJhNiXzGWGMA+olXVgAT9/th2fHnanwR6k5bq/rP67Z7pGX3QanR/f9/b/ow9cN/pfz0HNoC+ZxztftmEjK+9F+dOm2BlbJrftZIS652yPWkD7Ck+ZC7mu2SLr66uWo7zI+jomyqKqolVry/r4OCgjeCWpCRqicZY1d+bpqpacAgBweeGkEPZUGtGo65ZlFE17tawhqrqoagEEiIk+MvR1hKgHD+teval4NVKFDOrJZ5NojOZ6A0FvqoLEPUZJKpE8RL19GwS6fFvk7o4ZGjVaDRqQUGOUk7USWIKqfR71CbfXVtba3s2QbRdb1U1+o2EEaVwMuk2Ja2qth/Qan27BY3mnFTiLi4uWt/OeDyuq6urFlhK5KqqUVQ5toeHh9ra2qqbm5u2h5TAlJxxkvQf6k53GM3n5+cGgAAJAAfDilr2UtojUHDl9+wFG5FbwQyHO9CPTBgzEavq9BR1JFkSw6QxqyRZDaH76TTSLuTfSWvxuTx/VfX0z2cyCeaQJejj8bhVrTLIuL29bbaOTZjNZrW7u9vsbVJj2BeOLgOa1fr6hcEhsMttSthR8sJmJoov+JCgkGEJxZAVsrGx0fqpPn/+XO/fv2/BG0AwgyHBFjlUtccyGIKc6euz0pB/V3W9g1V/rZIPwdRMBjMxEgCia7IbjmmlPEskHSf9oV4ylRnXlPrl2vh4/jGroyp+Gat4x66DjeD/Njc3a2dnpyUD4o08fwIDVdWGjY1Gr/MHDg8Pm/7z36v19rW5udkG+flzfn7e3rmYk0/KmFacyg+bCL9cdttQ0TWgBUA1aYxV1QMZ+FS6c3h42KONApKyRUg8N5m80lcxSx4fH9uwOOAFG4JxR/5ycr1rzEQuCwfz+bw3IC9/PqSp+jMEZ/P3fCDQdTjYLa+jqv5ivwCnklgrffUwFh8OqPFucvown8zmSgT50qz2zuevfcVync+fP/8QfvRNFcV82QygCiPhz0QAIocP7MHf3Ny0YM5Ai6rXakg2rn4pSYM8PD8/183NTc+p5qS3PB/Hmo7A35rH9/b2mvGG0FX1KWKELwO6IfqSgvelP+m4XKNj5crg0/8dN52s68qgk2LaEoHi7O/vN4ee2xd4Rq5f71g2W3u2y+WyVQjH43Gj9LpGBsq1mgCXzpKSZ6/MMBFfra9bnBIai/etMiiAMRAqqTPesz2jUFS9H++eQ4BUVlWrKKcOpEMAJgF7OFRB0XK5bBPjshLIgTm3Y2Qi554Y7aRxZRLmZ6lTw4QuwZhhYunn+X0yPwRtEugZ2o0EjjhyejfUk+F1ZsWiqlqACDTjvE5PT9u1oBuZhun9eYaulz3LBAF1jl1ZrbctvbdJ0QdyqBCihZ6cnPR64uhGVdcWkBW16XTa/q/CdXNz02jkybQBLOSAB//HGHh4eOgNtjDkgf/NARWZGObxsppYVX+xDZl48o8S0tSbrNjTNSB02gx+hU8GvgLOUmeHU2DZjuXylYXz9PRUs9msPVeVPueQDHgGfOZisWhJgMFvGBmA7NR9dldCnv5THHJ1ddUA3ZeX14F+GaOoSK/W29eHDx/au+Grjo+PW7KXiRTgLdugxFXaK8yHAKpX9ffIxqyr6nrGszpNttjz7e3txsgiI4oa4jXXPR6Pmx6/f/++xepHR0et6KOQUtUBOjkUJq/ZcYdxaa7R6HXC8PX1dSvM5DYaGQOT/2yfsFL/2Tr/zj9f8sfOM2QU5eeHgFdVN3yvqno+3LNxH2yp/mG0fDEz+8yfShazgPQ9r2+yGZaXIfmo6vj2jDPhGPYuqi5tb2+3SqCAJOmSNi6dz+etCV0CqKwsqIKCDCuYrvXx8bEhuIQiz+nFU/JEFBl99wzRoKSJlObzyQpiVX8PKv8W1Gejsu+416S2MAruQ2KYI/PdB5qNLUuqXoN5AT5jlE3Ezo/WkO/VkBzncy8+9+effzaEE0345uamoUCeq/NBVXMM8Wq9fSU1hiNTSZrNZo0q/P79+56hFEgx2pKOlJ8cqLRYdKO319bW6s8//6x379713mWijYLd1Gt6C+kE8iSzQGLjmBzpcrn8C0vAz7M6Qu6GFbFEC4cVvrRVw32iUrfpQCarju37eb6qrgeJY3JOx5CYT6fTVunJihOHNx6PewyNvMbcZoSdS6oTW+b42AyJ4uoZl5QCllbrbYu9HwYm5FbS6HdkbOhjIPdsM3+VviGTOgBLVbWE1Dslt1nNrKpWmaSruZIZkP7QMZOiNawcDkHQDEiTlZC/z4Quk0H+lw7ldlZ8FVvItrAdQCs+nR8ErLFXGR9kIJrJegLRSTl0PxIMPnhtba0+f/7cAGoDTj59+lSnp6ft+Xi+nqUEXt+lRP9LAftqfd2ih+nHUmfm89f2HFVdcioWvru7a0UPwEBuPeP35KyqAyDpbOqc2DUraVdXV81n+gw5TEaeRc7IMj3f39/vgfbJxGMXsmVFfJ5zCPiUtD3kUcxAN+h1FjUAKfk9esr/SgrFoAlMpV8fMjOSmZAxZiaRfp+JrEQ+fX4+f8/Z87clHNvn3c7n8+Zbyc2PsN6UKGafRDoOQp5GXkJC6P74448mlIlU55AJCF4mb8vla/9hVhuhJ2mIvXjKaTmXlei932epeeh8MnHLcdZV/Qqr46WBH1YfhijMkDqTiRpHn058iLgktccG5VXdliWcm+scVna9O4GKd5glf+gSeqHzrq+vtyEpa2trzUhKZJ+enlqwDw2+u7trBsMgE8DB7e3t14rlasXyzg8ODtrEP0ZMtWk0GjXaYVVHlRZMZWCKukpuUI6BDSqXqsypN967QOfp6amurq7q559/bjKZwaVzQO/102a1QyCdTiUBEivpKFXV06P8fQatjHzaB7+T9KUec3iZXGbgnef1Xfo4DHx91/nS6Q6RTzZSoA7EETysr6+3n7nnxWLRbAQ7KYnIvfsE4EPaXj7L1fr6lXTnZKaws5ms+TmqN5ZIDoGq6uy0IDNpXwkWse050C2TxcViUZeXl3VwcPAXKqnAMH1H6nD+nD+kU342TBjJVTJz6F3aoQzAfCaT0+Vy2cDglFXnJ+OSZZ8l69n3LCHjq/Tf04UM7AEwktGk5OdK+yRYfH5+brMDbm9ve1Un135zc9O+t7Oz08BfswK0BehPXYGt32YdHh725iwk+KZ6nO004s3JZFKfP3+u3d3dnm5+/vy5MXhGo1FdX1+3dify7JjoqXwcsMO+fYorYmPUZbFiyh92H99Z1cW7Q5DGADyVahPO+UPswMPDwxqNRs23D+NdCZIYUKEhEzHXlzMS8npV1fViosQaBIRhNCxEpb9M25VxeILWADQ/8zxcJx+a9jX9Y/rlTIJ9N99LUot/hPWmRDErcJPJpNcjaKhFIgs+44UJKk3aE7BIwEajUUPWCLwHKwD1HdfAoTDqEAgCKujLIDKdbNLEXEMKv3MLyJKnnChiKp7vplDk8Z3bsxwiH6qNmoSHSGYGufaR8zz9njKNRqMWWGSiSoEkpJmg6kWczWZ1dHTUaDpJ6VVtyecuSScXnqstMZx/Npu18zruj4K0fO9re3u77fVTVY0SkRQMwZjg6OnpqXZ3d9vkQ31AKMj0e7lctr6aNJbj8biOjo56Oi5x1NvoHe/u7vYcBGObPVacJrlVWcQYYFs4SnYp77OqC7TpTyZx/v6v+pysBE/S2aSO53GHFcLUa+grXU063jCIzuvhlOmswN3vhoG79wAJTqfp+obOWdIwnU4b7biqGjCQ9nC1vn6Ryar+kJasLgAjya7x/AJWvU8JjA6pTvyq9+o9J0Uz5XhI61a5srl4+tAMyATMqRtZoajqA5pWVsfTT7JJdCiDrDwW+yKxIuNDv5s6mP2d9CP9oqTc95bLZQNAs683p1X6v3fLRmWQTd9Uj5MaL4lM1hKZ8Dn+2Xnco8oHf2+v1NV625KA8DO3t7dNl0w6zbYJ8dBvv/3WmxhMRsRxdNQQlLW1tbq5uamTk5MGACbVkm98eXlpSZcZFFmoYdttZcSWKG6k7mTFz7WJ0Xd2dnoMBhR48UKCR/SUvGWiWlWtzYE9GoKv6edT/wA77vHh4aElqc49rHDSs9Qfn83EUU6AxWTwHVvl/MnyoNPAKHY5z+u5JlAuwZQk/2i+802QcJbFb25u2oAEjd4pUErs9/f3bSw7yij0gpBWdX0Te3t7tbu7W3t7ezWdTlvfIMVDK0s0MquCLy+vezfK5hOxSSqB4+UipNBDSGYivLlvzrCs7XvDioFjZGnb//NPlvuhKJkspuHwb89bIJ/nGo/Hrb/CeRPpTboehZUQq1pkX8t0Oq2qrvcLLdE1pcFyr5PJpHHb0ZhUmkejVy67yvJqvX2pOHgfghQyJOCpep2C5j0Z/+zdShTRnBjYDIAY5efn5zo4OOgNeVAhIX90K7ef8ceU49QZziIHSrkfOirIAlgkBSsRzqz8Zf9TVT8Z0y+depRIaAa77tt0xar+dh0cvs/mdbi+DJat7A2EYCYYldVez8P/T05OqqrTc7ZqfX29IdEJdOk7FkxwcHQ2k9dMbFfr69dk8jpY4uPHjz3E2TvE3MhqWCLX/BhfTNdyCcQS4EiWjGDy8fGx9S9VddUGm7vzQXQ2fZSpqU9PT/Xbb7+1axvK9BAQqaoWtAGk/HwIsKYuJEDtWEltzUBtWPXT05WD+PIYm5ubrYpB3umE6qwe/qqu2pnA73L52o8vmEymgX9XvQ4UIgcZw7heyaj9+JIi7v3xw1X9ra+SJr9aX78AbQYF/f777+3n5PDy8rINixPD6kGdzWYtLjZ1HBsgK2Hj8bhN8lf5n0wmdX5+3nyKmNOgHCCMn9M1RQzXmXKZ/s498St0RPwlQRY/0HM9dgmMpt/Lqv/Dw0OzIXk9qXd8UFKn6WECTRm3DIsdOcU4/bkYNIsnrnOxWLQE+Pb2trctn73Lk73gOj99+tS2FsuCjOOKd9yva2evExT6Edab91G8v7+vm5ubNi0N7UHp9fb2tgUcUE97fglMVJjQOubzeZ2fn9e//uu/thdNYCWTghfBo8Any/aJtqeDzMpgVg+HwWQmivnic+BDJpgSxGHvo+U8w3NWfXl/tvzbs8sqSlX1EjLP3DNAqVEJEty799ws1HVlor2xsVHT6bR2d3ebMavqxnVzptAt3HnVyM3NzYawZpCbaC9jJuG4vr7+y3Yrq/W2pcqUgaGgyDvXwI4BIFg9Pz9vwRPdTvTOdwETVdUq4GwC3dzY2KiTk5PmECSprkuVMYO8YW9uVX+fs2QZSKY4mtQfzinBEfpHnyVazg3QODo66tkMKyuZGXy73rxmjo5zquqcXt7XsI8rHVQmBnQ9HbdjSoCxMdgl9ODpdNrrl2C3k5KfiXwGrkCHrGKu1tcvdnk2m9XV1VU9PDzU6elpjUajVrnIChzqeII4aOO5rZDgDWVL0Gl6o4pUIu0JTCbIIXhzvpThlEs6gFI3rOpl4jJMBqv6VcUEJPJzQ4AldTtBz0wSM3geVjIy+PO7jBGyrQQYJKHn5/lnlSV+M+2iYLuqmxI9mUwa3TTB3Dy/+0vmhKRgmAgDdHPPvtV6+0r79/Dw0AbZpA+9urpqScr19XX98ssvTWeqqgHt/n99fV0nJydNfvT2j0ajVk3GqmFzgUjkig1+fHzsxUxstqKISnP+nL7yV5Il7Sez2aw2Nzd71NfUxdxeiV9gcxxrfX291/eb9ikHTSoSDcHdqmrg0TCBzfjbO0qANYGktGvJ7MliUw7rEQMtFotWaUxWw2LxOoslddT381i+k7bz/Py8N1TwR6ksvilRNMFLRYgBplDb29u96Un7+/t1cXFRW1tbdX5+Xr/88svrRaz1JzGOx+OGhmelqqqagxQgpkBwrEMnlyhjNtMSHi82HRWBJ5z58n0mqwuZECbSmcFzri+VnzM5zZ9xQO6f8A0rqp6/YDerk0lhoeiufW3tdZyvRCEpEmhM6DCJ+FZVm1ibCukYjBgQAAKVn0eDwsGXXK6Q0G+zVPJzGExVv+cnt1ChAwAPeixpOjk5aZNOySGkUYLCsZHN8fh1Gu7BwUEvUcukx7UJqDhF8ioQA0qRs6xiZsJX1d+nMJFP9kFyKjGmM+Q9+3zo/jAxHCZ0jpWBLEANnaeqX1nJTZCH7y4DZse3vD8JsKEL0FbXzB4fHBxUVbWx3RKE7JPKZFoSqWcL4CYBXeno2xc5393dbdu+5PTOjY2N+u2339pgKHLjPT0/P9fHjx/rX//1X3sTCAF3BlA9Pz83OunLy0t9/vy5Tk9PW7Uk98YU/KK48j2q/CknV1dXveqazw8rh/n/BD+yp8fiF+m0gIrvr6peIPylpDIZTXSUf2P7MuFN6phz0Tf2Lf/PTiWoxn6g22eskO8OjU/VyPcSrBFELxaLFtN8CSwTGywWizZFc7FYNOB1td6+Li8v2/sbjUZtGwysE+AAm7i3t9eomFXVfJtiieJIgifePz98fn5eZ2dntba21tozJDB8jwFnVR34wN4nMEGuDJvDesmBNq7VvuHkKeNBckVX6YKKZtVf+5HJr1giW8BUaT1X8q0im5OY+Sufzwp8xiFJa03/NGQf0P+Xl5f6+PFjY0BVdTF3tto4pvcsrzHJlY3JWSD5fDwbRRP+XlL9va83JYr5Al9eXlpPk58LQC2CLAC7u7trL4gxhnwyoIxtVTcsgjFVlfBy0uCqMkqmOBnXkOeDWiTlyvGrqqE4T09PdXNzUz/99FO7nnSGBCbRC04wA9hcmcxJEjMZU6UTRHsm6LtouBSBQ8veJ4rHYCV9DV1I0J7OhUJWvZbl19bWajqdtkCAMvh3PjvBpPMxEgbVaJK+vr5unzGkSEVqtd6+xuNx6w+o6uSpqqM7ra+v1/n5eR0eHlZVNVnb2Niovb29Wl9fr8vLy4bKkxXHn06nbSsLyQsajuBIJdNQq0Tn6Ci9Vg2/uLioo6Ojhr4zxNBXiWSinYlIZtDMRgmksl+rqj8u23fdh2v1c5+nd1Vd0zsQJgM55x9WbtgFSGuipQluJRi1XC5b4M95Z9I9DJhdcwaaqGuYEhgBuVWJxPDy8rKqqoGB7GQG7av19Yv+ZXIDzZfI6Av37sgwvQCqCkRVGMjTw8NDXV9f1/7+fvOZfp4V80xkyIzEkU1IX5HV+FzuZwiisg3ZJ5R6l0yeqn6P0mTyStEVGzhvVr6znSWr3cmiSdsl2M9KPXsCKMkqXjIckkGRiaLkN4FTAanEMpkU9E0sQiYkttkTrI+SLgOg+FrvKW38ar198UvilGSXYcBha5C733//vY6OjposbG5u9vrnq6r+1//6X/X3v/+9VdeyHcDxJHyA1hzShqWj8pVyhtWV1bNMpOjhx48fG4OBbmdrl0KBxIpNwlbzLJJuqxJaVY0mK1mUKLmmlFOgZVYLXUfOO0nwiF4koJPvw/f53qFtWVtbq4ODg7/E4abvZ/EG6Ja9mX6PDZm6rVIr7iYfaQ+HCe33ut6UKEKuPWDIY3J0syyr/0hgiVufL94L5YgSIeBMGW4ove8nxTFL0ZkAZVWB4Wds3YcgisAz5tCiRCqtpOxwfENEI5FUyuoe00l+CYkk4GhKgmvCLwD4rygsnHfSRCEckOcPHz7U8fFxMzDer+/7meQhudZV1ZLIrKYuFq+9jNApNAvBjOEI6XRzAudqvW2p0HrfZELyd3Bw0EvqBR/jcbdXE4eUI8DRFvX8pM4YgALQ4PiyIjadTlui5hwCJM30wKKkzBwfH7cgzUbyVV31LVF3Rr+qP+abLqcd8fN0hvSPbOZ3U/fzXOwL58XOuGaB9/AdZYAuOK36MvMg6bHOmbT4BIQE+35OBjh3w6ZUONgHgXhO1OP8cvriar1tATEBkVUdks6Opy3k3wRmwEKAIH0ky/oOHfPu7q4eHh4aBdw7zip0ViSzop5/0jfR26xuDJNEx0jAJSsVyR5IeeOrc4BTVTVgwzPIY2WbxHBq82w2a9eYdE6VkAS1kvbumtmMBFRc4zDou7m5aSBzxhbsDbYSW5LbXHhe3gGQNsEt8kJf2RWBuZH+q/W2RS7YxgR17u/vG1sHGC5hVzEaj8f1j3/8o46Pj+v5+bmOj49rPp+3aaqAh2zHEN8ZKMjP8d18H19XVX9p3ZB4JXigiollwrckK21tba23N3ZuWaYCJr4lw/x2Vbd9CJuSrLcEM5+fu63zsiDETlVVK0qIU/ko+uk5+504PuPsjKkTSBULbW5utinIyZBStJIkVlUv0V4sFr3hgK7NTBTf4/f5TwMn9a8OY4nvcb2Jm/Dy8lLX19fNqXjh//jHP5riEFbORem96hV9+Pz5c8/JQPFQ5qbTac1ms2bgCeswgPWi7u7uerQRSpelasZcQEjAGARlZ4IGeTUo40vVw6r+1MZ0ss6dP0801b9xuQme86Uj8/vsfeC8bm5u2jVnBcX/Hd+0yfl83krnkuDh+Z1D2f3o6Ki9R9csYc+R+qo/qDGUi7NTdc5+MQbKSP/VevvyHjReM5aLxStlnJH07siChIaRU8XKjXyzyk3WUr63trZaZUOfQ1JdGEpAj+vjkCF46WQycYLuVXX9xu6hqttLKpNEuoY2X9WvnGcSmcmb89LpRCD9qeqqJmyPQJM839/ft8EwbEIGeVX9IR6ZJGYFz71MJpM2MOO///f/XpeXlz36TtUruGRIlGq95FUwnnRSLIvpdNoD0HKbHkHCar1tXV9fV1U3gEzQxa+QA0slOZF+wKhjCO4khdggAiafS/ZP6qjhcpKcqmp7/KJNzufzurq66oGO/AYbQ1esTBqHYKrry7YL1Qi+ATNCIidJc2zXx8YMK4mGy2QyRk/d+93dXRs+wtc7j36qrBr6jPikqutp29raakAc++SZJnCEeYXer7rJ7mYVoqqaL5coZ8XEu8RqWK23L/11VdX6y9jEBGDo5NPTU9uOShVtb2+v+c8ETyVR6P2SK7EsXw2MzQrZ1dVVT/6ruv1u7ZFM3/kwdntjY6P29/fr6Oiox2Sh72yRGIBeAGHcKz3a2NhoFc/05VXVA2RVR3P7M7Gm7+bzBnQD0wDU7ESeQxzPJ3o27GP66ARjqvpbdKisZkKXRZmM7bXXZXLvudBx7xjA517EGT/CenNFUZVQUPP09NTbaLTqVYgI2v7+fgtEJCL237MIg72CJHDX19dfRDBVDDk3iR4HU1W9/2cyJcnMKl5VR/UQ8Epgq6qurq56CVAiFUP03/8zSU0nRljwtbOqkeXy7D/070w4s7KYCSTlYIAYD0aBs+N48j4glfn8ksar6pRIciJGmVwMnS8KoUTD0BsKloHuan39MmVN8re+vt70kHNJmZHESwYFnfP5vLEAVAo3NjZ66PhyuayLi4teNYvxVu3IgGdjY6PRSKs6HWEfyGEa6KFeC5YT5avq71eIGpIgTyKuQ8onOwItrPor5XQYuDmelWATnXh+fu4FGnQ0A48EjyTrEtZEcSHPPvP4+Fjv3r3rJZ2Xl5ett83ESraSbifNEMPDezGpDgoKweawf5T+iu95DUHN3BYh/Yl/z2azen5+rsPDw54ukImqV7+Avjqfz+vs7KwHWrCvy+WyNwQjqwAHBwc95k1VNZ8LIMr94pJu6u/0P2Qyg8gESP17SOn0HUE5XcJQoRP8iM8PE+yqasdWnSTHOS0yQVLAZQI97GH6XRWf5XJZ19fXrQI8mUzq+vq69YK5NkHt9fV1e76qJjmDwf1LxIcJY9JbPQs+fdXn/22XQW5VXSWX/qku0527u7s6PDzs0RVV/b0vsiqpUSkks/wsvd7b2+sVW8jp1dVVAxP47ky67Jk8BHWxwehqVfVApqpO1oG6ji3ulFSyKwkY+z7AKGM/oK/r5AP5Yr734eGhzs/Pm+2zzZfPe95kPoFV1yOOcD3pz5Phg7rrnhWhPEe6ps8Tu+rk5KQVcJzX8cVGnq3/Y1d5dj/CelNFcXNzs7c5OtqgRJETenl5nbinipUCqwcJDcqCWqJGZZCouff29rYmk0mr+KmaML45HCf/cGJZMk4hInAUBwLDURJuwjF86RTBn6SuZuDrGUBajAMmnImaJoqZlR7BQSaGWbFwPZAmSLMSeSasjFAG2xl4anLOZymhyJ4pgSjkVIVoMnmd8nZ7e9uU/+bmpjl+SLckY7W+zSK/ZEJlTjDByUE1yQkdE6DQ96rq9QBzBPoPt7a2epWoi4uLHhInQAXU3N3d1fn5eevVEygNkcPsr80qILkXAPqZlXQTOsgxZdCagXAiplndU81InRwGpq6vqhv9n1sMeGbuKwNsz9K15nl9vqqbwri+vt50x/5bGXiqTOj9lVgILJfLZaPT7+3ttaB2PB63PcBcO2ePKZDVn9X6uqXipMKd/YmAVkwQyYJ3mdtVSKBQTfmJ3d3dBsZkcpP0VP6m6lVvgEhZca6qBqIIjJK1IthNUDNlsarrvUvQhQ+u6ih+mYANgZWsAPh/6nZV9a4pr7OqA26zTcU2OO6Zf3VPdEiCx+/d3d01X8quacuw9ErlNaVPF4xiZHkvnk8CSarNgB/+P4EGQC6bsFpvX9nvavp8xnJsKtssoWTnAWr5DsV8kj56IGnRfiOuyi3KnE+C5D37fTJ/kmnGT15eXjZmV/rvqmq6IBEjY5kEi2vteKDIQRfJIL/hGm1fsbe312ySOEGMzOYk+JwJoHv2PNNnDvV/CO5mVZF+J7sROOt9pe1yfD2pqv2+4x5VkFVd3UNuO2SYUCax3/t601Wur6+3IQecwPHxcQ/tz8Rte3u7J1QePsqj4BAKA+mr6iYGqYgQIEa6qlqAKrlBp6HomRwKSClPlo05LD+HBEhoKXx+hsBkT+WQGjQMYv0/Oe1Jj6OcqQyJSLkGSgad4qD1HmUAzsEMezcE4om+5OADlUPXBXlRGXbOHM/P4XvHGUCgPULaGFDPLPeGWq2vXzc3N824CxqBBabSSiKrOpkUmO7v7zfnk4EewKaqevq/tbXVm3pIXqu6JA3/n3zQGXpKl4ZUVdUQwdWXNvau6lcTM9lKR8GZCLQ5ILbEeS3H8IdepoynLqETcSJoc55Joq9Z3Ux6bVaTUi/9nZWk7CUE0mQVBDijOjG0R8liYFsyERfwZlC+ooe/fUkOAIPAVQkZWeZ7/LyqSyT8O6vUx8fHLekatiAAANOGZ9Ba1VXlbm5uqqrrjbQcJwHZrD5mcphACr0h99nz4xzomDmC3zmBja6R73Cuqo6RJL4YBpsSN8F6ftc9XF9f1+fPn9szwcjI6c58ZibqOd1SQD/UZXFNBsKeo+u3LUEOt0vw+PLysvWkbW9v1/X1dW8WBJ1drbevZHWIU/TMSWy8Q3FVVroMBJNgvLy8tMQkK3DAUnLtWJeXl+3n5GE2m/UA9clk0uiuVZ0PrarWtyhR297errOzsx54DKSix1nFzthM4QM4kX4yqbJAUWAlm0NXEviUOLtedit9p2tVQBILKE5dX1/X7e1t87l0bJjQZyzinYppAW/0xnfkCEBZILLrtjIZr+qSWtRUINDNzU3T6R/Fh7556uli0TV0pgNbLru9YaDSEqutra369OlT63cz0WmIbkt6jo6OqqoaAiBIqupoL3oYhhQtCpNJXV4rwyp440xHo9d9rLzIREoTFR06sy9xjpfLZS9YT0TE34ksCBpdT14XBZZIS+Qgr56TZ0V4Ce3e3l6v4Tl70zwr74jB4Lx9R8KfgYEJlZRra2urNVxzore3t43CwcgNUWFAwY+iQN/7IheSDTKYje+CL85kPn/tcTs7O2uGeWNjo43jJstHR0e93g06ByH02arqUUZ8Dq1cIkp3s3lc749+KsY+QZ6sylV1/U+ccgIgPpsVlqoO1BLQDYPwDPTyvFXV08+qavfgPAn+5LUmJVXgrlLCMdNL15HXM5m8bgmUuozunXZ5OORA4z5q3fb2dt3e3ra9obwbIB8miETTu/aOVuvrl2BDJc/7SpsomUEdruoSuUwALy8v6/T0tH0mdQyI4L0DHrUc8A+SHX5lZ2enHh4eWoUqaWlZ1R8OlkngJCm0VR2Q4h6yF6qq00WBFf9IPwVZ7oXdcu3DKkBubA7dr6qezumL4nfsHZzUUMmf6e7YRUA1QXz2Fea925Ot6nWKOPbQ58+fe/MBBOfaMaqqAWfL5SsN3HUBcD0vwMDa2uuWV6v1bdbBwUGdn583n+c9DgEIOsGfscP8xdXVVWsHSHCfbKZvYWcVX5LhBQwUm7ED5O3x8bH1QKYsus70XcAH8iQ+E2fmVh8KOoDdPH/G3s7rfHQdsyzjaklfUsIlohnzrq2tNZ+eesXeZELrWtN+eB9sCZDV/yeT1+23sGb29/d7oHPaqOHzzIR0aPu8MzbKhPgEa7/39aZEkZIwlgStqnMOiZ5n4+fR0VETAjxfFKmqaiVpRlpg5cF7sUPaSjrQqmoBT05eypXC4zoJnArnzs5OQ+0hr4n8D3sHCCjHRvmTypaBZVX1hJ7yMfg+5zlUdWhHBs2JmFAEAQIH4r1ZBlZUdVOz8vvQFnQn97ZcLusf//hH/frrr01pJOScseCYoxasVlWvuuL+R6NXahMDuVpvX95fVQfsJKAjWfCe0Evfv39fs9msLi8va29vr/dOyHL2P0oivf8EOIAOSWPTn0PHMsljK1DKJUAc7cHBQdOFDJR9v6pPMUsgxvnpb15vVrTpV1bnPcMh1Y0+OLZjpK77HKRTz+7u7u5fnllWMDjVtG2ZgCawRZc975za6HsJDrBlqqqCBoMMBM4+q1KCar5KFN++cj+9rNgtFov6z//8z/rf//f/vaqqVbNyejDbP51OGzBLNtli9C76wIdlskGOyDCboFdI9STlpqq/f5nj8KX/V5QqupWJHcDEvfPZQx+Yf9MBiXVWwfm8qo5CpnLq9yo9eqIzkK3qU2EtbJj19W5fOM9nfX29vQv3RK+SfZVtLJ8+fWoJJ9qwYNl9JusArdT7Qk+U1ObkyS+B1qv1dWuxeN2mhk4B3Pgl08CXy2UDV/gdlfu1tbU6PT1t28KpSAIhhgBuVX8CMj9Y1QeP+HT6qxKYfbF0ZGdnp1XSyQygUqWS3mWVPQfM0afLy8u6urpq2+44Hj+YFULPLXW/qpoOYazkPWxvb7dtufg1hQf3lb4x6Z8JyrqetEl0I0FWVdCrq6s2FdrCghoCWo55cXHRu4csZAHXEig3GfVHWd+ExC5A8QApiOrUfD5vDu7x8bFubm7q9PS0R/NM58CgQzm9JBSdrCh6mZQ3A8G7u7teEAo9T9RecAZR4SirqkcJkohVVe/7BCydZ6IlrsX3nIMDzOpioiQQmyGVx3HTqXtmDEI6/+yPlLAJHhgI78AeTZ4nOkAGwAaibG1t1c8//9xDf7N/CaKVypEV34eHhzbAKB09x/ejIC3f++IYDg8PazQa1efPn+vg4KC9E/1MEqrpdNomz47H4zo9Pe0FQfv7+/XHH3/UL7/80nSHI8TfR0FJhI9cefeTSbcvWuoA4357e9vbL81UOUh+JklDNNOxhg5DIJg0bmhiyrifZ/WPfbGG1cGqzunk9GL3lokn0Cvp6XkstoaOJ7rqGNbu7m6PMpTHSZoxipStgPTauG/BQPY17e3tta1VBLwZVK96FN++2Fl+TsL39PTUxuhXVUP5ofsYGpubm82fDgM7yQa/ktTuqmoJRgZ2ZIZ/0yuVgCn6GQZJAk9ZAXfu/4qm7XolZ6m7glz/T6o4MCn1OpNG3/GcVGCS3inhoqMbGxttUFAe3/lyqwlJJzBnZ2enxTp8vwF4YgeUYTYCQwPLx3vRdiOY5xcBVPZqk2wmECXZfHx8bMOnVuvtS4Ku4u9nZAsjh60HAoqtEkzxPVucZbIg5s3/S07y92QIGDSfz+v29rYODw+bbFZV+9t5c9pmVqQBfmQJIyZ7l+nQdDptyfFoNKqjo6Omq0CkpJWaX6IwxH/xuXR0OGXd9zPxBF7lFOHso6zqGD8JhGZC7llkgYjemrlwdnbWcoxM+NwznfZcrq+ve/T3/ONnaXs3Nl73p764uKiDg4O3Cef/j9abp556sQIriGVycCUhFMcGl1Vd0qVSWNX1R3kZlINhJAiJFEDdvCA8ZYlclpIFXY7pOzmkQfAroIUWZYCaFYkMuvwujYD/u85Ecf0t0dV8bLCE3/k7y/iOTXhV/gz6yURTeT+nYiVthwKpCGYlhtODZkpkHc9zZiAzGBEUS5AhsJkwM05V3Ybvq/X2paKXyJmAiqyQO7oLHTSeXpV/b2+vjf7O4E3/XSJ8iVSif7qeHBtNxhLYQaHKYJGM51Yrqe9ZWaRPKmJ+b1CPc2ZClklWVfVsQ1XngDwva/gzTttzz2tyzLW1tUbzpDP6VlxH0tbofdoevwMUJR0nmQsCZD/LBnoUVFsGqBYKODnKrIJ4F9PptA4PD/8flt7/9y8JiZ45siiRkESo4K+tvQ67UJEA8iSrA0JfVb2edQng5eVljUajNkmRXaiqlny6jpubm1ZBI0t0ftg+4DpTt6qq5+cygavqklb/T+paBuWZDNIF38/Po6oDRrKq4/wPDw/NNmQVA6C1tvY6AViFJCd2o8nxV6pB6WtVFSXR/G5SzOmpd2eKNCDNe7y+vq7t7e26uLiora2tOjw8rM3Nzfrtt9/q3bt3PX9sGrshPquK/7dZQLWsbrPL4hv6owUKcGC+hQR/PH5tpcqJmAAC/67qAF6JothIzCSmpW/z+bw+fPjQmzuwt7fXY79kW0LGAgAoMcCQFku36BCdE7Pzq5JXtoGvy21DVAedA3CbsWwWWjIW5BtzWJd/p69OWyT2cZ9fShJ9b2dnp25ublrin3F1Fr+s/Dc9lke4Vowg/8/45Pj4uMci+Z7Xm4bZ2ANqe3u79SKZcJhJhoelHC4gSoRF1m+vLgogCSXcHnRVfwNcCLffb29v95yZayD0mZwRmkQAMnGTeGY5frF4Hcph2w8CDnnJKgXHX1W9yaA+4/5zf5kv9TZNp9O6vLys2WzWm5aVybNnTnAFku6DklCA3C8HegqlYeCyQsFBejbOJxGF9qArZtDLcd7c3DSFZzxd+8bGRkuUV+vtC3WFrIzH4/rnP//ZUHDDjR4eHmo6nbYpjAIjDpDsMMxZSZB0mjzs9xKl/f39XtWBTJHh29vbFoSq+LsGDiUT0AwyE6whM+41UUD/F0BmgMr5JRKaRt15nDeDwkw8/V9T/LCyMuxdyopDDgXIiibdTj3La0zkmr4+PDw0O5FVDv/WA5VUpuVy2ahPDw8P9eeff7bgwntCE3p8fKyjo6Ne9XG1vm6tra21/tsM7jY3N1sP/3/8x3/0KuGCxfX19frnP//ZY77wo2SfnPkOu+6cAjnAIF3J31V1iHoCKMkKoAf0wvUappZ9TFUdBTx9eFXXMwWk4iOySsBn+G5S3hMUySScz724uGh7B5taenNz02PTiGVUUBaLV4p4gjLOKwjOwVEYG+vr6/Xx48dGK/QcJZrJgJBciFcODw9re3u79vf3a3Nzs1U5vEuViJzcyG6ur6/X4eFhb8ux1fr6NWSCeXf+n8DKEKjY2dlpsVhV57MkDsOJ0uTBkKusVNOLT58+NT/ofe/u7tbh4WEtFos6OTlpMweycsceZMJY1RVmgAtsvbkidLyq67lzrXwYH2guiQq9VrS0GxcXF3VxcfGX++XH8vNV3YwPFfiDg4MGatt14erqqrcPpcRazF/V2RqJn5g4QRu+O6e2qtKzOyqVZEJOYc/3m5ubBkYlgJzP2T1nBfl7Xm/y9FkRgIAYssJQ3t3dtWbeqo6eBUUzFCOncA6rXlXdS04EMOlhjHdWEqq6IRt6gSgq5UxhTG54VbVKV/Kyq7o9CTP4VdrPSqjANhNQzyA3Q02j4zqqqnePhCz3dnFO95TosXNxrCq6gsF01K6JERBI39zcNMRYEC+QTNTn4uKizs7O2jNzTYyZ4IUCMkaCC03irjuHKazW2xY9FAi9vLzUyclJm4JJhgUzUEW6LLjzznd3d1uiDzhAHXl+fm7DqAQ0HA05E/RxVIYX5bRcQVwGf1XdNDQ6k+jmsMKfdmYIAgk4bf2BlpvgFL0np+lQHMdK1FbQKDit6pxeXhuWRVLh87hZyUyQLAPmdHQ+KxCA1uazu7+/b8d4fn5uDp3dEQAsl8u2bZHnjTqXiUQ+g9X6umVoSVW1qtPJyUnTmdlsVj///HOtr3fbHgDdJpNJ2zBbQKk1IKmZGbAAFiQkzu8dkx1Uy9QxfiHteg7eGYK4QwQ9q4a5srIu8OJjExhx3/f3940h5LyZBNPj9DEAqapqscDLy0u7d3bQ+aq6JMwzB2JnkLy+vl7X19e1WCwa9a+qYxq8f/++JbveEyaOd+NZ3tzc9OjG2BMCaDRwemuPRpXO7e3ttj0ZO7pab1/j8ev+eYAbtnDomyRFKmzJehETqwKORqNmg1WhJEtsfP4uK2iA/6yW0dHj4+NGTdaGZIIu2+C7gD8yLeGtqsZ4oUv0PhObBPeHRY319fXa29trOiPhyiq6Z+J3YgAUcO1Ma2trbV9F7Jdk3Bg85d5UMpORUNUl6UNWDv1zT+If1+Y+va/UcQyA9MGekfde1e2jmNXaZBt97+tNFcWbm5uGuBEQRpdzQjNMVK+q6sOHD+2BcZQEN6cp3dzctCqlYNTncmuMfOCMek5Rkth4QVXVkABOKfc34kRV0igwww2Nh+hk74VgkTEwVSoRRMkf6sJsNmuoCKPAAbq/Id0hA2sC7LsEPqkMFCMrgt6ZQRUvL6/77khMGTz/vrq6avdb9ap8+k1df9JlR6NRo+261sVi0du76uLiolGCUHUY4NV62xIc5fAYSDM9HY/HrZ9UkEZ3AD+LxaKur69rPp+3jdclMBxjVde/k/+WcP3+++8tIEuqDaoVO0A2BVGZxGXgmKAOY59VlwwWq6o5mqGjgIgCfOhOIo/uNZ1NOg3XkP+v6mhzji3gXVtba1WGIQ0mE7BMgOlOfj6pfhsbGw39zf4YQFHSBAUxAk/DOVRnMrnOATgAp2Ff2Wp93bq8vGz9v4YhANPsrYheuL293YJRwZ53c3l52WR2uVzW+fl5Y6lUVQsaq6rJBGYKhJys5UAjwZrKc/biLBaL3nRy506Z5zf5Wj9P/yPhJG/J9gEYo65JoPmzqq4Kmcmx62Gf0ibZNgqAKSFjc/j/o6Oj1v6hcqTC6p5z2IbYIFtbskoi0Hx+fu5NVEfztqWJY9BBSbvze647OzsNbM+J8ILwFdj6bZbK1cvLS11cXNTm5mZ9/PixyWMmaybN+n/VqzzbpkoCw+6yySZUV736avGr46tYsQMXFxdt+BGbIKkkb2I07IHcxzzBfAw/Ptn1+SOOS3Ay79E1JiCk+qZNxXWLmfXwAoqBQFkdZc/E+gCa9P2SSNNoxdjsmHeTvjx7ILOocn9/32KEZPr592g0amDr/f19o6QDjRJAG8b5V1dXvaIMe/ijsHLelChCAObz1wEWw2QskXZCRrn29/ebQa2qJvSmdnFeks2qrhpZVW0sfBpEiYdAN4VAQMqJ+IxjVHV0z8z6BdPpxLLyxjBD47+U4Lh/CpXlb+fnqAXVkshMjJ2jqpoRETBWdeO+Haeqo7wJBtEDNjY2GtXVM6H0kK/RaNQUOxFU9yNY9A7QFk1SzMT2/v6+BeSup+rVmP388889o5SUp9V622KMDg8PWwCKUiGIUWnMKphAbDwet4qf4EYfQlX19gdkD5IORucmk0mdnp5WVUeRMuAKAo6WhaFgumlVp9tpA7KCmBWLNN5JASe3Q6oJOc2KX7IFqvpVQ39ntSSrfuxH6qbPZsKZepm/c42ui01J5+P/nDrnXFVtYNhwkBVUOas3knQ/Q5/JVgLvjM3KPvRVVfFtC0BjAqdppDm4oqraVgnZC3x/f9/8B9AhmTSfPn1q1HIUyc3Nzdrb22vUxo2NjUYXo7u5xUTSw1wXmcI4oUNDXUuZz2ArAzfyk20KkkZMpARa+LOk1+Ym4cPjZj+XASJ8rGtBkQXo0iXn1aPGJogHtra26ujoqI6PjxvrCMB6eXnZ20vRdQF+Dw4O2nt0LvqZgLNqTYIA4o7hgL+rq6sWVA9BrNX6+iWmMVBtbW2tUYKrus3bNzc36+TkpMnBctlRJjG5yFhVp6uj0SuD5unpqW0pVlW9aaRAPODH8fFxqyIn3TorgAAO1zeciKsAIsYF1A8LKBkPuxY6Sv/FqhI+xyG37Apf5Bnw3UnDBKbw31pTFHwk1/RGfJzbC1V1bDp+WUxS1SXRaQdy30fvKgHVLA5JmGezWZ2fn7deTXGVeEURKM91f3/f7H2C3d/zepMlOT4+bigooUsDTQjyZXiYHJ4EKPm7AlnIWQ66sIcbQU7UjGJCvTc2NnqN/hyd6/FvjiupWpK1IYUgk16c8QzwnN/Pq7okdTqdtqBsWLnY2dmpw8PDpgQMk6E8Oc3NdUi8CT0lreqcumRb0KrR/uXlpSHVFG1vb6+9w8lk0hCoLK8bbMDxXV1dtevxbk3oWywW9ccff1RVNeTZO5MoXF9ft7J8TrRboaHfZjFau7u7dXFx0ZuGCI1kpMk+Y5kU5Kenp3r37l2juyVAItERyAoms7Je1QFIWaFLND8pYR8/fmz0OP1bSfMZBqBZ0a/qnCFK1/X1dXMG7EhW37MXIasiiUhmIsmupaF3Pe7V9+iLa0/9z+SX7A8rIxl0D8ElSa0AuqpDSgXTGxsbbfgJirdBCeyyCpPqkWdj4In7MihjPp/XdDr9f1p8/1+/yMXZ2Vktl8t69+5dk42qbqqtxI9dFWhIZNAw+Y3RaNRojMfHx72pfylX5EiFLCvaqbfJEBHEHR8f9xI/fw9lvaofmGXFWnAsmEqE/fHxsVctcV3Zx7RcLpvM8r85uZSNMlp/KLeSNcDX1tZW7e/vNyAbkHZ+fl6Xl5dt4FBVNwwqnyu98T7420w+3Q87vL+/32xQVmXZrufn5wYgJDiePcZAA+euqpV+fqMFXK96reoaJkU/ABhs7fn5ea2trbXJs+ysid2SHxM5HZ8Pu7u7a7KEOWA9PT01wJZvSYoyNpcEZbFYtKKKRI2vuLu7q8vLyyajZNlnkpKdAIaWFL2+uX1VVtZch2do5ZA6/wZOsg3Ok72a7of8iz2ydUOCmoB1siCy0HF7e9sS1tRL70Fi6jhV1Wjjrm1/f7+Oj48bCO9YZAOF1jXLgZbL5Q81i+NNiaJhCSoAUDZlbgY7J+3t7Oy05naJw83NTe3t7TVU9Obmpr1gwxS8LMaQY5PdUzqO5Y8//mjGOScLJSUEWpoBVQabEjYB893dXZsmWtUhBFVd/xTniBZrVHVV1cnJSbtHzkYSmOgh4TVyOSt+DIggGm2AE/HckhvNSVdVS+jR9BKRyiqn92Yvm0S3//zzz+a4dnd3W2VE8g+JWVtbq/fv3zcDubu72+gTo9GoBe93d3ctqa96rWKsEsVvs8iSXmH65nf6HiQDWYWvqobw072kjI1Go/rw4UOTP1Q24I6AEkIn+ULBYmz1SFS9MgUY4kwq6XImcBxR/t/PyLweB6wH/RuJ5JN1OkW/kxqeKymXmchV9SceJ+WMfcrF9ng+jp3VywTb6F9Sxqu6gNnvvYPpdNqCZc35HBSU3Dm8j5zYRnbcv++7PyDRar1t2YgdaKN3Fu1bQnV8fNx0V+BSVU1mvbtse8hBGr5HVj9//tx0Kls55vN5m6AJAa/qb/30/Pxcl5eXvUEWSQvPVg6/J6NsQiZ+/Fi2k6g0COr4I+i9thc6iImS/o1O8J8AMNUD8YpKAYAV4OtZ7ezstORxMpnU7e1ts0V6ED3zbKEQkBtUxM8awuGaVDs9A/YgwbjFot+ycX9/37YzyuT8n//8Z2sRWK23r+l02qYBkzPv9uXlpT5//twYN9lqkTpKP4AzALjZbNYqdfzx5eVla/OQSAL1n5+f26AavbHirapuSww6R/7pHN1wLvsS3t7eNtCmqr+XqWMlHdWxE2TCSEkWn3vPwkdudydpy4QuWWpi44ODg9rc3Gz3sbu72+SbvUiAzXESTPZckn2T7Siq+kC1zc3Nuri46LWMeD5ra6/bYniOBoLRfQUtMXy2qO3s7DR796Owct5MPa3qJhZBAuwrktPLhgFeVTXjWtWNxd7f328TGVUXJTNJD9Nsy7BD1FBAJCUpRF44p0g43QdBzcAtaWWEu6pDLgR2jL1JsBRfUKwSlzTVpKL4nbL9zs5OQ1vS0XsGSUPKINlnICJ+Ljmn9Nvb27W7u9voCAIGCA5j9/T0VIeHhw3hWS6XDammWJ6ZQHs44U5y7b1J7NELE4FBs1lRT7/NSspGUk+gnILB7FvNgTMqfWRCgi9AM8RI9VoQ5/toitlzw+BLBBNdl6SgnQJPXGtSlskznR5OAuaUn5+fG1Uoz0ffM/FKehx7lp8hp55j0kvz3wkWuZZEWJ0nnQg0ecjGyApPVoOSHusP/UsHxB4CdAA3VdV+BpHGEOEsPWMB/vr66/CTRFBX6+vX1dVVQ/L39/fr4eGhzs7O6rfffquqqsPDw0YDFxgJWtFKBWyqi8kIoXMJgJ6fn7f3l/32mAXsctpwNgHoeXh4WPv7+z07TUf4PZURdiTlWGxAtrItw8RySS4/zx9Knsku0JmPyuA2mQBV/VaMbD0Bcg17kvg5AapqQE4w5qvplISO/fKzjJEyZlAFlNz6vamT19fXjU6a04bH49etFlD+nefk5KQ2Nzdb/+RqvW1hfKUv4zdPT0/r+Pi4bT4PmNOKpSouafrjjz96Pf5V3RZiQJmjo6PGfKmq1h4kwXAdqTvonOnTgQtnZ2d1cHDQYgBgCp3XH8v3fv78ue1znS1GfEcmtnwOdtDJyUnzfXQ3QX/soARBk/3Hf5LrrBTSC3RezxoQnFVBC8swe3nFP2JysYx3DTxSOQYI0GGf293dbcwc1+/dylmq+luaiIPFRj/KelMnZW4GyugqCQtKVZAEFYIU1UeCKBNnNFER9/b22gOWdDL8BFZQdHV11VA/zbBZ2avqgi30jCHqMuylkDCp+Pl9JpCcSzoLjjGpJBlICvgEzehC2cArqMzN0FEHndszyz5LyLJktqp66KgpWlVdoMjRqBKr4Ao6BStQ3NysGOqjarO3t9ejM3DGnreej/v7+zo5OWmTMq+urloAZAjDar1tJef+4OCgDSN6fn6dUKo35+DgoFU0OIHcs/D+/r6ur6/r5OSkBXAqfwAOsk5eOZgEKoAX9IXuAB04VfrB8CZCSo4z+QQYkTfVQMEcfeQ0DGCSvNE7elXV70vMqojAOz+Xv0tKHeAlwZ50ku4nK61Jj6FjaXOquj4ONrDq1ebYxFclNRc7oSJS1Q3YcFw2x/OEfHpvaOV3d3dtE+XV+vol+PIcVQrOzs5qPn+dAGjs/Xw+773b5XLZgj4Jo/YB9j1p1Ww1SiV9SlAEo0X1RFLEj5LB29vb1kPoc+Q4dTETtAQ5fJZejMevkyUzkZNQ0gF6TGfogKC0qnq092zDSDpZVbUJomITgeFo1LXRZBVEL3b2Uue0QwEnahq7q9fX8+fbJRLAV4mj/wv0yYfqjjgC+Jr93T4DXE+5Wq2vXwmiKAZsbW21OEo8yS+SKXrp/b28vNTZ2Vnzv3Tt5eWl7ZPJD/z6669/kZHz8/Pa3d2t8/Pz+tvf/tb2QUY/5eOSdSdWTJ8ppiMnqpv8s6RMcgj4J9v07d27dw1M5M/5k2HfouSWrzVhma9ms7KayH5cX1/XcrlsFfkEZCSKi8WisS0ODg5quVz2Yl72hx9L4LOq2j3kXraqvQnIVlWzbfQu/TSbwm6xC1q7Mtm8urrqxQXf83pTokiABZKC/42Njfr48WNNJpMWTDCiSRXJB1lVLbC7vb1tRi4pb4LXqmpISqL8h4eHrSIna+cEBcQmi2UglnS65Gi7Vkms3seqPmU0S9yplFBQjpFwuR/XAC3Mxl/OhAOHOqmCUtAMCFKhKT4H6/kzIJxdJpzu0/2glyZdyb+HqFIquoqEgB/lsapa0r6/v18nJyct6eQA8dAh46v19kXXqqoFFgxn0q2AKbu7uz0dHI1GTfeMX8ca4Ej+/PPPevfuXW8ENDmTROZUNQFbNrXf3d215HGx6DYBB/jQ56R+VlXTH7JN7iaTSW+SsXtIICaBoqx4WJLarABWdbSaTCx9lqPJQI1DSJponl/A4V0J/IFqbNPwvXLadHB/f782NjYaaMYOson0m966ZkHo58+f6/T09C9TFNF9POvsjV6tr1+Alj///LOOjo7q9va29ZGq+q2trdXt7W2bBpg+hO8F6iSrpKp68spfkik+LBO+DA4zoHU+/c78fAImmXRaKc+OnyCJe+GLM7ms+mslPyl8fKdzSNCen5/blMHcbiKTUgF4VQeiZlzCJ7JfCbK4pvR5mdCyp4AV02qn02kDj7x7ifj6+nrd3t72esIkvY+Pj3V+fl5///vfe/1wOfX0+Pi4PWfnS3rvan394i/YPj7Uc5b4sJ3JKrm7u2tVQHK4vr5ev//+ext8Q59U8VUmbfugQKLN6uTkpKq6AgJwSWwm9kofm2Dn9fV1A0MyvnaPZEviZCuaZOXxY9nKQhfEvFnIoENZAazqto2QdFVVj8kzGo3alPacSOzf2ae4t7dXs9msAV30M1mFeZ2ez3Q6bQyjZDsA0l03e8eOmUAMBJBAmqi8s7PT6PJJiU3a+o/CnHtToigIE/jlNCec/vPz83r//n3bPNeLINCCMMnJZDKpw8PDhqQq2WY/TZaQq14NvWRke3u7Hh8f20uSbEEybm5u6vT0tJ3LtVAYxyToXn6imRk8crrumyATqKqOCpqGG62lqpv4JpnMJDSRkaxIpmOF3BBGQWVSXzNJdW/T6bRHn/V8HUegC3nBRZ/NZo2TbeNfJXjPipMUhKoaZyC9vr7ettSAgHqeAp/Vetuil9BQ9BXvglxWvSJcx8fHVVVNfiDpVV1FCqAhqJlMJm3PJLRxumbwDUOcVXgySbZ9lxxmIEqm6KOqNHBpmOwNQRIyldXHrExk9YHupvxlQjj8vcDXs/BskspGvzOQTlog4IedygrNaDRqw3f8SWcKzHKcqldUVbLhHtfWXgcs5D6tesnJwMnJSZOXl5eXRoESGH/69KkODw+bjV6tt6319fVmO+fzeZ2enrZ3KRhh/w1f8N7p3/7+fq/P0d56ZAELJCvp6JumrjqmKgawDoCwXC7r48ePbeN3gWRV156Rtj4Rdv63qgODBYyZlPkZ28M+JWNnCCJlcka35vN5o5b5PoA3AUhJsEmWgE+VBP4Mq0jQmIM7np+f2z6s4gVBLx9Jj6qqlzC4/wRyq7phGJL1zc3NOjs7axUbNmKxeG2H0dMmqaiqFoCufOjbFzuuiojyLcb0fk9PTxuN8ubmprHbyAU9Xi6X9dNPP/XivbTRKmLL5bL1CkuCxMn/8R//Uf/6r//aZN7sD0y+qmrXCBg0IVcLFd3kSxM8WSxet/rY2tpqrL68Rr7QBvP/7b/9twZUiL/ZAjqrur63t9f0AdCJAeT8kinJsEJD0mpV/8So3k0+82TtqWLmwLakgOaOC1lQcR4U8yyG8Mvopo+Pj7W3t9fskHucTCZtNwCDHcX3P8J6c90TGsBASTxymwTVsqSo5IRBgaDJpnlsxljz6vb2dt3c3LT/o3Zllauq6390Tlk9ZeHMkqYjSZF4JT1H4Jp0NIErR6pykkldIqC5TJxSOcjqIyOBhkmgBIiuPRMz156DAhIpEgBLkNNJUTKBh+8xehTeM/YuCTxDSdlVIj99+lTz+bw+fPhQnz59ahSOs7OzHjUCcpMoz/n5+Q+jRN/zgmxltVvghCIjeXr37l2jP0Hpbm5uelUKTlIyKGnk7AQ3T09P9dNPPzXUjM5X9YMlvbJQ0Kpq1yT4rOp6fAR8EqX8me8kgKPnkkwul8s2fIK+cr5ZkUnQJCskmfhlgmh5Bol+ZmKYFQvH9zOOu6rbqocN82ycY3jOpK56P4vFoudM2TD2OJFnjAv0P8n0zc1No+RhZAiqfxTazPe89OGTP8GYJbhUzRX8sZk7Ozs9EEEVmV1Gn5zNZvX+/fseBZR+Oe/T01Odn5/3KGNJa1TZlExmRZktAN7wMeSbHyF/gBrUTkNg0PBU13LyIL1NgIR/9Cwl2bu7u+05umfXlFUBAZ3WGYG0xN1QEYFh+uPNzc02WMQzq+po4Ya1sbXs0tpat/3TZDJpQzGqqr1//hVtOIf3sHviHYE3m/z8/Nybzrpab1+oyPbaW19fb/s/A/YAjoYxksPpdFr/+Mc/mg3li8VA/Ii2C7os6VEFpGN3d3fNHqhAPj4+tom9kpkEapKJpn3AtZBNxRf+GcDLNqma01Vx+a+//tqOB6BJPfO9vb29Rh9dLpdtb8nsT8zEO+N6MznS//u8BE/PYVJI+aiNjY3a39/v+dqqajEnZod3uFwue8OJ0scO28+SzTRkJqRdZN+qXmMcRawfYb156qmJW5A3e6NJ5N69e9defk7H/FL27+98oImsC2psyyFI3d3d7SGj2Xzqd7k4CIadQb29va3ffvutUWyqqqEnFNlxs5qXykYIXSvFkrwl1UygJuidz/sbDxM0z43D8xkBgaBwmLxy1GisJqahdwrwszE6k0nTTd0DOo/gJAP7fFYSE5z9nZ2dOj09rel0Wjc3N3V7e1vj8bj++3//7/X09FT/43/8j6agruHnn39eoaHfYKnGQyX11Xj3f/zxRw9EOTk5aQMWBHFkNikcp6en9fLy0vY42t/fb7rNcaHO5VRV+iOA9I4Fpdn3I4ETyA1/l4idQM3vVehUDPTUJmjDiGe1M/XBSsCH7nMSfp/6r6ogqUywiA67VnZE8Jj66nmzJcOElWMbVk8T/BHIsqOOz5Hnd6tehwh4fjmwBEruWpP2vlpfvyREAiHItaQ/6YeqFhKD3JqqqgNtyQG9Pzg46PWFA07olWnexr1D2fXukWtVaj4vUfPsk4X2sxnQfLoscX16emrgIX8o6FM9ywp8Vify2Dksg87kdaZuZrKYflZgKQFbLBa9d5O6S+/ZjaTRqYSi7wmKBa+SOLMcgKXezfn5ea+lhg5vb2+3pD19tfeDzfHy8lIHBwe1t7fX24Zgtb5+kbkEFefzeW8vcP2FCcKRr7W1tfrb3/7W5M/79nsgKB8IaDX9WKLn30dHRw30Sd+UE8fH43GzE4+Pj/Xv//7vdXNz8xegczab1Z9//tn85Hz+unWH2Nz9JPg4pJQmsPz4+NgmqLq2LLhgkvFpQOEsuPBTjk+P3Zd/063MHzyryWTSYt4sHlW92ojz8/PeFO+qagAMkEjSnS1fnp/3qk+UndbPnXaQHvPv7i/3if7e15uucjJ57UGU5XNsXvrLy0tdXV21TZs5gY2NjXr//n1DGgSIOSp/b2+vrq+vazabtZfE4Ks6+RkF83vf+RLymIaawU1BhWIkH5nyvby8tPI/RVelSESIAidS6fOQ3iFN1Oehh9DLDPLsB5f3zIAR4ExWJZianVUg0QLQEj2XpNlCgBghaJUhGIYBMRyCEu9GAp/vSS/F5uZmTafT+pd/+Zcaj8f197//vRd0JgV4td62PHuUq/Pz86qqpntQOOjoeDxue0Q9PT3VyclJbyS7IChlX+9GDqACokDIBVUJpJAJvVB0XxWTbKaTFuRVdXKSzigTQfaoqlqg5RxZhRw6C/bA752jqqtOZIXP95NK7nqS1p2fZQcyWc5EMymmbITkfjhNMu0UWh4wKVFhy/WouHDAAh56S7czMEV707OyWm9btkxSrSNHhsA9Pj7Wp0+fGoXq/Py87u/vGxoN9ZaILRaLVqXIqd8nJyd1cXHRk229U2RJtQEwUFU9Bgp/lpM3JUCodeyDvi2ybSgSHU36V1a57Ttn1Hy2TtCBZL4ApySj/F1S5Xy2qhqgpAKiSsTHSe6SiqfiroLBpwsG2Vg+VwAsZqjqgBcJcdoT50PpZlfFAexlAsXj8bg+fvxYs9ms9Y2zlVltXK23r4w9xbtJpbRSbiQqAHOyRA4zKfqP//iPqqrWK6yipmBCRwwzNPwQ2GDAXAITyRJYLBb17t273twIMePGxkadnp62mNjUfnqvXzL7LPkKsl7VgaV8Q1UX77unqn4fPz013TeTKHaM75XE5XAcg3hQx8k8oGt/f7+1PylU0c3Dw8P2HnKLD+1r7BHbI15K6rlnVFWN3Sg/qKrG0pvNZg10U2DJ4ZU/Qqz7ph7Fqo7uhTqqksWoLZfLNvVPBdCIXoEbQ88JyMQlNMkLVrUiDBwVjrYsX+BHyDgFAzMIVlXX70eIGNsMjgmOY0IRsv8OAmNdXV21pljBXVVHvcvqSFU1hwVddJ2eEwOU/ZTpyLM/Irnvjq+ET1AdR3mdUXF+SWUiuU9PT22kOxTMhEfOUUOxJGOxWLTJfQKTjx8/1s8//9x6Qgw9QrFICvJqff3Shyrw+Pnnn1vCQVafnp7alGBVo6enp7ahdg4wgfRXvTrQ3d3dRlc8ODhoYANdeX5+bhVowS7HQe9Uv7KxG0PAUp3MyoDgLKtrVV3fUt4nGnxW9H02+yQTpcyVFdC0LVmVEPxlNSOviZNbLpcNuOIUE/Rh4wT86TwzEHBu/85n4DueNaoce+uaOF+UYfQ215jAlwTUvreS7dX6+vXnn382PdMWoWqg/+3o6KhVBFV8q6pNos7AiW0/OTnpVeK8S//nLwGS0G5ADB9Q1QEGBwcHTUcTEOQDM/jkCxeLRbsuPoS+0j1VOL5edf35+bkuLi7q3bt3LUCrqh6IxIelfxIUVlVv2jb/ZtGX29vbHpUMyJJT3dkaS8VR9VfSym963nn/jks3Hx8f6/r6ujFw0lZkrzK9v7m5abHQdDptWxEcHh7W09NTSyyurq7+Ag6t1tcvA6bG49f5DLu7u41dNay0JbCg4j+ZTBpl1ZZk9GRjo9tzXAIFTDDs7+npqQ3QSRASaJigTlW1yrz4Vaxm/1zJpipixoLuk7/HxHG9/Ml0Ou2BESmvKmpHR0e9mDIrb+JXfbxibombWBHlGoC0trbWtudSNeffPAMVTgki/5b9iK4FoJZMmnwPEkQ9+yimVd2WHfk+quovMcH6+nqjC/PJqreO/72vN1UUGSaUSRnyp0+fGuqRG+kOqZJexu3tba/kq68K7UmgkkFSfnY+nzdk7eHhoS4vL5vhv76+roeHhzYIQMOxlUlgbhWhApOouuufTqdNWFFW7HeFNnZ5edmUmMBw6M6bo4kFAtBEwk3QGY+c+ioQoBBoOJwwJEzC7nhVXZ+TKYmUN4NKjs951tbWWrVIIADVsseX5FLlkGI4F6f366+/tuEiufGqAPRHmQb1va8M/gQzxjILIBl7YAbkbLFYtMFFSROhi2iMEsikVNJVhpDhd03ZRyGBTLmTFHJqWf0CbtABCH0Ge0k9V6mng4YzZZDpvun6lyqUSZPNJFFgncfKvoXc2y7PQeY9f9+nj5xb3pfnRU+TkcApCU7X1tbq5uamHUNAnlURyZ9nCEySSLBJAlAVI2DRar1t6fMWPLCpnrVgJAcKeZdJ3ayq9r52d3d7lcjlctmAElU7Q6oEi+lXsGOG/fvAArrB9/IRdDxBYj7T9bsnIKMEEOLOT6mQAzIASxI2+lXV+ZysTLIBVgbN9IevRNXkuzOAA9AAyDKh59v0NFZVCyhzarBYiM6aJFlVdXx83O59PB739ofzftgF8YJ3LvDFkprP522Q0ZA+v1pfv1KezNEgv+wlGVZ1VjwAhFdVm3L66dOn2t7ebsnW2dlZnZycNFbW09NT/c//+T8bWFjVTS6VDGKkJCDrswlIVFWjk2KBrK+vt+086LgKPDlUYfd9exfOZrNaLpc9/5CAp39nzx+9JMdWgr7yiKpukM6HDx+qqtoUaD4OO853xCPi5JeX195601hRZ+mk5wQcU12kd+xsFl7Eu/IUzxzoxWYZeEQ+xEAKZFnQ8Ux+hPVm6qkgTKJjWmnVa2Igy86yu39DAaEF0+m0DbYg0NDJpGcmypfOkwAZiS1gm8/nrWeS0DhWJk7JgybgHGVypSmHCZLoe5w89PXo6Kg3thz6lIMGkqrDqXh26DzZB0HIsroisIXCZv/JbDZrDhGadHNz085DmJXCoZlJFfA5Dd0qgpDbREQY1PF43BA02y2MRqNWsZAcS9rv7u7aRtAS9dX6Nov8SyL0rUHsBE6qfZ6/QHQ0GtWnT5+a0dP3QIahkpyo3lTHZFzpFboXIEdF0qh3CWlV16eYPQiC1KzyCfz8nE1gc7AMBMp0OIdl5OezEu/8nJKfJXKYiWZWJAWLmeCxTX6ftLqqbjQ+dJqOZkUo/+1ZZlUyhxQtl6+DAxaLRXOwroedUM3Rs2aE+sbGRv3zn/+s8Xhcp6enrRK6t7dXV1dXPwQa+j0vW16kbHz+/LmBDJeXl/WPf/yjPfeHh4f693//9yarkgt+LWlaBwcHraIOyKvqqmGOMUz2BIxVHXsGWAiYSJaJpJUsZG8hfUkAiewlsJHBrcDSPegFe3h4qIuLixZk0XHUeP5/2IbhPvTIo+2hmgGaPSuB7vr6euvBdowMCv0bgwYAvrGx0Y43mby2YABY3bNqaVY91tbW2hYJwADn+fjxY+/9SCRVLgWvo9HrdNwE5lbrbStBeH26aIRo4KPRqAGtf/zxR2PDLRaLBpCTuZxI+/Hjx2Z3yX9OgOcvkwXz+PjYBjomoMhvGKSWSY5kVyWLzIxGowYcJXWTTPM94nHXLUakQ+wB+c7EMGNsfosu8D1V3XTQzc3NxqRwLtcMtPFcPFO2wO4KSUnPQk7qLvuUMc/19XWLUR1XDO34CXZ5/p7ddDqt/f399p7FEWIfQ/Xo7Y8y3f9NiSJ+8ObmZh0cHDRjpyGegKhoLJfL+v3332s0GrWXSTGqqiGqnAXFGQZdVV2gxjmiuClPU0yJTAZCXkyi/FWdM0suN+H3/+QtM+gSPImPyU6Jxqq6ZRBeVc0hZPldJc8z5MQdkwFisFR7DA86Pj5uyFQmoZLNDE6MIeYgKY5rE6Ccn5832tr6+nq9e/eu9dF4LtfX121zVAZgPp/X77//3t6noScSgPF43CpcBwcHvfe/Wm9fjCVKsD7CDx8+NAdD7sgbeUrZ17emElhVDSnTC7C9vf0XqmdWPxhm5/MzDgkgpNIsEVVxo6tDZI4ckXNoOn3hNJJmmZQYspgobCZ9+e9hX56fJbLJQWZVxHnYFveVx82m/Tx2JoGeOf0GAKW9qaq6vLzsUXJTv5PShj4MtGJ/gHym16IkswlZrVmtr190TkCmIpiUbrQooOEvv/zS6OSj0etkxfH4dWjY9fV1TydUALw/Poo/TPqq76U/ms1mrbqZNGaAoUoXmyGASoCEzJBBfhuw9PDw0CphVdUAEsGkYTuj0agODg6aT3cMLSBV1auK8O9V1WwCO5XXAyySeLF5VdXz835nuigdYGN8fmNjo02Qfn5+bsCshHxjo9srOuMZIKn7IB9ra2v197//vbdVDcrd//yf/7P++c9/tphnbW2tbaXxo/Q/fe9LlYgeiRdVktlwlfyff/65p09ittlsVtPptCWbgIFk8YgPz87Omg6ZUprx1/HxcWO0effiQnba/7Mf+ObmphUWqqolhckIJJN0SqJJFjMhBOzSq6pOH9inpIz7uUSU3Msj6IBkuap6MUHaFC1q0+m0xZtiSGAJPaDPKvnAobwX58Ty41v1/5qjghXAfiTIrEAi1vCMNzc368OHD+25q85mS9r3vN6UKELvc0+lqmqUU5NJvfD5fF4//fRTK3lLlvwuR10nMk/pEo33f9VHpXCCkOi8hEUAJygTuGq6z4qKaukwSHWtFO/p6ak+f/7cPiuYM1I+A8Wqakqbw3Uc4+rqqh2/6jWhMxlJc79ry8SPQ1fVRfUh6IvF656JqnRQsKQE5XkpcFXX/K8a6bkwDjk6OBOD8Xhc5+fndXl5WWdnZ82AeQcMBroM1JtRWq1vswT+Arm9vb1WKUQ/8e5eXl4a+qmf14CNl5eXNu5d4pi0VlVs+z0leyCDGMmSdz8ajRoCpxlfkEp3IXB0hzOTmCUdFgsgK4NVHVVbIFvV70+s6hLfZC9k9c//ky7C6WaymdQ6wItgwL9RhdJuOnZWWzw7NtC9OJeAuKrb/8kz5dxdI7vkc/kuc3pk0n/zmZiGe3V1VbPZrPXWrdbXLzLJB7DNepT4SLafTR+Pux5CwCMmy3g8rj///LMmk9fJehsbG3VyctISqmTGJBBL5sjdbDZr35XUJJ1NcCp4pQOolY6b1feqan5RW4pz0Qn/zmpg6r1j0oekaOqXJ+N0jl9JXUm6rwo8fVksFnVzc9OesfvmM7Ovuarbzsm9YuwAXxLcfXx8bICpGEZCzMalvgNiPYukjh8eHtZPP/1UVd2+x+jrHz9+/GGobd/z0v87n89b0nB7e9sSo7W1tVY1lhReX183JhXmheQOGGjwCRvL12AHzOfdUCtx5dXVVdM7YGAClUAPf4A6ACB6gBarT3lYPCGv/DY9SUCFTkqyyKzKmuo3v6J6lkO2JFkJkgCIq6oBIpmkmqqOgu3essopPqeP7FMm3dmvCLDjkxNYzd7Dz58/18vLS7NdWVjBfpCoTqfT9szRitmtjIV+BB/6piYTgaJEIocizOfz9vIZQ47q6uqqoZDZE5doo5dQ9dctMkwgy4qBRcAZUlOGsqKYKJApR4TRNWWfAeHL3xEyDo/Dl2QNFUewJyFVUYC2GBSQiiXpSwoggU4EI+k8kkY/E0xSKD8nuJ7JYrHoTXyTtEJ7R6NRXV1dtc2FswrCaApMBKOJCEFh0WkeHx9bBUqFgrxI4lfr7cvY+zSuAkIOZH9/vw1WQJfe3d2t6+vrppc7Ozt1c3PT3ldVtd8neslwAjIySco+i8lkUh8+fKhffvml6Td9Z0tUGwS/CRI5h/+ngxtSXOhw2owMQn3Xz5NqlrKe58zguqrbrmJIkfU79+Y6JGNJQWU3k24DnBHM0hE2LdkOACWBC4ekcszmClDzHg8ODqqqC0rZcnQd7x/yqydqtd62RqNRXVxctOe6XC579MPd3d368OFDm0zI1wmSBEjGtN/d3TUUXJDHR6MVHxwc9IAYPgQY8PHjxzo+Pq6PHz/Wv/zLvzT5Sf3ltwQ/zlVVrdKQ1bEEc6qqp0P6iLOtg/7QST/7Es3bNaGv0oUEOVyr/XoTsDG90s+AnwmCVnXJumngmViyq84lmXd9zuF9sG07Oztt+Ez6yel02p6x5F4MksAVcNV9JZPK9OrVetuSpEis+DsVegUDbVhsNjbGy8vr3qBiYO+JbJIhSb64z3v0+99//71Nin9+fm5DcJ6fn5u+f/jwoSVuemmT0Ze07qpOtsiUPVnJbrZ6VfX38KUbtsSQSGP3ZZ+eZ8P/qeiL+9I3D20E+1VVPdAYuGKLmYwjFJDyHapcigfkAvRWEebh4aEVcrwnMX/OAVFk8d2ko56fn7f/s1N/+9vfajKZtJwk2Y3f+3pToqiyJrHzciRYQ4FTqUiKlGAI+kEgCPKQ+qnULelkWAWpgsbZbNZ6Ev2pqmb8CUVVV0ETVAme0vBDKvU6Pj09tSBcFSORHcciYIIvVFJCYmKr4Nv9SJirOqSwqt8D6DknHbCqWlDh3sbjro+Foaiq9gzv7+97G6EyKre3t61CtLm5WUdHR7VcLlsCyPG6Lu/X9QhUVaqqqk1/guBub283aq2EeDKZ1P/4H/9jhYZ+g7Wzs1P/+Mc/6l//9V/r4OCgrq6uGo8eeuY5m5Q4TCKm02lv+IWKx2QyaZtC397e1unpaW8QBWADullVbTrf1tZWvXv3rsmTnwNtql7pGe/evWs2IClykkZ2Aa3GCPoEaQxIcq9JoxFYVnVDaThfNkniV9X17CYw4+dZxaQXACO/z2PRN7YI0ut4+Sdput4Deq8g0TvL76vy5yAhwUr2jQGEvMv7+/uGgEPBTS6ez+etQrVab1sJmO3v7zcqkgEY+/v79euvv9Z8Pq/37983EFDwJ/giH94/cFCSR7ewfxIRF5DOZrO6vLysX375paqqfv3116qqFiAlC0Ryl1V9Aacgmg5mRSQrejnkws/T92VAS4eGveuSKf6bXUjAp6ranmk+I1kUtJnuKH5I0IdfUunxDNbX1+vjx49tVgEAbrFYtL2Cq7pBQIJJ9D/tOoeHhy0u8DwllBJGuu55PDw81NHRUW8f3I2N16nVOzs77Vir9fYFbEw7ngkWfR2NRq2lZzwe18XFRf3yyy/1n//5n60SeXp62vZH3N/fb2BBJnMXFxctkaIfVa/6KAFFn1ZlVKhA4U5mHv1cX19vVO3ZbNZorWJXcWVV5ycAvOLSqmq2wHXlfI5M+rK30Mree/ZDQpstUv5UdT734uKi9vb22tDIpO+KObARPJthLE+3sazMS1AlxoQ0SyTZgBLh9PmeWV6v9yB+wHYUTwMM7Arw/3rqKSPtARmBjx6jOgSJqXoNoiQaXpCRvFVdk6uXqk/GflGLxaI+f/7cK+MaiAFdQI1U2aNc0ADoHseRE09dY1X1qC7b29tN2SWkSaH1OckY9NG1ez4XFxe1s7NTh4eHLbF2nTY/zf0mk0ZncqufJbLCoRHY3MBZdZAD43Q4TZx51wwROjg4qOvr60YjYsh6AjQe94wT1JMx5Vx///33BgL84x//qPn8dWjDp0+f2nPy/JfLZb17927l6L7Benl5qb/97W+NwnJ8fFyHh4etep374WUVP6sHo9Go7dVV9epEptNpnZ6eNgqcYAatJCmYKgyZtHBkHC5wpapakobyBiUU9GalQs/PxsZGa+7nHAEwEsbsARxWJQWYmZRmcpcVyKx2J/UzaaauoapL2rLS6Hf0zb05XlYbPbNM4tfX1xvYIoCk0xy1d+l8s9msUYmruv0eJ5Nu2xzfPT4+bs8oQS5JOce8Wm9bgBgVXQFiVhLQpCQfhqxomUjqFDs6nHBLz7PtQYAnoMHs4UvIEpnDJjAMi76mziR1PPdfzf7EyWTS7A/dyupn+pnUN/qblUfABfuBBiu4FcCqRJB1ILLEDqgLxKW/bMTV1VXbwse+l09PT23fQ7qo90rFFw3XNaefq+pXTXJDbu9Sr2rS887Pz+vx8bGurq7q6OiovVMBsu+KuVbrbUufKVn1zlSFgRvet89tbm7W5eVlra+v13/+5382OdAWIMn5z//8z9Y6JFmR7P/22281Ho/r8vKy9SLTDTMH9vb2er7q4uKiAQXAxU+fPtVoNGr9d9lTTOYyaeKLko1GN5LJhybqWv/8888WtycdNos5ijXZZpTAruTOz33W1Fh2hZ46nj3GJWMZf2bFr6qLYeg5HZYj0KXs81VUMQFWIpxsKMMi2YcPHz7U+fl5j17MFtzd3fV6Xr/n9aaKIsQLfeb4+LgJKAQD8vb8/FyXl5eN121vGuheBim5Ca6+Q0nUv/3bvzWDnvt8oVyiRlIAikSJTYtyPsLOWHtpSSvxf02q0Mnd3d1GB+KoKaHjeAaey7t371riiiKA0mraE5SX8jrecCiN55Wj0yWeqcQEvqrf2+E8EBrnyUD28PCwBfqQUQMSfJ+TQttxnRQDYqqafHZ21hLU8/PzNihluXydgrm/v1+np6erRPEbLA5E1WI+n9fl5WX97W9/q/39/ebQoF4oLZbJtbnhNKDn8fGxTUwb0kcymRKoffz4sSUd9Eegl4mVwNX53UcGrfRe8pqVQUhfUlWrOjpYVUebca0ZcJNzx3KcHJiRFcQMHoYUn6TjJrLqO3RZJc+Ex6TCZ59T0nCS/poor2DcszUExTNwDEGHgBlqnAj0H3/8UXt7e73R79vb242psFpvW5694TB80mg0amPh2XzAH7kHkkg8UEITuMkAjSxVVQ+kJCeux/vPVgHsgRx6IVj+Elpf9VdWkOPSFQAhX89Hkd+qjqqdcu2Z2IBcj7MqiftTJaQ7qrWeg+vynLMyX9XNOWA/6Y6q4tAvZr+UZ8/Gei6z2ax9z8TLl5eX9mzH43F9+PChMYwE6mIT7BybeF9dXbXKP4Ag3/lqfZuFbSFZsP8lGrd4Uwx1eXn5l33AJTWXl5dNR3Z2dur09LRtNXd6etres6q97eP4TbIKeNACon0pWXOLxaINgKJf/AebU1Wtqp6JVFW3IT2ae1b+3Dff8f79+17S4z4SOEm2jUSWH8MsEi9ky4hrYWuGDLvUcbbGMxYDZ8sZn4xVIHEesid+++23Oj09bbaXfWSztYNIKBMMq3plhBweHrZ+76QaPz4+tnj/e19vShQ/fvxYR0dHjT6TfTxVXSADwTei34O8uLio9+/ft6Qph6Wg1DCUKGk54KKqGnpIiLxo6F4O2klnwbgT7KwmSgopljHIjr23t9e+Qxm8fN/NgJTzzmsnpFnxIICc6Pr6ejuXz0OXh8hqIhuuM69LwG/bDc8798ORsHpn6EpJnTHQ4unpqbdnk+f76dOnluRRBgG5oF5z98vLS3PSNrDVk5MbS6/W169MVlT5UQiTYmXEd26ODUFPinkGmigsiVSSKZU7iadgCM3NZxnXpK1lJauq29wbskgHqrpx/eQsHcmwsibgzGqBY+R9DVeim19C/zgrti8pecPj57GgnJI2PcLZuzKktegTxLzg9DPx9d7/j//j/6j379/X5uZmG+sNFcakIAeSUu/Hfb5//7453Q8fPtS7d+8afSkBhdX6uoWZwpfQCXZd8oDGn/1/qpB8harx9vZ2o4InG4XeZyBJTun8crms8/Pz2tvba+BQboGT/tIAHXQswRNZSoZIAiNZNeQXkk0ANM1gMEEdgE5WD8UJFruXVO+kkgsWq6r1Dd3e3rZqYfbMV1XTTe/Me1PNGfr8ZAaoPhh4ApyzZU9VNQqsIUaAb7bt+Pi4BbDs9Hw+b9R2gDE7MGxHWa2vX/rvxCT8Ws7jyH2fVZrpgp7xZJXs7++3acK2NcskQ/JS1fXsYx6sra21XsKbm5v69ddf6+LiosmbNg4grgpZyv76+nqz37mVi2KOuFNrVAJJ/B15HLJkLAyApKBjIyRzSYLtHIBN18Au0f20QeLn5XLZii6SW6CmZExVlN2TfNPJ9M3sRzIOqqo+fPjQiic+w4eLS3Z3d9tEU740E//JZFLX19e9bcC+9/WmRBGVEQWTE3h8fN1gVhKZe/ZBJB4fH+vs7KyhcxwFdASNxEtD2dBLMxz2QECurq56mxhztNfX1/Xzzz+3HqaqboNNSqpiAdVNGmeex73mPkfDYPXu7q6hvhSUs6+qJiSEvKp6TjaVIpNYDn4yee1NhDYJ1DNhdCwKt1gsGi1In8p0Oq2qDkEiwNBYyLPKzng8bn2Frh3K4p1DS09OTnqJwHw+bxNxIUzX19e1WCzq5OSkql6NrF7I1Xr70ssiGRyPX3vbDg8PWwWRseUEqqolf7lZPLRSAmlwxnj8us2KXgG6xCHQccEL+jcqDbSNHkpKGWm9AgJarAPgkXNCc30ODSh7fKv6A26Wy2UDcfSXfKnyV1Xtfjg/zutLVNU8flX1ekAcH9UuE1DO0rvhfBxXj7QeDUAUcOz6+roODg7q8fGx/v73vzcbms+Mc3Y+z3x4D1ndmkxeNyaWvApQV+tti88hF2hL2gGqOppxVqz//PPP+uWXXxoApH9eJUryUlWNejpE4tOm0xM+g2+isymr9HGYyGmxYBOyPUSSJ7iitxlI+jl0PiuPdM35snqOsimBTJqrlVVA8UhWaz2jBKqcX++f9+L50M1k49BHx0jAy7N1D5ubm3Vzc9OuAxi9tbXV2Eu///57/fzzzz2dPT8/r3fv3rV4CLMj5Yk/Xq23r2RSoUxru1DVU+GzXl5eant7u0dZzUq9WFmc6TySOn4ze+JOT09rPp+3amZV1S+//FLj8bixtshPxqTJjnNufhVoJK7NzeezQpbxOMo7Wa6qNq343bt3vURQAs23ix2S5fP8/LqnqFiV/k6n0+Zr+GGU6uVy2Ybcud6qzs/+8ccfdXR01HIT15zx9JAS6/hAmJeXl97zAHpLnMUOALv19ddBnc4pAZ3P5z1QCPPxn//85w9TEHlToihhQkU8Pj5ugYQSskRKFSErYVXVKhBeBKPPqUD2NHVvbW3V9fV1awSFInI2wxece/SpMHIoWS7meChPVRe0edHuhdAlMiHYpPxQYhU8QRdEN3utOK5MQgk+ZNUzg4q6FijF58+fG5WVEgsgq7r9oCTO3hkHJ4Dc2dlp1VQJL0SMQmWyCNVVpXTdP/30U1MMCGj2vQwRWM9I5XM4tGC1vm69vLy0ShB9Oj09bcMyBCQ+m7RRyaL3k3ufQhOvrq7q/fv3zWnQZUsgxbh//vy5UWEyMMzeW1VrxxHgcngcSVXX08yZ0slMpOiPqk0mf1BODjSTu6pufHfSPHMlddY1CnqHk03T/iWzIPUdwMMO0H/Xp7JBX4aBLwAAYJd02Z2dnbYhMQeeeiuYMbwjneb9/X2dn5/X+/fv6/r6ug0uWq23LXR89nd/f78NloE8n52dtWSSbB0cHLT2DeBA0lP39vbq7u6u0aaycpgDpwQ8WRlIQFMSRU5UsFU0ABCAGsfwPQlj/jyZQY4BYKUX9AhQm5Vv3xnqTFW/j4gfr6peoKkXX8LpvAJrbS2q/KPRa2/3crlsdiwrG+7Ru/F8+Nas9qpE0UE2cHt7u46Pj9vzmUwmLbh0bd6Tym4mG0PAQZyxWm9f9IrcfPjwof7t3/6t+aIEPdAQ6e3Ozk6jCetfFWORR71xtpViD5KlgpWWbUuoj1V9JkxO0QUEDwFc+nx1ddUDCsltUiSxgcTNw3YNAGZuMJ8FhKquPWK5XDaQuKprjQFW576QfJRnwj9lcidOxKqTvP3888/t+nwP4GY4oxjIsdbX11v8kj2hCUAnUymTX4N03BebxgezHQYXsfWKON/7ehMkzMjqM/NgPFTj9CUyBtssFovWn6jBXKDiD4fGGaAoVlWjS0o2cvT0yclJHR4etiTml19+aYlgJlkSuapuKAYFIgB+n0kvBavqEruqagKMjkmRDY8gcEnLu7+/r9vb24bW5NSoPHZVP2EUcF5eXtbFxUVT5HSenC1lsS+eez84OGiVUtUmSeT+/n5TBu83kwiBqYlZlHVtba0ZQYkoRzefz9skKDRTgaZE4ktI1Gq9bU0mk6ZjZDxpZCcnJy2ZE5CQ26xOOxZHdXt7W5PJpFUhPn36VFXVQyzn83nbNNwWKhJDPRlkWgU7A8GqLghLuloCJf4vSIP+0jcO1HfTeWSlP52Q58AxCiQ5iqS35rH9WxN9JtwclZXVEU6QHrAzrs/zyHMP0egE1lSBUPo482QSvLy8tGZ+SW3SHV13VTW0XD8O0Gc1LOPti76gcRq+UNVVAodDJlJe2HOJFl/2v/7X/6qNjY1G8x/61gQ8MHYysUMpr+rAEksQdHd31/rpciiGanjSJyWJ6UsBJgnMJM0a8MlXJj366uqqga1ode5/b2+vDQypqvZsMRQEhGKWqmpBO71ju/78889mN/k5lD5VO8/TMejY7e1tz34eHx83m5n70jnf+fl5Y/iwp0dHR82WoLCenZ01sPrp6anu7u7an+zF0gu2Wm9bW1tbDRj59OlTb7bG9fV1e4/n5+cNWDDBmL2U1HvvYh3/V7SQSEguq6rXkiWRM1wRoCGmlLSRc0Ubid/Hjx/rH//4R4snDw8P28ReNj0TQD5TXJpAasZrVR3lOinehrvQI74c09BztNE9//PPf/6zPn782GzG/f19j5lY1e0bnO1P+Rn2IAfziD/m83kb8JVxKMDp3bt3Te8c0/u8vr5ufaZ+L2bPYo+CUdJyF4tF7e3t1dHRURuO+SOsNw+z0YvgRVxdXdXZ2Vn7nSxbBq9kDvmbTCZNwaqqF5yqoiUv2jFVAVDaVKuUsCEXAiZC7AVyEILBqldBPzg46E1a1UsgcEwKnOtzr55FIo1QDj8ToC+Xy79wlFFWMmh3jslk0qMKVlUzCBLE/4oC55nqVXIP2aTsu94BJXx+fq6jo6NGkRHQQ8H8DEq6vr7enFui17k3mB7GDG4SWc6K82q9bdHN3377rfb39+v8/Lwmk0kDNBhZQAzZy6EnKgZAGRUvgetsNquzs7NexY++vby81M3NTW1svG7jAGUdUl1R6ciLn5v8x2nRJ0F13mdWGIZo4jBBJF+pm5l0qTZkoDesSiY6S+/IuM9nQskhJSWPU8ngGLrqXOnwONasmrpnwJnrnM/nbRQ7B42WD1WVvELLyQYklI3OZ22D96wcr9bXLb1ukHDoPl9DzqD1WWlmg7Myhb74888/t4p2zg9gZ8mmd572mk9LJD7pn+PxuMeYGQaUeUw2BbC7XC7bpFDJriDWvVfVX/Q1A8Kbm5uaz+d1dnbWnmEGy/y78yegBJDk0wXIeT4+znWura21xE2fp8DQM6rqZiDwq8fHxzUajRot/+XlpT5//twAUzp7eXlZZ2dnjX10f39fP/30U4t9HO/du3d1fX1dh4eHdXl52XrWfM615jCO1Xr7ykrZ4+NjnZycNPuIPri7u1tHR0eNWkmOMdQeHh4aI6eqm4khbsXYYet3d3drOp32qvbs7WKxaKBB+o8cakSXq7p9rNfX1+vnn3+u9+/f90BOLSlbW1t1dHTUSwgzDgf+HB4e9pgBGcuhzib1e319vUd9z6oc3V1bW2tTnbe2tupvf/tbOx6WQbZjZCUxQTP3rEeTbhqI6V3msbMo5HnYKQCTUMw7mXRbnPj+eDxurI6MLdxfsvnYDSDal2YifI/rzdF4Oh4OzJ+qahO60CQJVDbpm/glcIUqbG1t9SYqEiblWigMtA2CJhAVSD08PNSHDx9647pdQ1U14+zz0FEJz9XVVUNrVE1TkZJeoim+qhpCw4AnMiswTLoaRIOyqwY9PT21qh5hzdI/apDjuc5EXji3y8vLurq6alXYpOBKKhgqDppDg45kAA1RmU6nLXjQvylg+D/Ze68dydIkO3e51lqGyMgs2dVkEzMU6NchwEfjA/CC4AvwmhgOQYAzPd1dWVkpIsK11upc+PnMbUfPzSkvNjLn7B9IVGWGh/v2vX8zW7Zsmf0YBVp0Ah/VKIKpdzJhovjrrkKhoF6vZ31NOEDpkiyR9LGPpct5Z+yrYrFovcdeTgEw88EFGy4Wi6btTyQSdk7fYrEweVk2m1Wv17P9wB4ggGIv7HOfrEmya/dkBzIyALbfV9gEe9xXMfFlfA9PXPC5VE19pcY37/vvgM/wCTDfzbOi/N7pdApMweTzfC8igBwGl+vFhunJ4ufYPxUd3gs79ZPbuF8QP3zX7Xar0WhkZ6qSPITrly//bIgT/DvPYjqdWkXN97BTPfLSZ4ALgx2o5vuqMvuCJBAQCtkHyQow46gJ4qAfCME+5DUQCMQG9i/XC6nIfsJupYsMDUUK7+Wvc71eq16v26Ae3ttXOjgqy9sIcQmVgbdTkiqvCPJnUPZ6PVPaSGdfw/PwnwFwJzGXZAkjBChEGb4ynU7bsVcMEUmn0+r1enb2GlVDkhMqJPRLRiIRUwfgAxlWF67rFwkBz5d9MZvN1Gg07IgSFHLEAxIFjqagd80PD8R+UWNRQffybPYjleJEImEkEDZOIYYWAQoD/sxrcLWPW8lk0qalUplnqv9+v1e32zXbQnWyWCw0n8/tPGZfTfeSbLAsR7X5gg/7mO/Y7/etTWy5XFoFEn9GvOa+4YckWUU3mUyqXq+r0WjYvATUABSrIKxQRPlKIz6D+AYpFIud+yWxc3IICAD+PpvNNJ/PA0ob9gV4C1KPAtaXopy7Co2juQaUfPr0yYAFzmw4HCqVSunp6SmwQekj8Fptfs/L5QheSE1w1i97mNLptEqlkiqVilKplJrNpm0WJr7RG8h1cz0ktY+PjxoOhybbIGlNJBLG4CBp5Tt4AIpBJZNJY2mY9OirHAQNLzMjWJNsId0kyHHQKMwp7+NBsJfREMwYIU4wfnh4MEYIsABDxXti0DiXWCxmfTL8kS7MFmCYaYj8/fHxUYfDufma7wG44bnCSHtyAfAUrusXNkWSx97nbB8cqXQGWFS12Q+z2UyTyUSDwUCj0cj2brlcnr1T9QABAABJREFUtsFE9FX5agSf6fcl+7VUKqlQKBgDhw9gD0A8Ud3y7+d7gFkET/4N8sRLpX01zlfPX4JbiCLeA8DOd/A9XbyXV1R4ealPKCGBCGi+0ild1BnYADaIj5QUAKHYEZVG7jOSNP7dA3Wex+l0sj6Wl2ypr6r6Kg3MOINy9vu9DasK1y9f6/Vab9++tWMT6AlHaSPJ9j/Vej/B9vHxUU9PT7YvAULEN+nSFiHJ+qCwY97fq3eYiIgvp5pGvzL7ntiALQFO2cMkk/+c3JUEFJKVygo25EkNbNdX8bBbXkc1hn56Km18LgSuvyZ/9AdVdpIzwLL3FVQhIH/xTdg395wqLjbok11wjh9y8sMPP6her1tisd1uA1UfEmeIG4D26XQ+huHx8VGSTA73/PxsCqBwXb9Go5Gk4GHxvV5PiUTCFGgka1QBJ5OJYSV/BIKv0M3nc6uO49uliz2SVEB40jtLnIjFzofEj8djJRIJe496vW7kAao4VHW+GIIfGI/HlsxylIV0jqOoA4lVVMNpo/L4AUxLsvlSNcDi55CUVMbL5bKOx/NQrWazackb94sYdTweValUVC6XzR59HPZVxOl0avcP/8YgNnCB78FGJcXZpdyLSqWieDxuBSmSYjCFxwQQNF7hgKqAlrGnp6eAWuhzX1dJT9lwvjwMY3I6nWxEdzQatWMwPKtAj1s0ep7axDjZ6XSqSqViwQM2BbDIxqTK5auUvteRTcdncV2ANDJ/ggzVFlg8JAQAJthH2H0MGedAQOJAc5gi6ZK8EQx4D1+ZJBggI3k59ZBAwT2G/fA6ay81xXAACWxS2FEMEKDom6Q9oB4OhzYciEZtJKwYHTIbrmu329lUW1gtn0AjWWXKKt/Xjw8P1/XrdDqpXC6bFGMymVgCxs/H47FNKwPsMFm4Xq8b6PBMOcGCQOdBo2/ghlTw55lS4cAeGMCy3+9tP2DXkFAkXZ4ggpRh/xNwX0pLGPhEkPaVCN6XpEtSIMn0FUAvsfOBH/+AvXk5K6/zFUnYUSrz+DXeYz6f2/fyCbBXQ7z0gzC0i8VClUrFSCoC3nQ6NSndbrezasRoNLLP4voAGbDWjHLHD+GL8W3h+uUrm83q/v4+MMl2s9loMpnYa6gW4GsBJcfjUbe3t5YEUdknRvhYxR7x/cjShSgBaLE/SVoPh4Nub28triwWCyNrX1b5mTgOmePjMNflCUCICoAkCRyJLfvrcLj0ZuFX2McAW3/ECL4IAEhFA2WMdJETAlb5/n7CMhI0Bk/QK+aHfYAPIMDwn0hGqTBxFAKxEF8CAcN0dGIgbR0Q1vRmskeQ8nPmMLgDjPX09KTvvvvu//Lu/f/Hwj9SOOh0Orq7uzPiwvfdopoplUqaz+cqlUrWnw8WQq2Vz+cDJATqHXzAZrOxITKoBMbjscmm4/G4yuWy2dxLImE8HtvE0M1mo+fn5wAhEY/HLflkcidxmPdnr4HdmOaPn8BOqJDhC5Bvk0ThK3wLA4uEkPtJsYnkVrr08pPw8TykSw+yV0pRdfXFoUQiYfccFSP3yvteSfYd8RHr9VrFYtFmcOB/OD7Ot3P41hF8Hj7pdDoFhpN5rPA5r6sqikwrTCaTgd5AnDxlaRILGn19PyHgCEcpyVgaggy6YekyAXE4HFqFDUBI8jIajYyp5Dph8Ehk+DweOiAKPTWJEaAWo4HJBdyxIUjMcPIEaDYHgVKSSUwwIK4VJ0NAQJoAOISt9dULHIivGkiX8+XYtBiWl776IE0VlWDV6XQsUfVs1H6/t+foJaZUXRaLhcnTMM5oNGqseb1eVyaT0adPnzQcDlWtVm3oEY38Xo4UruuXZ91IpH788UerlBeLRXPAJGiDwUDL5dKYQeyD/qnj8TzK37Pk/DuJDf4BO4EtR2WALUnBagR2AuOPXUoyW/HgD3UC1QF+dzAYGBtPRU8KTi6FtPAyVuwLEE0FQ7ocrUGi62V/Xn4qXaacEgDxXVwnNuWrlPjP6XQakADxGpLy1WqlXq9nVRX8VqPRsGsGbJKMc00M1ZjP59bHgUoiGo0Gkgq+M9M1I5GIer1e4H6F65cvGO3BYKBMJmMkXbVaNUUMR0JBwvCHeIZkDSD6sv8XEIR986zx3exN9heDz17aFxIuT1LAykuywVjEHKpsz8/PAWUBNkKyy973343Xsb8533C5XFpVlD2Kj8BOAMIAVdQ6AEXkfdgmR0YRx/l+DNhAjSRdjubgtQD70+mk6XQaIK+IYySrpVLJMJInS7kHAE7uzel0MkLX91uSQPtqE5UMyOPvv/8+HDb1Ky2kwvhMZiw8Pj4ameLVUkgMIQsYaEhFTLrMqpjP5xoOh/ZZ+/3ejpzL5XJ6enqSdFEFgMWQPNKWwewKMPR6vValUrEpnPP5XOVy2QoBvM4recAHxFeqh9Kll5J2IezvdDqfdoAyjIR3sVgYdvaJkCd5eT1FF4gtMDmfT+wjweNeeIkrRLYncFEHIJclaeP9/LPAX4BxiLsUl6gGQipTXfbT2/3/k0jyjGazmYbDYQA7kCB/CeuqRLHX69n4X3pWfAJDQCC791Ku0+lkE0B9UMIAkHWymTyLHotdRkfz4Khq4XxZ3W7XHhCSOaZYUW2LRqOB6WYeAPogiowTQMgGwyHTdwUTT6BAOispMDwCkMyGyWQyymQyymazlgSfTidLtNn8AEA/gAC9M5IG+kgof6M/B4B4o+50OiZfIPHkYFcPvnGEXrPPmYiwzZvNxpIKzvaJRqP64YcfAo3cb968MdY3l8tZlYOAHwa5X2f5PYzUd7FY2LmVOGRfUcjlcmq1WsaeERi8RJPEEwDnj5QBmJFAATCTyaS63a4FkP1+bz2zXg6D3RNMvAoANtEncZlMxvwPgQBWl89H+gVAhgX21QzpMj0U+6IyQxDzCZKX3fnKI3/4uU80fbIIUMW+8Ee5XE71et2AN6/zEu9YLKZKpWJsL9/Tvx++FkaUnmmejw+6fAYVFekyjdhLe2KxmBqNxhfBgn4JKxKJqNFoGAj156ZR/aPKTJ8MxCykK1U1zuQiuZMuZxx64EKcY2/6vcVnkfyRRG02G0sW+V1kY/4sZZImYsvhcDC5Mu0UvtpIbILQ8H2a3n4kBSSmEJK83l8f9gbOwG4hI3u9ntlQKnU+4B58gp8k+XrZKrFerwPx1ROsVF06nY5Go5HF0+PxaJJFyDkwkSQDp/RTbzYbI7bBMyT59EXxeWAfsABAH3wVrusX8xWWy6UR/MRN5Jn08DItmMII2Ix/x66kS6zxgxyn06n9P+9N9diTuVSuSEq9Es5jWCro7B0flyFiuS6vJnn//r2phRjsyPcG21Ml5zv5ggjFFuKSLyhIF6UN1wL2xfbxUySoXjkEHiFR42dcC7EOaexqtbJY6okhiOtMJqNOp2P3arlcajqd6v379+afwPLgXO7jbDYztQfzHRaLheU03A+m2YLHUAp8Kcq5q7RDzWYz0GtHIzg3B1beS8LYkAQLNmw8fh7RTckepg12kI1F4GSjELjm83mgL4DAg45ZCp7XB6uI1IPSOz/3vT0MEPByPQa4wHpwLpI//8YHaIKb70/iLEJJJo+TLiwz7+HBIEGTxl9K/N4BkAjyvbkvnv3C6TDVlUqON9zj8TwKeLfbGRtKhckHMMApiTjgmt5LSvQEQwKql/JJ0mQysfOqPOsVrl++mMSGvSwWC93e3pq0MBKJ2BmcXvZBwAK4ss8gcPb7vQFT/g3ABpOKvRFoW62Wbm5uzG8kk0nrxWH/YIfsCRy5FKzmATRf9i6yqDJIl4TJg2ZsEFBFkCFJgvQioPgqoP/8l9VB6ZIMkrQCrLlWFBe+Cst7+qEckux3SYphpQG+XAdBmvvtE1xYSy87hOTCX1FF5LqQVHGvIKd4BvTBheu6lcvlbEgMz9C3U0iyJFK69LIypZgkgyob0jBaGIjFnpwgGYKUgMQg5jQaDYtF2DOtG8Q6FoM8SE5JdknCXpIrEE5IuQC+vI7f8VUEf23EqXq9biPzwQNUx8EN3s6p4FAtofqCzXpcQQKWSCRMAgzW4H1JgpGf0u+NWsiTO3xvwDxytclkosPhYLiiWCzaQA0IOIb3cZA335nnyfXiJ0lu6bMK1/ULBRUSfpLzXC6ndDqtTqejXC6nXC6n1Wqlp6cnq0K/7CP3VXKGvRF/iLHYE7/z9PRkZw9jv4+Pj7q5ubGCCVV34hfFEPY378fegVwg1mAj7Eds2Fe3pfMcA4oAJDxefcNe9v15KN4gmvFt3pZ8EuqVM9jzbDazIYjgDEhtki2k4dzT4/FoChvI6XK5bDZJTJOk29tbJRIJS45LpZJhAyS1hULBMLXH68z84Bx59gFE9Gq1MtVHInGeJI5q8kvpI76qokhVDkaOc7vo1eOhx2IxjUYjS67YgCQ76XRak8nEgApO0rODvC8JpXRhGJ+fny0owvRQRUE+xUOHrUUWCpgEFCIZ5cEej8cAG4+hS+ezVkiISQgJJjT7+koEMgA2GAND+L7+GrhWDIKE3OuqMSCSLjYxPSecJYWT8JUhDI+GbN/riE4d4Mz9JHDzu9Pp1Jg2+hs7nY4F0f3+fLg7z1WSnp+f7V5Q1aLBGOCw2WysITtc1y2copcoUg3wfRCr1coqTVTpkVtw7iKDnHhekgI9S/QD8v+bzUaPj4/2XpAVo9HIGFhkcvzO4XAwXwEZQeCcTCaWFCFP8dLMWCym8XhsQQt2HYLFS+Y8s/kyOfNE0fF4NFWA76Pyr3/5/34AB2ASkOr9mq9iMjTIVwUJiP5Z8tm8L8kBQJUqPyQOgYjvDcsNYMbvUBHmvnIN+AEvS/aBNlzXLfwkzxswyh7ykwS93MuTdDDWvjrMM/LVYUl2riLS0G63azGFvUB8JpkE/EWj5zN1Yda9nBxQyjEQvIe/Zh/bSqVSIG76quF8Prcpn3wPZJ7EKYhE9rzviyQGEw/5PfxWtVo1qSb3FmBH3z2ADlzD7+JPIHtIvvE1JH+QOUhSeQYAXZ5bMpk0HBSJRKy6xM8BpHy3w+FgSbuv0vDcqWgwmCxcv87yRANECz7Y96lFo1G1221rD6DyD9HAswJLnU7nCcOTycTOHPbvfTqd1G63rY2D9/GDxIbDoVW9D4eDer2encMIBvW+nGsl6eP3KEh44tMTnP57kHghkU0mk6YII9bwPpPJRJPJ5C8qep7sJOZCfGFf/L1QKJgaENUb+BiiExzBVHVidSwWU7PZNPt8fHz8i2NMwJ34LaYOSzI1oHSZzYB/Jnnks6j484yRnEuyITbcc98C87mvqyqKNI561gTgmUwmjdkGaPBgyLTr9bqNhm40GoEpTZ5ZPRzOkzOz2axGo5EN2pAu2mufVEQiEZtUBRjCGHDWTDFF/gp48vp/PoOkk+vAMPg5MoCXh4hjMIAy5CIEEZw7lUbYUl+JQzvuARrBkffFAXhGxfc9Ebh9BZYzqGCJCa44DK/F5r0wumq1agCV4SNUgQnCSHR8czasC4GPxmz+zvMpFosGcMJ13RoMBoGJuThvkjL6kTwbSZWdSgasPWfy4eQZxACRQ48w0svdbqebmxsLEOPx2OwZ1s0PapEuPRMAR3pV9/u9OVzpklR630OF3NumtwP8AAoBkmUqa9LluBrp4gO8DM0342OPgFNftfSMKawogddfF79fKBTs3vFz+tX8JDpfhRwOh+YLkf/5Cgk+NJ/Paz6fazweq1qtKpfLWULopUMA1aenJ+vLQcLz7t073dzcWAWSPRGu69ZsNjOfiG0wSGE6nSqbzdowMaSY+HnAFXJi7OZ4PFpVi5HwVCeQyVEZREWwWCxMHQOZSCXRA0WUPsQJFCbsQ+ISYJekiAoKdsE1IVvHR/jKmI+hUtCWiRWHw8GIy3w+r1qtZomfdFEhFItFqxYAZvlsKhfe7rFherDxS4BCSBwvXcdG6RvF34BjiKH4Rr4vSUChUFC327Uqbq/XCwzLoWoMCQx2Wq1WqlQq9jmSDOOEqpzrF8/IV5A9ERCNRk0GDX4D45LgEz9Ho5FarZZV2bCjbDZr5IjHRJCI5XJZp9O5/WkymZhSCFumWMDRbOBVkkGqn+DU6XSqarVqZ5JDFrGfvL9hT3m1HfGpWCwa7vVqH/as/x4UPfA5xHDyBI52IX7js9LptJFTvs+TViv8IT6R+40ffNmjTIXeY2uwNwkkfg6/AukLqQyGAbegqpJkSiV/L+hZRZ5fqVRMLv8lrKsjve8f5MYTMMjM7+/vzaH2ej0DrlQpGo2GJRaVSkWr1crGc7O5AGu73c7GQRNUSUbZqPQA0Njv2RtJ9l5scphYPovgQrBiAxyP50EQkgxgN5tNuz4At93c/9dAuB9+uiebnuZzgoakAGsFECbpAgww+IAmZhI+WCGum74KrgemA0DpJcC8r5f1SjIQjmSBpBCpjCRL1n0SSy8N9zqZTNrY6OPxaM6y3++rVCppMBgomUyqUqnY9YXrugXjTGLHvmGP5/N5vX//3gKRB1hM1fPPazqd2vmiVMYlWRKCM5VkJAzAsFQqBaQZXAMLB0ww8tItbJGg7ZUAvvcCG2eIDRUCn9RQFQAIkmjyb4Bbprzh6F+SF3x/Hzy9FJWfvZS8+moM94D/EpB4BjDA+Da+czweV7VaDUgMve/ie3IvkAOPx2M7loTk1fsPpMmAWez34eHB+j+QE+GvwvXLV6FQMPabWArRyX6SLmeS0UZAXCOR+eMf/6jvv//eCFr2CIBLusg+2Usw4rSNSLJ4gQ1AlNAyAFkoKSAVp0rgCRPpvAepPhAfUBdQHQQrYCMkjV7+Dbn1EhT68fXL5VKDwcBYfpJSP2CKPQxgBgj67wVhTC+iZ/6Jbbz/dDq1gTOokE6n87EV/J2kmO+CL2Y6OmSYl/vv93sbHASApZ+N+E1VVpINBqRimUgk/oJAD9cvW0iBR6ORhsOh2u22VfAXi4UlYgwyxG6IdxDn0uXYo1gsZkQQ00xfKtBoxYGcZHK5ry6CkRkyQzLkJ9UjRfaYDrv1Ch2KPGBcyH2uW7oMUPKSd0hoT3KSwPJaMLZv0aAlhmICNk2Cut+fj+mirQtSzMc4iBPiKsQVZDAkD3EcsqpardpnkFx7Gauf5Irc27ew4FN87zD3C18CgcBrwEcQVh7ffO7rqkQRIEKiQTl2NBrZIJP7+3vbwDDnNH2i7QWM3N7eGltJTx/lW7J3GAHYTS/ZAHwBhNiQACg+H7mAJGN+YAj4NzYfyR5yIAIBDA0bFAPlXvgElcTXM6J8P0AoEh6keBg0RuJHDzPuHIaCZBOWCxmvdAbYfC+CJpWG2WxmSQTv5yuTOAcmY6ZSKZt86DX28XjcHA2HsRM4MYjj8fgXA4oAFujIGZLx6dMnff/992GQ+xWWZ/eQmNJbQGLYarXsrFF/z4fDoRqNhgUl9jzBgYBHZRgAhpPm576PgX3j+xM8yUP/KkGWar4PcPgZHD0SdC+RxE+w/172Qrys+vv+CP7Qi+HlddiZD04EN4IX703yyc/950HGeBaWoO97BX0y6n2a76ecTqeWfJMg+H4Y6RzoCVT8/WVlR5KRV9FoVB8+fNDDw4MxySTW2K+vrobrly1shErCbrdTv9/X4XBQtVq13hjIgvF4bFJI6SJH/uGHH6wy7pUxADbAWyqV0vPzs/Xl05eTSqX06dMn6zuezWZWfYTFn8/ngZ4olCuAMSoBgDqvMsDX+HjpwRYEELaOtJK2DBI8gBVSQF9hIN7w/p4c5r8QmKgP6Kv0/45f4Z5TneF7Y6+JRMJilk/oIXFIBkj6pMsB3CgCXh5pwjP1ExzBD8R14jBYAb8GIcYxJV9K/9PnvlDOZbNZNRoNrVYrm6bJPkGmjMQzk8loOBwGZmHsdjtls1ml02mNRiM1Gg1rHfIxAp9M/y+fI10mAfOswUtekRKNRlWv162IgBKI4gS9egzYobpIhZw4QhykNxb87eMcuBxyAjzth3LxfhBGzNCQZEofYj8tFL6lC5zL6yn0UKXkO+FrfI8i2Ja/Qwzt93sjgun3RupL/MafUPljeE46nTacSy4jXc7/JrlcLpeqVqvq9XoBoox9gk/4EnDuVekswM9reZGT9Xo963GSLqPy2cgwdgxKaTQaFlhINtl0JBTcXHTebJTJZKLVaqXn52d1u10ri/u+BqQwP/30kwU3qokEUzYgG4qkFSkIQJvlR/zDGPn+DunSawBr6PtD2Oz0R6ApPx6P6vV6BsA50ByJkf/+VGoBmQBQSXr37p1NyjscDta/RdD2lQv6MmE56TeFZQKoIiWApaZ/k+ou1wM75AE6KxaLqd/vBypAPMt8Pq+7uzvrpQnXdSuVStlZekhU6COl+kyyT7WankN6LQhSMIRUnQlUJIdUm6Vz8kKAgMzwySm9CkjHAEu+qkIyI12Gy5AQElzYw/v93mSVvteKfUilG3sjEUSqSnWNAOYlngRBvwiiOHn2speo8H5IUbzv2263NuSHJAwm0rOugAb8DOQZyVsmkzF5MKxyv9+3z/dAkqDKs+I+rddrTSYTS/449P3u7s5APrYMubTbhUfY/BqL2CPJkjv6tSFKiBUkHOwz5KnsPZJ8Yhp2djyej7shgby9vbV+cEkmz3r9+rUlT+1228a6U4mkcu+l5SSPDPwgPvsY7H28dLEt4jkx2lffY7GYgWGIGsC0FOwVYho5gJzY7T/P92nhC1OplPL5vMnf+/2+RqORSQCJcxDJTPX2bTa+IsJ1e0UBkjOuHTBOdRB5KfF0PB7b+4MPAMcosU6nk1Vlp9NpYDrl8XgZPBbGz19ngfOSyaQeHx/NJ9IyxIRbP5Fekkm3iQeNRkP5fN78OdgMkp5pmmC7yWRiLVK8loIFuNSfK8i+fXx8NCJkNpvp48ePAV/AtbJniSG+RYO9x3tLF/UgmJR4j83iKziWgmTJx0pIK4+TsRvk3R7jo3Tw6hUvF+dMSf7O+/rZHRDHkGgk2/v9PqCE80olPpPcJpVKqVQqWVsd9kkyj2/xUvtsNqvBYGDnL1J5Jvn/9OmT3Z/PfV1VUUTOhNEAQjyrmc/n1e12A03o1WrVblCtVrPhE5KM9ZBk8hDAGhuCSuLLSX3lctmkdR7QkgBOp9NAyZ0Nk8vlzLg9QyEpoANPpVK2wTxrSpJJRY1qpXQxLiQoGCDgkL/DpEjnIHhzc2OMJo4GII8UyPeMkCD6c+larZYxkZwJhWQWRhoWJp/P6/n5Wel02hIF36dBoGOiHmCWIxRgcTA0wAoVLZwKzci1Ws0cJNNfN5uNJcchG/rrLIIBIP90Oh/46sEm1QDfYI0MG+Z8MpmoXq9rszmfi4YUESbPTzAk+cHmfX/SbrezZ388HvX8/Ky7uzvzAf49qIBQZRgMBrq5uTHwRl8PLCGBggSSfRqLXfpzCVZU7giE2COvkS5VPAZ1YBcAYV9FYZ/7xBIfwz3E7iWZMkG6DDTxSTJVET+ZDjBB3yA2CICPx+NqtVr2vZGrI1GLxWJG4PlqIsELuS1Ek6/6kKhzuDOyoy8hyH3Oy8sxOU8tm81aQpFInEfN9/t9O8aGPYbsjdeyh0heiM2TycTICSocyWTSJFcQD6PRSPP53KoLKGQAnT4e+Ot4fHxUKnU5C80nRT7WeXIGm+X7814kYtjafn+e4kvfFD1gEDvsf0mBCevYFIPywA0QuCTYvjID+AXUUimRznEcKd9gMDBbwK6kS08zpAzflwQWIIoagMSf+7Pb7UwNBUDFxwDWiaGz2cwqwtimJ5DCav+vt6gIFQoFS/xSqZSKxaJms5mazabFCHw2eFQ678tarWbYhuOgiHU8W1Qh9Adut+ejNTi70VfZIfuYrkqi0+/3dX9/r8ViYQMDUSCAwagCkpwRn4idXrk2Ho8tZkNooZbBvvE1+CHfq0viiR8gwZJk+Hw+nysajRrRCa4mdvq4DolJXPSScYgbYnwkErGjK8DfJHFgdnIIMDt5BfeIFgs/L8VXfguFgsm+OWEBNR727XH7YDCw4Z+3t7dfTPy8KlF8fHw04PDyfB/P+POQqYpJ503Cz3CmTPx62STPv/meOQwS2WMmkwmAy9VqZQe5A8rK5bL1X8zn88AIXOnS+A4biw4d+RyAEwPzgIz3ZwKolwz4gTNsVKqUMDz0FkgXxtXLzQDeGA9MCtUVgqx0meCKNv6l7I3XMKKXMjy9gRgX8grYTqov0mXwBQEKR+AbmzEkfh/wDlhB4w8bw3hhn4iH67pVKBSM6GC0N0khlWz2NaSBpEASiW2ORiNVKhXd39/rdDoZoKWxHjkLe5oAQpLlZV2w7gRAqhEs5K0kh5xLxXVJFz+BNIXrx0alSw+uJ2UAvOxXgDo/4/2RhnvgSYAi2PgeCQAaA3MIGP598R3SZWgGsjOuE7/I1LxyuRzoL/MVnePxaKPZ6beAcYXYisViBqZrtZr5EHpcpEuyQPXXg0/+XiqVbJAC/WmhjV63vMTLS8Lpe2MfIRtmz1LFIkYwjIrn6Cv0hULBCEXAFIkniRrVBg4T9yCM/YICAVKT5K7dbtsef1kJI7ZDwBILPZEIcclACfYt/T3IpqXLcR2SLFEE2GWz2cAwHuIKcXO5XBpRKcmUQ3wGdsXPIWz4TJJHD4YhV4jdkNZelot9kxBy76iI4LcgvX0FH7+D/4hGo5ZAzGazQJUZkJ/NZvX4+Khms/nX3s7/IlckEjFsR3U3m81a9ZeEiATOq7iIF51Ox5RhHD/zz/ngYrFoxAYDGamSS5f5FSQyo9HIpoISg7Er8DBqOyqBkCnIlUmOkFvy75KMZPR2gAydmE0BBDKJ5IujzrwayGNQfo9eXN8mgc/wuJf76Vu0IFV83/zpdLLBebSfgHWwc4oekDkea4PF8R/EVI4M8efBegXfZDKxAlUmkzEfwLEj9Cnic0kuv4QYepX09ObmxiSKSJd8g20kch6NCwilGddXBJbLpYHC/X6vfr9vjdtIaaTLeGzpYiw4UJyrB1q++Z8HSkBBMvX8/GyBAXYdh8z/ExwwDEkBlhD9N4evplLng4/z+XzAcLgfGBqBnYDKZxEcCOh89/l8rul0qs1mY8bgKxUEGioDSO24fmQpsLVUAH1JH2Dgq0tIg0iqJQXOk/TgBekB95zKIMCdvhYSZp4XPZBUFZHdfilsy+e8IAgAn75CRjKHNNrbEL8LeMPJIoWBUODICpIeD1ywDwIhewU7gFX3IIfXQ3IwxdCDLewCaY8fcU2CRSDGTmEteR3vgwQMwM73gAjhMyORiN69e6d3797Ze0OE+WTREzL4OUlGiEmX4TfSRcIqyabRYQfYonSp7hMcmaA3GAzsekiOOYKk2+0GPt/LlWB/+R5+4Afvw+cDCEajkSk2kOOF67qVSCTMXqg84EuRLJM4SJczdt++fRuY5keChF3SukElzdsME1Lfv3+v8XhscTGfzxspALHAETYAYgAYiSi+BVvDbqk2SApI5HgNqiPvh3zVIRKJmJTck5MvJefEMpJZfuZlZIBIMAd2APDFV2GjxCZio59nEIvF1Gg0DExy1A/febPZ6KefftLz87P1XW63W5P/U+3lDwk4gB0/QkLNzxk65Ku62WxWtVrNfo/+p/1+b4qkcF2/SDYYyMe+xf683Jvpp1SU//t//++Kx+MaDAY6Ho/qdDrmr5FGksxRXYOgZJ8xKIVYzOf3+33N53OTf5bLZdsnxOrJZKJSqaSHhwernHnciX9hj1EYGY/Hms1mFu/pj2UgGlVKqu4+qZMuyR4xEKzOHkZ1gL/x1fbpdGqDpngPrypkoA/ECzaLPXPvmK8gKVDYIcYS51AsSJc2LKSzKA6puHJUFj4CzA+mR5pK0QsMQUErEolYlRa//CXg3KsqioDK0+lkyQONqLFYzJp0AaHZbNYOnIUFJXlEGlqr1XQ4HIwhh9UjEPjBCwSs6XQaOBOFwAQLS//Ox48fA1KRUqmk0WhkLDv9dgBTnyyVSiULBvQXRSIR1et1uw4YWwIefyhb73Y7dbtdHY/n4ym4Fj9ghkBGwsd99keCSLJKij8Kg16lSqWiWq1mQRUWicANUKASSoLp5Ssw2xiCP8TbX6tnr1OplEajkQUzgh2JLsk0pABMDa/DKNvtdoB5CtcvX8g7K5WKNdJDprBvcIj+WVDV4vmWSiWzPyqJ0qXSv1gsVK/XA+OykWdBesCuc4h1q9UyaTiSHD9QhwTVkwrSZRoo+4yeCCoRBAtf+QOk+sEYq9VKzWbzL2RmKBaokkhnGfybN28CFUL2NraHXRDQ8CW+N4M/kDe8Fnm6JOsDlGTJPRV6jgOSLkEIOTkVHK4XsC/J+lwg9vBvL6do+upnr9dTrVYzCTGDfQDz4dTT6xdj3wuFgsmjSB6Zgokfps8GGWk0eh4Qkcvl1Ol0bC/TXkE1ij0Pwck+ow+RvU6chgGXZCDJq0P4XCRUxBYSR99bzPTher0ekIxyLcQXMAP7nP4vSZb8EBPpOeS+EAOJnShYALActwGBSszz8c0TR3x/7BLyie/oyU+fRCKpbTQaKhQKRo77qiq9lsRC7gXTpKnW+u9IRYQknCmZxHViM/ecRD+Mob/O4n4nEgmT/G63W93d3enp6ckKIVS2sM1IJKLf//73isVievPmjdbrtSqVirXqJJPnKfBMj+cZIl0FB67Xa7VaLZMd46upRnkf7gsFVCiHw6GdS+1nZ/hEkUSLmIEkerVa2URWHws5WxBiCyKGZIj3A9+SxFGgkC5DJkkKS6VSoCUKO/MkLngDDDOfz80nvsSw/vtzljPPIJFIWOXXE0MvsQJx2WMPil2o7kqlkg3EQhYOxoYMAqek02m7XlpB/sUnip5JpxLGpmCoBJl6tVq1qoTX6MPWMOVpPB7bzcW5w/TB6HnHHY1GTQoF8CVZ8kModrudqtVqYHAAABhJHRVRNiXDXyqVihkiIBrgTDJ8PB6NTQKk+coDTGelUrEmWYABjAqsDEmY713AaPisl0dqwAoXi0VtNufDVP19JBGHoaJv1FdFMHAG6xAwSRpIHHB0kqzKAejlXvGeVDIB8dz32WwWOG8TRma3O5/hRzIRruvWcDjU7e2tut2uqtWqMds4SKSj3W7XCBv2Jgmkl0cgiyHp8JItLzsGvLDXYUtjsZhVS+iRwhGT6BCgPKmDDUynU5NvEjQghrx0eTabGasnXQ7/hXDKZrMmhfdyM5LIzWajYrFoh3+TYHkSiMDp5YOwy1w3Nky1nWDmE1mqRLwWQggf6SXdfoS6B/OeNCNBP51O+vDhgxqNhgFVTzDhc7Bl5Ec8i3w+r8ViYQGv3++bjwQcfQlB7nNftEp4sDadTlUqlcxGOOtMOpOxtVrNSAMvrZIU6PXl/T0Y9FUzgAt7CcKXigikYSaT0WAwUCaTUS6Xs6mKkgLkknTpT0dSCinqe5a9lIzqALbsr8MfzUFM5D4ArCFBGIwHuKYK41/PdyeJRobne3axX+4R9o/v8L2h+CGSOi97Px7PPVX5fN6ks0hO8Yu07iwWC5PXI2vEv/q+x1QqpcFgYFVo7g344OnpybAKyUK4rlsoKbAXkh4IENotIMEhxv3f4/G4nT/NvoMAIdEiJiIJp52K+R+0j7BnIOiJHQwyAxuSOBaLRZtojGxdUoAMJrZQ0MBm8vm8tY14GTfkB/EJkgk8TxxEyeMnfBL/wIf+fGISNOTy5BMQ1JPJxKq72DNVR5I64mMkEtFsNjPJL72NJI7SOVnt9/tqtVrmA/BL3CsvSx2Px6rVaspkMvqHf/gH3d7eBhRx+DzuH7bLNUiyWL5er60C/LmvqxJFDIbKIKwnAKzT6ej29tZuEMwpVTUag3HOOFHfnE4i4oMQWmrkoNPpVMPh0M6cofpHokbS5hvTYfs944izZ/PDytF4z/dESkIyDLiiuugbzKXLuY04bYyee+K/uweOXtbGZuX66FEESJAUArbpz/QsZKFQMFYTp+W12DgJrvHp6ckCOefQ+PO0+Fx6UrhGHyipPgEqYXJYMDHdbleNRsMAgx8IFK5fvlqtllXOYLeRmpIQRKNRtdttc/AEGs++0ctDUztJJWxZq9Wy/Q6wIlEDMDWbTR0OB93e3lqgRLpNpYOKIEykr1hy5qonjkj68AtcA/9GQIUIAVDH4+ejL0hi2b8vWUwCGBXIUqlkZzDxeVTg+Gxsg8/ku2K/SIP4PYAE/4+/6vf7ljRA8Mzn88BRJtPpNCCPJRBTYfzNb35jgb9YLNrnsA+QtDMB0kvtfVJLsEZmz3f/EoLc57wAoCRxfqgCxEo6nVa/3w9ITPHZ0nm/vXnzRrPZzA5y9v2zJKFIWYkxJG5eIQOQRUXCPuGPdDkzMR6P23RBYja26+OuB1rEXMhGABUKJD8DAAKR+O8luBBS3AufGHuixZNNfHfIS84oJuHz1QjePxqN2hEX+Xxey+XSZHgcjQF+AbBvNhvzE4VCwVpG8LX0V0HAHY9HI5Dpb2agkO8n5v/9kT3JZDIgBwaQ0z8c2uf1CzWXr5ZT+YVQw858ywSxhDYgSKBUKmVnJKZSKfV6PbVaLe33l7MXOVqBFghwZalUsoSkVCrZ2d2LxcJ6JrE7CJDD4aCHhwcj/n11kO8AbiPp8vaLr6C62Gq17NxlbNvPySBOYNuQGrweXEufINdC3CS5JEkDF0uXmQtcL+eusyBT2PucvOBxPdP58W3YrCTzPfgi8gyqv+VyWbPZTIPBQN988435DNQGvAdEO/hjuVzauZkUk/AxX4KNXpUokoGzIWExKBvf398HWA+SLpgUKoOSAqAJ5sRPKAUASTJnS0UulUqZDIbgA7AluaMXApDI8BZAD5U1qmkwGLwWiVwsFjNjJvFhs5CYcW/4LD8AQLpo3v3wGaqvDJKhogezJCkwRZSA7ht5SS73+70lhcfj0aQSlMvRhdObxftgqDRKM6QAuQzlfQC4157DKBHwDoeDer2eSqWSGQY9WM1m09hO9gFSWYaShLKZX2d1Oh3rPdrv94FzCHHU2BVAiD0Cq//+/XtLBGH42Oc+QYK0kGSADFC53W5NHkeSx76HWWOoDDYNY84hwEguOc4hn88bKQOpAyNIQCLRgQChf+/169eWIHlZCIFeklUKqKxuNhsNh8PAWVIMi8ImCZK+Z4P7Ayj0fV3cQ3wLgQWJSzwetwEjBCN8mfc3yWTS5LdUMZD1w4yeTic7iJnz4zxB5f0C/49fgRCEPfaqgnD98rVerzUajQIMOiAL0A/DLcmYf6T82IwfRMH+wW9PJhM7q9b3Cfvz1ZB/DgYDNZtN7XY7A6AAYWwO4pHpu1wvcRPfDXgkJvn+O+KmT/w84cPf8TWAOa5fuiiafCV1v99blZRqop8P4EEt8RnpHriD2C/J+rq4r3xfiCJ8BkQqr0dhlEqlTH0kXaaMx+Nxffr0SZVKxUg7T6gT+5fLpVarler1usbjsVUmidEAXy/PPRwOgd7wcF23iGMvWxuKxaLNu6DSDg5kz7FHhsOhtUJBts/nc2WzWd3c3Bj+lM4+oVgs6unpyfoikTHPZjO9efNGpVLJZOv4Yt4H9Q4/o80IHAkuRE7Od6JogT0Qz1AcUCzxrVaQG9wDpOzEFeTU2CaEDoMvaa3Apv0wLB8jJVnF1SeAFIA82cvzisVihmFGo5Gy2azNzSAppt2FPlHey8tF+Z71et2Sbe6bVyJ4oo1eVpJLFJMcH/j3f//3+rf/9t8aefa5r6uG2WAUOCSf2OC4vN6/0+mY3pnDLkejkbEGPBA2A8mEl4xhfJvNRv1+3x4W/Vc86N1uZ6Pzk8mkMWwkUwzJwKgxeIAnLAt9PHw2FUWvtyawkURhPL7nAkP1R3743gxYVt6DjQ4DS3BfLBYaDAYBIBqLnSdkcQgv4BHJGAaLodG4fDqdLHlD+sk0NYC816UD4AEkPGt/PAISmnQ6rYeHh4DRFQoFtVot0/NHIhE7xN0zMmi9w3X9gqnkLKFutyvpwnrBiG+3Ww2HQ5OQsoexbwKDZ+EgCEajkT1PnnUymdTbt2/NUb969cqSmF6vp1js3DAP+QC5QQDDfnz/B06bKiL2B2ML+PS24wkOpOZ3d3fGCrIAtMPhULPZLFBFZeJZvV63KW+SrC8XR++l7j5wSZcBNp6x9fJU3ouFT/XVI3/+I8mnZ46pFPPv+DvfW+zPkiKQkqx7dpmk83A42GAcSSZ9YyJyuK5bsVhMxWLRkjI/eIzYRA8PyYuXPyIx5lkxLRwAst/vra0CsgGGvVKpWMWSPcXxMxAJ0hm8+cPhuW6qcr7nl4E3fuCar9hJsp5i2gvYm8jK8EckcvgDPpe9CxgjOQPAYw+cr4hf4n08Mcp1cQ3ZbNaAMiQqY+0ZzoUKBhKF4RS+MnI6XSbRonwC4EoyMof34QBvQCdYqlgs6ubmxogDEtDJZGKKBdQWftIqQ/XCdf3yEklskCo9dsQzI9lKp8+HsqOWoorFxFSwlE/awH0obKhYv3v3ziTcqOW63a7FJRJI8Fokch4ExdEZYFXwKORFtVq1OEQ8B5cT6zlnlJkhKMOwU2KuH8zE333riSRL1CAovbSWqj09t8RJkkGUAS8nOKN+4d7+c7H0dLpMceVIDFQUHmeS6A+HQ+v/5ffJOzhmDnwCmczeoGKMX6E1gPcpl8t6fn7Wv/7X/1r9fl/D4fCLSBSvqigCwghsMM6U6L0WutvtWhAiUdvtdoHSMJUlvzmoslGG3+/3NjKe5MQnWgAfAB2SjZ9//lmNRsPOEPPSG/7OZzIcgk3/9PSker1u8iwch/89X1UDgGO89ENRyUFusN/vbQCBlwr5CY8EfJgKkiiakNfrtfX1+b5Ino90ZqiGw2FAt+7BCBuevjDP1jw/P5t+m6ANM0SVlTI795bXSVK5XNZgMFA2m7W+SBwXCQwDD3hvz9SF67oF6KIKgTYeQoOK22w206tXr8wGptOpJpOJHh4edHt7a4mLd9LS+fkCTP2Zo+v1Wt9++62Bn+12a1VuqmxIvukpYi8BQLFNQBb26ofOSLLXzmYzq8gjyfGN9Ngg+6zVapnfYb8zRRVlAT8H5JJ0YvcEX0kW6Ei8sEsCK8Ha3y8GhfjhGv59Ib3wc5AwVJ5isZj1rUC8ELBJXI/Hoz58+KBWq2W9o6gMGJwBCIXpbjQaRvzwTCD42u12AACE65cvknXYdRIkZGXpdNrin5dHE4ei0ajG47H17ZD0Ya/ERSTbxCcSOj4zk8kYYQhBkkgkrKLP/oOYYLDGYrGwfmASLhQ0JLdUvyBUeU/k1xCnyC2Xy6UkWazy8w+IaQBrYjSD2ZCTQvBQVfE9ksRHfB/XRRLpW1TwAygLaK05HA4aj8dGgPrBUB608x1JIojTvI7hfvR8k5ziQ6RLjxxVUfwm34WjG7rdrlUvIYnCdf1CBgpeWi6X1maEEmYwGOju7s6Sif1+rx9//FGvX7+2/Y8fTyaTarVatk/4HfYp1XtI0larZcUREhbO20Sl5qWvxF9kqChhKBawb14WJvx8DXz78Xg0Qpf+XvwSiZ50OYaKmMWAOkhR8gRJVkiiYkf8hORiZoFvZcIuwMoQKVTvIJun06n5AWKUl6V7ossXliDVkPf6thJP/qK+ICH3fhZFCFgYf7Hdbq0P0suSwQP/4qWnBAu/CQgWsAIASzY/Q20IAGx2qkoEMx6QBzwEC4IkiepkMgnoqJHPIZuJx88HUefzedvYs9lM//N//k/9/ve/NwfMEJhyuWxJ7nw+193dnSXAGDZVRV4nKfBzEtxisWjsiD/olOSL70+VxEtwcQIwMAyKgbGkKuQlO2w6wCZGivOC5YShpArx+PhoyTgBit41ni3/hvHB0uBkYG8BIrzudDppOp3q5ubGAjeB0LPlo9HI+hRxHuG6btGvRmApl8um4Sd4+KZ83ytUqVQkSf1+X81m0wIOTpmEnkEv2BdOkIFKlUrFCAFAGQkiDDokD7IOgBWVDqqK+AH2B/YmyXwGEnSWn+zGa7PZrNkd1wS5BMvPv2P7BCk+n8qcl7qyCKT4KN/LQGLuJ6xJCkxJ9CCTSp9P/LgOpDbj8diel3QmiZDVQHrtdjurIgFcqGxw/2C4qZB4ZpZkkQqzHxYUrl+2UNrgewFrDLMhuSCBAngQS+g9JYHDxiANSEwghwCc+O9Pnz6pVqvp+fnZVDf5fN4miUciEfV6Pav2e3kV7+XPOCRmUxXBtyMdRQbr44gk29ee/AEsepDqK+moHNizJKseixyPR/s+P//8syV2gE/iNzHSL+yVeQT0ZeFLfIUXoM53xz69soCq5s8//6xqtWpkEPEY3wrRDCD1gNtXUg+Hg/nY4/E8SR21EeRDuK5fVGY9cU/CBPCv1+u270go7u7uJF2SEklGmFBlJjaAm4mp1WrVEn9iE3sJ4uDdu3e6u7tTOp3W4+OjVquVCoWC4TqOnmNa8qdPn3R/f2+DeBicQ6xCikmlGxLDJ48QpZCV+CtiirdhT6JCSjJsBpKUokI0GrWWru32MvTQkzkkylTtJFnLCYkjth6JRKx/0ffwkxvgf5i8j43ib8HhxHfsrNVqWcyFiJpMJsrlcup2u1osFnr16pWRTZ1OR5lMxgpg+CQSTrD2576uShQBdpSEYSAxJpizRCJhYNJLGilj+8AC2y9dGtY/fvxoR1RQ8cOZoon2gYZBD5T40SdLl83bbDatwuEZOBJPmBECBWwffRLxeNwYkM1mYyCNa6ZZnTK0Z2Unk4kikYid80K1BABGMILtQSYKCCcpw8C455TFCVAkpZ6FIcjDfiQSCRtQQkLrm4g9ICbxxMAnk0lA8kOSgTHiEAh4HPLMPsDYIQb8d/cJeLh++eKgX57bbDazYNBsNrXf71Wr1TQej43BBIQgmyiXywYsqUICVH3vFHIV9lw6nVav17OhDRAeOGX6kH3fFNPGCKxIrHx/Hkydl0hSmUCmKcn2OAkcCRNBF/DqB9tIweM3GGTgwSf+wsv4+DvSeS+P9dIZ9rjvKYbcoReFJBFwiU3zM8AEwAV5MT4EH4t/w0dSGaS3GNkUUhyuL5vN2pAcf3xRLBbTYDCwqaews+H65eunn37St99+q/fv3+vNmzc25bNYLFryj+/lGW63W6s4SBc5JpVzZNxeSvySHacKSdyu1+sBOR2Kj2g0aj257EmSSEk2kAqlCOqhUqlkidvLqqH385LMjonZfAdva1ToAaTSpSfKE9I+oSI2Eo9evXplNoPyAckcSTc+CMD6kujhe+Dr8GV+AjNAGRIIspZK5FdffWX3hRhJNdj3WJEgoPRBKuexALhCuhz4jb8IB8L9OmsymdgQLySYTOfn3nuyALUc9ksRQpIlFmAe1F31el29Xs+wZiaTscmeVMx5nrVaTZKsoIFMFd/B0Tr4d9oX6vW6Ydd4PG4TNzebjZEc3j7BC5FIxL6T72fku5MMomQgcaSgIl2OnPK975JMOUbcR4rvySgSPWxQCvYngzGJo7VazXp0wTK+/QZ7l2TqIOTq+BswM/EbxU632zVSiVYvjvkiRvKHSidkdCKR0Nu3b/Xtt99Kukx0/hIInasSRd8Ij+xsPB7r4eFBnz59MjYbo+DMJ+lSKofRQKfMxvaVgjdv3thDR6JDoEHyQdLEJFU2FaVtAKzXg1N98I7d9wTCYsKIwvYTHPyhmgQKgguJJ86dnzOVdbfbaTKZWKJJkGfzk3BiMF4+AyOBcVEupzITjUbV6/Xsc6lmej01FV76GZCz0jzPc5UuY8V9lZTEwRsv7AgyPgYGjUYjSxboiazVatZvgROOxWL6+eefdX9//0UYz5ewms2mPn36JOl8bihAEXkmPS6cY0SPKvuYRCYajRrjzd9xsN5+GMJENZwhRfTbkHgQiAgoy+VS5XLZ5F/z+VzVatVsAmKJ4S7SRe7hBzUxWMOzgARDJF0wn77nRFJgGhkkDYkggBtizFc5sFdshqo5/mW/31vA9YMxuAdULUkmAYyATCaQwgQjEcQHIX0ql8vGAPukt1qtarlcGjheLBbKZrN2nXzXWCym5+dn6xuORM7nxFL1LxaLRirMZjPd3t7+1fbxv9TFNMKvv/7a9h2LcfY+AYLY6PV6ur29DQxCgTHHJiBnAJsA0/F4bEMd+HeAExI4ehvpUySBkWR7XLocZu8T3FKpFJCnSwqAJ0kWo4l7+AKYeA/ciEf8HJvGV0ASe8kscwfor/JkCPaMb8Ev8HuAYWItiSd2GovFrEc0Go2aP+OZSWcQ+Pj4aCoanh/3AjUF74eN06PlZYJgB4ZaQRKXy2XDAqfTyRILjgQIpeG/zqJqj9/Gvjab8zFwg8FAlUpFw+HQyD6SpvF4bL2K2Br4E+kqff61Wk37/d5amIgv7HHiMSQtVX72ACTC8/NzgJz1UnWmFDOxH1IULO8HIJEozWYzI5xJTPf7vSmISAj9pHHsioog/44tSTIcDWGCHyLOklwRkyqViqbTqfr9vj0P3svH3Hg8bkMVpYvSiHiaSqXU6XSs6sgQq0KhoNFoZNVNMIfvB2cYIK8Be3jSaTab2bPjrEWqh8xGgYyTLi1in/O6KlFkM7OxYSQ2m41puHG2VBVh29msiURCw+HQGBQeJIlMPp+3HggCBj0BbEScMKwblUQ2Bg3kJG6lUsn+PhwOtd1urWF8Op1qPp/r5ubGHDhlZi8ToaSNFJZKGiwLQyMwRjTQgEOSLDYzAUGSBTySPAChZ61IfNmQsI6SrNKHfM43GvMZvvJKqZ/EgT4nWBhJVsmg6Z7gfjweTUYhyUb4M+WJoMsk3FjsPFgHw6xUKvr06ZNd683NjVWNw3X9wsnRxwvIkhQIPuxdDhD2vQPsEVg0qsXsN/YATCCLM/1SqZRarZYNyyGx9EMpkNmxn9n7kCGweLw/e88TM7CuvkfJf5YHobCjXJ+fzsq/Qbx4GRjJJMGRQOh7UCBovH0XCgVJCgRjkjb8hJe/Ag6wXT4beSBBjMS7Wq2afyGZ8BUYr0SANJrNZjaNz6sXsP/j8WjXBwMMgMe2w3XdIumgL49/ky4zAHgGVMiPx6Pa7bZOp5MNpCDhl2RkCVUsqlz0wjHZlN4oPwyKPX08HvX09GSVCmwHAOdJDwAdVc35fK52u23JI9VzfA0Vz/V6bX4GQNrv903hQvXU+y0UDwB23yeE7dFzxdRgfIQk62nCZyDpRdWD7+j3+zb0DUALwQpeoXrDMxuNRkZ+S9Ld3V2gRQaixk/8RlVB4gB4BTAzKIzq0Wq1snYB6TLYSLr0j1P1ghQM13WLaaWA+81mY4Qi5CTTLakgUSWEhIAsANOS/LMH6/W67Qmeo++F8wouEjVwMbEB+/eyTeKOJEtsmcYLzkKBBrmAreNXsHdaGWKxmBGN4FRslcSVwpHvqZVkMZ57RLLn8Smx2tsDtk8lFCzu39OTS37gHEkkCR33W7qcJEDchSCiXScWi1m8BL9ybZBw2Cl9/0w35ZgPcoD1em2td+B45rp87uuqRBEGFGYf9gL5g5ex4CgBUT4wEhSm06klJCShbEoqVwQUHh7G5Y+QIMHxQY/rgjEkafRTyEji7u7urKrhJSWA0Ww2a6CY90W+46c14SC4PoD56XQy6U82mzVAyEaGKeQecM1sSvoYMHCv3Y7FYtYz0u/3NZ/PjcWQZCwrzBW/A+tD0PX301dKvESXxm6M0g8BAqCSIOJIpHPPJfI/pl3RL8czCKWnv85iih97mco7e6Pdbiubzerh4UHz+VyVSsXOPIQQQX4JOCSQSAqQMQQAwOft7a0xi/Q/4hRHo5EqlYqRPSQqkmyve3YR1YFnygmQkEO8h2fd2dNUyCRZMkqCjE2RDEM4ecke70Xyx7VyX6jEkNwCZgmEvj8Bn0Wviq+SUJ30wwh8Usy9AgTwXgBxArX/TIC6r+JSUW232+YPIPRIQJHe+iQBORMTLsN13drv96pWq5pMJtb/CQEDqcJ4dZbvs59OpzZ+v16vq1qtWhzm/dkX/F6r1TLwQnULAEXPD1MbAXuQtMR8Ep5U6nwmHIRiLBaz4RvsEaoXVNVJhIgLDMfZbDZqtVoW5+bzudmUdGl1wS45l4wEGrAJUP3w4YMqlYqplPzgikjk3KPN9fnrl6R6vW79YCRfXmLOPcJmJRmA5azWTCZj8xMgw7wSIZlMqt1uG3CkskGSDyjmmSLNQ0r7888/q1QqmX/udrvKZrMqFAp28Hq4rl+j0Ug3Nzc2oRKJL0eWFAoFm0bq5254/0sShaz0+fk5cC6w740nniGRzufzhjt97xzkIEQGM0MgSjyhKF3OCKTP2bcQ+N9B6eZ7FL1U1Es0GdDo8bq3V4gcSGNwM4lssVjUcrkMJL2+HcPnDJIsnvqWLeyRxNcT2PzB3oiX/ByfAfaGfALD+OP1mMrM94IgoMqL6oZKMoPxkLVz3VxfNpu1wtnnvq4+R3G9XltA8YNbpIuUk/PK6HGBhaOCBztXKBQMKCFpIbFi+hhZunQpKcNyIt8YjUYB7T7lYB4cr+fvnvUhaZFkG5/fpdzse5hgNpbLpbF4XhuNQXnngXTTJ3iM7eV7c79eytSkyzlLfD8cDMHbD+xAPjAcDnU8Hq0niX4lfh9mks/wzcdMJcV4SDjo0ySIIhf2TDYOhMpLr9czJhq5HEOOkLcCMsJ1/cIxUYUncLFHueeAJciLWCymx8dH3d3dmaSGKhlgkj+STHZOwkMFHF/g7YIKPoGNfUNQwaFLskCALZFEYbPIzQHC9L4yLY4gSVWB6/DVcoK4T2RJCJG88TveJ5FAjUYjSZfDxr2EBvv2TKmXwMBaAkCZRkk1hASOAAr5goydJBIwgiSIyiHVJJ4ZKol4PG6+dzqdajwe6/b21sAt1U3UCjw735v6JQS4z30h6UJW5eVdJCOQPOxNFDO+7x2p1G6303w+t55bJOaAwG63q0qlYvsXH8/n4afxwxDAJDLIqdl/sPCLxUK3t7fWnpDNZlWpVGzPIj2np4j9A0YACBLTAI/EYO4JEjn8hU8cGdoDuAaUYqeAQWSqtDxQ1SFpBuyCL7geL43FlyDd5xpR0xCLUVIxOIOkFkJKkiV/2CVgl2TZKw7AKxBtntwl0d5sNjYEJVzXr5fJDfbD8x+NRqYse/v2rb7++uvAZNrxeGy2k0wmDQOfTuejGCBu8KvlctmqfplMRm/fvlWj0TBpKXuXeEJ8xIYgWklMPMkvKRD3iLk+xhFXvH31+/3A2aIel/pp5356NvcIwpH9ziKmIYvl/fjDtXrZN9VV4qwkqzbyPbwtelyDDUEig40hZsDg4Gc+jwIHE4VJjCEJUqmUhsOhKpWKUqmUKRI90QyWB5vgb1EIfe7rqkTRy7Ykmd7egxZKwmjnGbgC84K0C5DFxuWh8xlsarJwHh6gzZ8fBasH00Z/DwMaqFLE43GbIMX5NqfTySpbMLqcR8Mm8/0JSG580oj8AMBI/50/PoTeBiQJjMeGNWVoDu/ph1+wkVOplCXhODMCqa/EJJPJwAQoghBBkalxSFboM/PHEeDkfHWH3yFocj+8RIiASMJAb8xut7NhPgAVz1Q9PT0FZIzh+mUrEolYT6gfAoUTJHGCWR+Pxwa4EomEjcZmmhhABcYTKc1gMDDQRRUcG5dkUhoP8ugnhohBggfZ1Gw2re9Jktm1JLNVXktgRAoD84tP8kmXvybuAxIRSWaTXvrpx3kTqKnqE/T94vNIXrn3/MxX/LDVjx8/qlwuq91uBwI3JBaVfYKRn0bnp6X6gEvg3mw26nQ6qtfrRhrws2q1qlwup/fv36tQKKhWqxm4hbX110GQ9pXlcP2yRZ8hVSd8LPGSAQwQoSRnMOvYBBVFgKzvE2JvIFukEoHEGRmbl6mhapFkwIszeEkEsRt6WWl/yOfzJq+EcPHKIggn9jS+hiEcXulAdRD7A5ASd9nz2BJ7knvjK+yANnwPRJAHo9gqFT1+RsWICg5250kxbAVfxyAaiBpiIpUYKq4cMYR9V6tVi6f4GEhcpijSukJCWiwWbTouiohQevrrLJ4jcmnITfZ3tVo1bPX69esAnttut6rVama7kmygSqvVsqEzxF1freQZvn792jAVe4lnDNbGH4DFfUsU9u6nn/vKmidPsDlshveiWu4rj6lUyo6L8b9LsYdqJvcPLMrnexv1Mzu4HmIMsdJjVfAz18ffPaHG9yb2gkvAChSu8C1Iz1EljEYjLZdLm7XC/eb9mN7MxPZOpxPAtxBAXDsFIU+Oo1z63NfViaIkqxDSy4Djxzl7NoMbjUOFgWT8PgCIhywp0PBKb5FnbKTLYJ1oNGpSHd8syxlgL4EmfUwMxOl0Orq/v1e/31e1WjV5qpedsVGPx2OAGaBySUJK4CK4YHz8PsAZeRGskm/sBcz5hng/+r/RaFiwRZIC+OfeMfCAEc4kogQ0klHkopPJxAwgkThPPpxMJiZT4x5y35AAwYQ+Pj5akzCBmWElyJvi8biGw6Hy+bzK5bLy+bx6vZ4qlYomk4levXr1FwxUuP6/r3g8bjJtJGN///d/r7/927+14EZiMZvN9PHjR6ssMXwGyScSKFgx7A/mjvOVIpGIJVMvWVDYQ/pa2a9UD5C5AHiozFPlQOeP88XZArSwOYgkiAyqI/QvID9nL0oXPwMAZkor00vxQUxfBijgAzjHiqoBBBKsLoGX+yFdhnrEYjHd3t7a5/O7VOe9r0MW6iWsHoxLl2NsAO/YHcQaCXIsFjMJ3s3NTUByA4EWi10m0VFB6nQ6arfbf+3t/C9uAaQikfNod3w1R0mQpKVSKXW7XaugQ7BBjEiyJC6ZTNowM2Rx9DdJsmohdusHo3BNEKRUq9k/+HwULT6Gkhx6mSaEKUkopCGA8HQ6WQIlyYgsAB3fD5kzEi5PeEK04He86oHXEj89Ae3jM8moJAPNvkqDvwEEUvFBOUNvJVOciflI/E6ny/nKEGi5XC7QZ0m1AmnqZrOxgVwc2F6r1QzY8r15HtwjgHmYKP46i2qWb4mhnaJcLqvX6xkBwnMsFAoaDAYmMy2VSnp+fla73bbXfPr0KdAr7O2fs7vZswxlkmSxy5/tOJ/PVS6X7b3BoJBGXPtisdDhcLCBcMQUlCgeq9K7S88z9kLrGLEvFgtO3wVH+ntFC4V06c3nnlKMoHBwOp0Mp/r4zmK2CTESm/KJF0ka0niO/CC+UcCB0CFZ80kzpDcDgPh+tMwMh0OLjRBGnlzDxsE3uVxO7969U7PZNCKK+/+5r6sSRZIxAhvABr00Ttjr+gGGTO7jQVQqlb84hNczdjhG6ZwcwSzyWkmBTB6WFgkGDfSw4wRJX+FLJpO6u7tTNBq1aWUeXJJ4wvJ52QfvB6PK5vcsMYwIsk7uCyV6HD8gk6SRKVsEuH6/b8BCUqCfgWskmR6PxyZhogcUKQvfi2qlT8RJ1OPxuA2xQYYGcB0Oh+p0Oja5j2f78PBgcjUALM6BhH06ndrZYe/fv1er1bJkw4PqcF23eKYkJIfDQa9evbLzlXjeOMvf/OY3xp5RiWfgAxUsnKgkk6lQkaTfiAQRm0VmA8vqzzHkXCTkz76qIl0CD0CXah9sIWoET6BggySDVCuRdAIK8WEkrwzVIWBDAKE8oArq5aeQY8lk0uyKagDJFbYH68/zGI/HJkUnaOAbOOeK+0ty1+l0AtIyX+3kuvDL9DHvduczFHkN9xXfSXKNegFGt9/v6+bmxgAJYMjLdMP1yxfVhZd9vEioGebk+4aRayHj5rnhyyUFJghDnmJTAC/sDjkz56kiwaSCKF32mJeoUlWQZImidE40mdBJwsJ3JOaxL/f7vV0rRCjSd5JM4h6LZBCb86AR+/T9Yv4PsQ9/gO35MxEBcKidJAViOOomwGsymbQJwDwfr2Did7Af/AC+h2SPRIEknuSAo1B8xZT9IV2G19HPtd/vAxOuw3XdInnBxiQZzoPwzOVyRqJil41Gw5QCp9PJ/K90Tp7u7+8t7k6nU/V6PUmXybnEJWIpKq7/83/+j/7mb/7GMCG42ktRHx4eLO6Dy4l/m81G3W7XYhtEYyJxnp5P/IHYxwYoWmCj/ggKiESKReQDzC6gBQJlA4QoPsmTnZKM4EHWi2IB+x0MBpIUsFs/hA9Mgy1wn+gFXywWAYKM74zt5vN5PT09mSyYATUMw1ytVlZI4jPxcwztBBNJsut9eHjQdns+QqtcLtuMhM99XZUoknTB5DGBDYkEQ1Vg0pCIAnjY4M1mM8DywWBwk9nslNsBsVTeCCKAnsFgoFgsFjgolb4+StztdjsArDxIAggT1PxxEkjxOGCTahgGT8XE67/pPZlOp9rtzkdVUK05HA42fZSghwTFVxK9LhuWyOvmCdySrJeKpMsnXgRUAIh0DjRMgeKoDJ/0E/wIbH/84x/1/fffq16v21Q8jBMm2Q/N4O8ME8I4AQH39/c2jIPX/vnPfw6lp7/Cms/nxqAxThv2Ubo0dE+nU0kywMKzZlAVQQs7jkajJjelMu2HsPR6PUswJAX6WPlckiwCFxVvKs2+Rwg2lAqLdA4mhUIhoFjA6RKokLmxH700zQ+AoWqDqoE+HwAZFf+XtrZYLCxYIsHxwI4EFrumEsv1UCHwknKCOj0R2BcJKYd1S2d7JYBRdaCaio9kEALM8mw2M8md/2yqPv5Q4ru7O6t0UEnOZrM2vj9MFq9bAKdisajhcGhEGlLM1Wql2WymZrNpsWe325mKI5/PG/niwRUJKITQH//4R3311VeSFOgxJH7TFw/BQRyfzWY2qc+rdtiTvtK93W71zTffSJIRvchEsUESOIgeXwXERjz5hNzTk6rYNESv/zfpIpX1oN0nt1TkSZ5ffuaHDx+USqVUq9XM7/EcIMv4TlQjT6eTnW8KOUyMl2QkEt8B8o2R/FQ3qEDSV0XbCEn0fD7XfD5XPp+3vn7IMIbWAb79AKRw/fIFuQcuwm6YAooaqtFoBI5vICZQPfOSS4oK9JtSxCD+4IvZ+8vl0ip+33//vcVAfAX7ECLXn0UM6cD+PhwOarfbgcSMfj16fInnxEn2Gfj8cDj8RQ8whQXwNv/mCy3YJDgRm6U4wp7nfcAs4E/uI0eJUIzabM7npYMhJZmtU7XHL9Av7ZNTsA1Vv0ajoTdv3gTUfdg1ySZ4lgGAtNtQlBoOhzaplR5Tcg6e75cy3f+qRBEGBQdKdkxWzY3FafEgPnz4YJNF2bwcmQBI9aB0NpsZQPNnFnnJKKw57IF0CcL8Lq+t1+vGlGD4/rgLWBwAla9UIg9DTpbL5UwegFSo0WhYYkfwpfrJhmPjIk+BRSbo8RlsfL4rwBlwTADHmQCuKbN7YyLoEuAxeqZiwkr6viSeEd8/FovpN7/5jcmHYYqpBsJYvwTGfmw0bAyDFGBbCYCr1UrffvttKD39FRbJOIfQplIpq5b7+4td4TwBWjwzEiqSBd/TFIvFNJlM9Kc//UkPDw+qVqtqNpuSZD00PmjCkALi1uu1Xr16Zeduknyxl9nj7EmfGEqyCgB72I/Gly7BCpvCL9CTANNIIGGgABJtQCG9vz4Q0V+L3CyVSpkcnPsmyaoBu93OfA/Alf/HvsbjcYCoAlz6QV4szrZkwAL3zEtsC4WC5vO5+Wm+NxVJJFSAISqKVDrYD/58MEiEL4EN/ZwXCQ9+EqYZf3s6nUyqRAKHpJvnSKJFAkeSgSxqu92aTHg+nwck00ilJ5OJ+WUICaSOTFYlOYQ4gRgBLDLBkGE1/n0A2b5HmhgBeCV2SArEZpI1YiWDfPg+ngDypDJtFJKMUAKM+uoQlYfZbGaSPnyNJFM1kSAQw6RL33Qmk7Ez2Hy7RTweN/losVj8i4r+fD5XvV7XaDSyagln6+H7+L4kEY1Gw8A4r+PzGH7SbDbtGLBwXbcg+yHgiUdUy9gPxAF/sL10wckMGUPpRbKIHdDGw98Hg4EikfNh9ygF2EvD4dCSDwgdWgWIMyjrSGqI+Xw+eMurFSaTiSWzJHf+XF8SItR0/rPwTfwhNvDdIIWJM+x3/AfXF41GbVbBer1WoVCwdg4vQeV3iftgbt7bq5m8zwPn8IxI/CiovHnzRovFQtvtVj///LPu7u6sl7Rer+tPf/qTXr16ZYWl0+lkeGG9Xqvb7aparQbawPjuzIHAtpvN5hdBtl59jiLsAxWiSOQ8BWq9Xmu1WllCuFgsTBZCosZDp9+Q4SwARipi5XLZgJrvxeOMRWSmMIyw3wQmNqCf9rlarUwfzoYB+OAMYFkwHL4zD55D43nQNLd6cOeDEiCOwCnJdM5UX0maPANDYPX9ixiXB+v+qAsMgkQVdjESidgBsePxWJlMRuVyWYVCwRLEw+GgyWRimxmQL50Tj16vZ1UmJDhUUdCyx2IxTadTIwAI2KVSyWR4nk0iMUdeQZAO13Vru91qNBpZIzmVcAgOD7JIdJCNM7hJOleda7VaYLIm1QqkGv/hP/wHbTYbs3UcNLI0JHBUIpGdrtdrq+Ilk0lls1k9Pj7q1atXkmT7arPZGPClkgE5wlERAFCOaWHvA+B8Rdufg+QnEUP6RKNR1Wo1S45YfI4PbnxXeovY3yRsJJXpdNoSQXyLdE4Y6HvyVQjsNpFI2DQ83os+CxID3woAQytdzmilsgLg5btXq9WAVHW9Xpuv7vV6BnCR+VBVDCuK1y8IBt8TQwWYZADGnOoyz40hCl7WKcneDxDKUQpff/21Aa79fm9Vf8gEEiYAGZ/H75CEcYg4AFiSxuOxJaPEXpQ9Xkru5Zt8jiemsClISgAutu9lbYVCwQhY6SLVI15TtSfppM/ITzokkYvH4yZXl2SELDaMj+I4DuybZNDL54m9YJpCoaDxeKxut2v2iiQO+R4D9EhYfT8ww/AikYhubm7s/UejkSXsfPfD4WDHEn0p/U+f+/L98ySNHIXGwLR8Pm92waRiyEKene/FJbnBbmnlAa/6ypfvFYf0xT4k2Z7BFlnb7VadTseG4SCXhcCkX9IfO8PAHpI95NdcA3GGAYqHw8EIlkqlIuk8yXm/35uiCDwI4SrJ9if4QlKgH5j2EBJZikCz2czuBZiY+Iey4ng8WpGHYgiYHbWOP5YnkUhoMpnYiQY+cXx4eDD1Ab7szZs3hunx1/v93nxYu93W09OTSqWS+b7FYqFarWa+F8XUeDz+IsjWq0o2vV7Pqgk8ODY5E8zoB6QSQfKHY2ZTUoJncAQbkEBCoJJkztqPlKahlMAGECP4UornTCf+DYaEQMZ4eD6/3+8bg+SrfExnQ7JKAifJqiFe1kfiJ8kSSMAlmxtQTW+UP2ORkr2XMHAd4/HYvsNyuVS/39d6vbbBOlwzkp/7+3tjqJEPEJwBmACQXC5nU2aR2HCMCZ8BMICh3m63+vjxo10/FY1SqWS9nL5hn6BN034+nzcGLVzXrXq9rlevXhlBQCUcp8x+QkrDkQkkiVSPYNRxsCQxVBRILHiGVPhIPpG0PD09BY5mmUwmyuVyxhpybhr6/c1mY3sskUio2+1aXyUMH2P3mThG4MZmYf2lyyQ0yA8q79wLfEypVLKg7ZO8l/Ix7AZnT9DE3qhs8DruFRU6b3elUsmCOEkk91yS+SHuC72Zy+VS2+1Wnz590mg0st/zpJJ0IeQ8M07F4sOHDxoMBhZch8OhBXaqTpvNRj///LORQHxWuH75Yh+/nHxKbznPEUABUQJR6o/O4M9ms1G1WlU+n7ek5tWrV+p0OoFeuXK5bLGLaubhcJ6+C8CCTEokEnb8DRUO/Ph6vTbwSkJFEggBCWlBX6zHBuPx2OTT7Ed6hrLZrEqlktrttgqFgg3oOR6PgT1LxZzKAbEWEgwbpgLi75WfI+ArD8Q8qnb1et2wA2QUMrPdbhfwlxC5p9O5NzsajdpxFshHiZfz+dxsmKSc4SCn03noCf6T65vNZqYcIgkmUS0Wi3YkVmif1y+SrMlkomQyqXq9bkeopdNpVSoVI/4Ph4P6/b6enp4MyzFnAkxHfyJ2zeAw344xGo0C/bbH49Gqa8TxyWRixDzYNpPJKJlMWq99q9UyvInqj+nlEJJgWH82ObjS9xUimUQmTaxEEvvp0yeb8srxEBBDx+PRkkKwBG1R3CMSqtFoZEkwsWs2m1kxgtdKCkz/Za4A8Q8S5XA4aDqdWixnwjpJIraNTyiXy9ZCQiGICuxsNtNsNrO2Fz+lfTqdmvqxXq8bOUuugy3SyvbHP/7RcPDnvq6qKCKRqdfrViVAxlitVm06IRIZ74gjkfM0zpea6Ldv3+rbb7+1jU1Q84frIsHwiY4/241ECsedSqVsghvJJUFSkiWJyNBw1jCgvI5k93A4qNlsGljE2ZMkeUbEVxZ8vyFMSLPZtHuAQwD8ATTpZfR9GVQ+CITIjrhfVBaR+xHcAIpMJn3J7hLc+PzxeGzHmdBfAgNNFZNkF5YIUI3xbjYbNRoNexYMRqFKixPBAGGNw3X9Go/Hljyxn/h3etqQrzEgw1fFGASDVJMKdrfbtQo6FWoCHckmTd/0ISNfeXp60s3NjQWI8XisRqMROCaFaav1et1kZlQbfUUNKQv7j0oDSSyVMEAtFVHpMnEUv0RChCP3cjbp0s8pyUAZAdz3Y+E3AJi++s73ky6JG/K8VCplwJ7XAWwJKJBiVEthqeljIyhjY3xX2FOSUPoYSSbL5bK1CcAgn04nO8KHHpjXr18bMx2edfrrrNFoZBWKP/3pT/o3/+bfWBWLtodisWj9xq1Wy2y2WCzaMQvpdNr6nZjaTXIxGo3UbreN9EP6DYgqlUomi/r6669NbeIBZb1et2vysQlChthBwuOrc759AoIGgAcpBCECYEP+BgGFHIzZBSTL/P9oNDKfQWxmpD2fj60TC1EgeJK31+vpcDioUqnYNVHdlC5+g+8hyQ7c9rMUkBSTyHspX6lU0ocPH5TP51UqlUw1wf0DZ0SjUSP6UDxwtAcD6iDKuE/4E753uK5bTGxnoMnPP/+sh4cHSwTYU9Vq1TDS/f19oG3Ct+8Qm6gY0naDvBsyln2KAmU6nVrVbjab2fRtjlWCaInFYtaCUalUbCgaMYjXgs2lYIwDB0rBCd2ZTEbD4dDUSR77QurweuIndgMehYxCkQJxJV2O2wMH8P1peYEkxe685NT3PEsyzMLnevsjJmOjJMe0oFHFh8yiJ/Ljx4+Ge+kD9ceGca0UoFKplHa7nQ3+oa0DifIPP/zwxRA5VyWKAE0kGbCBx+NR79+/16tXr4zlgwUoFouWvEiyYBaJRNRqtdRsNo3xQ44JkGHgBhUNpDGUzmE+MRZKw8lk0qqaMPfS5XgPnK4k25zSOfOnd8cHUxhLJKRsfI6TYGP6BNGz+iSfyAeQKFDliMViJhFArkoFiCZ4QByAEiMB7MN0UOGB9cBwOKeKoIwEl4lUBBkMy1dFSBpxgL6B2Cf9AHqcCewPTb1+OIY/BBapwZdiRJ/zoqmaKv94PJZ0SVKen58NaDAs4/7+3vZGv9+36hxODoYU9hyGjn3ikyWCC+ANVvJlgz/9ydg0FUbsmNXr9exMSPbm6XTuY2YwDPucw3aRqRKEvc3SN+z7Dkm2vGTN9ylyHADfh2mvvqcJQoy/SzI5XjwetwFWANWPHz+qVCqZ/IWg6QE4aglIOWyZI3zW67Wen5/VaDSsSgrxtN1u7Sw4AI4kUxsMBgOT8v35z3/WV199ZYk19wMghF8Je4ivX1T/IAG+++47TadTS4YgIABDJGuSDKzBjDMUh9exB9mzkUjEqg3z+Vzdbldv3rwx6TGEBIQdMX08Htv/E9OoYAOI+X2q9UwUJc6j+GEGgHTpHQYXEIOo5FUqFWWzWY1GIw2HQ93d3QV6ZCF/6Zenz4r4jGoG4oU4BxAmvqI4oPUCwMt3lS7j7l/2QpJIAmaphuK3GExDfzE+g0oFf39+fjY7p2LqCVjvBxn3z3dElQFh5PdLuK5flUrFpm9uNpvAIBhPjg8GA6vMQVCAo/y0U4j8fr9viRoKOV7HSQAULiAu2GOSjITAJzAEByIBHEa1HAw8HA6tkIFPoNpGPGd/+gE5g8HA2sCwO+YBSDJMTizEZohlkox4ggSh2EA8oWDC+4GlqTz6467AyolEwt6TPU+LC5JhCGz8IEpEfB73iXux3+/tCKhIJKJut2sEHXgAHOMHQOIH1uu1Hh8fVavVLPGkUIRsn2f8JayrIj1sn3SZQIj+ttFoGHMO0GDxkHq9npXQGcpAXx1BECkaGxbDI3ki6QPsrddrjcdja+4FJMICsCnI+pGGEMD8kBvvqDl3xgc7JJtPT0/6p3/6J2OCqO4BwtiAGIJP2qgMMr1uuVxqMBhYNZT7R5VtPB5b0OMPk1z9cBxko1QnMUzkb2xa6TIUCBlFuVy2ioIfcPPhwwfr82CgAp/HdXI9VHuoVhJkeaaSjJ3meSQS5zHL3ijDdd06HM6j9HFYvm/ndDqpVCrZc6rVaubETqeT9TLyd6rDz8/Pgamo0pm88b2n9Lfh5JFH//nPfzYVQKlUUrFYtADI+xEAXk4UTafTuru7s30uyYJ3o9GwvkkOLfY9e0jlSBxPp5NJvkj2CEA+aWSv07NAYort4Qe8/ByiAxsjMFHtQZq7Wq3snrXbbWMn6b2gr3cymVhfCEm0l65TuU8kEqrVaqpUKhZw6UVmAALydvo/IL7u7+/tXK5Xr15Z8n08Hu074MdJ/r+UiW2f8/KJjFd1ANIAZsRFjkIYDodGItRqNWPtIQUYjgMhA2HkqxZff/217WFio9/j9Fn5CjZ7nEE39GiR9HlAvNvt7Mgmr5rp9XqBij3E8XA4DPT6TKdTDQYDFYtFtdttGygB0PZkJAkddukHcdEmQlxHdkYVBNIJO0XuS+8XEj/fQ0iCy70ej8fWm0XMl879Wv1+3xJSSer3++YLSO4AswwT8sQU/dTc93g8bmAexQRYAZ/H/gnX9Qs5b6FQULPZtOPCvDqFGEOFEOKD5A/CDcwznU7Nl7PnjsejHU8DSUJ8gZykN5CBiRzzUi6X1W63VSwWJcli5OFwGYpDv2OlUrH4CVlPIcBjLgoWEJucx8r+Z/o1cnDiJTjw06dP+sd//Ec9PT2ZjNUPXNxut/bvvu2DxA/MQPWQuMq1eRVgOp02O1itVjYl1ieRVAPpQSQnwcYYxMUzff36tUnT/TAwJqCjssKPjUYjaxmQZLJfqpsk0IPBwHqUfY/m57yuqih2u11j/ZkoGo1G9fz8rG+++cbY/2j0fPZfpVIx57XdblWv1+3AYRwzw0wALbApXirHiNrZbKZ6vW49VDA6xWLRpjfBEBAsWq2WBUMvp6N64ifKAVoJEP4AUlhDpAV3d3f2fsjISJhgOaheeDYqHo+rUqmYDjqRSNjkRwAsgAw5J4ZBgpZMJvX4+Gj3CHmOn0wlXSQzVIQikYgN7UB6h+yGcc18v8FgoNvbW0tmqXYQ4OmLiEQi+vjxo25ubiTJnjfN0DDFDOJ4fHy0KXPSOdB988039l7hum7BvHH4L44ckgQpIlp+nsvhcLCeORIUwA8VRhhwGrsJPuv1WqVSyQ4ApnoHG8t+xt79YBz6ln1F0PfQsjd9lZG9gjIAUM05cL4BHydNnwSJpJeGM+KeyoVXLHAPJNl18V96KZF8AvhhQvlMfleSgWwGyBAUuc+Hw8FACfcMf8s1UY3ywwao6O73ezWbTRsGwoHEDJqCDKJKsVwubd8Q0Dm8ncSSawgrFtevTqdjxAlJ1el0stH73W7XyBz2KuQiFQUkqDD6VA5ILqLRqH788Uc1m02TuUIeckQD0lV8gyTrYwUMoa6BaCFZhKCNxWJW2cbX0NcFgYjvOBwO9pmn08l6rPjsXC5n6iQSJOnSpuCr4lyfPzYLcpK9StUcX0ciyx73EnRIF67DE6HIOyVZQitdJi9DuiIXxO4hSCORiCqViimfqMjQz//hwwe7z5KMCKenkYQBG49Go+p0OtY35oks76vC9ctXNBo1JQ5qDxISkik/CwLCB/w1m80CR0m8ffvWJoMT9yRZhY+KFDMhIJM4hxH7p8cPjMhEcT/UkHgJWcP34XQBL42lOOJjn28DgXjk33g9SRkxabPZWJz9/vvvDSeMRiMdj0cbEIfMlDjFZyJdR8bKPQBf0PPrZbN+oJPvz/eKOMhbCjqJREJPT0+SFPheVCFjsZjq9brNIkEyzvtGo5cBdEjCfZUVXEAsjkbPs0dyuZz6/b4SiUTg7OfPeV2VKCIFgXGjWvXw8KB//Md/1G9/+1s7X4bEg14EGEA/+EE6szf0LBAUvVRLkgFAzviKRi/DF2BUOFOI8jPGiIyUXkrPRPjzEGFM2HwEahgjStBo06ULcKQKhxEi+yLBI8Aj4SQQYaQkvkhTcP4YCsGMABqNRm2yFY6GHik2uK/+PT09GVNNI/JsNrPfRUI0n89NMtRsNg3YE5AAw1Q4YM3u7u4CTFg0GrWyf7VaNanpjz/+qJubm0DgJnjyncN13UKScjgcTM72+PioN2/eWBLvqwSVSkXD4VDVatWm6iElm0wmqlQqKhaLNvyC32Xi22azsXO/YEmxdwZKUVnmKADkGgC3dDqtt2/fmnSdgRE09iOxoTpKcgf7SeWLRBdQvdlsdHt7q9lsZgQVQJHEjb1JxR9wyM/9cAwvXQfEAQBZgFWSOn6HQMdrsc1Y7Hz+KwwrYJnPgTjzVU8CGaAdFpQA9fT0ZL1sAGXej0AGMKbnA/82Ho9tWANJCIl2WFG8fnlbodJH0tjpdEwyJclkkdgNyUSxWLSqMXGMHmJIhoeHB5OVUtHLZrNqNBr2HFEMMBF5sVio1+sFDolG3knVhBhIhe7lGHxaTbBtEi1iIEmnJGsTQRKKbe52u4CEHLuHWJFk/o33A3xKlxYTJhtDivqqJrEPLELVB7zi1QYk51RnqSLiRwDQkHC+F41BRb1e7y9UBF7Gl8/nAzK31WqlUqlkVRGuBZvme3H/wDXhun554py+UBQo+FhP6PvqPXsfIqFarapUKtlwquPxaP3eqHfy+bwVVYh5vV7Pzs/GxsFgYEESURQCvD9xGluWZEoRT5JgO7QtkeARnyFjGJgDPiUGY9/0QUKsIkuHCPaDESFx8B0s7Iziih/c1Gg0LFkjMeN64/G4TRf2pDNk7Xw+t97ESCRiZzfjR/CL3EcIUa4ZvEsvM7HSx10KMMxwQBo/Ho+NZKDv/EsphlyVKCKlgFnDgUWjUX3//fc26MaXcynB+mqX70+AGfD9AGik0U2Thfd6PdXrdQOwPCyu62UCStUTbTGJH2fTUBlh2hTJD1PmYAYZDkOlRFIATHINbEaYPZJOvynZWNxHgDGJLT0VkqxplsTWy2d5PRtckjXqYtAAQ6o0Xh/PNb7srfIN2zCf/IxqiB+CgwyWRmuMolarBSbbRSKXyZRIA2CaGR0crusXSR3PUJIljNjH6XTS09OTkQFUApDBbDYbG95EEzzSxFarZXup1+vZkTMkE7Ccvh8IZ8wz5ugNKvCHw0Fv3ryxa/EDGtj7VA2RsZOMsn9fTpHDdiUZQPWVCogJKvn8LrbDAriy76lY4Ps82ePluXwv/o0+SUgiSRasAQcAQO6Bl8fT1wGRRNDx/UxcI72f7AXkvPhiKpjv37+3AR6oG+gVhQSkWhqJRKw6Ga5fvqicAUbYlyR6o9EoEJuo+AM0YPs5t2s4HJoU+3g8mmzZ9/pQ9YhGo9bvyhEoyLyTyaSenp5MAkeVcLFYaDweW78qQAy790kSvcTIR4kdfA72il9iYqDvHWKP08oA8cQUQyRhxGSAKH6NzyLGYi8ksySAYIv9fm+/T0wkhtHKQlUTfwQ57K+dATWLxcJ6IPlMYiOxlQR3MpkYATYej42wglTy1SieE0no4XCwAUaQOqH09NdZxINOp2N9h8gJiVv08q7XaxsMxn5Op9PWRsA+oy8V4pK9kEql7Agk2rnG47EeHh60XC7V7XZ1f39vqi5anOr1uhaLhZ2byJ7HJpbLpal5jsej+v2+Wq2WyV4LhYJWq5Xm87na7bYlj+xzbBqVgZfS4p/wTdgp+5PCCvvZK9zAoJCp2AWKQU+g+X5EVAiSTCmUSJwHY93e3tpzo2ji21C4DkkBCThKHeyGggi+F5KKuAkJxuRmJqpDrh6P58nMrVbL5MSPj4+6v7+3IYOeVP6c11WJIpUigAPnH3JDo9GoaZJxtCQDVAxJShaLhUlAmYDp+6f6/b4dCAxwoXH/ZdkcFp9rBEBRmZDOgI+yt5e+Hg4Hq5DirAm69BQh1+GQTZ8gEoRWq5Vms5my2axNJqvX61apYGPCGnmnD9ADyMJ4xGIxffr0Sdls1oIFQQYH4Cex+QmoAHwkRCTA9IKS+MLgMPyE0j2Bx4N2BtRIlzNw6A0l2PL7vV4vAFioxOKA+AyMGAlSuK5fMIqFQkHT6VStVsumH2KPyEEgSgBDq9XKzu6iX5jgSMX98fFR9XrdDppH78/eo+J2c3OjXq9nLCoTVavVqp0hBZvPviQYcG3IPPAT0mVqMUkb/Vn4AkmmEGBCKTaPtNyTNfP53Nj9aDSq+Xxu0kz8GMki5yKyd73czQ+Pgm3kM6jS8joIEmwR3+ITTJI8/B/kChV5QCnAEfLI2xPTFekBY1LqYDBQu922e+sldb7aDwBhVPmXwoh+rot9BDEBGcjEYCqIDMjIZrOaTCYmfcvlcgF/LskGJDB6HwJkOp2aVA6ZJ893Op3aUB1UOLe3t/Z8iaepVMraN0i66FMcj8eq1Wq2T6fTqYFHTxoCFCExeW8qZOx3r7pB3uZlXoBT4hq/T5II6CTOYrteCsa5bPgdVAl+PoG3a2IxCiGuE1sFa1DVwdaIhb6/n+/Bdwc7jEYjSzqxZSq5t7e3lhQWi0WdTic1Gg1r7Xn37p2+/fZbdTodm6sQrusWWJEq1na7td5vYijEHkWG4/F8DBIVX0nWl1itVjWfz/XVV19JukiaKXggMWWPUrnebreG+9g/tH5hi+w/9uDj46Nubm5ULBYtZux254PeqZCCHePxuFXYaPGisIB9YUOo1bg/ECiQwUi/KWRwzrH3A9gr18ucDS9Pl87qQJI1iigQPFx7o9Gw6+HfyQu63a4l7/gPsMD9/b3Z9H6/V7fbVbVa/YuWFZRP+Go/DKdUKpn0drFY6ObmRp1OR+Vy2WyQ78wZ8pIMI38J66qrJJDhaJF24OT6/b5isZgdl+EfEKxlNBpVr9dTqVSyM15oXp/NZioUCtbszQbCaOLxuN6/f692u21sHFIsytoERJiFaDSqbrerWCxmbAB9fTh7pK4EJtZ2u7UN55lOZDewCPRWsgmoxsAe8P8w+wQRghEJGQC2UChYgztsFHIfjBjgSXJKgJZkbLDvx+DakNagzY5EIhoOhzY9C4fDteBAkKUS7DEcQDsApNPpqF6v2xlYTMglGaYSBPg8Ho8GksN1/YIc4UxNyBvpDCjfvn2rdrutRCKh4XBorCGBR1Kg4h6PX45MgS1sNpsBCRTBCuBLxRo2nSoIB9JKMsk4RIV0meoIkPSDOOjhwHawC5In9jPBgoSPvYdMCObWKxVIMpGD0ayPTyHgoVzgzCpsn+uij5d7zTmy9HAirSHQUtmDtUwmL+dWct+p8vshUxBkiUTC5IoEZAieTCajT58+6fb21p4r58Ltdjvd3t4GeqeZjEkSyTAgJPXIDsN13drtdpYssG+oLJOo4IfpV2MvYTvEQ9Q4DFjh7/TGEYfY10hDITlQwmCvKHQSiYTJRYmHHuChYuE4LAZMMT6exBH7gYSiwogcrNVqGVFIggc5RDykggHhgW0jkeM1AFZsfb/fG5CDhIbcZk4AZJRXuBDHPKEJEcxzQUZHdZ73xwfwXoBjfBP3ivu32+0smScpTSaTen5+NnKP+IvPQQpJC0m5XLYpqPjNcF23jsfztGqSKHw6Sdt2u9Xf/d3f6fe//73ZsG83Yu/5NiLi3+FwsGMvIHVGo5ElRpLMVmu1mg6Hg56fn22Gh99bkIPEBE4eAPsxqHE2m2mz2ejVq1dmJ8RO4iW2x/cHQ3L9VEB3u53hdGwb4gbVDzERYpG4iaTcy669DyMWUcHkvqNSkmTXwUAqVAp8TjqdVr1eNyUQfZ0kj+QEJLfFYvEvFHLEQS+HxU9NJpPAd+A6UWIQh71i8nA4WK70pSjnrpp6ioOEjSMRolydyWS0XC4NkPhyPJWmzWajVqtl8k403ASvXq9nzAfAFIYHZhVZJKCH/wd8kVSxiUqlkskBAJVU8KhQIqlhEhIBhMqE78Mol8sqlUpWPeA+INsCsJIIAn6pXHC2FImqb9wFABKQfKUUFoRKD+Vt6TJNj6AJY8V7AMYBznwObBMMDwca+6oioDgej2swGGg+n5vMld5Qqj0PDw+mz55MJgZSZ7OZFouFut2uyYCZxMhhr6Gs7foFYKDpmio6/UivX782AMPUNmTaVJbocfWMHcFot7tMJGZUP2QFDhMAh63mcjljyrEj9gyMIn4F6Yy3TUgWADZgGOKGqjZ25ittXpoKCwhphH8gWMKG4sz5fL4/NoEklIO0CZj8Dn6hUCioUqnYZEPIGkgqqkN8DhJd6Twpkc8leEUi57Hd9I8CFL2cHX91PB51e3trQZ7rHwwGBh5IQD3T6yuhHz58CIDmcF2/iIkkhTwDQIX3nYAnetBImJBWQwRFo1HrQY/FYmZD7CsSNk8a5nI5G+zGsAUGwvnpvIA83+NUq9U0Go309PRkKhOIBV5LbGbCIq0qyMbb7bYBS5Io2kR8W4SPuwByYg22CXjm/pBIQ8KSOEpSu902+TxJMfZP3Jdk9optQBgx5ZC4JsmO6ZFkyinu2/v37+0suX6/L+mSCFSrVdXrdXvuKDqYvgh55KV9kG7gCuyf+xSu6xfT9vGRFCjAZJvNRv/+3/9785kUQMAvEBIkQt1uV7e3t3bWbTJ5PiP07du3RspQFIAU4Ng4bLvX65kvxw6ZAYL88f7+XoPBQB8/frT4CAaQZLGD68M+wM60OREb2YPEaCTrTMkHz/vpocR98CzEje/9BctC+HLPfRyVZBJZSBhJJgFlaj9ky2g0Mmk8Pg77hQyDkKMNhKQOogY8jmovEjkPvOR4EYbXQQaAoyRZ4g5WZq/Qo8zAny8lUbyqokhFDIDFl0YWhqTtw4cPuru7M8OAUffSN8AgQzI8A47DWywWVpYfjUbWl+hZCEAqAI1NwINHLkaQwtDY1IzOxVABnJxRxUMHRCaTSX38+FG3t7dmDDCJyLUIEhgHjgQWENBHkssGo/rKZvd9j4AFNi9SUSqch8PBjgygmgSrxQRYJEEwPX6ine9vJChxPxns0+l01Gg0AmP0cVj5fF7v3r3T7e2tNRfjgDBE3isej1vTPhXen376KUwUf4XFPnn16pXJmhiPjSKACgX70fffIh3nOWGLsVjMKos4dIghJhCzf5BqUoHqdruSZJVyemaRhUciEc1mM3sN+96PmO73+7q5uTGWEFDqA44PRAQuXueTLogQ2FECCUyml4cBBKgMekkZAw14jZeo4IcY3Q0wJ3hwLYAB/Nd0OjV5i1cTEChbrZaen5/NpwCSIYf8FFtseTqdmn9jIh4/A9QTOLfbrSk1UI+8ZKjD9cvXfD63ygIMNdVi9jrPaDqd2s+xByq/VBjYX0hAiS1UA7E14h9DqEhAJpOJSdDY71QGqQQiTZekwWBgExyJW5wJyOdJlyOY2C/YGqoXkjPfswt56qef+mFYgFrkqYBFADH9j0xGpwLn8QrgHnuD9JFklUViF/8OWJb0F33M3FeUFJICU1O/++47kwm3Wi3ra2aAEO8FrmF43Ol00v39vSltDoeDhsOhJJmMHuk6z+RLmaj4uS9iH6R8v9/XmzdvrC+WZMgf8UWvIaQPld9yuaxcLmcSc6T+sdh5CCA9c75Cjf9mrxNbKXKgguP9SP5o54KkQalyOBx0d3cXGI7m52gMh0MrOGDvxFJJNlyLvcmwOCp0+A1iMwogqudgTq/0Yc+jHiSJA3dCNnu5KQpFyHDUBrSk7ff7gM9DEcc9hlzDB4JPOTcWDAw+kGRxGPzOEE0wNJ/F/fG95OQ+3Ce+55ewrkoUOagbKSJAQrqcnbher9Vut23TSrLNgbzEl6PZ4Nvt1hy8B7TSZTooE0R5CGw8FsboG2HpV+B3POiKRM5TkKQLy0/FJJlMBiZMkWTFYjE1Gg0b7OCP1vDDAzwbSnCUZAwVbAbXj3yI9ySR40DdaDRqjCTGzuRHyt+MSSeR9qwGMoZoNGrjwPmZr3bgYHheVCGq1aoZaKVSsaBPookee7FY6NWrV4ESPACG78uAjOPxPDp5sVjoX/2rfxV4luH65YsBFM1mU9lsVh8/frRpizDRDBpi2mK73Q6w8x5A4fh8fyxECmOtOTQaoLnZnM819PuGvUJyRWLGWYO+Uo7dYJOc9UdPL+x7s9k0mZqXdiQSCfNP9H94aTcKCEAnSaDvh5Bk1RgSMwAkfdr4PY6dAewCHCORiEnWvPQMhYQHjlRmCFLYK9eRTJ7PRoRhBWgTVP1URu4HklHe07OsPC/kbVT6P378qEQiYccqSJeBXeG6fn369Ek3Nzfmv7ENfHK5XNZ0OlW73bZJxFSpSeCxFSqHq9VKP/74o+r1umq1mpG64/FYqdT56Bj6/dlzftooIJKKNPJjBrHwh1jmK6JcH/ZNQudlluxxYiifSWJGdYLElb5c7g37lsq4JFM2xONxqzJS2QFT0N/lSWKAn6/CS7LEk2fB3keeRmJA5d33FtOrxnFSkOJPT0+mPkJ6N5/PbZAIbTm+mgOGIcGkX5XzM6nQUtXgaCrOTQ3XdWswGNizmM1mNhQGfwwhQKLhe/RTqZQpuZh2ylR/XyCRLr2Q2Crkhx8kRVzhWeMHKEwQIyUZJqTA4vt2ScCIZ1TYUfiBkVG3+UFwfAb/TkWP7++THxJXr5KRLoMwiU9ITwuFguUUxCB+nzY1+jxRHiGtJ7+QLkfJ8Tm+Io8P6vf7yufzur+/D5AB+DSUNmARvj+KBkgoCjwUqpjTAjGA0gPlHa08+N4vYV2FxMm2h8OhOUbPItKn6MvdJIfSRUqCg4SJY6w3CWipVNJsNtNwOAyM9B+NRpawSQpUvkjmfAP9brezoSrISqlOMsWJJAd2gNI/htBsNlWtVs0ACWQYNNcCs8t1EdBJFhmWMx6PNRwOrT9pMBio1+sFphaSoBHkqN5RufMLuQ5n0MEE0VtBnwvsC/ru6XRqEiTpwoIylICAC3vtASlVJO4xyT+VlNVqpfF4bM+Qhl96rAAZnMkYytp+veXZaj+xFAk2zp0qOmDHs+0Mw4lEIjYdEUbPTwBjr+D42Q9IogCwJI7SZfIqe5R+1263a77i+fnZkhiSNC8p4TqRrAMkISb4DCrr5XJZxWJRtVrNBmZh+14qA8GEP/ETyrhWSYGeZb5HLpcLHLVDACVQYcswlIADgiXMLv3OBHMA9el0Uq/Xs8Ek/rwpAtBut1OlUrGqrGeWCcZMee12uxb8J5OJJpOJnVd3c3NjoL5WqymTyQQqxuH65Qsygoo0RGMqldLf/d3fKRKJGOs9HA7NTrEFiDeADvvE9/z1ej0jKYiPvip8OBzU6XTsmnjden0+9PpwONiZm6PRyJJKwCXVa/4LAw9w8sNcqFhDKnlZLMCWqnoul1O1WjXJON91NBqp2+1qu90GDrn22AJgjz2WSiXV63XzhZA7xCr6tvgc+iIlmRKAKuRyubRq3uPjo5HEqCawc3wK+OBwONggIGLzaDQy/1etVg0A4x/4nsPh0BJP7g/Xg8KJ1wFkUR+F67p1f3+vdDptSQB7jPs7m82sYt7pdAwPscdICsCn+N7T6aQff/wxkGj+6U9/MjkxOBpVG9WybDarYrFoxRlsdbvd6vHx0eTRFCsgNrAFlD+oSGirkmSKAEl23jXvKckwsydcGJS1XC6N7OX74M8gk30Lk3SR9ZZKJe33e7uXi8XCZJ5gXYjO29tbi/X4HK+IkWRELqoY1AiJRMKGfxHTIF1pvZFk2Jb4vNvt9Mc//tGeDd+V79nv9y3+73Y7ffjwwXwIszhevXpl+wY79gqKz3ldLT1lM8CwUI2i2fvu7k6SjFHp9XrW9C5d+osk2aamN2IymaharVrVySciVAHYHADH4XBok/wYFIAEi8mFlMxhYwC5BDXYOQwdA/cb2TfGAr59VS6ZTGo6nQZ6raiCeh00n+k3M+Ca4yjQj+OgAK6AbT+oB6BHYuk/i0lUXkfvnQiBEaBwOBwMLPP+AHCqpCTGHO4MCCYR9eeuebkBEj/PAlWrVfX7fauGhmzo9YsknL4Z2HmklV6SjCyZqhfPCsIkHo/rxx9/tLOYAKg07rOf6bNlLD62g3QZhQDSVaSO2+3WKt0ECxJdJo6S9JHsrFYrq/ixN0mmqOx3Oh0DaFRJ6Lng35ClAzgJQvg4qhbsfYIuFVkfaCGZsDffh8ah9/gEJEOc7bharezMLi8b4r0kWSWk0WjY96BawrTler0e6JGg6uqleaPRyKr7/iiDTCajcrmscrls4Bd/dDwe7QBpSKVw/fIVjUZVr9f1+PholQYS/9/97ndWLfTnuEkX6WGz2bRq+3w+tz0NwUEFgL9jsySLsOvVatU+Gzkdg+WoYvA72+3WkipfzfNSSaSuJIz4EvbraDSy5BVJKz28ECfELWIqtsWEQ6qA7GcSU+xXUuC9sHsmpvpWGUhlEkXioY97kMmQ3qfTSQ8PDxb3UAD5vivfv8j3JA6SwCLnxrdRaTocLtNOibUklslkUt1u9y+OtuJIIwjccF2/wDIcf8DznM/nZiPEkJubG6tyoWBBYgjRAubJZDK6ubkJ+Oc3b97ocDjYkCXaHFAbvHv3Tjc3N2o0GkbUQurWajXDi5vN+Txj4hUKMX5WLpetd5d2KMiFUqkUqF5SNWNP+0FLEC+QMn7QGpVA9qckIzFImElY6T30lTtJgZ4/cKvH2+B/ngtSe4gnVIeepMLupHMbDPgC+2X6ajabNfxaLpf1N3/zN9bKI8mmEzPDgUXuAy4ZDofWBw0ZPx6PJV2mSX/uK3IK0Xi4whWucIUrXOEKV7jCFa5whcutsAksXOEKV7jCFa5whStc4QpXuMIVWGGiGK5whStc4QpXuMIVrnCFK1zhCqwwUQxXuMIVrnCFK1zhCle4whWucAVWmCiGK1zhCle4whWucIUrXOEKV7gCK0wUwxWucIUrXOEKV7jCFa5whStcgRUmiuEKV7jCFa5whStc4QpXuMIVrsAKE8VwhStc4QpXuMIVrnCFK1zhCldghYliuMIVrnCFK1zhCle4whWucIUrsMJEMVzhCle4whWucIUrXOEKV7jCFVhhohiucIUrXOEKV7jCFa5whStc4QqsMFEMV7jCFa5whStc4QpXuMIVrnAFVpgohitc4QpXuMIVrnCFK1zhCle4AitMFMMVrnCFK1zhCle4whWucIUrXIEVJorhCle4whWucIUrXOEKV7jCFa7AChPFcIUrXOEKV7jCFa5whStc4QpXYIWJYrjCFa5whStc4QpXuMIVrnCFK7DCRDFc4QpXuMIVrnCFK1zhCle4whVYYaIYrnCFK1zhCle4whWucIUrXOEKrDBRDFe4whWucIUrXOEKV7jCFa5wBVaYKIYrXOEKV7jCFa5whStc4QpXuAIrfs0v/9f/+l81Go3UbDa1XC41Ho+VzWa1XC7VbDY1HA4lSbvdTsViUbFYTIlEQtvtVsfjUavVSolEQtlsVpvNRtFoVIvFQrlcTvP5XPF4XJFIRIfDQel0WpFIRLFYTIfDQavVSqlUyv7s93vt93slk0lFo1FtNhsdj0edTicdDgclEglJ0vF4PH/xeFzz+VyJRELpdFqHw0Gn00nxeFzL5VKpVEqbzUbj8Vj5fF6pVEqJREK73U6ZTEaRSMQ+MxqNarlcajAYqNVqabfbqVAoKB6Pa7FYaLPZKJVKab1e63A4qFQqKZFIaL1ea7vdKpvNarfb2X3dbDbKZDJKJpN2vVzzfr/XZrORJM3nc0WjUVUqFR2PR0WjUR2PR0UiEUUiER2PR/v33W6nSCSizWajeDyu4/Fo9yUajWq/32u73apSqajf7yuVSulwOCiTySiTyeh0Omm9XkuSEomEjsejYrGYFouFUqmUffZ8Ple5XNZ4PNbhcFCr1dIf/vAH3d/fK51Oa7/fKxaL2esTiYQikYhGo5FyuZxisZjW67VOp5P+03/6T/aZ/7fX6XT6q3zOX3v95//8n5VIJDSdTtVut/X+/Xu1223tdjttNhvt93vF43GVSiUNh0PVajXbI9lsVtvtVvv9XqvVSpFIRKlUSqfTyfZePp8P2N52u1WhUFAkEtHpdFI0GrV9l0wmNRwOVS6XdTgcdDwelUwmdTgcNB6P1Wq1tN/vNZ/PValUtN/vdTwetdlstNlslEgklEqlFIlE7PpjsZjS6bQWi4Wq1art69PppM1mY3aaTCa13+/tvkQiEUlSLBbT8Xg0X7Pb7ez/4/G47cVEIqH9fq9UKmU2FY1G7dojkYj5qGj0zL9tt1slk0nzWeyx9XptPmK9Xmu9XisejyuVSikWi+l0Oul0Otk9xHfgP7mW/X6v3W6naDSqVCql7XZr/jKbzWoymSifz2u9XiuVSkmSut2ums2mut2uKpWKttutcrmcttut0um0JGk2m6lYLJrvnM1m9qzx07vdTslkUv/xP/7H0EavWP/tv/03TadTrddrlUolzedzbTYbNZtNvXv3Tq1WS6fTSZlMRvP53Oyt3++rUCgoFotJOscG4hC+P5/PKxqNmg1vNhsVi0Udj0d9+PBB7XbbYmWxWFQ8foYD8/ncfD82ttvtdDgcVK1W1e/3td1u1Wg0NJ/PVSgUtFgsFIvFFI/HFYvF1Ol0VCgUbP9JUjqdNpvY7XZar9dKp9PmGxaLhRKJhHK5nNkE/oP4uN1utd1uVavVFIlEbM9jo7vdTsfjUalUSvP53OyHn0ciEbMlfu9wOJi9S9LhcFA0Gg3EKWwI3IKNE/t8HD0ej5pOp/Z+6XTaYp+3RWyX693v9/adwCv4KfzZfr9XqVTSer1WLpfT4+Oj8vm80um0jsej2SV+L7TP69d/+S//xWLffD63eJPL5bTb7WxPgwX3+70Oh4OSyaSy2aym06mk8/OOxWLa7/cqFAqaTCbKZDJKp9O2PyeTiarVqsXS/X6vfD5v8ZL4cjqdtNvtDNelUiktl0vlcjktl0uL69FoVN1uV9lsVoVCQcvl0uKWjy9gS3BxJBIxrA4OwHbW67UqlYrZMdcEHjwcDobLT6eT2Szxkd/JZrPmf4htu91O4/FYsVhM2WzWcAeYP5lMKpPJKJFIWKwkDk6nU6XTaeXzeXt2xK90Om2/w3fh+223W/OVx+NR+/3eXjsejy3Wgz+2260Wi4XFS/YDdo1/YYEnttutTqeT+UTi/Jdgo1cliqPRSNvtVpvNRqfTyZKk/X6vwWCgXC6neDxuiVIymdSHDx9Ur9e1Wq1UKpUMLK5WKwNPq9XKkpP5fG7vywbE4DKZjAUoNtvxeNRoNFIikVAmk9F2u1UqldJsNlOlUjHQeTwebeNKlyRsOBxaEMnlcspkMlqv18pmswHALMk2PBut1WopFouZ0ycAEkwwqGw2a5+JM4jH4zocDpLOwccnmJLMwAmiXH8ymdTpdLLvnU6nVSwWLQhFo1HblMlkUslkUpI0HA5VLBaVy+U0Ho+VSqVULpe1Xq8tyT0cDgbIAQ7ca5xNuVy24JxOp5XJZLRarVStVrVarTSdTvXDDz9oMploPB6b41uv16pWq/r06ZNqtZo5VgD3v9Sg89de8XjcQMdyudTt7a2BiO12q2azqVgspuVyqUajoeFwqEKhoO12q+l0asEun88b8YKdAvaSyaTZD06aPZVIJLRcLpXNZvX09KTb21sjP/b7vZbLpZEnBFTIJgBvMplUIpHQYrEwwIg/gWTApkigCC6LxUL1et3eh4A1n891PB6Vy+UM9LGPfRJIACT4r1YrS0YLhYKSyaT5MZI73g9gzLUQhAB7kUhEmUzGnhUED0HMg1OCLkB1v98rnU5rt9sZqbPdbhWLxVQoFOweSedk+N27d3r16pUajYbi8biKxaIlHyQV4/HYgv/T05MqlYp2u5358fV6bX4Rnx+u69Z+v1cmkzFSr1QqWSz85ptvLOaMx2M1Gg0NBgPl83mVy2VlMhnt93sjOLvdrhqNhvl+nhcAt9FoSDqTpV999ZUmk4nFw3K5bIArHo8rl8uZn0gkEva8sfHhcKjJZGJ7kKQIsHpzcxMAssRuSAbIDwAY+3m9Xqvb7apcLmuxWJhvkc72mU6nDbhCmmw2G7MzHw+xGUkGQqPRqNlcNBrV+/fvJUm3t7dmc+l02pLjaDSq4XBoJDWJGNfK8yPR3O12Wi6Xgfu53+/tO0FaYa+SlMvlzFeBVzxRBLiuVCqKxWL6wx/+oHQ6rel0qnK5rPl8rlwup9PpZL7qJTEWrl++JpOJCoWC/vjHP+rVq1eSzmRaPB7XTz/9pPv7e338+FFfffWVlsulkR6z2UzS2Sba7bYOh0MAS4INST4piBBjSNKI1ZIsAWFvxWIxxWIxzedzi+PEEJ5/vV43+wBXS7LECxtiD5NsYnc+1lAk4JqIn1zXSzsoFotGonjymGS2WCwG7CESiahcLtu9hxgGC0AMR6NRK3hEIhH1ej3N53MVi0XN53Ntt1tVq1UjnsgFuIckmalUSoVCwTCu93EfPnxQqVSSdCFYP336ZH63WCxqMpkYtqaYgi36glM2m7WEHB/yT//0TyqXy19EHL0qUYSh8CBotVqpUCjYhsC5zmYzZTIZlctlRSIRqxiSiEynU+12O+Xzea1WK0nSzc2NJTYkZ7PZTPl83jYiC8e9WCwM1LKZqFAAcqlgwoDkcjlJZyaV6h4GBbAbDAba7XYqlUoWiPh+ADWqmVQCAY98Loa/Wq0scLJJMJbVamVJ8G6302KxsGrDbrfTcDhULpczB8LnJJNJNZtNTadTq9gS5DF8X+G4u7uTdGYYYMY8E4JTgXUBZHOdyWRSo9HI7qckTadTC8SHw8EcxXg8NkDy8PCg7XareDyu0Wikdrut0WikWq1m9wujDdf1i31KFXs8HqtcLluVnGQIAiSbzSoSiViFDtIERzkej5XL5XQ8HjUYDFQqlczBwkSmUilTEBAYt9utSqWSsa4EFM+cQhCcTiez88PhEGDmpUtCBdCs1Wra7/eaTCYBMEiShQ2hYCAIegDLfSDR2263FuhTqZTZ3Gw2s8SWlc1mdTgcdDgcLIDDYEK48JlUdAhwsJ2QVjwvqpcwl1wrSQEsJhX6SCSiYrFoKgVfUY1Go3r16pUBTgDlaDQyP0OywPOtVqvmnwGrhUJBx+NR4/HYfHW4rlv47uPxqMlkokgkolarpX6/b3ZQqVRULBaNCJBkVWDAIuqQ4/GoP/zhD/rd735nBNF8PjdiAvInlUopk8moUCgY6w65yv5PJpMWGwqFghE5qICoxqFEmUwm5kvYT1QGY7GYBoOBkRjYC9U5b+O8B8CQagmxi6orMZrvQrWAmIS/w89AWh8OB4v5jUbDkkjIWF633++tEiMpUPUgoYtEIkbcYOtgCN6D2LzZbExFEI1GjdAlTpfLZf38889GAHPfsUmI43w+r0ajoc1mo9Vq9c8qosAM4bp+ZbNZpdNpPTw8WJWuWq2aCmC/36tWq2k4HFpligSEOLpcLjWZTOy5EacWi4VhQhQFpVLJyEafHKFII1YRQyRZEkLxgWLFcrkMVK4pGiyXS9vLp9NJ4/HYKu7z+dySyOVyqePxaPGAit56vVYikTDfBd6HMAZ3Es9JriB1+E7ELpJIFmQt2BlMTIUS3IAPqtfrajabdr/Ax14JKJ2TY6p6p9PJEvrxeKx6va7NZqNcLqf9fq+bmxsjpBqNhv0bOQHPn++UTCaNXEWlNJ1OVSgUlEgkdDgcLO84HA568+aNJfaf+7oqUZRkwYBNfDwerYKAZCmXyymRSJgjWywWJpthA6xWK83nc0lSrVbTcrk0Fny1WgWkNbCo0+lUd3d3Wq/XtrGLxWKA1cGxejZnPp8boIPNLBQKxppQ4QSgYQx8D88KwkJKF9ZyOByaYQKmkPKR3JLI+bI00jM+G0dPYkqAJ/nGsPzfCYgYDODVvxcGu16vA0k1xkdlVjqX0anASmdDm81mqtVqqtVqGo1Gks6sW7vdtiQ1lUopm83avfFMFvujUCiYkSLBWK1W2m63gUpLuH75ArzMZjPb71QLYe2r1aolPiSJ9XrdmNRYLGZsOAzhZrPR3d2dYrGYZrOZnp+fVSwWFYlEtFwuDRRR0WSPsb9IViAEut2uXr9+bckSkh1AF8kRgQfZsrcBAtdqtbI9BluPlORlUPKSUC87hUAhKCNRYW+T3MLc8jtch69s8n3wk55Ywa9BEHnZy+l0UjabNXaZz06lUlb9A1CQvHO/uM8EXGQ0JKXIe6jg+2on1VRJVj0l6CFFBwSE67pFtSkajapQKFhsOp1OVoXq9XrGelOZr9frVpkeDocW+yKRiF6/fq3NZmOVrWQyaRXBeDyuDx8+GFlEvCmXy5a0kZCMRiNTAbXbbSNEqCRQ5dzv91osFhbvUJqQOLL/qErDutfrdUkykgmyCPuAUAEoYu9UCLBLyGl8Asl0v983mRxxnTjI/YBwSqfTBrS9BP1wONj7QSBThUfVEI1GNZvNVK/XzTcQhz228WoaKoLcr/F4rI8fP+rNmzfmBxaLhfkPfMFmszGyGuCOr4AM/mvJ2P7/snwLDz6UCn0sFjNly3K51HQ6VbPZNCxDtTkWixk2BZuBt6i4ET/AuciVfXzxhQhJVkAAy+H7o9GotVaRBJG8QBISLyVZrIRkgWBFOQRuRUGEXUMeUdleLpcmj+90OtrtdqrX6yZ1pQLXaDR0Op0M20sXKTb3hc8inkqye0nsIi6jfiNh5v5BxOLXIJa4HnD63d2dYX2vLALT47/4vpAA7A2S9vl8bjZ5PB5Vq9XMl1JVnM1m9ty+FJx7VaJIKVdSoL+MTcqmA/zA6OOg6V3iASFX8UHBM/4AIaSNbAB+n169bDarwWCgRqMR2ExeWiPJNjQSEhy5N9zD4aDhcKjb21t7DUAUJgkmZrFYSJJtbhI2epIkGcvL78CAAgTW67X1J9LvRSKI/jsajRrriIEA5kgEMGSkP1RwANqSrOrjKymwzjCYJBSASTY/zFG5XDb26OnpSfv93qTF6XTaDIoADtMMM8Rz5Jo9YxWu6xdOtFKpBKTNo9HIKmXL5VKZTMYqwiTwBLPlcqmffvpJ9XrdABXJItJL3hdG7k9/+pN++9vfKhqN6vHx0UBToVBQv9/X09OTvvvuO2UyGQ2HQ3333XcmlUNyRu8NFc1oNGrJra/gs3c8icOC1V+v1yoWi+bosQ9fZZDOyRukDESOdCZICMbYNoku1UPkOL76J8nIGgAzgA72l/eiCgDAhon1zC3SU6oIhUJB7969M/sGhFCVBGRIUrVa1Ww2swoIcpxSqaTn5+eAjBagI8mAKuws3208HofJ4pULAoCqQ61WU6/XU6lUsoSJyj4A7v7+3vrUsZUff/xR33zzjVXVIN6wESpfm81GrVbLKh/D4VCpVMpImePxaDaJj/7mm2+swgfRQM8qrR/b7dYSVSRYgCDiKQkSxNVwOLTq/PF4VKVSMTtjL0syaS5Akv2MXUwmE+v3kmREB0w+sZfY5ytufn6BpEA/pHSpQGDjVOFjsZj6/b4SiYTy+bwqlYpdl++hAqCSXPPcSCgAlNVq1dRWfG+qnvhjkhESfz7rdDpZUs/MBaom4bp++V5Z349HtS6dTms0GqlQKBjm8lU8fPLxeFShUFCv1zN5JXGDOEMsTSaT1ueL/aDmgCTCLiRZHPdqAKrvrVbLcCbVsnw+b5h9Pp9rMBhoNBrp7u7O4g6Y0s8TINnL5/O2H+mvpPovyXCEb5lAlUL8oHhD9TWVSllsQtHG9yYe8ix8byLPhiIKEn6wMkUU3/tMHCPhBCMUCgWbsUDcpjJbqVTMpmjRAW+TpGO/4/E4MDMBf8tnHA4HFYtFPT8/fxEx9KpEkYQM8A/buN/v1Wg0dDwe9fPPP1uPE5UFQCMlbkkGCHkgj4+P1lOIYfJfEicGXKAP7vV61ocHsCIIUJFgw9JzRCl+MBgYCziZTKwPIZPJ6P7+3q6b/iEYFAyXTUyQ6XQ6Wq1W+vrrr02i4vsXvQyA6wJ0IrWZz+cGrhOJhAqFgm1c33tGgCJgUwFEfsf3ATwCqmGoJWkwGCgWiymXyymfz1swJSlELkEQBHhQ7YDpnEwm1juCkXrpLxUffv78/Kx4PG6f6ZvAw3X9Ys94dg5GD4Ytl8tptVppMBio2Wwa+KQqMZlMVCqVVKvVTCqDJIQKM4mKdK6SfPXVV2YTlUrFnCSVru+//94AXaPRUK/X02azMeKAayYJJKix17kG+nOwIUkGprgWKgkAXd/viJNmj0N68G++V9f3GRCECGRUUVEeANYACshskPjhn4rFokmV9vu9ZrOZBV7ufzabNdsmQPKeVH9gcgEJXrLPdTw9PalUKmm325nsDXB9c3Oj0WhkleDHx0fd3t7aHmE/MBiB5/glyGY+50WsRB49m81UKpWMLItEIgY6UM3A3OP/9/u9fvvb3xoJinycAQ/ZbFadTkd3d3f23N+/f6/7+3vbJwAd2i+kS4IHMYO9Q7BibySLEK5e1u4JKeyGZAZgi6KH2IwtSDIWn4oghA4SbgAl/gGFj++J9IM7iJ34IwZUMJwLKSikJYCWhM/bJbIzhn8RY0kUeC2gmntEvyEYhL7OyWRi94hk/H/9r/+l3/3udwHbpheLZ0/likQ8lUrpz3/+s77++uu/6l7+l7ogUYkd7G3fitRut603jsQQkpa+Y+IOP08kEhoMBlb8IKYdj0fD1IPBwLA1/torg3xM3+3Og2Bubm40HA5VKpVMWSedbdzvfZRli8VCjUbDkjzIEd+24WWThULB1DapVMpiG/YIAesJHeyQf4dkTSaTWq/XhhtJ3ojHHjNKCvw735vrJa6SGySTyUBrB/ZFjgLmr1arkmS2j/2Sp6Cqo6JLXhGNRm2oGKq9eDxuSgueEQUgcAixc7fbGTn0ua+rEsXBYGCgElYSR4VDJBjBtsNM+8ELJFhIb3q9nl69emVJCJk6YCWTyajX61lPjnRmCzGo0+lk7CYbnWoWUzmRrs5mM93c3Jgxs4EBllQbAJWwqJ1OR7e3tyoWi5YgUylFMw0TL8kAGWVtZAaTycS0zl5mlslkrGoH8MUISaq5NvqUhsOhBRg29Hw+t+/imRxJ1hOTTqdt2iUGRwBGBgXoly66eQIvw3UKhYKx314OA5DFyAHR9OMgPyDZACyH6/oFoKPnD4cG8CIIEAgSifOEVFh+hhgBPHu9niRZf6mkwPvQU1ev17VYLDQYDCzAUknLZDIajUb2GVT/AaoASQIJVTSkHZKMuSNI4czpn0Iuxl7r9/vq9Xr6zW9+Y76EIITDj0ajVtWQZAGjXC5b3zX2Tf8iVXlf/cCHUEnAJ6J2wP5QOiAp85XMyWQS6LWAOaWCwedBVGGbfDZ+EYAC0cT108uNfWOXJIFI+gnq7KN/jqAK1y9f7FPuO/u+1+sZSUh/Ui6X02KxUDQatf6ZSOQ8zGk6nQYICPYKCVmr1TJCQzq3dxCLAa8Qh4vFwhQIgFH2C6/FFiFzIScOh4NV92KxmIHT4XBoPeuz2Uzlctn6lPxgDgAlzL/vJ8JPUaUg7nEPV6uVDdgBQPJzD9LBG8hufSWAOAd2QK0D+CU2evUCMmyqS/Qe0/PJexMnwT8kxBA+AFMAab/f17/7d//OwHQ0ep5gyVRMgC3tO/jq7Xar29tbk6WH67p1Op3M/1NJ5lmxZ4kZDIGin54WATDObDZTtVrVZDKxxIzEg4TCq2NqtZokWfxkeAx+m+ePXTSbTZOkz2Yza5ciXpDIIgOnrQRsjY1JMhITW6K3F6krVUsvHWVPYwu+NYQEFfvyijevICCekjxSsJEuszMgfrAz1E9UTcHR9H36ya4QSMhCUUDS6xyJRPTp0ycjY0jgyT2Yr3I6nWz2w2w2M/8KkcAeIP6iwKTgRf/il7Curig2Gg3T2GMkAAmmkiL/2Gw2en5+1ldffWWTngCNkmzUNo4f8OflFTwE6TIxDofPpvRVD0a9A7p4wAwDgDGF2fG9gTB0SHjYwJlMRs1m07TQAFYv88GAMQRAL4kdMpR8Pm9OfjAY2HuxsTFQKgeeIWUD+ooHjBGf7WW0JLMYZLvdtqDJvUZ/jySh1+upXq8bEEC3DkjB+CSZA/KyXc/gPj096ebmxkAJckAcGYYWJom/3iLxh8Em4Egy8MSz/e6774zsAQQ+Pz+rVqtZNezm5iYge6YKdnNzY3ZFskkF3TP/HCHDe7KHSRjL5bIlYVxfJBKxYRy+P7larVqP0X6/t6M3/BRJAm+5XFaj0QgMAAEIUsF+2avnQShHBpTLZZPVIEVaLBY2LRmWWDpXc/FP3IPpdGoB6XQ6aTgcms3jB5hqSc8YpA99hr7XgZ5v/JuXSC0WCy0WC5XLZZMbk6zik33vMFL4TqejdrttfhWfKcn6PnzCGK5fvlBmlEolA4Kz2Swgm/R9Mvh+qnEkGFSxactgiAskDc+P2IbklAofrwXkkdTwh6nE+HhJRmySEFH5J1aTqFSrVfuDvUkX2R3VO2SnkBoMjgJYEvN8ooc9e6m1B4WSAooK30fkZZrPz892byKRiFVFfbsG4BTABwEKiYL03vdKcU1I3qvVqknksFXskGfqeyY/ffqkh4cHUyXd3t7asyQ5kS5HkL19+1avXr0yUiFc1y9ige/7g5zkOflJpqPRSP/wD/+gH374QZL0/PysVqtl6pB+v28SU2zID2+Tzv21xKvJZKKbmxsbhEQ88RV2X8WkNYqZESRhvr8PNUg8fp6WfHd3ZwQGfh3fQIKMLRAn+XdwpSSLVUhUd7udxXrk0ly3r6wiLfd9wmBk7jML+8VXzmYzvXnzRqfTydRPJI7gbhJB5oRks1kji7km8EU0eh7+h81BfB2P52Nv+B18IQOsfAUV8gySCEyO/B9psf9en/O6KlEkcQBswKohG4WRoJclk8no9evXkmQPE2YNIyEA+GoUN75UKmmxWNhQBRx3r9cz0Hg8Hu3IBn9+Cg6Y6av0xSGzIWji9JfLpTXhrtdrC+IAaCZTcW2STB8OI+irZARgAj3yGBhKAo3/PV/dw4DYyCSMSF8A4gQ7/978DnIBgLY/mgBJBQYdjUYNPFPRpD9xPB5bz4V0BsSj0UixWEzdble5XE6lUkndbtdYYxr/YcFIJkloYXbpj7m/v79ma4br/104Yc5n8vZK9YKggQP0lW0q7VTpvS2ThHmJOE7wZY8De/r29tamo1IlmU6nqlQqNsabKj1VEl/t3O/31ifEZ5HMMRwDsMh1jEYjmzjn5S30ChCwAG3e1mAyAcCAM2Q4fA7S6ng8boCYAMG9IfgRUNLptJrNZqCnRJL5P+yce46UCGBM0uqJF0AMwAP74/6hEvDMK0cWZTIZjcdjtdtt8zlcP/eMnhKeT0jqXLdIsKiyHQ4H61f0CTwECRXlzWajjx8/KhY7T7DebM5n/s7nc+uNgbGfzWbWR+OHJVHd973yy+VS1WrVeo2fn58ttjKBU5JJX+m5eVlxRinU6/X04cMHiyVU3IhfVLwlGXiTZGqfSqWi9Xqt8XisYrFowBxZ2WKxsH+HnOS7Swr0V0Fa+fYJ/jSbTYvD7HdIahIEbBLADcCPxWJW5aOSQII/Go2sRxxQSA+Wj+H4HKTH+ATk3wwGRGbLs4PYwsfUajX9j//xP/S3f/u3f92N/C94QYaQpKHImUwm1jNPXGGQYbVaNQxVLpf18eNH1et1a8FCzgoeI+ZSPYZgTCQSqlarASyYSqXU6/VMQokSD/uh/YFYhrLFExjEp/l8rru7O+33e0umVquVVdMkWWEDe/WTsmOx85CexWJh/gpfRaJFfEVezt/Z/76SKF1sFgIMjIhdY0fr9dp8XzQaNSUU9wLMTw+knzbLa/i+3FvipCTzJ7TmcM30Z3NPeWb4Cz6T+QiSLL77/uZWq2X36HNfVyWKMOlfffWVbfRUKmVAY7PZ6PHxUeVy2Xos0AiTNCAD8/03BDH6KZjexhAH6TIif7s9nwUnXYZGUN4ul8u2MWEBmd5IJQWgRbCTZBIgElUY1f1+b3JXeqP84AzpLwElVUECsnRhIjwr77XbAGKkf7PZTMlk0q4RAwP0+zOUkLOxsXEcBCb/O0gmptOpJfUAFCRIvJ5+SjY7EihkaRhlqVQyJhRQ4xNgWBmSea/J514gOw7X9evp6UmtVst6hDww9YOdqBy+fFaSjMBhbxBUMpmMES/05tLrBFjy70GFzEuTIYmYcoadIB8lQC6XS3348EFff/21yWuwDxz/8Xg0MExAiEajgWMjmApKsKJ6QjUUUO6vwQcuEkeuzf+XKp4PkjCa2DTJHBV5lBX0EgNksd+XUlqkeHx/JPyARoAHJA6sppcBSWegTxJ5c3Ojw+FgCSM+i3uKXNBLBT3oD9cvX+yDfD6vbrdr/jKbzerdu3f6+uuvbejXZDJRNpu1KdEkZKPRyCrpJFHIk+lrRKKdz+dNZj0ajbRarQJnL9KjT/9dvV63OCvJbB2JtCdFANAAtsViYUkPfgfwhK35gTij0ciOAYnH43Zm3WazUbPZtFgBVqAyDkni20UA9LR3ACaxp5fJGv8P/qDCyv7G5vAp/Pv9/X1ArgfwhPzKZDLq9/sBUMlz91UpPheyIB6PazAYmL/ERrfbrU1zhxyo1Wr6+eefdX9/r0gkou+++86eRbiuX8PhUO122wgWKXiGNvEFpQktDeAvSdYKRbEA4gbMCtkP5sSfD4dDU4qR9BEj8Ov4ZdQm7E0IWfAe1XhIeS+NJCGCZMVO2e++qghO9Ukn3xnbphDCsVLYrCdo6K/1eJiqG0lrMpnU4+Ojksmk2QYkEDbMvWbPM3BmtVpZ4QSswzV4VRz+gJ/5RBMih/tF7sI50fhefBHFIi//73Q6VnTinmazWfV6vUB72ue8rkoUN5vzuXibzcYcvGego9GoOXukW17vH4lE9Pz8rP1+r9vbWwtQsNdUFwFzBJN8Pm/JBtId2FP6Fxg/vd1ure8Ip8sDLRaL1pguyVgjGBkMG8AHqGYDkySRdDHQAhkYCRqJHOV1NrPvuyBQUEn1LCiyHAIuoNeP+eW/kmwgDWASRoyfAyZns5kNGqLEzvfDgJAG0nhPtZUpb+jgkQJSvWi32zZNk2EIyKMkGahhP3Bcxul0MicRruvX7e2tOUmqRCTkgCwGNv300096eHgw7TzPD3DFYvjDcDhUrVYz4NTtdq2fCjsjEdputzaE4fHxUbVazcgI7DEWi1lQ5lqRocZiMX333XcmA6GPizH/vv+OpI2g7hNfBgwAoieTiZrNpiWQ2LvvzaOiSoJLAz926/sHJQWGTU0mE7uPmUzGgDEyVAIkiRvJHsEG0gmwTwJ+Op1Muo2chc/08iFJ1rQPo8vAGhJnfAf3G8IM4MC0Vw+seSbhum5B7vmhRfTA3N3dWRxAEcI9p7eFagHPkEQFchJZ8+PjYyA547OJU/y/P/zbt2cQ3/Dv+HESF2ySvlbiGeAUFRB+xCdm9CpDFtFnT9sFcniuC8C+WCyMfIYQJS5jQ8gBXyZogE2GqTGxEuk4R3D4HvxIJKJ+v28ycfAJcZvKRDweV7vdtmfAsT/dbtdsniFxyWRS/X5ftVrN/M/pdLIBIyQRJJOA8fF4rHw+b/79zZs3AZkg7Tfhun41Gg11u10jVCDPGEKD/0dhhxIDfCPJ4s9L5QqJn9+TkBvlctkmnLJQupBUETPZ4yjhUH+RTJGI+hYKr47x+9e3K3nVF4SkPyXAtztIsrhEHkBM89JtsLp0kcJjQyRStKBFIhGT4IJ5wdLEe/4N/0PCh3SV70JBimor1XgSTPwFikPuO1jVVx0hXtvtdsBX8TqGy0WjURvsududj867u7sz9UGtVvsiyNarEkVK1p4ZIFj5xAfwQ5+R75e7u7sLTBqDdaOqR+8dCRRn0fieJd9bRJBBs08i5uVsBEuGpsC6MxWQoEW1EYlcLBazgO5/jpOQZJuZ4EIAIwgC+LgukjaM7XA4BKbB+X5FKVjWJzGWLoMlALowV37EME3ZODYqsRiJ77uC5cApfPjwQbVazXpnSAKoAtGjxh6gYut16F4iQ1DEIKmicO1URMJ13aISUKlU1Ol0VK1W1e/3ValUrBcKCVW9XjfpGoGB/cAeApBSZfe9UMhV6HUYDodWCXj//r2++uorRSIRY2fZ1z4xoweHhIXz2GKxmA3GQZLKBE/IC8AzSZdPQD2IIgCUSqXA4AsCJ/t2t9vZAAwqstIlWGNT2D++j/vjQSrVDfquGMMN8CcZTCaTlqjj8/wh5Z44o68NHwKIxmcht63X6+r3+2Zz+FAIHpLEarVqSSasKZV/vut+v7cJxV8CE/q5L545/jqZTKrb7drRCdL5uT89Penh4cGGglGFQxJGgoGcar1eW4LFwCOIRV+Zg9iBcM1ms6Yu2O/3RnLQh0XVjriJHXc6Hb169cqAo2+/IAkFZEG2MBGQhHa73doZvcQ+CA5JFnPxS5z95hVDgGcA9GAwUKVSsZ+RaDG9l6E92BrVGekiOZRkfg7yhHYJsAk+AGKWFhnuDzJz4iPfBTtGqgrIpa+R704SAamNPyHxpT1kt9vp+flZr1+/tteG67qVTqfVarWUTqctASdxKRaL5nt5Fl5pQlGAmMO+QS3HIDh+lz3k8R0SbPY8+BmbB7NCXvD7i8XCpuly7iO9xr1ez5IpiCCqlNPp1GKWpECRwNsf5BC9xcQmSUYKQXJ5rCxd9jP3hN/3yaQkw82oLHxVjtdtt1sbMMS9Jgln+AxFJhJnYjiJLK8hGWSCLbJe5iVwDvJ0OjVyh+/oMbp0GcozGAxUKpVULBZtaBDklO/v/JzXVYkiVbpUKqWnpycb8035GHaNB4N+O5fLGQtA8rRarUzGisOGMeQhkEwxGhtWhuSIf8fBw3Sy+ROJhI34f//+vcrlsjH69IbABlBmpzqJEUSjUXvwNBePRiNjB9nMBB8AMEGPIMsfNiYyFKY4IeHDMGAgYKAI2jC6vtTtjRhwSVAG4FOhlGS9XBgpz9AnsIzaxmj4Xc6q7HQ6djaQfy805bCrnU4ncLwCSf9wOLThAolEwipJ4bpuxWIxa5qeTqdqNBpWRUAyQ+WYEdL0A/leHPYtLGqz2bTjaLAVD/z6/b5evXplgeW7776zJnt6AyFVxuOxEomEPnz4oB9++MEmm1IdIeD4gQ9Iv2ENfYWDRbABGEN6IHvH/ny1EIUBgBLg6/9Il95PklQO/cYXYL8A3kjkcrA3+xuJHn4G6SEj1zOZjAUWkjSAB/2YftgMVdLn52dVKhXzKSSm9FdLMnkQdg5Bg7yd7+D7UriXPMeXsvtw/X9f8XjcJL6Qb7D9TAxNpVLWi0N1jv3qK9kAxbdv3+rbb781EMsh70iS6adBNeD7+JfLpRqNhsnW4vG4RqORKpWKgdbNZmO96sRPrg+QCpGCTZHYsM8Y7kKcoifSn2PsQSG2S78Q98WTOZCikK3xeFz39/dmt6PRyHqt7+7uDF+gJsCPEVeJdYBowKmfuuqr8n5wCEOFqNJ7oAyJRq84BKpXGXHvwUcvz17l+WOHPtH4+uuvbY7AlwBCP/dFMYSzwSFOuLfEEZKkfr+vu7s7q15BGkKoSjJcBd6i15dBLbQeUFmEYKFC6SvyYDxfbV+tVoEjLPwMDooV/L4kIyrYb/w7rVrSRf3giX++AxV/yCUvT6f9BCzg75vv48MW8CHYC5U58CT+wNvl7e2tVfWIy2Bm7gMDcCDnuGdcP4UKyJxY7NxLCNEDMeb7/GOxmOEgzgwHn0PcQPbQh77ZbOzIOgpen/u6KlFkk8KWTSYTYzuQTCIRY7onG8s7UH7n5ubG+vHoW6JnimTPGwbswWg0UjqdNqP08k6ArmdCksmkXr9+bUmor5owKQwmFWDIhMRGo2FMAK+lURZGgYDP8hIeNjvBCMPBqTOhVZIZnk86WVTm6Mvis5Ei8b6wzejZAfNI1mA/MRyqeZ1Oxxpv/YABP0mq3+9bkOPZ0qNF0O31emZkPGPeh3sbiURUqVSssokjCtf1azKZqF6vWwWZ5IeKkB/lTAXas+hIJUnoAJsEFuRq2+1WtVpNnU5Hu91O7Xbb+me8rp8KJvuV4RipVEqvX782+ychobJMTyzXiYNlfyJ1YZ/D/GNLvtKATZFUYmcoFBjU4Zv+6WPgukejUaCqQX8JCTdEl19+qmQikbDzV7EvLyXn++P/CJ4QbwBhpNsE/WQyqW+//dYqhtwDKoEewANqT6eTnbe3XC7tuUM64RuY8OgJwHBdt/70pz+p3W4HeuEHg4HFFKSg+N1YLGYtF8QEVCaPj4820IyKMtJwqmGAIXqVSFwY208MJenjjDFIjWw2a+oPCBCSP4hfKobL5dKqWvQrkhC/7AckuWTAB+8NEQNAgxxmT0PeUv2gFYJ2D+IdCgTOoEylUgYgPejnTFg/FE6SDbMh1nIPJQXs0KtoUB/5I3HwV61Wy2IsPWQMxfPyOj8YB1LLywz53EqlYp/7+PiozWaj29vb0EZ/hUXrAX6auPSyBx8CEEUOxx5RQGC/SrLfAw+WSiUjZCDRSSrZS2BYsCXVQI9x+V1iKNe8Xq/V7/d1PB7VarUspvjCCPGTooyfPcC0a4gRjl/yAxDBqZ5UBHNjs36P83kUHkajkSVcqOsgdH3FEyUF38tj+FQqFSgY4T/wMdjhy+cryaTa/X7fqv/YLu0itMv4idFgAApCVCT9YKHxeGwKIJ4pLTxfAplz1UE7GMG7d+/sXBjG8KPtT6VSprWHOSS5oMoISGEsPFIMNgmJoD9LBXD6/PxsiRhVAzbjcrm0w3o3m43+9//+32ZQyB1x3AQeWHz6OAjOkiwAc0YkDIh06W/0kgCYCJI3gjTNw14HDjPLpsGQMUAqErAdBGXfO7nfn4f0AIB9TxoGjxH+9NNPVolB5sZrYEEIgIfDIdA3iGHicDjrZzAYBNhQAhmyPZwXDDrs9HA4NBAO+xUGuF9nMegAm3x8fNRsNgtUhmHGcarT6VTRaNQOEc5kMlalQlNP8sdB1SQRSNwALZAC+/35YHHYQYJdJpOxSjTPnWulqglQ7PV6Go1Ggcmh7H+IHvbZfD63IRYEF2yUahk9swRtHH2r1bJJwNgWgBTwi09gUdnBjrrdrt0DAAIklpdxYtc+OEuyIIeNE7QXi4WWy6X5GulyfpyvXgLAsWGfaPD50hnAvxxAgCQdOa10kaDjF3zPc7h++frmm28sIUJCWalUrPrwcigSz2Q8Hls8IOEgyWu1WkaeUBGXLskO9shE8VwuZ2w4VQd6xgFRVAmIYwBffgfyhyE4ED5++ES32zXbxlbX67URlj7ubjYbrVYrIzZ6vZ5+/vlnTadTDYdDq2qjfkEuTYWGBBiwR9ys1Wo2aAfSg3hOPEQi5sllP3CKIUCeXJVkEjbOmkVSC9AlMX56erI+SMhUkn4PwnnWzD6IRs9nK55OJztjjufA5yPXv729Nd8VrutWPp+3M6pJeDw2wza9Aof2Gl8FhESnAEFlnH0OMYltoiRhHkc0Gg0MvCJuSJeEk8/1x6qBJe/u7tRqtQIDoMCStI/5QTcey4KNISRIQGezme1XvictKhRsiJU+mYWQZC4GxSben9jJffUEJ0Q2VXZP0kB+ch8gi0hCeT/8mJeKc530HVJ1RZ1I1RF/4hPX/X6vt2/fGr5H0g+B7SupJIl+HsDnvq7yJPF4XG/fvlWz2bQMn7PMMAgeABuf4IQDBZSg+cbxwnYgkfG67efnZ93d3SkSiejh4cGYSLJ1NjLaZyRz3333nfVpsaGQiSAtuL+/NzYPnbMfWS3Jgi0VTy/FxFGwyagIENhwNExwwpAJniRjOH2vfQaE0u8BuwqLm0wmA2O6d7udHfgqnZNZWOQ3b95Ywo7shd4XL4mlh4teC6/HpymavjamYGLUBGl/zhsDdAAVsdi59wyZMMMESD7Ddd3yh20juxiNRjYO3j9fJrEh7YDEICkkgEkKAKRYLGYVavb/yyoZwYqBN7BsDPDgvfkcKgFegl0oFGxoFp/hG9EJEIfDwZj8+Xxuew5mnmmBPnlEEo9d+15fL//0UlUPJElUfe8tAVNSoBrggSP/hXQjiAIyOY8OFpV73Ol0VCqVVKlU9O7dO1UqFZMDwfISWJkiRw8pz2K5XKpWqxngyGQyev/+vUmTSJQJekgVCbRfyhlQn/NCgki8YT8nEgkNh0O7/9gIdkfvaDwe1/fff2+SuH6/b5JyzlNcLpc2NRO/TGLowRDSMgYxLBYL8wccB+Vl3qh+iGGoYrycFP9SLBatLwjpbKFQCPR7kSzhLyA6GC5BZRBVAf4LfIHdYbf4EWyT66R66QnVl+QN/0a85ro4X+10Omk8Htv9+/TpkxqNhlKplJ2R5yv9vBeEGRgnnU6r0+loNBqpVqtZlYL758mt3W5n02+RE3vZLMk1zzE8vubXWZ8+fVKr1ZJ0Gb5yOp1sqj77xcsSwU/gJWyXfQVupPLk9zJqEi9nPhwOGo1GajQahiO9AgabZ+958gW7J3EiLuDjIXe4NkgTKpa+gEDfJOQn8nYIT5JPSQHlCdjVy1CxfWIrhRJ8Edfkq/v8G4m1V86RV0BMgSXy+bz1eIKr+RmxGDxL4kaM4z4kk0mzO/zzy7OiyXskGeErXdSJ/vgMnufd3d3/1b37a62rEsVEIqEffvjBmJVEImGTRL3EUpIFoMFgoNevX5vjJHABBgmA/sGzcWHPOFsJvS+JIAbmq27IrziYs9VqmQyU6gTgKpPJ2HEeHCqNI/Y9l2wyz0bCvMLyeSkOjALnU0lBvTbA1stHYf9hNknwMF5J9jm+oghDRZDxzcpUc2GuMXicx263s+MO6AeDNZVk8gcSh+PxqPF4rPF4bFVEztShirnb7SzJJukHPHN/uKeAAW9k4bpuASiQKmWzWd3c3FjfGQkMexQnTAULOXa1WrUqFe+FA8U5cqQMIBc5BiCn1+vZfvSDlnDGKAsAO9Vq1QZRwRgScAi0VBAA1AQcAhYN6bCJ0uUAZZhE1AySDLAie4G9hGTxyTSJGYHX9yZ4xpl76IeJ8H2kC/FEwCGJzufzajabNgUT1vd4PKrdbhsgfP36tYFeghCVVirGSKUAlfgQwC6qAVhZfCnPFYVAp9OxYURUN8L1yxd7BTmn74Ej5sViMY3HY4sDxAvp0pPLXqIihYyYmOr7/YhdtFDMZrNAiwfPH2DF+2KLVOvr9brZJPuNqjhDztjv+HnAK3GKWM/3IYmlWuklqMgvIVX4t8PhYATHfr/XZDLR3d1d4CgtbIuEj+uBIPN9kdLFR3DNXpIPgeaH7GAT+BRvM/y+H0DkSdTXr1/bkVv4VlprttutGo1GQMbH50Jg0fJBxR/JeqVSMTsP1y9fnL8rKZCIsPCn2Jck84u+l5BkUlLgODDshJiAUoAzGolN9KWTSJHE+fNy8dnEb5Izquy8l8es0rmVApxGwgMm9IQK7Rue9CSR9BJbiBh697FdEkwfW7wqDx/Ez4hf5AnYIZjCz+EgOe90Ovb7/J4fMkdO4nucIXx4xmBchl5SSML+IHTB9CSkvAffCczA8xkMBpasckrAl6DMuSpRhHUnoHnNMRtps9lYtQhdPokNwJVEDfYBA4LN94dTeoaDCYIMx/HyFRhWDyqZ+ElyRcJJPwNJ2eFwsMZy388H6MRwnp+fzSGTAHkpjCQDrAQpgLZPltjMvmdPkjkPHAObj/I999Vr50mcYaJgZkgyMSxfzaQafDwercrnJTu8FwYKeGBi4t3dnWndMcRo9HwgOAbH4BrK9TC9VD+QUqDxDwHor7ei0aidvYZkip5D9jMOkWfO/qTKgQQVm+W4BKr2vs+IIVGARxJOJF37/V79fl+NRkOSAmc8Pj8/q16vW6WFxHEwGCifz6vT6ejm5ibQV0t1xPuh+XweaBSnakgwwdd4ggn7w5csFgvreSLQIIfFrgEB0gVAetma7+WjSoLsx0t6qPxB3HAOHuQPiSLfj0BLYCe54Lv54z54Dl6K+uHDB+VyORucQzKKVNHLj0iYN5uNWq2W9WsQcMP1yxdKCyYU4gOR9BNLqchR0WUPrddr3d7eqtPpaDqd6ng82vTajx8/6tWrV+abfcUOvxyJnCeA0wLQbrclySYzEmcAOvgKkhZsG1sAXIINfM8UrQZUpvEX0pmwgETq9/umesDGsSH2HfGLn/mjAPygGpJefBrJnJ8G7qv8fA/uO5OWmSLJmcP4DbCLP/6LeI26Ch/Gd8HP4DfBBNFoNDCjAXUP8XexWBjOwKaJ1RDdYDAv+Q3XdcvLpSEWiKVUEPmZrzDis4/Ho+FJfDozKth7ECskiRBEYFc+AxKI+EcbBnZAIYJYBOHBcSx+T1CcwH7Yu77AwfvjK7wqgXYTChrgQoa+QF4wII7ikMfZFGBI6l5+FsRmLBazogzfj3Y3X+GUZAQ27304nM8ix4eS5PG8iMWbzcYqj0iGedZepQSZjooH+TxTzJlaDfnKiQuxWCzQg8pe+hKw7lWeBOYeI6BXkA0O++mby5lCBpAjABFIGJgB4JLOfVar1cqMQ7qwKiRk8XjcMn4AGXISZCj0RfrgAwsDMOJa2Gxs2HK5bEwC1YuHhwc1Gg2TDpFoYdxcC+/L9UiXM8i8DOFwOOj5+dn01BgtDuVwOB/v4Su2BDffG8ih3t6omaxFpYO/U9GhiZ/fIQhjnL7aRKJfLpfVbretNwNgDEsNm0xCy5EMJC4AHbTmgAA/CStc1y1ABRVySfbsYbkJdn7YhGfYI5GI9ewdj+d+IfqLE4mEJYbr9doO6AYkzmYzY9E8e0Zln31NT3OtVrPA6nX89D7f3NxY0PRsZz6ftySH3mD2LYfF+15GgCIyU/Y7ASISiahcLqvValmfUDabNQaUYApZAiHDvvWDXwiE+IRcLmeHcfPdARHSJVFF3gaxcjhcBodxvhaTSbEzgAg93KfTye4dCS7T+EguAfXSxS8RaI/Ho51Hi+Qpn88rn89/MWdAfc4rmUzqxx9/tNH7vo9/OByajQC8SOyIs5lMJtBT3G63Ddx99913Rvh0Oh2LmcRD3m80Gmm9Xpt/li4Viel0askLfhtbAexAcgK4SCyZqEq1sFwuW0USAgNChT0YiUR0c3Ojb775xpQExKP5fG62QisExLIHdv4oCyp1/M5+vw8QJ/P53CSavq1iPp/bwB5eF4+fp7uWy2WzEyoRyBBjsZidBQ0egpBl6AXSN+wP20LpRIUfPwFmgKRCuu/JcAaNoKqQzrLmLwGEfu7Lx0SSn+l0GqhSSzIfTRzBLnjG7HHiEJV1+mI9doJQxAYpbiSTSVOnQPDh88G4VPxIzqTL/Ah6jcHN2ANJliQrqpTLZcORj4+Penx8NN9BDAVf+lkZxFNiPv4LHJxIJOzsY9Q2xBiIZ+nSFy/JPo/rls72QDxGvk4yKl2kqtLlnEZiNVPBpfPRYY1Gwwopvo8/EjmfnUqCPRgMNBwOA7ETsprkmVYNSUYUgQfYI8wr+VLmcVxVUeSgWM7bIqD45JHEjg1GMlKv123IC86+VCpZcyuHiJJ8UYnw8hjKzAxo8b05sJiRSESPj48GMDF4AgtySnqB/EANGBqSPL+J0Sj7gRdeJgPTQuKTyWQCJXySNhh+qhpI1/zEV9hjBvrwO4BxScaQ+N4pr0fnM30fCsbBOT04NpwLrDLNwDDJyFMlBY4g4Xsjb/CJMs7P96jABPkJlpFIxAwxXNcvHNlXX31lyTtVDC+vYr+THAKcADi+yR6JMJISwBiAkIAlyY6R4QgMHD5sPwHQDwTA/gBt2CpHfCC9IrjBsnpJJ/0POHuuD5+CpMWfzYTNcEQNZ135PgvArZ9IB6tJsuylfC+lSl4WK13kNvRrMv2QgNNsNi1xwLfOZjPl83mT+ZMU+2Z+AiqVDK/42Gw2gfNi8U8EusPhYL7Us98w0jz/kMy5fu335wFQk8lEuVxOg8FAzWbTzkDElhitjszaPx/pTJQMBoMAQcnePR6PNiBntVppNpup3W5bYoeqZDqdGnkB2GWfkYiQhBJXfEWNCpcHbCRAEMcQygCt9Xqt4XCoYrFoPUCSbL8BxGOx87RHqmr+SAqIYfwCBAsqBuK279fi/7G3/f5ybiM2i8176RokrSSLhSStTFvkGXniNB7/f9j7r+bIzyRd8HQgoLVOVWSRVT1VfeaMze18/+8wZmM9e053V7NIpoIIyISK2Avsz+OJIHfXhuAF0wyvWVpmAhF/6fLxx/1d6HdKN09PT9tOJDjn3blXjAnviy2UJGIFiJ1UiD2Pl/W8RZ9UzrI31dwNMWDGUGn3MabEp0lxTjZB7g1aVe3PxL9V1bMeyI4kTQWtqhoo4Vux4uieIgH/yH84J38/Hj9tHaHiWDWZIC45lXjmXsCemesj2xkbu84ETXyWXUk2Hn2SXIoVZreHU4iSJzgX4DNZOuJlCXcWvcRBwFh21LsBhqn+ulaJsZgb+MbeySckjV8DmPOsRBEnFxc5aVSSsNxDj3INBoOuDnqYEGwoA+SNcKGApSLak01TrVK5Kpj+IyPCUWEFoRxhVXWSaCAOvn8OhjFIRsXOtRJsaD1KW1W1AA2Hw0ZX3Y9pVpREUvnly5em/VRVVxwpZCa6hE91lfNbXV2t4XDYvWD9wgPBur29rU+fPk0hQyhBAhFV4Y2NjaZJHBwcTCHClNa7qKou6aNKGU1u+M3d3V2Pbxc4o7fZyPRlPX8Jbq6urjoBf/XqVQ85Wl9f70AtgzPo52g0akpzVhc5AlNP05iST9QpjqCqeu8wwI7eG3rDWfk/iitUXoJjorIA0WTA5eXlHgyBdm6wAyekSiAIp/uuS7CYNO2qascM9ZWAuZ6klaVTxEKQfHNwKEZVVXt7e01T8jyzAphVkExSR6NRT0AUKOhH8cxVYySUbHBOWxXoZIUmm+8/fvzYA6Yk+S8bej9/oYCjKlU9BRSolJIhCQ2AB1UqB1TQJX4z+9slMcvLy51wAkb4AvupAkH5DXrERrx//77evHkzhe6jWm5tbfU2LGdnZzUajZplIOjUI4havrLytH+rpEs7hesTtBq4plLABwvcJGXot34OCMp+6qwuuF72y/NFO1M5Sjo+gExSlgwc+nN8fNxVEDEN0MqUWqAYXZNMouKyGVVPiaX+MYD74uJi9zMDf8UQ+X5e1vMXMF/CyDfwl5KD1MdMFslQFjqqqgFTif/Hjx+7L9J+mPnexbt3d3cNHlQ92e6c0cE/8BsXFxdtN2x3U/Xka3NfbqAttgCwVmIkwaRf7EQy/PgUyW3aJzJvLS5OJhZnNS+TvJz+zUe5tvX19X72+o3lFvyjAZgbGxs9CyEH4bkHW5Q8Pj72ftL03XWdnJx0wshW2gbM+04gIOd88N0GF/ncH309i3pq8Ay6KGTx06dP9Z//+Z9NHdOc/vDwNJ7/7u6uPn36NEXFUl0UNCaVw3Q+UwwFZpTAhu2Pj481HA5bkFDiOJBMtDY2NvplnZ+f18LCQn348KErHmgjAkuCTyk5pESWBH7G/Se1ZnNzszY2Nury8rI+f/5c6+vrdXR0NDX+HFVAMJxIgx4EKA3KLsMkCRYIoulwtgbUoPGg2kJ8ILaprIkaLywsNA0uUVbIz/r6ejtAjg96JWDVqygJdv0oM6btzRrYl/XbVyZvr169qm+++WbKuApycrLZLCKKS68X13E5TUtPBGBhf3+/HZzjStgMl7q/v+8kRlCUfQG2gkDDJr9kMxFCgbHEMukf79696/6D09PTOjk5aSAoe5g8q3RyHMXNzU0jsSr7Eje2THWh6qmHOoEn9q1qQk31zH3u8vKyE17PqaoagFOZub192rR3bW2tpzgDcGw9YqjFysrKVL8mm101mcz4+PhYP//8cycZqj/6VvWPAR2ur69fqG2/wxIoGPS2s7PTwSEg8PT0tKvBWCdVT5UF03vJPdm7vr6u4XDYA1IEegAEVaqFhYU6Pj5u2+z9Li0t1cnJSVfNBMMLCwsNaiS1bW5urntxLFSzquokUMA1HA7bh7puPT8SU4Ei3ydpk9Dym/ZGdDwVEc9veXm5e34TFJKAsiE5sRuAVDXpUZOU63/0M0CyZ+E56Se8vLxs1oWqxeLiYv9/cXGx6eEAWtv3AFurqt68eVNV1dRXz5FNMRGTj37Rz99n0T/xIH/0+fPnBg3FPEBA+lg1GVgl1gOMkx9xGh/56tWrurm56WGLgIO5uadtcbLvj1xnkiKR4z/IpXaq5eXlbl0AEIofxdXAnOx5RAVNui0ASguGmDxj9KqnePnq6qqGw2Gdn5/X5eVlg8wJfCZLzvEkp3z17e3T1PLcg5DvBta47tXV1To4OKj//t//e7emiGOydUsCfnh42M8EqP358+fOU+bn55tJ5T0fHBw0OJsUX4BADqBECWY/vpY491kVRVSX+fn5RhBfvXrVTZ+Qkvfv3zdy/c033/SkL8IzO5VQ0zvKiklekiMConfRWGB7w0l8srcuk0KO1lAImxujv6yvr3eimg7i5OSk9vb26ubmppNQx4GkoI36neTTZptZvk7kJ4PTpJ2mE4JUqp6giHGYqUgcbFW1k5KsZsC4vr5eP/30UydnWVFJ5McIZveZjfZVTwGuIUIoAlXVJfw0IJ4XZYOer66u1v/r//X/qm+//fYFDf2dlqRNFQjFVIWebEr6s4/HBL6NjY0ekMEwS9ZQYgAEnI8kRiK6tbXVTgZNjSyS86pq+UMXe3h4aGAh+3lU8rAK0LjI3ng87qnIzqvant/NBnb3QvZ9RzKop9Z5OMKk21Q9OWp9kYlscr6DwWCqwV0CVlXdB101mcrm2VVV/ed//mc3zbMZP/7449TP3rx507YOUOTdSTghqY+Pj538v379up+JyqRzc5BZqcjk82X9toU9ggXy+PhYh4eH/b7Y/aR18UcXFxd1cHBQi4tPU4YPDg7qw4cPHQySGT4lgcHR6GmqYm4eTqbH43Ftb293O4npmWx99i/bXw6dXXDkeKPRqPdKPjo6aqaCQTVaEUajp2mnjjc/P9/tFCrhgmrxggTJdauwVU3aO9gaQTIbperAj6owALuy/wvwq8ohsP306VNXUOfn5+vk5KSqqu2mPjYVW+/k/Py8dnd3p/oP2eWHh6eprTs7O70HLaopP39+fl5VTz7YNkeSZe/UeV/08/lre3u7GRSe67//+7/Xv/zLv0xVybLKVjWpWmf1GlgorqQv+r/Jgwo0YBAoBLRNtht/PRwO69tvv239E0OnTGehg99RSEGrJj9iSPYGU04ChfUHVMnzuH/+go1IfUumXjLi0ic6p58DKQGgnpXYN9utAN7J1FDRTRuQLAzfAUpdXl72vooGyC0vL9fPP/881cPJz2PoAan8TPJvmyHv7WuZ8P+sRHF7e7tubm7q8PCwy9iEwYS0qmrEW9M1RyUo8nIhJisrK/Xp06dW0OFw2HuAeVkSLQGrAAwyIGNPCooELrneKim3t097th0cHPQ9nJ2ddcJmA+KsfhneAfl8fHzsxlwT6zyXqkm/WCod5VpcXOxG47m5uUYksodM5VMyCC0VSGiAVmE0yVGVMumAEtjd3d2eEgfFVW2smkxghcD6s7u723SlqpqaHMdIJbVC9aqq6ocffqg//elP7bgz6VhYWOjm35f1/IVWJfDXX4aOlqgWYIEM3t/f9+TaNOQc1NraWk8BPDk5aed2f/80sht1wznRtO/u7lqfq6qdZFIlOTJIO6AhKRsSLwAG4On+/r7evn3b9LaqSWO7XowcVOUZzCK9gnRAmKqnRIpO0WfnkZBVTYYFpGPLzyXAgzbEkUji0egHg0F9++23bYOurq7q8vKy9/iSoLoW1DZor14zzIKk1xhMpDL7+PhYu7u7HUBImEejp70w37171wHEy/rtS/+u904OJUQCUZMzFxYW6uPHj80I2NraqrOzszo4OKjRaFRv376t4XDY/U5VT/3+r169ar+sN/fz58/dX6MKBuFXpUgAUVVSYqbdIPvJ2XPUNdcNgAIMX11d9QRVSH9V9fRlwSUmAfnDlsmqIj/Il1ZNRvmzPyqidJdvRIXN7/G3KqeG2pgzQBd3d3d7GiqWkkR7PJ7sBycYpdNYM7OTySWi7k0AX/UUV52enrbuzs3N1e7u7tR+eZ6/IN53X9bzlpaglMncxxT7S7U8e7olOEmP5neAjwaxiF+zWs7G8pG3t7ftk8ks/3p0dDTVkoGJ5/xozcDZTGz5DLECfaPLWpDoDiDGRGB2i31J6nsyBBIUTiq3+FJMmBTf9MUm9EvyADTJqKBL4/G4ZxVgNxoEBtTyHOUBJqmKR7yj9O/shZhGQv3ly5f613/917q+vu64H6MumXxsjOFlX8N6VqLo5QiYDg4O6p///GejAQIVWbwSumyfIEv6IH93d0/7Eo3H4w5E//nPf9a3335bj4+PXSmA6DOcnAZUcWNjo05PT2thYaF74XLIBRSSU7i5uekx4qhc19fXnURVTeikS0tLjXwKBgnXzs7OVLWBszUMhvJIDCEzqGySYVUCwaRKn+DYkgBwUgwNJHZ2P5mqqnfv3v0iWGXcGIqsMKqoQlEEkBx5VgDR766urqqqepyzZ/Dtt9/Wp0+f6vDwsH766adaX19vms5f/vKXl/7E33FB4VWmgB6Qa4YXRQ0bwPslN5wF+jM0fmtrq05PT6tqUg2jiym/VdUOxAa/9NuQmXQoGaDS60QG2Y9Xr141ZUe/sXHVdJPecPT6Y/U43dzc1HA4rMPDw3bUqgJ5XjaKg7HdiyEzg8GgB/Xk6O65ucmAJntVZe+jBC8BJ88rQSPvZXFxsY6Pj6fGdetX0ddku5CsTkJi2ZnxeFzfffddB9V6ujlr71JwgC64s7NTHz9+fEkSf4eFZm1q9NLSUsukxFCiJcg6Ojrq9yv5QxEGjtgDF9sGOCsBrHoCcLOPka0H2Kkc+IMOrVKd26eQUb6E7U8K3t3dXb169aqZOiqCwM6qan13LvMLcm/i7e3trgyageC8+h2/fPnSFE1JmeeY/chfvnzpITqAYHr88ePHtgXJ/kn75PMCb4kngFbLC/uKuvjly5fa29urubm5Dhi1pkgKBLSLi4v18ePH3suWTTENEyOiajINXkzwsp6/JE8qt2tra03dZB/FLNgjGfdW1RTQIcHMNp/ZgS5st3YpNkE/PtnKPX2BMXyU/tmqmooL6Q4/4dxi1tmZGH7GR6NIp39171XV/iV7DTO+oDf0WWEjn7Xzi9E939XV1R5eKQZIEDnfAeo7XdQG4/6Apaurq83wMb9A68WHDx+ahWTv6MFgUO/evZvaEcCiu5eXl80g8I63t7fr8+fPff1f07CpZxFkbQz7P//n/2wUFP/XAzSRCGpiRDah+/TpUwuYBAlaSsg8VM4vRwLrC2TcGUrJyf7+fidR//7v/969NYy/nkJKu7Oz0032gmrT0O7u7jpAUlE0XhvqDzUQUGeQKwGjILmxfVYWKAokv6qm/qaIqiKuz9h6SNbZ2Vkrm6AjeeTQ4ZOTk0ZNPReJ9Pn5eSPBqrOul1IlioPSJqjxPubm5qZ4/rYd+Mtf/tJGx3My+OdlPX9BvsgzR4VaRq5PT0/r559/7oCvqppyhjIjECXfJsDt7+83LZHeC5boaOqAQTqj0dOgC/t/oSujguixcjxBGpkSJJJnsm5AVFK5jTWvqqnK/efPnxso4sA4J0G0IPP4+Lh1XND48PDQQ2EEcMAioIwKIBo23a2qDohVSvU8qSxIHNC6s+/KkBrPWh+xyoMg2TkMFEsarH4r96P/0VIV+uabb7o/bGtrq7a3t3tIycv67UuiAUiANGfgR9dsg+FngBbJA526ubmpV69edQIDQKmq7jEUCEkaJU2GUqWfUsUDDBqCBflX4Zybe5pCztdUVSeeglEAqeBJP5FtnwTTVdU9VuhmWUk3mANIVFXte5aXl3sv3ouLi6a/JQWcP11ZWanXr1/XYDDo/iL+amNjo3Z2dhoI95zYUTFDBrmeWdpMlV92Liebe1aCxs3NzXr79m0Hz6oZOzs7/e6xD3Z2dmp3d7fliA5nX+jLev5SFU8wJ7dx43dUrS3JYtWkbxAtMn/38PDQczroRFaqfS6BFTKnZzGBftek79Z3VcjFteJE5+BnsneS3uvFBNwAi5Nm6b6qagogoatsTMaX6fddJ7uikii2zaTPkrRm0SOr8Sj7/Lrve9bi+KToouOPRqPeGUDOwIbITbyTx8enre2SXsyve1bX19cd77B/X0uL1bMqioK2v/zlL1NJ3unpae3t7bXBMhXTSz8/P+/q2ps3bzpoQ9GCxJhgmOVqA1NQRfGIldTR6rLcC13429/+1seFBAkEITGUTiCFF+7n796968QGuiTII8DQ2+zDq6qm3ibFU6IH1c1r0ShMoKGV6Du5VYZgz//ds3fEwWQQqNdzd3e3jZ3vG6xgEFFVtZFw7Z4dPrfgmjHIYCY3HZZMozZwcL5rz8yX9fw1HA7r7du3XSEEBkgIjeSX1DDo6Nu2pPA3unRVNcLJMAoEGevLy8vu693c3Gz51VMF4EEzpvdAlM+fPzclW6XAmH8OIiueqp50fn5+vikn6ECj0ai3EiD7KCQQ4qSISE7ZpnSkgt/c362qOuEj45k0S+hUQR4fJ3u/qXhI6gUVhtPQd0GsqqlnzSZWVTtfCGZVdaIhMNjf3++qyc7OTttQwTj95hSHw2FXi2yr87KetwRzr1+/roWFhaZvHR8fV1X1ND0+QXA6Pz/fDAG2+/z8vKtUVdX+6OPHj90iIUnzufF43O0e2auYwdDd3V33ZAlSVeLIjnNtbW01o8agDXpVVe2TgKjD4bDG43G3pwA0gCXZH0k+Bcl6GO/v7zshy5YH1FIDYZJWKygUB2QgmpUOgf/x8XH3Vau4i0v8H+htKIbK56dPn2pjY6MrF+PxuJ/Z58+fm07Kvrl/SSkfK9mo+uWgj8Fg0AG5askLiPP7rIxr2NzcM3xpaan9ye3tbeuaqpr4iUx7h35PhhcWFurTp0/17t27pofnwKPsyRuNRlPJpUIMnwokkshkn69EUd85WaqqjhWdk9yJF0wdZZPEg64pE0n3LQ52Dvc/O7gwGWzo7WwMWwfE9TzF4gaAqbw6rusQ62t5c/1YT0Czq6ur2t7e7r3MNzY2mmmTz0XyRwb0MvoZJpBZAbb4A+TJAb4WHX12oqiKsLKyUh8+fOhg5uPHj90nJFg1WOLg4GDqYTL8SZGxdcSbN2/q559/7u8cHh5OoQeMN/RQE+7t7W1XNiUxnLLhKc5NqaqqkZGkpBKkqpoa6EHwcmpTVfVeVUZVV1Vzt/f29mo4HNbt7W3zmDkc6L+AmkPTc8CREjBJLKVPzrdrgdqkAxQM6m0SoELERqNRJ/qpGJ6N47lGtAK/V9lQ5fVcjTLPRF0AbyT0YDCo169ffzXc7T/6ev36dfftSdwTyZQ86hvidAyiId/ZeA2A2d7ebv1fX1+vm5ub7ociYwarXFxc1Pz8fFcOTBdDkYJgZsVkZ2enKxUcMGeXPRJXV1edwGWvBWcxGo26ydzQiu3t7Z76mA4Yskr/6RK6NccwGo36uTpnoosq+noZVWgECknnNvHxzZs3NRqN6vT0tPVkfn56Y2OBqGE5bDDbkWit7WiyrywrO4+Pj7W9vV2rq6s9jt/7TGZEotzQ1gxMX9ZvXwLOrBadnZ318Kizs7MOfoCJ/m+QE9upug58EOgBgegXndbPyk+en583mweib3Lwu3fvuupmgApfhQKrig4MAsxUVcsz3QBqYqkATlCkVStUSfi+rASiqbJLqhiCZfrqflVP+SGAT1ZPqyZ+fdYneh5iHIEjAObo6KhBICwcNozNBMBKAm3jc3x83DZRz1XVpJdZsCl5FZyyP54BelxVfTVB6B99AbdXV1fr06dPtbOz01vBsKnoiYa6VVUnVNl7L0aqmsSaqkraMthaIIl4T2Lm/YuhqqoTVjF2thewL5kk0SPbH2EIoEqOx+Opra/oL7lPwEYcbzaBZJNtEXPSIX2e2Wbh/u7u7rpyp1ji94Ze6TtWkMj7xS6aBW0TeNEqVlXdTiPxnqXjopBKuhWl9FCa/SA+zoqkZwGwPj4+7gFWwJ+vZRbHs2ezCugI1N7eXlVV05I4iuwDuL29rdPT01aYk5OTHveLakIZR6NRT3JaWlqqf/zjH+0g5uaeRt2fnp7WDz/80JNPUUoFPBCOwWDQwgCx4dTS8UBSoYdVkxHHEEvoh/sz8pZSHh0dtVCmsEJM0dBUepJypsxvAqFrRxODXOF8S9aMHea48bchK0kVEBhQcFVfSRwKgIEZ/l1VHWhLBqqq6b5V1Qk9uo3nn/S87MciO5KFlyb8328xUtlILvnApX///n0niaurq/Xzzz93EAJgqKp2HhwJR5fDUUz4wzCoqnayP/30U71+/bpevXrV1Q2BDyScIRZESoQ44Ezg5ufnmxaDQpJU16x2HhwcdJ8Betzu7m7LoATZCG96welnwpb00QwqUV5ULl6/ft12UGWfPama3qs1+0dUbe/u7lqv2ST3J9FkL/wfS0BQoK+QTqHjoLT5t98JOBJBZeM4TPecvVEv67ct7+Xx8bE+f/7cCQL9AYBWTYIeSPXOzk4Nh8N6/fp1ffr0qfc2TFZH1ZPvev/+fffmZN8sX/Djjz/2gI7Pnz+3DKgQAjyXl5fr48ePbe/pz9raWssadpC2hQSIJG5JZ5Xs0KHctgZCzxbNjvDnh1Uavnz50qAUfV5eftq+SRtKJpB8sCAQAAOQoWN8nuFSdAZtFBiT7R17e3tTdiEr9+izw+Gwbm5u6ujoaGrQl8BzaWmp23WyfSSHh2EE8b/e+4tu/j5LjLi6ulp7e3ud7GCSSbwAPeRALJPMFLKaMY5kwrAWLUOqePSJHx4MBlNb5lRVy21SpH0WuwTwz+7n9aF2a0fQ+pDxouuW8AGd6T8atM9JWsXbEmXgiham6+vrZs2gxLIROctje3u7fXbGtWIcvZ1+9/nz50666VbGGpJf1ygGZzP0PEt6xRRVE5ZkVlclo/y35+55YROY0TAcDnvHgT/6elaimMmDkfV6APGTb25u6s2bN7W8vNy8bki2ZvSDg4MptBp6aLNLigZxqXqiTc7NzXXl6+joqMbjcRtZzkA/oX8T/AzY9PpIZnJ/FijLDz/8UI+Pj53goY8IqAx7kHRSgGxmpTAMj54f98bwZC9Rlsx9Foo1GAxqOBx2BUZFQYUhqSnJ7YZ0UC6JGkfjOj99+lTj8binPUJ4BbGugWO9u5vsj+n8VTVlSH/44YeueGQvmORAReX09PSrUKA/+rq7u6uTk5Op/t1E4lZXV+v169fdGP/w8FCHh4e1urraCaRkX/XN4BZJFBRvZWWlqwpoXKurq70n2L/8y7809VOlIHv1JDqJLjK8HBRD69h6jOfmngYNnJ6eti2SDJFPeqBPgIyxF5wedFEPJ+d6dXVVV1dXLbd0SlAriBUEoMqoaEBmfVaFiOMC5ghKADsYEKjhbI6g4Pz8vKtP0NpM5lQN3aO9LBcWFtqeCko50IWFp31lAVSSZ72Rj4+PTRt8Wb996WNBnfbs2dUM/oAIEHv/v7i4qFevXtX/+B//YyoQYr/v7u56ry++Zjwe18HBQW9R8+c//3mKWaOnlz4cHh62bqKFC1ztoatyUFVT12EKIBaMioi9zPQOAV5cA11JRpApi4BQwahAe3d3tynoGxsbdXh42H61qnpPQ2CW4/r8zs5On4cNMbRKUokSXvWke+juuR0Bf8ZOoKBV1VTCDDQGanu3ae+qqhMSDKnsWRQzSCgkK57jy3rest8vm6kXTSLnvQDWyGSyW7IKSL8lH7N9eqZNs8ViSu/ez8V5igZ8WR6LXlVNBjEC+OkrYMPfKuAJHD48PNT79+/7nsm3c/A55E7FMYFm+/HmVFbyWlXdEiExBCKJDbJIArhin7IQAcjOQZlYT569RE7Rim5ubm7W69evW/fos5gk9z+uqj4nUNoAoOwlRpEnD8Dwra2t+uabb74KQOdZ1FPZ+vX1de3t7dXx8XGtrKzUn/70p07wJCpol4KZf/7zn3VwcND77yXCoroh8ZNwcBKj0WQPKIqgTKwUrGyfPR25CaYpg+mcOEaVRj+fm5vr4QCElUJw2BJHCi4Ih/jnBElBNNQplVvFBg1M8Oo+BJE5ICMnLqp6QDAND7m/v280LOmglNuUPUsSDjFmCLLK4t455sfHx+4bVf7PBueq6h4MfThbW1uNyFDyP/3pT1NDVV7Wb1/57lCzGV9Th1EU7X0Ixf/uu+9ax5L6wnmQFwwAgRFjmRRlAyX8vKqmKvqqJQKyqmrZ8xm6xIGRW048kysoY/YiCeAEVxmYA3cEr5x71aSXRJ9R0mhUEAWOsxQ7CTX6WtWkF0NyRvfZgtkAT5ABpMrBU6pL8/Pz9fr16z6Gcf5pO1ZXV/sd7+zsNGVc8J97Xp2entaf//znfk8+x47S1Zf1vKX3U/XO3ntsraSEXNjPNivZKGfff/99gyt8j0ocmurl5WUndkCj+/v77kUnY/wk/VlcXGzAgmzlVlUCNhQw94Aah+JlkEOupDdnX5RAjW9ImjXbAXh2vQJe1QW6Jb7AHAJm5rYUVU9JHLuysLDQDCRxxsePHxtoVXlxXnbV88rJy3mtpnyzOZ6ze0hQ13vyTAGsgNqFhYX66aefmsLr/OKYl/X85R2ikNpqxuCnh4eHHiIIzFPBymn09DIZOdnqxI+ImfgPcV/uQ4pqnhODJZMo3HyPZLBqEvP5f9JEyY/q5Onpaf3pT3+qubmnrZXsW57sFjYmAZ0ssvDbdF4/Jh28vb39xdTjLGDw+XwQv66in/RVepjtWX4nUcPAy5xCjOw5sEP6knMgje/SMffLLqqWygM8O4nvYDDZC57t+BrWsyCnxcXFrm6dnJxMNYWaBMqpJZq+vr5eBwcHvcmwJAdKZuT73NxcHR8f90a2hAcVEiJPEXd3d/vYhFXgarohweMAoACZnKp4VU145qPRU7O5fSAJgYBT/4CkEILKkAg0UW9PTk6aepP9VDYGTqPiD5SHYkJFodGCaMroWo+Ojurdu3ctmIJUvV93d09TL1ESVHcpHUqfxB/tQqKdNBjP6/3791N9J947urBNhQW+qLtQmJf1+ywGkezboyx/ril+YWGhEfL7+/s6OTlpGdcbqKJ2fn7efwSXCYZcXl7W58+fq2oy9AqCp9pMr0zuhKjnddNFzIWsiHBsSY0cDAb16dOnRiP1C9s/NIELQ6sSUJIMcVb2e6N/HI9kTpBqodmhC6pIpENR0UvKKket30H1wpAqVHqBtH6xu7u7rnzSNfR3wbH+joWFhXr37l07ruylUalkRwyr+fLlS1MR2TPP3CCEl/Xb12Aw6NHswDk6oZq/sLDQyVNW4weDQf1f/9f/NRV0Yp2QebJA7r1f/nF5eXlqKJoqHXmi00npEgipblZNAq7cRkXSpgLBr3z+/Ll+/vnnBjCrnlpTUPqAUhJlfqRqeh86AJife57sQALBAkqJNJpd1ZONMdFYVV1swfbxWUBWFQjXU1UdhNIPz0BFx3YF6HSZ1PK3/DEfKG7Kc9gSAUvBxGn3ozLzAuT8PksvGpDfFgjX19fdUsW3zIImwFL6OxwOp6pcbLCkwwLIOi8ad4JDKn9kTGLl9wmQVlXLEd+ZVWdyCZBcW1ur7777ru0GWQLGVlUzh/gwwBBQgw+tmsQfrkNVH0jseYjlAWP0cdY/+3dW6hKUHQ6H9fj4NDUZEwLDzjME0CwuLtY333zT74g93N/fr+Xl5bZNgPKFhYVuKfnw4UNtbm7Wjz/+2PeMwTAej5tanvYw26u+llj32fsoquwl/xkCU/Vk2P7n//yftba21skT5E2AYvCCIQmQf6iqBl40luXl5R5OsbS01LSYqmonSvCurq5qa2urjo6Opvoj0D4kJgZiZDLGUekpWFpaaqPsXIRH0zJD8vnz555spgoBbVINqJoEmtAGASWHxNH6PkSCYxd4jsfjdiAUg5IlKjkYDDrBlBgvLS3VmzdvGiVTHX14eBqPjJYrEefMBBL6U9BoDDagDN63ipEBKmjBglQyJUB6Wc9fg8HT6P0PHz7Uv/zLv3TfgWnCHz586PHv19fXXTWT2DNkWfEXgOUQmKR4mo5qAufFxcUUpUuyCKG1IIuc8OnpaTsbw2cAKCjKW1tbLTv0/ujoqO2PHqKkbquEQ30NlJEMsWF0SIIn4bOxNtpgVXXlBmgEqMkkjw7qw6iaOAqVpbm5p0m00GH0WYEDe8iuJG3WIAvO/+bmpof35JCBfJ/sV1XV6elp7e/v9/tQOZKcqJBIENHIX9ZvXwK3nCI7izQDFnMfVBO3//KXv/yC6oxpksBLsmTsuWhoDDklZ7k1EX+t+sX36GFWSaF7/JLKGSQdnU5yyc+7X0Ndsjro3vl/8UACK3QC0AW45b+TCgfJPz09nZpUzkaokgBDgMmWhL2q2nYkTTWnCmMUVVU/C6BOAuiqwZubmx3fSBC0ZfD73p0tO8QpqjoAJGBuUuRe1m9f5OTTp089AFDcap6E6hjfIWFkx6t+2Tua1bOqyRCljAOrqn1lJlr0BxNEFY2cqHrxD0m5dAw6NdvzVzXZtik/Lw7M4YkSVdVTcmuuwHg8vYWPY7E1qotZHKqa+FOMOffk3+IEvY/yCWw7x1KYwdpIJoy4O5+x9yARNRiKLZVoyiuArt9++22zDCTbl5eXdXBwUKenp31utgVrx0yXP/p6VqKY/GbG6vb2tgMdg1DevXvXtAuJzPX1dR0eHtbx8XE3ZB8fH9f+/n6Pzk/6ZCKHd3d3tbe314p4fn7eCdHW1tbUpCJB46dPn2owGPSLcRxCmkoEGVXiFhxCdBhhSqBJnjBDFQV5HBtFTIQked7QDcdIDrnJaBQqE1CBMichkGc0BPBzc3ONMs/NzXVSWPW0Hw8kRWCysrLSPU6zgSVnmA6PYZLwV01GS7sHz54zQ4+DvGV/1ct6/pL00wdGHA1tf3+/5cb+pvS0akJNMfJeMFpVbVjJRhrbrD5vbW21AfU9yaJqeFVNDc0ZDAZTvXmz9BIDcrIPWVX/7Oys0X39jeQve6cgrtkjlk36kFEIJ73n3PQ83N4+7QOnTxjVT0DsmatQsA3j8aSBPvtXOOBkPlTVlJ0dDoed9I7H46nA0VTMfD4qrd6v90LP9Hdlj5PPChKAXJz4X/7yl5eK4jOX6h9gBWiRwRekmtzm1iWSMXb27Oys36+qG90BwGGeYN6oaBnUQAeAko5lX7dkuah4JwUWEIm6CTzKZBT1XWJL/nPrJH+zG3waXSL3JgeakKhn0L1ntefjx4/1+PhY7969m/qd5JYPrJpsQaGvSnDNl4shAMBihH//93+vV69eTbW0SOQA3VXVAG8CvYA05/Wuz8/Pa2lpaWr7GxQ5dp0PNrDna6G1/dEX+3xwcNAAoyQcg0PScn5+3lsuse/stwRrdkgY2RfzOWdWowEYVZPqOT+teAF0optVk8IJQD+p0D4jLjZXBIifFUjXzxdmlZvfUi0Tt7reWTCJ/it0ZAtXJoF+z664Vv6H/+bvTO/3nQSdxMSAMz4ZiJuspKz4uV73qjKazAOTcJNdcHZ2VhsbG/Xp06cGVj07cmHv5q9hPcuScCBpsEyvVAoX6EHJIGKM+tbWVidpOzs7vceisfmQgkQuMhhFl1SlgqQY1oJGpmTselBcsmF3PB43HbSq2jh7mdkLRRiNSOYkUkg9B0Gjn0Ogqia0vI2NjZ7+pPw9y1//5ptvur/KM/M7Tg8C4jOUnyOE6KbTrqpOXNH1BN362gyp2d/f7/40gb5nA9HkTOfmnvbJMrhotrmZc/vhhx/q+++/r6rqiVNfy9jgr2Gtr6930vbly5cebKHKcH5+3sZWoPHzzz/31hbkYn5+fqrvEIovqMtBKDmsRrJydnbWe0ytrq62zKST5ASHw2FPPfVHEMqBkPmFhaf91kwFNo3z8fGxKfHAC99LJDVlmE1T4U+HlUkc58XW5D5JbNDd3V1XPzlQQQH2AtsCHU66nwBagsl+VVVXJlwPCjqGxPv37+v169dT05wlEMZ7C8w5csG1JH80GtWHDx/q22+/7QA2q0EvYM7zF7t6f3/fvo8f0Qf44cOH+v777zsYpCuqWvoas/LEh+gfBhwcHx/39kPYJUa2S3gkhFXVwK+gTzWDrFdNAldMltx/N/deJb9sie/yB6qVWSlzDlvRpF83WAJ1N4erCZ75YfpsjzoAkWfgmRnAhz3kvukDO2I/59RRSSZamjhCYOz9CUz39vbq8vKyn/+nT58afJKYbmxs1MXFRR0dHTUd2QAiwTpQ2zNmZ16GTf0+K5OLu7u7ZmjZVsZz/6//+q9mdUmmZkGHpHKywVXVssLXaVlgb71X8wQkVEkpn50LwEY4n+QsbX9VTSVieb0qpGQs26JU8rAgxJUA1oWFhal9fjNBznYPgBIdkVDx0ycnJ1NDbrSouQa6rIACFHYerVhJx07afQ5p856rJgCeCcyuN58tJs/BwcFUoefx8bG3xnv9+nVXFPl1oGAWgv7o61mJYvbIZR+ATF21LSdG7e/vd7+RCoWAqqqmjKOXXjXZG9B+b5IeCSJnubCwUDs7O02neXiYTIoTSJkaKNClEKurq91kLrHBZ3YdBuZAHCAYHGpVdVVPlVGgWVWtLIIuCbEqo31nMplKFJezzR6urET6GQfHMXFiaDg+q//LViBJP0Utur+/76pUJtecLOftOlFyvLfz8/M6PDysi4uLqWEC+kDevHnTiecLZeb3XQK/7HVSzWIgDSC6ubmpf/7zn/Xtt9/W4eFhVT1t1r2/v9/ylNU4TkfiiVZMXobDYaPbWAZZVVf1S+qLBEuV3PHoh2A2KahVNZV0Ol5VTU1YM5AAnQtIw86Mx+M+H31GocmqBqp8oppJQZXMSixVGzhxDvL29rYnO0umBdCSgc+fP3fAmoiznqScnJw9Ke/evWsgwLtzHEG1QBNwdnZ2VlVPA6ec682bN1PPQPCS7QUv67cv07ovLi46Sby6uqpXr161H/nuu+8apKHD9Ob29rZevXrV7RMZEKJK+yzf6Ht8IxBDAJT9QQLGn376qb777rv2S6mzw+Gw9QwoklUNQFRV9fA6sQKdBSAmiJMVEmCjJEtSKEinc44hmZJ0+V1WXrASMjhHQ/UMsuqi31PsIclFcVURQWOVtANfLTpsCIrEGTgLhMn93LR72IpD4O49XV1d1d7eXn38+LG2trbq5OTkJUn8nRaZlkRtb2/3OyEP8/Pz9fe//71+/vnn1rlkh5EDCZ8kkg3l3yR7VdWTNCUT+tMz4cyKmVhRrJp0TbEwkEfceXf3tIWMOM5WOIoZWA2q4WjU9E5LCV/l+bjetBNmmqSMKyywf2k3Hh4eant7u7+Pvebe3LP/06PZibFZkfdz7whNv2oyGIeP5veqpmeVJG2W7/ZcLy4u+v75aPmRZNa2YFiQX4OePitRZCAfHx/r48ePjYzt7+/X/PxTTxvjCaUU3KSCcVCSRCi9oTBnZ2e1v79f79+/76qhPivoSgaHlBrSQCAht/f39230T09Pm5768PDQwa1AiCOEir5586bpXXogKbnhMdnTqKLpGtbX13vQD6FNap0gjlAZssMJZ3WAM0tKJ6QV4lFVrfy43AzLw8NDHR8f98TYzc3NpjJl/whhzj5U17WyslLv37+vubm53txdUAIIYPCqJlPc5ubm6scff6yjo6OpybM///xzHR4etvK+rOet//zP/6y3b99ONYmfnJw0zen09LQODw/7ff/9739vBBGgoB+KoxF06XdUdeYwoOw7OztTiU3VZNBCUnjIQ1VN9XmoTLMzkksBGIQQyABoQZPjdFDBJbccT47YziqZMflVT0GCCcl6jh1DMOjeOIisqnOQ9J2es4UQVUOwBIyuLxFqz0rAIAD27CSgzuf37j/7MKuqaW1ZqRHwouNmgO04goSXiuLzl+Rne3u7e3Kzp1XQ849//KO3mVKtxwZIEBQQgAI3NzfXAJ3euKRSZ6+dwEcQLElcX1+vV69e9YbXVdX6ny0QKKbAI3Kyt7fXfhWFnR6krqB/A3Ukc6PRqLa3t/veVByAMwASVViVwQR7M9CjkzlKXzwg0PQ8XA9GUraQiCUy8fTugKBzc3PNkBFXeBeDwaC3DNvd3W1WUcY+wBmViKST0k0gAYZTVbXf/RqqFX/0RR68u7Ozs565oFpPT8Ut4ivP/+TkpAG4BCEw7BIEHAwG9eOPP/bWSFXV8iV2BvjzbxLQqmqQSPIyG2M/PDz0dc72HgMs6ZFWE7Im0QGy6tNEWUUbdT1i1az+iTGBu66bDaBPnqHE0ecysXJ9zuOZYFRkkg/YST/KxopBxAniF4UL9ubk5KQrmpLQpBlj27Hj3g37I0/Alpqd0/BHXc9KFAUiGxsbPeChqqYmZtpckmEWuPj+f/zHf9R/+2//rZF8iIEHmIEPVJGzQ83xkiVeqg1ZCq+aUEeTIgtF5LBWVlZaeauqG9UvLi66R0HwKNjmjByHQlJ+qB+KmEZ53+EABbxV1crNASbaT6Elof6fnHeGiOKqgKDZqQYbCERxnVvVktJAWYxOZljG43G9ffu2A/WNjY2esChxdV2JxJ6dnfX+fSbI2csxkZyX9bz15s2bNvrogyho79696yDv4uKiewm9y+vr697ChrxB5nKLFVNSE9wQcNG3q6urOjo66uEUOQJbQOdvQ1gkiejpzpfVUImdfd9++umnev36dfdiCdCAF+QNXW08Hk9R029ubppWRmcFlPSBncMMUE3nLDgeztp105uHh4e+Rw6f00lKjXu3r1QCRpLs7OnVr5FBdPa/sCcSD1Ur141+bPQ++ndSb028tL3G14CG/pHX3d3dlN6RU0EaHfn+++/r48ePPSxJ4HZ0dNRVfzL3P/7H/6i3b99W1RO75fj4uL777rtmsKRP/LXtIba2tjpo8jvJ0tnZ2VQ/Et1Sodjc3Kz9/f3edgMFS3ygTUMQKGHkn+x9ZsCL9gY+qGoCOlVNJi+qvIgBgJroetgEdJB9SoaNZzHLSjAQA3tJXJF9zkApOpY9kmIWCXhWbtfX1+vTp08d2CYdEdMnaYaWWQxV1dNPJZ9XV1ddnXxZz1+qQUDWHHrGH62trU2xok5OTmp7e7tlwHusemqrMGAtByhWVcdU+/v7XcH78uVLU54TFMyqIgBDIpqAv/75ZCMAggAyOcmUfuXG86PRqGcYJIVacmSv3gQ1UT8BPeJ71fIEcFUIxZ05UJKPxkYDFFkqheINn8tZJ9rFHEvhpqq6JQ5rho/1uZxVktXUtH+AumT1VU3HCD4DwPP7r2E9q5PS2HSBDUOqN0J1ixFN6ojK2d/+9rdGbKDkmbAIeIzIXl9fr7Ozs96fkZCkYae8FAhSD1U/Oztr+qtpRdnoqmkZ+gBVpeycknvjjH7++eemnik9S5qvrq7q7OxsavNWSkOZJJ4CNwq8srJS+/v73Sti9LlhAFU1hZZy+vo6PMscbawqkdS7qmonVjWpGFMcgStjgx6cgzkYEefwLqqqlTapxrlR8OLiYle3vJ+X9bwlSMptHjY3N+vo6KhHVAuejIBH7zap7+bmpj5+/Njvyf5hs5MP9b69fv26J52RyVevXvV3GfSqav3JHokchiQR9dnBYFAfP36cordJktbW1urNmzetu3Nzc43Ko+QY0KR6MhwO6+zsrGUW8MPRAZ3olKABWu++BaickaQXAkznLy4uGpG1RqOnrXdy43GUYAH74uJiByKqFcA0z46NFJBkciuA8bn9/f2pKohkwPdcH8BLILC6ulrb29v9rl4qFs9bpkqvrq7Wq1evulqodWJzc7MuLi6615ZsV00mptJlbRF///vfe+gJWeIPFxcXu1WjarLvp95VzBn2HLAheXz9+vXU9ELVP4kKOqQ2jqxGpL4eHx/X8fFx/2x7e7up3hnQ0h2UdIFeVTUQxE9K+NgpvmxjY6P29vZ6+xc6IYnLBF2MMBwOq6oa1BEM8ndiGM/HOfPaBb0CRInoxcVFA69LS0v16tWrpv7b/5n+Z+ALOGDPgTnkQCVSz9eLD/191v/5f/6f3b+/uLhYW1tbHetihDw+PvWW87ebm5t1fHxct7e3HRNlz2HaZnKNXqziJ9kHxKYfYZclPPTbyvh3Z2enq53pnwCOSatWKQQG6zvOBE08KJ5IGXVf7gXrKHWEr85ZGb5LhxVdxKmZiH358qVjafece0rSbTovFrcdT8YMvstuZVWyajId1jVWTQbhPD4+1uXlZT+LpP56X6urq83G+umnn+rh4aEuLi46J/la+oifPfX04eGhN/Otqn5wepw06SfiNhgM6vPnz73dQg6/USlIRBVCOIsIfPr0qQ4PD5s6ZaiOZIThZkAZ5qOjo56SxJmh7eS+jAIoCSjjfnd3146Yk9zd3W3KbY7/5+wM5uHAHJORyXK4RIlSJpJUVY3c5l5MmuA5383Nzdrb26vT09P+v2tnFASflFFwKAn3e4iWMj9k1LPOZBBqyyhIJjX8elfuVzP/7e1tN/3m8ISX9bxlu5akLUkIVLY/f/7clCWyl+jk4eFhB3tokTc3N7Wzs9OVBk5EcklGvnz50omFAKiqphKUHBZAFzhBiVg6Gf2uAt90VJwFFA8NT8Uze2c5UQGgSg4EOB1TPg/One6ipiaiijInEF9aWupBWRyxYy4vL3d1j+2RqCcdDrjiPQpaUfZUUdhaiDTWAeelAuPcbDLAAA3Ws394eGg7ok+5qhp0elm/fem7SQoipgekXXXNu7q7u6v9/f3WgbOzs9ra2uoWCbRFCeXh4eEUGAgoGI1G9Z//+Z91dHQ0RXGmh3RbojhLucYu4SuSnaMyBtzIa0dBBarqMZRUqQR6DgIv+pcAJ+q3REkVgl/Ui5kTgPVSk/cEiaqetokxqMa1AIWqasof84eC9fv7+078XasKvWqu7/KFqkkmxKaeeuaerWOxQXrELi4uug/8+vr6F2DUy/rt69tvv53abmZ+fr63P9vd3e39ZrN3O4cbDQaDnqHx6dOn2t/fn5rzkFVpcQ/2GbvO32Ti4v/ZOiFG5RsVLbJimP4ZMMnXkVnH4LsNkpFAzbZD5M+ramoIpMSMb0pwhp3AOjLQjS9mA7Powx7l+Sz6ItZNVqE4hn5mVS+TYawn9ghQVDUZeDcYDDqR9t7EwmnP/FzeYWeHjx8/9vv4GsDWZ1UUCQiBE9gQRg/YtCQvr+qJQonPXzUJEgV2+iGgdwKb4XDYBnBvb68FzjEkLJQVSsIh2J+NACUnWnl9bW2tdnd32/mh0TgXJI/RRlu1KaoATs9f1fQmoxrSBcUU3WAZFE39WAI0hkjlMfekUtFYX1/v49/f309ReRgfzt67QsVRkVCJZExQifCsBaemaCbKmk799vZpP0UotD8CCPfkngUdaEIv6/lreXm59yB8//79FMWFjq6urvbQlMPDw7q+vu5+itvb26lqJHrmDz/80COg6R0dGg6HDXi8fv16SvZRqCU95DIb7rNRHMWDc5H4ZHIK3eMoMyBOyiXnxtlmMEaeJZtV1XqfQwmAQEAtFQvOIaky6KxAIVUW9J2ksGR/MzpOVTWF3PdnR5fT4wwOBODLy8vtEM/Pz9uJSvw8F+BeDgyan5+v9+/fT1U33D9U+2twcH/0JQBcXFysjx8/tpwJ7NAPAbLeAYqw6cUCVYCISgD9osf6qsjx3/72tx60NB4/TSZ2LAPMBG+5NdN4PK6tra3a3d2dmi7uGt0Tfym50lNHT/kDspfbO0m2/B6ImNSz7G9PGqCYg16rFqIIVlVXWsUlWT2ng9nGIulV9WGLss+xqprCjWbK9qiw8Ps//PBDHR8f15cvX5q5VDXZ+uLi4qL+7d/+ravFKrZVE1tSVX3uTDIFyS/r+YsfUlU8Ozurw8PDBs5V4cUzS0tLPZDx4uKiwXj2XkHCtijkA5gophIH0YOTk5NOdMgWua+abC+V/b2uXYHA/VRVDzB0XfweP1s18c/r6+stT5I18Ro2CvviujLxTWAUrZReZwIKTMpppVXVz8g1ijGz/5A+AqslpWJ8Fb9k1iTtlr57nwBrBQ1gjQV0uru7q8+fP0/lIhm/pm+2s8Pr1687dvga1rMgp729vZ7E9cMPP9Tm5mbvzaWcq6keYuqlpHEdj8dNO6ma7JX2+PjYUxoZT0gk5TFQJsvOKfQ5pU9iCi1IB4mWlk56Fo0nNK6BYRb0JVoyPz/fiCuB5lTQRRgDFDOKnb0OkjfIs2dbNWn+dU7Pbm5urtFlz5LhoHyMxt3dXW9F4NgGgggqBoNB70fnvAIcVKg0WJ4phXQeBkni677Oz8/r5OSk9vb22jE718t63hoMBvXPf/6z9vb2anNzs4bDYeuCYIscLS4uduUoEzIj7lGoV1dX669//Wudn583SFRVUxuGC9I+fPjQgM7y8nIdHR3V8vJy75+qMumcgl3OFAjib8GUgCl7bpNSsry83PSsnZ2durq6atRW4Job0t/ePu2FCOySIHo+gBeoq+B4NmnzjDjXqonjVaXJnoZMuIBCQJSsJGTlI4El9m93d7dtATujNyWHEXHSQKj19fXeWy4Dk4WFhfr22297f9W5ubkGDyTaL33Ez1/aFm5ubnro1MnJSQckevqSUWPoCeAmK+0qWr6LLlxVXc0T/FVNb349HA67QgXwuLy8rL29ve65EgRKPA8PD6f0MvdvVM2gR4PBoF69etW+XVBIv7KCPhtsukc+mN5jv6jGkXG+mc66tgxW0QUll0nJVcVJEMw9CWIBTd6BeEKl0nkEnvaFFT9oe6maBL4SDzHHf//v/72qJtPVP378WAcHB31d7IDhQUBbsdPLev4yd8GWTuSsahLrVFWDGu/fv6+tra1OxPgJ4A6QX4yVbRhiU0WIquqBVRKiLDQkECkZTSqqz2TVL6+1qqZAVzoitmP7xWMAFHFr0qFTV7Pib/GzEjIsHM8vaafADhVF/xdXGiiENkvmTU5lL+mN3COvMbd8Sko7G+IYYgkUVr4f007LnN+LQ8S/OXDs5uam96w0JfVrWM+qKNrr5fHxsY6OjmplZaWGw2E/XENNrq+va319vXZ2djo4xNPVKJrjowm1KhfeLwfJiUFelpeXmy6SiklgMsiElqsSSHLm5ua67y+XagoBTmoXBSXMOWaX48rzVU0QE+iUvgnHpOCQ0OxxSgcp+WO4OMDxeNxVV/0Qgg7CmjQBPRIQ2KxgMFiuX0KQvOuVlZU6ODiovb292tnZqZ9//rkDWBUM18Zx6dEYjUZNW3737l0bBgbhZT1/jcdP+5NCIjc2Nuru7q77AAV4GxsbdXp62j1NJycnjVpub29PoaJAjaWlpU4UVRDIVFYd6KtgC63SeVP26ZkR86r90NKq6omm+XlgTNXE4aLfkbf8DicCndVLwSFwjvqgkr5DL9iF2aomJsLp6WlfU1JpIahJpZGgZX+IIDBtHhCMbbAZuCosHQXgsRtV1X1oaOjo9+hGkmHU0xzCgUIO6Eq7+bJ++xIwoT/Pzz8Nrvnhhx/6ffzjH/9o2VJ54lv0y1xcXLQfWlpamqLwC3oEusBTskrnUTL1Op6dnfVQt729vWYfVD35EuBEDsaQeCYowRY8PDz0XoCuVwLo82dnZ/X58+f2BVXVOpv3T/8Ettg8fLpEE8B5cnLSLCDA5+LiYvcwi0fcn2MBrDKRE+g7V9WTTzs/P+992QSUSS27ublpW2JLgqurqzo+Pu7nlj4d4AsQWFlZqaOjo37HelnZ2x9//LGWlpZqd3e3ex1f1vPX/f19vyNJiyrVDz/80Aw1QH+CMypa/J9YjO3PXn798Ww8/cdY4R/Sz4n98t+OD6z1e/40fWH2mdPD7IEG0hrmoydTa4YkbGtrq2MKxQjTfdNXAFl9NluMMCTOz8+nttHIa0rKNWDp9PS0qqqfk0KN7+lxlhOIDzyH7M1P+q7nLGHWn8o2qejz7YAj/z47O6ubm5ueYXBxcdFDx+RFKLR/9PXsqadelI3Y0ZokE9fX1zUcDuu7777rxncjsqFmBwcH9enTp/755eVlHRwc1Onpae9p5EFfXV31FDLCq2fj8+fPdXBw0HuZeNkCwqQ1Sjw/f/7c1UQla5UFiScHXlVT05VUOW9vb7u6ysHo70DDlbBCElQfOQyUT2gR56iCmZULCA6EKasZKgCmqmbCXFWtOJQFQpQVv9mKjAqgautg8LSZryonqtvd3V19++23rWyGHUk0bCWSfVEZ+GxubjZ/PCfbvazfvrw7cj8/P9+I9sLCQr169aqnV9KPtbW13psN6qnPKavEqGiSSMEsveDc/uM//qP1C9VCUJSVNUhlAhvuIRM29wNMUhHICkCCHBIjjnBWrwXQVTU1Ul6gLWkSgKZNsuiaZ5hDRATkrhd4lMyH2UBbdZdtElg4ns9AnqG4UEqJ4/Lycn38+LHG46f+N85tdXW1fv755ynaj/cmMJBIVlUzNqCot7e3Tb19Wb99CboMcZPAoyZtb2/31G2sm83Nzfrw4UMDlVlxlATqoVpcXKyTk5N6+/ZtV9kkhR8+fKiqyX5rVU+6xneORqPel89Eaokn8NfAFH5AL+NsdUS1S5AtcNMOklV7PkvlMnuN6evy8tOWTnqnVM6rJlVN5096qDiALQR2sjn07/3797W2ttb0W9eSPjLbWVRtDLKhf1WTvZc9e+0jSYf33oHiAAAT3hcXF+vHH3/sdhLDg6omDIN37971NWofeFnPX6urq/X69etOLLQKrK2t1f7+fvcLS5CA6Hwbdooqloqa95qFCuAgGaYz9ENFTWxYVZ1siVHpj3NYfAwfklN8qyZb9fBb2RPpWvh1TDvxG79LBwGMKobsieuUVDuX5CnBIc/LedkpdsK1Hh4e9s8z6Zv1wUntno3pPStJOz1lD+yGYH94el5VDQCjJ1v0E9V/NBrVhw8fplrKHOOPvp6VKKJVZAY+GAy6JO1hHB4eduAveVTKfv/+fe3v79f29nYPvtAzdXR01A/beOrxeFwnJyeNonOO19fXvcdhNrAKKnOaIcRvZ2enkzDnyUETSuGERSXEMSWrgtREKpPyIsCS0EnMsnldUI3GMh5PNjDd29urqukG4aSoUkLBA5QiKxBI1k4AAQAASURBVI3+5KAEFB3ocSYBelKgPAsLCz12++Hhofb29qZQV4lw9q0xFF++fOmBNVDk9+/f13fffTdlNDnGy8vLl0EZv9NSNUwDqxcC4CIoWV9f70Qd2rm3t9dTAD9//lx/+tOfOsC8vr7u/YBsKQPwoGuLi4v19u3b5vgDAwRgjKXr4/QEeunoPn36VEdHR10dffPmzdTvJW8CLfKcqCQqDUeS/RCC3ewrmpuba9CF/joeuXW8BHJU/JKhoOoq0EwWg+RV8qeqsLGxMWXPVD/cp+tk91Dfkpb2zTffNMXGvQ4Gg/rmm2+m6HwSZAlBDvRZX1+fSlagpi/reWtxcbEDj6urq9rf3+89yf793/+9hxwZHAX0ODg46DaB4XBYo9GofREa6draWg2Hw552WDVhkeiVX1p62vKCDgocsUJMVLXNjGAJPSwr5aZAqsbre82gGLAEvEwQBXCkf2tjY2NqQFS2TNA/lR2Bud+xC54LP5XJK110ndbKykp98803rc/uSTyjWij5ldQCUQWhEkngDf0ViKrO6Nvc399v+yeY9ZwWFhZ6WJ79NoF43qsKpGmPX0sQ+kdfKmkSb3EQPb2/v+/9uM/OzroYglHFZ/g/P3x8fFw//fRT/R//x//RjKsclgI8wK4hj5JVVfMEObKimOCo3yegmdVuiVqyf/hBflJsXDWh3DoOsCKpsEDH1Es5gvvj56qqj0dX5+bmGmQBcPqs58z3eW6/ViGVJEr+PKPhcNjsHM/G98Qi/u178/PzPaVYTM8usWUKYpJlthBrj91LP/5HX89KFCEfiRAm4gG9F9iZ/DUejxtR29/fr59++qn+9V//taqqg6RXr16143AsVScvUUVOVZKx5Hhk+JKzROsz4IHqQ08ka9kzqDKTwxySXkJZ0IFSIQWbHIgqqKEBVZMeK8iE5CqdaA4KSAUT5OU+M3jYhN3zEvx5DvluDOnhfFSQTk5OurfMZykGhRGU4+U/PDzUwcFBB8qCWgH5d99910krRZOg6Jt6qVY8f+klVdHVU0BeAR7+v7S0VJ8+fWoq8dLSUlMod3d3GxyS2AwGk546NmCW229vwm+++aYri3QsE0SOa7ZqXvUUvL1+/boDKNebwV9SZlL3HAfdPBPS7N1DLTHASoVSECgwy+NVTfZM8pkEmYAtKcv0TxKmQpmVGJNqs9cLqCSQz6pmMg7cH6BMILO5udmOCatAIMpO2Jxdolk1qbJAQvM5v6znLRWyqurNtdG5//KXv3QAKaFEt0JpAvC8f/++acXHx8fNyEFVYwcMv2GLAYsSMMdE6ayqHqbD91RVJ6H8hkCNXM0mUa4je5uAg8As5wOgZlAM1MxR+6oVmQSS+0z8kuZN7/0+90/LvmQy79hVE9obnWJbcpKxz0sIgWUqg3oqDdaRpEoOJACYNxnreJb6PFGWh8NhT9OUzKC5vaznr+zPraoGJ7zjx8fHev36ddtDbVdAl0xiLi8v28/t7e3Vn//857bxji92rKoG3flTOsR3ki8rK49k2J9k7Chk0H8VRDJIvuhA1aRS6Xf0KuNl8sw3V9UUuKPVwf/pqntgFxJYqqopvZV0SRQ98/S9KqAYQOnLR6PJwD33BwjLvl73x1cDmVVE0fc9G2wOIFD6cznDxsZGt52pln4Nce6zIOHt7e0ehkLQEpVAyUCpkFCpQlY9Kd3f/va3NryqdrMJkuREdQtyMT//tElvVu1Go1H3I3rxkkECLsmRyBIkNJjZ76Qzqprsp5YvmeFOtBO9JQfueFaegXHhkF7JqwTKzwzeGAyetrvY3Nxs9H80GtXOzk4PF+CoGQMBrn5QiWoO6GBcPOvt7e0ebIM26vmiIAhkBCLv37+fqmZIHAS29hby/LNXURL95cuXevPmzUvF4ndYw+Gw9/AUEEI4yYlEaHn5aXx3AhSAmH/+859tHCGc9gT99OlTVU02C/78+XNX+fX3QOj1BFZV0zSgooJF+pdIrMobOYbc+72KvEQQ4CQBJNt0INFP1Ub0aUGoZ0D3M3jj9DwfssoJnZ6e9jGTYkNXUTfpQrIJOOp0lJwVO5WDOfw+HSFGh6Dm9evXnSCwY2mfkjro/biutbW1vp+zs7Omnr6s56+keAFBxuNxT5zlZ758+dLtDZBo73cwGNTr168baJOULS0t1fv376vqKXE4OzvroBQKL5B9//79VCVRRYwv174BBK6aBMzk+ePHj3V1dTVF78zqAqoymZN0sT/kP/sa9TOS7aS+8rHA6rwmPs7nz8/PO3HNgBVwSqforABYEJt6ww7QbVuXVFV/l+1Dhzs7O6vHx8e2mScnJzUcDvsd3N3dNW1PMCpOUqWYm5ubmgKflHG6zN6qZLys5y9+KhOlBEIkYY+Pj83myj2rU7/5j6WlpanppllcEN9WTQAL+p7HI2fJdnGdVZNZH0lDJTvJdvM7vtV3+b2kjfJLyfii/3xCVgj5YcfFeFDNlxSKD/NcEknTvtMnZ8U0C1SW38kltre3m0n1b//2b32dqo3ZxlVV3ZucYGhSUdlmfh3lmP9OmzEajZqiz/4BDr6GJLHqmYmiF5TGlkGXjDGG6CgCqvv7+x4PXFX9b8ZNQLm0tNTUr83NzVpdXe0qIUHIvZo4CnSO3PuGMHA4Rk6PRqN2lGtra+1QqyaOJ6ktKaiqgzngAWL0a2ijayKAzk8hPUf/Fqh5bvPz8x3439/f91YVW1tbzZ3nUBgdfZ+JcqAKra6u1s7OTiuBZFST/9XVVX3zzTe1s7NTc3Nz/Z6SZ720tFQnJydNB0z6oOv1vF6/ft1TxAQLklLKtLa2Vp8+fXqhzvwOSyX81atXDTDQs6w2MnA7OztTG7sDU/7X//V/rcHgaf9TwQoEbW9vb6r3gs6SIxuJ2+PTZGTOI6sAjqECMBgMpuiahmWxKdDLpLtV1RQ9zr1JiMii/3NE+o0FmnpG6D1d5ZCrqsEvum34QFYLnMv3BN9J/fGugDBVk0nBgkDnlgiyL/Rc0O3vPIblmfu3YxkkUlXt8CXaVfULR2cC6st63sqkCCC6u7tb33//fVVV9w8DUgAdEv2rq6s6PT1tH0Knycz333/fFSvghO/f39/3xvLv3r3r6pWqnXfNXuiLI+f0h7xtb2/3kKqkuxnrb4AFRN3vJYiYCX4uaVxYWKitra3WU4ma8wBDjeina7M9Ytmnn9WQrOBUTfots0VGMsvO2H4kr2E2uDZlfXV1tdtH0NFsDyYxz2TENWIJYds4pl7N7LH2+/X19VpbW6uqquPj468mEP0jL8WFquqWjJz1INkbDJ4mTSerKwEPgAD5x6rTp0zm6Li4ERiCyVFVzTaxbZH3nBTJTLr41KqaAktVx/2/qqZiUJ/nS7PFIlk7i4uLXXGT+IqZZ4HavLa1tbWmmPNlkkH+h79PG8evAVISVJ5l53mHYum//OUvTbtPJhV/nFRyzyzfMcAA6CQpTXsFhLJFF9vInldN78f6R1/PShTn55/2diEMUDkBksAUtQMVhnJofF9YWGhDasPYf/zjH42QeSEEAl3Mz4+Pj3svsKSFVk2CHS8sqSqUbXFxsb755pupgRaSHd+rqqaVJuVLf4ehEo6dCuS6zs/Pm/pDKZPqQugYJtU1TiODCXs9CQgS3RQ4c6a7u7s9lUqFNelBFNs7db40iknNc968t++++65/ZghHVmEF2xQ9N/aWFGfF5PDw8KtRoj/y2tramuLCoxnmiGeT9DgpY9yrqhOaquohN4CX7e3tDs4+f/7clYTr6+u6vLyc6gOan5+vg4OD1k/ATdJTGNvBYNADKwS+rj+PV/XkxPQroEdm9Q8dO+noCZ5k0onac35+XktLS7W3t9eVDgmnCuLGxkY7OLrMydCDWQTSvaGZ3d3ddQA8NzfXYA5qL8bCLJjEZnqX6bjYHYilSafAmhxpntQ+yd/sNLqs7qIpsaMv+vn85V2nrxCQAUqrqgOQ5eXl2t/fb7lYXV3tScbkACBj2IsWkN3d3U4myPDDw0MNh8NO/ubnn4aQoU/xwZk8CaDQvwaDQfs0Qa+A7f7+vrc9SpCC33VcfsRxgS/ZQpJVOuyBDBhtbyFBAg4JYpPimkwcVYGs4kvAxuNxTzOVGHhPCSLPUloXFxd7I/aqmppKm3MIgOCqSxJnAXMmspJJe/TRYROWVR0FuS/bY/w+K/f7rqo6PDyc0ruq6mRfdXt1dbULDpkkqm4BZKomFUuMnqoJzTmrc0AXPqBqUsEGlvjc7PdmQZ/Ly8veCoZMVk0onlWT7SroTlbAyKz4mO/hX1N2xbPicmw2sV/GGp4nZp7P570kk0LcnG0h7iHPi82QAJf7mR3o5l59N/245+xZiWcdGwMiAeyqp9zBbgTAMhOSv4b1rB5FyR5hQS2DrnM2AkDTiRjK5A8nHeTs7Kz+l//lf5mqOlxfXzdSzwFBXP70pz811TTpqBI31b7stYDI618kNPozDAOoqikeOC6zQMn0J8hEKikUJ5FeNE1/2zcpkQoGApJUNVFgKBQH5bo9Owgvx69KAz0SRBsaIEFznd4lmtrW1lbNzc31FgMquJyRnibo0fz8fL1796733vNOOGEB72j0NDhBUOO6bOGQ06Ne1vPW3t5eDYfDRuMlDktLTxsDqxZJENOZvH79us7Ozrp3FMVEYrKw8DQ5VfKhcgChM4lYsLe1tdU07AyKqyYjvWcplVlJsZ0GNH0wGPQIf44pe+hyMJVzZpWOHeAQIfVVE2co8dI7SMfTIdEh/85eZ7rh8xxd0viqamryZVJrUOjZMwyOquqEmB1zb+wNW6QvVEKdzs552FvvL5kPGAYCotwq4WX99sU3eff39/dN9QUGpi99fHxs+u/u7u7UuHp+w36K/q6asH9sV5NoORv/ww8/NIWV/1hYWKj19fU6Pj6u7e3tPhbAB3X1/v6+qal6cfiTBDAkmpmYJVumqtpvZdJF5wAbkkp/6CZAVM8XXy2A9G/7PAs8s2fbMYErKjdVE+qdvQyzV4mftX9yVbUumqB8eXnZ2xCJf1ZXV+vVq1dt61Aagc9sCt95c3PTk8QFsiae6lkEZr+AOc9fKveq5aiWZJQt1iLEHxnkl7ToZPMAGxJM3NnZ6R0AgBSLi08zNZKuyJarSs3SS/kisVZSWAF+GUdnnEjHEmhJliD9I2PJ2nE8cajYLimkwBm0VLqXw+eycMF2pP+is845W1xJdp5CR+o5wIe/zUQ6Yw/37P5nE/Ds41QYc53eu/dt+ql8Z3t7+6vRz2dVFPOlCUBtXCvoEHBA5Rk31Yebm5tGBzY2Nmpra6sna0LDIQeSPgIGHRiNRr1no0BwPH4a1QvB39zcnOqJgnhUTRRYUnt/f1/b29t9P3o3Li4upvoWGXroEMEhGAQIwiSQ06sAlaIU+hJz028C6b4Sqc0KJyXBh07FSboPg8ThqQAmfU1Q6L0uLj7tmXV8fNyJNMREIKLHkFxUTWhTSSX48uVLvyvBZvbSzFKUX9bzFrRedUoypypcNaE7MZCSoY2NjX7fkonsYdCnA1l8fHxsZJuBNcHYtehXtoVDVbUzcy2cEOcAmLAZeFW1/CXNOWmnAqyk5ahWs02COXLsWIAuyVYO9uDwxuNxJ7D5XDnX29vb3g9OQgVckZyqktALSZ/7EJBwbvlu/M47cW9os2ym83L+0F4g1fz80xTF29vburi46Cormy2wSDR5tp/jZf32BSg9OzvrypytLaqqAxDJIiYMEIOcf/78uYHNrKBDstOPJKBCth8fH3vyON92fX3dg6y2t7drfn6+9+aUtPCXfARwUUXONWEKkf9kAGWwyqfbY7BqEmjRL76Nf7IvM0YNnTfTICnadJCPrZpsGZUBu3iG7waK8b+uxb3oBR+NRrW7u9v3arib69jc3Ky3b992QisZZtckvd4ToIq9Ypc+f/7cusmeAdNQ5l/08/dbJguLd9hpFUMJzcLCQp2cnHS/94cPHxq8U+xQSUYHv7y87FgUSIdeTNbJf4Ie3n0CLeQ3E5qqyYAcxYiq6hiY78qqoWKCaaPibDGpYyddVZtK0nQxylDB5QH0MPs46b6EFzMn26Yyrk2/bb9zNsFxqp4S9v39/Z4DIOGvqr4GILHEk23zd8bgdMtns8jFN7Kx19fXtby83C0/kuT/+q//ahv6Naxn9yhCyhgyD4eTYMQkd8kHFoQJQqGcnEnVpFfh4OCgg9vxeFybm5ttHEej0RQVc3V1tWkfFJGTmG0Ctxj9qmraGxrd3d1dTwxUPk6Hm+PzE7XhHAiXpCi505TaPWWSXVVTSHM20meFJHnfg8GgqweCWT0sDEwmmg8PD91jxpDkBs4fP35sA3V4eNgIqSDERsyum3GgEJJsvTGcMAVFBzo6Omolh1S/rOcviYXE7f7+vj5+/FiLi4t1fHzcNGFIuEDRcCW0Toaec8tN4/U7PT4+1tHRUe3u7k71BiwuLvYmupnAzCZJjC57AFzgrLJKh2qWCSZdRQXJAROMeq509uQ/B8xkT0YCX4Jj+5WSW6DSePzUWH9wcNBVGE5Ycufaqqo+fvxY19fXDaCdnZ3Vx48fp7YRoEdVk8pLBqIc8GzQC3hx7+jeEG3DUR4fHxvFRsVTPV5cnEzlQ6cyqfVlPW89PDz1BatQ8RM5uMm79A75J/pcVQ3aVFX7t6qnaiGqqoDm9va2q06oT97/+vp6nZ6e1sLC03YMh4eHU8wbARgdVNnjY3PbhuzrYfurqpNYsiWgE6ilH875AvRTApk6KTH0PHxHQsafJ90vWQAC7Kze0Z+q6anC/OTi4tMelXNzc7Wzs1MbGxt1dnZWc3Nzve9eVbV9dBwxgupr9nTxez5PBvhcPaxv377tOAKjJxNctlBQ/7J++7q7u6t37961zc6hQ/yKxJBustcm1PJLvue7GDJAQ9RoNnc4HHZilLKdveN8byYz/ibP/D95mI2zxQkSF58lr1UTMFbsRu8yueNvHI/vzSQrK/B0OKt4dBUoq3CBmUcXxKViccmjc15cXNTNzU2DLcClqup4Q0HHNbBr/J14I2NtzxIDIIHYpNezN5L5i4uLGg6HNRwO6+9//3u/n68B0HmWpxdQUQRj5ff29poiQbigY2hviSBwMip+EiIVCE7Fi/Dd29vb3mfMuTlPAoY2huoIKU+lIcSQTfdDeDjX1dXVdpwSXQJLwKxMhvI8nKMevZz2mVtvEHhB9Pr6elcuXJOGej0Y3gk+tnObqsoxQVxMgnPOVPqqJ17169evp6hDglMB7Pz8pFdNkK/Pqqqarur5Owa+uqEjSUlldF7W89fKykpTR8nq4eFhTzcF5iwtLU1t13J5edkUMkGUSjCaFHSbHibQALgQyOitksQw2hBFjqVq4oAzKH14eKj/+//+v3tqoMAoEy7Ogkw5v0l0gjufc51Qw6yaJTNCn3BVNSWvqro36OzsrO/9v/7rv+r4+HiKpcD+cZLZBwEggpwaLrK7uzuFcpoWDdTJrSq8nww2/ayq2lZJRkejyWCwqqq//vWv3dxvKFbS/tCDMjlnw17W85Zq/HA47MCe3OTUPUGWoOnk5KQZIEnBFHRWVQeZfG8OY2PDd3Z2GoSku7u7u1PUbEGr5HR7e7vW1tZ62qckkg/mmwVaCXgAl1w336//8PHxsel2kk6I/ng8rr29vQaOckoxsCST5MXFxZ6+LJBT6bMFR/ZHVU32YAR0uW5xjufEXgFl2SS+Lp+bvfAyPhCHZLtMBtuetRYQVRB7TCaQQIZmK0Z6Q1/W89Z4/DSU0YBFVdvPnz9PgYTeZxYkyJIYji0nr0B2VOjl5eXa2tpqn2B2R1JA2V9xYDK5kkVSVQ0+ZfU8/59V9kyUMuZLCqZYkR7PxmsYY8mkc548Fl0V0yaDUOyaQKbzZAUuq6GS9apJIm6eBh+qMOK7bFeCRdnCYWXVkz7NMpIGg8EUUF012V6uakJL3tvba5sxGAyazfdHX8/qURwMBl0217z76tWrKY68REVQ52cCTfRN06IEnpD4pH/Zg8ZefqhoX7586T2DEmXI5O3i4qIODw9rd3e3HRphSZocqiwlEZQR2qqJgEq4kq5TNUFjCDZH6fOcBAVB86OcehMkjolmULR0SAQtqQToJ5yXZyO4xAnHe8/AWkAICNCkiz5rAheaLWOTFZiq6u9VTTat5YDdo+mm29vbraSU6MXJPX/d3Nx0b+7l5WXt7e21wbq+vq7hcNh7bx0cHNTKykr98MMPTTEzOCoHVpHrhYWF7pOS+As+5+aeeuskafRdVU2w5thAGrJEl9Kwf/fdd70fqcqc6oSBAYmYZiVVYApFTMdZVQ0yAVBubm7q6OionQMHy/C719wcuarqz3/+c1VNKhAAI4hi1aTvwr1hWPjb/Urc6SwHJlhN/fB/yHM+ZwEx8CZ/L+EG4q2srNTp6WkNh8PeX+v4+Lh2d3fbLghuX6r+z19YFehlWX3Y2dmZahFIebbPMOolX8jvCqD4Q8Nb2G7+JKli2DJkO2nmwLysGPI1wEO+4dOnTw1IJT012TdAIlto5TZVrqGqOtAEHPm9BDH7fASI6RdVf1Tt3IdAlh1IUDaTRbbC50ejUfeeVU22CGNHfvjhh9re3p7y55nAJz0+7VfVpBdTT/Lp6Wn77gSFcnpr9l0nCCZmeVnPX4BQ79g729/fr3/84x9VVT1zgnyzmd4pXROTpkzs7+93FXhtba2TfrIvEcuBiWZlKFywCwmUkPMEYPlWSSc98XP2gt9IABU1nj5kuxO9FAPSQyAU+0ZOqyZ7Jp6dnTWQnK0kjo/Szk7yta6JDcqCkJ0BMA3znvOZsEE5GM7PZ1tXJLVyGfaRPdRG4JhyFZNpP378WK9fv67b29s6ODj4qvTz2RVFVbK7u7tusEaDIXyqEqpieP22YiBMm5ubdXl52aPXk9csaFXBMozFSzs7O5saKy9gonSqXgQRZ9iUIk5QT5HgKWkxnAcBSjqI3w+Hw6nekfn5+dra2urPZDVTwpfBr4Cw6mlwjXNR6qy6ST4FBoxElvf1QZyennagzhDs7OzU8vJyIy0S4eRcp/JICCjmLNrtura2tnoCG362hNsxhsNhLS8vN0KMBrW0tNTblnwNSMsffe3s7PRkxYODgza+5GNzc7Pfo0RddTspIg8PT3uBSkjy/dMF71mlWx+QYK3qSTZVBASm+Z7n5ua610dgp+qwtLTUiSXHDFxiV3K/tqpqUATFlS65X8GpCqIEdHd3dypQpmcS4EQnOWDPBB1Ir+T9/X1vJcKJ2E5G8rWwsNDDM9bW1ury8rIDVw6QDUmHnv0oeX3+L1DPwCATCc/DMBQJgHedNvju7mnP3I8fP7bDfVnPWxncCKxWVla6cohxkiCqygNZ02IBZFSRFKD4HHlTtYPCA+bYakBQDqnJivXZ2Vn7NjLPrlxfX/eUbZUFQG/2SevrkvSk79ja2prqA+KXrq+v6/Pnz10xxSTKikiyd/QErq+vNxAkAU56OVuH6spn0t0MxAXCGBSj0ahnIVRVff/99z286+TkpGMOuglwkzxLOvRZ3t/f14cPH+ry8rKBAgBC9maJYxLgBUqzny/+8/dZ4pOLi4va3NzsuAy4h9otkVlYWKjDw8OevyEJYbMzuTFAjo+RbPKzkhi2GztoPB53/3ACMdmK4Rh0gb3PCl3a8Ewck8qasgRo9TPJpPYIbQnpY0wuVVmlf65dDCheTPZN/j+vEXMhexZdzyxl1XmrJvsNe0Ym0WZVUSwrFvKMHMczob/ZQse+sIVZ2T06Omodd35Dsv7o61mJohHxHvJPP/1Ug8GghsNhJz+CL47KC4OuCGIEpXt7ey2oVdMjcY+Pj1t51tbWerrZ+fn51MQkaH0mO5yLwJBiQVvRW9ExCYCNvjMRswegYBYFzRCcHKecdJKq6kpKIsn5e9WPqprqSyS4rlHlrao62BOQSgCyJxJ/PgNlCnB7e9uBKxqLpNY5BNKekyRcA7PKkSDa/Xz48GGK224JXPV4eU/6vrJ35WX99pVDSTJwYaQlFVWTpCoTe05pZWWlE780+vTCtgl6ApJ6o5qIcmzIBMSUvlVV09/8nRTU/Az+v3sRIKtscGYMOj3kaLLimNMc19fXu7qdfR0cl+NxYCh2HIpnlQ6JHs0GeVU1FbTTUffL+WYVhB2omug9XWWfDMhKp+f6OGjVRs9Z5YrtVFEej8c9rQ3q+9e//rXv62U9b11cXPTk4eXl5bq6umo5uLu7a71SySK3WiP4Lv1NVU8+x+Ro9E52tqo6GQLoCjTpkmnVQCL/B2QAWVHPk/6JobO1tdVVGHoB6BAXkO27u8necir3ElBgtPMYbJFUseyFdP/pPyTBWBX2HXQ+gafPuuaqmrJ5V1dXTSUVDA4Gg67K6t8XtKKJs3uST9eerKOqCXvizZs3bdsEoY+PT/3LQGd+HpjkmrISJcF+Wc9baN4HBwf9TFNXyFq2DlVN9sUlU/wQBprPAEDRjb3T8/Pz2tvbayp3ArpV1dvckNXZ2I6MSZaqJj4u12zFMa/DvRq4ZcgOP8RHSLqS2cJ/5jY9VdP783omgC8+Ssw+20uczL6MqfMzAKbZ2FqM7HlKfvlOz0kSL74Xy0v02Uv36Vqyovrly5c6Pj6eajNh816/fl1VNTUv5Y++nuXpBYwCibdv33bF4O7urp2GF0zwVBGThlI14U8LwjLYEQTOzT01jlPGubm5HrtrcI0x2YIdweD19XVvKq2MnpUG1QiTTDmr2VH+EB5CnUgDx0ZYIS7+EA7IA2TEsQVr7pugug5OW78FJJSSUZTs16RsqgPuhfHw/rLSJLE+Pz9vx5P0J7+nuO6PYcDDfvfu3RRdbmVlpTY2Nur8/Lw+f/5ce3t7jeqq6GaS8bKet5ICApBJ8ASlkPEVSKEKPzw81KdPn2pvb68TkJzC6d1CKtfW1urg4KCqJk4gp5UOBoMe+ABsYZAdb21trZHVq6ur2t7engKMcnsOdkEQJmB1fk5MYuQ+9WQCY4BZ9NvPHYcuVlVXRugkfTWFuGoyHMsz8Eyrqh0Gx8rhctDpNAUgnFJWbyCqzuV9022fmaXBAoMASIC5ubm53npBspuIsj4Z55eYvKzfvlCULi4uuh9c4kM/vFv202RaVGLJyunpaY+9x+QBjmDsqF46jvOZ5CtITZtNrwBHVZMhc7P7cqYukEFBcA4zo5PiA34fqAiAUoEAjB4cHEyBMs7h/4AYOgAUGQwGPXjNsD33JXHNqkQmghI2ia4KboIz4h17HLpf/We2HHl4eGi2Al2nn0Bw1VR/ALL231MxEUexg57z5ubmVGLxsp63xJFpEweDp7arjBVt9aXKu7a2Vv/2b/9W/+2//bdaX1+vs7Oz2t3d7cRRsSBjxapJwuc9LiwsdJsGlgCdo1/evQQpGV5ZbWS3cwASuc8ihfka/LY4M1tMVNH4GSwBPsr36SC9z/56hRDXkRRvQFK2XIlvJbfi44xr0nalbosx+S8AW7L8PBPATtJPMx9hm9kEeut3OSMFrX1+fr4HxmXM8zWsZ1UUkza2vLxcp6enVTVBLSBdmaRoAvfCszQ723C/sLDQiUQmj8PhsBu6OYSTk5MWxmw+5SQ5i42NjdrZ2emsHzK0tLRUa2trPX1T83IKysXFRQeiWWGsmozUh+AQREFfVU0Fs4LDVBbHyQohx550FUrq+TuOf5+fn7ej8CerHxRWZfX29rY+fPgw1ZPIAL169aoTVHSH0WjUk/EYF4ZK0l5VnbhX1RStzfOEONvTT5K9ubnZPRwv63kLNUr1jx5Ap006vb+/70CT0aSrf/3rXzvRYYgh2gY2PDw8dE+fLTKSvgERR8kUtDG4nz9/boNKPjgw8i25zcpeIqqZFEMwBa0GUp2dnU01vs/Pz3elRA/18vJy6xc9pg/sHb2aRZE51HQm+VmOkx4LAgSM+Tn34jm6v6TDcFZAH887P5sBiECzqjoIhhhjItBV7y3tmcA6Bx+9rN++tFOoIGqzWFhY6CA0E4eqyRYtZM/7sR+idyXIA7zQJYCmvkW+E2tgcXGxq+qrq6sdiFU9+Xl/tra2WnYeH5+2xnEtABl6DKR1P2SfPvBHaHEZ+CWA7OeeSVZM+KMEl8kxnXHtVZO+RcO2MjBme6pqSs6BPkBl4JLvZgzCb2M57O/vNzgu8fUuVlZWutKJcUSvt7e3m86vGpP+EjXYBGQxgHt5Wc9bihBV1XEne39xcdHvYWNjo2MoNnZ/f7/BV/RyiSbmTSY2fp7xoeXdq3DyFRnjJmW0agIcph+zTUT6BX7K5zIhU0Shu/xSUrixGAw/c25yXFUNVFdNBlY6Nn9H51J2s9ikuMLmOK48wvWL0ek6/52VwATB2IdkKqj2A7XyeUh0FVUSQHUsz8gzTzuc8f3XsJ49zAbNkHM7Pj6eShIJg8x9aWmpPn782OiMh5/ovRecwy2qqg19VTVKMBgMeg8lzpbRTuRPwsWJCXLn5yeDW0xIOzo6qq2trVpYWGj6ztLSUldfsqzuGaCULixMJl/lUBzXLxhQpeR4KBeUJnuEOECVu6xuzqKpHJN7lOzaSzKpbxCW+fmnDXuzLC8xhRJRGLzvd+/etbJcXFz0O6qaTEr0Ts/Pz2t7e7tlQj8rND2DW8HDS0Xx91vk34AFiWCi5GjJgsJPnz7V1tZWXV5eNlqeTujHH3+sb7/9tqk30E49FRcXF3V0dFRVE2qLz1VVVxrpNGej0qEawg6oNKs6o1ipMNKhRNshd7NBGxn386oJzZvdQGkzzS6rOtm3lPrJntC7qic0OqtE6dDSNjh2UmxQ6AQdSRl2zWyAZwoQEGxnpTaDU9UhTpW9ApgJ2nPzcseTJKbOv6zftvij3Ig+e/yzD//9+/f16tWrnpgN8FF9SEDSdkS576Fkiy9RCXG8qgmFChgguFVxJE+J0GO2uAY2RCVNJVQlwT37veobmnMCRVlBz2pG0p4FynQmpxSLP1z77CAm92t5Fp4lnUu6KADb7wXsbEPOT6B3yeSxeTv7lBVOia3J4VkhSf/ompwTlbaq2n6+UE9/n5Xg6O7ubvfWZ9sMoPz4+LhjSkDo6elpz+OQaCXoyD9UTQ9RIadkTvvI+vp6DxvL1oJMgOhgVh8Bhc7v3BkTZ+VO9Z//VpTBuPEdMe1sFdD9JegqJnQNJjPnlHy+FLibe5lXVces2lwUFyzvg16LlZ0XiJbJKH3P4hW74flnhdZn2baqydRZoLPnKSlE+cfckAN8DevZ+ygyzOidVdN0RElHVXWf4uHhYW8ozCGhOKZwJvroIfu/BKvqaQw0YUr0Ag0Dopl9TF4SGhxK5Nu3b9soqBAy8tn7xPEQSE3OKgqqElXV+xJKOCH5kjdohPvW5yCZTiOiGukPGlLVZHR/Nk1DLCFC9kek9JwPg+E6PUvGLu87K55Vk/H7EFqJPiNgwpPpVkmLTSpuVfW7+prK8n/kpUeGkWXI1tfXmyoKYNnY2GiQY39/vy4uLnriWPY2Xl1d1Z/+9Keqql+8J8GNBCuX5IOsCI6BLGyGhDYdH8Q8Uf7sHZDIJGUvwZysEHBMArOzs7OuDFgcBMcK4KAvdIn9glRWVSfNRvDnVgGZ4EKj2SoOSgLnHjld9FTv0PXO0vWyUiiAYDugo7MU16TwQVITZa56Qs/1qECwX9bz1tnZWbdKZO88ar82i8fHx3rz5k0zQ7xne3VKWKomLSELCwsN0F5eXvbU3dFo1L4U4MFf5bklsGTPMrxOEkSvyCBQhD5mNZRsAXWSXZMBLb1ic8hi+nmg6nA4nOrP9B0Brypd1SQuWFxcnJpYnCBN2pGqmuoVFBibYOp++evl5eUGl9NOeM/ZZ+h60GnFUlXVAKq4hW/le1WFEpBmr8Q85ga8rOct4BlGjB5d8Usydd6+fdtJO9149+5dAwIGUmWfYtKsDbLi88iSZOXi4qKurq6m2p/oGCAvZU9iQ4bNrvB5sp9tKbkSzEyaKP9cVd3uBbj02fw3wJQeW+wO+RaXAnj5QLMvLKD28fHxLxJZ78M9ek5yC/4x35Hrch1ZRfSOEiyyk4HP5ntNoMvxFKH4Uz3pX8t6VkXRi6maIHf6k1QToaTr6+u1urpa//Zv/1a7u7vtwPQqOsbc3GSMPmVkACVsm5ubnbx4ydmvo7dpOBy2wCU6mQKZyojeoYdDRSF5x1WTkfrOi1qCp5zo5vz808CWDOYyEa6aoDrZt5DUXN/1jDgySZYKCepJbqJsQmzVZGhI9p54DxTS3jOmk6JMcIbptKA8FNJ1qFYkek1J9bUkpcozY1hMFXtZz1+QyQQPBG6Pj0+DVAzT2Nvba1CHLJKBn3/+eWoEf9LLklop0fO+Hx4eeq8ooEzSqBJc8P6zgu4essIgocUeEDRVTdDGpH5wsrPUzazGsUXZh5eUdSwAzqRqMiZ9MBg0SCNR5hSrJqPwsQToLH2UHAj2shKQ16pHe/a9un52IgE81yM4F+yyp5Yk2PlMKNZvpRJL5196oH6fhYooyFLFg5B7L6pQqRN0KeWQzPm/7Ynu7+/r9evXLQt8FpojAMf3MGwODw+b3ogVkm0bdJ9c8183NzdtM9bX17vyKRF13wJVFYVMCp2H/2RP+CzH9h3yD3xSqaH/Pkv+2RnH5qc8A8F8AsVpH6uqk/Gq6tgBM4F9UVEZDof9DIFkzllVrWf5bAW6dFVMhI0zGAx6ynhWUjCbXtbvs8h1VbVd5WtOT09773BywpdJgtjvbL9ImjPAIfc3JaPD4bCpyX/729/q06dPTXetmuw7LOFzbn4mY2e+KYF98p4xsnsmhyl/klpyq2BB/5PZIBkzUdh3BoNJj2PVZKCM+5kFiFxzgsIrKyu9V2hWEFXsxPeuiZ0CjLI93kGCLmwTIFe8o2IqHkn/TAeBsuyvQZCeh+fNLn0N61kVRcgaIcQ3njrB/KQ3YHFxsf63/+1/m0pM0JygqRAazlNfg6qcjcAFcjL5qmok4fHxsRNTn2PgE2VX+YBImuI6Go3q5OSkqiaGWZJIOaFMaHbQfog/gVaJe3h46HHhzi0wk7RmZY2CJ9XHM86JjkltSaNUVVMCmdWU5HireiQFMLnzKnwoRMnDZoCU2hcXF5s6ZEqeQOf+/r7ev3/faPTDw0Mb20R0XCdU+2U9b6VOkhmIoz2G7u/vm0olWTo/P6/l5eXudXn37l1XwVdXV/v33hc9TuoLGVaNPDg4aKrG4+Nj7e7utozrU4WOcgycAHtQNaFYQt2vrq7aIeX5ObTs3/OzdKhV1edOZ5CVyLwOFZitra1GZTc2Nvr/gmD2SPVhfX29x2Hf3t5O9TfS86zwO08O4RmPx/Xp06emf/oeQC2TYo4pnVFuEeLvqgm9ii0aj8fd8+b9kSUVqqwyvazftjAz6JLKND8FONDLqJ+V3n78+LHlMv1DIvR3d3e1t7fXduD4+Ljl/Pb2ti4uLppOdnFx0QHPwcFBB0npp/gBgGZOANbvu7q6WkdHR1MVAfqZ4Ag2Ern3PARcWARJl93a2urEUlLEd2RVAbOF73fu7OlVVayaTGJHt09ANnU04xetLaogAJnRaFTn5+fN0DAkr2pih4DpOUk1aeWqEHoYPae9vb223VUToHltba329/drc3PzFwyJl/Xb193dXX348KGHH+YWB8kEub+/b1uvJ00yxKdJ7BVFxItkF9uDHJn7wAbrI2bvUb8BstnXSJbEhVYmYvQ6Y8SqCVsQ4ELGqyZJVVbsMZOqqhk2joEpmMd3Dsfglz0jxYL8jmtQ+AEKsZupn4Bf+UBV9ffcg3fgjwQyq4/iYT/DNHQM8iFJ9325jJae9J8XFxf1z3/+c0qO/ujrWYniw8ND0yOrnh4c44V+aWJXVU05mbu7ux6msbi4WP/4xz/aOdin6fb2dooHTgCSEuIFQkYlPhIoAeznz5+nEhyB1MLCQu3v77fDI0w7OzudHGX5vWo6iUvajmvKY9n407PJCoYEEhc7ERqKnFWgRIgEhAbNoA1lgAv59x3Bpa1F8p48X8rhd2nAJLaXl5eNOA+Hw3aark8FaW9vb8rQmIoreBG46BH1nh4fH+vTp0+/MCwv6//5UmUgK1UTbr0KE3kTkJBNDkB1mZyfn593wAUIEKwKPAWsy8vLtbu723Js+xjgjb44CZTNcpOCXTWRT6CCvwXDAJwMmNkAga97S1qlZ5GVh0Ro2SoBG33yPc5U8JkJGGdaNaGFO4dhISryNsgGfCXzABDEBvzpT39q2+JaVRA9E3QgwbtneHFxMVXx9Hn34f9ZCc77YXu905f1/GVSJuYK2ULzzX0MBXRA1jdv3kwNjEj/pjrp3Qp6MDqg5dmXvrOz0z4ke4uy8lk1YRFUTca8Lyws9HYefAUbsrOzMwVcZOWbnvM/ZhxUVcvwYDBoCmlWPOjoLA2brZqtlvge+ZeU+q6ElT7TdZ9Pmqzk17ldMzDp4eGh/VpWkH5tqwCJ/9nZWYNKgm8DuFDbDOei7xcXFx2k5wyAF7D191l858XFRfstsvLw8DSkSAxnum3VhHHGV1VNBoilXaUT5Cf7a8la6j9/kft+SnToymxFzvGzGpaFhAQpZuM/S8zsXD4nFqDz2rhcd1VNAS6zAEYyfzw3z2hzc3Mq5k/7phDkuboXftHxPP9sn/I8fD+ZTeyfa/YcPYPz8/Opgo44KKmyEmN217V733//+9+7yvg1rGclilBQjfMSGi8kKS6CPwGIz6kE7O/vd1Czs7PTguwF23SbkHvZOT1JZeLDhw/dTK56YF8pCQrnJlgyJENSlQGf63bu7B30XYKX6AxBkfTmZDXGwGe+fPlSZ2dn7XCrJoiF3gbUl1T6qgkFzvOsmkyc4uhVVikDxRCYureqasodAUeFScc6iwDNzz9tBjsej2t3d7cDXwnl7P0nysvIzs3N9cCgV69evVDbfocFbKGHuc+ghE5Aw6Cen583epqVKc5NL+7Dw0PrjYBJlRrwgXWQ1FdG9ebmpjY3N+v8/Hwq6Kya9D5mYOkc+iGyugekcB8ZvKnGcDDpQKqqk1cOmJNwPv2WafCTjpqUP8eHFI9Go54Am05hPB7Xq1evan9/v4PYtbW12tjYmBrDDnhxz86hz8Rz9Tt6SdckjIKBzc3NduqCGIlA0hq9x4eHp3H+WQHyuf39/RcdfeYSPOVeitnff3NzU//xH//RfgigIeFH5fROgD2SDXog2MpeWsPNBEbs9SyKLlHi33KStx4h0yDz+NkntbS01AyAqmnqNNnyf759NHqiQNtXki/LgDUZCAkmJx1WjJB+M+8rE0+UNvYg9XuWhsr2uGY2w7Vtb29PUc6qqqnCCwsL7SOrJoyh7e3tqWm2WdlPmv38/HxPnUVTnJ+fr/fv3/fzzAEfL+t5yxA/A75SbquqqcSj0aj29va6ApjsABWwZBGQWT4xqdB0oqp6OBr/y8eQWZ+jN8keopPi1Yw9ySXfnWw4SZXPz+rNrD6Se/cBWM0EkA2SZKau5c9dUy7glWfqni3Xgu2HfVH1FMMOh8Op+Nh7e3iYbFOXQ320s0k+0X0zxvcO3bP3uLDwtAd7xj1yBRXlTED/6OtZiaIHlhQvFQIvZX5+vobDYZfPvQCJF6FHffv555+rqpqKJrja2dlpZCQVSKJKGTc3N+vt27eNSkoM9ep48bZugPhZqHGUqqqmklpKBx1Aq0mqZlJLNzY2pgLRrB5yUiprW1tb/cxQgaAjiUxQINcgkBRcEHSGgABDLfVUuOYcsENpCDZlkEBL5gQXSQFCO1U94Qwhu5TGVhs2T84Rxt5BUiVe1m9fSVuUZI3H456qt7i42AHHcDism5ubOjs7q9PT06mR/RBrsk0PciBNGlqybAqq4FJgpZfn9va2fvzxx5YPgIJzsTGqgpyUqWF0gMwCezIZdL0WhyYg9SdtxmxiVzXplVRJqJqgv67NNXP2VU8bNgN6BLGZGANtkqLEQV9dXU1VIv1OhTcdeU6UYwvs5ZT0JmBNVlJyEAEHDi1Fk5M0ugfff1m/faEmmgA6Pz9fJycn3Q86Ho/rX//1X6f61qqq2S7j8bgDV744Kwb8rV7T7e3t/p3gle9m/4EQVZNEVgXr5uamQVtBj2uxOT3AaTZo1KqwtLTU1wHQEKhlRYSeZqsFcIhPq5rouCC3agKeJtuJ3rnXpMP5N5nm+zP5U2GkpwnC8nn0FRjnHm19Mcsauru7m6p8ArWtwWDQVZWqSSIwGAzq/Px8aprqaDTq/aM9l5f1/KXannMg5ubmmsJNrhcWFnoAItCELokdyR0ZYYfJpgQC+4vcHR4edky2tLTUPZFVE1mqmmwRRT4TTOQrsz9vtvjimiTBritBnKqa0vOM/1XMbm5uGsCiv/zsbHxHftM30vEsnNDxrO67JoOC2BzHdY2AT3GH5+aZeE/0aDQaNeC2srLSgNhgMOgt49hj15tD+bynHFCW95rJ5dewnuXp7b8kSLu9fdqPT+ImWUR1qarm7ULtCQNj/Ne//rWdUNWTcJycnLTQXl5e9os39Eagk0FbvkRUAYimwHd2IiqURxA5HA773ggXlCSnGDHeAsikxlZN0As/d46bm5secMHQJLo72xeBTiMApeCeleBdz5Z7V5Vw7an8ubUFhd/a2uqNmwUySbW9vb2tn376qRXNPTMGuY+ihFxQ6/25B2haVmM5x5dqxfOXngkyzhAmtUJQhRJ6eHhYr1+/rvv7+wZyoGmAFImEoEQFA1tAUri1tTXVq8hAVk2Ajr/85S9tQDlkjsKSqJIv3P9MyuhD0rMYbcfL/aCyKplOwmc4mapqJ5A003QC6WizEqNCMdvHxGbRIU5yFoiSzPt8BsKpj2n/OH/HM4QoexoFKxxublXjOKPRqI6Pj6cSj+Pj45749gLmPH+x5/Pz8z1Y6s2bNy0HqscJiKpm0VkJVspi6g9ZpDOZSBgMN2t32eerq6v+tyrjjz/+WIuLk0FmwGFyx874WVY8sQxcLzmXuFY9TdclZ3rvM7hKJpD78LkMKn8tCKPTAlbPOP9kZQB45H1kskhnqybVDoBwsm2qJqCsae+ZmNJ3PY1sDNudQTmGBn3XQ356etrA++Xl5VcVhP7RF5DP8xRjGQaYtEN2em1tracRiznJpuQpk0T22ALmJ+hAl8goIIVPFKvNVqmTcef/CaDwfxmDJVPFd+mtqqR4gByruNNloEVW95ONk0mZPymz+VnXSA/Yg9QhzxHQlCwbyXp+1/ln7abzZPz9L//yLw1Ma9nJNjBxcFKStb1hNXnGqMmzcxL+yOtZiSInB21GG7Vv3sXFRX38+LGTFOP48apNeEoBQ2cT5CwuLrbjxAGXiBh0MztoBrKAWqfSoDqSKINKGCWWYK2vr9f+/n5XPdL5SRwFfpIhgSIBy0me2feQAWgqJjSKQnM8ieDP9lyg02SvYiKxgll0QYiKZNnUqEQ9OML379/X5eVlN8YL5B8fH7uZntNG9Z1NHFVVq6qrS4wcxMZiPF+c3O+3VHavr6+bijYeP23RkDRUwaCBDkCCqqdgUdCiYr24uNjDmdiAx8fHpiOS4YeHh9aN8/PzdiyOLWkUiEHi9NemM8hKWVV1r4CkNPUhqysS3QRJyOcsmrq0tNRVErbp14JPDsazqJr0PvkefbTRtnV7e9t2hj3M41ZNdCHHjkvM2BDMByCM91ZVU70rVdVDPZKSZONwCakKoneXAz30U+7u7vZ1qk6/rN++JCsQbSAF/+Hfs9vcZKVd0pIDU9LHsdXo47nnH5CF/U4aWfZOZYL29u3bTlYTwBRsVVUHllXVFM1MQCVrdIBtQq3MWQMC2GT/LC8v93d8rqqmgkr+yMp/u8cMOF1PUtyyZ5mNEoPQufPz86YJZ5Umr8vfknG9bDkkbnNzsynrGXhni47qxmAwaFu7uro6tRVGgkAv6/nLfoVV1UC39pqdnZ0G2FHIyVb2n3uHZIYfSpoxfaRTEh1AjM9LMLOIUPW0TRyfkds5JdCXbJUsapDZpLBWTeiio9HT5OPhcPgL2ZqVcbGoc2INVE32JZ2tTiabhQ6yRz7j++4778v2G6l7Sadlx5zXuTNWzjkh4gE2wv2ya/IO/jR7PPlh95osPvlI/vtrKIg8O1E09YthG41G3Ui9vb1dm5ubbdz29/fbuXmQo9GoubxJzeIAIfxGaqNaLC8v17t376aoY154ovSayM/OzlrgBa9JZ1OVM7gCIuEYkMScsFpV3TcgeYVsZsUmBVA1BhpsupMgNkv5kAjOLx1AIkQZXCaVSCKXDhAyldQbz13fGoP2r//6r1N7JEJaKb73nkaMEgokvC8InMTVc2FQPDvvMOmDL+u3r93d3X6/VdW6lsNRINmLi09j2w2esM3JaPTUKyQxE1wJShPJpCOZDAmGNP3T1+x3Vd1mhKsmk0j1dZB/wS1aZdXEaQi6yH46oQy4ZmkmCdz4jD+S5ET/JNJsSuqk6/k1RgFdyOpuBg5V1VTBy8vLRouzB0TAbGpi7mOFYaH3gj4JDqCZ3r3zea6Sw/v7+w4+2SqON0e3v6znLT2Jqon8D1stgKyaBDVJKcWUGQwGdXR0NDXenTzwCQ8PT9OM01fSA4mPVor0PTld8/7+vuWS3aabwIjcn1OQlv4maaFZZU/w1nTHZLLwm4LcTAgTYE2f5xqBI/knQaCs7FhiADYppzkmm2d3d7djEwP8gGP8euokUDhpwnrQMJzsNSyw9fy+fPnSNETAK/vsWdLzF/38fZZ3//j42PEKynBVdSFEzAfkpwOPj4/N8pqfn29AQVwFiOBnxE9VkwKDSqI9GoHzmWiw52JpSaJkxTFnaZCZXFVN9knNKurCwkJtbGx0PJ2tC0BF+uO+kh3jcwpKEqeqie+ebfUQ/ydA7Nk5dk4t5uuT5ut5en/yjqyYVk2KS/zy4+Njffz4sWONTL6rJpR0vtUxtLrl1nBsIjvIFmcP4x99PcuSEDwvUcJlWt7S0lKdn5/X5uZml6ApRXLuVZAEhY6FcjIej3svJlMXNeIz3IKYRAFUNldXV2t7e7uvm4GvmvRgZNkZt3x230TXLwBMI15VU4ZC4sVxZVCWTcyJiihXC4jTAFB6xsfPKZJ78l10zyzdQ6Ypbg5O8NxsXeD9mnqZyTIBd07vPZGiq6urTlLyun0OeizxzF6YdOAv63kLCi2Zy/4/v2fMc+gC2T08PGwjubOz08kRMIcBnJ+fn0pCOQvv0r/JXlbp6P9shZMOMMoSU8cj32l/qiZ6+PDw0D9nZ+h3jiUXHFfV1ATJpPokwptV+wz2PFd/OI4MiB8eHrqZfTbAyKDclMl8dq5VYO67en4TDU4aT/a7odtXVQM1UG16zube3d3V5uZmMzHu7yd73qoOfQ1o6B95sY3JzlhfX+93OD8/31tgbG5u1nA47MQu+0e1CqgqSq6qJgOhHF+SgdrpGP4PuEg/Rpb4DwETsDjbJfjwrIxVVVdOMjDKnqXRaDKoo2oyVMn01qoJwEFGEygSUCaNNauCVTU12dd5PCs6k/7WcCB26NcqG4AsSaxYh/0RIEriJMOulV3gB1U+cvsMAa7PVFUnDVWTKqJKh2t/Wc9fihXkKun72DkYNevr63V2dtaxFP9jCujc3NP2JpK83E/THI+kb0rw/A3MW19f77YOPgYgRL7obVa9cu9C8VhWwvwOoDjLghPLAvOtLHKMx+MuJtCTrPABcgFa9M11pB67BvELuyhmSWZQAjimuSuaeK7sbFY2M56nT7lDQe4cYA4HEE5ckJVicztUmPlwrC7nEE99DevZw2ygnpYKm8BzfX29rq+vmybx8PDQ04goTqIDMu4M6hjcTKogkwIaTgjN5vHxsTY3N6dK8AbmJCU1BYlz4JQYgqpqx6fyQSnToWRPXlZLGBdDZyiwyqYtQvSLqDhIxgTYWcpWsUmOdFZXB4NBO5eqSSNwKr1jWUkpUKEQnOrJlLQL5Al9okzLy8tTdN5U7DSyguEM8g0+eUkUf5/FiGZiltUmRl0AYmLp/f19T+DLTaSz/3R7e7v1DlqXVS99M0lfycWAGqDjOpM2d3d315v5+k46JX8z2GxG9iKqYCTt7eHhoSehuW4VHUY/m+MzIVJFT8poAj/k3bX62fz8fLMPnE/g+mtBb1JvqiZ0OXqX15uVfdcu6EVr5CjT0c3NPVH02UnONh0+vd3Y2Oh3K7n4GtDQP/pK2j46U7JXNjc3268Y6sa2AwLplgCJ//FvfVSCmLm5ua7YmaqNwmVgEf+V+2jSM9siSULTD1VNZJUuZfCbiSGfxp96Du4LWEMf/Mmgkt3IHsWq6YEbSTfzu1l/6Xqzd1hlgC4kUyiT4wyc6UYGp6Y7Y1O5/ru7uzo5Oem+au9tYeFpqxHPFHDNN7JNVdXxh2fpnl986O+zyO7m5mb7EOBmUjjJAQCczEqExIBphwFzAAEgj8oyvZeAfvjwoZM9MsvHkFc2BBBJphRWEjRJfcu4GthArhN4pW8pb1n9nmW8ZcUSEJt+ja/M/1vJ6skkE0U9q4JVE0o7NpvP0Jv00aaRu/dkb4zH4zo8POz3trm52a1o9JONEjf7XdWkPzSTdfcD6Jm91z/yepYlgTBDtyEi9/f3PVKdcaVABC2pb6hPhF8v49XVVU9Hu7q6asfnhfj3zz//XIeHh/1SbEgqqTs6OppCHxj/4XA4lRSaXsrJ+bm/HU8vT1VNBatJEeVIGHDGYGtrayoBzV6xnMRqs+PZoI9RcE3pDCkqJEWgQZgZAdQiaFj2OUlks0dkYWGhm/AJus8nZUeCnrQgSX/VU5JyenraiA+nq1dqYWGhZSiV6WX99iXolOjRRUi338/NzfXodjRhiGZVNeVE38Xm5mZdXl7WwcFBo60SH3KHIiWQoRscSdLr9vb2+jpcG3aCYVhkWyVz9ljpOBjgpJJyqow/upjrzwqmxCv7ZjPRZEfcHxQ39THRXoluBrzpuNNh0J10XpycJBegtr+/3+8Frf3Lly8NmGWgXlVTFDaTaufm5qaAPJ8hA2SHfldVO9+X9fyl1+729mkrG/u1ATuAiIK/7e3tKSAhgxWfJ2MZJNIBn8ng1ZRiwZjpw0mtzgRPG0nS2LRQ0DkVa9/1GUlhDsJDD0uqGnRej7tg0++TweM6ZhNCuvX/DdBIEC2ZTglooncmg8c90SHVhKQM6vtVDVFVSgbV3Nxc7ezsdDKczKGsOLk/VUW6Sde9UyD2+vr6V1Ot+KMvcaj3wo8CTpM2jI3G5/CbKWNiuarqLaIkZZgoWS02y2NxcbFevXrVP6+atDg4nrhJUsbvKiy4BvFoxpL8jSobEF9S5hz8s+8neyWvJePepMFmVd61kv0EgFLPxIi+PxtzJgWXLuaiv56Z5FcMIGlUxAJQ6TlNplAek9/PnQwk6OJm9u7Lly89+yCnQH8NyeKzKoocFP4y4y9gS7rL3NzclCMQSGUQxohqHhaojUajFhRG0vcXFxd7IAveuHNsbGz0BsBQCYEvJaa0KysrtbOzUxsbG1MVMwJmtO729nZtb29PIaJpkNOJoXom9fTy8rIdNAQxKax5/1XTgSXHmvQaEyCzpA6h3tvbmxpk4LlvbGx0BUkgzDhAz7IyYoBF1TTNhgGpmlRB9FZBgObn53v6rSTTdWRV1EQ9xmCWr/6yftuiIwzYaDTqqkQmftk3oBmdXEvOBKWc5MXFxVRiJVFKWpzAkB4ywN493WMzhsNhJ4aj0ai3h8mKNCeVVeyqydYdKaM5/CIHOdGVrFCsrq5OjdbOjbE5Neemn8AQ+gdk8oeuoSL5fupiIq70wXEl2QaGJW0t6fuZwEGsOem0TxJxANHV1dVUlSmTC3bT80jq3Avt9PdZj4+PDQ7Sw0xqgHHoqOPxuLfPSFSfH83gUJtAUjEz+OJvATpA0+xtVIUEwNKTDLwAo/QiKZmqzmQpJydKDvlJupJV+KxS5H0IrNN/JWPh/1eyKDDNY3k2/s1GCG79XEUjafT0H52XHifYSRctz44e7e/vt204Pz/v68zP0VP6fHNz09Vf17+/v9+xy8v6fZakTWKe7BYUzarqSrs4M22nmBXgnkAEwFBPuJiRv+KXskKWlXh64jg3Nze9z6prNDtDjMXnZCWQD8qqWtJH2f+qmorj054kWJuUWUWK9MGOwV/7f/rRrHpubm5ObU9ipZ/K+8rkNv245+Z6AVj+sGOrq6tTvhoAlvYsB1hlYpwxkGcqrh+Pn6b/k6evgZXz7I2wtra26uTkpIUje/OqJo6pqjoASRTE9wiH5EwSyhHMzc01qlNV/VIJrsZuSYq+GoKCXkfI0KhMAszeK0qcFRBOCZK+ubnZ06wgG5SScyZA6GaEz30JpBkhzyYnQWZyOz8/31QF9yJI8KxVCzmypO8kIuz5SEyrJhQAii+wzUlbVdXOUMDv2ubm5mpra6udl8RWcGACZCp5Dk/w85eNgn+/5R2qyAvOGMCTk5MGQN6+fdsGVoKRdJSsjo3H46bESfgEjHSbkyCDpqeS/9Qp9E8MArqzu7s7RdVmsNOpZWVBcseIk/tENJMqlBMM/T6DToEh2fQ33U0bMxtkui4OhCOkR/SbQ8zKSybGBiFkxYHdck27u7vtlNKGZV9hVU1NuB2NRu3QAG2DwaBHebvOrI5IajIQelm/fQkEBUbsbw574wNVjwxLEeCRHf3d9FCPKTufk4Qtupw9kUld5YtTBxIU8ntTB+lLsowGg0H7AbKUdLCqmqK4OU6CK2Q6e4sysZP8ZYCYIJDjVU1XEbO6Qf99JmlpfpZJZVYHr6+vewqmnwvUVYwl4lU1FSAaliFuwjASfGeynHGT+Ee8ARB2LAN1XtbzFzDbewEG0NncVgb4urq6WsPhsP0fmUh5IrP0K/UU0On9k3Fx18nJSesie8+X2M/v9PS0/S2dAQamvjj29fV1ffr0qX2NuAz4uLGxMQUapi65zqywV022XXNMtiOrh1XTe536jjgigSm/5wszZsnKpvOxodmKhUmTPrRqArrmO2EPJYdsoz5TDMa0j8k2EEeRIaA8/f4a1rOu0v4yhlioZO3u7nZgmBt8SxCrqimmyrA5TcgLz6ESJpQKuhIB0WAvOclKnkWYvUy0WN+nlNfX13V8fFzn5+dT/T8UI5VHpVOwBVnUo5l0gkQm3H+ivFVPFKTt7e1f/M49ufb19fWpoRP6WRihtbW1qd6Ku7u72tvb65+7DtQxBsK/9/b2umLjmQggUulyApZ367wcLFoTZ+j5pOMTyFRNpr59LQr0R1/eWVU1vSybrb/99tt2Iklfq6opNDGpmj7L+OlxYsxTDwV44/HT9hh0Q39qBnJpvDPII1fuJyuMScviyNAqB4NBV0+rJlRtTi8DNdecPR32E2WPEjkU1GYV49cqbBwjXUonl1ROgXXVhHqazi6DTAEgkEqA6TwZDCQ4Nzs1bra/NNkRkkbUd893a2urAybX/rJ++5Ksv3//vp8zu0pWPO+qJ50FxOmDEyiRIZSq3PAaTU6fHHAgh0Rg/0jOskIm+PMnV/rIRN0NQsJqAEBkMKcS6vqzMk7G0lckao/5IFnLZ+oYfu4YKhazn6uaBNkJ/gBE2TX3p8+YHxWDZHDKVqLhYluwIdlruLS01NOmM9DOPaddBx+ez1Eijsqo1/Sl6v/8Nbt3YfaRZlz08PDQe9ZaOYgofYbkjCyI88jT7HRysgBgGI2etqugX3RAEaGqug8SYyD7AAEKjlU1mRuxv7/fMUEyVzK+dU0JivI/7NHS0lJXALNyWTUBdn7Nh8wCPGLD9Lf0OGMB95KAq+vJnsaq6gTPtWQcmpVQ1zw7BAhwlwmfc6uCZoHMd8RRf/7zn6foxX/09axo3OCLt2/fTiVQV1dXNRo9NeoKJO3PxrlBIwxy8SAZTw308/PzvU8QZYKoqaox0lA75yBYWdnwImX7grMMnlQ1EoVRdUHZElByLO4np4pubGz0lMGstFTVFKpP0ShINspm3xOElUPPHijntC8bJZwdyIEGh0YkoPQ9CpCTngSYtinZ2tpq46FHldKiEhmeoFrpWlV8PQOokQDVM1XxfVnPW5yRwTTkUOAxi+pJ/IEgghMOUjIniByNRp08COBQ0JM6trKyUnt7e53gACdQXIFJdF5VmV4kwp8JW9LC3K/7EHgnZadqkhhmFY0tQeP2TDyXpKQ4BzmfTRgFtJw7lDLpQaPRaGqgDUeVFLWsWgjWPWfXDdlMeyCBZCs406SYe24os57dxcVFV3fZTO+bLTdB+uLi4qVi8cxlsvZPP/3UAcd4PO5Wh6wAmvytSpxoOR9V9eTLLi8vW5ZV8Q2gklQAWn2ffpNNtFg2AwBDD5I1lK0dZDyDtawEJh0N0OP/GbiiRpNTf9IezCaIWSHwJ/9fNak2+Ldn5nnk572LpJs7dyYACdomGO24kmA2gx0TRGfCcHx83MAykMA78r7H43H3JYtphsPhL9pQXtbzFz0Tu7DD/BXwUzuPOHM0etpvEa2cDVc1FyMBUvzRBoXFlRXF1dXVOjk5qYeHh45FDb1J5pdkbWdnp4F7Sa05IVmVc58q/oAQMTWQp+rJF+bvxaz+n4yAqoluJdjya3qZ//a92ao9cJNNlHeIW4AqGU+LKTzzbMXwOTEIX35xcdH36lrSHs3NzXVb19XVVVVN2AmzlFn6m/MI+OOvZRbHs4bZZMUO+jFbdp4twaKIZjUgExbBjgQLNUvQpKQ+GAy6b8pEMN/XmOqYhDgRkhS8h4eHTn5yyt/19fUUHSgVRUKDFppVB2hC0mYFvpwl4c/Jgek4GQfJYiqRY0s8KV5OYnT/nL7gPfv/cisK7zInanGmh4eHbVwkg/v7+x1cSCJOTk5qcXGx34UeNhQkyFDVE1V4Y2NjagsU97SwsPBCm/mdFsSK7GRgUjWRAYCABJG+2tR9c3Ozrq+v6+LionZ3dztAIYu7u7tTsnd0dDQVaDomoAE4kT15kqaLi4s27ICLubm5pnBVTXRQUpP9P/4N+YQUpk2CMrIl7ENWZxK1/zXQQuAHxEk622zVQtAm2aXjWe2TMNI/gefOzk7rU1W109bDrbeLLs7qL1ug/wkFnn1IRw5NZjclEldXV82eQF/1+5f12xf/8L//7//7FEpfVR2U0BP+ZHt7u32Sd0IWVaD++c9/1sHBQQ/XYP8Hg0FvzE3v6JkAh50X5C4sLNSXL196Wyo+2ecyaYPGV9XUzACMAf7q6uqqtra2OrB2HHKcwEpWDGaTrgRaq6Z7lGb1PStw/p3HzWogHU4ddw+qDvwuMJZ9sz3Yp0+f6ujoqKpqaphUxgDsQoLNVVXHx8d1cHBQa2trDdKg/7M3aIAGcAyHw37uSTF/Wc9bm5ubdXFx0Xqk6pdTameZG2JMdldcI+bMbRLI2MrKSsewW1tbbd/5P/HexsZGAz5ZnQS4SByBBmQqW5zIhutOAJRfMIW1atJn6fvpN1x/Mu3o/CwzLP/P7kiYPT/6ntcj3kiWTVbw8+diehVDMa6Ku0JExvSSPsmc/MR1SD7ZrIxZPWsDpNgN9nB3d7eOj4/r8fGx935mA5Ip9Edez6oozu4RBIH2gHM4BvR5e3t7iqZBiQRy6GIE8fz8/Bd0M4i/pIphlDTu7u52JYvj9N00npBCo75Ho1EjRqPRqLa3t/u6MvunLIKxFGDfrZpsUpolZg5R5Q3CQ6kSPcpkTwDrHBmgJVUvy/n5maSIZiJK6B8eHppKzPlVVdOHvdvBYNATbaEvDMXS0lJtbm7W0tJS7e/v97tP6tzFxcXUHpaC0rzvl/6n32/98MMPU0heTiVlYKumN72FFq6vr7fjYygBOI6pWpjMAAnN/Px8J38GUR0fH3ePJEeX+8YJNIE9qtKckqo0BFCAmEElvc7eu3Q+dMF9QGPJPHtBBn0vHReHhxqTTtTv8jll4jZLA2L33EcGxqljyZ5A3xZESjASgHP9go37+/vuWWN7OFHV1AxmqqrRUjoP3IIqv6znLcBb7oErIAT8CaRMwzbgqaoapCM/9OL7779v0M47law4HvmCsCd44f/J3snJxPSDrLEbAmI6BPzDEmBj0NiBHpI9OiJB3t/f/wWDJnWE/3XtqbsCwaxYOHYCQEl1yyTTn9kgNoFYeidp39nZ6aF4GXyzLXy579Iz1zU/P1+vX79uNoDrNPQqg173vbCw0LR+wXeyl17W8xYATtKUsi/Br6r2hdljSFYuLi7q4WGyF7BEPsHzqolvSaaLbWzoJfu9vLzc29oAHcSN2UtIZoFKCUqIBWYp5YZL0Wd6kjKd+lQ1Ab1yICKZ9rn0gTlLI//Mym0WQehNJoZVkzif3ItNssAh//B8M1nO+wGSsa3YdEAa3xmPx7W3t1cHBwfNwHOM7ON/fHzaXkMsjLWT8dcffT0rUfQgKA5EIekyEAcJnuDP+O3cbN2UtKpqoTD+3csm0IIbgZ5z+d5slSRppGgDKn1v375tR0N4ky7DGOcACedJx5cUlUwA03El/YRzsWUGoXH9nIUgLhM/CpHDOFxzCqlqSyqp7ws4s2qQiOd4PO7hNLu7uz18IHsioV56UHC27+7uGgmHQq+trTU67byMiT3hJKsZSL+s376+//77dmrknmGkQ2SbHJmwSWY3Njb6O5KDu7u73oNQb4YqpGEV0Erb4iwtLdWbN29aN01KFujS06y40X1BtKA0e6s4x2yqz4m6DDwjL4H0naSDJ5DjOVRNkrukiaOrzjpATrVqesNv15/or2CczclEM6mfkvTcA5NjzGCAnXQ8doBjSyqOd5kJwfz808As+1pigZyfn3fggaozixa/rP/ni38CRKLhCzbJnOqcakP2SaE/Siqrqm1sggVVNRXckgHU5Zzym9X0/F5SnrPyYJ9d1UbHFiBVTUCQxcXF7pemg2RJAJx9h1nxFgBmkiQOoP9ZxU+KrZ/P9iG6X9+pmqbBZcCfFQf2JYPcpNbqDUvwE5AD8NFHSs9QfO2nl3TaTA7pILDIM1KRyljlZT1vZUyZhYCqJ3nRagUw2NnZ6TkdYkrvxv9TZhYXF9svpxzTj+yHFT8D+O0NLs5yneLBi4uLX/SbO08WCVLXyWzGq3QlwZGqmvI3VRPgRazunnOlfxQvO3/SYROwyeeSdmE2bnetaUc9N4lh+sxM9BMIdv/ZnyrPmKXLV01asLxrhTKfU+n1naTafw3rWZ5e4MKwPTw8NEVJxWxubq4piVXTex+ZAJS0GpzqqknFiZAkUim4NX20avKiBZhV9YtqBc71LK+Z8iRVJpFEiR+03b1B+RiFqurEcXl5eWofxQwEJYKCtURJHQ8th6OFtqSDkoQmvWdubq77FHye8KYRScVZXV2tg4ODplSkMcvqi79zbyn0Bj0sjl81QVCBA5JbST36rurTxsbG1D2/rOet+fmn7UnQHBYWFjrQZLyqJohibioNHRWIJJIHGJlFzr1jwZugxj5wVZOKBuOdQVzSoSF/Z2dnVVVT+5w6XjIKslK+urra1bCk+VRNqCjpgGYrFCsrK11RzaoIHXCMNPrp5OgGh5iDN0ajUQcXVRMdYRfdK0CMvnuHrtN78wydW+BfNQHqoLIcHwRWtdV9+v3BwUHNz8+3A2QvoKO++7Ket+iNKiF7Sn7Y2RzDTj9ThgWSs1ODnWNxcbEDF4kYuTg6OppiGaQvWVhY6D5Ktnu2iv7w8FCnp6c1Gk32V3RtgkUymTpkCEwCwLOBaLJ1Zn1QUk7pfVLX8nfuLXU1k93UMfo4+x3Prqqm/L3kLIPBTCbYI3FI1SQRzwqT6hKdcw2Sb/3ifkdGcow/elvSWl/W8xY7TB+8HxVuPtRn2EZyA9whM2QBgJC+k53mXySmuS1cJhfsgGuhLzmxExgr2bm/v29/ND8/PzVwJ6uACwuTifd5XjqaxaD0P1UTgBQom9+dZSTl9xMoTfDVe8gkLucDZBXd8W1Bkft7Z6Lu/WQsbzl3TqS2n+vDw2Rrvdk2DecBCLmW1dXV3v82bcrXUhB5VqJ4d3dXr1696oBMmVzQ4yEQCIGUBAGKKTkROGbCIwlMAzvrAJz3y5cvdXl52ciCSoZjZemasEEJCSFHcXx8PJX9q5gRHAaAE0ff0b8gqBOcE2AJMsW9vLzsfrw0HhkAZnUEApkVoNkgnsIJOHLyHGfFySR6w/jp18zAmzNUWbDymjJBmJ+fr/Pz804E9Vh5NxkYUdS5ubmp4PxlPX89Pj42r17CDv3kMKomk91UfCUFVdPTxlRAqiYUGPSOx8fH2tvb63MAALAEbL7tuvQXVk2jhHojqyZ7SuXWNgliJEUskXuyRacTsSRvVdPoYVY3BMiApqSl5HfYALqSy3MT2NE/yZzBJJbnL8HOa/TcUdkFigcHBw16XVxc1P3907h8NiZBr8HgadK04Nwx3U/aT3aBDbT9RlY4sr/6Zf22xe6mzcs+NH5UXyE9y2QPiGkLnKrqrRIMQyFXqstZvcDoQG9M/1Q1STSh9FldWFhYqO3t7e43x0xJ8Chp2RKp2cQuj5fsAFWP1FGfJdOAogw4LT6UTaia9G1nZTKT8ASMMt5w3alX7I3fodqLbZIpwOZdXV31PneGDQlqyYNA9v7+vt68edNJOFuAzcO+rqys9KA6CetLxf/3WWKnL1++tM8DWi4tLdX5+Xm/X3Gbd47+rWLoTyZBCgpzc3M1HA6nEgg+RXxKviR/c3NzDWoClXx/MBj0vo18P/mWZLnO2cTPtc22TczqaxZU0kcm4OS4s4kgeU9wJvUxj5nfr5rsBuDdJJOHn8VY8nPHBIY7nxjj8fGxt5a5urpqe5w902wguyfuHwwG3Z98eXnZP8vYnf1dXl7urf6+loLIsyyJl7izs9OGmvCoDJl2mhNKvVwvXUDFiGrGnd1EPrng6WAJq3NmWT8pAdmrVFVTAZFmf3SOo6Ojqexf31ROLPOiM8HM8+W9ffnypRMlTeo3NzctNFU15fAYiizdJ2XVsTl0zy8TQMmjZ8bZei+QGH9DfjlSBkkAjgqFHqp6iIIIZXYOiYFzG0Sk8sxIJH1K4JMb2b6s376yYiaAq5qetqt3jWE+Pj5uOVNVurq6ar3NwVCDwVODt+AFfUpQJHhyzu3t7dZ9DogzVSnc3d3t4FYFS7CKzpw01Kpq+Zmfn1Aq0ZuxBug6o013szpIl9LWcO7+zy5kYsre+L37zWNkIMqBZ7Cez9b3JBBQZnYsbVCyHjgiOm3Ahd+jRF1fX7cOZkLoeQlu9B27n7W1tZahnIT3sn7bwiyRwAMdybuEbm5urrfFANgJSOjacDhsymrVZN9d75muADerJn4hgUrHTH+UlTq/933+ng4mwJhJJr2iCz6TyV/qooDc72YTS+evmqaTYhtldYG+iikyMM1nmDGMZ+XZZOIuttCOkUPZch+90WjUfjJZHQZnpD9mFyWZGVxmMuv5pW0CZgPdtXm8rOcvNpdc8gWo3d9+++3Uu5ybe5oS7d1li0FWrnNfP9VkLLusaIkr+WOyJikC8DtOUrkloGJUsirJHI8nk/pTlrJ6Lx78taTN+YCcyQ5KCmtWJPNvv0//Q7YThKRXfLnKbu6zmkmg55qMAfEOuyNhznYxAI7Yw7vHtvGcHfPm5qYuLi76nvVpp41xb/o+nUs+8jX40GcliqPRE4Uq+wagC14eCls6GAmBz3MmSvqSiqRY3d3dTRlXRll53MtxPD00vmOPRy8lnQeFl7i5J87i9va2NzgmNBmQCmAFeYPBoPsVs6RO+KGBGvqz6gcdTueQiOpgMNkn0b2kE0GrrZokmYL9WapO1aRPLZ+xZ+56JLockGfEAOTeiKpBl5eXbZA4V59bX1+vvb29dr7n5+d1c3PTU6q2tra6kvyynrfoVtWTEbu6uupeJk3yKg+QNVXBBCh2dnZaZ6Cms0vQqFpsixMoOH3OoReuMSngCULkNNB0OLZsSXQznXDVhMmQNJOqiVNOGgxnkYmb8wGwkkZOH40O97OsXiY1TxLIQTw+PrYuuTYUP47ZtXD8wKp8FoAX9yt5r5psveCYPgd4M8BHBSKT8qqa6p2G0CZtd3YQw8v6bUsCrkqRoAaZGw6Hv7D3BsTp9X/37l1VTYLLHJih+muQwsbGRielfCXat+AT2k0HyZVtAbJawF/yZ1WT7Y8AOoBHNofeJyiViaZ9UWeTQ4tty1H9GSTPVipnKW9WArOZQNLxrHrQZz7atfPzOTxjMBh076Yg3LV4PnRrtm86k1iJv+vb2tpqu/P4+NhbpfD3vm+o2Mt63spBZ94LoA54yo6rcIu3ck9DyZTKL5kSE2sLkZRknJsVS74mAYOkojue63Ne33cM/iAZDai0ZCnZQVXTOpX071nWTeqOn7FJed9VNRVrk3nfyQoi32PxgYBj38lkvmpCS5ckYkyg62YrmTg2ARoT+z2frAICVLPdQ1w1Ho/7337HvnpnQLw/+npWophGVyLlRVxdXbXjyY12x+Px1Ibwkor5+fm6vLzsSWj5olDUGOKqydShqglqkFMLOUv8/qOjoynqG2VHjWRwoSSOnXQTKAaDz1kkSuq6KQtKyMbGRvdvJJ2TY2H4BZO2FfB8CbdrTiqRe8o90QQZg8Gkz4VizFIBrKwIeka+Mx6Pu+pEAfSr7O/vtyEUhCYVd3a6F4CAodrc3OwN2J3/ZaLi77MEfCoKgBNTgb2blZWVrjLpE56bm+tkATWcPlRN9iCCWgJYOLGq6T6DWR31HUslK6saHGQGUZwcWhdHmv/PY2bFLVfaD9ct+JScpoMRILuvtCFZvadXWU1nE5MSCgzxbJOqQ2/0jzi+4CEpcEAuOm84xt3dXY/jNszIs0SdYV/0XXD8niNkPBHtrA69rOcvvk2yowJPnr58+VJnZ2dTPsbG2HzQ3Nxc2/ZknAB1JITkhj8DtAJdHCPPpbUg+2oEfFktQWnWA81eAFXp1cLCQjNXci9C+uZcGXyhk+XKykDVZK+zHPoyC7ZmpSKTQnFJMguqJuALmlvqR1ZhsqoHIJqlpWdQm5WepO7N9u8nayhprvluvaeqyXYHEvmX9fy1trbW8QlbOj8/34k4+8k+im+raspeAibJkerSYPA0qCxZIWIucpZtHvSDn7RNHHshRl1cXKz9/f2pGMz5s1qvAp7FnfTxVTWVUFVNVxeT7unvLEhkISP/0As+tGp6KnAWVyw5hp+LBdigWSAmgSnXAhiWW6QOu1esRu8tdbOqmpoKRBcns1e5x3AODaqqfv/arL6G9axEUbCmGkjI7+/v69OnT/2AsoeJAJviZmz35eXlFGUmz0FpJE/J86a8j4+PjfpXTXqRCCGly6qIgDaDRNz+rATu7+/X+vp6nZ+fd1Uxhdl1UliUoRRe59O36HlkeV6gCNH1mdlhGlWTgT2C/1mFrJqMDPacKLD3kBWYdNJK8pyYa2OkVB3SWAl8VZH29vamUOKc+JSBjEFIzum5ZcX0Zf32lUiodXNzUycnJ1PBhCAKmoiWidLG+JJPK9/n0tJSV6YuLy+n9JMeSIokV1n1zgBn1q6oMNCLdMb0mX5XTWxIJjWGDkje8rqqJpvrJiK6tPS0PcfOzk5XDjlLIJDzcfrsB9nX1+U86bDv7+87Ubd8N+kpWAvb29u9B2JSf9iIqmmGQ1ZGFhefpk3mflFVk2Q6Kb2cGkTUcwNapUN+Wc9bv0ZBYts3NzdrcXGxtre3p4CBTIoSPE0wBxBxd3dX+/v7PYXUO6VP6+vrTX3NtgnHFGxlsmTQVQZHKmMS0ExkLH44QQYUZ77JMRNwzSQpZS8DWp/jn6t+uYl3LrrhntOnJ6PH95NCqy9saWmpbYC2FUHv4uJi7ezs1NbWVq2urtbGxsYvbMFsb39WjyTSqhlsYtWEQZEUQXZnZWWl7evLev7yXjLhqHrSDZvTk3Xyx2b7XtIRqyZVbJ8BHEggvfNMcKqqwcOsgEv40KCzksf/J5vENZJTPtxnxJPuu6oaRMopoulHMw5Mpo54IYsbqWvJAsiYO0Edvi0HX7luSWq2ss0ms4AUdin39MZw81x9dm5uruNcQ6Rcv89iS0r6VPgBw47jHdNZ584E+Y++nk09TURLQLK2tlZ7e3uNZBK4pF1lyd0D29/fb+S+qtoAmxZEmXyHoa+qLulTuqymuT6C53wCKXQNjaij0agTRsqTI7hzjxqOOlEfCWImpLOO0DH9TTmrJlWgNDyuMe8jP0+AGQrnr5ooStIEKIskWTVBHyZHw5jldFaGyr1zqon0oOtVTQL5pGt4/p4FarCKkkmbL+t5y/sQYDJqkn49TxydBD+DFFX/x8fHOj8/n6o0MfL+np+f7+00fAaCXjWhXrAL0Di6zSEmpbKquspSNWkST2qP75BrwRa7Q0czsXW9sw4rKwRsjUl1goVERPNZc3SCA0GvbWyqpunAdJpuJI0tk+aq6vuQEGQ/Cf21ybJrw+rwbF2/KgXd3dnZmaoEp9OdpQxyuC8Dp56/xuNx277HxyeKqeQhWRlANxXppDmtrKy0HAAEqyb766pYVk1oZlnxvr+/r52dnSn5qpreWy19QfbqkKWs7PPVx8fH3duclTA2iLwCNNM3LSwsdJI8m2ym/lp0Zn19vcGQDEA966wuJkDrZ0nby6De+QTzafckaplQ+D6gSMAv0M7PJJNmfn6+W2AwmLyzpKmRm9yCS3CdFZCX9fzF7qe9w8hgQxUyPPuUnfSB4kC6IDZMSn9SSJO1R85SZgGZEhqsGjaAXNE78St/L/bMqhwqe/pGtoCMub8EWxOwyAKPz5BpzywT79TvTBTF2K43izoZC6ct4LPZtizW5PtJkMdz98zW19c7Bk2WEl1MhoN3we/Oz8/3ZHCgrZyD7Xx8fKyzs7MpUO6PvJ5NPU0DmRPGlpeX2/n4eTZsZ98U56DCJLi6v79vFGF5eXlqK4yqiQJnRYJhzd4ehpkAE7pMdE00ffXqVS0uLk7tKWVogM2qUTST9vVraEYajET8kqqaBgg9B4U0A/hZw0H5kwJHCZ070SjPIYP6VE6DfOxrmeV4SpF006xKziLQghb3isZn+wzHV/2B0Arw0xC9rOct1OqUn7m5p5H49uriKFTEyS152tzcrNXV1UZQq6oHGj0+PjYtR1C3uLjY+2KS06R4ZVWebmcQCYzIwDcrEOmUki6SNJEER+gaMGi2wpEJo+8y/rMJq5X9SXk9s8fOIRfuj5zT80wW6btq6uz9sjPj8WSQls+MRqOpIB7g45kntWhnZ2eKYp/vG4jALklQUBTp/guY8/wlmMCgODg46P5v8iVZF1Dt7Oy0/OSE1KSCsp8SEz4j2yZUCCw9M/f3952UZIUxk8XZveNubm7q+Pi4rwHV9fz8vHUle+3ouFaTlOsMBJ3X/8lssm9cEz85W03M72cy5bnPJo78afpYFfpkAqXOGqYH/PQz90qHBZCo4MkI8D3TkBP0cb0JEO/u7k4lAjkQhQ9+Wc9fWQGmQ2R3OBzWw8ND99JeX1+3fiQFWk8wX+R3ZFyVn+0W9/7a1lRYHQoq5CR9UlKh6bGYa2FhoYsymaT9WoyY/2Yrfi024y+zQGGR2UxEtVgpNORzngWFFIAkqgns/lpSS/az73eWKZBx62wFVuxwfn5e19fXzXjLhFEhKYGhBN+0e0nacw958Vi+/z/6epYlEah5+bNVraQpZmLo3xLMDHgkYRwJAZO9j8fjbt72gnyGYKTjZGBTSbLEzVElYjEYPPXdUS4laWV+lRhVgpzEmM69akJXSYHNqodnpX/EPbof3+WQCCIDlEixZCyVilOi7D6btLR0dkmHYLg4/Zubm64Sel6U2HUL/ClEOjDf0R9XNakwCV5Qd76WkvwffWWlivMZjUbdR+QzEpPBYLIHlMpA1S8rB3rfslKV1Tt6YWUFTRDkeFVPk5MTSMoGchWUdCIGJWUywzBzyElVBURl5TJRfX9zNvaZ4niqpidEqhLaG3EWmLLIc9LtMuBO2oxEzf14bvnzrJYmwutZOA+kmi5iC8zScnIYD2BOhYrdNdkt6X7z80+U/Bd62/NWAiN8ElYFiu/9/X1tbGz0EJqkiflMVU3tsTkYPPUWYgCoPAE/9OHoswKGshEAPrTQ9KsLCwtNPU1gD9BEH46Ojmpra2vqelXCktqZSH/2TGc1XeJGP/3J5C6rMZlczbJpPC++L3+eFLnZ2CODS++sajIVU0+Tc7tfyYFgUluH4VCCRcFobqmVCR+6q2RUQgmky2rT4uJiTzx+Wc9b3jOfw3fxZZIQ07+rqhO8BDNTJjIWJVP2OU3WlfdLVq+urrplq6qmKvnb29sdY5EbrJK5ucl8j6Q+Pjw8dEtVgqWuif74fPrN2UTTSt+TyR994jOSAZM66hlZfibudky+MAdtWbMFETGtZBdgmkCp63JftoSqmtDqPdPV1dWOPegjpp3nlOAwmytmFgd/LQWRZyWKHry+pqwKVE2Cw+vr60a5JH/QkqrJthAMMSRcssep+ZxkxYvOErTzCnIJFWpn9lcRDkJDGXNflaWlpaZlSU45q6pJryAl1/DqWRCYdIKzKD9lzFK8zwoeNjY2prbR4BQ5VQLMALl2ibngwbPynpJ2Q7lyQhdHmck9pCrRIwYxqQo5vVQi4Nw+n8F8BsBZQXpZv31BHWcbqrNilNVG7+/4+LiDHmAOx5iJlYCUsQSkoLDRA9+lp84lkGUTOM+sLJBPn6EfAmCBEtBifn6+A96FhYXeVgDq7xrSSVRV6z5HWzUNbHkGkrGkACZYkhWYpMjMBr7+do/JyvCODBrIgJijl5x6BrlvazrQDNIBZnltkGggl+sR/A+Hw6kNw72bl/X8lXpEzvU8XVxcdOJQNak6YOz4fjJHkuIpMOXbMF/4E4mUavEsPY0e+n5WqCSLQE6Bqkrj+vp6gy16YvkRQ2Nsl5TB56z+CLyyXSIp3Fntmf0ZWU2mQNWE2pZA6iw9l83yvbw+8QHGD3+uIjg3N9cAF3vB/mYFxDUkjd71sjEYEGkDxBInJyd9bQAywez8/PzLPqe/0+K35ubmmrZpEAnQ2zuRrLHzfEnqT87XUJmSgIp1FSju7+97fke+09wWgr5XTeZSqIIB+gFRVZO41x7B/IfrJDPJYlleXp7apzEBVn+y2jcbG87Gi5n8po+frSp6ZlnRz+/4jH9jSImBc6shCRwgJ+m29JVuO77BQO7XDgjpA8XXrjlBYcc3CT4Hionjv4b17ETRy6h6UgAbhgoSvZwcK/vhw4eph+llErBEVfJckqe7u7tWGOgkR1k1mVQmYKW4qQAMsmCzqhqVY7QlRFVPqJIKovOZyra0tNRJUCpNOpp0AJLibGDOzxFqDl3TvKCO0TAmO4Nv95n86XS8jsEocHZZZaFsnL3kUaBtmEaiJAJfATTFz1J/VXWF0TNWkp9VmHT+L+u3rwRMBDu5OfcsqFFVXYFXRcqgigHl0OghlA092/vPYIcuZeJis+JEUyGNc3OT3lk9jRmMOR75mq0OJrUECJIV1nROVRO7MFsZTCcqQPY8ZwPcTECT9bCxsdFDLTzHZDDkOTh4+iihzyoHvUmUVFUYQ6JqUvkQvAuM8xmyV/v7+20PMshwjc6DtphUwpf125ZBCfwOmVxeXq7Dw8Oqqn4Pv+YjV1dX6+rqqqmiZIjt3dzc7GCFHAAYcmDVwcFBU0arqgPDpD/7w1Zo87BtEp0zIyArXFXTFLSsJqavys9W1S98Vz6jBC1mKxd+PpssZuUxKzzpbxJQcw8ZYOb36R+Qye+B4FVPIGmeT6KbCXJVdRApgGRTsq8s2U0SbbGS4wHSd3d3v5pA9I+82HHT9/UQX19f937YGc9WTSbQZ/U8W4hub2+7+CGOvLu7mwJwqp7s9/n5eQMNuf+uHkkAUPZQzlb/ARrk2DWtrKx0cqvgwQdUTSYnSxZnY9XZOI3853WkP/QHcJu9mZmkJkvAcZMdk0laAq3JykO7TzsDjJm1M+JygFTaLiBQxvcZG/PHqqi+xxabezIeT9hFo9GoW3++hvWsaDx769KAebCcW477vrq6qrOzs365HrbP+lka0ZWVlaalVdVUgzflzL1pOJQcOgMFEjAKkASerjfRnKRgSohz/zHGWlCbJfR0MJk0eg6mLVHoWZQ0OeIqCL82+Syros6d/X9V086WsOZ1JoUtnblSelYonS8rlILKhYWFury8bCNAOXMwjkRWwppITNWk9+1lUMbvsxiitbW1qWb77AlNdO7i4qLm5ubqzZs3VTWhcDCESVMUHHKc+llz+i/kM51F1SQp29/fr6urq5YVDisrAuPx0yCH7KPEUKB7DHYGqK5LED7bVzhbdUgkMxPrtDXsE2fCkeaiHyo/v0b9dg2Onw7cMR4fn0ZvQ6W9B9eXQI13ury83FSi2WqhJFjQ8fj42AOHJILZo2FQAtTZsxNYvPQoPn/d3NxMDfDiq+bn57vCMAsksI05CG40GvUQFBV+Mry8vFy3t7e9TVXVxCcAQx8fHxuUpEePj5O9PrMCQKdV9l+/ft36u7293ecmswlgCtKAUbPAqBiCHUidqJquwGciWDWhhmfwmSt1PANex6PbVTUVxPJ1EukE1jJxFyiyD4BnupY0ecCyRCKrQtki4H0I3iUFl5eXnSigCDsPMDDZUy/rt698/8CWlDmVNhVCVUJyzPY7BoaGfkaxb+pzVTVYf3R01EAPW5/0aP4w99jNWDE/Q7fFstmiNB6PO3mtmmxtoYqfsXDVxIdbWSVPYDnj4NlYNeOPjEE9W7Zibm5uyo/S7YyTxeiz1b4EqIFj7kvSnu1vfG+CdxcXF51rAN8kg1dXV1MMg4xZHN+7AvrIl7JH/I+8npUoQiMEjhDIfMhp8C8uLmptba3evXvXDmJ2+l7SFzMQy2RjZWWl+f04+1XTVFKBWNWkQoIWUzXp6aMMjmHPqouLi6mKgz5E1BmC5pr18Qn8OMZZRDMTr6yESOyyElA1SYATbU3h9zyS682RJ4WUcnjOlBKi7FiOz/mhJGWF0D6JlDeNBR4/o+a+UH+hzRmwZuCfDd8v6/lrfv5pApfqn+ftvakAex/D4bDl4+joaAo4qKr+nvc6m+xkAqnPgpMhS9kT7HObm5tT08oyAXLeqmq6jKSG8c5kVHDnHLOU2apJIpjPabZ6gc6avQnsCptC59Pp5TE5dHro+Bk0ZzKatFF0G+wGn+dYHS8d6Hg8rrOzsw5qXatjc05zc3NTVSjvGWPCc5RAoCKy9YCul4rF81YyUbIfqWoyxCjf/fn5eeurz6pUZ2AiKJKAoCULAn2Xj8jKhHftevgSPmE2qEtqKL/O3osF/PE7ALEqHHtCxlJXEzBKHXU8sUHqxmwAauXvUmeTkcCuZLUv2zoEuX6evaGuLal/VZNeYxXY/Hm2m0gus/rgmGzgaDTqSZcAq6yUqAK7npf1vOW9o1BL+oGiYh7ghKFfEsyct8CHJGBuC6jcESBjanrLdieoyl77PZ8mEcEy4WvJd1V1siiupZez1fz0LeKFLHpkZb/ql5Rx/i+rhrN/2IAEcNiotB95joyvM170b3ExHZFHJFAr1nx8fOxhmcna86z5+VmGlRjAs/DckrmkQEX3q6pt89cyi+NZiaKAz7RMCYWqkheAvmTKl4foZTPYJkXpq8qem3w5gkv/r6o2vktLS83tzs2IUemytA7Zm1UIKGwmcok2LCws9J6BUPycbihIzcogYYbyuHaUhXTAv6aMgkfKJEi0JIAMSSJFlDCDkAw+MjFLhYUwZwWWYto/KBFeQIFjqkA4ruvB0XZNST/a3d2dus6X9bwFNLm4uKiDg4N2KNmraHqi4PL8/Lx1kw5xUmRVYLqxsdEUChUQsiL4ZDgzkEJNl3DQS3qQDg3goDrN0SaVpOrJQRhak1T3qmpZRXn2ec4zEzeymmCM6+fM6ZfrcF+uKasas4n1rF7nd7PymN+3INqz0yrdy83NTessICYrHzkgLEG1tHECICAfe+l9CWzPzs5eAJ1nLgHc3d1d207V26x0o49dXl52EkaPqiY9UpeXl7W1tdXMDAklfU+mDj1W0Uz/RVdn+3DJuuQkEX5ypiJWNakA6i/EwKHLmDSzlDKymcFXBsLJ1KGf+bk8RvqS2YTSzzLQzeqCnyfo5GdZUXH/SdnL/9Mpe0G71sXFxaYbZuXUOcUC2buWdH/XkJXM9Lkv6/mLfVUxRgNmT/kaQI39MumRpA0V/PXr152IVFVXtvg5sZGEhQ7SezEeWy5hRIMlD+ljc49U9iSLL+w8nzOrR+LPBGWqprffyoQt4/UsGGR8nElxgmOp06mbs8kiP5T6/Wt7iGb1nx9DIXa//CmQGjDjHJg19NLxFxYWamdnp+7u7n7BnhLLiuUlpg8PDz3IZjaO/6OuZ1kSjbYLC5Ox8xyHpM3PFhYWGmVjPL2QbBzPvX8Ip0XZBIKEyItAH9W8m6hAVU0di1L4k+X+vb29vnbnYxgycGNAkmqi1M+oC8AzmCOEEA+OGzrqvrNJNtHUrBDm/SeSM0tPFZRm1XZu7mnAAceeG44ycoyZ64c8pROsmvRxVdWUIuHpm4QJeXbMfE96NjP4flnPX6YmZmW7qqZoSsmvJ8OMpwCSfHJQ6ci886WlpSk6I3lEzyCDbAaZvrq6al3IxDJ1VwAHGRQkSx4TtCC/ZDirAI7r98l6mEVI/Yw+sgkLCwu1tbXVyD69yT7nZDokOppBagaiWan0PF3PLPVHVV4fOFuR/TD2y/QOVBiTTue6BSaui1POicUqs+zhCz38+SuDxfF43IPfZhF373RnZ6cBGN8TlG5sbDRgmxM4ydLDw8MUkOdnVTUVMCXbhY3IhHG2SjI7kIyN149bNd1jKKn17xzalp/Jip5j0J/ZZC+rnLMsHtecn83jskdJN3XtjpGUb9eWdPeqycCwrLywpfQlg1PXy74MBoO6uLjoqnHGRcl8mp+f70mW7uni4mLqvYlvXtbz12yFKwGarAA/Pj523+jKykqzo1T/LLbVVE2Vw83NzVpeXu6J5PzNly9f+rjOl7HzbBKYwF7GVHQSmDC7vVJOXubnElTEAEi2XNqYjFVnk8xZwMbPMtabLVbMAqezBYTZc4nRk25bNQG73KPr9dzpsneRvpp+eybouvyn+8iqYrJyVBH57wQPAA1fw3pWosgozc3NdclWQ+7d3V1PbSPcXjpBS6rk0tLSVH9DTkFNoUqFnW0iFkAx+FAg1NGkqqqSGUSD5uV3VdOIvmqoY0u4JFCpRKoWqSCuOye8ciyCXsYkz0kJOJ6sJqZCoaiq9FGunNgkECWcjJuqTCZxviPJFTRXVSNnj49Pk/koUlJqPKtEvtI5J10mDVv2SL2s56+kLybQkIHUxcVFG1gyxAADLtCxOJDxeFy7u7tNQ2Yok7Ksv6qqWi7o/9zcXFcT7u7uamtrq2UwAz0BGgdIZxONy4qZ+zOtLIc9CPhmQZBERLNXKJ0F+Xdf7JoEMpkDOdnZfczKflYkskri7zyn91A1vfl6IqyCEc82KegZVDsW28ouSjgF7bb9cD+eUfalHx0dTTnul/X/fHkn5P7y8rIHJVTV1BYx3quNtc/Pz1tmMvFaX19vulMyN9LW8kXeabJxyGru4cYeZOCYAXKCvLOVvkwWM9kRaDlHJj7uKX1y+j5yl2CS3yeompWPPL7nASDhf/LfGdhldT0DVN9XoRCTJNjKj25tbTXF/uLiot97zlkYDAY1HA6n+qHn5p5o4nrW+Hj35X7YJ4nlC5Dz+yx20HOtmrDp2OX7+/va3t7u9zVL/6VfEny/wxKomlSzqmqKWecde7eqx/RM4qpij84sKcLsSjDTZ/g6fj/7hhOUpA9V0/37fJjfZcUwAdfZin/6vdlEMu0M/znrQ9P+OG5SXOlx2hPHEXd6houLi1OgVsYVKrXiDTMS+EUxdc4BcN2j0RNFfH5+vm3paDS9xV4CCH/k9axEcTAY9LAFD12yINjU6Jt7eGXQ43spTIS0arKniRHBjOrNzU1dXl72xDFKKzD27yw7Gx6Thtz3BLWPj49d/aiqqR4Ogke4Mhnzu0QSCX6Wygm4ANxzSBTn/v5pM+uTk5PeK0nw6XOUlMJmhYQjV9EQZFxdXXUincNABJW43FmpFdhXVdPZUsmgXO7Ps3WNtjoRjAoUsi+Ds8T1T6TmZT1veQ8Cz0TMZuWzqqYqdeTUgBtBEWACgmdEOOc0GDxNhFPdgLR6t46d1GVoHeenuiX5WVx8GgcP2MjKnECXLEnkJESpV1k9dHwOpGpCM80KSeqZ+0yH59lwUBLUTGwz6Usd9T0VjXTmwCjoZ4Jsgs8EkqqenNvm5mbruPfMzqWtyb4z1+pY2R9FnxPw86xe1vNWIt8PDw89fRRAUfWkw2dnZ+2vcn8ySQr5Sn2rmoCJgkY+NClmrgOFjmwkQ0ZgJPAUtGag7FgW2aS/VZOEhv76f1YF2BXPJI+VlckEE2cDUzYlV9q8DDLTd7IDSetz/gw681qdF+iS223ww+4/ASlMm0zENzc36+DgYGp4n+/nO3b/bIFK83g87mDW9NOX9byVQEsyM9jpbHECvgHdACYp5zlYjN5oachtavgwhZPxeFynp6dTvi8BeWwAdno4HLY/ZDuur6+nWolSNqtqajAPoDFZfOSOrtOJ1LnU1QRL/Tsr5AnqpN+tmsTVyQbgN50ndTJ1wr8lvo6LAu9ZpS/nY62k7W9ubrZdzOok+619x+K/q6qHalZNgHtJJzv9R1/PShSTYyyA1Lt0f39fe3t7/bncB0UlzfcIgwybEn769Kmur6+7OVgmTuhl8vmS9dAIaGwFkCggZax6ckRfvnzpQTeEK53ExsZG7e/vd09G9goSvKppdDirpwwI45GGh+POoRmcGcV2P0ndS4WjWLOOLPtXKL/9XKomNBrJm2tznESCE00ytWk4HPZ7FdBn4H55ednH//LlS1eYXbPEmuGapRG9rOevpKB4PznUYm5urnZ2dlo+VCz8PxFOPcOrq6tNOZ+tVmUlAUBUVb9IEulgOq2kVCZSSOfTSdFDlQyOls5lJS4Tm0QZM7DNpLBqkgyyb0Cvzc3NX/Qi5DGqJiO7M8jMACGdqXMm5U4wnLSU3B4haen0VcCR1aJkLXCeHD8WRW6tkd+TuAtWHGNzc7MDl68FDf0jL7a3ahLoXF5e1sXFRQclWgNS1+7v71sWq6oBGjZeApIyc3FxMaXT6+vrUz1NPktnDdkYjUZTNHGyrCKZepmVgrThmaBh4vheJlwJws7aoPRDzpt+0bWlfnmueS0JqI5Go/Z7fpffyWSPbRMcquRkm4dAlR1KSjtfqOLLTiSgNhqNepop0Onq6qptnYQ+QSQ2KhNdfaAv6/krk7dMbiRaVdWJek7/T79GT7MSeHV11XbXJFLvdjSa9CSKUfW5Vj3JZ26bsbCwUKenp91+ZW4GIInPrKopeUx9NC15c3NzqurJz6fNTyArda9qGpDJ5Tol2GR61o+6v/xeXrfPp13wncxFJNv0VNI4a2O8o1n6LP/pXOPxuHZ2dppVhEXo3SSYIDeQl+TQR7KAkfdr9/9HW8/eR9EDrZqMfebYCD3kURJG+Pw/ecjpMHJcu4RMAMMoG9efSEi++ETmM5EiBKmk+MeOmcKaKCtFT2RXEGa4jeeR1Jp0PozJzc1NV1tdL2rJ9vZ2T5Kj+J63Z4SGgG7KGLi/rNrlPk9bW1tTFFxrMHiizUoAE70SNELCDEHw/q3ZaqlrSjoemTDoiFHV5JvVkpf125fghAPzzgR6uSegikFW1x4fH3t7m/v7+/r48eNUUjk/P99BZAY7VRNquqAVoMA5cYIAJoExpoBFh/RuOEced2NjowEb/YNAC3STdGwJ5HA+s6hoVgHZlNQFzyA/l9XBtGv5Oe8ldSHtXwJAeharakr3q2qq6uB5o/Eb+b26utrMBE7eM4GI0vkEbtLGeE7YB2z5SyD6/DU3N1fD4bBtq55xwalA7+DgoAMe/jNBOcFHVXWAcnd3V2tra1VVU1RGQVpWNqomeiEwzanUfHsuPktwlFXCWRCBzXAeLBn/zxaOTHBTr7LyOAsmzv58NklNvc4qhmO7n6rqipHrZh/ZxrQf9D1ZFmkz6D2gLH1/2i3BerZyeAb5ztOOXV5e9j60niWWV1aVX9bzVk4cnZ+f78n+OfE6K8b395Np2CmLtqdKmrL4jb/T18g3Z5y6tLRU+/v7U2B7gqhkw1ZS9s2le2Tz9PR0CohNnXGsbKXIJJPPqJreIzH1L+PsPM8s2GVlopm/S4Ann6NrSl2a9aEJ2s7PT6aNej+q/wk0sad0WgV1ZWWlGR25svCVAHmCNuQE9dS+uQkyfA3rWYni3Nxc09L6gP8fI6pqkUgdJeG8oCaEORORhYWFOjw87G0wMuHx+fX19anEDDKXCOPy8nJtb293dSOrl1XTlC1BUl4rZcvgOauESZHNRDUVKAdxJF9cNWdjY2OKmkOIoP6eCcF0Hf6erfwJNAUEFvpZosezExodh+J45n5GWTY2Nmp3d7ePBRxIVHV3d3cqSc3+UNfDCWcPzmg0quFw+BzRfFn/nyWZmEXKGUqBIRDCu5YUJOgxNzfZSDppyGQ8ewIlhSr8CwsL7WhzIqnEKOmgUED2Q5IrkZ1FCpOqliihvq1EQasmzthxJWBZFc3JklUTh5OoLJvFDuTnPWv6ANRRiUy7yAmqFjhWMgxyiFAm24L52Wpq2goJteRDkiAgTVqra/HOXC/WhueIrfGynr/QoLyvtbW1Ojw8bF9WNZl2a+CT4Ofq6qophuw/gCD7Zui8hM87NXEzA7gcbkbfDMERGPkOpJ4cu7bsN8+Kl6CTfcgkKAGaqpq6l0yc8o9gL//M+mhJaAaVsxVG95TV8kx+f80Peqb0FHC9trbWG6obWpd7MEv+qyagTY7tZ9swocQ59BponnRUyQo9zdaXl/W8ZfjLLBjBJueMCYlGVf3Czp+dnbXMq/rd3t72dPkspiTgWlXdrwjsmZubm2oxEC/z26n3fC4/dHp62vJCZ5aWlnqaK3/JD/J5uRNAtljM6lbVdG+w66azv5bQeUazTAB2IRNQ/trzYnPm5uaaeejZuUcsIIyZvEc2LFlxdJ5esgdiGM9ZJfbh4aFjGgMafQcrkP0COms1yKT5j7qedYV6zX788ccpVN4L9iIhKIkoZiXSd3Hr82e+j/e9ublZm5ubtbOzMzUMBeKdtNCsGjCcWSXLjYQZXgKZJXE/94Lz3wSOAHCQegAhhYl6+BzBVILPSlzVZO+k2alyUOFUnERIXXMiJlmV5CizOpqft2YRnURMBPLpjFZWVvq6JKj5nPMcAgXN/ZJNwMNLRfH3WSl3wBLyIvlSwfAu9TxAUSUS9/f39ebNmw5M0TLIxK8NqRAopeNLMGfWMa6srNTOzk4DTYnMSf6SOuec6aQdU/8sGwH5ywoBg51Dn9I2pXPLCnxW1jlLn3cO+pPOoWpCm5tFSNkN1EH2z2AS95/34FrZCo7LPY/H49rf329wzrkS6UyaalaF9Z5XVdPGVbCqqgPZl/Xb18LCQr1+/XqKEQIs419sdZH+q6q6yqBPKivFVZO9a9nrx8fHDmoEj6lP5FOyA6BNgGAW4YeSS66y4l01GaaWyZBEyWAm33NPAJcM2jK5SyCTDUnmChnPPwnkpq/MiodjC9ozKM5/53Og2+xG6mWC4EmHz6ST3RV45nZUaftmmQQShf39/alj8unZYvKynrckYxK7HIwmhkuQUBEjY6/r6+tmS6kqbW5u9nYp2HJ7e3tT1EnsruXl5d5ehS8aj8fdMnV+ft5ysLW11fMeALtV1cWD7e3tqX0B2RY2ga9KnUiAUvLmZ5ksWr9W/aua+MfMDzKZ9Bk+MnVt9vi/5kP5zfv7+2YIikPEBXTK99K+AAW8A7YjqaXZs5l22vHYiJwiq1gF8FlYWGiK+dfgQ5/NHdra2ppqmmWwBBoqdZlIEjbGDy3D/ynO5uZmP/Qs0wqIUmgFwnl+i9HOwTS+71g50IaDys3hE21PAeH4nJ/iVE33LFRN9k2bRQAJeVY68+c5ZIIAV007qVREiu9esxLi//n7/NtxEgFOyp3vV00ctnebSSalSuSGQeX4rq+vm6rs2lDmtre3Xxzd77AkT9lXKMFZXFyss7OzKVT64uKitra2ajAYdA8qvr1tL6om+54xmprqU34EXIwzKk0a3tkppCppjnN9fd1DqdKBLS4u9vTHBBYEnKYUpyH2u6y2JXsgEWCyncCVoM/1ufcMatmdDHLzuIn2+x25p98CafcEVMl9Ia+urqaGDQCAcoBFUucNMfBuVlZW6vj4uKqqk/LLy8s+nwTEuwYojMfjOj8/b0f3oqPPW4PBoJM372FlZaVOTk663//i4qK2t7f7ffIdGaTSIUMXLi8va3Nz8xc9/PSRDOeefGSObq+vr7d/rpqAhXQlK2PAH76Q7GfV+deCR/fEJ7i3TApnP5srq3uzFQeB8P+/5LCq+nkALBNQ8dmMN9igjGvEMdoyqp70jn0B+HqmvicJoeOufWdnp6/Pu/Zv1yOh/fLlSzOT2NiXiv/vs/R0kwc2cnFxsUFQceLe3l7PZkgQ9OHhofb393uqMUYLnaFH4/FTlV9yNhpNJlOLjdAo+fTV1dVun5Jg5iA3x+D/dnZ2uorGjyUVPQshdER8kMCn682EMnU848qqSfwrAeS7HCt1K2NUxxOHuhaVU9eluutc7q3qyU7Yg1bM7pqTdTc74dW52YdkOMhVDg8Pp1hAKP9A+e3t7ap6Als3NjYaTMoE84++nl3zXFpaqj//+c9d0bu7u6uff/75V40s1M9LJzAEUgALJTEUJ+mgSSMjeCpshIOzQT3LBMZ1qJqk4M5WPRhgw3RQeaDwNzc3jf5fXV1NUVacW2CepfdEU7Os7vwCSwqQTjBRZ8d0r5JkSGYqKQRHYO3YjEKios6FnpYV0Rx24RyeAfSZ4aPUDKvz5bNisBgticnXokB/9KVHkD4ZcjCbqAkyBCNVE12RlHFYm5ubVVXdP6eyTx7pclU16ON3ST+FrmWF3HTjwWDQAZtBLgK+HP5g+rHrSeSTvEm+MmjMwC/vWzA8m2TSL2hj1WQDcn2BqVN09Ncc6Wz1PwGV3E/LuVIvZ+k6aEzoNhxfJnCz12G0++bmZicgKqNshGd8fn4+de1ra2u9ncpLkvj8JQmrmth2iUSyPpKandXAHJmfCDxwlAzzR3zf7e1t7ezsNHiUlbKsZqceoInm8BfJTSZ3KuOJwAuqqiYU2ZQhfZGZvPlsVg+rJvriujKppHf83GzlPM+foHHVZLpl+mDP1ff4qqpJ/32yFRxPImhfTP6czzPh2XT4BMISHKK3ht25H7IgyM8JjPPz8x3MvqznL8+zajJ86vj4uC4uLqqqpvp502+Jl+bn53syuLYLcob6XVX9bvlJ+kZ+bY8iJvvy5UsdHh7WYDCo3d3dZv0cHx/Xw8PTdiz5efKX/769ve2q5mzhhf5UTfcPkr1k22S85nMZw87q++znks2XxY2sqCve8O/eRwI/WcChL67fv1UaqyY6L56Xb3gOikJbW1u1vLzclduMk9Kvs0mqxmwrOcoE2V7rX8N69j6K+QKgkgyqB7O2tta9UoKSDF6gHQIcSEFW02Yz/Ay4Eu3IlcnULDLpZ4LC2YmGgkcTyiBAEiXHcJ6q6dJ5Jl6chgQxp6c6T9JOZ5PGpLzMTiadLVsnWpNJYaJAWS1JRNQ7FISnQxK0ZtXD/xmi5Hn7nPNIWAT3Kk75rt0P5X1Zv89Kw8kIqx7Z3oZs0EEODUUFUFI1PQAiK/3kJRHBtAm+azhO1cSGSF44A+eFrjo+PVYVS+Ao9ZBN8fMc1gLhzz6mHLyVVVHyONsLlcFpBpIJDtG5pNX5nftO5Dgr9Ko+nu8s+LO7u9t65hmYjpjsAQsQILlXaRbgrq6uNkU1K7h00X0Ph8Pa2NhoUPBlPW+pEiwtLTVd8NOnTz0wY2VlpXZ3d2tpaakDi8Fg0Fs48S9Z3Udzo8OZPJrQxyeRUzY6q2J6WLONwCKbzuf3SVfOSnoGdrO+Kyv+WcGcDRhnAc1Z31c1oYpnspj+ih9iB7JfqWoCvCRldraqISFTCc4KR8Yk4/G4EzaVYzYi7dbCwtMETM9aL2lVTc0ZwFbKvk42w9Zf7Jh9F1/AnOcvspCxHIo/O8q+ZuWLHPs/P5oAUDJR+Lz7+/upvY2TPUdf9Spbc3NzU4PqDC+jB1mcQZsEKrLtqmX0Kv2f66V/QKQEhAHSVdMtGK4vqdGzxZv0pxmzpl1LRgHbR7f4WMegK+nPDXCc9eP6P2cr9b6b02ettLvsMtvF/rEPfHZuI6ZQ9LWAOc+6ytm9/QiuyUxemJcpW6+aBJu+k4Jrw2GDbFTy8kVVTYSPAM9SujL4mjWYAs2sPGb/j2OroqjEEFbB7fr6eg+dyeomp5HO0HXNOmgCn5MSM3HKit7sSlSmanqfPMbFOXzetVFA10RRkjefhlGZX5XVEAYVpqoJQuM6MsH88uVLDYfDKZQ27xn1QfD6sp6/VLxGo1Ht7e39IkBBPeNYrq+vO5DMSjeE+/T0dAol47AYbjYhaWRkUCAssEnbgHpn+qntVx4eHqYqjuQvwQb3l8Fd1SSJ5aDYqUzuZp1UIpqztoPD50Rcz2w1fjYo9qyzZ9vvswrj2rJimQipcyXdL+3leDxu6gubMeuM/CyrvBKC2et2zKzEXF9fd/L+AuY8f3nugkO2UnKQvghQSQbQiQEGZCcDm6wK0m/vHw3a/wW6GeSORqNOdgRf6V98n/yz5zk1mw7NtqJglWTAmr2OyQyYBYpnAyx+P/2/z6b/JvOOm9VB9it1hi9mw7AH2MwcJMTXCRTZ3rm5uQbR3BP/RveqJvuXsrXimUw22Tyxk6rJLA1WMvpryfTL+n+2+JvFxcVOpAwxoovkWmWKbcxhcLe3t90TrJWDDtFFn6X/5HdnZ6fjNQAAemOy7fT4qzJj7iToWDWhiq+srNTW1lbr22ySSP7JVP4/CyNAl1yp++lfZv1qxsXpe7OQ4Wfig/STjouJmK1nCSyLuX1mtoVtY2NjKtGvmt4P8e7urra2tjrxnrWt7J3k0rsFgnvuGFNra2tfDdj67O0xPOiqmgowvYQ0xpLFFIzFxcW6uLio+fn5HkHL2GWVLSlfSVVlLAnTrzkUgp0c6F/7XCYwkDyfo2yMf34XV121UDCoCTkni2Z1IoNEZe9EjqqqhY1iOJ9j5L9dTyKvFA26kZW/nP7o5xK87IlIRUul9J7Sibkf75jS6ZfIynFWVjhMScP/m70/+ZHzXNIEX/OYGPNIkaKkc05WAplo1KZRu/77e9mravSis6tyUKakQzJm9xgZk99F3J/58/nRvbil0AVEVLwAQTLC/RttfOwxe7+WkvwffU0mkxqNRnV5ednvtWrmwFQd0IGhmisrKwMEPJM0yUPqV9Wzjghe6Si9Jb8+k9/98uVLnZ+ft92QGCZldHl5uQ4ODgagVCKzVbOeI+ejA4I8Op0ghOswTCQrGlnJyKQokyQILgeQVXi6jk2RFNfsH4Emu27HzfviaNOhsyc2+9VTxomxJyqmknjODzLt3NkD4tyuE11H31TS9F/Xb19kRwLw5cuX2t7eHrzfDHI895wqvrDwTOe3sTNwlb5WDcESx5tnAlXVoHqosu1agBASOdXD3d3dPlcGj+w8GecrsgfIsabTaW/V4/r5sfxe+qX05SmL/s8/rq2t1cbGRvcHZeWEn87739ra6sRS/JIAm3tSwZmn3mfw57j8XVW1HeTrsvqS8w3mk3I2TazhZxcXF4N+p+Xl5drf3/9VltXr+h9fDw8P3Qa1vLzc4IA4j4ym7/DeULMdZ39/v9lWmcSsrKy0Hi0sLPTnMrmrqsFUbO9XbJd0bEORsnpuSRrRXMWzqtXpX8k/ORIfz9t+MTBd952Uwfm4O6/Z75LGWzVLlLOYk0URxxVnpK3JVjdVw3zmbI9ikK2JxBSqfvMMqYwPptNp9+rPg0t85eHhYcfYWQV2b1/DetFVZjDHUcwLTApUfiYfatUsMMoJgvNIt3/PO4V0OlkaT3QxK5gpvFlpSxQlaQRJHXh6mm2EmshmVgjne6TS+SaamXSA5FInPY6zvbq6qsfHx3Zq6UATiZGg58AdxiRH/qdzk4BziNvb23V+ft7IMIPiGatiqEZQ4Ken2Vj++WfJMG5tbfWzkhBmNUa/2Tw69bp++8pqw2QyGaD9UC29S1DKbJon1wsLC005FNQmauhPVjk4q6RJQV4Za3ZCEzhkNBOY+So/ehiqeFbsyR6Zvbm5aeeY9M2qmf2QMFfNwAyyzDEIzvWHJaWbo8oqKnAkqT85YTJBM3qYjALBtncIDWUrOPG0oa5BIC/4dA7UGM/eO+JAOczU19FoVJPJpIcFqGLMO/7X9T++2GSBfyLiS0tLDTw8PDw0EEAO0A0z4KC/kpjLy8uW2zdv3tT+/v6gakBmBE2CJRUJgaOpnL5HJ6tmWzXpZ9V3xx8BUFTG2B4BGB1L0BF4InHOAVLZF42OnpS5qhmDRxBID/gVvliPGHDMcRO49MzpT4LY7jGBzcfHxx5+AoxJnU72FB0EVgnW6aTjsTMYH64t7d48sIst8rpetviBq6ur2tjYqLu7u9rb26uq6udMFk5PT2t3d7dub29ra2urATtxMRv/9PTc95ZbNKlYZ/FFRRCQy5eQ/eyb5zfZ8gR2+KqqWaxtz8719fXa3t7ubRwkkWyI4yUY4phZDeePFYyyhSu/z4dlS0bG5xnX6vVLOnsWniSHfBMdW1paqouLiwZoXWPSdl2ze5hP6gFJCwsLPSgowQC2jR5mcp2f9c6zPzUZAV/DelGiqEco0bYU1DTAHmTy7T1MToBga9JnLNOoJuLNmCd9NVEASsr4Z3Do31mmToTP/SSNwwtPrrR7EVipKKahz+pAosOeSVZ3stLACWXlVNk9UVZKubAwa/aljGnEvKsc5OM6s3TumqtmyBFjlwYtBT7pMklb9HvPiWGbfweqWVXDvrfX9bK1vb3dk9YEE4yrZE6C//j42JTQqtm+pFWz7VaAORsbGx2EGqjhOAykBI1BrZo5FDoDeJlPGqtmfQ6SK3qTmxNXPdshCW8mxZxanjvtj0TK/aIC5YQ7tiOR/6wIVNXAzqlcAtA8D9ReSWlWCKuG/U8Sbe9rNHqmfNqXzTUneJUAlETPO2XD2JF85/Ta9eQ2C6pUzuMdC1Dmh+a8rv/xxeeQK+/x/Py8Dg4O2vcJ+P3Nf8xPv80edrKUW99kdT+BTgmX/5Mp/kSiSDfoMLnJRIePYmMkOH6XNEn3n/pVNdMpQXBSxdBD6Sa9cdyFhYXBHAD2xTPLvsKqGcAsFpAwO0ZuXfLw8NDy71z82ePjY08wzQpOVoCqZlUN908Xvde0Pd65YBX7Q8Jye3vbw1PQI/W7vlb8f5+VtOinp6fe1sAUSzIA7GT/q6pOTk5qdXW1bm5uuo/RZ5OOOO9vqqp9RVX1EEWtTlWznlXJ5fn5ee3t7fVWSMlQsZJJwHfTk7W1tcGgF/GdeHy+zcS5xXiqZvwFVgOZTnvDNrEdbFYWGMTD/KGk17Pyh5yzeWnDVAwlv+6N/ZJk0hvfl6AmhTttiGMDj7LSKa6SXH777bcDOrJEX7L/NawX1z0lNPf39y2gVgoJJKxqRlHNZMHLIng58IbAUVSVp+zL4bwSreQQ55O7TMRc5zxFxu8TNcnl2jnCLM9Xzcbu5ndTEShcJk5Vs2CY04eICtBznZ+f13Q6HUxOExQL9DgSz4rSpUJR7OSaZ2N00ggywIfYCjpUPjl8z1OV0MTEjY2NdnrZjyhY8fPX9fJFj8jp+vr6wGgnR58eJ5rm9wKRpA4LiFZWVv6GKUBGPn78WO/evev9oPR7GOBRVX3srLgvLy+3E04whJwyxmQ+9yxDjxXM6Se5vr5uhyfI1v9xd3fXPZSS6uzxYkuSaYC24z5U/TlNK0EQNs09Xl5eti1MO+R8qWuQVe/EFiCeEefjOGyKY6gMsX/satq7HJbCjj48PPQkVufI7TZe129fmegvLy93QMEWpq1OalUCp/wlmwtdJyfkB3Cj4jifDKJkOtav9c1aZDarh5ubm10BJUdV1f45gZAELyW7qWuLi4vd9+95YLEIkCVYCUYCj/Xt0s/0cZk4Oxd9EBT6XbJkvCPBMMA0K33eWVYjE3SVROdQGsdQqfDs3BemxeLi85Ylpm3mnrg5uIZteU0Uf5/luSqMACsTqCMX9krkI+7v73uwVL6fBN7sefjw8DwYyv8TzNPCIInhf1P2E/itmgEgyRqw9zAbUlUN7KuwZxtSVrUdP4H/6fSZCu24/J7nYXsrRRvX5t4kqbnvY7Jvcg/JnInCnvCnGW96J567mNXnfdY5gUw5TMg1ZMHLccXjCbwDDvSS0u9MsJ2X7768vBwM9vsjrxd5+kQVEgG8ublpoYb+ZWLmxWRmLjBT8VpaWmoK3OPjY00mkw78qmaVLqVf1QKK42UKLueRPudNqiQhJCAQvQzUKF06GNQXx6Z0lCyRRPecjit7Nqqq+ypMIEzhJ9iZTPs5Y1M1o7tRxKS/zveRWqkUqYwcj+MmMiKwTxpEcsMFyO7ZzxcWFnqwTdXz5ukCbf10r+v3Wd4dI0gWoHaQc0ZtnsbCSGbwl9UHFUOOJNkAUEvHVYmG9DmGxO7Lly+9WXFWshKVlOww0pnQqWpkRYPM5h5lbJJjGj1eNbNP8xV6gSKUdXV1tTY3NwcbMaPVCvo4U+fOajzHJMhOpkEmflWz7W2qqh0RXa6qDjZz8ExSgC8vLxsJ97uk0/hcDjkx5ABiDS333df1+ywBaMpJItUSRO+CXZVMZSKSMkNv+MiqGtChVfk2Nze7X50eCaTSlwlY+c0vX770AAj6IYECBllZtaZ3GeS57vx9tngIulHJ+GnXUjUDRPik1dXVBkklpILg0Wg0oK1KYj07ftdnk40EHKUL7I9ELvsSq2pgNxyjqv7GTmXPFYBAvHF2dtb7SnvnS0tLg43YUR4Fuq/MnN9nkWt7yQIptra2GsAB2gEJyOvu7m49Pj4P/APi/BqduWqWXNE1cpdxFRCPf80CyM7OTm8XpXIvpuPD5mOAqhnLh85h9gGKVOVTZ6tmg1kAza4/ffXT01MDG/f3960f4gv9++SdP7q8vGwd01/vubt3OvVrcwEsz7xqxuarms0W8L7kMd6bZ5/sHe/fdQL2gGpiH/FAbuMF2Eug7GtaL7ra3Ky3qhpJn/95JhSJSvsZA8t5KWEnFSoToawiuo55xJMgZMDpWm5ubrr/AYqR1UTGm8D73nwy57NJ7Uq6aNUsIU1nk0qUwwwgM2gAJjwlQilg4zAIo4CWoKsKauBPJ0Xw03Hn9eW/OUAJddVM4Xw3q642fxVsC34TCc8etaxooBDiur+uly9GykRFyb8BCORJ8E+3kmrqPQu8stKQyaH3yogvLi7WN99808BEIrOMPr2h/77HcfmOwNRxsiqRia+kC1WOnWBvBK+qgRsbGx08QhWThkdf3K9tdLa3twdDagS52btE5qtmw0GSauq+3HMmxvke0mbm9+ldVt+dO5ONtbW1PpaqP+TWd+aBrc3NzbYn6Ots+2j0POr7VUdfvgAd2XdDfj1/clhVnfxfX193QCmBvLi4aLlI32uRi729vWbaQNlV4HLM/Xwyl3Iyr1vknK9g8117JpGpj6mD/rj2tD35uwRtAFxoZXSUnUgdoSdZQRRveD78VYJEbFTStquqr5POVc22F8oqRPrYpMx5Hhk08reqnqPRqBkb9msmK2xZ+n7vLQeCvK6XLc8USA9czfkM4h3JmSqady4eJROp21XDff4y+UrwUBLFjrPvZDSZQ2Qu/S79Jos5wK1quI8o/aajmfxZ/LVESWwu/s/qXQ7K4Yclswn+uA825erqqpMxegk0Zefy2bmWLE65F8/G/Ru+JbZI5hzblvEGH+pnfKjq7u7ubtsHABKG3XwC/eXLlwZuv4b14ooixSGso9Go0TiIdaIu6TBy6pJgMlEwVMXM3hnHyWTS1DQBbW7Ym9XLquG2DSpWBNXLSnRmHhXM6mcqn89k8pXHye+m03TO+cpZNt4SaNftu46ZQWUG7q5xvjo4X3HISpHENgd15PfcWzpvx+FoKS1DmsGqd5jJ+87OzoAmSAYSlX1dL1sSQ3pgyiU9MjyGriaAkI3aVbNkDeUlq8uSGQ7A5xhff5NHyaIBHRJVo9+TNqcCN199qJpNUtaL7Ho4VCsTt/xuAkKJuGaVP/WBvUk7k4Ehnc2+MvYhA2TOaJ6SKhl0TZeXl30f0Myk23HiqvrO61o8b5Qo15sAD53NJPPy8rKfLf1HS0Rheq0qvnyRo+z3F8SpHALYMqjkHzI54I+zD0nVUSKVx0hKmMArZVRAM68nguHst0o6F9+aVdHsDRL0pa0BOmTFIAHoqpnPSt3ELmBf6Pn8HpDpE8Uknsv8u0iWT15rgrrJclCx8d2sGswH7XQOxTDBmkzsAEICcSyLBK9VgAXedFh7wPn5+e8jpP+TL0COdpyMwaqq9bVq1o+aw9AkfEkXl4j4ruIFHSLrb968qfF43BTvjPEyNvOH3LEB9Clj5ByCpXjDfwBtXGMy/xYXFzvWz8QtwSk2ybEdJ9tGJFvJ+jNlnR7Nn9czv7i46C2aMjbJqn0WohYXn2nC2EvT6TNFlD9LwIoOpp4B0XO4nvPyv87HHtK/3F+RHKSdwtT5GtaLICcvMbnCVTNlSR7+ly9f6tOnT42q39/fNzqWiQclETgygllqvr6+buOdSgCB4GQoQGbx2RxraQ5ODnGWqLOqOJ/YpbL6fAayiRhmVZNC+b9qjGskpChBkkX3nGik43uWOVAoDUkmlOnEEiXxHHwGFSGDZQKfyXNWTyjP4+NjO8Qs2TunhmEG1GeyL/N1vWxxSPqPyEf2gW5tbXWwKKGvmvUl6GUzoa1qFvDR/XSUOS1xOp32thZVs0p89jOSUwkmOaKDCwvPPcmCOtdwfX1dp6en7dgg/lVD2td8NcPnVD/Tsai+ZfCdlRRyLWHStE6vskk+70+QntU+VRKMiExm6aNkoaq6ssdJe94ZRF5fX7cueVa2HbI2Nze7b5NdyQTBNSdDwrP0XNmm1/WyJahSQaI/3s08Ek6WTELMgUKAkkw46Lwe4arqhAIinoEvWy1wpPP0KpkA+b2U16xwWFkRzFH1VbMgTzKclTf6lG0g9/f3A7+YyVxVtW7f3t7W9fV1XVxc9GfH43E/X3YwdTvH8ycbhs2i2zlVPBPj3Icx/XMmi1mJcSy2Nqu3zivwrKqmuifgYzuPTI7zebyul60ESqpmlXnPX5xK7shkJkaXl5c1mUwGTLMsjNB/dOK076n7ZCmp3KjVZCjjxfm5IORPgmKrD/8n02QzgYwER/K6EgRN0AcbMOcgVFXrmS3Wnp6eOklcWFgYPEODaLIwMT+0TpKWialrzev1eb/j9w0JEptILk1wlbMkACtutVDb6V8yNRYWFjrHyGop/f4a1osSRQ9fv87FxUWdnZ31y8k9umTlHI+qBvQ+g6pMZBjw8XjcFLpEI7Ivb319ffByM7FbWFjoSWBpvBNh/LWXli+VkFUN6Zn+Px/49kMOJ5oJlePntd7c3NRkMmlHeHV11b0VPu9PGp2sIBB430mkKekQrk1iScjdR95T/tt1VA03DE5ElsOEwFDwqhoYDGV7CT4DNf8MX9dvW4Id1E5BzXQ6rdPT09YVdGdUbIEhGiZGQO65mf3BSWP1b++UXko6E7zwGX8ATOTY0A0VyqrZNhACWEFf1czhcZhkbx7pzIpHbtXhWQBFBLD+7xzj8bjOzs6axpmBa9VzUpe6ke+DE1eJN6XQIBPgm0ogW+i5+Yzn7dwcl/6OpC2xXQLQeaZHgnwLC8/7eN3d3dXOzk6DdZPJpEEDif3retkSzEiKPNcEVtJPaZOQbJBrvYJV1YkU/8TuV1Xvtcinkm+DX+jR1dVVB7aZKAqMc5iHpTLNn6dMZUI071NT39kj33H9WUnkJ7JSmYOxBJrT6bQmk0mdnZ3V5eVlB+P8MT9JDwGcmXxmq4egn/yLbVSBsoqTfj4rEI6PDZVVD899PpHMdyk+SjvhZ+6PzHwto/f/6GthYaH9Z8ZQKmzeYSaSmTjxj1lQYJ+9b2ArNl7VLPlw7iyI5IT43CM7E89kEFg5NT/9sPic7+YvkmFG/1JuLfebSVUmawluVs1sFN+ZWzKlvcpnxkaormZSi8GWcWMC4e5pOp3W5ubm30zsFq/Sa/RZzzzjCudNluLR0VEDfklX90zZFO+16tdb5v6o60XU06xWfPnypZs9lWmrngVZI/bu7m47DAkdlNwLmkfT/C572pI7rSr18PDQ/XyOQzkZVC8fUltVA0WS+FbNkh7XkEhEfmcezUwFzEV404C4lkRN3WsmcjmAQDDt+gR6VdU0NOdP7jfHkgqWn82KcCIwnot7yzVPc82BQXok9SRSHN/JHrOkOEoe5g3c6/ptSzBk4AxZ02M3T3+QlNFP+kO/cqCE94YOWVU9OIl80TNTAclBNszf3t729K90EOl09NvOswNQZzk8iWvVcPASJzLf95FUTo7WNeQx6YBnJHiGJtMrcitRZYuyrwSKWVWD53xzc9Pbk+S5PVv6nwljJsSJZtoHa21trfskkh7vPbKpnmUem/1k57PaLNn+WhDRP+piH9fX1ztozPdTVQO5Zl8TBPVvFcWLi4umgGbv49PT0wB5BwKQKTpuGaaW7BHXRi+dl37Qs5x06vsqeSivmYCmXwPaCDITrGSPcpJpJpRVsz1K009mlWc+8OOPV1ZWmgovSKSrbKgtBOhMgr6uC+Uzt+XyztKvA2m9J/fGJ/KVWSH0nhJIqHqm5GUlimy9rpcvQB7/k74t41TvdmlpqcbjcQ8D4/P0zfu+5D5bqFTd+S7sud3d3bq8vGzflcmnKpxjZ9tPFjj4Et+7u7trxkHacXqZjDxJcdWwpz0LOqmnSS9Nv+Q87k/hiGxnsSL7ioFikkI6k0WQpaWl1oP050tLS81iyK24PG+67PfAOvGw4VXsSYJGVdU+3nBI8S17O88WwtDL7Tr+6OtF0fjS0lKdnJwMBliYHAj1kDgyqJK5LImn4GkwheRRzK2traqq7mHyvRzRn30BGTAK2FTOMtt3rRQvVyZMBFQgnChvGnEraTgEhODPl/RdawojI0LACWgqVU43E3A6j/dDYbKSWlXtcDIY9xk0gHmuuWdJ8CmL4/t7NJpNgRXwqIhYlPzm5qbHIDM2KEGv6+XLe764uBgES1lFY7zQSQ1RUu3NvoHUsaQPkwcUDnpaNduzKJ1mOlggEzROcuI7SYF1P5n4ZVBVVU1pz/0Q6Tg9Ju/6TmzPIwHOATGZPPm3JIzcVw17nOka1gMbIgDN0ebT6bSDct/L5BMSySb6f26C7voEtKr6nGW+76QQpz1Mu1v1XH3ybNP+SXrZn9f121cmVWx9AhgCUoBggi16c9fW1gbbMywvL/dWFRnc0Ed6vLu7+zcVOGyB5eXl2tnZ6c8/PT3V2dlZgxLn5+ddVayq3qy7alYBJBuTyaTBCjpBt6tqMJhinsrpfhLcSbDTdUtEEwzOwR2etWfGJrFVfFROixQjJO3VvWVF1zOrmg0bmk/kxQzeCRqge3Is9ohdyB5PATsGAcZCPif+3Z6Kr0DOy5cKuupXAoIJaLOb5F5cmJUk8VRWib3H1Pvl5eWexK3o4T2/efNmAJrOs11yaFMCNWkL5uO73M5JfKn6xre4N7JPBumOhEmcPf98fD+BHT7HM83v+eNaPBf3lp+pms0S4PuAram3WTxyz/7vPWV+AmDLYlACtuyLvCfB0/TfGW+Lf/I8f/T1okTxy5cvjYyPRrMGfE7JS9rf3++MG52sakZzypI242ngjNHPgqJ8QahwlheRe6pVVSu3IJhjSPqq683Sdwpi0kGyXJ8Um6oZNSQVMcv56eiy8pcIbCI1kjL3xBBkL8S8IGbpnnN07nSernG+36JqFhxL3AWZPiMR9PfT01PzyRcWFnoD9Ew0PYNEXhLx9h4Yntf18pVO4uHhoYEbCbppqFmxVzkUfKFecVgqk4JYOp0OhPOhAwAiMprDmra2tgbOqGrm5Kqqr4NOk016DPDhuNDoqmYVDg42HZMgLYEVn006uvMlZTrl0+/TQQiCVWwlZ/TMlMnsD8yqY1XV6enpQIcStXY9mUSbDlc12yQ6e0/93zEfHx8bsc4p0mRBRQLDwTsVaLP9r+u3r4uLi7aHWe1TFaCbWi/u7+97GMPGxka/v6pZT7GtVjKJ4COqZtRtIE/VLDCbnxTO5gto0sdWzWiV/KgEhQxfX1/X+fl5V+swCVIHExROhopkLn0V/c3+2EwGk9Kdvhv4lRMc8zNZ0Uiq3OLiYlPVsuKZFZWqGoAv29vbbevmq6aed1ZpxUb5O/rLJs1XEdkbyTEAm21i11796MtXgjEJuH/58qV1UbIBPMw+vZOTk3p6euoWInJDL8g1kIEs5NRrcRM/npVFYN7FxcWAQrm4uNitHRK5jEkVeNK2A1ldY/q8eYqtWG00mrUVJYMt17yPtNJn0sNMLulaFjTYDjExG5GsQs8n2QHAuPPz846pE0xhIwGrbB09YhfodE4Wli/Qa/3jgGz36nrdV4LLf+T1Yn7fu3fv2uFsbGx0EshwJQqeApAJV9Vs7zIN98rzVbOGfN9P40kJ9B96CQxkVh4SncwG2aRFQi3y35k4Vg2FO1FNSjgfGM5vF5GJnO+5L0Y+kztBZ1YSM8l7eHioyWTSWx5w3mmUONfsNclnn0kiJ+XZ+ZPUPAHu4uJiU4mSKsVRmW6Zzh+Vp6q6kphUQO/xdf0+C2L5+Pi8H+ni4mLThJOeJKBkBAUeZEj1QxIhAXQMFUbT2zjNqpkjQA0nm4zpvP5lgAiplBgJVDlU16zSZ2Nu95GMB72DjgUIqZohwoLKeSPOXvme4LZqxj5Ix+T7aHL0SFC9uLjYbAs2CrCjQuPZs6WPj4/d25HVQfqicpPJARoRepJgXT+55N5nPTfX6PjuAXggiHhdv30J9NC9FxcXu0fYoAkys7y83JVDzAvBlf7iqhkd0ebZVbNN2LP6nxUM8pNBLp29vLzsxJTe7e/vD+hZjlc160FX0d7d3R1MHcygEVhIT+liJoU5ej8rd+Sc7qGGph/JxM49ZfWUPama+fUEitwbMNd78KxUh9gVz8w7YiuS/ZMgugRQIFpVnZCwI96fcwLN2Sj6iw3hnXpWr+tlK5P4eVDCe1Rd9+f+/r7Oz88HCR65SLaAbXC8a8AiXSLLfFyCffl+yVb2qjtuVQ1iN7LElyejjlyR9bRPrvHu7m7QW6lVyLWJE1KHEqTKIogYM31YghsZe9DVfD6p25lU0j3PbD5+4BOXlpa6pz+BlbRDj4/Pe3un3ZpOp63znpnKc9oY+q/o4p6tr0VHX5QoLi8v1+7u7qCRHL0sy9pV1VUMn/Py0ignLW17e7uDmrW1tZpMJv3wOUhIYdXMuEsACWw6vqenp4Gy+COpJfj5u0wS0xAkPYQQp/BLlAhqJrnz/O6k2FXNJkfmeF0VIPeZFDA/ozCcRVUNqrWOhcaUSC5j5/8C/gyMGSP3meiOe2NYJBpk4+rqalDBoqS5ncLi4mKdnZ0N5OZ1vWzNBzxVs+FK+oUSHHl4eB7UYqVucCRQzjymZJSO0c/U86xkVVUbT8FvVuiTLqISmkEvtJDj9hlJEDkXfLIJjL0ALcEqOunniYqismUFMJkCSSvPZ+x5CBBS5/P9CII9r8fHxwEd2/ty7VXVaHbSlZJm67odO+kuqqso/XRdn2RWJhN19d4NJntdL1tbW1sNUAgok0IlgdjY2OjnLRmaTCbdU3dxcdF0Ku/WO6RLGWwlSCHw9Nn5z1fNBi9ku4HPV80qDuSDf1hbW6u9vb3WV4EsuZynnAnAEri0JJnuTZIpoUxmUFbBq4b99e4t/favBZ7+z2ZIGNiqpBSqxmbcwZYkawe4mhWn+R5QPjN1O6m5GU945vPvJKuXr+tlizyiWZIbSaBqXoImfNvGxkbHsltbWy0Pi4uLtb293eCdnrWsAlYNCyoZa1U964Opmo+Pz+1ZfCCZycEvuYWKCr+iBllR9deqAjismm0TkhVH9+saslKWIIdryAKIhM2zTEp4VQ18X9JOs2qZwE32O9IdSxyjxcP52LTcixUQ7Tx+R1cTHHd+QKpp4oB0lUXX5t2wY19Lxf9F0XgizPOByXg8HgRjWdKdTqeDZEYFYjKZtBBllerX0EcKJKERXJ2envb1OacXB4EjzF7aPE0nv+9FMvL+Pe/IEqHMZJLSZJXNPeTUtKoaIBrpgO/unjcGzmmUlMrnk9Kbx82piO7t8vKyjzWPkkgWE931s0Rvod/eXRqGqlmyyikKINLx5fPNvrA0uq/rZevq6mrQ47Oy8rxfkWmaqmschSlnqhfp9Oj2dDodbNjuZxxnAgyoZRcXF4NjAA2qZlQqSRc9ZmSz4iUgcyyV8uyTqJrpKIPNqSQo5TPplOerZPNI5zytnqPNbW08H9eYtkBSmcBSfiaTwgxEBRTZ7+16BYgcPqfN/jkOh+q9mJS5sLDQAcX6+nptbm4OWBXZZ5zOOpHd1/XbFoS+qgaBw2g0o5+q3APWyBjEX2KUDBG2my4nHZRs3N7e1sXFRYOQquvJQBiNRt1zxEcI+Ob1Kaspkh7AL7CR7NIDgaFz87PpG6pm1LVMvjLZ87OkqNMzgahn7D4cN59nJqzew3xQzF5KyDc3NwdgdLZVALOBsxkMJ3uDLUj9z213vBNge8YaEhgAbFadXtfLF3lRgZMsPj091dHRUVXNmFF+JxnjJ/hL7z1BnGTPOU8CKVXVMZz3zz5nBatqOGCQvc64MHUYA408i6lTF+ks/wYQ4gfcJ11Pf+P+khFXNRsaCcCcZxWkbrsvMaNziP2vrq7q5OSkLi8v+3rcDz1Mv8yv6jcFyGYO8PT01FOSVXlzlkHVbHiUc3oOrlF8vLy8XFtbW+2781xfE9D6okSRImT/Qx84kLzcSFRgcnV11cbPy9TgS6AySdSc7/MSjZOTk37oSXFUicwAWWNuCtw8JXbeUWTFkDBDGAg2gZsvr2cAns8nHWcme+kkBY+cnGvM6gVH4PlJ/iAcOSI5K49Jo6GwnIz3lc9FcHx/f9/GJn/PGLjuVGZB9tbWVlOT/dzxEpkTpM6jya/rty1URaiWHgaBBZ3LhD9RRfTDrAjSt2zuzySPzD88PNT5+Xn/jOOR7CWFFEhE1tGrlpaWBsMmrq+v/6baPw92pDxmZTAr71nR1/ORCWFW5DkutoFjyymy6ZD8zr0IVv2OPaKb7GfanqT/OL59p9yz4D/1TmCe15pO3XsWuCQ1fzp9Hl7gfaHvAoTS0UsqE1h7Xf/ja2FhoQeRkRfAIJtbNevZTRCgajipOxOpTBSMbnecBDVdw/39fX38+HFg8/0u9++EklfNgreqYQ8SeafTerPoPFZJDttxHPopURRIpm5nAOl89DwD0/m4JBNL8cL8MV37fAVyvuqZYLMqDqZTgrQmvXqX7ondBABk32YCRvQ5E9UEqgW2yWTyrF5ZOb/PUmDY3Nysy8vLTkLorWox+0oPbGxfNdt6RWJ3dXXVcgYw+fLlS43H45YHMnl+fl6Hh4edpJ6cnLScZuIm8chiRfbySgSzQDBfPReXJQulakazdQ3kMJPATBAzeU2dSn+YTLr5f+efeQDHz9xPJsWpH2LKZNDJHZKeK/6cjyc8/6WlpfZ1ily+4/PzIHgWidjtbOv42vzmiywJQ7u1tdVNm16ckb+3t7d/81JQJwmCipOeB0Jq+b/AMfs3/J6Qok1l4FU1nKKUVb4UwHSg6TgY/EwSGXvKlGVuax4ZzcpBVf1NCZrip8ImykExc6pVJmMMBiX1vUQ70BEgG67Rcv9JQ/Vsk9InCZWAeNdZvchr8A6SapcJbCaxqKiv6+VLzy85nNdHhk2gYkGpHx8fu+lehePu7q4rG3RYBVKVEJq+uPg8IVEwldQpVByOc54C4/o45EyyrKenp64aplymHSFz9M6/syqQgZXv0qNsbM+kke75v/OpygJefg0ZTTukHzHZEhkIJvUMdS+TNgmje2I7cxJlUhkFC0lxrKoO6NlXe0RWDbdoUD15DURfvsjQwsJCnZ2dVdWzzgJUHh8fazwetw7ybUm9enh46Kl7ghGBJ994dXXV1UOASwJI9PP6+roODw8HFQ+y51zpb9kDey5m1dxnsufZ/8lhVjOS3kr2E+ihg8AZv0/aW1Zl+ZuqIfCbepqgSuppLnYubYLjJx3VXopJuU8q29ra2gDc0duV/YxsR04tlZQ4Tm7wLonOe1SNZvtf18sWO6kyn4DMwcFB21hyhcFmb9ysdtEPdNXr6+sugswDBGLCrArOAyn0g+xgEIkJyUbSITOR8hn67W998hIoRZd51s18kWU+ySLrmfimflYNeyndR7IS5tkFGS9Op9Punc5jyTH4rUzUHh8fe+u2zBG8PzrJNrLF/KScxXe8l7Rdl5eX/Q5OTk46dgHUPz3N9pn/GtaLp54+PDwPUlHChqoxgqoXWWLPfiKG2kQyLzZL6R6yci5lWVxcrP39/eZbZ3UyVwqD41LCeQeQ1QGJofvMJLJq1uQs2JS4ZVXQd7L6kAlh1Uw5HDdpO5nUOl7SGgR3VTNDxEHlfft3Cnuij5TE/wUHAgUJZjpezjn53NnDmA7VYuTIT1I5XP+vJd2v67etDM6goozw0dFRP3tGNynK9jasqq4CnJyc9M8kcGQ6+xmqZnKclTvHnO8HTnpybutAZ3LyGD0wREswJaHLJJWuzDsFdiedf/5uPgH0J3U3E9D5akoGzekcE2XM58a5eU6OSxfQDjMhrJrpVuqYoNvnMC3Oz8+bRfH4+NjJgveT1aakwU+n0x6WQo5eKxa/z0qwjixiw3iHdDQDJn5KpSKpwSjjEpP9/f2BnBlOIxBC797f36+qWQUlq9hk0TZT5Orq6mpANXdtVbPWFADT4+PjYE9PAXjqe8qi51NVg2Dc31kRteb7BxMgog9Z/QCI0ut5Vs98jODa2AXnYGsEqOKerO75nSFfOTOATuXUSe+MfcvKZrIN+N95H/61DMr4o6+s7FdV04npCBCVbigi0GdFBgNg7u7uuk9OzGrrm3x/2g02NjZ6qxptI2dnZwPWCL+ncgbsw1Sgf9hA6a8lkORxNBq1PxZPAqnoBp2bBz6rZtVEvi9ZL35fNett9IzJNh/k5/MVyUzU5xNnjAz+1PNNYLVqSFGtGoJESfFPPcw2Nnbj+vq6ixqAPnkBOn8WU+YHdmH7/NHXizy9l6FJ1mQz0y5l3vnCOC/BXKJml5eXgxKu5CrRAeiGpGVra6vW19c7aM1JcK4xk6Os3iU9JCsG0NjkSwtCEwWdp90k8gh5TaVxDBXBTIgcAxWBA+DQsscyAzkKlMFn1XAPnwx2M4GcR1MZjeXl5cGQk0z4MxGcTqc9kp8sOL/hNbY5SaqQP47lulGRyNLrevlSLfCsTVm7vr5uXXx6emo9Jcd+zrBJUlTKvHMG1YbEVbNhEXRpe3u7N4BneAVTmfBBTdMgVz3vw5b9fhB1diMpbFU16L+FJNL7nBKaMpkVlGQTJEqfFckMDJOdkIARPZ1PltPRsomeu+cCkZyvXAqCJcwJrCTDwXvitDiz/B776nlJ1C8uLjrgzvaA7FVM2/G6fvtKaqnWCG0AbLx907JqJrFKICir50BZy2ANSUX6TsejgzYK11bhnP7tfHk9AuKcDuiaMgDkIySXSVfPwJFMZ5KVFYtkqnh+GZCKLapmupuJ9nzLiXPNrwRv089bGXgmKyZ7fJNF43pUNKqGW2vYt9bx6Bzg/f7+vn0zWw7MXlpaqs+fP9ft7W23w7yu33fRrflYjwwrbIxGz+1Sa2trg5YnOvH09FQbGxvdG25AlR70yWRSl5eX7Q8SUM1EK+PGy8vL2t7ebtvsu8fHxwNghW9nH8TAwKc8fs4wSKAi2Te/prupa5lEWskoSAp7+tJ8tuxAxvViSGCwpbo3nU4H7Ck5BJ88z1pzDap8vi8u8T1FK36WPZNA0nfXhnEpAa+ageHzz+WPul4035yQm5yXgrS1tdVGLl8kQ06wGMMMUj30NKzQkp2dnb9BCxN1z0TQdzMgnC+Jz1cKqqrLwylkVbMga74cns7eedwrZSa8rtvye0GaYwnu5ysViXZm0DCvqBJKzygDOxUPjjoNi0Q86WxJ/VFBRTOumiWcid6sr68P6AzLy8s9PnhnZ2dAkxX4Szpzb7DX9fLlXS8vL9f5+Xk7JZWG1dXVur6+HiT6nMTt7W1tbW11wPP27dtGyHJfKO8ZWp4OII00WZdo+mwmf+zD9fV1D7Txs8vLyw6WUn7nHQV9pl+Cr6pZYphov58nkAJBrRo6tkyQfi0JnHc4SY3FAlhZWRmwAwS+SX3xN2T5zZs33dvEWWWlIWnfWc1RTUQlenx8npB3cXHRekqHJ5NJrays1OXlZQc6nh/Q4c2bN30Pr+tlS8CZ7/r8/Lyf/c3NTSd3iVILKrO6fH193YmfQGQ0mvUpSTQErEBe/ar0r6oGwW6CHwsLC3+z9yY9SRDEH7KVMYAA7enpqa+ZvAqiv3z50glrBo5Vs2Ay9Tv1J2MMz5Z/zH6yDEqrZrTUPF/+7TNsAluRAbFrWV9fb1/uZ7bwUiV6eHjowW0JnKo6ScwFpNljJajHHvC+krHzWvH/fRbb+/j42Bvd05/JZNIxU/bhZgFEAknXt7e3azQaNciq5eLm5qZ2dnZqOn2mU2YVM2O5+/v73jIu25c2NzcHcofCXFUDX+c4o9FsSFLqt3t2nJRxMri9vf03v5tP4pzLSt3y+UxAM6bO2DL/Tx/4q6wy+ny2fABlTAhfX19vYMXn6HTua1xVXSX2c9tobG1t9ftms73Xqurk2nsCBqs4J5vvawFbf5epp6pfVbPhCYTSi2cgk56WqEFOW8zpYL5bVe0Y50vUuPoqJV5oVbWjyiqB/oQcTX1zc1MXFxeDRtSq2eTUFCo/n0cTCbEAUc+Uc+X1ZCk6x/pWzaqHqRBZDclEzudUVeadUyp4TkN9eJgNv5EAM2bJw2ckjcMX0KvcZsWRUUTHyPK/58y55nYbjKZjvg7J+P0WI6ZnCMWF3qT+pfxCxrLSSJ7oWyZYdGBtba3fYyYvOTkxqxCcKPtR9ZxgafbPJDATzevr6zo7OxtUBsgQ/XDc7CPiYOd1fL6a4FpV8PLaE03On1uO7xqy+uLYVTVIpOlgnlegnbRBtL+k7tmrFFJcVZ2Iog56BwsLC617noVnlEG5aiI0Gdsg7/u1j/jli41VWZf8oLexy5kcZYVtfhgYXa8aVuU2NzcbZGCXBbwq2vy5hA+zhP6x6fRynu6WCSf9VvUEIgrIXCs5J2uOD6CgM0nZon8ZAyS9jF772TwwmwmsxQbQ1V8L5OaZTvOfTxtC/6tmSTfdzaomm5XvynXm/mvJELq9ve12HzZBIo4+vLW19epDf8flPWDPTKfTtrnYL8n+qpoBImQb4CPxEBeRUQDB4+NjbWxstO/lazF8gDRVs60iFhcXe7rm8vJy76tdVd2/LJ5MuqnKM3+YjJME8yWzmdglqy6f07zcZfXRZ/L/85/z74yrM96mh1XD7UOSQeBdsGniTMwZIJUCSVYBgaJ0VWVXu5y4Pf0wPXYN3hk58a753pyN8DWsFyWKHI+kLhMpwsaoS9wyYfPw8iVZd3d37cAYw42NjYEzE+BC6gRVNsKUEFZVOylBV05BUkFMrvWvIZWSsOyXgEZkcsUJpqAIAnw+z1f1LNCMRJ4z93cRBKdzrJrtbZbOLys66bg54wyU5/s/sjLiHtIg5PRMx/LscbY5uQz037x5Uzs7O12NzB5Fx1GxyIbp1/XbF9Sfsdrb22tkTTDIkSU9AmAiUFRJSBBnc3OzhzSYlJoVOXIs4CVHOSVYAGlCnODO7yC59CYpqcCbeaMraEoH7g8b82tIp5XOyWdTb38tsXRvHHfVkN4GXZawZxKdDjEdf9VwOxnDESQSgKOkPLG5+te8q52dndrf3+/gv2q2P5gKi0Q/aXFZ7fJuUl5e18tWDnkhX1tbW/0OLy8vW0ckDFos5qtf29vbrUOqjwZP0TG0Vu0fVdWBTjJD8nqq/jaom06nA+aH+8hKSgItdFeQKxAG6kiiBFyoXUDXrD4kiJnXl4mfyqvvZsUiKxpVM9ou+l3GEqnvfLLvpo9PX5hVSM+kqnpgX7IcMkh1f86xtbXVIE4m/7mntO9JRPjU1/7E328B+6+vr+tf/uVfWleqZnIPNMiqMh3gM8lUbv8FkOV32VfgIfo5ENDnqmog/wnSGwxXNWTJuMaqWavR5ubmAITIRFDSJEkCShpCNw+gVtXAZ2ZVb97fJjMu/50gqeu3svqXVUu/oweZyLoOMUzV0KeqyrPBbJBnurCw0LH7+vp65yDZ30j3shA2Go06L2GTk4GhsPW1bGHzokTRqPpE1jKRgwComhGqHBd9fn7eBjQnK5l6CHVMGhgHyOnk0IvRaNQv0zXMCyVEIAUPIpGJbVYiCWLSa7KSAmHMCs1kMqmq6oDbvwWyehY5/TQwmQhLFBNVpuh+rgrpHuy76H6qZgGvYINTT4ooxfSsEzGFXKIzJLrqM2gwjGZVdYAjOc5qVMpJPstXNPT3WYuLi3VyclIrKys1Ho8bZeQArGzQfnx87D4YBlfPKbkUhHmviZ57j3Q39U8Qg0ZaNZOjqtlWK5m8ZMN8ynDKSOq542fCWlWDJDSZAVUzR+NP6tm8XqSjynO7vvxcOj4OKamhkmm67/+eLWcCheSMHM9zTNbD+fl52yF9TFmV9QxVFNmZTHRzb7Y8N0rwryXLr+u3LTqUCSP9kdxl9X86ndbZ2VkDtZkYJFAJZBEkXVxc9O/QxpPa7L3n1iz63apmYCvfCXikK4mm5z6nwJEEX4EMCWzQQT4sddO90EfgKyo6f+f5zYM2Wa3PYDN96a8lkb6bSZmKZNXQ7uQfn3HtGVBnEJ32BUDq58nWMRE+7RRgbt6/pm1+XS9figvLy8vdb7a0tNR76dkj2DtOmzkajbq/OCtp3tN0+rzn+DyoX1WDCmLVkOaZgAx/pwjw8PDQ24zRYbpKH9NeVA17A8mauBxgmwll2v5MDjNxy8SMTqSOZFwgDsyYOu83kz/gynxhKiv7WehwPTl59cuXLw1Szfs/4LljrKysdFxbNRuwlfGsAoke7WQ1JXiQ7KYECf/o60WJIr425yLoqKoOApM7r4qnuqFKlskZ2snCwnBrDcGRoIljyWQVJUADKuqc4JFAVs2mqTpnVuhcm/8nMpoUg0QvqmaCk+iG789XKObpfllxgSQlepnKlFVP53MdgoJMrrPK6FiZOFdVBwTZ7zifYHJUb9686UAl73txcbENFBTbeXMohsrOxsZGn9/zNukvEajX9dvXzc1Nra6u9rRBaKSRz5eXl+1ALi8vu//M+zw/Px8kNPSN3tqmRr8NOwCFI4Nk6urqqitdKo0p30lHZVSrZjogIEon6fzuKXt7M+GkP6r8VlYAfLZqtv9TBqy5sBEEmVbS3S2BdG6inlXDTH4BQ4KI+/v7mkwmNR6P+3hJZ2XLqp51bXNzs8E5upfAjyQgbZrn9OXLl+4VpqfsKjugyvxatXj58u6yQqUvUYJAljOQkUR4L1WzPVOrZtRHOpRgot/llOG0w5JJbRxs/+npaS0sLDQQS49QuqtmyYlr41NGo9HA1rhnflkiiPbFN+asgmS++C6/l/17rmO+XcR1eZ7pNzMZzCA2g0/viK6IQ9yPvlBxRR6Hz3ZMNsw1pT8HJiWzgN0GtGdin8879+v7Wkbv/9EXnyL2S50jY+K1+/v7wTRw7zP9lncrAWODxUZYY+PxuH2hXjlAXdXMlpObm5ubOjs76+/7LrAwCxNsO3lM8Dd9V+rKPMAiPqyasesSCMmkzZo/3ry+/Jru5UrgOAs2rtv55xNz7zHPzW+nLmccIAHM5+GasgKZTKW1tbWOCfL5T6fTwYTU6fR5D/ivBWx9UTQOeUAXhJBUzUbrZ69Q1Swb97I131te7pcvX5pbXVVNQ/FyKSaByMCS0V5bW+trlGRmj57vKwdnJSQHblByQjRfGvd/yKSglCEn1Il+CGLRY+f30UnaZyZ289ebQaZr4mA0MwuK00lBIimX7y0tLXX1qKo6WEjD4Rohmhbn7hieQaK7OQUSUsdQUKJEsV/Xy5ZkrWq2z6Z/k10BFnBnfg9DDowcSgarZnuiPT09dbCYSRB9y0pYbqeicgXY4Vg5zqqZvpBJjhhFUlA4T/mmy8AlCK2VlXDXPe/YUufmE8YEeHw/HWm+A5/PlcBX0uaz4sIGut+0AypJbB89YvfyfbCdo9GoTk9Pq2rWwyYIYreqqt+Hd5/BiAQgWwVe129bZBeifXR01EBKMmAAi9BuUzC9M3qg2pvggQm6KSMmhfMB5+fnrTe2TVldXa29vb323eiitqKS0KByjUazwTlJN/d998IWkCHnpJ/kVYI57+OyspeJpOv/tVaXXILFZL64jowP0uenTs/3QmXQLNhMVpLr8P9s/wDsYg+gq3mfycoSdNLDZFtlNWt7e3vwXF7XyxZbjGlTVQOgc2FhoeNYfzBzvnz50oPi+DBFE5Xgzc3Nurq6qsvLy04y7u/vG5DL/sCq2UR59hkIT57E43zs2traYPZGFjv4418DVNL2kGv3Zc2za9IXzrNy5v2lZ5q6lsBMVf3NdTn2r4G3qQPz70+FXiwOHM14+vHxsc7OztqH0ktMOvekOObe5DeAbr2mmD0Se5R67zfjqD/6elGTCXTZQ7y8vKy9vb2e7iPIIKAUC7VEojGZTLovgxAuLi7WZDKp/f39fjEQGgikFwCtr6o+Z1bPLE5HpdJLEghJHqF3fud65qkckp2svhHsTL4khJwqYfG9eeonxUx6D2F3nnnnR8lyilXupcb5usakGDAwjIFzccRZvWUo19fX+5lVVT/X9fX1Go/H3V8KDUsefqKsSvCeNYP3mij+Puv09LT+9Kc/1dLSUh0fH9fu7m4/9+vr6w7wqqr29vbamKJzc2Jra2sDHc3hNFUzCktWhCWBSdMS6FQNx8JXzaaVCnLnkz6yk5Uw+pUJL3vhGh2nahawsRfpcObBECvpYcCM+b5IDieXwDNBnwzis1Ik6cvfA9IE7VXViaE+a7rLnqLl0zc9aclqELjTS0GEKZvsEjDBgDFbnNif62uhzfyRl+TPNOicWupdZbWY/ErSBF8SfdU+x81pf1UzwNWx5wFSAYygOP3C5ubmIJlSvSLj5Dt7cUxhXFx8nvQJKMzpf1mFcH66nUBX1WxASPr7qpk+ZrWPzs4zAapmSaVrp/+SXkmaY6ad83/3XDXTpbRhnofkIEGkTPK9q6ohXdTxn56eeiKm697c3Kx/+qd/qn/4h3+oi4uLBpRUpLO6+rpetjJGqqq2vZKDlI+q2ZR+civZS1mWgAFv5pkvWZGnCwYubm9vt38UO52fnzdAkLRzceevgSVZCeMj3G+CFAnysi30Lf3zr/nQBF/n6dpZMZ+P1fPZ5zvw7/x5rnkWDz1lC8Q1CTy5dvrr/bCn2FaAWsNtxAiYTGyiBajNGAUYrNdzPqn9o64XpbNJpxiNnrfEOD09HdAoJHOQQmi5TBviUTULrAgYNIbhwxVOI6iMTghVG6A6ScWh1IScosxn9b4DZZRISuqqZs3AmZy5foFoJo2ukfIyLnjghBRlLJEfToaCJUXWtXBC6ZDSWN3e3tb19fXAsCWHOmlKOUxmdXW1gwzBbFV1YJK9lxzrzs5O79nlvXoWOYwk+2cgOIkWv66Xr729vaZDbG9vt16aGJoT9ba3tzu4I0coKUdHR+0ss+IskM1x/HRvnt5l2AI9oH8JNujrBf7Qc6gdfczjVs16DJMlgBYrWMsqvipnVizoWCKoZDeDVsdxzgz4oIW/hrL6v+NL9KqqmQWpvxgQnk1WadFaElxiY+laUnHzesgBnaua6bbkOo/H0fm8iuWvOfbX9T+22On5aXnpJ/mjrD7t7u5WVTUdbTwed+DIrksgyQsZenx8rOPj40FFme2WgJJR8kfHcz9Osi5wPjk56eOz9eSvqtqvZUWcP3p8fKzxeNy6lbpNF+htJoGpo/xgAj3z1T3/pstkmyyLGVROMiDNhIDdSH/KtjhObmwuOXcu9NqqZ//t/XlW+TsVqxy4s7a2Vt9++20PBnLP9/f3dXFx0YD563r5QvEUi6nset+2gqOL3nkmE/NMEXGVwYviWz4xK2/Ow2YnqCCeIsfJSmFLHINOZkya91Q1jCnJLDuRbLn5wkhWAl1H6k/Gc3wpH0wPU1fzHJlspj6mLlfN5l3QBdeQBZZsF+PLPUv20vuoqp7pkFtnZOHGfQBkVSDlQ0B3LSAZPywuLv7PMczm4eFhMKY5qxEEzwMitEkLScHKF1A1pOQk8ri9vd1JDQMp+OXoMsDyQgXE0MpMJjMYlORxuq45p3y6XkKZDiq3u8hKgt9xelkdJTRJDfT9LN/PO8yqITLqe5QjG5Dnk1e9SCoQpnqlcs4jRCZfeZcC1USUJARHR0cd/GbgCRzwvhlARi+rr6/r5UuFX4WAcWIwbQaccnZ9fd2T9cgsYGA6nXZFqepZLq6urtqJ0AMTwqqGmxTrQ53XUXLrushCovspI65FYEaPEgQZjUaDxDOdSAaYvp/OiNNJPaOfWb1ISk0mieno8/+ODcFMx5z9xZkkc+5J+amajUbnpKpmlQ0OODfjZjtsP+K7VdVb5VhJpXLM6XRanz9/bkBpHs19Xf/ja21trdbX1xuthmDzQfMTEKuq9U2S4j1JIMi/7yVIyYemHp2dnfXx+CysF/ae3oxGox6eZNFP+61mddDvq6oTUHLvnAnyoMLSbfFC9nW519TdTGhTTxI4SV+m3WNez7O6yF/NAzDsx3xFIu2H4yUglACUqrHnIu5BK86glJzMM59++OGHrnok2Ksy/bVUK/7oaz4p4fMkbnSUrRQLiwXRSOfllX6T//Sx/tjnFqi+trb2q3seLy8v1+XlZesp35mxbVbOEsSc94F8mQnbfHv66Hl/Oy/783+n73W9vpdr3qfSo0wSq4YMnrx+i1/13jJOzpg84/FspXJfihrpj1UN5QiSdc/b9/nc6XQ2CNI1OsbXUhB50VUKID3cu7u7NloXFxedAHjhDDPBqxo2oDKcl5eXg0l+i4uLHdhkbx7FYtCfnp57LRxXouK7eNtJEXHOdLpVswQMXdP/HTtpKaoX+vkWFhYaIUznkJWyRD1SeKuGqAenxbnkuG3PPUv4HEYG1VUzas6vKdVoNBpMMpwPHqfTaU+Vg5bhYFdVD0tRkRLweD+ZYEhKGVfvRiIvocwR0q/rty8B2/39fY3H407cBVQrKyt1fHzc7yh7Usn/09NT0yQeHp4nqp2eng6QvI2NjQEllE4CiZaWllpeEvHPoMxUOXalqtohcwwopuSXPCZ6n1XvqupALdHNZASkI8uVlNXUtdSj/DsZClnRSBuQwXNSw7JCmtVM98zOZX9TTm7O/qeq2fAvlZN00j43mUz6GGiEwLS0DwKf0WjU9EODi+af2ev6H1v0jH1G9wS26DmCdOd0YO9dS0AGqKkDhs4lc2d/f78WFhZ66vjm5mbL3fHx8QBoIZdPT091cnLSrB1JVdUswRIPJICTVUefV10E1rIB5DsDRBU4QWomyam3Ccz42+/4Wr26rjmBGotPvr+/r5OTk54um9eU/ppe0T065zkkDdV9ouU7bg4DQo+TLBowVjXb9sq7T0aXa7m6umqw4HW9fAHqJpPJgMYoiUq7DzjXekVOE1wD1POlEsDp9HnAiXc+30dIl7DoLIkkefL3fMwnrszWB7Jub06x7OPjY88ZySp/yppzzyd1vlM105WqmY79GhuIH5v/neNmIuqY6YvnE2A6zqbp7c4Cje+5j/SRWrc8S5VC/1fwyIJSxtZAWsc0IE47CaAMI++Pvl5MPZ0XfEnPly9fOlnkPFTacr8iiUF+D8pIeZaXl3v/vaxg5gbCVc8vWzLjD9RH8Jqo3NLS81TUy8vLwbVLnCAnuSdNClPVDMG8u7sbcJezKuH6BACcqoQrKxK57UcKHyfj2CqCWel0jRmAMmDpxBzfREvB4vx49qxuelbof4Kb9fX1evPmTW1sbNT6+nqfJwMP35H4Q3EcO99dVfVxX4PQly8JPqdgSeQ5sRz0lGADak32AEKr6ZhJtxxm0qY4MOd3HFQOAVnSaRIwSQCkagboPD09dfM/SjVdSPQX9S5BF/eY+ux6/Y6zoHfzzmp+QVuT7mKlI6WL7svPPZcMsn2OvrM16dTmJ8PmNetBnQ9CVZZNw6N3nonvs5F6F5eWljp4SLDvdf32lRQpCQSfoFokuRG87O/vDwbIJVsFeyDBxqzMV80mrQL09A3TAwCAz9GHh4eH2tnZaX/u8yrWVbNJwGRXb+vj42O3WWSymMfZ2tqqqplPyConkCV1wzky2MsgMqsafp7Bdw71cN4MngXoqdvzwSq7lUH0xcVFXV5e9rW5T4FjBtfulf1Vnbi8vKyLi4sO2lNW2NNkTmSyil3wCuT8Psv7y4R9ZWWlqf38ZLZrHB4eDoDTo6OjwTYVGeNZT09PnTimfyAXkouqGTU5wc7ckoFs+Tc7MF9lliTSQ8WBBCv5hfTBFpnPqmKCFu7h1yqOCaZWzRLS+eRz/v8J0KR8/1pCmtTUPL/vAkZ9b3l5tqfrwsJCxxjzBZYswrAXbIRKrHNKNH0fiOTnX4OOvihRNLqdETf+Wh8hHndVNeWTguBRe/FZgpd8JLKSzlJlMvcXhBYKiqBu2Q8nMBJ03d7e1vn5eT08PNTp6WmjOVkqVsHjODOYSnRXIrWxsTEQvky2EvkXyEkM56sCnD9HnbzoqhokpBQsFTyFl/PmaHw2S+MClexrnK+WcKyJKPu++0wE2fkgMzjbktUcpAKxyf7R1/XyBQDIqlz+ruo5qRdcbW5udhI5nT4PTRAwcpYqiPOghmAHOJD9t4YxkEXT/QBCEkbAkcmcDG0mSff3971lA1lifCV4CUDRxV+rBs7rZCKbVcOpbs7nGhIIEjQ+PT0PnkhaG3lOEIcDnL8Oeuqeq2a9vNnLgmWRAJtjCSLSLqKP0knnMHwIUJZMh6z4eM7uSVvAryXNr+v/98XuVj33w5CLxcVZ3+FkMmn5zwTo559/HrRrnJ2dDWyqReazeuBnSZciY/SlajgkraoGAE/abtUWjJHUTQkXmasaDi5jM9CxPA82iK1w/KwGJLAh0Et5zkCW/iUAk9VDvi+nQIph+GpVvgxG3WPqePovz4dOC+C/fPnS7yIrhcvLy2133QuQPfugBcZAHP8X6I7H41f9/B2WYkFS8Pm07PlL5ooKf1W1vRyNRnV8fDwAZfTESi7S9it6ZOEBc+7i4qJBg6rZcMFMPM/Pzzt2VmnO1ggFkvQXwCCxIV9H3pMJk/4sAdT54gYfmUBt/o7/nU9GxSs+a+XxsxCT/pneJIONP6Rj/Jz4dv4akh4rgaaT8gN2xTM0KNN5E6SuqsEATwDD16CjL94ewwjY4+PjRgSh0LnSsHlBR0dHtbi42Imbl0KpvBgIhr4JDx9dUSCVTiKrlVWzQRkSTk5kY2OjhUbygm4KzUwFvrm5aUodg+17ejAE0ImYZGDI0Aiys9fP/d7e3g6G+VAwDvrq6qpOTk4G+7y4B89gPB63cbAZqOCS8cqg2PPPADaTWNeVk7qg3DlZEUItqKWM+fmsGtnMlMHVuP+6Xr6ANh8/fhwYfhVbgWpWELLKRz9VnlXNVY9z/zNykqhd0nE4tY2NjdZzKKnPk/esfqb8AWEkZoIicukeErllI6qGlUl6UFUDJ/FrCB+HkFTV/OMzbMHCwsLg+PNO1HHyeiDSAgPvJukz8ywD362qfo+eX1ZrPEP/dhzgGVtGN/W4CdKTPidRTef9un7bShubCcZkMmlfcHBwUIuLiz15z3YXKeN3d3etCxn0oHTzG3SEP+MXsW3Ydt8j86hbAOHz8/Omn7MTGdCRHUM+5ivsfAP/w+bkfszkUpJaVQN/9GvbNqW/znucvy5tEHQiKxaeT/pG2xcApzNB5dvYiJzs7BjJJhKfuJfPnz939V5ADyxnu5aWljrxo7+SiawWsYtiidf18kWvqmZVKok9AEfctbW11bLjZ1nhp79V1WBbUkkXF5/3LhYP83FAQmyezc3Njh+rqsFCYEJWuPjX3NIhwZ9MMsluVu7ct/OkbmWCmP+25v1V/kn/OZ8seoaSvPnrqRrqbB4rq5YJ/Dp26nVWTsXu3pnvqB6K6z0fOQf7m0UZtnkymQzsL9nx/a8hSax6YaJYVT3mmoJQDhk3ZCKdYFbOfvzxxx6o8fj4PHaf8ZOAJgIyGj33bnihJycnbewpluA1EQ8B0+3tbaOSUL7R6Ln3xvYA7imdkgA66S4SvKT5OI/7ITw+nxQgQSWFyGBBokyQJpNJXV1dtcNxXlUM/z45OanPnz/3COOkNwiyOZSqGkzaSspDIiIXFxeD5Fl1lgIk8uPYOc0pldg9Ly0tNaLl86enp41+fS0K9Edfkoutra1aW1vrJIp+np2dtbOBkOaCkhq6IZDT01o163F9fHysz58/tzxmtRraryLt90lfk9jQ44uLi3ZIjLPgV0JYNUu6FhcXm4aaPVSZICVoIXhMnZ1PwjiaHEyQ1ZRMiB0raeP0mM0RaKZTdR2ej3vQX023BCYQzvmJz2yH/ZpyGqJ34nqfnp4GFXy9oEAbDs97r5rRCo+PjwfB++t62WIrgY2AHImbJCDHrVdV7e7uDiqCgJiUd76Qvvq8APf29raBm9zGSEKTFRRVrqRM3dzc1Hg8rqpqn+Sczs+nCJDTx/l3ArGqN/f39w1KJdDBdvlcAjtJiZ73IVnlFFyqyGYlPalirpd/9DxSh+lv2ow8XtWMmcD+VFX7VMG/mMZ7ziQ/fT2bBNyROCZo9fT01Pr/ul626B/mjfet5YJvE8tIArJSnFNP0+8kU8vUX//f3d3tBEWxQFsOneE3Mwl6fHxs6rNiC3tPdqqq2W+SRD83Z0My+v8JcMi4L49r/Ro4mr2383qawAv/ydZlZTITwvzjmbIVYm7Xx2YlwJr3lnbTdzY3N7tH3L2kzaLry8vLdXR01HIwnU7bD2vtyIoz4I1N/KOvF0FOqlICCmgk5eFg/H1xcdFC7vOUhbAK/kajZ27/2dlZIycQRz0Qehiz//D8/Lx2dnaq6m/H4lMsx8nJcuiUGaxS6EReCBC0V9JmETbJLoHKZNa1cFC+n/TSRJpVCCBQkkBVoel0RoNFG4JWcCIqexlUUlTvDGqVSTYqnHeTTlAikAH8aDTqfRQXFhZqMpk0XYkSZ0WCIZTUeh5JoXhdv31xFLnNCVoSvcqKm2EVSc9CSdvc3OwAczp9bu4H2kynz3Qb/VOJwOkTdI6sOlZV9zXSY85L8Or3SSFlVwxumO+zS+cEgKqaAThZOU86TTqtdCpZ5aD/dCjR5tRnQUPqTw7smK+aZr/ydDrtgJFDY0uwGDJByD4KdtG5JJsoTcCDTBwkje7Re2Uz02lnRfJ1vWyNRqM6Ojqq7777rlZWVurz58/tv/gKlcR8rxBsLJv5PQ0l9WQ49+HMPWrTHlfN6MpsOnQ8K28AJwltUi9TFzK5WVx8bilx3KRMS3Cz3y8r7qpkQFdyz2dkAsbP+HkGwj7DHgFE3rx505uSA7xcS1ZSAFD57tL/5edRS6ueKYD8oWN77kBp+uU5q+5g/Ui4c7ieBAXVPbcjcY+vgOvLl8SPbgFvMNkmk0nbfckAfQO+LC8vt6/K+InPU+BIarW4LitsOSxNwiW2M0yyasbaycqyf49GowYIk5ZZNSu2ZGUv6Z35TKpqwKhzjckeSJ+RCVjG2VnhS9+dhSfnBKr53nxFUzLq2S4vL/cQPc8JyOxZOAZWXFYR0+9dX18PtpTKfaKXl5d7kKdYGFjrevlisXuyLP/o60VXyWElIn99fd3ZN6fDmDHkqXA7OzsDuhVDV/XsCPb395uDL3BcWJjxwW1CS1CrZoEkWmM6LQHRxcVFC45yu1H6gj9/TJ+jmMl9vr29bSqQZ0L45oO5pMlQKA7Xz1DiLi4umhK0urpaOzs7/Xw5EQYgKTqa4QUZlJAAJ+WOcUllU9VIlNUzW1xcbDS3alaNhJo418bGRv388891fn7eSsd5kQvBy8rKSjtDzjqHm7yuly3v+df2u8wqGVkXbKi4cxpoTomir66utv7raxSwAmN8Znd3t9+78+cm4Mb6C8ZUE1wXYInTojMc9u7ubv/8y5cv9e///u+ti1U1QBjnkcqsintedFPgVlV/4wQzMUwKC12il/QMKmwJYDMoyF5iwXTSXeaDcnYm9zb03aTi516t2U/sXg1NSYqM719eXvb1vXnzpra3t3ts++t62VpYWKjd3d2WTVUL9vLm5qa2t7cb/ResvHnzpvb29lqOsz/W97VRZPVBVUJSYmK1YC9Bjkz0/IwOCNhStvT98zFVNdBV/sF3s8ef3PMX7BPbJPgjm5Ik15ygNJuWupbBLvsjmVTFzM9khYbf4/v4fffq88CYtK1685OO7rqSDnxxcdGUw1+jzwvaEwCzjZFj+RxK7qt+/j5rcXGxjo6OOslIAAaAyQcmUCkeA2KKyxLwT79jOFVOTBU76VtlA9Ln0Ak2hJ9I9gBfSp/pHn9nSOSPP/5YJycndX9/X58+ferjZmz9ayt9yXziNl9ZTF/p53Qnq4zzcSsbMJ+45ndQxOk0YOz+/r6Ojo46Vs/ENMEo1yK29jl6PJ1Om0FBLsjC1tZW5w6mxaf9ctzU568lzn1RopildwFTTthLHi/nJuisekZb/AyajUfNwSXdjaEWVHoZk8mkldCL1XwuUJVgelnb29tVVU2hRMt0Hww+R0BZsxKQ6AthVk1xnxm4pRKlsvj9w8Nsj7mFhYUOtDOwM6XO+TwDiFSW6LN/RRIuyaU8FMf+PxlQCBTSwUrQBbBVM7odx4a2sLGxUZubm52sp6NjwJwDXcn/vxak5Y++0Lpvbm56YJPeXvu3+Zkq/cPDQ52dnXUQlYAHI7u+vt66ube3V1XVTlMF7MuXL3V0dFRLS0uNhFfNtq3IQKyqGjwgtxKbquq/6apgy/S55eXnaZ5bW1u1ublZu7u7Lev0Ih2rBZxJna6aIaaStjT0maRJTJMKk0yCBH+yXzppuZ4tu5GVQ+fmsOeDbnqcztR9+X5S429vbwcDbO7v79sGstcZbEBm52nBXxMa+kded3d3g+mzdC4Hwqmkkznj1ecBjkT5BSnsum2IJF9ra2udfJJf1fzxeDzYdPrpaTjMpao6OczqYwZX2cJA/8g/uRFEAZOzJx8okVVGoOS8f8hKZuodfz9fufcs/V4wKaBPpkA+2zyW7yYLwLOSQNDhN2/e1GQyaUq5z2WP0vb29mCIjnvKoV9Z0UyauWsA5qHtps19Xb99PT4+t1bxISbRij3pQcqgWRCLi4v1448/NtBObugT+mG2Z/lc6ndVtd2WeKSvqKqWhdwWQ2wlRuPfJLvZnrS8vFzffvtt7e7uNoU8Czv8leuhJwmIZpKYoHL6v6wSVg0Hu2XCmL447YzjJxDkHOmbzeRQkLC/qGuer3Kmv3adzlU1o+pmwUYOlEWQy8vLppPf39/3UCEgIFn5moCcF1FPOYbxeNwBnoDf1gsrKyu1t7fXyIWAxNQftJqnp+f9W3LiIvTRVguQdaV9ju/p6anG43EHMBInyiCYhKxAJp2Pwd/e3u6XRzAlSomAcK6Q2OwxyB4v90oA56mV88mTz6UzTsooSpHzVs2MRz63RF0E6QIM1+V4KpPzSmj7jRy2U/W3Q4ky0Mygdmdn528qyp5rTmyV6FIgaOp8r9zr+m0LpUJClo3WEM+q6mdO7re2tvp79Hs0et6c++DgoNHPjY2NrpQJYs7OzhqMQZcRACddPIMvzs81ZI+Qz0p28rsrKyu96XHqz9bW1qAqV1VdcZFspvOjc0m7mQ8M5xF950t2QNJZOF/PlNxnlTIdr/eFMjoPyDgnsAaQ5FqzYmFvPfdkIIL3LXngvE5PT2tjY2NANSQvkGgIN9v8taChf+SVVYrHx8emd5PR6XRaP/30U+3s7HTQx58lPeri4qK+fPlSe3t7g0CMT1MBTjAo/VMmO+iQl5eXAx9UVW2v9VZZZCJlltwKmFT8ErClY6h6ZDf763zedSV92nFdwzwN3PkzoJbMZZUyK+3eC712/mQCOUcmpoJ49s25bm9va3t7u68pk0r/zved/aTZV5U0Rb5d24//A5MB66/r5Qvjpeo51t3Y2KjxeFwHBwcdb6r0VQ23aVhYWKi//OUvg2o+PSc3/NdkMmmwFOutarjv8OfPn2tvb69lL3vs6Cg5SODGtZBRviF9alX19T08PNT29nb7xXwWeY95bn6bD60aJozpQ7MY43OAI5/7tapjJo/5/UwkU3f4SzYwmQ4KU2lrxKT8m/tyz4phEnXUUu1hCWizy65VPMwObG5utsz80deLIGEPS1DGoF5dXdXW1lbzcJXZz87OGtW379719XUPS6GMuTdUVg/m+cqSEH0aa2trdXBw0IKSA3ZszcDB+l5VNQ3O8VQXvViObh75YIzX19e74kggcspZVTVyZEopIU5qi3NzwgICgSMHZ9sBdAQOQzU2naW/OdWkEqTiJcrr/yqXNzc3gz4nFRJK6Z25L0rtHgQ4iQRL4lU3oU0nJydV9beDCF7Xb19ZUUKDYrDu7+9rf3+/qmZ7Kqlmj0ajOj8/7/6MqhoEU1BRDlA1UYKRUxhTNzKxSfo0wIbDSQfmOBKyqhm1BYByd3fXY8PpbdWsOZ8tILeCvqy2q9BlMOhPBr+ZXLqfPKZr9tl0XirzWYnMIDMDAMeSALJBnl0eM3VLPzWAiAOEft7f33ciL6H1byj509NTI7Dec9WsEpQDq17Xb1vkMSn4KoiPj8/982/fvq3t7e1+5qptGWQtLy83OFdVDfagnAEgEp2na3oH+Q4+AfiYFLiq6n8nWJJ2vmq2TYtJnfypILGqGlzOKgrwUtU0dWu+ekpnxQvaOHwvKxyqMwkM0Z88f7ZdmI4qCMxgMt9dMgL83nOh466RDosXqmazCRYWnoeLuXbn8N6TwcSXiwFch+rta/vG77fST21vb9fq6mptbW01G2A0Gg16xO1fi4oozvSOyZclHtbzS47M80ALpcvku2pG33Zcviur93wEf0z+Uh6rqn1SJmr+vri4GLBxMhb2cz9zDZnkue6871z582TMpM/N5ZrnK42YEUnFBlAlsy4Bnbx2P8/40+fzM5JA8WtS05eWZjs1LCws1M7OTt3d3fWejABr1/o1rBcligIyDuXx8bE+fvxY19fXTWdTkrYSgfn8+XMnmT6n4ZeRY+AZdEnh1dXVAE3B30aZYYgJQZ7XHmw+k4lMVQ1Q+6R1uBZOJidJJsrueykEBDl7Ev07HSHnTUE5s6S+5ed8htMl5ImEZOCZwbzAYF6h9XamMjJuxudz+KPRcw+TEvvJyUk/W79/eHjo8ntWhrJK7D7yPl/Xy5dESxM1WptnrIKEesoRet+bm5ttUG9ubmpjY6ONc/a1ZRD59u3b2t/fb4YBuVR5nn//DGga50wqOeLUk0z6OD6TQN+8eVPX19fdy0HfEtGnH+Qtk8d0FFnRywWR5HwcN/t/0+5lNQKQ5OcSglzAmJOTkzo5OenkPR2f+zDMgj7PU/zZiqwAsYFYGQmiqdwsLy8PKHTsq+v7mqgzf9TFTrLx/jYNT0DovXi3SUEE7uQAl+yn558kYUlPMxiOn+FnDc9xbv5I8MqfJSOh6hmUOT09bQYJtlBW0TLgTZ+VQOj80IqsjpBH7ID0fTnAJXU4K3mCvkwqq55lGvXV+VVU2a0EplQq2A+ADR1PH+gZqYxKIFHX2RHJRe5l6Vn+WhU023L4UfbwawlC/+jLezQUJQeS5WAb73pjY6OrVWS0alYVEx8B0skddtc8kGpGhr2I6SffSt+zoJEFhyy2VFV/vmrWAgUwyq1sxJdiNHKeoKg1nyz+2mfmk2NgbuqP382Drn6f589jZsVyc3Ozn6Xnw25kpd97zApwVm8TWEqgStJNv5KRB7jx+QQJMgGWkKtI/tHXixJFiNl0Ou2eCg8AXTSnb6nwQdhM1kuk0FRTwSc0nLOCbHBi0BtOD4IimSRsT09PvZVG7sPG6GayyoGomiVa4V4InXsRUPqMIJJDQRODOtze3vZeTomMemaEnvNmVJLHzphI/lT10iEKElR/OFOOv2pG9/FOPXsIEwXW51I1q7S4J4Goht+qWcVZH5vpmVWzxu4s1y8sLNSHDx8aDHhdv8+S1AE4yA5ZQkGUgEkAIJxAFPrq/ZDJ7e3tAXABaeUQq6rBDDJLby4uLgZ2I8GNTEY45LynBFQ4Or9DmRZE2TA3E0dLUiX48nycn87PO79Ea5MWLwjOqqbANIGiedpMThFmX/b29mpzc7Pvx4Q915J9Y3SJTZ7fEysBJmwOeri0tFQbGxvNbFBN9t2sUFZVB8mv62VL4oGFA4hbXV2tk5OTQcLw9PTU4OvFxUXTvz9//tw22B6/gpbj4+P2Z4ZkLCws1Hg8HmzyfXp6OgAEABAPDw9Nn6oaVtNy71yVeHKUVLakmBoC41zzla/sr0xmQAaK/D15zEAvY4n0xfxV9n1h65hh4L5UUPi+tBXsw/Lycp2dnXXfIR3MKiUdTOq8qrHkNnsu6WhWXXO2Agpt9qAJtJNl4JpVc1/XyxabvL+/P5BXjK+FhYW2pey9WAddWHI3Go3qP/7jP6qqOjbTi7y6utqDG8fjcR9ra2urk77c+5q913euHzYTogRDUr6SoumP63H9vi8ezARufiCN55SMoQRM50HY1ClxiOvM6/PsE+R1TWLdZNrk/WZymIy2BJIAL/lM5ymnEju2cT7pzC2AMplGE2dvUgbEUjmB+o+8XlxR9IAI0vv37/vBra+vdwAKMUGb8ZBT6SCmqGwSMc5SlY/hzz2/FhcX6/T0tBMRzs3Ldz2cW1V1UgYZyHJ3TokiUBRa8HV+ft7ORMBGuV1/onqEMqkuVdVVQkjgPNKSipMK6njp/Ai96ZP2X+TAUGVVPSXOemNUJryfRKQEBzkxSwDhfIm8WGdnZ3VxcVHX19cd9DKwWa5PVDy//7p++2LYciiNJnUB0urq6mArhqurq0GFKuVUEjKdTuvs7Kx1kx4nCum9MrJZOaMDgiXXlN9LhJEOMOR03v8h9fNBWNWMGjrPLhA0J/WrqjoYr6qBDvo/PUwkNKuOGazmtbMn2UucwFU6uKTs5sAKvSuuMdkEVTXowUx74Hjeq16Up6enpsWrGHpOicqqBrv2nZ2dQRLzun7b4oPIfyYgW1tbHfxp2+B/Li8ve3L3hw8fOoEyCfX09LSqnvdaFGjygxB3x9LbKCn8/PnzADjB9OAffC7/r8omkXl6emo5lXCSw6rZYLSq2ayDBISrZuAKe5NJZlZLsoqWPbgWXc9gsmo4TGu+l3h/f7/29vY6ocxeemt3d7f1IX1zVjyS2lpVrX9sg2sbjZ4HZPDznktSUMU+nl/alwSTjfBXnXxdL1tLS0sNzKg2z0/7N3Vb8YRPePPmTW1sbHQ8uLi4WH/+85/7PY7H49rZ2ekETlyHTlw1a2Fg07Ovja9O6nZSZdn3jMvYmdRH15ZMMZV8RYj5YVSO6TyeRcpw/s535iuGmbhl0icOzopesonochZv8rrZhnlQlh55B/7vHADgrOrKCQBAVdXxxXT63O/vvWh1cT9iHowOcQe78zWsFyWKi4uLTUVDDdNDUVUDpVlZWWlOb/YhbWxs9IvIcqxqm88JjARRWeWAaOqTqpqVehO1yYTM97N6kAElxHwymfSLzxLzwsJC9yY6rnNUDZtvofZZHqc8idSncGcFgDIlTY4Du7q6asX/Nee5trZWm5ub/Zw846rqQND9jEajRkBzH8gMCBnAVGQ/owQCX+9McpqfeXx8rPF43I4Z/dBo79dhNr/PGo/HHezd3t52vwMDZdiFKW0qAmgTAjTyBCggWwIlBpQsZiCk4p1OQU9xyp7EJ+lcgjq9vYneO87l5WVNJpOWm9Sf7FvKBCynFXKYScOmo5K5qmFVMFHSRE4FxpLwDB7nqTw5lIceziezABqOHTNB8u2e3AcHl+O5837ynLYWAticnJzU9fV1jcfj1n/vhB1lk22Z8bpetlQeyCWWBnudVTMsnLW1tZ4BsLS0VPv7+wOZWV5e7s26c5DMjz/+2Cj28vJyTwTUapBgJRtP9r58+VLn5+dtN7RcYK2gLzvew8ND+7b0GY5PLn+NDg60EdT5N/843yP/9PRUp6endXp62rqZVYqkoCWNLs8vpsi91PLZu89MoHNCc1ZC8r15J4C45eXlGo/HA7+e95pAnZhkYeG5b9g02Jxmysa5V4Hx0tJSb7vyul62Tk5OGjTY3d1tZpUCgRjP1jQ54dT75RMnk8kAHDckbh4orKpBnJVJkM+TAxPIyYJ4d57OmWye9F/8nCFKKqN5XWSVX7bEkmm/rEwE+QrsofS/mQy6drFl+tvUr/liS9VsFwL37JmZ7J9MHyBsxgXO77nnfaR+sx3iYjLg++KntbW1npCbucHDw0NT9BNg/iOvF1cU//rXv3bPA/S6atZ74+VcX193YuhBE46dnZ1GwxLZz6ldjC6jaCJbomwSMsEqoUnaStWzAJ2dnQ1K1QJaAsFxJl2vakYF8R0jvbMKd3NzUxcXF4OpoRSgqrqnz/Ul2qOqZgoWBZ7vYcpnLFnMPstEqqtmgbZeBse9vb0dDBlK56WiK9mQWKKxUroMGCUFZELy6n1XVU+yzUB9b2+vk4R5auDr+u0LFVjAsrW1VVXVMm5lsqePCVAhKZEooCZK9JJSif4pacpqZNWMDq0vw7t+fHzsTYIlO0nzoSeOk72O6aglVGQtqZQcJIeStoYDrppVGrJSTy71FqXu+n5WUjMQFpxLINkBz8W5Li8ve+DMwsJsPHpSUlWE2Mz7++f9roz35/Tdf+o/QEZi6vo9M8Cb3uuktklmBCiJRr+u3760R1RVB0/Ly7N9h9nHx8fHpit9+vRp0ON6fn7eNt1Ap/Pz804EId1/+ctfWm/JJ7nPxGp7e7s+f/7cuqqq7RzZt3d6etqJCZsCwHR99BOdlU4koJHMJAknf141C0gF3QAP95w9lVU1CPRcRwbtbELqeupyPnv+LOOZPC420TxFLu1TAlzJNsrKg+/kZu2Li4vt3xNMAp5bqrdpW17bN36fxYdqlcBMy0nu3gngHFOnqlp+gED39/e9F18mKf7vb+wziSKARMHi7OysdYTPkHhmld515S4Azudnfp7Vdf4eLZYOzTPGMolzzvSZ6UdTzl2j2CGB5Py3zwCoswqfPZ0+I6bwh3/L82ZlMGmmQKTc4zVtiWfpHWUxRbwLzAPsyivYEqy6TKD/6OvF22OgrDDuHqrtKnIYDKFOGhwnJguHVGeT/8nJSe3s7DSlQsJzfHxcb9++7Sl/ibpcXl52kqJClhOlOJX7+/sOngm8CazT6bSrhqm4SUsTvBIax0+Ot3vmpFRAE8lZXl4elKUZFCOTVRL8Hm88A/BffvmllpaWOoiUyPlsVfUUy6SQMjzZoLu6utoOn8I5v+bqdKyuQd8Mp3l6etrNvY79+PjYaArDd3h42PuJpRF+XS9b9/ezoQ+qyaZ0oYhlHzGa2MbGxoAmnWgZeaWvkgnyMZlManNzsxNFVeZE6Okp+5DjwskF+UZ9EaiSPTLJAcyzAzKZ4yiqZtU195DTlVOmOZAEc1yXINC9J1o53z+RVBlJWlbiPROJQFLc/E1/DR9yL3d3d72/08XFRY1Go4FjSioRMMx3p9Np99GwN+7FMBXX7b6zf/o1EH35SmAk/y+QITPpWwV3mRz62Xg87kEXZAA4m6AqWTOdWEXC2tnZGbBrss/KdQku2W2+kP3ghyV9/DcQMyt7t7e3dXl5OQhaq2aJpuplVQ16pviuqupKTVZREiCumtFQ2Z4MkNOP+Rw7ISB1rPy9GQTeGb1zngxo3VMmiJlcsmeuKenlbFHVbEsywbPngUIO1H1dL1/eVVW1vdRLyD7OT7AmW6enp7W+vl63t7d1fX3dsQ37ubCwUFdXVw0c8Fl8NFnhL/goswGqqgGHlCO2gUxiFCksGKLCTtB9+qvAkb2D2TqV/qWq2ufy61UzcEaim9VFP3PceeZLJmiZPCZzJiuv8z49fXImvhI7n3U8+urZe5aS3Kz6e+fODzyrmm3/trq62sOPAOhstWvHpvwa1osqihk8rK6u1sXFRQegGxsbHVAZcpJDFpaXlzupqHp2FL/88ksfU0UiJ7R5kRTUWH8j/6HeqhOCmru7u650UCrXwqGhPRqkQzDmKxSqdoTWnkVVMxRzY2Oj75GwoSQQ4NzoFDorGOR8nE8/EcOSAstALSw8j+Hd2dlpB6QKRDEYgESfcsBO3q+kYmtrq0ezf/r0qREb23FAqh4fH7uKyvkuLT1P0YT+2spjdXW1Njc327lWDavHGdC8rpctgQhwAKjBIaluA1UeHh7q9PS0VlZWet+hDFqBPEn/Ilej0XPTt20X5pOnx8fHwQAVAE/VsD+BjpBZVXtG2GKok+7NMTgmPUlqT9Vs/yjJWtUsqRYAcohkEZ00kU2OF/Dj/AlYSbroeAaowDLvQHUiKbKOXzVL3LLiCETa3t5uIEsSkMhoVXWl0HNLOh97xEa7F9NkVa/o/2uP4suXSjI/4L2nTPt/bvIMxEDhlsTR8fmecwACmRU0CcJUpOmrica+N7+y2migS1YF0e8MsMv+pouLi4GOkmOxg8A7E6pk8bAnSeHW924bJwliVjqcP6lpGfCq/AsegaVJIU9WAvvAniXQlLbFZ+mg+AYzp2o4iOrw8LDfLZ+MLcBWkB0MhLu7u5pMJk3BTxri63r5UlE+ODgYAPQ5bVZsA/CYTqddhODnEnjHbJNASF7IWiY4gHWxFJlO8CgTt6oa9PKLB/nQpIqSa20pVcPKGX+Tw2BcQzJy0rYkyAqspOv0XULn566valbJ82z8ji3MRDKTU9eXiaf7T/2kw2lb+E6sCDF4JoWejWP6O/25a+OXcyiceAqzJ4/7R14vShTX19d7U1D9C7kXi0BUIpLUUcGWUu3i4mJtb2/XwsJCV9G8RIHJ0tJSnZ+ft4Di7Wdi5EUIgDIAzqCQ0o1GzxxmgbQkhgMg1FXVFZOky7m28XjcDm4ymdTh4WFVzYZ2UDzXiiOtTA01pUgMxZs3b7qC53xVM8ee/YB6xjjhREWqaqCI5+fnA9pooqn+TnpOImgWBBQFOIeHSLo9m7W1tcE48URgbm5uanNzszY2Nmpzc7OrJK/r5UuAyZHRB0aYwRbAVT1XE8bjcYMbKoJZzbb0oCZCKjlDiWMk6dT5+Xl9/Pixh+hIXJMukz2PguPcGmAemUz+f9LFyDydvbu7q9PT0w425ynXnD7HklXJTLbRUwSRhovQT3qSiVjVjGpEt9L52/wXGGPCrOebFD12CTOC0zk/P6+Hh4dOLJPZICh1DHbIfT89PXWivLS0NBiGcXd3V//9v//3AQVdAvy6fvsy8A3dNHty7QnKbxp6ofqbCaB3IWAkL6oO5ItPo8sqDeyCoAkDR6C3vLzcgKzKM1o3Ng8GQfqpqtlAF5Uy/wasCLKwahJoARRNJpO+Tt+/urpqsJIe5XfnQSoAEL2bZxGoekr6RqNRPwMB6KdPn3p6unPoVfSZrDBVDYHPnK66s7PzN31Sy8vLDbRJgP/6178OKqxJscdC0kayv79fb9686Sm2X0sg+kdeZJatJR90gQ4maJ7Da7JPdXt7u+PjZNJg3AH2tHLlIBo2IeNDgwrH4/GgrSNjVMkc/U65F49hAUjG6Euy5+irz/Ov9A0Q4v/pC/2e78kkMLekEZMkk+Du7q7bMrJambGod+IaAKo3NzcNvGRSix7s33R8ael5RoNzi5GzRS3P5TvOUTVrIVBU+uabb1pfvYPd3d0BS+SPvl68PQakeTQaDapEuY9Z1awyhrrC+eTgFI5ofX29y7V6ewhwIo4+9/PPP9fNzU3vyXh6etqJo2XiqkURTYcz1Sp7LQgRZF8FRSUPZQ3dU1IloHx6mvULMSzQ1S9fvgy2x3Bcwazv6k9MZ5gVotXV1aae3dzcdIUoy/GOnXQkvPN0kvf39+18VQ4E4Hj6rheS6TnMV0MletPptIMV79w1MEjelYR0YeF5at8rGvrypdLMoCXiVTUb586wSUxU+8ni6enpoG+IsxHA+TtRNXS2DAzJkNHtgspELx8fHzvJpFNk3LUmVUyyJhCUvHLUVcMeJdUWTp3Tq5r1D6Xjc51J6WLfVAvmJ6L5uUSTvuo35GzyHXAs7Ojq6moDUFXDiatAoQycOSABczr2HLAgoMj+FNeRjAX3cXd3V8fHxw3ksX+v1NOXL/sMAkKTRcNeLy4u9qAWMq7fdGVlpa6urrr6r6pIB6qGtLCjo6O6uLgYDK9KO5vyTA74wul02gCj9gK+yXf41M+fPw+YQex81bOcnZ2d9TmBipB8lbPDw8OerGyf5Ky4k2mBKz3LvqWqGvjXqmq9YCPQ/1RQq6p9tN+pBO3v7w/aLtinDEKrqoFwz9/iozOJc736VbP6e35+3okjEJ6vXVqaDSHa3t7un4ulDg8P+xpe129fbHZSS7PokLRihY+q2bT3TGgeHx9rZ2en4yP+R7KUVXT+dHl5uRly/BRaY4I12DpZpc5WA+w3MVjVkFYrts7/57V7Bo4vhk3/XTUrwmRMOP/sqmqgV9PptO1SfhbIpKDifcy3aHhuzu39vHnzpt6+fdsMmQSY8xot/t/5MmbHBHK/OT/BUMBkFohTvOtM5MnM1+JDX5Qozm/c7eYhXuvr6z0lilPxuZyOJqFMTjHax87OTr19+7ZL/NnHB5Gx3YPEk9HM/ccyuMwESlUjKagSzevr6zo5OenkSBClUV/vHcRBBUAS5jymfxI4CSdqwDwClQFuKheFS+oMahqUSYCZCWdS7yCkaHCcOeckcJSoTqfTOj4+rul0Wru7uz1aXQDsOU4mk0YxUWEsRgTyrcTvGRnTfnd3V9vb24Ppc6/rZYvR1VfIOB4dHXW/xPHx8YCqRC4SWOCAvnz50j1Jo9GoqxzsgM/QcQljsgg2Nzdre3u7N5PXDyngq5ptt5OTeemCpJDuZK8t3UnHQY5s6wTuAAC54klEQVQyyK6qRmfT9iQtqGq2x2JSUTmM9fX1DsYTBHl8fO7dVMnleIFF871MmBeXl5ddCXS8TPxcB+fPfub2I4uLi91zlkECh3R2dtafzV6ZpB9zcuhOu7u7tb293VsvsDMCotf121f2wxwdHTXglz7r6empKW+Ct6wI8imqElXV7RYPDw+d4N/f39e7d+/6/3wsvaP/eqbouWT24eGhtra2Orl6enrq0f90Y2npeQrru3fvuuKewCAfube316ARsJXfdxwVRnR3z6JqNtSGzrIDuX2Pz/lD11z7fEUzWT8ZbO7v73ewB5jJ6hF/nuwczygTaZRg4HjVbCAd9kaydm5vb2t7e3swMTwTYAwu1FTXiH6qMvK6XraSbUWWve/l5eUeOmjuA5ZaThhXEfT+2Fj6oEVnbW2tZSPjRf4TEORc4/G4Bxlio9B18z6y2u0eqmaU0gRRVeQlhPl538/+QHqX1c/882sVOMcDjM4DpewCmnVW/R2P7icQkmAxm8LOJTvOn2RQZNybcwjEvz7DxmSyCwx3rQmkZUIutpkHib6G9aJEsWpmiN+8edPUNUjdfNN4VXUghIIhWcseBwqSFDAOTBlaQFRVvefM1dVVnZ2dDfaEu76+rtPT0/rpp59a+AVZVdX9d4lWCrgWFxebCimJJHgSV0LCkCfNTWDoeRAwlVUKKZlEGyNcub8dGgqF4qwoLoOgksDpO74ANh2z5Bb1TWC9vLxcOzs7/f729/cHCK7EluOdTCZNXYCM5sRTVVUBf9KplpaW2rGhH/zahNfX9duW4EeS7vl7v8AciX1Stxi9HAOf1TXvV4+yc3BSVbPNahlHNuLDhw/19u3bBltUp6+ururi4mJAQ81rcZ0cG5ZBVXUSlT1DiU6yVXTN5Lm7u7vuI3DN9MB9siX6stgLlXfooopBVmnssUTH0gn5HGeW4E3SR5MimxTgeSodoE5woerp3WVgurGxMeiDEswaogU8y96bbO7/WpzcH3kJWKqqn/fGxkbT9flN70dQlomJ/jxJPp9hLPx4PO6AhkzxE1XVCQjZBFyQNdRq4GMCN/zBw8ND+y+yqBIgcBXAYQ1UzaoP5Ao7AQXT910HH1c1o5knqDOdTgcDtpIVANCVFJ+entbh4WE/u+zF9CwzBskglU3IKn/el5+pvtN/tgCI5LlnUInNkfRuweva2lr9y7/8Sy0uPg+vsjUKOVF99V5f18uX95L0xKpqkB4wfnZ21mDG9vb2AETlV/kr/gWTK5Oxx8fH+vnnn6tqxuhQAXN8xzA93nVlqxS7QsbIczJw5qmu/CQZT9C0arZHsKQwK47OQR59v2o43ZsuiSPoDf1NsNhK5sv8NWUiX1UDnXdMz1U84NwJ7mTBxHGwNKqqWwMWFxcHcwXYUte1trbW+4bLNUajUQNuVg74/KOvFyWKW1tbA/R+ZWWlJpNJo6JV1cYNks/4pYIsLi7W2dnZABm/u7vrRmBChiq5vb3dtIynp6d2qGiYVbPBFBQLGo5Lngmq6iEFQZWU7Ajs0nGrLCZif35+Pgi4Ez2htBQe0sghcYLzvUTzCA5nR5ElqJK0o6Ojfi7uR4CQdM+qGkxgymQY7VcPRdIuKAvhdw0SAc/4/Py8nSHFk2TPBwU7OztdZZLIv/Yo/j5LEAcMOD4+7mAzKTNACYlU0pyyemXiW1U1pSwpcjb3dj4Bop5H01AzweFob25ueo8418UeSHDoikofAML1mvwJbMJWyOTXvX/77bdtt3JScNUM9XOdWdEU3EqQ6cjp6WlTZu1furKy0rLsHt1/Bq9VM0pcVjlyU1+6m6yCZCewk57Hw8PDoDWgakYxZw+SQpp0u0zUJ5NJv5eqqqOjo///Cu3/REugogcRCKBSQb4levNBkKpwVtDv7+/r7Oys5wMAera3txsdpy/39/c9VIU8An34RiCgYAcw6Fz+rprZG9fK16iosAfzVDWAqnhBNRJQNS/vkr0El7FpsBDosoTSM5Kwbm9vN1CcFUjPgV3zc/eRQWXeQw6Kc16BJp0y6A7bwjNy7c4hYN3e3u7nKcj/85//3L1p4hB+HhBs8NbrevkiS/pAybKkb2trq99r9u5l1f76+ro2Nzc74c+kTaHDO+SbsoDCTzw9PdXnz58bSKCXSXWcBzmzIqeAgNmVSSY7wgbleRPUyIp80rwzccyqIr3TY6nIQ9/50mQwVVVTblOX6Qt9SLDVeTNhrZol+GxOAq4JKCebKIdviYP1hPqOa5Q4i3m8h7u7uzo5OWkG4uPjY7e9sF/Zd/lHXi9KFJO2InmTFDDWgs2lpaU6OTmp5eXl5iKrJEBgjo+P26hq+vUQs4pxdXU14P7as4bBpVxeuMSVQmQQRPk1y0JSq6r7AzkkFS+JmCSZMuzv7w8qhVnC/7VELcv9qo4cTU46VRn49OlT96r4Lpobw2QPtERZk7ZmQbkkDQLw1dXVprKqBnHejpdoEWMF4RKsn52dtbFiHFSLVIxWVla6ojSZTLoh2yjh1/XyhSKoUuFdra2t1eXlZe3t7Q3AHrSz0Wg0CDRSVxO4UGmqqjaIgtrLy8uWAfRi3/s//o//ox4fn/uJ9SjRpclk0oCJhDBpM0lz8x1VsaoZ6skBuRcVRAlYBuVJ2RNYptN3z6n3KpN0aGVlpQfAJFvAwJL5AJltSFpNUtrcV9WMOjqv10mb45ym02kHH6qBrl21wlAh//c8VSOTlrO8vNw9pQYyXFxcvFb+f4fFRwrI9B4uLT1vmE4OvQ+9eqhn3nVVdXJ5e3vbbQInJycNxKKjChD9YbtV2cgoUKOqBudLKlfKatJEszogoObTMhnLwE/g5LmoJgKpMHXoI1aLQC3ZQvMofQaBwE3Bn+DWfWT1MMHepJnSraxaZAVT4ItRk+f0e/p2eno6oO0lPdb/tQnku05WgdaC//P//D879mDTXtfL1ng87v7VrA7Ry6pq+efnyLvewhx4pEXn/v6++0+96/F43HIEyCd3ZGJnZ2ewXZQY8a9//WsD9Ol/MikiW+Q+K3pkPmNu4CHQl45mhZN+S5QcKxMycYOYmw7lfABxuUGNmXBnQSWP7c98z/x8lbFq1s4idk6QKWOTBL8TuEUd9yyqnkHTLIrR742NjQbcxCCes8KZ1r2vYb0oUfSCUZWWl5c7cBJQCfqTsrS7u9uoIWGBvF9fX9d4PK43b94MqgeE0M8pnYohR4LewWlUVSdix8fHfR2yegjk7u5uK6IEGB9cQsU4c9bJ/ZaM5abaruny8rJOTk5aWapm1TUGgZPSW0nQJZbr6+v1zTffNKqflQMUIxU8BiSVPDckxisXbObkWLTTpKE+Pj7W2dlZV/r0vkyn036mDM50+tyf8fbt2zo6OqrLy8veKsV1JH02ldGxTb19XS9faMDT6Yz3f3Jy0n0v//Zv/9aO4+joqGVyaWmpnSHZ+PHHHwcgC9kAyBhew5Ht7u4OhhQl8+Af//EfG63LxC6pH9lTyMjT0aRXZv9OJotkGFCjp44z5OQz4bTo5cXFxWC6m/NXVcusoCB7i9PBcSyCSgNLBAZsZn4/EdSkzXjmdM+5OEzJsfddNQO8OMPd3d16fHysvb29Ojw87MoiME5VmLxUzfa5ZV8ScHtdv33xX94PmSIjaKACzKyq5+CKquphFhJB+6XpRz45Oand3d16enrqPlbytLy83EmkIFKwI3gFOABlEz0XWAEo+Bj3x0/yrYm+o+yRq6SDpR+cp5TT2YWFhcF+x1nJtOiRa5uvkmTAxh74znzS5tzkn++VjItH6IhYR/UWE8IzYpfYO/cKVM1rVI3x/FRc2Pj//J//c/txNP/X9bLFnhosxo7OA+Hpc+inoWHiMBVFU+r5MkDN1tZW+2bv2nueTCZ1fHw86GXkmxVhgJLzTDT/dtyqahuTjJPUKyvp5XyRIgU594wyGa2aMeDofQ7p4rvFgmKK9fX1AUhE1vkjbBnPNuNtek+f6XEmlwk2J9DjurONw2fp79LSUg++rHpuXQNeZR4iafR87LV6dXXVzwYA9jWsFyWKaA/K72dnZ/Xtt9/27yROb968qcPDwzo4OOjkTGCZyaKm3J2dnQFSOR6P+4EyrA8PD135yv4NDb+MvUZvlZW7u+f9hqpm/ZL+hqgQzqqZI/dSDRqA8BJQfHUoOyWqqqZtZTJ5enraQpM9F+nQ5gPPqhnq4px46pQM8ssZC95TSapm/G+KzBF9/Pixe1Iyea2q5lxn6Vy1k/OTUDM+a2trNR6PazKZdBCTFcNUZMfe2dkZ7Kv4un77YljJxtbWVh0cHHQT/YcPH2pxcbErD6rCo9Go+9SWl5fr8vKyvvnmm050yD2KVqLpiVhm348gDdXz9va2Dg8Pm3a1tPQ85psDQcUmm6PRc4/jp0+fBsEndJah5szSQSUtKGl6UH3oXjpN91I13Feqara1BDDJOf2dbIisjnKkZD0nU7IhklPOWKUpacLj8bgDAk6N3UBr1zMKsDs8PGxUWIWCTGBNsHMXFxcdjAhmsDWAXhKH1/XbF5+1ubnZvXXeF9t/cHDQlWE6lawWgRefiD7Mbm9tbdWbN29qd3e3bm5uegBGAgiXl5d1fn5e6+vrHZRVPfsJVXKyUzXbjzgHpqDN0cmqGbKf7JkEGkajUTMeBHpZJUzGQFZWEiAmn+4lq5W5MvH0vABAVTMKrHOzJQL20WjU4CvwaN5XZ3UiA3M9YH7HR5smzta5p6S2C9QlnPP3YALyZDLpmAoQ/bpevrQj7e/v18LCQp2fn7e8JsND7MdW2y7JO8r2Ku9rHgDNWNPxJU22bptOp723brI6skrGV9GfPAc9BpYmoJrxXbYokWUFCPrL7yTd3DHIskJETk1PkIne0EP+v2o2Bd17EOsnGOQd3Nzc9F7eSTOlcxkzJDtoHjASy6aNcF38qmdKd23/NhqNmuHns+fn530sOzUsLi52y9XXsF7co0iIb25uan9/v8bjcd3c3HQvk/K1oTAeNqNLuARNGeygipkIJQlRBVteXu5mdOO1BaaoYFUz2gmUFaWH47m9va2ffvqpP4s6iRKnqsdhcHB+pmKJCsOYq0YmZYHCQSImk8mgsX06nXbiOp1Ouym2atjngSb0yy+/NDq1uPi8d13SXCFVibSks0W1hUJ+8803XdkzqMP1Ci4FBBTPe6NQ6+vrdXp6Omjehf5UVVeIUAz9PseTJ43gdf32pY+Y4YRQHh8fDyiSX758qa2trQG4AbDJIRkSPcnWeDyuL1++DCr9EEGf+TXdJn8HBwcDw727u9sOliHmhDipvb29BkwAK/oc2ZR0PqhyHEEmk/MBrCA59zurmm1hUTXr5ZuvgACC9EWmU81R2xwUu5FVSUkzB+b8AlMJre8sLCx0QG2ljVR1YneqqquNq6ur9fbt264eeveo5ipN6FOeycHBQQ9eea38v2xJaLBoPFfDY6DS3i8fIQkwAMq2FXTa9FDVikwy9/f3u6/KpMSqYZsCUG86faa2SlSdK/cAk1QmfU0gmb35AjqBFN29vr7uYCoDOP4rk03X5Nz0HGBjJR00f5bfScA3E1t2a56ux2aZlux9SDaB0gmy8r3AOsypnGHAR1fNJplWVSftgCyV16Sx5XVsbGw0cO4dvq6XLzGcxMpWCCrk5M57trI4IV7Sfw+81P9tCCO/tra21ltPZOWNn62q9rF+trOz0+eWkGbyl3/nvA0yKh50L/OsPXYlz+P3Kct0GJjk+1Uz/aqaFUYySX54eGgA6/HxsYfDAVbmq++ehfjVrA7nd792HnAf/paQA4Yck43J7YpysOQ86CufSfAswegcZscfqzR+DevFFUUPG3dXUFb1LMhJjbA/mP61bCY1vIZAoYppBEZdEzhCxff29lohDw4OamFhoXsuVD92dnb6JamwCVYvLi5qdXW1dnd3O3jb2NjoqhYkcTqd1uHhYVXNErjj4+Pu6VBurpoFr/ruoIMEjyCmE83ElSNAoc2paZ53VdXu7m59+PCh1tbWOtBbX1/v/SQJpFH5nB+6kV5BfWpXV1e9cbjvLS7O9mfUv6ZKpWK4urra72thYaFOTk76nQvy05gIBIxp9w62t7fr7OxsUMV8XS9bmRRVVVNX7FGKuowJsLi42EAKmSVvgA9/UEdRYbL/lvz523p6emoAourZ0SRYAWH0RwKo0pK9ECqZGVzOMwE4yuyvEiRWzfoqb25uOtgWAOSG8hycJFAwzwZaqgCZ7GWVZ2FhoYc4qQCRdwmbz3l39D+rK96N+/bcgUT0jiMFunnm9/f3vY9VVfU+lYLeDJBPT08bmHNPqiqv62XLIJKdnZ1GoQ8PD7vSJGiqqu6rzR69t2/fVtVz8r61tdV+KyuNVbMgVvVO0CeJRFkjWznNF3UbG6iqWhaAtCohVTXYXzT1HIsIii4Z5cfpcVLmgBoCr0zk+MmsRlj8KF1KOt18pSDjCuAOP8uGYTaweYJw30uwhu7QQQH34+NjnZyc9LUJXAHMqkvZB8YGLi8v90A+9wC8SxAhJ12+gji/z0o6cdKX2WBA/9bWVt3c3NTnz5875kOlPD8/798bgsimJoVYIWVxcbFjvi9fvtTx8XGDp0DLpCmjlPNz2YNI/snlfP8dGUv5me+JT4omX1VVg95gbVauQezqfGIJ9iKZhFUzRl9um0F36NI84KRwUjVLHvlQoDSfxncmG6CqGhD1Gc8pKbZ8Hh/t2U4mk7+xTYpayd4DCi8uLvZe8q73a1gv3kdRhU2lidEiqAJ+zbemohIED3F7e7snj04mk3YmPkvA0TL29va6FO9lCg7TGC8sLNSnT5/aYBNavGjVs93d3aqqgVBUzahBT09PdXFx0fsqCrb1RhEyqA+hT8OefYtK51tbW71XWlYA7c3DcXA8+qZUNFQJ0AKgQ0nHrXpWQgE5xYbC3t3d1adPn6qqBtevT8IgC43X2dcoQFVxkJBvbW21Qq2urg4QG8+mqvq4DN/m5mYb21dH9/LlGaK6bGxs9GRO6BsELBMTlSu9P4eHh4MADYrv/ZLzN2+eN/tGt15cnPW82Sdwd3d3QFHOATrAjqqZE5IAqsxxAJKcqmFiyEHRGzJJJxLRFAAnVY/DnB9IARlO2wZ0ysAx+6+qZpWMrOoDsQTQbAPk2fYdAhLvMt9TJoPshPubTCZd9aGz6QyhoxIOI/vnB2Ht7u4O7LSqxvyo79f129b+/n7Lh+BRawWZf3x8bFkQpKouAzOAl5a+V7rD12i7ID/0F3BLf/lD/iRpV9lzrmqpf5LN4Hf4p6xoZwIKvDVMzoA6IGwGdVhGGTDSHxWDTNay2mAle4ZPzVihqgbxBKp1BrNiEfeLUudZXl1dNZDquCqy+oMl04AnzyqnstM5DCJ6zx5lUCv2wC5K5tPrevmih4AR1bzclgYIAiARH2XVbWnpeZ9RgD5G28bGRseE7O38/uFpl1dWVprK+ssvv7Sf5Duz8gzopcsZf/J9fBKflv3P2Yvrb0ycqhowAxPg97zoAf18enrqBM/95/7Bef0qcpkcKra41mQGJiCbhYnRaNRU3aSlJh083xV/yud7Nny95yefSOYc34ut4N8m1XpeCwuznu4/+npRosioj8fjpkygNKkAQtqWlpYG++2l4cttLEajUY+sRmGT1BEKScT9/X1dXFy00yM8u7u7TXVbXV2tb775pnn8klpOgLJTIGOnCTxn/PT0PG0qp0RSbkG1ErfNbzc2NnqrjS9fvtRkMmmEJacsckoEkvNUkVVp5SR2d3c7wTNGPCsegoHr6+sBd7xqRgGCWl5eXjadTzLL8T49PW/0vLe31++EsVhdXa3T09MOMJXX0eUkqxyY4EFwkX1XOXSjqnpwz9egQH/0lU3VaI5oZ5xc0hk/f/7cAalg7vr6un744YdOxOgj2YVGVtUgMELHEGStra114smhsg8Q+6TISNigdYAGtiFZBmgqNon3WUbaebNykNQzK4EhoI17EBwLzgXVnHvSULOCzw467nz/pu9JFP0MmwECrf9Cj1lS5rKqSkdNh85hAd4XypQAf319va6urmoymXQyyDbngBRAjkrY63rZAhJI1iUcdPD8/LwTM0EXcCJH8usbzqTBLICjo6MGL7Q8SNr4PrqSbQVkP3U+t1XRY5U9VInAZ5CafcepF/zW9vb2oKdub2+v9vb2urp/enraidbKysogwcuepuytqpr1JYod+Gl+yvPkl5LWqX8XaD1vj9wHG8bHmkfw8PDQrSXen4R7vhfUtT88PDS7if9OsByTQaAq4PbcvfO0aa/rZSsLB3wGsF0VHuBIJ29uburf//3f268C6ejI9fV1LS4+96lJ/LIKn9UuYE+Cnuvr67W/v19LS0sNPqhOZzUtEy5/Uvb5ybQhSVFNfWHvszc3fZliBWYaX5f6WDXb4qJqBvACQ0w8zVgxmYsS7izEuNbt7e22XXKRTODu7u56wrDr8DzYPUlttsfYF1ns7JmgxwJogTqeSSatR0dHg1iXPHwtevqiqzTVycRQHHzZu8V4b25udvWC4eckEkG7vb1tVE4iImi7v7/v5n5OAM83KVHzL/zLly/1+fPnNtKZHEJvJZmWAEp/3mg06sEsBuxkLwcBycZ39E7BcU5RRe8RRHI4KIGEPlFEThjtU0WU093Y2Kj9/f2qGm48mpUQzlBFwz0I+CXnlEuCf3p6Wh8/fnwWnP931bCq6qeffqrxeNxImTI7+q1gk5EUcKtUZoUKUkaOXtfLVgbzy8vL9a//+q+NgJ6cnHSyaJDM7u5ugz339/ddmdjZ2RlQt1T/BZBkTfDEkFZVy5mAJwPa0WhU79+/b+BgZWWlTk9PB8Et9JwNEHySP850cXGx9y7McfxJH2PUs+9DYklOVeQYdwkm9J8z5ozG43FPhgRgCdbpFD3V/7eystIgDefHqXquCbTQqdzPUPN+VXX1lC7p685KZVZZVBSh2pJvFHABMLCNTZVo5hCT1/XbF9uOmkYegICbm5vdE8c30CHy5/15p6r4/JO+KO8r+8v5gdPT0/ZLZE4yxMf6uaQMeJvAZ9WM9gjEFbgBVauq6eoCJtUA+pPPZ3d3t+1PVhz4f/rjXgSRKe95bZI6fjEridn7LEB1zyofyfCpmg3p8l3HYV9cIx+OmsYu3t7edruHQJJfdi+SEcysHBok2K6a9YkCgV9ZOS9fKJUJZKRv894lCkAAwxvJJ5mtqp5GjOWWNExgAn2W8ABlJpNJXV1ddYylv5l/rJoNaKQzkkHgQtVsT28UVnGxgY3ixapZQpeFAK1R8wO0VEEz9vB9rU/YSMmKyVkdkjBLXCoRp0+ZNHoHdFeSllttyFmyNzEZSOKFhYWFjn34P8fNYZnuK4dM0f1MwN+/f18nJycDxoC45GtYL05nGd6PHz92cKlqlg3aj4+PvT9U1YziaHDFv//7vw9e1ObmZu8JmD0NuQ+UIRm5RxsBMlGUAk2n0+5hHI/Hg8oHpPTHH3/snqHLy8s6PT1tIcRPRuvQX5el7xxlLVi2ySrE9enpqbd/MADH9RFc56iqgePa2toaUOJUYgh78suhVIQbZZbAc9SqBqenp03f4cCS4jMejwdbbGSl2IakjBLjIWD1/h3//v6+/umf/qkDWM6UYum5fHVyL19kxDv47rvvWv4kHapXknVByvX1de3v7w8SDlV0wWDqOJmnnxk45WRhAR6n8Msvv9T6+no7Cn2zZELTNxCG83INVTWg0eUEREEgajwZ/bWgTGWE/kiYkv0gIRWYAWYMeoLuV82Q1wRhfH80GvUwMNd8cXHRxwUkQXnplOBfFQqi/Wt0XEGwgQq5hQA7qzI0mUw6WGUTJLL0lD31s9cexZev5eXl+utf/9pApeSJPqoK7+3ttW7yBfRaTz3/YSojgEGl2Dsfj8dt3/lhdHABX9KjVMgEYcbDG56iOm/98ssvDd4IFhOEzEQYu8c9q9jQG8wDPVxoazkoI2nmVTNaretPQCOBSZULMQBf//T01EN72C3PS4JAF7wPSZxnaAjd4eFhP3d2E3BUNdPRZGF5BkkX92/flcA6dlW1vgKtX6ee/j4LiJZ+0qwLSb/hX4eHh93Ty1aqQKVenZ2d1d7e3gD4qZq1COmXv729rc+fP/fvvH++L9lgZIav5mv4B/Eu+cnqeDLFsBSqZkNxXLt4Mtsysu8/WS5icd/B2HMcwKue/qrZTAVASFb/3EfVrM8eGLO6utp2gT6fn583WCXh037inSiGpJ0QG6DxKnK5Psk2GzAejwf39PT0vOWP2Nzz+uabb2oymTSTiMx8DXHuizbCgqKdn5/XwcFBDyXxIvRE3d/ft2GE1qtcCFQ/fPjQFTuVONl7Zt2mh6HeJEogIJOx6y2wHxQnoQLH8Qny/vznP7eSEV6BIgUS6BLI1dXVpnEJbDkchpwzwUnOsjshkeQKBgm7wFgSxZElegrB8j44aUtiSOnu7u4aLdK/AqX0+fng8OzsrH9G6SWCnstkMungOt+N4R2S64eHh/rHf/zH/h3D5DkBHF7Xy1cCBmRSIrSwsFDHx8e1vb3dVUMUjsPDw5Y9/YRo2XSKrJNnVA7J6dXVVe3v73dCyCmRdX+/e/du4ABU3yCUjHyyFNKR+O7d3V1TvTk/doGuQSuzxyEpn1WzqWbuFVPi7Oysk1jIPZ30eU5SZci5kpoKPU5qLqAFKnx7e1t//etf67vvvmu6MH1Ph8kZYSuwV0lDS4qNwEQCktMrFxaeBwslYsq2AKHYO7bkdb1sPT091XfffTeogOn9v7+/b/0BsqKvaTMQLGEBkB8JS1U1+0QwRN8BKqPRqAe0kBl6c3V1VdfX1x2ICT4vLi46WLy/f57AjVHy/v371tuq4QRrx62aja6X6NCXqup9WOkIH5s+q2rYN8VeeJYCwaym0x9ALRuR/YCqK64PeJO0tqQBApCrZgCqGOP7778fMDESnKLzFgCIrRT/SP6SYusetra26vDwsG28fYuBAK9V/5cvwNr19XVtbm42uKDKfnNz0+1Bb9++rfv7+wbP+QhVJ6DAwsJCx8H8JL+wtbU1sK/ffPNNJ4KAQDKCzZc98lkgUcHDBElmCNDe8MOsVKfMZeIn7tbrx0+LPekMEIk/kmxnH3H2fWZyWDVryeJ7U5c90/Tb6PH0XrEjgZaq6gR+ZWWlDg8PW1ccC8h0fHxcu7u7gx5hyaPqrhhKog7U9t1sHcnKbdUsVsohc3/k9aJEUUAoUUuE7/b2tvb29gaCMp0+TwDc3Nys5eXlno45Go2aYjMajerdu3cdYDHKKmaCGQb6+Pi4A03OT0Bq4/ik80gmIfQbGxt1cXFR5+fn9f3333dQlLSWzPoJRFLMXGcKat63hFOvEeGvqlZk16nSg5OuF4yDE2Bno/Gv/UygzBmhF6bSPzw8dP8ElCp7PDyLhYWFevv2bRsEAu9ZUHY0RoaJcnNejk92VCw5bfSr4+Pjr2Z/mT/6Go/HHbRY3u/V1VX98MMPnVgAD1BJ9vb2OviQCEk8fT5l3b+n02nv8YfSub293QYz0ftMEA1RonN3d3eDCWECPQATueZgV1ZW6vj4uPt7JWWGD6Qz9O8M3gR/5FLwXTWjwNHvBGKqqkEh65dffumBBHq4PV82KgNN14Nuv76+Xn/3d3/XDkal3TtAaa8abtzL1ghk8noTNV1bW2v7lbRxfVW2Rql6DmBt6JzI8et6+UrwQEK0s7PTgRzEGnApqBE0ffnypf2vZGgymdTe3t6g8owhkNXu/f39uri4aJTdMBS6wq5LguginROQLS8v1/v37/u6qqr9SlU1PV2lDQuFj/AcsvoBcBYg5xAR3/NZSaJrI+dZ3Ugmwa8Br2m7JMNVQ1CVfQK2rK2tdcLsGOwAuzN/b9knRhclHg8PDw3W5T16RgnCsQO2Gkmw6ejoqPvXvoZqxR99eV/2hN7a2mqGlViOrdXrrbUJW038c3Z21sNr+JMcOqaqzB8+Pj7W//1//9/1D//wD92zaj/Nu7u72t/fb//tWulgxq58aNUMuEldYTtcM6CH/Iljsw2BjvguXcteXLrAPiXt2+ezn9/xJGbiBHRY91VVnaymb0uqrD+Hh4fNntLmJl7wvJKu+vDwvIuDxPbp6al++eWXTtjZCX/oJxmRoALWvJulpaU6OTnp815fX9fBwcFXAea8KFG0B4yq1l//+tc6Pj6u/f393i4DMiDhsQWCIMUD0x/I+Xh4p6enTd/UD8kg58jh5eXlGo/H9e7du7q9ve0JndnQmgnQdDodbE777t27Vqqkd0EOHAtqS6gFy5zBly9fepPrvb29qpopZvYqZD9SVTXSxOkIyNAEja+fR0ezwscxn56eNjWIA2QUIDEEem1trRuPGTCUidPT0+51qqpOtAECAhsB5Pv379sQoBaRjaxkPT09ddDByEG9vTv9i6/rZQt9wrMkJzc3N/Xhw4cOUlJX/G48Htf19XVPvqyqpk1AzskiwIje/uu//mt9+PChDbh9ogS66OkCsMvLy0G1PZHQTE7pEP2jp4Jq8ikw9X2ymz1dqH2oJe6DXnheqEWGFEjE6GkmlQJE1FlOp2pmfxK8ElAIaiV6Wc2gGxy5gDh7Saqqp5ayRZw1oMa1qwSdnp7Wzs5OswA8K6ipimZVdT+0pBqy/LpetmxyL9nz7DOQMl7fe1bRoxeXl5dt3/kdepdgANnB7ABYoEZlFVpSopIvYRIUswPOwTfz3fTaObKPUjCcPsn5Un/ZEvpQVf1dNoCOADUljo5HR9kpK4Eqtg+Yk5/j0/nav/71r037PT8/7zjHknzyz1nBAHxJBBx7vrrvOUjWvXexCj8NJDg4OGi7pUebXLyuly+VdYmiIYXeof5FyQFGAFnNKtTKykptbm529f7p6XlKv+1hDLdJmf7Hf/zHZhiIpdl3+mU/493d3aY3svP02r0YUJczK9h/fhVFHADj3ElBpRN0zHczKfW77NfnlzOpA5y6ntShqmq6bDKYHM+5cheGLJ6ouiaTQVyqRaqqmskoxsfAur6+buBNbOB74mDP+ezsrDY3N3vYG3sGGNrZ2elEG4j3NawXJYperiQNSpjcZ0GNz1dV0yns17a9vd3Ch2aDCkOIJpNJvXv3rqqqqwWJROTETQEYo618DIlPtHBlZaXLv2hvVTVAaQSNSXOhuOh4iWpkwgex0MvBwHP8aAgS6qenp664MiZZfcgmX3Q29yyo9RxOT0/bIatSeB+CBs7o8fGxaU4U7+3btx1YJy1tcXGx+6fevXtXq6urvTcmnrY9vdIgCDIuLi5qf3+/nf3FxUUdHBwMHKmg+3W9bAlQBHD6Xzgc7zarg5rap9NpvX37dhC4oKGSz1wXFxcNjggIJUlPT091enpaS0tL3StcVR1wffvttwMUnczu7e11IpvUsdRhARR5EaDRDcFaIpKOJUCXuLJDSVVX4afHj4+PPabbtEYoIQpNVQ0qsWhLHGtS2jhF9iqphWkfOB7BY9JqBYo5jCT7qiWIVTXQb4CZ/uGHh4c6Pz+vP//5z+1g2YDT09OaTCY94MtxXtdvX1kR0L5wdHTU4Bow9MOHD/1+9A29e/eut5KC4qcOobHxo8vLy83Qma+A0wF+rGrWjwOMUN2rmu2hLNgF+FbNEH/JWFUNfI3YIIPh7KnivyRsSZ32//ljSKzdK1uWcUheC11jNzz77JFyrqrqOOK7774bVFLcI71GV3fuBIlUgjwLz07/Gz/PD1pAM8dOBoH7Xl1d7eE1bNkrK+f3WdqBzs7Oan19vU5OTur9+/fth8hOJnBkWPJCVsS6Ys/Hx8fa3t5uwN3PsrXBgDf6jU2zubnZwwYNzlGMyUEyQEa6kgmeJJC803OfcxzgTA6WS9ZaVvglUJbqJF3HNNNbaOWWM+J6vtj5Mxamz+jx+pg99wSTssKZ23nlADHMNr7U+wW++s54PK6zs7Pa398fFD0WFxe7BaRqppvn5+d9r+yQYtfXsl6UKKoIPD4+DqpGXiIhlyB42ITp5OSkOb6j0WwUOH63yhzOb1LPJFHj8bhubm5qf3+/t2xYX1+v//iP/6hvv/22ryOpJvjLOzs7VVVNk8P/VsnIwMpk1aen53HHprGaMFpVPYEwm4qzpI17no2ySW2BMErqfC8dcvaXZUVDAigg0GQsmE/6LLTW8Q8ODqqq+lnZ7mLe8O3u7rYjfv/+fU/jywD38vKyDQeDNJlMamtrq4cH7OzsNCXy8fGx3r9/3wH6+fl5VdXr9hi/05IsfP78ud6+fVsbGxu9fcrq6mq/G4kHmU26aI7UPz097e8mpRGCqYKxt7fXVBmDk5JJIDjkPLETyKDPZdU8qwfZd1H1rEfn5+dt1B8fH9t5AI44ENed04L1e6XRF3QJ1CRMkFpAVYJEKHJv3rwZgF1QZJ/V64eRQR/R3tkZDlwQgB6q+pTVH4H66upqB6QCX86RnQUIjEajwZZABmx9+fKlAxi0KcmA/TFf18uXIOLNmzf16dOnpjzt7u52rzeZAPZMp9OmBuegt9wjEU2SDmQLB+BibW2tzs/P20dDym9ubhpcgIDTMwktPcBQcF0qjK6J76QzDw8PbU8kYXyIQNMf951Vx6y48ZlsAqAkgzfPQ1BsZeWQf8sqo6U3FJVbTFBVbYdyC5OqGf2PjrjWrF64l6urqwGlnS/FUKDDbOXFxUXvvZY2W5BtYNXV1dVXQ2v7o6/R6Lk1SgXZs0bRB4L+6U9/GlDA2dGTk5NO3rKQAThQGf706VP9+c9/Hsg9ub66uup9FfkEVHLsGWy1BICSJqmSlTRpnzHUTMVRTEzO+FP6lgwloEj6N6ybpGI/Pc2mg9IV9gOjJiuJgFs2yzHnq4+2b9NbuLq62lVcz6+qWn/ExwkqVc3YUlrbslAzmUw6yV1dXe1e1fSp9BK7yrmzYvnmzZs6OjrqOQfzTIc/6npRooh+dHFx0ejm9vZ2l8MFVwInG9Z/+PChy7uCvcvLy6ZQao4XfO7v7zcNkjNSKTGtUBJiU2HbXBAwVZDT09N69+5dOzOB6MXFRVfLOAyb4kq60ugKrC4vL1soq2pwX4I/qEkGiUkDxU2HElXVIOG2Uokoi56mDAyMz6dYfp+9pBp5Hx4e/mZgTo6+51Dt34baAiFF91HR9fwZQ8aC4gs4vR8gArRJsJqJ7ev67cs0zf/0n/5TVdXAcB4eHnZidnl52ZXequokZHt7u6sT6+vrtba21lUMtBu9GmiJHAF6s743g2b0HHjfDL6fJXDj3IYxzQMfuTVO0nkcF7VvYWGhLi4umjGQfV7ooq41nZZg7PLysm1U9qYAapLultUAYI1gczKZ1Gg06gQgKyycIJtAl7NPJAMLwYRKEaCGjqf9cJ0G8qhCzYN7KSMCBO/VO02b/bpetthgtrOq2ld6h58/f+699abTaScXHz9+rLu7u3r//n0tLi72YLirq6tOPqtm4/X19pKfqmpmx9HRUQ/VoS9mDagq8pXz4+ZzYJIgVYBHrvN7dHF/f799c1W1bxDAOTewx5JUZoUyfa1kz3f4kUwCMynNqanAIXrMH4sDcnqzAFbQ5/6T9gu0whhKAB0jQdUpq5kopmzpPE3NOzw9Pa1vv/22qp57w5JC/8svvwxosa/rty2yhFqdsyjYyQ8fPrQ86l31vt6+fTvQD2Ci/QY/fPhQVVXff/99gwSOBcR4//59HR8f97AcVUS+HAjjPPoic39VdPMEJul51ay6Zjsdfiy3tAFmAp0UCAAcCgtsl5jSufgy9zVPq2ZnqmbtWBg82FDzFcWqWRyvPUK+IA5wn1tbWw3o5nfT9799+7Z+/vnnjn2WlpZqe3u7q6BiZvEsIBad982bN3V1ddV2dHNzs2NwdoU9OTs7+/+n6P5u60WJosAIH95Lz+bu1dXV3nJiaWmp9vf3W1kEmoJQLwFqaeT35eVlbW9vD6hYOeRB4KUBNvsAMqu/vr6unZ2dDh5R0kxYRe1IWl3ymtMhUgrBVFW1Q7u/n20xQfmgwRIvFQzIcZb8nS+Deg4GeutznpVjJ6XTtalmCLoNpiDwq6urjWajUbgGfxYWFgbTtxi7TH45RAi18cfoFs5/c3NTu7u7jVAJuNFqHx4eXnsUf6clqVFZzCqg/kMBE5opowwBk4AJTnZ2dvrnuWVK1YyO9eOPP9af/vSn+qd/+qf6+7//+5ZjeubvqlnzO8N5cHAw0K1MqMgnOauqbjinf8kaIJcqDBcXF4NJcejm884NEyATQEko3WcjULoFwaoAdBrAw6H8GgWG7VHh8cyBbt5HPj/vke1VCXXf7FsO3drc3GynSI8BRfRNwP7w8NAUYvfKDmdLwev67cvwGoPOAHoq/RKljx8/NkMGmo2JoSJ8dHRU29vbdX5+3rQoCWiyTLKawY9kpRr9eGVlpQE9OvDp06fBXrnkF3gkABTEJTPFeYEzl5eXXRWVNKXPI/dmEFRV+5Yc2KFyqXq5sbHxN/4zKar+z57pn8qqovNXPVPNMjZgQ1XaLy8vm9qe/Zbn5+eDbbRUCLM9hN8+Pz/vVg0J+jfffDMYNpR93kAwMZO4x/RZ9vp1vXzxH/wdWWCPJ5NJ7e/vt7zOUw6zqkefARTffPNNff78ufb29gYVLuDfxsZGjcfjWlxcrG+++aaTTcUHCZI4eGVlpfdYTFpsAp+SvKzQ5f6cVcPtJ/gDSV5W4ukcX+ea+FJ9f3Qs6aNVs+mmySbKFg62BHjsHCqgu7u7bXdsTYEpc39/P2i5qqquOlZV7e7udrIH8KKXP/zwQ8ek7j/jJv/mQ029FTMAbOYBLNcxmUxar7+G9aLRdSsrK43S43FTiv/9f//fe6N2AWoiegQ1p+1JZuyhyKh6yCoUt7e3dX5+Xufn5y1ckjibmELeJTSCQmV1wSmBNDAnKWZZsudoq2bVREF4KpDr8Hn3L6BeWZltbp9bDBBGFTgGg0EixCgNOM8Cwslk0uhR9htxRukEOe7t7e02ZLu7u72Za3KzBeiS8slk0oqS24UQekmw57q3t1eTyaSfvYD34uKif15Vg94ZdNhX2szLl6ocpFL1IBMYxvr09LR1YGVlpQeg0O008OR2NBrV8fFxByqOi84ssUggBEJJJwVQS0tLTa1xzQnEVD2DF+fn530efwuC6S1nk5XN+eq8qkBOSvYdekOmJci22oEcLi8v18XFxd/s0yTwnEwm9fHjx34GCTzRVRX1h4fnwTe5j2pSefJ5GZyQaGg24UsSFxYWesshTkzFxDE9ZwNSqp5pTWyBoJwd8k5egZyXL8kSm65ybkgKBsf79++7P1gi9NNPP9Xq6mpvn2JfTkOkyC35TlBiXt/IsGEOb9++bQZA1WyIzM7OTq2vr/dEcQGlKgSgT8CWYFDuE2qAD6AKg6GqBgFrVTWFz55oCXZVzXohbWFFj6tqUGWRVAqO7WE6n0zSezoi8JQMmqoM9MSMkOhroZD4Ssjd1+rqau8fPR6PBwwISanYJ7cJyv1aVQ2zt2x7e7u+/fbbrnYYjPK6XrZyixJg+P39fW1vb3fMMh6P65dffqnj4+M6OjqqN2/e1Nu3bxvYAy7ySyqUy8vPk7FT95NhlZPs6aAkKD9LNwxRUU1nMwywAVJmwoaVJ6YDyqS/EuPyy4BScQGWSfpf53GflhiZT5bMskmeEz3FdgII00/xq2cB9LHoqutmY7e3t5t9waflvXlOKLl+x19WzRhMBkGKg8mHBNGshOPj407qq57tnzaDr2G9eHsMFS4ooybr/+1/+9+60rC09LzXIjST8auqAb9+eXm5Pn/+3CXznPSZ1CpZfNUM9RPQbG9v93hoPxeIuj6JW2b5iQS5N5WuFHKOFV3Ey3c9WWW5vr5uNGVzc7OnDroen5e0osdm6V0gZ/KcBDFL9Ds7O90fyXl4VqqNgpGVlZXa29sbTCz1DDzz+YqKJNY9V80m3lIumzivrq7W1tZWy8F0Ou2qKUdu3yEO+PT0tCvC80MWXtfLFkNMjwwOqqrBfk2Pj49NG9YYrydNr+La2tpggBTdwAbQ4+D9ZT8S2pkmc4hbOojl5eVmEczTx8j6aDTqY1ZV6x2dEii6Dsa7atZcTl8BFiieqVc+i9IOJMrqI2fomXDgaWskVpypxFxCVzXbv1Di65mkU5SYTiaTpiJysDc3Nw3yYHd8/vy5+10kyhgAKiFVNXCsgv+06VWzDafZ6ouLi7q4uBgkEq/rty200X/913/tLSZMor67uxtslzC/wf3BwUH7QyADuqakJulxOYxJGwEZE9RWVVMeIeJ8mh67o6OjOjg4qOl0OgAXM5nFFiI3y8vLTdEUnF1eXvbsgRx4x+9YGUCSQQlg1WzYTdIsk5mTQ5/SHiT9zmfZGwEgfyeBBgCpyHuuaRM9FzZmZ2ene9AMF5LEmYvgnauOJOPBNY/H49rd3e0WGbR27CrPEJuCD35dL1sXFxddlZcEVFVXtk3rNjNDv9zZ2Vm/b7RgcZrjbm9v98/Ffto7UNGB7FmlrKqBj5OMihfptdYgcuTnDw8Pg5kc2kyqqkGXpGhKcMS8QMo3b960vQAOVc1YPpZ7YFPoHNCRzaJbvsP3iQfmaavsoThHcSXZhRLPjGfFIe4L69Ezq5rthcyW2X9W+8p0Oq2ff/65gbmqGsTOWd3E1gKqJXvua1gvqije3983Jzj35VKyhph7uAI5QsGJeCHLy8tNkzQS3Of1OjqmxNTDlgRJRu7v71spXQNBrZqVzSEg0ILT09M+TtUzknp9fd1N/qg+V1dX9eOPPza9UpCZggf1MFnVkJj5Uj7nBKWBFEEjXGP2B3KUFBJKks3ODMnR0VEndL6DBpeKyYgkIsUovXnzpoNkjfYqEygAgldOGQ2CguolzfewsLBQ+/v7PdJ5Op32XkOv6+WLHglicmLw1dXVoKq7vLzc281Mp9MOQiX2KsVJ86qqRsjJCgeh0ri09LxvWiaD+u3I3uPjY1PABaSCMPLj9wz9jz/+2IGwa/N5esuBsTlkUgVlc3Oz3r9/35V8gSDdQR/Xn6nnkw3g2PWGoZWRb4NJyLUqkcnHWeHn1FUZVYET9TQBEyIM4ZQEPjw8DyBBrzUcS9Crt/Ho6Khubm4a9ZUcq2xyollZYZ9UtV7X77MkWXxC0rGSIkavvnz5Up8+fWpadVbADXJ4fHzsHnsVBwDn09NT7ezsDIATCRodTlq3YxgucXBwUCsrKx0Eq2YllU1Aya9iuaS8C5iSAUBv3Jd+LIFy1WyCqucB+ZcAJlXMYgPoS7KbMjG9u7urw8PDTvYEqSaRJ602aYL+7++rq6sajUa9VcnDw8NgIvT5+XnT5/h8ACu7BOBVDRKEA+BVNquqK4363vj71/XyZd9E/mA8HtdoNKrDw8P68uVLP/fFxcV6+/ZtD0NR0NAzXFVdxfZ+tEDpnSNvyWSpqmbUaeWSRNLtpEKSId+TFGGcqEKaXp3VR39Ltp6eZnMw6FVVtcxL0pxDVTRZQ1XPwIs+varqqh1fi86bcwiy4qjIJGbABgKQ5ETR9O3zPZpaXOQtCiQoplrh6LXCCWos2yX3eP/+fd+rPEaBK7cQw/7jT9n1nPr6R14vShQ5NsGHREWPBe49yiJn9/Dw0DQnASdK2enp6QCVY/AlIErGXjhF/K//9b/2i6QseqnQTRjOL1++1NnZWY3H4zo8POzAVzKT/XQEh9N1Paurq/XNN990Y29SSzyT3C+GM3cugbDyfqJB2eyKOsfJU16Js+U5MkACzun0uREaBx59wTAKigbJhFD99NNPnWgLSCUWEEznzeubp/pydFWzqbAHBwed7Fc9O/63b992UmzIwet6+aIHZ2dndXd31xQIfT+MZib4EhWOSfLFuOLkq3bkcBWVMXQ5OswQS5r+23/7bw0+CFgNeIGsJrWGEf7pp58akfz+++9re3u7E1D6IFAFPOSeUJKdN2/edDK1sLDQVTcBd+otBzRf/UzHngF71SxolHwJtD1ztLwEwE5OTjo4YOPSFjw9PdXl5WUdHR31M2NDoNuAoJxUu7+/3+cBbKng65VOGit7dHNz06wEgEFSXZNp8bp+2wK8oEh9+fKlPn782DL7008/Dar46FX6GrUeXF9fd/J/enraAaVeQsleVXVfFd9GpxOwqKoOaCVZACY6oXqY9CmV75QTgSM9mmcl0ElJZjJqXD+KWVX1c/DZN2/edPIkeOTrsWvoY1LbLLpNX3IasuvIOCIpsvQqQZVkTdGnrGhgVDw8PHSlATU3p8dnJXRra6tbPRL0tTWAhL2q2p6/Joq/z8K2Yf/Y+Q8fPrRtX1lZqcPDwwYWFxYW6uzsrD5+/NgAIHaJBNH0d/Gq9/pv//ZvnUBkMcE71TdMvvRHKliIW4Hy5JKtUUjgR1FO+bGMlauqYwPHwEzwHBRExAMWe1FVPSmUf+ObUTvpD91MEMdSQRRXY+q4V+DqwcFB7e3tNYBlf27sG0PY7KNOf1UpPRe+MltTqmaD8DKuZ5skjVXVOl5Vg8+Ig76m9eKK4uHhYT/4jx8/dvJxcHDQSYKHChnjjLKCxTlpCmUQBVHJ3x6NRr1nEGMKkc0ScFW1cRewESoGmXALAjkQaIcNqfUbSIDwnSVfhJZicnCQ3fF43JVS153VV86J4nLIEB1GhtPNSkzyxgWMX758Gez/eHV11eeEsKgQ3tzcdHVDz8be3l4bHUG+ZyEIyMQWNdg9o17YrD0RLd/3LqtmzclV1ZXU1/XyRQ+8N1V9jml3d7d1TMVra2urEwNDJhg5Ff6qagpnTsLUc5S9ut69/Uo5WY5McqpSmPoo4OIw7aVaVQP9YUeqqoNQ4+yvr68b/ZUkkvvsXaqqTkwTJebsJGyOzfhzDo6V/ZOcYo6tz+oHR2u8PpBJcA8oyt4sdD9gnMBcv5qgNtkCKk0oNq4FyCOQFWT4jMELV1dXHZwLfl+HZbx8AQ6rZhUiFac3b97UN998074IsIpOlZtzV1VTwxcXF+vw8LA2Nzfr+Pi4Wx/W1ta6ErG2tlY7Ozvd265Kl20dQFvUOv5raWmp/fjd3V1TT8kMf8E3A1P5fCg/vSBT4oO7u7s6OTlpZD/vsaoGSWBWzSWFnqtrcgx2gn1J4Ddp43wg3wockpi6/qphD7H3BAwfj8et764P9fzhYbbHpX+LAfhF9sU59EMChLzXvb299t8LCwvda356etrB7ev67YtuPD4+NsuG/d7Z2enBUFtbW53sPz4+70t9cHDQMq1Czmew85n4Lyws1Lt379pP5+RgFSj6hnlDvvndzc3N3poJ0JsgPv9kQjYdkPjwPQBUcaQeY3qWrRq2qTNsKlvC6Bf/6VnOJ5UJrlbN9nYl73k9fDIflC0q2Am+53nZu9yz9LmdnZ3BoDp2VquOOJivPj09rfPz8wHd1u/y3lVJxUbOLelcXV39n2PqaVU1X3lh4XkqpiRPkPb+/fvuOfz555/rT3/6Uxt6dBLowurqau3u7nZytb6+Xv/8z//cmfnf//3fN4paNezHyyBI4gLJIYSSFA7Bht5QDp/NiYqcYVV1+TsDWteGlkpQJYwSPw7atXhOie5Q6nnHTbCrqof8cKw+wzEKQjnlRHElntlTWTUTaBVZ5+ew7CEkqETBE1RQAhUlPVC51Yf+yqRHfPr0qd69e9fgQh7na2ny/aMvQRjqI0ejglxV3WfA6QgeczuapLBmhco0MhRn9FXVkay8CWj29vYGI+85qH/9139tNNZ46RyLnQ4tg0IyWFXtFAXFkh92pWq2fYd/01kVDElgVXWCJBnjTCSH81QU95rBhXegauO9JM3IdUriodSqPRzZ2dlZ7xv19PTU+8ZybGjhVc+OHfXNtRjH/+bNmw5i6J1pyBJT1ySBEZgsLDzvK/Wqoy9fgIh8tmiOtjKBarOn3qsR+94XfSP3fM/p6WlP37Y9FLDj4OCg3/N8krK5udmULjKG5mbADN3R7pE9z1UzOZccZY9PVbW/0mfL3+3t7fU1OZ6qv/jC8cn99fV19+RK8PzxPfqbCRRZz5VBveBR0JoBvHeWFYeVlefpzfleBaUG/3kmfKJzra6udvyhpSPZTOzxZDJpWtx0Om2mBh+NffVaVXz5Wl1drfPz84E/qar69OlT96ft7e01S06SosVgY2OjZRywJ0blDxJ0887pt97B6fR5aje6ZzJYTF49Ozur6XTa+sMX0tH0dQBTi2wBSrPtoOpZJ1BGFUPoq2onZkpOxBaPqliSZTE6UNM1iM8z2WTb+My0AZJan2fz7u7uekijY4uTAVV67u0NzJ74vGfg72RQsinO7d9ssio/oNXzAfAB+76G9aKKoooEqhdje3Jy0r/nUL58+VJv377tvhi8Y0GfHh9Bmpf/v/wv/0v98MMP9Ze//KVf7tnZWTssf8vsKbOhGPqJlJzR5iRsNuh0vYJfL57yq7Co5mXDq54ACDz6HOclEYZEQi9RwDI5e3x8bAXIMrjAmjA6H847J5pUm6urq3Y6UCT0V4mkoPb09LSOjo466VctnUwmdXR01FtncJi5xx1qoBJ9KnI2/Uucs1LCSXoWqk+v1NPfZ0niBJD6Eqpmk9TQSARS9CMDNdtKLC4uNiiSfQqrq6t1cHAwcEZV1boPoDAEgzM6PT3tPeO+++672t/fr62trd7sHZi0sLDQvQccmt5h8kS+VBkwGugWR5LTQwVf6YRVyziVZDbQuf39/UY2JYlsmaBdgIkiNk9lpU+cz/fff9/BHcCqqroqOho9D/LRq7m6utrUGhRcdsvwKoE4BFYPnOdKn5eWlurdu3fdS6o6gi7+8PBQh4eHHYhnj8fr+u0LVVsCQ/7Oz887ALu/v6+Tk5MGTdbX13sK4PLycv38888DloBEjs7RCRt/O5dKocmbOeCBL8m+PjJnCAYbgT7OPyUYkswegIsAF2Cjb3me8sn3CdSyiifYzt7N6+vr1pN5qnjSS9mC1M+k0iVDIelxAJM3b940FfT+/nlLG20fwGJJtr3uJMz6napmFLacsu55GiDmWajAXF9fV1U1tc796ZfW5lM1HFT1un77urm5qe3t7d4DF2jJfmO/ianEwT///POgSLGyslJHR0d9TIWBrHI9PT21PgPV+SC2d3t7uw4PDztuMg/D4KStra06Ozvr+JZsVs2KAuRRO5NiSdUsaZUk8icqcqjg83TqLG7k74AoKNLiUSAUnXWNeR18FX3n2/lPrTPOoQovnhTj5rY89FdxZmVlpafGVs1ArmyfSiApn0lWaoHtQIBkJ3r/Sdf9mlhzL7pSiASDt7W11RvACjDH43FX/CB+NsO0h9SXL1+6EqXfiWPb399vehtaWCZNJnh6KQIgqF9SMiGmXjTj74VTTMvvMzHzgqEqEHf/vrq66j2oDLIRqPocgU4llxz5N2Rl3tkmZ911QWrTwXLYm5ubnSxyRJTc34uLi3VwcDBAUVHcOPbR6LlJ2HughKi0qhRVs/0XKY73iX6jaqUalU35kKz//t//+4Ca8Lp+2wIsCDwFSXTKgArya5gU2RX4OBYgQWWZY7y8vOzKhmZ77zSDTYOvcjsJzfy+m1Q1wRJ6lwC4ataoj2ZTNasW+oxqviAz9dQUSX27nGhWt7ED9G5wSHRTspT9eoAeVcekd3KInJlnCl1EwRbEApJQQDnGpaWlGo/Hg/MKFNkwQED2nZydnfX2Q5nQu48cumBSn3v905/+1N8Bpr1WLF62AALeD7uNlvbLL7/Ud999Vx8+fOg2Dnqgx0+1nPy8efOmJ2j6v0BQb2omlfZcBKTo+7dh9dXVVZ2cnNSf//zntttV1dUKFTOVstyOQ48rH4HyZs9AgSTwNKv6SZ1TjXPP/EgyYWw/JXmSGFbNhtfkxOGsXiQ4pFqSrTF8Wh6jqpqCV1UD9kBWitAB099//vy5qp43Y5d8A53fvn1bd3d39ac//akWFxfr06dP9e233/azTXs5/wwMTsnq7ut62WJX9fGtrq7Wx48f2waqjNGr4+PjevfuXcu1ROHy8rJ/Ln6S2FTNwJT37993VUpMZjKq7z4+PnZsurKyUicnJ3V7e1v7+/vdXlFV9d133zUTSBXv4OCgbQ59yRhcoSB9/8LCQgNU4soEXlS26SWQSQVNLAholngl002s7nk7Jjq7eJM9wRqY78M8Pz/v+EFMLKHj0xNMEo/QoQRaVUizPQZY7Nlntd81s6X5LNiSy8vLpp373B99vXiYTU4svbm5qU+fPjVKdnp62jQJaJuASBVC0oTa9ObNm/r48WOtra31hrO5z1pVdWVBcAPFyOoiZwVp9XkBo0EdEIWtra3umaiqVkRO6+zsrJMowktAGAsUhIODg+7jgNjksdBXoEDocgSeEmXPSaIPhJmj5+RVFtEBVHgEk1k+r5qNLIZmOqegFFUiKXLu5+eff+6qjmu4vLwcKBRlEiRUVdMS3r5920M0BKWc9cLCwmCT9tf12xew5ODgYJD421JFcCL4OD4+7uTq7u6uNjY2msbJIaJzZE+Od72+vt6OTKVZ/87Hjx8bQMg/GxsbjYACVnwHfWtxcbH3dUx9yL3CcqIhec4KNhmeB3vQclVQ0V8hhYbPCBh9Tl9yDg2QVPq9nsHpdNrDsySAbMfm5mZPn3MuiCRE03tUZUV997OqaiqQdzqZTPoYAkoTXpPyh5qEgiyAh8p6xw8PD93gX1Wvk09/h0VXJH4GNkhM9BCj+p+cnNTZ2dlgCqEEMQcqqG4A/WxPJRlip7MKKNitmtHRBUobGxt1fX3dbQej0WiwdcdoNGrgyPez2k6XT09P6+bmpgNT/Xvklc3PAIyuJ8ibVRIJGEAkga3sVUzmQVbC2QUVz6r6m8+TfZ/P9fj42Mwjz0NPFP3LZGB5ebm+//77+uGHH/o++d3sAQcs//DDDx1g39/ft38WaKoOu15B6CvQ+vus0WhUP/30U9teTDpbK41Gozo/P6/Pnz/39HlTUm9vb+uXX35p+aKTKZOZ0IiJxuNx22gyzf8AUHK4IR9SVR2vzjPdNjY2and3t4/l+OJZgIfkVqyY1FFTvAG3jmXYIRYdqqiEcn6iuKS4aralBLuXQHSyWySeSY9PBoTP6Mdm14Az2EzsJsA279fzkzyzw0CX6XTabSBWVo3nK4f5zhVTyJBK6dcAtr4oElfRgjI8PDzUf/pP/2lQXlUduLq6qv39/RY6zkWwY7z+2tpa/fDDDy3gEiXnWFtba0QTesd5UQLIHrSekFMGSSoHSuChO9ALSug6If2CWOfI6oq+yY2NjQ6aOQoCxbCglqXByKBOIEeQnDOnO/o+BfJzSo5yi5riffzX//pf669//WsbIu8kERSUN8NATDxdXV2t77//vhWO82IUsreDEURXurm5qf/n//l/mruvGp0VDPf5ul6+DMeoqsHApbu7532TTk5OukH86Oio93K7vr6uzc3N3s/t06dPtb6+Xp8/f+536++qGaUFDUs/HXBieXm5N4NeX1+vd+/etbFcWVkZVBj03lVVB7p0L/t56I1AVJDtmudlmUzRSU4yAZbsVXBvaDdAHhSaBHTogURXJaVqNhhrefl502z6n4kqAMvPIM6AE0G8d8g2ctjX19c1mUy6qoPaX1XdE1w1Aw6AR9nPlnRVCDinqceFg+VAX9fLFnSa/BraxieRoeypffv2bSfpBpGxxQIxesLPSCrOz8+rqjrpQWfLqkdVtU5UzahYk8mkg9LHx8cGlXLYk2oBP0yvtGLs7+93lRBI61rX19c7oeKDUcJQxsh+ovT04/+bbEoY2S1+Zz6pzAEfbJd/55AP7wJ1P0FRtsM7Wlx8nvoshmGTAFpJm5fwmRIrWRcPbW9vt19X+TQxFbAnwH1NFH+ftbS0VH/3d383ABlVjgz+WlxcrB9++KG3+ZJISpaqqpMq/nc0GnVCaHBUVbWcZVVcMkX+swDgGsnZ2dlZx2lVNTi/diwyTk5yLsXh4eFgPgXmTzLkcsiONg9g7sHBQduEZAtITN0Hn5MAkNi6qlr2xQj+8PO+k7YgWQTJ9pGfiNOTBWEQEbZA2pilpaXu+9TXPRqN2i8rkklGq4aAtSIVfRcjWF+LD30R9TQrBlC9jY2N+rd/+7d69+5dV4lMTWT8rq6uamdnp+lqT09PrRw///xzox+qUAYu/Pzzz93wrmdKb+Ta2lqPBWcgVSg/f/7cdA5Jnaon5MPACNUwmT4qF3RPdZOCcU6UnHJLMAmwBBWCq5opmeVsnL+quiqKSiAJgz77LFqr43kvqg1oSfZaenp6qr/7u7/r6kxVdULHgIzH4wG6ure31/2Nh4eHvW8ihfUeptNp7e/vd+LK2RrA8dNPP9V3333XzyiroxQMiv66Xr7G43GdnJzUt99+28mcYGY6ndbBwUGtrq7WTz/9VN9//31VzeiqAlUJwsnJSVcWLy4umhZ+f/+8/2jSPOmkIFWCIwk0sY+zMKRF4ibI4gTIfFUN+jpySd6yEgAI8XPOwDGyGkI305BnRT31w7/p6NLSUusa3fYcOG3XKzhPxJZdzGq/RFHDe+p/VvDR0CToJycn7ayn02kn3Rw756fHA6UJhXFh4Xm0O0T3+vq6Pn78WPf3940mAxFe18vWp0+fmoJfVQNq9fn5ee3u7jb1DH20apY40rNffvml3r592yCHwPHDhw8dCKGEA1eTKgVMQF3j+yQwdMA5ySFgTxzg3L7rWHTLtWcyh1FkOZ8qpECLbvC7fJ3rcjz65Fnwp77vWSfrQKKcI/4dnw8TmPouP06H3fPKykon5P4PCJhMJoPexqWlpd4OxYAxTK27u7uuDmLeSNbNYKiqrvQkW0j1+GsJRP/ISzVXwYHN39zc7NYIQKGJp+j5o9Go9fLh4XmyOJ8gKckJ/k9PT+03gbjkbmNjoz5+/DigimN5pXx++PChrx3L5erqqnWVD/f/vH6gy3Q67Vaj9LPkaWlpqbdqWl1drZ2dnYGeJaW7qrrgUzWr2JF1SRibJg7EUCLTEmaVuEwYk33DVjw8PPTwvKpqIFRRpKr6PWiNS/8rxn737l3LAKZVzhdwbab8o34D47ToeWdi8LRpf/T1okRR9Wx/f78WFxfr3bt3dXNzU+/fv+8R+wwWtFRWb4qUjBtSZyKiRGFpaamOj4/rm2++6T0UR6NRO0AN+gZMZDmZwYXij8fj2tvba+GVQCXVR7Kl8iIwzL6DqupqIEV2PPQc1FWBJ4RINQSSSvE8n3RykAuBruvikJ1PEus5C44lxd6VwJKyGFwBgSLYjCDqGQRWoI2+5pogxBcXF02lSt43Q/j09NxTKpC3kfLDw/N+UvYqMmL6ayjJ/9HX8vJyP3MomOALumkTdxRHSLXnTy5y7x8Gnq5Cszkc/5ew0Ue9F/SUTH/77bdNhUOLAzgAPaqeg+dvvvmmbm9v21gnsgnBdE5/6IsqN2fM6NP/+/v77t+knyr27EoinAmQZPCeVQm2JHs6JKo+IyjlVFQrcsiV98WB5xZEq6urXQ09Pz8fTKfTI8IhLyws9FAT4J7qrmDHMA3P64cffmiaLXDvderpy9f3338/GOD26dOnpnb/27/9W33zzTdtZ8me3pls+/juu+9qeXm5B07wr5kEVlUDpzs7O3V0dFTffvtt+xgVr6pq0LWqBn3o2UaBWpeMFODP0tJSHR0d1cPDQ717966DYkkr+hVG0vv376uqBqi+QIp+Ca4EpBlosUfuhW75fvY1ZiKYdlFwyYZIWD1/AWyycAC5AljHN/sAgCPO0V8MfP3y5Ut9//33zfhxbQJK1w60zXvgl9nT6+vrroycnp42GPW6XrYA8aPRqA4PD5tW/eOPP9a3337b4Kd+QO9/e3u76ah0NQcpjsfjjoUAqd4zYDcTii9fvgxaO8bjcb97flurhoJGDt7hQ+gp35tsM/fK/yeYT1/o4NraWlPGs9rnM3wpkDSr+Bn3VtUAvOWz6Y54UrLt96p42bOY/Y7ieXro3rWVYBpVVQ+dTDaR95Kta56D/9PzvEa5C7sJCNfeJibIuOCPvl6UKCqBQ1W8EIG+XoStra06OjrqgAVXmuOQzHAG9hbB5zX6WWKZnGQ9QGhmVdUbEFdVU19Q7fRFKalTws3NzaZuOo6BAPlC9WnZa8k5UFYgwBBP5eYcfkGwc2jFfLUHNW13d7edMCXJsco40AyR65Dw+lxSYozszuT44eF5Y3bJdd4DpXcPvudZMCTffPNN/fjjj/Xdd991EMoBux7IzMLCQqOlmdRXVQekX4MC/dFXItLGZ1c96+7x8XFv1ssISzr0WABZHh4eeh9E4EFVdQKF6shJMcQ3Nzc1Ho87uSMHkE3VZEmR/+dAhvX19Z6yym7kZLSsLrANOdRlMpkMKv0MPdszGg3Hejt30sUFB7nHJ31AaTc9Mnu1BGyeWQas9Hh1dbU+ffrUOuh5s0vsQ9UzjYjdcK9XV1edgOdU2GzyF4Rwxp6DoAOwlPSdyWRSW1tbDWKxqWSFDLyu377Y6+vr63r79m2DruPxuPcMPTg4GAx3wFAh497FwsLzFlXT6bSrCKpNdHQymdTPP//c9Djvcn19vQ4ODurp6WkwkZFvEQRixqgGVD2DN2hp2f9ouqDAGYMFI8UxbAeQw/EEuFXVPkLwm60XwB82QACbSZfkMXvBHFv1R6IusaIjKeP8sM+zJ559ThYGXvHF7EP6bm00gk7V5IeHh6ae//TTT/Xtt9/2sZOlIQi+urqqy8vLfp9aQV795++zPM/7+/t69+5dnZ6e1v39ff3lL39p2fzhhx/atrKXOZBmflgKOyvBySp3tlXwLxIL7xSYCdjItiWsrw8fPgz6ISUz9BrrK7dc80esyYe63uyTVfHL5M7v0VarZgNdXGPSwKuq9f3p6amOj49rYWGh9vf3+7v5bNDPq4bTURcWFmo8HndRpqpqf3+/dZ9toDfLy8tdZKmqLox4N+kH/ZwP3N3draOjo35ubAWGJP1mszES+WrXwUZ8DWDOixJFwcTy8nJXk7wIqJ6XqVdKY63AbHV1dZDUXV5etiAIOgU/gqpsXM9+CGgnoeBYsgL3+Pg42EfOS7KRatJY9vf3B6ie5DATOwkcJwmR4dCcOx2L6VKJ+OFj6216+/ZtK77vO4Z7kYwtLS01bezp6akFl7MllNkLUTXjzN/e3tbx8XFXZpPnnciuJN01JW3CPnuCGhUNwcfp6Wkn4xTVAABy89r79Puvo6OjrqIfHx/3hFA0K7rquRsyYRNZyH9V9dAMfQ7v3r1rnWWsBWm2axiNRj0YA6IHBeUAIHvzycinT586UKXLaBvj8bhBGddPHnMSYVV1D0HSU5L2CkziJFDjDI3hiOmA5NQ+kyqeCY6p/kkMPXNBNLRWkOk9OA8n7PeqPb6bFPUcGAIUS/tEh9OOqO4KcJMC/Pbt2wYRcsLjysrzxOqlpaXBfrav67evk5OTOjk5qb//+7+v0WjUk8NV46HYj4+PPUnY4DitCuvr63V4eFh7e3t1fHzcwKv38/Hjx2aBVFX9l//yXxqEoQsqFnRxOp02vViQmyCpxMaABig93wMoSsCFz86BMobw0G+fEczqlUyAmB//NRDTtbtXx6ArvxaUZVDoe4J036uaUbezPxkjIP0XG+Jck8mkqfoCVBVcCT9A+PDwsC4vL7uX88OHD/08xR76xIC1o9Got8lJQOFrobX90dfx8XGzcBYWFgZbA93f3zdjx/A/YDx9JruqSmSFnoqv0EMTsKuq1jsstapnsBZAIzm8v7+v8XjcQ1vYiMXFxbq+vm5QMKtx9/f39csvv9SHDx8GffIo6lWzrVkkgJJhurW4ONtHNenxCi/8DFq3WNn5Mqa0hR5ANpk5YnxxczID9EcqVLkPPd+qjcDwrNCzsWyI47I3qrGYgOIjz0AMod/SOxWTAIDEvXKN7DH9o68XTQxhTFWV9FLs7e01si5QkgxwTqiK0Iakc+hPMhBG8uKlolFCLNBkoBwMqUQq0UoVtKrql6hiIOCjoOPxuM7Pz+v4+LiRRUiMIPLi4qIeHh4GlRQJn0Z8wWH2N+RSPl9fX6/vvvuu6UZXV1fdy3B+fj6ggEJu9/f3a3Nzs25ubto4qQ5UVQ/1SDqbiqWge2lpqT58+NABJ+SY86+aVS4fHh66Ygv9NZ2V0aA8VdUBrFHrGoPv7u4adXZPEumcTvu6Xrag9VD9pHcKTCRIkDy9Rygl0+m0tre3e4ra09NTvXv3rqtWdJQ+a+KvqqZ3CmihagLPi4uLmkwmPTRJL+3S0lK9f/++9vf3a2NjoyciGrzz7bfftoMSLFZVyxlKXlUNArfb29u6ubmpw8PDqqpBYsXBqfKgaHIYjmmyseRLkmc6rCEdJiujhUm6JOD2eAWecC7Hx8c9tc57SZuVtDrobFZfIJ2SDaiopJH9MUyq6jkYurq6qv/23/5bU1sNAru4uOhncnBw0I74lR7+8nVwcNCUwfv7+/qP//iPHliiiq6nCQiicgGExNJJUFLyh6JM7lTvodpVVZ8/f+6KGrBUf076Z3JzcnJSk8mkfYGKqASWD3ZOdEv+MaspAj/3kOARGldW666vr3u6udgip5fnSspqUkoF1ZKpTBLFMpZYxXczOAVMAbJV9wSurn91dbXev3/fzyknMmIquJaNjY3BsCIx03zFRuKytrbWyYNJuBgM88/jdf22Bewmb+fn5x1rolHzIeLe1dXVevfuXfd6+2xV9f+zLYddr5oNgBHHkSMyJibTJ8iGLywsdD9eVuf15lXNBk4ZFsWX8RF8mIQqY8acwI+ZRxbpNR8r7sRWoFfLy8v1+fPnOj4+HlBrq2bTQ+3TWPXso7BaFJ48B61L8oFk1rnW09PTjpWfnp5a1zzPrGy6PjZSXIqxg+kkfpWYJygkKfesq6rtOJuDFXB8fPzV+M8XJYpLS8+DagyZ+ed//udG9ziXqhqMjPcgJWWMoOl7nBO6i4qeauR0Oq2dnZ3BMAUohKCJsVxcXOwmcX0C29vbndRA0FXAHh8f6/T0tJOgnZ2dpgNBMSCt2bsE/Sd4powmOvT4+Nj7K/qs+zRmOSuPiQhub2/3OSEbnE4+H88lERjPazweD7YpEXC7XjQ0dMHLy8t+JtBdQQRkRLLhmJKQ9fX17imtmg0FUPFFx/UcKCclrJpNdH1dL18SnPF43Aaz6rkS9e2333bixJipGlbNRu2fn5/X5uZmUzPRUKfT2d6HdE4vY6KBe3t7PcyKHKlok/3pdFpHR0cdmEEdsyp5dXXVCenV1VUHTlXDyoBk7PT0tM7Oznraqt5piKCANkEvDjuniiarQGLK2SbDwPX6varE1tZWXV9fNx0pEUxTUgEw7IbAJJMGCQLbkDQWlZ7T09O6urpqJ5lbA52fn3cwyw4sLy933/J//s//uRFQNicTX88H6PBa/X/Zur29rf/yX/5LVT1X/7e3tzuZADguLCzU6elpLS8v94AL8pubskPD+Tq2HMh3dnbWNtdehhgd2d+OhXN5ednytr6+3v385OD29raHJgFaswqQlbz7+/seoCT41LO3vLzcbSnJDsqAzndUVpNmRz9M/lSxYecMvtDvRD9tf5PBYrJpPFN+9+npqY6Ojhowc/38IQaBqqcKq/dxcnLS70qFiU0DsAGc3LfnJ2GQAGaF4vr6etBGUzXb0P11vXxJjIBtEvMcaAJksd2Lai+WiXaaqmdg5v/6v/6vboHiUwGOZPL4+HgA0Khg8Q3ef1U1IFFVg0qdZIxv8nMA5snJSVNjc+sliV+2mGTrE3DXUKz0pdhB4laJoMTy/fv3HbPPDz3MLTuSgWdSq4p5Jo1V1e0jnjsbp1oIeD0+Ph6wJLw/W/mlv/Zc5ApsngpxtmCQC7ZSrDMfyyt20eWvZeDUi6JxQYgJp//wD//QSgOBeXx87EQoe9AeHh56kubZ2VlXGfb29mphYaEODg665H90dFSj0aibfzkIhnQ8HtfZ2VkjNGiuUMSkkXFAkiCJCxRWIsnQQ9VzQIRqGeWeTCaDkjfUQCVDEvbtt9+2YFZVO8ZMnqpm052gKa5F4Ohat7a2anNzswM7iAyForioe5xM8t4FwIm6ChQkoJxVIj4EPvdtFFienZ21owIITCaTqnpO6gUbrtmfo6Ojmk6nTcN5XS9fniOQRBWPUQWMbG9v13/8x38MtqMxVIOxzqra7u5uV4cZ1KR3CZzo/PHxcU9I4xgg96p2ZPrq6qr1THJEJhYWFjrAIrfZE8Qxps3Rh+mzKtlkXbLHyd/f33fgCdDAGpCYcaSTyaQrgJBeDoidkEB/8803TdFOxJZuSogFDoAxlV5Vo4eH5ynDx8fH9fDwUJubm7W5udnHM6L822+/HdCJXJfkM52toEfv2MrKSldxM7E3iVpy+bpetg4ODrpaeHBw0CyZy8vL3pNN4GILqH/6p3/qbS8mk0lXnwyQyookmpdAF6VLi4DA7uLiov71X/+1KaDOubS01FMSyZag1/HmWzaca56Ktbe31xVvPggIaSy/CgOfAPxhdwRn831d2AX8KLCavmIspb6vrq7W5uZmVwQEq/xn+mNBpMTY8QA8CWDbB885VldXe/qi93t3d1cfPnxoCqHks+pZt+yryobaqiZBKnay6jn5SF+LkfS6Xr6A2IocABhVscPDw95SYnd3tzY3N3vIyS+//DKo+j89PdXbt2/rf/1f/9euFGayiX1HBrRZZMUv25xUDQ2xUUBYXHyeUq4ShuXDV2o1ePPmTZ2fn3f7UFV1PAxo4XdRNnd3d+vt27eDPRkxBNFG2Qb+gj7n9G8+ydyCZBVm9Z9/BDpXVT838bwCjm3aFG/evXvXQPXa2lptb2/3e5DworyqFgOxsn0OK2M6ndZPP/3UNgRgd3193Vuc0OOM/7NFJkFytuWPvl6UKJ6fn3eicXZ2VpPJpP7lX/6ls2Rc7vfv37eDQhNNpByqAg0UkCjr215jaWmpKS5eYpaul5eXuzl8/iW+efOme7IIeHK2k6amBwe6j+JJgbLvZ2dnp5FZG5pzWIkuCTShCaPRaLAxKPRVoEpRT09Pu3IhOE2qClTUtXEc9/f3XZqnfBcXF3V4eFgfP34c7IXDIBwdHXUAvrq62hu45nOGqqHcSjhVjXPowePjYyOd29vb3auhuoFTzkCoXAiiX9fLVyLLKysrPXnLnnsmiy4vL9ef/vSnpnWpCkK4GWoJxP39fX+Go5LEoB7rL4IifvPNNwOHlSjr9fV1HR8f13g8rv39/Q7yGFXOQBWUQ9UnRb9++umnQZBsEFZW/3JaJ7lFA5OYStjQa5OloBrPhhgCxQGrfrguiadqqArKDz/80EGBSut8kCsg+Pz5cx0dHTWKOxqN6uDgoB0+pBmaK0h33dvb242EzldhfQ6zAFMjaTqqKXTcPb2uly2VO75qfnL1v//7v7fNN9jow4cP3Rt8cHBQVdXVP0kLfyIQ2tnZafRfIMP3AU5/+OGHnpqaFFR7w/FfKvLzPYPZS0gOs/KctFO+PgMrwZokUD8k/1k16+fKwFIVQIsHOpzvYbSoVEr60iag0mU/VlUNKhJ6xYBvGBp8neQBtcxzlRxL3gDUdGh3d3cA+npWqis+i0UBlPb55eXlOjg46OePPfE6EO73WY+Pj82gId/80NXVVU8+Fe8qSqytrdVf/vKX7kUVGz09PdU///M/DyjOZJisiodubm46QdMjDuARR5I3VS/zKgx+W1paqm+//bb1mvyKi/k84I2K4jw7hl44l2P7HrkzKDJbE3K7uaTaqqCKeT1jNi8TVrZShVZsIP79f7V3J82NnkfaqJMkOI8gCQ7FqpJUstx2u8NeedX9z/s/dC+6N7b0WVINnAASBOeZ34LnSiTKO1FxQnXOmxEO21UsEHjxZOadd96ZT1XcKUBh5Bq3KmEqV1q0xQ/9XMUDdmooPs10e512u51SYoYshs2RORGRdzN/CT76omU2QKeBXg+t1WolC4KNdBUDrb57Dzc3NzOYKg4Fy8PDw9jY2Ij5+fkM4lNTU9Hr9aLdbmcHz+89OTmJ5eXlTES1UpcAasIYDAbJOK6uro7MMXidVqsVp6en0e/3Y2trKxkBd49hfQDO09PTWFpayoPO6QSIKr2rzCAdd8RQ2wwYf85A6HLq/Hwuj5FcvQYnMjuKddKdlKy12znqwcHBiDYdE0OHz7l0Z+r8i44yJgx4qVvzTk9P4+rqKjY2NuL09DS7TWbSvgQH+q2b776CLYy4TsP6+noSAff3z5s3JYOJiYlcdFSlGXWewjxElX/owjkrOta6z3xtenp65IoNgdvPRkR2v2o3rP6MhFOlWBKbbs3i4mIyqF7Hs8DsAo2eg+IX8VUZzZpEgVT/eXp63tBciSifTTKsHU6zoLq3/J9sbWxsLDqdTt7BZhjeM46IkfdRZ9cw256VGFoX5SASlpeXR+TmikQdDUBejPCdN/bLDTgRH6+vr2Nvby/W1tbi4uIi3rx5k0SsWUZF/8rKShb37XY7u3dAbV3SVH2ldtVcmYSM9XMV4GHCdfLksJqvgEC+7NqVCob9G6AawKt3Jstj9VzDD6TTYhZfU8CNj4/n7wQufY7x8fHsFJiB+nymSW7k54o9C39qdyBieIG5uKibeHh4mIsver1ebG5uJhFATQFAAqPuwqsAOiLi7Ows5fKuM0G+e3/eOwJ3Y2MjPn78GJ1Op/HPX8FOT0+j0+nE5ORk/Pjjj7G+vp7FB8XFxsbGSHdtcXExl8chxC1RnJ2dTb+uc7nIuqWlpTyni4uLI0uKZmZm4ujoKDd6Tk1N5WIa/g0HWjB5e3sbg8EgdnZ20rfGx8djc3NzpEv26tWrVBLIAfAlcrKqy2C8iEhsy++QRz4b/46IkQaForE+gyrprVvS5XMFsthU1UzIrMnJycyra2trI3nY9yY/Kl75vkJTYUdOasGkPO35U8j5znwG34Frq+Aw3xW5/pdgL15mA9A/Pj5moeZi34WFhej1enF4eJgHtpoK3OFy4bZEhPnvdrsRMQRDLjoVTEmtFGYOZT3YlkZY7GFphuQ2GAxyzs4hrXMGZigUZa1Wa2SI3CET9CWFCsawL3XofX5+PrdScuCIIQND+257FaYkInKW0O/WxbRoABPis5DgCFCABLBOLggkjo09b1MjiVKweg3ymMFgkEDCcyHPcEGwZ3N8fJwss8VF5qHOzs6ys2zAurGXmTlWQOrm5iZnH3QvnMt+v5+zvwIpBo2PkzMJ7MiGWtwoTnU0anA2N4B9l5S8R4CsJo+I544JeblzrZOvqOW/JGW62QsLC9HtdmN3dzdnqCQxwMyskA6CBKx7Ckz6LGKWrt/npAgwCfjWBTQ1tohjQGidVcYWK/7MrWGBq1zcMzSbIU6cn5/Hf//3f2fHybNTTEuG+/v7KVmivEAADQaDWF9fj36/H2dnZ3l26nxcY7/MAI6IiP39/bi5ucn7gCthOD09Ha9evRqZ4yYVxvL/+OOPEfG86l1X+ujoKDsTwJ7XFW8fHh5StgUEAZtV3bG4uBjLy8s5GwlMUSHoPpJoUwYZ2QCc6oytorR2Ay3rUTxbrkE+7/l4FogLvoCABYqraqWONPBJz7iOZHjGwPD19XWcnJz8k5LGs/R5yOdfvXoVMzMz8ebNm4h47h4MBoM4OzuL3d3d9FnfU5Xg3tzc5KjL+vp6Pk/F9eeqJjgHQTUYDOL169cj8bOxX25k3zr4sM7NzU0SqHBev9+Pg4ODvHbCtneKFQQEQhTxwN8QmpQf4oMcUkdzkIBUMre3t/Ff//VfcXJyMqKSe3p6ik6nkz7oPMths7OzsbOzExGRBbAuprNlTpOPaQg9PT3l+ZRf+NnMzEyMjY2lJJaf1oJpfHw8sTkzt+hnqGP4mnhS5/DHxsbi6OgoR0Wmp6dTuu+91BlKBR9frhhFHSGn1u5/JbGqCvL4+DgiIqXCR0dHI7sQ4HKx03dr5OS3bi/qKApMgmqtvjFqZiZqe9nhE/AUSRGRXSpS1cnJyVhdXR0ZkAeodAIqWAJcFaC6DAodP48NqKt2OVcFgj6bbXMCNKfFPpBj1VkIoG98fHgvoqCysLCQXQuSgDo3WYHo5wP3EsDMzEwyqzo5tbMjKFxdXeVmVJI9w/UkRDa4+s4Uiff399HtdnOzqUMfEQmMtekBkn6/n+BbgCJNEnAU0/X+PcHKBtfGXm62lbozyDr26enpaLfbMTk5mXecrq+vpzRSgTQ2NpbJznkyF+UsYM8ihuCLPApAjYjsXCAUXDhtg3Ht2EWM3qtqxiBiuEXM7+azCl/3/+lePj4+xs7Ozgj4qzJ3INE8LgCHIQYu3ZNU5TM1JgGk4oKfiYgsZD1D3RUgMSJSvhLx3LGkHPBv6yxWjT2e8f39ffyf//N/4vXr1+lzY2Nj8de//jVJMvGhyr19LjHL8xQPd3Z2Uj6MiKMoaOxlhoj88OFDvHv3Lvb29mJycjLPAWLNrD4/OT8/T1m4P3c/sa7E2dlZtNvtGAwGeZ8hEFmLLGfr6ekpTk5O8j4+IM4VVs4f0g+wIu+u8/h13k+nTRF3dnYW6+vrEREpzwY8/Tu+g1DUqakXV9dLsisAlZ8Vxd5nxHBJmudHEcUfIkYJZu+ZSqKqDHzOeqUPFcHBwUHmNrP779+/z2K8SoCvrq5yzMMYh7tfHx8fsyDwnYyPj0ev10sQ6plRiMjfx8fHDZHzK5iut7uIx8fH46effop/+7d/yzgpR6ytrUW/3x/JH7AMP1JQeW05wTkknYaFSUudUdj68vIyyUSE48LCQmxsbORZUFgifzR3nC3+wZ8tYTT2RQUGuypujUDUXMR/5H8FHr+o88owSFWoKJDrTB/Ma5EMfCFmVZWO7uynT59ieXk5bw+AexX4XhNB5TNHRH5vnhVMorsppvluEFa9Xi8ingkje04Gg8GIekBhSh0REXkzwm/dXtRRJOWwNIIMpIIMD10wxVD++OOPCewwEoo6QK+u08dSPD09Lz3RnaiSMF0wW9i0gbH+VZuMqen3+1nEmC06Pj5Odi8i8n9LGA6rGUKfbXJyMtfjWyxQZ720oL3fpaWlXJrh4DjYdSYD4PYZAc4qBfLnWNjKBvkMillFAEcHeHVlOYshYjNYnp0u4P39fQJHjmLj6crKSuzs7OQl5D6XArtKgQ3f29boAuEvwYF+66broHBz1p0LyUD315/Nz8+nLJy/ATHb29sj0mQFA594eHje8Lu4uBgbGxtZzJGK62I8PT3F0dFRyuZIHJE8YoP5YLI4AVoiErjNSVjCI8Bb4ENa58+Pj4/jw4cP6TN1FtIcCCBqbbgz+7lMVdzxPplnxmcVnv6Njr75JRtd+/1+RERem1O7Ofw8IlKyJpF9++23I9+J1yWfBTgs/PKd3NzcZIHgtevzxaQi54DVxkdfZhXMKcARIsjMhYWF+PjxY37/NtcCl8vLyzE3N5dXwwA04+PjCSZJ38wQYfgBOYsvXGOFxFB0OL9yuc49kAjsUtnUmXVzknNzc7kIA/nJ5yuA042EJ3q9Xs5qrays5BZC+c/70qEXS4BNsYDP8jsxD9D0Wre3tyMSevcLK1xrV9TPeV0gt91uJ9j2M5Q5gLrCT+f+/Pw8Dg8PY2LiefHVxsZGdkKQAmKMexZvbm5SatxqtVIN8Pm90I39coN9Jicn81nv7OyMFBIRkZ2it2/fZo5D9B8cHIzM3ioY+ZaO5OPjY2KwGmuRKM4Kkk+TBBnx+9//PlU8EZGLrnTckauaMX6udsR1PWFhuaTOJhvxODo6yvyI/NRlvLu7S1WP2OIMWzgjNslzimjYXQOi1Xq+K1wxCN+bv9ekenh4SKk3gsjyLB1MP8vvjVzZrlplsJ6Nz6C20Xjy3W9ubmaMg5XqeMnFxUWSXRR29bn/1u1FHcU6L4jt29rairW1tfj555/TKcwYqNIfHh7i3bt3CUqALMy2Q1SZPi1gEqzx8fGcrSKVw2IsLi7G6upq9Hq9HPiOGL083mFUIOpeKMwcXM7vwGLw/S4XmLvjyoGsumng0CIXc1PA8tzc3IgUDlD1+TlFTUikej6PbU8RkcBZt3R2djb12q6swHZwLkmb5ODp6SklwxyNjEBXyf15t7e3I8wSnbdiGBBYXV3NZ+3uLM4soDlXjfT01zHLE1ZXV2N3dzd9R+GGraxzp7p+/uybb75J38UEDgaDePXqVUQ8A5Tj4+NcrjIxMZFn2jysgOh6CCx5XQTFdyOGG5VXVlby905MPN+LJFlKGnXmSbHb7XZjZWUlpZtmo5AlACT2dmxsLNURzp3iyvkV6Ofm5jLmUDvwV+8HIaSAM1hfZTeSdGVcJfx2u51SWoUtX/Y8xBXPuc5dzM7ORr/fT6Cvi0kKyF/Nxa2srGT3KCJyjljHBPkmrgA0jb3M3LlGkl2loWNjY1mou8ZGDup0OnFycpLXm4yNPc/VHx0d5Tmvy2CAsYuLi2i320lQKMaQMTpX3g+Sp+ZQZ2EwGKTSAAHMN6lajDWYL4wY3r+JHI6IBGh1GzZgapEHcrfO6yNtxLSq+uFzcmur1cocDGx+LsflvxExImnnJ859nfvk95U0qUqj/f39kdgIPPqOENJ83nKNqampnB0le6cM0Fn23zc3Nzm//OnTp9zu2NjLjU9UIrDGScTK7Oxs+gQCiOLEuZSn+A3fQlDUeUS+5PeTZSOQ+DkM5VzJ8RoOfERRiRQlk4RtXetQCZvl5eURtV5EjIw1wf0KslrY8kdNCVhT4f34+Jiv6b3LtXXxVl0iVcnbfr+fhIs/4wt1JpBqyfdQ5x6rwgbeEMf8rLytQeSZ1rrEM1Ag2weyvLwcu7u7uZVcfXF3dxdLS0tfDNH6okJRwPLQDMOenZ3lQ6iXdpOg+HsMXpVz3d7extHRUSwtLcXMzEwcHx8nY0qyiNWIiAQ3ZuUkA4UmABcRKVeh/ZeEp6am8o6nWpByZLNb5hIEZCwN9uXi4iJevXqV84ecWHIgL5G8FJIOiz+rbCP21pIYsh/yFcEIg+vfWX++u7sbMzMz0W63R4ZzdTHqViYJ+erqKqanp2NraytZU+D6/v4+578UFsC47xOYHgwGsbS0lAt/tPg53Pz8fHz69Ck6nU5ERK4R7/V68fXXX7/kaDb2/9jh4WG8e/cuLi8vY21tLWZmZpIIkbxsR/O9STDT09Oxu7sb6+vr0ev14t27dzE2NhYfP36Mra2tvPcLewbsKYL8Tqwpv9IZn5+fj/Pz87xfzKXjETES/CWqOlfgnJGyIZfqcDkpHebXRuSI4TbDtbW1ZFnrELu5LQkHcBPLLOeKGK4Pj4ic3VPw+ixV9lkldZZw1ZmwWgj7d2dnZ6nAwPoClEgvsQxIqYu2zJ4C1YoQsS0issBF3ClOgRW+fnNzk7N0DZnzMiPFrzG+0+kkudFut3OBy87OTuZa5Ag/4F8RkUvhkHq1kJibm4uPHz9mfpDP5HI5zYIOOdRdfeRiwF9E5KwqeTu/qaMGlrxVv0WiUA/VXC1mfE5k+jtLeFZWVvL56UYgWut8oucmXpEKAsxyMCkn0lNhrAPLEDjk4bVr+3ln1EZSBenOzs7I8rZWqxXv37/PTeNAN/JsdnY2Pn36lIv9dDJmZmbi73//e85iwT07OzvZwWjs17G5ubmcMavNADlKh71et2a5Cd9R3DHNiZrP+AQ5Zt1yTYasQLGNHF7W9azjQZXIkKMYYrKOXEQMfULuM2pisZTRKHFCZxOmq1Jy+QTZEjEkYBVsVA2eK1/0vKpM199/rmioMlTzi55vlY3K72IIYlZn0s8iyigEYQPyfQQa1YD/Rlhp8Nzf38f6+nq+l/q5xJYvwV5UKD4+Di9l5kSSl9aq5DUxMZEHzqEyR/f4+Birq6vZzfDve71erK6u5rapt2/fxsXFxciCFgeOvpmT6Qwo/Hx5rVYrN5zW+SKJU9FqUFkiELQBsfHx4TIXbXHJQhHns7ngU8fCXVQYVk5YWScJC+PscNflI4rDiPgnZsafkyNFDBcHVIerTJT3LLjQXpP7ACNkPycnJ/leFQqXl5exvLw8skwA+0RWFxEpOdJl9OwmJiZywUljL7ft7e14eHjeaHt0dJQFisK+1WpFr9cbkZg5ZxcXF3kvlPuFSJR1AQeDQZyensarV68y4GLkBEqgt37HwFvd3En+sbKyMiIrU1hJJrp5OuS6avV33tzcxNnZWXb8P5eD8Ll6IXmVnPJz7GRNan7GtT4Rw8uEFW012XrOzj7wzv+Wl5cTKHwuZzWjaIsqxpbcf3l5OU5OTpJwq/Nk2FAxQwcV+Af0jQggfsjBAZTDw8OU1pydneWGZkusGvvlRhpqSQYCQ06VA8ir5AK+g6S07AGRCKR++vQptre3I+I5/u/t7eWMn58DjgDf/f39zLURzzllfX09Li8vc3NjlV8Dx3Ln3d3dCIGrwHPmFIO6G3XW1ZiIrsv9/f3Icibg0N4Dy3MqWYNAdTbFkiq7RaryTzkRGQr06mCKBWbUxIoqNYuIJLuqcgEBCt9Q0vi7u7u7ePPmTb7fqvSBH9bX11NNQQFwdnYWX331VX4XJycnsbOzk/OgtShp7Jdbneu3a4P/yU+aArXws72duqbOyvILs6417tciEZmINIH9xG6+IJfVO7l1OyMifZtqzCiUq5B03hReVICVhPI5yWbll3r/os+BoIH5qJQ8O36IEEViymGVBK0+GRGZv+pImTwMV5K4el/8vJK2rVYrFU77+/sxPj6eDRXvFzZVsMvX1UcrvqaM1Diq86EwurwfEV+Mj75oRnFtbW3kUDmIKnzSRQw1lkVgd6jb7XZcXl6mY5FldDqdTD7b29sxGAyi3W5Hr9fL15Es7+/vk8WfmJiIvb29iIjcwgrkSqSKF0UlKcv9/X202+08KDoukmmdS4gYblPlnJiIiOHm0ojnw21ttsCu28axzDQqvBzsOgOFTfEapDF+HyBemVizfwJYlRZ5P16HrNb/xm77D4A6NTWVRUREJGO7urqaHRGSCo5alxoBvpYS+TngQJepsZfZYDDILptlNvwQOeKc1tXTEcNrZ77//vtYW1tLuXSr1cpZjbu7u1hfX8+EY35O4vK96kKNj4/nhmGXhksQihkkgT+rMlNzU5g7Z1vXq9vtprRTXIkYAlAsp04/dvbq6io3E4on5hKxpjMzM7G2thadTmfk3EvuOo9VqgJESvgRke8PINbpRyjVQXezupWhfXwczg8PBoP0KeAVYeczRkS+JoWHGCGhT05ORq/Xy+fEDweDQW58vr9/HsT3PZtvbOyXm/EIQJJMCkgCgHwX4rozStaNCEV8IFNdo4AEWF1dTRmb8z829rysQ4fr9evXI/KtVqsV3W43O9O++4ODg/Q/xRaCUfe+3+9noSn2RESqF3S35SGbjW0dRshERCoE+DTAuLm5mYQjEBwxnP+Znp6OxcXFLHQfHx/zgmzKIH58fn6eM73UPAhPPus7qPNlcj+yVKF+dnaWpIvYKpePj4+nfE7BoZAGshG6pP0LCwuxtbWVl6T7/Qg8v0s8auzlVgk++UmOhFX56sHBQfzjH//IAmNxcXFEdYMkVMCRB5tjq91thRpMx1c+L5o0SSjWFEJV7syX+WDtUMqH5LPyrTwhf8m59R5in4mvUyOJUUtLSynDRCbBvD6Pz+rZwpIIGYVVlavqrPIjfnZycpLNooo9KJYo2pC6Yq+7TOvcYe1k1q5sbTLB6b4ncRu+rXFFvPLafvZLUOW8qKOIGasXjVq2UJekOHTn5+e5bbG2jvf39/Ohcq4qO6nDrlrMhv6rtBMgvr+/j42Njbi6uor9/f2R9f4SUA3Ila1RvJDOYZME68nJyQSwnL0uuVFIYiYeH5/vuNIlcdixwA67tjwWV4AB5CMiHcczcUhrQtANqnNJNTDVrVGC0vz8fHZ6FxcXc7YpIpLNqvKiOhfFgXRoqsS0zlpWLbjvgBwjYrhquNVqxevXr0dY2sZ+ublypm7grV00pAnWq3bBFPs7Ozsjna/r6+sEojZ8eS3nrDKftP98A1mDbUNwWHstAPPDGrid9do1cOn452ebdE9wR654r5K5901a5/0DiYpfc7U+v/vfBP8K0L1nXdRKmNSZKf5hYc3Dw0McHR3lgL1YYaBfrBWbLBGYnZ1NYkgBamZC/BR7fT6fi4xqe3s7mVzJ1u/t9/sjF6pPTEzE5ubmF5Hkfsu2s7OTbDrwIY85Z8CZ5XETExPJWLfb7Xj//n2sra3lCAUJV50fIhklMQc4+/1+rKysxP7+fqp3kLqAzdTUVC6g0SkjY60jDJUYcdZ1I+p+AiDWsiT4QN6oslP+o+OKGLbY5v7+PslNoFGO4V8RMeLzYht/rWBXMSA381OdjdodjYgc1zg4OMgOoZ9z9yWSS/fQcwLagUbvQVyq8/0Wkk1MPC/VWFtby84TtYU4QL3hPTb2Mqtd+jpLC2PZFk8KXUlJ2BERgMwRj33n8KyzjOC3DLEqcbxur9fLZg1FTcWCg8Egjo+P4+3btxkP4OcqLXWthA31JKm6kM6zc4oMkruQmzbw+9k6I+g5kojzEzOIXhsZBUtqNHmOn+f/2uygnKoFZo1LiDgKHc8R4Yy4qsXc3NzcyD3S/Nf3Se2gO+kqH0qEiNF70f3s0dFRXF1d5d3sv3V7UaGoKIwYbkSq65mXlpZys5iNmhXgHR0d5aINncmIiA8fPqSDmJ3o9/uxurqaLMhgMEj5pza0KyjOz8+j0+nEzMxMvH79eoQlULQ43JKUrgpHlCQwN9rlVTKAqTw7O4vDw8NYW1sbAXMRw2JakatI5YBeT3FNulL1zqxK57BIhm8lZsUfJ3h8HN7NI4FwKMsNdIAiItduRww7lJxJd8r2VqwraaB/J3jqwPo5bI4Ao6tJtqvgODg4SIa4sZeZrYN1lqHf78fbt2/zrNe7+wTZyuBFRBIba2trSeTUgnN6ejoODg6i0+mMbNwE4JxXAE2S83u9V9IqhVv1JV0wSa4uUSLx+VzS5XzX6xwUeHwyInKm2v1wEZHvuUpR/SzfBNwVqToA/E1RWOdPPo81lXQBVg3Ek/ZV33bdTZXckIQOBoORu5lWVlZGukMRQ/+s8Rto0Y0kiaKWAIqmp6ej3++PsMqN/XKrZEUlAM0fXV1dpdJmfX19pAPvnCnYqXOQfHXOsC51cd7Mvq6srGS+nZiYiA8fPuRWTWdGTtIVkG9sDXXVjYIKcKvdC/+Ri2sHDxCcnp7OcQl+U0dZFEXOrk5IBa58EFl0c3OTz9i9zwpJwNx4CrWOvEtFA7Po3iJCKSNq10QeczUYUBkxlJVW0G9WKuIZ3JMVev91hAPhiiiAI7w3hYjvpsmhLzeFBSVHje8KMHF8aWkpjo6OsoNs74W8qnFQyUWjH3wAPl1aWorT09NYXV1N4rLVasXBwUFsbW3FyspKtFqtfB/On/MxPj4eX3/9dZKNChznpe4CWV1dTWLK9Q1jY2O5Z4L/KfAoZ8zLiynyCf+Hs+uSqoihwsfntQTG+5HXFNKwScRw0Z24oris88n8V+wwP42g5j+e++bmZsrD+b+ZX7ExYtgUQiiLUb4v88zn5+eJR9Qfnsnk5GQsLS1lc+RLsBe1bDCTCgWMS9VtY/fMQlRpxOLiYnY86vKEN2/eJOtnSUXtrjkwFSQqdDDzHJGDHRwcxOnpaTq4wyToW4pRJSUKMgnO4a5sKonJ5ORkSlVor+twsSAuwZAMSVpkuJIH5tbnACCsPq7zhnXehAwo4rnoq1eP1PdGwuoAe/3V1dVMMBhn79H9Ojc3N3F8fBzdbjdnCpECh4eHI90N72dmZiaJAcF2eXk5AS+GDDPW2K9jdaZ3YWEh5ubmYnNzM8+6TqGrEWpSmZmZidPT0wQ27j67vr5OeVzEcDGM783guDMNuJBSRUR2A8jAnG1FGeCnyxkxHP43A2SjMfJlamoqJfDIo9XV1WQDFxcXR+50VFSenJzE999/n76NENLVIy2qIDgicj4TCwvUAtGSaS0Sa5dd7CL9q5tIJV8+hW31zPmMLY6Sf+0YYoK9Z8Wi56SABmx7vd7IBj0jAmY+JGWFbANCX25AEbkm4sT8J8Kj5keLXHQxqFBcieCM+a4QGDrRQNfDw0O8fv36n5ZJ8A255vj4OAtERIYCEPnjbDqPEc+jKTpfCj85KuIZJG9ubsby8nJsbW3F+vp65mvKArlVPiGfhwF0Aapqxey+cQp+pHirYyZ8G+iUG3Uq6pwVn4oYLqoZG3veNut1gF5xSi7m/xRJVdHj+yK1pdogY9VNJDX23YoTCgA53PMW9xp7mVn2VMG+7pLYzy8QPxRiJI91O2nFpu/fv492u505t5Ia9/f3uegvYlh8LS8vZy6lLFG8IhwRCHB3JT01HKanp3PJlLEfeccdoe5aRszI2YpLODLiuR7QnaRMWFlZyXEFObVugfW5kCpVTu815S35RuwTF9QZYhH/oS70ehVLIOPEEV3Qer1PXZrpd9edCIgwmOHu7i6Oj4/z+Wl+1bEexIKzcHBw8EXk0RcVivv7+zE1NRXv37/PACmBOFAeWNUiAzv+nNzKWnezFVVDjUXQ2ah3TVmDL5ADjQ7u5ORkOpxDhD25urqKn3/+OQsaSQewkuCs0q5/xiEUdnWY14H8PKHv7e2lZAgjpKPn0EdEgueI4Zru3d3dPKAAKiaxSk8EMUDdPJmizN2RDrBnR96wubmZ0lzPPiIyINnu9erVq1haWkpnWlhYiFevXuUderqruk51Q9bj42POWUxMTMTy8nIsLy/Hhw8fUmbV2MtNd2FmZiZZ63oXUi0KJACAq9Vq5WZLQNVZtRWXpEpCvL29TVaer5Bs7u7uZlK1iCIismiZm5vL+SNFirmim5ub6PV6MTU1layjZFI3wUmMgBNfqJ2RWkRdXl7G0tJSfPvttwlMEVvHx8dJ+JCEAbsUDbrr5Hp1AF4ii4hM2iTquopPT09xcHAQHz9+TMl8fa8TExOZQCVznUarzBFFfr/fqfuI8RRbapKuMzMWHR0eHmbxitgTU12vUSV/jf1yI78cHx+Pvb297DQBp+TPcpacqvM0GAxGCqe9vb0seNzLabnS1NRUrK6uRsSQqZcH7+6e79c1NoHIrUCv2+3mHY53d3ext7eX58cIgzmpOqtnRwB/kYcQJdh9RA1yBgAl74QVIiLP49nZ2T8tVRKDYA5XPMnTlWxW0JlLrHkUuQrcev77+/sJRP2391GlgbBAXbQHxyhODw8Po9Vq5TJAvo7wXl1dzfw+Pj4e3W43VT3i92AwGCkgzZN9Kd2K37qZm9UwsMSsjlnxAcqOelbv7+/zLlF5VfHl+6V4WVlZSfwprypA61VuyAznLCLyfRjbqMWWfPf4+JiNlZmZmVhfXx/p1Omm+d+1YeL3mCWG0eAFd2hXlRB86kwqZmEDr2kMwjOCMxAoln5REdTcX/3As6yz3a1WK8lfZJgmjwJdLtUJtHiyFvUwUK0VFN/I3zq248YGsQmZgHifm5tLxeNv3V4kPaWvffv27QhgfHp6SgnUYDCIjY2NDLCkGMCaZAAkYkR7vV6CPwWGYhIoxZpg9iRH0py65U+xBuxYFz83N5ebzCIiV9XXWSIzUBHDAdfPO39YPX/vouBWqxWbm5sxOTkZm5ub6WA2npK8eY+CPVCKNer1erG8vJyyBWAYIK7r9s0U+qySlQTlvqYqg/EMIyK/A6DWxtOISOd4fHzexqZ1b26FJNf677qRDvtbmV368JOTkxx8xnI19nKrXbgavG3nskZ/ZWVlZDsv4FKT3cXFRXQ6nfz3Cr3Ly8sscGoxQt6Jwfzuu+9iYmIit+ViQSUG5E9EJIte5xuRSLoD5qNPTk6yWKtsqc9uyRYVgYItIjLB8VsAa2pqKpnDetk38Ocz1vsia8zAGvIhyVfsqQXB0tJS/llEpPyWf5OS8l/viW963xbfIGfu7u5yQyU5qU4NoOEcUIBcXV3FV199FVdXV/G3v/0tCTvXNzw9PSXz/KUM4v+WTX56enpKKbYO38LCQnz//ffx7t27jPE60Ovr67ntVhy3XOzq6iqvPUHU8gudqIeH5wUO5oDn5+dzdKTmF4ue7u7uYnNzM0mGfr+fRZ/3xT/kRjnMFRZIWFJ4fw8ki0N1xsjCOvk9ItL3qSQQqeISHwFGqyTVa9cOw+fELrBKwVTlwDVPUyORGvJhIy1PT0/x/fffx87OThaVYoqOLgkazEO5AQ/VpWFra2vZdT05OUm5nb0KrglyPY7Y0NjLzDiCGe2xsbHcSHx3d5dYJmJ4BRssNDs7m8T55ORknhV/hiB1Pr2uGM2/7+7uYn9/P9rtdv5/Rafi6fMuv+JWbmTmD/ky4rfuKtD0QHxWiaVCtipM6gIZow1GnOosLaJGTIJvETIaHUjOh4fhtTjufa45Byk7NTWVOw+QahMTEzkfLHZUotgISd2SalRFPoR1xVOfNWJYWCPhkLee/6tXr0ZiC3L4c+XCl2AvKhSBD6wkuReg5kDt7+/nfIUDAezVzkC73U4JqVZwlXVh0+l8dR8kNJeKb2xsxNHRUUpCXM1gw5GiFusJ7LnHyO+zXEJBpKCzfACTArjVgfPx8fHY3NyMy8vLLNYUh7YxSp4c2WvVzoBn9PXXX2d3RVACECKG642xsQ7u3NxcXvApqSri9vf3c8B4Y2NjZJaTzEex7s9J6upl7T7T+fl5auoFrXa7ncWiwpdc1wZG81gHBwcpHWxA6K9jCqKTk5NkMAXsmtyAojpQXtlFiyYUG2aQIyKHs0loLKqqBQk/xuzzCYDJzJzkExEjG3H5hwIF2eKcLi8vZ8Ik7a6zyLoSEZHsr8IYeSEmAW6VadRZlYCZpApQVjn4/Px8doM+n3tWHGBKScYkkTorCnjUTgzfB4L5jGJOMQ6w6wy2Wq1kOs18u3/WIoDLy8s4Pz+Pb7/9Np/V3t5eAiIdLCRfY7/cdIydAZ0sap0//vGPeSfe+fl5gsh6TiMifvrpp/jqq69idnY2ut1uXpPwt7/9Lba3t/OqJ8trbLVFvkRE5iP+5qojxGslZ96+fRsRkTnfshvFG794eBgusNK1ts2x5oH7+/vMvXKyfAHkKQBrt0anUlchYrjQja8CfZbLkK/qKuh4+DdmEhGcnjsCpy7NiYiRpT6fXwvyu9/9LkmhqrQ6OTnJa7rOzs6i0+nkPLY83u/3U20jhiLT6p3MSFigX973/hp7mW1tbWVu9J05P/JqxKg8Uey+uLiI5eXl6Ha7MTMzE5ubm5mz5ImTk5NYXl5OElNRx3e8HiUJXO1nzdpVBQ1lyMbGxsh9gRFDuTZC5/FxeH8vBRpZZp2JrUvdSDTrPHBtcPAtGF0hVnOkz1ifHz9RQ0QMCzLyTkW2LaXiATlu9f+I5/rg6Ogoc5zPLd+Tp5rLJrfXudeokr+rfFW8iHjGWNvb29Htdkeu06MmdD0OGTLM8SXYi6SnEouWLXYAsDR30el04uTkJDY3N5MlBDYAzunp58u9B4NBXFxc5JwcBpT8aW9vL+7v71NuAoA5FG/evMkCBSOgiu/1eukQEcNtRL608/PzlPoI9BwKqPN7yGx8BodYMrAOd3l5OTe6RgxBal0K4/0MBoN/WtctcZuVrFLVuk2qJkbdB11CnwPTIkhZWa7LqxVf5zElUvIciYw8iUzn/v4+i3OflTQDSHh4eIiTk5MR8P74+JjEgRXJ/n1jL7d+v5/fsW4DwKXrZS6UfGppaSnJBUWfecDz8/Pc9HV0dJRnEsjzO53Ts7OzZGNnZ2fjP//zP0dm8M7Pz7MQ0t2uw/wYdO8V2aTTyM+d9UpwRMTIRtCxsbHs1CmuyMF0ZOqGxvPz8+xW3t7e5vUgNtbxdTHIjIMOIHVDlRydnp7GYDDIzqTlHYg1Pi3mRAzvgTL/dXBwMDKnrGC4ublJcqzOUj48PCTxRP6H7FIEYGOrPNbrR0QCEgTd1dVVc4/ir2DkYp/LGJ0tHUfn2obSiOfOgBy0ubmZeeTNmze5jOG7777LAmds7Pmy91breYutwsnvRyTwQWQqPz46Ooq9vb28tsXMubwRMdzMPRgMcv6mko9GVJxbKiKqgdvb28yLOu41N1eiJCKyC6+Qqn9HnSOOTU9PR6fTyc75yclJXrfhd9f5xsFgkGQUH5Zn5UKEl3zmXlhzpN5XxHCxxtjYWF6DNTMzk1d3IMJ1nKqsDqF1dHQUl5eXcXd3Fz/88EMCb3I5HaGIaHzzV7KJiYmcyUf2w0N3d3exu7ubDQ4Ei3yhi07pQU55fHycHTOxGnEuBkREziMiXuWaxcXFvM92ZWUl/10lUWC0TqcTj4+P6et1m7j3Zq+HThzCF5ZHpCBJlpaWkjjUSXfeu91uboLlSzr4ikckTERkzFEAI3RhTXFvbGxsZCmNq+koaRSnVErimCJbgazYIym3DZx/tlqtVFgZtVBHwO5ijK7x5ORkrK+vjxBJCnzYQLOsLtr5Umb9X9RRFMDb7XZ0u92RORZLFcz7mFewgrd2pE5OTnJL6djYWHYgOZGCYnx8PF6/fp0sDgmLL8KMQUSkBtjWUUzC09NT/Pzzz7G2tpbJaXz8+coASbsCK1IroK6u8gXEgCn/dmpqKjtsWB/yVM9Nx0z3pK5HxjI7ROSl2u4OtN9ZAavnILlgYiSuubm5bLX79wpwEuHd3d2Ym5sbuauGZEKHtg5mA7ikxKQx4+Pj0el0sqsiQfoOSNywzDoZd3d38b//+78NI/orGI28wuv29jaXM0REdsKdU1fXuNB5cXExrq+v4/vvv48//vGPMTk5mQuL2u12HB8fR6vVyvNCSqPjZSuws/Ef//EfCSYxgmRmQI73xP9mZ2czKYgFFWA/Pj5mRxLoqokCqKvdfj5psB7Aq2RMlcKQsejOmSOqs1N8sK4+n5ycHJEQmeklA6wyIbHDbLY44fnMz89Ht9uN5eXllHVjexXz/X4/OzbY0crwKo7FbsDDlTjiUl3upQPTaj3fn+lqAzGosV9ugJnYbNTCeSVJfPfuXRYy3377bYIuxAI53NraWnS73VhaWsqlcLpXFpvJEWRsg8Egu9kAZwVUijqKBDleVwvIk/MjnhfZKEZ9ToRI7Zbrlrl2wC4ABbKRhOXl5ZFuCjxR/bDK6AB0fmmXgHwPeyBEdDMAQfmxdg6R3BZpAa06o95bVdrUhX7eg+JOoVnxQ0RkPCUzrvOWZpEfHh7i3bt3qQa5vr6Oo6Oj2NraiqOjo4iIJIQbe5kh10kP+ax7vW2CNm8o/q+traXCA9H39PSUM/a+b7FVA4Ds3KKaKgkfGxvLuG4nSD1bx8fHsbCwEMfHx4mhERWKVP6s8O10OknywrfUJdWP4L660MxWT4Vf3R8A7zmf8lyVgsKun496KMb8u4hRlVvdM+DzRcRIQccfIyLjm9+pLnA/qX9n0RYM5H3A7rY0k9RS24nhfN33KDbCQVRBflZc+63bizqKh4eHMTc3FxcXF7GxsRH7+/sJurCVwN/6+npW7drQhnM5GlD69u3bDLyKHIOqh4eHmWjc28IxzeI9PT3lwLlCiFPNzs7Gzs5OzMzM5DpchSf9tcvDLVvRhfDaZnvosiVUf69DYVaIlI+DTE4O75u7vr6Obreb7CVghzWUqPr9fnz48CHZT/e31Q5o/XMzCpYEdDqdaLfbMTMzkyA8IrJIrpvfFhYWYmVlJS4uLmIwGKRkEGjt9XoRMVw4MjY2lgwXJprcqTo9ieL333+f14BUNonef2JiIv7yl79kgGjsl9vk5GTOlymEBPOI5864BDA1NRXdbjcDqrN7d3cXb9++za7Y7373u4iI+OGHH/Ks6GIjZXQRnSc2Pj6el15jDGsiI0W5vr7OGSygkFTc/COSAiNH/qXDTvIB9Oq2SxIY4dnZ2exs6uSTwPnfEiTVgE4kQB8xJIyqNMWCHXIzbK74qEAEYF2qjgyqrOjDw0Mu5QJoJyaGm+QUbzoxwMzu7m7+3cnJSZJW5Elkrb4X24dttiPVX1lZyYIjIpqtir+C1Tm6+l2bL3x4eIg3b97E//zP/yQIubi4SEKtdh9XV1fj06dPsbq6GrOzsym3cgch8KfTQVlQt/HWq3L44vn5eeY6591cYgWr3hc/ElssxAHWkClIEZ0VcciyDaqdi4uLzI2KZ11vRLEcPBgMsvOOaLHQC+Gqwyie8WOxyWIbnRBxRuwEeOfn59NXdFjroryajy3A8B7EEM+1+jgCZ25uLkdt7u/vY39/fwRIy+2VhLq6usqdCkdHR19Et+JLMNiFBHNiYiJndhV/+/v7+d3BNhY02jhuHnFiYiL6/X6SFZXsh2PlUfnQv6vfO7I+4rmIWV9fT8K35hHvE7Fk9tXyJGcMDpB7bm5ucq9GHamQk3URkaFyKuzGVyNiZGyjFqx8kkTcZ1Ko6uqJlRbNHR4e5pIgSj1dOqNd8rf3D3cgezQr5Hp7T1ZXV+Pg4CBfh5qobpZWGPs8GkMaNRFDYkfe161VGH8p/vmijqKB3Y2NjTg7O4vNzc3sIM7Pz49IzBSUOgIOkIOheMLQ6V71er2YnZ3Ni0XJFNvtdkxMTGQwlGS1/l+9epWzBhbfKAhrJxFoktyw7oorzvXx48fY2trKv/c5JVQSvcFgkM5JzjY+PrrRLiJS20zz/vlsEuBNEqOA0uWpRarXU+AKPo+Pjwnin56eotvtZlHHIWqgU+QpoKempvIuGJ2/h4eHZI5XVlai3+/noff5z87OUmbh+8Q8zc7Opubf6m8d5KWlpdSGd7vdL8aJfssGpJiLnZycjIODg9TeY+0Mgb9+/Tp++umn2NnZSRCpiOeX5ODffPNNModVVjUxMZH3Glb5FaJheno6jo+Pc8lLXRdvdkrXr87wKkycbWcf2+eckeAdHR2lxIu0FVgztK5zZz6IikFC4yP8oSZzf1eTmrmOu7u7nDFSVCJHJNyI4bU3OqKSdH2ftfDko3UoHxtsWQKw7zobsfj9+/c5pyxJ6ZI+Pj4mu+luVmC6dg4PDw9jfX09Y2VjLzPqiq2trTg7O4ubm5uUeV9fX+eYxh/+8Ic8OzaiOot7e3uxvr4eu7u7KXe6u7vLwgGIQdA6e/KF7vbS0lJ2Kn/66afcJm6+sap05FCkhEVNAO/p6WmSt8ZMFJ46c/I/aVh9LXNggLnOufdtbhgoU3gpTmsnRI6r8SkiRnKrLojiXCeVb9WOp6Lu6ur5wmy5XEGq4POc6xhJnaGu9xXL0WJrved1YmIiSV4Fsnub+e7x8XHK/8Vb76exl5mFaeTcyAKYTXGisBO/a/EFy1GowFGIirqFv6pQ5F5jXRQoyJl6ppeWltLPq0Rbg4Qk0wwjfOa+QHLM6qPiSC14qMceHh5S+UbV4rMp8OShqhyD7RSzJO+fK2wUxhRPZv6qAuDh4SGxhiaKOAeDz8/Pp//WUS8FtkIdnkc48TmvUxfrUUGoQ5iuqDNweHiYaklkD+L85ORkZFHmb9leVCjq6A0Gg5ibm8uLekmnJDOBzx18Kndfvi/C4dVu/vziTQ4yMTGRjCbgY0nG7e3tPyUb0jdSDAAHcMboArZ+j6Uf5igNHT8+PmZnU0GkgAV6b26eL0LWicDGKtI4FAkruQ3WQSGIacHMuqeGpI5jSCD+I8E41GSgErZi3QH23M1NcHLFguFf0iMzXAp9gNYW1263G51OJ4fyAVrzchHDRC1IHh8fx8zMTHz8+DE14I29zAQtnX2yK3K0p6enODw8TNJiMBjEu3fvcqECv0BgWJYhIX1+obvthxGRs01nZ2fx1VdfxfHxcQZwXU3A6OnpeQGLAo2EzP/2fsUGjGQt5GxujIiceXV9AN+WYIBXCc+8nr/ny/WZAd1iEj82T4LMIU8xr6hbExEpD/Lzg8EgSZb6e3RXLKkB/gBCMkMdVpezt9vtTPq6Si4Y3tjYiMvLyywCVldX8/1hXTc3NzMpzszMxPHx8cgVK+JOsyzj17GxsbHY3NzM4j9iOFsnv+gWK3ow3kggWzIjIkcXpqamcma81WrF8vJyFi8V2CHjSDzFi+Xl5Zx5jRiSMQgluVo8uL29zQ25VVqtm17JIp0DpI/zB4wCd/4ekUrhoBOnY1ilpfX9VcLHa1Tyse5J8OwjIonr2qUQA+ufi591l8Dj42POcpOvVul5XSBmQdj5+XmSz4jp8/PzfDZmmwF7rwmwI7pgFnH1S1mU8Vs3RYa4PzY2lrms1+vFzs5O+hvy296HH3/8Md68eRNPT89bjRVGzptCUS6Bkaenp6Pb7cb6+npKHI0U8APEC8nk/Px8jvDAz5WcqOfdOTTfCjtX/OucV/knCW6VtMKczj61DtzsM/t9r169ynhQN/PWMYtabNZzrnjUcKq+bgO/909tgTji/2JS9V3S+LOzs7i/v89iju+S94q5RsVqES+O9Hq92NrairGx5zvh+aLxH53aTqczEnd+y/YiNK4oIx+xFVFxtLa2lhIZhcbs7GwWbrXD5ovVfVTUOUgYd8BsYWEhLi4uotVq5UYzw6IOKgmsuUgSShIbshVFEekolsbnYTZB3dzc5BZEXYnKuGBKsVC1UwIEkIdKmuQ3OhwWRXgvDriuxsnJSaysrGRRhy3CsNbiGMMrgQPHki5mLCKi1+tFu93OQ/z+/ftMlPVzRgyZNiAcoD09PY1Op5NAQWCZmnq+c5M8QiH8zTffpJPZqEcm2NjLbGLi+XqXSg5ERGxubmaH331dfEyX0JmzqAnhwecUILpwZF58wHkj0TGP5DydnZ2NLFFBJmD1/T4F2/39fbKm/j8GD6v69PS8SAPAlSCqbE5Svr29TcUCFQGSBjvKsJWInoghI2mjnGH7s7OzlKZb+++ZSWiYYl1E7LCrCKzjBuaxyZ1OJ+MiXxaXzIP4Xs0pOgdAxs7OTkREyuzd59rpdGJ3dzc6nU6sra3F0dFRxkvgxO+XpBt7mbVaw0uxsecRkTNGzonCH/laiz3yK/4nB/v38hi/cXcokq7OACJygC1nFGHkSp0K5B4eHmJjYyNarVZ2+4x9WApBqinn6ZaJHYioiMgcFxG5FMLf83e5sgJAZBh/kmPlEu8Z0VQ75k9PT1mcec6eqc4+rFM7/FVFREonTsEhimNznT6L8Q5dYK+BAJeX+/1+yhcBWf4NgE9OTsbJycnIefI+G3uZwarT09Oxt7cXT09Psb29Hb1eL96+fRuTk5Px6dOn2N7ezq3t/PlPf/pTHB4eRrvdTowWESPKunq2bau+vr7OpouCqhZ4EcM7cCnCLNCBG5FPiki+4zXlPEWYAkbxpHiUv+RcjQkde7O9OpCdTmdkFvnz98svkU38NmK4sViOofSJiCSX+Dysy3e8Z8RUfc7yP8VDbTzBJvXKKTjc4h1xyoZT6jqqvdXV1VQXya/GOCh/4GDKBQt/vgR7UaH4/fffx9u3b7OSB3rIKgQqYAc7MTb2fNFsxHBG6uzsLC///OGHH0YWpvhysToKE6Dl/v4+Nw4BTDodFSjNzs6mQwNXmLrNzc2IeD7Mu7u7CfLqnJCEaWugIvHh4SEXAkheEcOLf2tbHQiuMhxMIfandnGwKBzU4dTZJG+TjCrTyuEc0IjIziGZjmdUkyF5wezsbGxvbyfrVQt7xbVW/NHRUV5svLy8nJp6CY1kY2trKxkyzieoWnzDMRt7ufV6vSRVbB3+29/+FltbW3F9fZ0dKGeQVFinW6Amt9LBMwOHOCAxdQ7JRGvXEBDCXFpItbq6mqy5bhrQZaAfA4+FJ/3SJTHvRCpT5yUWFxdzPtAqe0lC54JPKmQBRV0dxWHEsDsh4VX5T5WU3tzcjGxq9Oe1Ezo5OZkXZiNgLKZBaM3OzmZx6fPWeWnMpk3RKysrMTExEd1uN2OrRTVi0uTkZD7HKp+3hOT+/j6v9/GZdS2BigpaGvtl9vj4mIAO8LE989OnT0nyRESehZ9++inVGnXpCiLSoriI4dwuNtvsr5GI+fn5jOPuPiYjfXp6ythhuYqiE9GDVFGEAa4Rz2d9b28vOp3OSJwnj5Q7+UKVcEVEEqgKWICybvSmqNE9sywNdgBiK8GpMFWMAqfeo/8dMSRY3Bld9xB4z3K1cQqzUWKq92LWieqIPLfOcc3NzcXW1tbIXbN1fERMql0SJJKCuo7TNPZyqzkJ+SKOypEWx+geVUVKxcMUV8fHx1kIHh4extbWVp4nBUu3243Xr18nfrLXg2z74OAgvv3225x51xTwO/ibrZx1ZtGCHp1JCjb5Gm69ubnJRZVyZJV6w3mzs7OpRqndcCTj5/sBqmxWQ0PskIPN9VPiiYNe2+v6rIpfzRzFce3066xGDNVWVJHG3W5ubvLmBc0QI2ziYR0f8ftgAMqjils0Wfr9frx69SqOj4/z930J9qJC8dtvv82NeXTGHz9+zA4CRq7f7ycz+OnTp3j37l3Mz8/n3V4KLluItre3s8hQudMim58hZXUQ6JTdecjZHHqXuOt46Jw8PT1lYXN9fZ1OpdjTJYyIBEkSZcRwQNd9ge4oFKhPT0+TbT0+Pk5gQEpQ5xGr1KxK8BzKCkYxK61WK1kLGx+npqZyeQBJmfdk85ZZxKr3Bk5pysn26p1bwCrmxtpyG6KwKAoM0gPSJ4l4eXk5Ezn9uoRe721s7GW2vr6eZIou89bWVnbyHh4eotvt5qyN5w9gVj9VrEQ8d9G63e7IdrSpqakEj67ZQKII0iRYCsaZmZk4ODjIYuz8/Dy63W4CWMmlrud2xixgcZZqx8GZRy4hafiawlei4mOIm9XV1RxOBwz8blKfiOFCF93EStoAw1hUz4l/uypG0QZoK8x1IXUIqtwG+cTnbNm7uLjIGdSNjY3sZHjfOkZTU1MJVMfHx0dmWNfX13OmxXOjGvE6CszGXmZXV1fZPSRlrFc5IOjEx7u7uwSHdRmC7eMW2NQN2cgEMX5lZSU3av7jH/+I169fZ3HKqsyuyox1CjH2zp5zptiSU+osI5Apz15dXWW3sXZL5Ir6GS2SknfqDBNJvBhAls4XLafRlVHEyUXIV9tOvQbpfJXriW0RMfKM/bfxCUBcZ0fRDehPTk7G9vZ2/P3vf4+NjY0kbXTpFfWKSAQeSTBC1TNYWFiI/f39nOW2HLCxl5tiKyJyLs/dl0j3qozTnaKQmp2dTTmimOtcjo2NjWAhZ/bx8THW1tbi9PQ0ydQPHz5k9yriGX8jdBAEyEk+wRfFjn6/n9vtzQoaIZM35XJd+3fv3iVJVecoFYEKPoSI/88/+BpM7fOdn5+nsoDaB66t+Rhxo2uO4HV9RZ3rlmPlKGSQOWU1AZ+NiFQ41QJVDD0/P0+fgx2mpqayKSXOVZyuSSNGigOPj4+xurqaW90tJfoS7EVoXLGxtbUVEREHBwexsbGRRZ4uY21VT08/X7WA4XMZqEU0Zi8slyHNqFdXVMAIgAn2ZHK+REnXYdPRq12Bi4uLEdbO++FwBnYxmA4HpojcpDqEA2UuSMK39UpBCoyaMdDJkwBJcM0jcgaO6TPPzMzk7/F+MJgcjswAuKyriwUoTqBAfHh4yOsyfOc6N8CEdfmcXWfQM8Sg1uU9ft6MY8RQhqXgbezltri4mABCx8EVGBZT8NlWqxU//vhjvHr1KhONjjSftOBCYpQYIiJBqY6hM1+XPNQOHOmNAkcH391ROusKVAmR+oA/uJTY78A41q6C96srWiU//uP9kqFFPJ9trKuiUvcC4WNGm1zMc1FsiR0utsdEktvr2AOxtUuiWyChArvdbndE0i1W3d/fJztdQXH93wpVcbDOo3o9cUlM9N+Iu1evXn0xSe63bLq/JKTiubNhfEKXQGyXA7HoziXJcL/fj3a7nQsTjo6Oot1upxTNhsS3b9+OzPY/PDxfp+H8AXRra2v5HpwHBMLu7m5sbGxkPqiFk3jOr6r/T01NpeQdRoiIkQVOEZH+LXebHxoMBkm8yLuKOSoc75OkUwfIrK1CkETXv1HQVtKYLyKW4Qddmvv75+V1fEwcApZhH7G3zvpW0Dw3NxeHh4cZ05DVCnSkzcePH6Pdbsf6+npcXFzkQrqTk5Mkext7ua2ursb+/n4uD7q9vU0CRDfKkjFKOOSjXOOOS0WnfFbnjz9XniBn4aJXr17F3t5eqsmoz/wcufnZ2dnIfg3qN/jP77CIRR7mKxFD1QxfqF07/zHbiHCG+fiy/yg8qym03CkcMZwZJnWt8tTj4+PcIHt/f59LFHXlxKk6c/n09JQjI/JaLSTVEkagkKfyba/Xy7qBEkN3kxJxcXFxZNkPQov8nWpRbNWl9Ay/FFXOiwrFvb29WFhYSNmlgXiSCS12Mw8zMzPRbrdHBllJyIA/0jJSKQwlmdnJyUm8ffs2CzELMMxYAHX1OgrFSmUrPn78GDs7Owk46zC9uRCHNiIyEUgOJJbeu0Smk+DQkCDUhCYhYAPNLUh2fk9EpIZb6xqzWTdEAXtAhkRSASaAzSQx8hWMpucqCD49PcXGxkYGF3OHEqhZGcDlhx9+iI2NjQym9/f3cXh4mAWG+TEB1zIdBS19fmO/jim4kQCkSi73razbw8NDfPfdd/H4+JhnxdZF3TEzhGaSFYnOc0TkPA0ShgzSVmQJYn5+PkGrjXECrSVUlkTxB4lAEWUW8PDwMCYnJxPMXlxcRL/fj6+//jqvyJCozVB+vgkuIlLqhziRwDGAvV4vVlZWRginKjGR3CS7WjwqPmv3A8HDt5+ehptIScN9fsmFOoB8BbHleSCsJElSm7q2W4zsdrvZcW21WrkARZcCyBBPNjY24ujoKOfEG3uZKfjrHKJ8Bexjpv23mA+4rK2t5Rm7vLyMwWAQ3333XXY/zMb5Dt2pa0SBr6yvryfpCJyZF68AVuEodpB+Pzw8xP7+fszPz2cu9FpI3krK6OJRN1S1gc/jmego1EUetrEqqMUgzD/Ck5/7HUgxfq7b6NnzYe9FrgI2EcSexfj4eBazfKYqB/i6GSkjNhMTE7m8hAJJ7HCVWEQkkWCZnqIWgQ3c6syKTbVD3Ngvt8vLy5Sc1g49YtP3a2THXK4z5u5Z58Tik5OTk9je3s6zggwg/VbgUJCdnp7GmzdvUi6p2JRnzVKSg9/d3cVgMMglhp93Cp39y8vL6HQ6OZ9oyWSVyirmaoHjbDuD4hgc6d/L6xGRSrHqDxGRZFIdF+N7k5OT2Xyoc/2ekThixERzyesggBV8vkOFvGv+XEej8F1YWIiFhYXY29vL19fsqXFXAY4MEqPECV1Jyx9PTk5iY2Mjv+svwV5UKC4tLWWh+PDwEG/fvk0m7+joKLcbtdvtLCRpqGtFD1CRUt7d3eUdKX/+85/TwSJihNXRHaMLdhDMd6j0dehqh+Gbb75JUFr11ByfRLTOh3z48CH+/Oc/pzNVfbVZKge/dugiIgNJxHBYv85fkOV5BhExIkETjIBRzM/n68Erc1u7ouYlgFLvR/CoSdZ7rLKcGiwc+MfHx2SpMbE7Ozu57KeCYZ9JYDFbafENaa65uYYN/XVM8QR08BkLFCKGZ6jKrQC0iMiOtiJpbOx5oZQB7nrezBs6S/5ecNVNe3h4iF6vlxIbyaQWH/yPX0nEfNXCGoUc9o4M5fe///0Iu8vvxAOJCOOr+FTIOdcS2Pj4eGxsbCQgI2+rG9GwsORn5N1IkTpHpHCtXQCFnoU0Xh8LrYAn5Z+cnMyO/8zMTBbF5pj29/fjq6++yufuOzHzoiiU3L///vv461//muCaGqMuIgNYvpQk91s2d/7Nz89Hu91OqTVVio76jz/+GJubmxl7l5aWci786ekppeJAlXwkf5gRBzLfv38/0qVG4NYlEO12O56ehts9LbsyC2U5nNefnZ2NN2/e5OeS+0hn5fFKnvB7+QfYlX/FlbrhW+dCDvIZquS0xi4xSDdCB0dMquMrfgY+qZ3NSsaKQQjSwWCQM9FVbv75Jkn+NjY2Fr1eL+WJETGy6I901Pu5vb1N3/dc4BSdfoQyTOK7bOxlpuBGXNYxAmMQ29vbcXx8PLIURfMBFjIe5ZxTZ1HlGTNA0iF1P19cxFfkWEuWJiYmRmZbr66u8pqr6meKTIvMEMDOFUKTUsFZRtbIy3Le5ORkdr918ij+YNyIyAZHxcR1rMqZ1mVTlPJDZE1EpA9pWOh4ev6WUCr6qmyUnxt5IqmFwREyur1bW1tZ9D88POR4TSV4kT06vnx6cnIyJcSdTieLzojI+Pkl2ItuNAcudQcADl0yQdsAui7E2NhYaqEjIiVREtX9/X28evUq/uVf/iUB6sePH+Pp6SmOjo7i5OQkDg8PRxiPqhk2rwGgcSrB3dwi8FnvVHt4eMguItAFTC4vLydzg7UbDAY5L2XOiiNiXADw5eXlDBQSCEYRKKsteoVfZT84SETkfKDhWT/Dcc0zSo4RkS16TJVuBObGwT88PIzd3d0Ev96zpRbex/n5eYLjCsKPj4+zmPZ9m4Oh1TYYbNFIRIzMrDb2cru+vs4iR4d8YmJ4aW/Ec9Dd39/Ps0kKLQCaVVxbW0vgQt6IDHHmDatHDLsBc3NzuVzq9PQ0iyZExfHxcRZGCpOISEkl1hN7V+cPFT3et3+juJOUAT9/Lpk7w0dHRyN+HzG8LFdM40uuv+AzCmMdlNvb21RZKAzr7zo7O8tkRkXgGSKYlpaWRq4QqK8PvIphlgjoaiwvLyfoUDSY7SCJkQgpIEgE//3f/z3BbB3099ympqZie3s7CwdkQ2O/zL7++utcAnZ+fh4fP37MgqGqRTqdTvzwww95TlyPYtvhzc1N/P3vf/+nGW9bCy15OT09jfv7+3j79m2en0oGkG/pmgDFcl5dKqMYUSwphmoXxHm9vLyMT58+JSjVsQPO5DYKhToL77/rBeQkaXzR+0Buet+KTt0ImKN2M+TeOl4hhslzMA3l0vHxcRLPSBnxwMKsiMgrex4fH+Pg4CD6/X4SXsvLy7G2tpax0LPwbyhwrq+v4+eff45OpzNSYMvjn8sU+/1+SvN8xsZ+uZE6ugMwIrIwGxt7vs9zdvb5vlFkKtITUSOPKMjqDPDi4uLI7C+JdC0CqfUQ6P5ch8p1bfze7+U7CE95wmjE5zPMiAv+HhEjhDKlyvn5eapNpqamctEOwjHiubB2DtvtdsYT2JC/1OvSFJ+wLd9C+Ih9ruSBreU142pGV+qf1yU4p6enI+NqtdicmHi+n92z+/HHH3PhnFyOjHFNxtTU80b/et0H3IsIvLi4yEaWpXv/v5Ce3t7exs8//xz/+q//muu319bW8tJ0w+xYr8r6k1uSnd3e3ka3241vv/02ASxHqHe3AIgkHhg0l+8eHBzE5uZmJqs6w1NlYmYXMTeW2QCEfoaDra6uZgtfMSqgA6kRkd1Nizx0/+7vn6+oqJI02nPvC/NRGRUH0/IBPyvhVVmZQrh2PBRvGFMBAENEJqOY0NUAPmt7nMyUpLTb7cbk5GS02+0E0WRMrjG4vLzMGUbb9iRurLXnJlC5J66xX8ew7Zj3iOG1Efyrbj/VecAGfvjwIXZ2dkZmE7CBNPcSZ2Uf6zwvuTY/m5mZyfsaq8SrbkG9urrK+zirXMxmXpI0PlPl4QozAM51My79RaoAssgQwA9pJRbUZ2aJFnUCkGdI33KaiOEQPeArCVcFAaBfF/EoTnVmfI6IGCmkLcYw8+w5RQzvafSzukyk3ZhnYPr8/DxJNIUmtlWsIIeNiEYi/ivY+fl5nJ6eZk7D+ltegthcWFiI3//+93luAUkEQETEX/7ylzg6OkpG/9OnTzE/Px+9Xi9nIPnS5eVlkrXmisw1IlEsjEDiYN/rCn2EiTOtC18l2HLL+vp6EpmKpY2NjTyT9TzWXKaQ7Pf72TVA5iBsXUNjqVqVWOtC8AvP2VIoAJe/14Uc9eoSPuw1mHgnh1ERwDb8882bN/m96RzDB+IXiSLpoFmp7e3tLGBhAFd/KZJvb2/j6OgoyaGff/55pCPa2C8z/nZ/fx+dTie3euq8wTozMzO5pdTfU/Ocn5/nPO7Dw0Ps7e3F69evM9bDyLBhnXOzT0JHTWdbkejcUCA8PDxkcaKzKNc4x3WUwe/m15SCEZGfC3bwvlx1FRGZL5DE8qVFevKkxgFiSvHU7/dzQ2nEUGoND/udiBz4E8k6GAyyy+8z1p0mcHCv14u1tbXs7MMsh4eH2eCpDRaYdmNjIxdx2tp/cHAQ7XY757unpqbi7du3MT09He/fv49Op5M1CyxTG0kRz3fXfilk64sKxbm5ufjTn/4Up6encX5+Hp1OJw4ODpLtM/SJfTTMaQ7CnJSksbOzk4FQ1yMiso3vXkabGA2VR0S2i9+9e5fAx4C3hODQYhF0DDEKDuXk5PPK+vv74eIPy1dcXE2OV0EcBjViyFLWIV+dAwdnYmIiF3VoXVfttWTn4AsOAHPtTnISq/MtspE0MUzkd1hjjOPS0lJ2WiRRwHt8fDwL8lpkbm5uphNKjg8PD7G6upqBqtPpREQkK7u7u5ubwPr9fpIL/X4/tre34/z8PD58+JDXlTT2MgMs6/dtyUTEcEbKHV2KjePj4/SLN2/eJOEiuJOQ1RnezzcJmqGroAybenh4mPNJSIMqqUYy6VpUVl+R6HeSn1xdXWXHBYjFyjrf/CcicvkTyZbOZJWcI3aqdLcSQ94X9vDxcXjpsL/D+JuXjoh8Rq60WF9fT6LF4gPxxfwkQg2QFy/4dCV2dFvqhjrMse4uRhU5UKW7QL/ft7S0FLOzz5erk7BThzT2y21i4vmuQechYtgpvrm5yeUUT0/P8/5IUKMDrVYr/vGPf8Qf/vCHnNff3NxMmdzU1FS8efMmF90AVkYGSLPv75+vmJI75G5nGTuPbEAYVKmorj0fqX4WESMz/RQ/iiXvQW4BSj2HCnwRpX4/QCeHK0zrzgE5ssriIyJfQ7EFA1RwSv1Dyit/khZ67ToPfHl5mUASAdpqtZIU0IEhd48Yzv+7CoC6QIysm1QfHh5GNlV7/u53RoA3/vlys29BoVbPAKICyXl9fR3b29sR8Xzekex2dchNGxsb+RpiPrLUbLmzoxCLiOy+ySvm9RGcMCZ1AOLHjos6blT/DbxZmzcaA5oafKJiTPmGBLXKSvv9fu6dkKOrXFyOhE0QoK6nqFdc8N3aXOG3S0tLqXpRWyA262JIEmAYR7zSrBBn/HvNin6/HxGRuF/8guM9f3nx66+/zrrHd35xcRFHR0cp26+S/y/BR19UKHIOunrgLiJGmEDBemJiIr766qu4vr6Oi4uL3ChofT7WkGPW6x4eHp7v6rP1S/sYqMPWSCJ+n8LDkCrdcdUlYxoUkAI+SYvlHQ68eQkMARbVf8hdva4A4/1hmQC6iOHcgWTh+gxSn8o4jY2N5abFg4ODGBsbS7ZSux7r4z0ZtJVUSEE9I2BZUvRvAQm/X/GrEFYgY3nMUZg5PDk5yWA2PT2dEijP15KbiEjGqd6Z09jLDOtY5VHOLv29BNVqtfKSdZIQMg4dM8BOh2FiYiITG3mOPzdDqDjj97e3t7G1tZWSHtdcKPIEUoBNDDDrpENqzlWhJmHrxpCIiE+ShKJSIlJEWsDhWUQMB+oleOfd/wbmAEmfHWiVMMgBx8bGclOy98MXxDVyIEWz/08FUcEuQGh4HjDwTGpHlMydnHF1dXVEuqSYsGFO7LFNcWxsLJUSOzs7zWbiX8HkBFfD1M7xyspK7O/vx93dXbx+/ToXRB0eHsb8/HxKjL/66qtUbLjY++npKZUZigu5xrr3On9Ur7RR+JEu8j3dN/Jv/wFAnW0+cHJyMjL/eHh4GAsLC7GxsZGfX37StZSrFKYRkUSP3Oh1ER2KxYjhjJQ5XzGgdtYoJSqJ6s9I8+t2RfO/8mtEjPwMKXydF+WPtnwDmJZm3N7epvJGF0ZcVnju7u6OLC9BRD09PeVZELMUx4glyonGXm5TU1PR7/djZWUlZ3FhqZoXayesLiK6uLiI169f51jT6urqSPF3fX2dG8WNYnyuuDMTXBdAIjfhY3FDrnOW5EuKGWcGSVh3azjDCFbEh45kxFAi6t/LM/ISYpJveb8KbPPudTkjean3OTc3l/iDP/t9EZHFrT8zR40sjnju+vf7/SRbFKCLi4sZL/g0XKAjST0hv7vKygI/V8CJbxGR6h1+XDcZj4+P5y6Oq6urjMtfQjcx4oWFIqAyPz8f3W43tcWq5ojnL3R5eTlb5Dp/vlSyzrOzs3j//n1sbGyMdAsUaD/99FP84Q9/SDnj7OxsLC4uxmAwiMXFxTg8PExW35INHb6NjY3cOlQ1xQ6ZWUZFEzaTg0pcHCUiRtZPC96fsz1klrqBisMqVSMTAPgUUbqFdZYqItI5MFCvX79OYCrIkOfoNtR5QcFNIQbEA7atVmtkoYcEpluhSJyamspLSskUJE/SGW1/RaiASt+OVRaUdGkmJyfzfzf2MiM9dj/h6upq3gP19PQUP//8c6yvr+efOYN1+yKpC7mFLXD1DqTHx8eUoyBdSHTMAl9eXsbx8XF2NhA8zkldzgBs3d3dJbDVyTs/P49er5er/cUdhIOOTH1viAoJHBNszXXEcKOqpRv8DPkiAdaikUlytcMhuUmEZLt8XmdBolUs20rJut1uMpZii/ghzi4sLCSje3l5md3D6+vrXGTi+SLKLE4YH39ejLO+vp4gwd9VtrMuGtB9buxltrGxkTEVIHIGpqam8t4t38vExERsb2/nTGHtbHz11VcjZ7feaap7Vcc3ZmZmotfrZW5zf6HN0yTlzgLST8dRwYLx97PyeZVf+/O6XKIupLPxsS7sqDOU5F31ah53usnZikbyVt0+r6NgrF0N713uBsQVxvYQ+Bn5E4AU31wbc38/vPTb55LjzBUCynJ+r9eLzc3NfD9mv3Z2dhLHiMV1KdH+/n5K3zudTpJ2dZFgY7+OKfzN2FEBfPr0Kba3t1P+qKGhe4WgF6cRtDMzM0nQW2iim722tpYkve5XxHCXgF0XcgAFSF3sQinj98pBcGiVnfMtBadGzdnZWXbZjBghceVjON0d55pH1AJVFVfnacUpfiRfM8SkIrbGC7m7diE/ffqUCgp3rPP1eo2Qv5f3amyFhTWu+FNEjJBts7Oz6W+np6eZV+V+uOPzot77qoXrlyI9HXv6Et5lY4011lhjjTXWWGONNdZYY/+v2Yu2njbWWGONNdZYY4011lhjjTX2/z1rCsXGGmusscYaa6yxxhprrLHGRqwpFBtrrLHGGmusscYaa6yxxhobsaZQbKyxxhprrLHGGmusscYaa2zEmkKxscYaa6yxxhprrLHGGmussRFrCsXGGmusscYaa6yxxhprrLHGRqwpFBtrrLHGGmusscYaa6yxxhobsaZQbKyxxhprrLHGGmusscYaa2zEmkKxscYaa6yxxhprrLHGGmussRFrCsXGGmusscYaa6yxxhprrLHGRqwpFBtrrLHGGmusscYaa6yxxhobsaZQbKyxxhprrLHGGmusscYaa2zEmkKxscYaa6yxxhprrLHGGmussRFrCsXGGmusscYaa6yxxhprrLHGRuz/Ag30OCCVmO4OAAAAAElFTkSuQmCC"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Channel-wise images\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Root-Sum-of-Squares\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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N7XhR2teRI1U8msM9UtYPUZw5HB5Sf92xqfPctrAz5QyCRHtr+T4DX2OrmonMFl8OcoqGhSD/V+D2vT2xnrXknZTG97wwbe5lBGo0KJ5VdPAUTWRJRxNJxxaCBdf1UHstr69Z9DwmFdyRMGBrH/WxkGEhzPfYwmfaqeCMjDT89xJ8PGGc89oiGiufeMo6CrN6iosNXWSCc3tVXktEB9zPCTRP0W4jpHKKGeCNH1/TqAOPi0dDtOfJl4gGPB7aT/ZKtS9Vhgq3EfU/MpR0XnvfXEeVzAFEx9ByXZHRtE17Hl9uYwR6z20DXr1emFz7HtXj6bpt4YucEAbYhfXAoJYwX3tMHVXwECssarvOc9vglmsnV09OcOYEtFdPVLaKznUt8jpl69b1GNrm6LWNl5HDpUqpbwueofOY+h8zf6r4kb9z7UfXckb5tgJey+XaBUTKNyqTM45z+OdwrVLMdXHYBTym/ioD5qH4/Fs885jnGIryEbU0m83PkImqU0+By0dnYeNazlNkq3mbw0k8/LgcM1bkHfKznvUd1aPtKm34WZTxLE0Ppxz9vX4yjuoV1K1L6cMCJfL0IjpovZ6hEJ1fHgmDKuHGHqouaShe3lh5uHp8kzN6otPauP4c3rjvRaa8MdCx12venPTwzuV/eG1oXyIhHNHT2+rHCU7wWnlrWtUylYKHXxUfezLAm/d1ldVDjCiPdhqR0b7wONUxALw5FtE4+u31yzN06kDUBurTPfWRPvGezckhlesqh/n/fD6v1ReFnWVr5xSMR+hIkeSsRm+AvVCpVzYnWCNr01OC6iXpBGSh5jF+ZLGWZZndZxn1hWnh4aRKHt85BYrwcdQOP1M1yaKEIqUlKwStD/cUb8aF8eW2FSdvv7u+BMCjN9PdowPTVSduVZ38zfjmlHSk8Ook1zH+2rbXXk5YRXufdYz0CEt9RueJNz5MOxa2uIe9yriHsdacliplGSkqxT8q472rPDefPaia31VJod4OEK8vXn88o0hB5UyO17lOr33PwKuiU8TTfD+nF+oaiB7ugJy8KorC3YmyDez0bO0cQXLEzAmwKqHpHfOnZaI2IquLQfvjCXzcixR/HStxW8uxjpVb557XjredrG4bHh084af3cuOXez6qM+ItFSTexPSUUWRoVfFzDg/+joywnCHp4e4JltxBFDmoQ/eqOa4K2+un8otHX6+MtyUokhc5wf0YyPGRtpvDqeq5OoZeVE/VfPHa4LHK8aE31nWf9SBXNjKiPL6qM765uZ97Xo2SuvhuC49SzrzXLOd95AjHzOMlT7EC9ur0JnBOAKC+HFRZkFGYPWfta1/xXweTLVLGQevJMYXirddzz3BCjZfYkmM4TzFoX3RP+2q1csdXn61jfEERRf3V69saBJosqPVFY5XDwwujq5epBoHi5/E0LHcuq1uNIvyV7mqUML7eqX1cJuIJpU00h71QZFmWG6erKS20XW8e5xSWJ2BzPMFlc3uduZw+q+1G+NRR6GW5uR2PEwMVB8Ud5T0DNWrPbHOc6sjZnOGbMzp1/DgBT6NiOb3kzSNArq8Rb3F5lUMPhS+aEGZWLxSUE2Baj3e9roXiCY66z9ctF3kNuXL4X+fZqom6DURGwC7aqHoup2zq4Kh41m2/Dq/l6tzm+aqyqpAio8FTPtvg+lg+0ToVHuMt5BSlCsFIkVfNm0iIfgmInINcef6dM/Q8qCsXPXlTt66quj2oKwerlGMVeDwQOQde3VWy5yGQM+i2gUd5zrqlxSwfvuPfkWWh1mQdK1etdiZIdFa21ln30AEvcUctSAWU529lGBXOymyR9eeV8w6xYOD6Ik8isuL1v9YRCVGvPc8q1faVf9QiV2GYU3KKPyzunCJjGkdeuVr1apBGuJRl6XrOVbT3QNtkOrF3G/Gntud5RTq2OaEW8SzojnrVM/aELOO4jWHkHZLh8a2+fKSqXgY+GxzfelIX+sp1ePPE8xCVh3iPv4LHQ97e7giixEIPP73njZ967No3Tzd4c4fnvvZBt04yn3g8E40VQ5XDkitXV/7UgUcpZ+8wCIbI0gVoODNSJDkFw+3nhEy0/5rLsUDxDAhVMFF4u44i9bwCbxJFQjLqAy8N5IST1x+9D6h7FJ4XNlW8mHaRQeUpZs3K9xShZ+REQt0rq7TVMYuScBR35aXc/PCEUZ3/0b0qwyDiQ8aJvz1+rxKmXHeV4vd4LzLaPB6N2o3a0fHY3993j7j05lpOmXIoWOWd8mE0hz1c9Xpdwy0a86r5oe3WGWeVhUp/njdeQqaHs1cP48KGp9LYy0Py6uHx8foY3Vfw6BvN+W1gZ4eQ7Pr5KgFS1fE6deeYv8rYyCm93LXcBKvbJ8+CzD2fm6had66POXyidnmsIgFQBzyBkcM7Kp+r0xNcOqk9IyDCS5WzRpd061aOJjmBmlvnjMZAyyiwd4u+esq5SlFuA1XPe0orMjCrDBa+5xl72mada4CcLKjLix5sI2/rlI2MHw8nDyID0Pvv8WLUruKXu5eTKRFv172Wu57Dr6pfdWGnr4ysEjAsoMzqCWlPqKkA8piMBZfe87bQ6IlX2hcWyFXWb13lr4KtrrWFcppcpe1z//VeURRuQg3useKoSn7KWZ96YhXuVW1f8vob4V1nLBSiUK22qRY+/u/v7ydcMA4a2ivL0pbLZTq5CjyG8nt7e3ZwcLDRZoSrhu8Y19VqZavVKlSg3qlpSiOlA+pBaNIr421x0t+eIaVzMSew1SDNHX+JOZFTtHpNDRvPi84Jc8+Y07C90kGhbjme7/y6W3x7ZxREPKUGm2eYenLBq1Pnp45nHYMwJ8M9I5TL6Jh5OkEhMiYifYLvnCxkXYVtfY+BnZ6tXUfZenVEAsOsOhxdB6/ofjSBc3V6iqCKEarq5vseM+fK5nD12vWEJcpoH3N00ut1rO66dPIgEtqKrwqKOjTPKXdtA79ZwSKkqQfy85oq94Gf3d+/n4KewWb2aQ6sVivXYNRnIuORDSLtl9fPqE5+hoViFd9WzdW6Rlqda9vM65wyiOaNx/OP8Za2mfc5HD1cozJahypVfTbCTeW+Z2ipbMG1XJ+r5mQkq/h+XcWs97w8kJys5G++/xBe2OhD+RBJ+f+Dw8PDDYSyDWWsqcgD8qy/hypGLo+PJ6xUuOfajCDyJuoKKGXcaLJBwJttrutoOe6TKhBtL2ozwtnro6dEvPaAt1eHRxtMnG1P+IksblYyER9BgXrP5J5VfveMqf39fTs8PKycyFDO7EFze/P53JbL5Ub9+/v7n73yU+mu+OpJW5Hg0+0r+OZ1QL2mY6r0yAk8T9grKG949I6iSN5/bcubg6AT91mvKUTCPGd4qFGoY8lzNydLPB7jNrxtoDlDRPsUgddXb47XeVZ/e9HbOmfRR/Vtg0NuLFku/LucEBYxHxOGmQYTNgKvHDMNQ8QgPEj8bF2LMFJU3CdvMlVZajmlF00AtkoVH8/qxDXvXae5ya/P6xho6FFx9sJ4Xlu6dMCg1nek8DxA2LgsyxRm5vIwxLQOVURML3jD+/v71mw20+/oBC72blmB4xntU1mWtr+/bwcHB2nMcv2DckZ4nNvmaxzu9MYN7aM+/Acd2KvnazwW3h5jjL3iinene+PH88hTQPwccPSMUK0vEspcj5eM5M39nNHHuFYpEa7DqwfXdN+40s1bFqyrHD3jWHGoUrx1DEnPWMjJRw+q5DCX83CL5Huub56srJLrWl/EOw+BnRzfmSMAKxiFh3aiysphhq1bd2TJ1aknsqC8+nITOXfNa9ObOF4bikOuzuh/DqdtJ10Onzp4Rs/UEVJ1+sRKO8pUVx5Zr9cb67O4zl43v6nL7JN3C+XN7Wib6/Xa9vf3k+DTXRIHBwd2eHi44VlD8Ws/MT885ayGTUQDGB0szLhtXmeHcvaMZz4f26NxJCyj8nUUgTdvcgZsBFVKSvGto0RRPmdY6PU6deeMGf1fV/bo89so323mq0JOMT8UcrySu/ZY5VsFj1LODIo0I57b48fA3poH3iTyQhiRtxy1yfhgHY2ta8arrlLxLDzG36unbtitikE9D0KNljqKOBeyU8GsdeU8G699DwfUjXZzvKPJZoyHZ8mjXzzuTEuEhbm/UCYIRyOhi5O6IsAz7GFyQph3jenCSWd6BvtqtbLlcmlm9lnSmY4P+BtevtcO7oOu3D6XA97AA20tFosNTx7PmFnC9e7uzpbL5YaXjb7gtxd6Bz68nu8pW1XukVdTVzGrzAF4CUJeuaguxg911PHWPBlQpaD4Wc9J4Fej5iBnMPA17VMVXlE7XA5lPZnjLWls07bKxsco58cYC4CdK2eP8biMehxcrkp4a71KRO9ZjxH5uvZBJ3EdizAnIPh3TqkqPjnlGTFNlcCpq5jLcnPNJAc5vKsYOcIv6kcdr0nH2xMUHt4en2q2JZfBWnSz2Uy5F5zcpW2wEodygudsZklJ7e3tpTA66iiKwg4ODqzRaNjBwcFn7SBbW/FkQcbGVVnee7cebaBgy7L8LBs9mkfcFitnFaKr1cpms5mtVqtkaMDD5rXqCLw1fuWzaM5G/FOXz3IyJwKPtnXrjuqvO5ci/vbwqyN7FUdvnuUgMti17jplI9nB9x+qIKvGoY5O0OcfAjvJ1vYmLH/XsTYiiyv3DO5523K8gfaYq2qgvfaishGD5/rv9dWznutalxF4OHjGA388SzI3ptE1CHZvG4zX18h4iIwsLZujsSoY5lUo3EajYYeHh+m3eo8HBwfWbDZtb2/POp2OtVot29vbs1artbFuy9Y89wGKC+FopSvq4JA314fyUMiciMVbtnKga4N8jRUl46UeO9NxtVrZYrHYCMFreBveOhQ+eAI0QT3qOXMuA/CZz+efnZbF9PZ4I8djER/llISW13u59qP/Hv/yf47w5JS0zivPgcgZtd6c9OYqtxnJKE/GRv3blt45fD38onH36OONJdPAMwLq6Lq68G92CAl3KKqnqpOR1W62mfykCThcR/Q87xPVZ9nj0JCpMi3u5xJ8Itrwf8+izT2XmzhaXw5ARw1jRkpPGV/xhiDmEDKX98KvkXHmGRV1BCWPkSrasrz3IlnBHhwcbHjHRVFsKMvDw0NrNBrW7/et0+nY4eGhDQaD9ByUK8Ljy+UyeaP4tFota7fbqU41YLrd7sa6MSuqu7s7m81mCW+Umc/nn+3d5TFgxc0eMegABQo84OXz3m6E90ETM7PJZGLj8XgjIWy5XNp8Pk+hbBgTeIZ3e0RhcjOzxWKR8JrP5+ke0xN99ZQzxpnx5iRALa98pKBtKr963rE3L5mveT54bWNOKp2q5KTOYe969AzqVBnH+OpcjBRgJMMV79yzjJ8nm7w2VHYofqozvHajfnkGbl05Wwd2FtYGeNZLjsm3UfAKOULkrDhP0Xj1evVUPavtbAuPoQee98agLj7bGBR1y3tK1ZuIubaqDJQq6xvXIQCRcYznsCbMa8m41m63kyKCgoJy7na71m637fDw0DqdTlLGUORQ1svl8rM1yGazmRQ/FCgLAoTCWfCxl4xv9IuvseCAQQJjiWmhhhcrGFV6yCzf399PCpvXgIEflPNisbBGo7ERsvfGhpUSe+yceY7wN64j6hApyqpdIQw5xaGQ47O6yr0OTg95Jie3vhQOufZyMkdlQZU89uqKFH1dOZdz+v4c4FH7nL1EGJ3QLFhwnwkYCWnPI8xd00njWTKeANJnIuvN+63PegksikNUTx2r0aNTlHRShatakhGNq3Bkb0wt6apnuVxk2fLzWg+PIzwrKEfP4maPt91uW6fTSdehDBGi7vf71mw2rdPp2NHRUfKgNdTdbDbTejDucRY27vEpXnrN7D7Swv1jbxPQarU2lBI8SCSEARaLxWf7K2FsgGZeMpenYJfLpe3v7yfa8FYqVeLsoc5mM5vNZlaW94leq9UqeffwqjnJDX1eLpc2mUw2lDWP83w+T2F0fOCpo23QWw0OvqbeNPNiBDmvTcvhPkekFNQTjZRVlfGhCaxoLzLYuX3+zs0/T47WkcM6r9lT1ehFTo5EwPPBy0vgeqLoivbFSzCLEi2VTtzXxWJRqw8KO/OcI2GsSqsO40cWaOQRbmu15Ri/ijGie9vgEJXNCQfPKq6ySrftQ12LM6oX99UIAR3gdXr3uc46eKmy12UNnjxqMMIzhrLhZCusNR8fH1u73bZut2vn5+dJOaEsPpzRDYDXzQoba8GNRiOtV89mM5tMJmnseY8xymumNRLG8N/MbDqdpv5x2F4TrODdm1kK5bOXDcCbltA+jB4YJ4eHh8mjx+EnnuKfz+c2Ho83aINrd3d3Nh6Pk3cNQ382myUlO5/PP5uPCIkfHBx8tvd7Npul38qn0dxWxag86fEdf+O3p7Qi5e0Zl1ye64zajHBjeattev9zSruqPZZvOqdVKUeOBXhf5YBHhwgHfOfKeX3LyeecY6V1en3P6Z+6sPOwdgRANLIcUSZSXJ4y5I8yQF1jQMtH/6uYZBvIWbKeUMkpY4+R1UrMTcAcqAVp9rnFHtHNo5e37uxZtFWCUQXu3t7eRsY09uACVyhLrClD0UBJttvtpHzb7bYdHBzY+fm5dbvd5D1zqBvKjz3nsrxPatLTubBtCEoDdFgul0lJ4hsKfb1e23Q63bC6QVM9R3symaT1V7TJ26s84K1LULCcnAUPlaME8Gonk0lS3PBU+/2+DQaDjfG9u7v7LIyO3+v12trtdjJYoJxx2tlisbDj4+ONNXKmE2/Dwtr7dDq1ZrNpq9XKWq1WyhpXJe55p6pQIz5U4VvXKPfmpvfcNm0p/uAfT15UKeIInzqOkeKVi+ap02bm5xmwrPBkcV05FjkquXFQz7rqOc/h2AU8OiGsDiI8GXKek5aPykTWEntMaslWTTquL7L6coOQY5gqC5aTQFQZ829dl+O6vTCRtzd4G8bh/uv+1zqg9FDrmIGTXfh+JJCgODgkDUWtnnO/37dut2v7+/tpXRhwcHBgR0dH1mw2rdfr2WAwsFarZa9fv7bj4+Mk1Lk9HhMkhN3d3aUwK8K+d3d3Np1OkwLUhDBWhjc3NzabzTbO20YiFI8FFBD6ambJy0QYWscfBsVisbDZbLYxLovFInnvCD3zevHJyYn1er2k/Nbrtd3c3Njt7a0tl8vkBb969cq++uqrhD8UuibgHR4eWr/fT3hxZrqZJYOlLO+9cnzYMAA9Ef5G30Dv0WiU+jYajdJY8Dgqr0YeKvOcyrG6ylblWR1ZofIoKsPA+9yj8nW90QhHTzmjXi+kzM955xF49XjPcz2eZ5szPiKaewY/P6Nj7z2r7Wj/HgqP9py9ga6yar6EF6qWbxWD5cBj5iocHnO/Dh5RnY+lY05I5Cz1h8BjnvcEJxsenHkMwYRvnJYFjxmKHMICCV3tdtt6vZ4dHh6mRC8oXdTHWc+REcnKF+vKrHR0qxDWYefzeVLqZveZ0iy02PNG/6DE+YAPNlygkIA3C248C1zhXUJJ81oxPsAVCpKND6z3w4vjUD3/h0LGdX0fcll+Sl5jAQdDZbVapWcRteC6OP8A5XkrF8sILDnkeDXnISuPVgnzurKlztzzlPRjZNFj5PJjZFzUt1yddeRy3fbr1FnXuNolPCohDGtfWGNigcFQZaWph/sZkpmQjgprrjO6p9+Mt+KjeHnMxJPdu6d1R33mNnS9VLc2MR285/mbQbcMROWAAytAxX1bYI/O2xKSY3gW6nruM483DgWBl4YksMPDQ2u1WnZ+fm6tVst6vZ71er0U6kYS2MnJiR0cHNjJyUlSzlAKWI+ez+c2HA43hDqHh4Hjcrm04XCYFDO8WiQrwQOEwlZLm8N+UKDe+LHRwN4myiN8z4lcAD6XWzOlzWwjbA8coJz52tHRkR0dHW2sxwMQ2QCtu91uWorgV2+CjpoJDsXLYW1ONgPt0JfVapUiEaPRyK6urtJYTKdTWy6XaX16MpmkxDnQFjhEPKnGEvfTmxueHEI9XJ/efwjk6qnypuvITw/vKvnIz3uyx9sWpjjivso4lQGYfyqPI5kVecyo25Pr3tjjvteXbaKNDF9kzTlKyDGrXmCPwgweIVhxeO14BGRP21OQdQ2JqJ2ccs5Z6Gp1e0aE0sGjC+Oi4W/GVevhNZ4Il21A8dV6ODycs5z1w1t62JMEryGsur+/b+12e0PAHxwcWK/Xs263aycnJ/bs2bMNhTUYDOzk5MT29/et2+3a4eHhxhnUWI8ejUYpRIqsYc/DXi6Xdnt7m8KvUM6j0WhDYZvZxks1NPsddUMZ6uEbUKBmm2vJyITmLV51hD8Luslkkq7xoSdoG97per1OZdUYhdEEBctr9xCmoDEbKUi+g3HFBgSUPejEime1Wlmn07HpdGq3t7fJoIJxgPA86JNTMJ4MYcOnyrsDqBH+2PnF9QI4G72OYtPfddry5JHKOK9txlHxQX2oJ6fgdb57esHDN+qrjpGOPfOmyuUqh++xhtZOlHPEwF45/p2zQHYFOQWwTbs5hquyfLexpvkaTzTPoKiDZ64NvqdvGcqVjfqjypfr4wmcs8hRToUZh0dxD78hxIuiSIL88PDQer1eysrGSV7Pnj2zVqtlR0dHyXPG8whvQ7FCwUExDodD29vbs8lkYpeXl7ZcLm06nSZhD+XLyvnm5mZje9Dd3d3GwRzqtTHN1HPW66wgOQkOCh4G13g8TiFf3v6Y8yY8oeW1jft82hnvq8b9+XyeaIKtWZyop0eS6pGout3GzJKRpkoSCv3u7s663W6i397eno3HY2s2mxsREezbxvhFRiz3R+eL9jdSkhHdWdijjtxzej9yZlR2bGNM5CCSS96zuflep60q/DTHKJL5Hs0i2Yv7ubHaFv9tYWcnhHlhYQ957rhOAO9ZrtvzCLWdyFJia1dDbuwpRP3Ta8CbBWI0qdVjrlKU/DvKWmRFxveifijeDBwa8ixfD0dvonjfnuFS1X/1ioAjW+lQNNha1O12N9Y02+22PXv2zJrNpj1//tzOz8/t4OAgeZGdTieFV5GtjVDq3d2dDYdDm8/nKckICUXwlOH93t7epqQoJGUB79VqZbe3t5+teYIefPIVK1MN8fIznAmuilL5GPRHG1Cg7A1EoIpIlSCP7Xq93jgNjY0llMV9PnENyhnjwevQ7Xbbzs7ONk5cY1wQGQFefKAMDB8sVTx//txWq5VdXFzYaDSy29tbOzw8tMViYb1ezxaLhY3HY3v//n0KZ8Mo8OYVH7zCUQydZyz8vTnM5bQOlYvRXAH99Xo0npGM1S17EXjKVWVBzoBnGa6Ji1pXDgcur/3nyJMa+tx/L4lWx4vr1rFSekRy76Gwk1dG1r2nHdEO6jM5S0+VvHe9Lq517kdQZSVWebKPHUCGKprnICqnQqcuVBk2Vc9qm6yYORMYwh0ne6EMtkVhfbnf72+ciY09y6xUzO6FBrJ/Z7OZjcfjlAE8m81ssVikrOBIOZt9EhKj0WhDOTOwwuY1ZQ4R8npwURRpnZYVMQsoNRTL8v54UrSR40sALxOoYmRA+0jSQkKWCkYsEfBpaVC8BwcHqe8wHsxs43hSzupWA1uNd5RDXfCMeY0a2+PK8lPi2Wq1SniwANbXc+Z4uMozrSq3zRx7iLdWx3PeVb11eAzl+LuOvIyUbR3c68jdh9CAjbFdyfQvppzVO+Xy2yisyNLTwfEUtFd3Fd65gWZP1SurbXieTBUOXp2eVc73GL/IWvesVAYoJXw8CzPqH/5H45frJ+OuuPHYstKF98svp8ABGaenp3Z0dGStVstOTk7s8PDQzs/P7ezsLHnZnByFbUyc8QwvC+dFI4R9e3ubvGisM3NiklrxyGzmRKUcTcw21+wQ+uVkM6zVcj08Fnje4zUkVHnjwPT3lD68SXiMOQ+C+QaKlY0KlOczyBESZ+/6/fv3aVkCB58gytHtdq3X623gDXo1Gp+2gLGxdnd3Z61WKxlbJycntlgs7OPHjzYcDm0wGFi73d5YclgsFmlZQumonpY3D5TOSiPma0/m5ea755lF8iWSlZFjEZWpktseRHIB46/Pq9eZa59xjOS19zx/R8loEXgOg1f3LuBRytlb3K8imIYJFHDNW2PiMsqoWpcmHOUYN+dlR96bhtYji0wn3UOsO/Ukc33x8PX2fSugDJdlAcITSZWQhlc976qq36qcPeEPIY8XTEDgA/b29uzs7MxevXplzWbTjo6OUub10dHRRmY2BOxsNrPb29u0bxcnVEE539zc2Lt372yxWNhwOEzeHOiEZC9VbijDb2biEDU8Ns5WhhLh0CqfFsZjosYfK9OcQeS9WhLASWlaHydOeW++4vL8IhHUicNfuC+sPDUz++DgwC4vL21/fz/tU8ceaRgsPC+K4lPSWVmWKd+g1+sl3NbrtTWbzZRpPhgMbLFYbITVW61WyiOAEkc+gcfvLIdUHkX013nF9aBulVuR8vLa0fuRM5Ez4L22PbnmySE1BCLIKVTtn+LI80xpo3UxfooPR5kYp6jPdfCvem4b+KJvpcopqqrrkfVZF48o+9h7LrIYvWciAahbyZixVEFHOHv99IyQuvTS+1UGhKcQI2VZxYA5Cz2aEExDCG8oLWRK461RUGjwljqdTlLE2A6FhLB2u522/cErwlrybDZLa5EIVy+XS7u8vLTJZGK3t7d2fX1tq9UqndjFPIBkLzZg+L4qZPQ52tLGipo9QYw7QvUM3BafN63tok6U9dadlT95fRrjg/o9Ic+Klg1DPS4UbaF+rhtGipklfJfLpR0eHtpyubRms5meYQNgPp/bdDpN4ezZbLbhlRdFkbK+kXCGve0wHPCNdo6Pj9MSB6IgiJro/IjmoCfIPeXC9/R3NO+98aszNxWHurLZk2W58rgWyR5umxUz/mvZqgx5D5fcuHhlqmTcNrrpMbBz5Rx5dZ7gB3hCwhNc3vM5L5CVpYeT7pHzGEiZncPDvGXM21oRWZPKFF5f9T7+a+ShTpY1ynkKVgU4C3KmnZd85NEnN+G9icmvIeTjLc0sJXrBW+50OhuGEZTy8fGxffPNN9bpdOz58+efhbBbrVY6zvHi4sLm87m9e/fO3r59a7PZzD58+GCTycSm06mNx+OUZY2zr6+vrz879hE48J5YThTiSIQ3FhFfclJUt9vdeEe0mW2so+IeACd28UEhPHZYc0fEAKFylEFonw0mVqC8/q17kdmownNQcqCT8jm/pESBBTH2rsPg2t/ft8FgYIPBwPb399M1rhdLG4PBwJ49e5a87n6/n6IR+/v7dnZ2lrZXHR4epv3bi8XCms2mnZ2dWVEUdnNzY9fX1zabzezy8nLjpSJsjEWKOOf98n0vGSuSEZHhz8/pM4qDN0892VzHm+T72pZXzpOP3jNqyHu6gOdaRGsPqsoqfSKHRR2OXSjrf5OztbdBNNfJuvUo029LqBwjRlZVXcZH2SrPWelglg8fK9PmDKc6yjlXfpfgMTuu46AKeMxQLEgowpYpvDmq2+3aYDBImdv8tijQB97xaDRKwhaHU0ynUxsOhynRC54YjreEh8a04Cxr9ha9ZRlWYrrOyP3m5CdOeOM1XN7HDYDyRJidlUZZlukVlDjz2lOYnvGr48+GhxqkTAdNptIyLFCjZSzQEwlnZvdbqMwsecX83mvQCCeW9Xo9K8syrVHzqWTgKTNL79YGzyFrfG9vL3nPaF8z7+tCHW/Veyaq56FQJYOqno3w4vt1n80pco+36rTB5ar6qcZoDsecTtm1gt6J5xxZG55FpgpHFUpkJfIzujbpWVKA3DYDFjRemYiB+FkAhDaH07V8ZFXn+srXVDFwfdqPSMGjLAPX6yX7ePihTs8r4G9lav4N5QaAJ4VwdKfTscFgkMLb6/WnlyW8fPnSDg8P7eXLl3Z8fGxHR0f2i1/8IiWLtdvtDU/w4uLChsOhTSYT+/HHH9M+5ffv39tyubTr6+skfCeTSfI8+chN7guPPfMfe9dQmrkwHBSv0hHjBs+v3W6nUD0UNxsdaG+1WqW11Ovra7u8vNygA86aXq/vX9fIXr6+GELHHN8chsYatm6dwrfSAcaHJxCBA7fD9fFaNZYm9vb27ObmJmXrQ7ECNxhzh4eHNhqNkuEGBX10dGTHx8e2XC7t2bNntlwu7ejoyD58+JC2WOH5Xq+XojE40/v29jbxMGin+S6Rg+HNTQalA0PdZ3PykWXENoZCTkFGeFUpdA9vrZP74uUueXIIz+WcFa1DoxfRfPDq0mXNx8AX8ZwjZYff0cB59z1FxBMgGky9r3hw3R6uqvi8fkVeRlluvlPUS5yrY8xoGznFnnueGS5aL1Qh6fVPrcrIoNJ+oj09P5mTh6CoDg4OUji30+lYv99P9UI5v3792jqdjn3zzTd2fn5uvV7PXrx4saG8+OSuDx8+2Lfffmvj8djevHlj4/E4JYFhixSyc5EAxCFcj6beB33TFzroOMHbRmiaFRbWxIuiSGH94+Nj++qrr6zZbLrKns+U7vf7KbIAAwN9GI1GNhwOkycKXqhzvCDzEPeZeRv3GUdO9AJNeCnG++iSAGegw9vH0Zx8DCi2zUFBY3sbDmEZj8fW6XSs1+vZs2fPUni80+lsGAcoc3t7a999950tl8tkLJZlmV67+eHDh2TkMI74eElk6LcqAVUgOdmm9PZAaar3ov8qy/Sa1w7f82SC5yA9BCe+XoWbylRPr3hOnspCxSGnM7Sc169tYOfKuY4lVteKyj1fp9PbWIReG6jjIfXlBqeKMXPGSwR1aR7VHzFVrl5v0njlvYkDZc1rqdgOha0zuMYC+NmzZ3Z2dpbWmgeDwcYZ7ziFazab2fX1tU2nU7u6urLr6+v0hiKsL+OMaz4NzMvCV89HFZEKVt4bzJNdrX02XAAwDMwsJa0dHBykMLvmAphZWpOF0ldFB6+Z3zrlGWKeAcjA/fSMTE/B4FsTNHn8GRf0DyFzxYuVKBQgyiEJbLVapeQxLIkcHBzYfD63/f39pNgRCseBJUgSw4tPzMxOTk6s2WymqMrd3V3yphH2hnGjb9FiyHmbfL/qmo5HnXvatuck5GROJNPryiU25KPnPHzqGhF6rQ5uniG0rZzNGSteO9vCTrZSMagXoZaYnhscWTeeB8sd98IakZWkz+pzWgcLEh00zpytAo+5PEtT7yOMy/RSxvT66PUZz6mHo7hoPzGGXtRA2/FC+SjPyoSvIcsWr3LE6xq5Hn6F41dffWVHR0d2cnJiX3/9tbVaLTs7O0ueIpTtcDi029tbG41G9v3339tkMrGffvrJfv75Z5vNZvbx48e0N1m3RfEWIdBJPR4oAz6q0kvKY+XLSpLHryzLlDCFfkORFkVhHz58sNFoZO1224bD4UbmNnum2CrGcwz18Jq6hul1rBg3b36qAmWDw1P06DN7ULyWjpA96lmv1xvbl4Are6b8kgvQCYoSuQSNRsOurq7SEgn2u2NpBEl+RVEkxT0YDOyrr75K4etut2uLxcLOzs5ssVjY5eWlXV5e2mg0sj/84Q82Ho/t4ODAnj9/buv1Op0ot1gs0mE0wL1KhkX3mQ9BT0+R5GSRLllxnVwXG5w63tqGZ8jl7nt14zp/R4ae5zDkQs/eUqUH2s86zzC+2jb4U43Uh8IXTQiLBHaVoqyCnKLw/keWX67tqrJ1PNW6ffKYz2unqs4cTmoU5OpVevEE9izWXB2MG/+GgkaiF07xKor7tUK8Sarb7drp6amdnZ3Z8fGxnZ6eWrPZtH6/b+1228zss/f33tzc2OXlpY3H4+Q587GbOJqTgSe1er5MB9xnRcM05snOH00cw2Rm2vC6L4wHZBbzyyt47bnZbKbwOOrisDwrDC+j1Ruj3NpZjj95/LnPrMSBP17TyVus2MPnMWHlgutQ9uAXGCVFUaT3URdFkdqYzWYpygLc4U2bmT179iwlEvLbzXjtGm3e3d1Zs9lM53Qjy5sNLg/Y4NE5Ec1H5RmmcW6M9Hc0d7kNrz6vfpUH0TOeTMrJhqgtD99tINIZniGSKxcpZh7XqOy2sNMXX1QNUMQkVYNTpZD4Wc/i0rIeg0fM4pWpUsxeX7TOKJsX/zUykMOPcWLc9LnoGcUhZ+V6ffPw1D2/fABIt9tNHnO3203HaOr2lufPn9svfvELa7fb9vXXX9vR0VFalzYz+/jxYzq56+3btzafz+3y8tKGw6GNx+PkLX/48MEuLi5SuBtbfNiTNfukkFTJsQcH0H28uO8l07FyyR3kwnRVoYfXHQI/0BHKmvdgX15epnD4aDTaWHv3Qvbot44rJ+p5SsHzmD1eU8+NP2w84JhTeM4eTThKoQehcJ2cqT+ZTOzi4iLtbx4Oh8k7Bu329/dTpOHw8DC9FIWPhUWYu9Pp2Gw2s+fPn6cT5BA+xxhjq5aZufvOlf5VcoVprXTPKUgd0wgih4b/52SnthXJ4Mg7hQG3jbzxjMy6TkzO+Ih0kfbHM9pVx/27KmdGJBfm0rJRPWDgSJkDlEBsmWuZiJgejtoW39+WwfGMp1jVwuLnmRZ8T+t5qDXpTUSuLzfRc5akMiW/6B5KFweJPHv2zE5PTzdekgAlglDhycmJ/cVf/IX99re/tU6nY8+ePbNer5dO7ZrNZvbDDz/Y+/fv7fr62r799lubzWZ2dXWV1mevr69tuVza1dWV3dzcmNm9YoWHpjircmbFC4DHCr7jxKqIT6BMWYnz+jKe47pxHUlNjUbDjo+PN5Q8PEckuf3ud79Lb8+CkkDSUsQPfK6411dv/rHxofzBNOBERHzzMgI82qurqxT5wNo6eIMPo0EmNrxkKHfQkgX9/v6+jcfjVB/eSDUYDOzVq1dpXRo0/P77763RaNjLly/t/Pzcut2uvX792g4ODtIpZVDg0+nUfvjhB/v222/NzNKec0R82PvnN5tFQtxTzGzEqRxCPTo+qpg8eVpXLnMZ9Q6VDyIeqWPUa7+8+qpkn+LiJcvllLK2pYq+SlZ7PP/vqpwZqiyqbetShmJQJvHajwiTG5BtcGM8toHISstNsFwdD2n/MUyTmySeouZ9ujgXmTOYMekRvuYQNh8ugS1O4/HYrq+vbT6f283NjQ2Hw/R+ZXiMk8nEFotF+uibndRj5Mmkggf04snuZS17BheD5jFU0ZUFM4ejtSyUEpSVvsmK+1AlnKoEoIdfdD0nhDkEjTFCJEMjSigP3HD4CY/L/v5+KqNr3KzE8O5sJIRx8iGUORT/bDazRqNho9EoGZfY+4wT2nC+N04Og5I3u39tJeMFnEBrzxGIFHSkvJnu0TWuh8vouETwUHn+EDlT18GI5gOgjgytI8u8/5HDpgr9sbCTF1/UHbyI+TzhCNAkBgwMW+51CBG1jQ8neqkgV4HNe0F5MvBzXlYtC0oOjeIeRw4Yx6p+cNlIYOq13P+cNZ27pl5fURQ2GAzS9pbj4+Pk+XDCzN7env3yl7+0//Af/oP1ej37y7/8Szs+PjazT+N7e3trv//971Ni083NTTooBKd64eSvi4uLdJ9fUgHawMuCsaBjgJOfWKizp8pKE/h5ylkNKCgk5RuP1uAx0Ifrg9DHumdZluloSSRSmW2+0Qr8ncMPdWsiosf/nqD3aKAGGOjFLxnZ399PkQG0yVvuNPkLChRLIY1GI40jcgnKskzJb8ANCvLg4MBGo5FdXFzY3t6ePX/+3I6Pj63b7ab984vFwj58+GD7+/tpD/WrV6/s1atXdnBwkMLewANLJ+PxOJ3JDYWOkL2++MTjIR4L5gddImJQenMEU+e+Rkhy9ShveHINRqMaGjr2XK/HEzxGHuAePt5ykIcDP6eGI8thphcDy2+d9zqXlE67UNA795xz3kGufN2ynmUYKaxICXmCse7mcb7/EOKzsPSUsweeN5KbSPq/DqNEtMmBZ0ToNew9xZ7dg4ODDcGJ9eh+v29fffWV9ft9e/36tR0fH6fErtlsZu/evbP379+n7GMI7NVqlba5YJ8yK2X1OvlMaT7Ew+zz9yeXZbnh6WPMvGQlb2y8yQsFE9GNy3reASeCoQzKcVidjUgNk3t4spGKpCctW4efVPipMAafc0ifw768vY7pgL6occvhb9xHTgGHz4EH2ry9vd144QXOLG+1Wjafz20ymaSTwaCQYaxhy1W/309G4Gw2S/hgOx/4Bwaojq3HP96Y5Ix1pnluJ0mVMuQxQ/mcIVcFHs65Z715EPUhUsK5a8w7XvTCo1skC9VA1Xu7gp0pZ89y8pSqV14Vngo7nthswUU4VDEYt8sTGgOXU4IR3rrtJgL2CCBk8GxkLOQsxZzQZbppvyO8GDeludcecIWQRPgPIel+v5+Sb5A8BW+n0+nYL37xC+v3+/brX//afv3rX9ve3p7d3t7azc2NffjwwX744YfkEU8mEyuKIh25OBqNbDwep6Sf5XKZkqAQmmQhWBRF8niwh7osy6TUECLla/it9OH1Wbamlb6qlFRZaHngwOOGfbc4CIOVu9m9UuYtPN46mOLp8VGdsKvSQvmIBaGuv4GeDBrK9rwxNTD4ndEYO1bc2DMPRQ3ceY3/7u7OJpNJOpgF2dfgD67/8vLSyvLTCzOwW6DZbNrLly9tsVhYq9VKJ88VRZFC6GafksjOzs7S2edQ5Byl0ASnbRwPb156+++rQsGRjGaF5hkS/FtxUePD43vvHpfx2sut63qGjtcvlq0ejb2ks4inFa8It21gZ9naCmqFRcygykctXPUS1CKvg0sOqgYywp/xZiu+joLWdnSwEV6M1hu5vOdlcL2qGLz2WQjlQl6afLJer1PGMDwKnPD1+vXrpABxWhMyivE6v/Pzc/sv/+W/2MuXL+3Vq1f29ddf23g8tr/7u7+zt2/f2p/+9Cf7v//3/9pyuUxrg/1+3549e5bCo9h7+v79+421S36XMsYPyUAQwEiYwnq2dyiGejccisS3p0zwHzTT9VQuw/Xj8JWiKJL3xfTEM/Amoah03Rb3WdlH/KdhU+YrLu/xNitsVfSegufwrgdefbrGb3Yf5YCnCuMQXi2/zQr0gkfL/DsajdL3ZDJJIezz8/MNOr17987evHlj/X7f/uZv/saOj4/Ttr6yLO358+e2XC7tzZs3ad89nwSHw0qwq8Cbcx69dKz0PvNYZEgr7XA/5+UxPoqHp9DYKNB+8Zh6+RoeeApejTWPHhFekQJlHKO2+boncxVvz5F7COxEOSvh6kBU3huwKkZ6LC7/llAXt6r+RgbDLuquC8ys+PAWHw43QnhCeeOgh263a51Ox4qiSOvHt7e36Sxs3luKSQ8PEXuVWTHpliEoYSgpTUgDPdQzyHkras1HwjMC5dFImQFPnI4Gg02VG/BnxaP3c8anlvOES+45Vcp6z2vHqzPHz9E9zs7mCJSn3D3jmd+chW12WBrh922jTdyHwoXxh+192HNelmV6ixoMSZRDohj41MtP8WimcjBH9zrA9Xky1uMFj2/1Nz+fu18HrwifbQF1cj9yxoEH/PyuZGgOdnJCWI5gHkN5EClkLZ/zwNVq9KylyLr0iO79roOPN5G0PU2C44/u4dRJqB6x9ovb108Eet/zYMw2w/FFUWxksWL9rtvtprAivBecid1qtdIRnIPBwJ4/f269Xs9++ukn+9//+3/b7e2t/fM//3MKI2LLFU6TGg6HadvUTz/9lMLZOFqRFTMEJl6Gwa8XxLo31qz5QBLlGx0r9lp5LJn26hUzr6hRw94PvLy9vb2UeISIRKPR2FAcSAxjPuCkLg6ja0SEcdOkM+XPqF6MP5cDj6gg5PV6z6PyhH8UYlce5X7Cm8b+ZByxyREBphe28CExDd8fP360Xq9nX3/9dVqCOTo6svV6bd9//72t12v76quv0sEmGKd+v28vXrxIL9JYrVZ2c3NjP//8c9rS1mq1Uu4EvwscY8E0iQwrNUCYHlVGT2TY52Sg115UH4+ryvAoeuTh6ckk1TmRoZfbSqVGRWSQ8n0P/xxPPtSIYNhJtjZDZE1tg/A2irCudc9lc0rTw7eK2XGtSgFGDO5NQBb8nicAIe4p5zp90H54ghvXvUx29ZbhZSCMDW8VAnNvby9tj3r+/HnaRwpFjmMRb29v7c2bN3Zzc5NC2HzU42g0Sid/XV9fp/cXI/mL6QwcYDCwQmPv0zvWkhOTPAGo9GUaankt6yktDqmjH/xWLl4Ln81mGyFcXVdX/uJ8Bh1/bq8K1Osw+3wfdDS3OPKhvKQ0i+piHFRgqmGBTG5P8fGc4X34MIpWq1VKMHzx4oW12+30alJ+/zcOLOl0OtbpdNJad7/f3zDe9vb27Pr62sxs461YeBUp+sUZ9txXjx45OQua5MrlnvXoruPMzzLPecqX6885RlVGRiRbI5lZBZ7x4OGs8tArp0p8F/Ao5VzXOuF7OesMoMSo8vj4Oa++6Lltykdt4r+3Vqt4ec9zWC4yAiIlEFnV/My2tMsxtXcPYWp4pZ1OJ72sotFo2OnpaXqz0m9+85t0TCcyXd+8eWOr1Sp5FkVRpDU/9G+5XNoPP/xgy+XSLi8v7d27d2mLymw227BW0S4EYFEUG+vivFaMtWmPnlWemmbGKp01wY+FkXri+K3LAuv1OiW1leV9AhM8ZxxRib3CMFI41OoZgsAPoPNY5wXq4INlvH5xfTmDQNf89No280jnADxz5BuAnmb3uSFY91bac9Z7URQ2mUzs/fv3Nh6P7fj4ONEVOQ/dbtem02kqj6NB8SY1zkXBToIPHz7Y9fV1WivHwSuI3PA2Mu6fGnc5pQfjWQ1Dz3jTNpTOOjf0WZVdVfJD8eJ7dcfdu19HkSuOVQ5LXVxyxulj4FHK2ds3F1kWOSXiDbyW0fr0+TrPRBYZ+oDyOY+SgQe5TiYs3+cwpMdcdZiAPecczjpJvXKASHGolYhPs9lMLxbA+jGUTLPZtG+++cb+4i/+wk5OTuyv/uqvrNfrJZzfv39vf/u3f2tv375NdbdaLfvNb35j/X7f3r59a999951Np1P753/+Z3v37p3N5/O0bxQJPmiPk4JYOSOLHH3mAzD0fc2cQAYao+9Ma9TNiV7qEapC1igIwuP4jwMu+GjJ4XCYsnz12FEeR3zgeeFccq89NiY9D4Y9cdSP8SyKIu0lhrD1eMrjGaYdohdQJJ7AjAQlFKzyPtMT36ADQt1QijB6OAGQPVfwCI7mfP36dXol51/+5V/aYDCw4XBoV1dXVpalXVxcWKPx6XSxX//616ldHEc7GAxsMpnYH//4x7Sb4eDgwBaLhX38+DF58AcHB6kfvGOAjU8FlT1Ma82/UNp6hikvAUY8zXNEn82tn3NyrxpwPPZqOHE/tQ8MkaHh9U/rimSuJ9MVR5YXu1LQO83W3qXVUGXBAOootV3AtvVGg62gClOZQe8/BCe2mh/yvD6DCQOvBOvOeFkAh7f7/b4NBoP0kgpsOcFJXlhDRZYyH9W4Xq9TufF4vPHeZc1iByARrSjuPWYIZbN7rw/Cj8OIABZEasxFCi2iMZ5R2qn34Sk5ePYIu0Noe5n3kfGrODAe+tsrH/FN1BeliacQtK4qUFpVyRk2qnTpB/V5uxx4LzX+87Y88KkXeoYihfHE3vrBwUF6QQvyH8qyTJ42+BR44NvjMa+v29Ar4huPf/laHZrncNoFbCMbv0Tb/P1vATtVzizMVNCo8PA8Mb4eeb78TB3F7AneqM6oHb0X1cGT3rvveVQ8AaKX0KtXrx4Z16kTjsvl+qbjE3nVWM/r9/vJy8NvvMLx+PjYvv76a+t0Ovb69Wt7/vx52gY0mUzsH/7hH+wf//EfU2i63W6nbSnr9do+fvxoP/zwg/3rv/6r/f3f/306FQwnYKG/zWYzKXEk0LVarXQS2fHxcToEAoIWoXA+UQuKz6OxR3+lT85T4C2B4A8YIR7fL5dLu7i4SMqZDxLh7XWcSczeNHsZeIEEK1DmB02S1EQt5k/OOGaPF0sYbDTwGOl8gFfKeHkeh3pM6vWoYeCNlZmlNXrgDUXY6XTSfeAOXuJtV7j+7t07m0wm1uv1bLlcWr/ft7OzM3v58qWt12u7uLhIb0D73e9+l/ZADwYDK8vSer2etVot+/rrr63b7drV1VVKZMQaNV7iwklioHtubzwAPKWRD1XWXtRBaatt8HgweHI7pzS9eePJ2Crj0auPy6t+0PqYTsxLnr6oAnYS6uZv1IGdHULCawlmfjgZv7VMXYtaBQb/rsMMdcAzCvR+rk5lYFXMmu3MwEwDiAQm0zvHwEpjL5wZ9c+jN2fAYo0ZJyv1+307PT218/PztL58cnJivV4v1Y+9oH/7t3+bXnKBrVXPnj2z6XRqb968sYuLC/v+++/tD3/4w8br/7i/8NrZA4aX0u127fz83A4ODtJBJTg1bDqdpm1YLNA9einddVw9z5Bpzd8YN+CN32b3nhcf/4ixwuEjamzB4IHC4/ZVYaNtzRDX8eZjO3ENPOn9xpIBr98DJzUAmBY8jmxc6T0VojzXPeGuY8RGBfqHPfPaBvDjt3ihXRiHt7e36T3RnU4nrUXjxSqj0chGo1F6FSoMGORi4ACTRqNhb9++tbu7O2u32ynUDoMR44PcCE/o6/znOaK7PVCex7quAtQyVd66NxZ6P5LbOdka6RQPzwifqvu5uiPIOTSPgZ1ka9cZ2Oiap6CiTupga/s5C8zD26vbY94Ib8ahaoBYoESMys/qxGOGYoVdZUTksjZzuLICB87IesZxhsi2Pj8/t2azmd63fHR0lELYZpbWTH/88cd0mheEFbJdl8tluv/jjz/ahw8f7PLycuM4TRg1EKygBV5gsL+/b6enp3ZycpIMhv39/XTIiLeNh2mov3Pj6Ckqph1A91izkuB1RT5QBP2E8oEHpUqJFZbih3FT3NVjZ17henK5DOArRCN4DRxtKD4AjQCwRxx5LVqnzjXup8oFpgPojENCcF0NXeCotMZhJ8h5ePfunf3pT39K/el2uxtHh15dXdl8Prder2dnZ2epHfAr+BRwcHBg8/nc9vb2bD6fp/382kePtmz4R3xcZVzmyunvCFQm1lHoHj4RDvifw1/7nDMCWI7r8kdEu6jPVXpwW9jJPmdP4cByNostkpxg4boAmtHpTcQc5BS/B54CjJQlwLNwVTGrt8Y0Y6XIgkWtYpTT0B9/1GPz+sd9iqxMeHnYlwwPAJ7D69evrdls2rNnz2wwGNjR0ZGdnp6mV/KNRiP77rvv7L/+1/9qHz9+tKOjo+RRv3792trttn377bf2xz/+0W5ubuxf/uVf7OLiwmaz2WfZ1AcHBzYYDMzM0usF+/2+vXz50lqtlr169cpOT0/Tm7DMzIbDYRJ2rKR1fFSwqIJSHoOC0dAYl2UPGW8/glLGejLagqGBNXPmF29fNa+fA2dV4DzngEdkSCj/cN+9MrxOi8M32LDw1nuZtnzKl9bPuHl0YGWkeOqziE5AaSIZsNFoJCMRdMSSAeMDWs/n86SgDw8PbTabpf3Q/+k//Sc7Pz9Px4GuViv77rvv0n7nX//618mbbrfbdnp6ao3Gp1elIpESp5XhGNDb29tEC2z1Up5kurK3DJqyrPAcoUiJF0WxwXP6HI8VP+8Zfh6wzMHHW37BmPO1qB5uz5uT3rPePFH8PbpFuinSdQ+Bnb2VSidfjiA5T8Mri99RmV2CN9BRW1XGhJaNrDxm0qju6Lm6sC3N2TjAvmUcx4nkLXwgmJHsgnXIoijSVpHb21u7vr62q6urJBChhMxs4xWQt7e3yTtRmmgW9nq9TpmxnU5n48AOgCZS5SziugZcxPsePT2BxcpNFSIEiq4hevhyG6g7UlReH7Rtfk5/ex6MdyZ95KXkaPJQPq/yorzyUNAwIDye0C0/+GBppCxLm0wmdnV1lbxqxqMsy8T77XbbJpPJxpJWo9FIB+MgArVer63Vatl6vU48rPO2qm9azpOhEY9sK1N2DdvwrsK2jldV+4+taxfwKOXMHfE8EVXYei13L8eQ6mFGbdZpW8ugLp0U3v48T0BHVpfnEWjb7PVocpiWw6EUmqzkWcBqnaIvbFVz+6gf25LOz89TmBrbfBg/1NPr9ezly5fJIxiPx/Z//s//sd/97nc2n8+t0+nY119/befn53Z6emrz+dz+8R//0Wazmb1588a+/fbb9BxwhQJnA6EoPm2RefHihfV6vbSuVxSftvm8f/9+Y8yur69TEhgSfVCn0kH5SHlbx4rDxLiGpCPUzWd9Y3xRHnjDw2bvVsdFDQ4AogIIM7O3yvwIJcJ18ml0Gs4FDTnpDn1SYwHHWsIgY8XHoN4M9w20jrwg5mkkavE1fobHTpdGuA/Yp8y4cB3Ah9fPcaIcEhqHw6ENBgO7ublJRiI83dVqZR8/frTJZJJyLI6Pj1MSJPIx+v2+DYdDK4oirVm3221bLBY2HA7TizRU5jDOmhyq9K7yCPkej5fSWPHwnq3jTHntcAKk9wz3Sdv18hlU/kbtRk6SB140yINcomgd2Lly1kHE/SrrHOW8+57AxHeUZefVmWMcvacWJwsFr2/a94j562TzQUiysOL60GdOpOEEMy7HSSW4x8pZcWUFg1ORIFAUPzzLL2g4PT1NQmsymdi//Mu/2P/4H/8jvXmq3+/bycmJDQYDu7y8tD/96U/27t07+/jxo7179+4zDwz9QoYzPIr9/X17/fq1ff311+mVkXjH82Qy2RhDvKkKihmhVHjeGppmYO/dG2fQnukI/HgvNo8hDA0YQI1GI62Pa32gN5S4p7j0Gy97wMfLegYwD/HJbnqAC6+lFkWxwTugE3gMz3pzEnzlKRDPgOVvfkbXiRWUPgBWzoDlcrnBW55y5lA9DCGsW4/H47SP+fz8PO31N7N02thPP/1kjUYjKXZkemPcodwnk0k6bWxvb8/G43FqD/2KZKnSCNfrODBKN34e9Gbl5cnCqJ4qIwD3eR5qHxQnDUdHcljbi8ptq8sUf+ZbNQweCjvL1maIFNNDkM1ZbHUtHX6uDnjWYGRt4f829euzWm+ESyS4+HcV7uqdcLusDPk9zGafhO/h4aG12+2UrY2jCuEJNBqNpCC///57G41GNp1OPzs5bDgcps/FxYXd3t6mlw54+AMvhNHRdqPR+CwrFUKW+44MXIQjAZ6y8kC9B8545oQw9qL5Oisefp0hlGCU4c9CAoliHr9znxFpiLxWj3eBI15MwsqZvS70A9nDbJipUONvHQs+y5sFM+PFuHkC1Ru3SCFEfebneOmDhT8ra1bU/Px6/emtVhyuhnI9Pj621WqV9jnv7e0lYxIHzMD4arfbNhgMrNFobOypbrfbVhRFirzoGHDfvPmDPnvyQsfHo1X0jDcOOVnNv6N5V6UjPBnoGWAReI5eZMzknsO1SD4/VjGb7XCfc6Q09H/U8WiQ1fLn+tgCjiZjbhLnnmHrB5PBLPYKvMFj4Qz8PXxU+OeMDu67Mr0+42XzckiS6QCFAwV6cnJiL168SF7gbDazbrdrr169Snuau91uEig4hGQymdj3339v/+2//Te7vr62ZrNpv/jFL9JWq/39ffvd735nf/jDH2w6ndr79+9tNpulgzaUHugjMsURDoQS4ZOezCx5IpxwBIMB/0Eb0CDyrnhMmE6sDPlkJxgoCDODXzjBaH9/346OjtLaogpYVlbgbySOFcV9ko4qzm63m3gUB7UAB+6HziUe+263m07TYiMDSVIIt0MxcBid1/Rxn2mnr7IEbbiNyKvh/cbaH8+TMfv8THq+r3Uxnc0s5S1gvIA/DCR+pSfG//3793Z5eWkfP360i4sL63a79tvf/tZev369YZR8/PjRLi8vk7febrftq6++sufPn9vBwUHytFerlY1Go7R8NZ/PbTgc2s3NzUaSJNNTZYIqRB57z0P1ZB97qAqeoRQpc++36oLoWU+h4tvLdfDG3Cy/I4CNbU8vRBAZHXWM/irYWVgb/6NBrLImqhR01NloECNruS7RPMW3jTW07eBEFlsVbl57Xv+jssqIUDxI8uK1Q4S52+12Oh+72Wymw/xxktJoNLKLiwu7vr62Fy9ebCjT9Xpt4/HYPnz4kNaXES5VXKE0OATMW6SwhUUnIHs9rBBYIakxkxsvz3hi4QV6QxHDQ1flUBT3e7PZgFBjyfMiOWytuPIaN7/kIeqLWvjAHXiZ2YZBwbijz1gawDIBlBUUHysEpTGvK+YM6RyPa3/qgPZZ5wzTnctxGaYX0xFHwcKYRV3IYEdU6erqKh2MgiQxvEscBh6MUWT3wxiYzWYbmeccNVJ+3oYuHm1zdIroGv3n57aRoXVxVVz+rcEzrrfVFx58kbdSqVDisl7WqTKUWj4saHIT2GNMz5pCWa+M1qH4VGXNer/ZM/MsUc9bqCOsvHueYo48EvyHUsFbdqAAIYBPT0+t1WrZV199Zb/61a+s3W4nzxl1LJdL+8Mf/mA//fSTTSYTOzs7S1uqBoNBeusUPOvhcJi8QE1Ywffp6am9fPnSDg8PU3t8HGer1Up7Q1Efn/YFRYa92CjHh0wwbaKEEk6OwjeHfQF8tCgndcHbQigegpq9SY8vPL7xAHWZWQqXc/gYgLkERctRAPQNHro+C+XLa9FF8SmhDbTkhDHQlY0JTvrSujnBkZU48Nb550FujqN9zzDj5/EcGxx4Bs/DIEGf2Yi8u7uz29tbu7u7sx9++MHu7u7s9PTUvv766xQ9woll19fX6YUxWI7A6WUnJyf2/Plzm81mad8zDCgsAenBLZ4X6dGIjSLtu0JEb08h6T1vzOrg57WBe57ToXJD73u4R3Ldq6cois9kRa5u1PFY73nna86ecuYOalkdZBXSvHYXKT88HxEjwofb5sxPrhPXNJtU8YwMElWMuj0CdXo45hi5jpFSpZzxDWu/2Wza8fGxdbvdJKAPDg7SwR5ff/21ffPNN+kABWST4uSkP/zhD/Y//+f/tJOTk6TEe72etdttm8/n9u2336a38tze3pqZbXjAOl6np6f2m9/8xtrtdjrYhMOPnKzEb2ZSgYE1QGRsc/hVjSQ+BAS4RBnM/FmtVul0Jw4Bow6E/8uytOl0unE8ZDS2yu/MP6zkQBOsGyMZDdno+LCXi9AtvyCDlxYAHIlAfbzmjutsaKAPfMiKCjnlXbQBHFVR54SoZ4BHXrBnuGo9URscldAxgJFzd3eXokE//fRTWlrAyzB6vV5KCru8vDQzSwf7YLkIh/zwiWGTySRFNjDfZrPZZ4o2JwN5TAHaV5YVkdzwjBtPaTJEPK3PRI4F839khEQKX+VtpJxxTQ1l7a+nj6L+Pga+SEKYWX7d2YNcR7mOHBNUWYN1GNfDow5uXv2eEFLFoYLDmyDbtFXXuuP2IKARLuaXWeCdwlDaEOrow3Q6tXfv3qUDFPCCCyi029tbGw6H9vHjx88yT5nBIWRwNCLOxubjQeHNQzlD2eKVe6pYQAv1UFW55MZfcdRnmfYcTuf+4L83rjnegsDQxDevjwpFUWyEztVA1A/X5RlwjI+u2TLodVbIMGK8g0S8UD7XEV2rMzdzSiVqxyvDz/OYc6SCcwVwFCsSH5FngOQw8PPd3V3aYcAhbhjJMLgwpjhhDMYUv8sc/airLHO0qnqGHZwcLaNydXGLHKacnPOMLvyu4h01FlTv5PTMLpQyYGdrzuqBeJOan0E5BS3nKVhPyHlCBtcjHDwm1nq0v55ijSxNfHuCjD11VZaRco6sSW076ifqhneHrTN46QS2dEAxIpT813/91yk8PRgMNl6X+OOPP9p//+//3SaTibVaLfubv/mbRMO7uzv7/e9/b2/evLHRaGTv3r1LITmsnQFHeJu9Xs9++9vfpr3VeHkFXq4B/JfLpf3pT3+yt2/f2nQ6Ta/dQ1Y4BB7zIgRfs9nc8IA8o0nHvNFobCQKAZivkNmO/3t7e+ld10VxvybL3hYA4XhW5PziCx1rKH7GE/iY2UabnAgHYwFeM8aBeQx1Kz2w1xaAiAu3zQqfs+ahuLCvmMPgqIv7of3iNr3rkTGr3pFn5MBw4KQg3W8LvkM9ZVkmA9bsfkkDbZZlaR8/fkxbCpFQ+atf/cpevXpl4/HYGo1GUrA///xzenubmaXz5ofDoV1eXtp8Pk983Wh8ejXl8fGxXV9f27t37z7zCPktV14ibU4B8n+la+45VZgsJ6qUc27+RVDlfFUpaL6m46wOE8vOugbkY+GLec4MbG17llmVlcf15K7XsRa9Qapi1By+LNxzXgyXUcbgMpHxosrX6zPfV4WjfYXgwRou1m9hpSMM2+/37ejoKG2XgtcMgTsajezt27c2m83sl7/8pR0dHSWv4e7uzq6vr+3nn39O4W/eb839hbGA0N6LFy+SAoFCRYIMJgje6jOdTlPWNgQm7zFmIaGeo2atRvzJ9GJBzbzNwr0sy9QneLBeGFvHWz0DlFcDz0u04nrgZSlPcP/RNugQ8aJnfOMDmihu3AaehVGohjtohmd5LLYRhHXmnuckaFntZ6SoQEuME/oCemIJZX9/3z58+GCTycS++uqr5DEj4RLKG8fd4h68Y97eBlq3Wq20DzqSJ9p/7VeODkrPbT1CVWwKHl1zdSkenmKO7ik/1VGknuzkOrR+rrtuG1WwE89Zr/FkwDUNBaqi0Oe9dtRzUAJ6ArXKYvIEm6bTVylDzzpTYOHg1W/2+TqZgvZf++bRS/sMTwnrX1CIUNLHx8fpjOpf/OIXSUljixW81u+++86urq7s8vLSTk9P0z7oxWJhV1dXKfnrhx9+sOFwuBHaRN84Gebly5fpdXq/+tWv7OjoaMO7QnhwNpvZzc2NLRYL+/Dhg02nU1ssFhv7hZfLZcoE5z21TONGo7HhQavxqBYyymFs4IFxhjPojMQv3Nvb29t4axPaZ3w4QQsCGEo9Uhq6vq5CBLTWhLv1+v7NS4ie8DPKiwzgX3hwqJ95j6NPAOAAvoPxxEsOqvy8SBjfVzmDawradw/U4FJaMz1Z9uA6XmDBRiGiIbe3t1YURUqkBG90Oh3rdDrpQJPFYmHX19e2WCzs6OgoGXYvXrywbrebjr/lc+Jx1gBe5sFb7jzaga6RYefRTnlMx0HppIYleDhqKyc/mSfVWNL+efXotUjvMG4anfGMMq7/Icq/DuxcObP34C2ye3VEgxN11CMCM0POGuT2PMbVyajlPOODmdKrm79VSXB/qjJRua2c9QZgIYnnocSQOcpbpg4PD+309NR6vV7KzD48PLR+v59eIlGWnxKa/umf/sl+//vf22AwsPPz87SFaLFY2Pv37+3v/u7vkqDhdTTmg4ODAzs5ObFOp2O//e1v7T//5/+cDmPgxJfFYmE3Nzc2Ho/t4uLC3rx5k5QLh2uBH4QXEnOgGPf29pIhAnyglNn7NbONEDQnkKFNCEGErXlsEIlQL1H3ynOCFk7OYhz4efQP/cWbvqL1XywTKB+AP+bzeZqnnEPAx45GnhNCsbx9i9+6hTaZ30FjHMBSlmU6AhMvouCMWDUUonB0lXLmOelF7qI5qf2O5BTwxNjhdaRlWW4o6dFoZIeHh9btdm2xWNj5+bn99V//tTWbzXRcLQzb8XicDNf9/X17+fKl3d3d2du3b60sy3RW/Ww2s4ODg6ScmVe98x8UZ8icbWjI95lunnxUHgK/5eipwLzrLU16eEf1sd6InC1PiVe147VXZfTUgZ1vpeLrdbw//e3VsY0logTzFKQyWq4fOcLnjIAqvHN15PB6qHXGAo/D1/CcYcX3ej0bDAYbSgwTBAkuo9EoCWcOXY7H4+ThcuY0QJOQ0G6/309rxfxu4Ol0apPJxBaLxcbZ2LyOBqGvglW336n1roLJM9R4fDyvVO/zNiNdy41AE33gVaFv+Pb4Wt9UxHTG2ME7RjlPybEXrH33vFT0XwUbR4SUnp7iQ1/LstwwHrxoQdV8YNowREay1uFd5/uRQcy/NWLAfQGfwuBstVrpvd1YbjGzlNSIfdPgK0R6+MUYnLwJGvJSDY/XQ5VFlZe5bb11FLLXfp22uK85ub0LxVmnvcfCTl4ZaXZP7Fw4TCecCkXP8/S8gtwk4m/PG2Vict1eSDliSICHm3og/KynADgBKLLelHbchyrBokYJPOZWq2XHx8fpVY+np6d2dHRkf/VXf5X2OqN/UOLffvut/a//9b/Siw1ev369sb72//7f/7Nvv/02hddYQcEzxHo2DIHf/OY36VWUUDYXFxfJO8ChDfBC4C2wUoYgY8XD68sYX45QsJfh0U6NCnyjDr4GYYmXcKCffIIWH7/JZ1nD48I3n7cNvBWvsrzf8rVer5MHamYb9WCd3sxS2BNeGpR3WZbJi4ZxAP7VNtnL14Q+VsxVAhIfPmMcfeHkNYAnF9QjjsZOx9czzqNnIxmhz0GBQomy3GGjrSxLe//+vQ2HQ7u6urJGo5FOlWs2m7ZefzoG1MzSW9tw9sDh4aGdnJxYURQ2Ho83Xie5Wq0+e2+5Z3Rq/oInc5gGatR6Do/KUjWWeM6Bd1iueXRXUCPaGzfFp6pObxy9urgf2j+ODKusV133ENiJ5+wp3Fw5z5JTotaxkrTtqJw3IXmCV1mXXh1emWhA6tBD21F86gx2HfyxDaPZbCbh3el07OjoKL1nGS+5YGu80fh0wP+bN29stVrZV199Zf1+P4XtlsulXVxc2A8//JDwYEse3hzah3JGslmn00kCGsLn5ubGLi8vN4QivDymSVHcZyUD8NsTRGaWFIFHfwbPO/ImIEckuN9Qap4gwjNY7wd9oGB1HVaFKc5e5hPJmM4QhKiPj0dlo1BP7PLC4Aye1wya4hon4nG/uU/cDkcBeLw9Y9UbGw9XFZi5ceUx9XggEtZcxusv96EsyxT23t/ft8vLy/S6yE6nY2aftgeWZZn2QqNuzBu8xQoniMGDhsGl6+IebXJ0UfopvT1+NMs7Kkw3Xvb02ojwqIOvlq96po7y1P5Fyvih9edgp9na3mDmyniTK6e0c+U8UGGqbSqjaRn+zlnbXCaqU5/LGTQQbowjFFwdC1LxhsDHtqmTk5N0DQcevH79Oh0Ywh7NarWyn376yWazmV1fX9vJyUlSkEjO+vHHH20ymdjHjx+TAmArHZ4k6sWhIq1Wy6bTaRJGl5eX6YhDfi0f02Y+nyevga9xwhUrgbIsk2BjLw+4KS0h4OAJeWFj1FEUxUZ4sSw/P6ACnjNb2aADDCQoabSL+lkR81o4G1rAmfEys5SExnSCEWB2f4ALnxPOnrP2FW1q1IGB16vVA88Jcd7e1mg00vozG1uel+wZCJ4Czc3DnCzRelUWITkS0Rtko5+cnKQlBOUdXBuPx/bTTz9Zq9WyFy9efCYb8KrI5XKZ3vWMLYjIA0EehZml90nj3dFXV1cbeOfkUaREWJmivx4NI4PGq4fp6c0rb8yqDIu63q9XfluFr+3mDMRcDlEd+KJbqTyisWDkEBAmfk7pePWw98T/FQdvAvPvHOOqh6TPMuOxMlWB5FnbHpN6gg+eVBX+jC/wOjw8TPuXnz17Zufn56mdRqNhZ2dn9hd/8RfWbDY3Mrjb7baNRiP79ttv7eeff7b9/X07Pz9Pa2ez2cx+/PFH+9u//Vsbj8c2mUySkIA3hvdA87aifr9vz549M7P7Q0o4dM0KUSc0wt26RQrCkY/lBMAwAF4wfNhzZIUHA+Lw8HBDSbCyY0WLEC/wRiQBz7KCLYrCOp1O8rLxKkjONlc+4HVo8BgrdKYRcODrmF9IiANNiqJIUQzO2PYMEh5XrP0zTzMO3osqAJyohnaAV1nee38cnmXjRCGneOp4vGx4VNWvsgm8BFpjyQaRJ4SZsXuAjRokOMIw4hPbYKBcX1+n96DjtD5sJzw+Pt7g08VikV48c3l5aaPR6DPDwutLbq1fZSfopLLRk0E8Dl5doEUku3We6TVvvHkpKJfopgZWHdCcFa1HIWf01YWdZ2tH9zzrqi7ooFcRKDfZHgtad2SdR1DHytslQCgjhA0hDC8Kr2CEkoHRhO0d2OLBx1qu15+OE5xMJum1kNjXrP1kY4EnMwQvlBi/99h7qxEACpg9tKrELk9IwUvle/AkPUXp9UsBSlwNBNCNk+vwX42/Om15hpsmI6F9NejYo+XwN3vfGlJHffysLglEuCnwATTcTzYowWNVIdqoLeW1CB4z3yLFB0MDfcFLLjiihLIweDDP2ODkrXow9pgeMOyWy2V6MQaMUH6xipcxzYqWr0WA+1Uy1TOGqup9KHj4eArba/+h+iFy4PjeY/ScwqOUs67n8TVcN9scNGZqtW6qhFGklD0i1SEQT3zP6vPWRbifnleuEOHJOOgHwlzbzU0SbYe9sU6nY8+fP09JXuPx2E5OTlLy14sXL5KAxuT+8OGD/fzzz7ZcLm02myVBcH19bdPp1P75n//Z3r59u+H5svUKIYNveH84OAGnGiGRCVuoEApG0hn3B8IOWcjwSlkAeQYAe8sohxOYeDIxvhyqZA8O5SAQAfCQtBxHDeAlwztST0E9E1Zi7G2gL/hGZIQ9dbP7E734zVcIqeMoViSOcRsej/KJY5xlzXPYi2Z5b8dCFjInlgFn8AgMBD4hjfvHbUKZ6zzQPnjz1fPW9DkA8xa+VUa0Wi07Pz/fWMr58OGD/fGPf7TZbJYiAujLarWyt2/f2ng8tqOjI/vmm2+s0+kkZX53d5fC29haZWY2GAys0+ls5B1AweOtcYvFIm1FBG+bbb4yUg+QiRSQl3TnyWO+7hnYnqz3nlVjTGmfGycuw8sqnqHnQWS0qCz2yvDzj4Wdec6edeaBEt+bKFqvV1ddr8LDNQKdwDkcvMlepw2PNjrBNeS/jcGhShrhz16vZ61WK21JMjN7/vy5nZ2dpbCmJn/99NNPyZo/ODjYeMnFTz/9ZN99910KKZflffYt2mZlzf+Xy6Xd3t5+tq6IpBgkmXE/4NVjwoM+3tGAbHCxEFS6QFl6POQpf/bs8M2ZsYgCwCOFEsOaN85U9pY5VGix8gYOXI6/0RaEPhsrrNTN7j1m4MPr3BhrxpEFORQlK3NuE/cBeI7x5bC7bhkDfigPJc5JRNof5TMPcko3Uh4eT+XkEv5jfzLviri7u0uJlIw7xgmH66xWK3v58mXK3Ab98BYrXIfRBxphqQLzD8tSRVGk7HzuR5W88hwV5sVIceZornygsh7fmgzIOHtjoPVqv7wxU4NcgeuMFLRXd9TuQ2En2dqe4jKrjskDdNChoPh+XYjKVllvag15+OaIn8NRBVZ037PGvAQhfi4SPFA+eHEEJvv5+bl1u107OTlJb8PB1qb1em1XV1e2XC7t6uoqHfSBdi8uLuzHH39Me5l5XdDMNhQXDrbg5DJWIrpdBi+ZZyEP5Yv7+IZHiwNGGPA8e1eMH5+Gxm3jPitHrDkr78CrZvozHbi/nU5nI/muyvCEN6M8oYbJer1O3iv6zOF05TkWbjDAcjyr4XDlOxgmiju+I0NbIwb8LOdVcCY7tx2tK+qcjjwwrcfrW5V35vWTIxoYf4wjIhv7+/tp3iiuWMu/uLiw6XSacjVwRO58Pk9zmg/0geGNoz+Ry4BthziQB6ARhpzhr8oxUkyqzLwydZy1SKFzWZ0XVc/k7lUZJx7vRPyRU/IPhZ3sc46sTLaGIktTGZxDIZFVy55TJDxUcXmWH1/TCcfA91mo5SYwt8uZtjo5+L5nXUKhYd0pZ5Eqnr1ez05PT5OSubu7s1/+8pf213/91+mNU7D0j4+P0zuXcUoRXgaPNefvvvvO/uEf/sFms1kSGEwfeEN4o46GtuEp4SQxFjZ86AKUPAsWeLH8Fh4YB+z9scLWTGG82IPfq8yhUo4egO5lWSb68WljGAvdPgP8kbzT7XZTH9nQAj94YV/eF8u05WvM/3jTl/f2LfZY2VBifACadBgJVKatRi4YV8/AZv4EXuxFo4+INsBAg8HGOMMg4sQ5nQ9eH1Q2qczRsqiPDWs2dIAL8IGCZB44Pz+38Xhsi8XCxuNxogEUc1mWdnt7a999950dHh7aq1ev7OXLl7ZYLOzi4iL1G3Xi1L5ms2knJyfpKE+8s/zg4MBms1nKDUHo21POVZ6hp5j548lUhpwC1fGrioCwzuDneSxy3q2Cp+Q9gyEyRvQa0zfXlzrwb/Lii/9/h8hDrfPcQ6wnbi9nCETtcTiV175wrGSv10vWNysXWO+8hQmJYTgOczKZbLz2kdtVXCFMed3ZzDdwWPDplh4NZ3JGtwphnsBqdKnSYI9alQQrZxYgqCvKNsWzUPT8G/Wx0ozGuo7XEZVhgRU9o3izEaDRHfU4OTEpgtw9HduqjFlPYSr+KrDr4BHV57Xt1RkpIhzAA2PTzFKoGQqVXy+J52AIl2W5ccIeeBJGYVEUG4Y+jFMcfIP5jsNJ4FUzPjla5Lzc6HlPiUXPVpX9t4ScXM85QlV17qJvO11zjqwp9QBy9angjKyw6KxdxYf/qyD31sS4H7zWVUdYMr5e2IwtwyprEt98EpQnRPQ/1roODw/t7OzM+v2+9Xo9e/369UZiGCx6ZN6+f/8+vd4OW2VwfvPvf/97u7i4sOvr65SVDU+YcWKPD+uv/CYrVtIcVgZwBrfZvVfmKQt4reqN8nYT3jLFJ2JBSGGsEUHQ/dCqqNhI4HOTGc9G49OpTzgfXF/LyMJP5wLwZCWJsdcMcO4f8w/KctgZ7SApTTO01UNC4h3PY4y3evKauKj16dhx/0EvHNXKPKLJX1ovG3CgC+cqMI5okw0BXoNXD9bDVfusCho7Iebzuf3rv/5r8miRZ8BbCp8/f57e5oaIEZQ1lPJwOEzz9Pj42BqNRvKKwU/8ghXOrVgsFmkOHx0d2f7+vo1GI/v48WNajvEiNjnwjFAG9ZyZT7WM94wnF+saA8rHDLllRC8awPc1X8Z7Btc8vvF01DbwxbZSmeVDvHy/ShF71yMBh/vqOfFz0QTk78iIqLL6dNKygPLCbl7d3AcIaDYauC1lpFarZUdHR2nPMk7/wpttBoNB2kIF5Xx1dWXD4TCF3RAihSf9448/2s8//7wRhuV1S/2GUQHlDCXOwo33EPO4aPavp4DU2NH1XpwljRcQ8Ho32kRdyFbGh5Wz8gsba+jfeDxOpzphnPG6P04M03FFfzSawAqalaNGCTxjk2mjPM7eFXvzyrts+KiA4fVf5Uk1HNmoUF7l+clKk8cE13juaH4DjAV8MN7AFf1GP7EUwUYOliA4LK3zjPHnOrkt5BQsl0v78OFD8pxxAhgULBLEMOfMbGMegDexdITMa+yWmEwmyTO+u7tLuwYwVvhgHuPM+r29Pbu5udlY9sg5GQo5hezJMox/9AyDLtlUgRoCOQVeVV/OAPDmKfOzh1PVtW1gZ8d3eszM5TzLQyeCRyi1xLR+r95cXYp3hDOXY+8h6r9e4/85K7KOgo6sSWYaCCOEz1qtlg0GAzs+PrajoyPrdrvJeocggZCazWZ2e3ub3o08n8/t8vLSPnz4kPYxcxgZClb36bKXyOOmB1Yw8N5mCBZex1RjR8PD+PCaHIQ7yvFRlqwAOFGL94BHY8NZy2oMMKAt7h/3xeMbpqMqZE1wAy4a5lcjhz08KGO0AS+TaQc8tH1da1UFx/gzqFeTE6RMY976xdfN7tdpeV5wZMCLsnCmO3iIZQ+Ph3rbPPf5Ouc68DP8wdggT4Kz/GEUIgrDNC+KwqbTaXqBDI7sRBIgnkF9yJ7HXEA+CdpFoli73d7AHzSJZCvu83cVbKukqmRwdK+uzK2qN9eeJ6/r4FanfB3YyZozKwkW4HrNLLbs8a0TjsuogvTayFlCXns5geklx0TGgKdE9VmtX+vxaFO1DMBrm43Gpz3Ng8HAer2e/eIXv7CXL19au91OiWFHR0fW7/fNzJJCvL6+trdv39psNrPhcGjT6dT+9Kc/2d///d9vJGCx1Q1Pk2mOEDaULdqYTCbpN0BDUQjJrdefTh/jE5G4f5ylzIemwChQhYRvCGR4V3z4Sq/X29i+wuPvrX3zGOEFBZ73aHb/liGONHjGlbYDmjDdWVmwpwQloNnabIhwIpiZbQhtb0uZKn5WTJ5XC2OJx5T75v3WN4fpNc/z4vnOeQJQelDQvEfdi/ZoffyblSsbihxNAA/xWep4ls9Gx0tFYDTCQMZ7mPf39202m23QG/Pn9vY27WXG8pSZpXVrnnfAH0szSBIbj8dp/zPeNgeDmQ0h5kXuf2SYMb1yMqyOZ5qT23WUqtemZ9xzDok+y+A5ZeoUmX1+SmVU30PhzzohzPOY+d6Xbsv7r0yR89wVIiX9mAFlRcSHXUD5YCsVQtl6wAOSTJBBjQxpHJO5Wq02BBorIE1sKooincet46PGE287AQ1YyfCeW+4ne87oMytnFjDs2QMvhEd5HVqNvCrQcdQ6PCGh3zrRo3bUQ1VPVmmr+Ok6NgsWL7Sp6+zcbsSzanAw8L5p5hvcY2+RowIReEaQ1ovfufVGry/qRec8tcgz42gOynp7wGEscVIm34eS5nmJRDKs03NiJOOMerFOzQmhiKh4iqoKPMX2UDmcU2gPrbOOl5/TKVF9/D9nLOxSMZvtKKzteRR1vNccMVmQ5xSnt9cy8owj3CLLKMIFVqUnoLz2tIwKUhUInrDz6oOFjfOq+/2+HR8fp4NFTk9P7ezszA4PD9PpQpjYs9nMLi4uUggbh4t8//33dnNzYxcXF0mwQ1DA8gdO7HkB8CYd9YwbjfvXIgKQCQ4DgY/yZG8R9SAUjX5ospWZ722a3Z+WBUXA4Uhch8IHaJgbZTn0Xtfa5+S1nOVepRC8UDPjj+dYSbABgbHQ+2pkIHxaFPfJQ9EcUv5lg4C30WnkgMeIQ/d6Tb0f1Md1qXHFIWdeK/fmHf5rAh2iAd44ozzCy+BtjRBgyWM2m6V97zCisZYMzxkJmKzIy/LT+QIIceMAGRw8gn7s7e2ld7IvFgs7PDxMihyRndFolBQ2EscQYWBDzfOWVQZ6Y888EBkznmxWiOjNODGofogUf2QoK158jaNGik/defxQ2OkhJDogOcFV5z7+5+r1QgtevVVWTQ4XT/Bx31V48DMRE6qQUEGj4WwVyGBIeJg4WOT4+NgGg4F1u107OjpKL7yAUIAHibdAwUPG/suff/7ZPn78aJPJJPUTVjqHQNE2X8MZwpwIBZwRbmNvCVmq7B2wIOD+cp0QTAj5cTgQnn9RFO7LJ/hwCA7zamiPaezxjre/F2OnPIm2ue/KP/rt8Qs+HPr1wuEeTsqHHHFgZQ66aH3argeq5JXWakihXj50Br95D7MXueH6oFR1fqiHznXykhWe1XnO7ekY4Bk+e4CNAbP7vIayLDcMzqIo0nIK3rONecBvV0ObNzc3ZvZpTX0wGCQ+Z+8Xyh4vVcE8Z3pi3iCvBP0DTmjPW5rkfnvhbJW9aoRVKWdvjnmOjLanAIOKacMy1qvL60MOnyqcq3CsCzvJ1vYUVSTUomej+1qXp6iriJQzIuoAW+ysNHSNTRWKMnFkDXoDWscqgyCABwkFeHx8bL1eLyWBscEABTgej9MZ1uPx2K6vr9N6M9Yjeb1RDQbG2dvzzDTAfwge/nBbrEDZM+bnWdHgPwteFm4QClCYrIB07JjWqB/PeTztGWk6/kwTXc/j35FVrklfXD/6rErFGwMNG3MbrGg43Km08frHQhBlmIaoQ9e7WeiiXX6Ov2F8Ko04EsHzkmnqKQAoKzV+MdZleX8YDejiGc96DV40+G1vb29j3EAbPgObQ9PoBxscagwhfwPKnpMfsc6OV7Cijzh7e7VapejZZDJJb8TiCInS2FOk+ptpWwVVjop3Tfk6koVVbdSpy9MnVXqkTr8fCjtbc2aEeWLhGn/r71x9LBAixa8TjQWrZz1pG1yHZ1RwqBMTAQIH1/Ac7wFmxaG0YYEJvHT9letmixyAdWW8NOLw8NBOT0/tP/7H/2j9fj/tc8ZRfnd3nw7Rv729tfl8bjc3Nzabzeznn3+2N2/e2O3trV1cXKS1Zk5m0i1BrJwQyuZwJtMVVjyE0mg0Sq+0Uw8GJ5ZBMfOWK/Z4eO0cz/MhKjyGyIxlry5ai2U8eA8vFL+OFSdh8cs/2MNTZaChT57kLLDhSbGQB2BMUU7X8D0lB3yYjlB+wIWVAyt/zgUA7mqMq+JrNO7PL8d2HsaN8eBvVjYqKL0ogNKlKIrP5g23x+PHcxL1MG8BBy6HNjlnAfuKeWsTXhPJhu1wOPzsOE1OysLLXyaTSVqKQRvT6dTev39vnU7H+v1+Wq/GISPD4dAmk4m1Wq10NC+SQxE5wxne8/nc9vb2bDqdbhgSOrags8oeHgv0oQ4wv6uByO2pwcn3mbdxHXVrPaozIt6JHD0dd36G64/aewx8kYSwyIL2wLNUcl6j93zuWs6q93CM2vPaySUARfV5bSkzeYaJVweEKX+wvowMZH6PMcLZCGvBa8WeSuxzZgWBSajrk56hAdw8RWR27zlDgfJ9rpP346JN4MT0Zq+DFaQaQ/iwEvY86Jz3qTygXjUAPMD1alvAV69xHeo147dGcLyDM7SuCGBkqoDkuhqNxmd098rhPuOGe7rlzuMbFv6NRiPlUmioWo0/bavOB23wM9w/jgiwIogEMWgJXDDfYCjwwSowIFVJAXSPO7eFZ/f29lK0iT1zfvEK93Fvby/t4TezjUgbThXjEDi3Gcltjw/qgKfsQPsc73o0rzMuCl47Xnu55xW2pUFd2PkhJFEH1fr0lA4/79WTu6YTJYdrziJT5chllBk0nMuC1osc8DXPkvOsSS/8yt4jkkBOT0/t2bNnG2+aWq/XNhwO7fr62v70pz8lzxWTcTKZ2HK5THuacVA+K+a9vb3kybJyY89EcSyK+1cqIvkEhgHqhkfJ+7PhCfA6MJ/ExcIOSgNeh2550TD8arXaWBPUaIAaSizwNcSJcYMA1zo0C5wNG4AmpIFG3Cf1XmF8gPc874KBhS3a460/bDjwB94z8yCfGOaddY0lBgamF6DK+OV3E6OvGjFhYEXIkRb1ppXmqpxRD2QTryWrYuf5Dzog6QoRDdCM9zSjTY64oV/tdnuDz3HULngb4w3ewGlfR0dHid94hwN71Tz26/U6HUwEj3s+n9t0Ok04ga4spz1FyTTzZKUnRyFXvKUY79trU+uM5LWCZ2zl6vXGXNv0+ryNc5mDnSpnRi7yCvCclySAOvSaEhDXIgJFyjm67w1IRGSvHghitsrYM+D6NESvyl4tO89wQD1QzjiB6NmzZ3Z2dmanp6fWbDbt8vLSxuOxvX//3v7pn/4pnTjEex1Xq5VdX1/bx48f00RlowPJZt1udyNLmQW0hhpZWI3HYxuPx8ljVmEGJQ28OFTNYVS0x950WZYp7IhQIYdmOTsdgp6PEuW1RaU75xXgurcGjXbY8OJkJR47bz5AqGr4GIKYFRTThL3ryGjFljH+jexh3eLEv0EbVs6aaV6Wm2uze3t7G1ndjIcaNYon8xDGb71eb+yZ1y19AI6eQBmxscb3mXe0LuCBMeAwLY+f9oeXCzBOCEezwcG8y+XK8v4lH7xsgkRLTRQz+zQX8Paq9XqdlrTYa0ZGuBqjZVlat9tNe6zn83k67haeN8sYjUx5Ci2CSIEyDZXGkVL26gFeXC5yrvia52h5dXK+i4LqH3VadgE7DWtrp5UQdQbTm8C457VXhUd0X+vUAVVlo4oX3xEzeLhrex5tIpz5GXix7XY7JX4hUxNrfGaWDhVB8heSSGB94wUX7C1z33WdW40vb38vJh5Cd+wRshDlgyMgONQrMtt8KQVww394ClA8nlGmHjB7tZGxxGOthhIL12iCmlnCyVsj5b7xN+pVBaB90f+8lsp9UOCx8+akZzCjzxD87I0zPfg3Rwt0bV8hog3jqzTi+/yNcpH3o3jxPaVlTt5o/8GTqmBYDgJP1I+cDjaE2XNWg0brxnIUDhmB4mZvGB44Qtd4HhEG7LbAN35DPqic88bLc5D4t/KTJ5cjBZyThRFEuibHY/iO5rKW1d/6fB1dVwd28spInQCeotNn9DrKe894hNPJ6gkVD6I2dZ3SbHOfq+LgXVfGZUubcVScmY5ap4ZM9/f37ezszNrttr148cJ+8YtfpNPAzs/P7fT0NCnF9+/f23fffZdeAYkDCXAq0cePH202m9n19XXK4obSxWSFEoXHhiQxPu4SeMMAWCwWKSkLwoPpA6MCSlrHmtdRgY+Gltm7BF2U3nxsKHtl8B5xfCVv8dEksRxf67h6CX2aSKegwrssyw2DhoW38g7TQZdQtGxRFEnw6l5uKJa7u7uNYya57yhndh8uB59ACTCPwoiEIcmZwRhfNrzYowX99BhXnqfoF6+XMp/wNyslGGd65CobRDgjm4F3AcAD1eUG5idWpuyJI1yOt7zd3d2lQ4Kw1KR79XkMMHa3t7cpcbMsS2s2m/bixQvr9/tJcXuJcDjG8/j42A4ODmwymWzMYzbWVQ4pb6mxGBm0XE9kcKmR5DlQURtar8phfUZluTod3Fed85HR6xlkj1XQO9nnbLYpvFTgeJYWE7CqE0oYj1G07hyuVfcZP0wuzwKM8K5TfwRVVldRFOm1j8ja7Ha7G0lgZvcHfNzc3Nh4PN7YZwnBh+QwtuChnDgpCziph8Zrw+ijrqnzWjHGDWE89mJUGLHHoDRlwVGW9weHcPISCw4+5tMzFNWj3HZ8FFed7FUQCb1IEOhv9XLMPvc0PWGkQoQjEqzcoEhVcKHPupbL19h7Vq+W++/JCPCTF33wBD54QdvQcD1789p3b16zQcg0Ae68LuyNm/anKIoUrkb+BQxq3laIZ1gWoW2Um06nScnzMbswwnC4D9MQp4Uhkqbr9UpT9do9qOJznTee8mRaefPJkwXbyFXFIfodefIejnwtklcPhZ2+MlKvRZaUXvPu8cB4xPJCmFVKLVcuZ8XpwGFCRu1FijvHZPxf6YBJ2Wg0Ujb20dGRHR8f2/HxcUoIOz09tbu7O/v+++9tPp/bmzdv7O3btynzFR4DlNl0OrXRaJTWyECHvb29ZACgXdzjZCGsmWE84Dlz/zkrFIeQtFqtVI9ukWIjwMw+E6JQ7kozHi9+Dr9VWbCHg3Y9IQTFpOPihR0But7sGZOsFHitWUOabM3r8xzK9/DXbWPslTIfq7Wv6/EwUDl5z+x+vRchb7wGVL1Xz2ipAxhr0I8NClZgupbLPIRISVEU6XWZUNTAiZdNeGy4D5hD2m+NAjDgPytdJNaxJ851IB/Ey0Hw+GexWNjt7W0KZevLTJDdjfyURuP+RRllWaYT/+Bt4xRANhZ4DJU+uJdzSKrkHkMUZaqS33UUY473MDe4jrp8yvWz8/JY2NkhJKqcI2ZXRR1ZiFF5bc+r2wu3a12ewuX6UU9UrmoAOIGChYtnxDCwdcxtQpngwHwo5pOTE+v1enZ2dmbn5+f29u1b+/bbb+329ta+/fZb+/HHHzeO70SSSFmWNp1O035KCAd4zJ1Ox46Pj83MNvbRgj6clAThga1Z3E+c7d1sNtO7ZQF8prcKV1aKbABoKByeHiIDGsY0+1w5QzizoeFNqkgI6PjyuLEyRNsef+HDYWkYKoyTKmcN++keVVXMui0tEpg6D8En3G8Ns/J7iL3kGZUB3Cfui5bnJQHwAZSfygwO9ep6KdZSkSnNRqGHl4476FcURTI81uv1xrqwhrrBizy2RVGk9yhzexzlgfEDZQlPHErTg7K830uNcovFIhnguH93d5d2dcDAx3hheQlhcChnLH3pjhTPwGL+VP7S354s5PHXOY/6uW6lAcpExrXqikgf5ZafPPBk+TaGSBV8sRdfMILef4U6ndDBiSwbr64qy0vvbWs11YWHWnfwTBCSwptp8JILM0uhMmRI83GaAAgvWNlQasAJghxCrSzLDaXBuEPI8NYfCCMOYXMSFj/Hwh718jpZtPajk4INGAVPYebGJRp/VZCsJHQsc2MbteEpCVWmOR7ehl+5rxHuHh8ynaEYWXEy/VXZ8X3QLhKGrIiZl6L+RvOdjRLmQ6Uz2mTvnCMsijPzKV+PvCZVEBp1UTy4T1ivZ8WD9vA8G3e8JIFxwBzVY1+hqNfrdZItuMdLFVWyO5JZOh5cXq/pvbr8/BDlV6UfqtqPnvfmy2NgZ2vOOauGQQV0dI+9p2gtiMGzuiIryrO2vPtcBr/5nhcK1//qufOk4zYjvHGv2WzayclJ2qM4GAzs5OTEnj17lrZhXF1d2fv37+3Nmzc2HA7t6urKptPphjc5Go3SedrX19dpmxMEDjxd1I1JzaeFsWeyWCxsMplseNKHh4fpRKJut5vWl71QHurkRC0IR1bg8O51awx7qGb3W414nRnGDJ4B6Fq42f1+XXik7PmqcvaUBoMaFyqgGVRoK7+wl6URHc4F8LY8MT4cPWDAs7in26Jwn+mhdWp/zD5FR4qiSOFabo/7qEoJnjsrH9CBaaWeD3ip1+vZarVKBiz3Ww0enZOe0QDeLYoiGa24zspc6+Y+qOfpySNexsG68WAwMLP7oz85NI25iTO6x+PxZ9slOaqEc/bxmsmzs7ON12vCoIfxrnLMk/mYlzyeync5unN9OZ1SpeBzOOr8jXSJZ/zz72i+69juAnamnKvuRwMRdTrqZM6KU4/AU3L8u0p5q8Dw+qX3c5ZmlZcSWZRleb8XEiFiHFKAlz9gYk0mExsOhzYcDtNE49AUlOlkMkmnhLEgQWIIjgTFGlaEMyeiMEDZ8p5f9g7Za2bPFh4PFASEn05uro/x8sab6/aMIjzH65WRYcmC2Mt74DZ1DFGn95wmdXH4mJU248j1YgyV97zIRNQvvu+FeVV5IRSrbTKuUDDefM7NY1VUnmBm4PEAH/MnZ2TjGe6fjq/SUWWHJys8nD3e1b4jkQv1IuO9LD8dlatKn73j+XyeDhth3uH3ssNY1jwQGB04AEaVqqdQo/HQPuG5KAK3TV11+UjL6LpyBFX9zV3zdM9DYWdhbU/xqCCvUqres1WdjAQR2uX2o/aiwY4mT4RHJNhzCiSnlIuiSOtHeDdzv99PyWDY24zDBBBe5vXp1WqVvEZMUpwEBlyhRPFtZjYej1My2Wg02jhy0MzSlguexPBWYTxw0hdb1xxqBm4wCLgeFjz7+/sbb7ECsEAGaFYuAxsE/Fv5gxU5H+aB/muYniek50lyO2rBaxJTFWgbnkJgj5YVlJbn/kLAe3UwjUErbLfDWOn6JNOJIyXgUaanRkOY/my88RYnpj8vqYDGqBPfjIvuTuB+md1HVQC8Zo9neR2a5x6UI69/IxKhYwV89OUvaJNhb+/TMZyoG30GnXAMr5lZt9u1siw/i4ZgfRqKGv3vdDo2GAxsf3/fxuNxqo8NpEiOqgLX+0xbLustA+Rkr1dO68/BYzxaldl16n6sB70T5ewpUPZ2qhSjDqgKu5y1xPdZ2GrbbCB4z3h1c+isyvJGG95+afXi6tKx0fh00AheBweFjKM6kQzWaDTSKUKwfDnrki1v7K/kZA8kjMFqN7N0ahhPSqx7w4KfTCYbiqLVaqU1cVjk2LIFeprdC/WiuD9QBQqdTzYCrNdrazabG2/R4vAmA68J8kEnOhY8TjAwOPTJSglK0zMAOKTMRgqvbUYCjT2eaA1e+YONjSiUzEoZ489ZzwycUOXhikgK2lYFD2WBCEykIHm9kw8zYXxZCbNy1lC90kf3Q6N9PMdHi3JfMaZaPxt8+AAn7ivWae/u7lJiJZ8UphEE0IT7Cv4DPeAZt9vtNH/RTyz7wEDFq1GBG7ZMlmVp/X4/RdpgRCDxElnZmGtF8SkxDPOMlTMOMNHtXaCPJ2d1fLzrLI91+Yef9RwdrddrW4EjS4pbDm/8Z0Od8yHM4tPTtJ5t4YslhG0DXkeigd22nuj56Nq2BI3wjuqJ+hIZOPBoOREMkw4TC5Y5ry1BQXN4y+w+o5WVE08OVnosUM3uPQTdQqNCTD1HFXpqKKnn7dERfWJrn708xpH7q3Tl+vQ5j+eqeEKNSL0XPaPt5KDRaGx4pfxsTplX4RLRzmvHu69eNcaSackGTg43Nbar5qGGnT0jPwfMd2pse0sWzKswLvDNxhAvwzA9NHSrPOA5MmYxzVlhAD9ELzSTnOtEGdzjKBMOQoGs4UiaFxVR+kT92zV49MuBp+hzOG4ru3N4PgZ2vubMlokXdvMmKQtr7160TuF5tB5uYNyqtuoOOFt82udcQo43+XiyKRwcHNjR0ZF1u1179uyZnZ6e2tHRkb18+dKeP39uh4eHyWO+urqy6+tru729TQkwCGvDe10ul2m9Gfs20TbChTgpjD0KCJf5fJ7C4ZwIg9A71q1UwDP9ze5DhrDeYYBwmJAPn2g0GulFHvxSAfU4gRc8GLbw9UQoHU8dJ04W4/FRnoaQxj3lSfagPYPBmy+gP7eBMYCQxPoiaKCWPPNXHcXMdNe5C35HaJxD2LqnG2FT1MN003nH271gaGrEhPsPw4xp5K2Nc5uKA76RUOXRQ8ddZQz4Fvgw/+GVjFB8OoaMq3qgoAVojoQsTsJD/zudzsZxm3gGOSB4w5wuDTQajfTCG8zVsizTHvDDw0ObzWbppRvYnjUcDtN2MM4h4KgRj1kdUBnpRUbYcPJkd06eg7Y6vmxsasSF5wvf50iI14aHw7+rco4g5wnnBsSzfDls6BHYs3yrLLdcCEPvR6AClo2FyGjZxmvG5Ma7WfHhdWdWRpPJxEajUdo6hUmERDEcNoL1JlUAoDO8bz6eEwDlDmuePQles1ajxaMDH2oBr0NDwew18Jop1qBZQWMdmp9n+npKCmXYw9BPtOzhRQY8ry/HE/os+s/Pmfnha45iML/jtxq0wDnnYWr4nuuBQcSeoCoTPv1K8dbIBCskjrh4tOFxQHkeY+UVr34GVtLKK7iuXqn3PI8PkjL5HdTAgXHnfjGoQQB6cjieFTTmGb89C3SGMV6WZTJUOfLEkRi0wQZSp9OxovgUJocRw31m/FjuKa0ipavXvN+RXNb73EYk8yOcvPp4nnjtRpCTeQ+FL6Kcq5hQoWrwvImiirGqjkggRYaEMl30bNS+XlMhHfWjKIrkgXY6nXRM52AwsNPT07TGvFwubTQa2dXVlS0WC7u5uflsXzO/zYYPNcCEBk5sfWPyQ1hiErLlz2WwH5qFK0cp+FxhPAtBwYqdaRNlAAP0vGpdUyzLMtxfrXzA19AvXmP0spvZY+CxjfhD246EgqfcvbZ537nXLoA9OuDMb6VCW6yAIxzYg/D6WRRF2jMLnuK+qjIFDqyYuS/R3FHjDfc1eYohZ+jk+sj99IwC1MdLKYeHh9Zut5PRDOWHrU08j3ie8HIN+gq+hiHK7alhonwM/oei5p0TbCAjYoY+sLJHLgoidEjIQ3TNM548WejxfTQ++kxVWVWkCjmHKNIZ7CVH+iFXd9W9uvBFju/kicTlmLA8ETwLy/MgPMEaLcar0sx5Tp4i9urj6xym9AQw48XWsCf8FNdOp2Pn5+fWbrft9PTUOp2OPX/+3H75y1+mBKrJZGJv37613//+9xvbnTAB9/b2bDqdplA0TuJCIhdCe0VRJG8a+5x5CwqUKzKlgTuSjGBEsLfBVjUnUEAJo25e12KvDsJaeaMsy+Sl4DfKAjckk8GT54SnCNAnPkGNs4Z5rNSTZsXijSkfzQj6cJhaPSYOGbIRAHrwaWjK2/wcG0R4jpcxmMboEwwl7SPwVEHFig5eF/rHhhnwQX84JK7KWWmtQo7rBninfjGOHKFBNMlbB/bWsNVjBK+xPMD4AQ+UGQ6Hafmo1WqZ2acjc3lfMdOU8eQDRThBkg1oyCHOBMd1RL6wNxpn72NOI2SOZSrQH30rik9JadgHXZafThQcDofpVZXAlflNZaheU3ntyUcdQ67PU5TAQY0U1OMpWDyrPBA5Wt7z+qwnAx4DO39lpFl9pJjBH9ORiHgRftw2rkeD55V/DA7ec4oHKy4kgeGDdaay/JQxDW8Z3itb8WqksCCOgCcM46jeBASdlwDG5bTeqD3vOV5TVGHIwoGFLhSxp1irxhh1RWuNOXopRArWM0Bz+Hn3PGMX/+vMo5zBq7iBnzxjm4U6X2dhqTypoWceJx13FZRqxPOcY17x+I6VACCaC4rnNmXQJ/zmD1/j9tUDZceDy0XzJMdTyoP6LBsJUZgfSl8N1ojv68rBqI5c+YdCTkF7Zava83hw1/Ao5eytEXkeby58EA00C5pI4NQhooeLCnq+jxAuTyYO0Wk4U/FTAaVlvMmD8vBG8Zapbrdrp6en1u127ejoyNrttt3d3dnPP/9s4/HYhsNhCkPheSRvwJpFPxBmQzlun0PAvD+Zy2DSQri0Wi1rt9vuWiF7qxxGU88kWhdVb7csy8+SjIrifr8qto2hzxg/9IsFJtcJ2sOjV2XhjakKTr7PAo/vq2er66dcRtvlED/GBd6tnprGCkOXJjjSwLhwvVoX8OJ5ysYQ+hoJdR57T+Gyx8xbkDT/gc/M5vwAtI3x4yUM4A6eBr8AB/bkFbyxAH24nzA+GICHmaXthFB+ZVmmLYN8eA+iUly/Z1xrFIC/uS+aFKmRBIwFwtTI0Mb+bDwzn8/TaymLotg4nIi3V7GRkVN2Kic5GqBl9Tme95HR5o1ZZNh45VE3RwA9vLw6tnmmLuwsrA3wLGwPPEXOAoOf95Rf3Y5XeSMsHM02syr5ZCHgxThW1R0pZk9BQ2keHBwkxddut63b7Vq/37d2u50yKa+uruzjx48b+yO5L7PZbMNbY8EEQcg4sPfL61LAjYU16uRDQwA82Vh445v3woK+PEboh65BmtlG2IzHA4KGj/7UtUw2IHTyMI10fy0De9TKjw+xvpWHPAOSBRLfYy+RBaN6j54wY34oy80XR3CCGd/nObJerzfmRVEUG961ts1JblEUhPmF+Y63BLHy8hSqGlDAmY9uZcXP+3Z1TLk+Hj+luQc8VsiG5oNzeC2YceLjWUFT5gUdv5zxwHkezMssJzDHuRyPlZmlI0ERkeJdFTAuNCvfo6Eabh5fe0o3oi/Tha959PBA+Y+vV+kXTxdp/x+rlAE7eyuVXs8pLpTxLCdPMUcDHwlAj1D8rYonJ3B5Uih+/Bwrb2U4QC70i4NGms2mdbvdpPx4rYiP29TwGStDTFBMeGRyIwTOQhGCkb0OFWI8Xqrs0H5RFBvrxxDgLOzZi2MPgYW7em0cFlX6qVECxcGHigAiD0nr4UQwFcwq6Pie8iAbAx7vqVGjSkwVGejDRgLW3lEf6Kx95u1XfPiHPsPjnlNW6kl7ZRQHz+jhsefsYc8zYtwUmF8YvDFgAybynFnR839ONtQxZ2MSbfGYRod48LNMW14/ZaMA1zAH9TQ9pgdwB144eAgGCxthPPfhJBwfHycZgMgVH7TCONVxyPi+0ipSzHpf+5Zrm2Uy18X3qwwt7zkPvDaqaFEFO00IwwTMTWxvcDwLyLvv1Ycyep8ZnHHk+ljIMKjXxsrMA09Be5OVhTBwg2Lsdrv28uXLFNLu9Xo2GAzs2bNnaZKMx+P0CjgOLwNHKGQkcEGZD4dDe//+fdojzIlZUKoIacEa5kNN2LPHubwcbsX/wWCQXniBBCHOFp9Opyl7FN69ZorD02BlDxp5a3Cs8IETDBWmv57cpqFLjhyw8GMDRb1dj6fMLPWfFR0LAjUKyvJ+nymyZ+HVoG1P8eBZGG78DPBGm+gPQrxeAhdngTP9uE0A4+UZrhgn5hGtzzsZzVPCmvGv7eI5xZP7p4DxAG/qM1BAHE0Br3G9kCG8j17Hmo1HhJHNPhnk4E0eM+Yp5k9W3lCwbNiiHjYE+DCi4XBoRVHY8fGxO9/n87kdHh7ayclJytQ+Ozuz5XJpl5eXKREM2zIxh1WGe3LS4wPwL/MJl1fwDD3wCtpVp42vqZ7xlL33zfioXsC9nA57KHzRrVR1yz7E2njocwqqWFFXrn69VxcUR1Zs2JIEj5kzmff391O2NR+Izx6zJ5DBuPzeZH7bD08WXePV0J96zZ7XgH6wcuZ+8zYebsvzpKO1PBW0OoG86ATuaXkVfGo85iaYZ5FvwxOKB/MC+qn4oayui3OWtUYogJeuS7MhyR8+/MWjoQq/qE/6nN73vBj+qEeT83C8+rw+5MaH6eKV8wR2Dng8I77kOrltVc5KFzY+8SwrSe0D2sGuDuYFja4AV848XywW6UAjyA+NLGm/quZCjr9yUFVGnbE69QGfqLzXnxyvb8srETxKOeu6SIQQCxJv0JRAek8ZrY4nzUrFu+dZal7b/Nurg9vQ/ut99uBwvd/vpxdY4BxcnAiGU3omk0lKAAPNkYwFoXx1dZVO/uG1XExoPnwE4V/ddsQTHSEss/vX98Fa5z7u7++nsDu/zF4NBHj5WP+DEgBuRXGfFOR5fPwBXl6SF1vhrNy0HHvH6B+HGTnqoutSEb+zx8geMO7pOjzzgy5NQElrcl5RFJ+F7dEfREu8felmlgw0RCe80L3XF4wfh3LZSAD+3rID16lGjCoWtAfcEblh75aNJq89xo3fg8xRBTXw2PAEH2r+gRoMKrP0BDCul8vwISXs3ZrZBn4eX/E2OjbGWq3WhsK9u7tLr3EFD/GeZsyh/f39FOliZY351Wg00i4QzPHFYmHdbtdms1k67hNjpHRS4wP32Yvmcko7vRctQaijwd505GCxDOCxYGDjqK7RwPz2EAeOYWfKGZBTzhEBvOdV8XMdkYKG9anhNMXBG7ic0eB518yELKS8ejgEwwK/KD6dxnN6epoUH5TdyclJmoCz2cxub2/t6urKzMz6/X4qe3h4mEJl4/F4QzDxhICAY9oCD3jnTH/eC9lsNlPoG94vxgUTHHuekWjCmZygGxQzQubABcIGtIHw5CUAXufOKV1+HmPOp4/pvmMYHTpJmUaczayenaeU2CNhL8fbw8whOV3yUIXN+LHxofipooBiA91BE29tXsGjB4d48d+LMnjGb84D4fuqnEEXjSJ49YDubNgwzT3DWpd7uB2eR0oLgO7y4HpAK/akGcCDXp9UOYEm/KpXRKvw0gy+DwN/vV7bfD5PdS0Wi3TaYLPZTIcTmVnaAYIkVCy77e/vp1PDyrJMu0M0lM5GnPYpMnBYobGSU93hyW6MleeIebTUZ1XOM1R5zJH+ivTdNrCzt1J5EFkcVZaIRxBl1Jyi9ywkVbCeko9wVVBrKmd1mfmCFZ4RZxTjhRZ4WbqZpUMC+PWMmMxlWaZQNz683glLnV9dx2EstpYhBOFhsxLjAz148kAA6uTSfur4oU4Wlh6wQOZ6VcDpGOZOi2JhzEZVFT+qgGBQYcTtcN1MH8WfvV32QrxvvcaGUNQf5UH2hj3jQqMFVTTKQdUcQxk2WJTGEL4eXjymLKjZGGePnPHh/2p4c9RGBbp6h16Yl8eC8zuQ7cxtg8c0sRPPc5/YCWEDjucY+svJfmy8Mb+pIYnkLxhwbERia9p6vU7GejQXPdnrXfd4QWWsJ9O1Te+5qH7vt9KwSrky39Tt2zawkxdf8KRRhsN3NEBal/5XoY42vMnrDaYqC64zN/jeb1YyanFpO177uLe3t5e8TKwrt1otOzs7s263a2dnZ3ZycmLz+dzevHmzcS421qZ7vZ6NRiO7ubmxxWKRsrGxt7LRuD/iE9shNOTLYa6iKNLrJBEahXLG+d5sHSOEze2ZbSYKqeWLD2eK8nYMpjme4WxSXvOGF6HZvbyPVwUo8ymiBSrIVUAzTuo9ghf5mhoiXB9nrms/cRYyhKryqfJQWZZp/HRs+bhHD1/QDwrIE1DsoeAZz8Pm+9E9pgN7ODyPddsUK2lEPjwc0Tfe6sPKkPcTe2utaJt5hOkNz5HnutdfVqoqbzgpC/8xN1lpFkWRXsEJI5n5U/euA2+9jz4BHyR8cbZ+WX5KnMQyB2i3Xq/Ti3HKstwwdIFfv9+3g4ODJFfMNrdvgk65b44YKc/gvvKLV47HKjJOvTnEdfBv5c/cMwxVjuNDYGcJYWotKPGUQHU6EynPHLDy8QjrDRq3kVPMVe1G7XjGgXrOULrtdnsjc3e5XG5kNnNChplteLq6LYInpeLCypIFF+8pZQ+Z22NvoSqkCaHOSocFfTT5gKNOeFZ+uj4HY4LXMfkZtMdemPKDjqU3nnXBEzyR4Qr6qBHAoW6tyyy//SmiqwrGqH/cbiRMtXwkbOvOYzV0Abnn1FvWZSaNEFUBK/2qPmu/wJcRjqyoPRmBcryDAfVyHxlPxoXvsRHGbWDOsHfNES5e++a+oH14ziy/FI9IFyjkeC96JgdVfFNX2fLzD1W2j1XSj1bO6iHmBFDOUqqqm/+b5V+GUQU6Ecw2wz6qBCK8ta85wcQT6+DgIK3n9Pt9GwwGdnJyYi9fvrTBYJA8SxwCMJlMrN1u2/Hx8ca67nw+T56z2X2SGELZeJbXGbnvwHE6ndpoNErrzGVZbqwxsyLEMyq42PjivrIhwdt8AOwRISyv2chqJPHaLYQMH6bA46r4sOGmXh33gz1R9rYVHy9sz88rL+gyAhQy1hIVvK01Og9QD5+BzmOMJD7OLeD7rIwYf4ZI6XA0hceH/3u/0U7V/Nd5zkYf7vPaLnuwwFnfa6zffLY7lA7oxYYpngHPaV0e3UAjNryAqyZPcl0eHzMOzBfgNfSTowVcF4wC9Ovu7s5ub29tNpvZYDBIIWoodWyvAl0R8cOSW6vVsvl8vvGyHR4rT/57vJBzgtAfLadtMa/klsoiZatz13tG517O0HusYjb7Ai++4P8skNQCrDMxtd7IC/JwqRoAnuQYTCU2CwAOd3G/tM+egaDrnHhzDcLTUM4vXryw4+Njm81miemhYHGEJ8LgrJxxtjbefINnZ7NZOmifk1GUjmiDgTM5zWxDuKjwzSlDFXy8fqXCl9e+Iq+PPSO0BW+f6+RxUuUZeR48VtpmpHwZV3x7masMENYQfixYWbmxAIaAwto/2mPBzMqZjy5F8iBHLCLDQb2tyHPENVXOXkQiqkfnU9QG/+d96KxEtSzPaf5mxWa2mbDH/MKKWZUzl+fIgicPtE3wPhsVSN5iZaZzQGmF0D8bH7xswqF84KsRhtVqZbe3t0km9Xq9jbFarz/luvDyG846MLO03RP3mD7qtDDduA0eG+VP5X2dz8obnuGd46foWi5KxG2oAeKVfwzsLKztWT+etaFl+LfnLXkT2vut7ebai55Voc5lvU9UdwRgcoSvW62WdTod63Q6acuCmSWvF9sdMAkgjKbTaVLgZpvhNzAmJq2uMzMz8boU9wW0YAHhrb+ywuV1Ww+Yvrz3UycjABOEhV9O6XKyGtcB4Ptou2rcIsNDAdeVjl45FdT6rfh7vMgeLI83t8/ekrc1SMfTM1i8tVUtxwYV7jM91ED3jGu+pqFpVfzcD14a8XgvN77KQ14kqGrctbzSSj1ifo69fFVsoLtm96siUNpp27rkAcOL6wA/8XIW0w68xge1MF8xf6nhg3q0TU+xMj09nq8Lkd5QpZqD6L6nG3J1boO3Bzs/vpOR1MkDUOHI5QAeY0dKk9vWweWB9+pT4eIJRF238QYI7WrbuNZoNNL7mXu9nr169cparZa9fPnSzs/P03uaEWp69+6d3d3dWa/Xs3a7bb1eLx0EgBdfcBiYt1PgZDBOrmE8Icyx9ULf5VwU9ycQoc8oA3rwO6Hx/lpNxmJhYGYpXD+fzz+btN4+UQ6lex4qrkPYsXeEsJzigWf1NXz8zeWYd9U7UmOHBQMLVtAbNOPwPgQiktu0f4oX5w/gN481yiPy0Wg0UtIe87tnFPF4RNEkgCoQTtrisL0uHcBbxDU22Dg0rW3rGHF7PD6Rca144D+H+tkTZ2NTPSnPENUx43EG/6kC51eacjQM8wryAMduglaMA0f1PDrgJC+U03HHktd0OrXb29uNSMvd3Z2Nx+ON6JeZpYgadpesVqv0jagdaK0Gh+oBHhPGj6NPnkxVUH7xDHnugzosHq957agBrTipPHiMgv4iJ4SZ5deaoutq3TDDaecBOsA561kHluv3BLS2G+GgVrbeQzvwmtlj7na7yXMG8yCcbWYbJ4fhPt6rqmEqAHtSwNszkDhzE8CTmD159aI4qUQZntvg53jtlpWEp8j1eWzt0LA4T7pGo/HZtrEovKWek1emysJmPEF3s81tNcwXwEc9Z/U4OBQJ0H3Y/ByPtRc25QxfgCqrOoYv3+Nxyc2xXDs8N9RQ1zYVWJCjXh3XuuAZaWzI6noql1V8VRnlDBx9FgYuFLEqE57LiqcCG4ZsiBTFfe4A44ZQOJ/ip1ux2FBWrxnGReQVoz2lE5fznKttQMc+akd5TvUO86rHTyp/ImPhMYrZbEdbqaJr0aT1FK4OrMfQqljrtKvPKMFVINbpJ57LMSPuw6vd399Pb5jq9XrptCy8eQqn7yAxCG98QkLWer1OyV+wduGhoT3ddsTKgJNiOLTJQoYPFOGzgrkd7iN7hKgDiglCm2nshZ55PLgvrLyZpiqQeExBA9BQvT+1cNWa93DxxlbH2buPe3yCkm4RwhoxcNZtaFG9XDf67BlJXmhUPWcWNBryjhSLeoBsaHDdzGueAevNK+57lRDX62b3PMZrsrp8w4od84TX8TFnMSe0PQ7pq2GGfintlLe5n5ifPP5lWX52Br6Zbcx9lkOeMYV5iOUxL9qA+bpcLm0ymSSZpLtJwK9Mg1arZf1+38zMxuOx7e3dv4KSecJzIHT80RcvcqljzM9Gst4zntTrZZrllGnEu0z/HK4PhZ2cEOYhn5tADDqIfJ2fUyJEClPbigSB4uCFT6vaUPAGvijuQ8Q4+QvhIHjQ/X7f1uv1xv5irEHDY76+vrarq6vE/OrdsgelE5/v6ToyrsH6xRuxuB85r5aFHx/tqYKGBbq3Rs732YjAdfUyVNGiHmwtU3yjcDQrpyqjgNtmAyUSFBBoUL6qTHHc5nq9TqFpTkDSJRelv/cOYO4jZwPXUbLoM2iiwjKaX7x/mLfXsXLW+rkfPCaeJ8pziZW90l2NUlZ42n9vKQ18hjwPVYyRQ8AREB6HyBhS+t/d3aX8EShL3gUAQ4H3PiudlZb4hnL2gJUmtm222207PT1N8gBGtu6bL8syRfzMLJ2zgCU1jdp5RjAb27iuW7ceA9485TZ1zigv8nhzmLyOTtkFfLGwdgQs6KoUbu56nYHzFHwEkZXOFpK261lRfA/CBmFpWK7IeMQbnuDpYW2J9x1yXVXMgImuwsKzqvU/hz+jtSFVRCxw0DZ73HiWQ9uYGGzBqyGnwpaVua7tATwvJhJaEURWdRVo/Wo0sNCGAFKl6vGeKhB4hegjewegj+5pj/pcRXcVbBHomOh4A7xxe4iQy/VJyzHdlSeifnDfPZmg84YNAr7+UMHNyoOXa3TdO/c8/+YIEsAztgEcaePoF+7xeGMO8wt6op0WHt65vlTRTw1mr7w3Lz2HMZq/2+DjwWONi50eQsLfLOCVOTzBx9aKpxgYolCkV78KnEjoRkyoBPaEI1tgyiitVsvOz8/t8PDQjo6O0gk7r1+/TolUHz9+tOVyaePx2O7u7tJrI8uy3HilGysq7QMmxGKxsOl0avP5PB35GQl/tsxx4hfWKeHtq+DX87mxzluWn/ZG8/uizSzd4xCZenQalmVlxGFGftOV8hPq8NbTuM/AGW17CtBsMxmNPX4VPDr2eBbfHNbmc5H5unrruu4PD477DZqyMOezqEF7PKvLEpyYBWBjwTOyOAkQdYBH+DhWjt54e8N13LRfCp7RpPOTQ6+sYDQpUp9VHuNoFGjCbTM+niJnuaBeO57XJSf8Looi0Y7nHUdSOOmPcUDbupy0Wq1ssVhs9NOLgjEvDIdDm06nyYFgemLniNl97shisUgvxsA74/GpO54KdZQh8PaMYi3H7WjkDrT1ZKSHj7bHuoOvPdRAA+xkzdmzvvE7Z3lqeS7jEYaveWEiFdhqMEQ4RQo4wq/qGS6zv79v7XY7HTqC9VxkYV9fX6eDQqCckRzGYeMqj4GZAxNSw53KOKyQEMbi0B7wADNrvZxBisnLma5oRxPPGo1PJ6IhQgBAOS+czeFej6dAqyp+M/s8DBmNKQtM4K3A9OX7mqTFwljXn7l9Vcgc5uT2dC7okgFw0DVGVk7e/l31elE38GY88e2FftG27oKoMrz1HvOqZ3QrMK3Vc8R/XkdmGcGZ/8yvOXmheEReqcdn3vzk+jg5LOc5R44KzwsvyVABYwWHgPeUg2d1frPhjPveCWgRvXIOU5XMi+rEs9pG7tkqfePhzP/r8vU2sJOtVIyYehFm1SFCCIMoKStqhwWsThhtPzehqqy3qoHVZ4qiSGfyYj9zu922k5MTOzo6sqOjo+Sp4iARs/t90Pxmp9FolDxgPksaSoMnXVEUaf0Ka5yewoIAhqfASWk4NxdZ4iysMGFhAKhnACuahRy3Dc+OFRC/Ncc7D9oLs+cmkucx58aQhbYnaPk3l1Nl5NXJdTNerLBUaMJIQpueh+ThCUHJJ4Bx1MKblwB9qxUbOtw+TpBiXBkHXSuPhFtdTykS5AAeC/ZA2egBH2I9HLhyxISXkJgOjBcbF5584TYjw4Cv8bhjPnk8VZZlUohs5HKODK7ht2cM4jcbcTxW3A+cCqZKGHyC6BjPcz7DAYeUcITMM3CqlJg3/qzMeSzYgOPyXm4B+lrleEWecZUsifDfFnZ2QpiZH+LD9ZyCYwHgDWKuHZ2EOmBeuDQCHjAWxJGVFOHFewB7vV7a3/z8+XN7/vy5tdtt63Q6ZvbprVMfPnywVqtlz58/T+vTZfkpjHh9fW2j0egzBuEwntlm1uVsNrPZbLbxdin1PqCU9/f3097rw8PDdIwfe21s6aM+GArsTeM/n2Klggu04fUqs823ZPHWLT08w8zPNGZjQL1RFQ6c3ME8pJ4618H990Jp3ror95t5kIUo6tFMWvWgmAaRJ18URUok5Kx19hT1jV2c/ATA3lqlGScKoj0WfjyW4DHPoMgJN3yrYaJ1sIJjvL3x1yUC7b/yl+KC9nT5wVO6rDg5YQ/Ax6sy3lDO6Du+gSPq43P21WCAAmUjHvUizAwewEsuNHqj+OP98P1+f+NNeY1GY+OlN3x6GIwIPkeB+xwpU6a3ZwCD7jrvPR3BdfD85fq2AY9/c3kUEU7bwE6Vs0LOGt6V618F27Tj9Sf3fGRMmNnGwSC8/oY1Os7cBYDJ4YHy2iTqVJzYi+bDG9hj5n4xw+p+RQgwDYXyc/jN3gj6yNu1tG+ML3BkY4pBQ7TMM1U859XnCfeonofwpTdJAdHSAgSw0jTXRh1vA3XVAW8suD1VcvosG8SqiDV6wMpOjaJoDnn4RkLY+x/V49G5Dl8xHtFHDTJVfFxO+6blGDhZE3NLaevVp4YiGxD83wP16Bn4WcgNPfIU13PbVD2I5nD0X52nunNYnbGqsl77npe9K922sxPCVBFEgl3LsRD3Jh97wZ73kwtb8DOet8O/UY93j/vk9U/LYUvS0dGRdbvdhD/WnxeLhb19+zYdNsKHkrRaLbu8vLTr6+t04g7jj8kWeWzwevAch8jN7o0GPt8b7bK3bHZ/Sg+Htdl7gIePcCfC6UVRJOuaE7mAO7YMsdUPY4EFkCfUPbrrOMMr4Gc49Ospe+CjgkQ9OI4mwChSgathRXzUY+L6+NWEGhXi/nohe6YFwMuGZ15Roay8rQlh6rUyXUEnxUUjO7jnLbUorto3VeigneKG9mDkQsEoeEslyoMKwIG3JuI6761XHuJokCpIT/6wgQOcsMuj2+3aarVKL6pBPawUuF7ghTdJcZibaYF5DF4EbXDuNy9jgVb8v9/vJ08b16bT6cY+fh1Plps5R8fTM1oXl+PrVUtcKmMiGa/00vuMs6evHgI785yr1pnUe2HEvfW+Ou1G1hQzt4cDQJlDr3t1R/3igW00Gun8bD4CE4pxPp/bcDhMB4rAc0WG9Gq1suvr68+UEfcP3g4Dh4U5tMjhebZym83mxtundBuEpxw8YwfKkJPXILw0WQi4o5wKWB4LTmTxxs0bK/ZIvSSYSAFoP5kGOhlZKXmKhH9zCFLLML/gs+1ciICNDk/RcHhelYmGuvWb+8PruArgR577rJy8+iPjC9/4aEIbcAANcwJUr0eRGi3PBqbio8syTBv1XL0QuMpFlSlFUWyMi84pNtrVmGH8oGj1RTE8bzgSxglpypuMO5ZTms3mxssyeI4zeMY20yFygtSg9GR4zqDn+/rbw0PbjYxH715kSNeFnWRrewQwiwkcleP7yny5SRM9GwEnKkUCQEGtWb0ObwMnf/V6vXQ0Jw4eQQLYfD5P253gweKEMH5TEXBlocj9ViWyXq83LHheZ1MLkWmBCcreA5fRNVKlAScPYUIyY8KyZ69VjQvFT429KoHJSYUarmNlwBnHKjQ4JMn85ilMlFPFy/T1rHzG3WxznVv7jHHQ5BzPI1DDjQFJemwocd/4v9n9WqmXjMd9YV7wPBieZ4BozJk+ObmRM65ya/zKW8z3/PGMeq2Px8sLC2sUDm0qrdVz1jngORcoG+GrtOH/iFiZ2UYeghqGup5tZumwHETdWB6gDj7Kk3d+4J43rz065+a80sK75smo6FlV6JGBWBeXaO4/BHZ2QlgEOavFu5dTjJ6iATGjtZPcBGcrM7JYPZzVKMGn0+nY2dmZtVotOzs7s8FgkPY0wzudTCY2Go3s5ubGVqtVevHFcrm029vbdIweGF+tWMWDlTIUOx8gcHh4+FmijIYleX2IlQVnDaO/7BmjLg6ZYxKyJY8XdKigV8HMXnJO4IA2iuvd3V1KguP62QPnrUkaukXdKKfXQAvPK1GlEfEV05OFlyccWEnzWKD/qkRxT8PGEMr8ZjPul7cmyHkSmkSWU9jaBzUU2ONlJaBCjQ1GNoA84ak0VtrzDoP1er2hrNmA1aUjxoflgnrEzO+oU+cKzzlWlqgfGfW6lqx9Ae7gd1b6PC6egYCzErClE/WpoQ1+4CRFJKXipRfcR04wQ/RvuVxu8BDua0idceT/kZwAD+Db0yfeHPLkut5XiHSV4qdG8y4Us9mODiHJWSmAyCKtU/cuQQXoNnhFVhTqxLoQPvCkW63WxrosH6fHFjCOc/S2seTaN9t82UUUNszRRMsxnVhIsoWvHqUyviaDRB4P901/bwPeuGw7tvysJkV5noonWKI2Pfy8cLC2UzV+ZpuKQpUtxo5fHOKFuxlvFviatOgJp5zRjfq89WkP6no9Og6PkRUcBo/q8oS6mX/Ikveb/3v8VsdZYSMwN5+4PCt4NT5zRhaPAyJOutauRhh70PitBuhDoa6y3CVEjtqXbBOw80NIvDKeRYP/nsXMoGtHOQvHmxRqies1zwrncA2u59YCwYjYOtVsNtP2pMFgYKenp9ZoNOzHH3+0i4sLM7P0Or+iKGwymdh0OrXLy0ubzWapzqIoNgRupECg2LGFCl40ez2eN3p4eGjNZnMjDGVmSXirctYDQzBR1YtnGmvYULM50Z56UDq+3sT0DBEIBOUHpZd68iy0YO1rgpaOP4yPHO68VgcDDbh5QlbX/nV9kyMoqIu3vkSeCXhpPp+n7Wx8yIR6I6gDW3DYiMT+eMadn0E9ijuHk3VcdKw9j5vnMEdsdN6yl+x5M5hPiEhxG0xXxo376dWpa8moR8t5Zw9EhgHo5clGRFNUWUYKEH3G3EPUyEuWA10R7VuvPx0tDJkC3mk0GunwEeDA8mSxWKTEVD61jnHKQZU8Z1A6e/oip0O0LpSPdIRnSG3bThXszHMGeJZtZHHmnq/T2ZwVzb9V8PDz6qGwYmbBjUFhqx9loHSQZKUvtuj1elYUnzKYP378aJ1Ox05PT9MhH1iDHo/HNp1ON96lnEuIgmDFxMOaNRQn9itrHcCZlSWEnwoEL9QNOrByYsHEgpWf53a0PdBZx8WblHqfDQGEB/U6gAWh53mApvjkjDJ+JhonTykhDI5ngRfaxpoeK19kzXLWN4dX0Y63Bq74MC5sCHjzSd84xu1Gz+C3hnx1LFWZe7TDPVWSXHdk0HGI2KMJxoWVOn+8dqH4vbajyAX3kcdP5yPPPW8u8TjgG3gqLip7uT4YWAgzow6OFPH8N7N0ngGMNbP7RFCze56G8jazdKDRZDL5LI8l+s2yPuIJj+cifvGuebygfKX4KHC9jKvi/BjYiXL2vFKFnOXDHfIUsLd+EpXl78jKinDSayo4tS6UbTabSSkjo7Lf79vZ2Zk1m810LCfC2fBacWAAW5jqVZrFWX+81jydTm0ymaQ2mGYqHNVjVO+TgdeWda0OAIWNvnnMzQYBKwOPtnXGS4UngOtHv1Q5e0aZ3sPz2j7TyVM6Hp9Hc0PpwN6p2X10AgKfIxMMELK8XScyPIAD1h+VPhGgXnibHKpk+inwvUjgRs9oGVVCLFDRhtKHjRAF9eKrfnsKUL3lCICbrt+bfZ7VruPnRUM4qqP4Mc/hOtrxjIpoviHSUpb3O03KskxeNJ+zzW14xjGe1ZwNDzxvVXHl+Z9Til7f+BkGlQdVOOTu70JB72QrFVtx3uEXnvDSenKeEnsIZn6GrOLDjM73GLzJxgzPjK59YcGPxC+EtFutlr169cq+/vprG41G9uHDB5vNZjaZTMzs/pSsw8NDG4/HNhwOk8BDWJOzG3XdDwDFvFgsbDgcpiQzT8CzcEUbEOo8mXgsmN5mlvZBsidclmVay9zb29vYz8hjxRa/KmQVJjpGPDY8Pp7Xx/tpOTObBThAoyTMax6PeELY85jVEGIhHgFCgcxn/PpL9nBUKODkKD6tjdf7GC98I8qC89OV3tF8wfNIMgPOENKRgmbPLBeGrZqnKhdYMDN9PQNOo178Ww0zxV2NArSBlztAFrARq7IH/KrboXDgEOSnGjI4aYuXqfgUPuzQAE6Ya0WxGdHB9ik24thj13nIr17lMPr19bUdHBxYp9Oxdrv9mUGI6B3mAbzpoiiSE6I8rMrSM4giZRoZfjzmnlGn7eszXls5x4HrjXTdNvBv8spI9TbMqr0kgA6YN0mj8vrfI1aOgKpAtD9gZpwpy69Ow7XxeGyz2cym0+lnp+2AqTG51OqNLHmmASYCT/CcMaTtQ4joc0xrNkQ09IzfrLAVR7PPw8l16K/AIfDccyxwPC/SMxrreEpRu1U85An9aO3V4w/OoI0MxZzyr5oTPN68/1n7yFm8+M9KFwYf8PT6q96t58GokI2UM5fNyRHFRfk2at8bN8Urwln76sk7jZx448d91/VnXfeP+hgZuF49yssw9hHR4z3SumyQi8Dospa2V6UHIqiS/9tAFR9xGxEf7BK+mHL2mFEFulq8bA2zJcgWv9bPjKtWkgoeLh9ZNsBBmV6fxxnYeAVkt9u1wWBgZ2dn1m63rSxLG41Gdnl5aT/88EM6MQsMfnFxYY1Gw4bDYfKo0b73GkGz+1AWFDKSwBaLRUoGA46sMHmrD+gJyxb1LBaLZNWrF4Ky2KrFXhlb6bDudXyAjyanoS8Rz3j05/u6punxEQuRnNXL9Fae9HBQ748/LHSh7LzX57HAQp3cN4xPhDPzCcaDlSjq4b2tZveRG/bUga8XdtV74FHQWJU6G0c5GvI46WEcubFi+oFm0dqsZ7Rw9EZxw29VNMqzytPsPXJfvCU54MMvpOBwMCs9Bo06aBteyJgNcE6Cg9IFDzAOuh2Sx5DlyHw+t/F4vHHd2zLJmds4rIQdCS8i5c13Bs+AjnSOQpVi1fq8NiIcq/DeBr6o56zEYubnSawKVi1lLwxWR2hXPcOKA6BroloH4ODgwPr9vjWbzXTYSL/ft+Pj45TMNZlMbDgc2vv3720+n9vx8bH1ej27u7uzm5sbK8vSptOpzWazDYXHws7sfh1aFSu2XsFr5jVtFnzcf1ZkSE7BsXucqcyTEJ49H82HjF0+Lxx4ssDwPAW0i35CgbDAZMGrtOdx0TU/7renrBigQDylywpdlRiDZzwCB7P7/dcs3Bk4U1oFYi786ylnPIs2NRMXocm9vT3rdrvpNDo1HDxlxt9oH9uz0C7zkCq5SOkiqZDnIsY8mn8qQ3h8WZbgt27nUzxUQXveNO4pv6H/wIVpzoYOK1bwNp8/oHNbZSDTXJ2cnBHEfMdRGGRe855lPq4XdFMFzbRYLBYp2QvyQGW8Gu0oB2M/gpzi435yxLFKKUdOmrbh6RYPtyol/metnNFRHTC+713LEUWVqUdwrlPrriJa1D7aYYGNjGwkd7VaLRsMBkk4jkYjm06nGxMObXCYEmu5UAI8obQsABMNSpnX+pXBVDip8IECLssyefY5AcV0YoGitPdwxjcLb29MVDHlLFYvVKpteAlBGiHhsY/C8Kq4tP2q8J7nUWi/PW9C6YN2PYXpeVfclxx+3HcFpilfwzPRc5FQ5OtqJHn95Q/q8MLGKiC9ugBeHoQ3/z0jV5UEaMu8xkamGhI6LtqnSF7xdRjIajQoLVU5qwEAXJVOmCOsvPkaL3Uwfnt7n16Xy6FwGD45ozPqp45DNEaekvXGVfUSX68Cz2Hwnv93Vc4swDzmNftcQSvB2MvgwUZ9zAie8OS6vInK3hMEp25BQXvcLtenazJlWVqn00mveOz1etZqtez09NS++eYbMzP79ttv7ePHj3Z1dfXZqxvZakaoh/HGdgXGhWkEq3M2m6UzupG8wZMbeKvFDYHEW3rgBaEtjSDomHIWN9agsMeRhZ03AbhODvtxWA/Ak0BDoKxcOeTP5wbD6FEvmpOZuC3uC4ebmTd0AuKjoUQeA2+ND5EG7ud6vU4REM9jYd5GhEbHk1/RxwYKBLkagdHYMGCcEeUA3l7kg+vRpSbPSGUZoPRWGnOiE/dRcVYZoOWAK/bycrvctv7X53kHAxvRqvS4bo128DhwfgH6oNEVxoGTO9kA4NPH8ByOcUUiKfiS6+NxAr9gnNA25BX4rNVqpWtoE4mx2FtdlmXKveEdCKonVPHx+EVyQecwA8sh1R0546COAuZx1rLeHNoWdu45e1ZylXWilk3OMoqEvbbnTdgqSy0HOlh7e3tpCxUmCN7whHbwlqYIV1W83Jb2yaMNPGfN1mRBHzG7tgHhy5OWQ9TK4KpEeYmC262acOqJcl/5dxWjezTjScS0YYHkta0Kn5/jMmwgeoLD40f971n1Hp+qV8PGExucXrY63/OUYF3QNf46a8v8P6fwWNl7/BLRrA5fKN35N2in7VbJCZ1HXF+k5HNGqtJF+6tr14wD/9b2uV02jrx13qiPvNTDOwAwpnqoCpZWkBgL+egZcNqn3LWcwvRow99eXVXPVj1TB6fHwE5OCDOLEUc5ZfxIAXnCmC0eb0J6eLCCirwYrrOKwGVZJku10WikhLCDgwPrdrvWarXSd1mWdnx8bIvFIp0ze3d3Z+12O00gXRdlaDQ2X2yvoUs+DQwGgJ69zeWL4t57RCgedfLaL2/NUO8ZtNTXSOqBHVh/xgc08jwx/tax0DA5PnydPadcJEUNIY7GRIYiRzc46sEZyvxbBT/GFuCdWsZzgo0fjwasiBln0ETDlFhPxFowR06Ae9VaJdODjZUoFK/908iM0jgHnmHN9XB7oBN7i4wvgCMtbHBqUmJk0PO4guY6RsqfnqGJ3+gLzsPH/IEy895ApZFFbpfnnScjvTXuiO6eMahb83iuTKfTDeWr3nuz2Uz9K8tyI5rF/fGMOjWKmZbeWCm9dJ55ijunZCMHRdvV+usYjzn4Ysd3amd18mpn61gfngDkAfEY0lPcGsqJPAhPcGN9mc/ORkJYp9NJp34dHR3Zev3pLS7IhgZO3La+JxW00cQqMBqYng8v0WMb1crm8Nfh4aG1Wq0NwQKc+BVxsHpxH+U1SQRv1cJ4gLZ8NCTvgcXEZQUEgachbjwD/Hgys0Jiz4t5Qy16XQ/0BIIaAdx3fJgvOOTI/MRjyb+5vtzar/JkpJy5rO4thUeIyIp60ooz/mv76h0yPjljmRUf6KzCPfKQuN86jlVtMK463/CbaaChdfAiK0MV8KygGW+VT7p9UvHGN3ZLwJCFEc3PsMLD4SA8HvphenjKWWnOPOaNmSpu5jsktfL+a+43lDNOFePjcVkWKD15DFhnMHDUTsFbGuL/quy9cWK5oTiiDOsUNeYeCjt7n7PnEUT3vf9RR7zJH1kjda0UZrYcIb12obQ4AaLdblu/37fDw8OkhOfzeTrnmhUKKxNWeIoX+u5ZllDGugUDdauxAmXLli0MA6aJKg/1UnQCoCy8blY6es0T6ugrM7WOQaTEWEB4lnBkODKNvDJeXXyPP9oWylSFNjH+amVzu5HRGM0zpRt7/eCX3DYpbU/pUGXIqteCaxH99ZlIiKlX7o1VFXjGBf57DoT+9srlhH6OTgpsQGOeQs6oEsR9jCvPfa4jmksANTZ1+6qWBZ45WYxy2EECB8bMUsIaQt0Ij+O6l7UdjXOkI1CuShlGeic39riWMybVMItw3QZ2khCmzKrIVq1vcKdzxFcvSOvQ8K9XD1tBup9QB0IZvdFoWK/Xs3a7nbzPvb09e/nypb169crMLCVovX//3i4uLj475Um9dn6xgGYP8zNgZljZHNLWbGmdrPzOaOxvZW8bbbMQYFw9ZgUgbMoeAguR5XKZ+orJqh4Irun4cuib77OHEwlBDZkxvgw6oTyrH/XhukcDbpMnshpO6rWx1c+hScVT22CcFEB3jPFkMrHlcpnGPtd/tKN5EMAtJ3g8vDwvwzM4PePd2/vsCWqNVuSUuY6vKqXIOOOxjCI2jCOM1iiJSZUj5gwic0xrzhkAX/Fb7qbTaaoHbas3x31CroryD/Djt+blgCNhyK1Zrz+9JKPb7Vq32/1seQwZ3OBPyC+NjniGq84jvhY9E9Ge8ffq4W/mEQ8HjY5pfQ+FnSWEeR1jQVIFOW/mIZ3MWVGMo2cBM/FZeBZFkV42zgoCe5xns5nd3t6mCYNTwVhAY8LAu/Qmkqec0RYmvXrOijsbGJj4POmVKVmQARdP8XlCm71k9IU9aFbyHtMyjSMF7U0K4OONM7eZ83JhdKE95QUux/fUq1Hg/moonCcz+sPrs7hepaS1PS6zXq83jELmF+6vF3nQ0Dlo5BlrEeTmM+pUoavl1GDx6s+1HdFHaaVekecherh7hpqG7XMGJLeNst77sxnUuND1clU4Xj94jvI175vrBs6q8NWQKssyLe9xIhj6x2/M83D0aKqyQeVXTjHX6aP+9vg1V7en0B8Lj1LOubCbfuukU4XAgsGzoJR4njeuk92zjNQK4nY8KMv79Tt+49RgMLDz8/N0EEmj0bDFYmHX19c2m81sPB6nU3C0Lwye9c34sfeOUPnt7a0Nh0ObTqchw/JkR8Ykwkiz2ewzpeQpG15Pxf/Is8QEZYtcT2jSQyqAp4YuUT/jqGOI/7nwIcpx/azcUDeH70E3HosqPmG8PD6LlD6XYzrxi0SUX7324XnAaONQIRuDEIgQ/rzlDFAVumbgaA/3Meov15PzUL228F/XcLlNNvBYvvB4IqeC5Q17r94c1bZ4DuQUizeveCx0TblKqQL/qD3us3dmApfTsxfQL41W6bZFpmEUqUS7s9ksPY+1Zn55BiJpagxyX/Sa/tYxyBmDnrHu0dFTtCqXIoNSHZHHwE6Uc8Qg7A0o43mdU2uMQS1bzwhgxsIzvJCPcpzBl7N4cB+JX/wayKOjI3v+/Hna49xoNGw+n9vFxYXNZjMbjUYbJ39F7WjonCcE9x0hoMlkYjc3N3Z9ff1ZOFsBe3mB//7+fsryRru8t5lp6HmLECwqcPAN7wzGjJn/3mc+fIO9uZyg85Qej5EC84UuX2j9nCilr9hUJesJcC6nhmPOs9KICowR9Zy4Dx6/YGkESwwqPKBE+TQ31MnLPIxrdHqTKl7gqwpPvR02yFTgMw1grGl70Vhw3WzgRMITdfCzmJ85JcF1eZn3uMf8rcpO5xHXo3ukVcmbbe6B5jmm/QOOWDbjOc1jgKgXGzFY0uJnVR4zrXWceUkLypnf8QyPutlsbjgJSnc12CPdwMtAOVnI8t5TmpFyVp5l8Ix2NQ4fA1/shLDIUomIo/cii7pOu/w7ei6avJ4SZQ8UG+7b7XbKzoaARPhQ3/eq729lYKbyJhn/R1nPEsbzeA6TzTuFivvKws+7D8AkYAs6t2yhTArc2JPjunPeCuMXRWvU6NEJ8hD+yYG3peUh4HmQ7L2qJ8vtRQLJ8/ZVqUbjXdUXVmzAj4VY3XHE79w839arVqjCJYebRydvrOpGGXAtUrr4793XdpX+kE/RmQoeHh7f6jXPWOb5zGWi3AJ1INgI4m+OfCjU5akc7lX15fSOKnWP1x+CdxV88eM7zTZDD56y9hiS61DLT+v22gPwWgh7h1pWrSU8C88Zr4Q8Pz+3wWBgr169sq+++iptV7m+vk7rzXwi2Hw+Tzgg2QO4lmWZTvdqNBrJq9H+gMnVyvUmGU9gbJvSE5BgVWPbBp8ohWeZHq1WK4W0+dxtxs1s09ti65tpr4Kcw2/e+jJ7eVDs3glv+ixf57qVTuiv9iXnySsPcbte/Qy65uvVDY+WjTH28lXgcZ2axcsGGq/P4xkO6WoEI1KGGENeT2Q6MH4ebXPGBdPRW+7R8fRC2YwjaAJconHRsffWuNnoLYoiGeQMGjXwjNScsayv3tRlJlXY+/v71m63P/OENWyt9PUM/GieqALFS094KQB0QPv8khyUAa+hL6gb4W0kz6qc8OgE0PHXMt54c93K46qP+JoaJ2yY4Hken8cY7WZfSDnXsaA9iBSkEqNuPWabCQxMXLWG+FkduL29vRTSRhZir9ezXq9nRVGk9WUoZfVwYdFCuTEg05H3AwOYcTBx+DAJvucZOeo5a0gQ9/iwAF1n5pAorzlzeJU9aF7rxLe2yxNBFVTE0Oh/JNjqCvxosmq5CCBkVGkCcqG1qF2ze0UExRzhwQJYw7+6zMDt4MMC0vOC+bp67Fwfe8w6jl50JIp4eNei+a7zlw2yqM+453k7Oo/qGu3cb+0Hh6+r+qrAShDPwPhXHHCfw+N6z1PA2l++542RygKmOa5F7bGi1heEsBOhy0lR+3rd4wOvTDTmHq3UONVrXA/3XZctHqoDGXaylQoIAjwm8iCy0HUAvUngEbhOG959CC3+hmfQaDTSPuZer5eO6zS7PwGI39nM51yjflZowAWCAJv22ZNdLBafnS40n8/t9vY2HdfJk0OZiz1jWKUaWlcPBzSH98404ExvCAIIEfZocd3zcBhgkBTF5huwVHBo4o0aDjy+nkLz+Irr8/jL8ya8Cc6vCtTnqpSSZ2R4HiDoCG8FYwZ8+KhOT1EzbfCtyxL8PIcZcZ9fj4jn+HxurtNTZFXGis5p9ZaVNiwb+IUPKIffKugjo04VhV5TI175T3cLsAHK+Ryc5Kd0wVwFLZkeUNBqPPB/navr9aeTuNbrdTqkSGmu9QAPT2F7chpGIiI5LI/YyWBeLooiJcFBRgK/oig28IzmZKQzdGx0bmv/lVer9IcHkVGwK3iUcvbCi9EEVY+PO62dVAGBexyq07VStlYigntClHFUAYUMZ4SzkQyGY/XgKQ+HQxuPxzYejzeO4kNbnNEKvMCIOAaU8WMFjPKTycSur6/TJn9OqmFFWxRFwhtZ2siihsIDLhxmhFeM5A3QBaeK8QstzGxDOLNwR59Rt3e6E0L0RXH/vmM+kcwDZHmirwC1Vj1l6Y05GyDKYx5wnaycMdbgcW7HwwvXtB1+xy7PK9CKE3sAGEtdkogMZVakLNzQrtKEDQS0x0Yf084TjKxQ9b7naZndK370HXTTMUckB/1nhcE8wOF8Nvg8nlAPlGmkNGM8POWMcizHYGQhLAy6cnIXt6HK0WwzMsUKkekG3JRvVNYxvUAD9tRRVp0wfICLvrpUnRco8YODA+t0OqmvSFBF+FvXzJlP+L+nMJXHPFCH0XtGx1yvMx48H6O2H6O4v0hYWzuVs0qqkK+6X2X11LGIPByh5Pg0MGY0XuNBJrVuqI88NbNN78YTViogMNH0JRdevzhsrR6MeqAsXKGg1RNioa1teR+mK75VaUJwqQLIQWRw8W9PMXvjoPiqtxk9nxMOObyrIOoHL5Po+qM+5xkJ6BsbJB6wcco4e0aMtx4b9QX/Pd7ZlcfhzV9WbJ5wj8Yxhz/PJV2OUVDPmOel0k+Xexg39Wa3pVuuj8o7XtRCeYaVkufs8HXc41fQsiLnPJZc4mrUn235R2Vr1bW6deYU/GNgZ8pZPeLIsogEmycEUQ8T0LPClWH1WiSsvfuYRK1Wy168eJHe13x7e2uz2SxZgPv7+3ZycmKr1crG43Had4y9x+yJqOWK8DEADMvh8G63a3d3d6lNbM+CIeApaChYeLq8+R/eA++vhBJmLwThNfUuPQYELQ8PDz87zYnPc55Op6letcLVS0MdfEyhjin6zAqft0t59MZv9NPbs+0Jh8hrmM/nG88yaBQGfVShxnyIsWJjhY0x9pTMNnMxYCzi9Dj1hLF0whERphNHdFSgMG94RprOTTbEdM574X5tj7c5cj0aiWDDhbfrRcsIjC/GXxW1JxsAqqTU2OTnNMyrclCTKhm4PvTZ420P1CPm6JqW450ly+XyswgAaME8ozRGQlxZlumVkYvFwm5vbzfGHC/qMTO7vr5OMgOHOWEr6nQ63aAX8yfTT/l0G+OX5ZkX8Y0UaqRL9JrKnsfAF/OcPQKqUtVnFLTzVR4LW7hs5XId/EzkrUKgcdIXZ2AjHIMJgMNB+BWRUEZmm96prut5B0iAcZHxiEmJcLZ6oAwc4vKUK9/Dh8OU/FHvTIUcC05MKvXaIAhAE2+MMUk4ksD3mIaq4CAMlS7Ka56CqVLG/LzZfZiOFai+XUf7zoLYW9PjNrDUwAJWE+y0frNNocnhceAGxRx5JhySZNwiAy3qr46b0pgNZqUF00ONLW5TFSgrJJ3rjGdRfArzMk9DQCtvRaDK0us/C3wtw/JJjXc9E4KNVl06qqugc4pCFTnmiIbLFX+ek3yIjZ5rAMWu+IKfj46OUn4Ln8GthnqEP9OzCjw9Eilj/e85eTl9tK3RkIOdvTIyB5HnpXVUdVbL8TfXx+FJ/db2wPxcDs+3Wi1rt9uJiQ8ODuzZs2cpaxup/zjYoyzL5JV6/fYMCfaKWIBj3Xkymdh8Prf5fJ7KMdNzwo4aJ7jP3ie2V/HaJr9JBuCFQD3logKVJ3xZ3q9nmllaR9d1OR4/HiOuX0OubHxFAovLeePPr79k0LqicJ0qHj3IRT3NKoHDvKACyhOUvObHZ6WXZflZIp9GLVTxmlmKCEVGjtcnz6PFf+V3NtzUeFc+Yjw9GaHRBDV+1HtH/6GgObTq1e8ZFV42MT+TM5i1PI8f98VrIzKqeG7gN/eN6aJzCe1jGycnrUbKH+vRmqDGxi8SZnPywMw23gkAHHFICZ+5rfOX+55TfjkHMdJbUX0sW3PltJ5/V+UMyHksZpthM31GBagnyDxQL4/bZYtR19A8q4yZixmm2+3aYDBIyrfdbts333xjp6enKdQ8mUxsNBrZ7e2ttdtt6/V61mg03PAimJT/I3xpdh9+Wi6XNp1ObT6f283NTWoHzIpQ+2KxSGF0npxs4bOiQLi+3++nCaneDXsFkUHljY+GynhSQjhA8LNxwcpEJw0rKhYanIwFgauCn/vFa+ye8aYeO5fzwtEKqqzxTMSfkWLmKIBnIPBzTGfwmhprzWZzI3zPuAIPbOErivscCxaiPE9zyx06r5Q+nsHFwpIVR3TylXeN57ryJ+YLe2NQMCrcuT+coMVtsTHnORxeX9GmyiGOlPFcYY8XgOOB1TDWcpwcyv1C/1lBYs7hpTucDIcIFydo4T7Oxja750HOSm82m8lxgcyCo8GRoHa7nZbxgOPh4WF6djweu/OS+5Uzhpgv1WnxwLvv1e8ZSlX3Hwq7q4mgjmUSETaCaCJsU0eubtSvCQps0WGvc7vdTgoYgpHXd9QT95giihyA6Vhxg7G1PvVgckIDyhj9UTxVeHheZ8TYKnjVq+F7VWMV3feeVU86UoZVOHv1V/FWnTa2BRXqURlWZJow5ik/NcD0HpfRPj20n57yVlrm+MnjnbpQRT+zz09Rq8OXGs7O8Xed+niu8N51vm62+Z7uXH25/1H7niEYARufkeGkh5NoGS+CA4OIz1Ko4p2HgOKzbb2ebKxq5zGwk/c5sxWTC91VeR78O9dBbxJ4Qonve2t9nnU1GAys1+tZt9u1svy0renZs2d2fn5u7Xbbzs7OrNVq2cePH+3NmzdpLRrWra4RqvLNWVaMD7KzR6NRskDb7XbqG09YWLucDMbRgMPDQ+t0Ora/v2+dTmfDs+a2VVErbnUmvNKZ98SiTT4FiYU4PFWlmY6RdyoTPD4u63m8nrdWlvdbXpQ3tN+q0DyhyYJV69XfaMdTrgy87c3MNgxD/GaaKjAdeZzRP+YpDnN6uKvXyXQx28wk98rm1vW5nVzEQr0qlOdz0jGu3pIAR1NQD3vsnrJDG1xfVTga/KzbhDhPQnMLuI2yLD971Sz6B/7DfSyz8fnqrPij+c70QyQK+ON4YsYL2zOBb7PZtKOjoyRfkByGpEkeB+CNMyPKskx7smezWZrHOq7AmUHnaGQYVXnE+iza5/nhyXOPdtsYfFWwk7C2CjwWdnW9GJ4QOU9NCatWHwtOj+i5eouisHa7bScnJ4nBVquVDQYD+9WvfmUHBwfp4PbFYmEfPnxIazAIDeb292rbfF9DMZh0CGnzgSLoNzMtlCAfaoIPwkW4zwyn66MaAmZ8I9y5Pv1GnZFAVoPFG0+miSb5INynykS9d/SFjRvtH09Mz8BjnJi/VXiwIIvaUPDox/dUOUMYI2yIvaLREbCqcDTKwuNltpm8x/MaZbwx4P88F5R2nsHlrV2zQIy8u0joMm9pzgADh+p5D7jKCo+X1MvFmKtnqHuv9cP30E/mH+Z9PgRInSJ+NSg/p23nQrNMZ523PJ+5/N7enrXb7SSjcNqXrmWjHJS+7h7ANX7zmKecVad4/KT3+Bl1IJiOSqfIUEOdOo45XtsWdrbm7F1jqzxHwNyzVR2M6vEEavQMT04+j5rDwFBqt7e3dnd3Z9PpND2r+4mVISKlppMSTINQNmcHswDjyYf6YTioYua9hPoaORWKnmHDY6XjoUoeSgQ04ISkKNzljRVw4knJQp9BJxpPMl77VeNH+6u8ERlWnrHp8aAKdZ7QdZQ108KLRsGDwRoqJ89AUE+n041DIZQvOUyJ61H4G2PLygygffSiVJGM8ASfZ5wrTzBtoAC8fboerqxAlecVPKMgMkR57vOzOQXMcyeiI/MMR43KskzeMu8fVh72jA3PM4+UEHBhI5HpAn6D07Je328LhefOZ28D1ECE7DXbzNz25pfKJYA3jjrGalx64NWtukzrjnjoofBFs7VVCHpCipWA96y2401cLRMJQn2Wy0GBdTodOz4+TgoFr9lrt9s2n8/t7du3dnt7a9fX1xvCTUPFunWHgSetJk+V5afTwCaTSdrjDEVzeHiYJgh7fnt7e+nMb578sGJxCpked6g0YqHNa1Ggk7d3En3gQ1KKokhbs9A+jBwvWcMb4ypmV75hQ4Gv6clmfN9LbELdvGTAdNGMbDU8US/uQyhxHfggKzWnoJV39OAGVso8FjAgkSuhCpo9RaaDhrq9eeqtJ6oHofirMcn00mtKWw1vAwfeG47/UUY1jzfTWpO/GEeek9wXT+ly6JlPeOO5zX1Qb1ZlAMYQRhjzDitJJKuibvBepJjVMMOLJuAMMH+w4YM2+Zv7DlmF7G9EdLjusrxPCgWuLBe63a4tFgs7PDy0m5ubjbB4ZNxFihv3vDkeGYCASB5wea3Pi1Y9VlF/8RPCtgVVynU7mBPkaikp4Znx2cPjwxuYmafT6cbbllhYqeec66cOIk9STjJjBvOUEvA+ODj4zIJW61Tpwr89vKMJwPfVA+D6dCtPhAu39RCmzlm4oAMLxBweOYjomMNZaVi3bfWa9VnwH/cPwJ6OhvC9sGbUP69u7Rt/829PgXvXvWseP0aGPHBUIQk6eOU8J4GXeBRvDz81ShQ3nhtquLCCVVmg5XX7l9JCDckIIrlUJWdzipHlHnv13lIPR/y4TsaLE8N0uSTC15t/VXIrAo82dZ7x2n8s7MRzjiaeWsjeBEY5tpCjdrx4vloyatF4E0YtJ2wBwD7Povi0Xen58+fWbrft6Ogo4Yd9zWV5fyoOT3AGXZ9jJcahJAgM3h84HA6tLEtrt9sbWynA5EiswjoNvmGpKvDLMjy6apiLJx+vpyF5A0IB2764DAwF0JXXyhXYuFEBFgkbHj/vJDE1AtT7Ap4ev3hRBcbBe4GIN+7A0XuxB/MCgA08eOc6Pmp08TW0B1oj8oG64BXxmj34E9eR6MO8wMKVn+Ox4D544Alivc+5GgB4jPosaKNruUpj9IvxU+Xs4RQtleh9xhv1sJfMe3m5vI49v+QmCqOrglYljv6hDGjBnq/OK8xPnBoIeeYZKWqkQUbisKXFYmH7+/vW7Xat0+lsyDhsi+JjkBeLhY3H47SUiLYgXzqdjt3d3Z+Q6BmpTB+9p/PaK6fAMohlh6fota6cwftQ2Lly1gH1yvI1nUBRO0osXd9B2yyceaJw20xwFkw4Ts7sE5M8e/bM+v2+9fv99DySb1gQ6sRSIa8hVTAu48DMhJAkJgCSJHiyQ8G02+0Uet/b29t4CQDaxuQuy/sXXHjGEePIz3I50AdrSphAPJaYcFDSHI7XNnjccI0Vi7fnFeDtl+Z1Kk/o4uPdV7opX6Je9XS0DJf11tl1+4ryNq+V8/joWqmHG2jCJ4VhzQ/4KP+b3RtJWPpgBa1hUtCSIefpqvGq97k+bxyZNtzfKOFMv/m30tbbzxw5EWw8ekpU5xAfvQrAuDBE9SkOd3d3n70NjZ9DlI+XOjirm+kPWkAxHx4epqUPDuWrbOf2kBzLJxkeHBxYt9tNhjvogHA3cAFtIIvwDQMB76iuc/CLZ2h5ilkNN+2bPsNltV4uF83FKmOgCr5IWNuseqGdIeok31NQIyBnCHgClBUibwGAJalvdcI6MNZVveSTSGDwfVVMnueGkLanHLhPHC5mq5zr1lC9JsLgt7bhMRevNWEdiRM/OITtCVVcwyTndUMuWzcUz6Fb9la5Lwq6RJCrP+IbDzwPi6/zPeUBDwf2biBsWah6ypHX73jcOSsWOLEC1n2lUZ+Zbp4AiyBSeJ6iZ6WsyocNwIg3VADzN/qrEQf+VryVzuqtaltsCHv4a/tem0qTKvnmzV9VrjqnI8OVjY+q5Q8ugzGZz+c2mUw2eAUyjQ8rUbmnxj1ksL6xq0rhVc1R7jsbqdtAleJ9aL0KO9tKlfvv3fMsMwyOB56V41lFEWN6uEH4dTode/78uXU6nbT/DoeNtNttm06ndn19bePx2EajUdqPx0k2sCDZ89ItB5y1yO3D2sVaNvYq8jYETHq2aoE7vCM+dQiCGp4rrFzUw14dCxwdDwZ+6QbO+Qa+ZveeLHthPNbq9aly5giDjiU/x6Fa3WLExoryDgwxXFMBrTh4Ahbt5/YBA1fgGEEUHmYhpR68Gj7cZxiZ3EdVZjw32KDyhDX3g+cm8zHq05c5gCdVmSmtlLY8tz1Dl5OtlHZsGEZzntthhYRy7P1yhMuLHnB5KBxO8ERZ7Sdw1MQqLeeNM0d/eJ6jnM5l9j7ZwON6UQ7zWJNaddyY7npu/u3trd3e3lqr1UrLgVgKhFxVAx59RgTQ7JPnXJafvG6EzrcNb0fX1UjJ6Q6llefQePBYxWz2BT1nhodaJ7nnmaBVVnuEC3sYnNXKQhFrONPpdCN7miegJpAoeMqGccBvTCS1XD3mgbAEjry/kYE9RZTT5z3Bw/1hi5qP4eM2tT+qEFQoaBn9Dbp5yxeowxPg3liokofi8IwQz8NU0InrteFB1X3FgeuHQRJ5uADwgz6v7XMYWdutwsurs27fquarRgY8JatlGHT81Uvy8InGkhVmZCx4xoAaJGoAbAOR4aplPAOR8WfF7PEF989zevg6/4ecQjnIB4yPRhhVMaNPbPypge/xvvJFbmz5v3q1dccjkkEeXXYFj1LO6mXkhAH/18H3CKVKSCE3eRmUgKwYcBxnr9dLCRFYKwVzITsbW5vMbCPszf0321zDgqLVU8OKokjJLux1D4fD1A7vEUZdWMOB54YENk4gYmucacVKXwUz7jOdWBDNZrPkLSOMzUeKAnQ5gI0ODad7E1RBhaPZ54JIFRF7zp7C1LHicmpRcxmllxpc3C/l18hDVrzhTbGwUuHi7X0/ODhIHgnGkvuReyOVZ0zhujcOAH3FoBpi6sGxIcu0UlDF6Ckd7z/az0UqvLa0Xf3N46Lb6Mzux5bngp5LzjIAdXLdbGhE/Kz48toz82VZbm51U17ReYi8BChZNsR1jPCfx175BJE0ODt4nWyr1bKiKFLyF+/N57GDA8FHJne7XVutVjYajTa2V+UULMtaLc/4esByOWd8egaX8tFjYGfHdwKZyMvLTbTIetHOe5aKtoHfkXUIBsNpNb1ezzqdzkZSBCfV4F2k4/E4hX2gGA8ODj5TeJhcCMHwfj+EiThDkRX4zc2NjcfjFMrRw02QhY0kMbxaEvWoMmQPHAfcq6JAGVW0rOzn83laX0byF/BmS5gNBj4xTT1gz7P2BKKG8NgSV6WmY+4ZesCbBRVwxH1PQeMaCy9dV+RT4nhi63MRX3N4H+PBypmNFGzjY2HJwAoc99UDYzy9qIVnOHH4kb0ZFXyeV87jG70pC33jcLMnR7Q+9l5VKOtv5QmGKMLF88rrj2d88Xgh0qRGZZSNjvv4zy96AWBcmVZs2DG9cI3XevGB7OHneL6x4cm48dhzXyEjsDyIk8MwrvjGWDOeMBQgK3EoCV6GgYOfmNY8/0FfLyrGfOrJBh7rSHcoeAZCZFg+BHYa1o6ULN/3rA0PcpZKVJ4JW4cwvOUHE6jT6aTkMPZasQ6MRCgWLBzaVYXHwkKFMFvV+kYn3NcMZ3hKrJg9yxpt7grYolYB6HleOh7qJalQ8rwY9gK5XX6eDQOt17NitS3vGX2WaYB7KsAwNug3J7FEwPc0fA+F7hkdkRfMNGFPlRWzp3xVYEf1avlcf1hAaZiXlSiHRXmMorC1OgA57ybqi+cYVMmLnDLn37m5q+OtwHPIM5o4iqP8HikJrw88fiyn2NhBORiJXjssB/AMyzfIVMhMbUvzBzgywfIGZVgJRwqWceT/+oyCJ7v0XlU7ORnzEHiUcmZG9Ca9R5CHKgyegN7AeJ4Yt+d9t1otGwwG1mg07Pb21mazmT179sy++uqrjeSE2WxmV1dXySq8u7tLVp0OIpQJjtRjixWZ4Gb3VjhO+MELLiaTyYbniMxHvM95b28vvZwDlikYGROALWQ9BQoTgAU8r1Ny8gjTFd45J38hrM9WPo8LBK+XxKP4mW16fJjIsK5ZwLEHBw8ChgpvFcpZr553xIpEXzbA/eOwPgwqhOP4ABvQD23oMpAqLPX82OjCf95BgLFg0BA2nuOXoTBEysAzntT7ZrzVOGVamcVnzQNn5tlIliituF3tk2eoc988OaTLCJwDwgDFy9/MO162NtMM84KXe2A48XzgOcvjyH1hJcYGjXf4DJwKOCVmlhQn5IiZJbkHxYoxKooibb0CnTSKwy/FGI1GSWaCV4EPzmjAThhEBbBtlJU6OyxsoDDNo/GtMiI9fmBjW3kjiiZ4hs2/q+fsWaBemagjD2lPB4LrU4aNcFJmMrt/dyn26aEMPFv2nDGp+GAHteAxOdjCZesX1iF7zGBGDhvy5FPG1pcceIJd+x3RhQWw5xkrrTksjPYUFxYq6uXkQEPZPO6coW1270HwueEslFmwMh7ad+4bcGBly3zMHgAnxqEc7zWNFIGnnFnQ8Jjw86xceaxYgXuGl3eP646EmYe//mcFpKFh/sDYUd6E0tAx4HFhg4vrV8gJZQY2lPT5nIfrjZl6y7rmrGu6fI8jB2jfW3qAYlSlz88oPfi/NwbMc2zogE94rqF93NO2EH1kYxzGNnJVuE+QcRplZE9ZDaDcXOK5kpNvOYjKqNL3yuxSzwF2nq3tKVBvcue8GtSTu+bV7XlmXA6CE2vLOM0GkwEn3LTbbVsul+l819lslqzWTqdjZVlu7B+F8OP1ZbSJ5/gwCp4o2KY1nU6ThehtEdrb29vY5sWvqISHyUeKKs28SeWFiVEW99nSbjabKSkNSkrLo02eVGgXba/X63QoAR+Ij8nM4UDdBsJ9Y1qyYcNGgxde9ixuVrq4plvBIED4ZfCIkEBwIvOfhRTTlYWjGg7oDxsfGBf1GJju3A4EHEdfGJQfuB0WPp6g5/bZYGGlwXTibTw8Jp7w1KRJ5h+mg9JAcVc+1v5DieTOI2DFwDhxvY1GI62fmm2eBqZ1w1D1QtkKOi5ajxqr3B7LCk4YY6XZaHx6IxRO4OKz3efzeUrqZHz4G/yP9pTeWAdfrVb27t07M9t0hlR5A/gdBuz8mH169eRyudw4zMhL/IsMT/z3HDvmK0/BeoZ7dI0NhMcqZrMdec46qfU6fvMzVcrZa4sFi2elqLLQtsE8UG69Xi+FtXFvMBhYp9Ox8Xhsw+EwhZu9sBMLp0ajsbHVyux+PywfXM+4IgMRIXNMdp3cRfEpJN7v99PrH1utVkrW4r3RqhQgGHjSg5YcBlfrnS1+GCF3d3cp/IWsbSgAHn9MLoTuVHjwGHEYjj0O9QjZEFIvi0OP8HY56U7r84Qt+qfRBxaIEMBYhuCoB8Jx4AUO/UUKQ5W1B56xBSOAw/ugP4+pLm+A3qzIvLA7t8W8DlpwQpCG6jEeajDyuQAaaWKBr5EipYEqTNTF3xFwFISNTC8SoIai1lMUxYZBgaRJlUOKk7cExMYUz1P1qoEX6MByhAH0Bo7MA/v7+ykRdrX69OYyNjrhwGj4nHHVNWptt9Fo2Gg0so8fP9pqtUq7YZCBjSgh+oqX8kA5m90n1MKBMbN0epi+sY/bB234m+mlihf8wMaYGtXaDssFHmcuvwv4oieE6cB6RKsDnpDD9VyYQ5+BUINyhlADg4ChwfAQLjwQvM1K68YE8Dx4teogwP4/5t5sOa4eybJGkBQnSZlZVd1l1v3+j9YX9Vd1DvokcRQZ/4VsH65Y3DgRFENZDTMayTMADocP2x3DYTqbwsGoKM6ZK8kZNVNRDFxmgtMUazYudNKJkmgU2MZsTDgW5B3TWLNIJfWuyY9Bn3/cn7TTjHnrA9PsbZ6R0xQNYM3KLJLMPdJMvnF8CY7oqPis/24y2YDzGq/NH6b6OR8fcEUezvieutb4NcZ83/esboJHR59+5pDC55wxmpWMC0FT+kIAwutrZSY3aYtAoAUz7o8dP9swmDqktDabg2wfuqDNJSjPlFEra7aHfeHfbcz21eO6flc56laqMV5v1RijL4Dxe+2dMXYH1It9cp+/Uyxceefy8nL8r//1v8bV1dXyicUPHz6Mv/zlL8tnDhMx5+f79+/j5uZmJ8JMZDLGyzzoZrNZ0t4x4I5Kg1CzZy8n6jAiur6+Hh8/flwiMC6Y4TalHz9+vPqsJNPoFHobJUcn5hXHk/uXs4jj9PT01YKkRCTcUsR6WL9XshqJunCBC4GSIyoqOBer8OjAVveaUc298JsLVeKESHtAH7fDBfSxPWY5OCbhDU/dig40BE9HTd4x28N7dN7ke1sM6HZThw1kzpx/fn5eFk0yQtput0tako41hfQ4q9KmKTgukaEADso55ZBj5WizZZZmMsFMUJ7hvl0D6+hixoJOmLaEuku6+SEbTm/QSVJ+Emwkw0U9iN3ICX+pgwtdefIhP3ST+mn3Kb8EaJkipPyQvrz74cOH8enTp6XNm5ubxY4kM5h+54MaY/xcbMZ1HbQp4SdtRQtS+A753fxUc8aR64zFGtD+lfLbtlLNUBOdpp0BGT2rvzHwEKST58/Ozsbnz5+X/XepN+dqj/FiaB4eHhbh9jF+VEKm+7jAjIJox5S6uVgizya9c3Z2Nj59+rQTrdqIeDUjwVFLAc+iyhb55l4UMMYlSt8QrKPGljZtRqVFMW0MY1hZH9+lskR5maJrCtRSdK1eRsVtixcjxxjEp6eXjxW4z+QPo3HzgU6mRR8snp5watXpStZJWXD63fwy79LnyDZT/tyqyMxSm0rx3+YFU+KWI743k21OU7BegoI1oMbitlgHZS06S6DU+uWpCH6EhL9TbAt4j+9a7pn9ybtc/Z/pGa7fIJgx6HHbaffy8rLym/zxepZMl0WG2hoQrnFoAV2ez2/aa74X2sif5pj5e81OHeKH3lKOEjmPcViq0e++tSOz+tYYGIZlLoR7g/P8+fn5+Jd/+ZdlIViO6uQHyPNeBjXXU5rjIQ0U4CDXoFX2I+nqnN+d7VoRYs6R0AAyYgsdcRZEeA1ptgVI/Jv3CXKidIyCicQdCWQsPAcdnjJFb4VvfJyhWdLfwFwDhUyrMZq33Lb0toFNVvdvNpudrSh20gQynppIfU2e3G/O1zNao5zTIPF/9+8QwMvoNAaU8pwT5SgP+WpRIhrrsMeGxjT8GWMsejezAamLPy1V6vZSd/jJsZilcZn5MADMWNjpRidIA7NrdjazH2YAsgiVQCBbmjLHGz19enpavvvOftCm5CTEZO1sq2dykf5w/cV2u12i9PSJzp86Qr7nPkGJ77PN0NVAG3npfjiL4WJb0WSuyfExym+Zc6bizDpl5WuO1wq0ZpSbIaYRyqEdWXzA5y8uLsa///u/j48fP47/+I//GF++fBk3NzfLYqvNZrMsVCAdbn+MXUdGoSddDw8PyzGdXpgSYf3+/fs4Pz8f19fXS1qM31uNUw7S5ApN7rt9fHzcmVuPoaCRpmMgcqcRtGBnVTIdfIwAI6Zs+aIiUE7oIGI8mKrnat+WsrWMhAeOGDkG7DcBG51yDF5zXo6efY/7Q2MkLy8va//bqlPKcRbXGYC6zcgpsystlceodQZA8ux2u7vXk+X09HQ8Pj4uC6Fub2+XFexJb1OGmEm6vr7eAQqt/xw7rq0gCCbwbkY0/OO4pjDCJW/8bPSKjp11JLq8uLioqVTuxSfITx+cDk2/CVbbT3SEH57hF/UuLy/HGC8ZPH53nd8PYFo6spidIxmr9MWZAcqK15+k7iwMJJBlBLzZbJbsYd7ZbDY7n6OkbqYeLlJ1cNH4ShvG6Z0mB2sOfwZSZiD3PeW3bKVqpSnhDPk2RP+eEoGgY44gZC6VTosrp4P2udqV9KeuhtrcFwpYFCsG0IIUoYvQMuqiUV0zrkTZFEjT1wx+7vE5FjvGgIHMR5N35M+spL/sz0w+7HRMe/ruSJWloetWl9tfQ9d8Lyn1mSN3G61+yo0j4Nxv/ZhF3LlHJz4ra5Fz6vaCRjo3g5Y1mt5bZrTO+MQ+jLELVPK865+VmVzQ8dFhRVc8P82/3a4Bg6NnypojRLbHvnPdg2UugLABEj5nnUx71HVnsrgehtu92N88F+fp40sJYDwWlruZ37EuNb6v2Szb63b/vT7saFupWrHhbwq7zzlwsDkYa4zh/Qjnn/70p+VkrQhkruXTZj9+/Bhfv34d//Vf/7XMByc1lIVecUA0QESJVnoKXozY9+/fx9/+9rflAxefP39e0K3Tu1++fBnfvn0bf/7zn3fS6o66aHTTZk7eyQrHRLKOMglMOH/NaLopUKKW9D1RFOnK3kkbgfzNyJbvMRPACIR95epxGrI4HiqfETKNDx1KAz3kF9NxNkR5jvPRNzc3r7aBcesT0bzRO2Wbc2+M0A5xxE2HTk5OVle9WjfZBtdK5CyAGHWOr/mYMQgozUdmWDwWLM6eNGDSnCVBsd91nSmha41HpJUr07l4k2POyDCpXz7XnKQj+rYi3gsIye+Li4vl/0TOyQhmW1X0m/PL2+12ZzFZ6iPPmarPWhsvKky9sT1//vOfx4cPH8bnz5/H58+fx+Pj4/jb3/427u/vd1LyyTAkA5BMXGwMM2cBJ2l3FlQ08OOImHpnW8fn3MbMB76nHCVyNgKZOV2/056zs23338KICM/V1dX405/+tKRYxxjL8Z1J3cTBcA9rUGRWmqZ9L8LiKkr2y+nSpAKT1v78+fOSHuZ2KipklIyKQl7RyEdxx3hJaWWums4sRmSMsWzRYoqIfG7om0ifadTwyHNMLHSUUfD0P8qZe7mfPqTOMXZXM9MJzNKls5LnSXd4yj7a4DK16vpCY/pOI5q+53d+nBZtDsanRK1t2aJxiTM+lEdrUXg+BpPpmcyvu08NUJN2yqAjVzoZRngGW6TVvOV91mU+tC1Mbn/NRpF+Oqz89pxzZCfjTYAwcyDNWUdf45xiq8ivrFXhfmeuWs9RnWyb4D91mt/mMUFWk8nQcXV1NS4uLsbHjx/Hx48fl33WyRDShkTfeKZDFlhSxpoT3VeajK2NL/W03Zu9855ytMh5JrCH1GHlb4xaQ8aOMqjAUY4IBoXs8vJyfP78eUGxQfQfPnzYiV5ijMZ4vViERt1RAg1UBDBzRaGJQt0ELvWyXaJnGjqmqsbYnauLoOc6jV32UIeXNqLksx0MDURWcNOQULnZpk9Xy7QD740xdu7Pxp6847YUFkYGceYzGfN7KVyEkkMcvFo+95l63G5/LorhF7tioNkXGzWDIjtrtu/Ieoz5tsb0xcZmNkVC+hJR5cftpP+RIy7E5DZAZkHMZ48zp1DM4zF2HQPllg6M98xLynxk2vW5f/nN+m2MGSk3uj0mbSx4j9mO2A/qu99n1B5Q2VLkKd7WR6eZT5KSP+SNU+C0g8mQZPvl8/PzspLbPiS2ifPt5vkYY5EjTj9yLF0aYFu77rKv/kN83VvL0SLnNaFnaQK+z7HvQ8YpThFyXjmngTGS+/jx4/gf/+N/7OxffX5+XhZeff36dbkXYaeT4JxJBKuhuCDV29vb5TB4Roc01CcnJzvGPqiYe6lDD7czeT5mjJcjBTMffHLy8xSzoOXQeH19vQCXpN8YlVGhaRxC683NzZIVCMAhmNhuXz4BlywAV6EzoiD9LIxmqLjpH4EY5/ssg46K0j/LXrsfAJG9oM/Pz+P79+9LupBzgKHz7u5ux0Cz7xkDRkdE6C3VHXoos22xDSOn2bRLa6P9zt88Ie379+913pLjk/QkT+UjOCFvZzYkfeEz/CG/ZlGlHZHp5fUWLFB2eJ9p5WbrPNfMPjgSo4OPc+JiyNiH3A+wz7hwnFgir9vtyw4L8iN2gc4+iyGTGclHfiJD5gNtKnUv9Maxx1aMMZZjOgmEOLXl6bG0w+m5ZAQ45uFB8zsMOsjLxjdes2NuYO1QH/WW8ttOCEtpES3/PzbiYH1MKTFlkkGJoYhwxfi4MK1EJ8I+NIVOhEYkGmFiPX63RTrNYbRoomUUWpqVaDtgIdeihDaENu6pj8aQzzd+sN8t3cf+U5HI6zxPWhgVe8X5Go/2FfeFskNgwHFkROxxp9OYtWO9WKO3GY4mj7MxWesnC2mnQ+L7fId8StTmKLL149DiKK0BLoIZv2twsI+eX6WbtsJtu55ZG46a6VRcf9pgnXREDZgwQ8EpHgLytX7SvrS+sW1P87R6HQm7nuh42nM6fhYQHqMcaj/eamdaeZdz3oc6cr3ND9Hg0hH5nWZUmuI0ZmQBQpDb/f39OD09XeY9ss3n9vZ2/O1vfxs3NzfLNqqknscYO6k4bpGJsydybimmRJSpf4wXRMt0cJxfIo3n5+dlniXtkE8W4FnEQcfB845TmL7KlhCjSjtnGuq7u7tpRBCEnz6ljUTvTJXN+pV6LAs+dSmRI6No84UgwnxyhsDPNRp5fncyL4xomd7OFqNE2omA2LbBVe45uh3jJbXJjA3nCXlWcutrnndkyd0JqS9RVMa7Tbckig5Psk+f0XKbU+UYOdXJZywb3IVg3qQuR3XsZ641EOf6KNektc3F2mGEp61PdIxj7O6X5iLN1GHHGd7mWhZ6PTw87PCdi7+S/cj4ph924JHlp6enpV7rn0sDxqkr7W632/H9+/dlbQ+zhrEVBB9pKxmAMcYS3fNjGAYbBo8cc19f++2+tPpbeW/g+W7n3Axp/ncHW2lOwJFSe78hdV/L0XBJI8YpnZ+fLw4iAvf169flqM4IkNPjjApp0CJ47eg9Rk45qCGO6vT0dNzc3Lz64EXSNlEKRu5p0warGRgiTNLKFBmVkIcHcKtD2mJknFWTodGKQeecdGZOPaPxJ584h+5I0kbdUXKMitPjdnazfcXOcnA8ZsoYB5TsC+f0MiYBV3Ek2+12kS+e7z6LdjNulPGUyCTTeZzmiGHPdAj1I89m/FKfnUUMasAl57hDM/t8enq6OOSksrkGgOPC8eX/M7DPvzlOiSqb0bUs5Vr41A6Habxn3ZTxZgPJ4/Cc90Jz7lNmrT95ng6M4xxnlmklZmY2m82Oc+aHRyjjtrG8z1PDmO2hrpFP1FVnN6IHAXj86EbetaxkjH78+LHjiNN27HeCjtk4utC3+LmZHDID0ca8+cD3lHc5Z89fuaw5ZT/X3rOw8+/ZNSp/kHuitMfHx3F1dTU+f/48rq+vl+tU9GzqPz09XQ4AaWlIG5nmICNIEZxspWK0Z2GMwmThBB2z9ys3ZW4KYx7b0UXxozjhhbdq0RiTvjYVwMMXeOhB4xH5SfocuThS4Riw7hlytkG3EQ0v7DRaBOfpBoK5HDzCLSpj7K4fyDgxck5p/LQuNLCSwvFPqnK2aGmMHtXl+eiDsztsy1MV/EgLo8zNZrNDB8FVGwvOudKZ0ZizcExs9Fl/ZDv1xdh7K5KLgUiOmEx9BgoZb44Vgb7r9lQNQdts/K1L1t2s0h5jLECJH/lhn+3cWFci8fApMhWe0GE1HmasGRWnvQRJKdzBQZ7ww0Sx3QSf5IFLA6cOAHKd75jHszZmbb6nvNs50yHO7rHjsygk19Yc+iw6ZOQ0xovj4mrsIP9/+Zd/Gf/7f//v8enTp+VgdjrRu7u78fXr12Wx2PX19fjy5cv48uXLTmTIfbg25ryWRRB3d3fj7u5uSa3HeEXouRgiBjF0RXES3XLfcISXzomLoRjhkDekMYpm/qW0yIzzTI4kEjVxAVXq5bilnvzPA2JMI78Olno4Bq6X8kSjze1ldowEJ6m/OQ7WRz7w+RybeHJysnOSU5x26GAaPqWBKvKYmQcWg58Y1sgI+RWDT3BIIJh+Jf0YQEajFiCS/mZVbmSb9FuW6Zg5BcF+03jnPiM988jOKfoaWhJZRpeoNwEVjgjTLh16pifitLbb11kfAnc7pYwRwW/GyCnstB0H5JX4s+xQMluUm0+fPi30cwFp+JpP4ybajtw+P/+citlutwuPNpuXldJeT+ColHR/+vRpke+cgvjnP/95Z5FqgE+CpIxT0vKxm4mkOd4EUPQ/jHhbNoULc6nPfsel9ZW/31PevSCsRbf5/xACDw39Z/W1dqlwcWY8PjArZjPwTO94lWTblmNaGkCxoYjwOIKlUKRQQJrhmfW7CcYaf41wWT8L6UkfqABuPw6aK4Vn9LU+mJcERWt92Cdva7wwn1l/ayvPtuwRp0B8ulHeiQPMNEKT70N1IyXOfta/t6D70OhsinlDZ8bpkGScyDPKMJ1fA/Ls/74oZfZcHMcaPxh1eSwdvfGHWQQCFspE6k60t08vLVuMnNeeXetfaI0DjYxwbjt9n0WErIdZHdso1jcrtHdc10OwQVtDW0B7zvMaWlD0FpuwZlve+s4xy1FWa4c5RtXeN2dD1+qhYUsx4uQ9RxlM2ySlHTQY5Hd9fT0uLy+XLSF//etfx5cvX8bXr1/H09PTcirNX//61/H169edLUvZi+s0DrcXJMr48ePH8vlJnorE7zJHaShUjkrT5xhJ0tAQXRwBI96W4mOkxdRrjGfqd4TO8SHSTjqTUQjn4Wfj2IwSaXAUw8KMQCt29FFq0tKmAVpkwlSox518p7O6vLysC5dyGE2AY+bj3Rc6rvzfpj0YIYcuH+ZCGhtY4nx1Ikue/562KL/Rr4yzSzOS7F+cH20GpwfyrsfGYJb2JaDI+mGbxIhru90ui6TaqvIWqYdGyuwYL58qpQznZL5klBov0jb5tdm8fLaR7Qc0xTZx3BntJVt3eXm56Ca/dJe+JFINYImOeD8x7UB4z9/mXeqL3KXdtPf8/Dz+/ve/L9kRTy0Y9ITX19fXO4fhOGvk7Vwz0N0AU7MlDgJcF+XqWNHzbzlbOwPrdFmKGUKmRKhZj9HRDI3H0ObAkcxlcJ4k9y4vL8ff/va38ccffyyO+fb2dowxlhTLP/7xjx2Bz28KENEb0WWU4vb2dtze3u4ccMJFUanXyhfUzXk7p8HCW15jasyInk6pRTxR5hjM8JOZhLSZ/uf9pJ2yIpv92xcV8W9HGTFkPhkrJVGB5Yr/t/k5Ol3ep5w64k/f7XBmMhug6FQyP0bgaQ72mf0MCODBI6GD86tM1WZcOFfntKl5whX4Mf6R3fSVMtO+8rZWLAPN8abQidlwGqCz366HehO+pPB8AzqlFrWGX55+cbRMkJPnEziMMRZHbb1Pn+0c2rQDU9fcLeJ0ePo2xhifP39eHNvl5eWOI85xrElvc5prs3lZj5IzIDIG0VWuo6BDZLYhtJ2eni7tca4+U2CRWQctqY8Latu0EP8mYDPQtb43vWZZi7BT174A9C3laM55lg7J32tIxWXfs82w87ojrjHGjgH0PFsQ7RhjJyXHT99tNj9TXEHzpJXK4NRXlMdGkz9cRWlHlj4xSmIbFr4ZT83PFEbpoSHGq0Xerc62pakZ0dY+MxDmabvnd1ncVovM2W/TaOUi+LKstXrXlDGGhPOAMZ7J1jDNl/sEQmzDjqgZBcuQ9YP3afQpa3bKBHTRo+aYvahtNo7mOVPRjNRSbIQJyhvYoP6wxPBzxTvlnXwhzwjSaWNiH7h7wbYpfGVkTVkncOb4kS6PE1Przri0d3IvQC3yx6k38sLjynQ0+9f4Tzvg/x1tMgNGHtPJZ0Ft00XaLds0ts/xzd9rznpNp/fJ9JrtOqQc5XvOFGbPweQ5G4xZND1LHeQdtmNhyHtJaXN/chYAZXsHV3Cfnp6Of/u3f1uM5OXl5bi5uRn/5//8n+VEmzFelN39YgotqRquzg4iDU3X19fj+vp6QeAXFxfj9vZ2OWmL6eP0mSfmMFIi+p450DZmFGAawKTpQnf4PQNf3DrDY0D5HEEEx9nKawfU+kSlNkKngXIE7vrsvE3nGK8XHxkckSYbHvYnxiZbpwjcIn/M7ATgxBAahDDCPjk52XH4eZdpZvbZUR+BZKLILF7jHujQH5AbMMtILXQyvZ97MarhReM/xzI89fjbUc+MZzJPfp8RJvnJL2tR70JjsiDR1/AyKWcuFstWOaabDagboIgO0VlHFwkgttvtktmIDI0xdqJNZs/Ct9vb24XHmRr58uXL4mxjS8IHT1WkH+EVx5H9IGgK75ypiRxx/GOnueiVY56sHjN94RXlk4vh6GBpU+1nKEvOYroccm1foHRIOZpzXiN4liZwPYdcX3P8KZ67YEol86OMhnI96ZLr6+udhSLsS/5mn0xDjJ3ppSPPT5whV2A3JGfg09LZa8URRTNajZ/mt+t0RGUAQ/43XpKe2Xibfrc/xutTkWbyaEA46x+vGxTOygyBs4+cLqCcxNDG6NCQNAM4xus5eEdeocVbktoYh4dtoVPqiZFtskqdIq+cCTg0EmnA3dd9j89Qxm2nTPcY45W+u6T/LftF+YjzMP/ymxFpGwumfpkq59jQ8c90031nxJt6khp/fHxcghfbmcaTTCU1mWzB0oyftkPkcX7IWwYonJqgwyfNtAWH2HKXtXuHPPff6pxb42uLwMbok+ktqpq1Y2PAOdnN5ufiiaurqyUyzfajP/3pT+Pjx487H7/gqUdc9JKFYl+/fh3fv38fnz9/Hn/605922iOtRKmMHBMxc97bK1mJpJPijgKyvzPexIh7Xsr8zX0K/2xMUi+/lmVDECOSOdXwhcZnTTiJlmdGxqjXvODv2Zaq0NQM9SwSNl/slE2rU7gtvRiejTGWDEqyLImEGJGkDsq3+ZZ75+fnr7bGxIBb/+iII3+ZeuEHLfit8RjuLFpbS8FS1ux0HD2Fr6yPfSdfDYbNb8pe3qUOZUrBhWPreU62m4ya3+XXlMZ4Wa/y/PzzdL/87ZR50w9G2s3Ypx475+bk0xavcVslU+L5207WWQ7KTvhFoEZ+EpRYfiNzycwRkHg9DkEybc7FxcUS3WdOP1mmfMLUdsJZs0Y35XcWkMzuzZ791XLUBWFhcJS+zTtFiCica8iL7/m+5ybGGEvK7ePHj4uCPD///JjF58+fd5xz9j7HAcVIPj8/j2/fvo0vX76Mu7u78enTp+WzkhE8D2r+5t7ZrH7kQrIYVKbCyLsYbfLDitdSvWk/vy18+ZvGj8V1Ms2VPY52zuyTFzF5brCNaWvX0Rr73hZ+GXyYBkYfqYPGhsaqAUXW56jQfZnxkrRyCmSMsTho8jjTBEwrNpDL+V+Dk8g0DTejcKZM45xjMB2hJN0auuIcGmDy39EV8p3TMPmb6xbiSK1rvvRNQQABAABJREFULY1NB7LZbHbSmRnz0GybZJlO5BjaGMFlsWMcMuvKGPA3I90xdlf6p08cjxSDLAYEdKgEHw308IdjTTBMB08HTZ2KXJFP1CWvJGdpDppjz8WJlGXadU4tMduYdrP7ZYyxgNT0l3w0oGYfwwu2wXdmdoH1HRplv6UczTnP0N4YHS26Y7w/QyXNAESwOMAz1MXjOu0AIiz8ycpjKm0bMF5PFE6jFwPLQQ/i5vsUmJkwmDdrKW0aNQs/6WGxQSSPZhGLjWYzorP/Gw/WeLwP1EXBaPiJ5LkK1U63lRaNsI32jAsNIq812gkU16LFOLxmFCgPiS7WaCRA8fgzmnFEYSC21kacXeONZaDZBrZHeWS9pIsyNOsz60o2K/cs2wRypJ399v04Vq4JOMS+2Rk0h2Cn3upttmM2F9psIseGUTcBxww8mkbrLAGb5X+MF4fdbLnrSBBBO3eITO6jtT0zeyfvHdNBHyWtPVMwC3WYmWI0lYFaM8L5P/XmQ+9B9lmUdHFxsSzXH2OM6+vr8Ze//GVcXV3tpIPOz8+XbU8PDw/j9vZ2SYX/z//5Pxfk6KPtKLyhP5H4w8PDckb3ycnJcrZ3hIzfh47hiwInhZjTbygwPNQh9IcWZynCMyP68MdCHJ7w9KMoZBSE+73zLpWLytbQZ/7nfS4o4vOkyTLXFL3JSVvRSv4YDBJUUE65yMb9D53N8eS5MXY/h5f+x/Gl7mR6vn79Ok5Pf36ghWMVY+90XGSIziF/twyG+ZUIL+ls8iIfiGGbjHpSsjiKY+BUrkGko33ygT9+Zrt92ZK42bxs4/GK5Jn9YIlxp/NJsY4nTZ17lOOWDUgwwK1abQrHGRI6LvOd+hSnZD6zz5Tx2A/qLfUgwJW6mTR9PtaSsU8dj4+Pi12JfHLRYwCUgVymENinbOFLpoagkrLB8cx0y2azWWx9pgfNZwOkGdiaTXHNrjUwuQb4Dy2/ZZ9zuzYjeh+SXOukGczo0OnbGI8YMRtkGqj7+/ux2WyWr1fxw/IprJ+OiCtds8CH6G+M3f2HvJZn0g8j3RmvZsaH9MUZMBKKoafyMh3qAyts3Ok0eM/IfxbZ832PaUp7l7xqDpo8pfKTj0bsjW/NwLlNg6S14lTwGC+f5yQt2d7iOVD22ZHVGg85Pi4c9/CM4+0FYH6XZS1KJehOX9pzocGG05FfaEu7HudDjWP6aqfcAJunRqinodGggKuKZ1EwecffTCn7nTYWnprJ9fTHdKzJDUGCbXf6GPuQr+utRdEp5HN4w9XoAYdsm5ExnXPGwKu7OS771sCYnw38pzhgJG8p2677V8tRVmuPsT7P1iKbZkztNFn3DLnkeqLloPcMZj4UnlXYY4zxt7/9bTw9PY3/+q//Gv/5n/+5GPEsx099jJaIOhtaTtSZE2sSjW+32+X87CBpRwd5jvNIFC4qFeebHA1aqCi4SdFnMQXRORU3PAywsLNI/V4Y5/RpA2Mcc2/z8bMpLXXI5zkGiaLCs7bf1BmGVqhoM0eedsnDVndLC7JefoAg17ywhbxuBjXjzMNJ8qUsGyXSud1uF3nldEwyN3TM7DP7xf/b+DHTELpb9ozPWM7tqJtDMVhpjt28I33ho20U37HziT0wX2wLqS+2dflJ5iw0MDuRd2gfWOxQfY9yYCDNYIWgJ894XzHtFg+noZxH/yLT5Jnr4d8c62REoyO2I9S/fDbVgHIGzA3SWe+Mn37P48vfxypH30rFjq1FU/7NgeHzLV3rNjKA/MhCBO3y8nJZvX19fT1ubm7G//2//3fc39+P//iP/xj/+Z//uaRGuFeOkaudMx1anFcM3e3t7bi/v19+YijHGDvHicaJxCCO8XplL42TlcDpyyYwBAQ5UYenpjkiGOMl1fj09LSs1v7+/ftSp+fPqfThj40pZSB9ozIzUrKcOPWZth09c2rBHyOIwjrCsYMlyjYd5jv5R/l1dMyI1JFX+J1pjTzng0qy6CV9ZQrb19Jvp53trAK48kEWLgbjZz6TVncdTpfbIaad5kxsxCgP4Q339Nu5mh7aBMpG+EP5ttz6PnWctHr6xc9GnqgDoT32ydsJKfexL211vp0X2/f/ppF05TdX3PvgDmbZqEOklRk1LrobYyyg3rymvjWAHX6FD9Hh2EzbFIK+8/PznW/Un5ycLLteZrzLuDTQxnpMeysz0PzectS09oyght5bJMTn95WGcrxNKIrCnzHGYoiMnFr9NsZE9w2UeHuCBYOGhVt81tJyMzqbE1zrS0PVa4ApxsSpV9cxiwbas+132mtp8/buLIoaY77Ia82I0eg3BWtOek2hZ6X1LwaOCxVZL2WJetTkZSY7Ln6WxsfzkeZr61PTxVnbM56YtgY4Wz9oYKljAWXJiFlujmE8x1g/japF0Wtj5T7RIfpZZ2DYLn+3QIbTSRznmS67P3zemTy26QCCheA8vxPt8sfbXJtdNJhq8ruvND4eS0Z+tRxlQZgHkb/9vJlgQ83520McFe9nMZUPC0gUMMbuJxw3m82ymGCM3UUkjnR4xGajm0Yum/uzIMxOMIjUq3ITzVApjWhDGxEh6fDfszFwtGAF5Rx1FCeRfvoYHuXTbuGP6U5hFOkobIY+DV7o2JjhcMrP9TlKJvr3drxZCUqfybv5yH5w/Nhf7tOksw7NXKDFPaDsM6Ney0/qcSqZGSF++i9RB9P1ieQJTthnZoJynZGrbQK3aXnMowP5IZ9piEkXo1pG4CyUWer2rxjg0OCtkO25NgWWMaXNi31K37gOwOAwad/oJU9CzO/0P/KQNihzdrSUJ9Yxxnglo3TM4XdocbaL4NKONOPCRWbp3xg/txp+/fr11TinxLY/Pj7uHOBDvWf2JaWBmMiqbYafS3F94Q//f085+oKwQ8paVONr/N2QTUOG+fE2EEbO7aSZlBgqDrSV3bTZmWZhVUPVdM6kiwapgZP0q6HUNd7N7rM0pM3IOfc9zWBHa9TMFCPbsjNrPHYqzIpnRWqRCOmijHAbEsdgxqO8wxWoue5Uaq67Dspn+NqiOwKc1OWjWy1zlpXcC60z8NEAoI1baLdxtLFlinMWaTdnxr8JFmZymj4ZtNrgWrZa5GUAwXsGii3wcFq3FWcXZpE2Cx1fA0ZcuNl0yXxJgMG0temiTPEa244DIoAgkGjz4rlv2WJfLce0oUlZc589+RK5ZJ9avw4tHqu1bNjvLEc7IWymbIeU5hjyO4Iwa5voM6gtyCvzzPxCFQcwUR+VJfOzRNpZcBBnb3Sb7VNfv34dX79+3flIfH4Yuede5hoTLTCq9XxJojZGT0x1WqHG2FWK9IeKts8gERWHNwQfbCfXiLDpoN0OjT4VwIrFVNrMoBAIOeLic5vNy9YPZ0ncdktx00Gn2KBQJtlX3uccNx2soz+Cu8gNz1JmG5zf9D2PQeaaE3FznCnjBAyUH9PpjAB552d8nSCGskv5aQaW1xwtN9vAqChOwoCqRWap26uB8y7n+m3HyDuCqvTbP/uCDTpczus6gOG7WasQ+7HmaKxXlkuCSmcCDACy3iXPM+pOyfXQRd26ubnZ2VXC/nNVd+xo6GME70iYssP+8G8DkRYx+x0Wyuxb/aDLUb/nHKJSDkUrNoK81upuynx6+vIBhkSl19fX409/+tPioP25xhgpotQ///nP4/Lycjw+Po7v378v6ZL85FjQGKvHx8fx9evXcXNzM758+TL+/ve/L04z+66ZVk9UEADw9evXZW8x00qZZ6HhTP8okFzR2aKn0MI0XhTOKaoxXs/zbrfbhV9ctEQHGKPNOXQ6Mqa08r4dNiMPL4px1Ea5yjjwgyPp30yuWiTPtC6BAHlogz4DRSwtCjHtpMPRUeQg/OFxkZlqIZALv9xOaAiQySrtAMP0ObLatvGE1owHedGciJ2OI13LAx0Cx5wp88ZPAwmOecY2/OWhIOQ12/NY5b2Li4tXYCU0ue3cD8+dCWHEx8K228IqOl3veHD0nDqiv5QXrwQ3P9KfBshi//IhijxH0JMdMicnJzuLxOjgQx+DoQDSON/r6+vx+fPnne1S0fcxxjKlxuwTj0C2rlsHDTyYzicwMahyWQP6v1qO/snINaTR3lnrAFH1vvs0Dl59zI9dkD47CToqrxJtqc8xdhfs8MhBp3BSF41xc6izPkYpmkK7Pv4desITGiYixLWoi7TYGLtNO/lDAVqebX3zNbfLwiiJxp/1tEi+GeW30N3onV1fK6G7RYmUNdLNutdkg4CKspH3aPzfQrNpaX1ai1bYR/Z7rS9+hs7/ELrW6OC1xpNZyp6Fjirv7NPzvOeIrwUqTXdt35rerJUmT7zH376X6876zSJ12hJvUbMzzTXz0mNI3tjeRwabz5jxov39lvLf6pz3CXUzThwAC6HfZ90UgJngRrATFfzbv/3b+PTp0/j06dOyRWqM3dXaY+x+Du7Hjx/jy5cvixHcbH5+nPzPf/7zGGN30dYYL8qUuphiy99ZfJZtU5vNZqc9OjpGteTj+fn5uL6+3uEf53qaADHVGwefbTnss7eOmcceP27FcnTeUtmHzNmw7xzXmRFK/wyyGCGk0DB6zpy0OSVlI5x26CBJa5szJ7jy82wz/EwEkmucgtlut0tEwe0wziyMMXYW5jA1TiDJ+dI2D0nnwoiC7YRe84rOPka6ORcXGmOnq9PeDCzzGnnfZDK84Vw0pwZSF1PBsRlenez+2tEm2uP0D+W8ZWxYGhiIfLI+Li7k9I7lmsDMNpny1PY7296Ff/lJijv2jjpHeaa8Ud88ZZMFvLQ7pDN9tuxabsmDVuhjmAkxYGlBCQvt9v8TztkRR3PMfifv5XdzuO253F97lid6Jb3CVCkj3NSRFY9x7KT34uJifPr0aTFq7pvrooDToKZupqsaSsw98oUZAC80m/Ga6UI6/Ofn5x2DnPZcV3POjJxzjeNj57c2jxda8m6UzuPcwFneacdIek9znJuN9Yxnpps84Vi7TwQsodMrbmclfaXBasbGe6DZfugmPWPs7hXmAp6MY1tIYyeT59i/Q4GY5XxmwOxE+G5SqZkqoewaVLq0VDinUQgs04YdAI+zbYueGmDgmQHkWYtCw98cEJTSdMr2loA+/PHpfqyjgXCCY4IM6h9l2dF6+hL5TGrZ8sL6GTkHmLo8PT2N+/v7V07ZPzNnSBrZf96nfWkRM8E16/I45D5/v6cc9cMXKU1w2z0zrhlDO6z85rsNsRrteE6DRpiRB09qYj05C5uCFiXIpye5yrQ5E0ZcOQGHA2/HZ7pmDqM50bTXxqOV1ue198yrMcYOcvcYNiOce8w6MBKf0dBQa4vqXDcjuJmMuV3/pkGxk+Kzaw7jkPHIc+YVIwses9oMW96lU6GTMf0p5FWrz8+2+09PTzsR7xivDZd1qbVBAGDH5qxRnDavNV5z/DmeXkS0lkmYgYDwjdFkFt01vjOSzN9ceEkgQbtF20I+t8WQkY04OWZQyNO0H1tJoJxImpm4Fkzk3ocPH3a2AeYn/OW4MljyyV4et9TH894JMrlwL+80MMsxa200m+BAZM1uHKMc/XvOa06jGTpGdBbchixnTpmDkntMdwSBJapmmibo2HXxWj7px5TH4+PjuL+/H9++fRt//PHHzmlfTAlROPJM0j/5EEXeIYplWo2LyshXLoShYTFa5TPkdeMxjYtLrjUlSCq1OZWZrESZaWCisCwEJk15G0K2DNmRzLIPRsZNbvme0bczKa3fswic1xzhJgJ6fHzc2RZDeaLBIi/j2HnuO4Fgo5cy1Zw5tyiyLqd7yX9HHhxLZxnodPk8r7ViZ8q+kUbq+xi7n4yMbeB2QtoLpk3Z3+j4dvvzOF+eBNgcW/gbWxV9b/rnjBmdkmWW73gsyUuCAE77RZai56Hr/v5+5/Om5Gui3/A2C8by/XLynvKZqcL0P7JqgBg+5bPAGYvIQmi8ubl5JRMNLLc+8G/bVPK9vWvb+t7o+bdEzmtlZrTWUMm+dqlEuUZHnjpnKaUxdlfm5d2WViStFnAbXEdQNnBrEVyK+9b444gkf1vY3C8L3SHF0YPb3FcOEdpZ/45VHF3vu97uO2Wc0lKzLg30zIr1pRnVmUxbtprBb3QSDLCe9q7le63Y2M7eJSB4S0RCgGSnOSvNDrlO66n75P5wzy8B1j6ZnukSM2vt+TX94Ngxs5BrDaTnvRn4bfywTQzYYoYpbbofrS5epx02Pe3azA4eUg6Rh7Wyz56/pRxtQZgZ6sFtCCTXm4LasRBZM93LfW2Xl5djjJ9K8fDwsHNONrc1JEUzxoty5TSvnDKWe8/Pz6+ixNCcdFXeo7EMYmZUw1QfI5wsNCG6zvYDbp3yvljyyftvx3hJM5+dnY2PHz8u27HycQ/PByfKmgl0rgdRt+jTqUYbeYOGpLDfokCOuNhem9LI2Ddnxf8ZPZC/zoDYAK2le42iZ44uZWZgGg1c/xCZbdER3zOYZL3k0SwyZf3Pz89LqpT9TiRKvWmLqFKH+Zl6LJ8zY+esCn+nLurJvuLILnLKKHQ2ftwGGRtA5xxbMcZr0Gzd8Faq2CICAOqZo/qMlWU1/1N+ODa0rZwGCv0XFxfL2HthZOh4eno5/yHbpdJO7KDT+Lyfnw8fPozr6+tX60hir5NRpNzlbHiOfXjTeMZrvt9Kk8MGot9bjuKc96FNKv0aM3itTcAzzRCliRJz03scDBeBUUA9KNyLy4NMOBcyAxXcI0pBZoqOi3iMgmmcuGeQApx+tOg8xqah1LSRL1GdnJyMq6urcXV1taOMVHYqKB0Vxy/vho8tWmwCTh6ETwEVMxliCQ88Bi7NwbU52ZTw1al112NDNaMhMhkD6BTojG7SvtYnOyKv0l0rdIT538CTv32P42pnSrp9HKVBuKMpXntLZiHtErxSdiIzdvYz42n+8GS2tZ0RfJ5OmQ4x9Iau/KbjyP9NDrjqmPPBHCvKKIvT4bQ3HB86M/7OewxEcraAwWN4nm8zk29ph9NnaYPz8gFDp6c/v2t+cnKytLfdbpf9/pneiRzHF3A/O0EDeWY/wB+PEcd5n/M+Vjn6nPMYowpHe6dF02ae683vCGKciLdb8NQtl7YwbIyxID4achvkRLc3Nzfj27dvO1uj6IRNd3NS6SejYUZw/Jml6drWIBqszBdl+xQXTlE5zPsx5oslWGzoLeBtHFlX2jZtfJc8WnNs5inbPTTNaedDIEIHTt55y9IYYwecEYixDfcxz1AWZgZj1mcW1jFLrUZeSKOdA9c1WI4tT23smhw1G2FjSBk6BHTM6uZ0jufHx3gZS0dvhxbLpueVGQW7X3SsBMNciW7AwMJ+EGzTljTwlXfjsDjebDMn6nkNTfuxXIcXBDazaTpu6zOYMvDjOgxnESlDM9/ie9azma0+5P9jlqN9+CKlpd/4fEMljP5anWZ4lCyn9vB4zKRbklIhwyNAcd7cdxwF5cIaorI45u/fv4+Hh4fxj3/8Y/zf//t/xxg/T7H59OnTeH5+Ht++fXul+M1okSdEstvtdlGEpNizUMVAwuAivEyUnMVmvNZoC+qcpes8vh4jL+QxTa1Np1N55B5P+Qq/jPztjChDppm8WYsoGD2Q33mH5/pyXMM3Tp3wxKQ8M4u4eD1TMoxscp3yb+PiftPJ+2MlzApwhauNEgGwx5vG1ECL9NgQM3rloszwJ2PEMc+CqtDr9tJf/5/2eBxp+t0ifuphpqtapq85sQB+prXzk5XgaYMyEhr5qdvQ5+yLbYDBDG2IM2AGHJQl8islehg6Er1S7igbttHU49hZZy8cwSfiNW+YbUzfbm5ulkxgzmvwQl5G/2mvOXuOi+1VK/sCLf7/nvJbztZmaQj5LfXzfQsA/zaCbJFE6oyg2nH7GRvAMV6OvsvK1xgR0kMj0ZAlHbQjoxQ7sfTLpaXLnN5hyp31sRBFv6W0KMDXZ+2RDx7TtfZct/tkOmYIelZo6BlJmv78NiD9lWJZN2jY985a247GQ+shY73Wd95rz5l2Gtkmy7O+7KOTzrwBsLXCKNU8coQ9o3cfjXYILQNh+bQDm9kzF0eATa9st2d6F0eYfrrO5siaHV0D/QYdBqDkF+U418mXNVn1Nfap3TefeM/+aO3Z95Sjna2d37OIt72zjwm8x9/NwZDRWXyQD3Vnz92PHz8/5Xh/f7+g2ziuy8vLBeHy8AAuIMvWqXxyMojv73//+xjjp7Bky0CcYpw5Ixf3h0K83f78wHjO8I6DJphwHXTkWXyRvnB71iHKneeZxbCj2IcQqSQW5pba9fg5OjRAcft8pkUIRN88U5gGweia/CAtM4VMO61ffIbXSAP7yO07XizEd/wuwRxTuJwHbRESr6W9trfWURYjcEdqMx0dY3dx2FqmhvLgbYQe59yzc2cUHH2wgWfUm/cdbdI5UUfYNrdDJYPX5j1d1ubCo9OhhWMx0zHWE3sWO8bInaAmGZtms2mb0nZ+M+PG+xzDx8fHcXNzs3wEiHPn1OXQMcbYiaL/+te/7jybqTpmpdzvXCPv6OzZtzWdPQR8sbRg41fL0bdSUUje6pwbIsn1FjE1lDbGy8pVpnVzyD/T2RnorPSmc87HATj/cXJysnPgSBxvvvATp5o08vn5+fJ8CoXYUQxTe/wAQe7TGFhRouBc/OUUkgW51WVHRt43YDAb1za27Z3ZfFjLHKQf+ZmlrkhL6qJxpxEgzaw378UYr9Gadtq2EYKc3J/xzuDEzpn1jjGWBTNM5XmFc55nqpU0c2zZX04TUHZY58wBt3nFGEc6hjjGfRkiOiI6S8si2+SHOzztwnd9pOl2u13SpOwT5aXxOGNrUOTFjpTVjOvamhICBcrNLEPgtQ782E/sSwADAYqzfew7eUZ9o65b73gtQVHaSnFGh1MuGYPb29vxxx9/7PA9JzYSJHGKrvEp9NqPOGJnn8mDtekEj1cD8r9Sfuv3nPehDaPcMdZTAWYgDUEGxgg2RitRL4+1yz0e6GCUmAFNKpsHmRiI8Cc0tflB/14z2FagmQKzP1aet4xB+u+VnCmkwfdjxGycm0OYATD/PQNg5A0jB79npfTYcrGT02I0Li0qa7y00zZtVvI2ps1ozAxH6vC0Buk3j0grDRqdHVfvMyo2wCOf+buNUyscn7y3FnWzP+1v0tDaaXXOslms33Lv0oIRnqBn+bSDXpNlyqG/JsVnOKbehmWeUJ6T2jegMq9sG+xc2R+Pv53wmr2zLCcTwdJAzyEy4udsXxow8XOz59t77ylHcc5rziX3D7nWGJxnY0iJoBNZZj9z9sV9/vx5nJ6ejs+fP+8sw//b3/62RLFO+9Lw5GxtRg9JzXz58mV8+fJlp89MQzLqSQTORS7pz8zBWTiYFouBYUoqgsw9zYy4WGdT/FznvFt+8/g9Pps0e57LO23vpceX79CwU2m92pWF7eW+I6P8zhQD6+QJRk4lN4CQiHOz2VR5MW12wOT7LCXPKMhRnJ0KwZzTi4ygW2aAhjMG1iAq9xyhNuOetimf5oWfN9BjlMn6+A7TzQQeBjazSMbnaLM0J9e29rFvpHWMeSSdabVEjhmz/CQ6JOChk2ekm7Tz3d3dwouZXF1cXCzfEyA/yYf0OXRx+o3ZEoK3XM9vZiAN4NgGU+bmQ/hInWbbXCiXbCXHYLvdrk4LkKfsSwsY/D6faaAz9TQ7t88nHlL+WyPntxQbFyJQG7BEzfzmKVdop74Ys1l0xutcCMY6SF/7TRTo4oiF/Uwxkmz1hAeeHzX/Diktbb2WwvRvPruGPFudTaBnkQd/zwy/5YWInzyiwefzdv40xjPlbv1dU1YDtiYPrq8ZjTX5yv0m5xkH9zn84N+uw465RU0zGZjJowHDvnoacGylTZG0ttt7s3uWYYOFtWyCaW+ZG8otHajrbJHozBaQDwZpBOhND/nTIuFZdOpMHGUpskeaWE9sdABcC3CaXO0ba783k1nT4/utHMvv/bY5Z17z9XZ/9i6Nswdjs/k5N5GzW/N8tqJkL/IYY3z//n18//59QZ5jvMynsXgxSwQ4TplRBe9TWHONp2hFIL0ghWloLk7jiTrpazu3movGGoK0E6HC0hH7kA6PF9skDenrGC9oNuCIdLEO1t3S/0b4jrLZH8sJ+0hD4q0za2CF95vi00jZ8FlebbA9fuYNIxgavVlEEH6sHZBBIzhz4olu0o9kYMyD3M9PALBlgvJtWtrfeW+z2bwCQ62eWV1rTt+n3/lvL8RjfdTZfcWgmzpI55T+tsibbScryHGkA2WgQqBEcLAG7Lfb7XKqFj+MQrnL2gD2L8+Sl3T+uZf6Tk5Odj7cksCogTrSa/uZbZexh4+Pj8shVHmOMtS+A5DfBqZs1/pisNumZNzGe8rRVms35rLj7Z2UQ+9HIGnM45wzAHHOj4+P4/b2dnHOX79+Hd++fdsR6rYCmo6eKd58MMBzsUS27bpPGXNUQMeclYxZTDbGy4IV71d8fn7e+VJV26vKvhKZByDQWNiYtjFp0RXfT4qrLU6yU0rfKeReKZy+2immNOWwcmWcW9qwoXSDpiaLrIvKymtsu5XtdrsYmhgY85HPWtnDJx+AQoDEhWw06qYj/QkNWavBsSE/OW3DKSXqR46fbXpsZ86xJT+bMyQA8/21LMIYL/oe4x6+pS477ZbZafVnfBo4sWP2PQJS2hLKI8c617jQ0I5x5pi9c4N757PLI6lz0h7eGQA6/U0ayBNG+0lv80MYzE4YaPB6novchRfZA09QzzMj2piZVgZ+M3vj9zkVSjAzk5O3lqOmtW0oZx1r7+Ud19OebQ7I0VhTLjohMtXFfYhg0viYDivKPmcUoWBxajrX2Cc6eCti6wN5wn6zf3YE7f28SyRNoGBDS1Bip2k6mVodYze97OfSbzvb1odZP9zPmRMIr+1gWwrXba4pKI00wY1The19O0yWfenw1nf/Nl0EBo7M0mZzpGupaeoBQaONWwMllJGUfdM+jU92qLPnWsTVnqFxtn7x/Zm94diz7vQvf9MhWAdmoMJtWeYNRGc85zPU6SajlBPLtb+uZcBlsEQw13TbB6+YZ/t4wbFjva3MeLPvvV8pv2XOeU35WWxAjFgc2VAQ8juLLYLGgqqyPSrnwD49PS3P2bk7yvPexru7u/HHH3+Mu7u7JSrPnj0qltEUP8tHYd1sNksKJrQlRchFXewnz08eo3+/2PU1RM1+EX177BztU8mSSeDnB0PTZvMzpZSMRfry4cOH8enTp52oOM9mCsD8J93MWDQQwywI02nJOhgANgdqfnk+rBkiApB2fjfBVJ4l37kHnidM2Wkxu5BUnccr/A7vmsF1did84jhnGoegJHoVWhixUwdIAwsXdZn2k5OT5UMFlCfKCevhmDKNyjIDSORDnjP4ylgy+lwrdqJ2gIngCGbzHnWAbdn2JbOWug1Q89yac+Z4Ox3deJk+sE7X7z6T3vQ5H8jIttMs3L28vKxAbYyXcaZsxN6EZ6ExvMpzY4xlMa5XlpsfHPtDMr0zXWrPHwIU18rRvkrVygwltnfXoh8+w/dmSIxzYWRk2+bDd1JYVwagndfN9DBpZKpkhhAbcqdTMBihsEfwzFcjVEbbVAA7sVkx6meJAbdwnpyc7BzoQCfT6iFqbSCOq6gdecUIOPVNfhjkuX7+73HMD+8bkR8qty6zaMlgr9E4i5xTbJwPRfvkM2XX6X7X5ajFtDiN2wp1yWPJMhtT6y9patFvA2nmCZ1F9JLX3OfmmN1uK+wP6yd4SR3h08xZur+mz32mTWpy0gKqyDx1utnBFnRxv3tsFDMy5BvtdQCJ+8aomXbAcsTxbvLFfs18VHvONqsBwV8tR3PObRCNRJpz5d/NGexDIBnMp6encXd3txOR3t3djb///e/j6elpfP36dXz//n0nAvnw4cPOdgbXGYcYR5RtUdkX3eaabRCIlm2I3c/M47X0Ic+ejnKGrjWHNxOw9I9jw6iOfQkQ4AdDeO4uP9U52x8dEMVCoDSj19s3WGaOmxmGfY7ZRsQfKHA/uDDHdaYvzTjTyBMcEtU7dWmQRpAVWlkXgShlaNbejEehk0CRQNcAknWTHwG1bCNZF9NMXXK2yU6FOsDC1cwzm2GDyiiWMmRg73G0gbcjcgDg/s3ec4ar6YN3HTBr52JHzjYsp6kj613IT/Oe/OTfjMiZxeEWv+12u7MNi+NM/lhXc7Kj93Dn/awj4OmM3sbKMgOt5g3HaAbqWl370ur7ylGc8yzqiYJRkOiwZ8RT0A5BIhns1JlBv729HX/961/Hjx8/xj/+8Y/xxx9/7OxT/dd//dedVMgY41VKcYyfAh7BoBA4ZRInkhKBa6tvW6o1zpkLvfI7QIKp0WbQU5+NV0PGnOclLw0mws8fP34sh69wUVrSU7e3t+P+/v5V5oF8oKIE/DClbWTr/cl8huNkx0GAwmxHk8/IJR2ZQVfGe7vd7hiw1M9+cFzYbgxFmyZgqtE7BuycLSseO5YYSfIicjWLEujUKYu+72vWa9LjlPkY49W4h8eJqHiqm0Gjp2rybk6ianQ7KuNv0spDiZqNclRGnjCCZPtc8Bd5aREvIzM703w/3mCODm6mS62NAKX0IX3OYq20F57nHbbHcR9jLKlkRsTkQ9LS2f0Sfcg9BgIcs+12uxwAlZ0gBLEMmvLbq8FT2F+DQ5b0i/3k9bWy7/4h5bfucx7jdUfW0HqK0eTsmQw8jd0Y84VViajoXKKQDR2nrCE6GzcbJ4ISPu/33V8KvxFijEBT8NDrFNxbCyNdrsAlHwgU8lzozA8PR5mVJgtUBpeGUtdkhfWtyVwzuofQPEsztyj9EKW1Icg117k2t7hv7NdQf6PHDrnRNTNus357PNr7fpc6cGhZi/SYom56z7LPXrm8hcftubzv6J66x/cMNsfYXZvi8TOAzHNMIzd6CCBm/WsBW67FZgQoNt6Thhl/UsgXOtQx+jkYpHEmm3bos7Z/ZznaJyOb8WvpqDG68z3EuBGxBVnnPGuiro8fP46//OUv4+LiYlxdXY3Hx8fx6dOncXt7u2yxSmSdeuNYPn78OK6urhbhDdLPQrCgt9ARmqIESTMTnQWJ0qkRkf748WPngxlE/UGBnD/mNgRuvQjPk35vKU8WomALaaYFttvtzlw7AVDml7P4K4777OxsXF9fL4AoUweO7hnNjTF2IiXOj6e9FKflnOVg9OiIphmntcVFjFCYLufpaRwb100+u6yB0MiLU5fpSwATjRBBJGkN77kdMD8tRU0DSlBF3jhC9+LHtNsiE+7XpzxxvLbb3ZR4+sotXqQn9y8vL6dOv6265xZML9Rzn6NP5L1TxqGltcv1ERzTme0jPc/PLydy0X4wEMnBS1ls5b5yLOzo2dcsqKWOtZP1YnPDR/Y57TCy5pjyCGTSmrHzmqHYxjFeznYgr5PVS+qcMsK+NxlP8Tg4e8WyBipz/73O/LdtpXJpUUT7vfYeAUCYz7nYOEau1KbTy2rHMV6EPQssIjQ57pPKQSEm4nP0lHo3m5c0nOe1qbBGe6wnBjWKR6VIvxM9U8Dc/ozHLdJhYQouh7qwnigq54IpkFGgKPqsNLlgxoDRePo+y0KELhpD1sl6rJRNMdlnPsc2CMzYp9mUjfvdUqc2aAZf7rfBSYtaUloGiBEX6Wv6zGkp1sk+tyyJgVQDHy52pgEds8iZUZ8DA9bBvlKXSKejVk6X5Hoz+mzHgLIBRAYcdvBekdycKttPX/ZlqtivNr7hR9s1Qh43u0XetYiZWYA49maH2HbeS5+8hmKMl7l4+wvOOVOeqWP5v8n/W4tt7nvK0b7nvBYZ5O/GgNwzA1t9vBbHxQgxCxgopHFemV9hNJCTuHJvjBenkrO0eVxnouDQYkG0wEehuIWLRqk5NRoDoj/fp0Jn9baROo+7o0Cb7238QrvnnFhf2vZiobOzs3F1dbXcd4TDQsDRDFwbexoEp/5ttC1LBlOsh7yzYXFpkVKjj4bY7/F/y0CLKGNsGF0y+k/0NEtXMvKkvhHsUnfokEgbMxet8Lr53J5xSZsNuBAkm4dug/dSn58jMHc7BoRxWplTXesHQUizg+yLnbWLF56yfttA92mmVwRGlLHQwD5vNpsdHjUbxP61iJxjSdviA3faWhzKAG09i8EsAY2zi4cGkLzW5P0tgeWvlKOltV0ohC3qaSiHv9eMIoUy0XFS1jEqOeUog/nw8DBub28XB3tycjKur6+XlHii79x7fn4eX79+HXd3d+Pu7m4RxHzKMSluCgJRYH5nC1Yc1dnZ2SuUZwEPn6h4MwfDFCFXctoxNiTdlNZCHUVnCpTKfH5+vjhiLmLLaWdG/za4rJMGb+YcqfBtzj3XWgREg0mnk/a5Gr1FZs3phFekjaWlPc1/9iP1hVd23lx4xuxM2uLJcnY6XDTD9ymHqT+LbtKvgEr3p9mAyArpZv9skPl+niUYpKEn8PTuiVkEPqMjffP0Rtqjc+I5A9EJ0tXGmvWZjvSFPHVGI3QazLNu2gnWk6m32EA6xvDO4IxtjfGyQ+Tk5OVkL8uLAUh0inrBCJYykjS0AaYDEbYXHvHDMKmPtib9iP21c27yZx21HWqlga9ZG28tRzu+872EcFDX2hlj16C7Dt7n8zRqTBU39Efn5L3NfN5RmKNEOog2yK7TUaRps3NLXziP5baj/PxtdMnSjJujK9JLBO/76UsMbQMYs/9naUnyuzm8t8hhi379m+352UOKIzBnAwzKPI6h0/I2Q+vvKR7XFrmS9rVCpzOLWqwTv9InRmlv4YvpiXzue9+O4tBCuzOLZFO/ZaRNZbDemdywfj5/yHOM6v3TaGh/z4Irjlmb0tlXZjaBNJ6enr7aVmcbtGaP9hX383fo47ucM5WvKdgsJZW/+TvPzwY6/282u3PKRlGJvqKsuf758+e6qClbHLiEP9Ht7e3t+P79+7JFKIPAiPbk5GR8+vRpZ141NCWaTcqcAuloJX26vLxcztYOck8/8kMhY7QQfuVsXPaLEclMaXLPc7ZJldJoZ8HGbH7LHxlgO1z4xWcYUYR+K2LbIsNok4aaDsZAjPzcF/lGzhkthbbIA/tn5E/DHMCXPqXt8DRjxnEi+Am/ec1RSyuUnfQlY0TehL/OkBBwObNguXL/057paesiDD5nhancxu8ZcLOdoNxw14FloW2HHOMlI9V0ypHudrvdmd9OhsF7iamHtCWUNfLLZ9m3wCU0hC6nhbl7hRnB2B5H36bB4Nwf0GAAkz4mIxm9agEQ+cHfzR7xnPfoSqYkucDMdsX2Z2Yf2zPHAJizchTn3CKj1sF9ESTLWvQQZxbFYHpohuySvo4APj8/7+yNNj1xqlmlzcVmVIDMr15dXS3ppLRJw8t+8yeGMEaXR3jawcxQKR2e0W+ucTEJwUnep4GyU8izzTlTQc0/ZzAYnaxFvyzsp6N0OlUbuNa//G1nYr64bfaN6bIxXlbg0piwX5RLLxybzaV5HzSdo52mo1PL2RpfOXdKAOEIj2MU3lmGyC/znADaNDsF3njSCsfRPLUdcLTmrJq3AvI59s2RWLN/rI+ywDHkorXQT/kNLTy/PnVbzkKHnWOumwY+b5nnVBZlmrLXnBHvc1rJdEbG0qfHx8edNTzhpUEnS4uYYytjk3jMaQItTg823Uh7M7pb0DjTMb//q+Xdae21TrAYcZARfsb1uh5GEGE6nRkNAN+LMhsZR2iIWPMMf+IQiALPz8/Hp0+fxqdPn8b9/f2ChO/v75czZYkgaXTHeImYiMDZnvnC/63wKZzDSfG2Bo8F+c7I2Q6bSsj5uFafAcWMdtPtsc7fod1bbGb9oSLO0vrpU34zOmoRHI0fnQ0NcOomHzgWNIw+R5r9Ju28zzGwQWZER5nebrevPnua+uiUeZCKo2PS7+Jx5piQ9xxnZrIIAi0blCM6vrWV/BzPBjY5Pna21PH8vZbCJi0zvoQGrmsIbZwOI9/yLudrDYooB5SVjD3tYYumTSufo57ECXpul86M40T7bfBMvkWfGe02vjdbaKDMPkSG2mK6VtcamLWfYltN34/hmMc44mrtfQM/Qxjtt41t/s5gBR1dXl6O8/Pz5TukdNZ01BS6zWazc9pU7hNZZT8hfzivG6G/uLgYFxcX41//9V/Hv/7rv47v37+Pf/zjH+Ph4WF8+fJl2f/r/kaZ0+7V1dWykKc5G/ORxoQg5/n5eVkgx2ilpenS1hgvhtzPfvjwYecgEgKBbBFzv+xIiMAjI+E1nW/o5XhlrG3omXZzJMQ6HcFEkQ0IW2oyToxpzDwbWZyNSUt/hn/hN+XQTotKn/foLGPM23RCnA8jmPTl5uZm3N/f78g0p2iYsSE9rNv9bMWOJM+bN+kfnT375dRzno2sPDw87GTRqO8sXLlMOU8b3krF97P7w31x/+ggPO2SeiN/2TUS/UzEZ17boXKRn6PVOCLK2RhjmR4zX5ptCQ2RcZ6RHxvhMw8IFpodCN12vuZJFuVyfDK+TeaYcUi2wvYp+6g5TURA7fFJcRDD+83OtGzlWqB6aPm146P2FEYRvDZ79pBrvj9bXDErNvA2hA1FtQEyApulGWmEGg35beDQ+m/BmRUqKx2gabKBnKV1U2ejgQ63CeKacP6K4JJnjijXohq+fyiibbQd8m6LONiuFdcRoh1++2lyxcKsj8d3jNfz+ByLt47JPkC+Vvbxs9U9a4/XWx+o77Oy1veWOm8Guen6rL7Z+Da7YxvVbNa+fu17tkXillHa3EPsJUuzYayDWZB9ICJlFnCsvXMIz94a+b5Hh9bKb9nnzEgjKGSMuSMm4lpzRIxegpZ//Pj5GTKf6BOjFER5f38/bm5udtJKWYCVOhiFEdFtt9sdxB4kOcbPw9j/v//v/xvfvn1bjCLPqyXdjB44Z5nrObCD87l0Qo48zPe05XRg0GUiqdTpObYUpvTozHnP7bAwPbo23qTf10Mf95jn+lpGIcCtASmOAWWtpSRtoFIcNZAfpp18YkaC7VlPuC6CJzClTw8PD+Pbt2/LwTY8CSzRMbMyrDMyHnnmoqq8n/fct9DUwJrBEqP33G+LMTOlYwDjrAP5y3Fs6yxcGG2mbs5Tsu/c4kXgHBsym3axnDd5alNqXs/BknEzeMtnO2k3vC3KmSnT5/acfcqYcPqDU3PpBx2yaXCEut2+LMRzZJpvFmRMmB5PHUxhM/NJ3t7f3y+LwLLQl1m+jHHu7XOq+4JL6gBtCv3Ze8pRtlLtI4LE0lm3+77W6jo5OVnSNREgpk/ZFo08vysaIaZxoNJQOHONbUVhxxjjjz/+GDc3N4tjnUXmRJ42UkkpZn8wU9YR7PTdafnGnzFevmnKM8VtYOho8g7vzbIFVBoW9icCuza2rNdO1RF+k520ybrWUq4ESCn829Es6yc9NE5WcDpnr3Jl3+l0815kg8bIUy6hIQfopPz48WPc3Nws+0fp8D3O4YMBG+XAgKzJT+MheTF7hhFY+hm++l06mvDB2SbyjoVONz9MgzP7kvbZXxrwJus24LMAwxkr6xFlyLqa5xOUkCfkmVP7s+wi+Rheul+kmTIUGdxsNjtHFRPYRaZmtskOOPaY5yUElFIfZxmQ0JCTDJnGjo5xn3p42sZyNn7uC58jndTl95ajRc6HXJ892yLrtWh7jLFjWIikva2A9XHAUhpKt1PwgLG+McbO1pgMUKICInyWtJf7VqSZgEQJ6ZzpuOxMrQhj9GMRU5cdhCNAIvYGEGbAi/da1GW0HTTvMQ4aHqN/MnLGOyrNLOpbS9POInaOI0szLM1YUaljRHiAC6MaGsIsOuQcZNZK5D3LTaPbToI/fM+yydX4uc8ovOmvnSGvWU4sr3mO49UMZtMDrkjPc/7UauTKBppjHuNO/Wk2Yi3Kokw3UOh2KUMeI8u2nzEt1osZYM9905c2uGPFdKbErhnImk90qpHnmc134JF2rP/se9pnpNz0odn4XOe47LNnxyzvcs6ODseYLwZpzHA9UXA6RwshjRgFbLPZLGnq7BVm5EvURSXkiVA8no+/mwPg/Qw8F3VloVcWr202m3F3d/fqW7YxxkllE7VSGAk4vEhqjN0UtE+5yt8NcEQ5bcyZGkr7yVgY+HDhjseYzoljmB86fzrds7OzZXrAcuBIhulvyxP/j1Em72nEHbX5/X0l77Z05QxVM/X7/PxzQd/nz593nK4dFceM9T89/fymOfvBMfOYh68Bh20LH/tAfiey4UliMZQ8UY68icHnFJTTi7P0YN4JEG6glzrONny2e+jP/chzbEpATp6lw7O+UY5Jl7MzdKDJaEW+w0ODkvCN9MXGsA07Dssdo07qE+WIpTkbL1j78ePH8gGhBurC9+12u+N0nQ14fn4e379/H2P8TKd/+vRp6QPpoT00CGxZm7zPaQSDTsqXMxpr/Igusy8NDL23/PZPRqY0wXvPvTAxSsHtII7saJTawFC4aaj9bovQWO9apNn6RFTsSMWRHO81MDRDcXaOM1Sf9liPo8W3COBaFJH75qVLU6RmyBl1sC/mw77optHdFH8fUmak47bIX44h5SXPeE7YYxMHmTZofNmuHbwjgXa/AWrrlAG4o9mA1Bm/aDTXok2nnvdlOfx+A2nUh6wCn0VWrIdOws6X2Z5GR0qzP47Q8hz/JgiiE/Q4tL9JR5NhjgVlcwYWbJfNrzU+Wu8N+rzmgX2Z0WUayS8HkaYrdKz5IT/brq3J8K+UozvnWefaQDakYoXiOxFKOjxuR8jJWkknjzGWz+SZSTRwVPog2vv7+wVBz6JMD3IizdCY+eebm5slaovwJarOqTYtnWjBTruM/MfYdeSOuMM7Xgv4IFrk4pqTk5e92hS6luYKMs6YEVUSKa85rBTuxc67rI9Kn7YNunyNfGDEbyfv/uX/NWc3e58Gx3P4LM1oM4pNlM+oh3yhDITORFYtImSUlIVFPi8gxtkg0/2lDsbB5T0uHnIfDUDo0BkJsl1vtbLstEgq0wKUQYPu/G5ykzopE1xYRscYPjI1Tnoov+QPt4yFTm9dSjvMFCYIIN84PgZ15C3pD92hy3JO3WEWMu/G5lKuaE94EmAKx9tZUq4PCD+Z0QwdlgUeQsIsCbOUpJvFIJXX2nsMWGhPG9h5j2Me40hzzs3Q0sjakPAZ/tDxtY47Ms69pEyy55nG7fn55SQw09nSvHFKORmM+xdDX3OAY4zlu88nJz+P9Dw7Oxv39/dLqjHtnZ6e7nzSkoaRIIT8cvqK99kvGi8qPREvU3/uB1PjEe4W/VMGwiMuSPG83GazWRwCaaZBtoIbYadO08v6aCgCjmi8yJPmwFu2hcVGzTRyrJpetBSiHV/qj3NO2r4ZKLYXAxWD1tK+MeoEhZy/y3M+0Yvp2rTlKNFzi/sMYYv4Ms5OzY6xa8wNPilH+dvAiA7IQMfpXbZN58pCnco7Bg8E/eEtgZudAo/q5CE/3MvMvnPVOZ255Yl84f+R483m5ctTbY6efMv9Dx8+LLLJRYfsU+MFZYrFctfWD3Gh2NPT086e/yz0y7Mcf45nc8JNV5u9bfJJHhksv6e8yzm3DrX7jcg2eCxrjpyFSJTOba2elp6N4DCajmCwHhp5n6c9xovyEQU69ejFX1Y41teMf1vRStooSO0ZGmM7RgKQFunR0ESx2xyr6SdYM+hwPxsfUr+LAYefdSZinyzSgNEwz573/eZ81/qXezO6yE+OFd9ZM4QsNIzNQDan59LqpLyt9XM2DvvsCJ9rUXzoMMBj3W6DukLDP8brQ0RmIMNpbfMjdsABQJ5nKtk6O3Ngpm2NT6St0er+zeyix8rBEu3RPl1r9szgZIyx2FbaU9p4txenmb4TqKzR0K7tc9TND7V3/1uds1MnbdCNpGcd86Dlx1EPo7gMZhZWZD/b2hYCpoiY4uIe5ZzgwwVeEZC8c3FxMT5+/DjGGMvzQZCbzWbZtnVy8nNv4hgvQOL8/Hz5pOUYY2dLAnnGEqcYhSWCHOPF0dJZORoYYyzoMtsV0u8oRYAJMwbhXRYcpU9xzOET6eACLfaB9fk6UT75nucN9vK8077h0Wyvp9E96xtj97hE8yGF0wChoS2I84lOuW6AMosYsw+fxpv9TZ9ptNNuxiBtZRolAK0du2p5YR8tnwZ0Tns2frFQRvY5HMq3n2/AMiVZtMiqsy90MPlgTKbG2M7Jycu2zaZXY/zkI/uZMcjYElg9Pb18YCI6SZnI4i/KDQOC1j7BBhdpsk3yM3WGdt/zmNK2MOrebreLDebYe5oqdVonXc/z8/OO7c20GWnzlMB2ux13d3dLVtJTAt4bbR10EMGxN4+bDPK++fmr5SiRc+sYn2ko338355y/Gyob4zVaZlppjUlEzUTaVG6iMNLFd3mUY4TOdTUkydR8+sGyxkM6IRqrtb75Xtr0My1qNi+jrOHPZvPyMZA2jvybzmQ2Ni6mwwjXjqDJivudevks/2dhdqHxsqH/1od9zoe7Ctj2GLv77i1Ps7pbFMnV/pG/xg/2uxXrzKzfLo7S6fgOKftocoTIsmZIx3iRz6RKyQ/LwMwx+4e65GwHr7VMSIv6LOfNobb3CA5or9ayJKx/ZkO5cIs6ZF3w+zNdCm/NtwBT99WZ0pkOHSpfreyTm3bdduVXy1G2UrE0g9GMJBWpCcYaQ4legoydTo3z4gcoqIChn1Hz8/PPU5T++OOPJaokrUHfEZYgX54363miMV4iBM6Jk9bQwi0qLdJLv3Lm7IxHNnotIgq4GKNHhz7QYLN5iZxTX9A954AaHSncw0jamdZLmSFbXqeBaSCFRsPRnvk7UyI/n+csQ2to27Rx7pPFeuNMQ6LdZDZs4FvKb4zdeWwvPnJf+S4jJUde5gPvO0pufcr7zQFZ7vieo9ZZRJhnmO3iNJUdc9rihxf4jBeGemFZnvUCU/aLMpQsYAB26Ir9yNoBg88GUJq9yG+eckg+uF7ywdfYhzFewCJ/s2/UT8qgdbrZAmZ7aG8br82T29vbxa7ly1R0ztzvbHpmWa3wZKav7susj79Sfss+ZxPXUszeGM56+E5LITF1w1V9bDNtZHEX00qkn9efnn5+w/mPP/7YqYfvMQUT5+y90vmd1PHDw8PYbDbLpyVJX/qT9qmkdChJq4UnbfsH+2/k67nZk5OXT6zx05kpTKHRIMU5k8YYPX5MgQszMjbcWxqht2w0VM17drCRIRvdJm8cSwICLxRqPOS9BkA5jixcKd0c2b7C/mUcI/PmRe6bf+mrU9k8ctH9MHgKLaaNUU7S8h7bBtLIT/O6yTTH3pEgddPROfvZFsmx+ChUt5v/vdd8jJesBNOnDbTZQWdsPRXTFnpxbUdztASjWcyZ3SrOhvFdXptNVbaoMLLNVdG2r63fjoKZmuYiSNoq/mahbIaOfL6X9eeZyGaAkWWOemoepBDw77v2nvJP2+fMQqSaQoV0lJL7HMjc58KrVscY66c7cR6Kz9L4UglnhnmtUEAtYPtSLi1135yZednQn9+h8oev3n7gfjSjOUY/T9j9yPNU2kbvvrLGsxma9b32P0vro43FjDZHdmPsrmTeVxxdHiJvlkvSaIOb0hxnq/eQfue+69unh2vtz3j1lnfouPbR35wp/55NE/GHkRavWS5JV9pu2Q/TN0vbm+b2PO/PeGEgcoidc4DFumbFgcdb+msAQJ1z1mXmY2Y0HVLW5PItdmxfOcqccxNkDxiF1GiFyIjRDOs8OTlZ9jEH9Tw8PIzb29txd3e3fLjbC8KcuvbgnZycLKdh0RjSSTM1lGcchTSHQ6MQIMCFRC1lmnpCM+nm3HZzPA2UNBSYCJwL0q6vr3cOlWd/KHDensL0qFNIHIPQk/4yumqGwgbNMkO+mQ9MXZn+0OnSFPbk5GSHJ+zPbK4/hWlVytAYL2CoGZ+WKWrFupK5UsoZP2Cw3e4eW9sM9Sx1TNrWplLWeM46Z5E4C6Mo8paA0qnnlpE4OTkZl5eXr9rljgPqGyMrj2lLZbftOryfKJc7NzhGkfN8/MF1OBMwczaslzqc0vZfpw6W2dj73QaCLBsGBnzn+fn5VZ/ZTmyXT3czcPXWq/DV+/C5wK7pabN1pPtQoMh63uuof+tWqlYagszfa1FhBM/ziBF819GYZGNIx+nn+KxTku7HLDIzr5iSbFMCjU9s+1B+NwTd/udcpFeLtkJhZ11RDhtvOjH2x8rY6KUyz7Ie+e2xSBttjPx+K5ZFZi9mKH9Wd8bahzjMolkW64qj45mch37uC2addnSH0EFeNB7s4wtTiofIsSN2A7IWEJjW3DtkzGbv+x5/N4dEegio2zO51zJzs0LQ6/41mWUhOFnjx76IN8XOt+mi7/Me9dx8txOe0bHZvJzw1gCVx2Kfvd3X95nc/Y5ytLT2IQPKyLlFfmPMO0yh5XxENsJfXFwsn1xkpGJH6IUAicB58AgFmxHe6enpuL6+fnUGd+rMvF4clRUkzpkLo1I3DyQgGuYiHgraIYaQ/OQ1CykRd37bueW9RAo8g5kHJBj8tKiaNNAIN5rbIjM6NspU3rEz9ftsa2YE2A+v3p0txmO7zhbQOLC9Bl5mzpQGOPLGzFDGIDzifGjqaffbnF7adDo+su3xaPJI4Bw6zTvziW2ZLsuP549b5DzLTlAu04/z8/OF/5ynZrszOeEC1dTH8Z5Nd/BZ66XbZZ1tHy/PA7ce5Dp5zHpav9pCL9u0jOn19fWyxidbQ7nuJLKXOuzkaAv498yvWBZi8zebzfLpyNix/J22+Js0tLIPYLTrufbfGjmPcTjicDQ6xuuUQYsKWkQ7xovRODs72/ngRVbpZSFEW1hgg/D4+Lh8ai8ru102m58rKP/1X/91XF5ejtvb2/H169dlf3OEmalib86nQeEezyhmS/E4jUzH3ZCgnRV5GMF31BbUGWWaRVTZQzjG7mpu76lM4SrRs7OznVOprPSUjbUVxS31RyWmc6NTYh9SKAfkrRWyHUPoBWiRtfTXQMN8dQQ6Q/N+jlMpXNQSWfMBMQR7dl4+spWyxO1bDTy1Vc/Wr6enp2UNQ3uOY97GmbJu/lBf8pwXNhqgMUrND4Fv9tbe3t4uQNX0cmxmY2wHSyDkj9LkmYAty0OTC9oSvhMZbHuxE4Q0Hs+iewdRzALx2tnZ2fj06dPSfpxjtlgaoDQ7b31nH+iwW/Yuz+fd+/v7Zdqj2Vu37etrvoy0OrPD+t7rmMc4gnMO046BFA5pqzHTxXTMnuPzs7mWvB8HFmXLexEYRjSsM+9xLoSotTkr8nRfP/b1Lc+sKeEYfSVuihXC+7SpfOR92ps5+5miGfD596wPre68Z/4eqxwqa0032rOH9C3vZkwpR7P2Tk5OdhzzvpR2izh93/TP0rd2YgTY+d+yQl3n9Rnda/xtY9RAqu+7Xy3aa050n3zx3abrh9gr0uYtb6TZEW+LkFvgxHEg4HQ2i7Rb/ma2zMCczjfX46hjl2lf9/EnAMXBQtpuTnRNb2d21z7v2LblKGltCmhTUKIlo+bm2FrdJycny4KvIEZvTckP6+bpVS0FdXJysnzSLKfTOPUdp8rP4yVi3m5fPrrBhR+J3K+ursanT5/G+fn5EtlvNpvleSL39MdGw8iMPGnGKvRzfpupH27DCP9yj2g10Vj6zX3NTvelHhs2KmkTXEbJNixOD9uotzqzBcwpRBqrNWPCU9gik5Qbjn/rSxxN2+qTtgnS0jdPwVBfGj/HeDntLbzg9EhoTF9iAHl0qyNdtsEoa834u7Bep57DT5/4ZNrpEEhP1kZElh1dsT4bTdK82WxeLTRyn3wGAG2Vo97UyfYILHKtZWSokzMwbqBAvY3dsBPO/WTwaBtJezIEDYxx0Ro/32qwHJ4wyNlsNjtnNjjyJT3c6pWftJfMKMeFfSTI4imHNzc3O9c4Vt6OyEIaZoCJ/W6ydaxytDnntci5dZalGXnXzUhhlhZt7zeForOgMjf6I1A8VCTPxnhkv3CUIY4tQsrjOhPptH3ABBUUPs//rAlAQ/dUPkdQY+zO7dIZxribj8wgUFFnUY1lI/0hjUaiNBZO3dIQU64oH8wSxBCQz3Z6bNsG0On2mZzPjGnqSbHByv3ZdIoNQ+qMgcnvFhW0zMXa/9SnBqrWZI9RkGlJO3SoSfdyMU+eo6Og88nYOrXa+m9+tf6aV3yGjt/yv48PtnczOvZFW77neig/dpqRqTg/O2eOQbN/dPjev83fsQemwfJHm8a+uD/UBX4IhODY56CTT54iYbsOdBpN+/Tb/6+9855ytK9SuZPNSazVM3OQrC/ImWiRUWe+COU5l5mByXPZSpX5ZtJCQXl+fh7fvn0bZ2dn4/b2dqk/Z2pzRW4i5Ovr63F9fT0+fPiwRM4RMjtxKkLjHZ0HaTT/G+BoESj/JqpkqiwZi/wwEsr7VK4ZgIhTJfhgRiV008D7IBaemjSTo5bKInCgMTSgIwpvxVGJ2zAfCCRY2joI1teUnpFYA6StXR8KwvZSVwMKTl2uTYesGSXfc3TLLFdob/zntqk1wxkZslwajFinUqf7bJ7tG6tZ/5tjbvSs2cyZ4+RznBrI78z7Uo9aabJNWZuliElLG29GzAbSs35vt9tXmSQ+R3nlGCfCvrq6Gufn5+PHjx/j+/fvU+d8yBis8buVfWDrLeUozpmlOZN9xFJwZgg4xjpOjwYkA3V/fz82m82r1C0VlyUCcHd3N+7u7paTxFKo5FmBmBOyKBg5KYzo/urqavk4xqdPn8aHDx+Wa9vtdlxeXi40J4UTY9XS2i2F3aLgpN+NSpuSmeZEMJ4LTNqen2QjEGkK5D60dm1so8BczOG0IaOZZhAIbGaO1oaKcsrThFwcebR6Uw9PrnNkkMUpycgQqHCcybOAJYJFOzbr2Y8fP3YWhjnjZD45zWen5SiY+kG613gUHieiS1rV2RS/Q1lo6Uj2L+na9L9Fbo3HXExnWtg2gQ+BvvlpW5L60j7vN1BLGmdRJ4F+2rYN8ap22oX0mec8pPBsh8Zz9t+p5sgpQbcdcstepURHPM1E+0Q7fHp6Oq6ursbp6en49OnTuLq6Wux6VnFHzhowMLgzr5svm8n5sRz00U8IewtR7Py+99aMy8wBN6FqiJSObIZQSS9/N9QYY8I5spZKWxNQ8oQI1LT53iGpMvdrVkIft20cOr5vEdBD6Mjvt8pXgNx76OM7zUjOnh1j7vCcbuZUhp9da6cVO5S1wmxGq6e1O3v2LYUGt707A/4tqmNfmI6m/ruNll1Zo4E8WouCW2mRNqM/P0taDYAOLa1uv09+teAlz+TdmXNufZnJA2Uz7f5qmekHQXar/1iR7e8uRz8hbKZU/puKtZYya4x8fn7ZFhBUeH19Pf70pz8tc7tBbkFOQU9xNmO8RoZO7QRVM9LjojDTmv6dnZ0tEXP2XxtNs85E04yiWC9pGmN37tP1jfGyfSO/naacARCDGfKL+7czBvuU2nSn0CA3xJ0+mBeuq8kSt4Plb+5553RI6mj8zD1u0WBZS/WS1rYgpaUHHXlSJsn30DtzTOEtV3BT5klbIg5PL3j7nhcQMjvS0uTuB/tIZzNbg+E9yHl/u31ZKNT4l208nGdlNsKAKn1LFMx+c0vPrNA2tTO76YDIs4wnzztgliL3YuOs27a31jXbD68dCe2Muj3mrod6v1Zaho86xvYZ+WacvO8/bYaPzLhQZilTV1dXr2xPdD/1cBeNA6ymp5Z9vuMInHx8TznqgrAx9qe0moIcgrj4vg3VycnPVdlXV1fLvOQYLwPiOVx+sIFOuEU5NCZefBChcCSc/cA5sIRGzELO+WHSw48DWFFsaCzgqYNztKxjFiWkDhskRs8UdjoEF6bOZmM5xusTj2bR46HRCmXCgMJ8YP+YBSF97AsXAtkA+p3wMXXui3o4v+50qA2D+U1dsizOHBNlgeNIYx1amsE0LbM+zvg5xosh5/kAa4viAnbo8Jxa9oJMgrymC0wZZ3w5LdbkuB3ZGTqbfLkvPIiojUtk1rZiZhsNrn2P/HahjNB2kCbLjH/nHUbRuU45bEA+/Wy2xD6C8u31AbRf/nazC2WHfY4M087R+To7MNMHP/er5Z92fKeNH5V2ZpjdSaPPMfqhFHSSPADAEUk+hj5LPaZYgBt6en5+fjU3S6dGQ9IGzoPryCxtxHi0lCCNQ1Bm3rWSsBCgMLJsKJg8Ia3sAxGowVcrM0Xa59AInFIoG17dPcbuVowAAxvgWbp1BiJn8svMBQ0BV7nnPToJ9qml5xrSp5wRDM6MXfiXZ5yBWctYeD2HC433oalYGl63Q31oESDbXNOxtEN70KLb9uyMxsYnggH3P86DC54aKCQ9tAV03uQJ+0+aqMeUkWZr+XuteAxolxiNRqZtU8gn21A7/mQ2bGuoFwZI7X/2axbdc4xcbMfWbNohNu+Q8lud8z5httNi2sFIO4rEfZo2RHmOB37kOE8KRYTl4eFhWQjWIjYiaqZLadDp2K6ursbnz5+XvXnZYuVopqU1046Vey2dlP6Z7hwon/eMXCmYaevx8XHZm52FNBwH/2aalPe8ZzIOP6WljEgLUaqNu5E0013NsPqbx4laTk5OFmDGeji2zakYBOR33uHpTzRgM2NG8BK+05CQBkcpvHZycrLIAQ/3b9tM2F/WSQfN0iId0mB9dBTiqLs5IoOGtMP0NtOiAVizceFCJMppe4cy4TEiaKdOhl8GLNZVf6Iy40yn1SLHWYSbOpLNiLyw2MZQDpj9atmj0E7A4r7G+bJwcWhOBuM+5mQCHVmTlvz2ca/8YAxlLTRw37u/sRBZ4fQB358BR4PwmbNtgIZ8f0vw2srhKwx+oczQfoue31KImpshc5RIdL0Wsaz1YxZ18pkoABeBtUE6pL8GLnZSjZ8s7CuNcCtNWdszjT4DD9JtQDBzFocizbfICR14cwgs+/o+q39fPRy3QyPIBl4PfX/N2DgibqnJfX1qcjSLMvalFdv/a2Nj+hpo4D3q6lvkhsbV+j6rp0V+s+Jn12T/vdEX+2Aw7Wzge9qgjDY72SLkZsOaTSN4mdmgNTvrvu/ryzHLeyPo37KVqj03M465ZzTF54mYz8/Pd+Zkk0LOQR/eC8vFIaaR7fCrTFwgkkicAuCfnEJ2eXn5assRndIY3RHQUBKhbjYvJxkx5cwMgw905xw3ec05bEc4QaeMVHI/bRB0eOytnOyTIwzzYIY0GUWnX7nOtCrfz31+apCF6HptXoq846Iqtxn6GcG6z440SZdTk4wMzCe27ciRtGTMyENGtW9J01LGbTRp9D12NL5tTG0s2/tpY40XrU92yJRNO/DQlDr4bDJumfZqTnqWgWD9jATzf6JKZm4ctOTchjFedDpRfuMnx4iZH0f+Ls46pn/OgI2xu+Wuldi68I7P2p4b2PMe29put8siP34umFmMBszyE1ooc/tAewsgZ7pkO8U63luO6pxnSLWlH80UOo38T6bS0XEeIh9UyMDRMcVYOXXEQkOfVFHoyLOew7Oi0pHyJDBHkBTUZnRMS/q32Wx2lDntOmWc6zl4gONkIaMiG8DQ+HCVc8Yihs7GKP1l/ywjecd7FdNXOzKOPZ/Nc7P91DME3/YIsx2W9MUyyPSb++VrjljHGBWszSIApxeb0XX/0wb/NiCdgWjzhB/HOOTsAPfdpaXqZxkpGmgC2Ofn3cV5rVD3WvbC4D38Ci388hyfp7wzdRs6mfYNnw2s85NpF/Ms+hE9ig1oKWWCHU+XsHDnieWLYHwWSds2+zpBZuxlk4HmyJpzNtgOsGYQZSfNOpit404A8tk2MWM1063UT/t3DCc8K0c7Icydb8/l2fymoPh/MozbgljHGC/KlTnEMX4qTuZQORfUjHXqIBIy7UTWdNKpL3PLccxeoT0DMWPsRoRUzibEnAtq/Rlj16DN0DKd8iHpbxqwhjrX3m3XZvxocsL6iVhnyNeR2UyBZtdIC9ugEZgprZWac2kpa1MjjniaPLJeP7fmLM2LFjGEPtOSerminH20PpLOtg2t9aeVmVx4Hntm7KMvdrA05gSlPDhmTXfSzwYiDS4tr+5Xi/r5v0EVAQT1wXxyif1sckWQYYDe5Mn2lPbRIMf2fK2wnsa/tNkiZfLAdmkN7Lv9WSEfDHCbrT5GeZdzZjQ86yiVmdGMHVGKhSLIJw7v7OxsccLb7Xb5WMXNzc24ublZjtYcY4ybm5udT0B6QQQjxhzBaeFMP0M/I/P0J8dzfvr0aXz+/PkVqrfAOEqycLUoin8zEmgAwEpGo81+51B89puKkEIecqua20ohkDJtayXP2SDQ4FGeiJw5JjEQa47ZBsrjnWs0XGvfpm6OOHRTdpwFyPOMrFK4ypUgjnQ6K5SszczhtWwB98GyT5Qb9jm6Mou+OTY+dYtAhzJtJ25ZJ/hxOrllHJiFyYc2YkfIA55Slj42sN4AV9NNjjn3WqfNAPm03bJVtKmM8BndUR5m+/AdhfpDG6S1ZXZsozgGaSOyxvFORotgds1H8H/3n4vJ2BfWSRoMHB8eHhYfEZqZAWSdqbcB/hbUNdtrOt9TjvY9533XWmlI0R2dOf+0Q+E3QqZyzNocY1fhHBmwrVmUxtPAvH2L/JgNmAe09dmC6GdmkdsaQpz1031mepuKbCfA+gzCWn8PLexP/m9GkuXk5ORV2vCtqSgDAdazFqH6/dk47Hs3v2cyaxoP7VcrrU+z+pqhIk1j7AIWPksQ2NLWeXfWboBOS9HyXcrFTEZmetAAp9+jTpsOjrtBYuNpC07yt+vn33YOa/I1a4N9bfycARBe93RBCyxa26Q9f7svb5HtmV4e4jz31W96fM88OUZ5l3Nu0dFs3mzNIbS0DAXQy96D/rxvL5FgShAT07dMj7KELqbHWTeR2maz2Tn56+rqapn7tuK0wgjJhiYOn3wMgiR/1wxI6J0Zv4YcLVjhGyO+zHvP5qVSglhdtxcPUfFausrOuPW3OZQ4ZZ8ilEKDYqdL4JHCeddZf/MOUTwBFXnWgBvvhWZGra3f7Ffq5FnEoTvj7S1tpI91UD5nwNiLg/IO5/ocUbHdMV7OT3bKlwvjZuNu3q4VOjEu0GrRrO0C9c6/ow+bzWZZ88L9y4xQQ3P6GrvhCJDnI7TFdpSltX43R0E6Mj5tcSEj55Y29sJRFupKTlP0wTENtO+jtzlHA1MGEqHx4uJinJyc7HwCtaWo3SbpmkXWb3nmV8q7nDOFnujUcyJ0Ji1tPEOTERDP33K1dp7NoDCVEuVw6q9FQ0S6cUBtPjjtnZ+fj0+fPi1zzd5zvNY/tt0WgaXthmIbP8nTlopz++EBnbQRL/nJRWkPDw87fW1AbIzdT7oZbLA/sw9VMMJakx3/z/RWnDPH0kaAhoe8o3Niqtiyk/a48KSlK8Nfj0lbtMV3DzHAlqf0mfLENvPurH7qVzJCLbplX7guIc6OPGS6k2DBe7azL7XxKu2mTspLS8v7uiPaMcayTqQVts9In6eZJeXK/eWRCfKR45/xyVeUqBfRk5aBG+O14zwEsLY+ESTNpjQi0wxo2q4OA0lmEsd4DXptpw6NNGcBie1z5HG73S6LYznG1MnmnB2I7eMl61wLQt9ajpbWNrrx7zYQLYJlXe1njBf0GEPgU7lm82Fj7K5OtIMzCnO/0hcqn2mi0KcOR2NNwJiSceRFvjSa4jjztyNij43b5zwMD05pkVUEvxm0NWGcyQDT9Pvqa7LTQJBT/83Qz+TD19fGLM/T+bV+zqIF/p2os0XVjS4bt0Pkj/0hELAj8/y2Fzjxb/Yp73N9AOmPXBF8cPVt6mjpWvffsrGWhm7z9eSRo2PTzL7O+pdrjJxJOx0ex8j66euZpx9jvAJbLgYira98LpGz35kBWNLf+NXsSssaEcCsFcroLPvqAMWlRdetjVlhMMR32vVD6ntLefeCsDF2laddY2nXjKr4fkvx5HoWYX38+HF8/vx5fPr0aYwxllXa3EfovcHPzy+nYnFVN9G8FSROMO8mLZfomU7OAkM013gSumiYaNCao7XQcntZ+MmoOPV6Lvb09HRcXFy8SlGGTzQ49/f3r7YntLSoxzdpwDF2o7H2fuNPixJm8kUZcYTLyIVt5+/QOnMITRmjpG18afy8qK1F4nZODdgZaNDg04Bbrzabl33zjv7HGIsMb7cvR4w+P79MUdgY07HxfnSDTqntU/dnIGd1U14su37HMhjdJO+ia2mDetP4uNlsFlsRGtJO+HR7e/sqeuN4ZiFlsmwsBlSRQU8/ZerMz7UAYgY4SNMYfe6VfEqhXZtlGnKfWbNcy+/YZAMy9p/83253t1Lx1D+2R51mP7h1zfP/ifJtqw1mDBJDVwsqmu7+avltH76YKdAaAs7ANFQ5ixqztziR8xgvX5uatZUB8laimfFlXUR+jCCpyC6H1J+/OeCu03XTCNNR0vHw+tqY8D1e47SAlSJ8OVQIZyjTCHRfdMsyiyLyLtOrqZtRNQ0cFdwOzmi9tXdINMD+tb9p/N7C45ncuY/WU9fhrAOdl9uhXPs608BjvMiit7XYSTSj5hQuo33SYaCaejnWpDPXbNjX6Es7zmwxVd4MNn8YDZPG9MV6mDq8X93PuMz03W36enM6BuIuM51gvw1EWe9MHlN3A165Z4DN0iJnt0+Hu0ZPxmbG73369dZy9O857yuzQZw5U6PY1BH0yv3FMQaZq6FBaj9WPDOXK5QJHIy8Oeg0Pow+2A7RaStU8kQgM2Uin9q3o/lMaMrZtxbslBgC7hHP//n59u3bgjwzdzZzcuFDM17tbyrlPqfkyJJ95XymFZz0NpDgtikHbo8OzRF+iwydkg5/m2E2QHOkenJysrMQkrLgCMfy5v7zcAx/Fc18In2sm3yYOVxmdmwwHRlH18Kz9JNZCmd8SOOscB6dcuJxSd+9BsF9Tl8YOZLv1Ms2z06blcKIPnSSruZsWAzqOI5NjkwzafXvVphRpBzkkKjIqTNMh4Bx9pVj4CyEdS9rAfitAOo5g4+1QpuU95uskD+HBBdr5ejOeQ1VtMhx9h477rR2FCCRa+ac6ZyJcpoxscOz4x5jd3M+jXIMRtpvxoH7/Fxs6POu22Zp6Sv+T2PIDELjJxc4WVGYSjs7O9tJLd7f3y/pu7u7u7Hdbsfl5eUyNu1TekaaAT0pVJqmpOaTHQJBXXMGBEItcjfoCx2MjlI4NcJ37YDtoO20Iyc2ij5kh8CUfM27/MJWQFwMvOu28WFb0Zfsbvjx4+Vbwl4xTHljCR0BcdZn8qadOhZ+++Mhs0iLwDFjywVYnN8OPakzfWYkHtoa3ZTN1J3x5Lt09gZaTFO773mO95vzjPNjabqSj0+M8XJcKOtiW8zWeOW2p2HWCsfPOzViv+/v78f9/f0OsMi7rT8upnU2JZJ6OI3gffYZU2dPXUfzSzN+OBCbZRkOLf+0yLmhDJc2MHaYdJI8OIBRm9ttg5eB4eA050BQMROctVQHS0vLuJ+NzrxrZz8DFg0ttyiZEU6bB6ORHONl7i/GJwrJxUxrAtnASEs1NlS9r5h3Tvev0WR+OSKhIXa6ljTOIpnWh5kj8Hv7ZJIZAYOcjMdM/me6GLnwODRAQbpTn7cl2fhy7N3+oWnTGWhjxmamU3yWv9f407IhrU4Dt1n9pLXdz7gZnK2VtT6bbo85s0sz5zOLLimHnAZpwQ+BxpqekwbKkWV+VgjYZlkG65SLAek/u/wW5zwzsLOoZ8akDCbnk4MMLy8vdz52YYMZGnKakekY4yUNwy1XpJOLEmjorVSzbQ+t0Kmxj6TLkR/bDN0WXEbWnAenMWSUnCgnz+SeI+mkpRIJJDK6u7tbFrDd3d2NMcYyJhRqRyepOyvqycvGK77LfrJYidJn1h2DwYjRkQuNE8eIhstbn2JAbTjiyFK/jUJzBs6WpN3ZVAUXJz48POzwhrQ3w8RP7VnGCHSdgn1+fl62p7C/1Jf85qJJyzz7TD0ir5rstAVhzNy0vcGhj3ykrjSQtAbG2YeU1Bv7lLqTKbDcbjYvH8xpDniz2exsV2T9bp+6vwaO+Y5tleU9ZQ0cU66S8bi7uxu3t7fL9BkzBlw4ywV2ro8yTN1MO2mTC1Npm0lPpmjyOVyOedOr2Zg3ALvv70MDi1n5p6W1ef8tRM8EjkI/M9g0Hi65N4ucGwq1Q7RjnUUpbjeFwteAi9t0tESDy3l5HkSxRoN5y5RceMx7dPpMTXJfaoss7Zj9O+VX0kAzdHuI8jT6mMo7JAJeuz7LJFBOHEVw7FtUzLpbFNFAse9TN0xXwFdSgHxu1gZBNjNanL5o79Lx00GtRfuRz6af+5wT+9neaU6pOemZAabBJ2in83Q9Mz2lTWv0UcccTIQu/qxFgWuZjFYsVwSz3O1iO5C+nJ6e1ik320bbROtD5HSm081Weuzb71l/W/mdUfW7nLONBTtJps4Q20xQ+KyjkZzgdXd3N87Ozsbd3d24u7sbm81m2WYwxu5hDGtCR8WJ0WooiVEFFSoCmTk/9quhXIITpksZ1fEd1sUoZFaawfZ8V6s/hdkCKw0RcKLRjAkNZ6Lt5vxJJ/lOGomc4/jpIJrcOJJwis5GNM+RNoOOZoSZlmNbHgNHALzOyMwZFGdomgxlDMKbZpgjl3m2bX+h/HFMyKd2OlozcGO8nE6WCMXZCvLavGqf+WxRDXnDHy++ZP0EAelTon9HZnYA5n9+tz6lbUbO/HuM3fPS8x5ln3rR+MV+mabZYi3bI9dDcLTmyMzjZBwTnZK/BCk+RtnAKu02uaLjH+PFljH6DV3MmlCXZiAuAR7HnXyaAaoGgBvP3+u4j+KcHXX6GQriTED4PI0bGRa0lYVJJycn4/b2dlxeXi4pFKc/MhAzx2znHDqtmHmOQkGlyMlZTL/bCXh7BwWPz7YIgKsgTbPnkmjw085MQPlc+BTD6vHZbF5ScVxwcnd3N56fn5cFH1yw0SIPpmu93WaMF4Dg4w9dzF8aa67CpHJTkZkqbNmXfSkvo/vwMHRHHsOHtsDGBna2wto8oPFkdEbHzBPeWvSWOr2AieAz8k76W7YofeJKb8oB97wyNR3HnIVCjDZJi8EQn+PhQ+RPfns6J7wjsEn/uJ7CuhX+nZ+fvzLQaSfp3DzHVD953ewkf1sGGrhs4Jf2wWXm5EMXx8hAxyfsZRwzLZi9yOk37RM/ddsyneyPgQTBbMacC95Il8e18ZOOOXU9P+/uv3Y2kvSkjjYm+zIOby3/9K1ULGuIjvdn92hcG2qeFUcwvt6AwRpdfG+m1DF+s+1ENKwtzUUHY+FpqeRGn4ujFIME82eWMmypeRpTt8U+0VDNjFb6mML53DYWdBaNF1TkXJu1z/vtGcrKTAbXaJnRZ7paREjEv6YrHIvZcwRxBB0Edh7n5jCsk6Z9JocGp6TT2Tb3Y5ZNYd0G27PifrTnqYf7dGufjUtZo808tEM+JJXf2tp3jzQxYGEKexZwsY4An+Z899FzCI0cW9sdLmKc8WmNlkP1dozjO+ijfc+ZzmFGYIsYxpinr6KIRNJjjCV9fXl5uZOWIiKP4/L2lwhVrvuQDRrEhrabcWBql+iN88B23nTYTt83BeXiGqJQbinwfnDy3R/zyJgRaRI8OI3s9C358+HDh+Xv8CGf6cs2t9lYe9+j7+fksig4IzguPuN9GrPw2Nt3PO/lwnoMiqzss3oYyczuzwz7PqPAjAXPb26OLVtb2t7nXHdfI4fsqxdPcSyYFuV++KS4PTfIPlKGuc2PJ9MxZdp4zPOenUL1CWG0GUyFzhaTpZ78z4VMBGbkP3nHvoRGy4/PsScNPpHM2TaexrYG0mnnmtNiZie/KQ95JvVk3/I+QJnomjakbYVrgMjZME4fpp7c47HN5+fnOxkhTjE4+re8tKCM49yCNQLZY5XfckJYE+h9783uGVUzvUylpqHIsymmxxvPzfT22/2yArv9WV/aXjvSb+dNROi0KI0qTx1qzpkRO50xwUwE3dHSLBq0weD+6iiGHTPH0nRxzplKacQdHrFNGw+Od+sHVwnzWfLXoCmF7e6LCv2MS5NZR0qz9wgKXeygY6SaASFwnUVo+b/JA3WSTsP1zRbv0IG2aQmD3tBgQJ0+pKTPDhyaIaWcUS4ImEkX9043npOvY4yqf3zH35S2vIevTD/zfnuPhTJFHQkvZ9k62rbU4TUm7q/tZvg1c4D8u/HQeuEghgGXAQf752yb63Nx30wvr7tfxyhHcc5EjmP0nHyea+++B20wQqXht+I4kqLTozMK/TMERSWw8PkQeQs936NzorGwENA4pVBRQu92+zKP2uqyMjZjHLpiBHiNfWE0zb7xd97loRBt2wqL05MGUOlrUzwWt2Fl8sIzg6/mcF1nc56Uv8hCO1O68SC8ooPIs42u/N9ot5Py2MwMN8FdA0YtQ0Bw4a+AeW45xtPHk86ArZ2YnbMdB08JpNw4s8B+s13KhkEII3iDdNu+QwrHmG2mLduz/G5j0wKR/G0Z8X0u4uN12xzbioAx655Pq8u7tHOUM9tYy2azNa2Qd5ZlZ165tda2nnWZjvSbPEiZ2Yn3lKM7ZzsuM3sWqfiZ9lxD9lmAQVSeNCtXQHKBmPcvW9laFOXBHuPleD0qLtv0lgK3yzYMbPIuvz/LvtAIR1EihEn3OtKxkw79TDt5TyZ5RBCQd8kjGpG8l7p53J4Nzyz6bMiWBpRywSMh7dDJv/Yu+TIDZC7cruZVrCcnJzt7VDmOKR4fnjjHvuTZgBAbW/KGcpRsRsZ0lu5kFEX6OWWQ53KCWJMP0sooJfRFnplaZLvNBhBEWXepRwQFlqvcT9sGKHTOBteUJdbjgGIWMc6iqPCL9oBZsNA1c8rs5xjz73UbULS0tbNx0VmPc+oIyI6MeJoh5x+weKFd+MePmTSdzf9r0a39TbNJ+R27lfY4D+4AaTaWDSiEvha4vaf8UxeE2fHNipmcQqWfGVRHv/l7ra0ZajuEfgrPzMm09xnBEgHPhJDtOvL3fV6bCX27ZmQ8a5/0W3DZTwKVKEPuO8Lhc2vGbd8Y2VmR3kNKkzted1ut3kNR/lsiLhsMAxbrgAFgyprD2Ff8DNtoWSL3ay26aEa5PTe7xvfzP/VkrbgtA5+ZTLXS7EOjsTkT6qqzH67bUfWhZUa/dY90tDKzBwGFdHIt29H6s0abgXaz9WtyNatjbTzf4lyPGTGnHMU5NwPjSND3x5gLbu7ZUDJqfXx8XM57fnh42PkEIevinGecAxesEH3b4LEvTptwvo9bBZy6ZTaBaWdHG15YYYNLx5atHD9+/FgWXjE6CCJkFMbMguesyF+nvB2RhK4sirETSNT46dOnsd3+XBjG04PGePnAPRWKURvBRXjGRTgEJjEIdEo+Ec7ZDvM0hTxiSpaGkEbGEUnLmpi/qS885iI+R8wNkT88PCzZCNLH7UCMfpzSY90pBIa5x8xI2meKvo270+c8yMTtU+/IExr19MHvOqUb3afuMRKKDfCitVnJ1qDwgRGVbVL0hXLC/jltzgxJxs3rJMh3txm5caCS5xnxOm1MeQrtDCryrLM+vBe74hR86vjw4cOOzaVMMmrlOM8ctvnMbXrJXPFTvaErJ0om28MTIN3f5qB93/RQHkzz2tTVW8vRF4TtuzdzxPw9G6Dscxtj18Fy1SCfH+PFMHsgjKhobNfQIoWcKajQxzr5nufLmG7JszzSsjmr/B/B4MpqIkEqLunK+2mz8Yv8sNJQoehULbAZpygmFcr1tcMznIJuBpF1GMTY8DLVlOLFdwRQ5KnrcTRGnpvO0BPe2Ni6f82YGsjRaaVtgj6nD/lziI7SqVhevVeXfSWfWacjGspleOxnItttDEir+djS6aS/Zd1axBWbQr2i7TG/+D9LA74EJP44Be0EHZ7T7e5/Az+mh32xg+ZYWifZRvjJ+w4yzKfn5+dlX/LMGa4V60MWzhkUke7QyECKfXe2jmNou+jxNe3mE33Iex30/xOfjFwrViQKL3+PMXYEnNGJFZ2KSgNiR5jSjIydOt/1AMZZ0DDRIKcw6g2vSKsjXkbGjCJzP86Qh2LwS1PuY9prC2js3KgQjBKd3jw9PV22NYSeOJHQ40iIY+o27RhCX+qbASSXODOPqVfVUh4MRLhCPn0PH2kMZyVjw//bMwYgHhdGvIyO+IznaltdTE3a0ZoPTYaoX+xPA6s09LN6UhdX4uYM8US3dibmBet0toC0mk7LIA8Y8spj1kN+EgAy6ue3AMKHlLayvBXKaktt247RWTmT0hxsAyyNlkPbzkp+ZrnYR9sX89fgrWUVOE/ve00XDwUJbnutDuvme8pRTwjbhxbsaA8pNHJ0zjx5KAP++Pi4k+rg+1zk4APpiQab4bFzplNOG1FYD1ZozxYIGwhunxjjpyAnTW7Hnbat9HQaRObb7XYxZulf2+tKZcx+6A8fPux8p5m8JO10ZkSooS2LkR4eHpZToLi6lotKGEURxdqwEjwYFG02P9P+dlR+3k4p77cxZzSZcXQGh3RwgcuanHOsHPEYWLIYWMTJpz5GaxxzA1b3Je84ykx9lGU+z7HL33nXQGWWxfEOhDFe0uIPDw/LXm1+GpMOntkRG2M7S4ICZgd4VoF1Puna1MnFRJSXFI5JeJyxMIB0pis8pOw6y8JFcNmGlvsEy2mTkaMdu8e87YVvzrM58QYo8qGizWazTG1Rrlpm0227TgPxgH8uvrXuOJtiOtzXpru2TXyOIOwY5Z/+ychfeY/O55D67EjbfTPRAmCla4PSBKrR6+jH7bodG0c+Z+MRo+Q5QgtlCu+xjcavXDfaNh9nGQfSzcI29gnzWlqz1TsDiIfKn/nd7h1SV+hukeGaPPjePoVfM2ozOmf8m6VEx1jPeL03QnA71kM7vZn+WY7b/+ZVq7v1iXr6FlmKvjqTkfspHmMHAGv1ki7ywn2JLDVwNfs9xtt00Onm/KatYIqf9a3ZxzWeO03N0uz8Wl0zXZo9+zvLu5wzo4PGxDUhpLPjM0bpSV/d39+Pu7u7sd1ux+3t7bi/vx+np6c7HxNvX6lqkTAR/dqcIAU1CJ9RG1OyRpF0okTxzYG4vaS9HDUSabMdG6Hc52EB3G9spN5oyTx++ulIIotwuH8x/G4RhOUifPP4JJrzeKUwUuD4OIpkmaWXibxdmBWhwpJuP89xcqTjuuy8yT+CJy+c40IlZoMiL5FTjk0Wz2ShTO7ZSHr+n3zhPS6+NNCzLCVqY53OFjQ+UqcYtXJqhODx8fFxZ/Eb9c82x/pGejnGjoyZhUuU5ihz5tQZtbdUt3U6bTvNzfUt3D6ZNjj2bTzSD9urtWmMBlgazXmOn2YMrXkm8+zZltcAFNviOCfb0zJ4Y4xXU2Sk0/QymzgDbiykq4Ge31GOcnznGHOhzHN0gLnf0J7rjWPJNznHeDnK8uHhYSfF2/boNnRtQ0ijthbJxjhktSWdZQMFVAa+T0ExqnWE3IxonqUSU2hCG1FyUuVPT0/jw4cPC01WyhSmXOMw6SxIP1eKNzDBRVo2FnQUudYcfH5byZw+NiLPe56fM8/cd/7dnJdp47PtOd6fPRfjSuPGlbPkQ8bn+fl5+ehL+MPUJY2aj8lkejtt5P3GLx6tybQr9YaF+sNx4EJGP0+Zj+HNNFWe97oD60XLXJmmtcI+Ndq5argBPNuQ9JWy3SJpv7+WNnYdKeFNWxSZH05HzSL6taiQ75Df6Sf5kno550zgyz65TfoHA9vw0cCZ9iF08vp2u92xj7TRTR5d7CNmz67x79BylDlnCmEjqkWLfnamMHZeRkYcrLVIPgPpAaWDoLNu9RDpUTlsYI12DQJmjrGlm4zmrQRpg++S/kRUjHZ4KlCUZRbBkT9WICuoHQRpGGPsIFxnHdJ26udq4SYLeb4ZqJmzcLFjbYUGur1LA9PqyLuz9GTozPhxHJ1xsRPi6lSOuWVsrTQQRLpNr2mgo5wV86btcTdN5LuNufWSPKLjJ4hxX2OQSSMjz2a8W5DB+V2CR4Jmtk1nzf+pM2v8ZB/o8Bs/1/SADszXqPcpbXpmX3H70fXIpp9t/9sWN4c4kyM+Y9ltgIhZJ14/1J608h4n/S7nbCfEMiPKStUY7gGJwCeSYPo6EfXsEHYOCoWPA7Xdbpc0i9PcQZmz1CwNZOpm/xMJjbG7ajuflWuFirqW+vOn2ggC6HDp2B8eHsbd3d2SFqODdhseszFeK2nGiGl430+0nY9YhGfcm0hHRJrowK0kM97YUbWVxQRqaym8ZgzoBFqUsg9dc4xIt50xrzMajtxl4RsLjS6NoPvVQIMNGK+zn1ng9Pz8vOw/n8myHUcWAY4xdiJQpnsNfKNDBgYpBNZ2sMy4cSFo6uFY+G/qD+vm4lHu9w8ITiYj4zTLTjFj4V0X7mfGz/rHKat2LW1HFshj3qeOtcV5eW4fGOO4NF2KLbi/v9/RP4Met0+5iPy5rbX3k/Ww7NjXNCduoNKKdX4G1t9SjrZam8WD8pb6aDRnz5hpTmOx7RkNfJZO3Ma9Pb9miBhBtnbyfpQk1z2wrQ07oxiAfRHIGGPHATMSdoTOwrGgY7Gx4Xg1euwQaHx53/xuoOeQ0mhspcntW2W3je9amQEKFyN+p91orGayaeAbB5O/Z/S+B+27ntm4tchtjB7VNEOdd+lsaAeo007hOtpu7/Enz/F/pvfHeA3K2Rc7hPbsITxv+kHQdIjMtzqbPpoePufszj5aWTxmb+m3bX/KWh0zHjcH7TZnwHzt2jHLUQ8haYKZ0pzVLLVBJUiE6DnLFoXkvURk+dluXx/cwLO2x3hBVuzLw8PDKyFjOuv79+87H6NPVL/GI6fDnCoPguW+1MbPIMgIulPiTptnzodz5Z4rZiaAPPbzeY51uX+MhttpW/ntT+nlN8eX/HME12TM18PTtUJj7nZTaIydDnR6m/0jHaGvjZfbSF8JGpsezVC/I4wfP36Mb9++1f7PpliawaeuzBa+tWwV7zlCt+4/Pz/v7GPmQjbaBvKP0S0zYNHJLLT09rHWP46heU8Hn2uOllmnP/nI/fGpn6l3TvOQfvMxz3DNhnkZEO4sgAMAy110Odcorw08NYC9NqXC5wMwaT8s5wY5LarPferT4+Pjct43M0ns6yyQIm0e+waCZ6DuV8vRFoS5NEdkx9wG2QyK4IaxbR6NznmM8co5R9GJpC2guU664lS8KnOz2Swp5dPT053jAWf8oeDMjIjR6wwpUoDdpucbGVXEOZMH5ANTcA3phydj/PyWth2a+5mxt4Oy882HMRzFt/kw7l1kPfuiXjuBhnodnfLdgAyuYB/j9ZGfBktsj+lUy3/qIb8Y9flIUtNngMDxzDNZZUzenZy8fOTCkVHGtsk11wRYJ/nt8MZjbvtr0ZMN3Gbzc8X2bF2FeUn9Ch/Cfy9oJL9yzeDJ9LBkvL2XnAb76elpSa1fXFy8kvPoQFLuzYFmCspOxen40E560xdOpdnO5v1Z9D2LGBlYsNCWtMVprCd/OxhJG+Gx7QZtG2XdztnHwIbmNWfa7C7fZeFzzen/avkt+5xnhnCtWFhafc14EnXy2pqh9QEELdLMff42fUb7nKsxmqXCUalMd2ikUrbiCImHlDTh8f9GqelLjISjC/Y9CtHmct2Oo+vwgumthrBnMsPnW7+cVm8pRP5vutaebaUBMBsd0mLZzPMxjDQWjXbyylFki6Zt5LylLm3nWgNasz4H7LGPzSiRBjtj0nlycvLqM5Psa+NpKy3Csk1qAcMs2pk5tDFeA0Tet7Np8pHiSJ0Om3xpkaPro27lf8/RUt5YH+WyZa3cz+bA8sws5X2IA3tL5Jl+cFFldKP1fdaPJp9rwKzZiQY2f7Uc9YSwMea5/NZpFyMYCzSdGlN1Odw8ZTav5oji/Px8XFxcjDHGznaNONagWCMs05mtL6enp8vCNEeuFBj2n31jxOTTbqjUNFRJkeWkrex/bWMVQeUnHLfbnx+nSAoxvy8vL8fFxcUr5xKauW+zIV6mO9til9QT3uV+6mofgUjxBxjYriMWywwX83Aag7K25hztsEgXwQyzF2OM5SMlrHfm+Dx+5BsP9c89LqxKvZFfR2c5oYl76e080xc7XUbvMXyRezr+0NvAATM75Bs/lsB+pBiAkz6OJQG1AQlpy5hGt5ozIf2cWuP90E3nQJ6FnkStWRBnAGlblRP1NpvNzp5qf5Qi9913Zmn4O/1kEBF9sGO243V0SJCdujnOlP8Wga45Z/KDUW4KZS88CJi5v78f9/f34/n5eckK8WS25h9oN0gD6WW7pN98YiD2nvLbImc7WV93WetIAwEpTbBnjpT1xcj6h0ib12aoiMafRonKzjmkNQOQZ6jkjjxnfGXUx35Q0fIODSQRM+n48ePHsqq2IePsm6WhtSFI+9zGRR7aSPOdtUKHbbpbG20sWqTpv1v0sK+k7VlKfoyx2n+m7Wf1UzZTKA+NVspp/qehJs9m0ZDralHjjKem05EbQYEdwywD1oCb+TTGi6Pmj4GZ321/Nxs1C0byP3WOzsSyZf6ExgDh8IZ07bOZszGk7FsOcj992yfz7gv1zUDsUGdlx+d7abf1izSQ93aqjR7LsumZ0WLQcKxylDnnWWfGeC3c7VkWR0BjvD5AnqfjsB4qHBet5D6V3kgoKJJ9YrtGS83xB6VHOPI5Rypk6iKKIy2z9CYjgH0ghQpDgfX9tEkAwOdzLvcs9R/e2PG1Qpqa0bQxXRNy94mF9IXPnGLwfOoYu1MHawa/LdRjGnZm1AwE2iIqtk9AaDo4TtYHyyyBiOWOYI2yaR77b8v73d3dq7Hy+DWA4/vJFszWFMwiMPLBkTr1iu0TrKYOZhoM+BN5pV7qURtDAg3SHbvEetK+9WCz2SzZvIwxeZl3ZtsEPe/Pv7k2ojmdlKx/2Gw2y4I3O1tmviJf1ofwokWcqafx0L7A+kDAk/9PTl4+LBK5ypyzD96J3Mxo4fg3e9R4a0d9KCBp5egLwtau2anYuVPB2Fl/AzRCbwWncw7ipDHLgDC6Ycq1Oecxdj9VyX60/sSBcv9nBDOpZzrGtJ12eMqWDYhXrBNl2/mmfhp5R0ZpPxFyHHKAAI1E6mtGl4rXohAqtQ1aUwAq/2xeO31KKovFxpjF6VG/Y4VzFsKHtZycnOyAphQa8zFeHzAzi5w4NlnARecTGY78Pj+/fOknUxoBIwRCdBit7x4TR9izSIUp1fS5rS7m+4xi8oydjkHfzKmzWH/yQ7liJOpn4px5dgDrjrHlAtUm7wY8tBN0xC0NTgedKTfqhheSmrd5ZgZceT/9IehmXXHOkXvaQfOBtoHAIfU30NRod5/4uzn21M0PIZ2fny9TjDn6ebvd7gRE5InpaQEjbVLju52z6f+VchTn3IztIaUxorXByNHoKUJAlBeHmDmazeZl07vbp8F1u1Y0GizW4UKnOItMWlv8ey1i5XszYZmVGb08kGT2HCO2NuY0xC0KmyHktxaDOkdmM6fn9mc8PaRtOhOjeir8Wn3NSdFoWxZCKw2vjR37ygia9VDeDIZT54yH7lPjKwGTx6bJhsHVTPY8tgTeqYuLgmZ6QZ2kfSHwYRvuM6/NaPU7bpvAeq04amOgkcLsX0obP16fOY8ZwB7j9advXUfLkNgpz+iaFfPSNo/2qPXFWatDyqz/h9L61vdm5WiRs1OxJI6Mc5RslM6y2WwWh3x2dracbkXlz2Kwq6urJZXz8ePHcXV1tdSTE8Tu7+93BJ0njjWDQVSWrRxE1V4F6Eg8qZQsrKKRbDyM8GdbBlEgC6NhKwyFkWlnRwgsTG3f3d292oqR9wKCTk5OFrqaU9psdhfKtL7OIrHcI8DJu2yHmQ4b6Mgbtztx4Ut4nIyAF46RPkcgbKcVRhSppznQyEbGlqfIJesSgMkx46lwzvSwjaT0LEPkF/WUY7ndbpetP0zlO80cGhxZJPviz5MaZNh+hBbLtMc+fPAKbk9fJGpj2jO/I+/pL+sjiGky6YicvKG8OK1NXlO+bTPNazpHBirJmrQyG1u3wXMaXDh+7Bf1ynykXnPsPc0YOhyYzBwp2yCQyr0sKpytKfGCXNZrsMU+M8Di8+0ay1owdmg5+oKwGZJf+5vCYcdOZeAeUAtslCtzIxksOkYzjA6TiNa0U5jbddaZNnzwRtsSRQV3vxkxz+ZxzGumPs3rxn++xxRp+Pr09LRj1El3U6JZqm+ttAjLxYjbPGj3HCE6jdzScH7f/TCNVFIr+aERQoswnA6MDESunDbku6SHaW3zg+3byUTfQgOf5TP+m/THeHoBFP/O8xwTOkhHbByD6Dyfpf7SELeFVKaB48epp1k2xO94DPmcnZBtkHWfxc7d9jA8Ni9n77N4PDmOfj9jykWgLeiyzpGHKfsi2Xbf/iF/zzIBDi5cT0tTs89NVw4tbZx/pRwtcqYg7nuW1xyt8P3U1+ZKgtiy6CrbgU5OTsbDw8M4OztbtgU1lONBTSFqzXNMpzcFzXvPz89LxEFDlIUdKTYmMRZBsQQSMXbkodHbLO09AxttAVj+blE9ESTnUtvhAjQiHvcmtHyOW9DYP0dmdn4zI9Dmqz0fmgiTc3lj9HlOj7/56kU7qSe0tMM8QkMyQImK6XxpNKMDAao0lI6+8jfrcoSctr11yoBwX5qU89x2yOEfgXDbxkXer0VR5DlpIODimN3e3o7N5uccbjuEx8A7GQ07HPaPTo+2YpbNor1pGaBkA5PNyXsZMxY7RmaL1krbNWK7lmsz/tpm5/kGqnw/ARR//FxKA70EjeQFg4u0Eb5m3jm6wqwDM2+UQ4O2MV7vtFiTTb/zq+Wozjmloalct5Ns6NPIMj9hrPf05oi2HLeZ9LWd+xjjFeNnETGfjQHP300pIhxxzox6uXow92hs03+mZdjGDDXSWTKtxqhgZsAYeVi53E+m28lHR8rknefEGipnn6JQASp51hmHKJXHbIzXH2JpoI+L16KkWc3JdDRTwQaM5icdTvoSGgn2bNTofAMuOd6ph7zLfs6kv2fOgj8cWy4eI/h8fn6uXwpjhMZI1ZFk6M273vPdUuMEik4pewzN/zzLSNJZsNB8c3Oz0BXnnEVCpCnve/EXZZKBAekJrcwaUFZiPzhW6evT09O4u7sbHz582DlvgQA1hVM01lWv2qeckv90NHYy/r/x32sieI1ZB8oPHSPlugV0DqjYNvUj/UnfuN4gYCBTmnbOBLdsl312cMJnTK+LQcSvlN+yz5mlRcK/+mxjJAfMKDbPUQHpKNrgGxiwDgojr7d+tGjWBjTXqPyNpjVeNSMxo8eKZqDEZxm9eG43joZggPxxmUVdjc6ZfFhRCJwO5Z3HwwCGDsd0zQojw0P7GdrCvwZSnTmg7LXnSacdFOtk5EOnn+s0rIcWy6KLjXWjybzJs60t8obRuR0/25/J1j6bZN3I74wdn9kXhJhHfH/miKhrfn/2/5pTaAFCittY44vt6Vpb/ru1Oft/3/Ohd2ZzZ05yxqP3OtRjlt/2PeeZsVwzfjRErisObIyXhVxjjB0jw1OPsvKYPx8+fFicjYXFKa7UR8VOZNzQZCKPpKaz0MynbBF9592kPJuDa/zM30wNzVIoae/Dhw87jiCFqbXwlg46fSbazXNRBm7FIlInXU4/uTCtyv2m3qZkoOD2GNXR4aw57qQx2V7aiuxE/thexo77wcnXlNDp1JgjmPSZPOZJT+kPU9+py+nc5qAZbSdK//Dhw6szlznOjNCcmQiN7GNoSLv88Ex2U5DHlvd9AIf9Cz1poxnls7Oz8fHjx4WW1NH2vDoTRhljNoB6xOmQ0MDFkOFz6mYwwXGn7YocZprOANSBiE9AC384fg2AcwxaJor8tjxYnzkuqZPrbhwUefrK7RKctHZTMh2QrEN0hjpPuaR8MQhJmyzmNYvtaAMZvwJ0WY7mnHnNyMsRQXvHZYbeaMAZPY2x+8UXCnSUkSmhpsiMTjJwTgd5Ja77EaNBR936yWjTgr2PFw2ZryFOAg7PtZF/KRRYt0UQ4HnK0OIIlMqR3y2KX4uA7cha9Je6Wf9a1MS2c98rW32/yTqf97OO+MkP9q2BVhuivDuLthuA47UYfTrgta+oZSybfM6idrbNNDI/PmC74ChsltlqjjnXzVPSH2fZwBPHhfpvfs/6aHlkWpepWzuApktO53NKw2MRGkifF4LOQM9shXdzJuZH3s8YzHTL/aMemj7/bpmI/G5tGcyu2dMmU9b3tedD3+y5teD0reUozplE8XpT4F8hOMzLucCMRjOvkG1WRnE04qlrFnnQSEQoiappGElb3jk9PR2Xl5c788ysl0ZrjRdGk+QnDQLvNwOwxvts0g89LXUWo8ftFo4s4/D3AQtGDDPAQiPj+UQaQfaPvKSi2Tg0uryIpSkoHSe/SsVI3ZENecO+tVX0DbimZKGax5iREKNpylba4fg0vqVfnsowOOH4cBwbyGRpEbWjsQYAGjBxYTTGDJCNOtPDBIgzQEZn63HlPWZxOIYEF9EtArMAJOswfzIeM4fFNQMNnDe9apkPBzh5bs1ON6DhQl7mf/KrOV8X8sKyYKBF/QjfGSRxgfC+sgZC+cw++mfvHlqO/uGLMJQC2eaC9hHuDj89PY2bm5vx48ePcX19vdSZw/xvbm6W+4zcEi3TYPPTeaTRQmmHZedANHt6ejouLi7G1dXVEqVbsLgKu6Hb1GnnZWPs9qlYNIasg/RkMV0Upq3MpSFN6pM883eYneayQc4Yhg+O2NrqTfLE6WzWzf7RCTZnS2NEo9UihDzHz41yfNLftmhtFh2TVtbZxjYpaDsvjxdTs7OtgdvtdkcumaaN0WLUwX4y5c+ojjpDwGJHlrryjI/CJC3sh4/TJD85BilZLDdzzs6opG+pk6CdYIfPPz+/TPN4vCif1H3bFO8i8bPha+Ohx3zNeeT99JPb8aiL4Tuj9OYUDdIItGaZCOqrwTbtaws6Uq93r7AYwKTOOOTYrQY4rCO85uJnGiAi3X7nV8pvWxA2M3b7yuydMJ77PDmQcRhRQKN/FqI6ov/mKI2Kic7Y15mBauWt9xodrZgfNETtHRqsFv0QuPCdpnyHINL3CmujfwZyTOsY+/dSz2h0VGUjtVbfvrbslP2cI7HWL9LlKM+0NH6ZnrUyk//Wv8afffT52db+Ptpm/Z3JKHWA9bAOAqwZTebtPuDt/sz41hxI69/a/fy9xodWZu3m3poOjPF6d8yavLu9tWtv6cOs7JP3Q2lpz7yVz60c7cMXdgR0VI0JFGinuViIbrLdJNumnp6exsXFxaJYX79+XT51mNRy6mb0FyfPs4DPz88XB8+U0azPpI19SN8ZwSc6CH9sGJsTjMHwpwBdnD4yGnTUEdRtxH5IloD9Nv2scwZ4ZjSlztniohRvIWlzS5Q7ppYNPFjWHHgiY9PcxqX1KfcZATLr0KIFG7GW1Ul9kTeeKHdy8vJJT9Ln6ZVZ1OD2ncZlZOfPd/o56oV1Kms40pc8l/eZDZml1PlePrMYutI+P0eZ+rO1iVmpts3M7Y3xkgFjPYnybQvcXhYtcdzZhqcg3G6yGFxAlsIIk9fsJLw2J/S7TY4VbRnB/Gz+mv3jYlJuFwxdXigZGsIb2iFmADhekf/IY2Sr7Wl2nWzvEMBheZi9u29aZl85mnO2EW8Iub03M+ANdXC1J780ktXC2WNsAWDqmUZtjBeBzqBTUMjoWV9MK51cFM2Cb14YUXNQQ2tLyc2EKDy1Uw5vkubJKl0LkSNvAwCXFhHMkGPStK0YJGTcDPbWaLBhGuP1QpnU3cDljC5Hy3HOMZapZ+3d9DtzySyzqLfVY7op31yp6o9msC3P05k/4WczaDaQ4TGNsdPxKXbOXF8Q50S5s6z728V0pjHGBoft4JxmnK03qdMOkKv7yU8DUjt5jiXpJs8JyGj8mfLOuFgeml7Nxp+7WlqamjRxLMjDFrU3OuIcndZmn9lW6uBYsC32ydMLadvO2IDT/Uvdvr9Wmn1r/u9Xy9EWhM0EI789AGuGsTkwXs+WkDF2D+7IB7YpCHR+XM3nAWPbptuG0YMXYaHRSiGatZKEb04LNwdGZTS/bACMOs1Tz9/sa88r4AOKOM+cdzKH7bpMd3PouU8D6iiC7zfZ4XiTjhkA5LNtXpuFBxk0BXYEkHptGJPFIb3uQ/pOo2j6Pb/cANLsf/LJkXVknjzy+1yr0CIzOiz308bUToaRG51Bi3Sa0eb1RGv7DKVBSgA1I8amg7NpIDsarhhPfS3DwDop/09PT0sd6asXm5kPzXmmD7P1HdZRLpp0JoHvGHwkW+Co1XI6A6SUab5H/u8DJwRsuU+AYf+QdmfTgKZxFpS0Z3+lHHWfs9GghYPOkNeIxDxwNrabzc9Put3c3Iyzs7NxfX09zs7Oxrdv38aXL1/Ghw8flqMQk9oY46cTv7y8XI4qpCNg+xxADnrbYuN3qDS8l/+58CVlNoCO2puRIt+5krg5Zy9s4Xj4et5NPTQW7RON7IPba0ZgJgdO9dKItGxB6CEN4QNXztPprKWeIy/hdaK6tO2PLaTdOHavh4iRjxzk748fPy4y6B/KH40hF+zkhxkZ9mVm5CwboZ3y2hw9DbL5aHmnrJt3Lfql0ec4j/GSCXPJeEYnUwd1LfTTOBOYu690Iufn5zup8NST1HTTm/DEvNxutzvZDI5nFqBx/zWfy7Pkdejih1B437LG4t0JLgby7LNBFB1Y9JByl100GRf7ivb/zBZmrG13HITlWeqQxz/jnAyiedJ0xLSk2Ibx/lp/Di1HWRDWUPsY6wsgZvca2vMzdBJUyLbymDS21NMY+/fCWpgaAjSNFqT3zj+wbv/NaK+lYNmPtwiMoxBeY310SK2OfXM4jdZDinkRxaPDduRJXlGhCQxmkfGsbV4z8PBzzKKkkL8tGmrZjyZ7ds6zZ5pBsbFk2tDvsq8zXsyK5aRlbdyO+9HS3XzXxRkYPzfrh3lk0E6++d0mC3RCh4zVrL59z85scUrj35pd4d+mu+lTrje70MZgzaG5L01WXGbZLYP0Nefq66Z7DUjMbOFby1Gc80yRmsE1M41eXUeL0h4eHsb379/H4+PjuLq62omEWeiME2Xk5C4yj/PWswVhTj+3CCNIzfubZ7wgjXnGzxmZz4SH6VNG+QQxrM88TrsGNKybgte2PuV5prv5HtG102f83SLdmUEL7Z7fe35+Xj6AQr7m3Thxp14ZwTF1Tz7xAxUBAgRGlBHygmseuD1rbawTNeR0uxZRsV9tKoAOl22kzRaVcCzZPxrjMXYXxpkWb2E0aGX0nsWb3r/rwuyRsyakm9e5MDVz0t5ClOfCu5zTT3qpV5FH8/P5+eXc7tTtzALbCU8M2nyOferPgjfW4UwdwWZoa7LWeDUDqU2ueGa1zyCnrWI/Ek1TFhpASBvUe+oDMwfuS3Qrnxf22QxcoOa62TZtN/WFetBAGXn+nnKUBWENlb6FMDqMhjSZ5hvjJ/Nvb293lN6F72dAoyh05jE6PAKw1Wmj62tWAK4UXytG07OIqq3iTLvhIZ3IdvtydGCuJ/06S0tbgW2g6ehmjtn8YSqxLcwyILEjZNqpRQ3hScaM+7djNHzA/Ri7KerIB3mb8SNfKRsxwKbRfCPvY0SbMeLY29jQmcQgOqqZoXzWbfmKYV87JSzFAI6y4jFpBsvzjmPsLhxr+9TdNzoMynWTPf5vHXW2LTrBlb4x3FyFTV113SmZUqMMhs9p17tYDPQbkAqwCGhI/y1jzSGSZ+l/szd8Z+Z8OC6pI3qW68xUWdezGJIrxmftGMxxTA1wOMb8MEzGws72kOxYC67onK3Hdsy/kg1kOfo+ZyPJWbGjW3vHnaQRbg6FcwpByqHNwuJorSmcjcuaMaTit+eMBGf9bpFje47ok8qSvznHTcflsq9+p6ftuEOD63QkFMW0A+OYziJC0sQIOUrsKIX9aouKeG61+8RFTeZfG+8mN5QpGtH0Z82QeizoCJ3FmZXmvA4xSGwv/U5pBr2BBPKfAJv1pb88R7plV+jACKJmRpHXrPO5R3lI3eRps2N0BrOIiW3SiUYHGGX6/Qaq/D9Blu1HIsbWZwOMlBZhz7aSuu8zQM7f4W/rb3OQltUAJB/s5ADp5ORkyYryy2375D11zMBBu+aftXrfU47qnMm4mRNoTtlCYJTjd1NvTrpKCcp9fHwc379/H6enp0vamwaSdRMlB805beGVfWP0QWIdKQ35k1bXGQfDiLDxZ4zXKXEjtgjsdrtdFna0BSRN2exYGoL0AfPeBkO6qYhcaMW9zaE96NoOLe0ypcq+eBESMy0+5evi4mL5IELrfwxCjGn6w7Rcnru8vNzhB/ve5J2L6ggyCGDYX489o0zXHRnYN36u3yn56MD9/f3OlkCPA8ecDpTOmO1w0Z0jTDoeO4foFKef2jG8KdQHG/KMY+ptn630AtDQRhmi/lAmvK+atpBjwzoNIgkcm0Nu9OQciJyMFZnkynPzhnRT/5jRoNwk00C+cltmfpz94O/83bZBkcbIHI9Ezj7x9J9bGq+ursaHDx/GH3/8Me7v75ezMChb5CnbntnY8LhF3bQbBirvdcxj/BM+GcmOuaxFjq7Dzzcm0QA0pZ+9PxsYoj4LVot68m57n/W8ZeD2veOIdtaXRqtTxW5njU4ai1kbpK0hbo5bQ/BOR7l/GUPT3FbWe8xJRwMls3SfeROD4vTwoWOWtpyqdnEER8dgkOVnWEcDIunb2oESNmwtcj8kirAzspNgqnNWZpG7+0mn0CK61k/Ty/rW+stnmk7QOUc3TGsD/6TfjsJtG+ixNFlca7eBHrZDukhfK/t43eh0vQ6sWjEIddp5jRftuRmoyG/q0j4f9ivlKHPOY4wdpuSe788GyUxo1/xOnC8RdErmZri1iMiPiwRizGlYHfF7nq45eiNBP7PdbnfaTL02rEzxjvH6azUs7Ffj/cwQtfSQ+0LhJkpn2za0LdJt7bNwQRVT1IzCXAdpmW0vY2kRehxi7pufTr2mfkfnQfJ0bLOsEflhI+dCHlBe2T7XSTCKmrVHwEOasw842RWCE8pR6qa+hAZHh40Oj0Wi4NZv0p5xYR/Z/4zPjE8c3/StTWnwudQb23JxcbFziiADA/M2dTizZJ2k/GUsuAUqcjGzqS7n5+c7PJ3V48DEoIKZD0fW0R3WO8bYsbW0b7FlLZvkdg2SXTf7wZPlQmeyPJ42yHPMgjXwwTZmfom/m5NfA39vLUdbEJb/mwAZDfn9Q5EVkVoGxI54jJeUF68158y5Qh6oYKdkR+u+OoonMk6b6T/b3W63OzRQ+Ci4bXvNTGBIG3mfkqiHER/bbauwPUdMfqZPfs5Rq0v6S0BDutKWU3E23HSifIdj1g6emdEdg88FY6zb+1hpEGfRv+kmT5ojc0p6jJcTj+hA2kIuG/IGuOi0OAa8TyfBMQ1dbtvb12ysWVdzAjGoDVyRR1zQyHR07nM1dpubnIHS8N39jTG/vLx8BeApf+Q526Nsecx5pGdoyvNMH88chp3b+fn5YitSN+XCckDZaLbQ2xLTr1l6nPfIR7bPYptyiHOmjadzzjojrsROPScnJzvXm3M2rYf6quagc/29DvpoJ4S16/uISwec/18rVmAjH86RNCGdGZe1/rRBYl0e2Fmka3CR5/k7z1H5HB3yd1N8txkez1KRjuDoyMbY/ZpUi4hamrFFhWzbjo9KltJSrXyedTRHynn9WURJJSIwa/1zZsBAhf01YLVRM112pGPsfuJvZuBzz+3ZcHJsDXApb/5pzjJOKzTyvkFF01Pz5L1GrNFrZzzjU8bUmZuUtRR64y3H36DHNHAcZnrra67HYMNOsMmYf2btpR3/T6dKHu5714X2awZe044BAe2s20/AxsxEW2y6jxbLNZ/5Z5SjOeeGnMyImWOjoWoOpLUbgSS6Y6ru6elp3N/fj+/fv4/z8/NxcXGxrObLSWFZ3DPG7jyglZxOg4rYEHj6kQiZNM9WiK4ZJyPx8JURkI0E3zWCbA7PTpHXWCfbpADPnLMzCHmeSkKeGdUy/ejzkdnngLA46fTx4uJih1bOJ5OGyI354AjcK8FDj/f6brcvp4aFHm+tswFNuz9+/Fj2siZlzog17aWvNo4EOS0DksVKuT9z7ARk7i9XBQcArS1iayAgfFzbJ71mB6ybppdjTSMeWkNPFkzxvH6nmaOvjiITraVuzyPf3d290nmePsagJPaCgJEL1KI3nu7ymJLP5CHHoMmGQQZ/c9zcz7TR1ivQmfFZg3+PK/ueMygSGCRzwpPSuHd8jDFub2/Hly9fFj6dnp4uH0vyFCjpanI3+5v/z4D/e8vRvufs64eiDBr5htLdTruf6xwgIiWmlfNchNvG4RjIKArugffv0N7SmqwrhVmBvMe6Ul9714WCRVpYfD39ym86PbZHx9ccNOu3kzUoYB8ZadgQcazzvle8EpjkXTse8ofyQp7NiqO3WWmALmWWfrTMkF/tXpOr2bTDrC/mSf6fRZkpjM5nUZv7/qvGrBn/Ge9nY01nY50yeBljnimhzhPkNAfQ6GljaOBkmSU49nZPv+d7LLYhs4i20e4ghXWsjWvzG/xNsMBAhO/FlhMcBGjZJ1BuDymH6vC+fv5qOfqCsEMGf4y+8tWIz4pmAUuE+vj4uPOFpThcOuUgzIuLi3F1dbWkOnh+L+dhSUPmLGkQTcMYPwWFCLj1nWkpRussbRtCQ+58lqgydNNpGqC0wqh2s9ksn+PMD+fxOGZc1GX6QqMdedpj/+JMvRXFhoD84BoCR+S5xvlTT3O4/5Zpn+iUfubv7NtuYzHG7ifuQl/mxvhcCoGdv7KU+hhJxUGGhsheSzXaeYRm1tOMMmWWJTRaVynTnE4hcLEc5u82R2unEx4xq8J1GXyOOs4Mifexp77Gs/A6GQ3q1+Xl5Q6ttAf+wMlms1k+1BP7YzsR+eQWT/KHbWc8078GDMMDg83Z1JvHJrzk9kbzZmarG4iZTWPY7nNsmSFI9jN1JStKGfB8dXxC+kA/07I81BHLfNOLVo7hsI+2lcqK2RAnCw1cnm1o1feoeEwfcz8f05sR5OyRi3NOqiMlQhPhpbGikqXex8fHRSmymOX8/PzVPkUWC4WFl8aIJYrcymwuxspiZ+H0V0v1hp9cXGfljHOcCSrr5Jy1tzt5L3r4yhXuzbHTQdvQWQZyjc6LckZ6uNK2fWea4+x2M2US4520aaL6+/v7ndR0eEkAE75HvgM+bYTiaBzdJ61Outg/yprT6M04m0+OwggarMeUX45jc84xxATCpDf0Z/9+9NN9zCLRzD+OMXaMOE+Ai4x4lXKujfGyVz7tZSzSNqdV0tdMpVnPCFQM+P1xDfKS74d2T+tZ96NDsYF0pLMAojl40xka7Jzz47Frtr0Vgw9/lztgKOPCYMvAjj4pvoJg1DaFz5onps+8MIiy3P5KOeo+ZxNkhbYyNqTHYgPBdijYZCjf4T0+wzQJnRKdc56jErY0Femjc3QfDuFZG8hZSrjVYx4fKhj7ECAVxkJPId+XKm3I2gqSa41vTpvHEM6AC3nbDEx+Mq87xuspBfZrhviduk09bR7Y4LU5KtPHbIbBDkGk+9xobUa4RdQtYphFSWvFILu96/8ddfmZFt3xvZncU+bYRv63Y7YDmxnh5khbgMG/TaPrMK2tL281/KTP7zag5HbaGK45Xesbn2c7M5Cw1kf6Dmau7FxpM+yYTf/vKO+p92jOuTmZmbDm2sww8VpDz3w2abVEKBmQIMqg3Xwq8unpaZyfny9ON8aX26t84tPZ2dm4uLgYj4+Py8ICCxEH34bec7L520YidTrtFMPhSDvGmjSkvQZc2Lb56AiOwIVpXSsMDZmBEZ9jetfnWpsPLN7nnAVTz8/P4/b2dif7kWLDyQM+7JzZxywiozzOTlSjQbDMZhFL5IrymOc8T0aA6AVv+Z/9Oj09XbbgsG8+S9hyGn1xupDPhC8Ghs0xtdRs7lO/GWV5GoDPpg/OarTMQMpsi0za9TqD8C+yaH7GFlAv0mZ4F97GFlDPOMWWuvMOZcDgy/Jp50ybSb1vqVkGP0zNe1y84I+FmStHyZYVyltoTzaB+k5Zso1zn8Pr5qRprz58+DAeHx+XjBSnNSMbd3d3y3n4lKPmW9b8UgMXfq5d/5Xy208ISzHBbyF8DSFSsFlvBpRze94SRKHm+zFcmR9K5Eyjafps/FNs5OxgZ+CD/ZihZ/OhgZd90Syfa+g5BiZOrEWpnld0X1rh/Dj7kLYpJ+Qt0+ick52tpGb/PE758TxkozV1sx3K2BgvxtuLUDgvTvkivRxn/4SGGNjIbhyPt5+Ft2vzaWmbaWQ7tZmOWk7IZ78TGkKn0/fOeFHHmMFqkTSzXtZNOhICGILa/GafnVHjOwYZBCd8ziuX+a7Hi+MyG3vqpoGQx6PZi+aIPE4t6iVt5qnpdrGO+ZkGHlln6+8Yu9s8nXkjqEohyHIA4r7NeLFW2rP7bN+h5eiR8xg9rZNCJfN9KyoRlNvI80FLl5eXOyuvT05OxuPj4/jjjz+WgyJyWMTl5eUyf3FxcbHTZpzxdvsyn0nES6NH5WHkbCRPIGCD2dKmeZ6OhO/O0qzmY1PkGEg/Q2fhSHZGAw1dnmuOPsYqitIAlWklL/ju/f39skUlfUnUY0dORbWxYRtx9smSjDF2HGgKwYllgCV0Oe3uzwBSR5ItmY1LG5t8ocjgxe/mf4IGjzcdTwMpMzra3nemEqnTXMjT6mzy5PYjN8mIGeDa7liG7by41oH9IKggGLJzcpt8jpmPxpPmtFKfx8Z1528DcDvLyATrcbbL7Zj/Bo2kszn+5rAJcPJ/WxORegk+s/5ljNfrcR4eHsbNzc24u7sbNzc3i82mE2fmyfTtc8Skew2U2Jcdw0Ef7XvOzbE6MrWRn3WapTGPQpTFLFdXV0s6LEb24eFh/P3vfx8fPnwYHz9+HFdXV8sHD56fn8f19fW4ubkZm81mSY/++PFj+aBG9kGnPiJjCmNLgVEpgpaZria/HLnkPpWkIc0mAFRUKy3pZaFTYiEtNPh2eI56GgqNY+W7LX1EusnTLPC5v78f3759WxxDaM9UBB1i0mqNV3bOkdekH70wynzgymz3mcAuv7nALmDAuwLCXzqqWYrNc+0GM3SwqY+r1u3EN5uXE6ZYT2ufDjz3uSgvshsHxSkiyqQX8PDdgCs+5/trNoe2xToVOmj8yVc7v/SX3xB2IbDJc1wkxqkVj62NfAPM7B/5yXlUykJ+vCjSvCEwy7uOTA1oc91HjfoZyw3/b+NH2jg2yWCG9+HzGGPc3d0tQPXbt2+LXkTXcqQndZSybXvMMeBYtamR/Bhkko/vKUdfrW2DMHsuxYbN92alITUOdpvP8IIcI9ExXi8I4tw1z2Vt9Fiwidb2ITTXZWFpacYZjy0UvyIojPZTB9vzb7d3SH9n77Z7NiQcB6JjvscxbYjWxmqtny5r/fP4O5rzNdLCrInfm/FnH0pf68shMtXAVisEGPn/LXK/zw44hbxvbNZkaF/hVA1BtWlqhnzf2JiG2RjYadsOrAHOxifTxfqos363/czqMz3NFu3jP22zsxVjvKzoJy+YvWRpafV9Y7Rmlxqt7Ndbbf1aOcohJLNIyfM/+XsmzBx4p15y38+lJNLN4q2ko4K0spiLabjMKz8/Py9pUe7ZzSfYHh8fx83NzXh+3v2cWhNG7nH0qm8+ZwW0swmvnMYK7Y6aiERd4rxcmH5rY5l3aBxZp9vne+SxT/lq6Wa23c5ZZvSU+nPiTwDUZrPZ+aQcpywYec2cVRQ+zrEt8GtG0ml9nvWeiJn1su3mtFO8jWyz2T3dqhmYFpk0QMm22JfZyvcm602HN5vNq0V1Kc5ieIqA93I9BjeOmQ7akRd5bDsyxsuiREesM2NKGaIusN+hMWPOrXUcJ8vNGv/SngF6Fhryfuq03DWbzHn55+fnRTYpM7zv7EzkjxlBFgOW2EqOwSyTxTbC95xLkYV64d3Z2dn4+PHjTgaBmQn2N/abNJAn9lG2ZbbTazLTANV7ytHP1k4nbaD9TOswrxGF8O8Zo/xuUtN0tly9l3qjrF4kFkfLeWczvx02QqSW9/aV5lRTr51z6HCK0M6ZCm9jzpQq26ORm2UITGtLnbNN0sHUEB2Ho4HwznOkMUIZW7bDKIep6fCCjr2Bu/DU/WJ6lcfDtvlSGp5MsXjumga9lfSB45F3SKujGNfB8WjPthTnjJ41R0vAQePZwKdL+jiTqfSbh0c4rch+uu924A4QDongKIMGleRdHMEYu0CS9FGPG2B2IOJ+RZ4ik05Xp06CSNJAu0TbZLBAnWprLNKG+e0ApEWyaw6M9GQXAYEtbQEPc2FfG0htOypIC8dxBhr26VkDv+8tv2VBGJV1jNcD0AxjiyBsZGYMJFIbY+ygXQqJ0eb5+fm4vLx8hWydMpnR3sAE2+LWLhqhmWE2iLGzMLINDTRAoWdN8Zky4v9sa2ZUHZmwULHTpq/nbys7nZYdN/sfJJyx5VxfeMyTnMYYNS3Z+pZnbZT9neYxdr/C1OTRPOW74SNpbEaKTtqp7mZk3BZl3bpEozZzwJ7yMe1si8+xLmcdKK+ug44jf0efZtH/zJiyXp+sx7Gl7DV5WXP2+R1nwTHnWDGDN1v4R3pZr+8bnEQncm9mR7fbl29lJxAhiCJPCYJnTsZ2svFpNmb87bImh01HyEdmF7lDwvLTnPKMLgcY7f6svNdBH21BWEOJnkRvSI3/p9A42qE2h5lr2f96dXX1Kv1Lo566Li4ulnZzihO3TTWhYJ+tYDQEqWuzeYniZ2lNgoJZmS38yLVZyjuFC3gaD+kYbTwzVs1gsV2ngFNyvR2jyEV3jjDZdpTs4eFhWQSSdmIcN5vNst+RJSe80Ti2vda8zz4YIMwK6bfs8F0uBGvReu7N9rsb/DraoWzyNKxGazOsLNyPzTrdL390hvURrESXrT+Rbx7FGtkwqKHOmG8uiWjZN2Y/eCoYwRgXic2AKuUmtoTRIh1xm+6aOWaPUUup54cnFdJZUnYJyhv9dFRx3mmPp+Olzdg19iXPc0EmU9proMFyZ/tC2XGhzcp4Rob408axAUo6Y/uoNka/s/zT9jn/Smmoa4aW19Ca67NjfC9tMzRFgQ6CbhHcW9piacK6hjxz/62CxiisHSDRyhoSbcbOEZURK5WwReetP0bKbMdAgtdzzeCrpfBn/GYUcwh/2lg0gzSLdl2fDSIdmeWhlSarNGQz2tgP8m8WMbs48jJgOeRd/31Im4fUbzrMf48l07Gzd/i8/27/51rrX5OLGc1rEaBtm503gUwrBhCH2BwCLgYr+8bEckIwT/u7r+zr077yO5z3b3XO+xSlIaoUzvO6460+p5RSOP+TaKkZgH1pGNeb/2d94GKezBMZoVMQ3Q7RnSOFtlAj92fC0VKzzZCuRdEzdMvfHsc2Jl6YMktzMvJIBMQPXMwMBueaQwOVluPfxpsR1T4gEPkK+MpzpoE0N9447e+SNtr+66YjfIdj0t5tbToKtJzy+RbBUi42m82yhoBt8T1mNMKrRF+elmgR0BgvUXJAZH5n6ooRFtPsodFjRjvDTFsbq4DW7LlnPfk77UQGuXc3tCXKJ61ZEGXwY3lo8tlsFutqizPtpNiOo0k7dUbNeaaBE9u4MV4yXNn6moW9s4zidvsz85azLpJpub+/H7e3tzv23v1r/Gl9o4ywP67vEB/11nK0r1KxrEVLfm6GKm1wrTA2JkynjPF6wz7rpXLZUbR+HMJk94PGLYbJqdhZaQaMjqiV9JVGkLS1lCPbaIh+LQvBflp4Z3XS2ZHv7LfrYqqeRmymVD78nzxLepHv2wDHESQVbAPsa+Qt+3ooCnc/ZmAgpc0fN9SfflF+2liyL42ndj5r/ZhFxnTQLtRP0kHg5giqASo6m/zP4zbHGDvHgtqgho78GLiaRoMTAuQ13Y4ccxqLMkibkXUrmZLxs2nb9fAZOpg2LraH4afHJ+24jZnM2P6Yp3mOY5EpGH6kiMXZmow3U9cBOFm53vS8jf0MzLAt84b9a0DlveW3p7VtONY62QTEzGuOPM/lbOwMOh3uZrN5Nd8TxY1Q+Pi3CEwTRNJLxaIx86DTYLS5ICudgUUKr5sPJycnr1aJ0zCSXq+GpiMkLS1KT2F0ZYfAPhkAGXywPvbFCskxMU12yAZ4ft8yxdWhlDPPPdtQxtiuGeWW1bAzI8CalTUjMovU3Na+9HLT0dQ1c5J2spbdFvmx3jxDZ8T1JmynRTO8R1DX5MGOIc+xLQJsts356jj79GMmB5ZFL7SibNA+8Gz/lj2wfLe+cjx8aAj7Zf62Or210ryxo3cdpNtt0UHP1hJkLYDXAzSwzoWFaz7HvGCxDs2eNy/XANFbylG2Uh2CNmbC3t5Z6xhRLtsIYrJi8HNpV1dXO5+Xi+ATpd3d3e2c4GSn0lAjHc3j4+POfkqmeKIcpJEOMqsuGfXHUDkFHJ761KImTORTBHaz2SzpstCRBVcGIkwxpm2nOKmUScWRN456PY6OCFsWg0aLisn5KcpaUoTkpxWbBiGInfdpbGn4uZWLAIJ0ehxm6fvw33PV5FfTGxvV8JH0528DttRreWwAwkBlllHJc7NMQot8uGWOMsnTtSwvcdwNhEZns1q3TXc5S0abs89pjbE7RcQPm5De8CH1ckGYT+OzDBmgMotjgEF6ne0JH7bb7QJ6nJq2vISHDaQ02TBoMw/s7J0Zsl5z8SLlf7PZLMctsw+WkTFepjf8/j4n63EmnaRpJiP7QMBby2+JnGcO4r31GY01BGMFa/dpSPl1HqNeCxavG6n5d3tvVlp02KKB9h6N8qERUXv30EID5wiWhanAfQiytZ/+tD5ReRo6N72HGF2/PzPMrndtjNfuW2ZaIZDxc8fQL+vVmt40x9GeM237+EM6UqKPXCW+bwEiSxurmWGege19WZBZewZFja8zXaWj8rV9pQE295FlJk8zuW/OeVbvmg2e0W5HnTZTZ64bbDlA+pX2f4duHaOeo56t3QaYz/BeQygsa0bNAjQbiCzOODk5GXd3d+Pvf//7eHx8XE6dye8xxvj27dvYbDbj+/fvS3TNfcpWpmbsKVyXl5fj8vJy51CMlKb8Qcj522nAZmBaPTNDQZ74OSpe2uanEjMP5OL5fbbphVdph3wa4yWz0YwVI48ZUt5utzuR2pqi2/A5Tc3TldphJs5epA5GfulLPlFK0Bcwk4iedDKzwLPa23wwabTOrWUcXByBuJi37eMvea5ldeyo/ClIyoWB4tnZ2bi+vl54mYyOp1xybbN5yS5lzMhfRn8NbIcOLkQdYzcLkLoZsfvAmNAzRl+gRlmnTcxWTvanAUbbGm+fohPje5zKoW7nnch7+unpBNr4NQCQLauUOY5bAy88CKqdScA+ZXFtImPyPgvCWuRuWhtPOc6+PwMvrY3tdvcb679ajn4ICYlsCCjPrDnwlOZ434KMNpuX/YdR8JOTk+VrPtfX18sHMy4uLsbz8/Ny/GMc32yurPU5JY728vKyDvxM4fjVnjXEuq8w9ehDFmyobZzb3nQe8OFojs4r15JSXDP8bL/tibTxo2K1OaZWrDB8lvxIiWNknznHlWkH1tMQPZ0qwRBBABcFmYY4b9bjPpH+Zix5vaV2OWWzb44748I5Vht/G0TebzLT2knJu5xi4ILAZmssF4y8WfeMn2yHekK+0aGZz3wu616yhqWdckWZpPzwOgFOsxlplwvGUp9pYruU89THswc89bQG3qxbnk7I+2snhdkxN8fG8aCO0LekX20KyCDCY2+6Zk55n8Od6cCvlKOt1va8HO/vcy6zjrf31p4hMry/v98xYIxKguy4NYepbfajDYaddUNQpK0Jmn/4XHNENnDsa3sn9TGqS6GRyf8RSM6lNgFzFMz7XjBiw0XnTtBg3lFBSA+39fA3x3kG3ugY81ze8wrvFhWOMRbZaXJJ0EUD5QM8CFod4TBqTP857uyL5YwG2HLUDCuvtVXDTbfzHp2T5a3VP6M78mBn197jbgO31wBJM9D5uzld6gAdBGXXgK6BduoFnXJ7t9mFdtwr+0Ra2WbTCY6R9YtAOJmyNvYel6ZnBkczO+i/mw3kc27TfOApYNlK5e1T5i9/z8o+8Gi587v7gOih5V3OmUpiI8s0Fwk1gqcBG2P9cIvZgOf/DNjd3d2yuODy8nKMMRZHnO9+Pj09jcvLyyWdlBS4T28yinMkTQG3k2jRaACA57rZD0ZNdM52xDRUTJvmWnjAQmTZDCzL+fn5znXSQHqtNOkzwcHz8/O4u7sb2+1LFiN7UB1RMAUWpBwFJD/J/1zLKXAsrDvpMH6QItmSjGN+E/E/PT2N29vbBfS1/cZ5NuCQPGBpezfzPyN17/m14XOkHuNO+aTzaQAokRfHj6CvFRvj1ONMWRsD1uEoJ3ywA+XHDXyqmw3hDCBRT/keQZ+jHtJA20UbZt0h77ko0n01QAj92ZPNMU2fePodI+3GE4IZjw/Bcejj4kkvupo5UfKPmTLzuMkP9SwpdYN46ikB9Ha7Xb7pHn2/u7sb3759qztuyBsW2is+00AufZ1BmUGNdexXy1Ei5zUGvIe4X6UpwuKP23P/oNNkDS23/rTyHnQ0G+wIAw2+DRmfizLZABxS+I7HNM5x9ryjKvYrv71wbC26paDTAczS3mxr1gfW7QyE219DzYw41gppbzTv41lLHdsAz2ic0eM2/DwjrbV6jqHf5o/pCpgzH5jWNNBIHXYeHDtHpGPsptx5z6C88Twy1XSEwJQp5LXSQLr51jJWaxGcbQvfaY62Pb8vAqRe7Stv0blcbwFM7DntOo9+dWljuRatv7X8Dj931K9SNSGfoYwUR2YUKDJxHwpJ1BCEfXd3Nzabzbi6ulrqzuBluxNRYRQ/KC3GoaW789vOzwa5Lb4KqosxtBEIuAiSZbTQ+t9S0423hwpde5/thl6mmxsteY/nIwfFc+taQ5mOSGh8GekQTHEcP3z4sBNR0Bln3LOFLrLFA2w2m81OFiWFnyP1hw7Sb2cOPC402uzbbE7O0V2TOxYayWaEPC0xM+z89KG3qlxcXNQTo1Ivo+61aJrvOu2fuh3FhsfUC8/rO+LLPcsX9Y/znZS9tqeW9Oc3naazg2nf8tRAb9rhVjHSyHl4g5SWzg2feP5D9OD09HSJklmaTtJueRqiAU/210C7AfW84/T/2dnZuLq6mp4Uxo9bWO45LvsA7Vqg4Hca+J/5pvcEbkf/ZGRTDN/nuzai+e139zl5OtE4NqZ06Zy5QT11UwHinG0Y2gC6by0aYPqRK0tDV0OFUYb7+/sqDKmXiplrNmTNMLQow9FyA0SMEhpd4VnezzwQy8XFxbIAL8479dnQ05BGBjK+nCLIe5xCYJrWY81v2QYEhfeUJfKEq0q9WIn0kt8tbZb6zEOCD+qQMzzkuXXNBnsGcO0YDRpCv1PrY4ydBUgzx2+5pl5YvmZ02TmT3nzkhryzczbPmzON0ffaDNPP8SWQMriyXDWnQRrYx9ST9zyW3L2Q1evhZwOYY7xM8zC9nboCUAlMmIInv0MP9YKyR7mmLSXfaPdmxXWdnZ3tTH01587+mddrINTX+W7zQX7GOrjPP7y1HGW19j4iqDSH1tMcX343x090NjOWOSGMz3FBWA4I+PDhwyKIFvbmsOw81vq3L+oh/VEmC7QNtK/RqTSeWnDZDyoQlbP1wQLscaFhdeG0QnNgNOChgQaGbdvR5HdTFhsT86U5OkdLBD2UtVmKtP3d2l7jo2mcvU/+tXt8r8lxymzM/S5lf61PKcx2NF3m/cb/3ItDbc6ddbHMxmImJ634Oh0nnZ+fsR6Tp3ZInjrx+JPf/N0yeU1XKB8ECW3Kx2OVOvw8aYs9pR1Z00c6dNPnAISAZ2aH+T9l1XI706W14jrYFn+/1zGP8RsiZxY6RkZyHqyZ4Wn15bmWOkpUen9/Pzabnyntp6enJTVyeXm5pDyT+o7DTpr1+vp6fPz4caGbwkP6Q4fRsU8Vc/TF36E/P3RWTKvtQ5vkIZ2ht+nQ2ZvuWQRvvtt5MepgyjvttCMD6ZjbghV+ECCZjkxHBESRd6ElhpzXqIRB4jZkTKXxJzxiei48yWf6yK/ZNg7yzmPh6IJ8YASd5+x0bYAZtXiBoIsBUKPfGYQxRq2Xxptyx+iWWwV5ndke855RKscv/2cxkPvIeiy/dizNcTBKdb/d3yZLjddOB7cS3uRsaNvPvMszxFMXF6A1+xq6E4Rst9tleyCftd3bbl+mg7j4ywv00nZ+25lzbFJ3ovdMEzmjQX7e3d0tmThnQA0q2JZ5TVlpmag2xq6z9YvvrOndoeW3nBDW0CURWK61KNPvNjTk543iwyAqNlfOUim93zTOun1AwU7GQu/IgwJqBOdCp9wMJAfb6UJeo3FviD3PRKh5j2Bilg5qaW2PR8bZiJsKPFMqO3/yl2k+O2L+bvQ5wmiO0xHFTDlJS9637LnP/m262v9NZmZov8kp+eTCNG5L0+ZvOlrqw1qh3NHYslBGYqTZRltnwfEnYG62ZKavM/opgzMHSzBkvWtjss++mSds3+9Ql2ZRn8EN67KOMRVOoGL70eST9tOp99bv5sRYP4GZ+0P62/zyIWXN1zS9XKt75pj99z4dOaT8lrO1TZyRanuHz7Jut7fWJv/OYGZb1eXl5bIAiFFKzlPO3MvV1dX49OnTGOMnEuWnyIhIZwqe+dW29cdpnlxrjjIC1baWNWc7S+GxTbeRLMLMeHO7RQqjq9keavejnYWd8eKCG2dVyO/M89LAElnb+bIefjiARj373VMXaSdICa2cB/SWu/TFcujonQY2dYZvM6PL/wkeOFZtsQzfmaF49sXRjg2kx71lFCyzfIYZlZT8zQNOTB/7kTpOTk7qOdwtc7FmeBvwdvaF/QiQMAD04jTy1sDadDXd5E6TyKF1ibw1OMtPMg20MwE/GRPLLKNyjhOduaNz0zArBA2Zn+cJhOHZp0+fdsZ3u335PCRpzxeokgGwzsYWmE72i/rUQNrMCRM42C+9BTzMylEj5xlBZMAawk0ds8FtStau5XeccxYg+dkY/IuLi3F1dTU2m5+p8I8fP47n55+nheU0MUZGTlezn3TOEZg4h7TLFKx5w37l/xikJvicm6HTc6HRSaqWikuwkN82NDy0JW17m4ONFgU8/aDhz+Ki7OHcbrc7R/OlcB8k66YjDp3sU57lYq7NZlPT5DFgMWrkJ/k3i2zMbxt63uO75tu+yCW/3bcxxs7iOxuSluY3XR6/1N30NvfHeJmCSLGOeH0B+0aZY6q/RaV5jguvAiQbMFwbG/Im/CQw9Duhg9MuzABZvtO2QZ8ddpOTdmKXx8KgnrKRv5MyTqo8Mu5pJb5DcEXdtUw3W2NezgqdM8cyff/06dO4uroaDw8Py7eZ45wpB7Eb5sE+pzuTBY81ecDigMA2fMaft5Sjfs+5daI9ewjRjaluK/dnzpxKQ/qCDoO6YqybYnHuI3WsIcO0GwVYi2hIo42CnQufb5FVixJaobFiJEM+N6UiX4zYY1Rb5G9aScMaCCPi3ScvMwXzeOZ62qBS2WkYbbf3HG2ZHgPI5pTX+NWec/3sH+ltDo00kGamhw8tacOA2HR7rtRjZflo0cqsXTu8mTPwOPDvQ/vsaKvdt9F3FGd61koc54wWOn73hXyc2QoDHwJd22rK0xrtfnZGO3+nr3kvQQNtrkFX2moRvNuZ0XkIjX7+vc72reXoJ4S1jjcj14wX32tKb3SUv2cGnKlLp0I2m8348uXLeHp6GtfX10u0lIU+Wd2dtPfl5eWiLDOUHnqfn58XhLfdbsf19fUr2ohSPb/m9FzjbZSJKUZGkYx8wx9HELMN++YxESe3pxFYWEGaYWipOfI07yfLweMyHWWwtMgj7Vg2Q8PJycmyCJD84v5kRhehK5E+DQe3YZEvoSPpWILEGaBqkR4LDVJ4E2PGerjTgDQRnLa2YvC9r9rPEtjRcBG8kZYUZpRynVkc8qTptPeuJ8OV97kfN+No4LXZbF6dnDcDwW1RpZ1meEPQu1YvU/mtnkS5d3d3S/o+dPhgpea4Yrv8nOekKa+RfUbYdLbMOHGsCehdL8fJ9YSnzDbmY0Gxu8wGcYowusSFoua1wb3B9MxPeRon/Wrgq4E+juV7y1FPCHOZoa01BDJD42uIe61uCy8N0/39/ZK+TErVbXAfZK616Ir/R4EPXbzg/joqcGnzVv5NZOz2YwDaMXetDZY2z0ja14qjKBpPKgznhU1/A2LNYYfW5oCshEzZWQGZ6qaSGozZ+cUY+tn0vfHLgKalCAmG2hzrvnkwy5oj5rUIyfJwCLBL8Upn08i+z+bI3Q/qCIGqaeBz1o8WdbkvpqcZ/H2BRZ7Je/v0JfoZQNx0eK0wI+YV1UyN5zlOJ8xot3zyWdq/tf61eggemOJuY0SnPgMn5DP1Zs1h2u43PXYbLVjk++8tR9tKZcOzxggbAyOa9hz/txGaPcNtFkFeRPZBiU9PT0t6++7ubuedLFg4Pz9f5ig9J8O/Qw+jmixKa3ODs4H3XNUYuwuw+EwEhUpnY5J5XF5zhD0zik05SOtsaw2ftzENr9aiDdI4M6JE5KHVDsdZiPCLNOb5Ft22iIPyxx/yxMbbf8/0gGdrs08xtJxrpBFxuzRu6Xsr6bOdV3PgMwPN8WgRDGlmFmuz2f3YQ6K3GeAyDaQ5EThpmIFM63CcYOrwWesEpWt9p5w0YG7AaVuZvueTmXFYzOjwufAv1/KbW/0Mit1v0rzZvOxYsczPZN1jQofoMaO+coFusnipI/bq/v5+CXIoF7zWxoHtHgKkmr0gfxxUOGDI72M45ZR3OWcbqDFeK+/MefJZG3AypdVLBjVm5N2np6dlMQGjRDqEDP7379/H8/PzuLm5Gff39+P8/Hz8y7/8y5LKTDrz5uZmtU8p+a7pycnJ+P79+zg/Px9XV1evnDMNU1OcCG0zFjRgTbhiIKKsFqgYDxq1FiWQd6Qt42DHSCUy+PB4tgifxqAhZMpL/qdRtjzGoZnP6W+MEdO5XMRn3lq+m0OaObH02U6D19Muzwrmt37bNAjbdfsEcYyoWgRIemiYwxPSynrIn9DmOsJfg4ukpTM+lkGOPVfse5tXHBll29kHAwSu4s/+X358hIX0kf/8m7K+2WzqB0TaNrH8n+f5yVk6PTv89HmMF5uSYCP15DkCUDpJA4W8w/3Vt7e3S8Axk/fU0XTQz+Qo3DHGEiARrGda8O7ubmdPczKSeeeQ7N3M5xjwN7uy5mfcbgMt7ylHOyHMCNBl3/3ZO62tt7zr6KIpxBgv22ByVFzmmT98+LCkv9e+1EMe2Mkwxd1SVC40TjPkn76xH0aRDUSQF+3dhkRbRL0vgrLxXCs0sI3+GdibIeAY2/zQCR1SqMA2jrN62rU1QLPWx1y3A2VdvLYGBtj2Wl9baX1tYxQQlna8ZWeMnn1pkUyeTd18NwbVUawdtN/fxxvLzKzvs8zQmmztOwiGpRn51B05tgNhIQij02Wmh8+yDfOklUPlfu0Z6kHoDa0Oymy3zZ99TnONzrf02Xrc9PdQOt5SjjLnTKFuKQ0L2qzQGDYjbCTTmMbBZEoiK7M9tzLGz1PB/v3f/30nfcTn/uM//mNZJNGyBUaFpP/x8XF8//791T7b9k4K+cltG+FvjF5DsBwD8r5FFHku4IGGKu9sNj8/AmGn7ogk7UbZvLebUanTzGOMZdFLaOS+0Vwjvembo+GMW9JmcRaM3GwISFOu8Vu87offNbhgX9lPR2OJiBmtcIwyLonqUo+jJy5kY2bFcp72QnOeS93MkNAh5jl/1m+7ffmwSc4KSN9n2Q72xY4xukp+U9eZIqez4Vn4XLgUfTVYpt56O0/qYgYl9NjBNlDE9Gx0nmdVMxrlOJOn2+3LgRtOa4fHHCsuluKnaDk+uR/ZXiscu6zFyXtraWTbaZcEPbStPDs7e5u32+24vb1d+unpHWfo3P6+KHct0p0BkPDA47cWWb83ej7aPmciu+aY+f8+RvC92bOzAYiCG13Z4LO+s7Oz8ec//3nZD519gRmAL1++vErPNhpIO9vNt4v5rWEvVmoO2iWG3MaG/TZ9s0iT16LoVECn8MZ4Pfe4xgdGMW2saDDSTkAHnU9LszflYtsxZjzlzQpt3uddOgwfBEFARAOUerw6noXrDTx+HlPeo6Fv/U/7LU1uI9kMa/iU+vjNcMuO5z3bXlnLCx0HgUPoNtDJewbg/p9yz/GwM55FNgZzBGPhO21aAxqzwrl0zqHPdJEO2jx4fn5egHXozL3ZYlNmjNphSGsZLeoCbWbqXev7WvBl50Y5icMmv8JHA5cmG2tlXz/X/JH1Kb8NUH9XeXfkbIfk+362MevQDtrhjrE+ud8UgJGAnRuN8nb7cu5sQ9hJU9O4mK78HQHLZyy52MPzZmP0BUwzwXfk2AyYeRzFpVNxNEJ0az7xmUTKjDxTbERaio/zZOTd2dnZjtGnYraIy7/NDx+2YEecNsIHvpd2ufWsfaO3KTzfNeAxAKBRH2Ms20msN00OnLo0CMnvnL9sWXDdzRmyz5EfGuws4vFXjdq4pG1niAigOGZ8vmUtyH/LB58zH53KnqWfG1ie6WOuO9tFYMPofBbMEBjwHY8XeWswy3UCDnxa4bjTTnoMqI9+v/HINKY/qW+W5WI9ySRxMRjlfMbHtf7vC7h83Rkd93ONhreWd0fOIXIt3bH27r5Cpho1GXW3CIiKku84J13r1HAMY+aF80xSdj9+/Fh+23nzlCLSTgP//fv3cXd3Ny4vL3dQMI31ZrNZBJCG2qeMzcAOlcZoPKWBgIYabWT4bp4h7S31R/5y4Up47+9Vp88nJyfLoo/QajBEFM4Thtif8IGrsJ0CdjqaSp/UHg3Rdrtdpkg4Lk6Z2tGkfX4LOu+mXUc3/ja1nVvoYdo0tMQpsD1+HCFlbdwY2ecZprfpVMOn9IUGmbJM+vOTVLdTvU9PTzsZkPDFNsfAvB3+QwdN58PTuLxrwLaBvKPssATccwwo+1zkZ55zPBhB5t3Gv9bXvB97FsdG+l1X7nHcfSQt90M3R2TbFJkMrzjdkrGNfd1sNjtHsaau09PTnUOjclLhIQCBY04HbrDGsfb7vNdWiP8OxzzGb/rwxaGFzPKANlTqd/PbqKkVI/GgtNzLQHMlaeqnAyByP5RGtj/G2HG+pjH3aSSiDHaqjAosGDOBJdBpdDZjs69va8UR1KF1zqKt3Jv98D7laB8NM5DT5rYIEGlQ11D3Pvlskd2+Z9fa8N+Rn+aE7QTaffK4GfdZpOxUdgojVspxaJy952Kw2Bxm4/0+Gcozs7857mtRdWu7jUF+7wNJBlVr+tqi1VmZZQzcl1+Vz7UMEMc5NpjTW822remUdb/5h0P74rL23iF+6C3lKFupUow8mlHl3x70ZmRZt40v/zYCssDnM5JcMJE54IeHh/Ht27dl8UP6lmfPz8/Hx48fxxhj2e9MFNsi8BbREw1/+/ZtWYxDpE2nkLYTITKCYDttLDwu3G4zS1FTkX2PBpyAhfxu0RjnvBw5JarzdUa6ifqIvh2Rpb3NZrNsy2HGwsrdHDZT8+wnjQSdPsFc6moGgNFTM06e/yPvOXXSxjyF6fak5R2Vh0c5HS/tMKJiv/K3sw6h0U6RtCUKms1tUmYZ/Yd/nJ935iH9I1CI/Pj8AvKfY005Oj8/X6aYeDJdxoxj2exPc6htOsh0kA/MJDnypAy2VDj7zzGxXdhu+7fhE4E2/pgHqYfyZpnP2GTMIgfZrsjnMkWY8ebZ/Xd3d8vWLfafdDQ7lv+bb5oBnzVQ5mszO9t83nsd9dEPIWkphUPqWBN+P2/n3QTIzjl7jn1sYQb/7u5uSWFHkOgUk872UYkcdA62nbON+d3d3ZIKT+qS+wjH2F1oRAfG/bdsJ85yrVBAGbHkxwvVXEIf77eIi0Yp861Mkab9FpFQsdMnO+QGylJn5qsb8uazbNcK2ozCTN4dWTeg2NrIs3Z2rNfTG5ZdGmn+Zp2hjyt8I9/NOZM/Tue77vSHK8YDuDi+TUYo01y0FD5l2qeBBwK20MRpAY6BwQUdUHSbq9ZJD8eDvCNv9gUOBIm2jwYQbfza7gSPldt0xoF0sV7Kd+rnFBH11fQ2fQjdrDNjaRoyhpT9OOecE0G+GVibLveXdPlvP7PP58z+P6TdXy1H/fCFyz5Cm6DOnmlGdu29WZTTlGSM3RWvUdwITg4QCUrlnBvbIp0z1JnnmSrP9gjuTQxNnC8kQp6hvfTZbTfUTNo91zkDVjbKdBzsu+fNxuipM7bTaCR9nPMjMDGosMNzGw3gRS7yf5zijB4aXke3fm4GLMgXT1W4jy42zoxYCALZNxq4OK0ZyOAPZdl9ZPqZ0br5yeIIiulZ05NnmA1xMYCwwzPfIrMGvQQBlkWPF2n1/bwbR2OAMcbu3LoBEvs/08WZHJmP7DczKBm37EzxOLtvBiIzB0Xe513a1jx7dvbzy1OMnnmOPt8z7c2ZGkTMygwgk/5WT+svfczvKL/FOTPSyf8tqsj/NpBj7KYPjNw5GDNm01AlaqAxjQKljhgWOqf8f319PZ6enpa/Hx8fx+3t7dIe0WJobMeFssQwXFxcjOvr61eG1mmpFl2x2DjnGhXcihu+8UQkjxsNVEtTbjab5aSfTBNst9slrZi2qKw09OxXIrm8y3FIdiGnA4XujFtOcOM2ndDLyKmdyGaUv92+HHfanCp5mVQcaWAfvdrVfaaxjtEkfdz/3Axu6qZTzD1HjKyDfc0PU9h2duxzrgc8NsdsufNPxoj05V3TEOfszIv7mbqYAm6RU1LYdAysh4DDPDe455gZwERWWzo1C1RDJzNFeYZTW9Z96iLHtwHFMXY/1sO1M6GbpyjaubJuykIDP5Q5Rsc+Z+Hy8nL8y7/8y84WO+rdGLuZlbOzs1efhozjphxTDizDKQ3wzkCh9blNU81A4HvL0ReEvZcoo6B9z72lXgpHAwt2RjQu3jc7i2hSl/uyFlVxjmqMnjak4s5QHJ9z5O6IzMXGhUpowU49dKCzKILvtGt51pmI1qfMC/O6eTkDav5hP8lH8tVA0e+TRjoxlxkCZ7tOa7ayphc2UI66DIpbPWv3ZnykEXYbrY7GA8rsWt/dV9O9bzwbza0+t8dxPTnZPVu/OWU6uOaYU+zoZ2NC2Woy1mg/1A5btmdl5vjWnm1153dWkR9iw8Nz8uKfVdZ4vUb7W8ZgVo52tradWhPCJgAN8bhQKFs9zdHmN9FNFl5kDpRbcW5vb8fDw8POiTVExVnun8+aZUsU01Kkww6Jzo5R+vn5+c6nC8d4HaE6cpml23iNqNK0MP01xm7k3OZXnTIm0Dk5OXm1RSORSeRizfCRJraXfud6zjfP2oHtdvdc5BaNZgz5+blmDCkjNB426MysMPpLVE96+X+izPAuUcxms9kx9GulzXM6Mshc3Xa73RnTBiBapJA+Ur/yDjMRjCwT1fkd1jcbc4OpjFeLTsOnfXvvG2jkwqTQnO2M3qNP593S+flNeaXsOZ3tQifPrZRNx5nKzbQXxydOK5mk0M9r7Jv1ZIzxSncbULI95Xa2NSDHQCZtZu6ZWxC5wCzbvphRiN2+v7/fWegXmmz/STPlwPrSng+vxnhZmEg9bkDJfx/DMY9xJOfM9JE77A61suacHeVw4MfoB220+vJcBpsLvp6fX1Zzf/z48dVCiShS0F5OEssiMW6NMjKnkvleonF/W5YKQt7NoqAUp0VpXKiQnt8MLRQ6p6Eb2Mhzbcy4P7c5Q9fFQiMT3mUFMJWF/KDBCGhIXeSJF92RD+Qx+cFrzpzQaWa1P9OvLWXeeEb+NjBDOppTSr1c4T3bhtScOvk+Azpel+A9wqQ3v9fmitkn7qc17wjwqBsEmZEXP8uxps4ZkLLPzAR4S6ONvgudczPQBCQEbOxb6sk9nuBnGrbbl/MWMhacwybt+ZBGxtVBFccl7VB+TSP7T/tsO23nHIAUWhv4SpuZGsg+5zY3Pwv83CfWu+850t72Nrsu6+gaLYeW37LP2Y64ETpzMDMk5rotoH7fgpJrEQQ6aCphBiUCxBWjjB4iwFFsDg6Nix1BUw4bLs6bzhxsihF83t9sdo8cZHGU7OimRQfk75oyeFyJwtcUpDmkgCK+S8NJp8nSMgWmz21auYyEU0/Gw1GfwcwMFFmBzfOmN3aUrC8y5Ag9TsjGtOnEbBx5nc7ZYK2NZd6lk7BOWpcPLXZ8dqozkJ51AcyazYyoZabp3qwYNBpoHJJKJhDldE7en9WX3wE71EOOf94lyEl7vB9w7S9DsV7b4tDOxazb7XZZ/OroPePJ+vnjT0bO+M3SImdez99r7zKwbO/6OdLy3x45pzQha9HePnTTnKxTqRF0CqTTYaaNzMu2qe12u5yj7VWaSTVfXV0twhSEGsTdTrWhQpEeR6oGCfmWNNFa+kiDaMRt3iX15ftG107VmF/hk+lmf2IAWGyI8i4XNVERUyd/s4TPvs+zhi1TVMaG/N138p3/2/iHh1y4E2OZdrkmwePD7Eo7IcwgLu3aYOY6F/gwWs483sXFxbi8vFze8Xi7zoyJARsX0BkkEIS6hEYXAqkZeGyF+ss24gA3m83Olj3r3cnJycKTbJe0w7JMmt4GslthZmGMsUR9lnUfpUpnyZS7+xQ59Vi5/dCcxazNVmaRFc8bSL2k+/b29tV6Hcqnx/Hs7Gx8/Phxh5YsqI19pU2PLcwnfPOpyMfHx52T5+wEG5iljaLMNh1LoT7b8VpOHbywzpYV+9VytMh5DQEfi1iWhnoOQSt0jAYQLYVDh8mIhOkaAo9D+mqgweg7dBBZ2jHPnLNp5T1G44569kXDoeGQ4nZbBGajPTPQjlisIPvoDdhy/c2h8/rMCBMYcvzSzowO0vMWXfC7lovQxEKn30DL2hy3eToDhrMIhqXpkstb+bFGN/XRPLIet36t0bZm22ay4owRHWPuNwdDWSJopFNlu41+AgMe2tJoX3NatlGHro9I/wIwSFem8WY2iM6RYIXXZ31mfa0c4h+aXX1rfWtj85byLudMQdnHnDVHusbsZtxmiGjNABihnZycLMiMWyju7u7GP/7xj3F/f7988Pzk5GRcX1+PMcb405/+tERRX79+rUiJ0b6VKdcy9znG2HH2eZ91WZFpeB3V+nrueV7bqLWNGfcvu2/sSxNg9rd9B9sIvtEd2sd42abV6mpofowX58KzeG2MiLLZF8pU+py1Bg1c2fHFoCaSTUYmhXNtbGPmMAxUKAd8n7LE85zzjq+RF9xOl9OyXFpadgagGnDYbl/OJTe4be/kvUYD9cU6kumolMy5Xl9f78w3mwfkr+8b5DHa5lwwdT73fQpga4NO1Zkd8jo6ZRvKcXdWK20QHJydnY2rq6sKoiPTa86pjbWBLmlK5iK2KHPJ/pgFedG2hlnfTacj2dDDzJ77TDtg35J2XLf7yvtrfDu0HHWf8yw6aBGJ743Rz2Fei3Bm75hGGlqmk7lnNu/e39+PP/74Yzw+Pi77mz9+/Dg+fvw4ttvt+PTp03h+/vkRi5xE1aLnDGbbAkClHeP1amiWpKnJA6auGx/cfzrzlkpsDpSrKdOHOFTT6FRjyizdaYNuw+SS1K0NEoGXIxI6Iip9o6k57RYRZw1C9qhmHClXrMfzvkwBGtjy5Lm1KLwBJepd6uBCrYyb5cD7ibnAKwa0yYsNL/noyJr2IACG3ylOP3J/1m8aagMw24U8z33ncc4B25RtgvMmG5QH0km70uwes3Nr7bUIm895q2XAfaaq8i6Pwkz9kbm0TZ5xMapT1mtln722Hcw0QhbRnp6e7jhk/ibYnU3RNXpakNbAC4MR6g713c55BphTyOe2APZXyz8lrT3G+GWCbYwPaau9O6OlGROWNtBMGzqVRsPcjDJReIxV5oTYXooPWKBxp+DuSzk5wm4IkIUGuUWrszJLUTuycXS7Twmt9KS7pa5Jywztsi3y1fyhAaajn0VUM0U2CGh9au/wb/bJdBxSl3WnAUo+y+L7h8yvcYyoDw3A8/eMD7zvzIHrY3vcdkf6OXYex1Yn+8J32pQDy5p8ul8zeeLYEWA0B0JZmfG00RwgHFmnw2wywfZsD/NsQACBKuthOy2Lt89Wzey8n7XzbrqwVuwLmg3ZV8dbytHO1jahjfDZu37OzGuRZ1NuI9rZfSplDLujCCLuMV6iL54qdH5+Prbb7c4n2bK4iwYhEUzSOIlUc+3u7m6cnb1845mFpwflJ5FH65Ovk4fslwWMDi705f8456Z47F+L3p2Cp9FMNEdDaYOS+nxWc3jDZ9KGx9gLncjLjAFXhpJWyhyP9GTd3irH/uZd8oco3fS0QkNLQ+m99R4jj4WdUd6h42K2IfU5wmWfaZRbHywnrIcymvGlHBA4RwdIb8s4WRayqJBnaHPFMftC/oVfKe1kObZp2TH9potjwUWalulZJBc+NCccBxveJMVPXrqP/J1poPv7+2XxbPjlTJ4BcM4jCK9PT0/H1dXV+Pz580KLgQUXft3d3S39pq5Zbnl/LcKdyQX5yWt8tgUTzZkTuFD3/p+JnMf4tUUvLmZWi2Lcpts/pI219GZKG7AIGIWsKRBpy2C1yINK0wrppAGksWr9ssGisW39Mg8c5QeY0KmmzMab7TTQMNtmshZ9u17u6aUjJe9mkaoBicGB+0W62NY+sMjnLGPNMYcPrV6PieXKbc/knNftNNk/18v71DcCrX2l7Yt2ZoWF+rE2BcRCnePCpLw724ucbECr2845To6gaxY9Uy9n9LZMT7s/AxB8j9k40mx6Gp3eyvQWR0ObyAAn6XPba/Ky7RBJv9Z05tDIuV13vWtB3VqxfTlGOZpzpkNaG8hDCZ8JqOuaMZrXHCkRQXKxTASJEcKPHz+WAzVOTk7Gp0+fxsXFxbi7uxtfv34dt7e34+bmZmc7S9qNwZrNfQal3t3djefn5+WQAPYpxogOdq0QUUaJaYSMQkMXIwmDC0dtNHxElS0i5xi4zdwLP/IMF6J5QYgNkJE/aeFWlhahOi3s+lkoN+wzUTIX/xgEEUDw3bYdjP1h+6zLEXrbPsMSOeCJUKGFkdQYuw7QURJlyU7eB0R4xTj7bvoJzGio7UDZd08Feb41z3jMI2vMLpiuRicjY/KVckuZNBDlNcoNI8DwYAZU2nUDxPTJGbe2dYsfm0gUm+1MkYsWcJie8D5Rc7ZLeXGa7QuzGyxrtpw6O7P/axHx7Pk13Sdvm62gfJHG95SjLAibDRifyfV9aGdmJImWW52zaLiVGACnluicY7SIsE9PT8fnz5/Hdrsdt7e3448//hgfPnwYX758WVLWFGY6581mszOvEiFNOmeMsZOKopEILeGLVwWT57P0Yp4lj+x0Y7xDd/ZZ+tm0k9/eu+rI1O/awXIs2YeWfrPge8wZ1dNozmR1FtH6fupOROIxoiNp+51b6tXpPcv+WvRK50IA2HhCvXHUnffpsGLIOcbRBRpy0ugFTHZyHJs1QMJ2OLXSMi+kd4yx6B/HnFsfzZuZk/fY5j3KL59Nn8f4uQI5C7UIyFpGK8UOq61SbnRbv1LYHu0bHWD4yykd7zVm/5szTGG6Ogu/8jtTgOmT7UvetaPkLpqmmzN+Nl2mz2h+Zd87rJ8AvTnnffbkLeUokfNs0FhsmJqRzHUqYOreV39rr9Fl49LmB6i0nN9iHVzk0CIlD1LrX2jJd6KZipoZWypl42O7RhrWAEwzeE7lhZ4Yc6J8l1lK1X97nEjHW4W80W762X7ablFtM6Q03I6wWkr8UNqbQpM+1+exJJ2UxZZGnY0XHYLTpTSUcaKmf1/9M1r3pXP9jqeSWmE03sZxZlzH2F0o1VLVGRenmvPbDrb1Y8ajQ/hoW7IGWFvWK39zpTT/nunoGp2xlfwUJ9fn8NnQ64VgptsgcsaLQwK+fcFgaOPzM/43H/U7ylH2Ob/FMTuyakaGDmImrBTQGfObUsUZpp0slkj0Gvqy9eLq6mo5KWyMl0PaLy4ulkUOl5eX4+LiYqmX0UnoyPX0Of/zrOfsOcx2DxtJztVZyWkwWircSmr+RGnbGcf8CERKtkLQ8NmhzgSYY27g4npMH58jaGpRT0rb08pIjye9RQ6yoCWGI3xwu6w7MmIwZAdrIBJ+2hAx1clUf67ndz7bSL0hiGvgk3LIemksXVLPw8PDq48t0Cl58R6jy5lzoiFu4CrvEwhT98mTvBu++XOi5IMj2/QzjtX7hclP8pl88JGzLT3PwnHy1AnbM9ilcx5jvHKsuWagEnoeHh6WKbksykr/83vWJp+Lrcyi1pzClhPCAlTy7Onp6bi7uxv39/fj6elpoYE0Z1Eazyhg2eds1zIlLWhYc8T+7TYaPW8NJls5+vec1yI5R0rsaEODrb5m6Nsza23vi5zzHpEgU39R2vPz8+VIT8532cCuzSHFIOYnEbSBCPnC+poQkg5HJY568zyNgfnQrqc+rlRu0X5L57Z6XA4Rbjtz9p0lzrQZ4byb8bWTSTSRdpoDZDtjjJ136Kg9ZpZJOxgaZoI1P0cetMifhi/60+SLssIoy88n4mFd4ZnpIG+sA7zW5M58nYGbBiRSN/ePt0JanK6f0Ub+8D5labvd7oDp6Nw+mznTkVlK3UFMey7yk3veKpX09uPj4844zoIe8zKAhD9ZtW1ARNnPt64DCFkvo2mDoQbo2zXy0zTP/EYLBtmu9Wf2zsyuvaUcdZ/zGD09vU/g8jwZ3BjAH9dFGvy3n41hyeAnnUxEeXZ2Nu7u7hYF4GrDk5OfG+uvr6/HZrMZnz59qnPOY4ylPveB0cvz88+FGbe3t4shbYdSNDDTDDT/9zYnO7GmhB63NdS/T8BpxCzspIP3+Xx+uJCJc5F+h46BEYfntrMOICVjwrUHY7xkK8wf9sE8NlBjsYwnc5IzjGPoDFBDD9N9nAPOqVuRG2ZIWvuRfQMcAtAxxs5zAV/ZHkP+cy6ZERrlwEaONJleOhRHpn6v2QX2gaCrFT5LWilTzKjlmqc4XCcBH2n2eBAIef419MyiZ2ccmjMMz5PtiK1iWpl84I/tDfkTOc8hIwlYctjIxcXFDuhhewEDXGPQ6HeZOdvZtRnfPW4zIOJ7jR+pb2ZHf7UcfUGYkf7s2X1ow06eiminPUPUM8GiQfQeXhq6m5ubMcZPwxNBCz2Xl5fjL3/5yzg/Px9//vOfl4UUNtRcEU7aaAyTjvr69euOwOd3aGi8biuEieg3m900TotqzCNfn6FS1pn+WNjZPlOza8iSbXiLVPi83b4svOICrfwd3rT+p/6swqdjJL8D4PiFHyL4OKyk7ZgtmEVrbC/pu8jk8/PzspiG6x04PgSUkbMYPD5rUMdxyfuOOL2HNbQmNZpUf2hMXz12s0xMeNl44j5wvPyTkrq5uJM6yvpnDjp1Uh7zf3O4Y7xMXxhA8VpsYKbNHGVTPtku79keGogY1LT+cdyzK+T/b+/PehtNkixh2EiJ4ipqi8iIjMyqrOyavhmgezCX8///wQAzwDQGKHTXVOUSEdq5aCO/i8BxHZ445s8jiVmd7wcZQJB8Fndzc3fb3Nx8sViU5Ru+z3xJeTIr6uArHPgFtzZHavf7/djZecwGFvGoGNzc3JQDLSCcdZ418QmmTdN1LksVeaV3zajMXOKgn8qtl8CLLeeXIgCodYIT5E316js8yJz1wJasC5JAp+iWHExAuHF0gDE+TZodcIGbiYPNMo0twgfNODrw886NyNYKl/fUPq7hWANlzoyLWnVsoTXh56xzVeQYdwj0bJ8o198EypTxfrfb/YohqDafKZv6rFqVWb9llpGbGwpcD1s4T6G9vuO8Klwf49ymn7Pna+M4KxPX1XquzTV9X/sF8ysizw7G72b11cYC01afZ6+LekhcWTWeo3OQg8CQ9CWbYwD2BHB9ei+jbxNomQ7/puuZhZwJ8JoR81R4kXDWLTQR9ei3DPQdHWhNDa51gD7HH2hwu7u7MZlMotPpxHK5jMViEavVl/zZcDVhwI1Go40Ibly7vb2NxWJRXNzX19d2rZJd2douKAKLxWLDRQlaK8MA3YGL9oVz5+JbhTC7n5QR8m/W1Fl7z/pB956iDLZm+BqWGFgIsDKFMpmuTuFigNVXm+wQlljywNq/PoNvBBnVxrZjMCzY2K3c7/cjIr5yaXNZqBuKA7vBOYIZ9EM9SjfgwW3GfOD7eAeWPA7D0HzhNcB40q1ZaD/6S/dpox7g4OrjsagKg77jLHx81xgpjzHdseGEPvoD+PF1vAeLmecPK/X8Dlusnc7j8g7jom3jPPTAHwo/exgYWAlgr5ELMsNzMEr29vbKgRb7+/vlcCDuXy6b6YrxykoxPErs2WwDzE/V46SWs5NNKndwTetQK1nv4fdLBfRW0nfWtO0mzaWN64KJ91wLm+/xQEcQFtyIi8WiRO3e3t7Gzs7OV4NErQicXoW1loivXWNOOCuuwAv1g2HygFbByW5CuFfbWNl6DeU6Yc7MBkzK7bXWrSM8Abk8lMXfYFosfJR2+K3tYhetq4vpV9sjrsqbm6QqADPmldXhcATTauMJwLOcIAJKYkR8JdBQH68bswWnbda6dGsM48qMnNuaBWjpsgYrXKwgOqHK9WV16NgDbhqtr7TPIHOR14AVZfSPCljg7/oHwO0EHo5v8NxAHaxo4xkWlE5wqFGkc0IVJxasPD5wuAgMHzVQHM5OAPLSjXsmE5p6T8cm18/LBMxXav3txotTABwPeSpsJSAME0G1hpqwVHANUSFT03Kd9e2u8+DCfbbiIGz5RJsmAvO6MvJtay5exYetJx0A9/f3ZX0IuW1huavg4vZrhiaACmTHYJxgcgJK/3NZbkCqhQxA+5lxscCABZtZe8qoOJBGNWgVAKhPmSOEDwsvpaO6JTNFy/1X+rDgUMuX38nGsxOqbu456523hak3h4Um19MW2rjX8a39wUqtm6+63xr1YfygPM0HrrRrwttZ+bhXY7oqZLm9vPeZx7/zWKilx8oVP6vjCx6VXq9XrGTmcSqQuC+ckspls9KkbuzRaFQ8LLoOzkuEi8UiHh4eSqIT7he339rxE/7O+i8T6njXeS51jmn7me6ZMpvJsOfAiwPCVBPiRjshoc9pIx2BuJ5M6DM+ELLq9tIJj3egUcJK5aCcTNvjOu/u7orLCHtm2SXttFDOGqbW1+3tbdzc3JQoSATLcAS3lokyGE8wAaYR00TbwdeZ/lweu9+ce07HBJgK8I94ZFIKKIvbovcVZ7jBUTZHn6IvXN/pOFNLktfmAGzZujGeuXhBB20zjwOUyxYmmBXPG61bmS1A+1KZ8WAw2KAhaAdhAc8NysoUEMc4gSusKsbbCWwVOloeAwsyZq5qJXNgoJbpGDYLTFWQcE29PRmOminNlcOKiFqIbNXxtVqmO+AFJQXCWTOAuSUflIc+4mUnx8PBGxH8NRqNYjqdFqNGcYSb+ubmJq6vr8s+ed26BaOEBbRT4FhuZAZQTSiqAqWygd9XgyVTtt24folgjohojmhpgCYNxU0Oh3Tba9sEFkg6EbTT3YddbxhkTWtdmbalnQpmzmuC7v227czAWQvufffhe7X1XIeHq8tpnhmujLP2S1a+EzDabrWaMjezw9X1ZRPopM7e1THJUGNGqsiq2xXjll31Sleus+aOVPo/hx5aB5eVtSuD5/KPjJdFNGe9w/8mAcF16RKQG988FpW/6LhQRc4JWdx3dWag1rN+1BvGdIDiwJH1jl7senf41v5nOLcdB8pjXb86nJ46vtvC1tJ31pgzD2y1WFS4O+1Fy+LrjqBtFAVYCFhXxj0wKE7gDo0QGmO32y3WNYIgMKiw/WowGGwIVxa2bpLpGgvohe0GWNdm1zboxVofA1tqNavA9RNbVEw/9LOe8wpceKLzUkFWDq5x4gN+Xt/nccLKEEAT7TMtM3D3YHUrM8F2IHcfdNA9xExb/q0Wr7pQmQY8TnhpAzTDHvvMpQyr0gUrAVixZNqwNaZr2Yw/ewEAbPnzNkO0n8cIv8eWqrYhYjNDHVvqXC8LE7zP/EfnjOsvtpYxH9Sbgm8Whky/rA7c63a7xZOBd92+dscTmUYYm8j0tVo9HqzDc5T5DraI8nYnPKeGC9qJJTb+gB+inIeHh+JNhJXsdr5w2xAExkKc5z+3v6asZb+dgtlG1ui4UVrid5MMeips/VSqiK/XIvl+TYtpcglwo7PylcGzsFf8dH0D9zjIgddXXERsRMRwOIzlclnOZX54eCjZw9g94yx11oh1ImPAgvnyvl0WthFfKz4oB656HmQqnJkRARdeD9JsV1gCYMbBjFOFjqvHKWeZZeggs5B5wrBQBY4q6LPxyH2Pfe/oA74HYMHJ7W3DRNxk53LZ1Q3g9vC4wthwSo7i7LxGUP5YCQM9nDKE911ENfc3aMZ1Mt5qkWu/qCXP7XUeKj1BShUJxg918vMqnNmi0+BDfl+Fs/YPxzVwjgK8D8GK8lU4u6URdv8qH4BLmZV4Hgu9Xu8r97qzonWM8DZPzqHN21B5D7/uq1cFjo0Xxx8UHG6ZzODnVdDqcw6gQOE381LHT9p4IdrA1iznl9x30LZhT2HkSlQIGWa6eO7+/j5ms1nc3t5ubBXgNS0I68lkEhFfJvL19XXs7OyUd9GZGpSkjJVxYyGF925vb4vwZxxqDCfiMTCG6+NgC64/s4wdMNPjuthVim8Vus6Ccta09p9jwq6PXdk6Trhtri4wfdTrLBrQ0q1F4n3QQS0cpj+PCbUUlMaZt8K9D3DeC77naKkBdhqkhueYEXE9oFnNkgdjV2XX4YN62Xpl2ul4BP4637g8bo8qrcq4VUgCB2eVZ54prsspH6xY6dzjMajAnj9HN9Sr4xyCs9vtbghOHptMm07n0aMITx7yaKuihVgQAKcodhY6e6CYH9UU6Kydek/7solnOn6gMkSfrRmkz4WturVZ8GVaiWowPGjcc64ufqY24RzwZEbUoEY2RkTJYAMc4QLCRIDGubu7G2/evImjo6Po9/txc3OzkYHn6uqqDEgMct6KovUyk4XlfHd3V96BhsonYvF7DJoIIOIxyIgjzHXCRGy68TQIja05ZazKrFg4K2CbGvqLLRIVUOy1ADjGqG5mHpsAZ1FkNNTrKBf964DHnrafLQt9hq1jFSgol70WTGMXnQzgeemsF7UKYOlGRMkEhnJYoYEQV3e4c1NzOzCHdBsgt5EtQbzPS0QqnN0YZYVX6ao05bGlHhemO65xO1Spdoqheiwcf2IceE5yuaAJynbeJl3aUZc4W+/L5fIrK5CFJIDzPAyHw+j3+3F4eFh+qyLB43E+n8fNzU1xsT88PBT+xbxIo7S5fxww71chyWUoL8lkjtbHc1DHlXtXy/ldCOeI7UapaXlN5T5Xa2FXinYmCye4p3WNJOIxepHzyvLgU6bh2sXCI7sPPDudzgYurMUDn4wuEEqqpTIjUMsQtFDLDIyEXaWMe609GbNRyPpQmbajVwY6sZxV7ep3GjSDK8MJAfzmetuM1Zpl5LwYWRlN/cEM37XDvauC2JXtcG/Cl9/ncZ6NFx1/ijcL5kxg19qK+hlq44fx1r5W1y1b4bim72blO8NEBVQNNwDjqGMf85yX+JCERBVXx4uVz6qXrC3OWdvbWNhN7a9Bm/K3IfcYtpJb21k6DJnbxQkULrs28N1AqjFT1Qxh/cCdjcAw3kQPq3e5XMb19XWsVquSP5tduCh3PB7H27dvY7FYlCCM9fpLzmwIeHWBgtmwtYxymQaoG4L5/v6+uJigmStD0rUq/WYBp/3nrHJYSgDV2jHpXDSw619dD3Tbq7R84OaYJMpCNjB8UP56vd7oA3aBqpIAhYWtUqarS5CAbw5uw371m5ubWC6XERHF68K0R85hfp9B6a5zhi1eFWh8X+mm5aigB34qLFhB0/5VywP1Z/NUGTL6QscVZ7liz00Nfy2bAzIZB/3PbnmMVaa7KguMdw1cHziFiAU280H1CHG9+Nzc3JRjGNFelIn56BRqHiPMG8AXkWQJvAGubS6D42zwvVwuy/h2MoN3pDhvUaa4Nym3KlB1TmX3nTeiqV/xvI7Zl8DWLGfVvh0hIr7WHNltkLkd2mimTVqLMpn1+vHgC5zFvFp9iYoejUZxc3MTl5eXhSEgKIiFrLqzcBhGv9+P09PTWCwWcXt7W85njoiNSEp865IAJgRr0+v1uigQ2EvIwoz3VQN4kmDw86BBH2ANCdfA+HifcER8JajUGlSXp/a9m0zAGUwEApSf5/pUoKNenvT4RjSvuilVeIDJM5Pg9oA2bi81t1G9FpxsYT6fx3w+j52dnZhMJl8JYIwLZsbAA0KBmQePPV42wbMs3LSP1E3PZWlfsaLIcROakYz7CMDuccdkM+bHbWC82aXPp8RpvdwWxlvXVR1fYQWEk4ZACWH+xd8Ya3zNGS06FvU5xYmVXZ27ACiKTCOMOx4HrCy7oFB24au3DAdc4PCTbrdb/vO74JdYNkLUNsffcD+hLVluA26rE9rZ0kl27anPNCkA+h4+TPfnwtajtZ1QdQ3LGDZPjozJ1yzlGn6ZBoYBjcHE7j0ElmCLFALEmFHhGxvzV6tVDAaDEsWNAYwBqu85Swf39L5qt2xJa4IGTTqgVixw4EA3AASWWlAKLAh0DVEFVkb/rEy+n2mhbuKy1aOTJPPSsIBnumdeH8VRhQAYDkfZaypMxpuZjdJN+0EFoLoIVUlyDEwZNkM2l3CdvUbZMyib56q6bhnQfig1fN0l0MBYzoSa8qHMOuX3szJc+1hwq2Ljxjz3G9OlBkwPtaBVEVitVsU6ZQOixvPcNTaYeKyCb/R6vRgOhxvLdoone6EUB34ec4Q9fG2EZ3YtoyE/7+qoGYD6vl5XOafz8yWw9X3O6iplS5Wv82Tgxqg1DeBrHFDjJpeWh29mVowb0spFRIxGozKwEaF9f38f5+fncXt7G9PpNDqdTtmXyPUir3av14ujo6OCw/n5eezs7JRgCGa2qhWrkHD7HZUpQDCzy4sHDfYhQkBwUBkHqDGtuc/aDDYIMudmBGPhsVFzZaN/2HXMwWlsbTPOXB+PC42Ud2t7+gwfbOFwhGsdljbTHvs61+vN4yhxaMpwOEwtTm2HWtisoLl0rYiSZWHI64KZ9ZoJW7ag2Fp2feBc4VwOl6d1sMtTnwE9GX8ozqp8MI1UeVee5O452jjBBZqo5wH/ddzw7gW854Sks4wdTowraDefzzfOBnBtRNmq6Di+s7e3F8PhcINXDIfDOD4+Lh5GGCPwHLAVz0FeqE8D1VarVcmwyDg6YZrRpCZc3TM1OcH/VWHm98GHGXg8tMGpCbZmOUd4bQL/nYaRvQ/QCd62sfqeE1har9vzjEkPFw00UljYTungQAn+dtZGTRtzz3A7lMGwa0jdZWg370/c2Xk8ozrCH+enlqGjM9fDlmZN42zSUvU9tQxfArouqJYaT8Td3d3qqTjqfuVtPtwXaDMrQrpth3FiRo7lDcb3qaCCw7UDeDQxFl57zcaEKspZn6ni7JZIIjZ3DnD/OEHFZdfmmuLsnsmszrbl8vOOpk5h0XajjqzfWGF3vMnhqu1yQo95GcezQGjrdea5TqmBe54VbzY4lOc62mZ1/COgNtYAvyvLuS0SKmCayuFG6gQGZO7JrH6dINzJq9XjARMY3KiDByms1Lu7u1gsFnF1dbVhmaiGB+Y6GAxivV6X/dAc2OLaBJy0XRjksMY6nU5ZFwazZItCM/iw5s5CgrV8dacqHZ11pExEmQlbHNyXqsw5hs6CBcD1ZeuA/EFfqGvU0Z2VjIiv11SBHwti4I3yeU2atyRxIhvUl+Gg9TjasIXM17gdeIeZeFY315/tO9b28fok+lE9FzpWNIGNLsk4l+16vd4QDBysqG5/pSf3G4NTJPgeKw289TBzzTON+Fvb5JRwlzMceDivFsYatm4icBVWtNbX6/ViPB4X6xYfXmrTevr9fkwmkxJ/sru7G+PxuFjTEVE8RMCHjRhcAy9i3qSxMGg3z6eagZXJhcygc/3cRn65d1RBaJJvz4WtCec2Gn0NcWdtZ8R0zF/vO7wyHDHI1+vNE2LA/CBkO53Hc1EXi0VJOMJBEtgzjUmFQIqIKHtjsedPNVxmiE5hwbcmTQGgvsFgsBFhye5Rp4Wz5ahWNz/PdFCGq9YOymBrCH2gVjm+s/7JLBrFQ+vjIBMVViqc2LuAsjnqHLRhTV/HG+9hVqbkIuu1fYyXKiRuucAxMlc+8NX91a6ciPhKgdA6nFeFceckOdpOFs6YAyycOXKeBT/w6Pf7G+5sfDsFQ3+zF0Npz8Dv8tjWwDIeUzrutM9AI1ZsXKAX05LpyQoPGxGLxSIuLy83PH9MD1Zs4IbGMgE+mDtKNxxqsbu7W1IHj0ajEuDK50Oz8gfhrEoqhDPA8UBn8SuobOCxX1PAuHwuy42DjA/zGMqWU7YFWzsy0v1/ivBmwjrCZ/AULahWBruFEJnNiS/cRNO1MWiY/Dwidm9vb8sziKzltnK5PJhqFgAmg1p6ml5PB5LSWS0d/WZcnHBX/ABs4eKbB3bmZuU62VrR8pwr0OHE3yr0mB74rcJSrahsrCkj4LGj/eCEBCtB2mZtEwPGQWbl4xlVoCB8UBeXx0JRBboGYjnlSRkhW8mw1nQZCe+6Njvaat3KbzIGy3TMxrOzyDj+owbONevmsyqI2keqrOE37wLgIFZVeCMeFS32/rGl6wBb/Zj/qWKu7WNrnAU0K7OMIyuyTkGvWcMMjo/WynoqZO2tgSpXz4UXCWdXuXP51AQRX3cTwpVTm8C4X8NX8YAGent7G1dXVyXrDY5BQ3m8LYStJ0y6fr8fBwcHcXd3V967vb2N8/PzWC6XMRgMYjQaRcSjm1UtYtZieYLwYOPkKMgehuC1fr9fAo94H2imaYLpcJ2YDOwB0MHGk5v7PAvwcoJYt3MoA8Y6Fbvy1QuA8qHFc1magtKt7TnNm9vPgtUxGy7L4aX5h7nOjLa61KA0wtjhdkHwARQ3fk6tO051iToQ4AN66bxGW9XSjXjcFsc0hUBeLpcxm8028FOPEQv0iCixG7xmzwopnuX+RzY/brOjpSp6XBZ7Y7AswR4ZprnOGwXg6pZhuD8gbDG3u91uUfgBCGCFIQGvH48hzDd49WD5gibI8aCKVK/Xi8lksrGHGeOUz7kH/dm9Dtc6jBG42RV3tBM4uG1UTRa08jD85nuuTPe+A+ZD/E6TceLwei5sNSAM4BBlaGMNq7bUpqE6wWvaFJfPE473NEfEV0KLy4fFgnIwgCM2E1E4tx2Xx2WyUNCO5knEzBblcXIAt5VJacMTj59XK1U9B06zB7AFwLhze/Cu9ltmXaiAUOsJ0eu1MdF0TV3Erm0sAJyVwoK/ZjWz21tBvRjOna3XeRwwPm7pQD+ML7cfyhErSowDKxfOwse7LKzYquKy2ep3SyOqxCjj1HHVht/wM847pThoe5Qm2mdKLy5beRD3HT7w5DnPHdaKdcuU1oO6OdYBdfKWUaULYlTYE5NlO9Qxze51TfijijLj4Ay1rO8c1IQ4K91PKYt5Lv43jaU219vCVixn1RJqbkOHcBsh/BTXhGpSWR1OM7q9vS3ZwI6Ojsp7sGqvr6+j2/2SL/bi4iIGg0F8//330ev14u7uLq6urmK1+rIN6+TkJO7u7uLbb78t682Y1JPJJPb29oqmqfgz48Lg5UhoFhK4B+3eMTQAr6kqw4vw2alQjwqGTAnTvcHM0PBf61Bmy4oIr2fx2NL9l8jVqwLUWU+aTEUBuKhAZkaifQWmx3s51R2J+pyyx7QCqKWvDE0taFheaskpA2TXuXowuK/QHr2Pcpg+GfOKeEzcwmuRPI50yQJCghUAFjK8pY7bxVa7lumUXZSt/apt1qA1tuoZWFF3fEifQ1/wzgAoKy4zHdOQhZ8q0axUqXWPY2j5XdATKYj5gAsEhmH9Gcty2ter1ZfA2vl8vtEPnOQHdUNBYyVNBSG3g8dxBo6nOE8uP+vKyyzvDJwXNyv7KbAV4ewYQQbOen2K8HbPOe2vTTmujJubm7i6uioTHffW6/VXZ6NGfEnZeXx8HNPptGTGiYgSjAWBvlgsYjablff39/fLAGXBjW/VSMGQmCFHPAoM1nbVambrjZka7mfChtenarTlABm9B6Gq7zeNERXMWg7wR33wWLB7m92tyozUGkG9WgdfYxyAH/dLxNeubVyHJcFzRumkjAkMOhPGistqtSpeHwiziM1IYBUYztoCvsDBMWHMDxf05SxptgZ1jZQBtOMMdby8w4I58yo4Js10UsEMVy/PNZTHQtplj1MPhqMF46Br7BGbB5+A3jc3N0XZ5HVlF0THdWl/Yuzw2GU+hXdBb3wgoPv9fgwGgzg4OIjJZFJc6ax8ch/DuAHteOsmxifWyt2YZ+WGxwPXwXhnir7SP7POtV41KhgHrSez7n8XwhmgBOIJ0EbTUaI99bmMGFmnqquDiYzJgIhqMHvH1HhrB08MCMFOpxOj0SgODg42oqa73W4R0plLTQeOU3z4OlvYmcanGiH/zwZqrf/AhNWd6p6L8Nm8sjYzOMbjBCu+nWt0vV5vCNE2GrED3r6kzMlZSvjNdaubXtvKwFa/rlFyf+M641brv8wFqu1w7m5+Rl3iWflOmXAMj5VItpadIHC0q9GU3fEMSl9+h4EVJTdfeWxEeC+UU1xYocE3xwFwshkNotJysnHNdTpBBc8bL4lBYHNCHi2T18ddWlvGm3kVK5yZYHR11viakwMZT9F3HfBYd3O76f2XwlbTdzpm0KYxSgQVMpkw1vLYRZLV5QYClwc382q1is+fP8fNzU05DGO9ftwKsb+/X45Mm06nRfjig0E9HA7j4OAgbm9v49dff42zs7Oy9WE+nxctU7Vut4bHbeh0OkUTBV6ICEe6UG03rB3n7nO0dlYG9xUrFw5nMCznTXFWu1MsQBsoQVoP6AYGpuv7KAOMVfFjxsF1cjs5mAfvMbNhZuxo58rEMxqNzgwdLkm11lA/CxU8g0xabPFqv7LXgPuPAwiBh2ufeh0gVFS483q0urRBH92aB+GA7Ts8lzAOud9YIDMObmnAMWmXZIbLRDlw86pLli1QVX5RFvNFdZfjebbE2fPB4xnBX+p14OUmVRAYJ5TD9MdnMBjEdDqN3d3dksFuMpnEdDotOIPXwEU9m83K0brX19fFItZxjrmCw194qxXTQHkPj99MqebnHF/LDAKVV5li7QxDpzQ0KQHPga0mIcka7EAHL37XBGtmVfHzT7WM9HkwQwwkBFixkOp0vuy3HI/HJUqaXWTMUOBuhhUOtx6u1fYfM45OmWBNFINdI5SdIqNaqQpF7RPdOpPR11nIXK4qIYxHZuWhfl771HK5Hc6yYcsLz2oQmXNTKr1RFru5ua2ZEsljg6/z2h2Prcwdr0Ja15K5HbzmxwKB28fKhiphTohoG5vmWDafdb5z2zl2giPdnaeE6ebwU6teLe8az1DFFa500FzX9VlAOOWyRitnbbKiofU6d7DyEDe/3XjBs7wshg/c28ADAWo8fiGcOfJa26HbrLJgtCZ+pX2jz2ib3LsKyve0jEwxUB70W8BWo7V1oAAyweIsCX1ey9fJkFnAWWdlOCtge1XEF0t0MplsWErL5TIuLy+LAJ/NZkVgI5CFtWsIztFoFMvlMvr9fjmxCkdUQit2dHLaPH6zcEOd7MJzVgSDatlKGwiymiap72T9o249ZrJOu8U3v5dZ3WDuABWG+O2YMlvZShcAW2jAyQkxrtO5ZNmCUSuI+xNrg6q8sIKButAuvF9z4bIXRIWAE6a1MaPeDB0jytggfCHwWBnodDobGcB4uUj7UscqC2hVYFiINjFqfDsvC7eZLVS+55QnHiOOPk1CCtHZOhZUqeP6Wfhl/Je3WMGYAK/b29sr2cCY56oHBPjxKVjoD4wNWPyaFVH5tROOTkDXBCqusYelpnw5vqN94XB1zznF6CWw9a1UDrGMAXPDVcvDdVxjhuLWK7IOYbz4fhPc3385z/nm5iZGo9HGhINLGgIXJ1UdHh5GxGNwEltY6/W6pL+Dq/zu7i6Gw2GMRqNiVauLkF2KsIp5EkCbZo2U6agTFP2g2j5PPoBGwzKotQLB6Bg59wELfG4fC1ZlQK5OLoMB62ZZP6tiwO2EkAaNa/jjN/pLywa+qjAw83RMlLca8RnPzroCwKrD+/iGkuiYDwQexpUKDd4HrXMaY00VABZejiawALHswkoBcEBAEie9cN4l0C5jzmiH9pF7zs0HfYaVHk7mwfTivoVCy0ttPE9YsGuAJ9eNscgCkRUCVmy0fqaTPge6IwIb2cOQ3wGn6g2Hw4h4TBqiW7hwj/c0g9dyQCIiuDU/AsuGDJxy4f47g6GmoKjS7OplBUjL0Xp5TG9DQP8m+5wV2mgpOqGcgG9blxPMTy0j4tHt6PY+RzyuCcLiXS6XsVwuY7ValbUzLg/rnnt7ezEYDIp7G8JE13UzJYfbyQxFT6ZytHPWgbpOlY6ZYGBrxAkrbnv2O7NosrHC11xwjT6X9bmblLW1SX0edTOTdu3WSe0Yvyqn7JpXnFxfKoNyn4yWKvRwXet96hxS0HVRVkJ5OYjnAFuFTjHKxkwbHJXm+s1KcZsyuH60V+cG08EJDQcscB1fq80THSs6TqEUcVZDeDY48YnjM2wQZDTgrWFNgWzZWK4J7aytCipT2pTpynhqvS+FrZ/nHPE4ud0apOtwfg/3OLIT4AQIgIWaTmS844ID9De/u1gs4ubmJs7Pz8uazNHRUfT7/Q1heHZ2VoTz/f199Pv9ePv2bbFekOt6NBoVTfX29jZms1lp13w+L1uqEFjB7eL1HB1c6/V6Y4vXaDQqtHYCHpME6+uZ21vd2SrQMy2WGWoWscp7gTNwmq2OGw2AUbeZ4s0MistWQQ/cXftYcLhcyVwveyQcnWGpI9iGlSs9NazT6Wz0GR8awFYa32ehqNab5mzWPlJgYaP9inHF25y4r2AZ495qtSqWFe/p5d0PzEd4jz/w0Bzb3D8cHa8ud36W6ej2ofPaMt5hIdPGo+EEK6xbvcfjBpYq4l+AJ7ePt0aydQ/681GbmM/IgIggMHgrEF+DPc1wV/O4XC6XJfgLnkV4EHkJ5+HhoeSLmM1mZYkQY8u1myHj4arU6DP8nH6Yvvqe46n8vFu2Udycsv0S2Hr6Tndftcja88xk1AVZEwj8mzsAdbu1juxdHdiLxSLm83kZ+Eg4EvHo0gFjQqDX/v5+2acJwARYr9dxcHAQvV4vTk9PYzgcFma1s7NTNFMWToy/Y4yw8nHIhjIyZkagB69h1UDTlkZsZvzRtWC1nrO1Xyc8s37htrIwdONJ72ftU7zAjBmy8crMH5AJexbQuMcC3q1v4jlYMOwiZLdlZh2zEsZR1jrOdYy5tuBezcJRRovAORYWrn348PoyLwcok1bFS38zDjVw+Gs/qILG72RCWQWBzjsGnoNaL89NuLRdvgAeS2od65hAH3a7X1INw4OHADwoPBDcOO2KBeJqtdpIG7pcLkt9qsDA/Y3lGaWnG0tOkXc8vAlUKHN9GThBmykDTWXg90uE9G+y5gzghikjZ9ddTWvRMmvEcVais5bdf35XB8HNzU1cXl7G/f19zGazr6wZnmSz2awIbGxJ4I34nc6X7SIQ3sfHx7FYLKLf78fFxUWpHxqr61xmlCxoO51OqRvbPtT6YysOJ9IoU1ba6JYHpV8W8OJoiTqyLSX6n9uZTTYVDMooXMAMKxdu7VqFJv9XV7YGfbklBracuVxet1OlAgKLhatGrPMaI7uH2TLmNWB2q0KhdMoBb/FyoLTTNU318jghx0Jb15czgc5luPtcNujr5gBvvXOKKiuYUJxViXGWnDMC9L56UbRdjKM7IMLVp7jw86AVxsZoNIrxeFx2oXB5eBc8BktxCOZCuU6IMrBwZkWSDaw2hhaPJeYHTg60kRdtBGamAPI9/c0Kknv2OfCbWs46aNQSzrRqJY4OcmYwTgFQTS9ikxE44jm3Bb4Xi0Usl8sYj8cxnU6LhToajTaY1Gq1iouLi9jb24ujo6PiPuJ9mhFRtFXemzwej+Ps7Cw6nc6GK8p1sJuMvAUMGi/qVfpjwLs9tKBVRie+jknPW80QDId3OIEC6mBFhV2g2j4V8srQnFBh7ZzHETwWLJgx6TkQTxm5GzdctlqjKA/9qnhrveyqBD3hceG93SzktD4eLwDdisSWbMTmVjJdaoJyweUosELDa5W4hzaxoqR7r1VpQlnqrmZPQVuGyfW6cQO6Q3g4IcB9xzSCUgtauXnKY4XbyQFR+qwuKWGLEuOobYWVre1TJYH3L0+n0zg4OLAKKxQB9H+/3y8Kv2YWc7ijXsTiIKMZ44b33HxSwcp9kMkDHmt6LRsjKlj1GYejexfPcd/pEtpz4TcNCMsEi/vOnt9Gnc8tAx3D1g1SeDKjYbcLGAmexcRSFx0YGs5MXS6XJRBDn23C1VlRmJBuTy7+K4Ph9jq3Jg9+/ubIXccoHM6uvLZuIOeGxfttgOvR+pgO6kXI8MvqzdrCypVTRFlpcdYArHh8O4HlPAdgvjX8mmjo7mcuW91fzVYTu69VYLfFhZ/j/tS5o/3G494JV9DWCQFV/J03hNvMOCl93LO4BwGX4cjAPIBx5fZjTLEXD3XxkppGkiutoFxoX6qly2UAB9x34Pp6G7zclb/NcjPYRh1bSULSdhIx1LSapjp5YDgtpuZ2UmYAyNx3DHd3d3F6ehqz2SxOTk6+SnWHsrvdbiwWixIoNhgMyvoO1oRR32QyiYgvFtGbN29iZ2cnbm5uyrF6mKDKbNBOtBEaLyLLWfhB883ayn0BK8kFmjDwBOSy8S4HAIHpol7g6u4BF+03Z2UxoD62TJhmqjQw43FaL9MqY/TOE4Rn2HpXjwW+4YFR+sNyVsszIr5yN0MpQpm6XFRbcgBOCBxjgcnla3CVCiY8h3f0cBXUo5aPW0JRJsrjg+tFPzCdVDhlVha7lvkaaAtXPy8nsOKpQkuBlVamj6M9t5NxWywWcXV19dW2JZ4D/FuFodJUt0tBcYcHYX9/P46OjkqMA1zZqB+ZvfTIT4xf4IHzAzjhEtrPbebffM/R01n4zC9U+cks8kxO8bjU+Rrh+R+PbR6TXM5LBfQ/ZCuVgyaXQTaQ9TkuL3ter7tOqjF+nmxXV1flbGZ1/XHZNzc3ZVJcXV2VQaxBTHt7e+XQDN4Dvbe399V6jVpFTvhgrQe/4TpFNDdwitg8Y5aZIK9ZuknPjIpdhBAOvD2D3aIoG+VxJCzjA8HlLEvt+/X6MWJVJxfKdmvK7CpFWzWJAtfRRgFlxsJ9hTaztbtarTbOx0WdoAikPQQAAHAdSURBVJ0CC3sE16hCo65r54HJPCLM9BhnLoMVGR0TGAcahe++1VXPvzOmxmv+LIwYZ+CRMWwdN6xosGB0ChnKxrs1XLWeTDCoEsE48HnIbp3XCSA2XJRXIFiVT65jJWV3dzf29/c3ouk5uQjyZ7Pyj+huAOYP4819xEqI49+qkGtb1TDTb0f/rA6+pkLVlVMz6JyHYBuwtYMv2j7jCOomk4IjnCurSWjzM22ZrhOC8/k8Tk9PS3DXYDCIiChWz8PDQ3FVX19fx93dXdFa2WXJh6FPp9NYrVZxdXUV4/G4aK4apcm/XaYirIlhzTliM+0kW0paXhaEo8+xMsJMLSIKDnwPQtr1Gd5VTVqv4z0eC+xWZCGnbdAyeH0Z/zn/dpOmjXr4eVYu1PXuhBNwUGZaY1z832n0GFu8ru/K1j4DnTX5irYFSpU+x+vSrIQqrZhJM134Xf6w54DLYoWsJrRUkNaUdp5DTBe0Hc/xt/7m/4ybvo+ylQdB8PHJTYyHttO1nz/gAcgEBj4AbxcygE0mk5JwhL1wWDeez+cbgjni0SiBRY11Zt5tou1XOrHg5TY4pVz5E95TGtb6uHZdx0hNueP3VFHWd58LWxfObTREPMcdU2NOWja7EXjdSt/LXEluoNTeYVyhQZ6ensZ8Pi/nOXe73XLQxXq9/mqQwqWNCcDWacSXCfHu3bsYDAblTFR8IOgUL247tFZk48H+am7Dzs5OcVNxVC8zU3YZqQWqdUc8MhP+sKWOoCZsz2DBzv3hrBZdp1TQFIJMVy5XLQqMN/Yc4Fsnpk5YFd7KSJRpcxvwmwO1XBYvdQVqvWCw7LFAvbxfmK1y0AtlgXasKDBNmAa6hgilETRTGnCkODMt3qvMdTKN2XJ3W8Dc/l7um8w65nnP9XPbQB/kylcFkt3eUDy1bAZWRjUoys0pCDkIQ96mxJauCmAeq3x/Z2cn9vf3S0pheO9ub29jPp/HaDSKk5OTGA6HcXJyEoeHh3F3dxfn5+dxc3MT8/k8ZrNZ3N7extnZWSyXy688a71eL5bLZVxcXJRysWcfuDvLkvvJWcMKmdzgst3SZCb8cU3nNT+r34qjE84ANSqeA1tPQuKEoYITfpnm9JS6MzxqWo/DpwboRBaYLPCYYXPkI9Zg8BzXDYGFCHB2a7uk/8BflR3GUQctGHqmyLCio+AUA2aW2XqSWi54r2Y5afk1cJPiKZC1Vf8D3zZj0o07VTLUHV1r61PGJvdhplApU3SuXX2WvRP4wKKG67sJ57btUNyVPtmY13pqlm0TOOu4ptA/BbQcVU44TafyqKaxws9CiHKQKXsDwBP47GZeFlGlGR+8z7Ber4vR4qLRnwJP5f2KR8anInziJFfGS+rbJmztVCqHZHZfG8UCLRPyWXmZoNH6Mi0oq0vx0o6F22e9Xsfnz59jsVjE4eFhyerEAVEQ0FdXV9HtdmM4HJaJg+AM7HkejUYlo8719XU5JANabMTXKQCBJ1/nicQWzHw+LwwVz3MEJ9ak2NuRKQf4YEI6Cw5byVjJYDoynXj9CqBWPF/Db+Dg+jvi60xNOl7abtNRJsU4OM0b5fD/TDBn65zsBdCyeWmBlyo4Kpfrc4kgYD12u92NMrTNEBxssQMHdqWjP5geOjbV2mSa67r3U0GVP9CQac+xHCwEeZkD1i3PFQYdQ+y2Zy8E6JFZd6vVqhwKgexb+K+KlvYN8FBFGMFfvV4vDg4OYn9/vzzb6XRif3+/BIdNJpPCf3g/Nc99zE30F+i0WCxisVjExcVFyRrGwlnHfSb4nALG72cucTzLxgHz+UxQ1xS3NsalE/IvtZIdvEg4t1mfw30HsCpAXA58UOGagRPYTkg1KRI1i5EHP64xo7+4uIjFYhG7u7sbUdwY2OxuRjuxRxoTA/sPh8NhXFxcxMXFRfR6vTg/P49Op7ORDg9t1DbjvwqgbrdbgowQ1MFCGwJahTOYNgsOnjBgXAimAp6ok123mggDuHK0O7sp1dJ2oGuhTvCiDOCD9Xgumyd/lhGKBQorA/w8u+ZZYDsLVq+roGYaqkLAwgR4cT/inrqsAYiUZ0bG+56dQoLgIDwLWrAyAHyd4HU0BS1ZAKlQrvW/E5DsHVK66vPqLVDcQEfXDgesHKlwZiWAv1er1YYbG25k7lPtPx2zyuM4Mns8HsdkMonV6kuQWafTiePj43j79u1G2TAi1AXN/asKMNajIaR5DDE+jn8wTWu0zeY046eu8yYjT/ugppDX8HG8lnF7qVW9lSQkNcsT4KyJWuP0vUy4unfdO01t0PcdY+bOY9whmDC5sN6HBCBgZre3tyWAQi0IZqzD4TAODg4i4kue7Lu7u2KB6iDUtUhmBHd3d3Fzc7MhHFkgqvKig5uZpCpNrABhAkMYo82ZG6wNqAWkFqfinI1Bjo5nQeDGXc06UkHK+Li1+Uw7z4QNtwNCT3FT17hb/2ZlF+1VK3ZnZ6csr/B4guXE1nRmBUXEhkB2bWVgmun6v7aB6a39y32Y1VP7zwIjYjMKnhUh7T/HbDGOtE9d/zmcOECTt0w9Zf2TeRPGOnInaBIbrKUjlza/j/V0uKc5RSf4l3pUwF9Y4c1or/eZZspvm+jm2q/XM2PL4aHP8/xxdTtc2siyp8KLhLNj3g4UYRVubgBmDdZJ4rYZcLmOebsOaRLyHNTDnQyLAoEUsIQR3AW3Fd59eHjYiArmyYUArrdv30a3243pdBqXl5dF6GLScM5bTjgCpgvX42w2i4eHh3L0G4QnH6mItmXr0TUhif+YuMxc2PXJ1gQzaN12o/2jAgn1sKeFGWHGwJjh8rsaeFWzsnCfg9kYGC9msBwcA3CWOrfDCWhnRWu7gZ9z8yn+vN8azBWnq6n1p+Oi2+0W7xC7tZVWjkbcZgCEA1uKPD7VkmYrW+mpz3IdLGQwB9FWHg/4zVuFWMljRUKVXID+VyUE9xE5jR0diE3hNrKShXkKQYs16ojH41Kn02m8ffu2PAO6IGIbB/BgaU4js4HLbDaLq6urWK0205eifcAbYzxTtDJo4r+1d3ie6lZJpxg5ZdnNd1awVUnL3uHlHJVHbZSODLYWENZWUwFwAzJtRp9VAZspBG06ui3RnPKgwJYqjozktRsW5Kwh831mOnt7ezEcDuPm5qZoubwtCqdX1QYO18cpPNUNxMyehbWzInXAoRy2INhd2obebSdwG+3aWWKMf5v6GLROVhbYqnwJcB2g5XOsCB4/wBXgXLS8tQ7PMHPhMnhte71+jJTmJY8azoqbfqtigm9H3yb6NFk4Sq+MUWfxCa6tbceDMyLAKzRFpxvvNb6GPoWXDCmC0ce8vMTCBOXyXma25Dn9LiuSEY/8hcdMU/tr158jrLP3a0q2A+UdWl4TXi8RwhlsXThnGgqDWsjZGpMLGAHoRMg6VgehWmHOWlTc3cfVieMlEWgBQTsajcrAxnYDbD0As0fkZESUY9vW63XRgDudTtFu+dhA1vbV0uDAl7Ozs42Jy3sd4aJiXJgBMx14gjq3NTMDtB97v3VdERaMJmbJlCFWHhQHBVWMGG8dd5nlpaDuYV7fZcEGvGBxaHvgTmZw69AZTmytqWLHuOpWMWVewJtd3LjHdcKS421avB2MvQa8xOEsXCegM2tXPStqTer4UO+Feg0yWnB9EbGRQxpLUXyfgfmEWte478Yp5h+2HS4Wi/KczgWXRIcFIuJEDg4OYjKZxPHxcbx582YjwBLjFclIQCPwE2zf5O1QMALw7u7ubglUvb29jcVisbEXu0khrhlrDjL54axXfk6teH3X1dEWF1eWW/p5avkOthKtjUnBTMgRjf/XhB9bnvwOE8OVr52vZWYalhMG2vkqpNzkxoBG1q+dnZ2YTqdxdHRUBjaiMi8vL+Pu7q4EYcFNiP/A6/j4uOxPRvT2fD7fOLEGE5ituoj4ag2r1+vF4eFhjEajkjAA9OQ92ff39yVZClvGTAemp0bDdjqPB2Cw25OZKz7sekO5+M40V02jmClLrDxo1DOu6+RpWifnMYRyOSKfBTPTRuuGWxWWSTam3DgEbbS/FT+lCQMrFMDfrZkzfThoEAqXurTByJ0yy7hpvznB55QS5QPaVufSZ8GcMW7GG0fARkQRPowf0wY4cJvZLa/jgd2fsFT56EXli1yHth14YSnt4OAgDg8P4+joKI6Pj7+K1mfPHMqAKx0KAoLTVOhCOD88PBTehT3ZDhwfzpTubJ5nFrWOKeeRyPgC/68J8ew59ywUXKeYvAS2diqVI/hztCT8bquF1IjaBo/nElGtcIbVahXz+bxotFjLi3jcYoQkHcPhsDADtrJglfDeZ2xn6Pf7Za3brbm79S4IXk3vCOBAIHV5Zu3m9mPy6zYsxonfYybG7lxdZ0b56obnic4WU62/sr5m9zy3KxOMaqVCMXJrVKCDW5NsM/bcM09xqWs/uLLwzeWqtcrry25bHD/PoHOxhovDS9/T8rK6GJygcOPM1c0KK29vAl/JrP4M0O+cTYuzrTmeWuNh3e7j2cx8Ah57MBzNMJegVHIuBieIwGuwLs0JgDLA2Mt4ZRO9av2ZCfrs+dr/tu89FdqO9Qy2Jpyz9aEI7+rm953Frc+plqOMXjVMvqfMUTWimjKgAihrC8P9/X18/vw5zs/PS/T2YDCIyWQS4/E41ut1OR4SLqXRaBQRj/uC9/b2iuUMAX5+fl7WtOFeUoHFAT68lQPZfXCMZafTKdnKFotFzGazrzIycepGXgKI2MyDDZpyBCgH34AJMr1QLm+/4IxKKEezYPFeTLaIVLjwuKm5v9kCZmAPAeMEjwCXzVtQ1ELOFKUa6NJBGwGoLnpOp8ltAt6MA+YIByqyUGZF0VnLwDkTBOw1qbXZzXMuO8LzCoYa80c7ecyxoGZcsTNitVoVoXVzcxPdbnfDy8WBjln7uA4ItsViEefn58VyzqxHxlsV2vV6Hf1+Pw4ODkr638lkUvY46/qy8sf1el2s9uvr67i8vNx4Bm1Djob7+/u4vr4ugaY6hhRv9BF7EhwPb9N3Wq5u64pol2lM63H3HI9XJUNBPQNujjwVfrODL5z12uRGeKrFXNPEuZNeYjln95vKhJtqNBrFYrGITudL/lpEVMOljEM0ut3uhpsbAgPrQxDYsJwxsbDHOGLTJawWJQQI3Nc8cWHFs2sck/kp2h+YOFu5oIdj2jwudH8xrHinfLBCgHoZ2jA6fS5zoTJddQ0VSt9zrOCMESkebKk3WcFZXVouK7RqRWobOS7A7VXP1qm57DZQm8c1a1nbW+sHbhOPMQX14kT4WIWnMF+1WGGpZrnztR3ZuN3Z2SmpgTmpEbfBCUeeV5zJUOsCQImAotI05mueqjbj2Al7/e/Kcc8+1YLN8G5TzlM8A03wmwjnJoJl91yHNnWSDja8w5OIBflTCKZMjPHJ8FIcF4tF/PrrrzEYDIpw4UCa29vbuLy8jIeHh7JOjSxjWEd6eHgoG/2x2T8iyglW0MR1ndNlKFosFrFer8u2Cg4S4TUotqR4siutedLjnfV6vWFhMpOARq45pZWxMwNj65Q/br82JzNxnhvu2xpDVys/s76hWPCyhFoVeI73tTLtIqLEGXDyELZelEaMlxPuOtbVHc8Bf85iBQ054IuVPZTjEp04V2/NqgZ+Om/5OaYjvp03rGl+Kl3ZPYvnYB3DamaPSLfb/SrHtuMtKmTxfXNzU+YxCzqlB88r5Ys7OzsxGo1KEBgs5+Pj45hOp0VQZ+MWLurFYlHc1BFRPF/oK97atVgsylo02ucEUWYZu+ttFCl3Tfve3Qc/qvHpJnmgHoC2+IE2TR6yJti6cH6utqBMvg3R+Dd3GASSThYVtk3Wjr6jbawpC4D5fB4///xzOUIx4kuSEQSJQSMFIwBTACPEYREIHoH7CdsdMMF4XVvd08B5vV7HfD6PxWIR4/E49vf3i4BGLm8Em0GB2N3dLXukAezC5Q8zbjAutjjwgXeAaaz9iMGNdsG9XbPI1aJxlo3rM7bQGbQ9zMiZ1riv45LLY0tFPQFaHzN9tWR1jDlXPvBx1i3ecc9xmZrlyoEKJnXxsnXlFDHue47yb/KEMC31Ho93VV54nDKNtVy0GcohL6k4z5CCLtnw2NYUnepix8ctjTB/RO6C6XQah4eHRTgfHBxsjEfeEQJFCgoC+AbOCeAgzk7n8Wx0BIkhfXATqILk+LoaU9x+vubK1XmVPc91qiGnZdYUdVdPVi8rV23jQjLYyppzk5Bz7zA81RWQPcP4OOFb06Zr4Bh60zN8HS6j5XIZV1dXsV6vy9ozDx6sCw8Gg1gsFhsu7m63W1J+TqfTYp1dX19HxBclwCkl2UDkrVQRmwJdLU4ntNyaJd5xv9kicsxWtVSn6DjacllNTNPVzbhne7S5LvSVE1is+OkaPa4znRlPCIyMRhloW5311uR+dR4GZZRt5kvNKnL9r7TXe1kd+i7qY/q3wZU9ORCS6uLn5D5OWanVx9c58ArrzrV2omy1mGHVY00ZJ93hsByONVD6wAsAocwn5/E44mxhy+Vyw8J/DtTow21s4s/uWtt58hx+z/Od50DbOl8KL84QljFStYjwPGs/2ljXOY7Z4HfN2lYry5VdUwqcAFHQchxzW6/XZV3pl19+ifPz86LdDofDcqTbarWKX3/9Nbrdbgm6wJ5kZCr68OFD2Xd5dXUVf//732O1WsVsNttIo4cPBDgzRlgcNzc38fnz55LTGwetw02O7Vv8jrPEXSAM3NDr9XrDdcqubOfyYeGKbx0zvDbmXOMu2pTvO8tMt8nox1m3+jzXB5cxLH6GTqezsc9UXaR4RoGFPcY0t1WFhAo+fhfWIcrFPEY52i6+r8B1qNDiucm0c5ZiDZybm/f1oz52jTtBrcoV4jegbMJLAwsS5yBHRCpQtV5HHwRSIbDq8vJyg/bsema+xRnsOp1OyZO9u7sbk8kker1eHB0dxfv376Pf75fc/Mj4hfkXEcUwgHcMWQZ52xjmJQTx6elpnJ2dFbe2bhdiqBlC7j8DywLe7saKLtPG8XtVhDNoMgSdvHLLR/z+byWsX+zWzjRYvp8RxGm7qnk3acKus2qMxFnBzyGuMoam51arVUlu3+12Y7FYRLfbjfF4XAYkDsZA8BdcaNCUJ5NJ3N3dxdHRUfR6vbi+vo7hcLixLqxCJaM91pywfqUJKyI2hWyTJatrh5jw7OpUFy2Dc/W6seToy/dUmAK3Wh+78eqsBFcOWx0qbDqdx4AxXAcddLtatlas11BGm7HH7n3gkrXfzQ1AW6ujNv9c3ytNIvLkNirg2R2sa8YZXq4s3f+uCo3rF8YPCqQDvg6L+fb2tmzJ4qUdBVVsOp1OOS8eyjp2gYzH47KVSrO5cXlwpS8Wi5IABfuZWQDCmLi5uYnZbPYVH2iak030z9qK3679+pwrO7unZbfBJxszalX/lrD1IyOdNsEDRbVmfdaV6yY9a/WKA0NN48kEF/BtEki1znbthOBYLpfx6dOnuL6+LtYxA5KUwGXV7XbLRNT1IOAwmUwi4ksiFOTD7XQ6G24uaNK4BqaEw9Jh8SHZAEeBu7Yyc1WLBG3CtpOIr9fA+RtWvgv+4v8suJ27nRm93lMm7Jh7rb/VDY13XF/zc+gv3VdeE4ZMV4DOI82T7QA4ucxYTYop45Ap4ax04TrHGWRCGd+69pvhg/ZlShOeUWXd0ZHLw32kuoTQVx6BZ9br9YY3hC1nXeNHeQgAQyBWplQ4IYVERr1eL/b392M6ncbe3l68efMmhsNhSSqEuoEbtmLy/IFbXfNxM643NzdxcXFR4lkcL27i13q9SaA5emd11f5nv2v1OyW3pmRm7cqeeQlsTTjXgjhqxNGO0Yaz280xz6b1tJq2p9qlMghmMq5Nrl2uLt4TDAv6p59+2tj/zC5XHBs3Ho/j6OioRFPv7++XdWIWZt3ul0MyxuNxnJ+fF+HMbjNNjg/X12q1KpHee3t7cXR0FP1+fyPAjPtD+07bjzp4fQxCxAl5ME7W4CFwWGPn/c3MqJVZa2S59o0GzXE5XBbvuQYNeSwxk3U04L7HOxyl7WiazRkn4NiNjC05GePj/bjsVdG69D398HhCm3RpAeUheEqFOz+ryUxYoGfAypni6aLkWRFF2SwUeazo7gYeL2xlRzwK1vv7+0IDeLpAHyw1zefzcpgE5w5wigbXvV5/OVL26OgoxuNxDIfDcmjFDz/8EIeHh4WHRDy63rEWvV6vi6WO/AdYP4ayiL3riB6/vb2N8/Pzcu67m6+qDDF9lZfz+zAOuK0oU8tThapmsTsFGfWxceYEs1v21L5w9WUGpL7/EvjN9jk3aR1NkGnQaj3oNfdc23pqlkTb69lzDLx2itR5zMgwgVXTxWQBo8C+xojH9VYIQ/UYMGNmQQNGightrDejTE4/ycKJXXrOMlJrKgPuQ6z91SCzbrUu7ku0mRkzK00ADdLiMaTvZvgDtJyIsC7UjHG4a05BVQWqVo6bOw4UL7SN18Vr7tyI5qUEhcz60nu191nBQhm8Tow4AK2D26eBjtpXjvaYz7e3txGxmVMAwtAFXmm7+D/Pb7iwkacfAWC8p5nHPa8582EW6m3jOrH+zMJboWnMNN1rGg9K37b9jm9W8NwzT8W7LZ41vF4CLxLOPHBr1m2Ed0O2Yd58vy0hn0NwnbSq5bs63KBzz7s1MQTzfP78OVarVYzH4/jw4UM5lAKTA1mEkK/74eGhBH/c3NyUJPXQzLvdbszn87JexBHZsFAxcZm5YEvXer0ua1tImhIRJTiMNWdeP2XNmunCDEk15Yi6JRQRNpJWPRoAtuw0kIwt5k6nU/YV4xoLTmflRXxt6QFvZeAogxkHp2ZVhZBBrTW2AJSxg3ZgwBBAuk7qrBhnvfF9zZ3tLCanEDvLN1tuUEWRy3AAxRR4s5cF4wwCkutGIBN7dGDtssKJcl38A9fPfQ/hr/WuVquyZQr58LUsR0fQCfuX9/b24vj4OPr9fpycnMS7d+/Kb2T447EBT9jV1VUR0nBRg1dEPHqOMOfPzs7i8+fPJUGSuvedIlbjyywo3VzKLE0nE9Sa5nHj6uP5x885fFhucb8wjqqYOYNM51ltHLeFrbi1ldnUhO1LLOeMqWVlqobDBNRrmTXTRkty+GW4an3YO8iuZ2jiOzs7sVwuo9P5kowfWcWwTj0ajWI6nZakIcvlMpbLZQyHw+h0OiVhCTNMXksGXrxGBsYFC5yFFga6WsxoK7uCebCr4tPWesPEcQLRlQEcmZGwGxZt5OtMmybmwQKGlyqcksBeClXiXKpKt57NoEqMuvvZM+CURqYX01Tr5nayYO50HuMXdCxoXcxEcY+VK7VyuBy3Ng783fgBDfCtiiArW8CL26R9wAogl6UKl6ub761WqzIfEYi1Wq02FFkV0NxvSMcJtzVOujs8PNzYPoWgLqYXEhuxx433VaNfwXNYoENRd/OrplQ6oap8VCHrTy3HKeJuSUBBDUI3B1ihdnzetZ/HrT6jffkS+IdmCKsJUdx3Woe6aF+CkxMeEV9v86jhg85kdxKed213AxYDh7NxIVMY8muv1+uSS3s2m5X810gOggF2e3sbnz59KpGV5+fn5cANzU2tyocOUgiBxWJRjpkcDocldSgzMt720OlsrqmqJqoaKwQ5W0LM9DOaub5UYMszW1MCE1IhoXWwMqL96oCFcgZOcQE4ocm4cxQ8r1/iec40xgIJ4JgoAO9AOWNhxvjo0gCP+1oAWObSRTl8r0ZfVgzwYSWFy1dLl99zFpD+Z4UKgHnvLHXGh9NdorzMQgPdkRcbObKRJbDf78dkMilBoZqnYL1el+QiV1dXcX5+XpaoeG7yeEFSI1jLTklo4md83f2ujTcHbZQyh6OW74Sk431NOKggdm3Td7chmCN+owxhKgSca4GfdxaRMm1mErXJm+HkrrEWr+uPzATcHlS1jnTAMC10vyLjz1Gc4/E4ZrNZHB0dxZs3b2K9Xsf19XXs7OzEZDKJi4uLGAwGcXR0FIPBIA4ODuLbb7+N29vb+Otf/1qE6fX1dcn0tV6vS1AK6KhClS1CTpSAiPF3794VbRsCAcKB1/HYHc9CHPXA8uJzZiFMmNHyRyeR6wftY+DDzEmFKywbblNtzKgQ5f/cpxgP3OcKmTaPb47IR5m8lIDyuWyec3DTu3HLSpgKMHhKdnZ2ilubLQtYVFyvzhuuQ5kcf4MO6vLmyHwGXa+HB2i9fsyCxd4nBuYbGFM6J91zuMa05bgIjKGIx3HMe4exvOQUPuV13e6X06Uwtw8PD2N/fz+Gw2F88803MRwOy3nNEVGscmT3W6/XMZvN4vLyMs7OzuLXX3+N+/v7GI1GJTiMl3wivswPHKYDI8C1WfvP0U77V5U6Z2XyeNH/qhxz2dxX6urW5zJFg/lTDRctL3vWte8pMsrB1oRzk3Wszz5Hu2h677nlApw29dTynvo8M3OcesP5dvHNR9axGxMW9GAwKK4urFsjMpsnpptwik9EbNTBrrOaoqGTQ5mQWqisyDUBW8EauJXRnoUI9y2/m1lRruymsf3Sych18fdzgPviKQwxWxvWT83qzH47eMmcVoacGQCuHTy/eDw1Kf6OPzBNOPhLA8Ay4KWkfr+/EQSGbwR/advZa+A+2mfstse85sM3nmP4OKv1uWN3W1Znm3qalIam92vXt8ELtpa+s2b2N00+N6lcOTzItGynubk69F3XBmZUXEZGA9Y0VftWqwdla/tub2/j48ePxXqBSwsZgZbLZfzyyy/lLOfxeFwmdETE/v5+SYYPFzhcatCMuX0qONXax/+Hh4c4OzuL2WxWtnx1u92SDIGtHN6KwkIP67K1McJCgemva4ZgpigXZbMFx+5ZHSdgnBrghGv8HPc9Cy5nFSgt+fAK0JEtLm0/fuMZtImDnnQMunUyVuw42YXOQbV42XJmPNiFzIoZ009pwmXWhDjoovfQdhW++lv3xevZyAD0BWjDiic+oDELPPYAaNAZ3kcSIJ5rWJ7C844OTDdkCRwMBnFyclLWmzGnj4+PYzQaFeGP8Ygsfgg2w/ZHxgvLE+jz1WoVZ2dncXFxETc3N3F1dVW2Meo41j5RvqVziMdpk8JbGxcZDgxqCDiFMFM0Muu2CacaPq6OlyoaW7Gc2xCzyfLQ97NnMyHrtDe95wRR9jxPTNc+p0HzRGfh5Aa4Y1JXV1fR6XTi6OioBILBhYwgD2jRDw8Psbe3V4Qk1oVvb2/LoRqj0SgGg8GGQMN3TUAoXkj31+12i7LgliqAa0ZLpjXTQJUhN0m4fFYIlJZoHwczcVuVeTCOWEcHqHsVXoiMVoyPm5y6Zhvh3cK8dMAKipsTOqbRL3Dpr1arjchkxZ1prlulWGCpcGR6431e6uFrrs1cntszzgLIWci89MHXdG2VcQVdeA7wmIXg42UDDvyDcOe0qf1+f+OADGTgYuHklHK+h1wHo9GoCGmk6sSuieFwWHZUMJ0eHh5Kak3g1+0+nkftdhgg6RByZ7Mikint2RKKxt0wzXVuZgIrm0dq8Og95SHszXP4uPpqljPwzSzsmpx6qWCO2JJwzpi7TowMniLYs0Y3XXcCMYOmzsu0wsxCd/cdzhhgy+Uyzs/P4/b2dkMYAnB6FSeAwFoh1qJ3d3fj5OQk7u/v4/LysljPWONer9cbgpQHvGP4nU6naNqw1pGXGEwdwW0KTuiyZyET1hngXi3zmJaPdjjBrhYAv6/9xxa2tksZlStLBRy2+Wggla65K5PiOYGDGVy9Ov5VYHB5qIOFHlvMKnQdzRkHR0MV5rwGWlOAVcni/tV+5TIUbxUozvvA9bk1btAE2xJXqy/RzthPrONC+269XhcBurOzs3FwBeY1TpzCaXbAkY+yZGUA26WwdYrrA76IQ2GvmoL2aY2O3Ae4XzOS+L+WXbvm5pTyCC2/xmeV1zhwbdbna0sWT5E3GWzFrY3fTqjVOgtaSeYW5TqcgK4xdGZGKnQyXPg346CuL0wu5/Z2VoUbvDzhmGl0Op3iakK6PtAFzGB/f7+4KyEcsV41nU7jw4cPRSMej8dxdnYW6/WX3N2//vprzOfz6HQ6G+4+tiiVJriGfZu7u7slbSC2erCFA3c7ytC+Ybq6oC0uC98apALBzNnHgDfKYaGCcQZmym3UyGa+xgDm7CxybmMmOJz1x+kW1U3MEfaaGAblY6yirbpNjuvD2ibwVdy5XWgrJ6RQHJROLPDw3ykDXDcnudG57p7ndVSlbTbHoVBhnzPPR3Zx8zYu8CYIQa6HXdjX19fFeuWAOVWkNEtgr9eLg4OD6PV6MZ1Oy2+sLR8cHMT79+83eAiUcbiyUT9yIfA+ZrQHfTebzeKnn36K5XIZs9lsIzWnWqZOmWYhzN4QnktOeOt/pwhlvNuNYVZU3TP8zeWrAYDrz1mKcW13Y9W19SmwNeHs4KXIufqaLE/UW8PtKXg9tQ3Z4HtKOVg/gpsaJ1FFfBFGmnGIBz2n5BuPx2USw/3mmKROnKwNLOygKHQ6nXJIOwZrdryeTvbM4tJ68a0TS5U1BbWO1D0bsbluxkpA5mLPrCwGXkd11rcKFggCp8RkFgILVMWlts7p5kzWRmWITmC2oU9Wv2OuThl3eNbqcTi553ks8NIPt1sVAHZvczAV5qMqaQ4fzA0O9mKrGQFh8FA5tyrq5CMoOZ6C24hn+fxmXkqo8Upn3Lh52waeygezfnTCsCb4tw3blmsZbCUJieu4JgKqFallOqLWBLOWh4Gg1o3DVUHxVteXDk5lYm1o4gY6PpyD96effoqrq6s4Pj6Ot2/fxnq9jqurq42D2vkIwm73S3YhTEZM8tPT09jZ2YnLy8uSnITPaGWPBfBU+jCDurq6ipubmyL0sf8awo41eFhsCFhjWureVLYCQR+XpQp4uK1bAAgqlKlWKMpn16rWrdYha+2og+mj/czb1gC8donMa2zZQtmB5cqCMROmXDeWONh7kVlJDOy2xfPAkT016l7OrJFO5+uDV3ReADe1znFfy9S+4Wc4AFDLAC3ZSxexuZUPli9cwwxIs4vjH6GcItBS+QP/Zy8HrPfxeBzT6TQGg0G8ffu2bKF68+ZN8UbpMhEE8O3tbZydnRXLGdnAWIlCW8/OzmI+n8d8Po/ZbPaVYAZtuC/b8k8Hju/ztWz8ZWOD8avx0QwHLV/7p+m5Wpvd8sU2YWsBYY4xZdYH3tHn+TpDTePSd3nScoczQ3UDriaouX1NrmyuM7M0dDJoeRCcnU6nTMJ+vx9v376NiCg5cDHZ+/1+cS0jOX5EbGzNmkwmsVp9SRMKt7mLlGX3sdIXArHT6ZQ9nNhihaxle3t7G1GzEKj9fr9EnDKdWDDr+pXisLu7G7e3txsuQp04LGi1bG2rAtMDzzkXJYDHt45PZSRcP1s96CPQ0Al7FoicXYyf0/tQltoopKwUMQ1wDZ4bFbRNyi2iv7ktiivKqpXLy0r6PNOHhbhbD0TdUJhQLqKe0S9Yu8W6MJRNzLvLy8uSmwDz1B24osYI+gXbpXCIxcHBQbx58yZGo1FJz8kZAdGncF0jbS8O1ECWQShPbC1fX1/H2dnZRqAa95Ezlhz+DDVDxSlHTlg24aBeDFUAFU/FweGVyRZtm47PprZvWygDtpqEpNYR/Aygpm3q8zUBreUxg3Y4NZXVRjtsesa1Qa9ldGJmiQmKtSUkv+/1eiVn7/39l0PcoTGzOxlM4ODgILrdbtl2gYmKPNu6JzLi6zVzZn4YxGDeLpGHtgfWolNwVDBzHRkN2eLCc4io1v5GUJ0KW36XFRNef2ar3VkG+O3yc3N7IEQ0GxbTmoWX0pr/O9wYJ6cku/5RZRIftu6dC577Uet3irCL+nY4NTE8ns+oSxU5CN5af+MdKJE8jrkcJBnBsY/ZoRCgpfuPpC47OztxcHAQw+EwxuNxyZ09mUxKlLbGovAYR7Q2vrF1Ct4oPIvgMA4Sc4qnU4TYGMmUIe2LJmjitQ6nrO6mcaHP8Bx0v5vqcPd4/v2WsDXh7JhCk5ZRsyqdMM0YNL4zTZXvZ8CDomZloH3MZDQ4QWmhTJbxYkbBdIOb7fLysjCau7u7GAwG8eHDh9jf39+wapBTe39/v0RzI0BsvV7HDz/8EIvFIqbTabx79y7Ozs7i4eEhrq+v4/r6emMftGu7E9gPDw/F1QfrAutlOIda27Neb2Zc4/JVEHPAlMt2xRG0nCKxJlBd3epFiHjcG8uZtlgAcfAi9x+PIR6LCOKCNQb6QZnggDz2RqglzYIPTJmf0/aydcgBXRyrgLohQMH8oRxCQGMNlJUxCB245EEbBCNBgQONeQ7AsuZ3nbDQfgW90X6mLVuOCqgf+3rZg8HjEHXd3d3FxcVFCf5CEJULPnP8A7+Hw2HZv/zhw4c4OTmJXq9XlnnevXsXb9++LfUyX2GlAXMVZy7P5/PilYIicX9/HxcXF/Hx48cN4ZzxJsafFWW+zu9p3zher31XmyNN7zBteey7d/l+Vi63S5UAbWtmlCgfYsWV5Y3z3jwFfpP0ndzots9H1DtYhV/tnbbPuXqaBuM2IaMVTxK4zXDKFEeL4jlYwmDUELSI/Nzb24vxeBw7OztF276/v4/BYBDL5dK6bzJ8FUcIRrjPMUF0YKr1VavTTRi1OBnc2jPjjPpUwIL28DboZFTlSsvM6OMEDAsWbQ+vi2v7HW0co62tJaPt7jmUxwIHuOiyAQtWfp+tesWLxyhbzhgjDu+meaaMmtvolhqY5mrJu8QlEPIcSMXr0DX83PhjVzZvmxoMBkWRhYDlZDOgPbvcOQhN9ydzkNpyufwq+xcLx8yAqo29rC/aPOv4qjO4FC8V0E24tLlfkxltoamup5bnYOv7nJ3G0/ROU3lN7+gzPPhUSCiTdHU7rc61McMza4Nq2lpOzd23XC7j9PS0CNyLi4uYTqfx5s2biIiyjaLT6cRoNCprs2A0Jycn8fDwEJPJJN68eRPHx8exWCzi9PQ0/vrXv27k3nY0iYgN64sZHe5DaYDLDZYVXMrYYsV5m5kezGRAK1hrnHFJ6aOWIz6ox7WHhRILZtxny5M1d3WTOisNdGfrDtfX63VZz4QyBQWBhTSEw3q9/qo+rRfrk2yNKn5OKKMf2XLmrFa8Xa3J26EKMaxz/nBAFsrFu3jfuaG5fM7+pq5XeAgcbqgfbcSWOrzHysn19XUZyxw8qbhwe9VS6nQ65ZAKuK17vV7JOXB4eBiHh4cxHA439qmDxsBxuVzGp0+fyno3lGt4LPi42E+fPpU8BrC2m/hUZsEyLtm1TDA7vu8Ubqdk6nN6L1O+m9rSBCjfKS5aLj/L1/idbcDW3doqgPSes2rds03WtCMgv6sCT+tznc1EV+GZaZoOd/3tcHD1MrNQOuBcWESXDofDeHh4iMPDw69wGI1GxQUO4Xh0dBTdbjcODw/j7u4uptNpnJ+fx/7+fsxms/jll182hLO67sCsWVjqM3AXdjqdmM1m0el0SnYjuEWh7bvUmkwbCE1mtmxV8PPsvmYLCkIbAkj7hS0PLkuf4/rUbeyEJZeDb1VqODZgvX5MCANLJwtm47pQFtYh0WY8w7i5dWwth4Uz442+cJY348l0UQWIBRye5XZxHTXXIFu1vE6fKSZoo/a77iaIeNzvP5/P4+Li4qs+Vb6AazoOQDdk8BuPxyUYcj6fx+fPn6PT6RTXNtqDtmPM4pSpjx8/xmw2K/V2u91SJqKxl8tlfP78OS4uLqw16vgk85xMgPPSDV/LwM1PVqLQV5mC42issS8MvKTo3MgOnybDkI23TEa497i+bQjprbq1X4IQd4oSoGki8PtNWmIb/F2H1spgfJu0TS6nJtz1OWZ0fH7zfD6PXq9XtPTValVOmFksFrG3t1f2UDI+u7u7sb+/H6vVqhzsjjUquMwd/ZgZ6wdl8yTAeuN6vS6n6LAVysFrOsm4XLcOFvH11htmcCx4WbC4seIUIu0PdaVqW3lNN+tP4OHW0LlvGD+mEZiben9UAda5pEoIA4Qne0NcshKuC/2U0cqNDVxnBq1KhKOVAo81CCu91/RfLXtOwYnx6hiz4qdzmD0mOHMd7utut1uUL16rh1LFyhLc6JkrnS372WxW1puBi44HxlHHvkKNh9ZoWyuvTR85pSfD08kCN68zPGt82o1Dd83VocrpS2Ar+5z5tyOaChgFx1gyQcXPRWxqTq5DXee1bVM2SB1+2X83ELNyMYiZSaqlg8js+XweEVFSdr5//z6m02ksl8tygMZ6/eWM1/F4XNy80GKHw2H88MMPZVKvVqu4uLiIv/zlLyVCVZk6W3SsFfMeVV7f7HQ6JbhoZ2en5Ocej8fFzYcDPBwTZjoojWFpqbaMgDRmfEpHBbcmq4IXz2VCn78dvgxoLwQVzx2lBcrgduiYQWIadoWruxl9qevcEVFcvFDKYNHh+U7nMVsWW9nob3yYxhwMxh4PjqOAtc+WE2/XcgydYy10bqBepo3yDOVTSPKDLVIcnc7944KNmJ4oG7To9/txcnJS9i+Dnv1+P+7u7kqkNlJ1YikKaXsvLy9LLgFdm394eIjT09O4u7uLs7Oz+PnnnzcEOdOL+5nb3cSL0efqXVJhX+NnuMf9xJY646Ljtba84coEziocVVHJlNRMQXR0cTi5Nv2nCmdGLrvepGG0fUcHQBvtRuvTDtAya9qkllcT0E2C2rVHB01Gh/X60fWJVHxw0zFTRIQptHYVBDs7X86I3tvbi+l0GoeHh2VdmOtS/FUp4nbxdRbenB4UVgIOjEc2M9xz9K5pwioMIayYceoWJy5T8VWr101kZQIqpDNmyNeghHG9bYLldIww3QEsWLQspzywEoR+YKGLZ2CZqcKkNAB9edkj83yoxYPndXxpP4BeWgbc3VyW8/YA4G7Hei7nOldcXT/o2ODxjQMsEGfB3iLkpUddqA/KLBQGl/8aPABWM7ZHOnDzOGtXxr+aeGMb3pnxZ8ZlGwINdeG7Vm+GH7en7ftZWS+BraTvdIIv4uskDWqZ8LeWmdWj5THwJK2VV2uLg7blKaNxz2famzJ8xxy0zJubmzg7Oyt7mVerVTn4gtcwEZEKzR1WNU63+vHHH2Nvby8+ffoUV1dX0e/3S4IDZrBIbBGxmdmLrR1eOwOgLXgHQTZIKoJAMbj+2HrksiOiKB3skux2u0XI67ojaMieCFUG+EhF1wfaR9l4x/82jA/1szBiBUoFmlr0eIfdsgwcac1r8srEu91uWfLA87DosvbDSmaLGX3ILnv1qjAujFP2W4UK6oESg2dUsXB9s16vyzxAZi385t0PrLAo3Vkx1Wug3WAwiOPj4xgMBvHu3bs4Pj6O/f39ODk5KW5txINgX/PV1VVJbHJ6eloCKpEfH7gsFosS+InkRAjkzMApY0oX9yz3tz7L/aJlunHu+tLhpeDmVg0c/8/qdEZbpkg7utSUHnf9OfAi4awCpKlRtesqbLXBTpDxR7XjNppchpMLWnDvMX5t6uFrjvE5XJRJc5sxiaGZ47jIw8PDwsQ49y6E63g8LmWDGb99+zZ+/vnn+Pnnn2N3dzc+fvwYi8Wi0AOMj/P9wkrhAzR4O5UKMzBqWP0Qpp3OlwA3RK5i/VwFc0QUxsp9DIWDU4hyv7Arze1P5sxKrHC4YBjul2xSZwwPShFbfQB2AzO+Wge7j+HWv7+/34j6xjtQyFA3aMFjDFYc48vCV8cf3uHn0FeIjsd9uL5RLsY9lCEOMtP/TGMeR7jHaU25n2tufY6Avry8LCk42R3MAp9x43rUqufPcDiMt2/fxng8jm+++SZOTk7i5OQk/vSnP0Wv19uIpMZ2J7iwb29vy1GOrFyxcP7111+LO5uVVB1r2h4VpDpWXZtwXemo7zQJpDbCDs9pv9XKrNWl/FufzQy5DNeaAuGEcybnngJb3+fsoIZkG20I3zVtKxOmTfhkA/qpwJ1dUyz4elYO/24aYBFRgsAWi0UMh8OyrQZBJzs7X3Jqw5WmwT4QkoPBIA4PD8vxkqenp0W4u0njcNZBqQOcGRkYeKfzZf0Rru5O50vmJkR3c/BUpq3iPSdMWRiqG9vh+Nx+yXDjtjuccF8VNrUC8QzeZVpyO1z9KuQYB/7PdTgLRD+Onm2VWgUez1l/OCHB/7Xu1WpVlFNYo3rSlLaP52wT7wKuUPBw/jLOZsY564w7YgPQh+xW5z37wJ9PwYK7m99X2mTCz0E2VrYBTzVu3HjLlGM834aPann8TJPQzZRkLbP2zHNhawdfZIE7+j+bRHjOaW9aDn/zhMrq0zJcoIkyuIh8C1TGFFB204BxzN2Vy88qDgwPDw9xfn5esgfBZbZcLsuRkb/88kvs7OzE999/H+/evStbPSCs+/1+HB0dxb/+67/Gn/70p/hf/+t/lS0acLlFxFcHNaB+AK+roV0sBCBIQVv+Rtlg+DixB9YW77tl2jADY9zY+kXfcA5kNw50jKF87h8N2GN88K66qXXy8rjltX6mCbuiNUEJ6MWuY6ZNRGysEWP9mRUzHU8aFc7A9OJgO26H7qfOlBWdcxwkxsxO96lDkHFQWsTXR4PyfLm7uytHKn78+DGurq6KwsnC0ikebIkzYCwDj16vF8fHxzEajeK7776Lf/qnf4rRaBTffvvtRtpcxpEThSA1r/ZlxJf59vnz55jP53F+fh6fP3/+SqlgWjKNawoOzwkeh+75DHhON/FfFYRNSoHjww5Xhia8a4aFo5GTaa6ObQljha0FhKkAUVABivf0Ge1stTyaiNSk9SlTcu/wNW5Xhr9OBoVMELOAc8+q4HfPoTy4iYfDYckkxswNjHN/fz8mk0lxe0OIIoPRmzdvYjwex9///vdygMZ8Pi+CUwcs95VjcABWWpS+EY97TBnAiHmvLVyobD0CL3bxOkag1h5vX9HxpbTmyVrrP62Pn8X6uI4nF5TGeKqbl2mnwqP2jusbJ3SzNUynFCu9lME7ZTUTdhGPMQusRKgRoDTle1o/XNk4LAL7hblNaD9/g45qnXIbmK6DwSAmk0k5m3k0GpXkI+x9Yrz4lCkovwqr1arEf8zn841MgBlP4N+6fs/3VUl0gqrGU2u8NjM0tPysrKcI2qy8pwpNxTNr31Nk3Etga1upHJPi5zLtiK9lz+j7TuvX5xX4eVePw0s7wTFdtL1JcdB6mCk0PcfXVIiogrFYLOKXX36Jfr9fmBUs493d3ZjP5/Hx48eS7xc5kOFSw3rg999/H//tv/23uLy8jH/7t3+Lz58/F23fCQnF3SlsWMNcr9dfrUUygCGCsXY6nbKWzYwT1jTnwVahxIKGXZycucu1R5k+M38VTO4agNvJ63q45sYgWwcQEBpRzWVCKeEtYUpPXv/l+tnyx7PcVyqotQ281q9rxpz4BeVp4BXK1HXsiEfLn/HSlKLaHjyD4xEvLy83xq6uuYOe/J/pzcJZeQ5c16PRKP75n/85Tk5O4t27d/Hdd9+VVJ3cJzyG7u/vy2lROLyG67i6uir7l8/Pz2OxWJQtjswDakJVPyqw0Za2QpPf13vK+3R+1Ph2k6Bte03LUwUEUAugqykJbZShTC49B7YinJ+i5QB0cuA7ayQzCjBoh0cbLa3mEtL/PLC5o1kLBk5t28/4OyGndHiKgIaGzRHIOJqu1+uVI+aQeCTi0WrtdDrl+Mkff/wx3rx5E6enp2WrBtzmzmpRXFVgZROMI3uZEYKRa15gCO1utxvT6TSGw6FlCMCB+wZlRsTGYfYKKgzxLmfP0r3KLGBUeGVKHv+ujWFYWapoKM5ol6baRN8ykwTwdik+R5wFOaeB1DGP+iCgVTjjeV5OUKaGd3S/tCYD4fHmaIrrq9WXc4yvr6+LcNbodc3Yxgo2LyM4noN70+k0vv322zg8PIx//dd/Lb/fvn0b3W635BBYrx/TtEI5ZHc1gtK4Taenp/HTTz/F7e1tOeI141M6/7ifaoKNFU/3jvYTaJctffC7KogVR+eadzi73052uN+OFk5wKzglxuGj/Dqr67nwDwkIY3DC1zXEaTBNjXX33UR+KjiBqLhl11z7uDOfg0fTM2CK2Afd6XQ2jpdDTt7FYlEEpK7F7u7ulsQIR0dHcX19HTs7O4XhICFFrd0ZzqhDBzP/dpHSyqghtLrdbhG6vJWKXZPqrnVWIQNbydwepTU/7yzCJkaTladtdgolM9cMcE/dwbUx2zTG0D+skDpGh2u6pqttqLU7UwQZ0DYIPkRkY02XYxy4/CYDQec9Ppgr4/E4ptNp7O/vl8MrdLcAr4+v1+tylKPixmManiwIbU3nqkKvbb+1hSZenIEzItqUn5XRFpQGDv+nlNumHVkbtiFvAFtbc1bGkwlXp+1FfJ0NiN8Ho9boVX6frykOTYPYCU9tH2tTri6UkQWR6YTSTEYKqom10fgYHh4e4pdffomzs7OYTCZxeXkZg8GgbLV6eHiI//N//k/s7u4WBtPr9WI6nZatWQcHBzEYDOJ//I//Ef/yL/8S//f//t/4n//zf8ZsNivBNYw/CyME+MCdyUyat0kx0wFteVsQb3NReuE4vN3d3VgsFrGzs1PcjMi8xIKewblXmfbcP/yculzZ1Y7+Z2uUcca7bsw6IcsBXTonkGaSk7tk6994Bp4IBDEpXlBsVGgqvmwxuzZpXILixm0FnfkYTRfXwP+BB55H8hDsF0aGLSiSbgxwMBO3jecxKxewtgeDQXzzzTcxHo/jw4cP8cMPP8RwOCyZwCKirGtDAPPhFKenpyUTGcY27///+PFjCcK8vr7e8JrUaKxtcM8xOL7iwPFUV6azpnn8tKkv42mO5zpwyi/3s8ogxYv5lFPSUJ6TbaqQbQN+kyMjAZkg0cZlLuFMoGYMtUlg195v0vJqwpuFc6aUKLjIyMxKcwwqKxfvr9frsh8Ye6ERxT2ZTOL+/r5EkHKCEux5RaT03t5e/PDDD4XJ/Md//Ed0Op04Pz9PLSW2dpzyxcIMtHBjANZQRg8kdMC6OQeMISBOU3zyeIRgqDEW/gaDZuHN/dikADpaZBGyKrSVWcDKckyamQzXwda9U6jhXYBAd/0B/FkwA7Q+pYeOD3W3swuZ8XfAZcBiRkTz3d1dzGazcrhFxoOccqb9zrhAKTk8PIyDg4P45ptv4v379yUJEBROZOvj/NdYEvr06VOcnp5uxEqgPpwfjbVmLCHpureOyZoCrwYDf2f9m1nmAJ7XGW3d+Hf0bYI2vE7rrBlbbXkrxqUqRU7OsEDflmCO2KJwdkSvWXdthLZOaC6zyXLk9x3xa/VmA8ENkmzgZNdqZWc46gB0ONTg/v6+MIder1csp+FwGLu7u8X1vbe3F/f399Hv9wtjiHjU7Mfjcfz4448lc9ju7m7c3NxsZBJjfJU56DoffgPcOjxH0CpNXDmIeoVQ7na7Jd82W0DOktV1Vr6nFrICBAUYZq1/WHipEGb68do3a+4cVITfus6Mevk9uHzZUlbaN0Vrs2KQ1cXXUB4HxkGZglDFOjev5XN5fOYy6MT7gnHEI7b+cT5vt76c8RBWhJj3dDqd2N/fj+l0GpPJJP7whz/E4eFhvH//Pt68ebOxlMKWPE6MwhxBO5CZD7heXV3F9fV1yfiFfcxM18xIqQlanUMqpFigZQKsFvzF15t4oFMIXRmZEpU9X1PgMly5zbpcwjw2M+ycEl6j53Nha9HaANV+9XkmkHMf6MQAZIPSDQ6tg/Fy2lXTYONyaoO0qWNd5KebYBw4o7TJ2pfRICJKRiGsNZ+fn8dkMolvv/02RqNRXFxcxNXVVezu7sbl5WX0er04OTkpiUHAMI+OjuK///f/HrPZLCIiBoPBRsAYQzYBYJVpfzCDY5cxBIhzuTLThvUPq6Pb7cZsNis5xCeTyUY2LOcGdkyQr+EdDiDiPsVvduU7psrBb/wu9xuEvTJpFsxg4mqFcYAal71afUlWo4FXKNcpQAoYl7y3WAPhmEbqMVmtVhunlM3n83IgCxLh8LotnsdWJHzD7Xt3d1eya/HYYC8BZ7DL+gU0dYJ5Z2cnjo+P44cffojDw8P4r//1v8bx8XEcHh7GyclJrFarovze3t6WMfjv//7vJZEPdh0gEx62R+GgGjyH1LYM2XJFJghUMDs+y+Vof+u7oE8mcJSvM02Vlk1CXPvHWaqKoxoB3Dan8OOevsN41+rW/sieeyn8Q46MfI4WocK4qYwmjSWzvpsGSxto0gjb4O+gJui1bifEuQxYErCcdnd3i6ua10hhMSOgButsYMA4mxl7Om9vb4vAc8oHM8xMwXA0wz21bp11xvWoIqDMnRNouPVSZQBZvbimtM/awf3Egtm50vm/XuMAI2WYNQaC8lhQu3VXbZ8yXVdfTdFwSjGXhXchkDjJDcrDedW8/opMWZqCs61lqc/qdQhleF3G43HJEcDHQGpbeLwhMA33MB7RDswxTunp5lAN3NjD/20KijY4ZPeyudP07kugqe1a73NopW1w1vRLYCu5tXlAZRYzwFmb/IyzmGtWsuIS8bj1w9XtmLsKDr2f1emYsWura1vG9HkwZwOobb2cpQsMEG42COnBYBAnJydxfHxcop5Xq1Wcnp7GYrGIXq9Xgl1wcPx4PI4///nP8fbt2/j73/8eERHX19dxdnZWgsS0PWCowEsjfWG58DUoDcBdt5twNDZbcmCGsKavrq7KcZXIiobtZTs7OxvBTeyeVMsLVn+tv9brdcETH7fGrGOu5grHfm9OXLFeP27P4TpgdfI85HkKixbBUr1eb+OEJO0b3sKF671e76uoZE7kwgLXuWfRp4iDiIgS/3B5eVmEFKxhuL9ZyeQsX+v11+5N3sLGfYb+dQoj03B3dzcmk0n88Y9/jPF4HH/84x/jj3/8Y0lxOxwO4+Hhy9GN6/W6uLLZIobCgCUktPP8/DwuLi7ip59+ipubm1gsFhvrywo1npK9w8+p8uQ8c+5dV3fT806JY8XUtcUJtZrCyHjpmrBCFpMBnJqUby4ne9Yp+VmZT4Gtn0pVC+7KoElTbNNIMCgejBmuet0J5yatO2uDwznTrNwgxvdTNb9swMGViPsQkA8PD+WwgtFo9FUUMhgN7wfmU6O++eabOD4+joiIX3/9NQaDQQl8yfDlRAvu5CgO0ALu6FPei62uUrRHaYmJCQGDgB52uzIT5/ch8PkUJLYCawooCyusfWeWJYCFl8MFliO+VQEDLXQ9HWWzgsauZVY+QGtc4z5BOahTT7Ji4czBchpvgHLQF1A2+MQoCOPFYlGULE0+4ixx4MNjhxW3TNF3whnWMk6W+u677+Lbb7/dOIMcgpXjAGAtQ5mC4oBT4yCYz8/PywlUmTCqXXuqUdGk6NcURX2vjcBu0xbGwz3v+Jl7vkm2aDnumuLi+CvjoPdVQNeWAdrCVtecgbSzjvn6U4Wea6j+r7kH2xCdn3vq4MsmkaOH3svKyzQ+HVCZQqFWjGs39ipfXl7Gr7/+Gr1er6T2hODq9XplKwiEFEdFHx0dxX/5L/8lrq+vy/YlPpJPhTLq5lzKirsyYWa4rv1q3SrjYgYEhokgHezdRns0IAlroYonP6NrehGP6VK1D7XfVGAz/rA8me7Z2r6WC+UDzygurGy4dTu1tlnQqvLEDElxQ7mwgiHQ7u/vy9os75uHBcptV8aobXcKtwptxU/LQa75nZ2dODo6Kmecf/jwofzm5R2MYeCL/PNIE4oxjy1iFxcX8fDwEGdnZ3F+ft541KO2h/tWfz9VkedrGc/mPm0ymtoK6Ky+Gm7ueg1ffRblqFBVnqrv6DP8rM5dd+2lgjkiorN+QSmcYSkjaERsaJDMMJQhuPdrms9GQ4RJKkPnd3QdzIHrUAeKi3Zo08TJ6OBwcANLmbqzDrJ6QZvRaFRO0/nxxx9jf3+/5AaOeLQ8kZKw3+/H+/fvYzqdFkF9c3MT//t//+/461//Gp8/f45/+7d/K7mM5/N5YYARm+ud7iAK1uD5HWVO+HDgGK7BytSx5T7AYW9vr5yxy4FJ7CrGB+5fPpDDjSvgw+v6Og75WdABVhi7+iEQMJ8cc2fLmS1BNw7X63XZWsdzh+kOlytc6xERw+GwLAlgmUAVAPbYIKIaa6yz2awINAhqpNaEUFbByuVmQpnnPedfh5KI8cAeBkC/3y/7+v/85z/HH//4x5hOp/HDDz+U+TEYDEp7oNQiIvsvf/lLnJ+fb2RKw9i4vLyMn3/+OZbLZck9wIqi42cAHqesIDnaOMW0jTLDtMNv/m56N8PBeZpqPNzRwPEzvsa8RPkij8m28kavOborPuzh40BEPId581TYWkBY1mmZtvdUaNK2svJrGlUTPEczdDg14d0Gh+c+V9NCMeB4LZizEsFKY5cyBisE9t7eXozH4+j3+3F4eFgYL9bkbm5uUosX344ZZFos7jmFhSefY0pcLk82tjJZ8MJqBrCQBiDILKO9s+CdUOVJjWfZJcvPufV14A7gw0LcGMgUHcWV6+APcGCXsVoSeA+WMT6wNjHW+GAIpoPGr6jyyde1Ta6d3B68hzqgmI1Go5hOp3F8fFwOrsCWQ7QLcwNBXziKEh4YVnA6nU7JCIb82Lz/+rmQCd6XQDY/n1r2NqxGxUfLruFU4x343wbHp7R7mxYzYCtrzgyO8fBaViZAa41zg7AmdGrvgRnr/ax+4JZp6TX8sjY7hqM4umvKUNVacW1xdWvZEKLr9Tr+/d//vQS9HB0dlTSevV4vFotFzGazIqQuLy9jMpmUvZ4fPnyI9+/fl4M3Li8v4y9/+Uv89NNPxfJSjTZi8wi+iGbrSJk/C3+1Gp2g0f4BDnxyEFv7aC+782FZ7+3tbVjYXL8TUmgf9wdbYywIIbjgPoU1XVuzhkAAnhA8uh6N5xFZzAFhbGWyi5kjpfEsrzXjg7ZAACN7F9y+3BZ1XeuBKBgXTmBr+8FnMKbZElcrB/0FF/Z0Oo3vvvuuHGLxww8/FM9Ap9MpW6Tu7+/j4uKiZCFD0pPd3d04PDzc6L9Pnz7Fzc1NXF5exi+//FLWo9uAU2adNcz33XcNMr6kFqi+4+amm1dOKc+W6/Df8VSHg5sDtXa470z41ix+jmVwtK6t1z8Vtiqc2TLQhjkhpZPL3XcCi9/JrAEVgG5QZRq3K6emldfamQn2THFQoauWYUR9gGs9tQmID0cB43QjpMbEOtvu7m5J9AAmj8hvHJbx7bfflvSgyHa0WCzKPlRe3mDFQK01pg2vfSrd3XtKSxXIvAavk4sjgPEcC2esSe7t7UXEF0Fyf39fBDQEFvDmeQArC32r1jYLEz47G8kqkMoRApuBmSBwRSAfcGTFgp9DmRzkxQKT+4eVBrSfs3xBEKKvYS0iCQ4ENWd907mhS18ApiULYTe2GV+NGNelicPDw3j37l0cHR3Fjz/+GNPpNP7pn/4p/vCHP5SgyLu7u1gsFiXJyadPn8q4vri4iE6nU7LvwZLGOdLYj312draxj9rNTR6Lbr7XrNmnWLrKn5h2XG9Nyed3smd5/zsr38AxU7L4O+MLbWJyMho5YFpnz6niz+1xxlIbBakGv8nBFzUCZdBWQLpyVfjqdTcYubya0G8jlNu2rc2gqSkMToHR97Jrrk1OkQAzXiwWJU8xXNQRm6cYIaL27OyspP5cr9cleAwMEIx/PB4XZgkBACvK4cTWpmNQtb5RS4PLzYRzptzhHm8rQqITKDYQgrDIVPhywhS3Bow1V1ib6Au2oLO9zfxfU3qC1iyQmMns7u6WtnD6VpSL9WaOFAdObE2whQqrG27sbP9uk7LNtNf+0o/2n46PTqdTtoD1+/2YTCaxt7cXb9++LQlFDg8PYzQalTF8c3NTLOPFYlEUVigZvHwCV/1sNiupQ3FCHPpT28q4ZveUXjXBXKMtX2sa59lzGf91+LcR8E3CK2uvQo2fOSPoqZCNO1dfJmueA1uL1nZWqdN+2pSj/51W4+oDsNWp63OZ4pC5I9oOKNV4MyHbVjDjutPE2mqxjAtvOWKmohYmW23X19ext7cXd3d35QSek5OTsr0ILkrkCr64uIjj4+NiGfFRgnt7e3F4eBjdbresvXHWJ8YBwoWFczbWNGKYy4Cw2Nvbi16vtyFgVQvHO+zS0vECRQIR3rw+zcFRaLcKG9SL7VyAh4eHEu3L+5iBB+OloPOBrRasbbaxBnZ2duLg4KDkiMb2OlYMkFCDFQVOIarbnRRH0NMdCtKGAaMedisyDkwHVcg6nU5ZPz46OioHVnz77bdlffn9+/ext7cXNzc38be//S2ur6/jb3/7Wzm9DbTE0gKys0Eo42CLn376aePkKeCZKcs8J7N5zH2VgQt+dfUxPvqOU2K5/CZow6+0Pm4jP4PyWFnX+04JU1Beh/dqiokC46D18bILb3N8KfzDj4x8KTgCOosyG2RNmlhNI2wSzM+FNtphm7raWta1drDbF8wQzAnRqsyIYTl2u924vr4urlFMJHangvHz+uZsNvsq+lgtoqwd3GZmLM6qcozRWQOZ0qeKA55lq5SFDwtnCJMIn7AAa8rItQzhrAFReE+XPLj/lOmhj5QeymB4fZWPPoSywHjBRc/r9M6qd7szsj5smnfaJlXmtF9UoCFOALsSYCUfHBzE/v5+jMfjEkcA9zX27SPOghPisPcHSxHYRoW0oqwIPoVhZ8/W5rd7tzbP2xpLTTjVoA1ffArva9P+bZfx1Ge3gSNga0dGOs1eGYAyS2ddZhaCvuPqUWHs6tKyFAfGxWlkyigUD7cO0WbwKb6qpSpjdVpnbTJy1K/TllVAsVZ/cXFRmM7p6WkJfsG2quFwGL1eL5bLZZydnUXE43rk8fFxyQR1cnISvV6vJPq/vr6Ov/zlLxtJ/zVwzDFkZcTaz7jG66asAGib+Rpn3WIcIMD4v9aHtViN6NY+U6saOOo7bJXjt3OZI+MbrEhtn2bFUnzwgfv25uamHIaidF+vN3OaO+UHAHryfQh57S8noN1yB/bHM15OCYBygcjr4XAY33//fRwdHcXBwUF8++23JQEPMqRhvfj6+jouLy+LS5rp+vDwUNbRsebMAW8Q7FnQqVMY0QYF5T/MF1xAlPLhzECp1aHPuvvuXVduphhnZTXxY/ceX3djCDRxfNjR1wlYfdfJKNz/3QSEAXhQ1AKZIrxrQpkkX8sYb6ZdoxwmNNeXdYgT8NlzbtBppzLzaxMkwGWDEWcKg+LgaKFtdoKf+4K3EjHdHh4evkrJube3F999910cHh6Ws6Dh6oW7F8FIh4eH8c0338RkMonvv/8++v3+RpYkuAIvLi42ooPRh7BWmBkp44P1AqYMtztPTieceW8vGCSn7+Txifs6ZnkMsGAEsIsefaCCBO3U7F4QxLD6Op1Ose5Qzmq1KoqO2/+cjT9VUjqdzgbtOVubWvtsqbrtTkpPHqNsYWt2M1WYOMoaZbhlKnfwBoL3jo+P489//nPs7+/HP//zP8e7d+9iOBzG4eFhGSfwACAaezabxdXVVXFLMx53d3clEPLTp0/xyy+/2MM30H/KD934dUoo0xPXuS+1n5UmTbyJn+exzXOG62fgPnJ4unY6AY35p3xIeZy+l+HEbQCg7xgHhwv/dwKaeWZNYXBKwHNh62vO2X+9nmkxtYY9pdG1jnWDVDvFCT0uK2uvw5EZttaflZNpjg4X/t30vL5bU0ZcOyJiI4K40+kUyxkCGUKl03lcj+EkFuv1Y17lo6Oj4lJFNrKLi4uyvov3OaFIbQxhsvO7LGxU0eH2ssB27mcXJaz95WiZjTf+MM2cssrMWfdb85YrFWqOTnxNt6ExHXBfrQEVEqr8oWw+LYsFh8NJacR95xi14gKFEJHzk8kk+v1+vHv3Lt6+fRuj0Sj29/djMBiU2ACm3c3NTRGws9ls4zAK9ubw+ETgF+julGTFPfudXavxxdocrT3HPC6DGj/L6qmVVVMWHG98Kj4ZXs+RJ9mc5e+m/ngKfTLYinCG9hpRt375GpehFoWW7d7NBmHGKBwws3dal6svw1EtKzfJMsbr8Mzc+zqoM6sIuCo+qCtjEk4o8X1Yt58+fYqzs7MYDodxenoavV4v3rx5E9PptLgJIx5PGYqI+PjxYxHiONXnX/7lX0q2KGy9+n//7/8V1/fFxcXGuqbiiLagzboNCABL2Fm/8FS4cczCySkysDYZHygGKK/T6WwoF3wyFixmJHIBbdbrdXz+/Lm4+rHdqdvtluxcODQCSwu3t7dxdna2EbCUAQtfzVzGiUt4rRWeAaYR77sGDRHwhsQeEbGBE88XDlRkHJzlzN94FvVhz/KbN29iOByW4x2Pjo7i3bt3JUIb+KB8bHW6vr6On376qSRG4W1s2BYF2iInNgfvuUAgF8jHc71JAWchxgojaJ0JDkcvVRJqy2aMZ2Yx6rP8nIuvcG1mBdHh4pTYDF/Ure12/C6jj17nOpyXMeP1jkbPga0IZ+faei6wVlcTwE99L4PMysmuZR2SMY829WoZbcEN+qz8THvm593A5us8aRDVjUGLTGFIfwnmxxGtyKPMggl7hmE5IwgHdS8Wi4jwp505pYYnv04sp8yotehooMLLMZgMlLG4+mAN44So0WgUDw8PZf0bygfTTZOMuK1uTfEboKtawYwnyuMo6RqjRXt4nRz1qIJa6zcN8lK8FUe4/qfTaYzH43IwC9acQd9er7exfIJ1YmyB4jSicHdjnRnbqxBZn82/TCAyzk0WsqOtjrc2vCZ7plaP4qrwFN7F4z0zWByocObyHF2zMl37Ha/m++ohqxmXjNO2YStJSNhyaRowNWLpNdcR2SDma9lgyHDhZ92gcILfTcwmzU7fz9pUG+hKi0x7VtyymICsPoe/E+Z8ktCvv/4as9ksxuNxSc6B5AzIv72zs1MiW3FQBvaenpyclKMs5/N5XFxclGufP38uUbCIanZZoJxQ5fa5dTJOTarC1EVaM31cf7LQhQsVa8ZKTwg7pHXc29vbODAEdcJ1iiMVWdEAfgi0UmUC+DnXK1vOHCXOLnZuI6xoNw/Ye7Fer4vliWtsDTPPUP6hihIENccyYE/23t5eHB8fx2AwiPfv38cf/vCHskVqMpmU51erVVxdXZW9+ZeXl2WtGAdVgK58jCOsZERtawS2s2CbFLY2ir3ypOya62fHz1x9XGYm0Gprz04BduMiE2iouxZgpe3D/xp/d0oF48Ve0iywjp9twktlQdtx0ARbCwhTK6RJMNYaxuWwa8vVwe9webjPrr1sEihODhyh1eLIhHOmSCj+2eSsgWrWek9/s+WTtbEmeLR8ZWpnZ2exv78fEV+sYRyisbOzE/P5PLrdbgkcQ9BORBQhvl6vS4axy8vL+Pz5cyyXy/jb3/4Wl5eXMZvNSrYlKAVIDKFBTiyknKvbMS20VU+nQnkasFJTrFiYDIfDGA6HxRUMlyjc3YvFIh4eHsq+cig+UA6wPLBer8t6PPbQDgaDkjwkYvN4Rm2fBsiocGYhiOdVOVGrFjhqsB3cw0ofHmcaxAZLHt6EiMdMaZ1Op3hYkDRkOBzGd999F5PJJD58+BB/+tOfyrjr9/tfZezCUsnf//73uLu7K2vLfHQqrOPr6+v4j//4j3KClPIS4MnjinlPE29xNNG5nAnZTOjqXOUlFhY0GU9iYOWxtrxTE7AZH2NcnWIdsRkcxjg1la33WZhjHvNcqClLWoeTVYxrTZl5KmzNcn7uO05r08H6nHqaLL9Ms2pTrr7vtNm25Thc+Ho2MLOy+H5N2LcZPE3PaL/AXYg0n3BpR0SJjIUrG1tiYLVw4A8LCbjJDw4Oyrt4BhmbkNuY15u5/2trrww6kdlC4mfQVn4n0+Y5axYsOD5YhNcs8R8CjQPb1Dpmgc3ZuoALCxNmgryWDpxRJnAA48K30k+D7doIDuDBQsKNbX6W3fYRX07Bm06nJaENhPPR0VGMx+NyznK3293IA44UnPP5fOOULY5hWK8fj7VEqk62qF17dJy1Vaa5T1z73byrzcWa8qz/25TtePRzeHwN2uBSq7OJXnrfyRHXF23lwjaEbxO86MhITJqaVqj3Xai8ug74mg5+1vqbrikeT4Ws08HccD8Lr68NerfNgOnRdguGlp9NJNU4mUlyoI4LdnAMiLVKDQhCRrCdnZ3Y398vkbOHh4ext7cX0+k0Dg4ONsqD5QyBjEAkzve8Xq9jNpvF58+fN/I1g/He398X6wj32UJ1+DNwP7o+0O0m2q9uzLPVjTVkF7TGz2sgjQbZ8LhjXGtzAOVoYBz3H+pxeba5Pjc2nELNOGhSFqYDlDo8hzFwcHBQ3NYQwB8+fCiJRLCFD0IZ69wPDw9lbMzn8zg/Py/eCVjzi8UiVqtVWXZYLBbx6dOnWC6X8fHjx/j8+XNxy7MCon1cc+cyuDmJPmDLMRtjOtZ0vLBSpjhqOS4Cn8vGbyiLjJe2qa1hAFC+xwpezRDjcjORpX2EeYe2sLKtNM5wVHDzJuOFoHHbw04UtuLWZoKpIMY1JZpaGzXthZ9vq53WhHOT1sXP1bRSxwh5kDUpBvzsUzVTfScT2Nm7Dr9sQtSsIGcxQWDyM3d3dzEYDGK1WsVgMCjuXLhol8tlEcpgmqPRqDBPCHtkGbu7u4vRaFTWarHODYDrHELA0diNM7cOqmNbx1em0PA7zETVuleGAWC3qQpIBraQ3a4DdUfznINgdP3ITI3xdYxa26918zq8tsmVw9HeiLje39+PP/7xj8VKhhsaFja2RD08PJQALwhnRLyzGx3v7u3tlSQ48/k8rq6uvtrbz+1TpbxpXCmoIEJZTmA2lctjhxU95adOuWhbT6aMvgQyY6Y2TzPlV6/pOHVCX9vjxm5NhmR4bRN+s33Oet9dU6bm3nFMTkE7VstTIrZlLG5AN2mBDvemzlXIGFdbRaOJnqwU4Vqt/TVg4aTXO51O2dsMyxV7mTk3NZ6FYL68vCxBYYvForiz4bqOiJIOFOvU6/UXV/d4PI6Li4tYLpflaD9OJsHBZJyO0rmwlQbufkZrXHcBJbz3V0EZPr5rjLupj5wLHriooHBzR4UpLFRnPXA9XAasGAT/cd/zyV/YhzwajeLt27cxGAzi7du3cXJyEv1+P46OjjZiFSCI4cLG5/T0tORwh5KGutD/iGtAHnlYzrCqtS913ijfacOr+D2ed1mwoY45N5+5f2v92CTkHD9w322FUabcO36jdTbJDi0rE+isjDr8dT7wd9aWTMDru08xuBy8SDi7KFZ3H8ANY+1bhUD220FtELXR9vGs7ufL6kXnOheoY6xtBnIm1F29tfJUW3STVoN5uGyu2wklx3TcYOf/iMzudr/k3u52u3F1dRUHBwfR6/Vif3+/5HTG4RQImBoMBiWYDPmewbgRZIXgJQREIZAHqUQhnGFhn5+flzzWWBfHmqS22zFJtX5dv3FfoT/YdceZsQB837m6VQhkDIb7JWsL6tP+d4KG24x6sS0JbnJWbLgenesQvvv7+7G7u1v2dsOFjTVlWMbffPNN2R61v7+/0eblclkCvc7Ozsp/eG0gnOEZYMHPgYQ//fRTichGNDe78lWIZkIg41k1wYFna0KB57RT2FVB4j5DP7t6FU99LlMKMX7VgNBxo5B5SRQPhZrSl5XB7zQFqrl+a+KxTfjq+H8ubC1au0nr+c+CmuBgeA7OTiOtaXcOryZ83IRqGjyqBfO9GqNw9bv/GQ5OKWFhgPUrrAXCGsZ1WJQI5gED5e1EEbGxvqiRyXBV9nq9sk0LQh1CHxHUwAsBRJozWicZp6LUVJ1OGKmQhhDj/b8AMBHngs8Ep77/3LFdG088viGkOaIaOHEiF3232+0W5WoymcR0Oo1er1diESCAcR+R7VjWgKWMMYTlEAQEQvnCejIfbcnr2nzONPJmIxc2v+PGetO8a0v7prnLv9vUmfWts+ravq/1Zr+z+tpCG2Go4OaGK6OtYfTc59vg+FI5+KKAMN1H2sayUwbmtBdnjfE315n9d3W7323w1vKBMzNbaLbOzad4sEXlrJsmPFzZLqjCtV+3u7gJ6iyftvgrjq5vkWYRgrTb7ZYgMbg9EZSkz8FKxuEbk8nE0gG/Ix6DjO7u7srWLLjM1+vH7Ty3t7flGkfp6lYPRIrrnl38Z7ev9qlazmrh8Bjid/k+1wn8NKLajalMOau5zFmRwLvYugUBDS/GZDIpFjH6aTAYlPsQyDgMBZYzhDfPIaYTcMRWPQhmbHHCUgXogf6BoIbwRb7s5XK5seeZs5dpn+mYZrrxWNPxnllyfA3Afa59qOPF9bELYOK5q8sSteAubrta4qpwu/GU8Wgtk5WPzKWv0MRT2VrO+o2vObozPm2tfVcHt4FjYZ4CW7WclUiOObvfL6nTlec0PTfhXFnu3aye5+DsBiSX20YbdrTm8psCSwA6oF19XJdOymyyqtDHb95+w27kTudxOw9bVFwPr0/e3t6W9JX9fr8w1ohHq5q3YSESHO51MOj5fL7RDnZ1syXF5wVHfElFCYsfdWMNO+IxpzQEBaxz1KNCh+kPYat9qgd3oGylfdPYqQHTWxkzoNN5PFyEFSkoV9jmhCUJCOzRaFTc1jidbDwelyUNjBHe6oRgQWzPWywWcXp6uhG85QQKK6AYa9jnjOxeV1dXX+1z1bmf0ayNEGTFNRMqz+ErPOdqlm/TkqMrT8vg+xgHTnHM8HcC0vFjFpZt6MDlZULY0T2jN88h9cQ5ZVbvNxl2z4Wt7HNmcI3OBJ9j/NlgcZAJ1Qwfrq8NuHczAa84OI1VLUon3Fy97neGI665QVSDjHnoR/u3SVPW59ykiogSlAPhC+aPZBi85oztPpeXlxvWIwtyWHSw2vAery8Ph8MNHPr9fozH41ivHxN96F5k9KdaWSrEIZB5vRWu2Yxpgzkwg9D6+Drvc0ZCEigK7FrnLTusQIAhomy2kKEgTCaTGI/HRRmClQuhirJHo1ERtoeHhyVPODwf6D8O5MLpTkxDrB1DqMKFjUAv5LRm97W6sR8eHsohFnB7I387LGqXt5uhNl9qYz5j5q6uTDHQ99vMXX3GWaMOf1XuUJ7jHy4ep4ZbW36lykxWhuMvmUJaq9spCLjetqw2MuSlQvtFwlm3SgFUyDi3jFrXNSu7CZylrt9NQlxBtU/XVjeIcQ0Cggc0C2wty/12GqfiyLhosJIOwkw7zspnwcLWr9at73DZbrJx+1A2gnv4Hd4vDWt6OByW9UnsZ3XCkl2usJxZycApRUxj1AfBx2vf6/VjoBryOHP+aKxpQrAvl8tiHeJgBgRR4TxgtJVxQG5tvsaCCu593gLF5zkDB7QdngOkT8WeS9B1uVzGbDbbuMbR9O/fv4+Tk5MYDodxcnJSnkFkPeOFNh4cHGx4NLifr66uylGh8FJAwEI4ow0Yx9fX119ZyXBt39/flz3uiLS+vb2Njx8/xmw220jtCjcmKx+a9Y3nhlOedCw3CWe+rkquZhyr8SaUC0FWs4jVstR4BJ7H7l23ZOWerwVbZfi7/1yO463O6sZ3pug65YJ5YhtaMx7cb018s6n8trAVt7YDN9BfCk1W9VME+jahTWdl77XFuUnDZmjSttviWHuujUb/lPLW67UNxoqIIjDAOOD2ZEVH12ShIKkQBDNGljJ1NyuenU6nKAIIWINFyMdjQkDBqoewQlAT/q/XX6xjPq0LAAsT+3fBECC81uv1Rn18OIMeG8kBc7BaIXQjHgXxzc1N2Y4GPHFs4s7OThwdHcXR0VE5A1mFM4KsWMGBMqKehIgobcGeZKzt4xqCAPEOArgQC4D+5iAwWMNQPvjDApnHWm0cZvzK8R9nEDwFnGCpPavvKW5N9TwXnsJ/XloH15PV91Je76zoGvyW7U7rXL+glVgXBGSaDGsdtYAptQRxDZBZ6qUxLetzE1XfVXdfk6DK2qTt07a2DbTA9UyzbavFM6iVq+3R/65/M4tD3WquHsZB77MHotPpbJwVDQFxeHhYskMhsIjr0uQcqA+WM4QgnuEtWXB94ghHWKtwV8OCHQ6HRbjhPoQTAthY0EZ4TxJwRuINbjsE2Xr9mEebLUu0j61pKBBKZ/XsQKixtwHCOSLK1ibQvtPplC1JEM6r1SouLi7i4uIiOp1O6QusFUPAsgCGlQw6w1rGh5UPbI/jk82QKATXNOsaBLYbg26cRdQzQ7WxBrNxjGvMJ9rwJhX8Gc/Utjg+o/hkQsnxl6yMrP24r9tT+R3nkeDydGlH28fWtuKTCXZtE/NeF1DLvC7jla5sxjUiygl+T4UXW85N7gO93lYD085rI3yc8OLycK2NZVlzY7gJoHgr41Sc3cR0NGE3sqsvoyfj7minz8P1xTRqUp6cUqF0YFxc3dwfTjGDgNWDBTiRByxDBnaValQmB4VxO+CujohihUIwsRLBOaixNxu4YY3buabBELDFS8cYC1XergRX+nq9LsdyMr1xFOJ6/eh94MMwYMGibB6TvKYOgHCGMgBBDGHHa/+dzmMWsevr6w2mp+u9nHhmvV5vbH/iE7GgaKD/Li8vyxoykobgGisAKDsbq/y/Np/c2M+EYo2f6Rzgd1VYZzxJ8VbBwc9lQl7xdstrWqcqFY6vZzgrDWtt43eytju+gGttDBVcYwWb39cyHTTJi98CtpYhTJlvNhFch7hB7Jh2xuAzHJxQyvB311nQZ8qCG+C1jtR2NuGTdTzqZqHBApbfzRhNhnutzxyumWJQqzsrS/HInkGgELbHIIhMn+FvWLy4hhSjTEOcH40JjChhtnahNIDWmjYR23dUUcT9q6ur4lrHFiJ8IKw6nc6GYsFBM3rSk6MdBDa3BdZvxOZJQKpAsCV7cXFR7qGdo9GonLAFt/TZ2VnM5/NC506nU9azeS2c9x1jrRwKAvoE/7GmjKQysJL5OWXIbv24CWrCWp9ruv+UelGWE/xu7NcMAodbG36SlaPP13iJE+4Z3q7umoKTQVavU1oAuka+XtePjGyLU1P7ngtbEc7ZZKgNDkeMmmaoDFGFUBaG3zRhMq2MmTZrmrBMYCXheZTTxtLFu3wto0cWiAa80D7O1KSRutmArWmlTQKdwbl8miY9P1NTmrJ+ub+/j4uLi5LqE1Hd+GCfNAdGwTJmAcWpI8H40SaOruYAMwiJ9frLQRzOkkWZHHAUERuu4IODgzg+Pt5wy6Pu1WoVs9msnHuNADQuG+3AWqsqGtw/TH+4fe/v7zcC0GCpgp7r9bq4lJn+iODGfMA3EsagD66vr+Py8jIiHre4QfBC+PJ2qYeHhzg/Py8K1+np6cZaOujZ6XS+Oi0Kbefx7pR6N96ycdl0n2nCynrt/TZCt2Z8uHnleERb5RrfzgjhNjUpPTxvWNnjspp4IvMrfr9JYXDtVF7I8oHbxfzc0e0pCgO356WCOeI3DAjLoGYNtQEduDXrIXsm0/SyelQR0LLbtqOtBqk4tNHY21zPNPIavrXB6fqirZJWe9696yY2xwQoc4DiBCHGZxYz8HuwGFnwcZYyuJZh3UKQKUDYsDBhhoU10r29vRLcFhEbrnpEdSPqm5UFlI3n8VxmqesYWq0et5SxFQ4hiAA4KB/X19flXS0Lwhnl8jxB6la0G0FiiHrnbVNYKlgsFiXByGw221iagLeBme02mKADN/bb1PVcht4WVJl+KjyH//I8bZqzTTxH77nn0L9N7zTh+o+Gbde7tVOpmq7rxM4saNWWnDXwlEnZRjBnmhhrcCwkVDN0FuhTJg9riwpqMSrOrk1OedA17kwhUIWErZDaRFPt1JXvtHPnadC2M158DZZSxNdCF9Y1W6+w5tgKxlYrdndzwNfOzk7M5/MiGBCljLYi8xQO50DZWFPWaHD0BSxjXIcFj4AplP3w8BCDwSDu7++LcAaevEWI6anjBQAlZb1+3A+tLnONPZjNZiV6Gv2ArUtcH7u9gfdisYjr6+ui9HAyESgI8CKgb7GlDpYx1xvh05ty2xmv2jKPU/acQq/zhd/JFOda8BDeyZQLV6YzRkBz8A5tm1rEirvyg8wC53dqRg3vdAAOmRGltOP+z4Qy06PWT3qfeZeW48p2ZSofUrwVtqUgbCUJSca09b5q8biHbyWiDhp+3q0ftMGV68oGjwqlCC9AFfesfU2aZs0y5kHhrDTFsybkasK1hh8zfC1X8Xc0AhPRMaBlOqXCXVP8IGy4nbCEVUDyfQhfJM9gYQ/hDFyxJQnuczzHe58RPMaJRHgM6zWso6L8TudxvZppC8sdkdRwjcMND6UBwi3i0S3PjAbbnJQebPHiGp/YhTVwprdGQnN0NVzmWHO+u7srJ4XhGoS3Cn4nTBiamLceGpKNeXXT1gSpzk/HyJ0S7ZbaajyQ72fCguvKeGZNiAIvtwSmfK+GI4PypUwu8JzXdmqdNWHrcHBKGMZlje8pDlp/puxsQ/g2wT/crf1UraXWQfpOrTwmfJv3M0HM/zONtk3b8N2kkdZwcGXit8OtSTC3oeNT8NKJ5PowYzBaltKMaee8LY4BcH3M5NlFzUIS5fE6J1vEEY+R27yHmQUyu9lhwUNAKn2AK689495isSgKBxgO1oWhQEXEBh5qwfF+ZGcJgDas6KhrGni54DsIaLjtsRaORCE4MYpPfmI8WFHIxqJj9k8BN090fGT1N/EEd72NYFd88LupbapkuTlXw9PhrQqLw8MJbv1f49c1ftfEqxwPVTxqCldWphPgWV/9IwRzRLxsnzP2UXJnOKLWhG9WvStHXaCZduPK4kGs23L0flZPBm07zml3fDydC+TiMvU7G8iOIbiAjYxmYPYZA9dns6A1LscpDKqlt6UxwI27LLo+YxjAn4WpthPlIZEIX2MBrG2GKxzCDJ/pdFq2f8ESxz5uFegRUYQbeyCQiQtud1jXqI/nBbuvERWN9WXOYQ0vAI+B2WxWLGE8x14A7XtEYXM6TR1XmrHLMUUeVzyGdI84W0daho4dXhbSMdVkZalCp+9nv2tQUwp4XrQtpw3vrc1Jfr5Wd62tbk7W+EwbaMuHFYc2Cp7ec/czHlcrg8ElHGoD/ymWc6ZNOuHF9/W3vtNEtKd07HOh1sm1dzLtsaa96jO1Otto4g6vp7bDtSHD+am0duPG0S4Tzoqj4qDRvlo3hBqXz8FmKJMDmDgiHB8kGcFzvDdXt2x1Oo9JSFCWutbW6y9uZk4UwuOGT93irUocNc35qpmJX19fl4QesIgZB1WyIZx5TVWFaq3vncDS69nYeM5YahKwNSssmx9t503btrV9t6ZY6PM1AdUkoNsKOb7m5pP73WTc6O9sXtfw1zJ5rrTldf8I+E0Cwpxll1ktNYHKTJzrcsy2rTYDiwZlqaBoYhiZ9pZpi05QMLCF5LTOTLhlg6jt4M4EFtNGLQ2+39RvNWiawE5zzZQXrZeDZJzbz63bcxmwHHXts9N5TLahQik7apAnPD7dbrekxmQ3OtaN1WUMwcvbvCK+eK1Go1Hs7u6Wfd4qBLkMtZyBKwdR4R7wXK/XpWxY1ninFpyYBaWxAOf/PGZ4fOlWnIivk9G4PuRvft/d437k5ZG2wsSBu698QXFu4l9Nyre758Yjnmvim1yG1t0Gf6cwt+Gjrg5VuHVsuDKc7ODy+buNYFZ8IvKT/PiZl8DWLGeHsBMmmYbDZfB9x7TbapquLi5HJ2ZNadAACn7OuYnaKgvMCNndVxPq7nqm/Lg2Z/RxE0rLc67GDA8d9LV+y/B/ClNk5p89W2NynPmLXdwoG33E44Cjh5UmuI/3NbqfmQ5SknKiED1tiYUsR4djHZdp4Jgbu5lVYWC82G2t51rj3SYXsZbjdjZk/cO4qpLFSwa1vnTj0N2vzdlMeKiwcQKQ62l7rQY1/uIEb21uc9t4LNbqzpRaVvIifHT4U9vK7VBejfc1zoTfa+LDrkyHm+OVGPs8npt4zXNhq8K5rdCMaGdhvcIr1OCpY47fe847bocAM0PHpPkZ/K9ZTxC++mz2YStYcQBA+XNrs/xsE27atv+vQhuB9BR47jh08P8P9P3/MjgLf1t9+2Rc1v9ZNb/CK7zCK7zCK7yChXwB7hVe4RVe4RVe4RX+U+BVOL/CK7zCK7zCK/zO4FU4v8IrvMIrvMIr/M7gVTi/wiu8wiu8wiv8zuBVOL/CK7zCK7zCK/zO4FU4v8IrvMIrvMIr/M7gVTi/wiu8wiu8wiv8zuBVOL/CK7zCK7zCK/zO4FU4v8IrvMIrvMIr/M7gVTi/wiu8wiu8wiv8zuBVOL/CK7zCK7zCK/zO4FU4v8IrvMIrvMIr/M7gVTi/wiu8wiu8wiv8zuBVOL/CK7zCK7zCK/zO4P8HBnpQ/ScYEnoAAAAASUVORK5CYII="
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# 12-channel data\n",
+ "train_files = list(Path(cc359_data_dir + \"calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Train/\").iterdir())\n",
+ "print(\"Number of volumes in the train set\",len(train_files))\n",
+ "\n",
+ "# Displaying a specific slice from a specific volume\n",
+ "file_index = 25\n",
+ "slice_index = 125\n",
+ "\n",
+ "sr = 0.85 # Sampling-rate in the slice-encode direction\n",
+ "\n",
+ "# Load .h5 file\n",
+ "with h5py.File(train_files[file_index], 'r') as f:\n",
+ " sample_kspace = f['kspace'][:] # the key to access data is 'kspace'\n",
+ "\n",
+ "# Explicit zero-filling after 85% in the slice-encoded direction\n",
+ "Nz = sample_kspace.shape[2]\n",
+ "Nz_sampled = int(np.ceil(Nz*sr))\n",
+ "sample_kspace[:,:,Nz_sampled:,:] = 0\n",
+ "\n",
+ "print(\"Data format is x-ky-kz-nchannels\")\n",
+ "print(\"data shape:\",sample_kspace.shape)\n",
+ "\n",
+ "# We just want to show one slice\n",
+ "sample_kspace = sample_kspace[slice_index]\n",
+ "# Converting to complex\n",
+ "sample_kspace = sample_kspace[:,:,::2] + 1j*sample_kspace[:,:,1::2]\n",
+ "\n",
+ "print(\"\\n\\nChannel-wise k-space\") \n",
+ "\n",
+ "# Displaying channels' k-spaces\n",
+ "plt.figure(figsize = (8,6),dpi = 150)\n",
+ "gs1 = gridspec.GridSpec(3, 4)\n",
+ "gs1.update(wspace=0.002, hspace=0.1)\n",
+ "\n",
+ "for ii in range(12):\n",
+ " plt.subplot(gs1[ii])\n",
+ " plt.imshow(np.log(1+np.abs(sample_kspace[:,:,ii])),cmap = \"gray\")\n",
+ " plt.axis(\"off\")\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"Channel-wise images\") \n",
+ "sample_rec_train = np.fft.ifft2(sample_kspace,axes = (0,1)) # Only ky and kz are in k-space domain\n",
+ "\n",
+ "# Displaying channels' images\n",
+ "plt.figure(figsize = (8,6),dpi = 150)\n",
+ "gs1 = gridspec.GridSpec(3, 4)\n",
+ "gs1.update(wspace=0.002, hspace=0.1)\n",
+ "\n",
+ "for ii in range(12):\n",
+ " plt.subplot(gs1[ii])\n",
+ " plt.imshow(np.abs(sample_rec_train[:,:,ii]),cmap = \"gray\")\n",
+ " plt.axis(\"off\")\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"Root-Sum-of-Squares\")\n",
+ "\n",
+ "rss = np.abs(np.sqrt(np.sum(sample_rec_train ** 2, -1)))\n",
+ "plt.figure(dpi = 150)\n",
+ "plt.imshow(rss,cmap = \"gray\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You need to apply retrospective undersampling to get the samples in a proper way to train your model. We provide 100 sampling patterns for R = 5 and R=10 for the different image sizes in the dataset. The sampling pattenrs follow a Poisson disc distribution where the centre of k-space was fully sampled within a radius of 16. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-10-08T17:53:57.925573Z",
+ "end_time": "2023-10-08T17:53:58.579736Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "We provide 100 sampling patterns for R=5 and R=10\n",
+ "(100, 218, 180)\n",
+ "Average sampling rate: 5.007386322692224\n",
+ "(100, 218, 180)\n",
+ "Average sampling rate: 10.057139411117308\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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bN9He3p737+7u7ujt7Y1KpRJv3LiB7u7uCADYv39/zMzM5Pi6N8E7duzARYsWsfx/b3Soy56eniz/1hsd9pYpisLAwEBWJJTp/5nHZWZmYr9+/d74+/ut2Kid9tSg6Y8ygDbs097ejjKZjHUzoVCI9fX1OHjwYATQOoC2tja0trZmdTrmtXTb9u/fj7t370YnJyfs6OhAJycnFAqFLKMQiUScTlxQUIAzZ84knaStrY1lyAqFAltbWzEwMJDXEAUCAfbv3x+bm5tRIpGgUChEkUjEMcaUlBTs6OjA1tZWVCqVeOrUKTIooSgKa2pqMDo6GqdPn46//vqrXsWUlpbiuHHjcNy4cVhaWsp7DPM9URRFfguFQqQoCiMjI7G2tpYlD/M4fW0CgYDXyajVamxtbeUsCRjSF9/1hUIheSZm+5gxY7C8vLzHndfQPQAABwwYgC9evECRSITXr1/HlStXYkhICNFhZmYmrl27FgMDA/Hly5cok8nwl19+wU8++YR1fX06/KON91UM+m0kAMArV66Qgb9IJMLGxkYMDQ19rXfB1ycSEhLwyZMnRo8bMWIEPnv2zOBxGzduxDNnzhh9jtWrV+OVK1cM9mPaX/n7+xPfQd+TzxY9PT2xvb2dLCfy2VhPlq62bt2Kx44de+1+p88HGPLrtP83NzfnvSbTTxji77//Hvft28dp59OhLvP5On3+DwBQLBZjY2Njrz7yujrsqVy/Nxu1054adEtLC1mf1TcYANCOfOkXQVEUKhQKfPbsGUZGRiKAdubf0tLCmkG6ublhS0sL2traokQiIeu9crkcAbQz1x9++IEcX1hYiJMnT2bdVyqVshQil8s5Awa5XI43btzAlJQUznOvXLkSs7KyiEwXLlzAlpYWzM7O5hiAXC4nz8Z8Xqb8QqGQFZnge0+0QfO9R1tbW2xpaUEPDw8EAIyKisKKigqkKArz8vJw1qxZKBAIOOcOHDgQnz9/znoX/fr1w/r6evKcy5cvx8uXL/N2aloumn19fbG5uZll0Lo6nDJlChYUFJC///jjj7hr1y50cnLClpYWMiPUJysfm5ubY3NzMxnQDRo0CGtqalhGxpRfKpUS58TXpqsvf39/bGxsRDMzM706/KON91UM+m0kpi7e5DvW7Xf6+lhcXBxngMBnO0OHDsXKykriN8RiMQcXxccikUivrYtEIqytrcWBAwfy2lhycjLeuXPHqC1OnDiRNbn4/vvv8fvvvzf6bD2VwRBLJBJsaGgg/p+Wq66ujgzo+vTpg42NjayJBJ8/YfLBgwdx69atrywDnw51+bPPPuNgstatW8eKMuuyTCbrVcREV4eHDx/mRDffBjZqpz016LCwMDKaVSqVOHDgQKQoCr/++mucNWsWUU56ejpqNBocO3YsAdCEhoaSD4dMJsOwsDDiGGJiYjAtLY3Vpst+fn6YnJxMRrghISHo6OjIe2y/fv3w1KlTepUZFBSEbm5uqFar8ezZs+Sj6eHhgRqNhhwXGBiIYWFhJFR39uxZEpKmO/rp06cxODgYx4wZgz/++CP52yeffIKLFy9GJycnvHjxIgmnR0VF4aFDhxBACwyMiIjQqziRSIRhYWHEyVhbWxPDCwkJ4QUHzZkzB7///nscMGAAAmgHUZMmTUJzc3OiL1rW8ePHY0ZGhkFHMXbsWDx8+DCGhYVxgJFMfTk6OrLCu/7+/ujr64tisRjDwsJYH1orKyvMzMxEBwcHBNAOXo4fP44A2qjQ6NGjEUDr1MPCwvDw4cMYHx+PlpaWRC4AwNmzZ+OWLVt6ZAQqlYrIunnzZpwzZw4qFAoMCwtjfZDc3Nzw4sWLJHw9cuRIPHDgwB9uxL0x6LeReisj03cYYt1+R7OtrS1mZmaira0tAgDa2dkRADOTzc3N8eLFiwQAyLQxQ6zrO3R527ZtmJSURPzEgAED9ALonJ2dWR9Zfezg4MCarfr5+bGWJg4fPozDhw9/7f4llUrx/PnzLNAlRVE4cOBA4v+ZctGTBD57MsYBAQGs6G1aWhqJKuvq8FXZ29ubs5Tg5eWFffv2feVrenl54YULF8hAR1eHffr04QU3/tFsjHoMILx06RLExsbCwIEDoampCbKysgAR4d69e/DkyRMA7d3g+vXr0NjYCBUVFQT0kp2dDfX19aDRaGDy5Mlw6dIlArSoqqqCy5cvk7ZRo0ZBdHQ0uW9ycjJQFAVZWVkELJaTkwNVVVXg4+MD8+bNAwCApKQkCAwMhIaGBrh+/TogIkyfPh2CgoLA3d0dFi5cCBRFwc2bN+Hp06fQ1NQE165dA0SEKVOmgIuLC+Tm5pL73r59Gy5dugS5ubmAiHDt2jVoaGiAkJAQAsij2yorK1nnPnjwAIqKiqCtrQ2ys7OJrNXV1QQEd+PGDQI+VCgUsHjxYlAqleQanZ2dcOnSJQI0tLa2hj//+c8AAKDRaMDR0RE8PT1hwYIFBARUUlICpaWl0K9fP6AoCu7cuQNlZWXQ2NhI9DVp0iRwd3eHS5cuwdWrV6G7uxsSEhJg0KBBAKAFxixcuBDc3d2hoqICrly5ApcuXSIAusrKStKWnJwMPj4+UFVVBTk5OQAAMGfOHOju7gaRSAQzZsyAS5cuQUdHB8TExMCoUaOgo6MDrly5QuSqq6sj5966dQuqqqoAAEAoFEJoaCgUFBRAZWUlNDQ0sABZJSUlcPfuXRAKhZCSkgIODg4AAODk5AQpKSkgFAph4sSJEBYWBi0tLUTW+/fvQ0lJCbS0tEBWVhbMmzcPPDw8QKPRwKRJkyA7Oxu6urogLi4ONBoN3Lp1qyfmYaIeUGhoKEyePBkEAgGkpKSAi4sL73FM32GIdPudr68vAAC0t7fDlStXCCCtpqaGFzDW2dkJ2dnZ0NraCgDavpidnQ0AQHwHHzF9BwDAwIEDYdKkSeTvd+/eJWBmRITLly/Dixcv9MpK+7X58+eDt7c373HPnj1jAfwKCgpYwOibN29CdXU177m6FBkZCWPHjuX9W3d3N2RnZ0NLSwtpQ0TIysqCpqYmVtvly5ehsbERALQAukuXLrGAkcbo/v37LBBgTk4OPH/+HAC4OtSlqKgoiIuLI79p/69LRUVFcPfuXVZbcXFxj/pXaGgoCyxKU0tLC/ETAGwdAmhB6nzgxreeejO6379/P86dOxdlMhn6+/vzzr79/f31bkObMGECnj59mvx2d3fnzHA3bdqEH3/8MYpEIgwICMCsrCyMiYlhHaNSqdDS0hLDw8PJWt3p06c5W4GOHz+OkyZNwn79+mFOTo7eUevBgwcxOTmZJYNcLkc7Ozv09vZmHTtt2jTWGpyHhwcLGKVPLgDtlh++EaOtrS3eu3ePzJYBtDNjf39/FIvF6OzsjJMmTSJLFidPnsSEhAQMDQ3F69evo0AgQB8fH7SxscHg4GC8ceMGCoVC9Pb2RltbW5TL5WSLEJ8Of/jhB7J0IhAIMCcnB/v164c2NjZkhuDn58fZ+nTlyhWcMmUKS9bMzEwcOnQojhw5Es+fP0/0tW3bNvziiy84OlQqlbzgK6VSiXl5eSzgEEVR6O/vzwrJSqVSvHPnDpFPrVZjbm4uisVi3Lt3L86fPx+lUimrv9JyCQQCvH79OoaGhuLEiRPxxIkT5Lpbt27FNWvW/OGjeV1+F4l+9uTkZPzpp59QJBLhrVu3WJG4njC9vMNnx3S/M3YNut8ZO472Hfr6HZPnzp2L+/fv13stQz6R2bevXr2KgwYN+s370IoVK/Cbb775Te/B9B00W1tb93jGTPt/Ovpobm6Ovr6+5O/r1q1jRQb5/L8hZvpEAEB7e3sOSHHWrFkkkttbtrKyeuuiA0bttLcGTVEUBgcHY2trKzEOiqLIOvmLFy9YyHlDYaOff/6ZdEomzoCiKHRycsKuri7Wh5Y+pri4GKdOncq5Pn0u370MHcf8LRaLsaWlBfv374/Lly/HGzduGLx+eno6fvXVV6zrp6Wlsfbs0n+bMmUKlpSUGHw2mm1tbbGrqws9PT1x+/btelG49Ll5eXkcLMStW7dw2bJlGBoaii0tLaxwfVBQEC9YkPksCxYswPv37yNFUVhfX49Dhw4luubTob73VFpaigkJCaxjaB2OGDECa2pqjPYVAK1DaG9vZ+1p1u03fG2BgYHY1tZGwnopKSmYl5fHeWb6vLcVLwDwbg8Gesu6evDy8sLOzk7efAL6WLcvMn2HoeOYrFAoOP2up0yDJcPCwgzK1hsZXvX9GTrO0PWNXUfXJwD8j+9gtiUnJxsEVTNZ1//Hx8djZWXla8nJ5AEDBmBzczMZbHzwwQd49erVXl9HH8+YMQMfPXr0xq73JtionfbWoJctW4aXL19mrR+lpKSQWauZmRlZS1OpVNjQ0KAXTSqTyVAqlaKbmxs2Njaira0tpqam4s6dO5GiKFQqlaSTxsbG4uPHjxFAa5wikQijo6NZSPxNmzbhkSNHOPcJCwvDqqoq0mnWrl3LQglnZmbismXLyG8zMzMUCAQoFotZAJhTp05xEn3I5XJcsGAB5ubmcuSif5eUlOCIESNQJBJxPr5jxozR22lo+aVSKS9QJiIiAisqKlAgEKBCoWB97On3JBaLUSAQcGb1AoEArayssKamhqyn9unTB+vr68kzisVi8n9ar0lJSawPqa6sAEB0yHyfungQWodCoZA8W15eHk6fPp33XdA6tLS0JHoMDQ3F6upqFIlERIdBQUFYW1uLEokEf/nlF1y1ahUKBAJWf2XKRfOsWbMwNzcXKYrCsrKyN7L++kcY9NtIryInn++gfUJvrrNr1y4W0I7ud7rH6fMdTFt81UEi7U+YtldRUcHZbqyP9+7di9u2bev1fe/du4fTpk0zetz169dZ28OZ3K9fP3z+/DnHtzCZ6f9p5rMxvjZ9rOv/RSIRb1ImKysrfPHiBe/WQ0Os6xMlEolBsGNvmc/X/9Fs1E57a9De3t4cMI6Xlxcv8EahUOCQIUPI4GDkyJG4Z88eBNACXuhzZDIZDhkyBMViMfbt25d376iDgwOOHj0a09PTyZ5aOzs7lkH5+/ujRqNBhUKBp0+fRjc3N0xOTsZdu3axwHoqlYoFPOrfvz9nOYCPQ0JCUKVSoYeHB54+fRplMhlu2LABP/74YxwwYABSFIVHjhzBfv36YXR0NAFBDR48GLds2YKLFy9GOzs7TE9PJ7MbR0dHEhrcu3cvpqens3IvpKamkm1uZmZmmJ6eTgBPNjY2HBCiWq3GtLQ0FAgEuG3bNpwwYQLr75s3b8YpU6YQg4uIiCBhU6VSiUOGDDHo9Nzc3DA+Ph7T09OJYx4+fDhrtwetQxsbG0xPT0cbGxtctmwZrly5Ei0tLYkOFy1ahGvXriXnDRw4kJWzAQDw22+/xdjYWLS2tiay7tixA8eNG4dWVlYYERGBFEURHVpYWGBkZCRSFIX9+vVDHx8f9PHxwZMnT6JEIsHPP/+cbEFlsru7OwEohoeHk5wVjo6OmJ6erndA+7YZ9NtI6enpJMSrUqnw5MmT5OMyfvx4TuY7Pt+hj+3t7TE9PV1v6F+tVvcILEb7jjelp4SEBINI+YiICLJt0BgHBgbyRiXCw8MNLk+EhYWhq6ur0euHhoaip6cn798sLS2JPQEATpo0Cb/++mvWMfr8vzFeu3Ytvvfee6/1nkUiEQ4ZMoT3Q077jle57ldffUUGUkKhENPS0ljfJYqi8OjRo6TPuLq64unTp/VmkXwb2Bj1ujaBjY0NeHp6gkAggNmzZ4OTkxMUFxdDdnY2UBQFs2bNAldXVwAAkEgk8C//8i8gEGhvU1dXB48fPwYAgIcPH0JTUxP4+/vD+PHj4ezZs9DR0QF37tyB9vZ2Vta8oUOHwv/6X/8Lzp07BwUFBdDR0QERERHQv39/kuVp8uTJIBQKITc3F7q7u6GwsBDa29uhuroabt++DZmZmZCcnAw2Njbw8OFDKCsrg9mzZwNFUXD16lWws7PjBdXY2dlBcnIyCIVCyMnJgYcPH0J7ezsUFBQAIsLTp0/hxo0bcPnyZUBEePjwITQ3N0NDQwMUFRUBAMCFCxcgNzcXKisrobOzEwoKCqCzsxOGDRsGgYGBcPHiRfJOCgoKoKysjNy/qKiIZH7s7u4m8g8aNAjCwsIIkI/OLGZjYwORkZFAURSUlJRAbW0tODo6kox/T548AW9vbxg3bhwgImRkZJDrNzU1wdmzZ6G7uxtiY2MhLCyMPEdSUhK4u7vD06dPISMjAwoKCghYqKGhgZXl7c6dO3D37l2WrBUVFVBRUQFdXV1EhsrKSigrKwOpVApz5syBvLw8ePr0KXh4eEBSUhIAADx+/Bjq6+uhrq6O6LCxsRFqa2uhvr4eMjIyABHh6tWrUFRUBDKZDHx9fYGiKLh27Ro8evQIWltbobCwEBARSktLobq6GqytrSE5ORnEYjGMHj0aPD094d69ezBnzhy4fPkyhIaGQnh4OHR0dLBkNVHvqaCggID02traiC4AAGpra6G4uJhzTktLC5w9e5aAtNRqNUyePJlzHN3HmBkiFQoFyXp59+5dXrCYl5cXzJgxg/zOz89ngYCZJBKJiO/go379+nF8R21tLW8GQ5oyMjIIgJimpKQk8PT05Bx7+/ZtVoY/ml68eGEwu+KlS5dYvkQfZWdn633WhoYGOHfuHNHX8+fPOcfS/p8mXd9Bk64Oy8rKoLKykvxOTEwEHx8fo8/LpM7OTjh79iwr2yJNtO9gUkJCAmg0GqPXffLkCdTU1AAAEL9O92GaHj58SO7b3t4OhYWF73am0p6O7lUqFUqlUpw7dy7+/PPPKBKJMC8vj+QEd3d3R4FAgHfu3CGRA29vbywoKDA4WoqPj+dk1hs9ejRrH/wPP/yAP/30EwqFQlSpVCT/PXNG8csvv2BCQgIqFAoyy/fw8CAzBrlcjvn5+WSGEhwcjHl5eWTmsWDBAjx8+DACAPr4+JDQt7+/P+bn52NAQAAJ1YvFYlSpVEhRFLq4uHDqJDg7O7Mygnl5eaGZmRmam5uzwlmbNm1iZVGk2czMjBP2Ysrl6emJGzduxAMHDqBarcaCggICVgkJCWHJBaDN237v3j0yG6N1SMtKyyWRSNDHxwcpisLU1FRcsWIFGQXfunWLs+5Jy8oEPuqT1dHRkQO0dHBwQGdnZ7SwsMDCwkKSHS0sLAxv3brFmyeCqUP62Zj6UqvV+ODBAxbgiykXfZ5KpcKCggJUKBS4Z88efP/999HLywsLCwtRqVTizp07cd26dX/4aF6X30V6E3JPnDgRf/nllx4da29vj7/++ivLBpm+A0A7M8/JydF7DUO+QyaTscBxTN+hj11dXQ1uk6MoCm/evEkinSKRCFUq1WvhVzw9PcmWRjpbqqFwvy7b2tpyInU9Yabv6I0Or1692uPlOdp36LYz/SQfHz9+nGz7/K1Y1/+/LWzUTntj1PpShS5duhRv3779mwmRmpqKBw4cQHt7e+zu7jbYQYcPH4719fVIURTm5+fj/Pnze3UviUSCra2trKUQkUiEzc3N5GPo7++PnZ2dqFQq8cyZM/jVV1+xrqELIKypqcHY2FicOnWq3myDTI6Li8Pq6mpWW3R0NNbV1aFAIMDCwkKcM2cODhkyBF+8ePHKDkMsFuPLly9JiE+tVmNHR0ev1rqOHj3KAhA+e/YMx4wZg5MmTcKysjKODpnnfvvtt69dzEMmk2F7ezsrxCsWi1k6DAwMxPb29je6JvhH8btIf/Q7A4Ae+Q4mG/IdAwcOxJaWFr15Ufj4woULvGnG9bGnpyd2d3f3eCmBjx8/fkw+fJaWltjV1dUrhPuHH37Im5zsbWB9voPp//+oZ9P1/28LG7XTnhq0lZWV3vU7qVTKWlM9duwYrl+/HlUqFT5//hyVSiUeOnQIP/vsM/Ty8sLa2lqShGjSpEkEkPbw4UMcOXIk5/oKhQLNzMyQoii0srJCiqJw8+bNrEQ/NItEIjKit7Cw4IDbsrKyOEZ+4cIFXLJkCWo0Gnz27Bk6ODhwZLWysiLGT4PvALTr7PRHhqIoLCkpwbi4ONYH1dLSkmTRYiYf2bVrF+7atQvt7e2xrq4OnZ2dcevWrbhv3z4iw+3bt3HKlCkoEonQw8MDa2trSd0FpqzZ2dk4Z84c1jNnZmZiSkoKhoSEYGVlJe+sgKlXplwAWvTvtWvXOOcwdWhmZtYjWWkd8umV/n3r1i1MTEzEyMhILCkpQYqi8Pr16ySpFZM/+OADUtmMlmH58uUksxhTLqFQSOQ6fvw4mfELBAIsLS3FwYMH48yZM1kzxf379/NGbf5ofhdJnyynTp3i1Cahfcebfm9M39GT4/l8B83M/tRTZvqJnjBti8zndXFxwbq6uh7POi0sLEhSMVp+euIQHh6OpaWlrInEgAEDsLy8nNiNTCbTC9gUiURYUVHxm+fmf/DgAcbHx3Pa+fwJ/Vy6+JGwsDAsKyt7pUmTWq3G6upqFoDbz88Pnz9/Tu6v6zt0feLbwkbt9FUM2sfHB3/++WcUi8X4ySefcLbqhIaGYkBAAJqZmWFMTAyKRCLs378/qtVqVCgUGBMTQz5M7u7uJFXx0KFDOaFkAMBFixax0P4A2tmebqQiOTnZ6N7wQYMGcfa/hoWFkf3H0dHRrE7j5eWFP//8c49Teg4fPhwdHBxw+PDhuGvXLoPHBgUFYVBQEEokEhwxYgQpYsLMZhUREUFSEotEIoyJieHtaIMHD+aExwYNGoQqlQqtrKwwOjqaOJYJEybo/dAJBAI8ePAgqtVq9PLywvDwcKQoCg8cOEBm3wqFAkeMGIEHDx7Efv364dChQznFW5j8/fffk8xilpaWmJaWxllaoWXdsGEDfv311yRcGB4ezosU9vf3Z2Uk/PLLL3H16tWspYzPPvuMk7Y6NDQU/f390d3dHY8dO4ZxcXFoZ2dHZKWPCwkJeeNFUH4Pg34bCUCb53/ixIksWQYMGMApkUv7Dmbb2rVrWXlA3jTv3LnTYC0KqVSKR48e1Quy+71YJpPhiBEjWL7IysoK09LSep2pz87OjlMa3sbGhuUnDDFFURgdHW10m6eu76B57969PRpIDBkyhIAgDfkOQ2xra8uR1d/fHw8dOmQUoGphYcH5JiiVSoyJiSHn6voOmh0cHDAtLe2dAR/3CkA4atQo0Gg00NnZCc+ePQNEhLq6OpKZiqIomDJlChQXFwNFURAVFQXHjx+Hzs5OuHr1KnR2dkJMTAwcP34cOjo6AEBb6vHcuXMAoAXryWQyAACQy+WQmJgIcrkcGhoaoKGhAWQyGSQmJoKZmRncvn0bqqqqYOLEieT5mpqaWICRMWPGQEBAAABoQUCJiYmQl5cH1tbWEBMTA0KhEKZNm0YyYTU0NMDJkyehu7sbRo4cCcHBwdDV1QU1NTUwZcoUUobYysoKEhMTQSQSQVRUFISGhpJ7nj59Gp49ewYvX74kAJSJEyeCh4cHqFQqVtnlmzdvws2bN6G9vR3+8Y9/QGtrK+Tm5sKNGzdAKpVCYmIiXL9+Hdzd3WHo0KHQ2dkJx48fh5aWFggNDYXExEQCyLGxsQGFQgGOjo4wZcoUoCgKLl68CJaWlhAWFgYnT54kIKDm5mYOeKlv374QGxsLiAjPnj2Djo4OKC4uJgDN6upqorOWlhb4xz/+AeXl5dDW1gYvX76E1tZWSExMBKlUChERETBo0CCiw+bmZgK+6e7uhmfPnhGgjbu7O9FhRkYG3L17F3799Vc4ffo0AGhLONMAM1qH1tbWoFQqCWgSQAvYunnzJikvDaAFrKpUKhg9ejRpy87Ohvz8fNKHT5w4ASEhIeDk5AQ3b96ExMREEIvFkJOTA3fv3gWlUknkMtGrU21tLSuDHQDA5cuXOSVys7OzOWWO6+rqSKY7JqlUKhg3bpzee06YMEFvRj8mPX/+nBeARhNtE52dnRASEgIxMTFGr9kT0vUdNPn5+fHK1draCv/4xz9YWfl07YmP3NzcWBkSAbRZGU+dOsVqq62tZfkJQ4SIcPLkSd6y0brE9B3MNjoLKZNo30HT2bNnCQjSkO9gklgshsTERFKa/vnz5xxZOzs7obq6miWrnZ0dTJs2jQDeAbQgTfqbQFNTUxMcP36cPAfTTzKpq6sLnj179u6Aj3szuk9LS+MktnF0dCRhGYFAgPn5+RgaGorTp08nYBE3NzeUyWQ4efJkzMzMZJ1vZmZGtsrdunWLjNAdHBywpKSEgNPkcjn27dsXS0pKCHBkyJAhnCIfMpmMrAtmZGRgYmIiOf/x48fo5+eHy5cvx0OHDqFUKsVHjx6RbTsSiYSA2H7++WdcvHgxAmjDggUFBWQkGxAQgF1dXahUKvHHH3/ENWvWoEgkQnd3dzKqZsp1+/ZtHD58OMbHx7MSW9ja2qKtrS0KBAL08PBAoVCItra2aGdnh9bW1lhSUoJubm748ccf4+7du5GiKHR3d0eRSIRr167FkpISvH//PgoEAjx79iwmJSVhv379sKCggIxalyxZwto/zdQXgDYyI5FIMDk5mZUd0sHBgRMKtbe358wE7Ozs0MbGBl1cXLCkpARtbGxw27ZtuGnTJo4Omezi4oJmZma8OmSys7MzGVnz6ZCWgY40MXXo6OiIK1euJMsJbm5uJPTL1BetQy8vLywuLkZ/f38SFnRzc8OSkpJeh4V/K34XyZhMYrGYZTs95XHjxnGqBSoUCjKTvH79OmdrGd3vXvX9M/sdzZaWlpxaKRYWFpwop7m5OQv0Rvc73XskJCRgVlYWq43pTwyxXC7nbCeMiIjAvLw88n6tra15lxronC/MNqY99YSZNvYqTPsO+rerq6vekLs+36FUKrG4uLhH28WZ3LdvX3z48CGJvPDpUB/r+v+3kY3a6esadFZWFn700Ud6/05RFL548QKHDRvG+/fExETerHy6HB8fb7RcJYA2TN/Q0PBKSgkJCcG2tjajSwLMwQDdplKpsLu7m3xok5KSSJIkfbxv3z7ct28fOjo6IiKiq6sr7tmzR28KTEtLS+zu7n6tNJfXrl0jDogGEDLD7TT/8ssvrPTBANqUn7rFgY4dO2Z0OYSPnzx50qOEKAUFBQZBoDKZDDs6OkgYkqnD7Oxsgg8QiUTY0tJCcjrw6ZDur/X19aYSxm+QjMmkVquxs7PzjayzTpo0yWCZ7J72u97wmjVrONiaFStW4K1bt1ht7733Ht67d++V7vE64GNd3rx5M6anp3PaBw0ahC0tLazQeWhoKLa2tvZoF4JQKMTGxsYeJ1PqCZeXl3Oyl/5ezKdDfazr/99GNmqnvTHo06dP45IlS7Bv375YVlaGUqmUgMj8/f2xoqICFQoFHj58mLUty87OTm9nkslknPWu+Ph4AirMy8vDMWPGoFQq5V0Xs7S0xKqqKrKeJ5FIOGtKAwcOxOLiYqMAEpFIZBSck5ycjFeuXEF7e3sy4JgxYwZev36d1SaTyThI4PHjx7MyFVpYWKCFhQVSFIX29vYoEAjQ3NwcLSws0MbGBp89e4bu7u74+eef448//kiO60kdcyaHhIRgaWkpisVitLa2ZjldOzs7FIlEOH/+fFJLAEC7Fqn7oaRrCXh6epJsgJaWlj1aE9u6dStr14GtrS0nq+L169dx6tSpGBERgY8ePUKKotDGxgY3btzI2rp14cIFnDt3Lvltb29PwJ1MHdJ9U7cf6urw4MGDLNCanZ3da5d9/aMM+m0k+tnfe+89PHv2LEcmoVDYI1Acs9/pO0afn+Drd7q+A0A7e6+srOTgigyxQqHgRMz42uRy+SvvDuDzk3Tff/bsGZnBSiQSo/gBpVLJ+w7FYjHHd4pEol6t0Rvy9YmJiXj9+vVeyW1ra/takQaAnvv/nugVQFvWvbKykuVb6D78TxMZiIyMxICAALSyssIxY8agQCDAdevWYUJCAlpaWuKYMWNQKBTi4MGDWeV+AQBXrlyJM2bMQAcHBzxw4ADHSR84cIB8VDw8PHDEiBEIADhixAj87LPPMCUlBS0sLPCnn35CW1tbTE5OxqVLl6JYLMb4+Hg0MzPDpKQk1v7WL7/8EqOjo9He3h7j4uKQoij8/PPPSZlcsViM+/btQy8vLxw3bhwrwvHhhx/iTz/9hJ9++imnIwwbNgwpisLvv/8e1Wo1qlQqDkCFyd9++y2GhISgp6cnq+jSwoULceHChbznSCQSjI+PR4VCgcHBwZw9/tOnT8eVK1ey2iZPnswbdrSxsSHy63tGPz8/TqGXmJgY3LRpE1IUhXv27CE6NTMzw/j4eKOzBSsrK/zpp5/QysoK+/Xrx8pStnXrVjKDMDMzwwMHDuC0adPQ29sbHR0dWeFdjUZDAIgAgMOGDeMUN/rkk094UccAWoDPvn37yAee1iH997Nnz3K2h76t/C4S/ez+/v44ZMiQHsnp4uKC+/fvZyHwe9rvespM30G3iUQijI+P50XRM33H78W079D3d6lUivHx8bzpyn8Ppv1/T4718fExGnF777339KZG5mPad/AtRdLM9P90m5+fH/7www9kYqXr/w2xubk5xsfH92pr6dvAxqhXAEKxWAwCgQDq6+vh0KFD0N3dDZ2dndDd3Q0URYFEIgEAbca927dvs87t6OggpXx1gSOIyAKYPHnyBM6ePQsJCQlw9uxZePToEXR1dQEiQnt7OyAidHV1QWdnJ1AUBWKxGCiKgs7OTtZ1Ojo6oLu7G6qrq+HYsWMwYcIEMDMzY4FtmNdjntvZ2Qnt7e0c4EthYSGkp6eT6+P/y06VlZUFCQkJIBaLAQBYwDX6HiUlJXD8+HHWPeh3AgAQFxcHXl5eAAAsuW7cuMECxvGdy2wTCoWQkJAAVlZWAAAgEAhAKpUSUKdarSb3mDBhAtjZ2UFBQQGcOXOGdT3mO+no6CDvrbm5GQ4ePEj+5u3tDbGxseS8sLAwGDBgAJEdQFvumZmljNYNTW1tbXDixAkoKiqCqqoqOH78OCQkJIC5uTnk5uZCfn4+jBs3DiiKgvT0dCgoKAB7e3sYP348UBTFej6BQAATJkwAW1tbANACjzo6OmD8+PHg4ODA0iEAwLlz5yAnJwfMzc1ZOjTRm6OoqCgQiURw9uxZVntAQACrZDlNTFunyVi/A9BmFmSWtjVEHR0dcPDgQWhubiZtnZ2dcPDgQQ7YkT7+TWeY8/X1hZEjR5Lf4eHh0K9fP/Jb9x3oUltbGxw8eJCTHa8nJBKJICEhgQDtHBwciD3xEdN30ET7f0M0duxYcHJygkePHsHJkydJ++DBg6F///6c6+n6NZqkUikkJCSAQqFgtet+T8RiMfEdAFqw4pEjR1jvke5fNOn6fwCAPn368PbNxsZGOHjwIHR2dkJERASEhISw/q6rw3eGejO6P3nyJAHV6bK/vz+WlZWhl5cXSiQSVCgUJLzk4OBA2pjhQCsrK9ZedLrN0tISHRwcsKKiwuCID0Ab6quoqEAPDw+0sLBghXXs7OxISFwul2NpaSlnK5MxFolEBBxkY2OjNyTu5eWF5eXl5O8TJ04k64gODg4k1CUQCNDJyYlTuMTJyQlv375NZrfW1tZYUVFBwDtSqZTzLiQSCTo5OaGTkxNr6UAmk2FpaSnZohUcHIwlJSUoFotZOhSJRFhcXExmHrSsPQ110XIlJCSw9tl+8803uHnzZoOy6laRpGWgdWhubo7l5eUEBDRgwAB89OgR67yQkBB8/PgxGaErlUq0tbVFsViMJSUlGBQURORydXXF4uJiEp0QCoXo5OREZFUoFNivXz+WDuVy+VuXSexdJEO+Y+7cuUYzC1pbW3P8BABw+h2AtvAXcynO0tKSA/7ka9P1Hb1hut/p+7ujoyOJZuj2u8TERBZYMDU1lRON1Gc7MpnMqH80xGZmZlhWVkYyK4aGhmJRUZHeZUhD/l8fUxSFv/76KyuyR/OePXtw48aNPb6Wra0tVlRUECAln08EAI7veFVesGABa1mLz/8fOHCAk6nUkA7/SDZqp70xaGMsFAqxubkZw8PDceHChZifn88CECYnJ7Mq9PGBzw4ePEgK/PSWt27diqdOnSK/jYHPesIajQY7OjpQLpf3OosYzXQGQgAt2hYRWehjJoBQ3zWio6M5mbWGDh1K9KO7N/tVuLdArtraWt4kUTQ7OzsjIrIQ1Pb29oiILISytbU1IiJ6e3tzdNgb5gNy6eqQbtMFEM6bNw8LCwtZ5/UEBPp787tIryszH3C1p0ynMme2fffdd3js2DFW22/R7wB6DlztCev6jvHjx+st6/vPwCNHjsTa2trf7X6v6v/fFjZqp69q0CKRCIuKijjrWc7OziiRSNDMzIyM2pycnFAqlaJCoWCN5GxsbDgjdGtra07b5s2bcfv27Whra4tlZWVkZDh06FBWzWwat0APQhwcHNDMzAwHDBiADx8+RIFAgL/88gsmJydjSEgIFhUVoUgkwtOnT3O2TKalpeHSpUtRJBKRj5mtrS0r13dhYSGGhYVhYmIia8vk999/j5988gm6ublhWVkZa6uaQCBAFxcXzMvLw1GjRrHaBAIBfvnllyygHc1SqRR9fX2xtLSUJOGRSqXo4uKCLi4uZHbcv39/fPToEQqFQjxz5gzv+ltKSgrL8R09ehSXL19OZGUOOHx8fLC0tJQ4r6lTp5LtXE5OTrhv3z78/PPP0dXVFcvKylgAKaZcAFr8R15eHmnbuHEj2TLp4uKCQqEQLS0t0cbGBpVKJZaWlqK3tzeuXbuW5dBpHerKZW5uzgt2YupQt425FdTBwYGksY6MjOT017eB30XqrYwzZszAixcvsvyEMZR2Xl4e73q0lZUVZ8ZvZWXFAfLR/Y7Zptvv9LFuv6N9B/2b9ol8/a43rGtPMpmMs6UxOjqaVWL8k08+YZVwzsrK6vFuimXLlnEGTb8F8/kOBwcHLCsrM2h/UqnU6Na/nvqOnjDT/7+LbNROe2PQH3zwAcbFxSGANvyTkJDwWmHUlJQUnD59Otra2uKePXv0huDDwsJw0KBBKJVKcfLkyWTm6ubmhuPHj2cd6+Liwinb6+joiAkJCUhRFI4cORL79OmD9vb2pC0mJoZT5jQ6Oho1Gg16eHjgd999R8J80dHRBGiSkJCATk5O6O/vzwK8RUREYGhoKJqZmeHkyZOJIwgLCyN7aMeOHUs+6hYWFvj999+jtbU1Dhw4kBVS+/zzzwnQTiwW4+TJkw2i9x0cHIhcI0aMIFn0RCIRfvvtt+jl5YV9+/ZlARmjoqJQo9Ggm5sb7t69GyUSCS5fvhwnTpyIFhYWOHnyZCK/r68v6QN8skqlUpw3bx5vCmFPT08cO3Ys+T1gwABOCWaaaVktLCwwJCSEBfjLy8vDJUuWoEqlwm+++QYFAgGuXbuW6EAoFOKuXbvQx8cHR40axQrjLV++nBfwNHToUPzkk08QQJuhcePGjSRHxdvE7yLRzx4bG8sqWa2P/f39DQL1tm7dysloN27cOJKp802xbr9bv349y270Me07enIPZr97E+zh4YHjxo0jv0NDQ1k2FhsbywHf6mONRqMX8EfbGL3Nmek7evvMTN9Bt8nlcpw8efJr1xPR1SHt/5nHMH3H/5/ZGPUYQBgXFwcuLi4QGBgIERERgIiwd+9eqK6uBl9fX4iMjGQd7+PjA0OHDiW/Bw4cCIGBgeT3iBEj4E9/+hPIZDKgKAqUSiULuCKTySAuLg5kMhlcunQJHj16BEOGDIHU1FRoaWkBAICnT5/C/v37yTkajQa8vLxg3759AKAtfezj4wNVVVWwd+9eQERoaWmBtrY2qK6uJm3Hjx8nZU4pioLRo0dDdnY25ObmgkAgAHNzc4iNjQVbW1sQi8UEwNLc3AxdXV2Qn58PZ8+ehbi4OBCJRJCRkQHZ2dnQ3NwMqampBKgiEonIuT/99BPJrMeUv7W1lQUGMjMzYwHampqaCGDHwcGBBT5Sq9Xg7+9P5PrHP/4Bd+/eJX9XKpUgEongzp07kJWVBbGxsSAQCODUqVOQm5sLQqEQzMzMgKIokMlkIJFI4MWLF5CamgpRUVEEfHfkyBFyTV1Z29raQCaTkUySAADR0dHg7u4OJSUl8NNPP5H2y5cvQ0ZGBqvfaDQaGDBgAHR0dEBqaiq8ePEC2tvbic4BtFnJCgsLQSQSkfemUCjIe2K+T6a+ALTlbSUSCZibm0NcXBwIhUKiG7lcDgAA+/btg+fPn7NkMNHrk1gsJu+YSRYWFhAbG0t0kZ+fD0ePHtV7HTMzMxCJRKy2AwcOwJMnT17r+RwdHVn2lJOTwwKayuXyHoFLT548yVsOWaVSwZAhQ1htzH5Hk5eXFwwfPhwAAGJiYsDZ2bnHMjx58gQOHDhAfmdnZ7Ns7Oeff4aCggLOeWq1mpX5DwAgNzeXBfjTJTMzM6Izpu/QpZEjR4KDgwMAaO0vLi6O9R6ZvoOmly9fQmpqKiszpEQigbi4ON4+NGzYMAK+ZpKuDtPS0jjloHV9x+jRo1nZTfXRqFGjwN7e3uhx7wz1dHRfU1ODGo0GFy1aRIrB0Dx//nwS1rOxsUGxWIyzZs3Ca9euoa2tLVIUhT/99BNrBPzw4UMcPnw4SqVSEsazsbEhI0tHR0esqakhYbBRo0Zhfn4+674SiYQVWvr0009x//795Hd2djbOnDkTxWIxOS4zMxPnzZuHIpGIgH6srKzICFQsFmNZWRkGBwejXC4nBW9KS0tZW+MEAgEWFxeTtUBvb2989uwZ76ydKZdAICDvRPc4AMAtW7bg7t27ef9mYWGB1dXVJKIwePBg1v7ZNWvW8Ib1mLLS3LdvX6yoqCCjcVpW5r3oLVcUReHjx48xPDyc7HfWlcGQXHSuCKa+rK2tUSqVGtUhgHabJ11ymWaFQsEKHTN1qI9tbW3Jcoqvry9WVVWxtpUxZWDK/zbxu0jGZAoICMCqqioS8ZPJZJylQplMZhTgZ25ubnQtnu53fH+LiIjAoqKiHofwbW1tecF2lpaWvLib5ORkTmZBPp48eTLevHkTAQALCwv1zs51bac3suqyPt9hTC59stLM9B0A2uhgdXU10S/T//fkfdfU1PBmYszJydG7/MH0/wBc36F7LO3/de/N3Eqo6//5/J8xX/97s1E7fVMGDcDNQOXn54ddXV0Gw9rMDIR0+dueCtdTAEl4eDg2NjayDHfgwIH48uVLFIlEePPmTVy1ahXnvN5koDLExgCEvzX3JIvYsmXLWKk9+TIQAmgdGiJid3c3y4g9PDwQEQ0mKGFmkaQzwb0qCGjDhg0snIY+HdKsC+TiY2YWsePHj+OOHTt+Nx31lN9F6q2MCxcuxAcPHnD6HRN8zMc9AR+/qQyEupkvmczMfPlbsjEAIbOE8ZtgZvZSmo1loDXGPc1A+zqs6/91fYcx7q3voNu8vLwQEV+rDPWbZKN22huDPnr0KAHaiUQizM/PJ9u3aPbw8CBgOZFIhJ6enrwjo9zcXIyMjESlUknyYbu7u/OOMjdt2oRbtmxBGxsbfPz4MQGDyeVygkq/cOECqYqmVCqxqKiIzKBlMhlZTzx9+jTOmjULpVIpyTzm7OyMlpaW2LdvXywsLCSzeEtLS9YodP/+/bh8+XJUqVT46NEjvSPib7/9lmUgzMGAUChELy8vFAgEuHnzZty8ebNBBWZmZuKECRMwIiIC79y5Q97lmjVrWI7vzJkzOGPGDNa5J06cwOTkZJasup2c1qGurBkZGbyDAXNzc/Ty8iIyAGjXa69evcpqi42NJXUYrl69iqNHj0aFQkH05ebmRsq69kSHumxtbc0CD+nT4Zw5c/D48eOkb/LNlFJTU/GDDz5g9VcHB4deV4J7Gwz6baTeymhhYcHZWWNubm5wtw2AdqeKsWx5dL+j7fvx48e9yr1PM0VR6OnpybtG7uzsTGa+QqEQHzx48JuU+jUzMzP47ExZmazrO/Tx4sWLWZk/mXLR7OTkRCYFAoEA79+/z4qgGmOm/9fHtP/XbY+MjGRtI9XHTP8PwPUdPWF9voNmvm+dSCRi+cQ/mo3aaW8MOjY2loRPBAIBJiYmkhmuq6srbtu2jRhHZGQkCyy0YMECFnBrypQpegE//fv3Z+3TjIiIwMjISJTJZJiUlMQbvh0/fjzJISAWi3HGjBloaWmJ48ePx/fee48cN2bMGJJJTywW49atW4lB2dvb4/Tp01EgEOCyZcs4oJIRI0Zg//790crKCmfMmMEKGzk4OOD27dtRLpfj8OHDWRkDJ0+ezPsxjoiIwIiICLSwsMAdO3YQQ9NoNGT/7YQJE0jJXWap6AEDBrDCh/Hx8ZySu7dv38bly5dz7jtixAhcuXKlQR3GxMQQo6YoCr/66iu92xf9/PxYup09ezZ+9NFH5MM+ceJEAjQyNzfHHTt2oLW1NU6bNg3nzJnD0WGfPn1w69atmJSUxKvD1atXkxLHhnRIv0sm4BFAC9piznCio6N56zOEhYW91qznjzDot5F0ZRAKhbhly5ZepfzV5Tlz5nBKp/eWJRIJJiUl6Y1c6va7V2GKojAxMZF8fJydnXH79u3kwxIREdGjKEJoaChu2LDB4DGJiYm8SHm1Ws0q/qPrO/RxSEgI2fHEZA8PD9y6dSsnAx8tq+7Ond7w7NmzOcBd2v/zPceUKVP0XmvdunV6AcpMXrlyJcl4SzOf/3/X2RixUTgGKDo6GjIyMqChoQEsLCxg4MCBsGfPHgJmE4vF4OrqSgAkSqWSlPwF0JaHZGZ8qqiogNbWVnBzc4N/+Zd/YYFcFAoFODk5kd/Mv+3cuRMAtCV3JRIJ3L59G4YMGQLHjh2Dly9fgp2dHQQFBcE333wDAACWlpZgZ2cHQqEQhg0bBqdPn2aVXHZ1dSXgkerqati1axcAANjb25PSmRRFwfDhw+Hy5ctQW1sLSqUSysrKYOjQoXDt2jV4/vw5iEQicHFxAYqiSPlduVwO4eHhsG/fPujo6ABXV1fw9fUl8tD/WllZgaurKwHjKBQKAhqiwZAAAHv27AEAbYarx48fQ2FhIQwbNgzS09Ph4MGDAKAtZfznP/8Z0tPT4eLFi/Do0SMA0GblGzZsGFy5cgXMzc3B3t4euru74bvvviPXZ+qQmSkRAMDZ2RkkEgl4eHiAt7c3nD9/HoYOHQq3b9+GgoICKCkpgejoaDh37hxYW1tDTU0NHDx4EKKjo+HgwYPwr//6r6DRaODx48dEVisrK7CwsACxWMzSYVBQENjZ2cE333wDiEh0SJODgwP8+c9/hoaGBrh58yZHh99++y0MHz6cgECLioogOjoaTp8+Dd3d3aBUKgmgafjw4USHCoUCBg8eDGfOnIHOzk4wMzNj9WETvRpFR0fD5cuXoaGhgbS5uLi8VqZHW1tbFohwyJAhcPfuXaisrCRtkZGR8ODBAygvLweJRAKRkZGQkZFBALrt7e3En/CRbr8DAPD39wcrKytWNk362L/85S+Qnp7OysiHiCwbY/oJAK6fDAkJgdbWVhbwF0AL1mP6RD6ysrLiZOcD0IKxXVxcyO/Lly8bvA5NOTk5vO1isZglg4+PDzg7O8OlS5dYstI0bNgwuHnzJinpboisra1Z3wkA4ICMaXry5Al8//33eq/l4OBAshDSFBYWBuXl5VBUVGTwOKb//6ehno7u6+vryfpYYGAg1tTUoIODg8GiOQKBgBSzEYlEKJFIyCi8uLgYo6KicNKkSWRfrLm5ud58z2KxmDWC37JlC+7duxetrKywvr6ehJMjIyOxtLSUszShUCjw+fPnJHpAZ7uj/y6VSvUCxoRCIVZWVpLZo5+fH9bX12N9fT0ns5aFhQWRwcPDA+vr60kYLSEhgeRFMDc3J2v4FEWhpaVlj4EmN2/exKSkJAwPD8fS0lIUCASoVCpRIpHggAEDsKKiguiFlksikWB1dTXRoUAgIPKbmZnpzW2uq8Pp06eT0BytQwBtSLK+vp6E1sViMapUKqyvr0dHR0fcuXMn7tmzh1yXKb+uDnVZJpNxdLNmzRqSK8HCwgIFAgGRVSwW47Nnz0gODLVajTU1NeQ6zJwPZWVlRIc+Pj5YV1eHFhYWqFAoXntb02/B7yIxfYe+Pva6+7cfPXrEmcUWFBSQrax2dnZYX1/PWmqg7a43YVxmv2OyRqPB6upqEhmVSqWsEL0hG2PygQMH8Msvv2TZia5PZNrOm2Rdn6ivTZdTUlJYwEimrBRFYWlpaY9m6EzurU/sKV+5cgUXLFjwRq7F9PWG2t4WNkY99i66SpFIJNjS0mJwfcjPzw+7u7uxu7ubZCB8+PAhUTZT8QBaAKFuSJfm+Ph4rKqq0ttxDP3max84cCC2tLQQxX3wwQdkjbun9+Brq6urY4Xg9D1bRUUFyZHg6OiI3d3dPapXru/d5eXlETwH8+8ffvghXr58mdMeFBSEbW1tKJVKMTMzU284XFeHzOsYeu8TJ07EsrIyvbooKSlhAbkMGf2mTZt4U9ZSFIUymQzb29tRo9GwdKjv2c6fP89agtJ3XFpaGm7fvv2NGOGb5HeRjDl0tVqNHR0dr1XCmO8exvyCtbU1dnV19TptbU/8y/Lly8mOAL5+11Ourq7mhKuZvuNNclhYGAdoHRoaii9fvjQ6+GDK/ssvv7DSDL/KB93Z2Rm7u7tfa8mhN7rrLQsEAnzx4gVr+YLP/79NbIx6hRk4dOgQK72vSqUiI0BfX1988OABmU1NnToVf/nlF1SpVKhSqch2Dn2zPwDt9jxdwEtWVhaOHDkSlUolMdoLFy7wDhrWrFnDSm98+vRpsqYkl8vxwYMHZO1aJpOR///888+4atUqFhjnp59+Ih9XoVCId+7cwZCQEExMTMQTJ06Q43bv3o3vv/8++e3j44MKhQLHjRuHGRkZpH3Lli2sNb/KykocP348RkdH47Vr11ClUrGM0MrKCgsLC9HV1RXXrVtnFNnu6emJH3/8Mf7www+sdltbW5ZctA6lUimqVCqkKArd3NzIjF4gEODt27exf//+CPA/M3xah8xrX79+HSMiIjA+Pp6UP87Ozsbhw4cToCHz+KioKMzOzkYALdLWwsICIyIiOGVNw8LC8ObNm8Rw7e3t0dXVlaND2gBVKhVKpVK0sbHRq0Oa3dzccOnSpSwdAmhxHcwa7y4uLm9d9sGeGPTbSLoyiEQizMvLI9ECiURC+iKfzN9++y2uXr36jb9LgUCAKpUKxWIxx3cAaHcT9QRoN3/+fDx06BCrzcbGhoWJcnNzI+BGgUCAubm5PQLaeXt7c6JifH4yMzPztde45XI5y7YA2H4SQIvVOHLkiMHruLq66k1G5+7ujgUFBSTawPQdTBYKhRyfSDPT/1tYWGBBQQHL7pVKJRYUFBj81gBoozf3798nWCi1Wo337t3jDHyY/p/Z7uPjw4ke0v7/TffVN8FG7bQ3Bj1+/HgMDQ1FNzc33LhxIyscYmtriwsWLMAvvvgCVSoVajQanDx5stEH7N+/P37wwQcIoJ3FBgYGYkhICAEfTp8+Hf38/FCj0RCgTWJiIvr7+6NarWbNaCMiIliZy6ZMmULAgiKRCOfNm8dCiItEIty4cSMuXryYg/YdN24cC0CXnJyMLi4uqNFocNKkSeS4uLg43iIcffr0Yc18Y2JiWCWCZ8yYgb6+vujr68vaBTBx4kRMTExEmUyG8+fPR3Nzc4yIiMBRo0ahTCbDTZs2Eafi5+eHGzZsIE40LCwMx4wZg2KxGL/44gveSAOtQ336oGWlEb4ODg64adMmlEqlmJyczMogOGvWLPTy8sKAgAAC+klKSuI4lLVr12JwcDCqVCqy1emDDz7Afv36oZeXF8lW+P777+PAgQPR09MTZ8+ejQBah0xv6dHVoZeXF3755Zf45Zdf8iLNaR26urrixo0biZHr6hBAu/TFB0aysbHBTZs2vTUG/i6SrgwCgQDnzJmjd9Y3ePBgFvA1NjYWw8PD0dbWFjdt2sS7fEP7DgDtB+LLL78kHxym79DHur4DAHDQoEF6I5VMDg0N7dVMnbYxvp0AM2fOfKVZ//Tp0w0WYdP1Ha/K/fr142R47Q1bWFiQyQgAsHxHT5n2/wDaD/r8+fNZSxkSiQTnz5/P2fmgy0KhEOfOnUsGLg4ODjh37lzOAITp/3v6jKGhob/JAPZ12Kid9sagQ0JC0MvLC1UqFZ4+fZozghIIBHj8+HHe9UGNRkOqYzE5KioK9+7diwCAhw8fxrCwMBwyZAgnJzhzjzrzhTOT0fj5+WFgYCAKhUKMiIhgOQ2BQIARERGsUbZEIsH09HQW0j08PBwpisLg4GBO+DAoKIj1oRs8eDAnYcagQYMMbkmTSqUYERGhN23nsmXLOHt5aTYzM8OzZ8+SThkcHIwnT54kefSZ90hPT2c9P0VRGB4ezoucDg4ORh8fHzQzM8OIiAjWGqq7uzuePXsWFQoFbtiw4ZXW23766ScOGnjfvn1kRwDNqampnMJHe/bs0euQ1Wo1njt3Ds+dO4cqlQq9vLxYo3dahz4+Ppieno4SiUSvDt3c3MggialDR0dHPHfu3FuTk/xdJEPyeHp6cgbiY8aM4U285ezsjOfOnePdLkf7DgDtAO7cuXNkf/egQYM4M/dXZR8fH852an2sVCo59mSM169fz4lmvQnW9R1M7t+/P2dg0q9fP94dUIau31tZDbFEIjHoJ992jomJ4URp/2g2aqe9MegLFy7gihUreG8kEAhYH1qxWEw+xmZmZpiWlsbat65QKPSCD0UiEfm4KRQKFIlEGBcXRyrI0W3McxQKBW7ZsgWPHDmC5ubm+OLFC1SpVCiRSFAmk6FCocCGhgYMDg5GsVjMeV4A7ce+trYWJRIJZmRkcJLYnD59GtevX0/kraysxPDwcBSJRCiXy5GiKCwrKyMfOYqiOPdwcXHBxsZGVgiaPo4vTMonK20sMpkMlUolvnjxwmhpZolEgrW1tRgUFMTSDQDg2bNncc2aNdinTx9saGhgDSx0ZdDVjVAoZLUBaAcjunty+doAtKPznsy6dfVFy0/3L4FAgCtWrMALFy6QY/Tp8NNPP0W5XM7S4ezZszE3N5ejw7eN30UypMeUlBSydPR7sK496bM7fbxu3To8c+aM3r/TfRFAO1itr68n/VTX7mh7MgRU5fOThnynrj1JpVKjwMWcnBxOddcrV67gokWLeI/n85098R362vjYyckJGxsbDeYD0JWV6RP0sVwufyXwJVOvTJbJZO/MgMWonfbGoAUCgV7DUavV2NbWRpQzf/58vHfvHgFVDB8+nHVudXU1Z28nzRMnTsTS0lIEACwtLSWFd2hlFBcXc/YY//rrr5icnEyOoY3liy++IDWphUIh3r59GxcvXowDBgzAxsZGzoeWPo9PVt02+l5z5swhGdOYHcbT0xM7Ozs5Gah0DdnR0RE7Ozt5R+2PHj3iJBMCAPzqq68IqtnQjg6++65cuZK1Ts+US/dafn5+2NHRQSIKs2fPxl9//ZWlw8TERFap3wMHDrB2DgBoZ/26aYYBtDkPqqurjT57eHg4vnjxgjzfRx99hBcvXkSZTIatra2o0WhYfcSQDt977z2yg4U+nnnu25IkhI/fRWI+v0ajwZcvX5KBoa7Ofmtm+g5ra2vs7Ow0urbMZEPPKxaLsaWlhRXpYNpTSkoKq6IgANt38PGzZ884yxdlZWWsYkRMjo2NZQGtt23bhmlpaQZl6omvM6RDPlkBuL4DQLu019nZ2aMUxMb8mq7v2LJlC0kwpo9p/9+bPiMSibCxsZGVO4bm9PR01u6Pt5mN2umrGDT9gnJycsiSgFqtxs7OTjIYsLW1JeHYgIAAVCqVOH78eDx58iQCaCuT7d69G9euXYtOTk6Yl5dHOoiVlRWZ6fr7++O2bdvw888/Z3Uy3fUgX19ftLGxwYEDB+KVK1dIR3Z2dmYZu0qlQjs7O1QoFAQ4cuDAAbJGrct+fn6Ym5vL6vje3t54+/ZtIquNjQ0r9LxlyxZcvXo1isViVKvVpFOPGjWKDEwAtHn4P/30UxSJRKhWq1EkEuH69etZERRfX1+0trbGsLAwsiuAlishIQGzs7NZzik4OBivX79O7rlgwQIOCMre3h59fHw4OlSpVHj79m3WTEUikaBarSb3sLGxIcs9/v7+aG5ujtbW1qQS2sWLFzExMRFnz57N2gHg5ubGCkWeOXMGY2Nj0dzcnBXVeP/993Hbtm3kd1paGk6cOBHNzMxYSY8cHR3R29sbKYrCPn368M4IfvjhB1LCWSgUYk5ODgYHB6OdnR0H15CQkMDrNOly08ZK6L4tBv02Ul5eHqkUJ5PJsE+fPkZn415eXnjnzh0yi2T6Dprj4uJYoE8A7QeCb+cJn+8QCoWoVqvJTFHXdyxbtowDKkxJSWGVBGYy3Rf1zfT5+p2u79Bl2sZ02/T1RwsLC5Y9ubi49Crc3xPuqQ51fQcAcHwiky9evIhDhgzhtH/88ccs/0+zru/QlZX2HcxzaP9vTEam7wDQfsP4IpheXl5vfMfDb8XGqMdVCwEA5s6dSyoRdnd3ww8//ABjx46FqKgoqKqqguXLl0NHRwfMnDkTBgwYAA8fPgQAgPv370NTUxMUFBSQind0ZbLLly+TqlV0MhAvLy8YM2YMOe4f//gHXLx4kTzH3/72N1CpVKxnKywshNraWigrK4N9+/YBIsKiRYvA29ubVAcE0FbQ6tOnD9jb20NCQgIIBAJW1UKhUAgffvghbNiwAYYPHw51dXXw97//Hbq6umD27NkQExMDDQ0N8Pe//x06OjoAAKC2thbq6+vh448/BplMBr/88gtkZ2eDUqmEyZMng1QqBQCAoqIiOHToEHmWixcvwsWLF6GzsxPu3r0LnZ2dkJWVBRcuXACFQgEbNmyA6upqqKurg/LyclaFxoqKCrh8+TKRNSUlBcLCwqCqqgp+/PFHkvjk1q1bcPr0aRAKhbB+/Xpwc3OD6upqePToEUeHtFydnZ0wc+ZMGDlyJFhaWsKkSZNIgpja2looLCwkuvnb3/4GkZGRpBLagQMHICsrCy5evAjp6emwYcMGMDc3h6dPn0JpaSl5/sOHD8PDhw+hsbERSkpKYMOGDWBjYwNXr16Fc+fOkeOOHj0K+fn50NzcDL/++it89NFH4OjoCFVVVdDV1QVr166FBw8ewMyZMyEyMhLc3Nxg/fr1IBQK4eTJk3Dr1i1Wf62qqoKamhp4+PAhUBQFa9euBZVKBQUFBXDs2DFy38TERIiLi4OmpiZORTUT9Y5SU1Ph+fPnAADQ2toK9+7dA0QEAG0CrYULF3LOoatl0jbG9B00/frrr3D48GFWW1FREasypi4VFBRAfX09AAB0dXXB3bt3yT2YvgMA4Pr163DmzBnW+Tdv3iRJxQAA5s+fTyq2IiLcu3ePVNpzdnaGjz76iCRHovsdk2prazltTMrPz4fGxkZOW0NDAwQGBsLKlStZf3vx4gXk5+eT3+Xl5VBSUgIymQw+/vhjsLW15b1PTEwMzJ49m9Pu4eEBH374IUmIBsDWYXJyMkRFRfFes729He7evctKwtTR0QF3796Frq4uzvH6Kk9eunSJ5f9pamxs5JWVJtp3MOnhw4dQU1MDnp6esG7dOhAI+D+BTN8BoP2GMSun0lRcXAwVFRWsNtp3vHPU09E9AOA333yDM2bMQDMzMwwNDUWKonDbtm2sWXX//v3x+++/xwULFqBcLsfQ0FAUCoUYEBDQ4/28w4cPJxW0QkJC0M7ODm1tbQk47MiRI2Qvp1AoxAEDBpCZu5WVFQnT7d27F8eOHYsWFhZkq1xqaipOnDgR1Wo1ZmRkoFAoxMDAQIKeF4lEeP78eczKysKZM2eynmvLli04d+5cVltAQAB6eXmhm5sbZmZmstbDnJycMCsrCy0sLNDPz8/gDECXLS0tMSsrC2NiYki6YKFQiKGhoWQWbGlpSeT64YcfMCEhgSUrk8ViMV64cIGMpA3pEABw8+bNuGDBAvTw8MDMzEy96/obNmzgpDymZbW1tcWsrCwCxmPqEP7fKJ2e+WRlZZFlEqYOmSyTyTAzMxPj4uLQxcWFpcPvvvuOoIwvXLjQo3VBiqLw7Nmz5JlkMhnpr5999hkuXbr0Dx/N6/K7SIbkmTRpkt6Z9usy7TuYbcHBwW90yyjd7+j+FBoaSgCOKpUKL168SHwTvRvJ0PX8/f17nKY5MjKSFc1SqVQkQqfLSqUSMzMzWXUABAIBhoaGolwux7lz5/Lm/+/Tpw+eP39ebyIdPt8BoI1cGpO1tywSiXDAgAFvZI2e6Tt6cw5flMXJyYkl67vqO15pmUCj0WBjYyPp5EKhECUSCQqFQnz+/DkOGTIERSIR+vr6YktLC5qbm2NaWhprbUUqlaJAIEChUMhZe6KzyQFoK29NmDAB4+LiCI6AyVZWVvjy5UuyFDB06FB89uwZUhRFnmnQoEH4/PlzvYrPzs7G5cuXI0VR5L4SiYTXAGhZ6d8nTpxgJdjQZfp6Bw4cwJ07d3Lk1z1Wty0/P584G0tLS2xpaSHOIiIiAqurq1GhULC2F9bW1hJZ6cyP9PXp42gdMrN8MeWnWbdNV366jXnM3r17cc+ePaw2sViM48ePZ+lwx44deOTIEdZxIpEIY2JiiA7pNubHPScnh4W4Zspl7HkN6bVPnz7Y0tLC2TnQ0zKwb4NBv42kzy74wsxisZilH6ZN8PVP3XOZ/YT2Hcxjfv311x5teX4VFovF2NDQwNmPTvOCBQs4VVB1++eRI0d4P8o94V27duG+fftYbbq2w2SlUonNzc1GwcfGbIeP586di7dv3zYoa2/Zzs4OX758yVvYiPb1xtp6ynyynjlzBj/++GPOscnJyRwsyNvIRu30VQyaoijWi1q8eDHJtiUSifDs2bP42WefIQCQjigUClkfuqqqKhwxYgROmzaNU5o0ISEBnzx5Qq5HZ/szlKqY79nu3buHc+fONXgufQ+BQIDBwcHY3NyMEokEMzMzWYWWaE5JSWF1cl25mOzp6Ynt7e1oY2ODQqGQ1TFLS0s5IKDi4mLO/nf62fTJam1tja2trcSgdWVdt24dZmZmokQiwebmZlJoiqIolMvl2NjYSLbUqdVqfPnyJSsK4Ovri21tbSwAYUFBAesZdXUoFApx8uTJRIcA2i2C+/fvZz2bUCjE0aNHswBPmzdvxuPHj7OO27hxIwvBzXwnMpkMW1payMicqUMA7bovEyx57tw53oIvc+bMwfv373McJ1OHf7Qx98Sg30biszdmv2PymTNniO+gKAqfP39O8nN4eXlhe3u7XvDZ/v37WVsSad+he+/fsr68oesLBAKOH6L7HdMmXvUDxnfu1q1b8ejRo3rP6Q2y/vLlyz3eO88nK5/v6C3re96CggJOJJf2/69yHz7/r8/X88n6NrJRO+2NQe/Zs4ckiGGyk5MTC9zl6+vLm1AjPj6e5AXQaDRoaWmJtra2nGp7NjY22LdvXwQAPHXqFMl/z+RVq1bxzshDQ0Px/PnzBMxDhwRlMhlevXqVzKoDAwMxKyuLZTwKhQKDgoKQoij09/cnYWuhUIiZmZmo0WjQ0dGRgGcuXrzImgVQFIXnz5/HgQMHIoB2dBkSEoLnz5/H8PBw1nMGBgaitbU1Dhs2DE+fPo0A2gHC5MmTMSIiggU0XLFiBavq2JEjR0jyH3oQQy8daDQazMzMJHK5urqiv78/UhSFQUFBnK0/QUFBZGlDLpdjcHAwq8PLZDJWm729PdHXL7/8gmFhYSwdnjlzBiMiIlg6BNA6cr5lIktLS1aIzcPDg7Oc4u7uzpujgikDDdpi6pDZN2kdTpgwAd3c3NDLywuvXbtGznNwcEC1Ws2rw+Dg4Fd20L+3Qb+NpE9nfFvMdH2HRqMhkRpjuvD29u7VzgAmL1u2DL/66itW23vvvccCs/Jxamoq726fxMTEHu0zp/udvr+fO3eO+A4HBwfMycnp1cDU09OTZU9M32GIf/zxR04yIKZPfBVm+g4mOzo6Yk5OjtEkQYZYrVZzsh4y/T+T582bh998843B672KrLq+421jY9QrAOGlS5cIUEwgEMCKFSvA3d0dKisroaamBlatWgVisRj+8pe/QEBAADlv+fLl4OPjA8XFxfDLL78AAEBubi789a9/hYEDB5IKXYsXLwZ/f3+ora0lgL4zZ87A06dPISAgABYvXkyuefv2bbh+/Tr5PX/+fAgJCYHq6mo4deoUAfM8e/YMALRgoePHjxMwTm1tLZw4cQIQEWbNmgWDBw8GKysriImJAYFAAGFhYeDn5wegfYtw6tQpqKurgz/96U8waNAgANCCTGpqaqBfv34wb948clxVVRUEBQVBcnIy5OTkwIkTJ1jV1Ojnr6urg7KyMgJS2rhxI9y+fRsqKiogPT0dAAAWLFgAYrEYrl69Ss795Zdf4PHjxwCgBcbduHEDEhMTITQ0FOrq6uDUqVMEtFNWVgZNTU2wYsUKyM3NhZaWFhg8eDDMnDkTKIqC6OhosLGxAQAAc3NziImJ4YCFbty4AUuXLgUfHx+orq6GsrIy+OCDD+DixYvw7NkzeP78OdFheno6VFRUQG1tLRQVFcHq1atBqVRCcXExFBUVgUKhgNWrV4OFhQWMHj0a/vrXv0Jubi7RoYODAwtQNXfuXHBxcYHCwkIQi8WwatUqUnHQ3d0d/s//+T+Qm5tLQFstLS2Qm5sLK1asAFdXV6isrIT79+8T3Vy5cgWePn0Kzc3N8I9//AO6urpgypQpEBwcDHfv3mXpEEALgrpx4wYv4MlEPaMPPviAVTUPEeHmzZvQ3NzMObawsJAFNM3NzYUXL14AgHFdFBUVscDCNOn6Dj7Ky8uDa9eusdru3bsHV65cIb+Tk5Nh4MCBrGMuXboEv/76K+d6v/76Kwv0Nm3aNBg2bBjnuGfPnnEqFDLp9OnTxHe0trZCWloaC8xqZmYGq1evZlXdk8vlsHr1arCysoKSkhKWPTF9hyG6ePEiR678/HwoLy8HZ2dneP/991lVIx0cHIj/p8nW1hZWr14NEokEALQVRflkpeViVitUKpXEd/SE7t69C9XV1aw2pv9n0oMHDyArK4vT7uLiAu+//z4IhUIia29I13e8c/Sqo3uRSIRZWVlk9qdSqfDq1asok8lw27ZtrKI5Fy5cICFB5uh+48aN+P7775O2jIwMvdWtIiMjDSb7+Pnnn3HMmDFoYWHBmmkamlXSTI/uAwIC8MqVKygWi/G7777jrQ0+adIkPHDgAKtt/PjxePjwYVZbbGwsAUEyWSwWY3BwMCusJBKJSDIk3eOPHTvGm3Pc3d2dNeI/cOAAZ4khICAAnZycUK1W4+XLl8k9Z8yYgampqQZ1yLyOrg5dXV3x+vXreuvA0zM0Ozs7zMnJQTs7OxIZsLa2xpycHHR0dMRVq1bht99+S/RF65B5rUOHDuHixYvR19fXYHTH39+fbPERiUR4+fJljI+PRxcXF5TL5SRa4Ofnx1lz/Prrr3Hx4sUolUrfqigAH7+LlJOTw4oc6rKxiJRKpWLl+e8tG/MdPeV9+/bxpqzuCe/YsQMXLlxo8Bg6Wtqb69rY2GBOTg5rVmxpaYnXr19HZ2dnTmRAH7u5uekFH+qyv78/Zmdns9b/+XyHp6cnXrt27ZVSeTN9R2/O0/X/NOtGUPmY6f97ci+NRqPXBwK8e1HFXg8GdNdHdNdR9K2Z0cfxrfu5u7tje3s72tnZcda9dK/Hpyhm27Bhw7CmpoacY2i92RAbk0sfFsLY87q6umJ7ezsrfOXg4IDt7e3o4uKid81QFwvw5ZdfcvZd6/KNGzf0olp1r8fUqyEZenKc7totAD9mAEC7L5yJGWDKT1+vJzq8du0aJzsmve4XGBiILS0tKJPJWJgBXR2qVCpsa2sjYem3cS3wXSRjMhnDqhw/fpwTwv+tuad+gu+8nmISmL6DoiisqalhVbx7E2WKjWEGaN6wYQOeO3eO024Mb/UqbEiunvo/fcfp+n+aFQoFvnz5kuS7eF29CgQCrK2tNVia+V3DG/V6MLB8+XK8cuUK+Z2RkUGKgNC7CejCMkymdxPoQwTTI7a9e/fit99+S9qZiGA+NKnubgLmTgRaiYaQ6Pr44sWLpICSSCTCuro6koHK398fm5ub0czMjLWbgKIorKqqIhgHT09PfPnyJacz8I1O6TY+RDCAtoZDZWWlXoQ9HxtC0+ruCFm0aBFJVsTUIXNHCIC2kAoNeKqoqMCYmBgE0A7oXr58iXZ2dhxUNwD/bgI+fX311Vd47NgxNDc3x5aWFlSpVD3SoT40MW3EurtE+HSo2zcTExMxPz//Dzfi3hj020jGZDK2i0V3h8HvwXfu3ME5c+b06hxjuwl0WXcnEnPnhD7f0VvuiZ+gj+ND+vfv3x8bGhreyMAEQIsdevnypV58gK7/p1l3N9mOHTvw4MGDnON0/QmT9e1g6cm7Yfp/Pn3p43dpJ1KvBwNubm4sYJharWaF8Pr3709mVsw96nSeARcXF8zKyiIflxEjRrBC7Lr78UNCQnDz5s24bt061j7T5cuX44YNG1h5BhYvXswyLjrPAPOFaDQazh51vhenK1doaCiRi86fIBAIWHkGsrKyMCIignR0qVSKAwYMQJFIhB999BGuXLmSXC8tLQ0jIyON7hU+ePAgjho1Su/ee4VCgZmZmSzglFQqxYsXL6JKpcJZs2bh9u3bDeYZANBGLMaMGYOZmZkYHh7OkZV+T05OTqRQS79+/UiERyKREFk//PBDXLVqlcE8A8xcEUz29vZGf39/Vk6FhQsX4ubNmzk6pO978eJFVlZEOs/Ajh07CMJYKBTiL7/8QkKI+nTIzBXh6OjY46I0b4tBv430W70LXd9Bs26/i4iI4E1Tq5vfQtdP6Mssp8936OYZMMZ0v+P7G9N3AABGR0ezirLx8fvvv09qpxjilJQUFiDZECuVSpafSExM5P1Y95RpufT5XX35WPTlKHmd/qPrOwwx0//rcnx8PO8E7m1jY9QrAOGUKVPAy8sL7ty5AwKBAFJSUqC2tpZkjaIoCgYMGABWVlYAoAWyDBw4EIRCIfTp0wd8fHygtbUVLl26BP/1X/8FPj4+UFlZCQUFBbBkyRKQy+VQUFAAEokEkpOTAQAgJycHsrKyID8/HyQSCYSFhYFUKoWHDx/C/fv3oaurCy5fvgxTp04FpVIJzc3NsGDBAgAAuHHjBpSXl4OHhwekpKQARVGQm5sL5eXl0NjYCJcuXSLZxsLCwmDKlClEVo1GAx4eHuDg4ACLFi2CnJwciImJgYiICHj58iVkZ2dDd3c33L9/H4qLi6G1tRUyMzPhypUrJMOZVCqFsLAwEIlE8ODBA5K5DwDg6tWrUFNTAzU1NXD37l1YsmQJKJVKePjwIXR1dcG8efOI/JWVlVBfX08ATklJSaDRaAAASNZCZnas7u5uyMrKgubmZigpKYHc3FygKAr+8pe/EECOUqmEsLAwkoGrrKwMsrKy4NKlS5CVlUVAW7SsNGirsrISHj16BEuWLIE7d+5AXV0dqNVq+M///E+4fPkydHZ2woMHD6CgoADa29vh0qVLBBj0/PlzuH//PixZsgTy8/M5gJ+ZM2eChYUF5Ofng1AohH//938HuVwOjx49gry8PBCLxbB48WJ4+vQplJeXg6urK6SkpMCVK1egqakJAACampogKysLuru74fbt2yQjGSJCVlYW0c3Lly/h6tWrMH/+fGhubiY6zMrKIrI6OTnBn//8556ah4l6QI6OjrBo0SIWSFWXQkJCYMaMGZx2S0tLWLJkCchkMgDQ9kUmiJgm3X5XXV0N2dnZnOM6Ozvh0qVLJPMpk3JzczmZ5WjS9R00ISJkZ2dDU1MThIaGwrRp0zjn2trawuLFi0EsFhPfwUdtbW3EngC0QEMmiJiPCgsL4cGDBwaPAdACLfWBFkUiESxatAjs7e0BQGtP2dnZRNYnT54QwC8AwMSJEwmgmqYJEyZARESEQbloG1Or1azMhwUFBbwZGZ8/fw45OTnk98OHDznZBfkoMjISJkyYwPu37u5uuHTpEjQ1NXH8vy5lZ2cTn6hL5eXlcOPGDU67hYUFq7++9dSb0f3evXtxzZo1vHntAbQhmmvXrpERHDO/+Pbt21lrullZWWTLDF2bgB598eUXt7CwwAEDBmBeXh4OHjyYE2Y6efIkjhs3jpVfnM6CFxISgjk5Odi3b1+USqUkrz3z/Dlz5pBSygD/k5uaWZtg9+7duGjRIpRKpdinTx+D+QVcXFxYee09PDzQ1dWVdZy7uzu6u7ujra0t5uXlERxBZGQkZmZmckbo9Ej4+PHjmJCQwMrXz8y5zZevXywW482bN8mamb+/P966dctgEhC++hIA2ujQnTt3yEiZmSPe39+fM4Jm5lK3trbGvLw8Uo2MmV/85MmTuHDhQvT09EQzMzO8c+cOenp6kvoScrkcb9++TZ5Jo9FgTk4OmWU4ODjwZm9j5lL38vIi9xYIBHj16lWcMGECaxbo7++PSqUSx4wZQ7Z9vi38LpJuXzDW7yZNmsQLvtXtd2+amTbGx8by2jNrEyQlJXHKsANw65rovpueRhUAtEBBQ89L1zXpzTuQyWSYm5vLmi0bqrmQmprKAUbu2rULlyxZQn4zfYcu89WXYNamYbJuLYme6FC3voQ+Her6f33MV19CH79rdU16vUygW/GOjw1VvKKVytdeWVnJql3PPG7MmDFYUVGBAPxVC3XvDwB4//59UmxCoVBgW1sb9unTh1S843teQ8hP+jh/f39sb29HpVLJW8kwLS2Nsz/50KFDJLxG3+PHH3/EH3/8Ue99mW3R0dH4/PlzUjWNoigcMmQI1tfXo0AgwLy8PLKOLpfLsa2tjbWcY0g3fPILBAJcuHChwcqThnTIrO5GV57kuyddeYy+L1/lMd3Kk/re0wcffMAqh0vfQ6PRYEdHB8rlcszIyMBPPvmEdR6z8hid6IZvCeNt4HeReiLX6yCujZ2rr9KgbtuwYcOwtrZWbx83VvGOr2qhIWbanb5+Z2hNety4cVhWVqb37/oqnhp6Dj6WSCTY0tJiFAuhD/hXV1dHEkf1RF/MqrVMtre3x87OTs5uIOb1XleHxuTiqzzZ2/f5R7FRO+2tQfPV5NblEydO8GZ5A9BGCxobG3kBJMw63br1rJm1q3VrkjN5yJAhWF5eTjLsMUeRdE1q3brXWVlZuGzZMgwKCsLa2lq9s5bTp0/j+vXrWTW509LSWBnTysrKcPTo0RzgiFQqRalUii4uLtjY2IgODg6kzcHBARsbG1mzUxsbG2xsbCS4Bab8t2/fxpkzZ6JQKGQlDOKTVVeGCxcucFD3Z8+exTVr1rBqktPgJlrXZmZmmJaWxqqoyMe0DidMmIAPHz7Uq6/8/HycNGkSCoVCdHV1xcbGRvTy8uKtSU63KRQKbGhoYG1Tk8lkWFdXh4GBgZy+mZGRgatWrWINBuj64/7+/vjixQs0MzPj1CQ3VC/+j+Z3kYzJxOx3vX0fbm5u2NjYSPABfBwXF8cqsQ2gHVzrfnCYNsbHujbGx/rsjo+ZvkNfv3vy5IneUu/GnteQn2RyTk4Ozp8//7XkMqRDMzMzIpch/0+zSCTSK5eZmRnrQ2tlZUV8x5vSIZPVajXW19cTn9ST79+hQ4dYGKe3hY3aaW8M+uuvv8Zp06axbvDVV19xRp8ajQZVKhW6u7vj2bNnWcqRyWQE0MOXjpRmiUSCERERRnNZK5VKPHfuHPmQ2tjYcHYzaDQaPH78OAoEAvzmm29w/PjxrL+HhISgl5cXmpubY3h4OOlsEydOxB07dpDjgoKCOCEijUbDCqkNGjQIt2/fzkK7HzlyhGSlkkqlHLloWekBRFhYGKalpWFERATvzoPQ0FB0dXXF4OBgPHfuHJ47d05vmdLp06ezOiYtK/OY4OBg9PHxQTMzM4yIiECBQMCrQ11ZAbTpjhctWsS5r6OjIycT16BBg/DQoUMIADhgwAD84osvCDCUKWtISAimpaWxjF6j0eCpU6cwMjKSlblOIBBgREQEbxgyODgYvb29UalUErnovykUCoyIiCBOKjY2Fvfs2UP+vn79ekxJSUFHR0c8d+7cbxaaftMG/TaSPt9BM7Pf9fZ90PbEdPA2NjZ47tw5gsS3t7fnIMHt7Ox469Mzec6cObylc9VqNZ48efK1B4x89qTLYWFhJKxtZ2eH586d0xv6X7ZsGa5Zs6bXz9G/f3+S9VEqlWJ6erreonKTJ0/mzcrYUx3S/v9N7Q7R9R091SGTt2/frrdeBZ/vMMZ9+/Z9bXDjb8HGqFcAwkePHkFVVRXY29vD3LlzQSgUQlFREVRVVYGtrS3MmzcPRCIR5ObmwsOHD6GtrQ3u3r0LM2fOBA8PDwDQZpvKyMiAO3fuENCXubk5zJ8/nwAt/Pz8YPLkyZCRkUFAQCqVCpKSksizDBkyBEaOHAldXV2Ql5dHjjM3NyfZD6dMmQKBgYHQ1NRESm7SJSydnZ1hzpw5IBAIICcnB4qLi0Eul4NarSagumfPnrGycP3pT38CJycnANCCJZOTk+H58+dQWFgIVlZWMH/+fMjOzibARZoePHgADQ0NAKAF0NByRUVFQVRUFLS3t0NGRgbJLPbixQu4c+cOZGRkQGtrK0RERMCoUaPI9fz8/MDW1haamprg7t27cPfuXWhtbYWwsDAYM2YMiEQimDdvHtjZ2UFVVRU8evSInEvLytSht7c3ODo6QnNzM2RkZEB3dzdHh11dXZCbmwuFhYVgYWEB8+fPB6lUCk+ePIGysjJy/aSkJFCpVFBVVcXJ8vXixQu4f/8+AABcvnwZrl27Bk+ePAGhUAj/9m//RjKVNTU1kayBNDU1NcGDBw9ArVazADnd3d2QkZEBI0eOhH79+gGANjvmnDlzoKysDIqKiqCpqYnIBaAFh/7tb3+DjIwMAmSqqakhAM/Zs2cDRVFQXl4OHR0dkJeXR4BcJno1on0HHzH7HR/FxsZCeHg4+T1r1izw8vICgP+xJ7oMMQCwSoIDaAGEly5dYl2zpqaG06ZL5eXlrGx9EydOhODgYJY/AQAIDQ2F8ePHs84NCQmBhIQEVptGo4FJkyaR37Q90b6D7v9MunTpEtTU1ADA/5T/1dcXS0tL9QISaZo6dSr07duX1Xb16lWS9bG7uxvu3bvHC6oE0PpEPoAfnw6tra1h3rx5rKyEtP/XJ0N0dDRvpkYALSB9/vz5rKyEnZ2dxE/yka4O+ejhw4e8mQoBgOM7AABsbGxYcgUGBrLAh3fu3OkRuPGto96M7mkOCAjAe/fusWa3vr6+eP/+fRJCsbKyQi8vL6QoCm/cuMGZJdIzUQBtucuCggIy4h01ahReuXIFfX19yeg7KioKr127hr6+vmSrXmpqKgcwFhYWhjdu3ECKovDUqVM4ZcoUlMvlnBl9UFAQ3rlzhzW6V6vVeO/ePQ74DkALPDl06BBJ4iMQCPDmzZskuuHp6Yn5+flkhmppacmagbu4uJAlD5o3btyIGzduRKFQSORi/p3mdevW4fbt28nv9PR0nDhxIioUCpZcS5cuxdTUVJTJZHj//n3WFkUaUElHH5g63Lt3L7733nsokUjQ19eXzMhpHdLXcHV1RQcHB3R3d8f8/Hze2fKVK1dw2LBhCABELr4ZlLe3NznfwsIC8/PzWTnpBQIB+vr6smZ8crkc79+/T0BAzOOPHDmCc+bMQalUigEBAZiXl4eBgYFoY2PDiZokJiYSXIJKpUKFQsHqrzk5OTh48GCODt8GfheJTw6VSmUwGxyTd+3axSqQc+3aNYPJXn4rTktL412Dnz9/PmfP++zZszlbAZn9jsm6voPJPj4+vQIVMtnLy4sDXqN9R0+vIZPJelV6XaVSkW+Al5cXPnjwgLcGBYDWJ9Ll2Wn+6quv8JNPPuH1HTY2NlhQUMDxozQ7ODhwQNq9YalUiiqVine9nymXj48PPnjwgES8J02a9NYBjfnYqJ321KB1X5Cx3wsWLMAHDx7o/fvz589x5MiReh/c2dkZu7u7Wevo9vb22N3dTQAkI0eOxOfPnxt9CeHh4djY2Kg3rKebRe/ly5fYv39/8jeRSITNzc2ssKIxgEhSUhIWFRWR34cPH8bvvvuO91xHR0fs7u7m7Hc2do+hQ4diQ0MDJ4TFd55EIsHW1lZeEBB9vFqtxo6ODtLJdXV46tSpXpVX5dMhfa+SkhISNuZ7XgsLC+zq6uKgmun/f/zxx6wdF/TfQkJCsLW1lQxUly9fTipq6t5HKBTiixcvMDIyEufOnUsqqtHHzZgxg6XDt4HfRdKVgQbaGVom7A0AqyfHvmlA1+tcrzfnVldXc9KR9/T8oqKiHgEIDV07NDQUX758qXednXm8UCjExsZGTlE2fXz06FG9BYP4fIex+3/99ddGs7IaklXXd9DM5//fRTZqpz016Pr6erKNMDAwEGtqasgsMyUlBbOyslg3lkgkrBHt4cOHWWs3FhYWvDPh77//Hnfs2IEURaGlpSVLYbptIpGINTvduHEjWZNmslAoNLjme/HiRdZWGAsLCxQKhbho0SLywaHbALSJMWpra/WOeA8cOIBbt25l5a1WKBQol8vRxcUF6+vrWbnE+WS1sbHB+vp69PDwwC+//JJTD+HTTz/FtLQ0jlwDBgzAiooK3oEPUwYmp6en4+rVq1EgELCup6tDhULBmc3t3buXA5bRp8PRo0cTUKG5uTmKxWKMiorC4uJi3vdoaWlJBjphYWFYXl7OKl3MfP+0DnV1LZVKUalUolAoxMrKShwwYABLh87OziT7mlKpRIqi8MmTJzhs2DAUi8UGc4+/jQb9NhKfHPr6IoC2PryxHUs0u7m5YX19vUEAIbPfAWh3rOhuI9PnO/g4NDQUKysrX2nd25jv0GVzc3POfQoLC1m7rgyd2xuwHJ/vMOY7ad/RE73qMu0T+f7G5xN1Wdd30CDjV9WhIVl7I9fbykbttKcGHR0dTUJOFhYWGBMTg2lpaejv74/e3t4YFhaGAoEADx8+jH379sXY2FjyQTh48CDOmjWLFM45ceIE+cgMGzaMBdzSaDScLXGLFy/G5cuX8wqoVCrxxIkT6OTkhH369MGQkBBUKBR4/Phx3pDR1q1bMT4+HgMCAvDnn39GoVCIAwcO5A2F+fj4sJY3PvnkE5wxYwYqlUqMiooinWPkyJG4c+dOclxwcLDesqQymQyjo6NRIpHgsmXLcNmyZeRvqampGBkZiQMGDMDDhw9jdHQ0yuVyVKvVGBwczLpOQEAAa5a/bds2jIuLI2WRaSOaOnUqbty4EUUiER47dozIqVKpMC0tDcViMYaGhpIZuK4OT5w4gcePH+c12gMHDmBycjIpH3rixAlSKKRv375obW2NJ06cIMs/Tk5OGBkZybqGg4MDDhkyhPyeO3cuSW/NZBsbG7L8AKANuTKR2Hw6/OKLL0hhGYqicNiwYeRZdHUIoA1bnjhxAkePHs0ph/q28LtIxmTy8vLCtLQ0Mrnw8vLinYXp+g6mPRn66On2Oz57on0H/Xvnzp04evRo3uvp2lhvmK/f9ZYjIiLIjNnKygpPnDhhcDAUEhLCyXmg0Wjw0KFDLBmMybVp0yZOMTSm7zDGMTExr5W9kOY9e/bg0KFDOb6DZj7/L5VKMS0tjSwZvo4ODfFPP/3U43TUvzcbox4DCE+ePAkNDQ0QFBQEkZGRcOrUKXjy5Al0dHRAUVERychVVlYGbW1t0NjYSEpvlpeXQ2ZmJty/fx86Ozvh6dOnBJDR3NwML168gKSkJJDJZGBnZ0dK6gJoQTs2NjZQW1vLep7BgwfDsGHDoLu7G54+fQqdnZ1w7949yMnJYbWFhoZCTEwMOa+yshIaGxuhra0NysrKABHBwcEBLCwswN7eHqZPn04AhI8ePYL8/HyYPn06CIVCePbsGdTX10NTUxOcOnWKgM+amppYJYrt7OzAysoKlEolJCUlgVQqhaFDh8LgwYOhtbUVTp48Ce3t7fD8+XN4/vw5Oa+iogKam5uhpaUFiouL4eTJk/Dy5Uu4e/cuJ8PV/fv3oaKigmQ5q6qqgqamJqirq4Nz587Bf/7nf4KNjQ28ePGCgGPodwKgzdL29OlTkjWNBs/p6vDp06fkOABtOVgaLFVeXg5ZWVkEYEjrlS6VPGHCBJauKysrISsrC5KSksDMzAwAtICkixcvQlJSEpibm0NdXR3U1NSAWCyGGTNmgKWlJQBoS07TZZ0BAOrr60EgEEBiYiJQFAVZWVlgaWkJsbGx5Jiqqirw9vaGuLg4QERIT08nGROjoqLg1KlTMHnyZFJet7OzE0pLSyE9PR2CgoIgLCyM3xhM1GuKjY2F4OBgAACiN0dHRwDQAuNoWwQAKC4u5gX36foOACD2xAQQyuVySEpKArlcDgDafnfu3Dnyd117Gj9+PHR1dbEy3FVWVurNOFdXVwfp6enkeYODg1n9zhDp+g5dGj58uNF+l5GRQbIj0r6OeT2pVApJSUkEaNfa2soC+eprY8oVFxdHspzSVFVVxXknTN/BJLVaDWPHjmW1NTc382Z1NDMzI/4fQAsWnzhxol75aT/57NkzOHv2LOfvTP9PE+3X6H6iq8NXpejoaBgwYAD5XV5erhfM+NZTT0f39B7w+fPns3LpA2jDucz1bkdHR06BDbpNJBKhl5cXa53bxcUFHz9+jDY2Nvj111+z9rJfvXoVR48ejQqFggUY+/TTT3nXm+RyOaumwOrVq1khQRcXF04oKC0tDefPn499+/bFX3/9FVUqFVk3CggIwIcPH6JUKkUnJye0trZGsViMnp6eSFEUr6zffvstfvTRR+jm5oaPHz9GKysr3L59O3722WcoFAo58vMxXeFRJBKhra0tq8qhq6srmpubY1hYGObl5bGuJZVK0c/PDx89ekS2t0ilUhaIztLSkrUWR8tl6Hk8PDxQJpPhpEmT8MKFC6y/KZVKguNwd3dHhUKBsbGxePXqVQTQhnLpsKiNjQ0+fvyYdX9LS0t8/PgxS79KpRKLiorQy8sLbWxsOEAjAO2MJz8/n4T6UlJSOBXa5syZwwFtzZgxA8+cOYMCgQAbGxsxMjISzc3NWTMJWoeG3skfwe8iAWjXh+nttiKRCPPz83+zug8ODg74+PFjls3Q9sQ3I79w4YJBUB1FUejp6ak3+sDX7wC0y2y0n+A7z5DveB35ra2t8fHjx5zkPL3hEydO8JZw52Nd/w+gjdwxqyDyyUoz0/8DAMt3/BHs6enJwQ14eHjwFh1KTU0lBe3edjZqp70xan3hj2XLluGdO3fI719++YWTnOb06dO4ZcsW9Pb2xu7u7l6nyYyPj8dnz54ZPS4qKgobGhr0GmBeXh4LH6DLYrEYW1tbCYCQyRcuXMDPPvsM/f39saurC5VKJZ49e7ZX5VXd3NwQEXk/bky2tbVFRO0gbMeOHawB2K+//qq3otqgQYOwubmZ5fRCQ0NZwJhVq1bhjRs3yN+zsrIMOiCKorChoYEVpmdyYmIilpSUIADgs2fPcMyYMay/l5eXczIQ9oY3bdrESU/9Jpg5GJg3bx4WFha+8Xu8aX4X6Y9+ZwBa8DEisgacPWWFQoGdnZ16l/70cWBgIEl2xff33vqOt5WXLl3K8v98TPv/P/pZjTGf/xeJRNjS0sJbjfddYqN22lODtrW15QXMnDhxAtevX8/KKGVhYYFmZmaoUqnw2bNnqFQqSZtAIEBbW1ukKApTU1NJGlgAwIKCAk62rby8PBwzZgxKJBLOyHL48OEsYNCnn36KR44cIcdlZ2fjzJkzceDAgfjkyRMUCARoZWVlNIMUU1a1Wo2VlZUok8nQ0tKSZAmj1+houby9vfHZs2cEcDZ58mSCYgfQrnNt3ryZJf/OnTtZWIO8vDwcN24cAmg/wLa2tigQCNDMzIwVzbC2tuYA+bKysjA5OZlEEph/E4lE6OzsjBUVFajRaFAul7O2HFlaWuLy5ctZKZoBAGfNmkXKVdvY2PDOjFJTU3H79u1kcGdjY8MZVVtbW5NRta2tLdbU1PBWiqN1uH//flYbLb9CocCqqir09/fHVatW8W7Tog26rKyMrAszdah7LC2XTCbjZEXT1eHbwO8i9VZGZr8zxq6urlhdXW1wzVzXnnr6HB9++CHZHmhra0sG2P369cPS0lKjAEKmn+Bj2ncYukZhYSFvamxDvuP3ZrlcbjCjYE9l/b1ZKpViRUUFZ5DH961jtvn6+mJVVRVLHl3//zayUTt9FYP29vbGAwcOoFgsxsGDB5PiNzSvWbMGExMTUalUYlxcHIpEIly1ahUmJSWxjgsNDWWFCmNiYjgfiejoaHR3d8fQ0FA8fPgwHj58mBiYk5MTa/Cg0WgIWhxAu/XOx8cH7ezscNSoUZyXIxaL8cCBA2QvuUqlwgMHDrA6gqWlJcbGxqJAIMB169bh1KlT0c3NDQ8ePMj6uDBlBdCCoIYPH86SVRe0FBISgiEhIWhpaYmHDx/GiRMnkiUOc3NzPHToEAGyBQUFYWpqKuv8vn374g8//EDqFKhUKgwICMC9e/cSpzdx4kT86KOPUCAQYGxsLDFapg7pDh4ZGYkCgQB//PFHVKvV6OPjw8opDqCNvHz99dd6dQigBXwuWLCAt0NKJBKMi4tjDcjMzMzw0KFD6OjoyNHh5s2bMSYmBgG0zjU2NhYtLCywT58+ekfqFEXh6NGjyQCFqUPd43744QcCWHV2dsZDhw4RRLKuDt8GfhdJVwahUIj79u1j5cFgMl+/08dyuRzj4uJYA1Bra2s8fPhwr6OPuty3b19eIKOtrS2OHj26V+Az2nf09hliYmJ4t9fRvoP+HR0dzVoe1cdbtmzhHVxMnTqVF7j7R7EhHe7YsYOTZ+Lrr7/mtdXExES9WRlpn9jbQkLm5uYYFxfHir7q+v+3kY1RrzIQRkdHQ2BgIHR1dZHsgRcuXIB79+6xjmttbYX29nZoamqCI0eOQGdnJ7x8+ZJk2KMpOzsbbt68SX4fP34cysvLwcvLC+Lj4wFAC1wsLS2Fzs5OaGpqgqamJgL6qKyshH/84x/k/NzcXLh8+TL5febMGXj06BEIhUIwMzMDiqIAACAoKAiioqIAQAtqQUQIDAyEqKgoIhdNAoEAlEolUBQFLS0t0N7eDt3d3eQ5hg4dCiEhIUTW+Ph4cHV1heLiYjh9+jQAAIwbNw7Kysrgxo0boFAoYPLkySCTySAnJwdycnIAEaGpqQlOnDgB7u7uMHjwYNJGy9rV1QXNzc2sZ2Pq4ezZs6T8Md02YsQI8PX1BTMzM0hISIBjx46REr7M46KiokChUMC5c+cAEaG5uRkiIyPB2toazpw5AwDasqTOzs7Q0dHBKpdMl/aMi4tj6V8XRBMWFgaDBg2C9vZ2OHLkCPz1r38FHx8f8vempiYCPiwuLiZlR1taWgjoh6IoUCqVIBAIQCqVgkKhAKFQCJMmTWKBThERjh49CqGhoaDRaKChoQF+/vln6O7uhuHDhxMgG61/GmjE1CuAtpyrQqEAE715am5u1guie/ToEel3TFIqlTB58mRWpr6XL1/CkSNHoL29HQYPHgxhYWFEj/oyGgIAuLm5cbIG6tKdO3d4gYzPnz+Ho0ePssBntra2MHHiRAI+VqvVMGLECPJ32nf0lo4fPw4VFRXg7e0NY8aMIe2076Dp5MmTpJQ8TXFxcaBSqVhtTHtiUnt7O8uu9VHfvn1ZgGyarKysYNKkSSASiYxeoydkSIfNzc0cGfS93/b2dr2Avu7ubvj5559JdtieUmNjIxw5coTVf5nfuneWejO6P3HiBG8eeqVSaXArlpOTEy/4wsbGhje8FBcXh7du3TI60qFBffRvS0tLtLGxQYqi0NnZmYzcBgwYgA8fPiQzwxUrVnCygy1atAhPnDhBftvZ2aG5uTn26dMHnzx5wooC0GF3iqJw//79ZERNURTm5+dzts/l5eVhVFQUyuVy1Gg0WFZWprcU6pdffompqamcLFtMWR0cHEiIipaVHpFKJBIykzhz5gzOmzcPg4OD8fHjxyQKoFQqWfc/evQoLl++nCXXTz/9REbUFEVhQUEBDh48mPeZJ0yYgNeuXSO61g3HOzo64q5du1hZFK9fv86pEUFzeHg45ufnI0VRLFnlcjmWlpain58f0aFUKsWSkhJOqM/Z2RlPnz6NS5cuJXIBaPNdfPLJJ5z+amZmRgBndH+dOHHiHwpk4uN3kV5FTmZfpNs8PDywrKxM70xux44detfgBQIBuri4EB8wdOhQvH//vkF/omtPANpwN5/tBgYGYnFxMYlQzJ8/n1Oa93V47NixmJOT06tzrl692qtsgz3hlJQUPHXqFKfd398fS0tLjS7B0qzvm9BTtrKy0gtINMT29va8GR3Nzc159cr0HYb65rvARu30TRi0LoCQyRRF4YsXL3jBZ8eOHWPVmu4tjxw5Emtra8nvrVu34qlTp9DCwgK7u7t7vP+Vj7Ozs/WGzQICAgiAsDfXnDp1Km9pTl2Oi4vDmpoaVlt0dDTW19cjRVEsAKFSqcSuri5SyW/QoEHY0tJicB+zLoCQZrVajZ2dna9UPY7m6urqNwogLCgoMFpRTZfFYjG2tbUREBCzaiEA4HvvvYf37t1jnUMDCCmKwvr6elMJ4zdIryKnn58fdnd3v7E12J4ACIcPH84BH4eHh7MAuRs2bGBlvjTxq3Ftba3BDLTG+LvvvsNjx471+jx9API1a9aQCQ2TdX0HwKv7/z+ajdppbwz65MmTuHjxYlSr1VhaWkoSOaxfv56Acx49esRa8wXQzmQlEglOmzaNVW/eysqKBYy7d+8eWR92cHDAiooKdHBwwC1btrCqB964cQPHjx9Pyv/S7RYWFmhtbY0URaGTkxMrMvDo0SO94KEzZ86QbU9isRhLSkpw6NChqFQqsU+fPlhaWsqa7QqFQnRyckKKovDAgQO4bt069PLywvLycpbzcnd3x4qKCrLuJZfLWTPSbdu24bZt29De3h4rKirIrERXLt02Ozs78sHWlVUsFrN2KixfvpyzFdTMzIwAYoqLi8nao0gkQkdHR+IMk5KSMDMzkwxABg8ezNLhgwcPOIM8BwcHTE1NZWUltLe350QLaB0OGzaMlfIYQJtUhf4wM2WlOSMjgwyGpFIplpaWciIDjo6OJBJCy0X/TaFQcGYBzDa6vDSfDv9ofheJ6Tv4ZOKbVTJtjO+cPXv24MaNGw2+q9zcXJKpTyAQoJOTk0EAoUQi4didRCJh9R1zc/NXmpHS7Ovri2VlZSTalZiYyMrempqaip9++mmvr3vr1i2Mj49/5edavXo1Hjly5I32VR8fHywvL+f9aDJtjMnffPNNj8r/WlpaGgUt8rGdnR0vkFGpVPKCPXV9B1/f1NXh28pG7bQ3Bh0VFYVqtRqtra0xISEBBQIBDhs2DN9//3386KOPkKIoHDduHHl5zs7OpHDO0qVLcf369Swg33vvvccCFY4ZM4aM3GUyGSYkJKBMJsMBAwawgDyxsbG8JTYTExNxyZIlKJfLWaF2R0dHHDduHFHehAkTcNWqVSgWi/H7778nWfQAtE5jwoQJpGPQstIf2+joaPzkk0/IPSMiIjAkJATNzc0xISGBfIAiIiJw586dmJCQQEKHYWFhrI4+cOBAHDhwIJFVX4ht+vTpuGjRIlQoFJiammp0WyKTAwMD9W4JpCgKJ0yYQD6C7u7umJqaSozUz8+PADTHjh2LTk5OqFKpiA7j4+N5szyGhYVxBoQAWhT21q1bWTp0dXXlODFnZ2ccO3Ys+T158mRcsWIF+R0TE0MiIUKhEBMSEsjH2tvbG/fs2UOWTUaPHo3r16/XK/+uXbsIANbJyQlTU1NRLpfjokWLcPr06WhmZsbS4R/N7yIxfQdTltWrV+P48ePRysqKZWO6vHTpUk7548GDB/Nu/2VyXFzc71poSigU4u7duznF05hsYWGBCQkJLGQ6c4YcHh6O/fr16/W9+WTdtGmT3oJOarUav/nmG+ITNRoNJ5vfuHHjXmsPva6sPeGe+A4+/uKLLzjLs5999tnvAgDW1SHtO36vftdTNka9AhCeOnUK7t69C3V1dbB3717o7u6G9PR0yMvLA7FYDIgIBw4cAH9/fwgMDASKokAikQBFUSAWi6GoqAiOHTtGricSiViAk46ODgIYaW1thb1790JraytcvnyZBeT5+eefoaioCJydnWH06NGs69FlJaVSKVAUBRqNBvz9/eHAgQME8CMUCslxEokETp48Cd3d3RAZGQmICG1tbeRYWlYaLMK8B4AWiNfZ2QmNjY2wd+9e+Otf/wpOTk4gEAigvb0d9u7dC1FRUeDi4sK678iRI+HJkyfw5MkTGDp0KOzduxf+9//+3+Dp6cl577py0eA7BwcHVuazvn37skq9Dh06FNra2iAnJwfi4uIIgNLf3x+GDBkCiAj79u2Df/u3fwO1Wk30RVNBQQEBaP70009QWVkJDx8+JDo8ePAgJ4vZqFGj4PHjxywg54gRI8DDwwMEAgGRg9ZhWVkZHDx4EAC0AFVvb2+oqKggYEylUkn6iVAohDFjxpBslra2tjBq1CjYt28fBAcHg7+/P0cGgUAAdnZ2MGbMGBAKhRAeHs4q4SoWi8l7oSiK9BuxWAwikQiam5th7969rwT+MtH/EO07mCQSiUAgEEB9fT3LxnSJ1gWTLly4AFevXjV4zyNHjhgs6SsWiyE+Pp5kw+Qje3t7lu0YI9rf0URRFMTFxYGdnR0AaMt47927lwDNCgsLIS0tjRx//vx5uHbtGue6np6eLOBe//79oX///uQ3n6xisZgAGnVJIBCw7CQ3N5eTzY/pr5hkZWUFY8aMYV3b0tKS2BhNTFnDwsJYGQ1Hjx4Nzs7OnGtfunSJ5TuYz8t8lpCQEBg4cCD5TfclJhmS39bWllevtL6YgGR9FBsbCw4ODhwd8vXXd4J6M7q3trZmhWzt7OzIqE8oFJIQ+OHDh1nr7XZ2dmQvNx1is7OzQ4lEgjKZjMzCHzx4QGaiAoEA7e3tUSAQoLm5OWs5wdbWFmUyGQ4fPpyTKEYikbBCwOvWrcPDhw8bHTUtWbIET58+jWKxGEtLSzEoKAgVCgWZcdIyyOVyVpjw4MGD+MUXXxDg4sOHDzkj7Pz8fM4a9L179zA2NhZjY2PJ+vXt27fJjJiiKLS3t+cNsV+9ehUTExNx8ODBWFRUhAKBAG1sbPCTTz5hZULLyMjABQsWYEhICJaWlpKoxXvvvYfnz58n+jp+/DiuXLmSoy+mrHQbUzf0rEKfDmmmc0XwvXemvq5fv45Tp05FiUSCXl5eWFVVRUq4WllZoUKhwIqKCpJZsV+/fvjkyRMUiUR47tw5fO+991AkEhG5rKys0MzMDPv06YPl5eUok8nw6NGj+MEHH5D+ShdEeVfW/95Fop+daU+GWNfGesoWFhZ6MQa0PTGXCSwtLbGqqopT4prJAwcOxOLi4l7lJ2CyUCjEJ0+eGKzQyGRLS0teGcaPH4+5ubnk9/bt21mAXEOs6xNfhe3t7Ymv79u3L5aVlbHC/P7+/lhRUUG+D7o63L9/P27YsIH8LiwsfK1ZOw20ftXzmb6D2c70/8x2pv8D0PrAx48fv1OVDI3aaW8M+tq1awRhTpf6pUM6+sBnTABhcnIyPnr0CAH+B0DCzF7HZGdnZ0REdHZ2xh9++IFVte/JkyecsCHNfCCgV+UVK1bgrVu3WBmoDIHP3qTi6AyEiMjJz8DH9+7dw/fee6/H1w8KCsL29nbOQINZhnThwoUE1c8EgepmkdSnw56wLgiUT4dffvlljzMQhoSEYFtbG0okkh6DQN+lTHDvItHPrg+4qst0v+vtuzl48KDeD8TrZCD8Pfn48eMsfNSb4OjoaKyrq3tlnyiRSLCtra1XBXheVYdvI5syEPIYtKOjI2tbj7u7O1lLFYvF6O7ujhRF4b59+3DVqlXkODc3N5TJZKhUKklSITc3N5TL5WhmZsabjU4gEKCHhwfJ2Mcc2bq4uBAQiKWlJZaUlJCEGzKZjDcnt1wux8ePH6Ofnx8uX74cDx06hFKpFB89ekTWjPv27YsPHz4kMllYWBDcgZubG0qlUjQ3N8dBgwbh48ePycBHqVSytp/s3r0bP/74Y/L79u3bOHz4cIyPjze6Ve2LL77A7du3E/k9PDw4gJcLFy7g1KlTMSwsDO/fv48CgQCdnZ05M4pTp05x0hanpaVhSkoK0RfdnpycjKdPn2bJyjRoWoe0cbi7u5PZEp8O4+LieMvQ2traYklJCXlfcrkc+/Tpw9Ghv78/FhcXo7e3N1pbW3OAXbQO6d8nTpzABQsWoEQiIXIx+6tQKMSCggKyHkvLQG9fpAc2FEVhXl4eRkREYEJCwlsHDHoXiX52pj0ZYnNzc6N17Hft2sUB2tnZ2enN+Efb05sqQ6vrO94U29vbvxZAMSoqihVBoO3JUJ2Cs2fPGp1wMH09kw8fPoxLly59JR32lL/88kuDeAFdViqVxHe8Kb3QPvFN6vr3ZqN22lOD3rVrFwugIhQKcdu2bQQs4+bmhjt37kSJRILR0dEccE9KSgqn/OW8efMwMTGR1ZacnIwzZswgvzdu3KgXKBQYGIjbt2/HxMRE3tDahx9+iEOGDEE/Pz/cuXMnTp8+Ha2trTEkJARjYmJQKBTitGnTWKHwadOmoUAgwBUrVmBcXBy6u7vjjh07UCwW49KlS3HcuHFoZWWFiYmJeoExQ4cOZZU+pjMLqlQqsrf+iy++4AUKDRo0CCMiIlCpVOKuXbuIc1Or1QR8GB8fj/7+/uji4oKTJ08m544bN45lmLGxsbhmzRpW+edRo0ahRqPh3JcuWUz/Xrx4Ma5bt443vG9vb4+7du1ChULB0eGmTZswODgYfXx8cMKECaQ9KSkJk5OTUSaTYWJiImuAI5FIiA4nT55MBiuJiYlkecjPzw+//vprMruhdUhfY/To0bxy0UxRFE6ZMgUdHR1x+PDhBrOtTZo0Cd3c3NDPz+8PS/H6qgb9NpI+WZYvX653+cgYDxky5HcP0a5duxajoqIQADi+g8m072C20b6D7otff/01We4yxnPnzuX4SX3s6enZ6228cXFxva67QPOIESN6FTFg+g7dv9G+Q7d98ODBeoGQTNbnO4zx6tWrOdscjelQlx0dHfGbb75hgcBtbW1x165db00aZmPUYwChg4MDBxTh4OBAQB0ikQgcHByAoiioq6uDxsZG1rHW1tZgbm5Ofg8bNgx8fX1ZbUOHDoV//dd/ZbXZ2dmR0pYSiQRGjBhBfkulUrCwsIDvvvsOGhsbQa1WQ79+/ci5tra2IJfLQSwWg7W1NXz33XdQV1cHOTk5cPz4cejq6oLdu3dDbW0t+Pr6gr+/P+zevRu6u7vB2toalEolCIVCIpe1tTWYmZlBfX09fPfdd6REcp8+fcDMzAxiYmJAJBLBmTNnICsrizxHdXU1tLW1wcOHD2H//v1ELolEAi4uLjB06FBy7IsXL+DFixf/H3t/HhXF1e2N47t67qaBZp6hA/0AD3SACzzCRa7II4K8isp1iteRbzTiMk7XIboSE4dlolETjVeNRuP0OM8a44QTRAVFRBQVEAWJIqMCMg/790e/dW5VV3XTqInye7PX2kvrUFVdu/Y5u87wOZ8NFEWBvb09AeTIZDKws7MDAB1wTyQSgZubG+zcuRMAdCmdAwMDwcrKitzr6NGjUFhYyALDHD9+HF69esVKkxoREQEtLS1w/vx56N+/PwiFQrCysoLi4mI4ffo0sYsW+p0IBAJQqVTg5+cHffr0AQAd4EoqlUJhYSHs3buXXGNpaQmWlpbQ0dEBFRUVBCzm4OAAvXv3Jj5UKpWgUqmgtbUVtm7dCrW1teDv7w9RUVHg5OQEAwYMAHNzc+JDgUAA8fHxcPHiRcjOzgZLS0uIj49nAYcsLCygX79+8K9//Qs++OADCA4OBhsbG1b9VKvVBHz5r3/9CxwdHUEsFsP+/fvhL/ljhG5jfMJsT3ySkpLCyw4IoIsLAwYMAKlU+taeFQBI+wcAVuzgO0/fLua1AP/bTkwRuu2YIsXFxbBnzx5Wmb29PcTFxRm85siRIxxwJwCAj48PC6QHoEsvzIwdv/zyC4sJEQAgPDwc/Pz8eH+LGTv0xdA7uXz5Mly8eNHg89PCFzsAdN8BmnGWT6ytrTkgUlN82K9fPwIMZX4nTLH1vZSu9u5lMhkZhatUKlSpVJx15+PHj+PXX39NEgMBTy/l8ePHpJdN68OHD0kPjaIoVKlUZCQoFovRy8sLX7x4QbazicVi1rLFqlWr8ODBg+TZBAIByuVyFjjM3NycNd2jUqlQKBRySCfMzc1RJpOhUCgkNiiVSpTL5Sy7jh49isuXL0cvLy+srq4mCTmYPd/CwkLs378/SiQSTm81MTER8/LyyPGGDRtwx44dnfbyvv32WxYwMj09nbMkQNvKtAFAB5a8dOkSOT59+jR+9dVX6Ofnh5WVlaxn12g0WFVVZRRgN3r0aMzNzSX+ou1n+lChUKCZmRna2tqyfNivXz98/PixUVu/+eYbPH78OCoUCqysrGSNqGQyGVZUVJD8AkFBQVhWVoYSiYT4UKvVYkVFBcpkMjx58iTvrMAnn3zCYnjbt28frl69+p335vW1OwrdxvjsoZfe9Mv56h3d7phr3/pxAkA3Snvx4gXZgqsfJ/TVwsKCNQVOxw5T/EHXsbftZz5bmapQKEwmB+vbty8WFRUZxQzox0kAwAULFnBYFPVjB5/S8f9tvAcLCwuDqaNN1cjISHz69OlrgUD1YyfTPyUlJQZZWd9H7Uy63BlYsGABZmZmEgAhIhpMf+vj44Pt7e2vxSLGBBAC6KbGy8rKWBU6ISEBq6qqeCtQe3s7ent7c9Lf5uTkEAYqmUyGLS0tGBQUxOkMXLt2DZcsWYKBgYHY0tKCcrmclcK4ra3N4AfyxIkTvEhfUxkI35ZmZ2fj/PnzSQrjN21UxtTd3R07OjrQ1tYWDxw4gDt37mT5cOfOnSwQ6J+htA//zN/8o7W7iqGp5M8++wxv375tku186c/d3NxIvTN0HR07DP394cOHmJycjADs2GHKM928eRMXLFjw1v3s4eGBHR0dBvEDe/bswT179ry131u5ciVevnz5nddvfX369OkbpT9/U2XG/3f9Lt5UOxOTo0tBQQEhHKIBWl5eXqjRaNDGxgZ9fHzwwYMHrJcmFotRo9GQD/ioUaM4qPChQ4fyVkKhUIgajYaMKJRKJYdUw8zMjJdURCAQoEajQbFYjLa2tixiHHd3d9LToygKvby88PTp0/jll1+yQDaurq4kHa+XlxdSFIUuLi5oY2PDsWvMmDF47tw5cq2TkxOLafDGjRvYt29fNDc3J8977do11pq3MV2yZAn+9NNPaG5ujvn5+WQrVM+ePTE7O5vV4w0NDcU7d+6gUChENzc3ku6YxnYcPnzYYEZBfR0/fjwBFTLVzc0N8/PzWbMcIpEINRoNYXpzcHBg+dDBwQEdHR3R2toaCwoK0NHREZctW4YbN25ECwsLzM/PRzc3N1y0aBGLovrs2bM4btw4DAsLw5ycHJatISEhxNZjx47hp59+ynpO2oe+vr54//59MiP08ccf46lTp1AgEODt27cxIiKC40MA3XauzkZB71uDfh9Fo9EYBF8x40lnyqxjxsr0lS92MNXDw4PUZWbsMOWZ6DbWFR8y611XbGWqg4MDi3wsLS2NYH6srKywoKCAlxCMVv3YYWNjY/R8Q7pv3z7efDX6unXrVoMERk5OTlhQUMD7HtVqtcFBV3R0NCdfQ2RkJN66deu1dk5otVq8d+8ey/fM+N/V+7m4uGBBQcFrMSX+EdqZmBxdpkyZQhDdbm5uuGbNGhSJRDhz5kwcOHAg2tjY4OTJk1EkEuG0adN4gUFarZYA3r799lvUarXo6+vLSu05YcIEHDt2LOu6cePGsRidlixZwtm3u3DhQoyMjERfX19cuXIlKR86dGin3PbDhw8nIxehUIirV6/mDR4zZswgoBKKonDVqlXo4+ODWq0WZ86ciWvXruWdMvz4449RrVZjWFgYGakmJSWhRqPB4OBg3ik1MzMzXLt2LVpbW2Pv3r1xwIABKJFIcMqUKWTK083NDT/55BOkKAo///xzjI6ORmdnZ5w0aRKpvAkJCawth8OGDcMePXqwfEj/zdnZGX/44QcyZRoYGMjqlU+ZMgWHDx+OFhYWOGXKFN4Av2zZMqNAPrlcjlOmTEGlUonR0dHYv39/lEqlOGXKFLSwsMCoqCgWmGfUqFEYEBCALi4uxK758+djTEwMOjk5YXJyMmG+NORDW1tbnDx5MulYBgUF4ciRI5GiKPzkk0/Q1dUVtVotJ6mLn58fpy6+a+2O0hX7DMUOUzU0NBSXLl36Vt+5RCLBH3744Y3Q8fqxg653zHNWrFhhcHcCM3bw6fjx40lKaJlMhlOmTDE6I8uMHabaMGfOHM4AZsiQISQW0zFRo9Fwrh04cCBrSp2O/wC6ztqUKVO6vNyiVqtZYHMA3WDPlK3Ynp6e+P3337M6W/b29jh58uQuLydERUWxds/Ram5ubjBOvgvtTExGNqxbtw7Ky8sBQAfQ8fb2BoqiwMPDA+zs7KC5uRkKCgqgo6MD3NzcCNgNACA6OhpsbGzg7t27BPCm0WhAqVTCgwcPYPv27eRcZ2dncHJyAolEAn379gWJRAJOTk4QFBQEUVFRAADg6ekJFhYWrOf74IMPQKVSgVKpBI1GQ4AcdnZ24ObmBgKBAGJiYsDMzAw0Gg2EhoaCQCCAPn36wKlTp6C6uhp69OgBFEXB3/72NwJSZIq+XRqNBuRyOdy9exf2798P3t7eIBQKCeshLVu2bIGioiKwsLAgaXsLCwuhrq6OVRYZGUlYuYRCIfj4+IBYLIbnz5/D77//Di0tLbBu3TqScrO+vh4KCgoAEeGDDz4AGxsbePbsGfz000/Qp08fUCqVYGtrC+7u7kBRFPTp0wfOnDkDlZWVEBUVRXwIoAPQ0WUCgQBCQkKgsbGRBUZydXUFBwcHqK2thXXr1pGU1M7OzgRU5OXlxQHe9OrVCxwdHQFAl3J23bp1EBQUBHl5eXDy5Elobm6GdevWwYcffgiFhYUsNq/Hjx/DixcvoKGhAfLz84mt1tbWUFpaCps2bYJ//vOfRn3Y1NRErgXQsUo+fvwYEBEKCgqgoaEB7t69C7t27QIAgN69e4OtrS3cu3cPduzYwakHf8nri7m5OfTp08cgqEq/jQHoUo4z2xPtHz6xtLRkpcbmEz8/PxYbHp9ERESAm5sbAOjY77y9vU0G/PXo0YPzDEy7EBE2bdoEv//+O/k7RVGg0WgMsiEy4wSALkWyVqslx9u2bYO8vDwA0NX3devWERC3ra0ti5kUAKCkpAQ2bdrESsPcmbi7u3OAt4cOHYL09HRiw9/+9jdeG44fPw6XL18mx3T8B9Cl/123bp3BVMOGpKioCDZv3swqe/LkCfz000+dXiuXy1nfCQCA8vJy2LBhg9HU1wA6cGtMTAwBd6tUKvjggw8459XV1bHi5HsvXendKxQKzl5Tuszf3x9fvnzJSzr07NkzsjWEoigy7SOXy1EqlZIyfRBQXV0dmQobPHgwPn78GM3Nzcl5QqGQ3MvMzIw1vaN/P4VCgS9fvkQ/Pz9cvHgxnj17FmUyGVZXV2NAQAB+/vnnnClhiURiFKSjVCpRKBSiWCxmbR85ePAg/vDDDyxbAXRTf/R5Dx8+5IyA8vLyMCkpidND/uGHHwgwkmlrdHQ0Pnv2jPRkpVIpyuVylMvl+OLFC9Z2IYlEglVVVRgcHMxL/jJz5ky8du0aOT558iQuW7YMBQKBQX/R544ZM4aTDpZpa0FBAQ4fPpxVdv/+fc5W05ycHPz4449Zfs3KysLJkydjZGQkPn/+nNgqkUhQLpcb9SFdN7VaLb548YK816lTp+KNGzdQIBBgaWkp9u7dm/iQoigsKSnB2NhYjl/fB+2Ownz+wMBArK6u7tJo6ejRo6ykRE+ePDE5q6RMJuO0pw0bNuC+ffuMXkfXu87uzxcTL1y4wBop0nHidfytVCp5tzBv3boVt27dyilntjFaY2NjsaSkhHcWQKlU8o6E+ex6E6Vjh7FzzMzMDG7X5rOLGScA/jf+Mc/hK3tdP9A+9PHxwZcvXxqMDabY+i6003balQadkZHBWfdJS0sj09yGpleY5Wq1Gtva2tDKygqPHj2KmzdvRjc3N2xvb+eAgPTv5+DggO3t7WR6bcCAASTVb0FBAU6aNAkBdCCg1tZWDgiIvh9FUaRh8JXROn/+fIOMaQKBAGtqarBPnz746aefsti26Ht5eHhge3s7AQGNHTuWMPXxvSuBQIDbt29nffj5ni0vL48EKuZ9VqxYgRcvXjR6fwB+JjiKoljX0L/p4+ODra2taG5ujqdOncK1a9eyfGjIV6NGjSJgSfpvQ4cOxefPn3f6fHFxcYQxTSAQcHwFoFuOSE1NNerDq1evkqlVfdvoY/pfpg/psgkTJhDGzPdFu6MYiwemqL5fu3L9zp07OR9+vrbO94ymTKFnZmZyAITM+4tEIqyrq3ttToTy8nIcNGhQp++E1sTERF6wJN87UyqV2Nrayst3cOvWLZw3b95bq7d07DB2TllZGWdvP63M2EErM/4DAK5btw5//fVX1jnff/89nj9//o2enc+HxuqgIQD5u9ZO22lXGrRarUY7Ozv08/PD27dvEw55e3t79Pb2xjt37pBe2JgxYzipcwF0oEJfX18UCATo5uaGTk5OKBKJ0M/PD9PT07Fv376YkJDAC9wSCoXo5+dHeo9KpZKsk2k0GvJxEggE6OvrixKJBOfPn8/pQc+cOZNFW3rw4EHeUYCtrS2LxWr79u2sNKy+vr5oZmaGVlZW2KdPH7xz5w6rp0rbSvcoLS0tOetp8fHx5KMGoFu3Z64lnjt3jpPVj2krUx0cHNDDwwNlMhnm5OSgj48PzpgxA3fv3s2xi4mJ2LVrF0nhLBAIMDMzE3v06EFAdbS/3N3d0dHRkeVDAF0ASklJYf2Gvq3ffvstbt68mdVBW7JkCa5bt45jh5mZGfErU+VyOd65cwc1Gg3a29tjYmIiZmVlGRx10fWVrjtZWVmE1MTLywvv3LlDZn6srKw42eZUKhXv+uf73KDfR+nMJv3YQfvuzp07XZqZWb16NYv5E0AH4jLGvgcAeObMGUIG1lVVq9Wd8v77+vqSeqZf7/R1w4YNhPKdfjdd2Y1lbm5u8k4IZpzU/5unpycvm+PkyZPxwIED5JgZO/jeDe1DOnZ0Vg8M2cpnFzP+A+iyjtIsprTSMfFN2x3Th0zdsmULi9QNAMh37U1/821rp+30dRq0vb09zpw5kxWEbW1tcebMmeRDHRwczGHNioiIIC9u8eLFJA2tSqXClStX4pw5c1Cj0aCvry/Z6vPll18aBKTRU/708ZAhQ3DixIkolUpxxYoVaGdnh9HR0Th8+HCUSCT47bffoqOjI0ZGRrLY8caMGUNAMEKhEJctW4bu7u7Yp08fVkUfMWIEAcFQFIVLly4lHwuVSoUzZ84kDSssLAxXrlyJK1euJJUoNDSUjCK++uorDAoKQh8fH2IrAODIkSM5gEo+drDExET85JNPiK1MZDEN7LSzs8PIyEgWCHD69OmcBCEjR47EyMhIdHJywpUrV+Ls2bPRzc2N+JC2lW6M1tbWuHLlShK8tVotC7QzduxYznag+Ph4TEhIQDMzM1y5ciVaWlpiXFwci/XQkA4aNAiTk5OJXXTwdXZ2xunTp7NmFPR3StA+pCgKp0+fTj4M1tbWhK1s0qRJOHjwYLS1tcUVK1aQaeWwsDBOQ3/X2h1l5cqVRncM6McOAF3njNmeTNGEhASTlw+YOmHCBAwMDHxj3zBjh6FzmPWO7++JiYnYt29fk39TqVTiypUrTWbbmzdvHosdtasaHh7OWt6jYwffucZ8yIz/fGpubo4rV658rW3pr6N8sYNPo6KiWAPCoUOHmsSO+D5oZ9KlzkBQUBCp6BRFYUREhEnOCg8PR5VKhYMHDyZTdidPniSV0t7eHlNTU3mJQY4cOcJpHKGhoejg4IA9e/bEkydPkvJZs2aRj+/ly5dJALK0tMTo6Gi8fPkyqtVqVKvVGBAQgAKBAHv27MkafdAZ8Pz8/HDMmDH4888/89pEURSeOXOGjDRlMhlGRkaSDlJCQgKmpaVhamoqaaj9+vUjfPrHjh3jrUSff/65QdSwUCjEyMhIlMlkOHPmTPzuu+8I/4GxrVPm5ubYs2dPpCgKN23ahElJSahUKklZYGAgqtVq9PT0xEuXLhGiHnpWhKIoPH36NEHrOzs7Y2pqKvG9g4MDax/5kiVL8IsvvkCxWIyRkZGswKdSqTAtLY3sTLG2tiYdsdDQUDJ6oG2Vy+U4bdo0XLNmDblHcHAw+agzfZiUlMRJ8mLIhwqFAiMjI1EgEOD3339POgqpqamkPiQkJPzp3Ahv2qDfR0lLSzOZeteQOjk5mUx7y1fvmKrRaIx+iAB021b1820w6x1T3d3dMSgoiBU73tTPUqkUIyMjWR0kiUTCscvKygrT0tJMzkq4Z88eg1Pxr6uurq6cLH+dKR3/9WMHrTY2NpiWlmYw18Tb9iFf7HBzc+MMRD/66KNOsyX6+/u/dzOKAG+xMyCXy/HKlSsk1a1IJMIXL15wtvjJZDIUCAQoEokI2KysrAz79Olj0gNLJBJWZadZAIVCIRmx5efn45gxY1AgEHDAQXxlvXr1wsrKShQIBCiVSnHZsmV4/vx5lMlkWFtbS0YFFEWRa6VSKash0nbR5+mDUjQaDdbX1xM2M1P3KTPtom1l/p1mEATQdWrq6+vRy8sLxWIxp8fNLJPL5WRNsUePHvjy5UsUi8XErqCgIKypqUGpVIoXLlzAxYsXs+w6ffo0fvvtt536RiAQ4NixY1ksivR5jo6O2NDQQJYW+J6NmbUwNzeXbBUyNzfH+vp6suebaWtmZiaZsTHmQ7qeMsE89LP5+vpiXV0d+fALhULe8951A+5qg34fhfn8+v7hU32fAegYIpnZQo35h1nv+P6+ceNGDi5HX2/fvs1ZOmTWO6Z+8cUXmJaWZvBexmKHfr2j1d3dHRsaGlikQy4uLtjQ0MBJ2mVM+eKEqefp+0E/JtI6a9YsFmEbnxryl37sMFXftg/5dO7cuZient7lZzt8+HCn+Ih3oZ22U1MbdGtrK4aFhXEIP5g/JhQK8eXLlxgVFYXTpk3DO3fukPNM3c+6b98+3LZtGzl+8uQJjhw5EocMGYLPnj0jv0NRFA4YMADLy8tZ18fGxmJVVRWHspR+1uzsbJw7dy75wDJtCA0Nxfr6epRIJHjlyhWyBCESibC2tpZMh/n6+mJzczOHDIO+1/Hjx03OsjV8+HACtCsuLuYg7B8+fMjiWKB/Y82aNXjq1CnWud9++y1euHAB5XI5NjY2Eope5nXXrl3Dr776ilUmFApRIBCgVqvFpqYmVCgUpIx5/19//ZVVycvLy3HAgAFIURSrE8P0If0bO3bswL1796KdnR22traS3jmzM0D7Vf+Zv/vuOxYhkKHz9H0IoAOBMolJLly4QDo5zOumTJnC2hHRFR++Tw36fRTm8wcFBWFDQ4NRtDUzdjDbMLOOHTlyxGiqX0OodADdgKEzEKJ+HTNURt/PEG6ls9ihX+86s8GYXXy6YcMGPH78eKfn0bGDWbZkyRJWJ4cZO4z5hk/1Y0dXrv0zfGjoN/6oZ3sX2mk7NbVBBwUFmcSFHRgYiEqlEu3s7DhTg8OGDSOgwkuXLpH80I6OjpiVlYUqlQrVajUL8EGzHqpUKrJ2furUKRw4cCBaWFiQshMnTuDQoUPR3Nyc9RHUV19fX4M9a4VCgUFBQUhRFHp7e7NAIIGBgWQUKZPJMCgoiOVwd3d3vHnzJiqVSvT09GStkV68eBF79+7N+q1z585hbGwsyy6tVsuhH/X39+edKnN1dUUvLy9UKBSYmZmJarUaXVxcCDNiUFAQa00/IyMDRSIR+vj44FdffYW7du1CkUiE6enpZFTNtGvTpk04e/ZsVKvVePPmTTQzM0MvLy/WFJtWqyVLIMZ8CKBjeaMBfYj/m1ee6UOmmpmZ4c2bN9HDw4PYJZPJMDMzE728vHDatGn4888/o0QiwRs3bhAgEdOHALolDBrEee3aNRwxYgQv0xoNjKWP9X34vmh3FObzy+Vyln/4lC926Kunp6fRtXmmxsTE8CLKLSwsMCsryyjzXmhoKF65coXV1oOCgvDatWsGPxTbt29n5QkxFjv0693r6unTp3kZTd3c3HhT+YaFhWFaWhrxA93GmOc4OTmxQHs+Pj7o6OiIvr6+eP36daMzDiNHjmTlTtGPHe+T7t69Gz/55JN3/hx/tHYmJpMOZWdnQ0NDA/Tu3RuWLFkCCxcuJBnFIiMjYcqUKQAAcPv2bRg2bBiEhobCgwcPAADgiy++AI1GA48ePYIzZ84AgC7b1bNnzwAAoKGhAQ4fPgz//d//DQqFAoqLi8nv3r17F168eAEuLi4wYMAAAAA4deoUFBcXQ21tLTx8+BAWL14M169fh8ePH0NdXR3k5OQAAMD06dMhPDwcAADEYjEsWrQIqquroby8HNzd3eHLL78EgUAAkydPht69e0NDQwNkZ2cDIkLv3r3Bz88PHB0dYeHChZCbmwv19fUAoCP1yM7Oho6ODhg/fjz0798fJBIJ/Nu//RsIhUJ49OgR2Nvbw8yZM4mtpaWlAKAjD1myZAmkpqbC77//Dq6urtC/f39ia3R0NIwcOZLYn5ubC5GRkbBkyRL4/PPPSXlgYCBERkZCW1sbHDlyBF69egVPnz6Fhw8fAiJCdnY2NDY2AoCOZOfo0aPQ0dEBeXl5cP78ebh48SJ0dHTA0aNHoaqqCiIjI+Hjjz8mdqWlpUFOTg7U1dXBkSNH4LPPPgOKolhEKQkJCYQkyZgPAXTZ1IqKiqC+vh4WLFgANTU1kJiYCAkJCSRj2syZMyE0NBQAgNhVV1dH7Gpvb4cjR45AbW0t5ObmwuXLl6GjowMOHz4MNTU1EB0dDWPGjCE+/OSTT0Cr1cKDBw8AEeHYsWNw7do1ePr0KdjZ2cGiRYtAIpHA+PHjoUePHlBWVgaLFy8GqVQKEREREBwcbGrz+EuMyJIlS8DFxQUAdKRTtH9ocXBwgEWLFpEMqBUVFSR2AACMGzeOtH1aHj16BE+ePDHp93///Xf45ZdfOOUtLS1w+PBh0q41Gg2rjdHPcvz4cdbzVlVVwbFjxwwS9ly6dAlyc3PJ8e3bt3ljB33/e/fudWpDUFAQzJ492+Df6ZioLyUlJfDo0SNOeVlZGYvci25jTCktLYX8/HxynJeXB8+fP4eXL1/CkSNHSOZRPiksLIRz586xjpmxY/78+eDj4wMAOqKoJUuWgEKhMHg/f39/mDdvHqdcJpPB4sWLWdla9UWtVsOCBQtYBEPM+H/hwgWTfMAndPxnyujRo2HQoEGvdb93Kl3t3X/88ceYkZGBV65cIT3DsWPH4q5du8g569evJ+syFEXhhQsXMCwsDO3t7VmoXY1GwwK+nTlzBqOjo9HGxoYD3IiOjsbTp09zejvm5uaYkZFBgCKWlpYE1HfgwAGyZUgmk+G1a9dILzkgIABTU1NRKBTijh07WFPxALotI8nJyajRaPDq1asolUrR19eXNVoMCQnBHTt24KxZs9DNzQ3T09PJCGDQoEF49OhRlq2enp5ob2+PGRkZZAagb9++rOn+r776CpcvX856lvnz52NGRgZrdDN79mzeaTdzc3MCyNFqtWR2g6IoDA0NZYEl6TKlUsnxIYBuVK3Valk+ZP793LlzHCQx7UMA3Tpkjx49DK7tLliwALds2ULAR4cPH35tcJO/vz8uXLiQtY2U9iHf+Wq1Gq9du4ZyuRzXrl3L8eF3332H8+fPR4lEgj169Ojy9Owfpd1RMjIyWCN9Zr0D0I0ar127ZhBL8MMPP5DkYoZiB7ON0cdBQUG8s2pqtZoX4BUeHo4XLlzoMg+9i4uLQRrhrmhISIjB7Ir9+/fn3ardmarVapO2Grq5ubF8xIwdpqhcLsfQ0FCD0+M+Pj6s2UJm7HBwcMCMjAwWh79+7DA1/gPolgJCQ0NJfQoJCcHLly+zno0Z/43ZpVAoMDQ01GCdoGMHs4yOHW9aH962diZd6gyIRCLOy6PLKIoijmOex/wQTJo0iUXOc/jwYULOwDxv5MiRhJxHLBabtP5CnxcbG4sVFRUslkJmIBeJRKz7icVio42fadf58+fJh1ogEGB1dTVnRwB9P4FAwLLpwIEDuHnzZpOcxvxNfRsMfVhp+yMjI/Hly5coFAoxMzOTBFGJRIJ1dXUYEhJCAJ70eqY+CJTWqVOn4p07d3h/09hziMViFAqF6OjoiC0tLUb3F/fv359DkmLMfkNlGRkZ+Pnnn/PWTUNlpgAEPTw8sLm52WD2uD9bu6Po29BZveNT/fbEjB207t27l9UZLC4uZm0hpvWnn35iTWGbqvqxg9Yvv/wSr169avC6zmIMXecrKio4ad278k742s769evx2LFjRtsOAODy5ctZgw1m7ND/Tb6OsVarxYaGBoNLySdPnsQffvjBZNtMiR36ttJqYWGBTU1NbwXRHxAQgPX19aRjwffOu4t22k670qCvXLnCSchw6dIlXLJkCasynDlzBlesWIHe3t7Y2NhItqAJBALWOhP9QXJzc8OmpiayPYZ5XlFREW+D1tf79+/jxIkTkaIo1m+sWrWKlXnv5s2bpCcnk8mwrq7O6B7jgIAArKurQ5lMRj5y9N8kEgkHqFheXo79+vXDpKQkFkqWttUUpw0aNAifPn1KjtevX49Hjx5FS0tLbGxs5JDjAOiAkTNmzGDZb+h558+fT6iH9W1gqlAoRK1Wy/IhgG600dTUxJuNq6SkBJubm0lQ7gzJrF8nAHSzJcwOnb4PAQCXLl1K2Bb5bAUATE1NZRG4AACmpKTgsmXL0NfXFxsaGkwitXmbtKxvqt1RDL3TrozAJ02ahPfv3zfanvTLJBIJ78e7K22RqczYod9ODH0gRCIRvnz50iQGQkPPa0iHDRuGT548YZXpxw4+W1evXs3aks1nA197AgBW7GCqftzVV7FY3OV33lm769+/P4eV8HXrlyHVt2vGjBmYnZ39xvd9F9ppOzW1Qaenp+OwYcPQ1dUVfXx8MC0tDSUSCfr5+aGbmxvK5XLs0aMHYbWimfD0dyA4OzvjtWvX0NzcHFesWIGff/45SiQSDAsLw5SUFM5IOzg4mEz1qVQqTE9PR3t7e/ziiy9wxYoV5LygoCACDDQzM8Nr166hu7s7enh4GJz+oigKe/ToQXqzvr6+mJaWhmKxGDdv3oyTJk1ChUKBPXr0QIqicMOGDbwZEIcNG0ZYuUJDQ1GlUqG9vT1Z6jhz5gwrYxeADl0bExPDKjtx4gTGx8ejlZUVWeoA0H18fXx8UCgUYlhYGEqlUpw9ezarpx0QEEDskkqleOXKFdRoNDh58mT86aefUCwWk/3eLi4uOHToUOJDAN1Sz86dO1EgEODFixfJ1D3Th2vWrME5c+agVCrFsLAw3mAREhKCYWFh6OXlhTY2Npieno42Nja4aNEiXLp0KcuHhiqtpaUla++xvg8BdNOafn5+KJFIMC0tDb29vXHixIksTgF/f38OaMnPzw/d3d1RLpez6ibThwC6DsjbpGP9sxr0+yhvw24HBwej2TDfhoaHh+P58+cNfkS6OnVOa2hoaJfJc5ycnDA9Pd0omZCNjQ1nf79+7OBTtVr92rwPLi4uvIDfd6EqlapTW2mVyWR45coVXjBlV/3CB1B3c3PDa9euvXe5TJjamZgMILxw4QJcvXoVfv/9d6irq4NLly5BR0cH3Lt3D0pKSqCxsRFu3LgB//3f/w0NDQ1QXFwMTU1NkJGRATNnzgS1Wg0AOgDRhQsXoK2tDXJycuDevXvQ0tICGRkZcPHiRZIZkZaePXuSjHetra1w4cIFaG5uhvv370NjYyMB6WVnZ5Nr29ra4MKFC+Q5mGCku3fvgq+vL4wZMwYQEa5fvw4NDQ0AACy7bt68CY8ePYKGhga4fv06ICJkZWVBQUEB592UlJTA1atXAQAgMzMTXr58CeXl5ZCdnQ0AAGlpaVBRUQFarRY+/fRTUlZeXg6+vr4wbdo0AAC4cuUK/OMf/4DevXvDzZs3yf3//ve/Q2BgILS3t0NGRgY0NzdDXl4e3Lp1i5yTk5NDQIodHR1w4cIF+Oijj8DBwQFu3rwJHR0dcOnSJQLIu3r1KrEVQAfwu3HjBiAiXL58GV6+fAkAQHzY0dEB2dnZkJeXB83NzZCRkQHTpk0DjUYDAQEBBEB68+ZNyMjIgMLCQmhpaYELFy5AS0sL5Obmwt27d1k+1JfJkydDUFAQ1NTUQE5ODsybNw+sra2huLgYGhoaYPbs2UBRFEyYMAFcXFzg3r17LLsePXoEN27cIPfLzc2F33//Hezt7eGzzz4DkUgE9+7dA0dHRxg9ejSxS9+H9PvMy8sDS0tLmD9/Psjlcs7z/iWmyfz58wnQlE/s7Oxg3rx5BEAYGhoKn3zyCeucsrIy0p7+KKmsrITLly8TYGDfvn1ZYN67d++SNiYSieCzzz4DBwcH3ntFRERAUlISAOhiAp1B0MbGhmWrIaHjZGtrq8FzqqqqICsri1X24sULVuygRSqVwvz588HKygqKiopYMXHixInQo0cP3t9wcXGBuXPnkiyTT58+JYDfzsTa2hrmzZsHEonE6HkWFhacNmZmZgbz588nGRB9fX1h+vTprOtevnzJaystkyZNIoDk9vZ2uHDhAgFy8omTkxPMnTuXZCPkk9LSUgJQZ0pDQwP5rnVbedPevUajIes6AoEA09PTWaM6iqLw6tWrGB4ejjY2Nib1SFUqFdluk5KSQihGhUIhBgQEoFgsRjc3Nxw/fjyL19/FxYUFKvLx8UFbW1s0MzNj9WZnzJjB4jLQaDQsOl99uwB0owK61yeVSgmDof6za7VaMgoQi8UYEBDAYiU8e/Ys6/y4uDjW/t4lS5awMrQB6NYk165diwKBAAMCAshoXqlUErt8fHwIDz+t+/fvJ8BIiqJQq9WiXC5HBwcHznqanZ0dC2jk6enJAuVotVpUKpUsH1ZXV2NCQgIOHjwYL1y4wLKV6UM/Pz/eJQUA3foeDb46efIkDhs2DAF0szu3bt0ioKPQ0FBMT09HgUCABw8exPHjx3dqF+1DHx8fvHnzJkqlUvTy8sJ58+aRNWOmXUyec7Vaja6urujs7IzZ2dkGgV1/tnZHuX37Ngdgp9VqyYycRqPBrKwssi47atQoFviW3jZr6jsSiUQYEBDwxqDPuXPnGsT5SKVSvHnzpsF4NmHCBN7MiJ6ennjr1i1iu36941O1Ws0CLhtrT4ZUqVRidnY273bZw4cP49ixY0l7CggIIL4ICAjAGzduvNa7VKvVeOvWLRI3DcV/R0dHvH37NssmW1tbvH37Nlk67tu3L2tZUF99fX05YNFjx45xaNEBdKN7vqVWf39/zMzM5F3yYcZ/U1U//r9r7UxMji76BtHHqamp+M0337D+rk+6QAMMk5OTMT8/3+B5tI4cOZIQ8TCVSVizefNmPHLkCOvv69atI2thQqEQ7927h1OnTsWoqCisqakhTIb604A3btwgzIq0HfqkQ8ysVfrEITRxBkVRWFVVRTovHh4e2NraitbW1gbfiTHn6b9zS0tLbG1tJRU5JiYGX7x4gQKBAHNycsgODvo65m9IJBJsaGjA0NBQ/OKLLziMYXPnzsVbt26Ra1NSUnDVqlXEvurqauzbty/LhxUVFThgwAAE0E2Ttba2ksbL9GFJSQmnUTKJoyoqKlh/M0REwlcmk8mwsbERg4KC8KuvvmKtZzJ9SCuTdEggEODLly8xOjoap0yZwlqTPn78OG7cuPG9acimNuj3UfRtoNuTqQBCQ4Q1htTR0RHb2to44DNDvuwKEU1namq9pVW/3vHpwYMHWctfT58+xeHDh3PO44unXSXAkcvl2NTUZJSrRd8mU4mD9OP/29T8/HyStbYz/eabb/Dy5csm39tY5klj75cZ//8Im7uqnbZTUxt0fX09WZ8KCgrCly9folQqRalUivPmzcOMjAzyo6dOnSKoe4qi8Pnz59inTx8UiUSs7UMHDhzgZXkTCoUGc1ArFAoC6tBnMaPLzM3Nsa6uDrVaLUEA0/e7ceMGh46SBgcGBwfjixcvUCKRkDJmI2FSijJRs8nJyXj79m1yHt0wmOft3buXxZj2+PFjTjZCptrY2GB9fT2HWIW2n66ItF0ymQxFIhH27NkTKysrUSgUYnp6Os6ePZt1LY2G1d/GJRKJMDQ0FGtra1GhUKBUKuW1n+lDQ7bq+5B5Hq15eXk4evRoXl/HxMRgaWkpK0D36tULKyoqDOZe57NL34cAYJJd9HmTJk0yyA73vjbo91EMtWNTP1JSqbRLIE66LjLrj62tLdbX1/POMNy5c4fQYL+JisVirK6uZs2MikQirKysNJgcSL/emWI/X3sC0O2S2LNnD6ts7dq1JB+Kqar/7vSVGf8BdHTEzPhvSE2x9XWVjn+m+skYAyafMuM/U0+cOIErV640Wg//CHtfRzttp6Y26KioKDL9rVQqMTo6GlNSUtDf3x/d3NzIHtPTp0/j+PHj0cvLC93c3PDixYsYGxtLUu46OTnhxYsX0dzcHLVabadTZAA67u+FCxdyyiMiIsje2927d+PAgQMRQPchioqKIh+ZoKAgPH36NAoEAgwNDUU3NzfUarV49uxZVqMyNzfHXr16IUVRuH79eg73wJo1awjXNUVReOrUKQwODkYXFxfs0aMHOW/ZsmWsjzCAbgqKaWt4eDhnWv/gwYMYGxuLkZGR+Ouvv2JUVBQBCzJTs+7atctgtj9LS0vSgw0JCeFlaRs3bhyrY7J27Vr85JNP0MzMDHv16kUq/dChQ3HHjh2sawcNGsRJiRwfH8+aEl20aBFnm5++D8PDwzkjN9qHVlZWJHju3LkThwwZgiqVitMz5/Ohj48Pnjt3jnzwR48ezTvV6+HhQaibV61axQsMdXJy4nArvGvtjtIV+77//nv89NNPTTpXv94ZU7FYTNqT/t/CwsIIMFChUODFixc7ZZ+USCSYkpLCWpaiKAojIyNZYEG6zNRlJr7YYar6+vpylmO8vb0NAv7kcjleuHDBaIpfPz8/PHfuHOtDq1QqSZwEABL/33XboNVUH74NDQgIIHWAoig8efJkl7bM/pnamZgMIAwODiYMUQqFAoKCggizloODA/z9738nzHe//fYbFBYWQnNzM2RlZcHly5fhxYsXAACkrK2tDe7evQt5eXnkN5KTk0Gj0YCvry8LQPT48WN4/PgxOZ4wYQL4+/tDTU0NARXl5uZCZWUlAAAIhUIIDg4GmUwGAACvXr0irGeZmZlQUlICr169gjt37sC0adPAzs4OwsPDYcCAAZCamgqICHl5eYQhkZb8/Hx4+vQpOc7Ozoba2lqws7MDf39/Uv7w4UMoLi4GCwsLmDlzJshkMsjNzQWhUAgTJ04EAB2DoLW1Nfj4+MCkSZMAAODOnTtQXV1NgDGXL1+G5uZmKC4uZrGD3b17FyoqKjg+6tWrF8TFxUF6ejrMmDEDiouL4cmTJ+Dk5ATTpk0jIKDS0lK4f/8+x676+npITU0loLry8nIOM1dFRQWLXW3QoEEQEhLCAhU9evQIHj16BGZmZjBz5kwwMzODx48fg1AohOTkZAAASE9Ph4CAABZTF+1DCwsLCAoKImVlZWWgVCrh3/7t31gsYq9evYJbt24BIsKoUaMgPDwcGhoaICcnBz799FNwdHSE58+fs2ylpampCbKysqC9vR0KCgqgpKSE/G3KlCmgVquhtLQUMjIyONf+Ja8n4eHhMGrUKFZZaGgojBkzhhzn5+ezfGFMqqqqWPVuwIAB0K9fP95zW1tbSXvSl4yMDAIMbG9vh6ysLGhqaiJ/F4lEMGPGDLCxsSFlHR0dcOvWLcLyCQCAiPDbb78RsCCzrKamBgB0oLoZM2YQAGFQUBABGgL8b+xgysCBAyEuLs7ou6DjCrNtAujepyHAX0dHB8tWFxcXmDp1KquN1dfXkzZGy6tXr0icBNCBbzMzM8nfhw0bBlFRUUaflxZ/f3+YMGGCwb9/8skn4Ovryyqj478h4fMhLWPHjiWgQj4RCAQwbdo0AlrvTHJyckhspr9/NPi624mpvfsHDx6QHqafnx/euXOHTF1NnDiRQ2xhZWVFwHwajYZ3usTFxYU1Orx69SrGxsbi6NGjSVIiLy8vsr1GKBSSbY2JiYmoVCp5iSWUSiXeu3cP1Wo12tvbs3qIarWarOFIpVK8c+cO+vr64tSpU/HEiRPo7e3NmiKTSqXo4+NjdNps9OjRLHYsV1dXdHBwQBcXF7x//z4BxgwePBgvXbqEAIBpaWkYHx+P8fHxJBkIbSvTLk9PTzKqEAgE6OPjg2KxGB0cHNDNzY2USSQSnDt3Lu7cuRNlMhnm5uaSmYigoCDMyclBsViMHh4eaGtri1KplGMrrd7e3hx/0T5UqVSs7Tnr16/HpUuXkrwHzFG6nZ0dPnjwgMyAxMXFschZvv76a1y/fj3LLgDAyMhIzMzMZD1bjx49MCsry+DU8uHDh8msjVgsxpycHLLuqe9DY3WToii8ceMGRkVFoaWl5RtvRXrb2h2FfvbJkydzpqz5Ysfr6po1aziZNo2pfr0zpHK5HHNzc98KiY2Xlxfeu3eP1Dn92MGna9euxW+++YbVFvW3Kl6+fBkHDRqEAP8bJ/mmzT09PQ2CD0NDQzE7O9tknIy3tzfvcu727dtNZuAbMmQIC0Dt7OzM2r6ZmppKcEm0Xrx4kZep1MHBodP8B8ePHze6JNRZ7OjO2mk77WqDBgDyYgy9IIqicMaMGXjv3j2kKApfvnyJffv25Vxz7NgxMoXLLB81ahQhznj69CkBn9nZ2WF7eztx+IABA7CystLos6xevZowa1EUhTk5OThnzhxeO0JDQ7GpqYm1PhcUFITNzc0k7a4h25nHJ06c4LCjGXpPzOtKSkpwzJgxmJiYSLIxPnr0iFReS0tLbGtrQ41Gg2vXrsWzZ8+ihYUFtra2clC6+mRI9P8zMjJw4cKFGBwcjM3NzSTNNP13oVCIdXV1rMRKdDbKvn374uTJk/Hhw4ec+3p4eGB7ezsnr7qx90SrlZUVtre3o1qtNvqOjb07Y78RGBhIfAgAOHPmTLxz5w4KBAKsqakh6bX1f3fChAn46NGjd96Iu9Kg30cxxUdvW025P7Pevc3n+qNte/bsGaFZ51MbGxtsb2/nnf7Py8tjpfU15Vn5zhGLxdjQ0NDlKfHOfu/AgQOcpUlj1zLL1q9fj7/++muXf9fY3/Rjx5/l4z9CO22nXW3Qn332GaampqJIJMLy8nLeNdUTJ07gd999R7ZiKJVKFAqFOGHCBAK0A9D1uGUyGbq6umJtbS3ZGiISiQhSX6lUkh4uRVFobm5OHME87/bt25w1fgBdz06hUKBCocAXL15gaGgoSiQSDA8Px/LychSJRJiWloafffYZCgQCVo979uzZhCAJQAeMXLp0Kfr4+ODLly+JfZMmTcKsrCyOXZ05Z+vWrSz6VKVSSZi6aLvMzMxYIxfafqlUiv3798dnz56hpaUla8Qsl8uxuroatVotfvXVV5iSkkL+plAoCNMZbde5c+dY6/nm5uZkdKDRaPDly5fo5OREWMpou4uLizE+Ph7HjBmD9+/fZ/kGQNdTr62tJds2Bw8ejIWFhbzvgr72+++/x8OHD6O5uTm+fPnS4Ghs8eLFHFZCAB1JVWVlJatDp+9XiURCbKBtpX1IURSWlJRgbGwsy9b3Rbuj6NtgLHa8DdWvd8aUWWdjYmKwpKTkjQP9+fPnccGCBX9YHWDGRD7Vj5NMZcYTpVKJL1++NIrbCg0NxYqKCt7ZE2acMEX14z+fGoqdfLEjPj6e0NYD6ECEfDMVkZGRWFpaanBWkY7/fH/Tjx0AwIn/3UU7baddbdBeXl4YHh6OFEVh3759eaecQkNDyUiVoig8fPgwBgcHo5ubG0lbzFSZTIZxcXG8FW737t0cVkIA3eiO+QHr1asXurm5YUhICB46dIjTEIRCIcbGxpKPrEqlwr59+yJFURgREYFeXl7o7e2NJ06cIM/h6emJERER5B49evRAHx8fNDMzw7i4ONIQ3N3dOeC2+Ph4PH36NJ46dYqXfWzPnj04bdo0DAwMRJVKhadPn+ZNqgIAOH36dNYWuS1btmBCQgLa2NgQFsONGzfikCFDSAWOjY1Fc3Nz1Gg0GBYWhmKxmCyDAOg+8rStYWFhBpOZ6NvK1OjoaLSzs0MXFxfCsLhv3z7yLqRSKcbFxaFUKsW5c+fiDz/8wPKlvg8BdEtQISEhKBKJMC4uDhUKBSYnJ3OSN3l7e2NYWBhKJBI8ceIE2W5pYWGBsbGxSFEUrl69GseNG0f8f+zYMbLU5e7ujr/++isJPEwf9unTh8xwODg4GPTh+9ig30fRt8FY7Fi+fDnZIsaMHcxzDhw4wOlIGKp3+vffuXMnmaXUV1tbWzJL1Jnq1zumhoeHk6RJJ0+eZHVM9etdZ7p//36Do++oqCjO7oGuqFAoxLi4OKMfNUtLSxIn37TuGor/pqiDgwPnO2BnZ8f7bdBXa2trDtsrU+n4b+qz6MfEgQMH4pYtW8jfFy9ejNOnT3/j9/W2tTMxGUCYnJwMdnZ2UFhYCOnp6YCIcO7cOQKWsLa2hkmTJoFIJILMzEwQiUTw0UcfAYCO3a5Pnz7g4eEBqampAACQlJQEHh4eAKAD6HzwwQeE+Umj0cDYsWMBAODJkydQV1cHnp6eMG7cOPI8VVVVUFZWBlKpFJKTk+HWrVuECbG4uBgQET766CPQarUAoAOVnD17Fl69egXh4eHQu3dvOHfuHCAiXL16lQAei4qKABEhMTERbG1tWax0Tk5OYGVlBfX19XDmzBkYP348uLi4wJMnT+C3334jdrm7u0NdXR08fvwYioqKCCDP29ubgKWePHkCV65cgdu3b0N7ezs8fvyYNyXoqFGjwNbWFsrKykjZ77//DrW1tVBVVQWXL1+GSZMmQUNDAwEpdXR0wNmzZ6Ffv36gVCohIyMDEBGKiooIgIppK20XABCGP2dnZwAAYivfs128eBEqKirg6dOncPnyZWLXq1evyG+cOXMGmpuboaKiAu7cuQMXL17k+JAWmu2tvLwcxowZA2fOnIGGhgaoqqqC58+fg0gkgkmTJoG1tTVYW1uDk5MTsaulpQVCQ0OhT58+cPbsWUBEePbsGQGuIiJhxQTQpa99/PgxAUE9efIE7ty5A8nJyZCWlgb/+Mc/ICoqClpbW1k+/EveXPRjB1OeP38OVVVV5Jhmn2TKkydPOExyhuqdvpSUlLAAfkyprKyE8+fPAwCwYgctw4cPJ8BWZr3Tl/T0dHj48CG0tLTAo0ePWOA7/XrHFDp26NvFBCky5dWrV7xpnGUyGSQnJ4O5uTkA6NL1MkGKtLS3t8OZM2fIu3RycoIJEyawAIQ1NTUkTgIAhISEwJAhQ3ifRz92qFQqmDRpEgFLlpSUkPjPFKVSCcnJyQTwzSdlZWWs2AGgAzMzyyIjIznphAEAqqurISUlxeC96fhvSGi7aBZN/ZhYW1vLSs9cVlZGwOzdSkzt3RcWFnK2qKjVavT09ERra2v08fHBgoIC9PPzQ5lMhklJSXju3Dly7rZt21hJY7Kyskgv3NnZGQsLC8n2w8GDBxPyGHd3d1QqlRgfH483btzg7bkWFhYSkKBcLidrgCkpKWRkSFEUqtVqFIvF+Nlnn+H+/fvJPVxcXDijlEOHDnH4CPbs2UOAMQKBAO/cuYMRERFoYWGBbm5uSFEUZmdns9bbAXTr6QqFAhMTE/HKlSuk3N7ensPRb2dnx5revHz5Mo4YMYJlF1PNzMywoKCATKfLZDJy3smTJ3Hy5MkolUp5r6X1wIEDBEdB20WPRsRiMVnLBwBiK9Muc3Nz3i2MQqEQPT09USgU8tqqr5cuXcKRI0dir1698Pbt25zRiFwux4KCAvT29ub4EEDHLMkkonJ2dkZPT0/O3nJLS0teoJFarcaHDx+iubk5btmyBZcuXfrOe/P62h2FbmN0+2b6548mZDGl3gHoZgWYYGZm7KD11KlT+Mknn/whz2kodryOWllZYWFhIan3vXv3xuzsbIOje1dXV5IPJDc31+jU/9SpU/H48eO8f9OPHZ6envjw4UPemQc6dtD1gBn/Adixw1S7lyxZwlp2NaadxUSmCoVCzM3Nfa+2T76OdtpOu9KgmSoWi7GxsRERkSBd+cBnb6rPnj3jpZQ0pHFxcVhTU8Op+AqFAtva2nj33GZlZXGyMXZFP/30U4PMWhRFYWVlJS8vwJ49ezjTfNu3b+clCYmPj8cXL150Ol3Xq1cvrK+vZzWi8PBwbGpqeq3Umz4+Ptje3k6mySdPnkzW7mg64qSkJNbaHa1OTk6IiOjk5IQ7d+7kfLz/aL127RoiIt66dYtVPmvWLMzNzf1Tn+VtaXcUAMDr169zMkheuXKFhZL/I9TUerdhwwY8derUO/fvu9Dc3FzebIx/pNKxw9DfmbHjj/j9nj17YkNDwxtTVncn7bSddqVB66uVlRVaW1uzQBtWVlYoEokwOTkZ09PTWecnJSWxgHYAup0D+qCSYcOGEeY3lUrFYR/Lyckx2EEw1Bmgn43+SIaFheHTp09RJBKhhYUFymQyDAoKwufPn5PfmzlzJou28sSJExx6WwBdL5NJKrJ37178/vvv0c3NDauqqtDT05P3Q2xmZoZjxozBBw8esMo++ugjLCgoQADAW7du4dixY1EsFrN6zsuXL2d1Gq5evYpTpkxBkUjEGYHpl82ZM4e1nQdAh0ugtzgC6HZ6LF26FAUCAWv0xrRVpVIRNi96ZiU/P580coqi0NraGh88eIDjx49HpVKJtra2WFVVhc7Ozrh27VrctWsXrx/Nzc2xsrISvby88Ouvv2ZtP0tLSyMkQVKpFMvKyng7eRYWFmhtbY0WFhYoFArx6dOnGB4ejjKZjOWvw4cPY1VVFYeieezYsZyOxLvW7ii0L/TBXXxlAMAbO/R1165drKydhtTMzIzghIwpPcP1Oj6ZN28eC6RLq1arxbKyMoIPmDJlCmtr7eHDh1lkYl3RzZs3c8i01q9fj9u3bzd6XWZmJmdrnaWlpUEMQ2hoKJaWlhocSJw6deq1BlJ07DD0dzp2GBv89OvXzyAguTPli5MAukFuaWmpydkQu5N22k670qABAEeMGMFJpEOrQCDAffv2oVarRU9PTw64Q61WEyDHzp07MTQ0FN3d3TmAHldXV4yLi2OVRUREkC0nsbGxZKpaqVTi0aNHydS6vb09yQ0AoEOwLlmyhPOs1tbWOGDAAFZlU6lUmJiYiIcPH0Zvb2/09vZmzXL07NmTMC4eOXKENKD+/fuzaJXDwsIwMDAQ5XI5Dhw4EMViMc6fPx+nT5+ONjY2ePToUfLxdHFx4djq5OSE/fr1QwBdgg56i5BCocCjR4+ik5MTarVaFrAoOjqaFwQzevRoXL58OYpEIjx48CBqNBr08fHBpKQkPHToEGmQGo2GZWtERARqtVp0cXHBI0eOoFwux4ULF+KkSZPQwcEBjx49ikqlEj///HMWY1y/fv3QyckJo6KiyJRdXFwcmZaXSCQ4cOBA0vnSB4IlJyfjV199hSKRCAcOHEiSTDGBnNHR0WRZRCAQYEJCAlpaWuLIkSNx5cqVxFYmKJLOg8AH0oyIiMBBgwZxwGMeHh4GwWbva4N+H6WrNvLFDn0NCwt77ZTGU6dO5TBkGlKtVov79u0zSp3s4+PDC4yzsLDAhIQEcq2XlxfLLrqN0ce7d+/m/QjRsYNZFhISwjk3ODi406nsPn36mDw9DqAbQOnHSaZGRkYSsDgz/nfFH7a2tnj06FGj6Zr51MHBgRXrAXRbwffs2cN6XlN8SCsdJ/g6Cny6ePFinDhxIq8P7e3t8ejRo90GfGwygJCWxsZGqKmpAYqiYMyYMWBvb0/+hojw4sULaGtrg0ePHrHAHbGxseDo6EiAHC9fvoS2tjZ48uQJnDt3jvUbv//+O6SmpsL48eNJWsuWlhYCBjt79ixhKevo6IDq6mro6OiA8PBwCAwMhFOnTpF71dfXQ11dHYhEIhg3bhyoVCoA0IFKfvnlFwKMCQoKgsjISDh+/DhUVlZCW1sb5Ofnw6VLl8i9LCwsQC6XQ3t7O1RXV5Nrm5qaoKOjA8aPHw9isRgyMjKgsbER+vfvD8ePH4fW1lZ49eoVvHr1Cjo6OuDFixfQ0dEBUVFRoNFo4PLlyzB+/HjC8CiRSAig79y5c+Di4gIxMTGAiFBdXQ3t7e1w9+5dSE9PJ3ZlZWVBYWEhODg4wJgxYwgIqLGxEWprawEAiG/y8vLgwoULxIb4+HiwtLRk2Xr16lW4e/cuy9a6ujpoaGhgvfO6ujqor68HMzMzGD9+PFy8eBFKS0uhubmZAMTOnDlDADYtLS1w/PhxaGpqguzsbA7DX0NDA9TW1kJbWxscP34c6uvrQaFQgIWFBbH11q1bhPWro6MDTpw4ATU1NcRW+j21tbVBQEAADBw4EAAArKysQCQSAQCApaUljB8/HsaPHw+PHz+GY8eOEfAYLVKpFCwtLeEveTsSGBgICQkJnHIrKysYN24c8Q1f7AgPD2ddk5GRwZvS2NPTE0aMGGH0OV69emUQRCgWi2H8+PFgYWEBALp06C9evGAB/uzt7VltLC8vjxcYV1tbCydOnCDg08LCQpZddBuj5cWLF7wpi+nYwZSbN29y0vdmZWWxmABpcXV1JeDc8+fPQ1FREeec0NBQiI+PJ8eDBg2CgIAAePHiBStO6vvwt99+I+mQmfG/K8KMJ4ZErVYTQDotZWVlrFgP8L/+4itj+tCQICL88ssvrHvQ3zq+dNV0TKSF6UM6dnYb8LGpvXtXV1cUi8Vobm6O9vb2KBKJ8OHDhwanU5RKJQsIt2vXLhYgy9nZGWUyGSoUCgLccXJyQrlcjgqFAgMDA7GkpMQk8A+tX3/9Ne7cuZPVc6SnCOVyORYXF5M9tRKJhAUsmzVrFslzQCttK3188OBBXLBgAYpEInRxcUGKotDW1hZVKhWq1Wp88uQJ6QWOHDmSlUGPVqFQiK6urigQCHDdunW4bt06tLW1xZKSEvIe+vXrhzk5OeSa5cuX4/bt25GiKHRxcUGhUIgqlYqkZy4uLkaNRoOWlpY4YMAALCgoIKA9c3Nzjq0Auukwuuzo0aP49ddfE385OztztmUxy4RCIbGf+feSkhLWkoJAICC20j5grgFaWVkZ3E5J2yoSiXDRokW4f/9+lg8tLS1ZuR1oW/XvM3XqVDxz5gwKhUIsKCggMxEajQZLSkqwpKSEbElj+tWYD9+ldkehn33GjBm8pDC+vr5YXFxsMDmZfuwwpomJiXjz5k1ybG1t3SWQolKpxCdPnhhlngwNDcWHDx+S9WYLCwtWnLCzs2ONcl1cXMjSo34de9sqk8k46+zR0dGYm5tr9DcXLFiABw8eJMdnz57lzRFhyIfG1FA8MbQ0oVAoODb0798fs7OzDf6Go6Ojyfv+TY0dtOrHju6qnbbTrjTq0NBQ/PLLL1mNzZDOmDGDtRaur3zgs4qKChwyZAiLjvhNNC8vjzcBDYBuyr+xsdEogOTzzz/nXTP28/PD9vZ2VCqVmJKSYtLaJa2urq6IiCYRovBV4o6ODhYDIfPvK1euZGEcbt++jfPnz+cFEAYHB2NLSwtpkPPmzcOcnBxeEChFUVhbW4uxsbEIoJvGRcROp9L0QUBDhw4lzIoAOtIlQ8hkCwsL7OjoMMh/sHz5chbG4U1BoAC6DkJHR4fJSWXehXZHeZfva9euXXjgwIE/9DcWLVrEwpukp6eTpUmRSIQNDQ1kGYEZO/6IZ0lMTCSsrO+D6scOWo0BCF8n/hcWFmJycrJJ5/4RsaM7aKft1NQGTc8GmJmZobW1NYpEInzy5AlZo/L19cVnz56R7SJyuZzVI9+3bx8LLGNra0tSBdOjQ5ozXyaTEdKXnJwcDg/1rVu38KOPPsLY2FjMy8tDAB1aedy4cRgdHY0PHz5EiqLQxsaGjDYUCgU+e/aMrG+JxWLSO0xJScFZs2ZhUFAQlpSUkF68QqHgHVXQo26KolClUpGGTVEUFhQUcAguHjx4QNa2BAIB2tvbo0AgwI0bN+LGjRvRzs4Oy8rKOFn8AHSAn9GjR5P729vbo1AoRHNzc1SpVGhmZoalpaXo7e1NtmAWFRWhUChEa2trVCgUKBKJOBkSRSIRazTDtNXOzg7FYjF+8sknZCuknZ0deS+0DYZGGlu2bMF169axbAXQgf2YdMXm5uacD++3335L1vxoW/l+Q6lUsjoj1tbWZGQgFouxpKSEQ1bDVG9vbywtLWWNJph+fdcN93Ub9Pso7/J9WVhY/OGdOzMzM1ZdtLKyYtUruj39GXVMv40Z0l69emFhYWGn6+ghISFYUlJiEOx34sSJTvEXzNhBKx3/+c5nxn9TlY71zPhv6Nw3jR2mqpubG5aVlZmMP/ijtdN22pUGvXTpUhw2bBgC6D5Mw4YNQxsbGxw4cCBu3LgRP/roI9y5cyf6+fkhgG4qaPfu3SiTyTAqKoqzpDBnzhzCNqavKpUK9+7di+PGjeNwbCckJKBarcaEhASsrq5GAF2eAi8vL3R0dGR1HsaNG4cLFixAoVCIw4cPx02bNnF6pLGxsbhw4ULcsGEDDhs2jDSQwYMH47Jly1AgEOCOHTvQ19cX+/fvz8pf/fnnn+P48ePJcWJiImeKKzExEV1cXLBXr164bt06BAD88ccfcdasWQTdTnMJAOimIX/66Sdil7EpS5FIhCNGjCDTknZ2djh06FCDjYHpQ1oXLVrEuzvD29sbJ0yYQHzIdz8+H/bs2RPDw8OJD2mwZFhYGO7duxf37t1rcHkgJCSEsBky1c/PD7dt24YCgQBXrFiBCQkJ6OPjgzt27CAdhmHDhpEdEHTd5PuN/v3746ZNm3DEiBGsmSFHR0fcs2cPyuVy/Oyzz3DixIloZ2eHe/bs+cNGcm+7Qb+Pwnx+tVqNO3fuJB+XgQMHcpILxcfHGwQp66uNjQ3u3bv3jUFaH3/8Mc6bN4/3b1KpFHfv3v2npMQ1VadNm2Zw1tMUdXBwIIylnb1fZkzU1z59+pCkPq+rXfXhunXreGMEAHDif1e1s9jRmf78888E2GpmZoYjRoww2On5s7Uz0SF2XkMQEQ4cOECOa2pqYP/+/QZTiF6+fBk0Gg0MGDAAfvnlF95zBg8eDJmZmSw2p2PHjsHLly/Bzc0NQkJC4OjRo3DixAkA0LEeHj58GAAAfvnlFwgJCQFXV1c4ceIEjBgxAn799Vdyn/b2dti/fz/84x//4Pzu2bNnwdLSEsRiMcum15EjR46Q/5uZmcGAAQPgyJEj0NLSAl5eXqxzr1y5Aunp6SzmrbCwMIiIiOC9t1gshv/8z/+EkydPwqtXr8DBwQEiIyNh3759AKADQfr4+ACAji2NTz766CPw8fEh6YzpssOHD8OePXuAoigYMmQIpKamQn5+Prx69Qr++c9/AgBAVFQUvHr1Ch48eAADBgwg797T0xMSEhKIX7oizs7OEBYWRt7bzZs3wdHRERITE+HIkSMQHx9vlB2MTzo6OuDAgQMQExMDz58/h99//x1iY2Ph0KFDLNYw+r317t0bamtrOSmr/5L3R+jU5oZiR1dFv969qVhbW0OfPn3g0KFDvICx6OhoePHiBS/osTOJjIyE1tZWk9Jpu7q6wj/+8Q+OXfqxo6ysDA4dOsQ6x87OjrR1fRk2bJjB36TTqTNjR3l5OescOnbogx47k0GDBkF2djYnrTOfhISEgFKp5Nhub28PvXr1gkOHDnUKIqRjh6lCx46srCzO3+rr60mM6RbyOr175hSztbU176hJoVBwelfjx4/HjIwMcqxSqVhAm9zcXOzfvz/poTk4OKBAIEBLS0scPXq0UaIYW1tbXLt2Le7ZswfNzc2xtLSUbLUTi8WsKXFj00TMMqZd9vb2nKkye3t70usTCoXo4OBAlg5opr7nz5+jlZUVx1am2tnZ4fPnz9HR0RHXrl2LP//8M/nbjRs3yDKBhYUFlpaWkq1BvXr1wkePHqGTkxM6ODjghg0bXnv09sMPP6CDgwM6OztjQ0MDwQww7dq7dy8uW7YMPTw8sLS0FC0tLVGlUuGkSZNYOJLNmzfj2rVrWTZaWlqypmttbW1RJpNhbGwswZbY2tqSqb6CggKkKAqvXLmCEyZM4PjQFP31119x3rx56O/vjyUlJazZDaZdBw4cYAHU+KY13xftjvI27NaPHYZUJpNxlsT4lFnvuqL6sYNW/SVGfT169Ch+9dVXvH9j1kW6jI5/FEXh1q1bcc2aNSY9X3x8POFoYap+7ADQze46ODgQHTRo0Gv511DsYCodO7r6vm/fvm3ySH/58uW8uRoiIiLw0aNHRpdE6OXvrj6ffuwA4H7X3hfttJ2+ToNmgs8MsYh1BiAE0OWWZiZ4YCoTfGYKCMgYgKR3795YV1dHppNNAZAYAwEB6BprXV0d2ZvOBJ+dPHkSN23axLrfkSNHcNu2bW/dwebm5tje3v5ajdiY0A3ax8cHOzo6DE7hGfMhU/V9+OTJE9bySld9+KZqCMhFp9zW37/8vmh3lD/z/bwt8LEh1Y8db0P5gKtqtRoR8bWnq01RpVL5h8aO7qT6INA3Ub74/z5op+3UVAczb8rclkZvo9HfHkRvLaQoCu/fv4+9evXCsWPHsrj5bWxs0MrKqtNtafrbg65fv47Dhw/Hvn374p07dxDA8NaSRYsW4bFjx9DZ2ZmUGdpaEhgYiI8ePUKJRGJwe9Ann3xC2PuYW2aYW4ZsbGw4uQ5oW+njW7du4cCBA1nn3LhxA4cMGcLZWgjA3h6UlpaG48aNw8jISHz69OkbNVxDUlFRQch/mFuhDPmQ+awbN27ENWvWoL29Pdkequ9DR0dHVCgUvD6MiorC+/fvs0ZKUqmU+DAlJQUnT57M2eLFtz2UVv3tQUy79uzZw2KWpLc9/bW18O0IgA5oZozyViAQYH5+vsEMfaYq37Y0up6WlJS8Mb2tfux4G8q33ZC5BdnYtfrbkpl67do1g0ytf0bseNdthan6W8v1VX97KK1+fn5YVFRkcpZJuq7xZeR819ppOzXVwdu2bSNTTB4eHrhlyxbWtLlWq8W2tjaymyA2NpbMGIwcORKdnZ3Rx8eHNeUzffp0TEpKQjMzMxw3bhxvylE+pUeVrq6u+NFHH7H+ptVqccOGDaRh9ejRA6dPn44bN24kZcOHD+dFwNrY2OCYMWNQIBDgggULeAE2/v7+OGjQIFZZTEwMJ8VuVFQUARquXbuWg04dPnw4hwls2LBh6Onpie7u7jhixAjW31xcXEjDLigowPT0dDx79uwbNdrO5N69e7ht2zbcunUrymQynD17Ni5dupR32s7a2hq3bduGSqUSe/fujZGRkSiXy3HcuHGs/ePm5ua4bds20jHQ9+G4cePwu+++I7YuW7aMwwI4cOBA1Gq1aG9vj6NHjyZ+DQoKIstMAMDyIUVROGrUKN4tnbGxsdijRw90cHDArVu3kufVaDQ4adIk3LZt23uTu7w7CoAOtKnPGPjFF1+wAMmjRo0iHzVHR0dS7/Tfwbp160xiH1y9ejXpXMhkMhw3bhyJT0xdsWIFL+PhqFGjDOa551OhUIibN29mAX4FAgFu2rSJ9RGiKAp//PFHArR+EzVm15AhQwhTJ63Lly/Hbdu2/emxg/59Ozs73LZtG+/zAujyvOgng5o0aRJOmTLljd6TSCTCsWPHdvkjbWVlhWPGjCEzkv369cOvv/76T23zb0s7E5MZCFUqFWEIEwqFhMmPlrq6Ojh+/DgBaEmlUsLitWfPHnB3dwepVErAHfHx8aDRaMDMzAzq6+th+/bt0NzcDKGhoRAcHAxSqRQGDRoEUqmU8yw0A2FraytJ2wsAEBAQAP/xH/9Bnq1Pnz4kLalKpSKMYXK5HJRKJQgEAkhISCAsc+3t7YQ1z9zcnJNSs2fPngCgAzUyhWlrfHw8ODg4sMpUKhVJ40nL/v37oaioCFxcXCAuLg4AAA4cOAAqlQrs7e05wJOnT5/CgQMHYODAgWBmZgZhYWHQt29fzrt5m/L3v/8dxo0bB+PGjYPBgweDp6cnPHnyhOVDmpVLIBCASqUCgUAAly5dgqKiIujVqxds374devXqBa6urgCgAxmpVCqIi4sDNzc3+P3332Hv3r3kN+VyOUilUuIHCwsLkEqlYGtrS9KTHj9+HO7evQvl5eXwr3/9i4CCGhsbWcxyTB8iIuzatQvKysrAy8sLoqOjyXlnz56F69evg0AgACsrK1JPHj58CEeOHCF2/SWvLydPnuSA55RKJcc/z58/B4D/jTHMdLq00GyUTOnXrx9JMUuLpaUlSCQSANCxhG7fvp2TDpm+H1+coeOEqULXbf1nM7UMQAcgDgwMZJX16NEDgoKCQCaTwcCBA1nPasyuQ4cOEaZOkUgEAwcOhDFjxsC4ceP+9NhhZ2cHAOw4wSdmZmZgZmbGKlMoFJyyoKAgCAsLI8cxMTGgVqtZ5/Tp0wc8PT0BQMdCuGPHDlbabP34zycvXryAnTt3sr5rdGpoWtRqNfTp08fgPbqNmNrTA+AmFqGTEoGBnggzyc3BgwdxxYoV5G+FhYXYr18/VpIbAF2q4x9//BEdHBywqqoKHRwcDCYb0U9UsXLlSta6dEZGBqeXyUzKQSe5iYyM5E1UBKDr7dPT4PqJivTtpygKHz9+zMtnr1Qq0czMjLwTejSbmJhIsBUqlQq3bNlC2Aatra3JNCGdSrijo+P1u+tvKEOGDGH16GkfMn1N28VMNnXv3j0cOnQoSiQS4mtmsil9W6Ojo7GoqAgpikJLS0uUSqUYGRmJJSUlLDZDGsdAJ6AylDCG6UMAdsIYvjpsZWVFZr307XrX2h2FfnaZTPbawCr9ZGD6ykyQpa/MegfAn7zodRMV0UnO3pZ/6SRnzLKdO3fiunXr0NnZGauqqowuU4jFYs7o932MHW+q+knOsrKyMCkpiXUOX/zXr1PGkpwZYsTU14kTJ/LiDbpb7OhSZ4CZhpROYcxMIKOv+ulv+dRQ+lv9xvC20t/euXMH586dyyozxkAVGBiIra2tnIqhDyDsTGkAoTEGwpKSEhw7diwC6JYsEJEsJcTHx79mM3y7QvMk6KubmxsiolGykKFDh2JZWRmn3MrKCjs6Onj5FB48eMC7n5oGcslkMmxpaTE6bdwVH+oDCOkg+jpI4z9Cu6PQz24qeymfMlNnd1X1693PP//MYb583RTG7xt7XWJiIlZUVLDK3vfY8T7qtWvXeBPcdUW7W+zoUmfAxcWFNcJSq9UolUoxOTkZf/31VxQIBHjnzh2yTkf3SCmKwm3btnHymQPo1pDDw8OxsLAQrayscN26dbhq1SrWOfb29hxwx/Lly3HTpk1oaWmJhYWFvIQgKSkpOG7cOFaZq6srWlpaYkhICN67d4+Ad/R704cOHcKZM2eiRCIhNvA5Wy6X49ixY/H8+fOkfMuWLbh06VJ0cXHBwsJCVKlU6ODggHZ2digUCtHT0xOFQiGuWbOGtWXI3d2djFgEAgF6enqiSCTCpUuXYmlp6Rs1xLclNTU1eOnSJQTQBUL6QyoSidDT05NF2KQPvjMzM2MRSNE+ZNq6ZMkSku0QQNfJ4BtNWltbo7OzM1IUhWq1mszmaLVafPDgAUokki750FCZvl3vWruj0M+uUqk4OTL4NCkpCc+dO8cqo7fq0sebNm0yuFXNzs4OCwsLyQhav97Z2dlxOuO2tra8ILzOlC928OmePXtw/vz5rLKdO3cSoB1FUZidnW0yEl8/dtCqUChYtnaH2GGKD5malpZmEmHSm6izs3OXmQP1fdjdYkeXOgPz5s3DDRs2cCp1UFAQDhs2DCmKwgkTJqCzszNGR0ezEKX9+/cnCWEAAFetWkWmZ5RKJSYnJ5N950wwz7JlywjlsVKpxA0bNqCVlRX27t2bLDMkJyfzzj589NFH5DckEgmZagPQAZQmTpxIPhADBgxgMZAlJiZijx49EED3Yf7hhx9Qo9FgTEwMfvnll0hRFK5evRp9fX1Rq9Xi9OnTcf369SiTyTA+Ph6joqLQ3Nwck5OTDQIjY2JiONTFtJqZmeGGDRvQ2toaN23a9EaN8G0LPUJLSkrisEOuWLECg4KCUKPRkFmOznzIvD4yMhLnzJnDYqAbNmwYbtiwAVevXs35oIvFYly7di26ubnhgAEDcNWqVfjJJ5+gQCBg+VBfHR0dcd26dcQ3vXv35u2svm/aHcWQLXPmzMHBgwdzyrVaLQcYrK/9+vVjfTjpegeg+yAmJye/0bT0woULSRzSjx2d6dy5czk7hQYNGsTZKTFgwADs2bMnOU5KSkJ3d3eTfsNY7GBqd4gdVlZWuGHDBtbSjTEfjh492uCugLetrq6uuHbtWg7HDDP+M8u74sM/WzuTLqGinJ2d4YMPPgBnZ2egKAr69u0LKpUKsrOz4cCBA4CIUFRUBE1NTWBpaUlAYwC6dJPV1dXk2N3dnYBCXr16BT/++CM0NTXB2bNn4cGDB9C7d29yHg3YkMvlkJycDEqlEiorK6G8vByam5vhxx9/JMAxW1tbAubYu3cvtLW1QWhoKFAUBWq1mgD5nj9/Dlu2bIG+ffuCubk5WFlZgbOzM3m+I0eOwPXr1wFABwzy8PAAmUwGlpaW4OLiAgBAyu7evQsHDhyADz74AAQCAZw6dQouX74MdXV18OOPP0JzczMEBQWBVqsFqVQKcXFxIJFIICUlBe7evUvAbL179ybPIBQKCTDFzc2tK276w0WhUEBcXBzs3LkTiouLwcXFBaKiogBA5y+lUgkPHz6EHTt2kDLah0KhED744AMQCoVw6dIlOH36NLlvr169oLi4GC5cuAAeHh4EPGZjYwMffPABqNVqDqCM9iud9lksFsNPP/0Effr0gQsXLkBlZSVERESQ+kqnhpZIJPDBBx+Q+1lYWJD62qdPH7C1tQUAXZ2Li4vjAED/kjcXJycn4g+m3L17lwUq5ZPTp0+zUm7T9Q5Alwb7xx9/hIaGBggICICgoKAuP5urqysBAOvHjs7E2dmZY9exY8cgPT2dVfbLL7/AlStXyPHWrVvhyZMnBu+rHzvodPCGpFevXu997ADQgRvVajULVMj0ob7861//gry8PJN+z9fXF0JDQ8lxz549OUBDWiwsLCA2NpYVYyQSCXh6evICWen4z5TOfPhei6k9OtDrZYhEIiwvL8ewsDCUSCQEHFdaWsqaAaD1yJEjnOl/fVUqlYRrPz8/n5TLZDKUy+Voa2uLtbW16OLiguvXr8e9e/cigG6pgR4xxsTEYElJCTlevnw52Xtubm6OAoEApVIpKhQKlMlkWFVVRbi1BQIBa4aBtsvQ85qbm5MtJ/rXisVi1rW7d+/GH3/8EZ2dnbG2tpYsewwZMgQfPnyIALr1cXpERFEUmpubY3Fx8Zt0xP9QcXd3R5FIhGPGjOGwQ4pEIqN8/kqlktPbvn37Nn788ced9nDNzMyMMgSKxWKsqKjA0NBQnD17NqalpaFIJMKysjJejItCoSAzBBRFYUlJCcmy5u7ujjU1Nd1m3e99FGZ7MjZaZ7YnpsrlctbsGh0nTHlfmzdvxu3bt7PK6HjyLv3IZ6tSqeQto23Vjx18KhKJ0NzcvNvEjrf1Lg2B9JjxH0BHJjdjxgzec4ODg7GysvK9ZR99U+1MXrszAADEAZ999hnJNW3IKRRFdYqqLCsrI3vYmefu3LkT9+3bxyqn72dlZYXt7e0cmk3931UoFNja2oparRaXLVtGWMSY54aGhmJTUxOpDPPnz8esrCzeZxUIBFhTU8NiIGxrayOI5wkTJuCjR4947dd/D3zlNjY2fwg72NuUjo4OHDBgAK9No0aNwqdPnxr0dXFxMYeB0FTUbU5ODs6ZM8foOfp1wNj9z549y1p/NeSf90G7o9DPvmDBArxx4wavXXyps2k9ffo0i966srKS1LvOlC/ubNmyBY8dO/bOfKgfO+jnfPHiBYf5sqKigsVr0lldTExMxI6Ojne6c8AUYcaON1G++G/M/529v/eprb9t7Uy61Bk4cOAATp06Ff38/PDOnTvko2ltbY1qtRoFAgFmZWWRdVq1Wo337t0zOLpev349i9dZo9GQ0aSdnR0+ePAA7ezs0MnJyeB6nUAgQB8fHxSLxfjVV1+RbH+0zp8/n2zV8/HxIeyC7u7uKJVK8c6dO2TdRyaTobe3N6kQNjY2qFarUSgUYnZ2Nln39vLywnv37mFQUBAqFAocPXo0XrhwAX18fAhYxNLSEiMjI/H+/fuoUqlw7dq1+M0336CDgwM+ePCgU5pRejfB+y4lJSUsH169ehVjY2PRwsKC5IagNS4ujmzp8/LyMjm1LO1D+litVpOROtOHU6dOfa3c9W5ubmhvb48eHh54//59UgeHDRvGAoa+D9odhVmn9TEmTPX29uadOXB1dWUB/phxQr/emfIOHR0dTQIy6uvx48dxwoQJb8WPGo2GY6tGo+HESj5bmWpjY4MPHjwgswVKpRLDw8PftctNkoSEBBwyZAhhdAXQASOZWSxTU1ONdhqY8R9AhznKzMw06aOuH/870507d3J2onUn7Uy6hBk4fPgw3LhxAyorK2Hz5s2EiCEwMBCGDh0KiAhbt24l2d9qamrgp59+gtbWVgAAiIiIgM8//5zc78yZM3D58mVyPHbsWEIS0dDQABs3boSGhgb45z//CX379gUzMzP47rvvWIRHHR0dkJeXB62trfDbb7+xMhUCAFy7dg1++eUXQETIy8uD6dOng1arhSdPnkBbWxts3rwZqqqqIC4uDpKTkyE/Px8QEaZPnw49evSAoqIi6OjogC1btkBpaSkA6IgoNm3aBLm5udDQ0AB3796FnTt3Ql5eHnR0dMCECRMgNjYWcnJyYNOmTdDU1ATnzp2DS5cuQX19PWzcuBEaGxsBQEeesWjRIgAA+Oqrr1jrW91B9H24Y8cO+I//+A/o378/yTa4cOFCCA4OhsLCQoIj+K//+i/429/+Bv7+/rBkyRLWPX18fODrr78mx7QPJRIJrFy5EpqamiAiIgKmTp3K8uGNGzdIJkUAgOnTp0N8fDw4OzvDihUrQCwWw5QpU2DgwIHg4OAAK1euBIlEAiUlJVBeXg41NTWwadMmaGlpgQkTJsCHH34Iu3fv/iNf3/9TUlVVZTT7XH5+Pu8a8e+//w5lZWXk+OHDh/Dq1SvWOTt27IBHjx6Z9BzPnz+Hp0+f8v5NKpXCqlWrCEkOUw4ePMghTpo5cybExsYa/C2BQADLly8ncY1pg76tDx8+hPr6ek7Zq1evIDQ0FL788kvO/RsbG0mcBNDhrwoKCgw+z/smDx48YLWxc+fOwYULF8jxv/71L0Kc1Fn8BwAoKSmBrVu3dpqdEABYsUNf6NjBlBMnTnBwH8nJyZCYmNi5od1ATO4MREVFwS+//ALp6enQ0NAAt27dIqk6HR0d4R//+Af8x3/8B6xbtw5sbGzAy8sLmpqaICsri3Qa7OzswN/fn9yzsLCQFRwCAgKIo+vr6+H777+H+vp6+OCDD+CDDz4AsVgMwcHBBMhjY2NDWAEBAM6fPw+XLl0iZaGhoVBQUMBKSenn5weOjo4AAICIcOvWLWhsbARnZ2fw9fUl5/n4+BAwHyLC2rVrSQCprq6G1atXkwqYnZ0Ne/fuhaioKBAKhaDRaCA8PBz+/ve/w/fffw9NTU1w/PhxyM7OBq1WC99//z0EBASAnZ0dWFlZwYcffggAAB9++CFYW1uDra0ty673WQoLC6G5uRnCw8MBACAnJwdsbGxYwe/DDz+Enj17gkQigR9//JGU2dragqWlJQfgZWlpCYGBgUBRFMuHAoEAgoODQSaTgYuLC3h7e0N7ezusWbMG3N3dobS0FPbs2UPuQ/tQoVBAcHAwUBQF3t7e4OLiAjKZDEJCQiAqKgpUKhW53/fffw8tLS2g0WigtrYW/vWvf0FUVBQvW9xf0jXx8PCAkJAQVpmbm5vJHWCZTGbQFz/++CP5aNASHh4O9vb2nDK6/dMSFhZG2AuFQiGpY/qyY8cOyMzMZJX5+flxmA+DgoIISI2iKAgKCiLgRoVCAb169QKhUGjUVqlUyrLVxsaGxAmmNDQ0wPfffw+vXr0CX19fiIqK6jaxQ6vVkg8yLb/88gsLVLxp0yZ48OABAECn8R8AoLi4GNatW8cqU6lUEBkZySqzsLCAf//3f4cffviBpGBmCjP+07J//35ITU1llf3tb3/jmsLw8QABAABJREFUgDS1Wi14e3sbtf29FFOndOrr67Fnz54oFosxKCgIX758yQL1+Pv7Y21tLSoUCjx16hQuX74cvb29sa6ujgDrRCIRi63rwIEDuGHDBs50hlAoNAjwUSgUSFEUSiQSTExMxJKSEgTQTfGLRCKMiYnB0tJSsudz8uTJKBAIyP3o8+j/19TUYFhYGAfMJpPJUCwWE7wB/N9pJUNlXl5eWFdXR/bEJyUlsVKJSqVSTEpKIsDIx48f49ChQzl2AejW/bqTtLW1ERbF4uJisl2Mfk8UReHPP/+MO3fuNDqNxfQNrbQPjV2nUCgwPT0d58+fjwKBoNMtZXQ9FAgEWFZWhr1798bk5GS8ffs2AugAazSQy93dHevr6/8CEL6B0M8+f/58VpIrAF1yKVNSEwPolofq6upY+/qZbVFfCwsLOTk+8vLySEpwWu/cudPp9D+zfXamly5dMpiox9fXF+vq6shygH5MpNVYvWPWT6b+9NNP2NTU9K7d3SXRz3gqkUhMBvDFx8eT+E9fy7eNu1evXlhRUcHa7x8eHo5VVVVvDcTI1EOHDnFSuL8P2pmYHF2EQiGmp6eTSs5XGZnIevrFM89LTk5m7RJgnsfUkSNHspxMq52dHba2tqKrqytu3rwZjx49Su6fn5+PkyZNQoqiSJlQKESKojAqKgpramrI2v/s2bNZz5yZmYlffPEF67euXLmCixcvxsDAQGxsbES5XI4XLlzAb7/9Fn19fbG5uRmVSiWeOXMGV69ezbGV+RwAOjrmrVu3cp4NANDBwQHb2toILqK7dQYM2eXo6IhtbW3o6Oho0NdMpX2oX6eMBWGZTIaNjY0YEhKCFEVhaGgoNjQ0GA0oM2fOJFkh6fvT/qIoCquqqlhArreVNvnPaNDvoxhqE4bKjKn+uW5ubtja2srLfMlXd0wtY6pCocDm5mb09/c3+RmN1XWmDVOmTMH79++bZCutT58+xeHDh3PKBQIBDh069F27u0ui3xnYv38/Z/eHIdWvO+vWrcOTJ0+a/C7/qHZtSqx7F9qZdAlAqNFoeGl0k5KSWMCtzZs3E7Q3RVF49epVDA8PRxsbGwLWSE1NxaioKATQfTRu375NevwqlYpk9EpJSSGBWSgUYkBAAIrFYnRzc2MhSH19fdHGxgYjIiLwypUrrMZtZmZGyIe8vb05rFa0Xf7+/piZmYlisRg1Gg06OjqiTCbDgIAApCgKvby80MnJCaVSKQYEBBDmPPojTqcX5ttaqVarCVDt9u3bBEAYFxeHqampGBAQQBj4ioqK3qiB/dlSXV2Np0+fJrauWrUKly5diiKRiNil/z7OnDmDgwYNwqioKExNTWX5kPbZrVu3WIAzmUyGWVlZrExsFEWhVqslMz9yuRy1Wi3x/4QJE3Dfvn0oFAoxIyMDg4OD0c7OjkNaMnLkSEJRq9VqX4un/n1o0O+j6NsgEonw+vXrGBgYaNRWZuwwdI5YLMaAgAAS2Pv378+bn6KreuzYMZI7QyAQYEBAgMEcBJMnT2bx5HdFbWxsWHVx7dq1ndIb+/n5GWQ97G4DCf3OgIeHBy+brCnq4uLC+iYwffg29Oeff8aZM2f+Ke38j9DOpEsAwocPHxIgj0AggMWLF4O7uzvcv38fzpw5Q847f/483Lp1C0D3BHDo0CF4/vw5VFVVkfWfI0eOkDX4hoYG2Lt3LzQ3N8PIkSOhb9++UFJSAl9//TWcP38enjx5AlqtFubOnQs5OTnQ2toKJSUlUFRUBHK5HJYuXQplZWVQVVUFz58/h4MHDxIAyYABA2DkyJFw9+5dANBl2vv73/8OHh4esGjRIhAIBMSu6upq2L9/P3R0dEDfvn3hww8/hKamJrh79y4sXLgQ2tvbwcvLCyZPngw5OTnQ0dEBUVFREBwcTGw9fPgwARqqVCr4+uuvQS6XQ2RkJISFhUFjYyPs3bsXmpqaAEC3xnXw4EHIycmBtrY2uH79OqSlpXXFLe9cHj16BCdOnCDHv/32G6Snp0NbWxuxa/jw4TBy5EhyzvHjx+Hx48fw9OlTkgXxwYMHEBkZCePHj4fW1lbYt28fKwthe3s77N+/H2pqaqBfv34wadIkQES4e/cuNDY2Qp8+fWD8+PFw9+5d4v979+7B6dOnARHh4MGDUFFRARUVFZCXlwcURcFXX30FXl5ekJ+fDydPngQAHenNf/7nf0JiYiLLh3/J68vkyZMJGVhHRwccOHAAKisryd8pioKFCxeysCbM2AEAYGVlBUuXLmX5orW1FXJycgguqaioiJNVtDOZPXs29OjRg1X266+/EiBeR0cH5OTkQFNTE6l3TLlz5w6cO3eOZat+VsCJEydyAGkAOlAlk0AnLS0Nbty4wTpn9OjRMHToUHI8aNAgcHd3BwBd5sevv/6aECR1R7G0tISvv/4aFAoFFBcXQ0lJyWvd5+nTp1BUVESOmT4Ui8WwZMkSkmX1deTChQscACmADocyc+ZMVlloaCjMmTPntX/rnYipPTgA3YjJ1dUVAXSj9NTUVE7Gp9DQULJuLpPJMCwszOCUibe3Nyc5zTfffIMLFy5EGxsbTE9PJyPF6OhoViIRT09P9Pb2RnNzc7x27Ro6OzujWq1GHx8fFAqFGBYWhjKZDGfOnMlKkLFr1y4cPXo0BgQEYFpaGkZERKBCoUBXV1eWLZs3byZT1kKhEC9fvoxBQUE4cuRI3LNnD6snb6i36OTkhOnp6WhhYYHLly/HBQsWkL8FBwcT28RiMQu30N1793y6aNEiMltAE1UB6GaBQkJCyHlz587F1atXs3zo4eHB2f4zZcoU3LhxI1IUhT169ECFQoETJ07En3/+mZTpb8kKDQ1ljfgpisILFy6QLaNMXbVqFc6bN4/lw85s/DO0OwqAblQ1ceJEg3YJBAK8ePEiBgcHGzzH2dkZr1271qVZGy8vL9ZMEp8eOnSI8Jvoq4WFBat+0PXO2P2YsYPWDRs28CbcMkX1Y8evv/5KqIitra0xPT2dzHZ2x9jh4OCA6enpZLbD1taWVQ8M+VA/dhhTmUyGV65c4U2GRquZmRn26NGjy1wDw4YN42xpHjRoEB45cuRPjw/GtDPpUmfgypUrnCkssViMEokExWIxCoVCrK6uJtP/3t7e2NjYSBqvQCBgreUeOnTIaMNinqt/7aZNm/DQoUOs89auXYsnTpxAc3NzbGxsNBgEhEIhisVilMlkWFdXh4GBgfjll18ScJNEImER1einNGaCDWm7mc9synrRkydPcNiwYQigS8TU3NzcbTED+p0BkUhElgb01+5tbW2xqamJdCr79++Pz58/57w7pg9XrVqFZ86c4X3nTB8y/15bW4s9evQg/hKJRPjy5UuMjIzk+JCv3r2Pa36mNOj3UQy94zcleNGPCXy6bds23L17t0HfdnZ9VFQUVldXd7q+rB8nTFH92PE2tLvHDgDdkl1RUZFBH9LKjB0AurhjrF13piEhIVhXV/cXA2GnJ/7fl61feS9fvowtLS0kNSmNtmdWePr/kyZNIqhzQ/ej1dHREVtaWkgmMebOAQDdR1kkEqGVlRU2NzcTciD6I2SsUnz99dd48eJF1vPS14rFYqyrqyPESQEBAVhfX0/WC2fMmEHYFgEAT506RfKPUxSFFRUVnOQ7fCoWi1lBifm83b1B7927F7du3crxIZ+tFEWR4wcPHrDyj9PlTL8C6DKezZo1i3UeXwIjiqJw3rx5hPWOLtP3IVO74sP3sUG/j6Jvg0gkwtraWqNYAFN0/PjxhMrbkAqFQlaMKSwsJAm0mLHD0PXM+mlMX+dDwowdb0u7e+wA0HXymO9c34eGfEMPBl/33Znq6+6qnUmX6YgnTJiAO3bsIMe+vr4YGhrKQtpu2LCBTJ1TFIWXLl3CsLAwtLOzw/j4eLx+/Tprqs/BwQGvX7+OKpUKly1bhosWLUKxWIyhoaHEOVZWViQr2YkTJwgrlVAoxNDQUNaWEjMzM8zIyCDZo0JCQjA1NZV8fF1dXdHX1xclEgleu3aNBeChKApDQkLI1h+5XI6hoaEs5D9zOcHHxwdnz55NZimCgoLQ0tISBw8eTChPz507R2ZL7O3t8fr162TLUN++fQn47uTJkxgfH9/tG7SXlxeq1WqOD/v06UNG+Ewfmpub4/Xr1zE+Ph7t7OwwPDwcL168SN75rFmzWFtQ/f390dHREQMDAzEtLQ2FQiHu3LmTsz3s559/xsWLF3NQ4Po+1Fc+H74v2h1F3wa6jfEx6zFjB61r167lZX6ztbUleUUAgMQOY+8vMDCQ7DzQjx369c7YPeh6R5cpFAqyo8VUX/r4+LxRhjtm7KC1u8eON1EPDw/09vZmlenHjj179pDOIK18sQNAB4zvbDt0d9LOpEsAQgAdiPDatWvk+MGDB5CZmQkVFRXw+eefg1gshuvXrxOgICJCSkoKVFVVQUVFBaSnp8Pp06cJYQ8AQFNTE5w5cwZaWlogOzubgAQzMzOhtbUVBg8eDP/85z8JeOPy5cvw+++/A4AOGNKvXz+Qy+UQHx8PI0eOhLa2Njhz5gxh86qqqoJz584RUNnvv/8ODx48gI6ODjhz5gzU1tZCVFQUfPzxx4CIcPPmTXJtY2MjZGVlwWeffQbu7u5QVlYGd+/eBYqiYO7cudDc3AxpaWmEjCI7OxtqamqgpKSEZFW7cOECREVFwcCBA4mtzc3NAABQWloK169fhy+++AJu3rxJwIfdWQoLC0GpVMKUKVOIDwF0zG8XL14EALYPW1tb4fTp05Ceng4VFRVQWVkJKSkpgIgwefJkMDc3JxkkAQByc3Ph+fPn8OLFC+LXq1evckhn0tPT4fLly1BZWQnz588HkUgEY8eOhQ8//JDlQ7VaDaGhoTB58mQA4PfhX/Jm8vHHH5PMlnQb02cRBABW7KAlMzMT7t27xzm3srIScnJyyDEdO4zJ7du3CXCxvb0dMjMzSVtk1js+mTRpEoSHh7PqHS00Edu8efNIVlOmjB07FmJiYlhleXl5vBnugoKCYOrUqZxyc3Nz+OKLL0i219LSUtKe/v9BZs6cySJ+Y4qvry8HpAegI6L64osvQKVSQXFxMbS3t7OAe/fu3WPFjitXrnCYKvliB4AOGH316lVOua2tLfnW8clHH30E/fv35zfyfRZTe3Dwf3sXjo6OrJG0t7c3Ojs7o0ajwczMTNb2G6lUikFBQWREbmtrS7YMAuhAgO7u7oTISCgUolqtZm0n02q1uH79ehZfNYCOlMPT0xMtLCwwKysLXVxccP78+SyyB19fX842Ql9fX7S3t0czMzOyzuzj44MLFy5k9QK9vb3RyckJAXQjiGvXrrFALfQ2Qno5Qd9WfV21ahVn1KJWqwnPflZWVrcFAR09epQz+o6Pj8ezZ88a7KX6+/vzEqq4ubmxchocOXKEtcUrKCiIVccoisKgoCAWSRVFURgYGEjIaLy9vfHGjRsolUpx8+bNrFkr2ocfffQRnjp1ivjQ09Pztbc4/ZHaHQVAN/pKTk5+a++Bjh2vcy0dO+hjPz8/Xp4Cfd2/fz+OGzfO4N9FIhFmZGTwzjox611nmpiYyJruVqvV6O7ujvb29piVlcXbbrRaLVpbW3e72MGcGbhw4QIreRNTY2JiePOEMOM/AGBERASmpaW99YRDgYGBZLZYrVZjZmamQWK8H374gcNb8z5oZ9LlzsAXX3zBYgxLTU1lJaoRiUTEET4+Ptjc3IxWVlZIURQhHaLXf48cOYKbNm1iEYfs27cPt23bRu735MkT3r2imzdv5kVrUhRF7p+bm4uffvop6++3bt3C2bNnY0REBNbW1qJIJMIbN24Q59HXpqWl4eLFizn3FwgEvMQpvr6+2NLSQlDnzOfQV7p8165dvPuTu1uD7ujowMLCQvL8fKQr+ut+jx49wnHjxnHek7F1P4VCgU1NTejv70/8IJPJsKGhgSwhAQAL96HvL/1nY9ZXjUZDfHj8+HFedsx3rd1RDNnSGTmPMaVjB58fO1P92JGXl8fCqnRF38QGOk50BiI8ePBgp9PpRUVFOHr06G4XO97mMoGpyhfDjSmNceHjj+lO2pl0uTMgEolY6/NSqZQEc4FAgJWVldirVy9S0RUKBZaXl2OfPn1QKBSir68vNjQ0oEqlIrsQKIoivSy6jL6/TCbjdRy9i0G/vG/fvvj8+XOkKAqlUinnWvp5BQIBGWHSZSEhIVhTU0NoLfk+5tOnT+ekNZ48eTLeuXOH1VNMSkriZRZzcXHBhoYGtLe3N0i92d0a9Pbt21mj9d27d3Ma+datW1kdH9qv8fHx+OzZs079SqtcLkeKonDJkiVkpCCTyTgfArps7ty5mJ6eTsrPnj2L33zzDamv5eXlGB0dTeqroXr4vmh3FEO2XLhwgbfDbYrq++fJkyeEBrsz1a9jfHHCVL127RrOnz//tf05efJkwoZpqq18Sren7hY73kVngBk7TFWavvzPfta3qZ1JlzsDALpp14sXL6JEIsH169ez9tRGRERwUtOGh4eTPaQymQwjIyPJh7Z///548OBBgwYcOXIE+/btiwC6faVpaWkkXSeADnyWmppKtuVZWVmxUMrTpk3DNWvWoEwmw0uXLrGQwxKJBC9evEi2ICqVSuzZsyfrw6LRaIitALqPuf5eaCcnJ9Z+12+//RZXrVpF9iefOHGC9ColEglGRkaiWCzGhQsX4sKFC1n3+vzzz/HevXtv1MD+bNmyZQva29tjamoqWlpaknTCzNGXj48PTp48mbD8Aei46jdt2sTy16xZs3DlypXkeNeuXSSHA9OHarWaBR4D0HXANm3ahCKRCM+fP49+fn7o5ubGmjUICAhg1QG6bg4ZMoR3+5KjoyOmpqb+xTPwBmLIlsDAQA6Sf82aNTh16lQE0HXOTp8+zcsDoa9hYWG8acEXLFjw2h0OAB2Y9OzZswY/BEFBQUaXK4RCIaakpBhkW9SPHW+q3bEzwIwdfDbx+XD+/Pm4bNkycrxv3z4cNGgQ6xxm7GAqX+yglRk73nVbf9vamZgMIJwzZw7J9lVfXw8ZGRnQ0dEBubm5LBDM1atXoaamBkJCQiApKQkAdECuly9fAoAOLPjbb79BW1sbAACUlZXBzZs3yfUDBw5kMXVlZWWRrFKtra1w7do1aG5uhn79+kFiYiK0tbVBeno6YfR78eIFZGVlwezZs8HKygqKiorg3r170N7eDhkZGdDU1AS9evWCkSNHQkdHB2RkZJD0n0qlEnr06AECgQBGjRoFkZGR0NDQQGz96KOP4G9/+xsUFxfDrFmzCIDEwcGBlXnv/v37cPXqVZLh7ObNm/DixQvw9/eH/+//+//gt99+g9bWVigoKICCggJQKBQwZ84cUCqVUFhYCL/88gv8z//8j6mueady5MgROH36NLS0tEB6ejpMmjQJOjo64MqVK5CVlUXOy8vLgytXrrB8XVhYCFevXmWlBX306BHcv3+fHN++fRueP38Obm5uMHXqVLh+/To0NTVBUVERVFVVwcyZM0Eg0FXjp0+fEvbB69evw6tXr8DFxYVkQJwxYwZUV1ezWMrouvn8+XMWu1hiYiLExsZCc3MzYVP8S96u3L59m+ULAB3gi5nJ9MaNG1BbWwsAOqa62bNng1Qq5dwrIyODNxXtw4cPIT8//7Wfsa6uDm7cuGEQVJidnQ1PnjwBR0dHmDlzJicbIV0Xa2pqAECXaY8ZO0pLS1ltwpD4+/tDcnIyp5wZOwAACgoKum3soNuYn58fAfMC8PuwsLCQBTS9desWK801wP/GDn0pKioyCDRlxg5Dou/DoKAg+Pjjj8nf6djR7cTUHlxubi5qtVq0tbXl3ZcrlUrR19eXjKrHjBlD1n59fHwI+IKpbm5u6OTkhCKRCP38/FAoFOLq1atxw4YNZLSu0WjQwsICzc3NWdtGlixZwmIWZKpSqcQ7d+6wnpNe15dKpThz5kzeLSNarRazs7NRLBbjnj17cNq0aSy7tm/fjnPmzEEvLy+8c+cOAaiNGjUKf/31V9a9VCoVh/Ro0KBBeOHCBQTQza7Qo00bGxvMzc1lzXjY2Ni8dm/7z5SEhASWrWlpaWQmh1bah3y+AtBN1/v6+pKpUDMzM07ugB49emBmZiZrhBYUFIRZWVkEeGpra4symYxVDydNmoSHDh1CoVCIWVlZZBQmkUjQ19eX3M/KyooFXNywYQOuWbOmU/a6P1u7owDoRmP6YF5DscOQurq6Ym5uLqlLfG2MT/VjR1eVGTv0y+jlMWbsMHYv/djBVxc7ix1M7e6xg+8dJSQk4KVLl17LV3yx421rZ/F/w4YNnW5xfRfamXR5mWDBggWExIUZmLVaLba0tHDSidLMb/ofCADAo0eP4ubNm9HNzQ3b29sJonfUqFGEYKikpARHjhyJQ4cOZbFN0cp8Bjr7nH5DprELbW1tRveX82lgYCA2NzcbRI4yf5P5LBMmTMBHjx6RMv1ne/bsGUmvymdDd2rQSUlJ+PjxY44t9P9pHxrym42NDba1tZHOW1xcHL548QIpikKBQGD0I0HfJzMzExcsWIChoaHY2NjIiztg/qavry+2traSve5Tp05lZdSkfcgERr4P2h0FADAjI4OzJGYodtB+N9auAYBV74ydZyh26NcJQ2UKhQJbWlpYO2ZkMhk2NTWxlqBeVzUaDba1tZFOzpvWO3t7+3ftcpNk0KBBbwX1z/QXM3aYer2hevO6572v2pl0uTMgkUhQLpejSCTCyspKDAsLIw7RJxHRaDRYW1uLjo6OvAAdmUyGUqkUKYpiXSsSichMgpmZGaG31Z9d0M9nvXr1as4Og2+//RZPnjz52p0BPruYeuLECVyxYgV6eXlhbW0tadAikYjkLre2tsY9e/bgjz/+SK6j7RoyZAir4W/atAl37drVrToDIpGIBPKioiIcNGgQjhgxAgsKCli2Mt/bgwcPcPTo0cSH5ubmpKEJhULi66ysLJw8eTLvuw8PD8eKigoUiUQol8sJ1Syfv0QiEZaXl2PPnj15/crXGWDa9b5odxQAIP4xpY1NnjyZMJoC6AYN3333Ha9Pmf7ZuXMn/vTTT7zn8c1MqlQqrK2tZc0gmpubY21tLWdkrlQqOZ0EvrLXUb7497r1buDAgVhXV/euXW6SNDY24vr169/o3en7kBk7TNW0tDScN29ep+edPn2atXOuu2ln0uXOwOjRo3H9+vVIURRGR0ejpaUljhgxgjfHgEKhwL59++KJEycwJCQEExISWLmqFy9ezKKVNaTz5s1j7dvctWsXxsbGoq2tLfbu3ZuU+/r6YlBQECoUCjxz5gy6urqij48PhoSEoEAgwJiYGDQzM8MJEybg999/jxKJBE+dOkWmh729vfHXX39lTfV5eXnhqVOnWIHM3d0dz5w5gzKZDIODg9HHx4dlK809IJPJMCYmBsViMQYGBqK/vz/a2tri2bNn0crKCgF0bHj07gsA3f57Ok1zTEwMPn/+/I0a3B8piYmJnP3ZUVFRaG9vj46OjsSuvXv3EsS+paUlnj17FhMTE9HZ2ZnjQ32NiIgg+/1lMhmePn2aNHyVSoXR0dGkEzF27FgWz8QPP/yA48ePRwAg9dVQ6lc3NzeMiIhAiqLwyJEjxIfvm3ZH0bdBKBTiyZMnDYK03N3dMSIighwHBwdzElXxaUBAAKuzz6x3fCoSiTAmJoY16ycUCjEmJuatdAL5Yscfrfb29ti3b1+yq+p9lcTEROzbty+nDnz55Zf42WefvZEP9TUwMBCPHz9usOMWFhZGeCdEIhGePHmSt76Fhoa+d8uGXdHOxGQA4ZQpU8De3h6qqqqgoaEBkpOTITU1FWpqajigrMGDBxPwXUpKCuTl5UF9fT28ePECHj9+TM57+vQpC+CRlJQEXl5eAKADxkyZMgUUCgWUlpaymPkePXoEtbW1UFlZCdeuXYMpU6aAhYUFODg4gJubG3R0dEB+fj60tLRAXl4ePH/+HCZOnAjnz5+H+vp6qKiogCdPnpDzaAaypqYmyM/PB0SEoUOHQlhYGKuMFvq+iAhZWVmQl5cHDQ0NcO7cOXjw4AFhL2xqaoKUlBQYO3Ys1NTUQG5uLrS1tUFeXh4LQHnjxg2YMmUKmJmZQW5uLmFgTElJgY0bN/Kyr71Lqa2thf/5n/+BM2fOQGVlJfj6+sK4ceMAQMcsGBwcDFqtlrAyFhYWklTE7e3tkJeXBxcvXoRnz56BUqkEjUYDAABjxowBf39/AACQSCQwefJkuHfvHnh4eMDgwYNZ/goPD4fo6Gi4ePEiICIMHz4cPD09WWDWJ0+eELY5RISLFy/Cy5cvISgoCEaOHAkURcEnn3wCrq6uUFJSAlevXgVEhIcPH0J9fT34+fnB2LFj/7T3+v+KICLk5+cT4K6+PHnyhMX8lpWVxWEl7N+/P0RHR7PKcnJySKpyAHa945O2tjZISUmBxsZGUtbe3g4pKSkGn60rwhc73oYkJSWRNkPL+PHjwcfHB8rLy+HcuXNw7ty59z52nDt3jvN8z549Y8X61/GhvtTX10NBQYFBP2RkZBBWQkSEgoICAkhnSmZmJmEqZMYOfRkwYAD07t3b4PO8t2JqT66goID0uv39/fH+/fukx2tlZcVia9u5cyd+/vnnKBaLUaPRGFxncXJyQnt7exSJRITBMC4uDgEA7ezssKCggBd0pFarSW4DCwsLzM/PRzc3N1y0aBHvvtWwsDDMyckhPUMbGxt0cXFBiqLQy8uLBQyi9cCBA7hkyRIOC51KpWJtJaJHt7StfL3PGzducDATarWaTA3a2tpiQUEBOjg4IIBuWp05dblp06bX6Hv/caK/njl06FC8fPkyOV67di1reyCtSqWS2EX7MDo6GjMzMxFAt++cxhYolUrMz89HtVqNX3zxBQfwOWfOHNy7dy85PnbsGH755ZckGyLTrxKJBL28vEg9/Pjjj/HUqVMoEAjw9u3bZDssk/kSAHDEiBGvDWT6o7Q7CoAO/MfHnMfnHwDoNHZs3LiR8EW8juq3MQDdTKaxFLfM2MEss7a2JvWuK0rHDmaZk5OTQaAlrdeuXcP+/fuzytLS0jg8Cx4eHrh379537X6WMGMHHf//7DbE58PO1MvLi8Wlwowd+uf+9NNPuGTJkj/drs60M3ktngF9/eyzz/D27ducch8fH2xvbzeIJD927BgBEHZ0dJhECQqgIxihp39fR5ctW4ZpaWkok8mwpaXFIAho/vz5eOvWLVbZrFmzMDc3lxyfO3cOf/jhB9RoNNje3m5wr6y+lpaWEgChviYmJmJFRQU5ft87A6bq0KFDsays7K34kE8XLVqE169fR7FYjE1NTQTPEhgYiC0tLUanEj/99FPMy8v7Qxvj29DuKACA169fN4iw1mq12Nraypqap2MHM6HZ29SEhASsqqpilcXFxeHLly+NgsTo2MFX77r6DHTsYJadOHHCaFr3rujjx4/ftes5wswyScf/P7sN8fnQmIpEIqyvr/+LgZCcCIDnz58nbFsikQhLS0uxR48eKJVKeRutQCBAa2trLCkpwejoaPz4449ZH1czMzNUKBRIURSqVCqjjXDQoEEEkGZhYYESiQT79evHQhMD6DLjFRcXG72XTCYjo3KVSsUCN4rFYnz+/DmGhIQQu4RCIT579gwjIiI4tiqVSpTL5SgQCFjr0WPHjmV1kHbu3MkCy1haWhrchiQWi0mn4vbt25icnIzDhw9/o0b4NoXZGdi9ezdrnf7hw4ecLUP379/HoUOHsuyifUj7oLq62mAq2W+++YZFVnTlyhWcNm0ar19p3zD9KhQKOVgBHx8frKqqImAjqVSKFhYWSFEUFhcX8+5+eR+0OwqADphHj6yYsYOOE/r+4St7E7W1tcUXL16QESGzLtIqEok4ZZGRkfj06VMy48eMHXz1jqlarRYrKipYI0qm0rGDWUbHRPq4sLCQMwtgqlpYWKBKpXpvYsfmzZtZA0N9W7vqQ774T9e1qqoqsr5vig9pZcZ/ZrlKpTJIL99dtDPpUmcgMjKS7NelKArj4+MJEE6tVuOxY8fI1OzAgQMJt3u/fv3Q1tYWPTw8cOTIkXjixAmDzpg/fz4roceOHTswKioKnZycSIDeunUrxsTEoIODA8bGxiKAbmqGToFLLzX8+OOPBveyAuimJ48ePYqenp44atQo/O6774hdzEBElxma5mTqvn37MDQ0FN3d3VnAl9DQUA4L2dy5czmpWWfOnMmiN42OjiZJSkaMGPFGjfFNZcGCBThgwACOXQEBAWhnZ4cnTpzAoUOHoqOjI/bu3Zukuo6JiUEXFxfs2bMnZ7q/R48eePDgQRwwYAAqFAqcPHkyZwTp7+/PYins1asXenl5oa+vL+EQ6MwvAoEADx06hP7+/piYmIg7duzA/v37k2sHDBhARmRxcXFob2+PcXFx74Qu9U0a9Pso+jboxw5TND4+nnengKkqkUhwwIABBj/MhtTGxgb79ev3Wr9pYWGB8fHx5COUmJjYZfR8bGwsWT58XX1fYoepO7mYscOYD5nxn6kikQj79+9POvqd+XDNmjX40Ucfserm63ZE6fj/Jv76o7QzMRlAmJSUBA8ePID8/HywsbGBcePGwdmzZ+HFixcAoANylJeXE5BGY2MjVFdXAwDA6dOnobKyEoqLiyElJQXKy8uho6MDYmJiIDIykvU7L1++ZAFGKisroampCUpLS+HcuXOkrLGxESQSCdjZ2QGALk1xY2MjVFRUwJkzZ1hlTElMTAStVgugeztQXl4ObW1t8OrVK6iurgZEhFOnTsF//Md/QEhICDnv1KlTUF1dDVqtFhITE8n9+vXrB+Hh4eS4vLwcmpqa4MmTJ3Djxg1ISkoCqVQKmZmZcPv2bVAoFJCUlAQymQxqamoIMxkttbW1hHENAODixYvg5uYGgYGBcPr06bcORuqKZGRkwMOHD8HW1hYAAEaNGgVlZWWQk5MD7e3tUF5eDqdPn4bnz59DU1MTAe+lpKTA06dPobm5mbDEjRgxAjQaDTQ3N8OzZ8/gl19+gYaGBnj16hW8fPkSxGIxJCUlgYWFBeTm5kJRURGMHTsWKIqC1NRUKCwshLa2NqioqOj0nVhbW0NSUhJUV1dDa2srNDQ0QHFxMZw8eRL+67/+C5ydnaGxsZE8m52dHUgkEmhsbCQ2/CVvT+j2RMcOKysrSEpKApFIZPCapqYmXoZBMzMz0sZokcvlkJSUBHK5nJS1tLTAL7/8wgsMYwqz3gHoYsjp06e7ZF9CQgKEhIRAbW0tnDp1Cjo6OgBAl+aYzwZjcvbsWQ6zHgBA7969eUFqHh4eMHLkSFYZ3S7fVezYuXMnHD58mAXuNCbM2AEAEBUVBdHR0RwflpWVwdmzZznXt7W1wcmTJ1kp7I35sLq6mpxL182XL19CYGAgDBo0iHWufvzXFzr+d0sxtWdXXFxM+Jy1Wi0WFhaiVCpFBwcHg+vkIpEI3d3d0d3dnbdHvmXLFlyxYgUKhUJ0d3c3umdXLpdzQB8xMTEkyYezszMqlUqUyWQsMI+VlRULpHL27FmcN29ep73to0eP4pw5c1AsFqObmxtZdpg4cSKmpKSQ83bv3s0ZydrZ2RFQUXFxMapUKrS1tUVra2t0dHTE4uJitLW1RRsbGxafurOzM5qZmXFsXbZsGW7btg3Nzc2xqKgIW1tbsbq6GsvLy1+nk26y1NbWYnFxMdFevXrhmDFjyHpbTk4OxsTEcN4dbRuzzMbGhoUJSU9Px2HDhrHOoX0IoJtCLSoqQk9PT7SyssJBgwbhgwcPUCAQoKOjI++0rKWlJfGrm5sbWYbw8/PDR48eceqgQCDA+/fvE+4B+L8jgzt37pDZD1Pq5p+p3VHoZ7ewsEBHR0eOTb6+vvj48WPWlLlIJGK1O0Pq7OyMxcXFrFk7e3t7LC4uZrV7gUCA7u7uZCZILpfzgv6Y9Y5ZJ9zc3MiynkQiMZremo4d9LGrqysvSNne3r7T2REXFxderMvatWtZy3O09uvXD7OzsznldOyg2/KfETuePXuGHR0dvG3V1dWV2NVZG1uzZg0ng6i1tbVJGDP9+E9rZz4E0OGITp06xSrTj/+m+PB90U7bqanONfQDV65cMYjq9fHxIdfzTefQ6u7ujoho1LlDhw7F8vJyg38vLCzE5ORkjIuLw5qaGhJE1qxZw6HxXL58uckAksDAQGxtbTUKPtPXkydPstKrAugSLjFTMwPo9tAyEfElJSU4ZswYDoBQXwsKCnDKlCkYExPzGs3UdPn+++9fq9IdPHiQsxywa9cu3L9/v9HraB/ql+v78M6dO5zlFQDDAMI3UbVajYjImwTnXWh3FPrZv/zySxaZkDF92wBCOzs7RETyAUhISMDq6mqTrtUnLOvZsyc2NjaatIYsEomwoaGBxSVCa0pKCgdAqK+VlZUmZ2Psiv4ZscOYD6urq8kSrinxX19//vlnFo7IkPLF/6760Jia4sP3RTttp6Y6FwDwzJkznCCsUqnI2oxAIMCioiIy0hIKhWhnZ4d2dnakV+3h4YFlZWVoaWmJO3fuxNWrV6NAIEA7OzujowCpVMoKyGvWrGF9cKytrVEmk6FEImFVKqVSyVr/uXz5Ms6bN49Vdv78eZw1axYGBQVhSUkJiyREJBKRrT7Hjh3DhQsXore3Nz5//twg05WlpSWHRY3Or0Af5+bm4tixY1mAGhsbG5RKpSiRSNDb2xvLy8vRzc0NV61axcqoZ21tjXK5HMViMarVamxvb3/9VmtAEhMTDeI6mD4EABw5ciRJ63z//n0cOXIkmpubo4ODA5aXl6ODgwOam5uzbL116xZZp7O0tMSysjIMDg7mnUHS96GVlRUBHkmlUnz27BlqtVpUKBSkl25ra8vb0A35cMKECaxUx7SaUjffpwb9Pgr97Ez/dKZ07OB77/n5+QQXZKpSFIV2dnZk9KkfJzpTOzs7MqsgFou7dC299ZgvTtBtjKIofPjwIWemzcbG5g8hLfozYocxH9ra2hK7XqeNmZub885IR0dHY2FhIbmXfuxg2s/04dy5c/HMmTNdfo9MH9K6e/duXLVq1Vv32Ztqp+3UVAcD6JD6NGOUUCjEXbt2sRKAUBSFiYmJvPtkv/jiC5wwYQKamZkRZHnPnj05YItZs2aRFKa0Tps2jQUqBNAB13r27IlmZma4f/9+k/erxsbGchJZxMTEoK+vL1pZWeHw4cNx7969qNFoMDExEVesWEHO6927NwYEBKC5uTkOHTqU9bFxcnLC/fv3k4+UWq3G+Ph4g8+RkJBARimWlpZ44MABtLa2xk8//RRnz56NEokEhw4digqFAkNCQjjbWj7++GP84osvUCQS4dChQ4l++eWXr9WAjxw5grNmzcL29nYcPXo08aGzszPu27ePNTPC9KG+rQMHDsTvv/8ep06dijKZDIcOHcr6wFtYWOCBAwdw7NixZPeAWCzGoUOHkg9zUFAQbt++HSmKwjVr1pB7S6VS3LdvH2t6TyAQ4JAhQ1gNXigU4u7duw368PPPP8fNmzezfKjRaFggo/nz57NSc78v2h3FkC1Llizh5KwwRQcNGoROTk6cclNjx7vShIQEo7NtgwYN4l1G6aqam5vj/v37WXFYoVDg/v37Wfd/W7EDUbeMTN/HEE9CbGws79KGMd20aVOXtvQ5ODi81kyKn58f9unT5634OTIy8q2mpX5b2pkYRuzwyPnz51nHLS0t0NHRAX5+fuDh4QGnTp2CI0eOkL9bWFhA//794cCBA9Da2gptbW1QX18PBw8eBACAK1eucH6jra0N2traQCaTQWJiIhw5cgRaW1tZ5wwePBhycnLg0aNHYGZmRhgEAQCcnJwgMjISDhw4AAkJCZCfnw95eXkgFAph+PDh8Ouvv3JAeykpKaDVaiEsLAwOHToE/+f//B9AROjo6AClUgkjR46E/fv3g0gkAoFAAHV1dcQGWhARmpubYdiwYXDu3DkoKioirIxDhw6F9PR0EIlEEBQUBEePHoUTJ05Az549wd3dHe7evQstLS2AiMT+lpYW8htCoZADrqLPa2trg4MHD0L//v2hsLAQ7t69Cz4+Pkb9+OGHH4KrqyucOnWKlB0/fhyuXr0KQUFBcODAAfJOEZE8Gy1MH0ZFRUFTUxO51/Hjx+GDDz4gDIt0ms+IiAgQCARw+/ZtAgSqrq4GFxcX+Pd//3fW++zo6ICWlhYA0KWtpgFYAMB5lo6ODjh06BAA6MA9rq6ucO7cOXJeR0cHq/5cunQJ5HI5WFpawsGDB2H48OGQmpoKAoGAPCv9u3+lLf5jpa2tjeVbWnx8fOBvf/sb/PLLL6RMo9GAn58fHD9+HI4dO2bwfvo+MxY79IUZOwAAQkJCQKVSceIeU+Lj46GkpATu3r0LAoEAhg8fDufOneMFCra1tXGehymG7DImdJw8evQoAUvTsQj1AIP6ZXTsoMXX17fT2AGgA+TGxcXB/v37ob29HQB0rJH0vfr06UNSyTOlo6Ojy22qubmZt44AAG/sKCsrg6NHj7LOc3BwgKioKDhw4IBBEOW9e/c6ZWuMiYmByspKkuqcjh3Pnz8Hc3NzGDBgABw8eBB+++030w18n8TUnh+AbspKqVSiSCRiAfA+/fRTsi7j4OBApn+8vLzw2bNnqFQq0crKCi0sLFAoFKKjoyOZxpHL5aQnaW9vT4A29vb2WFpayjviv3nzpkHCnujoaMzPz0eKojAtLQ0nTJhAfqekpITDOe3g4IAikQjnzJlDwCIODg5k1Ovv748lJSUok8nwyJEj+NVXX7Gut7KyItP/FEVhfn4+p4d579497NevHw4fPpxMpwPotj7++OOPBBRnCECzYsUK3LlzJ1IUZTDp0+XLlzE5ORnFYjHLN0qlkoCraLtmzpyJZ8+eZV1vZmbGWRdXKBSdTodu374dv/vuO2IDc6qP6cN169ZxcBQAutHC/fv3EUA3FUvPQOjbKpFIOKBPfVuZPmQqs75aW1sTfwkEAnz48CFGRkZiUlISXrly5Z333jvT7ijM2GGKjRMnTuRgesaPH8+7jNNVNTV2AOhmLg4ePGj0fufOnSMzD2KxGIuLizE4ONikZ2HGDqYyYyKf2tvbk9k2Ozs7LC0tfaMZBVtb2y7t99dqtVhSUsILigTQYYYWL17c6X1UKhWHkE6lUplM3MaMHUzVjx0RERFYWFj4xiBgZvxnxg4A3ezos2fP/jCSrLehnbbTrjRomkUsODgYW1paOOu7QqEQ6+vreRPPnDlzBtetW4eenp6IiGTtMCkpCYuLixEAsKKiAocMGfKnvRyZTIatra0sBkKxWIzNzc0mg8/eBoDEwcEBEbFTikxLS0vs6OgwmiyjV69e2NDQQBrC119/jdeuXUOJRIItLS0G98DOmzeP7MygdcaMGfjgwQOTbHgdEJC+MgGEFhYW2NHRQZahevfujfX19ayOkKkgoKCgIAICvXz5Mi9VcnfR7igAxhkI/19VQ7Fj4sSJvGQ6tD5//txgh+Z1NDc316SEcW9bjx8/zuHx4AMfd1X1Y8dfqtNO22lXGrStrS2am5ujWCzmXbMD0K2d8wFerK2t0dLSEoVCITo7O5Pet0KhIKM2R0dHXgDZ2rVrcePGjWhjY4NPnz5FZ2dn/O6773Dbtm1oaWmJT58+JfkCevfujQ8ePCAzAx9//DHrXhcuXGClxHVycuKAe+iyadOm4dmz/z/23jyqimtbF5+1+45N33c7sAMc5AJXOcJQrsKxQR7Y8Gwiw5YXjTLswogdwySexGGixsQYr8Y2NhyD0dgSW2xQREEFaRQBexEVQUVRULr5+2O/Wq/WrtoNxiR6f2eOMYfWoqp2zZprzVrNt755BMViMV6/fp10EPR6PVZXV5PRNNu7ZRgGKyoqBNeeNmzYgN9//z05Li4uxkGDBuGgQYOwrKwMPTw8qJ6rvb091tTUUNufGIZBDw8PFIvFuHDhQszMzES1Wo3V1dWkgyCTySjf2NjYkA+0kK2sajQaArC6du0a9ujRA9VqNTUz8/PPP+NXX31FjsvLywkRFOtXrg0uLi5YU1MjOLtz/vx5XkBzcXGhctq7u7tTMwOsXawPZTIZ6nQ6vHPnDkF6h4WF4a1bt1Amk2FWVhbOmTMHJRIJuZbrL3O6ZcsWXLJkyV/egDvboN9G4caOP/r9JCUl8SjEhbRfv35YXl7eqXsbxw5zysYO9njXrl34+eefU+eYqosqlcosBsrV1dUkeVJ8fDyWlZXxyrVaLVZXVwuyfDo7O3c67e+bUAcHBx64z97eXhDwl5iYyBusmFLj2CGkEREReOPGDTKQmDlzJh44cID8nY0df/Y7+SPVYjvtTIMW0s8++wxHjBgh+Dd3d3fctGkTVXGdnZ1x8+bNpPL16tWLgGpWrlyJERER2KNHD6rHHB0djb169UK5XI5jxoxBlUqFPXr0wNjYWJTJZDhmzBgyBenh4UFQ6oMHDybLAjKZDDdt2oSpqanYpUsXq15eSEgIJiYmIsMwmJycjMuXL8fk5GS0tbXF0aNHo0QiwTlz5uDo0aPJNSNHjkRPT0+MiYmhEKUxMTE4Z84c0iEYPnw46nQ61Ol0OGHCBNyyZQuFtGZtXbNmDcbExGBYWBiuXr2a/L179+7Yt29flEgkOHr0aDK1FhgYiOvWreNNiUkkEvzpp59Qp9PhkCFDqGk8rg9ZW9kOGteHffv2pVL7fvDBB+jl5UX5EABw2rRpOGnSJFQqlThmzBjBbZlDhw4lqaNZXbp0qdl0pawOHDiQ+FAsFuPo0aPJUoijoyOOGjUKRSIRxsfHY3h4OPr4+ODGjRtJRyg+Ph6//vprZBgG165di8HBwdivXz/q4x8bG0uxHr4t+i5KZ22Mi4vDRYsWmT0nLS2N19EHMEzXDhs2zOR1y5Ytw+joaPTy8ur06Jpb76yNHVybfg+oLDo6mhpMmFIfHx/BeCyVSnHMmDEWO2QymQw3btxIQLp6vR43bNhg9sOamJiICxcutOjDPn36vPasnK+vL4+XJDw8nMc/YEm/+uorTEhIQGdnZ0xOTiaD0rCwMArwzcaO1/XX26iWxGoGwqSkJLCzs4OgoCAqdahSqQSZTAZarRaGDBkCYrEYoqOjISQkBEQiEWg0GmAYBnr27AlhYWGkjBWxWAwqlQoADGmLJRIJ+Pn5wbBhw8g5L1++hJcvXwIiwvPnzwER4eXLl9Dc3AwtLS2QkZEBPXr0AJ1OB/fu3YNt27YBgAGQw6Y+ZRgG1Go17N+/HwAAevXqRdkXFBREMXrFxMRAR0cH5ObmwuDBg+GXX36BZ8+egUwmg6dPn8K//vUvaGtrA6VSSbGfbdu2DWpqakAqlYJarQYAQ7rVyspKKCgoILbu2LEDnJycwMnJCXbs2EHeU7du3SAyMhJevXoFGRkZgIgglUpBLBaT+wEYgDXNzc3Q1tYG//rXv+Dvf/876PV6EIvF1PsNDg6G3r17AwCARqMBiUQCUqmUYmfj+nDw4MGwfft2eP/993k+PHr0KJw7dw5UKhUkJSXBrl274O7du5QPAQygJoVCAc3NzZCRkQHNzc0QEREB3bt3B5lMBklJSXDgwAG4fv06uLm5QWJiIvG/TCaj/NKvXz+S1losFsOQIUPg5MmTcPnyZXB0dITExETYunUrYbtsb28nbGIHDx6E4uJiygYAA6iRfV61Wg0SiQQkEgm4uLhAUlISSCQSOHHiBOTn5xNbjZ/r3/JmJCAgAPr06UOVcdsOKzqdDvr370+O2TpmLLdu3eKBe7miVqtBKpXC3bt34ZdffunUs2ZlZcHly5epstjYWAgKCuKde+nSJQoAefjwYSgsLOSd5+fnB/369bP42xKJhGpjpuTOnTuwfft2XnlraytkZGRQ7K4SiQSSkpLAxsaGOlej0YBIZPg0GLcdIeG2J26ZsQ+FzgMwxB/jNsYCI1kf3759mwA7uc9v/BuWRKlUglQqhbq6OsjMzCSAwpKSEgpQzcYOIbGxsYGkpCQQi8WkTKPRkNjxzoq1vfv6+noMDw/HmTNn4rFjx0hvw9bWFlUqFQYHB2NtbS2qVCrcs2cPlcLRwcEB9+zZw5t2tbGxoUBFDg4OKJPJcPjw4dRU1+rVq3HDhg3o6OiI9fX16OHhgT/88AO1tnThwgWSBY9hGHR0dCSjY6lUSjGUff7557h//37qWebMmYPZ2dnk+NChQ5ieno4hISH44MEDMruhUCjM8lY7ODigVCpFuVxORvpVVVWk1ykSidDR0REZhsF169bx+NZXrlyJmzZtsmjDokWL8NdffyXn5efnC05hzps3D48ePUrAgay/jM9TKpXYo0cPkz4EMEw1qtVq9PX1xbq6OvTz8+MtCbE+5PqBYRhBHwIYRhDcTGZqtZqaOs3Pz8ePPvoIAQzTp7W1tWS2p3v37lhdXU2m+lQqFcbExOC9e/cEl6ocHR1RIpHwfMja5efnhw8fPqRGT97e3lhXV/fWsIy9i2Ku3k2fPh1PnTpl0e6UlBSS6tpatbe3NwlyM1bjemetHj9+HGfOnCnYnoTihFgspoC6EyZMwIKCgk7/ro2NjeAoXyaTWZVDhbW5tra2U2vrSqXSIsCPbU/W3tPT0xPr6uqo9+Lq6or19fUWmWKN46SpstdVY38BAAYEBGBtbS1lo1DseNvUYjvtTIMWUnMMhKxjnj59KshAaAwgefjw4RsBEAqBzxobG61KaGNJjVMYG9va0NCA8fHxJkFAXl5eiIgWK7mjoyMiIlnji4+PxydPnvCIOTQaDba3txP+ByGNiorCly9folQqNQnkEgIQGisLAmVt5bKICfnQ3d0dEdEkvkRIf/zxR8EdAdaouVSyXCY4Yx/+T2IRexsF4K8BEN67d89qHoPfU++EdN68eYLYhaCgIGxvb7d6Z4UpzczMxMzMTF65JfbS36vWsEgeP378tdlLO6v29vbY0dFB0Ue/SQDh6zDQvq1qsZ12pkEDGHryWVlZ5NjNzU1w1JSSkkJmENjcBKNHj8aTJ0+Sc1xcXKhel7e3N6pUKhwyZAjpLRcUFODgwYMxPj6eVMIzZ86YXRdkGAZ9fX3x1KlTOH78eFQoFARgePjwYZw0aRJGRERgRUUFGVWmpaXh3r17yT12796NM2fOxODgYLx27RoZYWi1WjKqBTCkJv7nP/9Jjn18fFCpVKJGo6HAf2vWrMGlS5eiWCxGnU5H9VqdnJzw5s2bVAdBJBKhTqcjz6dUKrFLly548+ZN9PX1xS+++IJsN/T19RUEBh44cABTU1NRLpejr68vAhg+0EIjFltbWwIALC8vJ+vlfn5+eOPGDVSr1ejq6ooODg7o7e2NN2/exJCQEFQqlThixAiyLc/b2xs3b96My5YtI7aa64T16dOH4lJ3cHCwOkubsQ/t7Ox4HY8pU6aQWSAfHx+SqpjrQ9Yutu6UlpZahV14Gxv02yjm6p0p5dY7478VFxfzQLqFhYW8zHReXl6C13/zzTe4fv16qsxUvYuKisLy8vJOjzLZ9mRcLpVK0dfX1yTb3k8//WR2cMUqy+xqXK5SqXic+7GxsVhaWkp+k40dr1P/bG1tqTb266+/8lhpjdtTWVmZIHHQ+vXrcfHixVb9Ljf+c9U4TrK/6evrazXVcFZWlmBKdADDTIs5fxnHf1ZHjhxpNeX9n6UW22lnGjQAYNeuXTEpKQnFYjGuXLmSAoExDIMrVqzAwMBADAkJweHDhyPDMPjDDz9gcHAwBgcH49SpU3Ht2rVkyrBnz54EaPLdd99h165dUa/X46hRoxAAcNSoUajX61Gn0xGgXnJyMn711Vc4ZcoUVKlUuHbtWnRycsJRo0bhjBkzyPOMGDGCB/gZOnQohoWFoaurK6akpBAnd+vWDYcMGYJisRhXrVqFaWlpGBERgY6Ojvjhhx+a/KDFx8djjx49qLJp06bhmDFjqLJ+/fph7969eddHRETgypUrceLEiahSqTAlJYU33T9mzBicNm0aymQynDhxImq1WuzRoweP4XDYsGHUlGVSUpJJEIyPjw/++OOPvAbDMAympKSQBm9ra4sTJkxAqVSKM2bMIFTDEydOJFPxgYGB1Aisb9++vO2lH374IU6aNAltbGxw7dq1pAPp4+ODY8eO5T2fQqHANWvWkAAdGBiIK1asQIZhcP78+RgXF4eurq44adIkXLNmjWDSkblz5+I///lPTEpKImUzZ84025EEABw3bpzFJCZva4N+G8XYBqHYYazcemf8t7Fjx5LOPbeNCCHlhbR3795mc6Vw1d3dnYoTplSn0+GqVat+N9d9XFwc9eFctmyZVUC2JUuWmAS8ent747hx48ixUOwAMIAFV6xYQXV8/Pz8cNWqVSbj36BBgyym7B0/frzgtun+/fsL5myIjIykWEPZ98sFao8fPx6nTJnyRtpUUlIS4YZg479QXRKKHULxH8AwA8QC2d8WtSRWox3i4+PhzJkz8OzZM3j48CEAAHh4eIBUKgV/f39wd3eHM2fOkLJLly7BpUuXgGEYcHd3B5lMBsXFxfD48WOIi4sjABWVSgVubm4AAODm5gZKpRKKiorg2rVrAACwdetW8rcHDx4AAEBmZia4urqCo6MjiEQicHd3B4lEAnZ2duDo6AgSiQT69etH0lg6ODjA3//+dzh8+DDs3LkTgoKC4L333oONGzcCAEB0dDQ8ePAACgsLQSKRgIeHByxfvhwqKysBAGDDhg3kPeh0OvDx8YFTp04BABDQiUqlgt69e0N2djY4OjoCgAGsEhMTA0ePHiXpc1np06cPlJeXg0qlAq1WC+vWrQMAADs7O1CpVCCVSqFv376Qk5ND7GppaSHnnTlzBpycnKB///4kjaetrS1J6QwAhA3Szs4OoqKi4PDhw9CjRw+ora0FRAQPDw8eMAgRYePGjRAZGQm2trZQUVEB69evBwAAJycnaGlpgcbGRli3bh3069cPSkpKoLKyEm7fvg3x8fFw7NgxOHr0KAAAyOVy+Mc//gHHjx8He3t7kEqlIBKJwNPTk4Bv7ty5A1u2bKGewcnJCaKjo8HDw4MAcqRSKXh7e0NCQgL4+/vDpUuXoLa2FjZu3AgJCQkglUpBr9eDq6srYbZ0dnaG4uJiihXT2dmZpM4FAIiLi4MLFy5QjHGbN2+GiIgIsLW1hevXrxMfmmOP+7d0Ttg4YSyRkZHw9OlTqt4Zi3F9ATCkybVWTp48SR3HxMTAjRs34M6dO7xz79+/T+IEgAFobGdnB/n5+dR5EokEPD09rX4GVnx9fUGn05FnYtOvs+Lm5iYIlDQWNnYKSXV1NWzevJkcnzlzRvA8hULBs0EmkwnGCVb27dtn8dk2bdokWM7GLYVCAbGxsXDs2DFoaWmhvgmscBldAQwxjU0zbUp69+4Nt2/fpq4TEm58YBgGPDw8KFA4K8axAwAo0CFXKioqCHj9nRFre/cNDQ0YHh6Os2fPpqb6AQxT7Fz2NrVajQqFAkUiEQGbqFQqwb2xUqmUB7owLlMqlZicnEyAZizXgUQiIaAfGxsbMlK1sbHBJ0+ekL330dHReO/ePRSJRKjRaHDRokV46NAhcv9Tp07h7NmzUSwW80BEIpGIKps6dSoF+GFt9ff3xydPnpBzpVIpdunSBRsaGtDe3h63bt1KbYOpqqoiI1aGYdDW1pYafTg4OGBDQwNZdhACX8XExGB1dbXJKUyNRoNyuRwjIiLw4cOHKJVK8cSJE5ienk7O0Wq1aGtry/NNVlYWfvvtt9TaprEPb926RaZmjUmHpFIp6vV6bGhoIKN7rl9ZH3J/k/Vhnz59sLq6mrwPhUKBarUaVSoVPnr0iAAIjf2Vnp6OJ06coHwjl8t5PpTJZKjRaFAkEmFNTQ2ZseHW1+3bt+Py5cvRx8eH+FDoHf/Z+i4K1xeW7MvKyrJqmlylUllcxzVVx4zLiouLMSUlxar3/8UXX1Cxw1pl44Rx+UcffWR1JkdzNphSU7HjbVIvLy9saGgQzAzaGVuN9cKFC1QWVK1WS2KlUKz/n64W26m1DZr7oRKaMuOWHT9+HL/77jsMDAzEtrY2tLGxwUOHDhHwGVdTUlLw1q1bVNmoUaOwpqaGHGdkZOD27dvJb9y+fRvHjx+PAwcOxEePHiEA4LVr1yjHGz8je1xaWoqzZ88WtCciIgJfvnxJIdHDwsKwpaWFCjzca7Ozs3H58uW88gkTJuCNGzdMTi9yy11dXbGjo4O3xsies2bNGgqnYeo+xlpcXEw+/Ox53POlUik2NTVhR0cHLwAzDIMff/wxRfdp7EPuvYw7A6wPuecMGzYMa2trKR9yf5PrQ+5133//PaG75pb37NkTm5qaeOuF7P/Pnj2LCxYs4PkwLS2N7Fbhnq/X67G9vZ2Hln5bMhYCvLudgYKCAsG11c7UZ67u3buXt+5vrDU1NTwAoVC966x/X6c+5OTk8Ka+X/d+9+/ft5ojwVzseJvU1DsQ8uHr3FOlUmFrayuVhto4dvxPV4vttDMNmtUuXbpgeXm5ydSanp6eJG2xXq9HkUhEyry9vbGyspL0yrRaLVmfyc/Px7i4OLSxsaHWbNzc3CjQik6no7aDsWXff/89btiwATUaDVZWVgqu+/j4+JBRnlwux/LycoLEl8vlqNfrqUokVObv748VFRWoUqmIXWzlKywsxN69e1N2CemZM2fIup1YLEa9Xo9isRiXLFlCZfY6ceIETps2jeooZGdnE0yFWq3GiooKsv4aERGBpaWlKBaLKVu5OmXKFNy9ezcCGD6Aer1esFduZ2dHgIdcvwrZI5FIiA0AQPkwLy8PBw4ciBqNhvKXcc9cp9Ohra0tRkdHY1FREXnnzs7OgmuOCoXCLDWzt7c3SQvN9aGxXQCGNeecnBxSX//qhvu6DfptFNYX1m55s0Y9PDwsAk11Oh1v1lGo3pnTyMhILCkpMVkn9uzZw1u73rFjBy9TopeXl1VU3WvXruVt6TVWPz8/wd0IxrEDwADSNh5kfP7557hx40bBeysUCrxy5QqV2VUqleLly5fJh5Sr27Zt41EZZ2RkUIOQoqIigoMwjv+WVMiHr6MMw6BeryffLEux43V/g43/b/K+b0otttPONGgAA8BlyZIlOHXqVApU4u7ujsuWLUOZTIZTp06lQFsAgKmpqTh8+HDUarU4ffp0/O6773iVa+LEicRBWq0Wly9fjlqtFseMGcObxktOTsaJEydSZbGxsTh48GCUyWQ4ffp0gl7W6/W4ZMkS8jEYOHAgzpgxA8ViMU6dOtXkB65fv344a9YsFIlEuHTpUvLBdXBwwGnTpqFUKsXU1FRqO+TkyZMFOwEffvgh+YADGGYOAgICMDw8nGr8/fv3x4SEBHI8fvx4Hghy3LhxGBoaigCGhjpt2jQSaD08PDA1NRUZhsHZs2djv379UKfT4bfffkv81b17dxw5ciSxi/u8DMPgkiVLrGooX3/9tWCASElJocA+H374IQkuGo0Gly9fTnwTEhJCZiU+++wz7NmzJ/r6+lKzPKzKZDJctmwZ1TGUSCT43Xffkc6Cj48PZSurXB/GxsZieno6sTUgIADDwsIEgYwODg74/fffvzVTre+iGNtgXO+4seN138vChQsxLCzsjb9vLy8v0p6E/p6cnEyxcgIYgMuvy145aNCg197JYhw7uKpQKPD7779HJycnjImJodL8pqenk9+USCQ4bdo0quPCxkkhiuThw4fzAHRJSUkUMHDy5MkE8MnGf3bJKDIykpoxMo4dndHhw4fz4kZSUhJOnTrVquutiR2W7mEq/r8NakmsZiBkxcXFBbRaLfz3f/83tLe3Q7du3cDf3x/kcjlhrHvvvfd4ABCdTgceHh7w7Nkz+OGHH8DPzw80Gg24u7tDjx49AABg3bp1oFAoIDg4GCQSCYSEhIBEIgEvLy/w9vYGqVQKsbGxIJfLwcvLC3x8fMj9o6KioKqqCnJzcyEqKgp++OEHCAoKAi8vL9BoNBAaGgr/+Mc/QK1WQ3R0NAwbNgwQES5fvkxSf9rY2EBMTAwBy7i4uMD7778PDMNAcHAwYbt6/PgxrFixAlpbW8HPzw/c3d1BqVRCbGwsrFu3Dm7dugUeHh4QFRUFAAaAYrdu3cDLywvkcjnExsbCli1boKqqCuzt7eFvf/sbsePIkSOEJRHAAL5pb2+H0NBQUrZ582Z4+fIlhIeHQ2trK6xYsQLef/998Pb2hnv37sGPP/4IiAjvv/8+ODk5gUqlgi5duhC7zp07B9u2bQOGYaBLly6gVqvB19cXunfvTsr+67/+C/R6PahUKoiNjSWAP09PT2LX3/72N8J2yNollUrBx8cHvL29QSaTQWxsLGRkZIBYLIaQkBDyLwse02g0xP6AgABwcHCA27dvw7p16yA2NhaUSiXo9XoIDw8HkUgEISEhFLiHLWPBU0qlktTDbt26gZ+fHwAA5UMnJyd4//33KRtKSkpgx44dlK0ABuDif/zHf7zbzGJvmXDrHQBQsUNIwsPDISAgwOw9//a3v/GY9F5HxGIxqXcAAHfv3iXtSUgyMzPh3LlzVNn27dt5AMOuXbuCXq+3+Pv79u2DEydOkOPo6GhwcnKy6tmNY0dUVBR4eHgQu0JCQkAmk0FOTg6V5peNEwCGtMYrVqyA+vp68vf29nb47//+bwIc58qOHTt4gMTdu3cTgDUAwOrVqwk4k43/bIp0e3t7Km0yGzvMSY8ePcDd3Z1X7unpCTqdjirz8PCA9957T/A+IpEIYmNjSQzjxg6uGMd/45jo5eVFYuLq1astAhbfWrG2d69WqwWnyo4ePUqRiZg6T6FQCPb8x4wZQ61LZ2Rk4KZNm6iRmFwuR7lcjk5OTtjY2Iienp4ol8spQE5ZWRl++OGH2LdvX7x37x4yDIMXL14k2/RUKhU+ffoUg4ODccGCBZidnY0KhQKfPHlCRhRdu3bFx48fk+eUSqUUVsDYBrVaTXqR/v7+2NjYSKa/xo8fT4ht7ty5Q3jKPTw8sLGx0eT+e9ZW9v7sds2dO3dS53377be4b98+cnzhwgWcPn06ikQiam+1TCbjAa2M7QIwrKNzU8QeOHAAFy9ejHq9Hp89e0ZSgk6cOFEwEYqXlxc2NjZSyw1ubm7Y2NiIbm5uuGHDBmpvs0qlMrteZ2Njg8+ePSOzOtwkIqxybVUqlSiVSqmyEydO8JLDsMowDDmP9auxDyUSyVszI8Dquyhc/1hjo3H93Lt3ryCJzZvyD7cucuudcR1j25OpJEHm9PDhw1SSL1OqUCgooCU3dlhSbuwAMGQjNMZH/B7tjA8B6Db2JrWiouK1Zw+M3zU3/lurQUFB+OzZM2Lb5MmTKa4UgHczdlgdXdra2gT3k4pEIjKFJhaL8dmzZ4IpjA8ePMhbz2IrDLfzIBKJcMyYMVhdXU3KMjIy8JdffiG/AWDIArhnzx7B52Dvxy3jXsv9TeMPMvc4PT2dQvqeOHGCUCqLRCJsaGigpvS413J/w7hzJBaL8d69e4IJRTZt2oQ7duxAR0dHbGtrI4QXxvcQem8Mw2B0dDTFtrhgwQIe+cWnn36K58+ft+p+bMV+8uQJ9uvXT/BZhOw3LhOJRNR1t27dovY+m7ufqd/s0aMHNjY2okQiwQsXLuCnn36KERER2NTUhDKZjOd/rgYFBWFLSwtqNBo8cuQIAYFybZgwYQJev379L2/EXH0XBcCAB7IGQAhgIDbjMkSa8mNKSorZVL/W6tWrV3HSpEmC9TgmJgafPXtGyr7++mur6JON1Vxd5GpWVhaVkKwz+BU2dnT2N63VzvgQwMAN0tra+sYpet+kXa/LSmsq1rP6LsYOq6MLyzYHYAikFy9eFBzpBwcHo1qtxtGjR1MjV51Ox2OHW7FiBZU9j1U7OzuyfQzAMOo0JoHx9PTkEY+8aXV2dqbWznU6Hbq5ufFsTU5OphC7K1euxPnz56OHhweWlZURdPqgQYPw6NGj5B3a2trigAEDMCcnh7LVx8cHxWIxhoSE4PHjxwkmwcbGBktLS4ndUVFRmJ+fT1VElUqFXbt2xZKSEgwICEBXV1f08/NDqVSKRUVF2KVLF3R2dkZ/f3/yEbWG1CQ4OJgHWsrLyyMdPzc3NywrKyOAxcTERLIDQEgDAwPRzs4OY2JiqG2pn332GRUMs7KyCNZCqVRiSUkJ8QmbEwPAMDPj7OxMmBpNBYtNmzbhzJkzUS6XY5cuXQiDmRBlsr29/VuXE/1dFNY/QmvOH374IW/Wy9HRURCz4uXlhaWlpWTmxt7engK6serk5IRlZWVk3bt///687dBcDQgIMAluVKvVFNU3257M+Wjr1q04bdo0k38XiURYUFBAUqJz1dfXV5C5kKs5OTk8tkX2/bzJmDhjxgxqNs+UDwEMuKySkhJqRkcmk2FISAgVn4x9aK1a8uHBgwdNZs99XZ04cSLVueqMvouxw2rMwKVLl8ja+pMnT2Dr1q3Q3t4OAAbSjo8//hgAAMrLy+HFixdQUVEB+/btA4Zh4MsvvwSJRAL379+n7nn8+HFqvenzzz+HkJAQaGhooAgboqOjqSyD6enp4O7uTpGEzJkzh6zbcGXgwIHw0Ucf8cp9fX1h8eLFsHjxYvDw8IB+/frB1KlTyd+nTJkCXbt2hRcvXsDXX38NEokEbt26RYiPuLZWVlZS5BvHjx+H/Px8eP78OWRkZJD1sRs3bsDOnTsBAGDYsGGg0+ng1q1bcOjQIVi8eDFotVq4e/cu3LlzB+RyOYwZMwaOHDlCCJhaWlrgX//6Fzx79gwADIQo27ZtA0SEtLQ0iI6OhqamJigtLYWMjAx48uQJ/Od//ifExcVBR0cHbN26FR49egR1dXVw/fp1UsauBbq5ucHXX38NUqkUPvroIxg4cCA4OzvDokWL4Pr16/DBBx/AkCFDiJ2//PILVFdXAwDAixcvICMjA16+fElsZbOMzZs3D8LDwyEkJAQ+++wzAACorKyEhoYGqK6uprLHFRQUwLFjx8gxN/Mkm6GxoaEBAACampqgvLwcAAyZIUNDQ6G5uRkuX75MrfG6u7sTH7LZ4169egXl5eXwz3/+k9RNJycnWLRoESgUCkhJSYGYmBioq6uDxYsXW5Ux7t9iWq5fvy645myc3Q8A4NGjR6TOc6WxsRH+9a9/QUtLCwAY4hBLDMYVbrZMAH7Gu+HDh8O4cePIcVVVFcl6aSwvXrwgdQwAoLa2Fm7cuGHOVDh48CBcvHjR5N8RETIzM6lYwsrt27fh3r17Zu+/Y8cOwXVpNnaoVCpYvHgx2NvbC14/ePBgmDBhgtnfAAC4ePEiRYJkyocAQGVyZaWlpQUuXboEHR0dpMzYh0ISHh4O8+bNo8qEshZyZffu3VBVVQUABgzKokWLKKyFVCqFr7/+GlxdXU3ew1jKysooDEZn5MmTJ+R53hmxtnffo0cPwrfPItlZNU5y06VLF4KoZBgGs7Ozcfz48ejn54dyuRx79OiBYrEYg4KCKITm/v37yUhTKpVijx49UCqV4vz58ynE/e7du3l0mjt27CAIWbFYjFFRUahQKPDjjz+mlifCw8PRw8MDQ0JCMC8vD/Py8jAgIAAnTJhA7Vteu3YtfvTRR6jX6/HUqVNkLc7Dw4OMpCMjI9HW1hbd3NzMjq4jIiLQwcEBnZ2dSU7zrKwswq/u4OCAeXl5FILX1tYW8/Ly0M3NDf39/amZkvDwcGqGAsAwGhHaj8vS9XLLjH0YEhKCPj4+6O/vj7m5uahQKHDFihU4Y8YM9PHxwdzcXFSpVLhs2TJqG1H37t3JTIBMJsMePXoI4gD27duHU6ZMwcmTJ/OyRXK1a9euJreLabVaaiTl6+tL7WTYuHEj2XHCMAxGRUWRmQy9Xo+5ublkyyd7DcMwePToUbL85eXlhbm5uahWq3Hp0qU4Z84cdHNzw7y8vLeGoORdFGtti4yM/FPec3p6uklOfG7sMP4bGzuMy729vXkxkatRUVF/av3hxg4Aw0wrd8dDWloafv/99xbt+r2qVCoxKiqKTKm7ubkR2l+uGseOuLg4alZZSPV6PRUTuapWqzE3N5eaTVYoFJibm2txVsc4dryOGn/X3ha12E6tbdDNzc3YrVs3TE9Px/z8fAqoMnPmTGoN+vDhwzyCjaysLFy+fDnqdDpsbm5GOzs7/PXXX8mUsEKhIKyFbMVpbm7mffS4jVQkEpHnkMlk1B73pqYmwanGCxcu8PYAG6tcLudNM7NlM2bMwIsXL6JIJML6+nqMjY3F1NRUQVAdqzU1NThw4EAcPXo0YVFkmfGMz5VKpTyQzsqVKwkvAIABLMndQsN9XoZhBN8JV9PT0/Hs2bPk+MSJExb3NrPPxv3Y379/H4cMGYISiQS9vb2xubmZdGi4vgEwfKy3bt1K3U8ikVC2lpeXU9tFuXb16tULHz16ROxZuHAhWYZgz5NIJCiTyVAqleLTp08xIiICxWIxymQygnuIjo4mZabsMlX2Nui7KAqFQnDZhvULgOEj/OjRI7IljVuPWRUqe9P6OrHjs88+M5mUxly9s6aNWaum4gkA4IABA/DBgwcml84uXrxI5XSx9p0LxUmuBgQEYFNTE8EMfPTRR1heXs47zzh2CKlxPFm7di3++uuvVF16nfV/Yxu4scOSrabe+e7duwUJ9v5qtSRWRxepVEqAEhEREfjixQviHJFIRFVgsVgsCJpjy9gPgFgsRrFYjN7e3tjS0oItLS0UP4HxR9HZ2RlbWlpINsDExETCaFdZWUny3gtdy6pEIjELypFKpdjY2Ej1pCUSCT59+hSjo6MpWyUSCXkn5howex77wQIArK6uFlzj+umnn3D79u28d8et6FwbNBoNvnz5kvSSo6Oj8enTpygWi7GwsBDnzJkj2LAs+UtI9+/fT6X6lUgkuGfPHly7di3vnScnJ+OdO3dM2gAAuG7dOty1a5egXVqtFl+9ekWCMvfdsTaIxWJUKBTY1NSE4eHhOH/+fDxz5gz1zufOnUs6qmxZWloalpSUkHsdOnSIh1bfu3cvRR/9tui7KC0tLWRGjKsnT57EhQsX8toJgOFD8urVKwp8ptPpsKWl5Q+nhu5s7LC2/RvXOyF93Xp369Ytkwh747ZjrV0AhtmaxsZG3juRSCTY2NhokU+Be52592Rpl8LQoUMpVlrjeGIc/61RbuwQ8hf3mI3/XFseP34syAkhFOveBrUknSYdmjBhAu7atQu7du2KDMPghg0bzJI6MAyDJ0+eFKw03333HX722Wcok8mwW7du2K1bN16a00OHDhGwjEQiwW7dupGKY2trSxwZEhIiSB70ySefUIC0bdu24dixYzEsLAzz8vJQLBZjRkYGTpgwgTxv165dybaRgIAALCgo4E0dMQyDp06dwoiICPzggw+oj9r333+P8+bNI8fHjh3j7bAICwsjQc3BwQEvXLiAzs7OqNPp0N/fH21tbfH8+fME2NazZ0+Kd59bKbt160ZmTDQaDXknwcHBvJkV1ofGKUzHjRuHmZmZKBKJMDc3F7t164bJyclU71uv16OPjw96enri+fPn0cbGBvV6Pfr6+qKrqyteuHCB+M/BwQFjY2PxwoULJnv8Op0O9Xo92tjY4Pnz5ymWQdYuoVEJ60Ouv5RKJXp4ePCmDt3c3CgAGIABBMYlcgoICMCZM2dSPmTt+qsbcGcb9Nso3bp1E9xmFRgYKMgsCWAI1N26daM+UmycEAq02dnZr03Ws2fPHoo4zFTssFY3btzIyzwqVO+E1FS9S0xMNLvEFhoa+kYZHllVq9Uk1nPLjeMkAFCx400/h729vdktgKbivznlxg5L54aHh/OWDsLDw9/4Tok/Ui1Jp0mHrl69CocOHYKioiJARDh9+jRcuXIFXFxc4PPPPwepVArjxo2DuLg4AMMTwKFDh6Curg4ADCQT8+fPB4VCAefPn4eSkhJoaWmBwsJCKCwshP79+0NSUhL5vezsbLh79y4AGDJoJSYmEkIQNrvZ/PnzoaamBrp37w6jR48GuVwO8+fPB0dHR7h8+TKcPXsWpFIpfP7551BRUQHXr1+Hx48fw4EDB4gNV69eJc9bVFQEL168AAADScZvv/0GFy5cgGHDhkH//v3Jsx08eBDq6+vh5s2bFFFIQUEBlJaWkuPDhw/DgwcPICwsDNLS0gAAoKSkhGTAevXqFWRlZcHLly/hP//zPyEiIgJaWlrgt99+IyCo2tpaCswTHx8PY8aMAYlEAgkJCYRw5fnz53Dp0iX4/PPP4dGjR4IgpatXr0Jubi6IRCKYN28eeHt7w/Xr1+HUqVOAiHDgwAF49OgR3Lx5E3Jycsh1165dgzt37kBTUxP89ttv0NraCteuXYPbt2/Dy5cvISsriwCDHj9+DOfPnyd2JSUlwfDhw6nnCAsLg7///e/Q2toKv/32GzQ1NUFCQgKMHj0aOjo6CNBvwIABFODr5MmTcP36dQAwkKkkJCSAra0t3Lt3DyoqKkAkEsGnn34Knp6e8ODBA3j8+DF89tlnhDjovffeowCpVVVVcOrUKcqHrF12dnYwf/58kxnh/i2WpbCwEJqamnjllZWVUFNTI3jNy5cvobCwkAKfsXGCBS5z5ciRIzyAMgDw6l1aWhqEh4dT5xw/fhxu377Nu5aNHZ2VvLw8wYx1tbW1cPnyZZPXzZ07FwBA8Fmqq6tJNlAAAwB52LBh5Li0tNQkCPJ1ZPLkyRAdHQ0vXrwgsZ4rxnGSLWNjhzWSnJwMgwYNEvybRqOB+fPnE0KgJ0+eQElJCfn74MGDITk5mRxfunSJfGNYmTp1KkRGRpr8fdYGNsaak+LiYnj+/DmvrLGx0eK174y8Tu+e3b5mnOSloKAAFQoFrl69GhcvXizYC+aOKtkytscvkUhw8eLF1HZDbo/X3t4eL1y4QIHMuKPKuXPn4ubNmzE6OhrPnz9PbbNRKBRYUFCA/v7+6OnpiYGBgbyeobFdxqPKtWvX4scff2yxB8luJTQeyYwbN47wJ3BnBrgzHl988QXhMuAqdxYEAHD27Nm4cuVKVKlUeO7cOdTpdOjl5YWBgYEol8uxoKCATLFze/dBQUEELCSRSDAvLw9DQ0N5tgYEBKC3t7fgCM3YVkdHRwrM5+fnR1FyhoaG4qpVq3DRokXU9Z9//jl+9913VNncuXPxhx9+oGYG2BGa8SwI+47z8/MxMDCQzAxIJBI8c+YMeaaAgAAK5zJ69GiyFMP27l1cXMj5XB+6u7vj+fPnecmL/ip9F4V9dnd3d5OgL3PKzki9zvtasGABVe/279+PAwcO/N1++L2jSqH75eTkYM+ePQVjh7F+9dVXFkmMQkJCLK7DG7cnVn/++WerMzm+rrIzw0J/c3JyMjuraBw7hGYGdu7ciSNHjqTKgoODLea0+J+qFtuptQ2au+4XHh5OMAOm1punT59OQHXGazDG60MsZsDJyYm33nLnzh1BlDz3PO79+vfvj/X19WSdjCWo4K5VffXVV5iTk0OtGYlEIoyMjMQXL14QkI/QerOQDexaGMMwWF9fj4mJiSgWi9HX1xdbWlpIZ2bMmDGEJIWLGXBxccGWlhaziN74+Hisq6ujiJWMA8XixYvx2LFjvGvZdT+VSoX5+fk4f/583juZNWsWFhUVkePs7GxcunQpb+2Wu+5XW1uLiYmJOH78eIpg45dffsFNmzbxfGj8m+awEEKYAVtbW2xubiadTOP7cTED7P3NYVe4635TpkzB8vJy4sP4+HiLa8FvY4N+G4V99nnz5lHpv61VY6wKq0Jr4ZbWx19XjWOWqfVmYxVab7aGxc84dlhS4/gEYCBT+vDDD01eY4w3suY3XgczAWA9Lul11VrMwMWLF3HmzJmUTda8S2tseN1Uy3+WWmyn1jZoLiKYizA1hURnkbMMw+DDhw+xb9++CADUbgLu+WzvNDMzE3/66SdSbgqxyaJJ7ezssLm5mYxEuajT0tJSnDJlCg+JzqLO2fszDIOfffYZnjlzhlqjFkKiAxi2jrx48YKsl3F3E8jlcty7dy9Bk3Lvx302Y7ssUZwao2mXLVvGW0Pk2sVVhmFQq9Xis2fPMCoqCiUSCYaHh2NjYyO5pymEvTGamIsIZm0Qi8XUOcY7ItjzkpKSKGZJY0SwNbsJVCoVKYuKisInT55QgE6uDadOneIxpmVnZ1MjRa4N3DohEokwJSUFKyoq/vJG3JkG/TaKcXvqrM2mEPbjxo3Dqqoqqiw5OfmNsBJyVaVSYWNjI2+m0xKaXug849hh6Tprn/H69eu8QRM7WLP22Sxpbm6u4EjeeDeZkLK7yf6odmHtbgLueQqFAhsbGyksgqndBAD82MFVvV6PTU1Nb80WZCG12E4706DXr1+PEyZMwMDAQDx58iRhmGKn8EQiER47dozsJWX3qMfGxpKPP5dngH1IV1dXzMvLQzs7OwwMDKS29ezbtw/79++PAIb9snl5eeji4kL2mYrFYuzRo4dgwwkPD8fFixfjli1bMCYmhrfPVCaT4alTp8iUeGhoKEokEszJyeGBzrh7hdn9s9xtkNwRgvE+04MHD2KvXr0wPj6eolD+4osvqLwOAIZtSnl5eRQX/5w5c3Dp0qXk+JdffsGZM2fiuHHjMDs7GxmGEeQZ2Lx5MwW044Ig1Wo1RkVFIcMwuHr1arJVkevDESNGYGZmJnVP7l7hQ4cOkWlNrg9NVUZHR0eqkRnvFe4MzwCAYYmItUHoGm7d5JaxAC1jngFjdXV1xYSEhH/zDPxO+aPehYuLCw+s5uzsbNKf5nTWrFm8JStWRSIRRkVFvRGueePYYY1+/fXX+Omnn5o9JyIiwiyATqPRYG5uLtmJZUm5sYPV0NBQHhMsgGHpVwjcx+UoCQ4OtrjHn8szY06FeGa4KsQzIKRsTOR2zMzxDJiLHQqFguJUeBvVknQKQFhcXAy3b9+G58+fQ15eHnR0dMClS5fA3d0dUlJSABHhzJkzhCGuubkZ8vLy4MyZM9CvXz/o168fvHr1Cs6cOUOBgF69egWnT5+G1tZWqKyspNjHCgoKCOtVa2srnD59Gl69egXXrl2DiooKkEqlEB0dDQqFglwjk8lg1qxZcPv2bTh9+jScPn0a8vLy4PTp0xTgpaOjA06fPg3Pnz+H6upqePjwIXzyySeQn59PgCEuLi4wc+ZMuHDhAmH+a25uhvz8fOjo6IAPPvgAunTpAsXFxeS+LEhRq9XC7NmzoaSkBB49egQPHz6EgoICADAwHDIMwwMaVVVVwenTp6msZ1VVVRTw6MKFC8QmFuB04cIFqK6uBi8vL0hLSwOGYeDixYuEIRARCSsigIFZLT8/HxARSkpKCKMZ14f37t2DmzdvwqxZs0AmkwEAwIMHD6CoqAgAAPLz8wlYiOvDwYMHQ0JCAmVXYmIiREdHQ3l5OcyePRtsbGyIDxUKBcyePRuuX78OtbW1lA9ZFrVnz55BUVERzJo1C5ycnKB3794waNAgYgMrbm5u8Mknn4BYLIbw8HDw8fEhPpRIJHDp0iVwdnaGDz/8EBiGgcjISLC1taWedcaMGeDn5we1tbVw5swZOH36NMWs9m/pnMyePZuXxZQrzs7OMGvWLJLJEgDAwcGBqndC8vDhQygsLKTK6urq4MKFCyavSU1NhZCQEF75tWvXoKysjCrr06cPjBw5Ejo6OiA/P18QBMkKt96xwq13rBjHjj59+pi8JytXrlwRZLMLCgqCKVOmAICh/dfV1YFer4dp06bxzm1ra4O8vDyrwHIAQMUOVkpLS3llAAA1NTUUuI8VNv63t7dDeXm5RebGgoICki1RrVbD7NmzSaZArpw7d44HFvzoo48IMNRaW9mY+OLFC4iOjoaxY8fy4iRXLl26RIE7ud+6ly9fQn5+PvmuhYWFwaRJk8z+/lsnne3ds7z2wOlxcPnFWb5+AJqbes2aNdR2u6CgIMFtGd7e3oK9V61Wy1vbsrGxwcjISCwrK6OuUavVWFpaSnpx1vKLd+nSBYuKisgUt7OzMyYkJGBxcTGZeXBycqJmLtatW4fp6emUXWyv0jg3AVe5/OISiQRDQkJMrrt5eHigr68vikQiDAkJMTvV2q1bNywsLMTQ0FDBpQeWX1yhUFAc/sZ2sarT6bC0tJTqPUulUgwJCUGxWEy2GnKv+e677/Drr7+myhYtWoTffvst2tvbY1lZGbq5uZH8ElqtFktLS9HLy4vYyvWhu7s76nQ6KjdBWloabtmyhbLL2dmZ8mF+fj4uWLAAAwMDKR+OGzcO9+zZI8gRzzAMnj17FqOjo99JfvG3UcrKysxuqRPitReqd51VNv8Htyw7O5swlVrSuXPn4oYNG6gya2MHAPDqnbEaxw5Wra13xnlNAAD79OlDESCx7UnoepFIhF26dPnDiZxM+cbctjzj/BKW1FJuAoZhKFu5eU0AAKdMmcKbBf09OnToUDx8+PCf/l7NqSXpdGeAm/HOOKuccdZCc1mr6urqqNSc7L22b9+OmzdvpioswzA4dOhQvH//PnWPxMRErKurE6zk3GM28xj7ERXKPCaUeco4ayGAgcqTyzbIzaDFMAw+evRIcArLONMW99jV1RXb2tooACEX5LZmzRrMyspCW1tbbG1tRX9/f7OZF5VKJb569QpDQ0N5drGZx8LDw7G5uZk0jtmzZ+PFixep3zaVeVGn02FbWxva29vjnj17cN26dWZtNaXGmScBDGyLLBaCtWvZsmV47Ngx6hmM7WKzFnLvlZeXR/AsnZm+Y+/7LmYeextFyI7OTqeay5ZpSqurqwlw1dK11t7fOHZ0JoOetb9hrt519r2tXr2aSqLGVY1Ggy0tLZ3a4dHZrKWm1Dj+v2k1fhaVSoWvXr0inVJuxlOh64X8am3Z26oW22lnGzSba1wikWB9fT1GRkZiWloa5ufnI4BhFM79mJjq2atUKuIwLy8vbGxsREdHR15O7mvXruGIESNQLBbz1uyEyvr27Yv37t3jfXidnZ3x6dOnGBwcLJiTfN68ebysWMZ51YXKsrKyqPV8rl1czczMpHIE3LhxgxCdsO+JfeZBgwbh7du3EcCQk3zSpEnkednzvv32W9y3bx9qNBp89uwZr0GzfvjnP/9JZQ9kc5KLjPK0s3aJRCKsra3F6OhonDJlChYVFSHDMHj//n2SS4HrV+P86wCGlNPcPA+mVC6X8/zA+oabV14mk2H//v2xtraW1K0FCxZgdnY2zy7uvZRKJZmdamhosCoPPcMwWFNTg3Fxce9kTvK3UYxt4MYOa+2eOHGiWcpvIVWpVCiRSDApKckiqPD777+nKL9NqXHsyMvLw7lz51r1PNOnT8cLFy5YPM9UvfPw8MDGxkaTmQOtbWNc5cYda3Tu3LmC1MtdunTBp0+fWt1eTMXJN6Hc2GFsK3dwY27W6cSJE/j5559TZYcPH+Zt5zSO/2+zWmynr9ugGYbBmJgYtLW1RV9fXxw5ciRmZ2eTijd06FBqexmA4SPHMt/t2bOHsBLK5XKMjY0lwbxv374kdWTPnj1x+fLllGO2bduGcXFxggY7ODgQfnMAw/TPt99+iyKRCGNjY6kKIJPJ8MiRI6jX61Gn0+GoUaPw8OHDvI+KWCzGAwcOYGhoKA4fPpza7RAeHi44pZeQkEBx8YeEhFAf7OjoaNKo7e3t8dixY+jk5IRz587FVatWkZ0LUVFRglsOAwICMDw8HMViMcbGxppshH5+fhYZwX744QfCwAgA2Lt3b7Szs0Nvb2/io169elm1zWnXrl2YmpqKwcHB6ODggMeOHSPX9erVi5euNioqiswOZGRkkD3grF1sx8vOzg579+5NrvP39xdMehIYGMjzoUajwdjYWBIIjH1orL169UJHR8e/vPG+ToN+G+XYsWNUqmFu7BCyUSh2eHp68lhMBwwYYNXUrrOzM7W1T0gDAwMpENzmzZsJNbpCocDs7GyKO4PViIgIwXIh9fb27lQHyFjZOPk6OzI6q+vWrSOAZKlUiocPH8bAwEDU6XSCAE21Wk21MSEfCqm1PuyMGseO19Fu3brxloO6du3K62CYiv9vo1qSTgEIP/jgA4iKigJnZ2eYMmUK5ObmQv/+/cHT0xNOnjxJpausr68n6UUnT54MPj4+8PjxYwKEuXLlCgFpyGQy+I//+A8CvmloaIArV64AgIHNiwXHsVJRUQFPnjwBnU5HQBrjxo2DkJAQsLGxgeDgYHLuvXv3oLW1FSZNmgQ5OTkEQOju7g6pqalQXl4Ozc3NcOvWLcjNzSU2jBgxgqRERkSSrri+vh6uXr0KDMNAamoqPHr0CJRKJYwdO5b85qBBg+A///M/KdCPr68veHt7k+PTp09D165dYcCAAdDW1gaXLl2C1tZWqK6uhvPnz8OFCxdg+vTpcPnyZQgMDIRBgwaBTCaD6dOng62tLVRVVUFxcTG0t7fDiRMnoKmpCaKjo2Ho0KEgkUhg2rRp4OTkBDdu3IDCwkIQi8UwdepUcHFxAQADaGvq1KkgFovhxo0bBLgHYGD4i42Nhffee48AGU+dOgW9evWCmJgYct6kSZNAp9NRdeTKlStw5swZKC8vJ3ax4Ltnz56RdLApKSkQFBQEz58/J75mU8n6+vrCxIkT4cSJEwQEpNFoIDg4GBiGAQBDOtWioiJil7OzM0RFRcGgQYOoehgREQEDBw6EEydOUHWTZZwEMLDU9e7dG+zs7GD69OmQn58PPXv2hNjYWPi3/H7hpj8HMLSnnJwcePr0qeD53NjBSk1NDQWqBeCnMI6Pj6cYQlmpq6uD06dPU2Xc2AFgYEPkguCqqqoIOJYFSrOpyLly4cIFuHXrFri4uJD2ZEqqq6sJgBjg/9U7S5KQkEDA1ydOnKDS/6pUKpg+fbog0G78+PHQpUsXqmzcuHEQGhpKlY0ePZrHynj16lUC5mPtZ+OkEEDzxYsXvDZm7MOQkBCKSRTAeh8CACiVSpg+fTphJTQlbEzk1jk2Jjo6OpIybuwwlsLCQh7gsaioiJdau7i4+N1LVWxKrO3dAwD++uuvOH36dAwODsbLly+jTCbDzMxMKq2tXq+nemQMw2BhYSHZgsaqv78/qtVq1Gq12LNnT6ysrBRMQOLn50cwB2KxGAMCAsg6T2xsLJ47dw4BAI8ePYojR47E6OhovHjxIgYGBpLRYWRkJBYXF1NrXV27dsXS0lJCDmQ8Ety+fbtZtkGRSIRFRUUYFRWFo0ePpsAia9as4XEvrFy5ktqj6u/vj+vXrze5ncnOzg4rKyvR09MTFyxYgGvWrEEbGxusqKgg2+VUKhXVU50zZw5mZGSgQqHA8vJyDAwMRCcnJ/Tx8UGpVIplZWWEZY/1YUhICCoUCnRwcKCARlu2bMH09HSUyWQYEBCAIpEIN2zYgPPnzyfnnDt3DmNjY1Gr1VK9aA8PD3RzcyP+YqcDNRoNAZ/m5ORgUlISqtVqUqbT6dDW1hajo6OxsLCQmr409qGTkxN6e3ujXC7Hy5cvY1BQEE6fPp3iLQAAnDx5MoVL8Pb25m3B2rhxI3766afo6+uLFRUVqNFoBH34Nui7KG/Cbjs7O4sj8B9++EGQvVNIubHjTWhISAiWlZUJEs8Yx0Tjemfp3saxg6uOjo5YWVkpuCX3+PHjPFDdkSNHcNSoUVTZ/v373xjboClbAQwcEEePHn1tHzo4OGBlZSWVb0UkEmFAQIDge9fpdGSrs1KpxPLycipecmPH77VbKpWSOPmm6tSbVovt9Pc0aOO1JrFYjI2NjWYThrDXPHr0CAcOHIgpKSl469Ytk+fX1NSQ6SpnZ2fs6Ojg7TYwfg6tVovt7e1k+kZoTYwL+issLCRkGubOFTo29X/j3zC+5/379/GDDz6w6r6myvr160cyFJo6Z+HChRQrH/c8qVSKTU1NGBUVhXPmzCEZ1bj3CAwMxLa2NrPIX2Mf7tixA7ds2YLu7u7Y0dFBki0NGzaMZJlkdeDAgfjo0SMEMOBDuKmZza1lLlq0iFq7NOcjrubk5JD02ub8Y80z/BX6Loo1dlnyX2pqKkn//XvUkj+t8Xdn6oREIsEXL15YXKb4o/WPqMem4r81XAFvSu3t7bG9vV1wh0dFRQVOnz79D3lPxufp9Xpsb29/a2jLhdRiO33dBi2RSLC2tpZK9Qtg+BAfPHiQt7UMwLAFsbi4mJzH5rPnfmi2bNlCUuICGMAgbK+PpaRlGAZXrlyJmZmZaGdnhw0NDajT6fC7774ja9K2trYoEolw0aJFPDTtF198gYcOHUKFQoH19fWEtCg8PBzr6uqoNbmQkBCsr68nWIipU6dSlKp79+7FJUuWoL+/Pz558oSQ04wbNw5LS0sRwAAWTEhIoJ6BaxerV65cwZEjR+LAgQPxxo0b1N/69++Pt2/fJpXwm2++waysLPTw8MDHjx9jYGAgLliwgCIrAjD0fo2BMrNnzyZgSa1WSxgENRoNikQivHfvHgleIpHIYgU39qFKpUKlUkn5S+g8th6x78zGxoa8exsbG3zy5IngdkcAwzou165Tp07h7Nmzye88fPjQ5NqmQqHAwMBAfPz4MarVauJD43O5Pnxb9F0USzYFBwcTdkkAw4ffmNFOJpO9Voa4devWUbuTLl++zBsZs9qnTx+srq42+0FgY0dnnoFtY39VnTGOHW9CzcV/a23NzMzEFStW/K7nYGOM0Ihco9F0Cl8hFP+FlBs72DJr4uRfrRbbaWcbdHJyMv7www/IMAz2798f7ezscPjw4bhq1SpyTvfu3TE9PZ36qC9YsAAXL15MgcDmz5/Pm4oPDw/H0NBQtLe3xwMHDpjMXR4SEkKSG8XHx6NSqcQuXbpQH4B169bh3LlzeRU2MDCQsIDFxcXh5s2bMTk5GW1tbTEuLg5FIhEuW7YMx44dizY2NjhgwAAUiUS4ZMkSXLhwIdXLj4iIwODgYFSpVDhgwACyhOHt7U16yH369LEqOUZsbCx6eHigq6srQe6z6uzsTCidAQwBNCIiAsViMQ4YMAA1Gg0GBQVhZGQkymQyzMrKoqZVJRIJ7tu3D/V6Per1euzRowd1f64P+/fvT0B/Xl5euH//fmrqz83NDQ8cOIAajUbQh/PmzaP4vzMyMnijhc2bN2O/fv1MvguJRIIDBgygGpxcLsesrCzBfdM9e/ak8hjExcXhTz/9RFjUxGIx7tmzhyyTaDQaHDBgAIrFYuJDDw8P3L9/P/kocX34tui7KAAGtP7o0aOJL/bu3UumZ9k2xn5EdDrdGxtJh4WFUeygMTExJtMmOzk5ma2T3NhhXK7X63Hfvn1W5UVgGAZ37tzJy2uwY8cOk+yJffv2pXg15s6da/UuBuPY0Vnlxg6uDWz8t+YeCxYswClTplBl3bp1oxKcZWZmkuVkofhva2uLBw4csJp7wJSmpKQI7gDgxn8AwJEjRwp2Vrix403U0T9LLUmnUxg3NjbCw4cPgWEY8PT0BLlcDo2NjVBbWwsMw0BKSgpUV1fDuXPnqPS59fX1UFxcDCdPnqTKnjx5Amq1GiZOnAgKhQKKi4uhtLQUOjo64O7duzBy5EjQ6/Wg0+lg9OjR5NpLly5BYWEhtLW1wcGDB2HQoEHQ3t5OgVsePHgABQUFcO7cOVI2dOhQUCgUcPPmTRg3bhwcOXIErl69Co2NjfD06VM4fPgwdHR0QG1tLTx79gxkMhl4enoCwzDw8OFDuHjxIgEjjR8/Hu7duwfl5eXQ1NQEhw4dglGjRoG3tzdUV1eT9L8eHh6gUChAr9eTtJujR48GPz8/8PPzI3adOHECAgICIDQ0FE6fPg0TJ04kYJm6ujqSwvSDDz4AAID79+/DmDFj4NChQ/D8+XOoqKiAgoICQESoqanhsebdvXsX2tra4Nq1a3DmzBlSPmjQIPjb3/5GQIRHjhwh6VBbW1vh7t27gIgQHx8PPXv2hLa2Nrh79y50dHQI+vDRo0dUGtMHDx7w2Nvu378PL168AB8fHwp8GR0dDfHx8dDW1gaHDh3ipUitqamB1tZW6N69OwwcOJD8zcXFBbRaLTg7O8P/+T//B7Kzs6GqqoqwRiIiVFdXw8uXLwHAkOr50KFDpM6wgEfW1ri4OPD19aVSOP9bXl/Y9sTK3bt3obW1FQAMMYX1BQDArVu3eIA/Y+nfvz+VhtqUlJSUUOygOTk5JtMm19fXQ3Z2ttn7VVZW8oCMAEDqjrVy7949UhfNlbHy/PlzKkWzcRszJ9zYISTDhg0TZGXkChs7WEFEOHLkCGHg40pCQgIBX7NSX1/PS7FcWFgIly5dIsf3798noD82/nNTWLNlQimsAf5f7DAWliGXBR83NDQQVluucOM/eyx0Hjd2sKLVamHChAmERTMoKAhGjhwp+JxvrXSmd89ViUSCFRUV1PYukUiE5eXlgj1n7nU6nY6a1vHw8MCbN2+S0aharSa4gIKCAhw8eDDGx8fzCICUSiXhnz558iSOGjUKlUolj49eoVCQssOHD+OkSZMwIiICKyoqUCKRoLu7OzXF4+vrS6aKgoOD8dq1a2QvvVarRQ8PDxSJRHj58mUKGMkwDJaWlvIwExcvXsT+/fvj0KFD8ezZswgAeP78eUxMTMTExERqSvTbb7/FLVu2YFhYGN68eVOQjTEnJwfHjh2L0dHRWFZWhiKRCD08PAT58+3s7CjAjbu7O683/+uvv5IpdgADp7hCoUCtVkuNorZs2UKB6ry9vcmMAevDsLAw3jZHLy8vVKvVqFKpeHzhffr0wdLSUtTpdCiRSHDBggXUtC6AAThkPLMyd+5csv0UwLDfd/r06RgaGopVVVVmp/pYH3LLbGxsKFt/+uknwaWuv1rfRbHWNrbeWXPu+vXrcfHixVSZs7Mzb9To5ORklrPfXB0DoGMHVxmGoeIEV4Xa2J+pIpGItCcAEIyJXM3OziZbi1m7zGXgk8lk1Oycra0t1Z4yMzNNpib+I1UodgAYti+Xl5fzlhLM+VBI3dzcTM5U63Q6vHHjBlnKGjlypCAfw1+pFtvpm27QltTPzw87OjpMvlQAQ775e/fuWbzXwIED8fHjx9RaWFxcHD59+pRyfExMDDY2Npqc1ikqKiI8BlKpFF++fGlyP/Ann3yCly9f/kOdlpSUJMisaE4vX75M7epg9auvvqIAhOfOneMlR+IqFwT08ccf45UrV0ye+/jxY15u+IcPHxIyJVbv3buHycnJOGzYMHz48CHvPvb29tjR0WEykcn3339PESf9XhXy4bRp03gZ8N5GfRfFGrveBPjs119/JTwmrGZkZOD27dstXvvjjz/iwYMHeeWmYodCocCWlhbBFMYsDfZfVUccHR0REckyYXx8PD558sQqzIBSqcTW1lYMDQ01eU7Xrl2xpaWFDJDmzJnz1mFrrFFzPhTSkydPvjMEQ0JqsZ12pkFnZ2fjnDlzeD8yffp0iqJzz549pDEwDIM3b94kjVwkEqGjoyNVMT09PbGuro5s75PL5WY7C6zKZDL08/PDuro60lOVSqWo0+nw4cOHZMuaVCo1S5hjZ2eHX3zxBWG0c3R0FFz727dvHy5ZsoTX658wYQIWFBQgwzB448YN7Nu3L44ZMwaLiop493B3d8f6+npqtOLs7Iz19fVkBC+Tyajn/e677zAzMxO1Wi3W1dUJbrGys7MjoyqlUokPHjzALl26oEqlwj59+mBNTQ1KpVK0tbVFlUqFoaGheP/+fdKguT50cHBAqVSKCoUC7ezsKB+mpKQQFjWhzoCDgwPKZDIcPnw4YYyzt7dHuVxO2VVSUkJ2iTAMg46OjigSifCbb77BX375hbqnWq3G+Ph4vHPnDunkff7557wUzgAGzMm9e/dIbz8tLQ1PnDhBnWPcGdi5cycuW7aM51dTPnybG/TbKNznN653xnWHOyI1jh2W1MbGhpdtTqPRUMBDbr0zrmNCM2vmYoejo6PgAINtY8axw1wnnBs7AAyzbnV1dVbFQaF7se3Jkg2dsYtVsVhMbcVWKpV/2ExIWVkZb3DBVTs7Oyr+d1aNbZVKpVhTUyNI1Cbk13dJLbZTaxv0rl27cOLEiRgUFIR+fn64fft20nADAgIowFt0dDQFDBk4cCCuWLGCylUPYOD+nzJlCiqVSkxKSkKZTIazZs3CGTNmkHPWr19PwERarRZ37tyJjo6OOHXqVExPT0epVIpJSUmUk1gKUjYwhISE4M8//0zNFgQFBeG2bdtIRQgKCuIFnaFDh+K3335Ljnv16oUhISHo5eWFv/76K/n4+vn5EeDRwIED0dXVFXU6HcWSOGfOHJw2bRoqFApMSkqigqFcLsekpCRUKBQ4ffp0asoewPCB69GjB7FVrVbjhAkTqD3/y5cvx0GDBpHGOmTIEBLcHBwccPDgwcgwDC5ZsgRHjhyJdnZ2OGTIEPJOWB+KRCL8+eefKf+xdrm4uKBOpyMppRMTE9HNzQ1jY2MpqmUAQzBjczRwfcjqgAED0NfXFyMiIggwatWqVThnzhzs0aMHKpVK/PXXX9HDwwNTUlJw1apVxD4AA/0pl2ly6dKlOHz4cJ5dgYGBhBktMzMTg4ODUa/XU4Cq6OhoaiSUkZGBXbt25fnwbdB3UbjPb+yfpKQkXLZsmUl72Xo3YMAACqT8usrWu9e9fsyYMbhw4cJOX8fGDnPnsLEDwLAjh42Jxuf99NNPPN6WdevWUeBsSxoaGopbt241O1swatSoP2WpzMnJCXfu3CnYGYuPjzebilgo/v8eZRgGBw8ebLETxqaNF5pBmTdvHqampv7h762zakmsBhA+f/4csrOzoaKiAtrb26kUj1VVVVBYWAijR48GiURCUr4OHjwYAAzpKNva2igGrw8++ACcnJzg5cuXIBKJQKPRAMMw8PLlSwpE09TURIAriAgvXrwARCTntba2wu7duyEuLg4CAgIAwADm2b17N3lG9rdHjx4NdnZ2pOz58+eAiJCQkAAymQzKyspg1KhRIBIZXktrays0NTUBwzCQnJwMFRUV0NHRAQMHDiTXAhgYFFkGsKysLKitrYVbt27B4cOHiR3s8758+RJ2794Nr169gujoaIiOjoZXr17B7t27qXNYSUpKgufPnxPAn0ajAZFIBK9evaIYtpqamqC1tRVcXFxgxIgRsHfvXgLY4r7fpqYmePXqFSkDABgwYACoVCo4duwYecd9+vSBiIgIcn+1Wg1isRhu3boFR44cAQCA3377DR48eADt7e0EIDhixAjw8fEhAMoxY8YAIkJbWxv4+vrC8OHDAQDg0KFDcPv2bWhrayMgwaamJjh37hyx9cWLF9DR0QEtLS1QXV0N+/btI88jl8tBpVKR43/84x/wt7/9DcRiMcXGVllZCSdOnCD3a29vB5lMRl17+vRpKC0tJcfseSyQbcyYMWZT6f5bzMuYMWMI81tDQwPs2bOHgLTYNmZKsrKy4OHDhxbPAwDo3bs3REdHmz2HrXevKy0tLVanAebKqVOnKLCckLCxA8DQFnbv3k2xDbLy4sULHjiYLfPx8YERI0ZYfB7jGN6tWzeIi4ujzhGyNTQ0lJeevDOi0Wh47QkRqXjKlYMHDwqmTWaFjf/cuiGVSmHMmDGg1WpJmUQioeI/VxISEggrIyLC3r174cmTJxZtEfIDgMF3poCgb7VY27v38PAQBJU4OTmhjY0NBgUFYXV1NQGUTZgwAU+dOoUMw2BlZSWv11peXk5G056enlhTU0OmspRKpcWteHZ2dtTU19mzZ3HMmDGk1+bu7o5isRi1Wi06OjqiUqnE6upqiiOd1cOHD2NaWhqGhYXhrVu3SG9co9Ggs7MzisVivH79OkZGRuKkSZN4aUNTUlLMgkXc3NzILAIL9hOJRLhq1SpqtOPq6opKpRIVCgVZMigoKCD7orVaLVZXV1PLBKyt7LJGZGQkXr9+nZr66tatG968eZPyX0hICN6+fRvlcjnu3bsXv/rqK2rpYseOHWRak+tDlUplNlFKWVkZmRFwcXHBmpoacn5iYqLg2iLDMOjh4YFisRhtbW0pv7q4uJDthVxb582bR5gF3d3d8fjx42Z9yJ4nk8koH7q5ufFGX25ubiiXy1GlUmG3bt2wpqbmtaZr/wh9F6Wmpobi/bekarXaqmQ8KpWKihNr167FH374gXeeQqEwG09cXFzIyJIbO17XR2xMtHSeo6Oj4Gj492h8fHynEzoBGJbddu3aZfG8tLQ0Hs8Ct41xlQVnc2cffHx8sKam5g/dk6/RaLC6uprCIFkT/83dk40df9Qz/xlqSToVXYT2wObl5b3xqSRrAIQbN27Effv2Cf5Nq9ViR0cHBgQE4PLly3lrxtbqvHnzSFrf36P19fUkh7qXlxciomBwqq6uxjFjxmBSUhLW19dbdW+NRoPt7e1Ubu7X0blz51oFAkpNTX3jaX3t7e0REdHPzw9//PFHKthUVlYSFjGVSoVtbW3UdKtCocDW1laTICDWhxKJBJuamqilBZFIhI2NjdQSF8Mw2NDQgPHx8Thx4kSL2e7+bH0XpbM2fvzxx1hRUWHxvJSUFJLd05yaAq6yev36dcJ8yY0dr+sjawGER48eFey8vGtqKnYEBgZiR0fHa5FFvU0qFDveRbXYTq1t0G5ubmRkGRISgtXV1SiXy9HBwYGszYtEIrx+/TqP0IarPj4+eP/+fbS1tcXNmzdT3PyXL1/GhIQEVCgUvJ5mYmIi1eO1tbWlQCv5+fl4//59/Pnnn5FhGMKNb2NjwwPPfPrpp7h3716Uy+VYXV2NISEhOGvWLB6aWK1WU0CZ3bt34/z58zEgIABramrIiDUlJQXz8vKQYRisqqriEQa5uLigXC7HESNGYHFxMbq5uQkyZjk7O5OUwOzI6Ny5czhq1Cjs06cPVlZWkl72V199hb/88gtlqzUV4uDBgzhr1ixSyW/duoURERE8WwEMaWONZzxUKhXlG2MfAhgwAlyyjuLiYpIBjtXCwkJCx8y1QavVUqNwJycnatTm6urKA3cKlQEYONcXLFhA7HJ1daVwLjU1Nejn54cymYz4kOsvpVJp1ba0t6lBv41ibAO33gnZqFKpqC2CmZmZggMOU/5xcXHB+/fvkzakUCjMzjQ4OzuTGc3OticA4MUObkzk6uTJk6k06fb29rwPZUZGBqHLNqWlpaU4ePBgq57tm2++4e2wsFbT09N57K1CKhQ7AAzYJTc3N5O4hDcRO/4s5cYOAMO37tq1a4LkWNb48K9Qi+20sw06KSkJV61ahcnJyYL7NkeMGIGurq44YMAA6oXMmTMHx48fjxqNBpOTk1Emk2Hv3r1xzpw5uHz5cgQwAPZMgUV8fHwwJSUFt27dSj7uXbt2JcC1QYMGYXJyMrXHf+nSpQQAJpfLMSMjA728vDA8PBz79euHYrEYk5OT0d7eHkNCQgir1KZNm8hOBNbxGzduxKlTp2J4eDhqtVpMTk4mH6CAgACCqh8+fDjZc+vk5IQZGRkkMOh0OjJDsGrVKoyKisKoqCizwKjBgwejXq9HDw8PHD58OAIYuAjS09MpW5csWUKm51n96quvCOhOIpHg5s2bMTU1FUNCQlCn02FGRgaOGTMGnZyccODAgbyAGxgYiImJicgwDK5fvx63bt2K48ePp87p3bs3RkZGoqOjI27duhVtbGwwOjqa6hAmJSXxdkAMGTJEcCthSkoKD0BprIsWLcLExETBv/n5+eHmzZtRIpFgXFwchoWFoY+PD27ZsoVqzFwfpqen48KFC4kP161bR2YajH34V+u7KFu3bqV8zTAMjhw5knzwvb29MSMjQ3CHAYCBmZPbcVizZo1g+mpWFQoFJicnC3IW/PDDD1aP8MaMGYPz5s2zeB4bOyydFxQUxKMlN9aYmBgeY2paWhpOmjSJak9CIMjJkyfz2EC7d+9u1W6M5ORkHjdAWFgYAQtzVafT4ebNm81yEVijbOzglr1u7Fi2bBlvEGZKR44cSbaSc5UbO6y5z4gRIygOF3M+fBvUknSagVAsFkNjYyNkZmZCR0cHxMbGEvYqRITt27dDbW0tiMVicHBwgGHDhoFEIgGpVAoSiQSeP38OmZmZEB8fD1VVVXDu3DkCJmltbSXAIoVCAcOGDYNhw4aBj48P3LlzB3bu3AkymYwwSTEMQ67dt28fZGZmQnl5OQwZMgQADEASFgwIAOTa4uJiyM7Ohvb2dsjMzIRu3boRxjvueUFBQdC3b19SdvToUSguLoZnz55BZmYmJCYmgouLC1RVVcGJEydg2LBhsGfPHrh37x55PrlcDgAAPXr0ACcnJ9izZw/1bCKRiLBWJSQkgK+vL/m9YcOGQXZ2Nly7dg3u3bsHO3bsINcWFRXB5cuXKVslEgnx0dChQ8HZ2ZlKqSqTyeDIkSMEyCSRSGDbtm1QX18PYrGYPAeAAYylUCjgt99+I/eXyWTkN1g5efIkFBQUAMMw5PrTp0/D3bt3ITExEQAAdu/eDS4uLhQrGetrd3d3AjRln4m1ZdiwYaDRaCA8PJxK9SqVSnmpYvv27QtBQUFUnTh8+DCUlJSASCQCuVwO//t//29wcnICACA+bGtrA4lEAteuXYOsrCzynljh+vDf8nrCbbMAhjjB1jsAuh0LyYkTJyhmUe79lEolDBs2jLr+5cuXkJmZKQjiMo4JrHTt2pUHPpRIJLz6LiSXLl0iscOcVFRUwP79+6kyvV4PAwYMIMc5OTkUY6rQc+zevRtu374N3t7eFAun0POeO3eOx6IZHh5ula0lJSUELGws5vzVu3dvCAsLM/l3VtjYwZXTp09T7Ki7d++GW7duUefs2bOHl15YIpEQv3Jjh5AYxzpWLNVDY9m+fTvFssuKkA/fCbG2dw//t3ehUqmoadz9+/fjp59+imKxmDdlp9fr8cGDB2RUxabKBQCsqqribdu6cuUKJiYmolwux+DgYHz48KEgiQ2rMpmMjC4cHBxQoVBgbGwsXr9+XXBqigUDqtVqaonh2LFj+Mknn6BEIqFs+Pjjj3lkN0qlEh0cHFAkEuHNmzfJNJGfnx/W1tZS034ikQidnZ2RYRjctGkTrlixgiozfr6SkhIy+ndwcMCHDx+SmRKuraz27t0bb9y4wZuhUSgUeO/ePQwODubZKqR2dna8ZEa7du3Cb7/91uz+ZCcnJ5TJZKhQKHjThMOGDcPLly+js7MzikQi/PHHH3H9+vXk7xcvXsSRI0diXFycINmPjY0NPnjwAP39/XHBggUE3MT6EMCwrYh9J6wPhexigUx37twh7JhsfWX9wPqVa5e5d/ZX6bsof+T78PT0xIcPH3ZqH72QLlmyBLdt20aOHRwcBFPxGrcne3t7wW1txnGSVeN6N2HCBMzPz7fqGY1jx5AhQwQJ0BiGIe1O6D5ff/01xd5prQrZaip2mMJMmIt/QuexNsjlcirG2NjYmAQhcmMHt5wbO6xV428CV52cnMjsiLFf30a12E4726A///xzHi0wgGGqrK2tzex+T2vBZ51lIASgQUBCygWfLV68WBD9HxERga9evTL7IegMA6FOp0NEpCqxOQChOe0MixhXjRkIhdQUCNQSkIslHTIF5HJ3d0dEJCmMf68aAwh79uyJzc3NJqf1WCBXWFgYtra2UsE9ODgY29vbSUeVZSDkAgjfxDO/aX0X5a9+Z6+jXOAqV41jB5e9lKumwMfG9a4zam3sMGYgfFMqxF7aWQC5NQy0APzYYQwC/emnn0wCyIVUCHxsjQrFDgA++Fiv12NHR8dbnbnQYjvtbIPWarXo5uaGEokEq6qqCFOTVCpFb29vZBgGt23bJthANBoNjxMewMBrf/v2bdK7V6lUFE/88uXL8ccff0RHR0e8ffs2qSBKpZJw93t4ePAa2IkTJzAlJQUBDL1lb29vlEqlaGdnJ9igZDIZGYnv2bOHAO24amNjQ33cfv75Z7x9+zbm5OQgwzBYVlZG1vLFYjH6+PhQPXTjsoEDB5LO1blz5zApKQkHDBhAUj2zqlAoiK0nT57EcePGUX8/evQoj9QJwAC0FLI1JCQEr1+/jnK5HF1dXUklFolEWFFRgVFRUTxbMzIyqB6/l5cXKpVKVKvVxK8XL17E+Ph4TEpKwqKiIp79XB8uXboUN2zYQP6Wm5tLMttZ8qGxv1gNDQ3Fa9euoUwmI3axrJRVVVVkLU8ikZD6ytZN1lYvLy/ctm0bLlq06C9vwJ1t0G+j3L59myJnMY4dppRhGLx8+TJvjb+0tNTqLHxs7Ojse3Z3dxdEwRvHDjc3N8HtgWycNC43rnfGumnTJpP1TiieCKlIJEIfH5/X2h6pUCjwxo0bJKMkV7lxwlyZOR+y9gvZwMYOrg3seUqlkvomODg4dCp7oXHsiIiIwKqqKov4AO53zfhvXl5eBOdiya9vg1oSq6PLhg0bSE/T19cX169fjxMmTEAXFxeMj4+neozx8fEYGRmJbm5uuH79egrI4+zsjBs2bECVSoXTpk3DlJQUVKvVmJKSgqtXrxYMEL1798bZs2fj6tWrqfOnTZtGnTdmzBhqv+jQoUN5W+6+/PJLHDBgAOr1ely9ejWKRCKcP38+Dhw4EHU6Ha5ZswYlEgkOHDgQu3Xrht7e3rh27VqUSqU4Z84cHDFiBHW/AQMGYEpKCg4bNgwBDLMaXl5e2KtXL5JIZfny5QSQZm9vjxs2bCABRKfTEXrUkSNHol6vR19fX1K2ePFiXjAcNmwYBgUFYXBwMK5cuRIZhsGkpCT88ssvcc6cOSiTyXDdunVUkiOJRIJr1qwhPnR0dMTx48ejWCzG9PR0shTDMAyOHTtWMJDFxcVhVFQU5UMAA+iHBYsmJyejTqdDf39/HDlyJLl24sSJmJqaigqFgvgwOjqaCurDhw/HoKAgDAkJwRUrViDDMJ32oZOTE44fP14Q3Dp27FgqkDMMg6tWrRIMfP369eOxvL0N+i5KSkoKFbgZhsFx48ZRCH/WF8b7wMeMGcNLOTxq1CiTQOOoqCgKod67d29e4jBWVSoVbtiwweyOkcDAQFy1atWfFuSN6x03dpjTpUuXCiaIGzduHMXoylW5XI7r16+nOvxisRjHjx//Rnk1hHwopGzs+L2/p1AocP369Ra5JcaNG0f8OnjwYPz000//FB//VWpJrAYQuri4QGxsLAQEBIBEIgEnJyfYvHkzPHz4ENRqNTg4OJBznzx5Ao2NjSAWi8HFxQUYhoHIyEgICQkhZSKRCOzs7CA4OBiioqJg48aNYGNjwwNr9e3bF65duwZ5eXmgVqth48aN8OLFC9BqtTw2KY1GQ5Xt3LkTGIahgGv29vagVqtBIpGAs7MzMAwDDg4OpMzFxQUAAB4/fgzPnz+nbGCv1Wg0kJCQAGKxGA4dOgQbN26EX3/9FQAM6UJbWlpAqVQSsJqzszMoFAoAMLABsvZ37doVHBwcYOfOnZCYmAh79uwBjUYDTk5O8Ouvv0JiYiJ4e3tDZGQkREREgEQigYSEBDhw4ABUVFSATCYDZ2dnADAAbW7evAn29vbEXxKJBAIDA6Fnz55UGYAhBeqmTZugvb0d7O3tCdgGEWHLli3w4MED0Ol0EBMTAwAGlsLCwkLIz8+nfAhgAHGxDHP19fXw8uVLuH79OuzevRsSExNBLpeDra0t2NraQkdHB9TV1UF7ezs0NjZSKVB37NhB7GL90Fkftra2EhY3VrRaLSQkJMDjx49J2lxWnJ2dSZ1Tq9WQkJAAEokEsrOzIS8vD/4tv182btxIwIIAhjrGxg5WGIYBFxcXHrArIyODl3J469atUF1dDd7e3tCnTx/qbwqFgrQ7AANIjWWgZCUsLAwiIiJIWzQGo3JFKpWS9i8k0dHRoNfrTV7fWTGud9zYYU6cnJxAqVTyym1sbARZ9wD+XyziAgfb29th06ZNVjHwmZKoqCgIDg4mx0I+FJLMzEweWBDAAHjs2rWr4DVSqRQSEhIoRlEhu4zl4cOHsHnzZsJ6qNFoqG8YVxiGgfj4eJN/50pcXByJXUqlEhISEgTBim+lWNu7BzCAtNLT00lPw87OTnAqat++ffjVV19RZTt37hTM+DR69GizjFlXrlwho25Lyk5Zc8u+/vprsrZkZ2eHIpEIFQoFWVIwZUN2djYuWLCATBXa2dmRKaXAwEB89OgRqtVq1Gg01B7l27dvE2ZFkUhEEv0AGKa1uVOKGzZsICOTJ0+eoLu7O/7444+4ZcsWdHBwwCdPnqCPjw9+//33+Msvv6BWq8XHjx+bzO4HYJgB4E7bffbZZ3jkyBHeeWKxmAcsZJ+XPU5NTcVz584hwzB4584dMsLi2sWmJmavuXbtGg4cOBBlMhkGBATgkydP0NXVFVUqFarVanRycsInT56gp6cnrlixgvBCsL7hPg9bJuRDU/aHh4djbW0thfsICQnBJ0+e4JMnT8i2JWNbAQD9/f3x8ePHqNVqiV3GPvyr9V2Uztool8t5U+/Gbcea2GFK2Xr3Jvxx8uRJ3lZYGxsbwW2NcrmcWnrgxo7Oqkql+sOT5nDjJLfMePnE2C6h+C+kxrHDlK/Xrl2LmzZtIsdarZa0bzs7O3z8+LHVMwo2NjYmt7CaUrFYjPfu3TPLnwPAj/8+Pj74+PHjd4a99LVTGEulUmxubhacmvqrdNWqVYJpSAEMjae1tRVDQkJw0aJFmJubazGFJZe97sWLF4IEE9nZ2SZZxHx9fbGjo4NgIcaNG4fV1dV/6DuIjo7GFy9eWFwv7Nq1K7569YpqGCEhIdja2moxyHBBQHv37qV2CbBqDAI1l0pWKIWxVqvF9vZ2igmO68Pf+56CgoKwra3NJJArKysL16xZw/PhX63vonTWxhkzZvBSZ6empuK1a9f+8vdvjZ49e1YQTW8MPjYXOyxpZmYmZmZm/qF2LFy4kAc+/uyzz0jWUlbnzJmDJSUlnb6/udhRU1Nj8ro7d+7w+E6s1dLSUos8Jv9T1WI7tbZBX716lReE/f39UaFQ4KRJkzArKwtFIhGWlpYSIgk/Pz+srKxEtVqNGzduxH/+85/o7e2NVVVVgqCbgoICjIuLw8GDB+PVq1fx6tWr1Hqeg4MDXr16Fd3c3HDRokW4Zs0a1Gq1WFVVhd7e3ujs7EwBFI8cOUKAdgzDoL+/P8pkMnR0dERPT09SJpfLccaMGbhr1y6USqV4+fJlDAsLQ3t7e7I2ydr64YcfUh0ODw8Pk0AWiUSCer2ejGRtbGx4PdiEhAQ8e/Zspx07f/583LRpE6rVaqysrES9Xo8Ahp678Zaa8PBwvHz5MkqlUty1axfOmDED5XI5+vv7I8Mw+Msvv+DMmTNRJpORMgDA8ePH4+HDh8l9WB9y7XJ3d0cXFxd0d3fHq1evkl6wsa2urq4UDuHUqVMEpyASiVCv11NgHrbMOKUt60O2TC6X45UrVwiuICQkBMvLy00Sovz88884d+5clEqlqNfrTY743d3d0dnZmefDv1rfRemsjba2tujj48Mre91sgwMGDMCCgoI/zUdeXl6CnUc7OzvKLnOxw9PTE69evUpmr5KSkqhdDK6uroJr4v3798dz585ZfMaePXticXExVa8jIiKwrKyMDCQcHR0p3BEAUDHRXJmxsrGDW8bGDuNzheIkV3U63WtTHPv4+PyudMvG8X/z5s08sqa3VS22U2sb9JQpUyjHiUQiXLZsGep0OuzatSuOHDkSGYbBSZMmoaenJ8bGxuJ3332HqampKJVKcdCgQdi7d2/UarU4ZcoUXL58Oa9zkZKSgv7+/hgQEIBTpkzBKVOmUKNUpVKJU6ZMQY1Gg7GxsZiQkIByuRynTJki2LkYNWoUL8Vkeno69u3bF/38/HDZsmWkMXTv3h2HDx+OIpEIJ0+ejK6urhgXF4dz586lrg8PD+flQ+/duze1eyI1NVUwZ3pkZCRvxPDBBx/ggwcPyPGYMWNwwoQJqFarccWKFejg4IDJycmYmpqKSqUSV6xYgc7OztirVy8cNGgQSqVSTE1NpaaipFIp/vDDDwS04+rqipMnT0aRSITDhw/nsWMNHTqUzPAwDIPffvst6vV6DAsLo+xgfShU0TQaDU6ZMgUVCgVOmDABx44di1qtFlesWIFarRbHjRuHH374ITl/3LhxgsC9Dz74AFesWEEtKQ0bNgynT5+OMpkMf/jhBx7gKTU1lQRVFxcXTE1NRZFIhDNnzsTExET09PTE5cuXk3SnxsDAadOmEX4HAAOFa5cuXf7yxvs6DfptlBUrVpCPoLe3Ny5fvtxqljdrddGiRSZn+Pz9/cmOFEuqUChwxYoVZumLjWPHm1JuvbOxscEpU6aQmbugoCDeDiJTz8ZtZ6xyYwfrh48++ojqDHt4eOCkSZP+kCUxc7HDnC5cuNAkbXVISAguWbKEKgsKChJcjn4dtSb+Dx48mJoxfpdjh2V6rf8rK1euhO7du4NGo4EbN24AwzAQEBAAKpUKHj9+DLdu3QJEhDVr1gAAQPfu3cHDwwMOHDgAHR0dsG/fPvDy8oL/+I//gJUrV8KuXbt4DFEbN24k/6+qqiL/DwkJAZFIBKWlpbBy5UoAAAoUtHLlSujZsyfcvn0bmpubITQ0FE6cOAFbt24FAAPgLDw8HE6cOAHvvfceXL9+He7fvw9BQUHQr18/OHPmDJw7dw4qKiogNjYW1qxZA4gIDg4OhBEQACAiIgIaGhpg37590LdvXzhx4gSEhYVBVFQUvPfee+Q8Dw8PkMvloFAoIDo6Gk6ePAmtra2g1WrB398fAAB69eoFlZWVUFtbCydPniTXuru7g1KpBLFYDIGBgSCVSsHV1RUcHR1BJBKRslOnTpFrfvzxR+o9ikQiCAgIIMC42tpaWL16NQAYQHp+fn4QGRlJ2L927txJrmUYBt5//31Qq9VQUlICJSUlAAAQGxsLeXl58OjRI3JuTEwMXLp0Cerr66GtrQ0qKyuhvb0dPDw84NWrVwTAKJFIwN3dnbALxsTEwLZt20hKa4lEAjExMZCXlweurq4QGBhIUkcjIjg7O4O3tzdll16vBzs7O7hw4QL8+OOPEBkZCVqtFm7cuEHeh4+PD9TX14NCoSBskidOnKBAiwAAXl5ehPmSYRjQ6/UkDbKxD/8tnZfAwEACgpPL5RAQEEAAeTqdDtzc3CA/P1/w2tjYWCgtLaXqnZD4+/vz4kmvXr2gqqoKrl+/DtevXzd5LRs77t69S+oYy0THjR2sKJVK0Ov1FKhQq9XC3//+dzh+/LhgKl4AAF9fX3B3dxe01bjeNTY2klgHYGAvrKioMGlDdHQ0XL9+HW7cuMFj5wMAKnYAAFRXV8PatWupc+7du0fit/Gz/eMf/4Dz58+TtOhcEfKhj48PeHl5ETZBbvpxYzHXxvz9/cHGxgYAgMSOU6dOwatXr0Cj0fDAm2wZGzsA+D7kxg5zYir+Z2ZmkrK9e/dS74nrw3dOrO3dAwDm5ORQXN0ajQZFIhGmpaWRqW6NRkOmmfR6PT59+pQAZaZOnUqyW7HnSSQSHujPuGzr1q0WGbNKS0vxww8/xL59+2JNTQ3Vu42OjsYHDx6QnrxMJiOpgh8/fkxmD7p27YqPHj0i09AymYyamTh06BAuXLgQAwMDsaGhAdVqNe7btw+/+eYbZBiGWn+WSqUYFBSEz549I+yIXFDRtWvXBJkVjc8zpWwiHVNlrG+4NrBlc+fO5ZEuGduqVCrJqIRhGLx37x7GxsaiVColvrlz5w7ZF+zl5YXPnj1DR0dHng0sCyCAgbXr2bNn6OnpiQqFApVKJdrZ2eGzZ8/MTg2KxWLyflUqFS5atIgCRubm5uLcuXNRJBJRfmDtkkgk+PDhQ+zZsydKpVLUaDQ8f3HrHGu/j48P8aEln/wZ+i6KOXu4scNYufWOW86NMeb06tWrvK3AbBvjth02dgjdwzh2mFLj2CGk06ZNw/Pnzwu2sd+rlZWVgrORQnHCGlWpVFQcfPTokcl8EEI+TE1NFSSnM46TAHTsMPdM3NjRGVuMffjll18KgqotKRv/ra2HQrb+lWqxnXamQTMMQz6yUqkUX7x4gVFRUcgwDIpEIhSLxfjs2TMqMQbrgIMHD+LKlSvJcX19PSYmJmJKSgovTeyoUaMooJ01nQFuYxVquNyyRYsW4alTpwTP5R6np6dTFZprP3seW6bX67GtrY0sV0yYMIGiCt65cydu3LjR7DMCGEhHfv31V4uO/eGHH6j1fADDFNWJEydQqVTiq1evMDQ0FBcuXIh5eXkok8mwubkZIyIiKDtYnTNnDkV0dOzYMWq/Nvu8qampePXqVbPvbvv27bhlyxZSXl1dTQUq9rwNGzbgnj17zL4PVmNiYvDZs2coFouxpKQEZ8+eTdnA2hQREYHNzc0kkHF9yP5GWloatre3Y2trK2msU6dOpdgWWQChNc/2NjXot1HM2cPGDmvaNats7LD0rkzdl1vvrPGvtf63dJ6xrUeOHCFJ2n6vmvrtNWvWWJV50FgvXrxITZGbs03Ih6b8qtPpsK2tjYewf1Pv2JrrhOKfNWoc/58+fWo2OZKvry+2t7e/MwOJTnUGfvnlF5w6dSq5eVBQECqVSpwwYQLu3r2blLEjTJ1Oh2VlZahWq9HHxwfT0tLIBywwMBA1Gg3a2dkR8Ftubi7269cPbW1tSRmAAUzDBbIcO3YMhwwZgjExMbweaXR0NBYUFCDDMLh//37CaKdUKrGsrAz1ej26uLhYtRXFyckJ/fz8UCwWY1FREXbr1g3Hjx8vSIMpk8kwKCiIVDquXQCG3q+Hhwe6urri5cuXSS84Pj6edEwADOt2Xl5eaGdnh5cvX6YAkTY2NlhWVoY+Pj7o5uZGgZH27duHc+bMQV9fX2QYBoOCgkgqZJ1OR8oUCgWmpqbyOleOjo4Umt/X11cQoGRvb0/ZtXLlSh4OgrWVPdbr9ajVarF///7UjISHh4cg8IjrQ7ZMpVIRjIGfnx86OjpieHg4FhUVUb1zhUKBQUFB5FrWh9z7Ozg4YHBwMOUve3t7Cnjp7e39xmiU/8wG/TbK5cuXBXeAbN26lSIJAzAAzYzXaY2VjR2v+w5N1bs/Qrmxw/hvbDt+nfsuXbrUqrVxd3d3QVujoqLw/PnzJj+ubBt7U+9h3bp1+Nlnn5EZ07ehgy2Xy7G0tJTElZCQECwuLrY6G2NQUBBvVpurrK2vwwT5R6gl6VRnYNSoUYJ7Lbt3745jx44lx5MnT8ZBgwahvb09pqWlkVFaWFgYTpgwgXcty/yUmppKfWgADLkQjIFBH330EXbp0gX9/Pxw1qxZuHTpUtL70ul0pMOSkpJCrpVIJJiWlkaAZjqdDr/++mtSKePi4nDGjBkoFotx0aJF1IeWYRicMWMGenl5YUREhMVtLR9++CHFjfDll1+SYKjRaPCTTz4hHabAwEBiA5tWd9y4cahQKPCTTz6hgJFyuRzT0tJw0aJFPBDc+PHjTQJtAAxBafHixejt7Y1RUVE4evRoFIlEuGjRIoLSdnd3xyVLlvCmOhmGwYULF1Lb/FgdMmQIfvbZZzz2rq5du/IoqQMCAjA1NRUBAD/99FPyvEql0qQPAQwgHaG8Ex4eHjhjxgwqsHh7e+PixYtfqwE6OTnhN998Q3aNWMtv8TY16LdRPvnkE8GOZXJyMm+77ogRI6xKuSukY8eOFZwqt1YDAwNNJtgxpWlpaWRfuZByY0dn7mtvb49Lly41uc03ISFBMB1ySEgIL3+AkPr6+uK0adM6PULu168fL02ysbLxn1s2dOhQiymGv/jiCxInbWxscOnSpSZ3DXzwwQeCoFBrfJiQkIBTpkxBsViMaWlpBCzq6uqKH3/88e/+eL+rseO1eQYYhsEePXoIOmv58uUUVXBUVJTJ7RwJCQm8aXFnZ2eCeN+9e7dgY9Pr9RgcHIw2NjZ46tQpMhK1t7enuA/8/PyoUUnXrl3Ry8sLQ0JC8OjRo8TxKSkpuHbtWpRIJHjs2DFMTk42uZVJpVJhdHQ0+Qi5u7tTPf8lS5ZQeQ2ysrJw0qRJGBAQgDKZDKOjo6nep729Pebm5qKTkxPOmzePV5n1ej2FUM3MzMSkpCS0s7Mzy/MQHh5OgpBUKsXjx49TCH6xWIzHjh0j78fPzw9zcnLIer+Hhwd269YNGYbBQ4cO8TobUVFR6ODgQPkwMjISHR0dsX///mS2SEh37txJ8AbGPjTW6dOnU9OprA+NbfXx8cGgoCA8fvw49X7VajX27NmT+MvT05Osf7J1093dHYcMGYKnTp1CtVpNfCiXyzE6OvqNo9//qAb9NsrvtTkkJIS3XRbAELy5dXLBggVUp7R79+48quGIiAhexyQiIgLd3Nywe/fueOjQIZMfSKF6t2HDBjI4YBgGe/bsafW2t5CQEJMEYm5ubnjq1CnBXVLmtFevXq+1LABg4PawRKwzfvx4QW4ArhrHf1bd3d3NDliysrLIjgNHR0fMzc01OTuRnp4umMPBkg8BDJ2VlStX/u56ybXLXPx/W9RiO+1sg5ZIJCiXy1EikeCTJ08wKiqKlAEYpmmN12dqa2uxT58+KBaLBc9jGIYCuSQnJ+ONGzfIsVQq5Y1W16xZQ3Ui5HI5isVi7NevHz548IBUhqVLl+KhQ4fIeRcuXOBNTXLtYp8tNzcX58+fz3s2iUSC4eHh2NjYSKaIUlNTCTDS2H5Wf/nlF1y7di16enpiU1MT6Y2KxWKzgEG5XI6rV68mKXyVSiWxLTY2Fh8+fMjDS7D3KygowFmzZlFlQrYK+UEmk2FaWhovYRLXh/fu3cP4+HjKhjt37uCQIUOoaxQKRad629znZf3K/k2pVGJhYSGmpaVR5509e5Zix1QoFOQ9hYaG4rNnz8i5M2bMwKKiIhSJRPjw4UOMjY2lfMjaL5VK0cfHB5uamt6Zdb+3UYTqHVeN25ix7tu3D7///nte+dixY7GystLkdTdu3MAPPviAKquqqsIxY8ZQZZcvX+bNWBrXO3Oxg1WpVIoNDQ1mP3hc3b9/P3777bd/eZ1itWfPnvjo0aNOtVX2W2DNuR999BEv46ul+NfZ8yz5sDNqbX01jh1vq1psp51t0J9++ikhtWArwezZs8nabUNDA28/qUQiQYZhcPLkySR3/cOHDwkIyNvbG1tbW8kUPsMwlBO3bNlC5RoHMHwwuB/Bqqoqsm/WmLyGey+xWCzYa5w/fz6ePXsWpVIpNjY2YlRUFIpEIgwLC8Pm5mbi/LS0NCwrK6N+g31ehmGwvr5eMP0t93m5144YMcIsK+G1a9fwo48+QpFIhLa2ttjS0kJGSca2AhjW21mgnVgsRpFIhJGRkdjY2EhGy6wPJRIJsRXAMFJ5+fIlqlQqzM7OxmXLlvEaE9eHrM3jx48nnTeh93vnzp1OTd/2798fHz16RDKesUsGKpUKX758iWFhYWRmqrGxESUSCbEVAIgPuXwKQv7i1k3jOrdv3z6S7e5tmRWwpkG/jSIUO7jKrXdCNnN9y1VjnwldZ1wXrS3j1jtLsYOrnakrpuz6q1QonljSU6dOUQh7S/c39hc3dpjToUOHWpXW3pIPO6Pp6ek8tkUAA1bg1atXBLdiqR6+LWqxnXa2Qbu5ufEyi7m6upLp57CwMPz5559x5syZqNPpsLCwkIygnZycKKY4dgpMKpVieHg4isVi/Oabb3DBggXo5OSERUVF6OTkhL6+vqjT6dDe3h6LiooE1x+Dg4PRyckJe/Togbm5uVSjjYiIwLy8PKrhhYeH49mzZ4kTWbsYhsGwsDASmJRKJYaHh5P7ubi4EIBabm4uj8AnNDQUtVotJiUlkem6EydO8NZBs7OzsX///mhnZ0ctY3z55ZcUMKhLly5kqkwsFmN4eDjprUZGRvJsVavVGBYWRv2WSqUiH1BjH4aFhRH/KBQKDA8PJ+x/np6eZn2Yk5ODvXr1QgcHBx7RRnx8PNm+ExISgvb29ti3b188duwYdV6vXr0wJycHAQzThMOGDUMbGxuy3TMoKIhM9YpEIgwPDycjBCFb2cbJ9SH77s6ePUttjzL2oY+PDxYWFqJGo0E/Pz8CIiwqKnpr8pS/i2IudhjXuz/rPXLrnZBy692foQzD4KlTpwQpz1l1cXHBoqIiapbKwcEBi4qKzD5rdHQ0njx5khzPmTMHV61a9UaeOyAgwOTynjUqFDuE1DhO/hk+5H7XuGpNfeXG/7dFLYnVWQsBACZNmgR/+9vfoLKyEkQiEcyfPx+8vb2htraWEGKUlJTAwYMHobS0FBobG2HXrl3Q2toK48aNg6ioKCgvLwcAgEuXLsHAgQNhyJAh0NraCsXFxdDe3g4FBQVw4cIFePnyJezatQumT58Otra2cOvWLXj16hXs2rULmpubyTMpFAr48ssv4f79+1BfXw+1tbWwb98+ivijrq6OV/bo0SPYu3cvKXvw4AE8f/4c5s+fD5cuXYKmpibo3bs3jB8/HoqLi8l577//PvTr1w8AALKyskjmNUdHR/jyyy+hsrISnj17Bjdv3oQjR44AAMBvv/0G9+/fp97lgQMH4O7du+Dl5QUJCQmknM0MqFKp4Msvv4SamhpCuNLe3g7FxcWQmpoKkZGRUFtbC1lZWQAAMG3aNOjZsye8ePGCEAVNmTIFevXqBY6OjjBkyBAQiUTEh8+ePYN//vOfcPnyZXjx4gUAALx8+RKKi4uho6MD/uu//gtCQ0PN+vC3336De/fuwePHj+Hy5cuUfdXV1XDgwAHi6ydPnsDdu3fht99+AwCAmTNnQnh4ONy7d4+UHT58GG7evAmNjY1QWVkJX375JdTV1UFdXR3odDqYN28elJSUwIQJEyA6OpqyFcBAUDNp0iQQiUQwZMgQKssYIsLevXtJ9jxnZ2f48ssv4ciRI8SHz58/h927d0NrayvcuHEDqquroampCXbt2gUtLS3wb/l98uDBA17sAKDrnZCMGzcOEhMTyXF6ejoEBgb+rmfh1jtW9Ho9zJs3DwAMJD91dXXU36dOnQrR0dEm7ykWi2H+/Png5eVFykQiEXz++ecUeY2QICJkZWXBgwcPoFu3bpCWlsY7p7m5GXbt2kXIugCAxMSXL18CAEBQUBDMnTuXuu7BgweUrSUlJbyMnAMGDIAPP/yQ95ve3t4wf/58ktkxJiYGUlNTyd+rqqrg3r17Zm379NNPeeRArA+NY8fIkSNh6NChvHs0NDTApUuXqDIhH3KF60OpVApffPEFySjIldTUVJKdlSvc7xpXLNVXAKDi/zsjnendb9y4ET/66CMEMIy0cnNzSW9NqVRiREQE6S25urpSPbkffviBlyBi6dKl+Omnn6JMJsPu3bsTHnguqGb//v0YFxdHXRceHo5OTk5oZ2eHMTExWFBQgB4eHqjT6aiRR2hoKHbv3l2wd6fRaMjaXpcuXdDDwwODgoLwzJkz2KNHD1Sr1ZiSkoIZGRnUdcnJyWTJolu3bmR2w9vbG/Pz881uNXFyciK7G8LDw9HBwQH79etHuK7Dw8PJLICtrS0WFBSgu7s76nQ6Csm/c+dOHDZsGGq1WgJc2bZtG28qPiMjA8eNG4chISGYm5uLEomE+DAwMJDwDwAYQDBcf61atYq3PirkQ2MNDw8ne4ilUil2794dpVIp+vv7UztF9u3bZ3avuFqtxvz8fLKro1u3bnjy5EkUiUT4888/45gxYygfAhjWJDdu3IgSiQRzc3OJX4W2tel0Ojx79qzgWnW3bt3empkAY30XxdgG49hhSY3rXXZ2ttkRNFeN6505jYqKwuPHj5tcCmDrHQAQTgvu7BO33nFtPXXqFGn3CoWCipNCOnjwYIoHoTMaExPzWoQ606dPx9WrV/PKubEDwICU37x5s9l7BQUFkXbLMAweP36c5Kux5MOvv/6al8PAWjWO/1xVKBR49uxZQcDmli1bBEmnTMUOVrnxn1v2rsaO19pNwDAMby9mSEgINjU1kcYxbdo0k+lFja/19vbGV69eoZOTE27bto2Q8xgnqWGPr1+/jmPHjsWEhASsra0l56xYsYKamikrK8OWlhbSOKRSKWnoPXr0wKdPn6JEIsGCggLCrCi03iykIpEIHz9+jLGxsSgSiahnZdewjW0YM2YMWR978OABD9x08+ZNHDVqFO89rVq1Cvfu3ct7hj59+uCjR49MBhaJRIIikYh6d9xn4543d+5cHmMY1y7uu+M+H0suwp5XU1ODSUlJCGCYFm5paUE3NzfcvHkz/vLLL9T7YBkouffklpnyP3ter169iA9NncdmnrTkG9YGhmGwrq4OBwwYYLK+vs0N+m2Uztpo3J6sPU+obm/evPm1svtJpVKzH2yFQoEvXrwwmQ/BlAYFBWFzc3OneBKE7OrMexKK129KhZ7tyJEjbyw/QGfUOP4DCMcYa9/J3LlzKcZIrq3c+M+9p3HseJvUYjt9nQbNoum5SEuGYSjEv1gsFnzZOp0OX758ydtqyF4rkUhQIpGgm5sbvnr1ipByJCUlEaCdTCYjgDzub0okEuo32V0IUqkUVSoVNjU1kV4793mlUilVoWUymVX7b9nzUlNTqY7Pnj17cNWqVejr64svX74ka3zc5xXqDLB2ARi21rx8+RJ9fX3JOxGq0OboT8+fP4+zZ8/GyMhIfPbsGUqlUszLy+Pt/8/JycGvv/6a569p06ZhaWkpqeR9+/al/v7gwQNMSEjAcePGEVZCrg3Gfv3ggw8osOTatWtx586dgg3axsYGm5ubyaiud+/e+PjxY+KnhQsXYk5ODs/+bt26YWNjIyk37gwcPXoUFy1ahEFBQdjU1ETtCGF9yLXB2Id/tb6L0lkb2Xpn6TxuvQMwbLnlsnxy40lnn6GsrAynTJliVfvvzH0ttVkh3b59u+B2vuHDh+OdO3csXj9gwACsra39QxIQ5eTk8LZCG8fTP0uN4z8A4LfffstjagUwzAI1NDSYrRvG3zA2dpjzv3H8e5vUYjvtTIP+6aefcOLEiahSqbB79+7kRYwdOxYzMjJQJBLhiRMnBDmsly9fTvZts9Nx5qb6pFIpRkZGEmc4ODiY5MY2dvKxY8coJ3Xt2hVPnTqFUVFRJPMhSzXL6sSJE/Gnn34ix+vXr8dJkyahXq/HvLw80vFJTk7GzMxMMv3F7lvmAtkCAwNRp9OhTCbDyMhIlEgkuHjxYirVZdeuXXl7aLOysshOBIlEgpGRkSRw9OjRA7Ozs83aPn36dIKABzDM1nh4eKBGoyH+CgkJQS8vLwwMDMTc3FyUyWQYHBwsyFLm5uZGgHwRERG86a9u3bqhnZ0dOjs780ZIAwYM4PXSjX3o5+eHAQEBaGNjg2fPnqWWesRiMUZGRhKwoFarpZYEvL29MTg4GGUyGebm5pJlFLVaTdVN46m+4OBg9PHxQaVSiZGRkdSyFguyPHr0KNlvzfXhX92YrWnQb6NYssnf3x/z8vKIr7n1jqseHh549uxZso/fuN7p9XpBPgIAAwAtPz/fZDbC2bNnU9sXw8LCXpsdEMCAkudScltS43rH1YCAAMHpbUdHR6tiop2dndXbHTur3NhhLv5bo4cOHeLloTCln376KX7zzTcWz/P19TW7TMz9TgQFBWFubq7JGQM2dvwR7/HPUEvSKQDh+fPn4caNG9DU1ATnzp0joLrbt2/D+fPnARHh5MmT0NDQABEREfDRRx+Ra4uLi6GyshJevXoF+fn5cOLECQLm0mq1kJ6eDkqlEgAAunTpApMnT4aCggKSxerx48dQUVEB6enpYGNjAwkJCTBs2DBy/9TUVEhPT4d+/frByZMnARFh0qRJJNPU8ePHoaCgAJqbm+H69etQVFQEEokE5syZA66urnDjxg04f/48uV9hYSHcuHEDXrx4AcePH4f29nYAMADj2Gx/J0+ehCdPnoCXlxdERUUBAEBaWhq0tbXBrVu3QKlUwj/+8Q+QSqVQVlYGV65cIfcvKiqCR48eQVBQEEyfPh0AAPLy8uD+/fsQEBAAU6dOhYKCAgJce/ToEZWpsH///jBy5EiQyWQwd+5ccHR0hKtXr0J9fT3MmjULGIaBS5cuwb179+D58+dQWFgIs2bNgkePHsHdu3ehsbERcnJyoKOjA8rLy6G6uhoADJm3Zs6cSbKOsXZduHABnj59Cl27diUAosLCQujbty9ERETAjRs3KB8+ePCAZCybNm0adOnShefDv/3tbxAaGgptbW1w/PhxaG5uhlu3bkFlZSWIxWKIjY0FlUoFAADPnj2jsoxVV1dDeXk5dHR0QE5ODjQ2NkJ0dDQMHz6c1M1x48bB+++/Dw8fPoQ5c+aARCKB8vJyuHPnDjQ3N0NBQQEBAdXW1kJJSQkgIpw6dQr69OkD8fHx0NLSAgUFBdDW1mZtM/m3WCljx46FmJgYeP78OdXGHjx4AKWlpQAA8Mknn4BOpwMAA4Du+PHjxBd1dXVQXFxM7nft2jWT2QlbW1vh+PHjFPiOK5WVldS9SkpK4MGDBwBgAJ/NnTsXnJ2dedelpKQIggpv3bpFxRNLwtY7oeyMVVVVgpkIHz16BEVFRRbv3dDQIJihjxs7hKR3794wduxYqiw6OhpSUlLIMTd2cOO/kHBjBwDAsGHDID4+nhzn5uYSMC8rbOwwlitXrpA6AmCI/+Hh4bzzbt++DU1NTTBz5kwqy+Tz58/hwoULFKicGxOFhI0d/2OlM717vV7P29an1+sxNDSUR1U7atQoCgSj0+nQ09MTZTIZhoaGolgsRp1Oh15eXujm5oYlJSXYs2dPtLW1xYSEBDx69Ci51sfHB319fdHe3h5LSkrQ1dUVv/zyS4qVbv/+/VhSUkJtmdm7dy8mJyejWq0mo8OAgACy3UQul2NhYSEGBQWhq6sr6vV6Mno23vMcEhJiEhyYnJyM+/btI1vV2BkPDw8PLC4uxh49evCAJsHBwWhnZ4dxcXF4/Phx6m+xsbFky0xQUBA6ODigjY0NBUxKT0/H1atXo0qlwosXL5KRQ9euXbGgoIDaMqnX61EqleKFCxdwyJAhglszAf7flplz585hREQE5cOQkBDUaDQ4YsQI3L9/P7lm+fLl+M9//pP4kLv8I5FIMDQ0FE+ePElmPMz5EADIdka1Wo0XL15EX19f9PDwoEZGXB+y/lIqlZiamopbt24l52VkZODUqVMxMDAQCwsLBQlEWLscHR0p8NGKFSt4yylvg76Lwj4728bY482bN+OMGTNM2sowDJ45c8YsyyZXfX19rc45oNVqrc47r1AosKioSBCIuG3bNpw0aZLZ683FDlMqlUpJnDQu485SsW2MWyYWizE0NJSHiQkNDaWWKIxjBzdOAhiWa4xnNyZNmkRhMPR6vckZFLlcjqGhoWT2zVTsMPcejh49KsjbYqz79+/H4cOHC/4tIiIC8/Pz3/j0PRs73uQ9/0i12E4706DPnj2L8+fPp37g1KlT2NbWRq3LGqtYLMaDBw/iihUrUKfTYWtrK9rb2+Pu3btx3bp15Lz79+8T8BlXhUiHjO/P/t+YAEIkEmFsbCw+ffoUxWIxFhcX48yZM3nXcUmHXrx4QQEI5XI5NjY2UssaXCCJJUKU2tpaHDx4MPX3mpoaTE5OJteaWmO7du0afvjhhxgfH4/19fVU1ixT13KPFyxYQCUHMvYh99yQkBB89eoVqlQq6v4Mw+Djx48pWmhz75xVNzc3bGtrI8HCHDkHW75y5Uo8cOAAdd6yZcuoziHXhwqFApubm3nLFOz1Qr/JlolEImxoaMDY2FicMmUKXrlyxWL9+qv1XRT22U2RDlnznoXatXG72759u0WkO6uJiYlYV1dn1bmd8b8QONc4dlijvr6+2NraSmFVPD09sa2tjerMu7q6YltbG7XX39HREVtbWyk6dVtbW2xtbTW5jAJAE5ZZ+5zmSIcCAwOxpaXFanrmN6nmbDAVsztzP27seBP3/zPUYjvtTINWKBS89RS5XI4qlcokVSTDMARoxgIu2FG3XC6neqpKpVLQiTKZzCToxs7ODhsbG0kWwr59++L9+/fJR/Obb77Bw4cPky1kCoUCJRIJRkVF4aNHjyjkOmsD+zEEMGxPfPr0KTo4OJCyoKAgQkeclZUliJxNSUnB8vJyk3YplUrMyMjAzZs3o4uLC7548UKQvEOpVBIUK3cb3HfffYdZWVmo0WiwsbGRrIv17NkT6+vrKcQ81zdcH0okEnz06BHZ9sP1zcGDB3Hx4sXUc7D263Q6bGxsJLMAY8eOpdL/cn2vUqmIL5KSkvDWrVtmfSiTyTAxMZHyoVQqpUb1rA/ZY66/2PMfP36MEREROHv2bF5my+nTp5OdE6xdEolEsA77+Pjgixcv/g0g/B3C9YupOCESibCurg579eol+PeJEydSIN0dO3ZQ+BhLccJYjduTKe3VqxfW1dVZFdy59c64DXf248Bti+bK2PpvDGSztszYB9a8E67K5XKTa+ymnvePVhsbG2xsbDS5pXTBggXU4MKSSiQSrK+v5yWHM+XXnTt34ooVK/50uy2pxXZqbYPOyckR3MO5YsUK/Oijj9DPzw+PHz9OGvuwYcPIFFN0dDTZe+7u7o4nTpygeosuLi6Yk5NDAGr9+vXjocx79+5Npqy3b99Otm9IJBLs3bs3+V17e3vKaXq9XpClTqvVYnR0NDIMg+vWrcOcnBzBfbZqtRpjY2MxOzsbQ0NDccSIEbhjxw7s1asXmY5jKx2bNjkqKgrd3d1x8ODBmJOTg1qtFhcsWMBLzRoUFIRBQUEolUqxd+/eKJPJMD09Hb/88kvUarWYk5NjcgqOXZ5ht9ixjc7W1paMQjZs2ICjRo3C4OBgzM7O5oHgGIbB6OhosoTB9WFYWBhvFPHNN9/gxx9/jAqFAnv37k3u5+bmxpvK5fpw586d2LdvX3R2diYAqc74cNq0abhs2TLeOwgJCcEjR45QyaZWr15N7LKxsUEfHx9e+lg2+6TQe+X6EMAQ7Li2/tX6Loq1tkVHR5tMaObh4UHtVQ8JCTG5p7yz+vPPP/Oy7LFqZ2cnOKqXyWR49OhR6oPDrXfW/O63336L06dP/8vrlCmdMGHCG03o83uUG/8tqVgsxt69e6NSqRSMHX5+foJbQgMDAzE7O5vXuWH9ai1/QEhIiGCG179aLYnVAMKioiJoamoCAAAXFxeYMWMGiMViqKqqAg8PDxg2bBgUFRUR8MXDhw8JU93p06chJiYGYmNj4dWrV1BUVEQBslpaWqCwsJCUPX78GG7cuAFpaWmgVqsBwACCYQEjZWVlUF9fD35+fjBx4kQ4efIkvHz5Evr06QMxMTEUu9a1a9colrqxY8dCREQEPHv2DE6fPg2ICOXl5VBUVCTINvXixQs4efIkFBUVwfPnz+Hhw4dw/vx5yM3NhdTUVGhoaIBr164BGN42FBcXQ0NDAzg6OkJAQACx68aNG3D79m1y30mTJgEiQkVFBbS2tsLJkydh3LhxoFKp4MaNG9De3g6FhYUU4Ekul0NaWhrY2dnBtWvXoKGhAVJTUyE3NxeampqgV69eEBcXB6dPnwYAA8jmwYMH8OLFC7h48SIBy0RFRcGoUaMAEeH06dPwv/7X/4Lo6Ghobm4mPiwpKQEbGxtIS0uDtLQ0kMlkcO3aNaiuroaXL1/CyZMnoa2tDQYPHgyhoaGQn58PAACTJ08GvV4Pjx8/hrKyMuKvx48fQ11dHVy8eBHS0tLg1q1bEBERAYMHD4a2tjazPqyurobm5maYNm0aDwTE2jVq1Cjw9fWFiooKYldjYyPcuXMHCgsLgWEYmDZtGnh6esLdu3fhxo0b8PHHH4NUKgUAgPDwcEhJSaF8CGBgeGNt/be8eYmIiIAxY8YAgCFOGIPPpkyZAr6+vnDv3j0C3AUwsFpWVlZavH98fDzFXjhhwgQeIO3y5csEzGwsDQ0NpD1xpaOjAy5evEixoXLrHYCB5fLjjz8GiUQieO+rV68S8N2bFIVCAWlpaaDVagEAQKfTUcA9VqRSKXz88ccUUydXampqrHrH1kpYWBgFPuyMcOM/gAFAPXjwYMFz29vb4eTJk9Dc3AzV1dVQVVVF/f3GjRsUWJSVpqYmuHjxIg9AyPr16dOnVj3rpUuXeL/5Tsjr9O6Dg4OxrKyMTMlNnDiRkOIEBASQUapMJsPAwEAUiUS4fv16CpCl1+tRo9Ggra0tNQL18PBAd3d3dHZ2xoqKCgIUs7GxIb1wf39/tLW1xdjYWCwoKCDXzp8/H9etW4cikQgDAwMFpwz37dtHMpQxDIOBgYE8YFlAQAAqFAp0dHQkyw/GKhKJsLCwkIwgpVIpsRXAsAeY5eFnbeVen5ubS4AxYrGYMAIOHjwYNRoNNeJwdXVFLy8vtLGxwStXrpDtLT169MDCwkLym7Nnz8aMjAxBuxiGIXalpqZSMy+ZmZk8tkFvb2+cNWsWVlRUYHl5OQFA2dnZUWC+VatWUWuGZ86cwf79+5vsnTo4OGBFRQW6ubnhV199xeNInz9/Pv7888/EfjYnRffu3UmmQZ1Ohw4ODqhUKkkPfNeuXZiamsrzIXvM4kXYGQF/f38sLy8ndXX06NFUdksAwyyLqRSzf5W+iwJgWANnE5FxlRs7jJVhGLxw4QL27NmTV+88PT2t2vr3zTffUFsGT5w4IYhL+iM0KCgIy8rKBIGrptTLy8skwNdatbOzwytXrqCnpycCGEbVQlgNlUqF5eXlZnEExuro6MhL7e7g4GAyTnI1OTmZtz3aOP4DGGZtLT2TUOxg46Q1dnBjx/9f1GI77UyDZpW77sT9v1gsxsbGRpKUJzAwENva2tDGxoZkhmPPra+vx8TERExJSaHWkXfs2EGWF7jnDxs2DB88eIAAgLdv3yY5xIVUq9ViW1sb5Wz2Xtx7KhQKfPXqFZkyYlmpmpubMTIyEtPT07GoqIi6h9B9AAwf/La2NsGppLq6OgIgFLre1dUVOzo6SEVOSkrChw8fkr+vWbOG2rNviTxEqVRiS0sLNRUmk8nw5cuXgtPjQvc7duwYfvfdd7zy1NRUvHbtmuA7MXVfU/XF1DPExcXhkydPkGEYvHLlCi83emlpKc6aNQt79uyJL1684E3hy+Vy4kOh3zd+LlN+nTBhglUZ1f5MfRcFwJBO+3VpZoXq3d69e3lEPObqoikVOt/asj9Cs7KyKA4Uc+3lj3gmS/f87LPPKFY+AEPiI26q8848m3H8BzDsRKupqen0e1+1ahUeOHDAqnNNxQ5L78aa+PW2qsV22pkGzTr+1KlT5Dg7O5tq5DY2NhTSnl0/2717N5W7W6PREMYo7qhZqVSiQqFAV1dXfPbsGeklSyQScp5Go7FIJWljY0NGzNHR0Xj//n0UiUSYn5+PH3/8Me+89PR0sp2PLZPJZNSWoIMHD+LChQsxMDAQGxoaqL9xbTVW1tbk5GSCWL927RoOHTqUVCQbGxusqqrCkSNHUrYCGDotXGDPxYsXceLEiRbtz8/Px08++UTwnXDV2IcAhlGD0IhGKpVSdm/btk1wXfGDDz4gqY6rqqpw+PDhOGTIELx+/TrvXDs7O3z69CkZXYjFYmK/Wq3m+VqtVqNMJkOxWMx75127dsX6+np0dHREkUiEM2fOpHZTCPlw3759uGTJEvT398enT59S2TQ7uyXsr27Qb6Ow9amzzHvm6h0bJ7jnZGRk4Nq1a62+p3G9Y9tIQ0MDNTOnUqnwyZMnJFvnH6nGdt26dUswh8fGjRt5bIu/V3v06IG1tbVmkfgymYwHCjSOk1lZWRRTnyW/Gs+acuMfGzusuZdxnDSnQrHDkrKxw7h80qRJ1KDxbVWL7bQzDRrAML3KBYtFRkZaNd0SERGBwcHB6ObmhgcPHiTO7t+/P7WX9dNPPyVMhXFxcSiXy3H27NkkdwCraWlp1Ads48aNmJCQwPvdyZMn47p16wiVbs+ePQWntPR6PWXX999/j+PGjUM/Pz/cv38/SaYUGBiIarUa4+LizDaa+Ph4ss1p+/btGBUVhR4eHti7d28EMHAJGE9zxsTEmEwHGhERgb/++isCGDo3xkxYa9asIZ0LuVyO+/fvx9GjR6Ofnx8GBQVhVlYW6QUnJydTaFdzPvTy8sIDBw5QjYzrw65du2JISAg6OzvjwYMHyYfU3d2dzBDFxMSgu7s7urq6UltxWB9KJBKMi4sTbMjr16/nbctcvXo1L0CsWLECk5OTUavVYnx8PO7fvx8DAwPRz8+PYnUT8mFERAQGBQWhSqXCuLg4lEgk+OWXX5rdA/+2Nui3USzZpNPpcP/+/VTnU6jeWdLw8HCrkx8BgGC9Y8uM01/379+f+mjJZDLMysoyOZ09cuRIwU6yj48PHjhwwOSuCmONjY0VZE0MCwsTBEYLKTd2ABhmWb766iveefb29tivXz/BHCRZWVlWT6uz7QnAMNDZtWuX1ayEbPxnj9nYYXyecfy3VoVih3GdyMrKEgSnsrFDyKed3Tr6V6glsRpAOHnyZHB2dobr16/DtWvXYPLkyTB58mS4desWVFVVgYODA0yaNAkkEgkMGjQIevToQa798MMP4cGDB1BeXk7Y+caNGwe+vr7w/PlzCkTz8OFDqKurA7FYDO+99x6IxWKoq6ujmKlGjx4NTk5OUFtbS8ru3r1LAB5SqRQmTZoEtra28OjRIygvL4ejR/+/9r48KKprW3+dnrvpCWiGZuwAF7qAQi5wkac8hieKlIpyEb2UyvDigGU0UmryLGOMsUyi0RhjRdHE+TqgIg4xzkM0cUyM4hAHZq4gKjIoKAqs3x/9zv6d0+c03ai50ZdeVV8pu8+0ztp7nT18e60jAGCK8ufu7g7Dhg0j5w4fPhw0Gg0hwQGY0mM2NjbCs2fPoLy8HBAR9Ho9ODo6QmtrKxw8eBA6Ozth8ODB0LdvX9BoNJCXlwcSiQQAgJDXAIBEvKutrYUffvgBAAB8fHwIOVIul0NeXh5cuHCBkw501KhRYDQa4enTp+R6P/74IycS1p07d4j+XV1dUFlZCcePHwedTgeDBg2CyspKQiBsamqC2tpaEAgEMG7cOGJDAFMEwrFjx4KHhweEhoZCeno6OXfQoEEQGxsLHR0dUFFRAV1dXYQMmpmZCZWVlYR8U1dXB+fPn4e8vDw4f/481NXVgYODA/j4+HBs2NHRAQcPHoQnT55AbGwsK6Xzv/71L2hpaQEPDw8YO3YsUBRFdHV3d4dx48YBRVFQW1sLTU1N0NLSAocOHYLy8nJob2+H8vJyEgkRAOD8+fNw8+ZNYsOcnByoq6sDkUgEQ4cOhYMHD0JHRwfU19dbJJXZ5dXIsGHDoHfv3vDs2TNW/QQwRQw0L7Mmly5dYqW5zcrK4qTOZQqz3pmX0WRpABMh7dChQ/D48WNShohQWVlpMbU13cbM5dmzZ1BRUcGrV25uLmkftBw/fhzu3bsHgYGBhGgJYIqQyCRGA5hSeCcnJ3Ouy/QdACaCNh1dkSmNjY1w+PBhzrPRulqK3mguP//8M4uMTUcBtEVo/0/LiRMnOOnfAUzRF5n+nylM/28uTD/JlKioKEhLS+tWV9p3ODo6woQJEwj5uLq6mpdk+saJrb37srIy0usOCQnBsrIyLCsrIz2+0NBQ7OjoQIVCgZs2bcIPPviA9AwvX75MtrDREcIuXryI/fr1I70xPz8/1hS2i4sLlpWVoYuLC7q6uqKrqysKBAL08/PDs2fPklEwEzqdDt3d3VGpVGJpaSnGxcWRkSpFUWgwGFAsFuP777+P27ZtI+ft3bsXJ0+ejFKplDVroNVqCQkHwES0mzlzJuueGzduxDlz5qDBYMDS0lJUqVTo7u7OIkv5+vqiQqFApVJJRvTnzp0j0386nQ7LysrIkohCoSAknR9++IGT0Mjb2xvVajXK5XKrxJ3p06ezRgVMiEQivH79OrGhRCLBgIAAvHbtGsbExGBubi6L8LNu3TqcO3cu6xpubm44ceJE1lS8m5sburi4cGyYk5ND1htPnTpFbEjbVSQS4bx583Dt2rVIURT6+fmRJYKYmBi8cuUKq45ERkbi9evXyYyHo6Mjy14eHh7o6OiIEokEDQYDGfFoNBr08vJCgUCAV65cwT59+mBWVhYhfAIAx4avC95EsaTL9u3bWaPAl4Gvry9nSefMmTOYmpqKDg4OHNKbJZjXu1cFvV7fbawKiqLw0qVLZDbNHGlpafjTTz91e49FixaxSHXe3t6/e8AfDw+PF47BQfsJW47tiQ1p/28LqZHG1KlTsbi42KZjAwIC8Pbt2xZjKLypvuOFCIR8YHYGLB0zceJE3jVjb29vRESLL3Djxo24bds2dHFxQUS0yBhdsWIF7t+/n/x948YNso9XoVBgR0dHt9OIUVFR2N7eTtY2Z86c2W1kRUvYu3cva+3ywYMHOGzYMMzOzmZl7bOEtLS0bqOj3b59GydNmoRJSUnY0tLyyqJd2WJDc+zZswdXr17NKtuxYwdu3LiR14Z813B0dMSuri4WW1ytVmNnZ2ePGL9z585lsabPnDmD8+bNw169euHz58/JdPC0adPw2rVrPbLh64I3Uf4d7+XevXu8AwQAE/mYmeq8O7xIvbMFP/zww789rW9FRYVVbtHL4qeffsJPP/30hc7l8x2W0BMb/tF4U33HC3UGwsLCsL6+nrXGJxQKSWAhANNa/dmzZ5GiKKyqqsLExESUSqWEbV9WVkaCzggEAnRycrLIyHRwcEClUokURbEiAQ4cOJDVuVAoFKyesEajYT2jo6Mja51fKpVifX096SCY6yCTyVCtVqNQKMQ7d+5gTEwMTpo0CU+fPs16vnHjxrE+QkqlkvVB1Wq1JJ2ypaAqTIjF4m6Po/USiUTkeU+fPs2bcvWDDz7AAwcOoEQiwbt372J4eDjOmDGDkw+BT39L8PX1xYaGBtRqtahUKjkjMr7OAG1DZtmyZctw06ZNLLsuWrQIt2/fzrE1bd979+6h0WjEOXPmcJjDcrkc4+Pj8e7duyiRSMjsCZ9d+XZ9+Pn54YMHD1CtVhMbent7Y0NDg03v5XVo0K+j2KKXQCDAmpqabtdemfXO/De6jfGdZ97u6HpH/33x4kXMzc1FAODUu9jYWKypqeF0uM19hzWoVCrSGRUIBFhdXW0x2iITpaWlNsXm54O5/2MiISEBq6qqWHrFxMRgTU2NRS7UkSNHOIHT6DbGLNu9e7fFEMVM0L7D09MTGxoaSBbXjIwMEr3Vkg0BuP7/VeLQoUOEp8b0/7aca+7/XxdYbac9adCLFi3CzMxM1Gg0OGTIEBQIBPjZZ5/hmDFjODf28/MjZLGUlBTOdFBKSgq6ublhUlIS2SK0YcMGjImJwfj4eJuYsm5ublYbytixYzn5trOzs/HTTz9FgUCAQ4YMwTVr1pCpeJFIhDt27OBsSxw8eDA6Ozujv78/Jx61wWAgSx6bN28mEe9cXV1x165drA6Ks7Mz7tq1C7VaLU6fPh2nT5+OWq0Wd+3axTvdtmrVKhwwYABGRkbi5s2bEcA0A5KSkoK9evXCrVu3IkVRmJiYiP7+/hgSEoLbtm1jhU6OjY0lumq1WgwKCiJERgBg2VAgEGBhYSFxcp6enlhcXIxyuRw/+ugjnDBhAioUChw6dChu27aNbFXU6XS4a9cukmo4IiICHR0dcdeuXRY/pOHh4fjOO++wOg5hYWGsRrd8+XKynCIUCjE1NRVVKhUGBwezPhxffPEFjhw5ErVaLambCxcuxFGjRnHuO3ToUFy2bBlSFIVbt24l6XIdHBwwNTWVtd1ILpdjamrqK582/r0a9Osou3btshgalolBgwaxZgcpisItW7aQLbIKhcKqLWzxHeHh4axohklJSRanlHU6HS8xmW5P3UWlMxgMuHPnTt5dFOa6WkJKSgqHaLxu3TpOp2nNmjUWlxj44OLiwtHL2dkZBw8ebHFQFhcXZ1PUxz59+vSIyEm3Mfo9eXl5YXJystXzzP1/Xl4eJ3cOn/+nIRaLsaioiDeWSGxsLIsESft/8+M+/vjj33325VXBmvQohXFLSwsEBQVBXFwc7N27F7q6uqC5uZlFwKGlvLwcLl68CDk5OXDkyBFCClEqlZCTkwNHjx6F+vp6ePr0KYk61tTUBM+fP4f29nZWJLK4uDhISEggf2dkZEBAQADU19fD/v37AQAgPT0dgoKCAMBEIMnJyQG1Wg2tra3w6NEjEIlEkJ2dDVqtFtra2kAul8OoUaPgu+++IwQ/ML0xePjwIXR0dEBYWBikpqYCIsJ3330HDQ0NIJPJCDFl9OjRoNfrobKyEo4ePQoAJhJOQkIC9OnTBzo7O+Hhw4fQ1dUFiYmJEBsbC11dXdDY2AhdXV3w+PFjePz4MasMAMDX1xcyMzMBAKC5uRna29vh+fPn0NjYSMqePn3KKtNoNCCTyeD58+fQ0tIC2dnZ4OjoCAqFAjQaDXR1dcHevXuhqakJbt68CVevXoWsrCwQCoUsGyIiNDY2koh7tA6ICI8ePYK2tjZoa2uD3bt3E/IfgIm0SOv6888/Q1NTE4wYMYLoxWfDx48fc6LOlZSUQE1NDYwaNYqlK/0se/bsgUePHoFCoQCVSgVCoRCys7NBLBbDkydPQCQSgaOjI6mvbW1t4OTkBNnZ2SAUCgEAWHWOqWtrayvs2bMHOjo6ICkpCfr06QNPnjyBPXv2kFTadum5MN8xU1JSUiAyMpL8vW/fPg5ps7Gxkbz7trY2q7Yw9x18cunSJVY0wyNHjkBlZSXvsQ8ePIB9+/Zxyun21NzcDOHh4awoh7Qw24650LoajUZIS0uz+Kz79+/nkP2ampo4xEW+MqbQPlGlUgGAKf0zrdewYcMgJCQEGhoa4LvvvrNI2jx58qRNEQlPnz4NV69eBY1GAzk5OYRoZzQaIT09nXM83cbo5//Xv/4FBw8etHofpv8HMNWPlpYW1jG0/7cklurmjz/+SEiQTP9vLrRP/D8htvbuAUwj3Q8//LDbwA4eHh5kasrf3x+rq6tRqVSiTqdDrVaLPj4+WFNTgxqNBp2dncnUPU3ooq8jEAhI2bJly1h5A86fP48jRoxg3fenn34io1ulUonV1dXo5+eHGo0GdTodyuVyrKqqIj3bqKgoLC0tJaNAtVpNtvDQqZYnT56MBw8eZN1n/PjxeOrUKfT29sZbt25hbGwsOjg4sKKGrV+/npXkB8C0RY4vvj4Ter0eFQoFDhw4EEtKSnrU6zt27BiOHz8eAUzT4JWVlWg0GvH999/H/fv3E2Kdq6srqtVqDA0NxYqKih5FR6NtyLS1pS1SaWlpJBkQABAb0nb95ZdfODakER8fj7/99huL8Gc+ipo5cybu3LkTpVIpVlRUkHS0vXr1wvLycjLKUKlUmJiYiJWVlaxnFYlE6OnpiRRFcfSyZMPXAW+iWNJl165dnGnnnkChUFiNQujk5PTCBDe+eqfRaDiznNOmTWMFBesJcnNzWXFbnJ2dbVpKtAXu7u5kCU+tVmN1dTXvDAjTdzChVqt5CX5isZhF1GXCkv8HMCU0644Eqdfre5woyQ7bYbWd9qRBnz9/nsMmZ8I8AiETBw8e5Oy7pQkkPj4+iMgmEOr1ekRE3j2mPcHSpUvx+PHjVo+jyWdisRifPn3KmkY0R3BwMHZ2dpJKPnnyZBJg52VQU1PDu+TysoiJicGnT5+iWCy2asPuYG7Dhw8f4pAhQ3p0DZoEamve+Z7YkA+zZs3iJYEybXjkyBH86quvXvl7/z3wJsrv9S5yc3Oxqqqq22M2bdqE27dvf6Hr89W7BQsWsHbOvGrs27fvlZHPXpZA+Mknn3AyfgKYAns9e/aMMxDozv/bgvv371skgdrx8rDaTnvSoB0dHdHBwQHDwsKwpqaGd1Tp4uKCYrEYx48fz+oFarVaVKlU6Ovri3fv3kWNRoMajYZExXN1dWWtVdFlzNkCZ2dnrK+vJ4F5BgwYgDdv3kQAU0clOzsbAUyjwbt376K/vz8qlUrennZ0dDRWVVWRmQEHBweytu3i4oIikQinTp2KR48eRaFQiFVVVWQtWygUsp5XLpejs7MzUhSFt2/fxqSkJBw9ejT+/PPP5H7r1q3Dr776CvV6PdbX15Me97Bhw0hqVp1OZ3GknZSUhLdv3+as5zk4OGBdXR0GBgbiRx99xMrsdezYMXz33XdRJBKR+9E2BDCNjqurq8m6v9FoxNraWlQoFLhz506cN28eBgQEYF1dHXmPTP6DTqdDiUSCmZmZrAhcq1evxq+//hpdXV2xvr6eFTSFoihi16VLl7ICTgEALly4ELds2cIq47Ph7NmzLY7GxGIx1tTUYEREBCoUCt6RIdOGWq0Wp06dimfOnGHZ8I9uvC/SoF9H6amO5r7DEuj8Id0do1arWev6v/76K/7jH/+w6Tn46p1SqewRmdTcdwCYfFt5eTkvWZL2ibZe38XFBevr63lnSJydnS36k7i4OCwrK+t2JxLTJzIhEol4AyHRz/Oi/BqdTmfTTGVPbAhgCjRXUVFBdLXkO8LDw7GmpsZipMxdu3bZHOjI3P+/DrDaTl+kQTs6OuKIESNQIBDg3LlzMTMzE729vXHz5s3EmIGBgbzkPgcHBxw5ciSKxWKcMWMGTpgwgfy2atUqjI6O5pzzzjvv4NSpU1EqleLIkSPJVJKHhwdJOjJ48GASDUwkEuHIkSN5G9Xnn3+OQ4YMQZ1Oh8OHD0eKovDTTz8lPVKhUIgbN27EgIAADA4OxvHjx+PmzZtJ1L7Ro0d3+8LT0tJQr9ejn58fK4xobGws9u7dG+VyOY4cOZI0Uh8fH06EPQBTh2bLli3kI67X61kJVnJzc3HmzJlEV7VajeHh4ZiYmIgSiQQ3bdqE48ePJyQYcxgMBqIX7VA1Gg2OGDEChUIhJiYmYnh4OKpUKhw5ciSLVOfq6opbtmwhnQqDwcCaIejbty/GxMSgTCYjutI2ZD5DdHQ0WWbZsmULurq6YmRkJEk/umXLFtbMkFQqxc2bN6O3tzf26tULJ06ciBs2bCDs54yMDJw/fz4KBALMyMhgfSgEAgFu2LABjUYjDho0iLPNKyAgAN9++23csmULjh49GvV6PSYmJr52ecnfROmpjny+o3///rh06VKL51jyHeYYMmQImSpn1jvz47744guy2+llQFEUDh8+nDPdnp6eTpYW3d3dccuWLRanyKdNm8ZKwsUE3cbkcjlOnDiRk3CMhkKhwC1btpB7urm5/VtG4RRF4dq1awlJt6dYsWIFK525uQ2ZePvtt3mXnVxdXYmvBzAtJdKEbyacnJwwIyPDYgeJ9onm5cnJybxpkvnCSP+RsCY2EwhHjhwJTk5OEBISAn/7299g27ZtnFSPtCQmJoJCoWCRO+Lj4yEyMhJaW1uhsLAQBg8eDB4eHhbvJ5PJYOTIkSCTyWx6vu+++w7KysoAwBRFrLCwsFviyIMHD2DHjh3dRji7fv06HDx4EBARioqKOOSm9PR0cHNzg4CAAEhJSQEAgOLiYvjLX/4CTk5O8N133wEAQFpaGlRUVLBIS7RUV1fD7t27AQAgNTUVDAYD77PU1dVBcXExp5zWtW/fvvDkyRM4fvw4+e3AgQOEBCMQCCAjI4OVrrSzsxN27NgBDQ0NYDQa4T/+4z9g+/btkJaWBteuXYO2tjaIjY2FwsJCGDp0KLi7u/M+W2VlJezduxcATGSkqqoqOHv2LDx9+hQKCwsJAdBczp8/D+Xl5fD3v/+d9/fu5PLly3Ds2DHe37q6umD79u3w17/+FUJDQzm/G41GYi9aSktLCaGqqKgI/vKXv0B0dHSPn8su/NK/f39eW/DJrVu3WL6jp9Kd79i7d69FsuDLCt3GnJ2dAQBAq9VCRkYGFBcXs6LqAZjqmKUIegAADg4OMHLkSBLRlClDhw4lkQrpNsZH4u5O6uvroaioCABMRE6afG0uzs7OkJGRAQKB6VMREhICAwYM4Byn0WhgxIgRhKT7e0pPbXjv3j3i6wcOHAjt7e2E8M2Uhw8fwvbt28l3LTg4GJKTk4GiKBg+fDhcvXqVN/Uxn5SXlxP//8aIrb17eo96fn4+Hjp0qNseSFFREX788cesnllxcTErecWNGzdIqluBQIBubm5IURRqNBpC5rt79y6GhISQUbSzszPevXuXtUxw48YN1r0lEgmrp69SqThTXZam+pjTX05OTpx98TSEQiHq9Xoy1ff222+zYg9s3LgRFy1aRPS6efMmGeno9Xq8e/cuGSnIZDLy/0uXLmFGRgZKpVLOSEIikZAymhDJ/P3kyZM4Y8YMll7Ozs5k9C6RSLCmpgYTExNZcQFcXV1RLBbj5MmT8fjx42QKs0+fPoQsSVEUlpaWkrVAoVBI7EXrQPM9rl27hoMGDWLpBQBkWYj+m14SGTBgAN66dQvd3NxQKBTiggULsKioyKoNmXB1dUWRSIQODg6sJYHvv/+eNVKgdc3LyyOkLVdXV95pQdqG3dXzPwJvogAAHjhwwGK0QZFI1OO0vVqtlkQX5asPd+/etTiV/apA1zv6b7FYjNXV1SSiZ0hICNbU1BD/pVAorC5r0PD29sa7d+/y1vsrV67wzia+KE6ePIl5eXm8v0VGRmJ1dTWZ+rfk/41GI965c+eFCIBM/9/dcRRFoZubm9Uga/RxfPESTpw4wRuPhQ/MZeLKykqb4wx0Vzf/SFhtpz1p0C8CiqKwpaWl2xz3TAKhecCa2tpazMzMtPl+ycnJ2NzcTCpWT0hAzAiEZ8+etbg/1ZxAaAkGgwERsVsHMHLkSJKamUZaWho+ePCAVZaSkoJNTU1kTZuvQi9atAh/+OEH8vfly5c54ZOZBEI6XTMzkY8t8PPzQ0QkjoqPyDV69Gisra0lf5sTuaqrq0kaakdHR0REst/XFhvSkMlk+Pz5cwwPD+dEIGRCJBJhW1sbK9CLQCDAR48e8U4Zvq54E8WaTq8q8uW/E8x6Z+s5tkS+/DOCj0DOB1vJx2q1Gru6ul55FElb8SpJoK8SVtuprQ26pqaGlSVLJBJhaWkpCbBjNBqxqqqKt2fo4eGBO3bswPnz57O2Fq5ZswYXLVrE2lro5OTE6g1b2m6yePFiXLNmDWo0GqypqSEx/6VSKSvzn63bgwDYW2ZcXFws9u6Y29KY5RRF4fXr10lQIvMtk8OHD+d8rORyOVkXv3DhAqanp6NMJiNlZ86cwczMTJTJZEQv5pYhBwcHrKqqwoCAAI5erq6uqFKpMCIiAsvKylAsFpOthfQx9DbKiRMn4uHDh1EgEOCtW7dIL9h8exCtF1N/8y1eK1euxG+++Ya13m++xcvd3Z04f4qi0NPTk/TkbbEhE56enigWi8mMklgsxrKyMo6j9vT0xL179+Ls2bNZdZPmuRgMBqyurmZxTby8vLCmpuaVbff6vRv06yjWdKLbHV2fxo4dS9KJWwK9Ldnatc23JVvCJ598womaCWDaiXPr1i3e0Shd72y1nVKpJCPbGzdu8BIIe7KlNTU11Wq49AULFpDsqT3F+++/j7t37+7xedu2beME/wGwvLWQb2s5H+htycwRv7n/B/j//oQ5a8MH823JtiIgIACrqqosdl5f5fbQVwmr7dTWBp2dnc0a4VIUhaNHjyYfH61Wi1lZWcQAAwYMYMWs7t+/P/bu3RuVSiVmZ2ejRCLBxMREi6NSjUaD69atI1PLUVFRLDJX3759cdq0afjtt99idnY27yj9s88+w/79+6PRaMSVK1cSZzNixAhWSuS5c+d2O+0mEAhw1apVJEaBh4cHrl27lpf5mpmZydmDu2zZMoyIiEB/f38cPnw4AphGu+bbFzMyMjjRsNLT00n0NrlcjmvXrmVNf4pEIszKyiLvKSgoCFetWsVJ+jR69GhWma+vL65evZo4s9DQUExNTUWKonDUqFHk465WqzErKwvFYjFOnz4ds7Ky0MXFBdetW8dqDE5OTrhu3TpUKpWYkJCAsbGxVm0IYGLw8qV67Q7z5s3jJaempaXhnDlzUCAQ4OjRo3k7EAMHDiS7J2i89957OHr0aFSpVERXAFO8g4KCAszOzu5RPIY/skG/jtJTHY1GIw4bNgwpisKCggKbotktW7aMY1cAE3GXGW3TEmJiYnh3kLi7u+OoUaOsTmH3FJmZmbzbpvv168eajqZ9B981DAYDJ1ZHZGQkq43FxMRwZr7GjBljU4KoiIgIm0IhDxo0iDWLmpyczGuLoKAgFgnaVkyYMMHi1L5EIrHo/61BKBTimDFj0MnJifgOW87TaDSsb92bAmsiAhtl/fr1EBcXB/X19XD37l2Ii4uDzZs3Q1dXFwQEBICXlxds2LABBg0aBOfOnQOpVApqtZqcf/jwYQAAUCgU0NjYCIhIyG5yuZyQUuh0twKBALRaLSGuSCQSEvlvwIAB8Ntvv0F7ezuEh4fD+vXrAQAgLCwMFAoFSUWsVqtBKpWCSCQCrVYLFEUBIoJcLgelUkmeTaVS8ZKNAgMDwcPDA06ePAlarRZEIhH4+/tDbGwsuR4AgMFgAH9/fzh69Chs2bIFevfuDTqdjqQY1Wq1IBaLoaysjJAcNRoNicwllUph4MCBsHfvXg7ZrqWlhURcoygKHB0dQSgUQmhoKCiVSjh79ixs2LABEhMTobq6muhKi9FoBJ1OB//85z9Z1xUKheS42NhYePDgAezZswcAADZt2gQAAH5+fuDr6wsbNmwAAFP0yEePHnFsAwCsshMnTgAAgKOjo1UbikQiVj0xtyEAQGhoKKjVapKKWKPRQGRkJDx69AhOnz4NgwYNgpMnT4JMJgOVSgVdXV0sfdVqNcTHx8O+ffvgwIEDRJf/+q//gn379oFSqYSQkBCoq6sjutJ2EQgEpH7Z5d8jN27cgBs3bgBFUaDRaEAksu6mLB1nKbWsm5sbREREEKIis74BAPTr1w8qKiqgvLyctAdrQlEUDBo0CH766ScSGZQuO3PmDCuC3ZYtW3ivYU5so30Hn1RWVnKIdGKxmNX+zfUCAI7/syQXL160egyAibDJbMOWogfevHnTpgiGXl5eEBISQq6jUCgsvoNnz57B+vXrISkpCUpLSznvw9x3MKWzsxM2btxIdKCjM1qT5uZmlp/gE19fXwgMDCTfvTdCetK7pxNVmCcqevfdd/H06dOo0+l4k404OjqiWCxGqVSKvXr14iQboRNVNDQ04JAhQzhJKcyT3JSUlOC4ceM4vUE6yQ39t6VEHXK5nDUVrFarUSaTcRLa5Ofns9bgAcBqoiJHR0csKirCJUuW8CZgosucnJwIcc3FxQUbGhpQr9dzdP31118xKyuLo8OCBQtwz549FhMV0ZEd6URFzDLza+3fvx8//PBD1rkikYgkmzI/ngnzREW2JowpKSkhXBBmcpgVK1bggQMHWGWffPIJma6kdaATFVlKGMPUNTQ0FOvr61l7roOCgvD+/fvk2a0lm3pd8CYK/ex04q8X1d1aQjPzOsbnO2hYS3Jz7tw53qh83UEsFmNdXR1ZOgXoPsmNSqVi1THaT9qi16sCMykTM/EZDfOyl7EhM1EdANd30OBLVGQNzGRTTDB9Bw1z/98T8CU5M9cLwLQk8iIZb39PWG2nPW3QlkCTgMyNS1EUNjc344ABAyymMDaHOfmML/3tmjVrcM+ePd1eh5nCmAlzAuHFixfxww8/5KQw7ikoisKmpiYytWYwGLCrq4u1Vu7l5UXeKZ0ciYn169djUVGRTffr378/bwpjuVyOz58/Z+3tlUgk2N7ezjt9x0RPo4iZE7leJJUsM4Ux3RngSyVrSxpqALApiqQ1vKlpSF9HoZ/9ww8/ZIWo7in8/Pywq6urW66ALb7jdcHhw4dJ5Etz32EOW9Of9wRKpRI7OjpILJK4uDhsbW1lDRiY0Utf1obm/v+PIoG+TBRJPhLoO++880oi0P7esNpObW3QZWVlGBoaipMnT8Y9e/agSCTC69evk/UsiUSCBoOB9Npzc3Px8OHDCGD6KMrlclSr1YQJevHiRc5a1oULF3DgwIGoVCpZhBBXV1fONiEXFxd0c3NDjUaDZWVlLIapUqnE0tJSjIuLI71YuVyOt2/fxsDAQHRycmIR1Dw9PXHu3Ln4/fffk2AWRUVFmJ+fj0ajEW/dusWZYRAIBHjlyhXs06cPZmVl4dGjR1m6Aph61X5+fvjrr79i//79EcD0sfXz80M/Pz9Ox+n06dM4fvx4dHNzQ0dHRywrKyP8g4SEBLx06RJrVCSXy3mDb1AUhQaDASUSCU6fPh137NhBno3WIyQkBG/evIkSiQS3b9/OWkM0GAxkFO3n54elpaWsZ/X29saysjLUaDTo5ubGIi36+vqig4MDDhs2jBXKdMmSJbh27Vr09fVlNUqa3+Dn54cikQh1Oh26u7sjRVHo5+fHGinRetlC2mLqaglMG9LnlJaWokqlQnd3d5uyyr1uDfp1FPrZtVqtxZj2tC26275Ft6fuiGbWfMepU6deONjO/v37yWyBVCrFmzdvYnBwsM3njx07lpXrRK/Xs3hYTN/h6emJZWVlZDbAXC8AU6C1c+fOce5j7jsswbw9yWQyjj+RSqWssu5saA1M/w8AHN/BhzNnzmBqaiqnnPYdL/Ic5v7fEiZOnMiJVKjRaNDLy6tbvV5XWG2ntjbovLw8dHFxwcjISExPT0eBQIDjxo2zuD84NDQU//GPfyBFUbhkyRI0Go0YFxdHiCa5ubmsDwMAYE5ODiHQqdVqXLFiBarVaszNzSXLAitWrGCNDKRSKebl5bGmfcRiMU6YMIE1dSMSiXDChAmELDJ9+nTy28yZM3HOnDkscktaWhpGR0ejs7Mzjh8/nvSWk5KSyJT622+/jZ6enhgaGsra/jhp0iRW6tycnBzSoLRaLa5YsYI8b2RkJGEPjxkzhoyEZTIZ5uXl4dKlSzE2NhZ9fHxY02AjR47EKVOmoFQqxa+//hrd3d0xPT2dE4EsOjqal7Sj0+mIXsOGDcPevXujh4cHfv3112RmJDExEb/88kucMGEC6wOsUqkwLy8Ply1bhqGhodinTx9O/vKAgADW8ka/fv1Ih4hGQkICDhw4EBUKBS5fvrzHH19/f3/86quvOMTIZcuWEXLPwIEDWWTR/Px81seAtiHd0PPy8l54Zuh1aNCvo6xYsYLT1s1BURSOHTuWOGk3Nzdcvny5xXC65vj8889t2uY3evRoThreefPm8TL7zTFixAhyD6FQiOPHj+9Rne3VqxfvbCAf6DbWXYfW39+fhGBngvYdLzoV3lMkJyfjBx98QP7Oz88nROmXRVZWFm/6a9p3dHfuRx99RHZ2SSQS/Prrr7vtBJj7joiIiFemx+sAa2JzBMKKigqSHvTOnTvQ1dUF33zzDRgMBggMDASlUgkDBgwgEaiuXr0KW7duBQATmUKhUIBarQYvLy8AAFi7di1UVVWBp6cnxMfHAwDAunXroLy8HAAARCIRvPXWWyASicDFxQVcXV1BKBTCW2+9xYpy1d7eDgUFBaxog4gIlZWVrFSnHR0dsHLlSnj48CFotVrQ6/XkNw8PD7h16xYrwl9dXR08fPgQGhoaYNWqVdDZ2QnR0dHwt7/9DTw9PQEAoKqqCp48eQJXr15lEYLc3NxAp9ORv9etWwdarRZCQ0OJXrQOSqWSRBPbuHEjSCQSCA8Ph6dPn0JBQQFotVpQqVRQXV0Na9euJdd0cnICd3d3EAgE8NZbb0FiYiJERERASEgI9O/fn5Abz58/z9IrJiYGAgIC4MGDB0SvXbt2wblz50AikYDBYCDnajQacHBwgJUrV5J36e3tDX/961+hoKAA3N3dwcHBAZRKJXh7ewOAKfqkm5sblJaWEpJNQkICXL9+nUOmefDgAdy7d4/oQBPAdDod9OvXDwBM5EZfX1/WeX379gWDwQBSqRTeeustSE5OJuQfqVTK0kGr1bIiXXp4eLCiMK5evRr0ej0YjUZobm6GgoIC+M///E9iP7lcDsnJyRYJTHaxLm+99RZIpVLe3wwGA/Tt2xcQEb799luora0FABMRjmlHc4mIiGBFNPTx8bGJFPfPf/6TQ2Lz8vIixNbuZNu2bSQCXWdnJ6xatYoTlbQ7uXz5MhQWFlr8vV+/fqTePXr0CAoKCqC9vd3i8WVlZYTcmpCQQOo57TuYPlEkEkFycjIoFArOdWJjYy1GPrVFNBoN8YkAAO7u7iQKIy1RUVEQHBxs9Vrh4eEsu27YsAFKS0s5x504cYKQgWkJDg5mpcT28vIixEaKosBgMHTbjs19x8WLF2HHjh3k9+joaIuRGs3Fy8sL4uLibDr2tRFbe/ctLS0YERGB77//Pivl5uHDh/Gjjz7CkJAQbGxsRIVCgQqFwuoUrVKpRJFIhGPGjCFrMHQZgGmkoFKpkKIolMlkrFgDSqWSM1Xs4OBAylQqFTY1NZEepVAo5PSS+coEAgEpO3r0KM6ePZtVtn//fjICFggEWFtbS9bWmcfRYOqwefNmXL16NSEzWdJ15cqVnP3OTP3p48zf5/nz53Hy5MnYp08frK+vZ637MXU9ceIEzpw5k/W8fPaiy8z1Gjt2LF65coWUyeVy1uitvLwcU1NTUSQSEV1v3bqFGRkZHF2XL1+OW7du5dgwKSkJa2pqkKIo/OWXXzAvL4+lw9mzZ0meA5lMhg0NDRgWFoYSiYQQsugEWJZswyzbs2cPLly4kDxfTU0NCZLl4+ODzc3NL5wG91XjTZTu9MnPz+fNjMeEXC7n1M+tW7f2eEuqNQiFQg7ZkM9P9BQqlYqXuMtsd+b1rqe4ceMGJ3kP0ydqNBpsbm7mbF0G+P++w7xcKpW+MhJtcXExLl68mGNX85mfjRs3vvD0/5IlS3Dnzp09tg3fshOfT2T6f3O7mvuYnJwckoDudYHVdmprg2Z+gKz9/+jRo/jFF190+2APHjwgiRzo8+rr68mUtl6vx66uLtTr9bhx40YsLCwk51ZVVZHodTRKS0tZSY+Yz5WQkICPHj1iNci+fftia2sra69oVFQUPn36FCUSCSupRXt7O8rlcqQoyqLuAQEB2NHRwVqa8PX1xc7OTsKAzs7OxurqagQwRVakpwzd3Nywq6sLPTw8OPcAMAXx2bt3L2o0Guzo6OCdNrP0XAAmEtCTJ09QLBaT3yIiIrC9vR2lUimeOnUKP/nkE9Y5tA2DgoKwo6ODVdH9/f2xs7MTHR0dcffu3fjtt99y7j1q1Ci8c+cO53lcXFyws7MTvby8OLoybUiX0/8ybcgX7AnANC147tw5ElmRJhAybUifYzQa8fnz56hUKru1K9/fr3ODfh3Fmk7W3u+BAwc48Sn42snLIjk5GRsbG636jp5AJBJha2sr7zLEoUOHWMmXXkYfvnPLy8tx7NixVq9vqXz+/Pk2ZY+09fnM72PuO17Wrj09VyaTYXt7O+/y0unTpzkRaJnXFwgE2NzcTHhvfP7/dfIbAL/DboKJEydiUVERikQivHz5MoaHh+O4ceNY2ze8vb0JaYeiKPz555+xb9++mJmZSUiFAQEBnF44s0woFGJQUBDJA5Cbm0u29Pn7+6NGo8HExERCoPHz87O49UahULBY6dOnT8fi4mJOuEqZTIaBgYHEiHl5ebh//34MCgpiGdbf3x+vX7/O6jWLxWIMCgpCgUCAK1euxI8//hhFIhEajUa8cOEC9uvXD9VqNcmsGBAQQD6wtK50x6R///6s0ZKbmxuJ0BUUFIRisRhnz55tkYkrk8nw2rVrZG2U1ot5jFQqJbr6+PhwiDy0DSUSCRqNRrx48SLJHsbU1dPTk5M+denSpVhQUEB0BTCt6X755ZdE11OnTnG4DHw23L9/P44ZM4ZjQz7odDqyNh0YGEjWTvlsKJFISNnatWvxgw8+QF9fX/ztt99eKIDJ69KgX0cx9x3d6bd+/XpOCG0vL68e5y5gIjk5mbMdmOk7aDg4OHA62pbqnVQqxStXrvBmBS0sLGRl6AwMDOSNosr0k9YwbNgwTlTGlJSUblnxfn5+nC1v3SEqKgovXbpEOj46nQ59fHxQLBbjlStXWLt4mP7/Re3C5zvMcfLkSd7sf5988gkuX77c6j1mzpxpMQIjRVEYFBTEO4vt6+trlQ8SEBBAvgFMn/ii7+P3htV22tMGHRMTg6NGjUKBQIBTpkxBvV6PUVFROGbMGKQoCj/77DNOg5o0aRL6+vpir169OHtBo6OjSXjYjz/+GMPCwjAyMpKVN3rUqFE4b948TjINPz8/ktpz1qxZ2KdPHwwKCiKj3P/5n//h7ZHHxsayiDz5+flkek4oFOLChQvRx8cHo6OjeVMWOzk54dSpU0nSG/OP2rBhw1jRzCZOnMj6MNIYM2YMjhkzhreSMVOWZmRksHr4M2fOxNmzZ2N6ejpKpVJctGgRcZZ+fn64ePFizM/Px88++4yzY2Pq1KmYnJyMAKbe7YIFCwi5Ua/X46JFi3gJdLQN+/TpwyLk0dBqtbh48WJ0cHDAwYMH48CBA1GlUuEXX3yBKpUKU1JScPDgwejg4IBffPEFzpgxA0NCQjAkJIT0wGkbMq+bk5OD4eHhGBAQgJ999pnF3jbThub1i2nDSZMmcdjJ6enpmJiYiFqtFvPz81EikeDYsWPJ0sbrhDdRzH1Hd/plZGTYFDHQ3Hd0h4CAgG59x4tAKBTiu+++y8uGz8zM5KTdfVkEBwfjuHHjWGVBQUGs2dDuIJPJcPHixVbDek+ePJnTxmhdmR0ypv9/1XWc6f8tpWEfMGCATcmaEhISWO14xowZhFT4KqHT6XDx4sVk2SMmJoZFqnwdYLWd9rRBdweBQIAHDhwgvUW5XI7x8fGkp+nh4cHa+x0aGor5+flkCWDXrl0YFxeH/fv3Z40gPvjgA/zoo49QLBZjfHw8SqVSDAoKYvVUN2/ejKmpqRgdHY379u1DiqJw48aNmJ6ejlqtlnQKoqKi0NvbG9VqNcbGxiJFUfjNN98QVq5IJMLDhw/jqFGjONtswsPDOWtuixcvxilTprB0DQ0NxcDAQJRKpRgfH09G/G5ubmTrVExMDC5btowV7IcJkUhEdJ0+fTor4+OmTZtw2LBh6OjoiAMGDMDjx4+TEXFYWBgePHgQBQIBrl69muN8CwoKSIdMKBTioUOHMCQkBL29vXHEiBF47Ngx1jqeuQ2HDh2KmzdvRgDTUgu9NcrV1RVPnDiBGo0Gg4OD0Wg0opOTE544cYK13q7VavHEiRPo4uKCQUFBOH78eLJ9h7ahpUZN74HuzoYqlQrj4uJYDs3BwQHj4uJQIBDg0qVLeT8Cnp6eGB0dTfRatWoVa8fJ64I3UV6F3ua+A8A0MmYuH74MAgICbAp7TEMgEGBcXNxLzyIx6x0NvV7/UjEy+ODg4IDHjx9/4W2BtlyfbmM9Oc/d3Z2zndTc/9Ng+n9L13N0dOy2I7Z27VpO4rvIyEiLu13M9fLy8uKN1eLl5YXHjx8nW7AHDx7M4kO9DrDaTm1t0AqFgkXIoqdHpFKpxX3fgYGB+OjRI3R1dSVbEZmkiu3bt+M333zDmUITCoWsMp1rqzoAAAwoSURBVIlEghKJBHU6Hba2tqKnpycWFBSwog2aQ6FQkA9CXFwc3r9/HwUCAV64cAHz8/MxJiYGGxoayIdaLBazPoInTpzgxKo+ePAgfv755+Q4uVxO3om/vz8+evQI1Wo1FhUV4bJly9DHxwdbW1vJx3DUqFEkOEVFRQXvthWJRIJSqRSdnZ2xtbWV7C02fycApqnO+vp6VgMUCASs45h6WbIhgCmYBnPalLYrbUMmZ4CiKHRwcMA7d+5gSkoK59k2bdrEWsKQy+WoUCg4Mw4FBQW4c+dOcq5MJmNxOHpqQwATF6KxsZHcSyQSYXR0NDY3N6NMJiN6URTF0j8vLw8vX76MFEVhXV0dmT0xP+6PxpsolnTpzneYw9x3WAJfO+ErM8fSpUtx165dnHpnCTKZDJubm0nyNvP2xFd3RCIRhzBH1ztmWW5urtUIfLRPtPY+aH9iXs7nJywdZ6n+M/1JSEgItrS0dNtW+PTPysripKFnQi6Xs5YtaP9v6fikpCSsq6sj9rOkFxOnT5/mLE3RCAsLI74DAHDKlCkvFTjrj4TVdmprg+7o6CA9ovDwcHzy5AlKpVI8efIkZ485E2KxGB8+fIj9+/dHiqJYRByBQIBvv/02lpeXs87JzMxkRdvasGED6WUx2ZuWeqFqtRqfPXvGWuujz2MS0JjPMmfOHNY6vVAo5FxfIBDgtGnTsKSkBAUCATY1NbGmnPiejXkPpv58RDgAU8+V7uQwz01JScEHDx5wiG7mxKbY2FhsaWkh5fPmzcNTp06hRCLBtrY2XhvyXevIkSOE/Wt+D4PBgM+fP0edTocURWFOTg7Lhua2qampwY6ODk5PWSAQ4NChQ/H+/fsIYNp1QE97vogNzY8DMC2p/PLLL6Ts2LFjuHDhQjQajdje3k5GdpZs4+vri8+fP7fvJngJsaSLNd/BBF9d54O57wAwLQMxoxLyga6zfPXOEpjPExUVhW1tbawPdK9evfDJkyfko5ufn48lJSVW9bJF102bNuGmTZusPmNBQQFvpNakpCRsbGwk7XThwoV47NgxznExMTHY2trK6bSJRCJ89OgRa1Rv7Znz8vI4kfqs6VpTU8MayVu7h/n1lixZgkeOHLFqx+46f5Z8+JsGq+3U1gYdFhZGKnVERAQ+e/YMZTIZBgQEEBKIQCDAs2fPcqZRQkNDOdNpJ0+exPj4eHR0dCRrQidOnMCkpCTUarWsyF4+Pj7o6+uLjo6OePnyZbJ2FR8fz9rmOGvWLFy+fDkKBAIMCwvDAwcOcKaEduzYQdahz58/Twzr5uaGAQEBKBKJ8MKFCxgaGopjx47FwsJCFAqFeO7cOYyIiCDT20y9MjMzeRuch4cHXrp0CTUaDS5ZsoTDTqXh7OyMly9fRldXV/T29mZNWX3//feYlpaGKpWKpNrcu3cvJ3jJzp07MSsrCx0cHFjTne7u7hgQEIAURWFoaCixoVwux9DQUIuNwM/PjxOgY8WKFThz5kyUSCQYFhZG3h3ThjQGDRpEGmFwcDCGhYXh2LFj8fjx46zj1Go10ctoNJJlB9qGtmYL3Lp1K+/6qaurK8ux+/v7o16vR6lUimFhYd1Oa6alpeGRI0dYuv7ReBPl8uXLvGlimb6DiVGjRpFROh9o38H3m7nvAACydMUsM/cdNMzrXVRUFJ49e9bq9Ddfe5LJZBgWFkbKmL7jZeHj48OJSDhv3jzOLi4vLy/eKKVKpZLlJ/R6PS+vSaFQWPQToaGh3c4EGAwG/PXXX8nUubOzM+/6f3cIDg7mJYZ//PHHrJ0YluDh4cG7ndIaNm7ciO+8884rsdXrAmtic9ChkpISePLkCQAA1NbWwuzZs+H58+dQWloKQUFBMGXKFEBE2LFjB9y7dw8ATMFj5s+fD7dv34bHjx9D7969Yfr06QAAUFxcDHfu3AE/Pz9ITU0lZTU1NdDU1ATXr18n946JiYE+ffpAe3s7bN26Fdra2gAA4M6dO6yAOr/88gv88MMP0NXVBSUlJVBcXAy3b98GAFMQk3nz5sGvv/4KN27cgAcPHkBxcTHMmzcP9Ho91NfXQ2lpKXR1dcH27dvh4cOHcP36dThw4ADRKy0tDaKioqCxsRHmz58Pt27dgsePH8OtW7dg3759AADw4YcfksAUra2tUFhYCM+ePYMff/wRzp07R5515syZEBYWBgCmICFbt26FyZMng7OzMzg4OMCsWbMAAGDv3r1QUVEBHh4e8Pe//x0AAPbt20cCcUgkEpg/fz788ssvcOPGDWhtbYWrV68CAMDkyZMhKCgISktLARHh6tWr8OTJE0hMTIT//u//hqtXrwIiAgBAfHw8TJkyhTxfeXk5GAwGyM/PBwCAOXPmQG1tLVy8eBGePXsGJSUl0NnZCWPGjIGEhAS4ceMGAADMmjULQkJCoLKyEnbv3g0AANevX4eSkhL48ccf4dChQ/DJJ5+QIC8tLS1QXl4O8+fPh/r6eoiNjYWcnBxiQ0tBVwYOHAgTJkwgfx88eBD0ej1MnDiRddy9e/fg1q1b5O+ysjKoq6uD9vZ2KCkpga6uLvKbs7MzzJ8/nwTIqaiogKKiIqKrXV5Mtm7dCg8fPuSUl5aWwt27dznlN2/ehO+//97i9WjfwSfmvgPAlGXOvMzcd9BiXu/u3bsHO3bsIO3EktDBx5jHPX36FEpKSkjZ/fv3WQGPcnNzYfDgweRvpu9gyujRo2H48OGssurqaqiurmaVnT9/Hk6dOsUq+9e//gWVlZUgk8lg/vz5JBjQ48ePiZ8AMAVZozOqMqWtrY2jFy1Xr14lvphPWlpaoLCwkAQsa2hoIH6ClsjISHjvvfdYZeHh4TBz5kwAMPmOpqYmzrXPnTvH0nX69OkQHR3NOa62tpYEsuuJHDlyBK5cucL7m6urK8yfPx8kEonF86OiomDGjBk9vu8fKrb27uF/e4LmcZkBTOs+zEA5RqMRfX190dfXF0+fPo0KhQKNRiNOmzaNEEMiIyNRq9XikCFDeEcBYrEYe/fujWKxGOfOncuaTgwPDycsXqFQiL179yY9eY1Gw0vwkMlk+NNPP6Gfnx96e3tjcHAwSiQSPHXqFGvkSFEURkdH8xKDVqxYgVOmTEGDwYCnT58mo2w3Nzfs1asXUhSFR44c4TDi+fD999/jxIkTWTsv9u7diykpKZiQkEAyDdLo06cP2ZbJhEKhwNOnT5Pev0qlIvpv3LiR5PqOjo5m5Y1Yt25dtzYEMIU8LiwsRIqi8OjRo9i7d290c3NjbSf6/PPPWbsLDhw4gImJiajT6UjeioiICDLi12q1ePbsWdaWKpVKhWfOnEEPDw9877338MsvvyS/9erVC93d3VGtVrPsOmnSJFy5ciXreceNG4dr1qxhlXl4eHCIYXq9npXEia6v3t7eeObMGVYeBolEgr17935tcpe/ifLveC+RkZFkKYfpOwBMu3/outgdLPkOPtB+4mX4JMuWLSPhw7vzHQsWLLBp10R3UCqVeObMGV7/HRYWZtOuAKb/pygKo6KiOH4yKiqqR1kN+fz/wIEDOTkBrNmwqKiIN+w6nw7muoaGhlolVkZFRRHelLn/58PQoUOxuLj4pWz2qmG1nfakQZ8/fx7nzp1r9abHjx/HJUuWsMoOHjzIihj28OFDHDJkiMVr6PV6RETeSlpdXU2CDjk6OiIikqmg5ORkbG5u7nYNqLusVWKxGNvb23vE5p08efILZa3aunXrK2ecxsXFYVtbGyfzWHt7u81kre7wIpkna2trOcs1tuLmzZs4ZcoU1m6Cnpw/a9YsTipR88xjR44cIdnjzGEwGBARWQll/ki8ifLveC/3798nOSfMfcfw4cPx3r17Vq9hi++gIZPJ8Pnz5y+1z/51wbVr13DatGlWj2P6fzqwF7PzIhQKsbW11eaMpz2BrTa0hitXruB7773HKqOz1lo6RyQSYVtbG8bFxf3htnoZWBPqfxurXexiF7vYxS52+ZOKzZwBu9jFLnaxi13s8n9T7J0Bu9jFLnaxi13+5GLvDNjFLnaxi13s8icXe2fALnaxi13sYpc/udg7A3axi13sYhe7/MnF3hmwi13sYhe72OVPLvbOgF3sYhe72MUuf3KxdwbsYhe72MUudvmTi70zYBe72MUudrHLn1z+H453yAIBdV40AAAAAElFTkSuQmCC"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sp_r5 = np.load(Path(cc359_data_dir)/\"calgary-campinas_version-1.0\"/\"CC359\"/\"poisson_sampling\"/\"R5_218x180.npy\")\n",
+ "sp_r10 = np.load(Path(cc359_data_dir)/\"calgary-campinas_version-1.0\"/\"CC359\"/\"poisson_sampling\"/\"R10_218x180.npy\")\n",
+ "\n",
+ "print(\"We provide 100 sampling patterns for R=5 and R=10\")\n",
+ "print(sp_r5.shape)\n",
+ "print(\"Average sampling rate:\",sp_r5[:,:,:Nz_sampled].size/sp_r5[:,:,:Nz_sampled].sum())\n",
+ "print(sp_r10.shape)\n",
+ "print(\"Average sampling rate:\",sp_r10[:,:,:Nz_sampled].size/sp_r10[:,:,:Nz_sampled].sum())\n",
+ "\n",
+ "plt.figure()\n",
+ "plt.subplot(121)\n",
+ "plt.imshow(sp_r5[0],cmap =\"gray\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.title(\"R=5\")\n",
+ "plt.subplot(122)\n",
+ "plt.imshow(sp_r10[0],cmap =\"gray\")\n",
+ "plt.title(\"R=10\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-10-08T17:53:58.547671Z",
+ "end_time": "2023-10-08T17:54:00.889786Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ "\n",
+ "Channel-wise k-space\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Channel-wise images\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Zero-filled root sum of squares reconstruction\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sample_kspace_r5 = sample_kspace.copy()\n",
+ "sample_kspace_r5 = sample_kspace_r5 * np.expand_dims(sp_r5[0, :, :], -1)\n",
+ "\n",
+ "# Displaying channels' k-spaces\n",
+ "print(\"\\n\\nChannel-wise k-space\") \n",
+ "\n",
+ "plt.figure(figsize = (8,6),dpi = 150)\n",
+ "gs1 = gridspec.GridSpec(3, 4)\n",
+ "gs1.update(wspace=0.002, hspace=0.1)\n",
+ "\n",
+ "for ii in range(12):\n",
+ " plt.subplot(gs1[ii])\n",
+ " plt.imshow(np.log(1+np.abs(sample_kspace_r5[:,:,ii])),cmap = \"gray\")\n",
+ " plt.axis(\"off\")\n",
+ "plt.show()\n",
+ "\n",
+ "# Displaying channels' images\n",
+ "print(\"Channel-wise images\") \n",
+ "sample_rec_train_r5 = np.fft.ifft2(sample_kspace_r5,axes = (0,1))\n",
+ "\n",
+ "plt.figure(figsize = (8,6),dpi = 150)\n",
+ "gs1 = gridspec.GridSpec(3, 4)\n",
+ "gs1.update(wspace=0.002, hspace=0.1)\n",
+ "\n",
+ "for ii in range(12):\n",
+ " plt.subplot(gs1[ii])\n",
+ " plt.imshow(np.abs(sample_rec_train_r5[:,:,ii]),cmap = \"gray\")\n",
+ " plt.axis(\"off\")\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"Zero-filled root sum of squares reconstruction\")\n",
+ "rss2 = np.abs(np.sqrt(np.sum(sample_rec_train_r5 ** 2, -1)))\n",
+ "plt.figure(dpi = 150)\n",
+ "plt.imshow(rss2,cmap = \"gray\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The test sets are already undersampled. You only need to reconstruct them and submit your solutions either to Track 01 or Track 02 or both! An example for the 32-channel data appears below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-10-08T17:54:00.838026Z",
+ "end_time": "2023-10-08T17:54:11.405802Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Number of volumes in the train set 50\n",
+ "Data format is x-ky-kz-nchannels\n",
+ "data shape: (256, 218, 180, 64)\n",
+ "\n",
+ "\n",
+ "Channel-wise k-space\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Channel-wise images\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Zero-filled root sum of squares\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# 32-channel data\n",
+ "test_files = list(Path(cc359_data_dir + \"calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/32-channel/Test-R=5/\").iterdir())\n",
+ "print(\"Number of volumes in the train set\",len(test_files))\n",
+ "\n",
+ "# Displaying a specific slice from a specific volume\n",
+ "file_index = 35\n",
+ "slice_index = 100\n",
+ "\n",
+ "# Load .h5 file\n",
+ "with h5py.File(test_files[file_index], 'r') as f:\n",
+ " sample_kspace3 = f['kspace'][:] # the key to access data is 'kspace'\n",
+ "\n",
+ "print(\"Data format is x-ky-kz-nchannels\")\n",
+ "print(\"data shape:\",sample_kspace3.shape)\n",
+ "\n",
+ "# We just want to show one slice\n",
+ "sample_kspace3 = sample_kspace3[slice_index]\n",
+ "# Converting to complex\n",
+ "sample_kspace3 = sample_kspace3[:,:,::2] + 1j*sample_kspace3[:,:,1::2]\n",
+ "\n",
+ "print(\"\\n\\nChannel-wise k-space\") \n",
+ "\n",
+ "# Displaying channels' k-spaces\n",
+ "plt.figure(figsize = (9,12),dpi = 150)\n",
+ "gs1 = gridspec.GridSpec(8, 4)\n",
+ "gs1.update(wspace=0.002, hspace=0.01)\n",
+ "\n",
+ "for ii in range(32):\n",
+ " plt.subplot(gs1[ii])\n",
+ " plt.imshow(np.log(1+np.abs(sample_kspace3[:,:,ii])),cmap = \"gray\")\n",
+ " plt.axis(\"off\")\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"Channel-wise images\") \n",
+ "sample_rec_train3 = np.fft.ifft2(sample_kspace3,axes = (0,1)) # Only ky and kz are in k-space domain\n",
+ "\n",
+ "# Displaying channels' images\n",
+ "plt.figure(figsize = (9,12),dpi = 150)\n",
+ "gs1 = gridspec.GridSpec(8, 4)\n",
+ "gs1.update(wspace=0.002, hspace=0.01)\n",
+ "\n",
+ "for ii in range(32):\n",
+ " plt.subplot(gs1[ii])\n",
+ " plt.imshow(np.abs(sample_rec_train3[:,:,ii]),cmap = \"gray\")\n",
+ " plt.axis(\"off\")\n",
+ "plt.show()\n",
+ "\n",
+ "print(\"Zero-filled root sum of squares\")\n",
+ "\n",
+ "rss3 = np.abs(np.sqrt(np.sum(sample_rec_train3 ** 2, -1)))\n",
+ "plt.figure(dpi = 150)\n",
+ "plt.imshow(rss3,cmap = \"gray\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/projects/REC/SKMTEA/README.md b/projects/REC/SKMTEA/README.md
new file mode 100644
index 00000000..fd7b608d
--- /dev/null
+++ b/projects/REC/SKMTEA/README.md
@@ -0,0 +1,45 @@
+## **Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 Dataset**
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the
+Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 dataset.
+
+For more information, please refer to https://github.com/StanfordMIMI/skm-tea.
+
+Related papers:
+- https://openreview.net/forum?id=YDMFgD_qJuA.
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](visualize.ipynb). You just need to set the path where the
+dataset is downloaded. The
+[original notebook](https://colab.research.google.com/drive/1PluqK77pobD5dXE7zzBLEAeBgaaeGKXa) is copied from the
+https://github.com/StanfordMIMI/skm-tea repository and provided by the SKMTEA authors.
+
+### **Preprocessing**
+The SKM-TEA dataset is supported natively in ``ATOMMIC`` and no preprocessing is required. You just need to generate
+train, val, and test sets depending on the task you use the dataset for. For example, for the reconstruction task, you
+need to run the [generate_sets.sh](projects/REC/SKMTEA/generate_sets.sh) script.
+
+The preprocessing script can be run with the following command:
+```bash
+bash ./projects/REC/SKMTEA/preprocess_dataset.sh
+```
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/SKMTEA/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` to the generated json files. In `exp_manager` please set the `exp_dir` to
+the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/REC/SKMTEA/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/SKMTEA/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/REC/SKMTEA/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/REC/SKMTEA/__init__.py b/projects/REC/SKMTEA/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/SKMTEA/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/SKMTEA/conf/test/ccnn.yaml b/projects/REC/SKMTEA/conf/test/ccnn.yaml
new file mode 100644
index 00000000..fb1b8400
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/ccnn.yaml
@@ -0,0 +1,122 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/cirim.yaml b/projects/REC/SKMTEA/conf/test/cirim.yaml
new file mode 100644
index 00000000..702077b9
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/cirim.yaml
@@ -0,0 +1,156 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 8
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/crnn.yaml b/projects/REC/SKMTEA/conf/test/crnn.yaml
new file mode 100644
index 00000000..125fc989
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/crnn.yaml
@@ -0,0 +1,122 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/dunet.yaml b/projects/REC/SKMTEA/conf/test/dunet.yaml
new file mode 100644
index 00000000..98ac04f8
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/dunet.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: VS
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/DUNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/jointicnet.yaml b/projects/REC/SKMTEA/conf/test/jointicnet.yaml
new file mode 100644
index 00000000..53757c5d
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/jointicnet.yaml
@@ -0,0 +1,132 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/kikinet.yaml b/projects/REC/SKMTEA/conf/test/kikinet.yaml
new file mode 100644
index 00000000..4812634a
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/kikinet.yaml
@@ -0,0 +1,132 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/lpdnet.yaml b/projects/REC/SKMTEA/conf/test/lpdnet.yaml
new file mode 100644
index 00000000..5896ad81
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/lpdnet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/modl.yaml b/projects/REC/SKMTEA/conf/test/modl.yaml
new file mode 100644
index 00000000..6a7043be
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/modl.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 10
+ residual_blocks: 15
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/multidomainnet.yaml b/projects/REC/SKMTEA/conf/test/multidomainnet.yaml
new file mode 100644
index 00000000..a3de9e8c
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/multidomainnet.yaml
@@ -0,0 +1,120 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/MultiDomainNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/rim.yaml b/projects/REC/SKMTEA/conf/test/rim.yaml
new file mode 100644
index 00000000..6403ea47
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/rim.yaml
@@ -0,0 +1,156 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/rvn.yaml b/projects/REC/SKMTEA/conf/test/rvn.yaml
new file mode 100644
index 00000000..01131c06
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/rvn.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/unet.yaml b/projects/REC/SKMTEA/conf/test/unet.yaml
new file mode 100644
index 00000000..16c70074
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/unet.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/varnet.yaml b/projects/REC/SKMTEA/conf/test/varnet.yaml
new file mode 100644
index 00000000..675adee0
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/varnet.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/vsnet.yaml b/projects/REC/SKMTEA/conf/test/vsnet.yaml
new file mode 100644
index 00000000..d4325651
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/vsnet.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/test/xpdnet.yaml b/projects/REC/SKMTEA/conf/test/xpdnet.yaml
new file mode 100644
index 00000000..745fa768
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/test/xpdnet.yaml
@@ -0,0 +1,134 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 20
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 2
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/ccnn.yaml b/projects/REC/SKMTEA/conf/train/ccnn.yaml
new file mode 100644
index 00000000..523d900e
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/ccnn.yaml
@@ -0,0 +1,178 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/cirim.yaml b/projects/REC/SKMTEA/conf/train/cirim.yaml
new file mode 100644
index 00000000..aa574ea8
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/cirim.yaml
@@ -0,0 +1,212 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 8
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/crnn.yaml b/projects/REC/SKMTEA/conf/train/crnn.yaml
new file mode 100644
index 00000000..42dc47d1
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/crnn.yaml
@@ -0,0 +1,178 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/dunet.yaml b/projects/REC/SKMTEA/conf/train/dunet.yaml
new file mode 100644
index 00000000..7f82e015
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/dunet.yaml
@@ -0,0 +1,181 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: VS
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/DUNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/jointicnet.yaml b/projects/REC/SKMTEA/conf/train/jointicnet.yaml
new file mode 100644
index 00000000..d1cdc113
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/jointicnet.yaml
@@ -0,0 +1,188 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/kikinet.yaml b/projects/REC/SKMTEA/conf/train/kikinet.yaml
new file mode 100644
index 00000000..e99ca25a
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/kikinet.yaml
@@ -0,0 +1,188 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/lpdnet.yaml b/projects/REC/SKMTEA/conf/train/lpdnet.yaml
new file mode 100644
index 00000000..d564cdd6
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/lpdnet.yaml
@@ -0,0 +1,191 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/modl.yaml b/projects/REC/SKMTEA/conf/train/modl.yaml
new file mode 100644
index 00000000..0d14163d
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/modl.yaml
@@ -0,0 +1,179 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 10
+ residual_blocks: 15
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/multidomainnet.yaml b/projects/REC/SKMTEA/conf/train/multidomainnet.yaml
new file mode 100644
index 00000000..f0ab2552
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/multidomainnet.yaml
@@ -0,0 +1,176 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/MultiDomainNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/rim.yaml b/projects/REC/SKMTEA/conf/train/rim.yaml
new file mode 100644
index 00000000..e01afe6b
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/rim.yaml
@@ -0,0 +1,212 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/rvn.yaml b/projects/REC/SKMTEA/conf/train/rvn.yaml
new file mode 100644
index 00000000..fba59254
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/rvn.yaml
@@ -0,0 +1,191 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/unet.yaml b/projects/REC/SKMTEA/conf/train/unet.yaml
new file mode 100644
index 00000000..1a4651ca
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/unet.yaml
@@ -0,0 +1,180 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/varnet.yaml b/projects/REC/SKMTEA/conf/train/varnet.yaml
new file mode 100644
index 00000000..050fc3d1
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/varnet.yaml
@@ -0,0 +1,180 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/vsnet.yaml b/projects/REC/SKMTEA/conf/train/vsnet.yaml
new file mode 100644
index 00000000..fca26e2f
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/vsnet.yaml
@@ -0,0 +1,179 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/conf/train/xpdnet.yaml b/projects/REC/SKMTEA/conf/train/xpdnet.yaml
new file mode 100644
index 00000000..6bc5e2a3
--- /dev/null
+++ b/projects/REC/SKMTEA/conf/train/xpdnet.yaml
@@ -0,0 +1,190 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 20
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 2
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: false
+ dimensionality: 2
+ reconstruction_loss: l1
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/files_recon_calib-24_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1+echo2 # skm-tea-echo1, skm-tea-echo2, skm-tea-echo1+echo2, skm-tea-echo1+echo2-mc
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: poisson2d # the mask will be loaded from the dataset, but we need to specify the type here
+ accelerations:
+ - 4 # 4, 6, 8, 10, 12, 16
+ - 6 # 4, 6, 8, 10, 12, 16
+ - 8 # 4, 6, 8, 10, 12, 16
+ - 10 # 4, 6, 8, 10, 12, 16
+ - 12 # 4, 6, 8, 10, 12, 16
+ - 16 # 4, 6, 8, 10, 12, 16
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.SKM-TEA_poisson2d_4x_6x_8x_10x_12x_16x
diff --git a/projects/REC/SKMTEA/generate_sets.sh b/projects/REC/SKMTEA/generate_sets.sh
new file mode 100644
index 00000000..7502002f
--- /dev/null
+++ b/projects/REC/SKMTEA/generate_sets.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+echo "
+Preprocessing pipeline for the Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 Dataset.
+
+For more information, please refer to https://stanfordaimi.azurewebsites.net/datasets/4aaeafb9-c6e6-4e3c-9188-3aaaf0e0a9e7
+and check the following paper https://openreview.net/forum?id=YDMFgD_qJuA.
+
+Generating train, val, and test sets...
+"
+
+# Prompt the user to enter the path to the downloaded annotations directory
+echo "Please enter the (downloaded) annotations data directory:"
+read INPUT_DIR
+
+# Check if the input directory exists
+if [ ! -d "$INPUT_DIR" ]; then
+ echo "The input directory does not exist. Please try again."
+ exit 1
+fi
+
+# Prompt the user to enter the output directory for the generated json files
+echo "Please enter the output directory for the generated json files:"
+read OUTPUT_DIR
+
+# Run the json generation script
+python projects/segmentation/SKMTEA/scripts/split_sets_json.py $INPUT_DIR $OUTPUT_DIR --data_type raw
+echo "Done!"
diff --git a/projects/REC/SKMTEA/scripts/__init__.py b/projects/REC/SKMTEA/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/SKMTEA/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/SKMTEA/scripts/split_sets_json.py b/projects/REC/SKMTEA/scripts/split_sets_json.py
new file mode 100644
index 00000000..19ae937e
--- /dev/null
+++ b/projects/REC/SKMTEA/scripts/split_sets_json.py
@@ -0,0 +1,45 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+from pathlib import Path
+
+
+def main(args):
+ if args.data_type == "raw":
+ data_type = "files_recon_calib-24"
+ else:
+ data_type = "image_files"
+
+ # remove "annotations/v1.0.0/" from args.annotations_path and add "files_recon_calib-24" to get the raw_data_path
+ raw_data_path = Path(args.annotations_path).parent.parent / data_type
+
+ # get train.json, val.json and test.json filenames from args.annotations_path
+ annotations_sets = list(Path(args.annotations_path).iterdir())
+ for annotation_set in annotations_sets:
+ set_name = Path(annotation_set).name
+
+ # read json file
+ with open(annotation_set, "r", encoding="utf-8") as f:
+ annotation_set = json.load(f)
+
+ # read the "images" key and for every instance get the "file_name" key
+ filenames = [f'{raw_data_path}/{image["file_name"]}' for image in annotation_set["images"]]
+
+ # create a directory to store the folds
+ output_path = Path(args.output_path)
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # write the train, val and test filenames to a json file
+ with open(output_path / f"{data_type}_{set_name}", "w", encoding="utf-8") as f:
+ json.dump(filenames, f)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("annotations_path", type=Path, default=None, help="Path to the annotations json file.")
+ parser.add_argument("output_path", type=Path, default=None, help="Path to the output directory.")
+ parser.add_argument("--data_type", choices=["raw", "image"], default="raw", help="Type of data to split.")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/REC/SKMTEA/visualize.ipynb b/projects/REC/SKMTEA/visualize.ipynb
new file mode 100644
index 00000000..e167ed0f
--- /dev/null
+++ b/projects/REC/SKMTEA/visualize.ipynb
@@ -0,0 +1,1481 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "oeT5uJROMl6L"
+ },
+ "source": [
+ "# ๐ต SKM-TEA Dataset Tutorial\n",
+ "[Paper](https://arxiv.org/abs/2203.06823) | [GitHub](https://github.com/StanfordMIMI/skm-tea)\n",
+ "\n",
+ "Welcome to the SKM-TEA dataset demo!\n",
+ "\n",
+ "**Dataset**: The *Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset* is a collection of quantitative knee MRI scans that enables end-to-end benchmarking of MRI reconstruction and analysis methods. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies.\n",
+ "\n",
+ "**Brief**: In this demo, we will walk through the data and how to use [the codebase](https://github.com/StanfordMIMI/skm-tea) to run pre-trained models and perform evaluation with your own methods.\n",
+ "\n",
+ "- Inspect different data types in SKM-TEA *DICOM* and *Raw Data* Tracks\n",
+ "- Use pretrained models from the [model zoo](https://github.com/StanfordMIMI/skm-tea/blob/main/MODEL_ZOO.md)\n",
+ "- Perform clinically-relevant quantitative MRI (qMRI) evaluation\n",
+ "\n",
+ "Interested in learning how to train models with SKM-TEA, check out [this tutorial](https://colab.research.google.com/drive/1LUC0MqFYK39xG5AV9kQi5hIBsi9eCpS0?usp=sharing)\n",
+ "\n",
+ "**Time**: 25-30 minutes\n",
+ "\n",
+ "**Colab Runtime**: We recommend running this Colab with a GPU runtime. To change the runtime,\n",
+ "1. Click on `Runtime` on the top navigation bar\n",
+ "2. Select `Change runtime type`\n",
+ "3. Select `GPU` from the dropdown\n",
+ "\n",
+ "**NOTE**: This tutorial is under development. Please contact the arjundd \\ with any bugs or recommendations.\n",
+ "\n",
+ "**Acknowledgements**: SKM-TEA is built on the [Meddlr](https://github.com/ad12/meddlr) image reconstruction and analysis framework.\n",
+ "\n",
+ "**Coming Soon:**\n",
+ "- Tutorial with detection (bounding box) labels"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5g8MtjY5Vs7c"
+ },
+ "source": [
+ "## ๐ก Downloading the Data\n",
+ "Let's download a [mini version](https://huggingface.co/datasets/arjundd/skm-tea-mini) of the SKM-TEA dataset from Huggingface. This mini dataset was created for building demos/tutorials with the SKM-TEA dataset. **Do not use this dataset for reporting/publication purposes**\n",
+ "\n",
+ "*NOTE*: This download process can take ~5-8 minutes.\n",
+ "\n",
+ "> If you would like to set up up the full SKM-TEA dataset on your machine, follow [these instructions](https://github.com/StanfordMIMI/skm-tea/blob/main/DATASET.md)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "mLWsy6TS6KGX",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "422b0934-856c-4bf2-9c92-692be5371d02"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "dataset_dir = \"skm-tea/v1-release\"\n",
+ "url = \"https://huggingface.co/datasets/arjundd/skm-tea-mini/resolve/main/v1-release\"\n",
+ "force_download = False\n",
+ "\n",
+ "if force_download:\n",
+ " !rm -rf $dataset_dir\n",
+ "\n",
+ "if not os.path.isdir(dataset_dir):\n",
+ " os.makedirs(dataset_dir)\n",
+ " for fname in [\"all_metadata.csv\", \"annotations/v1.0.0/train.json\", \"annotations/v1.0.0/val.json\", \"annotations/v1.0.0/test.json\"]:\n",
+ " out = f\"{dataset_dir}/{fname}\"\n",
+ " os.makedirs(os.path.dirname(out), exist_ok=True)\n",
+ " !wget -q $url/$fname -O $out\n",
+ "\n",
+ "\n",
+ " for fname in tqdm([\"dicoms\", \"files_recon_calib-24\", \"image_files\", \"segmentation_masks\"], disable=False):\n",
+ " !wget -c $url/\"tarball\"/$fname\".tar.gz\" -O - | tar -xz -C $dataset_dir/\n",
+ "\n",
+ "!ls"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "TfqPa76WMl6P"
+ },
+ "source": [
+ "## ๐ง Setup\n",
+ "All SKM-TEA code for training, evaluation, models, and more ships as a Python package. In this tutorial, we will learn how to use different parts of this package.\n",
+ "\n",
+ "> To use the latest version from the `main` branch, use `pip install git+https://github.com/StanfordMIMI/skm-tea.git`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "s76_F_6FYVoK",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "outputId": "c9e80dea-5841-4e4a-b179-c81fbfefc9df"
+ },
+ "outputs": [],
+ "source": [
+ "# Download SKM-TEA from main branch on GitHub\n",
+ "!pip install --upgrade pytorch-lightning==1.7.7 skm-tea"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "968_Ac3sMl6O"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.environ[\"MEDDLR_DATASETS_DIR\"] = \"./\"\n",
+ "\n",
+ "from pprint import pprint\n",
+ "\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import h5py\n",
+ "import matplotlib.pyplot as plt\n",
+ "from skimage.color import label2rgb\n",
+ "import pandas as pd\n",
+ "from torch import nn\n",
+ "\n",
+ "import dosma as dm\n",
+ "\n",
+ "import meddlr.ops as oF\n",
+ "from meddlr.data import DatasetCatalog, MetadataCatalog\n",
+ "from meddlr.utils.logger import setup_logger\n",
+ "from meddlr.utils import env\n",
+ "\n",
+ "import skm_tea as st"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Set the default device if cuda is enabled\n",
+ "if torch.cuda.is_available():\n",
+ " DEVICE = torch.device(\"cuda\")\n",
+ "else:\n",
+ " DEVICE = torch.device(\"cpu\")\n",
+ "\n",
+ "print(\"Device: \", DEVICE)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "B6st9z4rm1tO",
+ "outputId": "66c126bd-fadd-438a-d2a4-f5581cda00dc"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "hb5pCSZzMl6Q"
+ },
+ "outputs": [],
+ "source": [
+ "# Run this setup phase only once.\n",
+ "# Otherwise, you may get multiple print statements\n",
+ "setup_logger()\n",
+ "logger = setup_logger(\"skm_tea\")\n",
+ "path_mgr = env.get_path_manager()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Some general utilities\n",
+ "\n",
+ "from typing import Union, Sequence\n",
+ "\n",
+ "def get_scaled_image(\n",
+ " x: Union[torch.Tensor, np.ndarray], percentile=0.99, clip=False\n",
+ "):\n",
+ " \"\"\"Scales image by intensity percentile (and optionally clips to [0, 1]).\n",
+ "\n",
+ " Args:\n",
+ " x (torch.Tensor | np.ndarray): The image to process.\n",
+ " percentile (float): The percentile of magnitude to scale by.\n",
+ " clip (bool): If True, clip values between [0, 1]\n",
+ "\n",
+ " Returns:\n",
+ " torch.Tensor | np.ndarray: The scaled image.\n",
+ " \"\"\"\n",
+ " is_numpy = isinstance(x, np.ndarray)\n",
+ " if is_numpy:\n",
+ " x = torch.as_tensor(x)\n",
+ "\n",
+ " scale_factor = torch.quantile(x, percentile)\n",
+ " x = x / scale_factor\n",
+ " if clip:\n",
+ " x = torch.clip(x, 0, 1)\n",
+ "\n",
+ " if is_numpy:\n",
+ " x = x.numpy()\n",
+ "\n",
+ " return x\n",
+ "\n",
+ "\n",
+ "def plot_images(\n",
+ " images, processor=None, disable_ticks=True, titles: Sequence[str]=None,\n",
+ " ylabel: str=None, xlabels: Sequence[str]=None, cmap: str=\"gray\",\n",
+ " show_cbar: bool = False, overlay = None, opacity: float = 0.3,\n",
+ " hsize=5, wsize=5, axs=None\n",
+ "):\n",
+ " \"\"\"Plot multiple images in a single row.\n",
+ "\n",
+ " Add an overlay with the `overlay=` argument.\n",
+ " Add a colorbar with `show_cbar=True`.\n",
+ " \"\"\"\n",
+ " def get_default_values(x, default=\"\"):\n",
+ " if x is None:\n",
+ " return [default] * len(images)\n",
+ " return x\n",
+ "\n",
+ " titles = get_default_values(titles)\n",
+ " ylabels = get_default_values(images)\n",
+ " xlabels = get_default_values(xlabels)\n",
+ "\n",
+ " N = len(images)\n",
+ " if axs is None:\n",
+ " fig, axs = plt.subplots(1, N, figsize=(wsize * N, hsize))\n",
+ " else:\n",
+ " assert len(axs) >= N\n",
+ " fig = axs.flatten()[0].get_figure()\n",
+ "\n",
+ " for ax, img, title, xlabel in zip(axs, images, titles, xlabels):\n",
+ " if processor is not None:\n",
+ " img = processor(img)\n",
+ " im = ax.imshow(img, cmap=cmap)\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel(xlabel)\n",
+ "\n",
+ " if overlay is not None:\n",
+ " for ax in axs.flatten():\n",
+ " im = ax.imshow(overlay, alpha=opacity)\n",
+ "\n",
+ " if show_cbar:\n",
+ " fig.subplots_adjust(right=0.8)\n",
+ " cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
+ " fig.colorbar(im, cax=cbar_ax)\n",
+ "\n",
+ " if disable_ticks:\n",
+ " for ax in axs.flatten():\n",
+ " ax.get_xaxis().set_ticks([])\n",
+ " ax.get_yaxis().set_ticks([])\n",
+ "\n",
+ " return axs\n"
+ ],
+ "metadata": {
+ "id": "rjVszyf4aDBj"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "2sDPAp83Ml6Q"
+ },
+ "source": [
+ "## ๐พ Understanding the Data\n",
+ "The SKM-TEA dataset consists of two *tracks* that are based on the source of the input image: the *Raw Data* track, where inputs start from the complex-valued k-space, and the *DICOM* track, where inputs start from magnitude DICOM images.\n",
+ "\n",
+ "Note, the Raw Data track supports all reconstruction (upstream) and image analysis (downstream) tasks available in SKM-TEA with the caveat that all downstream tasks are performed on the image reconstructed from the raw data.\n",
+ "\n",
+ "In contrast, the DICOM track only supports image analysis tasks -- it does not support the reconstruction tasks. Read [this paper](https://arxiv.org/abs/2109.08237) for more information on why DICOM images may not be good targets for measuring reconstruction performance.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The `skm_tea` package simplifies getting relevant data paths and metadata using the `DatasetCatalog` manager. We can load any of the dataset splits:\n",
+ "- `'skmtea_v1_train'`\n",
+ "- `'skmtea_v1_val'`\n",
+ "- `'skmtea_v1_test'`"
+ ],
+ "metadata": {
+ "id": "0GVy26yPEaNB"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JCJHRjU9Ml6R"
+ },
+ "outputs": [],
+ "source": [
+ "# Load list of dictionaries for the SKM-TEA v1 training dataset.\n",
+ "dataset_dicts = DatasetCatalog.get(\"skmtea_v1_train\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "QIjEQX_gMl6R",
+ "outputId": "0d02e6a3-d938-41d6-f7ad-8618ec29b76e"
+ },
+ "outputs": [],
+ "source": [
+ "scan = dataset_dicts[0]\n",
+ "pprint(scan)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LFeAVfimMl6S"
+ },
+ "source": [
+ "### Raw Data Track\n",
+ "The raw data track consists of (1) multi-coil kspace, (2) complex-valued ground truth reconstructions, (3) sensitivity maps, (4) gradient-warp corrected segmentations, and (5) localized bounding boxes for knee pathologies.\n",
+ "\n",
+ "While qDESS is a 3D sequence, the SI (axial) readout dimension is fully-sampled, and can be reconstructed without information loss using the 1D inverse fast Fourier transform (ifft). Thus, reconstructions are performed on 2D axial ($k_y \\times k_z$) slices.\n",
+ "\n",
+ "Also, note that the reference segmentations for the raw data track are different than those for the DICOM track to correct for DICOM-specfic post-processing. See [our paper](https://arxiv.org/abs/2203.06823) for more information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "iYPX8955Ml6S",
+ "outputId": "aee45171-f99a-4965-a850-1a8707563631"
+ },
+ "outputs": [],
+ "source": [
+ "sl = 200 # the slice to be plotted\n",
+ "\n",
+ "# Reconstruction data\n",
+ "recon_file = scan[\"recon_file\"]\n",
+ "with h5py.File(recon_file, \"r\") as f:\n",
+ " kspace = f[\"kspace\"][sl, :, :, :, :] # Shape: (x, ky, kz, #echos, #coils)\n",
+ " image = f[\"target\"][sl, :, :, :, :] # Shape: (x, ky, kz, #echos, #maps) - #maps = 1 for SKM-TEA\n",
+ " maps = f[\"maps\"][sl, :, :, :, :] # Shape: (x, ky, kz, #coils, #maps) - maps are the same for both echos\n",
+ "\n",
+ "# Segmentation data\n",
+ "seg_file = scan[\"gw_corr_mask_file\"]\n",
+ "segmentation = dm.read(seg_file).A[sl, ...] # Shape: (x, y, z)\n",
+ "print(segmentation.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 597
+ },
+ "id": "h_G_os2pMl6T",
+ "outputId": "ffd7c9ba-732d-4af7-b6c8-e2b1a8d1a534"
+ },
+ "outputs": [],
+ "source": [
+ "# Display kspace per coil\n",
+ "n_coils = kspace.shape[-1]\n",
+ "nrows = 2\n",
+ "hsize = 5\n",
+ "wsize = hsize / kspace.shape[0] * kspace.shape[1]\n",
+ "_, axs = plt.subplots(nrows, n_coils, figsize=(n_coils * wsize, nrows * hsize))\n",
+ "\n",
+ "for echo in range(2):\n",
+ " kspace_coils = [np.abs(kspace[..., echo, idx]) for idx in range(n_coils)]\n",
+ " # Scale the kspace to avoid over-saturating the image with center kspace\n",
+ " kspace_coils = [get_scaled_image(x, 0.95, clip=True) for x in kspace_coils]\n",
+ "\n",
+ " titles = [f\"Coil {idx+1}\" for idx in range(n_coils)] if echo==0 else None\n",
+ " plot_images(kspace_coils, titles=titles, axs=axs[echo])\n",
+ " axs[echo][0].set_ylabel(\"Echo {}\".format(echo + 1), fontsize=20)\n",
+ "\n",
+ "plt.subplots_adjust(wspace=0.1, hspace=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 327
+ },
+ "id": "EGowchm2Ml6T",
+ "outputId": "fc3497cb-2e68-4da8-e38e-dd3f46c6b51e"
+ },
+ "outputs": [],
+ "source": [
+ "# Plot reconstructed image\n",
+ "mag_img = np.abs(image)\n",
+ "seg_colorized = label2rgb(segmentation, bg_label=0)\n",
+ "\n",
+ "\n",
+ "_ = plot_images(\n",
+ " [mag_img[..., 0, 0], mag_img[..., 0, 0]], # echo1, echo2\n",
+ " processor=lambda x: get_scaled_image(x, 0.95, clip=True),\n",
+ " titles=[\"Echo 1\", \"Echo 2\"],\n",
+ " overlay=seg_colorized,\n",
+ " opacity=0.4,\n",
+ " hsize=5, wsize=2.3\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lpmPd77iMl6U"
+ },
+ "source": [
+ "### DICOM Track\n",
+ "The DICOM Track consists of (1) scanner-generated DICOM images, (2) tissue segmentations, and (3) pathology bounding boxes.\n",
+ "\n",
+ "**IMPORTANT**: As mentioned above, this data should only be used for image analysis (segmentation, detection, classification) tasks. It should not be used for reconstruction tasks.\n",
+ "\n",
+ "\n",
+ "Let's visualize a sagittal slice from both echos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PwtalV6eMl6U"
+ },
+ "outputs": [],
+ "source": [
+ "sl = 60 # the slice to be plotted\n",
+ "\n",
+ "# DICOM data + segmentation\n",
+ "image_file = scan[\"image_file\"]\n",
+ "with h5py.File(image_file, \"r\") as f:\n",
+ " echo1 = f[\"echo1\"][:, :, sl] # Shape: (x, y, z)\n",
+ " echo2 = f[\"echo2\"][:, :, sl] # Shape: (x, y, z)\n",
+ " segmentation = f[\"seg\"][:, :, sl, :] # Shape: (x, y, z, #classes)\n",
+ "\n",
+ "segmentation = oF.one_hot_to_categorical(segmentation, channel_dim=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 309
+ },
+ "id": "8eV1c8szMl6V",
+ "outputId": "31c6bd8d-1ac3-4121-b630-a73cf7841cbc"
+ },
+ "outputs": [],
+ "source": [
+ "# Plot reconstructed image\n",
+ "seg_colorized = label2rgb(segmentation, bg_label=0)\n",
+ "\n",
+ "_ = plot_images(\n",
+ " [echo1, echo2],\n",
+ " titles=[\"Echo 1\", \"Echo 2\"],\n",
+ " overlay=seg_colorized,\n",
+ " opacity=0.4,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xtEvV8GFMl6W"
+ },
+ "source": [
+ "## ๐ Model Zoo\n",
+ "Interested in running a pre-trained model on your data? We got you!\n",
+ "\n",
+ "We maintain a model zoo of pre-trained models that have been trained on the SKM-TEA dataset for different tasks. You can find a list of these models on [GitHub](https://github.com/StanfordMIMI/skm-tea).\n",
+ "\n",
+ "And loading the model is as easy as 123! Just use the `skm_tea.get_model_from_zoo`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bUp06YLTMl6W"
+ },
+ "outputs": [],
+ "source": [
+ "# Load a scan from the test dataset.\n",
+ "dataset_dicts = DatasetCatalog.get(\"skmtea_v1_test\")\n",
+ "scan = dataset_dicts[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LRzGe08hMl6W"
+ },
+ "source": [
+ "### Reconstruction\n",
+ "Let's use a pretrained [unrolled network](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664163/) to reconstruct 6x accelerated qDESS scans.\n",
+ "\n",
+ "The reconstruction model was trained to reconstruction axial ($k_y \\times k_z$) slices for the first echo. You can find other pretrained reconstruction models [here](https://github.com/StanfordMIMI/skm-tea/blob/main/MODEL_ZOO.md#reconstruction-baselines).\n",
+ "\n",
+ "*Aside*: When reporting results on the SKM-TEA dataset, please use the masks provided with the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3PTee3KIMl6X"
+ },
+ "outputs": [],
+ "source": [
+ "# Simulate 6x undersampled data\n",
+ "sl = 256\n",
+ "\n",
+ "with h5py.File(scan[\"recon_file\"], \"r\") as f:\n",
+ " kspace = torch.as_tensor(f[\"kspace\"][sl, :, :, :, :]).unsqueeze(0)\n",
+ " maps = torch.as_tensor(f[\"maps\"][sl, :, :, :, :]).unsqueeze(0)\n",
+ " mask = torch.as_tensor(f[\"masks/poisson_6.0x\"][()]).unsqueeze(0) # TODO: Fix\n",
+ " img_gt = torch.as_tensor(f[\"target\"][sl, :, :, :, :]).unsqueeze(0)\n",
+ "mask = oF.zero_pad(mask, kspace.shape[1:3])\n",
+ "\n",
+ "us_kspace = kspace * mask.unsqueeze(-1).unsqueeze(-1).type(kspace.dtype)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VCLy0Gp6Ml6X",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "85a46aaf-42a6-479d-89ea-5787924c979f"
+ },
+ "outputs": [],
+ "source": [
+ "# Fetch the model with pretrained weights.\n",
+ "model = st.get_model_from_zoo(\n",
+ " cfg_or_file=\"https://huggingface.co/arjundd/skm-tea-models/raw/main/neurips2021/recon-models/6x/Unrolled_E1/config.yaml\",\n",
+ " weights_path=\"https://huggingface.co/arjundd/skm-tea-models/resolve/main/neurips2021/recon-models/6x/Unrolled_E1/model.ckpt\",\n",
+ ").to(DEVICE).eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "MGXj7huvMl6X"
+ },
+ "outputs": [],
+ "source": [
+ "echo = 0 # the 1st echo\n",
+ "echo1_kspace = us_kspace[..., echo, :]\n",
+ "with torch.no_grad():\n",
+ " pred = model({\"kspace\": echo1_kspace, \"maps\": maps})[\"pred\"].cpu()\n",
+ "echo1_recon = pred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3AGVZPP4Ml6X",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 398
+ },
+ "outputId": "2c567e15-a693-416b-e023-37795498c06f"
+ },
+ "outputs": [],
+ "source": [
+ "# For visualization purposes, we scale the ground truth and reconstructions\n",
+ "# to get rid of very bright outliers.\n",
+ "gt_abs = get_scaled_image(img_gt[..., 0, :].abs(), 0.9999, clip=True)\n",
+ "recon_abs = get_scaled_image(echo1_recon.abs(), 0.9999, clip=True)\n",
+ "err = torch.abs(gt_abs - recon_abs)\n",
+ "\n",
+ "plot_images(\n",
+ " [gt_abs, recon_abs, err * 4],\n",
+ " processor=lambda x: x.abs().squeeze(),\n",
+ " titles=[\"Ground truth\", \"Recon\", \"Error (4x)\"],\n",
+ " hsize=5, wsize=2.3\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zOBE1rQAMl6Y"
+ },
+ "source": [
+ "### Segmentation\n",
+ "Let's perform segmentation on the DICOM track dataset using a pretrained [U-Net](https://arxiv.org/abs/1505.04597).\n",
+ "\n",
+ "The segmentation model was trained to segment sagittal slices for the first echo. You can find other pretrained segmentation models [here](https://github.com/StanfordMIMI/skm-tea/blob/main/MODEL_ZOO.md#segmentation-baselines).\n",
+ "\n",
+ "**Note:** The volume has to first be normalized to have zero-mean and unit standard deviation. In the near future, this will automatically be done."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "X3PziENNMl6Y",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "6446b963-e37b-465d-82e6-9f832ee8c930"
+ },
+ "outputs": [],
+ "source": [
+ "model = st.get_model_from_zoo(\n",
+ " cfg_or_file=\"https://huggingface.co/arjundd/skm-tea-models/raw/main/neurips2021/segmentation-models/U-Net_E1/config.yaml\",\n",
+ " weights_path=\"https://huggingface.co/arjundd/skm-tea-models/resolve/main/neurips2021/segmentation-models/U-Net_E1/model.ckpt\",\n",
+ ").eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Xhy1kVpzMl6Y"
+ },
+ "outputs": [],
+ "source": [
+ "from meddlr.data.data_utils import collect_mask\n",
+ "sl = 88 # the slice to segment\n",
+ "\n",
+ "# DICOM data + segmentation\n",
+ "image_file = scan[\"image_file\"]\n",
+ "with h5py.File(image_file, \"r\") as f:\n",
+ " echo1 = f[\"echo1\"][()] # Shape: (x, y, z)\n",
+ " segmentation = f[\"seg\"][()] # Shape: (x, y, z, #classes)\n",
+ "\n",
+ "echo1 = torch.as_tensor(echo1).unsqueeze(0).unsqueeze(0).float() # Shape: (B, C, H, W)\n",
+ "\n",
+ "# Ground truth segmentation\n",
+ "# Medial/lateral components are aggregated into the same category.\n",
+ "# 0 - patellar cartilage, 1 - femoral cartilage\n",
+ "# 2/3 - medial/lateral tibial cartilage, 4/5 - medial/lateral meniscus\n",
+ "gt_seg_sl = segmentation[..., sl, :]\n",
+ "gt_seg_sl = collect_mask(gt_seg_sl, (0, 1, (2, 3), (4, 5)), out_channel_first=False)\n",
+ "gt_seg_sl = oF.one_hot_to_categorical(gt_seg_sl, channel_dim=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JT4D1Mp-Ml6Y"
+ },
+ "outputs": [],
+ "source": [
+ "# Normalize volume and run model.\n",
+ "echo1 = (echo1 - echo1.mean()) / echo1.std()\n",
+ "echo1_sl = echo1[..., sl]\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " logits = model({\"image\": echo1_sl})[\"sem_seg_logits\"]\n",
+ "\n",
+ "prediction = oF.pred_to_categorical(logits, activation='sigmoid').squeeze(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "_, axs = plt.subplots(1, 3, figsize=(10,5))\n",
+ "for idx, (data, title) in enumerate([\n",
+ " (echo1_sl.squeeze(), \"Input\"), (prediction, \"Prediction\"), (gt_seg_sl, \"Ground truth\")\n",
+ "]):\n",
+ " ax = axs[idx]\n",
+ " ax.imshow(data.squeeze(), cmap=\"gray\" if idx == 0 else None)\n",
+ " ax.set_title(title, fontsize=20)\n",
+ " ax.axis(\"off\")"
+ ],
+ "metadata": {
+ "id": "IQNltkfZiUd-",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 216
+ },
+ "outputId": "78e7d8ab-fc8d-4e2a-f124-3e3fddbd8105"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "t5dNaY7VMl6Z"
+ },
+ "source": [
+ "## ๐ qMRI Evaluation\n",
+ "SKM-TEA introduces a new family of metrics based on quantitative MRI (qMRI) endpoints. In this section, we will explore the utility of these metrics and how to use them to benchmark your models.\n",
+ "\n",
+ "Specifically, we will consider a qMRI knee analysis pipeline that uses qDESS reconstructions to analytically estimate $T_2$ maps and uses automated segmentations to get region-specific $T_2$ values.\n",
+ "\n",
+ "As a proof-of-concept, let's dive into how we can use qMRI endpoints to evaluate a segmentation model based on regional $T_2$ accuracy. We will evaluate the same pretrained U-Net model from the [Model Zoo section](https://colab.research.google.com/drive/1PluqK77pobD5dXE7zzBLEAeBgaaeGKXa?authuser=1#scrollTo=zOBE1rQAMl6Y&line=6&uniqifier=1).\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "G3QOQzciMl6Z"
+ },
+ "outputs": [],
+ "source": [
+ "from skm_tea.metrics import QuantitativeKneeMRI\n",
+ "from meddlr.data.data_utils import collect_mask\n",
+ "\n",
+ "from dosma.scan_sequences import QDess"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "atqdug_EMl6Z"
+ },
+ "outputs": [],
+ "source": [
+ "# Load a scan and corresponding metadata.\n",
+ "dataset_dicts = DatasetCatalog.get(\"skmtea_v1_test\")\n",
+ "scan = dataset_dicts[0]\n",
+ "\n",
+ "metadata: pd.DataFrame = MetadataCatalog.get(\"skmtea_v1_test\").scan_metadata\n",
+ "metadata = metadata[metadata[\"MTR_ID\"] == scan[\"scan_id\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load the DICOMs for this scan.\n",
+ "dr = dm.DicomReader(group_by=[\"EchoNumbers\", \"SeriesDescription\"], verbose=True, num_workers=4)\n",
+ "volumes = dr.load(scan[\"dicom_dir\"])\n",
+ "\n",
+ "# Filter out unnecessary dicoms.\n",
+ "volumes = [v for v in volumes if \"T2\" not in v.get_metadata(\"SeriesDescription\")]\n",
+ "assert len(volumes) == 2\n",
+ "echo1, echo2 = tuple(sorted(volumes, key=lambda x: x.get_metadata(\"EchoNumbers\")))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "ca289c7160af4c159ad7fd411d912ef7",
+ "015d4fbb69af450585e5e2fa60412047",
+ "ea4b2048a1ec44e099bb923bad633b9a",
+ "51a3a30ad84f49918cbda78bc33ccbd4",
+ "842b85d281ce4c239af9436d6e57958e",
+ "550602d30f904c8f8123b8366342950e",
+ "1717cc1ff8934b0b9020c7a0cd2b595b",
+ "d0e0ecc7cc104ba1a7d2918a3f585b41",
+ "67aba4eecf5141e0bbb4b0da63ff8c51",
+ "2ff53edd8c7a453395cdad88414d1212",
+ "699878da64014757921c9ef70119e50e"
+ ]
+ },
+ "id": "Mx9ngDEOx-sm",
+ "outputId": "55581b63-ec42-4305-a645-612edfd382b7"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load the ground truth segmentation.\n",
+ "seg_gt = dm.read(scan[\"dicom_mask_file\"])\n",
+ "arr = oF.categorical_to_one_hot(seg_gt.A, channel_dim=-1)\n",
+ "arr = collect_mask(arr, (0, 1, (2,3), (4,5)), out_channel_first=False)\n",
+ "\n",
+ "seg_gt = dm.MedicalVolume(arr, affine=seg_gt.affine)"
+ ],
+ "metadata": {
+ "id": "q68mUZqFz7Qz"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Run the model"
+ ],
+ "metadata": {
+ "id": "qS22VFNe8Asz"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def run_segmentation(\n",
+ " mv: dm.MedicalVolume, model: nn.Module, normalize=True,\n",
+ " batch_size: int = 4, pbar: bool = True\n",
+ "):\n",
+ " \"\"\"Runs a segmentation model on the qDESS volume.\n",
+ "\n",
+ " The model should be trained to segment sagittal slices.\n",
+ "\n",
+ " Args:\n",
+ " x (dm.MedicalVolume): A 3D magnitude image (single echo).\n",
+ " model (nn.Module): The segmentation model to run.\n",
+ " normalize (bool): Whether to perform zero-mean, unit-std normalization.\n",
+ " batch_size (int): The batch size for performing segmentation.\n",
+ " pbar (bool): Whether to display progress bar.\n",
+ "\n",
+ " Returns:\n",
+ " dm.MedicalVolume: The one-hot predictions from the segmentation model\n",
+ " where last dimension/axis is the channel dimension.\n",
+ " \"\"\"\n",
+ " mv_ornt = mv.orientation\n",
+ " mv = mv.reformat((\"LR\", \"SI\", \"AP\"))\n",
+ " affine = mv.affine.copy()\n",
+ "\n",
+ " x = mv.to_torch().type(torch.float32)\n",
+ " if normalize:\n",
+ " x = (x - x.mean()) / x.std()\n",
+ "\n",
+ " x_chunks = torch.split(x, batch_size, dim = 0)\n",
+ "\n",
+ " logits = []\n",
+ " for chunk in tqdm(torch.split(x, batch_size, dim=0), disable=not pbar):\n",
+ " chunk = chunk.unsqueeze(1) # add a channel dimension\n",
+ " out = model({\"image\": chunk})\n",
+ " logits.append(out[\"sem_seg_logits\"])\n",
+ "\n",
+ "\n",
+ " logits = torch.concat(logits, dim=0)\n",
+ " prediction = torch.sigmoid(logits).permute(0, 2, 3, 1) # make channels last\n",
+ "\n",
+ " out = dm.MedicalVolume.from_torch(prediction, affine).reformat(mv_ornt)\n",
+ " return out"
+ ],
+ "metadata": {
+ "id": "9_AnZ_0qNiRr"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "torch.cuda.empty_cache()\n",
+ "\n",
+ "model = st.get_model_from_zoo(\n",
+ " cfg_or_file=\"https://huggingface.co/arjundd/skm-tea-models/raw/main/neurips2021/segmentation-models/U-Net_E1/config.yaml\",\n",
+ " weights_path=\"https://huggingface.co/arjundd/skm-tea-models/resolve/main/neurips2021/segmentation-models/U-Net_E1/model.ckpt\",\n",
+ ").to(DEVICE).eval()"
+ ],
+ "metadata": {
+ "id": "CrnRYP5tEGGN"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "with torch.no_grad():\n",
+ " seg_pred = run_segmentation(echo1.to(DEVICE), model, batch_size=4)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "SBoA-EfcEX2N",
+ "outputId": "c5f873c1-9ebb-485b-97e6-6f9626b2f7d3"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Computing $T_2$ Maps\n",
+ "\n",
+ "Computing $T_2$ maps from qDESS can be done analytically, which is much faster than traditional fitting. To do so, we require a few scan parameters as well as rough estimates for $T_1$ of tissues. Scan parameters can be found in the DICOM files or the metadata file shipped with the dataset.\n",
+ "\n",
+ "An open-source implementation of the analytical fit is available in dosma. To ensure standardization, dosma should be used to perform all qMRI evaluation in SKM-TEA.\n",
+ "\n",
+ "IMPORTANT: Do not use the scanner-generated $T_2$ maps (available in the dicom folder) for analysis. These should be used for visualization purposes only."
+ ],
+ "metadata": {
+ "id": "E7cTiTk8Hqps"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "As mentioned above, we need rough estimates for $T_1$ of tissues for the analytical $T_2$ estimation. From [literature](), we know that femoral, tibial, and patellar (articular) cartilage has a $T_1$ of approximately 1.2sec and meniscus has a $T_1$ of ~1sec.\n",
+ "\n",
+ "We can use the segmentation to fill in the expected $T_1$ values. Note, we will have 2 $T_1$ maps -- one from the ground truth segmentation (`t1_gt`), and one from the predicted segmentation (`t1_pred`)."
+ ],
+ "metadata": {
+ "id": "_MMDFtcY1Q0R"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# For reconstruction, this would be based on reconstructions for E1/E2.\n",
+ "# Estimated T1 values are 1.2s for cartilage and 1s for meniscus\n",
+ "def get_t1(seg: dm.MedicalVolume):\n",
+ " \"\"\"Build T1 maps based on the segmentation.\n",
+ "\n",
+ " `seg[..., 3]` should correspond to the meniscus segmentation map.\n",
+ "\n",
+ " Args:\n",
+ " seg (dm.MedicalVolume): A one-hot encoded segmentation mask, where the\n",
+ " last dimension is the channel dimension.\n",
+ "\n",
+ " Returns:\n",
+ " dm.MedicalVolume: The estimated T1 map (in milliseconds).\n",
+ " \"\"\"\n",
+ " t1 = dm.MedicalVolume(np.ones(seg.shape[:3]) * 1200, seg.affine).to(seg.device)\n",
+ " t1[seg.A[..., 3].astype(bool)] = 1000\n",
+ " return t1\n",
+ "\n",
+ "t1_gt = get_t1(seg_gt)\n",
+ "t1_pred = get_t1(seg_pred)"
+ ],
+ "metadata": {
+ "id": "JfMeAPjrrAwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "y6k1z3LtMl6Z",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5c302053-03fc-4087-fcef-d4697dc9f8bc"
+ },
+ "outputs": [],
+ "source": [
+ "def compute_t2_map(t1: dm.MedicalVolume):\n",
+ " qdess = QDess([echo1, echo2]).to(t1.device)\n",
+ " t2map = qdess.generate_t2_map(\n",
+ " suppress_fat=True,\n",
+ " suppress_fluid=True,\n",
+ " gl_area=float(metadata[\"SpoilerGradientArea\"]),\n",
+ " tg=float(metadata[\"SpoilerGradientTime\"]),\n",
+ " tr=float(metadata[\"RepetitionTime\"]),\n",
+ " te=float(metadata[\"EchoTime1\"]),\n",
+ " alpha=float(metadata[\"FlipAngle\"]),\n",
+ " t1=t1,\n",
+ " nan_bounds=(0, 100),\n",
+ " nan_to_num=True,\n",
+ " )\n",
+ " return t2map.volumetric_map\n",
+ "\n",
+ "t2_gt = compute_t2_map(t1_gt)\n",
+ "t2_pred = compute_t2_map(t1_pred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "sl = 60 # Sagittal slice to plot\n",
+ "\n",
+ "plot_images(\n",
+ " [t2_gt, t2_pred],\n",
+ " processor=lambda x: x.cpu().A[..., sl],\n",
+ " titles=[\"T2 (Ground Truth)\", \"T2 (Pred)\"],\n",
+ " cmap=\"viridis\", show_cbar=True,\n",
+ ")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 352
+ },
+ "id": "4k3lVDg52trB",
+ "outputId": "4d6622c5-3dc1-4257-b050-136c477e347e"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### The `QuantitativeKneeMRI` metric\n",
+ "\n",
+ "`QuantitativeKneeMRI` metrics simplifies computing and tracing qMRI related metrics for key knee anatomical structures.\n",
+ "\n",
+ "We will use `qmri_gt` and `qmri_pred` to track regional $T_2$ measures extracted from the ground truth and predicted segmentations, respectively. These regions will correspond to the four segmented tissues: patellar cartilage (`pc`), femoral cartilage (`fc`), tibial cartilage (`tc`), and meniscus (`men`)\n",
+ "\n",
+ "We can also choose to compute qMRI measures for anatomically relevant subregions in the these tissues. To do this, set `use_subregions=True`. Note the subregion division can be time intensive.\n",
+ "\n",
+ "**Note**: The metric is stateful. This means each time the metric is called, it stores the results. Use `.reset()` to reset the metric and clear all stored results.\n",
+ "\n",
+ "*Aside*: These metrics are automatically computed under the hood with the [`skm_tea.evaluation.SkmTeaEvaluator`](https://github.com/StanfordMIMI/skm-tea/blob/main/skm_tea/evaluation/qdess_evaluation.py)."
+ ],
+ "metadata": {
+ "id": "zCn3xtvfuBtt"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "TrdR9XsOMl6a"
+ },
+ "outputs": [],
+ "source": [
+ "use_subregions = False\n",
+ "use_cpu = use_subregions # computing subregions is currently limited to the CPU\n",
+ "tissues = [\"pc\", \"fc\", \"tc\", \"men\"]\n",
+ "\n",
+ "qmri_gt = QuantitativeKneeMRI(channel_names=tissues, subregions=use_subregions, use_cpu=use_cpu)\n",
+ "qmri_pred = QuantitativeKneeMRI(channel_names=tissues, subregions=use_subregions, use_cpu=use_cpu)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3Tnrul8RMl6a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "50572ff7-b3bb-4d59-9fbd-3c68cd340d9a"
+ },
+ "outputs": [],
+ "source": [
+ "# Reset the metrics\n",
+ "qmri_gt.reset()\n",
+ "qmri_pred.reset()\n",
+ "\n",
+ "# Compute regional qMRI estimates using ground truth and predicted segmentations.\n",
+ "qmri_gt(ids=[scan[\"scan_id\"]], quantitative_map=[t2_gt], sem_seg=[seg_gt], medial_direction=metadata[\"MedialDirection\"])\n",
+ "qmri_pred(ids=[scan[\"scan_id\"]], quantitative_map=[t2_pred], sem_seg=[seg_pred], medial_direction=metadata[\"MedialDirection\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "kUwnBDOqMl6a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 99
+ },
+ "outputId": "7209be8b-5d89-43a1-fb48-35103eba8fe9"
+ },
+ "outputs": [],
+ "source": [
+ "print(\"Ground Truth Regional T2 Estimates:\")\n",
+ "display(qmri_gt.to_pandas())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(\"Predicted Regional T2 Estimates:\")\n",
+ "display(qmri_pred.to_pandas())"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 99
+ },
+ "id": "d5dqjITe5IkJ",
+ "outputId": "03b07adc-1123-410c-8d90-0fc5c311890f"
+ },
+ "execution_count": null,
+ "outputs": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "toc_visible": true
+ },
+ "interpreter": {
+ "hash": "2eb31e4132ee4926db264fe71a873573f5351ed39181c53ae251bffe4e1faa2d"
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
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diff --git a/projects/REC/StanfordKnees2019/README.md b/projects/REC/StanfordKnees2019/README.md
new file mode 100644
index 00000000..7b2468eb
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/README.md
@@ -0,0 +1,48 @@
+## **Stanford Fullysampled 3D FSE Knees 2019 Dataset**
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the Stanford
+Fullysampled 3D FSE Knees 2019 dataset.
+
+For more information, please refer to http://mridata.org/list?project=Stanford%20Fullysampled%203D%20FSE%20Knees.
+
+**Note:** When running the preprocessing scripts please make sure you have the ``ismrmrd`` package installed. You
+can install it with the following command:
+```bash
+pip install -r requirements/requirements-ahead_stanfordknees.txt
+```
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](visualize.ipynb). You just need to set the path where the
+dataset is downloaded.
+
+### **Preprocessing**
+The preprocessing pipeline is implemented in the
+[preprocess_dataset.sh](preprocess_dataset.sh) script, consisting of the
+following steps:
+1. Convert the data from ISMRMRD to HDF5 format.
+2. Split the dataset into training and validation sets.
+
+The preprocessing script can be run with the following command:
+```bash
+bash ./projects/REC/StanfordKnees2019/preprocess_dataset.sh
+```
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/StanfordKnees2019/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` to the generated json files. In `exp_manager` please set the `exp_dir` to
+the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/REC/StanfordKnees2019/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/StanfordKnees2019/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/REC/StanfordKnees2019/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/REC/StanfordKnees2019/__init__.py b/projects/REC/StanfordKnees2019/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/StanfordKnees2019/conf/targets/Test_SENSE.yaml b/projects/REC/StanfordKnees2019/conf/targets/Test_SENSE.yaml
new file mode 100644
index 00000000..b742bbc1
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/targets/Test_SENSE.yaml
@@ -0,0 +1,106 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: none
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: /output_dir/atommic/reconstruction/targets/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_AutoEstimationCSM/SENSE/targets/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/ccnn.yaml b/projects/REC/StanfordKnees2019/conf/test/ccnn.yaml
new file mode 100644
index 00000000..01067362
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/ccnn.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/cirim.yaml b/projects/REC/StanfordKnees2019/conf/test/cirim.yaml
new file mode 100644
index 00000000..a9715a21
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/cirim.yaml
@@ -0,0 +1,159 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/crnn.yaml b/projects/REC/StanfordKnees2019/conf/test/crnn.yaml
new file mode 100644
index 00000000..4a6cdc74
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/crnn.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-6
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/jointicnet.yaml b/projects/REC/StanfordKnees2019/conf/test/jointicnet.yaml
new file mode 100644
index 00000000..dea12ec9
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/jointicnet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/kikinet.yaml b/projects/REC/StanfordKnees2019/conf/test/kikinet.yaml
new file mode 100644
index 00000000..2ae5df80
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/kikinet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/lpdnet.yaml b/projects/REC/StanfordKnees2019/conf/test/lpdnet.yaml
new file mode 100644
index 00000000..64e86a2e
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/lpdnet.yaml
@@ -0,0 +1,138 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/modl.yaml b/projects/REC/StanfordKnees2019/conf/test/modl.yaml
new file mode 100644
index 00000000..63d5d005
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/modl.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/proximalgradient.yaml b/projects/REC/StanfordKnees2019/conf/test/proximalgradient.yaml
new file mode 100644
index 00000000..724f3f4c
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/proximalgradient.yaml
@@ -0,0 +1,120 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: PROXIMALGRADIENT
+ conjugate_gradient_dc: true
+ conjugate_gradient_iterations: 10
+ penalization_weight: 0.05
+ dimensionality: 2
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ProximalGradient/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/rim.yaml b/projects/REC/StanfordKnees2019/conf/test/rim.yaml
new file mode 100644
index 00000000..4a6cd213
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/rim.yaml
@@ -0,0 +1,159 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/rvn.yaml b/projects/REC/StanfordKnees2019/conf/test/rvn.yaml
new file mode 100644
index 00000000..613ca3cd
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/rvn.yaml
@@ -0,0 +1,138 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/unet.yaml b/projects/REC/StanfordKnees2019/conf/test/unet.yaml
new file mode 100644
index 00000000..d5439fdb
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/unet.yaml
@@ -0,0 +1,127 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/varnet.yaml b/projects/REC/StanfordKnees2019/conf/test/varnet.yaml
new file mode 100644
index 00000000..86dc0f3c
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/varnet.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/vsnet.yaml b/projects/REC/StanfordKnees2019/conf/test/vsnet.yaml
new file mode 100644
index 00000000..3f0be423
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/vsnet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/xpdnet.yaml b/projects/REC/StanfordKnees2019/conf/test/xpdnet.yaml
new file mode 100644
index 00000000..411a3052
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/xpdnet.yaml
@@ -0,0 +1,137 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/test/zerofilled.yaml b/projects/REC/StanfordKnees2019/conf/test/zerofilled.yaml
new file mode 100644
index 00000000..0bbc7973
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/test/zerofilled.yaml
@@ -0,0 +1,117 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ coil_combination_method: SENSE
+ dimensionality: 2
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/Stanford_Fullysampled_3D_FSE_Knees_2019_Test_gaussian2d_12x_AutoEstimationCSM/ZeroFilled_SENSE/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/StanfordKnees2019/conf/train/ccnn.yaml b/projects/REC/StanfordKnees2019/conf/train/ccnn.yaml
new file mode 100644
index 00000000..765da9db
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/ccnn.yaml
@@ -0,0 +1,186 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/cirim.yaml b/projects/REC/StanfordKnees2019/conf/train/cirim.yaml
new file mode 100644
index 00000000..1efdb298
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/cirim.yaml
@@ -0,0 +1,220 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/crnn.yaml b/projects/REC/StanfordKnees2019/conf/train/crnn.yaml
new file mode 100644
index 00000000..229deaf5
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/crnn.yaml
@@ -0,0 +1,186 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-9
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/dunet.yaml b/projects/REC/StanfordKnees2019/conf/train/dunet.yaml
new file mode 100644
index 00000000..b6b361d1
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/dunet.yaml
@@ -0,0 +1,189 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: VS
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.5
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/DUNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/jointicnet.yaml b/projects/REC/StanfordKnees2019/conf/train/jointicnet.yaml
new file mode 100644
index 00000000..f812e7bd
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/jointicnet.yaml
@@ -0,0 +1,196 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/kikinet.yaml b/projects/REC/StanfordKnees2019/conf/train/kikinet.yaml
new file mode 100644
index 00000000..007577fd
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/kikinet.yaml
@@ -0,0 +1,196 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/lpdnet.yaml b/projects/REC/StanfordKnees2019/conf/train/lpdnet.yaml
new file mode 100644
index 00000000..bf658f60
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/lpdnet.yaml
@@ -0,0 +1,199 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/modl.yaml b/projects/REC/StanfordKnees2019/conf/train/modl.yaml
new file mode 100644
index 00000000..15427588
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/modl.yaml
@@ -0,0 +1,187 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/multidomainnet.yaml b/projects/REC/StanfordKnees2019/conf/train/multidomainnet.yaml
new file mode 100644
index 00000000..54936759
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/multidomainnet.yaml
@@ -0,0 +1,184 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/MultiDomainNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/rim.yaml b/projects/REC/StanfordKnees2019/conf/train/rim.yaml
new file mode 100644
index 00000000..887fa192
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/rim.yaml
@@ -0,0 +1,220 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/rvn.yaml b/projects/REC/StanfordKnees2019/conf/train/rvn.yaml
new file mode 100644
index 00000000..e5ab6e6f
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/rvn.yaml
@@ -0,0 +1,199 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/unet.yaml b/projects/REC/StanfordKnees2019/conf/train/unet.yaml
new file mode 100644
index 00000000..fb3e24a5
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/unet.yaml
@@ -0,0 +1,188 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/varnet.yaml b/projects/REC/StanfordKnees2019/conf/train/varnet.yaml
new file mode 100644
index 00000000..b3901f25
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/varnet.yaml
@@ -0,0 +1,186 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/vsnet.yaml b/projects/REC/StanfordKnees2019/conf/train/vsnet.yaml
new file mode 100644
index 00000000..434f2f98
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/vsnet.yaml
@@ -0,0 +1,187 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/conf/train/xpdnet.yaml b/projects/REC/StanfordKnees2019/conf/train/xpdnet.yaml
new file mode 100644
index 00000000..57fb2d42
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/conf/train/xpdnet.yaml
@@ -0,0 +1,198 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ wasserstein: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: stanford_knees
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.Stanford_Fullysampled_3D_FSE_Knees_2019_gaussian2d_AutoEstimationCSM
diff --git a/projects/REC/StanfordKnees2019/preprocess_dataset.sh b/projects/REC/StanfordKnees2019/preprocess_dataset.sh
new file mode 100644
index 00000000..241880cc
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/preprocess_dataset.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+echo "
+Preprocessing pipeline for the Stanford Fullysampled 3D FSE Knees 2019 dataset.
+
+The data download link is available at: http://mridata.org/list?project=Stanford%20Fullysampled%203D%20FSE%20Knees.
+
+Please make sure you have ``ismrmrd`` installed.
+
+Starting the preprocessing...
+"
+
+# Prompt the user to enter the path to the downloaded data
+echo "Please enter the (downloaded) data directory:"
+read INPUT_DIR
+
+# Check if the input directory exists
+if [ ! -d "$INPUT_DIR" ]; then
+ echo "The input directory does not exist. Please try again."
+ exit 1
+fi
+
+# Prompt the user to enter the output directory for the preprocessed data
+echo "Please enter the output directory for the preprocessed data:"
+read OUTPUT_DIR
+
+# Run the preprocessing pipeline
+echo "Running the preprocessing..."
+python projects/reconstruction/StanfordKnees2019/scripts/preprocess_dataset.py $INPUT_DIR $OUTPUT_DIR
+echo "Generating train, val, and test splits..."
+python projects/reconstruction/StanfordKnees2019/scripts/split_sets_json.py $OUTPUT_DIR
+echo "Done!"
diff --git a/projects/REC/StanfordKnees2019/scripts/__init__.py b/projects/REC/StanfordKnees2019/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/StanfordKnees2019/scripts/preprocess_dataset.py b/projects/REC/StanfordKnees2019/scripts/preprocess_dataset.py
new file mode 100644
index 00000000..40414fce
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/scripts/preprocess_dataset.py
@@ -0,0 +1,81 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import os
+from pathlib import Path
+
+import h5py
+import ismrmrd
+import numpy as np
+from tqdm import tqdm
+
+
+def ismrmrd_to_np(filename):
+ """
+ Read ISMRMRD data file to numpy array.
+
+ Taken from https://github.com/iasonsky/meddlr/blob/main/datasets/format_mridata_org.py
+
+ Parameters
+ ----------
+ filename : str
+ The path to the ISMRMRD file.
+
+ Returns
+ -------
+ kspace : np.ndarray
+ The k-space data.
+ """
+ dataset = ismrmrd.Dataset(filename, create_if_needed=False)
+ header = ismrmrd.xsd.CreateFromDocument(dataset.read_xml_header())
+ num_kx = header.encoding[0].encodedSpace.matrixSize.x
+ num_ky = header.encoding[0].encodingLimits.kspace_encoding_step_1.maximum
+ num_slices = header.encoding[0].encodingLimits.slice.maximum + 1
+ num_channels = header.acquisitionSystemInformation.receiverChannels
+
+ try:
+ rec_std = dataset.read_array("rec_std", 0)
+ rec_weight = 1.0 / (rec_std**2)
+ rec_weight = np.sqrt(rec_weight / np.sum(rec_weight))
+ print("Using rec std...")
+ except Exception:
+ rec_weight = np.ones(num_channels)
+ opt_mat = np.diag(rec_weight)
+ kspace = np.zeros([num_channels, num_slices, num_ky, num_kx], dtype=np.complex64)
+ num_acq = dataset.number_of_acquisitions()
+
+ for i in tqdm(range(num_acq)):
+ acq = dataset.read_acquisition(i)
+ i_ky = acq.idx.kspace_encode_step_1 # pylint: disable=no-member
+ i_slice = acq.idx.slice # pylint: disable=no-member
+ data = np.matmul(opt_mat.T, acq.data)
+ kspace[:, i_slice, i_ky, :] = data * ((-1) ** i_slice)
+
+ dataset.close()
+
+ return kspace.astype(np.complex64)
+
+
+def main(args):
+ output_dir = Path(args.output_path)
+ if not os.path.exists(output_dir):
+ output_dir.mkdir(parents=True, exist_ok=True)
+
+ files = list(Path(args.data_path).iterdir())
+
+ for fname in tqdm(files):
+ kspace = ismrmrd_to_np(fname)
+ kspace = np.moveaxis(kspace, 0, 1)
+
+ # save the kspace as h5py file
+ with h5py.File(output_dir / f"{fname.stem}.h5", "w") as f:
+ f.create_dataset("kspace", data=kspace)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path)
+ parser.add_argument("output_path", type=Path)
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/REC/StanfordKnees2019/scripts/split_sets_json.py b/projects/REC/StanfordKnees2019/scripts/split_sets_json.py
new file mode 100644
index 00000000..b98ace67
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/scripts/split_sets_json.py
@@ -0,0 +1,62 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+import random
+from pathlib import Path
+
+import numpy as np
+
+
+def generate_fold(filenames):
+ """Generate a train, val and test set from a list of filenames"""
+ # Path to str
+ filenames = [str(filename) for filename in filenames]
+
+ # shuffle the filenames
+ random.shuffle(filenames)
+
+ # split the filenames into train, val and test with 70%, 15% and 15% respectively
+ train_fnames = np.array(filenames[: int(len(filenames) * 0.7)])
+ # remove train filenames from all filenames
+ filenames = np.setdiff1d(filenames, train_fnames)
+ # split the remaining filenames into val and test with 50% and 50% respectively
+ val_fnames = np.array(filenames[: int(len(filenames) * 0.5)])
+ # remove val filenames from all filenames
+ filenames = np.setdiff1d(filenames, val_fnames)
+ test_fnames = np.array(filenames)
+
+ return train_fnames.tolist(), val_fnames.tolist(), test_fnames.tolist()
+
+
+def main(args):
+ # read all h5 files in the data directory
+ all_filenames = list((Path(args.data_path) / "preprocessed").iterdir())
+
+ # create n folds
+ folds = [generate_fold(all_filenames) for _ in range(args.nfolds)]
+
+ # create a directory to store the folds
+ output_path = Path(args.data_path) / "folds"
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # write each fold to a json file
+ for i, fold in enumerate(folds):
+ train_set, val_set, test_set = fold
+
+ # write the train, val and test filenames to a json file
+ with open(output_path / f"fold_{i}_train.json", "w", encoding="utf-8") as f:
+ json.dump(train_set, f)
+ with open(output_path / f"fold_{i}_val.json", "w", encoding="utf-8") as f:
+ json.dump(val_set, f)
+ with open(output_path / f"fold_{i}_test.json", "w", encoding="utf-8") as f:
+ json.dump(test_set, f)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path, help="Path to the data directory.")
+ parser.add_argument("--nfolds", type=int, default=1, help="Number of folds to create.")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/REC/StanfordKnees2019/visualize.ipynb b/projects/REC/StanfordKnees2019/visualize.ipynb
new file mode 100644
index 00000000..a247c8a0
--- /dev/null
+++ b/projects/REC/StanfordKnees2019/visualize.ipynb
@@ -0,0 +1,186 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-22T14:14:52.336934Z",
+ "end_time": "2023-09-22T14:14:52.377789Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from atommic.collections.common.parts.transforms import EstimateCoilSensitivityMaps\n",
+ "from atommic.collections.common.parts import utils\n",
+ "from projects.reconstruction.StanfordKnees2019.scripts.preprocess_dataset import ismrmrd_to_np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "stanfordknees_data_dir = input(\"Please enter the (downloaded) data path: \")"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-22T14:14:52.377538Z",
+ "end_time": "2023-09-22T14:14:52.378359Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "filename = f'{stanfordknees_data_dir}/raw/1b197efe-9865-43be-ac24-f237c380513e.h5'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-22T14:14:52.377589Z",
+ "end_time": "2023-09-22T14:18:03.304962Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "100%|โโโโโโโโโโ| 81920/81920 [03:10<00:00, 430.64it/s]\n"
+ ]
+ }
+ ],
+ "source": [
+ "kspace = ismrmrd_to_np(filename)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-22T14:18:03.307080Z",
+ "end_time": "2023-09-22T14:18:14.241055Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "imspace = np.fft.fftshift(np.fft.ifftn(np.fft.fftshift(kspace, axes=(-2, -1)), axes=(-2, -1)), axes=(-2, -1))\n",
+ "target_rss = torch.view_as_complex(utils.coil_combination_method(utils.to_tensor(imspace), torch.empty([]), \"RSS\"))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "outputs": [],
+ "source": [
+ "csm_estimator = EstimateCoilSensitivityMaps(\n",
+ " coil_sensitivity_maps_type=\"rss\",\n",
+ " gaussian_sigma=0.0,\n",
+ " espirit_threshold=0.05,\n",
+ " espirit_kernel_size=6,\n",
+ " espirit_crop=0.95,\n",
+ " espirit_max_iters=30,\n",
+ " fft_centered=True,\n",
+ " fft_normalization=\"ortho\",\n",
+ " spatial_dims=(-2, -1),\n",
+ " coil_dim=0,\n",
+ ")"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-22T14:18:14.240798Z",
+ "end_time": "2023-09-22T14:18:14.241427Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "outputs": [],
+ "source": [
+ "kspace = utils.to_tensor(kspace)\n",
+ "sensitivity_map = torch.stack([csm_estimator(kspace[:, slice_idx]) for slice_idx in range(kspace.shape[1])], 1)\n",
+ "target_sense = torch.view_as_complex(utils.coil_combination_method(utils.to_tensor(imspace), sensitivity_map, \"SENSE\"))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-22T14:54:38.470875Z",
+ "end_time": "2023-09-22T14:54:48.328222Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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QEAqFsFgsVj4DAM/z4Hme/M9n2L/5v/5M/9bv1u/StFgsVp6n7+X99nvbVnuvfuajlmnQz+O4sJ32M90eVzv0+11j5HoOx4nX2Xmwz9fX8jffpcforDZzzObzOebz+blj9LkmPcfkDz3O/K0/s3ylr9H0SubfvkOPqZ5ny7v83rbHziHn2T5Hv4fX2rnTa9m2JRwOP7Qugtan6zp7zXw+X/le98Xeb9e4q+0u2WH7Yftv58WOke4jx+VxUqPRwNHREV566SXMZjMkEgmEw2FMJhMsFgusra1hOp0in8/D8zzEYjEAQLPZRCwWQ6PRgOd5mE6nqFarmM/nWCwWSCaTaDQamEwmiMfjmM/nGAwGSKVSmM/nyOfzAIBIJCLl18vlMu7du4dCoYBIJIJEIoFarYZ4PI7ZbIZcLofRaIRYLIZOp4PRaIRcLgfP85DP5zEej9HpdLBYLDAcDpHJZFCpVNDtdjEcDhGJRDCZTHDjxg30+33MZjO0222sra1hNpshm80CANbW1uD7PlKpFLrdrugkAOj1ejg5OcG1a9cwHo/heR6Oj4+xubmJxWKBwWCA4+Nj7OzsoNfrwfM8lMtlHBwcoFKpIBqNotfrIZFIIJPJoN1uo9/vY2NjA71eD7PZDNFoFPV6HeFwGOVyGUdHRygWi4jFYqjX6xiNRohGo8jlckilUnjppZeQTCYRjUZRLpfx0ksvIZPJwPd9jMdjaX8qlcJgMEC320WxWMRisUCpVEKz2US320UqlcLa2pqMX6fTQTabRT6fx+HhITKZDDKZDPr9PtbW1tDr9ZDP53Hr1i1sbW2dy2uP7BmwSsEuXCscrIAFVpUtP3cJNP29FpZcmBYQ6O/44yJXe/WC9zxvRWnoe6zwdwEJ3Sc9XlbYu4SeFoqusXG9Uz9bj4Hrejsmtj/63a622Dl3kW2P6z2XQbY9WqlaJasVqhY2VvlY0mPjGlv9neZZPt+2yUW2DRYcaLBu+63b6GqrnnM9765nWYAUxPO8xgUo+aPHN4hHg8CLBXr6Wpdyt9+7wIJdx5dFnCMCgdFohNlshul0Cs/zkE6nsba2hnv37mEymaDf7+PevXuIxWKiCGKxGNbW1tBsNuH7PobDIfb29jAejzGdTpFKpVAqlRCJRDCdThGLxdBsNjGbzTCbzfDgwQOUy2X4vo9bt27hwYMHmM1mmM/niMViyGQyAIDpdIpyuYxKpYJMJoO1tTVMJhPM53PMZjNkMhlMp1MMh0MUi0UAQK1Ww9raGra3txEKhRAOh9Hr9RCPxxEKhVAoFJDP55HP5xGJRNBsNvHyyy+jVqthsVhgPB6j2WwikUig2+0imUyiUCgAWIKGtbU1lMtljMdjxGIxpFIpbGxsIJFIYLFYCJDZ2trCfD6XM2R6vR5CoRDy+Tx2d3cRDofR7XZRKpUwn8+RSCSkTclkUsBZNBqVeRsOh6jVaiiXywiHwwiHwxgMBgKkYrEYcrkcyuUyYrEYWq0WptOpKHIAuH//PiKRCOLxOCKRiIw7wUMymcRgMEAsFsPJyQmm0yk6nQ5arRZyuRzy+TzC4fCFeO2RwIBrYVgl7kLm+lr923WN/j5IAVoLSQtVe40VEGdZEsCqQLFeDgDnDqzLUgsSNhR6QcLdJYSs0ncpDquM9XtcSjpIgbmAjFaMVthqJeISzJcFCFztDhpf/blWsvo+e60LBLuer5WcVo76+XasLcDT3gLXetRzwfZbYHvWOFnetc/W7dCf6+/5LPsM3Sd9DT/T7XQpcsvjLlCq51hfq5+r/9bzFgRiL8MjQIpEls7bRCKBSCQilms6nUYymcR0OkU2m4Xneeh0Okgmk8jn8zg6OsLJyQk6nQ4ikQjS6TR6vZ54eKLRKJLJJCKRCLrdLprNJsLh8IqSXywWmE6nYiF7noejoyO89a1vRTweRy6XQ71eFys6n8/D932MRiMUi0Vsb2+jWq0ilUqh0Wjg3r17CIVCyOVyCIfDWF9fh+d5GAwGuH//PgCIRdxutxEOh+F5Hnq9HjqdjniSEokEJpMJarUacrkcMpkMGo2GWN8AxAsQCoUwHA4xnU5xdHSE4XCI8XiMbrcrY+z7vrwrGo1isViId4Peivl8jvX1dYRCIUQiEWxubgpIy2Qy2NrawnA4RDgcRjweRzqdRjgcRiqVQjQaRTQaxWQyQa/Xw9bWFvr9PkajEZLJJPr9PlKpFHZ2dpBMJpHJZLBYLNDv98WbwjH0fR9ra2vIZDLwPA/tdhuj0Qie52EymWAwGMD3fSSTSZmvi8rdR/YMBCkflwXtUjJWCWnBawWkvoffWcUGPCwA9DPsQnYJFP2/tQ75fLbXuja1JWXDIfpe/u0SUraPuv/WU6E/P2ucXddZoKKFnkt4W7JWqCV+5xLStr+XQecJfZLuB8MbQQqJ97oUbZDS1mOk5922QbfDPsP1t+UJfqf74gKNwCqP23v5ve2Tq5+WF+3fHE87zlbputaRJsvn+p4gkOfyOFjArMl6SC6LMpkMotEo8vk81tbWRPGsra3h+PgY0+lUlFm73QYAjMdjvPTSS2J5A0tAEY1GkUqlVkDGcDhEq9USK5cW62AwwHA4xGg0QiaTEQv84OAAvu8jEongxo0bEq6o1Wrihu90Ouj1emg0GpjNZvA8D+FwGGtra6J0h8MhcrkcWq0WxuMxAKDf76Pf72MwGKDVasmzM5kMYrEYIpGIeDJmsxmOjo7Q6XQkXHJ8fIxIJCKWPUHxyckJotEoPM9DMplEr9dDNBoVzwewnO/pdIpQKIRoNIqjoyPcu3cPnU5HrPxGo4FUKoVWq4VerycW+f7+vrxzbW1NQAE9JL1eD5FIBGtra9jf3wcAUfhsLz0ivu8jHo8jlUohl8uJ3mEoggCKfZ5MJgiFQuJhiMfjyOfzAtJms9mF+OwVJRCet7Ds58DDQiQcDjsXuBYCNu6pFZp+no5xupA+v9OxxiBB7bLs9W/bJyvwrJWj22aFu+5v0DO1Vcjn2efoObFKhWNtrT7tVTnLnWyVCckFqlyg8CIg49UmPVZB1rULINjf1nK1zwcejs/zM1IQ7/I5/G3XURC/6uutqz2Ih12AWwMaV0hEK3DbJrs2LNjUvKb51/bBRXq9a763bbDvD5JDQaTnXs/bZXoFgKXrvd/vI5FIiKVKd3okEsH6+joGgwEqlQry+TySySTG4zHW19cRjUZXFBxd4PF4XCz+cDiMbDaLVCqFk5MTxGIx7O3tYTAYyHWlUgm1Wg2RSGRF8bZaLfj+0joeDAZIJpOo1+sAgOFwiOFwiMFggH6/L0pqPB7D9310u12Mx2PUajXk83lx0c9mM0wmE4TDYcRiMYRCIfksGo0ik8lgPB4jlUoBgHgCJpMJ6vU65vM5Op0OotEoOp0O+v0+ptOpgI7hcIhUKoVbt25JP8LhMJrNpoAQysT19XWsra1hMBgIWOn1ehiNRgIEEokEcrmcgLRQKITxeCzt6PV6EhoZDodIJBKYzWYoFouIRCJ48OABarUaut0uRqMRGo0GcrkcGo0GEokECoUCNjc3Jd+KuRTj8RiTyQSFQgHxeHzFowAAL774ongFCPzOo0c6wlgrUpf1wGtIQQLT8zyxts56pv1Mt8Fl3VrlpcmV9KeFBoWNjZnbvth7g5Sd7sNZY6nHxvUMV/JX0NjbdmgF6GqjfYdtr1VWQYlbehzsOLsU0GUQ22JzWCw4A069VPpzza+0dFxA97x5D6KzklItrwT1L8gitn/rfunfFlRrcq2zs9rjui/oO8ufLrBi22X5TfdFgxkL6Fzr1877Rfr1OCkUCqHdbiORSABYArNkMimWZTweF5fz3t4eUqkUOp0Otra2MJlMMBqNEIlEJBmPbut6vQ7f9xGNRhGJRJDL5dButxGLxTAajTAej8UC5XHshUIBvu/j8PAQvu8jnU6j0WhgNBpJIiI9CZ63dOmnUinEYjFRkvl8XpLxOOb5fB6j0Qj9fh+TyQQbGxvodrvwfR/9fh+hUAitVkvmq91uYzweY7FYSOJdNpvFdDqVtToajdBqtSS5kuBmMpkgFothMplIQibzC2azmYxXOBxGp9NBoVBAu90WL0O/30cymcTa2hparZbkEwwGA5RKJWSzWQyHQySTScxmMwkXeJ4nYR16NXK5HGKxGKbTKQ4PD7G1tYVWq4VyuYzJZALP8yQE0m63JYxRLpfR7XYxnU6xWCwwm80QCoWQTqcxn8/lfo7RhfjsURlTC3hrgZCCBIBLkfB6a93welcMUF8bZCXp9un7XRat7ZNVYmcpNK1Mg9ph+35WjgOw6hFxWa1W6bjGwiXMXCELKyTPskit8tDtOAsQXjYQcIETy7e6/8wLcY1FEB/ZH32NVXiaz118cF779GdBC9017meBR7umtWLluFkvF/BwfN/l7bNgya4pl6LW9wcp9bP40Y6V/l+vF9sO/e6gxNHHSVSiAES50Svg+z4GgwGazSbm8zkmkwmuXbuGZDKJzc1NeN7SHR+Px7G2toY3vvGNiEajCIfDYoXSgzCfz5HNZpFMJkVBURHXajWEQiFRThw/urCTySS2trbkPVRQ3PVAC5n5CM1mE61WS3IL1tbWkEgkRGHncjn0ej1R8uFwGPl8HsPhEPP5HNFoFK1WS5Q7k+ZOTk4wHA7h+z5OTk5QrVYRiURweHiI+XyOarUKz/PQ7XYxm82ws7MjXg16JVKplOQZ0Aqnou92uyuyfrFYSNLmcDiUNTsajTAYDDAajTCZTHBwcCCJnicnJzg6OsLh4SEKhYLwYTweRzabFY9MOBzGfD5HvV6XvAOCiEQiITs/otEo0um0eH+YPzCfz8WLNBwOL8Rrj+QZINkFeBbysAvOZU1ZqyhIaLneGST0eI/9nEhUW9z2PS6l6mqTq71nWd/6f1d/rUCyQpHttvdpS9ZaRRYcWeWu26SF/nkxcNvecDj8kPud/7vA0uOmIOBjlY0FgppcIQDXOGs+s0DJAkLXM/Q8u/pheddlFdt+6v91Py2vW5Bj26+fFwSCXf+fxbu27dbad60f22bXvNlxtf23vEuyXqHLBLRUUMViUeL/TBKjW5gZ5cDSXd5ut8V1DUAUFRUfLVparq1WC6lUStzvd+7cwc2bN2VnQTabFeuZijYUCqHT6ciWt9lshrW1NaTTaRQKBRk75h2Uy2Vxoff7faTTafi+j/39ffE6rK+vSy5Er9eTraylUgmTyQSRSETc+JwnWvOMj3e7XaTTacRiMVGC3E1BUBWNRgVAZTIZ2T7JHQae5yESieDevXvSt1gsJoCLnkF6XgCIFyGTyQhgYQ4GQwD9fh/z+VwSKOl98Lzl9kcmgsbjcdnKOR6PMRqNUCqVUK/XsbGxIc/hOI5GI/GcbG5uClCkp+aiuwkeCQyctfCtALQC46zFpC1WPjdIofMzKzCt4LDv1M+xbskgQa6Zzvb/PBBjhdt5fXBZNecBExcIcoEXl+JygY+zxiQIpJF0/QB93VkhisdNmid0IptWBrbtVum6lINL4ZM/XHzhUl5BCta6rm2dBvtcq3D1HOrfLiWvn2f/tzwXBHRsu4MUtSunxmUo2DYFAdogZR8EboJAMQHKeTLocZHv+xLX9n0f2WwWh4eHmM1maDabKBQKGA6HkiR2fHwMAOh2uxJPT6VSODo6wmg0QqFQQDQalWx5WtO0shmn5n557mKo1WqYz+coFApYW1uTLHb+zxDAYrEQ67xer+P69esSDmi1WhKLp4JiLoTneZjNZqjX68jlcohGoxiPx5KnwPwGAoA7d+7I+weDgdQz4A4H3/clZyIWi2E2m6FcLksOwfr6Ou7cuSM7KEKhEPr9vng0GGpgWGUwGMjWyHA4jEQiITse6Lr3vKVFXqlURGEDkGTJtbU1SV5Mp9Myb4lEAtlsFvF4HNFoFHt7e+LRqVQqskukXC6j2WwilUrJ+5hUOZvNMBwOcffuXWSzWbmf2xgvQo/sGXAtQJ145FJSVugFLS6rWK073SXIzhMA+hq7wIMsDNezXEIy6DsX8HEJH+0KPgto8DPX2J8Hos4DVa62ucbwvHe73hMENC6DtBKwygDAQ8DMxcd6u559tp4f1xic5SVwtTXoGpdys7zhmkMLcD3PWykipO9z8bMFG64xCgLGfL/la71Lw74raH0FAYaz/natf/5vP9MettcC7/r+Mj5eq9Wwvr4uVvnW1pYopul0img0in6/L4qLLn9as9w1cHR0hPX1dYzHY3GFr62todvtIpfL4ejoCPF4XCztfr+PfD4v74jH42i1WqhUKhgMBlJ/4P79+7KXH1gqQFr0vu/j6OhI9sX3ej3kcjnMZjNsbm4im82KBUuvB4FFKBTCfD5HOp3GSy+9JM++ffu2XOf7vnhGmGwYi8Uk1NFutyVEwf33d+/exfXr19FoNNBsNnHt2jUJPQCndQba7TZeeuklvOUtb8Hzzz+P69evI5vN4vj4GKlUSrwJuijQ/v4+ptPpSg7FeDyW3Qz0RrBeAXMmms2mABtgubOi3W5jPp8L+KpWq9K+bDYrIEbvAGHYgCEHei/Oo0dOILTWv0t4cDFZAaQpSLFrIeZakK5YH8laRa5n6PfrNuq2uIAAn+F6nhVgFqi4PAAUjPp5LgtKP4fFJoJCJC4rMAgUuKyvs5QLr7U5GPo9+pnWMrzMrGzdNtt+l0I4S/nZe/X/Z4Fgl4J28W6QcrRtoZB0rUFXqOAi4DBIwWrwSovOuu05x0FjYEnLCPse9iHIq3heH1y8q9+h77fvIT/YMXQB/MdBnres3hcKhXBycoJwOIxisShxbCo9KpXFYiF5AfV6XWL98/lcqgvWajVJtKtWqxiPx1K0ZzKZSDggEolI7QC64wGI0huNRnj++ecl3r++vi6Wa6vVwrVr13B8fIwbN27ghRdewHg8RjKZlAQ7KunFYoF0Oo1UKiVub1b6m81mUguBFi4VfDgcRjqdluz7ZDKJRCKBRCKBXq8nyZC+7+Pg4ABPPvkk1tfXcXR0JGGSRCKBzc1NjMdjAVb0SNAD8La3vU0KKlGJ5/N5HB8fCwCjvDs8PBQFrD0TrAnheZ5Y8ZPJRCpBJpNJpFIpyV0gcIhEIlgslpUIj4+PcXh4KLUYQqEQGo2GVJe8c+cO1tbWMJ/PMR6PkclknHl1QfSKcgbIpC4LlqSFrN3yxPtdgti1QF3PJFkhelZWtiUXeNBts2Tj6K7MdD02FlzYfts26jHV79MCKshqdQERq+CtIHRdr59nAZAFgdZz43qfHZPLID0fFoyR7HxY3tXfuZ6vlaEGQrwvCLza+LSdI/1u3Q9b+0D3xzXmDIUE8ZFdx5Yf+IwgfrY8YfnsrLmwa+gs5ey61zUvtp0uA8a1RuwYvBZoNpvh+PgYw+FQLEDyJivVsagNAClUwy2G2o08Ho+RzWYl05xb+ACIFR+JRETpETQw5MAkt9lshieffBKtVkuUdigUEkUFAPV6HZ7nyXZDJgGORiNsbGyI25y7F7gVjvvz6Y1IJpMSpiiXyzg5OYHv+0gkEqL4isUi4vE42u02Go2GAA7u3c/n8+h0OgCWuQnlcll2BoTDYQlzbGxs4OTkRKx91nTo9XqoVqsIhULY29tDqVSSRL9EIoFyuSzJndPpFJVKBdPpFM899xxu3LgBYMm70+kUkUgE29vbsu2yXC5LaWT2ezKZSCEoVmUkyGMSJvvs+z5qtRqy2SzG4zHS6bQkE04mE+zu7l6Izx45Z8AKAfu9SyHo70kuxWSfa4W33t4FPBzTdQmoIM+EfafuX5DFcZYVqZ/jAgGkIMvatvEscGEFqquNZ5Hur0sJ2Gv0eAe1EXg4jnzWeD5OsoI+aM5cIMfFsy5Qo8GSvjZoPfD/II8AP9P3upLoXO3n/5oWi8VKv1zA1t7nAgeu/r2SNa+fGQQkdNvYZj0GQQmdtk+uuXSNnQVf+rrL4uHFYiEu58lkIi51Jo/lcjm8/PLLopToPgYgJYbr9brU6ec2O9/3USwWMZ1OcXJygmKxKDKWipB72YvFopwd8PLLLwuYuHv3rsxbOBxGv9+XGvij0UgK38znc3G/h8NhqXy4traG4XCIer2O4XCI17/+9fB9X1zcrOhXq9UwHo8Rj8fFG8Ls+2azKdsSw+EwKpUKarUaZrOZbL+czWbY3t6WnRF8P/MAMpkMjo6OkE6ncXx8LMmQvu8LoOH13BXAHREbGxuS1MjQB133yWQS169fl5LPzz//PMrlMkajEXzfF4A1HA6Rz+clzyAUCiGRSKBUKknCKLeLsnwykwsJ9LgFEliCMAKp9fX1V6/oUJDwsYrIfudaUEGWqH6Pvs4lJPT1LuUYlIVs22GFnatP9hmuRES2i0BEgxk7NswkdQkq/S5tLbnGxgroi25bCwrfnHW/DZPofloAFNSvyyQ7D3rOgIcBKMkqv7MUh0sh23nW/7vWSRDPuACnnksqfX0N79fzRKFj+6J51vIvPwuFQsK7ui9ngWjbPz2mbHcQYHM9R//WY21BiR0D13i41tOjeBgfBw0GA1Gs3B3Q7XZxcHCAW7duiSJgMR5a4dwRkMlkxP2ueT4SiaBQKKDVakl1QM4tXfqetyx1nEgk4PvLw3VYhpd1/VOp1Ep2/c2bN7FYLNDr9ZBMJsUzxWczmbDX62EymWBzcxMPHjxAs9nECy+8gNlsJgAinU7LDgHmFDB0QhDAbP/Dw0MpWkRe3djYQDqdRrFYxPr6ugCE+XyOXq+HBw8eyJkAs9kMjUYDw+EQb3jDG0T5ZzIZxONxdLtdOXOBBxuNx2O0Wi0J4zBUMxqNsFgsz0/o9XpyYNAb3vAGseIrlYrc32g05JAk7goZDAZS6Ijto/VPLwF3cRBUcZthKBRCt9tFv9+/EI+94gqE+rdVBEHWgGsRBwEKq2iZmel6t1b61lJxCW2t1FwKDFitNqeVoxZoVhHzt3U/23fYz+3YBllpQX0/6/1BCty2w/Wd670uC8xlWQUBwMumIAVhM8iDfnMsXV4SrYg1ucIRvMeGgvS79P86t8UVgtD5L5Zf9fv0/1xTdgz4Dheo4P9nZdy7gI++NwgA2zboz12yxq6tICDAe13rkn3VbbN5SZdJ0WhUtgJOJhM0Gg1JEPR9X04mPDw8lFK8PHHQ95d1AqjAuWVvf38frVYL9+7dA3A6P9PpFDs7O/JuDRg3NjYkgZA8lkqlUKlUZF+97/sCRoDluQrcy7+5uQlgWbSIVfN6vZ6ELbhjgEqvXq+LW1/rBOYr8DwFWs8EDxsbG9je3hZPied54inwfV8OY2L7qVB9f7nNkfvzmbnPbH+S53m4d++ehDIWiwXq9brsqKDV32g0UKvVZA70WQ+Hh4cAllse7927JwcnjUYjAMswD4FFr9eTLZO+f7qzJBKJoFgsolQqyVkH+XxeANRisTyEyfM88UCcR6+o6FCQtaeFCq/VGdguRaLv1d+5rMsgxepKznMJIlc/gt4blPDmEowuoWv76xI8Qcon6Jm2z6732DFwCVb7TDteWiDbZwcBEhegOG/OL4NsfzX5vv8Q6LR8YMf4ou9zATj9PK3g9W/NDwQb9nwMkitMw2uCwKu+TrcpaCeP/Yxr3AJYFxjXz7ZhCk1B350FOPR4aQoCJPoZrmRIfe1l8i7bVy6XJb6eSCTk6FvuCqAl3Ol0EIvFUKvV4HmeVL5jxjrDAYVCAdVqVU4vpCcBgNQKILBgrX0qXoYdWIqXcX3f9yX5jcf7stTuvXv3cPfuXXzmM5+RPnW7XdmpwLyBdDotMXhg6fI+OTmRsAFDCPQyMH9CH0DE3AqOm+d5Mi4EDXxOqVQSbwGVted5K5UdeUAQwxWs4cBxqVQqePDggVzHQkH0EhBA8bmsc9Dr9UTBR6NR7OzsSGVBekOYv5HL5SRUwtMm79y5g3a7jaOjIwFE3B66traGw8NDeN7psdbn0SOBgfMEO4WCVSyuhcjveZ9+hl3oQUmKfIZLcZ6lzK31wQxptt8VT7VK2gUkbOamdUNaQa0FT1B2tv7fjjsRrR3fIIsoyFqz7z1Lebn6bhWGfeZrgbR14QI0AB6a+yCLXn+mecO6zjW5QKflc/t+l1KywE3zrg47kayVa5/J3/pZun2uPruAqQsEuHhRr2kXUDiLX1yAWX9n3xc0F5bHbb/O4v/HTYvFYiXZjwVw6Ormsb6e58m+8larJS5zAsRisShJaSy5O5lMUK1W5ZAiZrDT+8DvWPu+3W7LeQie5+Hu3bviks/n80in01IPgKcKsiJePB7HW97yFuzu7oprPZ1OS+ldegWYbKhL+PII5MPDQ/Em9Pt9eJ6Hw8NDdDodCWfwGQQOnudhc3NTPBG09NfX11EoFETZ85hgYOm96HQ6uHPnDjzPk2RKbo0slUoSsslkMnKc8fb2tpxgyLMCIpGIHKoUCoXkuGPy1nA4RCgUQq1WE8DCcyNmsxmy2Sy63a6ECrj1c21tDdVqVYpG8SCrbDaLxWJZ64Ft45kU59EjH2EctGCtRakzl4GHLWH92VnWK5/tUvraguF1fLdVoGclarmEg30f/7ZxVP2dy3IBVkGGvdcCBvvMs4RWUH9cCty2yTUGWlnaZDj9W19vrU1Xe11/P27S4+2ydoHVfmqL3cWjLmUfxN+uudJgz/KrVd62Lfb5fIb1kNn2aT50VSVzgW5mMweBbhef2e+CgJDlb1f4w3WdHtMgwGQ/Owuw6DbZUM9l8iwAqXaXy+UwmUzQ7XblsCBmyV+/fl32s3ueJ4V13vCGN4ini4ftUIloxc2YfrlcFhe653milLjLgLkKPPgmFAqhXq8LYGG9/sViWRUxFFqWLOb5BM1mU0AI4+gAcHR0JMWS2u02Fovl8cONRkMUfSwWQzqdRjabRS6Xk4RKuta73a60u1Qqwfd9OUqYyh6AbGlk6GAwGEgeRqPRwMHBASaTCRaLBYrFIvL5vFRn5DY+fSAQk/VY14GnRdbrdWQyGbmXXhdm/XPbIZU3zxJoNBqoVCpoNpuy/VDvDuHZEPq44s3NTal0eO/ePdmh4PvLA6FelQqEWlC5lJ+9jtdQAOn7XADAtf3HLnotnBmCcAlB2x5NLotCAwn9OS0+/W59r7Ui9L18t+2Ty6rR/bT3a3JtZ7TjZdvjGg+XhecaQ/s87eK1wtnVD/v9ZZCLN88aIzvGGghqxcLxcD3H1X/Nuy4F52qzDbHp59sse70u9LV2Xlyfs9/63fpaPef2Orumdf91G9mfoHfY7125M3q87Hq0bbLz4ppzO2audl0mIKArvNPpyCl13HvOUsWNRgPRaFTK3Pq+L1vfarUa4vG4KDa6+HUJXu6fB4BOp4PpdCqVDbn9j14Jxut3dnbQ6XQQCi33uqfTaVGsfNZoNJLEPLaf1mwul0MkEkG/35dTBnu9nrj10+m0FFDiSX6j0QjD4VAKLLEGAkEQPSQES6lUSmoZMH5eLBYRjUbh+0t3fiwWk8JLrLDI0sv8rlqtyhZGFm4ql8uo1+vodrvY3d0VEMOtn9vb2/A8T4BIKBSSo6gnk4nkVqyvr6PX662UMp7NZtJmFn5i3ki1WpUERp62SB7lscnj8Rj1eh2VSkUKH12EXvHZBPa3VaguhWitJC3I7KJzxRVd4MMFMlwKzv7tWuAusMPfOts5FArJFhz9PJuU6Hqmq39nWTd2XM4DYC5g4nq2VfoUwi43tRX2Z+1scP0dNN6Pi1xKQH931ti52h3E93acbBJe0NZMDTJcgEFf7wJ9rpwZFx/qttu5cq3ds9aB5RPdHutV0s93gVn+7QopukCW/d7Fuy4vh+67BWTWCHDN02URzxMAIKV+O52OnIgHAM1mU/IJWNa21Wohm82iWCxKZT4q9uFwCM/z5P9kMonDw0OkUikcHh5KGeBOpyMlcRkjj8fjGA6H6Ha7Yv3yZEHGs1mb3/d92ebHRDweN7yxsYH79+9jY2NDQg2e56HX66Hf76PX6+HGjRsYDAa4c+cOFouF9IelfHkQ0u7uLh48eCDnG8RiMXS7XZRKJfT7fcmN4FY7nsQ4Ho+xubkpWxa5g6LX68kBRgw3XL9+XY5BZnEjAinyEsMXzA2gNZ/JZHBwcCDghfqDOw6Y+8C6B9xCSr5LJpNIp9MyD9xmyuqSx8fHUnGS5Y273a6Ui74IvaJyxJZ01qldzDbeboUnEHw2gVXyQHChIGvxBCksfe1Zz9ECwxVn1u/idTo72zVuVhm4tj3q97kEvxZoWgheFCTYErSuMdDP0VaTbae+Vys+e78ep9cSaT7RwNQqC9dOFQtC+b0mFyAk6fu5fiyvByn3oHXkelcQQNYgxCp6q6wJfjU4CQIaFvRY8KFBhwUN9vl2TF3A1gVA9ZjZnQIuwGfn5ix58biJ72ZcGIAkA1Kps3od8wWonHlM8XA4RKFQkPMKeFoeaw+Mx2M53phZ9qz4x3AErV7fX27rm8/n2NzcxL1796Q08YMHD7C5uYlOpyOFb2azmeyfp/JOpVJYX18Xq5vHCfu+L9bzvXv35AwEKjkq5WeffVZO8MvlclIemHv16Tnp9XpyUiBrLjBXgecQ1Ot1AU/ValV4ivyZTCbheR729vbEvT+dTtFqtfDiiy/i1q1b8H0f6+vrEtIgYJpMJgJwWEY6FFqeIsjdArqiJ8MsBFQsK5zP5+W93OLJ/BAAePDggZz0WK1WV0IDrVYLmUzmQrzm+RfkdMYZbVxPHuRQElrg6IVJAeuKS1thwb+DBJ5LeAUpIZf1Y9uu2+MCOCTbJtc7g0COFfoX6Y8ej4soIM6Zy43Na/i9VQq2Dbq9tu3WQtTjZa+fz+cXdll9Lok8G2TN2nlx8Yb1YFllrnmIddaDkliDyAUGdB+CFK1uH/t2Fu+6AK21rq2HwsWvrnXsAuFBIsbVTzsWZ42Vi3ftNSRmn9sDtfRv2xd9r/79OKnVauHg4ED2xPv+0rJkRvzR0ZFY3KxdH4vFxErmHvznnnsO5XJZ3NNcj8fHx1hfXxdXf7PZRDqdxsnJCba2tkQx8bhceiYYO2cogTsYQqGQKHDuKtDVDFl+mOV56c6nAmNBJSo3Jtzpw3m4za5arWIymcgxwozl04VPq9nzPKklMBgMsLu7i3Q6LVn70+lUdiGUSiX0ej1sb2+j0+msuOKZlMc6C8zj4E4AHt3M+5h/wWx+hiKYBMjyw41GQ0o4Mz+CFSPj8TgajQY2NzelmiKLCBF88LmszphKpSRJMZ1O44knnljZMhpEj7ybwC6UIOQNBC9oq8iswrSK1KWArJCymfxBgkiDEv0se5/rXtcYaIFr32mTkfT9GoHae4IUPT/TngO+14IR26az5iZImNr36b9twaQgK/WsvjxuOqstQQqJ19ukMpv8p6/VIEu/T19n28S/9fy5xlLzuavNZ31+Vt8siKclxz7atrnm9iw+cvXdAjTX2rPgUoPjoP7a69l+DXh4DfnYrsMgmfC4ifvjB4MB2u22VAI8ODhAKBSSU/W4T50VBWnFsure+vo6ksmkVM7r9/uYTCYrme+pVEp2Kly/fh2TyQTT6VSSAdvtttS7Zwig2+3i6OhIXN1UhLFYDNFoVNzhLHzj+74AC2bF+/7yWGRW2CuXy7JTgsCaGfGe50kC5N7eHg4PD2VLIZMXWcPf95dnEjSbTTz99NNIp9N46qmnBIzwxL98Po9cLoednR0BHlxra2tryGazKJVKcjwwQyIMtZRKJQwGA7z44ovSHpY05gmRBwcHUnI4FAqt1ANg4uZkMkEulxMPCHMISqUSgCWvtlotLBYLZDIZ7O7uSu4HvQ1M8GTIiCDqIvSKKhDqBep6URBC1wtT/+8Sbi7XrBVCvEe7d+07g9rF+zQ4cAk8fZ/+zmUR679tH7UwooWsn6t3FdhkK5dFZhWUKzHLBaZcVqEFFa6+27FxKUfXOJ1l/T0usgBMz7cLeOrf1n0flANglbr+HQRMXWOv11ZQX/QzSWd5z3i96299v+t5dh1qgGvzaPTfrnVgQdN5fbVrXv/vCqvYMdLvOSsXxgI613svi2hddrtdeJ4nJXy3trakHC4P5Nna2kIul5PaALFYDMViUTwK+/v7UmKYpxjSOwAsY/lUwLRc+/0+RqOR7HVvtVqYzWYSv65UKrh27RpCoWU1wGw2K/F0jiMLBTFTn4WQmPfAI311uJlWMz0K7XZbzg9ot9uIx+OiUPf29lAul6XQEE8kZPiEhwFtbGxIsZ79/X2J2RMULBbLA5N4CBEASdhk7D2fz0tewc7OjpQb7nQ6UschEonIqYoMjbAQFGP+g8EABwcHaDQasotAH06UyWRkxwNw6rXi+5lk6Ps+crmceDjoJeIOCQCvTjniIAQetHiCLFT+tta8vRd4eO++FZhBSt8lNIOsPyvwXELTKoIga8/2JyjzXm/XAlazxnmtTaDU7QhStjbfQAtRV5jBCmffXz0Ex/bLZfXZ9un3uvrwuClI4WrF5SKrzEgMmbnm4Dz+d60RC1aCeOsiY+9qv+YZC2J0m21+TxAodQFhC8Zd7XF5tM4C0RaA2nEM6qu+zs6RvtfOhQVQVvZcBqVSKQwGAwwGA+TzeSkbHAqF5IAbKqrBYIBarYZms4kXX3xRtuZNJhMpv5vJZFCtVjGdTrG+vi4hh2QyKQcQ0ZW9WCwko30wGGAymUiBo1arhXw+j3a7jcPDQ9m62Ol0BLQwRt9qteD7vtTl97xlxj+P6k2n0/A8T3ISCEBoNMXjcdlOyJ0HrO5Hy7pcLiOdTmOxWMgphMwfAJZJmM1mE+12e8X9n0gk0Gq1ZPdCNpvF0dGR7LjgHv+jo6OVXRwMpbB0cSwWk/MDmCAZCi1PjNzb25M4/8bGBqbTKXzfl50HTK4sFos4OTlBIpGQQk4EEwxJtFot6RtLQrNMtPbgxGIxyVcgaDqPLiypXQvHKjh7rUvAkayFAawqZvusoDa4ku2skj3rWXyvFTK6vdabob+z73ONgb5Wf6//Pkvwu9pkFVDQe+z3ur0cP1finx27oO+t0HaN0WsFEJC/XKEYPU6ap6wCA1b5wfKXS8G52mFBk2uOrPtek2u+XcDurHmxCtN6l1ygUz/DlfPjAoK2X0Ggxip9Pf6ua1z3ugCPq69B4Etfb+fzMohW6fb2Nmq1mhTdYU7K4eEh1tbWpEwtf65fvy6lcWk1UzlQqfE0Pt/35VwCKnWCkEQiIRY3cHp8MXcUUNkwy56HJzFMwNwBlgEuFouyVc73l/kPPG43Go1KPf7xeIydnR3s7OxgMBiId4S1BdLptCjl9fV1vPzyy1gslkf9FgoFcaVHIhHs7e1hsVhW7kwmkwAgSXUsz9xoNNDtdjGfz5HL5bC7uwvP8zCZTFAqlaSOAN87m81QKBQk679QKMh2P5ZeZtIntyhy+6TnLUMA3OY4GAxQKBTE0mdRKJ0nxP+584JbJY+OjtDtdhGPx1EqleB5HiqVChaLBfb398XzcRG6sJR2CSiL8K3lBTwMFDS5FJQ0LCB50LU4g4CI65og4WgtFv23tphc79NK0b4vCHTYcbBAx/bZ1S6XkHKFPIKsKNezgubNpQCsAOb3LgXyWiCXknJZhUF5MJoPtOJklrC9jt/rdROkPDVpftLlke36su76s4CnnV8LQDS4sUBY98O15oP4S4+3BYeWv/U46ntcytt6KvQY6OdZ8Oxap5qC1uZlEttMxcyjd3u9nihfHs7D43EJAuLxuOQGcP88TyMEIFY/Y+1M9tvd3UWhUEAikUAymUSn05ECN77vS6b78fExJpOJHIQELJMYu90uDg8Psb+/j3q9LlUFQ6GQuOv5zEajAWB5BkOj0cBoNJJqhPfu3RP3OSkWi2FjY0OSC7n/fj6f4969e2LBcwcE6xKwKBNDHsPhUArzMGY/HA5xcHCATqeD4+NjxONxVKtVHB4eotFoSC4Edx1wDFnvoFqtSg7GZDLBzs6OJGxOJhPZMun7vnhLQqGQgB0CP84vd4jEYjF87GMfw2KxENDFHQ3AaSVaHg/t+z6azSay2ax4Zy5CrzhnwFo1gNuNqIXWec+ywtml0M8SeHx3kOLWFo+9T1OQwAhSwFbouIAE3+vyfJwHllztcF2nx9MFrKxA1m1xZdrbfgS16TwL9rIFahBfWeAKrCan2oz8IB51fa7f7ZpjrQD5ncuDYr9zAcqguTkruVd/5gIFfFdQbsxZYQHbRvsu/Xyr0K23yfZbgzU7lrYfeuw00LBtdgEzmzR5WcTDb7QiTyaTcgJhPp+X7HNWq7t+/brsp2exIBYTSiaTspedseTRaIR2u41QKIRisQjfXyYG1ut1ARXpdFoK2nieJ96HdDotlQSB0zFkMiABCt33pVIJk8lEziBYX1+XY5bX19clae769euoVquyJY/vYJGiQqEgtQT4rpdeeklCHKPRCNFoFKlUCtevX5d8gX6/L1n6zEMAIMcTZzIZVCoVJBIJdLtdNBoNZDIZTKdT3Lt3T5QtgTq3c7IscKPRkBMZfX/p+SgWi3IYk+ctEw75Peez0+lICII7AlieuFKp4Pbt21KLYLFYCDghT0ynUznkqdvtIpVKyUmPF+XfVwQGXIJN/20Fo/5bux5dVrAVSkEK2N5r2+USdPZeLSDOsgrOUth8nhZQQfUGXMDE1T5rEbm+s4LXWrDaijpLGbsUm0tIW3Cl3+sCdfoaPv8yyaVctGLRc2MzzEmuWDuf/Sigx6WUXHysr9FJqNaSdrWJ7bLPcV0b1J6gdRe0Xuw6Zntt/1yJsbpv+jPLT642WvCggZxrHejtk7pPlm8JmC+ajf1qEV34lUpFtg563rKaHgBks1mMRiPk83kBAayIx0RAnktAN/xwOJQSt4vFQrbLkebzOSqVCrrdLtrtNsLhMAaDgcTueXQutweGQiGk02n4/jLkQKs4l8uJ0t3b20OxWEQoFJIjgdmX+XwuCYaZTAaNRkMSCgkomNnv+754FahcU6kUvuiLvgjz+Vxc+ZPJZGVXTLFYRDKZlPBGsVjEdDqVMAUAOc2R2xBZxCefzwuY4DHHs9kML774IoBlEiMz/fmucDgsitrzPNmWGIlEEA6H0W63MZ1OEY/HZUthpVLB7u6u9D+RSEhuAsd4Pp9LUSGeicC6Ajxwyvd9HB4eSmnqi9Aj5wxYZe36bS1Q4GF36lmx2CAwwefo91nhpK+3goUZmVZguhSmC1i4Pud3WsCdlekc1PYgj4FVOhwnl5J1WYiusXP9H6TIXEmetp1WELuAzmVaV7YtZ31O4e8KT9lxsMDiPN61OTL6GhcAsHzHBe9ScnYOXGSBj4uHNM9Z3rOg0CYc2ne5+CBIXpwFSFyfuwwK13vsPXrMH3U3yGUQM91nsxm63S4ASCyeSX2JRGLloB+eAcD5YlW8fD6PGzduSFb+YrEsJFQoFKS4Dk8Q9DxP4v6LxQLPP/88YrGYFDE6ODjAc889JwfkzOdzRKNRVCoVyYZfLJZnDDz55JPIZDIoFAo4OjqS0AXd5uPxGOVyGbu7u7JXn9X7eDYAaxDQxT8ajZDL5SS5kddziyG3Ofr+8jjhBw8eSJiAlnooFJLjlH3fl5wAlvENhUJotVqSyMcth4PBAFtbW3K4kO/7sk2Spy9Op1M5+bDX64knJpfLwfd9bG1toVKpSC2IxWKxUmK42WzKYU++70tlRe6k6Pf7Ajju3Lkj50YcHx+LF4eFjz7nRxgHLXaXwHApMOtydSUf8T0uha3/DhIALuGjrSeX0tVCwgoka3W5hIJWCq5xsvdqi8MqlPOEtIts/9m/IEASBA5s/4IUup4zFxCzz3M953GT5lOrtK1ScvEulXBQf22/bYKiC0xqAOnaSWLv1ddbsgBEt5Wk1ys/JzjWbXf1SwN5bXHbNrjWrIun+Dz9uav9dkzsmNrn23cHkQUTVp4Egf/LIN/3JVTgeR7u3LmDl19+WWrlU5ElEglEIhFRKCxQw2OGB4MBUqkUQqGQnOJHBXVwcIB2uy1VALvdrjyLYIDbB33fl4I+6XQan/jEJ7C3t4dYLIbxeIzBYIBer4dnnnlGXOAvv/yyKCsActIisAxR0HXPhDsWFaIl7vs+tre35VjfxWIhgALASl+4q6Lb7Qog0hX8tra2MBwOsbe3h2g0ik6ng1arJYWHmIMwmUywsbEhvJBOp2Xr5ebmJqLRKDKZjJQj5pkL9+/fh+d5UjKY4YfDw0MkEgkJFfT7fQlzMK+B+Rb0yhQKBTnzoVgsIhaLyQmPDAGk02kASz5ttVpIJBJot9vo9XpSQfKicvfPnebtUuT2c32ttS6sUrGf6XutcLRC0KWMVjp7jpVrk6V4TZAw52dBOyDsj+2zfr5uj0sQWlCjr+d1rnPuLWCzfbXjEKTg9BjafrruOwsYXBa5wJYdp7OsUxff6Xtt0huJn9nvLchwtZXksrQtCLT3EGxaUOgCd/a5Lp7QfXHxR9Acu452tuvJJvdpvndd62qb67kuoHoW6AoCvJdFdJXHYjE5RpfueSonHqCTSqWkyEwotNz332w2USqVUCwWMZ/PRXEzNs8Mf+7HJ5ige51V95iMt76+jkqlglQqhXK5jOvXr6NUKonHIJlMYrFYSH2EwWAgY8ktdYzFDwYD2fqn75vP5+IRyGazGI/HuHfvnsgeeh5CoWXp5Pl8jk6nAwDyDrreI5GInB0AQCog8jTBSqUiYZbBYADP8+Q4Zm5rHA6HkuTHxMTxeIyNjQ2EQiFEo1E899xzMlYEMYVCQWQy6wywEBRDNboUOXcRrK+vIxQKyTHJ3J7IcAfbxbGgtyeRSMg2VI4Hj0S+CD3S2QQuBWY/15/p61wZz6SgBR2k0CySd7WTwMMFKPQit+/QgOCiwoHXnKcMgsbTZkLbZ+i+sP0uj4Jtp2s+LKgCHq7CqNum/3bNU1C/zprvyyY9FhaAuuis64L+1/3W1rTlJ7bH/s9rOY52PF3vDAIE/H1WH/XY2HtdoCFozeu/2SYXQHQ9134exJ/6f1322Sp1V6LheYDI9sU1po+TqAzYRh7Jy+z/2WyGWCwmOwJOTk6kDLDneRI/j8Vicj3j/KPRSLLXmRPACoSMtzM/4Pj4GNeuXcNoNBIrdzabSQKgroWQyWSwsbGBO3fuoFQq4cGDB3JULy32nZ0dSZbjKYHMFahWqxIj9zxPjhduNpuyJ388Hks/Od+tVkvmnWct6JyBTCaDk5MTxGIxaSsPJGJ+Q71elxwIHhhUr9cltHDv3j05f6DZbGKxWEhBJmCZ38HCUKwjwDoA8/kcW1tbqNfrAnju37+PYrGIZrOJXC6Hk5MT2brJcsm+vzyFkiWpgWViKctOczfEYDDA7du3AUDKG3Onw0XowmDAWiJWOLi2nNn7SUGKxqV4rbUQJIh1PkBQ22wfbF9sgpjtO5nTJkG63ukCIq4xcX3mGiutkKx1bvtjyQIbC4TOoyCw5iIXiLrIfa8mWUVik9r0/LssShdIPOsaq5wtD7vAgU5q09fqg6Xs+nIBgKB1Y/Mb+C57rcs7RjrLGLDrSz+Da1N7DS04sbLlLKWt30HLyzWW9vmW/10gXo+j6+/HTfQe6cp1VOyslMeKelR+xWIR3W5XxuH4+Fi+4/Y07v+nYpzNZvC80wqHAKSK4Gw2E/f9+vo6AODll1+WLXb9fh/ZbBZPPPEE6vW67L2/desWptMpnnjiCTlDgGGLQqGAvb09OTSJBYZGoxFOTk5Qq9VEkXIceHri4eEhqtWq3MujkVlKmKWOqWRZ2phbDvlM5hrwfAVu3+TpjIPBQHYK8NhjVl5kCAGAbEuMRCKIRqMCEh48eCBbN+mxuHfv3soBQ6VSCe12W8IQrBPheZ7sYtCFplhgarFYoNls4ubNm9K/aDSKk5MTOb9gbW0NvV4P169fvxCvXRgMnGcdWiXzKAvICiArnCnAXQrcJZi0srbP1wmM+j6rIIJCBlZIBAk0+ywKdZeQ0/2w73Ip77PutcBFk33+eWBFf2efY8cjCNRYxXfZ5EreI50FqFz3WB7Uc2B5177DAibrAdDt5Ti6QK0mF++7QIPmE8tbLuDjUpJBAMUCC/v+oDboz2y2vx0r199B68OuV9fcatAW9J7LBLLhcBiZTAbtdlu2vDFhLpFISDLe+vq6hAmSyST29/exvb0tYzoej5HNZiV+X6lU5PAgnm/AzHcmxDFDnqcQciveE088gaOjIwAQ9zZj7ty6SEUdiUTExc4yyGtrayiVSohGo7JGms0mrl27Jif35fN5cdOvr6/LVsFQaFmvgEcSl0ol1Go1OQPA8zypCFir1SR8wth7p9NBu91GNpuVUAS3Z3JcfX9pjR8dHUmthEQiIRn9PCDK93054ZH7/cfjMba2tqTts9lMEgJ5EBIPlZrNZjImw+FQjiWmQudYzedzKcaUTCZRLpeljHEsFpPrCIS0N4Rlo8+jRz7C2JJLCbgY2l6rFaW16F2I3QpVawG5lNx5CsgKItsvq5DPsmTOIpclaJUGn3WWMAoCJi6LR7fRNWYce6vE9PdBisy+0/UOO2evBSBAOqsfVknaXS/2Ozt2vIbEz/U42q11wMMJti6es/fYvlwEqLg8Ca7xCVoXdl3q6128a8GpHV/bbgsqbLutDAjicT3OQTKKn7s8Cfa6y+Jfz/Mk5r1YLKT2P/egM4v/+vXrkpkPQJRmrVaTnQetVku26NVqNSwWy8OFotGoVP/rdruYTqcol8uyl50KJRqNyiFHsVhMDj5ixUFW0ptMJvA8T45OJiig0kylUuj1euLhYEVFAhWWCabHYGtrS/IfeMAQzzTgVsFkMoler4dcLicnITYaDSllzO2JfA+3PE4mE6nbwBoKwDK2z+qOb3rTm7BYLAQQeJ4niX0s6EQgxJLALNtMUJZKpeQ4Z4KXo6Mj6R8Asebv37+PSqUioIbJlQwDVatVKdpEQMdQy3A4xP7+vpQ09v3lVs+L0CPnDAQtDBeqtgJVP4f36HuD3mWPiHUJj7Oe51Lsrr65nuESXEE7Ic7qi+v/IKXtiuG7rtOfuZS5bo/ewWDvd8VW7RgEvcuSiweC2v44yfKt5glgdd51HNr2UZ8/bnnSBciC8lZc9wbxtb6Pf7vAie6Hq4+6P6551RTURstfduyCnme39bra6WqDbjPwMLC2/XbNsesZtg0ugGYB4mUQlRQVZq/XW9mn7nmebDPjPvx79+6h2WxKtvnBwYGcTMgkPbr7Z7MZBoOBlPhlsR4eI8ycBX5fr9dly9poNBJQEgqFVuLrACTzn3F7bgtkfQHu92dRIt/35d2VSkV2BaytraFSqUiFQhbfoRUfi8WkwFEsFkOj0ZCtfTyemTqEoQSencCEQuYvTCYTFItFOcmw0+mgVqtJ7gV3A7BOAncBdDodKUPMsscsn0wvCRU3AFy7dg3tdhv1eh3j8VhqDnQ6HZTLZQCQsEq1WpXzCG7fvo1YLCYhFuZhAJDQD88sIIDijoPz6JGPMD6PrADTW91Ii8Ui0OLUgtFavy5rhde44r1WMFjrRV/jElL27yBh6Nq25erPWRaZFlBWMFmBZt9hhayrja6whx4P15ZL/TzbJ17jslRtbPi1QEEKjOQCr1bhAJC4q36G7ifv1TweNH9ngVZXu4IAs22Dq+9s01mg3T73PACtrw8aS/6vXf+uz3m9BaZ2bZM0KDgryS8oj8jVh7NkwVn9fzWJyorWMI8y5nZDegoymQzC4TAODg4kiY1JZ3rbIZPrms0m+v2+7PWPxWKSnMbvut0u7t69i+PjY6mvT5d1OBzG+vo6bt26JWGB4+NjARtU8JxT31/u46di1DKCdfcHg4HE+lkTgFsLP/3pT6/s02diI+vuMzGSdQ/YTuYK8MhlKmkAUj+AgOX27dtSK4F1BorFIjKZDDzPw7Vr11AqlaQeAQFWoVDA7u6uhDH4rtFoBGDpqucBUDxZkHPAHREMWdRqtRUPAw8n8n0fmUwGrVZLFDzHmOGb+/fvYzKZSAghEolICOQi9OcOE2jLkovOJri5FLC1LrXgdVm2ekHbHAJ9bZAVdFZ79TU6SVC/n9/xfxtXdSkX+ztIKbgsy7Msp/Pep7/TbdYKywpde12QpaXJBVb4bFffXgsUZEme1z72zTUe51m7+m8bOglSZEFrQQM/G4fXz+T1mnf5ucvrYfnDBVxd/Qta2/r9tr8ky4cuhe9KyrRhCp1RftY82j7az1zXXTbvMpmM8WbmBySTSbTbbVHQ3BZH13qhUJCte1SAa2tr2N/fl9MBOZ5ra2t46aWXUC6XV7bQTadT3L59WyxdftdoNCT2nc1mZQsf974z2c3zPAEYDE8sFsuCQjxJkVY94/TcDZFMJnHt2jXcuXNHDvFhoh77yYz/QqEgHof79+9LHsViscBnP/tZ7O7uSiEmHupzdHSEWCwmCYPj8RjHx8d4+umnBXCxrwBw/fp1dLtdAWbcUcGqgnpHBz2LnucJqOFn8XgcBwcHWCwWAuAIFAhkxuMx6vU68vm8vJO7PwhcFovFyo4KnqHA0s5MSKR35iL0iusMBKHtIOFqLREb+9PPPM8K0ve6djG47g8iK7hoabi27p11r0t4uLK19bu0QHUl/PEdti26TVYw8jmuZDM9vvYzqyg0WWXg6qN+P9uqrbbLsqw0WQtZf0ayQM1a1FZJBoEy1/OslW2tZ/62QI3v4m8XkNbXuRSsa41o/nB5QxgSse8AHq4L4Gqr7ad9N5/v8mzZdWPHTr/HtiVoHbneYdvH9gT16XETSxFzHzytSZYXjsfjYoGura2hWCwiEonI6YbMA2g2m+j1epJ1T+8CFQpj1ACQy+VE+Xa7XSm/y+OP8/k8FouFFMUJh8OYTCaSwLa2toZCoSDt6vf74s6uVCpisdP1zqp65XIZsVhMwg48XpmHMrHi4GKxEECUzWbRarXEzb62toZ2u43Xve51KJVKyGQyOD4+FlDU6XTwkY98BI1GA/v7+3jxxRfRbDaRyWSQyWSkoFI4HJbExWKxCM/zpBgTawhotzzLEfd6vRVPAMFPv9+XOWLSIBW17/uypZN5E4VCAZVKRbYZzmYzAV5MJgUgZaFZZTKRSMjJjaPRSKofXoRekWfgPOGpySVA7fX6GUFxbZeFDpwqL5dlG/RelzLUAvYsxWWFu/7M9ks/W+9i0G0KUpp2fCzosd4RjfS1orbv4pjZrWwuReUaR9JZ7leXsnutUJDyOq+dQbwVBA5c79Hzpq/RlgRwWmiGil0XJtFA2ipACxxtOzSPu8CFvobf8XlnhfX0fbzHtktfa+P+5Eed1a/HK2gO9HMsoAiah6B5tuvDjtdlku/7cqpfLpeTQ4pisRju3LkjFemYwMc4NuPMnuehXC6j3+/jwYMHKBaLKBQK0jcm5kUiETlEhycYavd9OBzGyckJgFNvDGPwtMx5SE+/35e6+tvb20gkEnJcMLPlPc+TE/iYBZ/L5cRrwIOXgGXN/Varhc3NTSklzJP9GJbgtkp6Deh2D4VCUjJ5fX1dkhOZ/Od5Hj772c/KQUWcfypatolufgCo1+tSzdDzPPGeLBYLHBwcIJ1OI5/Py/WtVktyKvb397G7uytbBmOxGI6OjpBMJlGr1TCdTtHr9dBoNKSeA+eFZ0kwCbFSqUgYo9vt4rnnnpN8AeZFvCpgIEgA6r/1YtbXupSxvt4KMbpbrJLTwlC/w7Urgd+dpdSCPqcgcllhVqjpfuk4mbW+dHvsOAa1nWMRNJ4ucOXqixXwrjGyz7VWvqsNej7t9a7+XxYFATYXOLTXky6iWFwgV4MvHmKlyfKzXQv2Oa7147qG7bCeAtd9Fgzrd9h+8f+g+Q0KC2jga3nwLDly3nX8+yLz4wKsrmTQIBn3uGmxWFb+Y4Y+M85phdP65HHCtKSZbU83/GAwwHQ6lQp5BJmHh4fwPE/2tycSCbzwwgsS+wZOD0LiaXhMbKOl3O12sbOzI6GAdruN0WiEjY0NCRm88MILEl/v9/viHQiHw0in01hbW5PiRKlUCnfv3kUotKzCxwOGmIDH0wGr1aoAo0QiIaGNYrEoYxYKhcQa9zwPJycn4j2ZTCZymmOpVJKSys1mE9vb21LJr9PpyPbAUCiEv/SX/hLa7TZOTk6QSCTQ7/eRTCal9DK3MTIUMBgMkMlkcO/ePQl/8Pjp+XwuOwX0KZIMP1CX0LMwGAywu7uLdruN69evy4FJLM2cTCbh+75USsxms5/7g4pI51k+rutdlqq+3iVANQDQQirofS5h5rqWwsguflebrQJ0gSFr+WghGZRX4BK2LmXkCqVooW8FZpBFp8dVKxqbVe1SjK42BgEkJs25kuxeC9aVbocV+JpvXIog6Hv9mSv5Tb9T36PvDfpOP8PllneBNPKNy3Vu32VDU9YKD1rjdgztugsaH/tsy1u+f1pSW39veVf/re+3/XMpflcb9P8uz+Rl8m4oFJIEuEKhAACSNc9YNPfvM5udFfIIHDxvuRWO/WDSnOctT0Lc3t6Wsr6TyQTb29tSDpgueWA5DgQWBHRsH/f3N5tNSWqs1+s4OTnB/fv34ftLVzitfc4fq/9Fo1Hs7Oyg1+uh1+uh1WpJSV0W0uEBQNwNwYOJCoUC8vm8bBPkyY30JNBSprXOfrDGQTwex40bNxAOhxGLxXDr1i2kUilUKhXk83nk83nJIchms3jhhRdWdjLM53M0Gg0888wzsgOCQIeHQmkemkwmslOD2wGZi0CQRm8gT51kUiXDHiyRzF0PiUQCyWRSyh5ns1nZ1ukKe7nokcMEQcoPeLh4h73GpbDswuNidHXAhdr5uSW6HV0CLciqsoLFJZhd7nnf9x+Kr7oEz1nWlO2Lq316jG177Xtduzi0i1q3S1/HZ9hr2TZXX7RScoGr1wKdBRKttWld49YDFJS8yt8u5WXn0qWg+E7rRtf3u5I/LS/a53ueJxngljRPu9aW7VPQ/XYdWbd/0Fpjn4NkhB5LV2KjHR/X+rFjc9b1LiB4mcQ5SyQSaLVaKBQKoixzuZwcY8tkwWg0KhUHi8Uirl27JifZ0Wqs1WqIRqPI5XKo1WqinJgncHJygo2NDQlN0AVPrwPf43lL1zRd6+12W+LljJ3TW8DPgNN1xTwDAgQmwgHLvAUm3NVqNbHC6ZZnBcZarYYXX3wRvu+jWCwKEKbbnUV/WAAoHo9LiV5djpkJhfSO8LwHPmN9fR2+70vFQG4B5HsASDLneDyW6oPXr1+X72m9V6tVKRJUr9cxm80kUZShj0gkIuCn0WgImOLWRnpW2HbWg6DnhjsQWCzqIvSKcgZsNjKFlEsIWgoSgi7hp9+jFypRE+9zudit0rPKTv9t3x3UT95j79UKQgs1V99dXgkriOy9LnChrUCXhWjb7QIRrjboz/TYB8WMXeMIPMwDrwXB6hp/YHU89ZwGKUDXONnvgFUlR2Vm4+MuJedK4nSBGPbHxS+6fVwv1tPGa11ue/0sy9P2N/BwmMsVHtB9cPEYn+Pqo+UnF9CwbXJ9ZsfSNXYaCAeN8eMkZrxPJhM53IZxc8aS0+m0KNtMJiN9GQ6HcoBNPp/H3t6eJLXxhMEbN25gMBhgbW0N3W5Xygn7vo9UKoVMJoNeryfzwBh/q9WSqnmcB1q5PEOg2WxKGWCOK4/tZS0EhkAAyEmJ2WxWkvy2t7clDt/pdOTo3uPjY0ynU+zs7MjhP+vr65jP53J8cLFYlBP8hsMh+v2+bJVkCWEqf8bZeeJjLpdDr9eD53lSYXBvb08OaQqFQrh27RqAZQ4Bizd1Oh1kMhnk83nJx+AxzZ7nSWIfgRLDDIeHhxIaIRAYjUaSSHh8fCzghF6FTqcjBYeY6NlsNuXwKZ3ncB49EhgIUnB2AerFZhN79H36fpeS1dcBpwLGddqbTdCzVkWQBeB6D9+v47O6f2d5LYKsjbO+cylplxXpEsxB4xgEEOz/LhBkx8hlkblivkFgwtWex00uwGNJuz4BPKRAXWQVkQWdWiHr92qetKWq9XUuUOKaa/u9PdMgCGzrObbP0L/1u1zA1/Kd6zfJehLsO2xSaxCwDAIAZ81xEODh9UE8fJm8y8Q9uqWr1Sr29/eRy+WkOuHR0RHK5bIUH+Lpf/v7+wiFQlLalq5o7kO/fv36ytkE5XIZnU5HShffu3dP2kD3N3cZsKQvy/guFgtxj3OHAYFKKBRCp9NBJBKRcwc8z5NYOOsG6BMDZ7OZJMxx14Dv+7JtsFKpSMId33l8fCxlmT3PEyBMi933fQETBA3cacBrwuGwJGGyQNJkMkG/38fW1haOjo5QrVbFGzMej0XxcocFwRBPguROA13+mWCIXgImYjabTeTzeWxsbKxUW0yn0xIWmc/nUp56Mpkgm80KACoUCvKMaDQquR/n0SsKE2ji4rOuVJfys/e5/ub/ruQ9YFWQMOOVi5ixFjK2tVh1vFyDBldCHfBw1TRXe11g4azxcVmdWgja+JIWuBrwBFmttg/2HUHA4rzn6XuA4Ixr2y/bl9cS6bnh/1qxubxJ9n77v7b4gYd5iWNONM9ruBXJHkPtmmtt6Vv+5P8MC/B+6+U6CwzqvtmQiYufeZ1rd4ttl372Wfyk23geuXjMtSbP+87O52uBd1mTnpX/+Fk6nUa73Ua5XJbjeunSXywWK4fj8EAinjLY7/dFSZOX6vW6JONls1mZ442NDTkBsFwui3ueJX2B0yJFVH60rhk2aLVaqFQq8P1lHf+bN2+i1WpJPQEqW8bfeSASCyc9++yz0pbj42Pkcjk0m02JycfjcakIOB6PMZ1O5VwDAJL3wAQ9Vv9j0h/rDfAwKO5g8H1fgAITHXli5N27d1EsFuWMhFBoWaDo4OAA0WgUAARY6OJABE0ABBAxLwFYlpHe398Xz8/x8bHkS6TTaVQqFekH3x8Oh3H37l2Ew2E5XZHrjZ6ai9Ajg4EgC8BaDvqaIFQdZAkFLUoKUf7QxUNFyb24LkFpLTh+Zj8PEkZakGhwYS2toEx9V2a+FbjnjZFtd1AipEvAuvoTJCTt+Nn79dzbtristdcCBYEjO/e6Dzpn4CxXOu+hNW6BH70NjAtyyw951/OWrsPhcCgAwLZJ/689Y5Zn9cLXc6JLLPNZZ+UmWBCq+84k0SAFqdsUNO7n0VnXuQDoWaDEtivoPbbdFwXJryZ5nodsNotisSjKfbFYiJXr+z7q9ToAYHNzU4rcsOrgzs6OFKGhEuGugtFoBM/zMBgM4Pu+bEtsNBpyKA7d3fQ+sB4Bv9f1+SORiLitO52OKLgbN26g2+3i+vXrWCyW9QZ4TkG5XJYTFkejkcxHt9tFLpfD/fv3JSEwkUhgd3dXYug0/NLpNE5OTrC5uYlwOCwV+OhB2N7ext7eHvL5PO7evYvnn38exWJRdhMwu59Km4mT+hwC7jIYDofY2NhAt9uVNrDoE89+YE4F3fw8WjmbzYoHJJlMruiMwWCAcrkM3/fFym+1WhI2YbJoJpMRT8+LL76I7e1tydegt4PAj6dJvmqegSDBwf9d1sFZgkEDBn6vLSi9KGhN0dXFRAsmZgDLTFkOln62tcrte/V3VomeF/+2StAK8SCA47rPjosWyHp8dRxVKxA7xlbpBwEdKxS1m1m3yZIeW/6v2/paIt0HC9js53Z+NJhzgR/9nfUKkHdZkMXzPLGgptOpJErRSiPvAu6tiOcBQLt2LKjT46Hj4y7gbnlL851+l05APEuhBvGj7oN9vwuU2+fZNrjWtu2fa12e9fllEXmAljAtx1arJdYn3dFMDGRM3/eXSWQMGyQSiZWwFN3+lUpFaga0223s7OzI8b4cj0wmg2effVY8BdVqVfIXWGaYx+4++eSTUjaXiYR0+RcKBeRyORweHgIAYrGYlOllmxaLhZxDMJvNUK/XpUY/axZcv35dFH6r1UI8Hkez2cTx8TESiQQWi+X29FqthnK5LHv9o9Eo6vU65vO5FGEajUbY3NzE4eGhnKPAEsysjTAajZBKpeQMAwIlbu2bzWaSLMj8DfazXC6LUmdyoO53t9vFaDRCo9GQmgbM5WB/h8MhqtUqjo+PkUqlZNvoZDLBzs6OeDtCoRBisZh4ci7Ky3+u3QSuRWrd+1qAuBYysGqR2lgqBQ/DASxOQVdMLBaTwQ2FQpIxu1gshFH4fCbF2AHSp1WxHS6rzKVIXcLT3nOWkLuoJWItH/2Zq12u/4OE8FnPtmPiAghBwl6DlcsmC1r1b60U9Xf2ew3M2De64/VnFGiWd7k3PBaLCS9rIUne5RYvzT/crsm65yRWedNjzjbqvtv+BgFPfX3QNXp96vVlPXFWAWuAaPmf3wfxqOv7IBDimmP7N0mv9YuChcdJ9CjxREK69gGgVCqh2+3iiSeeEPc0rdl+vy8nEdKt3+l0JDEuGo3ipZdews7ODqrVKrLZLA4PD3FyciIFfgBI8ttisaxl4Pu+KJ56vY5yuSzJdcASIBLMxmIxSVRkRj9rJEyn05ViPr6/dGmHQiHZScA6CpFIBLu7uwCWfMNYO63wcDiMmzdvilGYyWRQLBYl059rm/UF5vO5KHV6Mng9CwURtDP8wb54nidzwPDG4eEh1tfXBZDQa80Kj6zc+KlPfQpbW1tSyIgJjcPhULZGsi2sGcHaBtVqVXQaT5FkJUjWdohGo3LWRDabxUsvvYQnn3zy1QkTWEHpUgYURPxOo1DrVgcg5RwpHEkaQFCwajBA1wczL/k/944CEFea53kYj8ciYLXbhAPFxA62TSeP2TafNbi8l4DEjpXtm+t5Z1lD1vNis69d1p0WwmcJOOtGDmrLWYLTCmX9rMsitok/1lLWvKn7oUEj79PP5GEoAKTeO3AKEAAI71Kou3iXCUXAKYAir/JvuoiDeJcZw2y3nW8b+3dZ5a65tsl8ejxdpN/lWvf83DU/QZ+7wl16Ddm16gIhLo+Bbgs/C8p9uCwe9jxP9pfTdX3t2jWpRPjgwQNMJhPZd+/7vihb7psfDoeIRqMoFouo1WqSYX/79m1RoKzBzxoGwCnQnEwmotBp+XY6HaTTafi+Ly5/KnWeHMgxTKfTcgIgk+08z8ONGzdwcnIiQCeTycD3fdnZMJ/P0ev1pEwxgUW1WhVLOhRaZt/TGxCPx+X0xde//vXwPA/379/HgwcP8PrXv168J5lMBmtra2g2mwK2qV/4PJ7+F4/HkUwmcffuXZkTevG4pofDoVj/AKTKIQ9aGo/HWF9fl5oMBP8bGxuo1+vodDpIJpPineAR0ny/53mS18HxGAwGODo6kvBLNpuVOg/pdBqpVEpC6hfiNf+CXB4KhUSw0RLiQuEBDcCpm97GGbUw5YJlDFUvOIYCtBVPQKAzVskEwFLps5AGrTAW3ACWApDMDEAQ5Ww2k5PAuMeWwITP4Pv5HP3bChRXkhSAlcnQ32sB7VJOQQlZZ01ZkKfCCmOXMHcJe5d1aIW9ywqzFhqT41xu71ebdLze8q5O6OHca74kaT6w/+txYbxOKxeGCDTv8oARzbu8ltYQAHGXalcxrZvpdPoQ77K/XIdaOVo3ula0Vunr9WhzCzRPLRaLlZCJNgasMna9w5JdS2fxlwYFek26eNeuTQsaXIBEf8/xe9zU7XZRr9clJsxwEpMIKee4NY5b6ABIDJ9n23PvPHMEmAHPU/SYKMdMdWBZFIiymNZ7u91GqVQSuRyJRNBqteREPypz7uPnXn4qULrFy+WybKHzfV/6wgx/xuOZE8Cjh3kokzb2AMj2wkqlIlsAb9y4IWc4AEs5NB6PJZ+BoIL5Cmtra0gkEtjf38discDm5ib29vawu7uL8XgsxygzFk9PHb0wkUgElUoFACQMwj7E43GpvpjNZsWbQ5Cv8wN4H6lUKuHw8FB2S1y/fh3Hx8c4OjrCbDaTnBL2kecs7O7u4ubNm7h+/fq5vPbIYQItYKylxU5xIq0S0i7MtbU1YQBuj9ACiAJRHzPJGBABgxYyBCtkRp1IwYIZjI/RtUQm50ES1tXLd/Dd/F5XSSMRFHEcNKMSoPCdZH7g1HKkK841riT231rdQVYa26X/toLcZdEHWWn6Ga7n6zbp32dZko+TqMBdwJRjq3lXf295l+7QRCKxso9X8zC9BXTxa97VANHyLt+jDzoheNVAluEynkfPZ/Ie9ov94TOBh13/FEq0zNgHetqocMmnTIKkUNZgRYMMvov91FssLWDUbQrygLn43SZ3BnkE9Heu6yydlTT6OIm8YeUGY9Xcy87YOQA5sY4xfCo7Jq4RiDJOzxwE1vpnnJ2ymc/kDgBgOW4s4UvLnfKaZx2wngDBL5/Fvf+pVAoAhP94mJGuZcAKhSwgxGN5mcBIxTyfz3H37l1J8uOZDclkEvP5HM1mEw8ePMAXfMEXIJVKCfgneOchSpPJBJVKRZICeZ4BvR5cj/RMUE9ks1kMBgMcHx+jVCrJ2iTVajXRQ9Rv/X4f165dEyufgIcnFtLTwKRLJjeyqmM2m8Xe3p6cxshKk+PxWBJKLyp7LwwGrBDVi4wWibbk+bdOhtILkkLKCo9QKCQurlQqJXWetcJn7InX07rid9xuopU7hZqeUFbAohWmQQjzCLQrmJYdra7FYiELlLEuCn+iTQBy8hTHhbWumfhDVMlkMm3p6PGnRUkXshaYek6soNUeDQvO9Pcugamf6yKXJeW65jIFqgWvbJPneSvKkcCMc2O3+fE3gYDmXT6P8Ua6LLlXmaCQhUmAUxCgE5a4rYk8oKu9kXfpBmb8VV9PIc9n67m3vEvB7HneivWm22F5l3xqeZcgQYNlPptE65QgXH/vAp9WmVtgq0Ga5W32+SwwbD0FrndcNmkgxZwT7QVlNj3d9FS8yWQSuVwO0+lUQqSUa/Qg8LmDwUCSWxeLBZrNJrrdroQBptMpJpMJjo+PpdY9zwGo1WpiZLE8MPmSruzFYiF74OmN5bzRy0H+yWQyAigASPycWwDJz6yayLDxfL48Avj27dsik3O5nITMCBy2t7cBQPREKpVCp9PB1taW7P1n31hciPv59bpnPgCPbWZxosFgIEmDXCN02TMcQsBNANTr9RAKLZMvaUDU63U5jIj6jvlGxWIRvu+vbAUdjUYCuAjSPM8T8HcRujAYIFNygdF9aBUNJ1hbnjr2r917vI/WOgUyJ5i7BjipdKNSgVMJc1Ap3IvFoqA9ACuWGweVzEhUR1cb43LaNUaGZblKLczopo3FYiJQ6dpl9S3P8wTxZTIZ7O3tSayKtaWZ4UoBbfMu2EYKMiK+8wSY/t6l2II8AvzfBUz0s7XVZq05yzuXRWwn20Fe0nOsga0O0bBv9PoAq7xO3qVwJtBkghP3/BJokHepuCnIwuGwHLJCjwB5j33IZDIrOTG0GCmM6QGjIGVb6SlgvzifrGRG3tUHpXDPeigUkp0P2WwW9+/fl5PpCoWCWGm1Wk0ABL1XXPPsN4nnsp+lhDX/ubxVFmAGgWL9GfDw+Qsu40Y/T8/1ZRHd57SmF4uFbIXTISEAsqUNgBglDCnUajWJlbfbbTmNkDsSPM9Du93GbDaTUsUMfY3HY9mxQL7URhTzX8hjzWYTpVJJDByGDTzPE29avV6XnQgAJJmQyl/rjl6vB9/3ReERiAJY+ZyV+LimdB4YtyjSY0EPBisDMimPVQSZwMhaC76/3H7Jsc9kMnI8c6vVQiwWkwTEarUK3/elSiMrQZKXeCohEwcJQKLRKLrdLo6OjuB5nuQjrK2tSeVGVqOkYbCzs4Nut4vhcIher4dcLielkVmq+iL0isIEVNpckBRs+hoyMSeULlJOBpU1ESmFBa177Z7U2doaUeoFbmPBBAZ8pxbu2tVGNxGFPQU5AEFz7F8qlUIotCzWwUxQuuB4mARLWbI6F3c0RKNR3Lx5E+VyeaVoDF15wDIJjZXGtMXDMeL7eSKXVhRkeJ1Ypd2c+hoXOLBCWSd+BpF142oLLcjiuyzSbdJ9owdL8xJ5gv9rDwCFDXmFAECHtQjaNM/qOaEi5FhpQca2kncJDAgKqcjJ70w+ZE1zF+/qwimpVAqj0Uh4l0Le8i7j0QS6N2/eFOHOdpF32YeTkxMRlFyjmncJYnVypAVjLuWsrzmLd/X/QfPv8mRpxW/BwmV7tSKRiBS7oWKgV4ilgLvdLqbTqSiPTCYjxgaAFc+qTs7jsb60zNlXxrZ1JUAaXOQXjud0Ol0BCbVaDaVSCb7vS8VC8jqp0+kIyKDip5yja1wn4PJ+ZslzTYRCIXmv9khzbfI6rgMCD4YrmEfAtTEYDFAqlaRNPJHw5OQEg8FA1gR5mhUVfd/H1taWeAjoyif/RyIRWVP0ltDjRlnCmgIHBwfI5XKoVCoCtniqo+/7ODk5wWKxkHMYGCaZzWaoVCo4ODgQI5fbG5nDcC6vXZQp9WKi8NTxP/7WCpsTqi0j7o2ksKM7iQBDC1/9bGC1Cpp9vi7EQqJwpYfBWhuRSEQQJwDZFhIKLU+nIsPRU6C3ydCi4mRx+wlP4WJyDvf6hsPLyl0UvIz3pdNplMtllEolDAYDOduaCobVtGhhtlotiS3TNUVyWeMUsDrBiovLlVR1nhC219r3WWHq+vtxkwsI2PHQvAucggLNu1T8HDu6/Dm/vEa/l8+y3gcCUQtkeZ8Whvo5OnygD37RYTFmZYdCITnKlLzLtRfEu+l0WoQe48+ad8vlsoAEulHJu/F4HIeHh5Isye1jrGjXarWQSCRk37QFjEEK/ixw6fIq2GfyxybqnvUc1/+XQeRDegSYVMctb9xCCJyCGSaUMbGUc0ve5296DVjgR7vz6Z5nARwmttEy5hG7lLHMNaD1S28V206LPxKJYDgcolAorOR98VhkKnANGPlc6gUqY+7t10nmnufJezh+DLMxTKITdvVa29jYwHg8Fv6lnGC4lyGRcDiMdrst3oRyuYzFYiEhEnpa9vf3sbm5KcYuwXaz2UQqlUIikUAul5OETD6f/3c6HeRyOYRCIdy9exfpdBqTyUS8hxyTk5MTeJ4nHgQtp5i7cBF65DABB1gLUy3QPO90dwEHjr8ByN9coLzHKhxNOp8gSFDoWLtunxam+lkAJMkrHA5LEQhaYZFIRDI1mVvQ6XQkmxaAVPGaTqeCPrmg0um0JGCxGtazzz4raJhWHBdELpdDMpmUvbtEjVycGxsb8vyTkxPnTgyt6Dk2nDst3LQFZq0jDQSsMLSWm36HnTsrPIPm9nGT7Z9W9jrOzq1Btl6Ado3yfvKcBkJ8F+Depqn5n6SBG9cTcLreNO/Sg2Z5ly558i4t+W63K8er+v4y8YilW/VOGwICrg3y7nPPPSfuYp5MZ3mXSWcshMJT4tbX10WR1Wo15+4a/q3Bpx03/Zm14jW5vAUWrFpvmV0ftj2XRQRejA+vra2h0WigVCpJzFvv5qJXkdtNqQCpzHWuFl336+vrEp4slUoyb3Rd05PARD7KJnqF6C0g4ODxygSd9CLx3cxB4TPpDdBJsbTaqVsikQjy+by0hX1jMq7eLUSwQJ5mmJmAg9Y9CypNp1PhC3q9uOd/OBxKDhqT+mq1GhKJBA4ODqSdfC4BBctH63LJk8kE7XYbJycnorTb7basyUKhgFgsJkCdHhiGRAjwCDyYT6BzGjKZjHh66O0JCvNaeuQwgd4tsLa2tuIC1YJSC1ctMF3KmZMYtOi0oKXA1EBCKyptRennWyuNQpPCVMc0NZoETmvBM5FGl/XUe3q1giVIoPdE50XwGrp9uc80EokIsmZ7eCzlm9/8ZjQaDQyHQxwfHzuz3jk+LsWswwUuABAkEIM8APZ+0nmg4DKJwoPufu2F0r95rfZykb91SIELzfI4/3Z5VSz41UBXCyQ+315DoKC3JlIp8xrddraJyoS8W6/XV0JzWrEya5u8SzAcxLvdblfixnw/6+WnUim86U1vQrPZxGAwQK1Wk2dZ/tN/W+PD5anSwMDF/9Zo4bX8TpNdAxz3ywaxlJfao0PZRFDG+DHDsjRcmDiXTqclVBCPx4VvqXwJfikHPc+TpDZ6cgGIrAMgSorKHYCEodhOrg3f90UW6twVz/Okwh7nQ9cyIC/pmgn0SOtcFHpDuObI18yfIK9y/eocM4Ya6JHQ/et2u9jf3xdLmzsB9JkOd+/elTGljqD3gWui3W7LeDM5koBhMBhgZ2dHZHq5XJZxpSeAYI7FjdhPHpTE8e/3+ytJ92yn1m1n8tpFmVILQ/7Wi1qDAq2UXYwNnC5aojr9mf1bC2UysgUP1togA/BaK7TZzul0Kqd5EXUOBgMpOan3jGvrjCAiFAoJwiWa5mlijMcRkVslRFcXmZDbG7Vbh+9m8giPsdQ7EHgd50nfa+dQP9OCBF6jkaR+hlX6WmDqMXJ5bi6jvoBuq+Zd3/dXBILlWQ3q7P2W//RnGoBa1yVJ8yGJ12jgasMYep3oMdWuQc9bugpPTk6k7gDnWCcVapBwEd6lsCPvEkRr3qVrlFYN+wpAiq2cnJzg6OhohXc1mLfgxwVsg0CDC3RaPtagwwJcDRz0Z/Y5j5sIBOgVIA+TyGtUGHSvc67It6wkSJBHxTwcDlfCrVTWDK1yyyqNFP4wLq5l6cbGhhSAo/IDIO/iONKCDYVCKztReLgQdw5wXdEDRp7ld+wj8LAHmKEH8pM2XtlmlgOmV4L5MQAkh0C3GTitoxOPx+U4ZSboFYtFOYDJ8zz5PRwO0el0UCqVEIlE8PTTT+PBgwcolUqoVCro9/uo1WrY2dnBYnGaHEpqNpuSazYej3FwcIAbN25IMSl64tiXwWAgeSPaK38ePRIY4G+rTFwAgILQxmX0ZOjELE6otY44ES6B7XIjamvehiO00CYiZLIIz5imy4ZbcLQ7mOCASSHsy3A4lEUQiUTkqEtdDYs/+gAQnXWuBVu73Zb29/t9jEYj/MEf/AG2t7dx48YNHB0dod/vo1QqodVqrew/DlLeJJd7VAtDq8wtDwR5HoL4hfN32aR5gDyneVLzDoUo/9bPALDynVbW/N4CUw2W+RwLfLUC1PFM/V4NCizvNptNyX9hkp5+p7bMLO9qQKF5l/umXbxLAKHDVcAp74ZCIeHdP/qjP8LW1hauX7+Oo6MjDIdD4V3GYzUv2vG2bnuX10/LJJfXymXpB3nCzgMZj5OoyDQA1AXVdGweOOU9fQgWPQYEogR7dEPTxU0LnnlKDB0w76DX64mCy+VyK0mHBISsPshncxuizd+i4s1ms/IMXsd1Smtf53JpA49WPz0ANKLo/WOfmZdGi5pghd/RoNJ1aQCgXC4jFArJ2uIefpYN7/f78jmtfWb067odvV5PQmzk+1AoJLUOUqkUtre3JWeCoGw6ncqzuVWRXjrmm7EdnBPt+eJ4vCq7CawVZRevFopBSkkLOmuJ8Xvew+cBp+cKaASorTGdzKgtLY2w+D2ZkTHT+XwulaUoSLlI+B6iXDIlY0pkRj7T85YhgpdeemmlQAXjWHrcyOi9Xk/GjYlgZAoKv263i/v372NnZ0f2mWazWdkmpKvUcS4sadd10DVW2bsErYtcXoizrr8s0krbgiLtodKg0cXLVrkDwe5q7U3gvfp78rbneSsuf16r300hSCtvMpnIllSdqU9Xq95axXXn4l0KGfIuBTN5V9c6oKv2PN4FIImO9+/fx/b2NkqlkmwpY8yUWxxd4NXlOTgPqJ51reVvO7caDFjgfBnEeaJlu1gsJPue8+h5nriUWduC2wN5DRPOqLCZIM3wA+efoR16z2iwEDwwNOr7vigzJuxFIhEpjcx99FTYlJs8MZFrIRwOr/DaaDRaKWJEd7s2+FgIid5VvUWY1+odB/QS6ERd104srq/FYiE1MbjTi2ssHA4L//KcAcpnVhTkGmX7yeuhUEjKNBcKhZV+0ThcW1vDvXv3RI/t7e1JGIXXXrt2DZ1OB/1+X3aE8Ijo2WxZUZdhIc77ReiRwQA7oAUhf7QFqP/Xil8LRS2AbcyV7hjrIbBWE4mfWYXHzG17TSi0LFZERUqXkEap3ALJylvaa6EzzHmMJ13QnufJQmk0GlKOslQq4fr16zg5ORGXHtEfF8H9+/fl4Ip2uy2C+tatWwiFQtjb25OtPHqvKRGprganyeUmDVJ0+rdVakFA4ixrKkiAP046j3e1wtY8ovmHf9t4Mxcd7yUotApUCyer8Gn1nWWt8jO6hbm3uN/vAzhdHxSuDBXoxEcdZw3iXVojdDNmMhkkEokzeXc0GuH+/fsYDAYoFAqSZDudTnHr1i1EIsua7HRXc03Rrdntdldit9YbYD07JJenKohfXbxrnxUEhi+Tf6mAgdOtdrQsddEzm0fEevf0FtGjQIVFj2YoFJJjiil/GXbQfJtMJiUurfld17egcmcbdCiUyXHMjQIg99K7wDVKixkAWq2WABteS0tfx8S5fjlX2nBkmEJ7P0la5+i55lojSGIiJ7fyMqmvVqthY2NDEswJygksWGmR+QYsOtdqtZDJZDAcDqVYVDabRTKZlO+Y76HDNgTgXEsMu1Hf2l0ZrP9xHj1yAiEHD3i4Cph18/M7DqwWhpw4u5+bQosuHH7H64lyNQCxQELHwa0iIvP0ej20Wi057UkzB5Ew3WTcccDBpuXDOJMFG71eT4Qdk11YG4AnjTHTm9t2tra2sLe3h8PDQ0ngovuP7lbuM+bY3Lx5E8PhEIeHh7LIWOWKsVzddz1WnBP72yp1lzC1Vhg/I1224reklalLcXBcbNhAu6M1AKAgpGCxW2Lpjtek3ZwWzJK3bV4C76NQ0qGjdrstSad6TpkQRve7LrClAfpFeJfCnTFIzbvc6WJ5l7XXybu9Xk92O2je3d3dxWAwkK2I5F2G4lisxsWbVmFbb6Jr3u2c85nn0WsByOr8Fip+DRwJQGmRJhIJseI5r/Qm0Mhi3JvufFZcpRwnke/Io/T+AKfjp9375DXOqd6fT2BA4MK/ye86rLxYLAsXcT8+E7AZpuX7qRPo+gdWvWDa2tfrDzgFzwQfLDSkkxO55pLJpHiwjo+PsVgsZIdEoVCQUAaLO+kQXLlcFr7tdrsynjx9kOc80NtHYMUqpmwv60owNMETCvP5PHq9niQX9no9AfAXzRcAXuERxvxtlYGOV2irXyNOACvo1VqnZHCWU+SkudyywGp81sZZyQgkxqlY/Yr7M3VRIl6vUaRWDlyEbA/fp3cKsJ1E3UwIGQwG2Nvbw2KxEDcaK1GNRiPs7u7iT//0TzGfz0XQMjOXyV0UztynHY/HcfPmTZycnAA4jRV2u13Je9AZsjonwGWhWteonV8durBA4yyL7bIFKrDqcreAFlhV8hQuFIycX91PLVR4H13v1rrUwsnez7/1NXb9UAhOp1M0Gg0cHx+vAFK9PvibCkILWP0ukot3KajJuzzX3fdPS6l2u90V3v3Upz4lwoiAmtYg90uTdwFIMSPyLq0+AgjNuy6viVX0mqfPUuKu76wMOsugeJxERaXlEd3JrOhneUbPLwEq97hrkMkkaIYKCA5peQOnCa8MIxD0kh8pp/heJjky2Y0WLQ0ofaQw998zgZtb/wCIxct5oNeAfda8ToOLfaOcsp9zLslL9OABEC+XnneCUnptuasgmUyi0Wjg7t27KBQKiEajaDQa6HQ6qFQqUluDnhqer8AzC/h+6gFuzYzFYnLSILdxstbD3t4efN8XDwJLSpfLZdy/f1/GjTsS2C+eL3ERekU5A3rAKWhcjKiFj54oNlpbmFoQcgI5mZrh6QLXz7bt0sKWAp7/z2Yz1Ot1QW8UWlo46nAHUSw/Z5tY+YuMqhNG2E59iM1isaw/8Mwzz6w8k/cfHx/jiSeewLVr13Dnzp2VBJjNzU0Ui0WxrKrVKo6OjtBsNqUCWLFYBADZUsRkE8aXddVC3T/OK9tivQVW+Lr+p/B0WWh6jl8LxPZokEfeteBRk87G17FHze+07MmjOi6qhZAGZTqHQXu8tBWjrUHyLt9HfnQpN4YH+B0FeyaTQbfbFUuRFpAOV7h498/+7M9WeBfACu9ev34dd+7ckT7OZjNsbW1JxrXv+xLbZBZ5IpFAoVAAcMq7BLnkXbpdrZs3SEm7lLjrepeXSN/Pvy+TuJbJU3R3UwlrDw4TnjWY4XrXRamsB4tykEpdgw8S5Qm3sbFt3N5IcEDvBL0SHEueEkjSh/wwT4ZKi/kDoVBI8rf4LCbika9t6E4DeL5byycCAK5Behv0eRzaC0xPVyi03PlAMMDn1+t1qSUwHo/x4MEDkcksrsRTI7e2ttDpdKSWAutFDIfDFcBOlz/zFVh5kXqx2WzK+NGzUC6X0el00Ol05Nput4tkMinr6zx6JDBgF4ZdYNqiIrlclBa58jPg1H0JQOJCmnk1Y+gJBbAi0LgQKOgYK+t0OqjVaiuJKS6woskmDwKQhD0KbCYiaoVAVy1BCKsRktm1mxYA9vf38fTTT+Pg4ECel0gksLu7i+FwiKOjI8kT0Alk0+kU3W5XBDUXJv+nZ4FuKwpW3V9tGWpha0krLN6nAR3dbtqT8FpwtQKrwp48qEGXjblqPuN9WnlbC18fI0yQSUHH99hn8rkaXFNosT3kl16vh3q9LnyugW+QYtNhOMu7HAdmUH8ueHdvb0+el0wmZQuU5l3Gr8nD3NPO59LlyjXLpDhaaOQ/DdBs311jHOSt0vNir7ls3tWKizyldwewNgAVOxUZjSqOFwGB9bB4niceJ10f3/d9yT/i/zSs6KbXoJdGm97tQKXN0E8ikVgpxcu1po0/DcjJb0ycpLfBhip0kjnBjAYE2oDUIIneEZ0EyffMZjN0u10AEG8HQ6/D4XDFgie/c6smeZX6IZlMIpVKYTKZSMhtf39/pf/cmru2tjyi+dq1a/D9ZZLmycmJgA0CCx6u1Gq1EI/HpfohPciHh4fy7Itu635FOQNkIq00tGXOSSBR0ACrJVVJHGgyNXC6BYXksvj5Hq3INBrUljwtcLo1df0AC1b4HjI4hQ6fyUVo41AUZtqVpomoWfedoGSxWODBgwd48sknUSwWpYJctVpFNpvFM888I/GmXq8nSpcxZFYe40EbHLNQaJl0UiwW0Ww2JaMYgNQ5n8/nIiTYfzum2itj58PlCeC9do4ukzRosS5lkhX+ri2qwGoyoeVdAjHdf8u75DftPSB/AadhKvIud7zocqoU0mwnnwM8zLv6mWwTFT09CH9e3r19+zbK5bLwbrlcFt7tdDoSAtDgmnkCnrfcGmULITFhi0KPeTzkXebFaEveBYxsGCDIU2A9CpfNtxTozG6PRqPiatfF0shXBI/8njxLntGykwpGh2JIeosfn8+qfgT4bIfmFXoB6KXgOiCIzGazGA6HUhCH79SgmcrfyhgaNqFQaKUMs+YDrkftitfeP224cUsl88O4i4KhrkajIfUQmDdAbwTDLtlsFp1OB3t7e5KQ7vvLMxpYBnpjYwO9Xg+FQkEqfvJwLwJfnktTqVQQj8dxcHAgOxd4GBnXNHMU+v0+isWijG2z2cT6+rrwhfYOXoQeuc6Adt2TSeRhKiNZ/9YVkIKsTe0OdV2jhRAZ3KJ8bYnxM15PpMuMe+3K1H3g9WQo/Xwdb9PAgOMQCp1WLuS92n2l9wNrgEGaTCZotVq4efOmuILpVuXBMhwvPp+IlO9mHIrxLwr2a9euodfryfV6zBeLhZSv1PkTus9Brv4gQWv79loIE2jrHFjNMbkI72o+saEw7ZUCVvsexLt67GxI7DzepVDT/SGRN3RbNJ9a3uXn2sNm+xjEu2wneffGjRuo1Wrw/WVIgC5t3qNzHOie9bzTEubMVmd7QqEQtra2ZK80r9P9YMa0LitLsOyaCxcvusCsHq/LAgUuXqCio1VK0oCevES5pCsUkv9YvVJ7o/S5EnqsOV8cX10fhd4kxvWB02qFoVBopR4FAIl502sAQLbPEVQTKFOG0bvAd+odCzqMQk8H72ef9RZb6gkdgpjP53Jss+d5K8njOrzKrbv6hEW67ZPJJNrtNg4PD6UGyG/91m/hXe96FzKZDA4ODqR+g+8vDzdKpVLIZrNoNpt4wxveIDF/HkwFQA4W47HNxWIR/X5f2jYajbC9vS3JhawBwa3nQR40S4/sGdCWt1Z6dLHouBQHnxaEtsI0g9E1aC0hLeT0gtC/SfpeawESNZ6cnMiJWZoxKFg1GLFgR8eXtIvJumD0wBOY8DMyOi1DvpteisVigcPDQ9y8eVPGk7kC9GboMeG4kqn5HmaEE6UDy32yTJ7kfGlFx/85XnTJaUXimguXwOUcaMv4tUCad4HTkIf27pB3teVsd8YAWOFdVkTTCsoFDFwAmnxg45zaA7FYLFCr1cRtqdtMgafvsUBZC2zeY3lXx+PpkuYYAaf5CS7eJX8cHh7KNsLZbCa5AszC1kReppeKfeBOmtlsJrzbaDQkNu4KRTKcQN6l504rRz0Plgj+rSWsAcVlglnNm5RN2pUOrK5ffk6Lkx4CKl8dgtHAj3NOcMa/tbeJ/KnXiFbK+h7N2+RTvWWRz+QPw526YBIACTfoMw14L+eIPEfQqr1uek1RZjFBlnLf805LMEejyxM4GR5LJpPodrvijqe3gNsLOc6FQkF24ty/fx+TyQRf8AVfsFJemd4xliWmvNjY2MBkMkGlUkEikZCkSgI0ffgSgUC328XW1hYODg5kLDKZDOLxuCQtjsdjVKvVC/HZIx9UxMHkz1kuCMaONDNpRQys7tnnPWQeEpEbhZjLEnItdC5oKrdWq7US09aWs04a0f3ViJMLgChY9127zyhoea1uJwUoF6e1wk9OTnD79m1B/dFoVOKtAIQp2GZ+xrYzsYT9r9frK2PLRcHtQIwN6209jE9zgVIJ2PHRvGAVv7VwXwuAgDxncyKCvFXaA0a+s679IN61zyTv2s9d8Ty27Sze5TN1QpQm65XRcVWd3KW9Y5Z39Xyz37rAjPa0TCYTnJyc4NatW1IHIxKJoF6vr3i02C/yBr8DIMCAY6+P4dVu7kQisWJVat7NZDJSs53uY+3l04DMfubiBW1cXAZpY8TzvJWaA7SuXV5bKjpuM+Z4udzGXL+ce+2lBU6BoD6Dhc/QwFC75TlXencWtz1SrlkwQS+R9tYBq0eO2/Cu9Xboe63nmO1ijgTd9VwPPDCJeVUasORyOakcyOOG2X4WEGo2m2Ktb25uIhQKSa0OPmt/fx9ra2u4ffs2wuHwSq4A9UUymcRTTz2Fw8NDrK2trRxrXy6X0Wq1ZEdPv9/H8fExyuWyHPrEXAZWFrXGXBA9UpiAHeLkWESo0aueTE6W3rpCpcPfQeCCAsPuVDhLGbk8CrQULOPqNmvrTFt3BAqMm1o3q7ZUyKz6M16rLUwbYqCy7fV6kmTChCnWmnfNAduvE98YKtB5GtyiQsE5m82QTqdlbHgt3YW8Tifv6EOatEWo58OOu+afy6RXwrsUosCqIuP3mnf1szQA0u5O7Sn4XPGuVvQaVGpBrr1a9l5+T6BxHu9qzx3HjbxA3mUZ4/F4jFqtJlYY28F2csz11lcAsp2RbWw2m1K1jkqe+6y1xUTloHmXnzEExv85n3o89Lzo8dSfPW6iEaQ9Ohx/LZsWi4XkANhrafFzTEhU3HpOtAzWhg9j+Xod8z068U7H8TW/29Ay+SYWi6Hf76/wKcGoBil8PueJPE/vhp4zjoc29jT/MeRE3qFHQAPUxWJZR4CFlvj++XwuBbUIIJiHxnFjEt98Pkc+n8eDBw9wcHCAQqGAbDaLVquFe/fu4ebNm9KnwWAgHgSuxevXr0tiYDKZxMHBgfBEsViE5y2PUL527RparRbW19elnkcmk0G73ZZEzouQ+wxQB1kBptGYtSA4OdqaBlbdSNa6t9fqhCh6FzT6s+2gALAuUhb24b58/mjrJwg5aSUNQKoUcgKpOHmtRsVcnGREol6id14zmy2rWpHBmSCoT69irF97A/geJjJy8ZOsgJxOp1JUg5+1Wi1EIhFUq1Vxj83ny73h+XxeYrfxeBypVAq5XA7pdFqyVrUVrOdE84xVcJdFLp4B8BDv6mss72oFZp+tr+UzLe/y5zzeJf9a3iVZy92lqMhrF+Vd3qPDBBTozIPRHjS68rXlycQqJjjxfw1k9Hvo0aJCYT90tjrfo2sXUCAzvko3LXmXVdy47pLJJPL5PDKZDGKxmGRkU7lppe/ilcsmzrUFJjqBVe9m4j2cHwJ5Lc+0fNXeGuvx4dzrz4BTl7eW9eQ5ncRIhcxQEXM7tHHF43f1emDbyAP8n1Y8x4OeAe2JXSwWwpfM4KclrwExsNzOSsDQ7XZxeHiIw8NDtFqtFa9xo9EQnovFYqhWq7KjgAl8uVxO8hV830cikUAul8O1a9dWStNnMhksFgvZqtvv9yU5sV6vSx2OwWCA+XwuibXb29uIx+PY2dlBpVIRr+50OkW1WsVgMJATC/k5czsuQq8oZ4C/tYC0FpdeRFqBBT1To1/9uf1Mxz31+6zFQ6HGc9WpEDWz63tJ2lug302m0AKRz6LCpwDRyFQLXv1MPkfHcvl+MoCuCqYtNq3ANOIFIO5Ajql2OdN6Yyy21WoBgFhxPIubmbZkfpbJZOiA1pnORraA6iwvwWURx9H3/ZXkKM1L2qoGLod3dbKS5l3mnGgFxrbptpMXyRMu3tV8HcS7WhjzbwpkPke7qHktSxTbcdA8rpUJ28mkLj3uvH8+XxbiYry20+nA8zzxnlWr1RXepUVFIU33qVZAtJisYmHbSJfpGQBOwR+zxMm7er7IU4vFYgVM0aDQwAdYrf1iPWU6P4T/a8BrPS/aS+TyXmpZSXBJWazrs2ieoDfDeqnsdnN+rteZ9qLqw5boYVtbW0O325U1w1oC3W4XnU5Hymlz3346nZYtjYPBAJVKBd1ud6WEPcEzK/+xdgD5qlKp4OWXX8ZsNsPrXvc66WMikcC9e/eQSCRkffNZBFDcSjkcDuF5HvL5PKbTKSqViuQOfOYzn8HGxgY2Njawt7cnORb5fP5zn0ComcgmZgCn+QHWatLWkmZGK3S1ENLuL5erTi9WCnb9LjJUKBRCtVrFyckJBoOBCDW6qEg6TkoG0ZYecIrCKfBoCdHNyTZry9HWBNDEd+qx4TiyWAXrhXNsdLa03hGg3cy5XA6NRuOh93meJ/ez1nepVJKjZNfW1tBqtQQ5N5tNcafOZjMpbel53kOna2lXJd+rx/O1Aga01aF5xfKuBYlBvKu9C9ajEMS7mq98319JpuJnvNfFu5ZXyZOPwru6yNBFeJeKQVuA7LO2+Mm7+Xwe2Wx2xWIj79GVrPNmOIZ0oeo8CC1fwuGwJHCxBCvXYrvdFt7jSZ6cJ11wh8lg5F1tZVJx6PWo+eCyiOuOPKblhnata5e65jdtqQOnoFQbJ/o+jr8GEFbOaECncw1IfAeBnOctz2shSKSy13xrPcgkbQTp3Vo6dKfBgb7ergXf9yUngGXmeaohc6gODw+Fn5jMSGs+EomgVqsJOGVy397eHqrVKkajkZQgZoGhXC6HTqeDmzdvCs/euHFDToIsFApyLD3XSiqVEtDCNWsPxKOXNhKJ4HWve50ACIYddnZ2nIZaED1yBUId+9HeATKrVfLaYqEVoMGEXnTWxa/d+sCq+1VbSFTKbBstYIIEChjgdGuSZjg7WGwrcFo8hsqfk8+FxIXG52h3qgYOwKnrHsCKsNJMPp1O5SAKnkSmDxnhmOmEKu2iYzzLujr5m1sI2+02bt68ifX1ddRqNSmYwwQY3/elpvhsNkO73ZZ9uHQ/8ZksIKI9GRbsXJQhXy1y8a5W/q+Ed3kNf1No2s+Ah3mXgpRKUvMurVoAAsoonF1WkaXzeFffy/dYgEtgp122HCcL9PQ4Mv7KIiuWdwlANLDnc6ikLejiuiJfdrtdXL9+HdVqVRIUaTUSwJJ3GWZjbX5aWuwzeVd7MjiG5ImLWlavBlE5A6dnFHA96WqAHCf2kRY077OhJRvust5Hzhefp/lOW/F6fvgcghJWnaQ3QOd3sA3cokdAo4EpSfOwXqMaHLFd9FgSyOht5MBpqCGfz4s7PRKJIJlMIpPJoN/vo9vtSnu4O4AekmazCd/3V7bukd/C4WUtAsrvnZ0dtFot9Pt9bGxsSIjhT//0T/HMM89ga2sLhUIBpVJJvFRcs+wbD0iifOC8EhRwTAiO0+k0nnjiiZXdEgRe59Ej7SbQloBWQFa56wnTLiRdyEKjfu0q1AzAidLMQwuZ76Olod2RPNf98PAQAFbAgkaMVORcbGRE9km3XVuJLoVhQY0GStYdp1E4v9MMzVgqk1uA07O2tfvW1SataNg+/W6Nvu/du4f19XUUi0U5EtPWH+e1XFj0IlC5shASFSWFVygUklwIXdb2sshasTYMRHoU3qWQ1DsKNPA5i3c533Tpcsws72pXreZdKnedXR3Eu9Y9b/vL6/T12trn+FFZaDDAaxjzZ+xax3epjNhGnatAz4SWGzr8qN9D3o1Elke7VioVFAoF9Ho9yZzWoIrgLBwOr/AuFaGOszNmyzZTidBbcFmAgOuMMXC2XYd9aERo61/POX8zxKeVKj8HVstna88TsHo2h+URHVrRYSU+Rx8sRCWmk8vt+uG2OutNY/t0rslwOJRYPOdIV7ocDocy/3xmJBIRb0C73RZQNZ/P5dyaW7duIZPJSIG3wWAgfMqDh3q9Hmq1GpLJJLLZrIwL6yr0+30BJtxxAABf/uVfjueff17ay7wvhsmSySTC4fBKyWbufPB9XwpxEQzo8NbR0REqlYoYjKybcBF65DCBdX3qRWwXM0m7FykUyNTWnUpBol3PvF67Y3VBCy3g6JJn7ObBgwfynW4n36OFnRaiGqDouCqJQkwnPtF1pQWSFXh8nh5T3qsXj7ZI2W8ufC5w6xbj2LKvVpBa63c+n+Pk5ESUJBcxE3p0eIP9JCjRiY8kejJoxTLWzSSWy/YOAKvHnAZZ/o/Ku3wucLqFkPdq64nXuXiXn+u4I3lXz7m22M/jXQ3MgniXz9XzzGdrT4EFBxpYamuVz2R7CBzsmGjepQLR1q32OHCctSdsPl+e08C20CtFD4CO53JMaFVRsVre1eNCxcB76Dl43MR+a8+mDvtwTHQODEGXlql6bEmcLy2H9FhrT6T1MloZROLzOMbcSsh7mTVPY0EDDh06ohfBykDKKX7O37rgEb2fTNrjM2ezmQCFRqMhYQJgmTd1fHyMTqcDAA8VJKJnYzKZrNRxYeIhsNwFk8vlMBqNkM1mkUgkpLIhDc9MJoPxeIzNzc0VWeB5niQz0tInsNCeQb6XbeKc8dyIVquFer2OQqHw0No7jx7JM6CZQr/EKmsKJV6jAQJ/eB87QWWorS6tyLQlTXc2hTeZg3X6geXir1QqODo6WmF4LghOsg0XWJRKFM1FRaYig+kFQIa2i08LF+0ZAFaTbKybWVvUdH/pOJkOT3A8AawsJC1cqdQ0MJhOpzg5OZF54kLK5/OyfYvgS7va2FfODxcFx2k0GmEwGEjM7bJJC61Xwrv8zMW7VJxUutbaPot3OZ/kXfIleZdrQvOv9ciRH/ScA6vbP/m95l1aSZp3Nb/o/AK7/Q84LSPOMdEuf+29Y591LoDL0gQgiWX22Xw+54jPrdVqAla63S4mk4nwrj5Ome+j4mRogc+hBy6Idy+Lh7WyZ/8JxrUy0HJEjxHBqfUG8HoqR/KQ9j6w39qA4BhqT5DNOdAGF2PY2ktmKx9qzxYtWj5bzxvXJj2TBBP0Dmj9wnXC0xaZz7K2tiZFezR/MIGPIWWWu04mkxKKun79uuQOzGbL3ViJRELc91T2fBZBD4EF+xaPx1EulxGPx4Xv+Ewto3VhKAIBXXWS/aKcotdCe410Iarz6JE9A2SIoO/YMU6+ds/LSyOne5VtTFLHQ/lsLVyYgUpG0rERCi8AknTFQ05YCIXvImPxXXqhadIgSDO8BhEa3dl9vFrB8F1a4djkNQCStUo3JnB6WhkFOJ9PYvu11cXvda0ArVh03In95slhXBRaQVAxktk5/uwLdxmEQiFB3XQX6zjx4yZrxdrvNOl+2tASEMy7wGplQf1ezbusra6tPVrGLt7l8aYcZ604+Q7OvQZs7AuFvPa02dwTPkPzrrbqNSi3oFrf4/u+HIileVcXPuFYWS+RDlNoT4gefw2W2T4NUrhPu91uS7xXKwZmWNO6ptIh71KWaN6lNXhZvMvYOy1qCzzZd+vpIshj/2y1UYJJKn7tedQ/OlSj+YXvdiXkTqfTlaI8wGrSra5bwO/Ju5Q5bC9j9r7vy+l9nDfeS0BOgERPjud5Alb4HJ7gyjHkoUONRgMvv/yyuNW5HZH94ymEdNWTh6iPUqmUrDOX8cfxTSQSODo6kvyrUCgkgGI4HMox3r7vCxBgWIQJh1wT5GHWFqAni2uCyYesJ3MePXIFQs2AXGTaktcozlqQWolpJtMKE1gVDLS0Ocj8XKNAuoR09mU6nUY6nZatFXRb6XO6yZgUlNpiJxrTliP7oJMI9ZhoK4yARXsJOInsF4WaRt5ra2tyPDJwuuea7yHK1NaYtkYt6bHSuQZacACrizESiUgpWSrzer2Ofr8Pz/OEIdlHIlEmDGmBTqFtrbzHSXp+rIVtvVDWpW55V/OsBnfafcnnUMnT0rWeGX4fxLu5XE62RPn+afEY8h7Rv15PbA/nhkTwonlX84D2lpHn2F7NuzoBUofFKCDT6fTKaW96vGzlQxfo15/pPmne5efsA4Uf4635fB7D4RDZbBaTyQSNRgODwUC8BzozW/Mu+2d59zJJK1EmRWsQqg0AWtI6hKWBnFbM+jMttyiTNEDQHjHOu+YdqxOA1fCPBnRa2WtZptvKOdEhWFbgoyXN9UDXOtcYK2HmcjnRHTRout0uSqWSeAva7fZKmWuW9U0mk1KGeDabSZJfu92G53ni9mdsnmuAhzDR46rd/STP85DNZkWn6WTWRCIhxYuo1K380PU/uCWcHoh0Og3fX9Y36PV62N7ellDJRegVVSC0QkQLDc1EWtlwAvm5dk1yUetrNWO44vKM/9BVm0wmEY/H0Wg0MJ/P5VSncDiMJ554Ag8ePBC0yrZyIogkabVRSOgYse4DP9NuUf0dlaoVuFQArA9O4aO/i0QiEi/moRYEDtrjol3P1oWnFyyFmbbueS2fSyuIYQKOH91Y2WwW0WgUzWZTXGtE2OFwWOJyWuDY975WyDVf/JttD+JdbVEBp7xr+VdbsDp2S2BBfiPvplIpiWNq3g2FQnjyySdx//59STIiH3B7J93arNtOq1BX7WT7rXUNPHyypLb+2Fbt9tUKXcd0CZ7j8bjwrgUAtFb18/X72V7dTxdg1l4u8q7nncapybu0JCkXXLxLwK9BBsfqLJD9uIjbxXq9nhzmQ7lBpcB2U35pMKN5Ws8v58MaCvpa7c3R48A1oGUK51nnlEQiETmyWHs0YrGYFPWhrNWGXqfTkb7SI6Bd4p7nodFoSAY/K+5xX34kEllZE2xDOp1Gp9ORxGf28/j4WNYTdwDQCk+n06hWq1JWWOd20WNH8MKQC/tIUEoAro+K5tyQT+llIIDVybDsN4EUPRp8FutPMFeu0+mIHC+VShcGtI+8tVArMWslaQvDClMtgHRsiM+lUOUP79GudiI4HQvltb1eTwalWq2KhVKv13Ht2jUUCgUcHh5K4QnGsuj24Xt0DE23gcRFxMVj48YaNVOY6e1bnnda0Y2TpN3FpVJJDsrIZrMATitJ8RoyB8fOWqjAqRLmQtdzwPnT91vQwkxW7qUlEKlWq1gsFqjX6zKW9BjEYjGk02lhcApnXSznMkl7akjawuT/QbzLuQfwEO/a2D5wuj3tPN7tdrsSz6xUKshkMlLmdHt7G/l8HkdHRyu863mexLQ510yS0+uRfeL8cpsUeZexfM0PWlnripU2OVLz+GKxPCSFoJFggJ4P3UaOp1Zk+nMNJG34UM+NBmFaJjHZz/IuTwBtNBqiOOhV4H5wxpJ1Wd/LBAR6riaTCdbX11fCTlqJa+8UQaE12sjDum/W08px1MCevKxlB4lKiqEs8owGApRfvI7Kj3PABFBtyLACH3M8ON/0BHNeqGRns2VSc7lcFqvZFdplSIh9y2azwhNPPfUUnnnmGWQyGZTLZanc6nmnyampVArRaHTFZR+Px2VbIMNVnBfKReYqcO1yDbCGy2g0kqqGug4D5SfPTZhOp+LRYOl6huIYAqlWq+h0OvLMi9AjVyDkYAKrh9FYb0AQEAh6Du+h0GCM2Sp7zej6KExa9iyTS8U+m83EbXT9+nU0m01xf4fDYWSzWTlLmgUh9MLRng8uNjIoka2NKRIVW2WvF5zLXe37PsrlMjzPk72qFEp6UWjlr1142tp3KQOdeMUFr8eZi4sLju9iIiFdWdpVxkVFJcJMW13d7bLDBJp0IpOLd2m9aDCoeZdjaXkXOAVj2gMFXIx3o9Go8O5isRBkf3Jygnw+L/XH6SaNRCKivI6Pj2V/v1bO2jvF9tFSoYIlP5PYPs27nDedX6Bdwpp3CSD1OBCE6HwcDUC0188aAyQNrPg3lYp1l2veTSaTkoPBfBgmYek96KzvQY+NtowvM1TAd5NPaLUCD3to9XhpK5rX2pAA76HM0wmm5B1rkRI46ForHEf9XnqmOPZU1rT62YZsNovxeCwn7BE468RDHZIlWEgmk5IMSEMMgBzew3mkcajd98wbAZZgq1gs4qWXXpISwzQS0+n0SmIiZVm320WxWFwZd81fnB/Ka3o3MpmM9EGDbV5HgMTn6hAl5ROrITKnazQaIZPJSKjA8zyRE9Fo9KFw4Vn0SGCAi5HMxcHUL9MJQrYRWrnbQSET6RgkS40SDdHK5OELFKIUNDwL/eTkRJIumITVbDZRKBSQyWSQTqexv78vRSfoBj85OcH+/j7i8bhMorbiuSAWi4UwmgYwbJ91yWm3m06m1FY6FUy5XEa73RZLcTQarSBxW5KWY609NBrAWIWn+8B2cU7pAiNaZ98bjYZYI6zepV2sOnTDedauV231XRbpfmrXKYWb/py8a9t7Fu9SkJJ3eew0ywkTzDGJiUCAVg29K7VabaVK22QykYqR6XRatmqRd1OpFDY3N9FoNLC/vy/bmTjfGgRyTjh/emzIuxoEUaFq3tZKhGsVWPJlqVQSLwddo1ohad7XmfB6TDXvksirOlxmExG165onulEwsh9UEJp3uc41OLAeTr77skh7VYDlOI1GIyQSCekblY2WM4zN6xCI9fBowMh1QDDFZzKZVXu2KBt13gh5nEYCn0twyOOJCQ6o1LjNjm2mp1UXyaJBoa+jDOZct9ttjMdjJBKJlW2DjUZDDmnT3jOGOLmeXve61wkoz+fzYo3Tw8FQAvmBAIaWPN/JcaQXOBQKScIg+5FIJNDv9wFg5chmPc9c/zrsx7UcjUbRbrelUNJ4PEaxWBRPhwbKrwoYAFYPbLFxJTIfX64TRygUtMWklVkoFBLFP5/PpZCIRsZ6UfJ5w+EQtVptBWHRNcU4KxEbXTEbGxvodDoiGHzfF0Gg41u6jdqK0gpZW9vaWrKhDovMycgahZdKJWSzWXzyk58Ua4rJY2QkvlOPrXbzWSuPpBlUezmsVUu3FZW753krLjXNrIxrMQ6rx4t/W7fuZZJeqAAe4l2icuBh3gVwJu9S8dNy0PvcCRB1vgiwFMSad3nWOxUaBSPbOBqNUK1W0e12RQmQd8kTtDB02IOfk6cI9DifmnetV4S8q3NodIEbzbu5XA6f/OQnReA2Go0VviSA0BaTBmOad3X7SdrTpUG2Dv0w6YvPcvEuhak2LGzIU/9tPZ2PkxaLhRhFdBPTgzQcDmUMqaxdOUVUanqcdeiQMpWevVarJflK5A9dCI1hKr6THqf5fI5MJiOKi/OwWCyktDXHkjKD9x4fH+PatWsPbZfWSbMAhPf0bqb5fHkeBcNUnF8adVrGMV6vE2l5CFa1WsV8vjwRMJ/PixeU7WB/6V3g58zmB049pL7vy84E8jpDH8zup2eWyZRc+9yJQzDB35rvc7mc7HBgdVg+i2CXBsdFw1yP7BmwVqh2fesFY92ILqtQuyIHgwGGw6FUtCOi0aUgtaDm8+lB6Pf78H1fDn/ggBNFhsNh1Go1AMszpckY4/FYYouRSEQG0e4k4A+RHieS/SITW8Wtkbp+Hn9TiIfDYdy4cQPRaFTOsaaS4QJyjaEee52cqYWpDSvovA+OIcdc36fjtHStck4Zt7NJYfzR2fcaLF0m6XCKK06qlaC+56K8yy1ttLxoKWjeJV9zLpgcRoGZSqVkm5IGJOTdg4MDAbjT6emZ7ORdvYeb7ee8kUe11U+wovtrw1icY+3t0bwbiURw7do1RKNRHB8fy5qlV8u6q7UVpPnR5RXg9TrpTXsBNO/qhDgdyqCg5PwzV4L94lolT+jnXDbvcu86QQGBHWPWtCqZVa7HRnt/dChAV4mkR+Tll1+WBFDmKrH2Pa1nXX2USpzP4vu444HykQYiXfu5XA7xeFxCiXwWjwomH7O/mhe5jvg9z6mg7CPgJR9xfjWgJIihRQ8sc0vS6TR2dnbQ7/extbWFcrksAEDvPiEw9n1ftntGIhEpv00+1Ymp9J4w0Z35HzQ89QFbnndas4BjSJBA2cE1x3XO/xn+BlYB30XDXI+cQMhFw/+50DX60pMHrCZl6bgqUSktKQoX7W7RMVwdc9VWNieb72MGLvekApDYu46XRiIROW89mUwKA2lm0oqWpAUkBQY/1zFWEvvNRUq0qi3OcrmMra0tHB8fo9lsijeDDK5DD9oro118GmhwnLVg1G5jbZ1qzw2fwT7oxEjOL/vEvuq+aBRq3fCXSdozQtIADTjlXc4v59Z6XzTvsowzLV5mQQOn/efYa97lOGqwCJxaQDwPne/TAobroF6vS7yV/MyyqRpIaGUOYEVQ8Xk6DKZJ8y6tHPaDzyuXy9je3ka9Xkej0ZC1pF3KGijqMeW46nwjrj/Olx4/HZrQ+T16XZC0ImJ72Ce9rvTcck705wwxXAbRmgQge+LpJWDSGnlVFykDTreg6nwYjgHB3PHxMcbjMba2tlYSC5n0R4UNrJ66Sc8r38vn8xq+Qxs15DEmvXGLHRWl9m7Qq0Beo+zWz6V3R3tI0+n0Cm8Cp6EQ9oUudNbEmM/n2N7exsHBAdbX1yVEQQDA9nCnANvDNRqJRMQ4CoVCEuLlWDEcwb8J5JlnoKsnarCjP6PHjyCJY6V5NZPJCNghWNDJm+fRI3sGtOXgEhzaBa3diTrms1gsJPbMieLk6gxYHZKg9UI3D4nPAyCIim1Ip9NoNptSVpf3MZGw1WphsVgeYZlIJFAqlYTJNRORQTUj6Dgs+0uhpONw2q2vlbYGMNFoFNeuXUM6ncbHPvYxTKdTOcOaC12727VgA05d/Npro9+hBaQVltqjw7bwfWQ0KiT2lcqGc0hmZdu0ouEcBVnYl0Va0PN//nZZQeSz+XwublsqGWYIa97Vz+Oipgtb84nmA/JYKLQ8Xa/dbstBP+RdJhI2m03M53MMBgPE43EUi0WxMqi02TfrCdLCk3PHedW8a4GABkV8biwWe4h3uQ6td4i8pi1tbTxo/rb8Yi08AgFtzdudQWfxLot6ad518YX2hl4GTafLE+yYD0UBr+P4ehspZYH29rDtDCuQ7+LxODY3N6UomJZZelcK+Zchpm63u7I7gB6pfr8vY8kMdiolz/NQr9eFX8PhsBw0RYVFLw5lSqFQwGQyQbvdlrMjaD0vFsvdK+wvjRb2Q5/qqgt9cS61PE2lUshkMhKDj8VikqDI8eh2uwI+KAOo20KhkHifCDR07hgte1cOB0MWFpRqcMt+a2OW61vvCNLP1mG0i+a7PDIYIHPpvy0ooHLW1gGwtM71wR/aWtXCQccjtZuQglm71uke4v3xeBztdluSLxKJBCKRiBR0YeJKPp9Hs9lEsVgUJMfFpV2sVlBp8AGclk+11o62bPT/unY/n7+5uYlqtYparYb79+8jHA7LYqI7S3sAgNW98FroUiBQyPJettPmOFBYskATgBV3NxmNe3ZpDTARTlsGOi/EhpMum/QiC+Jd8hf/1rw7Go1E0OnKYrwWOC3swzmg0NHKX/O3LV4Vj8elKA551/M8EbJc/LlcThJi9S4aHTvX1jbfr/mWzyIfUnhY0muTbbC8W6lU0Gg0cP/+fXGZ8vk2gYntYlu1R4n8aXlXu1Q173recudAOp1GoVAAcHHeJcgi7+o1qr1sl03s53A4RKfTEbc080+YMEa5xJCB5j1av7Q4acUyhk/jhwoMOK13r0885JwCpx5C/mbFP73Frd1uY319XRQeZYaOkQOrCazM89JFofL5/IpRSUtcg2eGUvhMylkAEt9n/2iVkz/L5TKazSZ2d3eRSqUErLAvvu+Lx4F95rO0F5Xzor13bK/ess7xbLfbKBQKDxWWI+/r3DXtTebYcV1wPoHTrahMJNZzeh49UtEh67rTSWfaHa3d5lS+XMhWIFM42HfogdH3aaHFwedkExHncjmMx2McHh5iPp8jlUqhUqmsoGY+j4frMPub7dSJThRInCBtNemcBL2QaK1QoFCRaPQdDodRKBRw8+ZNZLNZ/P7v/74ks1CA0f3Le/W4kTnIALRcbfiESk273igoaMnpIkPz+Ry5XE4ECvfNsh2aJ9guHcYgUOM9GihcBlneBVYPhtLtpmACsMK72rvB3y4XPMnljdFueMu7dCVmMhlMJhMcHR3J2JfL5RXgx7nntZZ3tZVEntUJgORd8rfmMV6jw2V8rga25N3d3d0V3iUfkSeCeJd8y3ZxfCx41H3SXgQqpng8jkQicSHe1XLHem70+Oo5oiy4LN4NhUISJkgkEhgMBigWi8KXTF6j5aj5m+NKBcztyjp0QstZyw6CMPIOAEmaC4VOy+cyD4PAhPxLDwL37zOXgcqalfKAZV7CcDiUbXy65gATFemx1IYUlSLBHdvBWgD0jLCd5EVtifM9HAeCWIZegNMS2xxTzgllB//nGOvcFeZ4cL1ovTAajVAoFMQDwfEnD3qeJ0CM79Brj6CMbbMlmQk4NMg9jx75oCLgNI6sXR1cnDr2ycZxUrTVApxuQ+T3vFZbCDoGTleqtqjIpHxvr9eTTEu2ldmnTz75JJ555hlRzLQ2FouFJOPoc645CdqasZYl3xtkHeo+2EzfQqGAN77xjahWqzg+PsZLL70kSWSMPWsBqZlPx5w58Vo56fihTSDh9b5/mumey+UwmUxkuybrCMzny0JI9XodnU4H8/lcFjDHhYtDLwbtQSEa1wrycRLHjwCWbbYeLNJkMhHrQSsu/Szt0gZOwZ61bjXvhkIhSZ6ista82+/3ZQsh20rX5O7uLp577jmZOwp03sftULR+tSdLK3ZtBVve5bomr2ve1Vnei8UC5XIZb3zjG7G+vo7j42O8+OKLUk2Re5417/L9lnd1DNsV+2Z/SVw7VCy0HDXvptNpiU+XSiU0m81A3uU4uHiXilGHER43eZ4nskB7cej6pgdEzy8VE5UssHoUNq/j82iN64QzvoNrgztfyDN8Jy12lvkdDofyXia/NRoNVKtVFAoFSdZmW3Q/gKVso6eDfK5zCPTOMHrDdGyc8lB7pPR61/1kWHpvb08K/+iESfI+x1OvI/0e3QYdWguFTvMHGHLQ80Oep2z0fX+lQJfLoiew53XhcFhydPge6iz2g7rwPHpFdQZsnJGTSEbSi1wvOH5v3cfaOqPA0O4XvpNClMmG+p0cFApYMnAul5ODdabT5SlUJycnuH37tmxpoatsNBphfX0drVZLLC0NeKw7EXj4NDhOsnbrUMhpz0G5XMab3vQmVKtVTKdT/Mmf/Il8ztiZ7p+OjXLMtELSyoyCQ1tT2hug8x6025fovlqtAlgquHa7LWNJpaarYBUKhRU3sEa4em7Zzssk9pdzqQEWieOgeTaId7WSc/Euf4jk19bWVmrgA6fKlfkE+oho8i4XdSKRQK1Ww82bNzEajXB0dAQAEqusVCpotVorJazZbq2MSZZ3NZAnALK8O5/PUa1W8aY3vQnr6+uYTCb4kz/5E/i+j0qlIryrrz+LdylLSJZ3Of6ad7WlrreYxWIxVCoVAEuhycIsdKdflHe1p9IC6cdNVFBMwKPXkyBhPB6j3W4jn88jEomg0+ms7C7RiYC6iild1zqezSx7AkECVYI6KnGOJ61UKjwAKyFEABKm9X1fMv8ZIiMfcCuztrgXi4W46ymHKRN5YJBea7xXez7J31qpUh8R/DWbTeRyOeTzeeELvpM/mkf4bHqldEgUOD2XhuPMcMd0On0oWVADIO0t5jhyXWi5pdep9ohoI0WvHbbxIvRIYECjGgpFLQz1ZOiMTu0yt/FDKiJtwerJ0ha2dg1pAQ2cCoxIJIJer4dqtbqSQEQXYiqVQq1Ww9HRETY3N2UrDCfH8zy89a1vxbPPPotmsymDrReN7hfboK0/PeEUxHpRVSoVAQLz+Ryf+MQncHBwgEKhIO7OZDK5UmKZY0RkSKbU8WmCAi44bfmRWXgtXXLJZBJbW1viHaGCAyCxvbt3764oz1BomdxDTw33IOsQBRepXsyXSXRlco41ONJuPG0Fc57JR1qocNFT6Fi3s4t3GZvV1hkBLnmk3++jUqnIu8i7s9lMTlc7OTnBxsaGhJRYZz0cDuOtb30rnn/+eeFdXS5bx8BdvEv3pQ6X8DsKpmq1Kt6s2WyGT3ziE9jb20OpVArkXb73IrxrQ2MasHG+eFjLWbxLvrx79+4KIPE8b4V3NdDlvXrNauByGeT7vpyjAEAsP32qHg+4oUGkiysBEIXLNcn+6uJq9AJQ1pBfuY+eHlZa5ww78D7KZIKAcDi8UmZXJ5YSGHNeqAz5uU5etD9UqloWAachaQ1KWJOBfKYLyPGz0WiEYrEo95AHKbPIJ8Ap/3JNaY8v8090cSHrTeQYavmqZQTXmfbKaUDB+7Uu1YaW9g5pHf05zxmgcNAd1JnHwKlXQFtU2oXN+2zMhddpREehQOGsETqvjcVi4hblol4slnXz6/U6yuUybt68KdZVIpHAc889J+h6NBrJqYa5XA7tdluQ5Nve9jbs7+/j5ZdfRrPZlL5qNyX/18UedHIZXXVkZN/3Ua1W8fTTT2NzcxORSAQf/ehH8YlPfELinnwXgYVWSBwzrfB1IqBeyDrJSisBClPuZQcgp38x/qrRr+4jz4nnomJCKJlfmMoANZe793GT9j4Bq4cMsY061khgq/t2Hu9qzxl5V+eNAKe8yyQ2Ajx6dhqNxkO8G4ksT1J7/vnnhXcnkwkKhYLsPCHvrq2tPcS7OnfgPN7VIR9dSAUAKpUKnn76admG9tGPfhQf//jHJelK8672AlGp6nHXyl/zrvZUaPDKH827lAFBvKs9AYVCAel0WqxjF++64qs63HUZ5HnLksrNZlNq3zORkBn2bCcBEOd6Pl8Wf+J2U15HZcZdBADE86qVGNeCTtDmeOmQhQ0TUhGzcFYkEpHQJ/+nLtHgXLeBMkavSeuJ4I+WyVr/2G3k2kM2n89Rq9WQzWYRDoelAqgOAXDdaEOBY0WFzbFk/wkY+A62j4qc/dCAm323oVt6q+np0V4Tkk5g1+EJtpGA4yL0yGECvUDZ+CBQoButrVUdE7FZn553mvWr76XrhSUcfX954AgXP62uXC4n1k0ul0O1WsXm5ibm8zkODw/FtZnNZqWmc6vVkuId3W4Xi8Vy28qtW7eQz+fxwgsv4OjoSFxgetApxMkc7CfdbRyzRCKBjY0N3L59W7wWx8fHeOGFFzCbnZ45zcQgDQg4sXwWBSYZRAt3zoNGs1rg6UW2ubmJUCgkhZ4Wi4WUzw2Hw2g0GpjNZjJWFCBsi1akGoVq60MvoMsKE2ggoEM/OqShQYLlXQoUjjV5V+/BdvEu76MCYi6K7/toNpvyXvIuS6ZGIsvTyyqVCra2toR3NzY2MJ1OkclkZD5Y613zbiqVwq1bt5DL5XDnzh0JJwTxLvmNn2veDYWW2xzJu5VKBZFIBMfHx3j++eclDg9AQms290CHsPiZzp8hPwbxLu//XPKuNmq4PsjDlnepJC6DNIhiKIQ7CgjWeTor+UzLC54VQouVxhUVFLB05XO7oE4u1DJN17ingtF1/AlCKJvoIeUa0Jar3kXANum8B17L9WbrCzCDnvxBhay3U+rwNEGvBjTcq88QXKvVQiaTEeuc3hTmDpEIMrVc5+cEozocx3HWslwbRwRu5HngVAdQh2kvM+Wr9ubp/60XTHv3zqNHAgM29mfjpVoZAA/vOacg1TFVDg5dTIwFcYEzB4DPZXyJTKA7ShBQLpeRyWTwBV/wBdje3pbkwDe84Q2Ix+M4Pj7Gyy+/LBOoLQkK1BdffBFPPvkkNjc3kUwmsbe3h/39fXHZcksS42q8n8JPuzzX19exs7OD3d1dJJNJRKNR7O/vY7FY4IknnpAyzK1WS6pIcQEzmU2f0qUXOxlfzw8Znm3RliotK8/zcHx8LO1k8hXPfac1ymQeYHU7qI5V8d2cW86JVqqc88siLlK2XXsJ2D4dK7a8q113AJy8a2tqMA5LoUbLiHytrR7Lu29+85uxtbUlbvennnoKiUQCJycnwrvcfkrrgLx79+5dvO51r8PW1hZSqdRDvMs2kHfZdh0ndfEua76zgueTTz4pSWPNZlOy1VnEi7yrhTTHmuOox5ykeVd7CF8N3uX32iJ8LfFuOLzcZZLL5dDpdFbOfPB9Xz7THi0S+6AT63iKnd7SSjDBbczMTyGPM0bPcAJd9Tp8RhBBbwz5i2uHSZv6zAAqa+2h4z1cd1qfaBlD65nbI7nVUOsLDRr0WuVa51rt9Xo4PDxEu93G7du35SwCHR4jkNbGsAY+OuzGcIfOPaJu4DWed7pbQo8diW5+Hc7kvZxbei7YL4ZaNA/YnImz6JErEDLBQgtP7WbT6JMDr+OVRGzsEJmJyJ7ol8zNPbIUDto6ZqIK41h07cRiMYnJk/mi0ahscVlfX5dtLy+99JLESumCozB48cUXsbGxgVKphFKpJFXW7t27h1qthtlsJu3T1gRRcyaTwebmpjyDW8Hu3buHVqsF3/exvb0NAKjVavIZ+8hELPZfu/q05aVdhToGzTnhd8Cy8E0ikRCLgUKBCDkajWJnZ0cWtW4PFwKZjP9rK9paerRALxMIsO0601aPmQaxlsd12zXvAniId6n4mNhDS1kLFS0ALe+S9xmTp7LWuzuCeHexWJZnpUC5c+cONjc3hXevXbuGk5OTFd5lMqPeax8OL7O0Le/ytLSXX34Z7XYbvu9jc3MTvu+jVqvJZy7e5Wd67VLpkDTvakOB3wEX493t7W0Jceh51u8L4l1ttGgrTj/nsoh8xNDAcDgUw0InLVNZ6/nkeLdaLXGL61K/3A56fHws1+qdVvQqUMHpinb6bz2HHGOtfMn3JA0C+L9uNw0LC9q5Lqn8dI6BTuqjVc/xAFaPVyZAD4fD2N7exnQ6XakmasN7uj/sK5XyYrE8T4TrlXxPHcV+auNOyxltRGgQRJDDCrpcH/RU6lohwGlCrfawXJR3H6nOAIUhBSZdavoQDNdE6hi2tsh4HRcmB5qIiucMaEYgwiUjMblFZ3e22200m01hcqJF3/cFeQKQuOyzzz6Lg4MDQc77+/sYj8cYDAZyClu1WkW5XEYul0OhUEC9Xke73cZgMJBjZcmM6XRaCqHk83mx6vf399FoNNDpdCRRptPpoFgsolQqYTAYSJ179ld7HejmpruIe02BU0Gmk034DM/z5NhNWlAUpKVSSZg1nU7Leem9Xm9lrixD2YVAsp4h3YbLJB2b1PypgROtBc1jOg6rQZHuj3bfUQBp3qVw0tYmBS75XfMuw1Z0U9JKC+Jd1iQYDofY39/HaDRCOp1Gt9vFtWvXUKlUBIyexbuxWExO9STvMkmSvEvAEY1GhaeKxeIK73L8aCzosASVGhPNNOlMbo6V5y2P8y6VSsK79KIUi0XhY8u7NoGLfeR4WyOF3+vQAHD5vEtDQFcGBSDgkJ5UgktdQGg+n6Pf72N9fR39fh9ra8vCavqMewKLcHhZN4JjQg8wQRkz7amweSYGifkqTFZl23VIgGuQ+Snao6kTOemt1da7zgXhM3lCId+l3eJch5xTgpzpdCqepNlshkajAeA04ZHJl1xrOrToeafHY+scLnpUCBp1Amez2ZTtrNrjxLCF9hASFOg54PkH7Btwesidlina68E1pnO2LkKP5Bmg4teuNZskpfdTEklZFwcZgEhIC2gS0Y52MRKZcsC1tcfnEADs7e0hEomgUqnIhDUaDakjoDNbt7e3USqV0Ov18KlPfQqz2QzHx8dypna73Ua9XpfiQNVqFcViUWKQ3W5XFg3boBHi/v4+9vf30Wq1VkIeFHxktvF4LM9nlm4ul0O/3xcG4Nhp9EfSCVFUXHp7EatozWYzFAoF8Wjk83lcu3YN3W5XKjWSwbQ1rD/X4I9Mzd9kQI38LxsMWN5l23T/tJtfx65peZC00NG8q9cAcApAyMfM3CaIuwjvApAjuHO5nJRzJUjZ2dk5l3c3NjZw8+ZNyaEJ4l2CWb3XmrzbbrclM5sKSPMuKyOur69LbD+bzUr1RO0a1vF4YHUbFcGE5d1UKiUellKptMK7Ozs76PV6UktAW/Na9lBJaVBHBQFgBVy/VnhXh7fYDp5Wx6Q0nd+hFSyVMQ2aeDyOVqu1EiZg3JseJoZidWiRHh7gFNhxjrU7X1uutLop/21YjvcDDxuaOmaudytwXTIvhMYoAAF42lNG0MC1x/mlVykajYoxyWOA+TzynM6XYI0EKmJa67Ty6dm2+/0pOxjOI1+T2CfKAa3ktYufIAJYrUzLrcR8L8nljTmLHrnoEAGAzh/gS7VC1BPM3zrblJPCZ3Oy7R5v7aYlGtYDzEGl64tbaxi3unXrFhKJBNrttigslmqkYN3Y2MDLL7+M27dvo1Qqye6D+/fvy6lyR0dHOD4+Fg9BPp9HMplEOBwWwEEGmUyWZ9C322202230+/0V5mSck9aLdvMx1kbBy7PqNTNrd722GLj4iOSZIUv0TCHOcczlcpJc2e/3VxQD54Vjq61fbTlzDnSbbP6ABo+XRUG8Sz7TCpH94nfaNce+aKuSi1dXSANOE2kBSAa8dT0CD/MuhZmLd/VJm4lEAuvr6+h0Orh9+zbK5TL+6I/+CGtra3jw4IGUsD08PFzhXRblWltbk9oALt61hzBRBuhEPfaD48EDdMi73M2gwySWd33fl0JFVM6ad+m14DjO58ujcjc2NgBAgACfz/nkXGrQRhDE6/Rebs41+/Ja4F0Natg2YJn0l81m0ev1VmLNkUhEvABa8ZD/tBJlyIHyW+ce8N2UJzyaWGe1a2sVWAUu2s2u15T10lDRa7BNeXNyciLnblBR8j7eq7fiUa4yTE3PH8umazc8PQeUAZlMBv9/e2fe21h2nP0iqYXivkqU1OplpideBg7gcYD8k3zjfIQgARIHiWHYDhDHjj0z3e1utVqiuG9aSJHvH8Sv+PD0vezuQTLyC+sAgiQu9557Tp2qp56qU2c0GnkiJXkSjAf5PzBVHLIEYIF9vr299VMQYazVqJOcyPWVPUFHMc+M8WQyWduJgR4ibwh51RwG5oNx+5iWWHxkQAFam8WJUlUErUKiwsH/qlRRyKAiHh7FAQ2jsVu+w+Bq0gW/MeQgunq9bj/60Y+cKn/27Jkvkuvra3v9+rX97d/+rSsFTqL64x//aL/4xS/sv//7v63dbruiQ4GRoMSBQhpH0p+dnR0rFAougLqVizHK5XJObZqZHR8fe87BbDaz3/72t/b69WsXas3eZSzNVqUpYRnUuO/u7lq5XHYlwQEzd3fLLTZkumuclgWEoOpzzeerAknE33h+wAiiNZ8vd3tgXL7vFspuKL+qrHhWGkAW9gujwrNpLguLH+WqBkcT16DwomQXTwjZ/fGPf2xmS4P39OlTl13i9x+S3Var5bILw6Gyq54cOQ6a60Bp31B2UT7ZbPY92c1kMm48fv/736/JLvUtwjHSEMjt7a0Nh0Ofi52dHQ9xIbuPHj2yu7s7P/xGQawCNq6B1weNXqvV/P8/Z9klQZNnwimg7C4gTmvyT6fLw4Q4a4SxYY5ms5mfz8J84j2brWrvh8YJAAijqTpa5RkjxN9cRwEEfysrFL7PjhvCD3jmyIEylRhZ7kusHZCAjDPHGqaCHZhOp2thqOPj47XcNFhwWEYNfeMshcZXHQjGCQCjZZpZT+QT6DUUwOh4M2bKRPK3MseM34faJ4EBtmIoXa30H5OBgVL6iM9HUdzQ+Hg/IHnQHYKpGcYhYmYA8I51ALg/5VJzuZwdHh5aNpu1yWTi3tL29rZls1krFAqWTCbt4uLCfv3rX9tvfvMbe/v27dpeVAxAPp+3o6Mje/LkiU8YJTmJ/WvoAGHAQPE5KH4oWrNlgRdyCc7Pz32LGIyEKnF+dDxBndls1kqlktXrdatUKtZsNl2ZNptNX4xKa2sWLQonPMa1Xq9bu912ZM6iYGyUmiU+fZ9gQJE+cqMeZMgOqDeDLCtDhgyq7CLbhIjiZBelESe7Wk7bbF12j46OLJPJRMouVd6azeaa7FKUx2wVhy4UCnZ0dGRPnz51mSbuz1zDRiC7SkeiODGeSlMSjri6ulqT3Vwut0aFfkh2M5mMVSoVq9VqVqlU7OLiwkFsKLtcN8wAv729XZPdRCJh9XrdLi8vXRHDBETJ8WQysfF4fC+ye3d357tAAKR4nhg3ngnWZnd313q93tp2QBwCZQfIOdCYPDu6eD1k0xgnwJzG8M1W9VDou/ZN9b0yjGbm+QK8j3E0Mz8xkOTuVqtlpVLJSqWSy5ECONYOuRCAH2LzbCukn9vb254Lw98kJvKDE0v/NGyDTseYs8axlRh/KkYS+lK2w2xVwp8QCtdiHJDx0WjkgEj1ko49oDaRSHhtmA+1TwYDUIDcUMGADpDGKqK2X4Q7DNRwRF1DHxRhMVuvCY+y5/3d3V07OjqyTqdjd3er/dC5XG7tlDcomFqt5nuqoSBvbm7s9evX9uLFC/v222/t9PTU46AIdDqdtp/97Gf2xRdf2HQ6tRcvXvjzkehHf/FEksmkb78iO9psVS8bBByWJWZxalVFErt0uxDjQanNfD5vP/zhD+3w8NBevnxpk8nE2u22o00AinqveKDj8XitMh2oWj0wDQ0oGNDwyXA4XDv34ftqyK4ibjXEKrtQo8igUpnKYvG6ym4Yiw4T6GhxsqsVDeNklyNW42T34ODAGo2GU6bI7osXL+z09NRGo5GHo5Ddv/mbv7Hnz5/bbDazly9fuiyGHhSMAclW0PkYWi2Dy354xlWTuZCp+XxunU7H+v3+muzyfZXdH/zgB3Z4eGivXr2yyWTiOyJSqeX2MirCYajw5qJkV+ltva8CCc09uE/ZJeEN+lpZK01uxZiynjHSjD1jos6W7saA8SOfAHlUsIHcma08XlgnjJeuH+6rIMDM1mRBE50xmjg4sJjKDi8WC7u8vHTZh5qHYUV3qT5lLGBQYE/0ACLdncCz7O3teTguTIrkuwCOqCR1xpmwtRbF0vAkfWMM0CmwwIy3Mi+sb0JAXCuVSnkxPjNzB16TPePaJ4cJiO3RITqAV4WBVPSvShL0jcBAYyAYmvym3hvX1NCBerBK+ypdkk6nfT/nT37yE7u+vrbLy0sPJ5iZn0/PoBMjy2QyViqVrFarWblcttFoZO122168eGFv3ryx8XjsAl8ul61er9vV1ZV9/fXXlkqlrFKpeOzo+vraET6TrqcFYvyvrq48OYQJV0oVOgs2AfoW1MscYRj29vbsyZMn1mg0PA/i9PTUFwC7LTg6V+k2zfDFsKmhVy+Kpt4KAgp9TiLT991gWzBOcbKrMU4FnGarjGddyDAAoPDQE1LwilyqYlTZNVvtCcZr2CS7AOXJZPKed4zsFotFz28ZjUbW6XTWQAGyW6lUrF6v22QysW+++caSyeUuE+LzoexiLFDGgJlQdpVaZYxUdjXkpGshl8tZvV63TCZjjx8/tsPDQ5fdt2/fukxiCIibI7uMbZTsojd0xwKNtUVDtu9TdqGuYZmIH5utH/dOrQDAQRhKHQwGfswvn2FLK029d+ZE+6EhA4wiOh3aHGCiYTUFgwqc9Z4aJlZwAt1vZl4GWdmjvb0932ZJKARAYma+ewL5UBCv4AMGT8fVzBxI4MRR0XGxWNjFxYUlk0l7+vSpAzXd8qr5DFTA5flgEhgjvgMbzjjpzhvWIzaR65Lrw/ySJ8K6Y51+qH0yM4CnqoYgpE2j4qpMvNm6V8ZgKF2iYQUejvuE2ZGK2MzW68hvbW05ncZ2q+PjY99CQuGSMJtUFTuJOvl83kqlkiNsEhBRxiia2Wxm3W53Tcmo4PN8TKoyLbwHFYtwsbBBrLe3tz7+CNDe3p7TxMfHx/b48WN7/PixFQoFN/avX7/2bWiMG9dDgWh8mwWsniQKVGOIPKMqlpD2I0xwn8wABWuUFkbuVH5UnsN5VISuOQRmq6Qo/VuLlWySXQUmJDXBBkwmk7XwwNXVlV1cXPgcYIQxtszb3t6e5XI597Chign5sEtFZaHT6ZiqBIA5jXEDeIZsnSpS5IYxRtYUvJB3sLe3Z8Vi0Y6OjuzRo0f25MkTKxQKNpvNXHabzaYrSrNVfJy50uQpzTKPkk3d4YA86Psq7yQR3xczwA4L3eLKc21tba0VrxkMBr7bArlOpZbldvEgMb5436wP9faRXY3to28YS4AX40V5dxqZ/fQjXHtmK88dY4vzw7rSXAQz8xAJz8E2QO7Fb0CDFmvLZDJ+wifPBJBSm8T3NFxEvzudju9subm5sXK57P2dTpfHL2O8uabW3NEdRMqEM76AFM2rIBSnSbbIRhiGMFuBGV6jVPeH2keDAaWuGTCNU0D78J7GOkKvXSdYwYAuSgaE95WOoj8aN8f48H4+n7fr62urVqtOhVWrVadsMLIscqhskC2LTRkPjDILiAnWPoM0o8aOmB7epaJTmi6+0FNVpUaMmX3heP+1Ws3q9fpawtzXX39tnU7HY54oUAyJxku1HwhW2EeMEMpXEbUCBh2bq6srGw6H95YzoLkYLDSMsyY54TnAxGjuS0jxIZtm75/oiRwgOx8ju2bmMb6bm5tY2YUCZDwxDrAXUI5mtmYcUUyMhcqqetL0mXGA/VDZDZk8xoAWxbKofJBkmM1m7dmzZ3ZwcGD1et1qtZqzBIlEwr755hvrdrsuN/qcWvBLwzn0Ay9Y9YLKro6PhrT4LnMCTX8fsgsYuLu78/uz9jkqmLnV/mMUZ7PZmiHQrd86J+Q1zedzr4KKzCPvbFfFwKp3qkBJ1w994m9AK3vvs9msyx7JkoDU/f1929rastevX3uy8t3dnVUqFV8D+Xze5bLZbPoWaeL1hGMVTKknroYb44peI35/e3trg8HAP3NwcOBOHON9d3fnTBjyxn1VtzAufI/3YaC4hxZSGo/Ha2tOmU2uBZBjpxx94zyO/3UwgDdgZmuCopQoA8si073divA0jMBgItQahyUOxYCqYo7ytJQe4p4UbcDzh8pBCKGcdKB1kQB66KfeS+PBmmGrxkAzPLm3xv91dwELh/6okeCzmUzGwxJHR0d2cnJiiUTCer2eC0CxWLQ3b96sxXn1GVEGemAL9+dHjZWCEWVPdCcFwq2eC8/CIr8P7wrZxSADSj8kuyqvSvGHITDGKE52zWztvnGyyz1UdvHYNBseGQ5lV0Nxm2QXb1FDdZqprOEQNR54cHwWw6Axd0BmmAMEiCYhsF6vW6PRsMePH7vsAhJKpZKdnp6+J7saT+V/Be0KlJW5VHnmO8o4mtl7c6nPcn197Szg992m0+la/QQK7bDOlZ2lYVD4Db0N0MMZY460mBAygWHD6PB5rs/3lCVUXZBKpbysL2MPla7b6wh1DQYDKxQKLmOVSmVN1xKTx4HS8xRYA+Vy2VqtliWTy1NVAQQwBs1m016+fGn1et3y+bzVajVPxiOHTNcX1S5LpZL/r8+DLgW4wswpQGPM+Q5rRUPdrDl+1HnQ808YC+SfcWb+KCtOoaLr62tntEmq3NS+09bCkAnQ2D7CokpVPa+QEueHRa0eBQPG99XjZmLCkAO/8YCU4kFwMcg0NYRKrymqZUKYOH0+XUSqnHTC9Rk0jLJYLLOwKQikYwtgQZhLpZJls1nfZcAEk+mscd18Pu+oNzQSKH2ElzFAwNVDROBQCqB2BRQYfZS/2Xr8FdR8nzkDeJvq8dNvKEWzzbLL/2Yr2cUoqTEKZVeVAfS9yq4Cqbu7O0+EVQ+WvzFmNAXTagx5LZRd5lXZrjAkxXMqGxYnuzs7Oy67Om4YUWKjxWLRcwGQ3URiuWcbdg7ZzeVyvo5CcE5fGHd9XvqvLJaOP5U3NSzEM2kcGy+N52Dv+H2BAc4u0XwXjTmHMswzMGaAHgw0ToUyW8osIAtm5o4KOggDp4aN8QplS50BgJjWH0APaSVM8qfa7bYVCgWbTCZ+gBDPgFxj7DCMmsiK3qzX6z6/PC+sCWFfZAI7xPNzoi3gA2BFYSF15BR8wrohd8oghLZA54t8gpBNUdBG2IVr0QADgD3usb29vWYrNrVPPpuApohUFZouPoTCbD2DVBdzuAgVrdPUIOvWMH4zEbPZzJWKmfnE0l/6gILTREOUmSJhRYChh7e9vTqIAuVPlT+lfFhA6jHj4WBAp9Op9Xq9tcQfXVTE9JTe5ZhmvEMVjMVidYCJGi8dL1WaShcrhc5z6/ODREO6SlG8yosqDZWf77vFyS5jbvZxsst3Q9kNPSPuqQBXqT/eV1nFePK/XkNj48hVnOxiGPieAh3mFtnFuCO7yKPKLmMGiNYYKbKrwFFlF6+LEON8voy7cqpgKLvz+dxp3jjZVUWqssszI7s6F2arfdgYFXUCmFdd5wo+7quprKhRI+QFta+GnTFQ3UdsWes5KHMQAiQ+v1isqr2qvOJoaMEcvsv36Gen03FWSEGdmTkIIPRAGKFUKrkOS6VSNhwO14wutgDDZ7ZcM4DO2WzmOUI4A2GOk+ZHcSwyz6rGVEPhgBI+E4JnZcv5vrIl+uzqLAI4cDbNVoWO0DMkfeoWRH2P+VB7+imy+8lHGDPh6umr98H7fF4pjRDxoJB4EAZUkz5QRhhn9ZxVIaPElMJW7xxalP7ihSsVDKI0W5VDVoStiIvPsG+avxE8qCsyTzEoevQmVf8YExgGElSU2jNbPyKWnAX2waq3z2IJESlKMGQ7eEYNeTDmurBVuWqSobIfINNQqapxvY+mC4Q5/FjZVSXJPGkSnP7W/dlxsmu2in2jCFR2NZ6NDKvnrUCW8aUQFfczWy8frcaCfpKIRbKibidDdnXOUULz+dxlV40zawDAqPf8WNmlxckuBj+cI1XYPFOU7Oq6CNk/rhn+f59gAFaNOTFbVRJVtsZsxXIwRgoCdfy0hgSyAsiDcVCjpl4utDWGCCOKoe33+74GSApUQHl1dbW23Y+QBfKCQSYJUEMSsGas2el0at1u12VIyyoDlgEQyCT5NJPJxHq9npmZ777Z3t72Mu16DwCPrmPkCTsRhl9DJlH7wmd077+G2xh3rq3zq8536NRhv5if0AZ8qH0nZkARsyp5pQjNVkZGO4xRVMOMIuV6OiAMBgZHi5PovQgB8B734BqKXKGBEDSAAYkaZivki/eiRgOPSdmDu7tVgg8eoFJSt7e3VqvV7Kc//ak9e/bMWq2W/du//ZuXJaafJFbVajVXrAiyeiuan6BKUReeZnMrZa3Cqx6yerJKeaNYlD3Q7zL3YY4Djfvfp1LVhpyFsqveYSi7IZuggCIEFXjs3IvFj3FCHpXmVjlGdnXMzd43fshuMrnaWqYUbyi7XJd1gQyQmEjfFXSw7XZ/f9+++uore/bsmV1eXtrPf/5zl10au1lC2dUtasjJx8iurv0wtMG4fors6tib2do6AuToWOs17rOpMVevk8Yz8ywhm8M4KnME2KNqJc+OJ4q8Il/Q48lk0o0q8s8OgMFg4AmLvV7PisWijUYjn3+MPHM3GAx8eyPhGJgABZkYOGQASpz985rDovJrZmu6FWCCPB0cHNjNzY0X5cFgq/OioBYDrnKHbOCRq05RBkFZOXWiVFbVvobzjv5HjqlAOZlMvDYNzCJ9HA6HViwW15yATe2TmQGMEX+ziENqlEUXGhAmi4FR70eRDIKH8gBEcE/6o0ZGQQjKFoPM4KLk1CtWuleLP6hHoUoU1Iv3geLiftwzm81arVazR48e2fPnz+3Zs2d2eHjoFQ4fP35s//iP/2jffPONDQYDm06nvvWFsABJZEr9Mv6KDkNEqp66AgieRcdbQZTSXnpts1XdduaT8VOPS+eCz+u43GfTxaoGKM4IqEJSNonxVNZJlbMeIhKCVDWeel/d4aG/mXeup/SvGgBAALKrc4fsMtd4c3j6GA29B7J7cnJin3/+uT19+tSOj48tn89bKpWyJ0+erMku+/03ya6ZvQcANskuf6tHhTLVMJvKqtlm2dU5CBks9aqZWzVE99GUQl4sFl5zoFgser9DXYAsMJ70n/g4Bl31K+EG3XLJGuC6gAjNkYHCLxaLfg3A6N3d8kh4trVS+IvYNv3GedIiSYAVQkyJRMLrSWQymbW1SHE0WCdl08I1BgNA/hC5TN1u10/q5NnV0QlBZCijNAWumt+iDi2eu7KoqnuRd83DgxEh4ZH1CqvCNRQIslY1dLupfVICodaFVsSsxmATrabxxJBu1u8RAlDjgtI1s/fioLroGXwGRBc7r2k/9Ue/o30KB1npSwSTex4dHdkPf/hDq9fr9sUXX9jh4aELGfEeqgVyfPDPf/5z+4d/+Af79ttvvfhPsVi04+Njq1QqPvYaY1KjFlKganCUQVFgps+r9KzS/qo89bv8KODT6ys9jTLB+7yvOgPIrsqUGvxQ2at8Kxugi1rjj8rasFD1HvyvOzdY9HqNEOiGLWQzVH7VA6aFsqsAJ5vNrsUdj4+P7Qc/+IHVajX7q7/6Kzs8PLRKpWLlcnmtwE+c7EIRq+yGHpeuc5U1nkFlV/sc5gqo18j/GJGPlV2ur4yWOgcqu+wo+L4b26ApcBY6JioLeNYYR+LVACHCr8ospVKptc/icUetc+L2rKXRaGT5fN6rSWK8ptNloSS81VQq5fVeSOzjf4z7fD73o4W3tras0+n4rgLk/PT01A4ODvwwomKx6POEzQCIEEYDtFNwDGeUNc/WzT/96U92cnJi+XzezGwNdKvMKlutoRc+p59RvcDryKcyNTT6DjBSeWZ9KAtEmAFGWucUULZYLOzw8PB/dzcBMT7dBqeDoqjJbLV9SSli9R7V+1EQwG/9rA6cKkNQoSpBXRzQXKBZVZp8RtG0VpPjNT7Lc9NXDv45OjqyfD5vnU7Her2e3dzceKLM9va2b+14+vSpffXVV/azn/1sbbsQNOq7d+/sn/7pn+xf/uVf7Ouvv7bRaGSVSsX29/ft6OjIE3/wuJUyUjCA4GjSm9l6ESA11CGqVXDBtckMZ8ubKlD9LEqUfuhnqGJ3X2CAH+TTbL1IkNlq+6COscpKyHxEya7mv6g8K0VpZmuUY6h0UewoV7w5s3V2TuePZ4uSXYAka2d7e9sqlYodHh5aPp+3brdr/X7fz7lHdnO5nJVKJfvss8/sq6++sq+++sqm06nnCsTJ7nA4tGq1avv7+3Z8fLx2Roh6Syq7qieQU6V61VjHya7Kr8ouRpLPoUx1PXAPZXO4JgWa7kN2b29v/fwPvHPmXvUXMXKy71mvyn5RoIckZr5ntpTnXC7nuw2YJ3JYMMDIvO52aTabtrOzY6VSae2sCa5Rr9dtNlsWj5pOp747QPO90um0Hy1vtmLKtHLobLYqDnd1deU7rJLJ9XMXyKXhHsiR6gGenfV7dXXlOzfy+fwaaNCmhp7/zd53PBU8INcKZPkOv0MmkiRfZQ/C68DqMIaa1M2OBzOzQqHg+UGb2ieFCVhkoeetnlL4ng6Sfh5B0Z8QPHBN0KLZKjkK2oQcAl7nQAuUcbFYtEKh4AelJBKJtUM+dB8uYIMMV6VWUd6VSsWrMF5dXdnr16/tzZs3nuCjQAlEvLu7a7/85S/t7OzMdnZ27Kc//alv0aPoRi6Xs7/7u7+z58+f23/913/Zf/7nf9qLFy+s1WpZIpGw/f19y2azLvQoUPoMFRRSWwoK1FjreKshp6kXjLeLkdQFqwLOnIVoVg3wfTQ1OspmoCR4Ro0vhp/T74ceeZTs8j3q9fMZUD9xTzNz5bS9ve2yRznhQqFgzWbT55VMacIvXI/+6umTGtbZ2tpak93JZOKlqUl2ZU0yHvl83tLptP3qV79ak12qbnJ8ai6Xs7//+7/fKLvEZdXo0k+VZ31NQQHyqQyOyi7zzOuaOKayS/VRZcPUUVHPTd+7r3Z3d+f9v7q68vr3qVTKzxFYLBZrsXP0GUl4mkipAF3HMpVKefEeDivC4ychcGtry50YZJbEPQozaVGs0Whk5XLZer2eFx2Dtchmsx6SZcw5Tpt70U+2n1I6PZFYFvSh1n+r1fKzOdCr4dkYoRMZ5l5QIOju7s4rPgKi1aCrjlSjrk6x2gHeV4ZWZUtZavqFExDunGMOWT8KVsifoISz2Sp3KWTb4ton5wzoQg5pPTobxkKUpgzjKbwOncNgMOnEjEBKs9nMjx/t9Xpr8RA13BhvzoJXhU+8H+Sp5xQQpyITloE2Wx4RWy6Xrdvt2qtXr+zs7GzNCGtSJJQYgl+r1bzC1tXVlfX7fVfKAJ5MJmNHR0dWr9ftyy+/tN/97nf2z//8z/anP/3J+v2+1et1KxaLfjId46fFWBgHxl6FTZEyHrDZ+0VaEFz1ovAuUCgq1LwWMjQhJRtFe3+fTQFU2B+VbX2GMB+GcVTFgOyqd05cVmV3Op2uyS73ofgKxtJsCXb7/f4aSFssFu5xmS0XO8ce8x4ggecj3looFFx2X79+baenp2tsGTFYvqde0ibZJWapsvuTn/zEfvvb335QdjHkqjgV4NIAUgpsdQ6j6H9kNJRdPF2VXfW8aBpeCGXl+2yLxcKNrdlqP/lisXAvOZfL+dZk5BfPkPnlMCe8eowNaxcWAB06Ho89sx9mFUYomUx6aVwO4IGFYM3Axtze3lqv17NyuWyNRsOazaYlk0kbDAZ+pkC/33fQUC6X10qwcx12npRKJT9yHf1XLpc9EZK+kleh3r+yASHIC4EB4VxyDAAD+nnGjWuH9D92AL0DQOfzfBcAp3lhyCjyrEWguA7rW0EvlRHRG7quP9Q+GgzoYgnpDVWMIQXI5/CKNJlPQwBR2dNQfKPRaM1rOD8/d6PPIPMTxrz5n8FNJpPvsQlUQ2s2m26YQbwIBglRzWbT2u229fv9NRaE/mriF4vv0aNH9td//de2u7trv/nNb2x/f9+rbQ2HQ2u32/bu3Ts7Pz+3VqvlivOzzz6zH/zgB/bv//7v9q//+q/28uVLazQatlgsHCTpXOg2FwSM91S50UcWDsJptvJSib+p8GosVcGPZq8jfCgmM1tb3PfRlI1S+VLgZrai9KJkl88rK4BiUdnlswAtjr02Wy768/NzHy8Fxpokx3WYm1B21Vvb39+3g4MDazabThWPRqM1BQ1QaTabHs5SShJakfsAMpDdn/zkJ5ZOp2Nl9/z83M7Pz+3y8tLq9bqHFqJk12xVWEbnAi9GvVvGX0N5mmCpgEDngaRe1Qn/v8quGi6o8ZubGysUCq4HwqRTAIFu3cOI4zVqeEATUMkZMFuXT83yp/gNYHd7e9t3owAgC4WCb/nL5/MOSimcNhqNrNvtWrvd9kOydnd3rdvtOjjOZrPuuFG6GuDJeMznc7u4uPDEazPz0AFzH54zoDlerG9dC+hvs1XOBmOgYWSV05Dh0nux/jWcHjoYABVl2ZWVhBHSkILaRP2NjCDPuoVxo6wtlPfY0FAourWCh2Fg1CPVBcd7AAIeUql/jI1eU5ENvxlkBotFqgtY22KxcEqK/1lA2gcUQ6PRsEwmY71eby2Gqoqa7VbE0rgWg76/v2/lctkuLi48b+Hg4MDMlttrDg4OrFwue42Bi4sLGw6HZma+D7dUKnnG68nJiT169Mh+/etf269+9St/DaFX4VCFGBVnAixg8M3Wt8Bo+IR5RCEi5Bpv1Nc4LdFsvfTybDazTqdj3W73XuKumnXLOCm1p7JJQ3lhHJA3Na5mqyx1XegoGpVdBaoh1c39zdaTlfAK9RhhLZ6iBXRms5nt7++/J7vq+aq86t9cF0q/VCpZs9l0ELy/v+8huU2yCy39MbJbqVTMzDbKLvOhY4vsRcnuYrGqfc844iV+iuzy2p+D7A4GAy+k1uv1fM4Bg7BHGNytrS0HayShpVIpd6hY42bLkNNwOHxPB2pZWwz9fD73MzNIDKTAmtnKESJcgeN0fHzsR63f3i4Pd4Md07wAPoNDQ4VANeTUdNHwiNmqbLcaX9YU46CyFG79NVvVldE1ruGq0WjkITbWMo31rGwsoIG+8Dm+q/lJWi8kBBmqCxSwaBK9Ni1OZmaePPkxgOCTzyZQwaGT+qBKlWjN65BCWSwWViwWnYbiOggwn6OIDyh0MBjY7u6u5fN5Zw00XKFnbrPgNQQAUjZbFpsoFoueBY3HxCACIhKJ1VneJOcQi5vPVwcGYRTwri8vL9eOddU8CZJDVIjNVkdumpnXw04kEvb06VPL5XI2Go3sm2++scvLS8tms3Z0dGSlUmkNLCk9ZfZ+BTel9NUTRvB1PjQkwGc0BgvtCgU8m83ceOHFQhUOBoN7OeyF0BNjrHSfLl4ze092FbzqIqPcs45zWD0zlUq5F7y9ve2V9dSz1vCO0oQoCJQhBYDoJ8l9vV7P0um01/bn2ZBdQMNisfBDXTSOjOwy74CS7yK7bBszW5fdJ0+eWD6f3yi7yFeU7IaUa5i4qkygenzIrrIum2QXAACoUNkdDoeez/F9NorjzOdzB12AP6W9SczDCEI7s++cODhe43Q69e2JACuYJ7xuQkWz2cxPkCQePx6PrVqtrulqzU/hJEDkhK2sMAjD4dBL/BJyQH7oDzu1YLs4i4FnRF75HDS5MiHKWGOgNWzE+CE7GkbgeXAUVCfyzApi1VgDYLg3+kTvw3usrbCvZtHVUmF4VI/xPIALvsv4aa2PuPbJYABAoItUvXNF6QyWUq14Z4nEMgkEAVc6TKv/cV3QH4yDVqpSZc4gkLGqoAEvAdTHgRXj8dgrWUHHsHNid3fXzs7OHJVCwSaTSRsOh5ZOp63RaFij0XCP+urqygaDgSUSCb/e7e2tG0MMg9JBUEF4c/1+38zMFwz0LftgiblRs5xjljE8jL0qTcaUMeB/+hfGTzXkgEFSpZtMJq3f71sqlbKjo6O1kAzldTXZ7b5OflMwoLKrtL+GmkLZ5fWdnR2fi0wmY6PRyBe/UnRmKyOmsqt90UQfpS3Nltm/ZPMDGjT+jezmcjkbj8fW6/VcdgkhILvn5+demW00GrnipRjMwcGBHR4erjFdHyO7Ckxms5llMplY2TVbAgXqa7Bzgcx1jDH9Rq+oHCqjxX3VwKu+CLOvVXYBw/Rja2vLDg8PY2UXwHBfsnt9fW29Xs/6/b4fPV0sFtdCSxiFkA4n4Y6DjVqtllUqFY/z8zmSnzWRFd0Kpc/1+/2+M8QYwlar5c4ac7pYrBLwkEV03eXlpScp4uBQgQ97QLKn2dJmkKwKgzWfz133kSmvO74IkyB/anwXi1W5dgCxhlF1XPl8eB0AAWEm1reCUQUJgAze1zBZyBzwdxgGU3aDfgIENd+H+8OKkHPxofbJYAD6VOP9YbwP1K0ejqIrVcLa+PzBwYGf187AZDIZ3/oCUkXB8T31VOr1uicLctLX3d2dZ7xqsosiMvqkMUZNctKqfiwGTdJQ5IoQ5HI5y2Qya2cXQHWy0AAq0+nUDQELFYoW+i6fz7uQsRsBILK9ve10lho5Ne70M/SoFBzQR4w+fWFsUJSwGAcHB3Z3d+f7oekf19Zs4++7KZDV+D3zwzOhYDEMWtKTMUOW1ZNlke/s7Fi9XrfLy8s14w4FjYHhYB/kEG8GT6Vara6Fo1Dmw+HQjRqyq3F3Nc7qKejrNJQ0IBN5QJajZBdjj+wOBgMzW9HqyJ96kWaruOvOzo7l83kft1wu957sAlbUyVAwYGZrAEGZrCjZJfciTnYXi4WDgSjZBSDd19bC0WhkV1dX7swoyMfLVR0AWEQHaVXV8XjsO6nUu8XQUTdAc2Bms5n1+32rVqt2d3dnrVbL5QLwdn5+7usEwLS3t2d7e3t+n2w26wad0BdhKA1raS3+3d1dZ7Lm87mVy2XXH+yqArCarRJ5de1CuxP31/wL7AafwbCarcfyo9YPxhpPPNStyC/PD1DTcEpU/hzzhj4idKNgBvlmLGnMJWuU65Jk/KH2ncCAAgKlTmgIgXo86nEprRgm4VUqFTf4mnzBnuizszNbLBaeNYlHa7ZEZ6PRyJNOUKYI1nQ69RgbCiWceKVa44YmpHT0s2o4mAwAA6c+5vN5y+Vydnh46IuXfoUJP2arfeIU7UFIksnkGnOgW2GYExS+xvx5D89VwQBGyGyVNHh3d+cV0DqdjveZzyEfCqA0pjufz73S130xA6lUysNNKrtqRPCGkb0wbqe0Ishe6cNQdmmNRsNyuZydn5+77KKgWPTIrh6YgsJCdtk9E/bLbH37kHoVoTxH/a3f4z0MMes+nU7b3t6e71k+PDx0WaMMLeOjsovsc4Q140eCLmAlrOMRJ7sYaV5DRpkL2JZPlV0Nkf05ye5kMrFWq7VmeKghACtHzgDZ/rqlDvnSkAk6SUsFX11drcl/IpFYS/L78Y9/vMZOIW94xGyT3trasslkshaKmE6ndnl56azGfD63Uqlk5+fnDmJgHahyiZME0wVQq9VqPhckJ5qt55kdHR3ZYDCw0Whkz58/d3mmr6lUyrfThvIV5pKEiaMAWbN1Rk/lUb1zxhq7h47UsAEONUyFgmDNVVInE+CiTjlrHgeSe+vW/E3tk8AAdAMXDqlm7ah63WooUVSJxHK703w+d2oJxKneDJmrfBf6CppVqRS8eCZLlTwCoxRk+J5OoA6uGv5wuMLcCW0ISNR3AAeNRsNOTk4885tM7lar5Z5XMpm0R48euXfCiV5QgVoQhGM9Q3Yg7JeODUqQH81aDreyQctqA5Gql6ZxM6Wf70OhEtrS6pkh1RwnuzyfMlmJRMKVEAaOHBP1QKBN+R+51RCBsjMAapS4thCAhusKZWi2fhqafp8WAtmoph6RXgPFlU6n7fDw0GUXzz6dTlun0/FQwdbWlh0dHa3JLmGPdDrtmeNm5vU29Jh0TexUr0gVOEYf6pi97MgxMhAlu2bmNPmfo+ze3NzY5eWlH79cKpWs2+1aoVDwkCQGGKAJJQ9QS6VSa1umZ7PliX540mwXheElvr+3t+fhib29PT8OmMJGhKeg3Dudju3t7XluDOAQ0Ev/t7e37eXLl7azs2OPHj2ydDptr169stlsZkdHR3Z7e2tnZ2ee0wAbmkgkPO8L3VStVtfYIAX15PBks9m1+gaE4UJKnbwLchQA7Hd3y22K3W7Xqx52u11nsTDufBbGAyBJPgSs+Ww28/FhHfAenrwyAyEwBrAoq6NOtjrYmjz9ofZJ5YgVvaCAVElphTcWFmhGt6ZowytQQ82ixusnbhd6YqFyDA04/ysA0fdoivZ8YOSaNB0q/lZKLU656nNFeWPE+aFiK5WKVatV92i2t7ftyy+/tK2tLfvFL35h3W7XhYA9yFtbW7a/v2+LxcJpN7PV4THkOURVtdIxvbm58aRM+gs4A2ipsCkToKiY67JwyRm4jyQswIDmUmhIiIWki0llF7lW5aGeM4sUD4zkK8YllN0QiNLCpajANJSdEBBsMuzhZ3ktNPbawvuF64vvw7ZQ5KVarVqlUlnLkfjyyy8tlUrZL3/5Sz+62Gwpu+yeqVarZrbMhYG+z2azHqZQgMRYqqyZ2VpCMf1Uz+1jZJfP8D5zeV+ye3NzYxcXF2sVS0OdeX5+7iV9k8mklUoll0Pq+RcKBWu32x6qubq6ci+V8cBjBtzhlcOwAvSm0+URw+Px2IrForXbbaeyS6WSjxmf+93vfmcHBweWzWat3W57cis2BVnh+hhjErZhQ25vb61QKFipVLLXr1/b/v6+1Wo1Ozs78zCwmdnbt28tnU7bycmJdbtdy+fzNplMvJ4BjHSj0fA+IEMkPfMMsHWPHz82syX7R5gJfaKMDCXnAfuAA2wQtD42UWsDwBaElSM1/KH5eaH+1lN3aciK7qCIa59UZ8BsnSo1W6/eBW2iVBQIiFKVZLeCxkCqSuuFSjM05LQ4+lM9pVAJRv29SZHyvoKKcDz076gQQtx1mMybmxunItvttr1588bzBqBlp9Opl3ctFAruLfBdAMHW1pb1er21OCF7gvEIAB6aVasJhFDp6uWarfbSqhDyzMqQ6A/CqIlO99VQeJoYaLbK6I2SXbwCGCnkGW9HGZEwds09o9gh3uP+ocyEcqbfUXAQAoSoa0e9p8Z003iFLQyvaMW5RCKxJrulUskymYxNp1Or1+se6mu1Wl4pDh2A/qB4jpl5jglKd29vL1J20R14XFCoOtdmq3oNIZhADkLAZmb3Lrs4A2rsWafb29s2GAys1+t5/6rVqhs+s6VhbDQanreDcVJAgOExM3vz5o17qmbLEFev1/NMfnaekO8yHA4tkUjY4eGhDQYDm8/nzlp0Oh1nC9LptFeB3d3ddbrfbJlTQ50BtncmEgnfXXB5eWlmy3k6PDz0Gi+DwcC63a6DH8AHW65fvnzp8lqpVPzZSVx/+/athzY0vp/NZr2+DAb43bt3tr297XkXqdTyRE9AyHg8dibQbHX0NEYecAUDAUMAeGBe0dMwGRhx3UoZ2jZssm6fRydvAvxh++QwAeiFRQYwYNFoyACKgobhh35TNB6lmBigjQ8gyin8fOjdR3lj2sJrRHli4Wt6vzgmYdM9VREp0xJei2zxcrlsh4eH7kklk8ttJoQUlFFhniqVis3nc/+M7k/f2dlxZKqJoMyNhl7UCDB/yjBo3JXPMZ4oqPvwrjTfRRMq8epZmHie+jqNbHueXRPNwhhhCPjCOY0yzFFA0ywe8EZ57dqXKNZB5UsBiNn6YWP8H5V7EMUOKIsQyi5KfW9vz8/y0JPh2C2RSCTWDuOBlSmVSjafz333DV4cHpRW1TNbbZNTj1/7rHP3/4Ps4iRwtsf29vJMAChvYvGUKi4Wi76fn/krl8uWy+X8GmxphTEjlyqXy9lgMPAkZIzZ5eWlpVIpK5fL7izgcdOn/f19393UbretVqu5gWRdzWYzLx1MQt14PLaDgwPPtUkkEr7TwMyc5SSkTG4C/cAo393d2ePHj51BaDQa9s0337gcslMLz7/f71uj0XDjf3Fx4WPFrhvyWgqFgu8cAsiiO0lUNzOvw4BnD2NA8uVsttx1w7ZV+s/zaniRNQSji6HXEJqCCnZZaJIpyZUweB9q3+kIYygqYiRQyGYrLwuPF8NEwobZ+6U9ozwks1VBkfD1sD9xyjfOcwqVlb4W56Hxf+hNhPeN8trilHT42bAPOk5aCvbNmzeWyWS8xGy5XF6jmKC38LiazaYrO7ylcrlsg8HA0TVbdDCWeriM9lmpRR0LnSulzcPxvs+m4BXFr3FGZZTU60fZcA29ns6xykE431HGO/z8dxmjD8mYMiBRhlrnUJme8Fk3MRAqs+FcY0wnk4l1u12X3Xw+b5VKxYrFoitFPCjdBow3qbs9yuWy9fv9tdoK7NBIJBIObkOgrQyGhoLU8/9zlF0N0W1tLYv55HI5e/funZfLXSwWHl6pVquWTqet1WrZbDaz09NTz9cwWyW+4RFTWyCMic9myxoMT58+tdevX/tYsJuJOWd9zOfLmiucIwAQ7Pf7Pt93d3eeK3J3d2cHBwd+bgGsBk7ndDq13d1dd3w0gZLKnhSvevXqlXvst7e3/szPnj1bs1MY9nq97kCA8BKGtlQq+ZbHVqtl7XbbFouFb8NNJpPW6XRsOBw6a4FBL5VKLlecd0BeRiqVcqYEXYQ9xXEmD07zFdgtoofVadIgu0awy6xlEgn/1ysQagKhxlhA56o8oVBRuGbvn+4U59lEeS2hstHr6WfivCJtUYt7k/ILgUocMIm6bwhSojxHnjuKivyY58aDxSsolUpWq9UcMZL8RL4ASgCkChWu+R70Tz3jxWLh1e/M1k+SU6+Kz4aU+fX1tXW73XtLINQf5oJkK2RWY/zIr7YoAx8a/6iQQJTsh8BTlesmgMrfoUe+qYWsQnitOOAb9QwKXvW545436j0FX8SMS6WSn4NAyIbtqLrzQsMDoVdEAzTo2lMPFXmlj7pDA5lV1uvm5sZ6vd69MAPUGdCS7IRbWcOTycQKhYKl02kbj8d2eXlptVrNWq2Wx5Y7nY7V63X35AuFgtP9f/zjH/2odR2jwWBgtVrN1wy7lgiZ8TpzSPE2Ehuz2awbpUwmY5eXl+6IzOdzB3N7e3vOXEwmE09upN8HBwdukDW7nxoKhJpubm4cYJ6enlo2m7W9vT07Pj62u7s73xnx+eef22w2s1evXtmPfvQjd5bY+q11K5DTyWRiBwcH1mq1vMgRBbNwejudjidXzudzH2e2DhPyYtxgCHgfuQcoMN8kiXIaLswBdph1yEFFyDrsGjb6Q+2TjzCmOAQdUTqNeEjoGavSChWFKreo16O8+SjP/kP3oUWBjFC5f2yLAxEf8qg2Ubnh9fU1DWHEgZpEIuExRuJneF0oT3INUILqKXFNgACvAQQSiVWGtxoEBX0YU82IR6kRJ/w+m4a4QOD0mf6r7JrF7xL5GPmIArsKEuLAbhS7pNeLM968Fl4nrm+0kNXS96MAi/YlXON63aj7awJm+Eyau4HsVioVr6hHDJvYbFj5kb91B4ICFS2hTj+UNdD1GBUGuk/Zvbq6sl6vZ+12ew2wkwsESKK/5A5hUDHcxNjr9bofHJVIJOzdu3dOIbN2S6WSV7gjZ2YymVi5XLbxeGx3d8vyvOQU1Wo1PxsjkVjuYKhUKpZIJJxyx3iR78TYk7fw9OlT37mAjcEoY6BhEzqdjn322Wc2Ho+dPZjP59ZsNq1QKKxtJa1UKu41FwoFy2az9uLFC0+qZozQreycgJmg4NF4PPZcBLaoTqdTOzg48PFV2RuPxy6rJBbe3d1ZvV53fYRsaZVSmA/GRhNxzZbON8mk5DuwjrHFZkugRH4YTs+H2keHCdRgYgzIEtfYf5SC+JD3EnpTqojUAwqVKveIYhkUVGzydDZ5MXFKWPsYF/IIX1Olx/e1j2p84rzLEETo9xQkkFA4GAzs9PR0bX84OQedTseNIF49NKOZ+algKGEQcCqV8v2tzLuyCEq/QoNpaOE+ms41447s0jed63CsQwOqIRGz9+U8lFmuF275C+ed3yoDUXIcgtgwLq7X037p/4vF4qMM+qZ1EfV+3FpXsBgFfGhkwcNmqeyWSiVPaIPFwYsnHIlBJEygyVgquwp+FfjyPOg4ZQ3uo9GnVCrlFU+hyM/Pz61YLNrbt289BEi4ZDAYeO2BVCplvV7PDg8PrdlsWr1et729PWu321YoFMzM/PTATCazVjcil8u5UcI7TyQSvvXQzPyo48FgYJlMxg4PD213d9dev37tIYFisehGGm9bAQJbI/HAz8/PfScBmf3JZNLzGzgOGYaj1+vZ0dGRyzXeO/9TwKjX69mjR488Kfv09NTq9bqZrYAh9VyKxaL1ej0zWybwjcdj99opkjQcDq3Vavl2b9gITlLc2dnxctIwJ4vFwovmmZkDPHWeyHOi78gsLIFWkFR9APggj4QaDh/TPnlroS7mKCOHAMcl/30MW8BrUV7Jh7yuTdfY5PVEgRFVsuEzfaxXtElxRo1ZqCjDPobXj3uOOAAD0i0Wi1ar1cxsFZNkb7YqSgQOVgF6ljk0Wx2eg8JHEfM5aMP7SsIiUYo+M45xNPsmMLlJVvif78VdK05G9TObvPLQq94EHPW1qOfhtfBeH7s2eU8BUNS9onJjonRE1H3QLzs7O5bL5TycYLYCZwBgVaiwP4TFMJjaoFQVxBJmSCQSXqnwvmSXHUaj0ch6vZ5nq2spbIDT/v6+JwlT5e/m5sarYjYaDUulUg4iNE7NFk/WMAWJGAcSCol5EwenDxQGajQaTpVzuA/gO51O29dff23Hx8c2GAy8pgSe9XA4tNvbWy+zred4cLIhuySm06knFV5cXHgOFLlUsBGEQvv9vp94SBKi7mJptVp2cHDgCajj8dhqtZonFlJ0C3nO5XJu3CeTiR/6hCeOd895LRj3drttZqs1ByOgVWUJfRB+hxkJ1/5isdqqSKiCZEPA0N7enm/R/VD7aGZgPp87OgsVRJSSCw1plIHU/2lxXv4mdiEOCER5IShKfQ2FEnefTd5PHLMQKvWo59W8AcZMPxMHVvQ7/K3MQNx3FouFb+fq9Xp2enrqQk6+QaFQcGSMkkSBKguBck0kEmsllumLbq0JDcv33fAc4wx3+FqUp817UfOyyXuOks3Q6w/vHyW74e84MKp95dnj1uumtaVyFYKTkPVQOY5aB9of7UM4znHPwXrFuHU6HWe8wmTE8XjsgFbnXMdd81s0UZY+AAD+HGQ3kVgm9hETb7VaXpiMMSc/gkQx2A9Oq9ve3rZGo2H9fn/tnBOSujVkBkDjLIl379558iZ79/GQF4uF725g373Zyjlgq2i32/VdS1988YUlEstwxLt37/yESwojmS3nYn9/34sXDYdDBwp47tQ5KJVKXjuAmirUOCAB9YsvvvC4fDK5LJlMUaHj42OvlAibcne3PBqcXRQ8Cwc3AZDY5oq8sF0T8ADI6ff7vsPg6urKCoWC7ezsWLFY9PBKp9Px5+J+bGWGCWLLZjKZtG63a3d3dzaZTLwOBw47Ow84gyIsYBYra4tN7qo03X8dp6A+RrA/dLvQ0EV5PuFno7yJsD9xXvOHYrkoImU/Nim8UHGq4TdbVzphIZsoDzTKU9w0dtqP8HV+axKWAqJUKuXxrVwu57sL9LCccDsW10UR8F6YmQ/Ndh9nEwBm4mQ0lGVtccBN39e5Cj1kBXhRcxLKa9Tfm2Qh/Dt8pihjvuk+2jf+hulRwBwHhKKeI3wtqr9Ra1j/ph+6JpkXvDvOgEB2E4mEn6Gg4aAw34CELJVd3iMcMZlM7kV2R6ORvXnzxs7Oztx7NzPPDYASZvdApVKxVqtlhULBWq2W3d2tyhNjkBnD8XjsHj8x5vPzc2dder2e5yQcHx/bzc2NG7xut+unFmoIcbFYOOjQegLz+dwLD52entrbt2/t5OTEhsOhXVxc2Oeff25mq62fxWLRbm5u3Nix5TGTyXjYgqJVhCyoS4GDAqB78uSJjcdjN/bv3r2zbDZricRqGx/zjDxVq1UPfxwcHNif/vQnq9fr/iwwACRUamJgs9n0EBf2slar2bfffusG/+Liwh49emSTycSLH3GCI9toi8Wigy3NKeCayGMikXAmJZlM2mAwcPDDybzsJNnUPgkMYDDMor35OG+V99SohspIW1S8VDMtozyROC9v7WEj7rfJyIbPg6L/0OfVo9L7hSAhyghFAaHQyMcZgtBghPeJe67wPUAftFomk3ElslgsPKRAjgHzY2a+z9VslbiFsrivkq4YEZRV1DhpCw0Or2mLA8JR1LfKZFQ+AdfbBHy5ThzwjQMEIXWvCb96jah76/+bQlhRTddK3DirQY/SFeEzhM8VleSJ10tdDg6/QX8ATElCVNlVKlZllzj1fRxUNBwO7ezszG5vb73Yzvn5ue3v77sXeHl56SAIqr/X6zmAy+fz9vbtWw+znJ2deeVIQgWLxcLLGnP66e3trbXbbatUKg48OBdgf3/fE+4oyQ1jgZEi/wOjq/X4MX7pdNq63a4nu9EoQz8YDGw4HFqj0bDxeOy5DWbmBYpILhyNRp5TgtHFM97d3bVWq2VPnz61nZ0dr+xYqVS8L7BFe3t7nnQIqCS3SssvM26aXAlzRb4DLMvW1pZdXFx4qIF6EGar0yBTqZRVq1XL5/PW6XSsVCp5mIty1NVqda20NomONzc3nv9B/hd1abLZrJXL5Q/K2neqQMjC1AUfZcDN3jfAmvEY99mQ8uZv7qeeZ/iZ0COJ84zC6/P/Jq867Huo0OPAUJh9zzhEAZkoxRwa+ri+x41FmJ8R59HpHBNOgE5LpVJeIQzUCdpEwer2SO5ZLpft8vLy3hOxzFayF+e9qvHiNdomb1a/E14jNJ5hKGiTlx013x8CMfp3KA9mK4ZK/44Ckh8jX/rdsD9xCZhRfQ7fj8ovCPsS9kGvhbEfDAa+ZQsamUx1diog5+gUnT9K7d5nAux0Ol07Yp21SCJcu92258+fW7fbtfl87vFrnLdEYplT0Gg0HLwD9KGuSfKD1oZ9YHvgu3fvnKImXNFut61arfpnMd6z2cy3L2Jg6Qf7+yuViucRbW9vW71et8Vi5enWajUPSx8fH9vt7a01m02Pgw8GA/vyyy+9dPGrV69ssVi4wTs5ObFer2cvX770Z6UU86NHj8zMHARR5ZCDk3Z3d63dbnvFXMYMWQJcjsdjKxQK/v3hcGjJ5LJA08nJibNVPFOn07F0Ou1hDJgmQrPsKOBZOVNCkxVJpmSuSahkLkkATaVS3l+2nX5M+2hmAPokLPEZ16Lej1vcm74X5f1Eva8t3MYUfl5b6DnxWpTRD6+hCjaqb1EKM0q5h9dKJFY11DeNVXivjwmpRHmkYR8Zg7jvEXtDweoplYAmaosjNxQKIZ71fbZEYlVQSOcyShZ1XDZ53Xwm7n60MGQQJtvqtejnJi9ZDbfOTRQwD59Hrxn2JQSwceMTXiNqXKLGKAqoxDEdUeMS3jt8/vB+UeAGBa2yq6dU8p3RaLS2Txu6+j5kt9lsWrPZ9MOfYCqurq4sk8msFaAB7FBELJ/PW7fbtVar5ZQ0NUn0BE28db7PWQzEwdPptF1eXvpBRbPZ8rhnkvru7u6sUCh4/8zME994rVQq+VbBwWDgeQW3t7d2dHRknU5nLamPxMNsNuvnA0ynU6tWq/bmzRt78uSJvXv3bk33EHeHFeIQLFid29tbOzg48HyDVCplJycndnNzY81m0yaTiR0dHfn4zGYzHx9CqFQZJF6fTqet1+vZ8fGxb0El+x+WZXd31zqdjs/pzc2NnxcxHA49obLdbtuTJ088URXAQT2FYrHoeQD9ft9zN0gYrFQqfkw0OqFQKFg+n7eDg4MPytonVSCM81LM3jeqocel19jk/X7IGwsVa6hsN4GHsG9xFQ6jFKgyH3GsQKio1fMKk6XiPMfwvjoW4bOFzxOl4MPvxrX5fP4eiIoam8Vi4aEADjVCwXBudjqd9lwBWIYPgcD/yxbl6Zut09OhHEcBgdCDjwNW+nfUmtBrh975ph0OIZjc9L9eM5SJKBlS9ir8rjI+m2SU78WBmfBzUe+HQCoOLESBurj1qn3Hy0d2NaSgv8mr0bop99E0L2A+n3uWfyaTsfPzc8+An8/n1m63vQJgr9fzZL/9/X1POHz58qUnB6q+wPhTXleTBPH+8X5ns+VRwjgs1WrVjwze29tzz59yvowfcXlyDYjHFwoFSyQSvgOCTPper2eFQsGKxeJacbCTkxObz+d+7Xq97vF+8jzOz8/t6dOnniR9fHxs7Xbb+v2+dTodazQafuBbNpu1fr9vR0dH/uycegjQoPARp+oCNABCgKJyuWzJZNLOz8/9JEe2GMKeAmDYRfCHP/zBGo2G14Sp1+t+oFM2m7XJZOLPxT339vasVCrZdDq1i4sLu7m5sf/5n/+x+Xxun3/+ufX7fXvy5ImPy8e0T2IGSCKM8nDN4j2EKOWj34lTonHGPAr1RylB/TsOfOj/UVnkITgJlRVjw/U/luLU/8OwSQgqwvtHGbGoPm7yyuK83XBs4uYubkzNVgWqWFCLxTKuNhqN7iXu+qGs8E1AZZOHHYbGaHGyGwUCNo1v3HeigG/YV90FQh8x+Gbvy26YUBo+sxr5OECxae1Gjf0mgBs1lh8ju1HjHToO4fv6HWSXaqvI7n0lEHY6Hbu4uLDLy0s/0REqOMx1IIse+phku0qlYovF8nRD2I3d3V0rFoue6c4aub6+9kp2bCEklEJYIJFIeGIhzAk5AWbmNPZisTxBNZPJWKPRcFZDwwbb29tWrVZ9Z0E2m7VXr17Z8fGxTSYTu7i4sGq1aqVSyY0isXLuS3IzpZlJikTfaBEltmdWq1X74x//6GHP+Xxu5+fndnJy4iWJcY4IKREuSafTdnZ25mwHBYCo+HpwcOC2gARIyjvzOXIM2PnBIUc7OztrRY8Yr16v52EGyjvDrMAyLBYLz/OYz+dWLBYtn8/bycmJPXny5IOy9slnE4QKib95n//jjId6GVFGR5VklLKNMmzha1F/R3l0+t6mZ44zFoq4wrBEVJgiCghEGQf9HQKLOEUd1T6kpDf9H6Xkw2cPx0bjWMoaUJ/ivryrTS1uHFX+2oOmFQAAC5tJREFU4oxblMEP10XobUe10BiGYJv3osBq2Nfw/xAghM/E66GBj3o+fU2fKQSFHwMEwveiQK/Z+7kHUbKsYxXKpYKfMF9EX+c75BsQUtCysPfRyHdgJwHb7ZLJZVnfZrNpNzc3nrWvnvvNzY2Vy2X7/e9/b59//rknxFE50Gx1jDTUfCKRsGazaYeHh76dcLFY+G4BkiqhuMlRIKb/6NEjZxk54Q/mgvNStra27PDw0NLptA2HQ8vn83Z5een3YVthIrHM20in0/bixQt7/PixTSYTm06n1u127cc//rG9evXKcx0AcWzBhOk4PDy08Xhs33zzjTUaDTNb7tI4OjpyAMTzA7SoIAjTAtMJoGELItsveW2xWPjWxUajYfP53OtAmJnnGwAEOLhob2/PkzKbzabt7e05GODgJZJDy+Wyn+HBPL99+9ZDErVazdlbQMXHtE8CA9pCYxEu9jBuuVisJ+BFXUsXvHo/m5RF1H34m8+G34tSFlwr/Fzc86oB189E3fNjcixCrytsoUGIGr+o54wCXp/S4jzpTSBEv6d1/u8rgZDnDucrSl6ixlE/H3XdOBDAPaMMpYLfOPAQtStBr6HXjAMK4bNFgb3w2lGAO25dRXnucd581D02AY/w+lGvR63LMFym74fsx6b8CjyvTf37v24km5HTcHBw4IV5Wq2W1+Ofz+deES+VStnl5aWfTvr8+XNnPRKJhCf3AdrJjOeQs1qtZovF6gAdACPJjBwWRTwfT1VzhfD62cmRzWZtPp87GNEdBf/xH/9hjx49skQi4f3X4kCwGLe3t1YsFt1DHwwGHpYATACc8vm8lctlu7q68rGoVqs2n8897FKpVJwFIQEvmUw6CNja2nK2gLLGegTx3t6evX371haLZfhpf3/fDftgMPAdII1GwxaLhc/Z/v6+l3UmiZODjyaTid+bks8coU71SAAixYloOzs7NhqNrFqtWiqVsrdv3/oJkR/TPgnyxnks+r8a1FCJRBlPVVRKXYaGm8/ooozz8FUpcc+o7UncK1QmUYYhahtTCGK4liarRRmcsP9QauGzhc8VZYBDj1vvq+MY1+eo729S1nHGU0MlUf3fBBy+jxY3nuFnomTMbD3zftM1w89HjUdcXkcoe8hRFFDcZFjj5jf8vt6L+2koMG6M9FrhT1SL6p/eV++/aT3q9VnPUbsjwu3PUWtA/9bxigMt99UA0+yt5yAiYvKZTMbPTWAf+s7Ojhdi2t3d9a2BuVzOFouFn9RHEZx8Pu/17jOZjO3v73sxJsIKsAYwAuQBKQvANjpyANiGN5vN/Djf+Xy5R7/f73vJ4XQ67aEY8jSGw6Fn62OAKZFOOWHOTRiPx3Z8fGy7u7teqGdra8vPkqBGAdsEOYERo51MLg8iIuTB9meAxs7OjrVaLZvP5w6GiOFT8fDw8NDDNZzAeHFxYcVi0T/Xbrc9mRpwwE4HgNLR0dHaLoFMJmPlctkrDZ6dndlsNrN2u23j8djOz89tOp36aYnkVfR6PT9W+mNPLfxkZuBDXm9ULDtcYFFUpb4epYDC11QZxHkh+p0QpIRGT58tzmOK6ot6b5u89igQE2dY40IMcUo6TmFu+r6OBQo4Ciho0/6Fc6VbssLfUJd/bi1qruhzlFceZ/AYQ/7W3+H39b1Nu1D0Xgpotd8heNF+6PVVzjQpL7x/KAdRxjRObsPvhIAnaudC1JoI7xfKYxQoDa+lch21PjfNZdiXKN3zfTaK4nAYD9vjiDVvb29buVx2b5s5ZlsZoVn2ubNdjfAAHjdJbYVCwa6vr70SqRa4IfOfnRhaHOjk5MTMloYXg5hIJGx/f98PR7u7u7NSqeTb8QjFnJyc+GmA1BqA5ubMAuLvb9++9Zi7UuGcy3B9fe3ljBeLhec6dLtdN7LkMgEmOEyJZFJyI6g+uFgsfAcAx0OzVZHdC4CcXq9nuVzO5vO5VatVq9frdnp66gCFbazE/tPptB0eHtpkMvESzWdnZ1ar1fzazWbTWq2WHR0deQ2FZDLpWz/NzOe8Xq87gByNRlYsFj86TPBJzIAqd/6PWihR3pQquDDbmGuF29nCpoosPJmM97Wv2r+431EGkmQqveeHjHDYzzATmvGI84RCsBSOTdhPHdc4bzJ8L+x/qHB13EJFH763CXSE43+fylRb+ExxMsFn1EtlPENjqgY0fNYo0KjzTzZ2OFZxwDGUwxC0htdQOdbv6LOF/SXLW7e26ud5Zj0mWMc2HDN9L2oewu9GyWfYotZs+Hf43RCoh+MXpzui1vH33fD4MUCcP0BxGoopNZtNS6fTNhqNrFwu+wl+ePIAgdvbW8vn8/bZZ5+5cSYGPRwOvXAOh9ywta1QKFiv17N+v29nZ2eWTC5P8GM7I9n5k8nEKpWK5zEQ9y4Wi+4YLBbLLZwY8/l87of3vH792qbTqTWbTS9axHqpVCoOhubz+Vr1RZID37x544WMOE6Y3U6Ap1qt5rse2AWlFQ7x4mEUoPopD9xoNOz29tbK5bJ7+t1u1xqNhlUqFWdEKGlM4iNhDxyqdrttqVTK2ZvRaGT7+/u+/qigSe4C2zk5b2A+n3ulScDR9va2JZPLSoQfCwTMvkOY4EMAQFtoxKKMkyqMMAlKPxdn1MJrRyH6OGWv91dvIux/eA0FRfr9sF9xLEJUv0JDFZdbERWXDsdnE6DQa/H+pi1c4VxHyUD4zPoTde/7apvAkb4XBVb5W42dFo4KP8v/mzzXEMzq9zYZL76v9w/j5GF/4uZR+xACFp3nEAzpewCEUObC8di0ZqPkJm4+4tZleO04wB+1DqPActR3v+9GX3d3d53mZmteq9WyyWRiw+HQyuWypVIpOz09dSON91soFNzgUOq22+1av9+36+trGw6Hvr+erHTqBUwmE7u+vrZut+vevdkSoDSbTavVau7RLhYLT3BkJwYJbKVSyba3t200GtnLly/t8vLSaX+SCNneSJiB+wBKSqWSF91hux4Z/Nvb29bpdCyXy3nWPeEBqP/r62vPzodJKRQKVq1WPVGRmD85FBQD4mexWPhuhb29PTs/P3djf3R0ZKPRyMseX19fWyKR8AOdGAN2+mSzWd9ySI4FTMtisfBciWw2a4PBwKrVqrVaLbu9vfVn43CiZDLpgIj8iWfPnn1S8ut3SpMNjY0uJvV0VZGEizwKUMQlTIUt6vv0J0wCVCXFd0NvXL8Xdd8oBcU94vrH88QZz1CJhR5nqJi16bVCJoC5UUUfZeg3PXuU0dT7wspwL92yFoKmEKTdR4syqlFzGhp7jF+UHMbJxCaZ/djvMq5RzJE+Q9T3Q3aAz4fGMcrA6lxFyaVeS/8m0zqO2YsaZ55R5S1Or4TzFQV0w+fQ17VPzHFU36Jk9b5BbCKR8EO/NMY9m818y+DV1ZWdnZ35VkCK/bA2MSBmyxK+2WzWDg4OvAJerVZzD5jEwlQq5YYLj3k2m9nBwYHlcjmnp9+9e+fx+dvbWyuVSl6ch51EZmbn5+fW7/ftD3/4gx+gxMmHjDHHBV9dXVmn0/GCSefn59ZsNj3RT0/oy2QyNp1O7fLy0o6Pj71UMdsbKcizvb3tWzMBO9QU+Pbbb+3bb7+1fr/v8kOhJ+4DUCKBk3EuFou+M2I0Glk+n/f4/vPnz61arVqlUnFQkc/n/VCm3/3ud37IUzqdttvbW+t0OnZ9fW1v3771cwhub2/t8PDQLi8v/TjvZrNpFxcXlkwmrV6ve4Gom5sbr+pIuOCjZW1xn1r6oT20h/bQHtpDe2j33u5vA+1De2gP7aE9tIf20P4s2gMYeGgP7aE9tIf20P7C2wMYeGgP7aE9tIf20P7C2wMYeGgP7aE9tIf20P7C2wMYeGgP7aE9tIf20P7C2wMYeGgP7aE9tIf20P7C2wMYeGgP7aE9tIf20P7C2wMYeGgP7aE9tIf20P7C2wMYeGgP7aE9tIf20P7C2/8DfXAjJkylir0AAAAASUVORK5CYII="
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(torch.abs(target_rss[80]), cmap='gray')\n",
+ "plt.title('RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(torch.abs(target_sense[80]), cmap='gray')\n",
+ "plt.title('SENSE')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(torch.abs(target_sense[80]) - torch.abs(target_rss[80]), cmap='gray')\n",
+ "plt.title('SENSE - RSS')\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-22T14:55:56.052161Z",
+ "end_time": "2023-09-22T14:55:56.224507Z"
+ }
+ }
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/projects/REC/__init__.py b/projects/REC/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/fastMRIBrainsMulticoil/README.md b/projects/REC/fastMRIBrainsMulticoil/README.md
new file mode 100644
index 00000000..30a5b146
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/README.md
@@ -0,0 +1,39 @@
+## **fastMRI Brains Multicoil Dataset**
+
+This project folder contains the configuration files and visualization scripts for the fastMRI Brains Multicoil
+dataset.
+
+For more information, please refer to https://fastmri.med.nyu.edu/.
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](|visualize.ipynb). You just need to set the path where
+the dataset is downloaded.
+
+### **Preprocessing**
+The fastMRI datasets are supported natively in ``ATOMMIC`` and no preprocessing is required.
+
+Note
+~~~
+In specific training configurations some files might return nan values. If you want to exclude those files from the
+training, you can run the |scripts/split_sets_json.py script to exclude the files with nan values.
+~~~
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/|conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` to the generated json files. In `exp_manager` please set the `exp_dir` to
+the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/REC/fastMRIBrainsMulticoil/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/fastMRIBrainsMulticoil/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/REC/fastMRIBrainsMulticoil/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/REC/fastMRIBrainsMulticoil/__init__.py b/projects/REC/fastMRIBrainsMulticoil/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/targets/Val_RSS.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/targets/Val_RSS.yaml
new file mode 100644
index 00000000..3887272d
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/targets/Val_RSS.yaml
@@ -0,0 +1,103 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: novograd
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/targets/fastMRIBrains_batch0_Val_GDCC/RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/ccnn.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/ccnn.yaml
new file mode 100644
index 00000000..8110d5fc
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/ccnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/cirim.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/cirim.yaml
new file mode 100644
index 00000000..42a2f9de
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/cirim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/crnn.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/crnn.yaml
new file mode 100644
index 00000000..76c51314
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/crnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/jointicnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/jointicnet.yaml
new file mode 100644
index 00000000..ca655930
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/jointicnet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/kikinet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/kikinet.yaml
new file mode 100644
index 00000000..7212356f
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/kikinet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/lpdnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/lpdnet.yaml
new file mode 100644
index 00000000..054f2de7
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/lpdnet.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/modl.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/modl.yaml
new file mode 100644
index 00000000..c6b93616
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/modl.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/recurrentvarnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/recurrentvarnet.yaml
new file mode 100644
index 00000000..19950a4b
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/recurrentvarnet.yaml
@@ -0,0 +1,138 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: ???
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/rim.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/rim.yaml
new file mode 100644
index 00000000..a6281e6e
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/rim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/rvn.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/rvn.yaml
new file mode 100644
index 00000000..29a1c48e
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/rvn.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/unet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/unet.yaml
new file mode 100644
index 00000000..3ee9fac4
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/unet.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/varnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/varnet.yaml
new file mode 100644
index 00000000..d2fddef4
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/varnet.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/vsnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/vsnet.yaml
new file mode 100644
index 00000000..c7af3795
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/vsnet.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/xpdnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/xpdnet.yaml
new file mode 100644
index 00000000..2eff8c3d
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/xpdnet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/zerofilled.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/zerofilled.yaml
new file mode 100644
index 00000000..4068113c
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/4x/zerofilled.yaml
@@ -0,0 +1,106 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ center_fractions:
+ - 0.08
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: novograd
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_4x_NNEstimationCSM_GDCC/ZeroFilled_RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/ccnn.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/ccnn.yaml
new file mode 100644
index 00000000..292a3d77
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/ccnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/cirim.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/cirim.yaml
new file mode 100644
index 00000000..79a9e31e
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/cirim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/crnn.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/crnn.yaml
new file mode 100644
index 00000000..21867357
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/crnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/jointicnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/jointicnet.yaml
new file mode 100644
index 00000000..d7bb1a2c
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/jointicnet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/kikinet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/kikinet.yaml
new file mode 100644
index 00000000..21036cfa
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/kikinet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/lpdnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/lpdnet.yaml
new file mode 100644
index 00000000..26d65bc8
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/lpdnet.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/modl.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/modl.yaml
new file mode 100644
index 00000000..4d1e6d6b
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/modl.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/recurrentvarnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/recurrentvarnet.yaml
new file mode 100644
index 00000000..aecb6a24
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/recurrentvarnet.yaml
@@ -0,0 +1,138 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: ???
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/rim.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/rim.yaml
new file mode 100644
index 00000000..d893246f
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/rim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/rvn.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/rvn.yaml
new file mode 100644
index 00000000..c2d4dcf4
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/rvn.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/unet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/unet.yaml
new file mode 100644
index 00000000..7712e1c8
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/unet.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/varnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/varnet.yaml
new file mode 100644
index 00000000..23ca8c91
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/varnet.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/vsnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/vsnet.yaml
new file mode 100644
index 00000000..b4145afa
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/vsnet.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/xpdnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/xpdnet.yaml
new file mode 100644
index 00000000..248d2cf1
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/xpdnet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/zerofilled.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/zerofilled.yaml
new file mode 100644
index 00000000..59d80076
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/test/8x/zerofilled.yaml
@@ -0,0 +1,106 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: ZF
+ dimensionality: 2
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 8
+ center_fractions:
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: novograd
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRIBrains_batch0_equispaced1d_8x_NNEstimationCSM_GDCC/ZeroFilled_RSS/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/ccnn.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/ccnn.yaml
new file mode 100644
index 00000000..2369fcf3
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/ccnn.yaml
@@ -0,0 +1,185 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/cirim.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/cirim.yaml
new file mode 100644
index 00000000..e56ed3dd
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/cirim.yaml
@@ -0,0 +1,219 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/crnn.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/crnn.yaml
new file mode 100644
index 00000000..975016d3
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/crnn.yaml
@@ -0,0 +1,185 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/dunet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/dunet.yaml
new file mode 100644
index 00000000..b40b4e39
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/dunet.yaml
@@ -0,0 +1,188 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: VS
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/DUNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/jointicnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/jointicnet.yaml
new file mode 100644
index 00000000..0adbca68
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/jointicnet.yaml
@@ -0,0 +1,195 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/kikinet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/kikinet.yaml
new file mode 100644
index 00000000..45c3aa8f
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/kikinet.yaml
@@ -0,0 +1,195 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/lpdnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/lpdnet.yaml
new file mode 100644
index 00000000..b3812792
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/lpdnet.yaml
@@ -0,0 +1,198 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/modl.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/modl.yaml
new file mode 100644
index 00000000..0f68d005
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/modl.yaml
@@ -0,0 +1,186 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/multidomainnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/multidomainnet.yaml
new file mode 100644
index 00000000..04d5ef79
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/multidomainnet.yaml
@@ -0,0 +1,183 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/MultiDomainNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/recurrentvarnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/recurrentvarnet.yaml
new file mode 100644
index 00000000..0eb1048a
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/recurrentvarnet.yaml
@@ -0,0 +1,198 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/rim.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/rim.yaml
new file mode 100644
index 00000000..14f97034
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/rim.yaml
@@ -0,0 +1,219 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/rvn.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/rvn.yaml
new file mode 100644
index 00000000..0eb1048a
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/rvn.yaml
@@ -0,0 +1,198 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/unet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/unet.yaml
new file mode 100644
index 00000000..ab86b322
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/unet.yaml
@@ -0,0 +1,187 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/varnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/varnet.yaml
new file mode 100644
index 00000000..d6acc4b4
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/varnet.yaml
@@ -0,0 +1,185 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/vsnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/vsnet.yaml
new file mode 100644
index 00000000..7e0a9d1a
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/vsnet.yaml
@@ -0,0 +1,186 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/conf/train/xpdnet.yaml b/projects/REC/fastMRIBrainsMulticoil/conf/train/xpdnet.yaml
new file mode 100644
index 00000000..ac8ef8dd
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/conf/train/xpdnet.yaml
@@ -0,0 +1,197 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 0
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 0.1
+ ssim: 0.9
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/batch_0/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.0
+ apply_prewhitening: false
+ apply_gcc: true
+ gcc_virtual_coils: 1
+ gcc_calib_lines: 24
+ gcc_align_data: true
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: equispaced1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [ 320, 320 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRIBrains_batch0_equispaced1d_4x_8x_NNEstimationCSM_GDCC
diff --git a/projects/REC/fastMRIBrainsMulticoil/scripts/__init__.py b/projects/REC/fastMRIBrainsMulticoil/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/fastMRIBrainsMulticoil/scripts/split_sets_json.py b/projects/REC/fastMRIBrainsMulticoil/scripts/split_sets_json.py
new file mode 100644
index 00000000..f7cad0a0
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/scripts/split_sets_json.py
@@ -0,0 +1,45 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+from pathlib import Path
+
+FILENAMES_TO_EXCLUDE = [
+ "file_brain_AXFLAIR_201_6002914.h5",
+ "file_brain_AXFLAIR_201_6003003.h5",
+ "file_brain_AXFLAIR_202_6000508.h5",
+ "file_brain_AXFLAIR_202_6000531.h5",
+ "file_brain_AXFLAIR_202_6000539.h5",
+ "file_brain_AXFLAIR_202_6000554.h5",
+ "file_brain_AXFLAIR_202_6000420.h5",
+ "file_brain_AXFLAIR_202_6000486.h5",
+ "file_brain_AXFLAIR_202_6000586.h5",
+]
+
+
+def main(args):
+ # read all h5 files in the data directory
+ train_set = list((Path(args.data_path) / "multicoil_train").iterdir())
+ val_set = list((Path(args.data_path) / "multicoil_val").iterdir())
+
+ # remove the files that we want to exclude
+ train_set = [str(f) for f in train_set if f.name not in FILENAMES_TO_EXCLUDE]
+ val_set = [str(f) for f in val_set if f.name not in FILENAMES_TO_EXCLUDE]
+
+ # create a directory to store the folds
+ output_path = Path(args.data_path) / "json"
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # write the train and val filenames to a json file
+ with open(output_path / "multicoil_train.json", "w", encoding="utf-8") as f:
+ json.dump(train_set, f)
+ with open(output_path / "multicoil_val.json", "w", encoding="utf-8") as f:
+ json.dump(val_set, f)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path, help="Path to the data directory.")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/REC/fastMRIBrainsMulticoil/visualize.ipynb b/projects/REC/fastMRIBrainsMulticoil/visualize.ipynb
new file mode 100644
index 00000000..8bedf4c5
--- /dev/null
+++ b/projects/REC/fastMRIBrainsMulticoil/visualize.ipynb
@@ -0,0 +1,425 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### This notebook shows how to read the fastMRI dataset and apply some simple transformations to the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.521955Z",
+ "end_time": "2023-08-28T14:56:16.599617Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Testing if integration works"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.525598Z",
+ "end_time": "2023-08-28T14:56:16.675773Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import h5py\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fastMRI dataset is distributed as a set of HDF5 files and can be read with the h5py package. Here, we show how to open a file from the multi-coil dataset. Each file corresponds to one MRI scan and contains the k-space data, ground truth and some meta data related to the scan."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "fastmri_brain_data_dir = input(\"Please enter the (downloaded) data path: \")"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.545316Z",
+ "end_time": "2023-08-28T14:56:16.676675Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "file_name = f'{fastmri_brain_data_dir}/multicoil_train/file_brain_AXFLAIR_200_6002429'\n",
+ "hf = h5py.File(file_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {
+ "tags": [],
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.549656Z",
+ "end_time": "2023-08-28T14:56:16.678047Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Keys: ['ismrmrd_header', 'kspace', 'reconstruction_rss']\n",
+ "Attrs: {'acquisition': 'CORPD_FBK', 'max': 0.0009159000657805458, 'norm': 0.2906827581143191, 'patient_id': '120a9ed15c7402b4d558d0e522ed2dcb77b53d365ce5ec1eabe0a4137b12207d'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Keys:', list(hf.keys()))\n",
+ "print('Attrs:', dict(hf.attrs))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In multi-coil MRIs, k-space has the following shape:\n",
+ "(number of slices, number of coils, height, width)\n",
+ "\n",
+ "For single-coil MRIs, k-space has the following shape:\n",
+ "(number of slices, height, width)\n",
+ "\n",
+ "MRIs are acquired as 3D volumes, the first dimension is the number of 2D slices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "tags": [],
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.556657Z",
+ "end_time": "2023-08-28T14:56:17.548101Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "complex64\n",
+ "(37, 15, 640, 368)\n"
+ ]
+ }
+ ],
+ "source": [
+ "volume_kspace = hf['kspace'][()]\n",
+ "print(volume_kspace.dtype)\n",
+ "print(volume_kspace.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:17.633379Z",
+ "end_time": "2023-08-28T14:56:17.642450Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_kspace = volume_kspace[20] # Choosing the 20-th slice of this volume"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's see what the absolute value of k-space looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:17.638866Z",
+ "end_time": "2023-08-28T14:56:17.650801Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def show_coils(data, slice_nums, cmap=None):\n",
+ " fig = plt.figure()\n",
+ " for i, num in enumerate(slice_nums):\n",
+ " plt.subplot(1, len(slice_nums), i + 1)\n",
+ " plt.imshow(data[num], cmap=cmap)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:17.649943Z",
+ "end_time": "2023-08-28T14:56:18.058876Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_coils(np.log(np.abs(slice_kspace) + 1e-9), [0, 5, 10]) # This shows coils 0, 5 and 10"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fastMRI repo contains some utlity functions to convert k-space into image space. These functions work on PyTorch Tensors. The to_tensor function can convert Numpy arrays to PyTorch Tensors."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.058499Z",
+ "end_time": "2023-08-28T14:56:18.059322Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from atommic.collections.common.parts import apply_mask, to_tensor, fft, complex_abs, rss\n",
+ "from atommic.collections.common.data.subsample import Random1DMaskFunc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.059736Z",
+ "end_time": "2023-08-28T14:56:18.212542Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_kspace2 = to_tensor(slice_kspace) # Convert from numpy array to pytorch tensor\n",
+ "slice_image = fft.ifft2(slice_kspace2, centered=True, normalization=\"ortho\") # Apply Inverse Fourier Transform to get the complex image\n",
+ "slice_image_abs = complex_abs(slice_image) # Compute absolute value to get a real image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.155202Z",
+ "end_time": "2023-08-28T14:56:18.563048Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_coils(slice_image_abs, [0, 5, 10], cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see, each coil in a multi-coil MRI scan focusses on a different region of the image. These coils can be combined into the full image using the Root-Sum-of-Squares (RSS) transform."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.564688Z",
+ "end_time": "2023-08-28T14:56:18.567792Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_image_rss = rss(slice_image_abs, dim=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.568683Z",
+ "end_time": "2023-08-28T14:56:19.540857Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(np.abs(slice_image_rss.numpy()), cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So far, we have been looking at fully-sampled data. We can simulate under-sampled data by creating a mask and applying it to k-space."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.434416Z",
+ "end_time": "2023-08-28T14:56:19.541208Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "mask_func = Random1DMaskFunc(center_fractions=[0.04], accelerations=[8]) # Create the mask function object"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.434705Z",
+ "end_time": "2023-08-28T14:56:19.541312Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "masked_kspace, mask, acc = apply_mask(slice_kspace2, mask_func) # Apply the mask to k-space"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's see what the subsampled image looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.434926Z",
+ "end_time": "2023-08-28T14:56:19.541404Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "sampled_image = fft.ifft2(masked_kspace, centered=True, normalization=\"ortho\") # Apply Inverse Fourier Transform to get the complex image\n",
+ "sampled_image_abs = complex_abs(sampled_image) # Compute absolute value to get a real image\n",
+ "sampled_image_rss = rss(sampled_image_abs, dim=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.435213Z",
+ "end_time": "2023-08-28T14:56:19.541751Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(np.abs(sampled_image_rss.numpy()), cmap='gray')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "name": "python3",
+ "language": "python",
+ "display_name": "Python 3 (ipykernel)"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7-final"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/projects/REC/fastMRIKneesMulticoil/README.md b/projects/REC/fastMRIKneesMulticoil/README.md
new file mode 100644
index 00000000..c6fee22c
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/README.md
@@ -0,0 +1,33 @@
+## **fastMRI Knees Multicoil Dataset**
+
+This project folder contains the configuration files and visualization scripts for the fastMRI Knees Multicoil
+dataset.
+
+For more information, please refer to https://fastmri.med.nyu.edu/.
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](visualize.ipynb). You just need to set the path where
+the dataset is downloaded.
+
+### **Preprocessing**
+The fastMRI datasets are supported natively in ``ATOMMIC`` and no preprocessing is required.
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/fastMRIKneesMulticoil/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` to the generated json files. In `exp_manager` please set the `exp_dir` to
+the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/REC/fastMRIKneesMulticoil/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/fastMRIKneesMulticoil/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/REC/fastMRIKneesMulticoil/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/REC/fastMRIKneesMulticoil/__init__.py b/projects/REC/fastMRIKneesMulticoil/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/ccnn.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/ccnn.yaml
new file mode 100644
index 00000000..0f39d46c
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/ccnn.yaml
@@ -0,0 +1,128 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/cirim.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/cirim.yaml
new file mode 100644
index 00000000..f53302ef
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/cirim.yaml
@@ -0,0 +1,162 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 8
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/crnn.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/crnn.yaml
new file mode 100644
index 00000000..3173e406
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/crnn.yaml
@@ -0,0 +1,128 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/dunet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/dunet.yaml
new file mode 100644
index 00000000..a2b6f2cd
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/dunet.yaml
@@ -0,0 +1,131 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: VS
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/DUNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/jointicnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/jointicnet.yaml
new file mode 100644
index 00000000..a5696151
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/jointicnet.yaml
@@ -0,0 +1,138 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/kikinet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/kikinet.yaml
new file mode 100644
index 00000000..f09ef055
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/kikinet.yaml
@@ -0,0 +1,138 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/lpdnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/lpdnet.yaml
new file mode 100644
index 00000000..52c5aee6
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/lpdnet.yaml
@@ -0,0 +1,141 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/modl.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/modl.yaml
new file mode 100644
index 00000000..417dadf3
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/modl.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 10
+ residual_blocks: 15
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/multidomainnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/multidomainnet.yaml
new file mode 100644
index 00000000..5b46654c
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/multidomainnet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/MultiDomainNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/rim.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/rim.yaml
new file mode 100644
index 00000000..5d2336d1
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/rim.yaml
@@ -0,0 +1,162 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/rvn.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/rvn.yaml
new file mode 100644
index 00000000..f569944d
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/rvn.yaml
@@ -0,0 +1,141 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/unet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/unet.yaml
new file mode 100644
index 00000000..f7e4bf73
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/unet.yaml
@@ -0,0 +1,130 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/varnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/varnet.yaml
new file mode 100644
index 00000000..1710d5cc
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/varnet.yaml
@@ -0,0 +1,128 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/vsnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/vsnet.yaml
new file mode 100644
index 00000000..f78a7995
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/vsnet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/test/xpdnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/test/xpdnet.yaml
new file mode 100644
index 00000000..c4cc7aff
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/test/xpdnet.yaml
@@ -0,0 +1,140 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 20
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 2
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-7
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/ccnn.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/ccnn.yaml
new file mode 100644
index 00000000..99aa5371
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/ccnn.yaml
@@ -0,0 +1,189 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/cirim.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/cirim.yaml
new file mode 100644
index 00000000..a49c4117
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/cirim.yaml
@@ -0,0 +1,223 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 8
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/crnn.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/crnn.yaml
new file mode 100644
index 00000000..c0146bff
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/crnn.yaml
@@ -0,0 +1,189 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/dunet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/dunet.yaml
new file mode 100644
index 00000000..f273016b
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/dunet.yaml
@@ -0,0 +1,192 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: VS
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/DUNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/jointicnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/jointicnet.yaml
new file mode 100644
index 00000000..a9d42623
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/jointicnet.yaml
@@ -0,0 +1,199 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/kikinet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/kikinet.yaml
new file mode 100644
index 00000000..c31e8f31
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/kikinet.yaml
@@ -0,0 +1,199 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/lpdnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/lpdnet.yaml
new file mode 100644
index 00000000..d261c091
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/lpdnet.yaml
@@ -0,0 +1,202 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/modl.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/modl.yaml
new file mode 100644
index 00000000..8b2bc4a8
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/modl.yaml
@@ -0,0 +1,190 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 10
+ residual_blocks: 15
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/multidomainnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/multidomainnet.yaml
new file mode 100644
index 00000000..44d86755
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/multidomainnet.yaml
@@ -0,0 +1,187 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/MultiDomainNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/rim.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/rim.yaml
new file mode 100644
index 00000000..699eddda
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/rim.yaml
@@ -0,0 +1,223 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/rvn.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/rvn.yaml
new file mode 100644
index 00000000..8977bed5
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/rvn.yaml
@@ -0,0 +1,202 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/unet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/unet.yaml
new file mode 100644
index 00000000..17acde8a
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/unet.yaml
@@ -0,0 +1,191 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/varnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/varnet.yaml
new file mode 100644
index 00000000..0290cc18
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/varnet.yaml
@@ -0,0 +1,189 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/vsnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/vsnet.yaml
new file mode 100644
index 00000000..609c3037
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/vsnet.yaml
@@ -0,0 +1,190 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/conf/train/xpdnet.yaml b/projects/REC/fastMRIKneesMulticoil/conf/train/xpdnet.yaml
new file mode 100644
index 00000000..5643eb36
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/conf/train/xpdnet.yaml
@@ -0,0 +1,201 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 20
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 2
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: false
+ dimensionality: 2
+ reconstruction_loss: ssim
+ normalization_type: max
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/json/multicoil_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/json/multicoil_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: true
+ coil_sensitivity_maps_type: rss
+ coil_sensitivity_maps_gaussian_sigma: 0.0
+ coil_sensitivity_maps_espirit_threshold: 0.05
+ coil_sensitivity_maps_espirit_kernel_size: 6
+ coil_sensitivity_maps_espirit_crop: 0.95
+ coil_sensitivity_maps_espirit_max_iters: 30
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: max
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-7
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_multicoil_random1d_4x_8x_AutoEstimationCSM
diff --git a/projects/REC/fastMRIKneesMulticoil/evaluate.sh b/projects/REC/fastMRIKneesMulticoil/evaluate.sh
new file mode 100644
index 00000000..8678cdcb
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/evaluate.sh
@@ -0,0 +1,14 @@
+python tools/evaluation/reconstruction.py \
+data_parent_dir/PD/multicoil_val \
+output_dir/atommic/reconstruction/predictions/fastMRIKnees_multicoil_PD/ATOMMIC/VarNet/default/2023-11-10_09-56-29/reconstructions \
+--evaluation_type per_slice --output_dir output_dir/atommic/reconstruction/evaluation_per_slice/fastMRIKnees_multicoil_PD/ --crop_size 320 320
+
+python tools/evaluation/reconstruction.py \
+data_parent_dir/PD/multicoil_val \
+output_dir/atommic/reconstruction/predictions/fastMRIKnees_multicoil_PD/DIRECT \
+--evaluation_type per_slice --output_dir output_dir/atommic/reconstruction/evaluation_per_slice/fastMRIKnees_multicoil_PD/ --crop_size 320 320
+
+python tools/evaluation/reconstruction.py \
+data_parent_dir/PD/multicoil_val \
+output_dir/atommic/reconstruction/predictions/fastMRIKnees_multicoil_PD/fastMRI/varnet/reconstructions \
+--evaluation_type per_slice --output_dir output_dir/atommic/reconstruction/evaluation_per_slice/fastMRIKnees_multicoil_PD/ --crop_size 320 320
diff --git a/projects/REC/fastMRIKneesMulticoil/scripts/__init__.py b/projects/REC/fastMRIKneesMulticoil/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/fastMRIKneesMulticoil/scripts/split_sets_json.py b/projects/REC/fastMRIKneesMulticoil/scripts/split_sets_json.py
new file mode 100644
index 00000000..4e17a3ec
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/scripts/split_sets_json.py
@@ -0,0 +1,43 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+from pathlib import Path
+
+
+def read_h5_files(dataset_path):
+ """Read all h5 files in a directory"""
+ return list(Path(dataset_path).iterdir())
+
+
+def main(args):
+ # read all h5 files in the data directory
+ all_filenames_train = read_h5_files(args.data_path / "PD/multicoil_train")
+ all_filenames_train += read_h5_files(args.data_path / "PDFS/multicoil_train")
+ all_filenames_train = [str(filename) for filename in all_filenames_train]
+
+ all_filenames_val = read_h5_files(args.data_path / "PD/multicoil_val")
+ all_filenames_val += read_h5_files(args.data_path / "PDFS/multicoil_val")
+ all_filenames_val = [str(filename) for filename in all_filenames_val]
+
+ print(f"Number of train files: {len(all_filenames_train)}")
+ print(f"Number of val files: {len(all_filenames_val)}")
+
+ # create a directory to store the folds
+ output_path = Path(args.output_path)
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # write the train, val and test filenames to a json file
+ with open(output_path / "multicoil_train.json", "w", encoding="utf-8") as f:
+ json.dump(all_filenames_train, f)
+ with open(output_path / "multicoil_val.json", "w", encoding="utf-8") as f:
+ json.dump(all_filenames_val, f)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path, default=None, help="Path to the data directory.")
+ parser.add_argument("output_path", type=Path, default="data/folds", help="Path to the output directory.")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/REC/fastMRIKneesMulticoil/visualize.ipynb b/projects/REC/fastMRIKneesMulticoil/visualize.ipynb
new file mode 100644
index 00000000..a64d5781
--- /dev/null
+++ b/projects/REC/fastMRIKneesMulticoil/visualize.ipynb
@@ -0,0 +1,425 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### This notebook shows how to read the fastMRI dataset and apply some simple transformations to the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.521955Z",
+ "end_time": "2023-08-28T14:56:16.599617Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Testing if integration works"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.525598Z",
+ "end_time": "2023-08-28T14:56:16.675773Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import h5py\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fastMRI dataset is distributed as a set of HDF5 files and can be read with the h5py package. Here, we show how to open a file from the multi-coil dataset. Each file corresponds to one MRI scan and contains the k-space data, ground truth and some meta data related to the scan."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "fastmri_knee_data_dir = input(\"Please enter the (downloaded) data path: \")"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.545316Z",
+ "end_time": "2023-08-28T14:56:16.676675Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "file_name = f'{fastmri_knee_data_dir}/multicoil_train/file1000108.h5'\n",
+ "hf = h5py.File(file_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {
+ "tags": [],
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.549656Z",
+ "end_time": "2023-08-28T14:56:16.678047Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Keys: ['ismrmrd_header', 'kspace', 'reconstruction_rss']\n",
+ "Attrs: {'acquisition': 'CORPD_FBK', 'max': 0.0009159000657805458, 'norm': 0.2906827581143191, 'patient_id': '120a9ed15c7402b4d558d0e522ed2dcb77b53d365ce5ec1eabe0a4137b12207d'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Keys:', list(hf.keys()))\n",
+ "print('Attrs:', dict(hf.attrs))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In multi-coil MRIs, k-space has the following shape:\n",
+ "(number of slices, number of coils, height, width)\n",
+ "\n",
+ "For single-coil MRIs, k-space has the following shape:\n",
+ "(number of slices, height, width)\n",
+ "\n",
+ "MRIs are acquired as 3D volumes, the first dimension is the number of 2D slices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "tags": [],
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.556657Z",
+ "end_time": "2023-08-28T14:56:17.548101Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "complex64\n",
+ "(37, 15, 640, 368)\n"
+ ]
+ }
+ ],
+ "source": [
+ "volume_kspace = hf['kspace'][()]\n",
+ "print(volume_kspace.dtype)\n",
+ "print(volume_kspace.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:17.633379Z",
+ "end_time": "2023-08-28T14:56:17.642450Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_kspace = volume_kspace[20] # Choosing the 20-th slice of this volume"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's see what the absolute value of k-space looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:17.638866Z",
+ "end_time": "2023-08-28T14:56:17.650801Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def show_coils(data, slice_nums, cmap=None):\n",
+ " fig = plt.figure()\n",
+ " for i, num in enumerate(slice_nums):\n",
+ " plt.subplot(1, len(slice_nums), i + 1)\n",
+ " plt.imshow(data[num], cmap=cmap)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:17.649943Z",
+ "end_time": "2023-08-28T14:56:18.058876Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_coils(np.log(np.abs(slice_kspace) + 1e-9), [0, 5, 10]) # This shows coils 0, 5 and 10"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fastMRI repo contains some utlity functions to convert k-space into image space. These functions work on PyTorch Tensors. The to_tensor function can convert Numpy arrays to PyTorch Tensors."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.058499Z",
+ "end_time": "2023-08-28T14:56:18.059322Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from atommic.collections.common.parts import apply_mask, to_tensor, fft, complex_abs, rss\n",
+ "from atommic.collections.common.data.subsample import Random1DMaskFunc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.059736Z",
+ "end_time": "2023-08-28T14:56:18.212542Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_kspace2 = to_tensor(slice_kspace) # Convert from numpy array to pytorch tensor\n",
+ "slice_image = fft.ifft2(slice_kspace2, centered=True, normalization=\"ortho\") # Apply Inverse Fourier Transform to get the complex image\n",
+ "slice_image_abs = complex_abs(slice_image) # Compute absolute value to get a real image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.155202Z",
+ "end_time": "2023-08-28T14:56:18.563048Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_coils(slice_image_abs, [0, 5, 10], cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see, each coil in a multi-coil MRI scan focusses on a different region of the image. These coils can be combined into the full image using the Root-Sum-of-Squares (RSS) transform."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.564688Z",
+ "end_time": "2023-08-28T14:56:18.567792Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_image_rss = rss(slice_image_abs, dim=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.568683Z",
+ "end_time": "2023-08-28T14:56:19.540857Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(np.abs(slice_image_rss.numpy()), cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So far, we have been looking at fully-sampled data. We can simulate under-sampled data by creating a mask and applying it to k-space."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.434416Z",
+ "end_time": "2023-08-28T14:56:19.541208Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "mask_func = Random1DMaskFunc(center_fractions=[0.04], accelerations=[8]) # Create the mask function object"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.434705Z",
+ "end_time": "2023-08-28T14:56:19.541312Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "masked_kspace, mask, acc = apply_mask(slice_kspace2, mask_func) # Apply the mask to k-space"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's see what the subsampled image looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.434926Z",
+ "end_time": "2023-08-28T14:56:19.541404Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "sampled_image = fft.ifft2(masked_kspace, centered=True, normalization=\"ortho\") # Apply Inverse Fourier Transform to get the complex image\n",
+ "sampled_image_abs = complex_abs(sampled_image) # Compute absolute value to get a real image\n",
+ "sampled_image_rss = rss(sampled_image_abs, dim=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.435213Z",
+ "end_time": "2023-08-28T14:56:19.541751Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(np.abs(sampled_image_rss.numpy()), cmap='gray')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "name": "python3",
+ "language": "python",
+ "display_name": "Python 3 (ipykernel)"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7-final"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/projects/REC/fastMRIKneesSinglecoil/README.md b/projects/REC/fastMRIKneesSinglecoil/README.md
new file mode 100644
index 00000000..427bc74e
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/README.md
@@ -0,0 +1,33 @@
+## **fastMRI Knees Singlecoil Dataset**
+
+This project folder contains the configuration files and visualization scripts for the fastMRI Knees Singlecoil
+dataset.
+
+For more information, please refer to https://fastmri.med.nyu.edu/.
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](visualize.ipynb). You just need to set the path where
+the dataset is downloaded.
+
+### **Preprocessing**
+The fastMRI datasets are supported natively in ``ATOMMIC`` and no preprocessing is required.
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/fastMRIKneesSinglecoil/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` to the generated json files. In `exp_manager` please set the `exp_dir` to
+the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/REC/fastMRIKneesSinglecoil/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/REC/fastMRIKneesSinglecoil/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/REC/fastMRIKneesSinglecoil/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/REC/fastMRIKneesSinglecoil/__init__.py b/projects/REC/fastMRIKneesSinglecoil/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/ccnn.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/ccnn.yaml
new file mode 100644
index 00000000..80b9783e
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/ccnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/cirim.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/cirim.yaml
new file mode 100644
index 00000000..4218a0be
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/cirim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/crnn.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/crnn.yaml
new file mode 100644
index 00000000..26d45d91
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/crnn.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/dunet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/dunet.yaml
new file mode 100644
index 00000000..d62e880b
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/dunet.yaml
@@ -0,0 +1,126 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: PROX
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/DUNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/jointicnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/jointicnet.yaml
new file mode 100644
index 00000000..78bc547f
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/jointicnet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/kikinet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/kikinet.yaml
new file mode 100644
index 00000000..a746c997
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/kikinet.yaml
@@ -0,0 +1,133 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/lpdnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/lpdnet.yaml
new file mode 100644
index 00000000..8f383268
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/lpdnet.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/modl.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/modl.yaml
new file mode 100644
index 00000000..83214e4e
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/modl.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/multidomainnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/multidomainnet.yaml
new file mode 100644
index 00000000..68163a94
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/multidomainnet.yaml
@@ -0,0 +1,121 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/MultiDomainNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/rim.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/rim.yaml
new file mode 100644
index 00000000..407211c4
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/rim.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/rvn.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/rvn.yaml
new file mode 100644
index 00000000..c1f44f1b
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/rvn.yaml
@@ -0,0 +1,136 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/unet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/unet.yaml
new file mode 100644
index 00000000..56852e89
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/unet.yaml
@@ -0,0 +1,125 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/varnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/varnet.yaml
new file mode 100644
index 00000000..e1e999aa
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/varnet.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/vsnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/vsnet.yaml
new file mode 100644
index 00000000..f7c61ad9
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/vsnet.yaml
@@ -0,0 +1,124 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/test/xpdnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/test/xpdnet.yaml
new file mode 100644
index 00000000..8b5c7817
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/test/xpdnet.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 2
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.predictions.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/ccnn.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/ccnn.yaml
new file mode 100644
index 00000000..3320e7d9
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/ccnn.yaml
@@ -0,0 +1,178 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CascadeNet
+ num_cascades: 10
+ hidden_channels: 64
+ n_convs: 5
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/CCNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/cirim.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/cirim.yaml
new file mode 100644
index 00000000..7d4f3110
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/cirim.yaml
@@ -0,0 +1,212 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/crnn.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/crnn.yaml
new file mode 100644
index 00000000..3a62ceda
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/crnn.yaml
@@ -0,0 +1,178 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CRNNet
+ num_iterations: 10
+ hidden_channels: 64
+ n_convs: 3
+ batchnorm: false
+ no_dc: false
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/CRNN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/dunet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/dunet.yaml
new file mode 100644
index 00000000..3abd2528
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/dunet.yaml
@@ -0,0 +1,181 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: DUNet
+ num_iter: 10
+ reg_model_architecture: DIDN
+ didn_hidden_channels: 64
+ didn_num_dubs: 2
+ didn_num_convs_recon: 1
+ data_consistency_term: PROX
+ data_consistency_lambda_init: 0.1
+ data_consistency_iterations: 10
+ shared_params: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/DUNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/jointicnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/jointicnet.yaml
new file mode 100644
index 00000000..6dbceb86
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/jointicnet.yaml
@@ -0,0 +1,188 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: JointICNet
+ num_iter: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ sens_unet_num_filters: 16
+ sens_unet_num_pool_layers: 2
+ sens_unet_dropout_probability: 0.0
+ sens_unet_padding_size: 11
+ sens_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/JointICNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/kikinet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/kikinet.yaml
new file mode 100644
index 00000000..81963960
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/kikinet.yaml
@@ -0,0 +1,188 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: KIKINet
+ num_iter: 2
+ kspace_model_architecture: UNET
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ kspace_unet_num_filters: 16
+ kspace_unet_num_pool_layers: 2
+ kspace_unet_dropout_probability: 0.0
+ kspace_unet_padding_size: 11
+ kspace_unet_normalize: true
+ imspace_model_architecture: UNET
+ imspace_in_channels: 2
+ imspace_unet_num_filters: 16
+ imspace_unet_num_pool_layers: 2
+ imspace_unet_dropout_probability: 0.0
+ imspace_unet_padding_size: 11
+ imspace_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/KIKINet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/lpdnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/lpdnet.yaml
new file mode 100644
index 00000000..0c456372
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/lpdnet.yaml
@@ -0,0 +1,191 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: LPDNet
+ num_primal: 5
+ num_dual: 5
+ num_iter: 5
+ primal_model_architecture: UNET
+ primal_in_channels: 2
+ primal_out_channels: 2
+ primal_unet_num_filters: 16
+ primal_unet_num_pool_layers: 2
+ primal_unet_dropout_probability: 0.0
+ primal_unet_padding_size: 11
+ primal_unet_normalize: true
+ dual_model_architecture: UNET
+ dual_in_channels: 2
+ dual_out_channels: 2
+ dual_unet_num_filters: 16
+ dual_unet_num_pool_layers: 2
+ dual_unet_dropout_probability: 0.0
+ dual_unet_padding_size: 11
+ dual_unet_normalize: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/LPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/modl.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/modl.yaml
new file mode 100644
index 00000000..c1a9c0d3
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/modl.yaml
@@ -0,0 +1,179 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MoDL
+ unrolled_iterations: 5
+ residual_blocks: 5
+ channels: 64
+ regularization_factor: 0.1
+ penalization_weight: 1.0
+ conjugate_gradient_dc: false
+ conjugate_gradient_iterations: 1
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/MoDL/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/multidomainnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/multidomainnet.yaml
new file mode 100644
index 00000000..9d760d05
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/multidomainnet.yaml
@@ -0,0 +1,176 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: MultiDomainNet
+ standardization: true
+ num_filters: 64
+ num_pool_layers: 2
+ dropout_probability: 0.0
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/MultiDomainNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/rim.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/rim.yaml
new file mode 100644
index 00000000..bd554884
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/rim.yaml
@@ -0,0 +1,212 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: GRU
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 1
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/RIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/rvn.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/rvn.yaml
new file mode 100644
index 00000000..f385902e
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/rvn.yaml
@@ -0,0 +1,191 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: RVN
+ in_channels: 2
+ recurrent_hidden_channels: 64
+ recurrent_num_layers: 4
+ num_steps: 8
+ no_parameter_sharing: true
+ learned_initializer: true
+ initializer_initialization: "sense"
+ initializer_channels:
+ - 32
+ - 32
+ - 64
+ - 64
+ initializer_dilations:
+ - 1
+ - 1
+ - 2
+ - 4
+ initializer_multiscale: 1
+ accumulate_predictions: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/RVN/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/unet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/unet.yaml
new file mode 100644
index 00000000..1bcc9e2c
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/unet.yaml
@@ -0,0 +1,180 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: UNet
+ channels: 64
+ pooling_layers: 4
+ in_channels: 2
+ out_channels: 2
+ padding_size: 11
+ dropout: 0.0
+ normalize: true
+ norm_groups: 2
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/UNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/varnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/varnet.yaml
new file mode 100644
index 00000000..30797713
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/varnet.yaml
@@ -0,0 +1,178 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/vsnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/vsnet.yaml
new file mode 100644
index 00000000..f34c4ae9
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/vsnet.yaml
@@ -0,0 +1,179 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VSNet
+ num_cascades: 10
+ imspace_model_architecture: CONV
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ imspace_conv_hidden_channels: 64
+ imspace_conv_n_convs: 4
+ imspace_conv_batchnorm: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/VSNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/conf/train/xpdnet.yaml b/projects/REC/fastMRIKneesSinglecoil/conf/train/xpdnet.yaml
new file mode 100644
index 00000000..4ce5afd3
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/conf/train/xpdnet.yaml
@@ -0,0 +1,190 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: XPDNet
+ num_primal: 5
+ num_dual: 1
+ num_iter: 10
+ use_primal_only: true
+ kspace_model_architecture: CONV
+ kspace_in_channels: 2
+ kspace_out_channels: 2
+ dual_conv_hidden_channels: 16
+ dual_conv_num_dubs: 2
+ dual_conv_batchnorm: false
+ image_model_architecture: MWCNN
+ imspace_in_channels: 2
+ imspace_out_channels: 2
+ mwcnn_hidden_channels: 16
+ mwcnn_num_scales: 2
+ mwcnn_bias: true
+ mwcnn_batchnorm: false
+ normalize_image: false
+ dimensionality: 2
+ reconstruction_loss:
+ l1: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: complex_abs # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: RSS
+ ssdu: false
+ n2r: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: true
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/singlecoil_train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/singlecoil_val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: fastmri
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: RSS
+ dimensionality: 2
+ mask_args:
+ type: random1d
+ accelerations:
+ - 4
+ - 8
+ center_fractions:
+ - 0.08
+ - 0.04
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: [320, 320]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: true
+ fft_normalization: ortho
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM/XPDNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.fastMRI_Knees_singlecoil_random1d_4x_8x_NNEstimationCSM
diff --git a/projects/REC/fastMRIKneesSinglecoil/visualize.ipynb b/projects/REC/fastMRIKneesSinglecoil/visualize.ipynb
new file mode 100644
index 00000000..a64d5781
--- /dev/null
+++ b/projects/REC/fastMRIKneesSinglecoil/visualize.ipynb
@@ -0,0 +1,425 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### This notebook shows how to read the fastMRI dataset and apply some simple transformations to the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 72,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.521955Z",
+ "end_time": "2023-08-28T14:56:16.599617Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Testing if integration works"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 73,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.525598Z",
+ "end_time": "2023-08-28T14:56:16.675773Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "%matplotlib inline\n",
+ "\n",
+ "import h5py\n",
+ "import numpy as np\n",
+ "from matplotlib import pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fastMRI dataset is distributed as a set of HDF5 files and can be read with the h5py package. Here, we show how to open a file from the multi-coil dataset. Each file corresponds to one MRI scan and contains the k-space data, ground truth and some meta data related to the scan."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "fastmri_knee_data_dir = input(\"Please enter the (downloaded) data path: \")"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 74,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.545316Z",
+ "end_time": "2023-08-28T14:56:16.676675Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "file_name = f'{fastmri_knee_data_dir}/multicoil_train/file1000108.h5'\n",
+ "hf = h5py.File(file_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 75,
+ "metadata": {
+ "tags": [],
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.549656Z",
+ "end_time": "2023-08-28T14:56:16.678047Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Keys: ['ismrmrd_header', 'kspace', 'reconstruction_rss']\n",
+ "Attrs: {'acquisition': 'CORPD_FBK', 'max': 0.0009159000657805458, 'norm': 0.2906827581143191, 'patient_id': '120a9ed15c7402b4d558d0e522ed2dcb77b53d365ce5ec1eabe0a4137b12207d'}\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Keys:', list(hf.keys()))\n",
+ "print('Attrs:', dict(hf.attrs))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In multi-coil MRIs, k-space has the following shape:\n",
+ "(number of slices, number of coils, height, width)\n",
+ "\n",
+ "For single-coil MRIs, k-space has the following shape:\n",
+ "(number of slices, height, width)\n",
+ "\n",
+ "MRIs are acquired as 3D volumes, the first dimension is the number of 2D slices."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 76,
+ "metadata": {
+ "tags": [],
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:16.556657Z",
+ "end_time": "2023-08-28T14:56:17.548101Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "complex64\n",
+ "(37, 15, 640, 368)\n"
+ ]
+ }
+ ],
+ "source": [
+ "volume_kspace = hf['kspace'][()]\n",
+ "print(volume_kspace.dtype)\n",
+ "print(volume_kspace.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 77,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:17.633379Z",
+ "end_time": "2023-08-28T14:56:17.642450Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_kspace = volume_kspace[20] # Choosing the 20-th slice of this volume"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's see what the absolute value of k-space looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 78,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:17.638866Z",
+ "end_time": "2023-08-28T14:56:17.650801Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def show_coils(data, slice_nums, cmap=None):\n",
+ " fig = plt.figure()\n",
+ " for i, num in enumerate(slice_nums):\n",
+ " plt.subplot(1, len(slice_nums), i + 1)\n",
+ " plt.imshow(data[num], cmap=cmap)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 79,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:17.649943Z",
+ "end_time": "2023-08-28T14:56:18.058876Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_coils(np.log(np.abs(slice_kspace) + 1e-9), [0, 5, 10]) # This shows coils 0, 5 and 10"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The fastMRI repo contains some utlity functions to convert k-space into image space. These functions work on PyTorch Tensors. The to_tensor function can convert Numpy arrays to PyTorch Tensors."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 80,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.058499Z",
+ "end_time": "2023-08-28T14:56:18.059322Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from atommic.collections.common.parts import apply_mask, to_tensor, fft, complex_abs, rss\n",
+ "from atommic.collections.common.data.subsample import Random1DMaskFunc"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 81,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.059736Z",
+ "end_time": "2023-08-28T14:56:18.212542Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_kspace2 = to_tensor(slice_kspace) # Convert from numpy array to pytorch tensor\n",
+ "slice_image = fft.ifft2(slice_kspace2, centered=True, normalization=\"ortho\") # Apply Inverse Fourier Transform to get the complex image\n",
+ "slice_image_abs = complex_abs(slice_image) # Compute absolute value to get a real image"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 82,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.155202Z",
+ "end_time": "2023-08-28T14:56:18.563048Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "show_coils(slice_image_abs, [0, 5, 10], cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "As we can see, each coil in a multi-coil MRI scan focusses on a different region of the image. These coils can be combined into the full image using the Root-Sum-of-Squares (RSS) transform."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 83,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.564688Z",
+ "end_time": "2023-08-28T14:56:18.567792Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_image_rss = rss(slice_image_abs, dim=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 84,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:18.568683Z",
+ "end_time": "2023-08-28T14:56:19.540857Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 84,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(np.abs(slice_image_rss.numpy()), cmap='gray')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "So far, we have been looking at fully-sampled data. We can simulate under-sampled data by creating a mask and applying it to k-space."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 85,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.434416Z",
+ "end_time": "2023-08-28T14:56:19.541208Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "mask_func = Random1DMaskFunc(center_fractions=[0.04], accelerations=[8]) # Create the mask function object"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 86,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.434705Z",
+ "end_time": "2023-08-28T14:56:19.541312Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "masked_kspace, mask, acc = apply_mask(slice_kspace2, mask_func) # Apply the mask to k-space"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Let's see what the subsampled image looks like:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 87,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.434926Z",
+ "end_time": "2023-08-28T14:56:19.541404Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "sampled_image = fft.ifft2(masked_kspace, centered=True, normalization=\"ortho\") # Apply Inverse Fourier Transform to get the complex image\n",
+ "sampled_image_abs = complex_abs(sampled_image) # Compute absolute value to get a real image\n",
+ "sampled_image_rss = rss(sampled_image_abs, dim=0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 88,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-08-28T14:56:19.435213Z",
+ "end_time": "2023-08-28T14:56:19.541751Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 88,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.imshow(np.abs(sampled_image_rss.numpy()), cmap='gray')"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "name": "python3",
+ "language": "python",
+ "display_name": "Python 3 (ipykernel)"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7-final"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/projects/SEG/BraTS2023AdultGlioma/README.md b/projects/SEG/BraTS2023AdultGlioma/README.md
new file mode 100644
index 00000000..fbb84b14
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/README.md
@@ -0,0 +1,57 @@
+## **BraTS2023AdultGlioma**
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the
+BraTS2023AdultGlioma dataset.
+
+For more information, please refer to https://www.synapse.org/#!Synapse:syn51156910/wiki/.
+
+Related papers:
+- https://arxiv.org/pdf/1811.02629.pdf,
+- https://arxiv.org/pdf/2305.17033.pdf.
+
+Data need to be downloaded manually due to required registration. Download link:
+https://www.synapse.org/#!Synapse:syn51156910/wiki/622351.
+
+**Note:** When running the preprocessing scripts please make sure you have the following packages installed: argparse,
+json, nibabel, numpy, pathlib, random, tqdm. Those packages are installed by default if atommic is installed.
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](visualize.ipynb). You just need to set the path where the
+dataset is downloaded.
+
+### **Preprocessing**
+The preprocessing pipeline is implemented in the
+[preprocess_dataset.sh](preprocess_dataset.sh) script, consisting of the
+following steps:
+1. Crop to the brain region, as there is a lot of background around the brain resulting is slower training.
+Important note: the cropping is done only for the training set.
+2. Normalize the images to zero mean and unit variance.
+3. Updates headers and save to NIfTI format.
+4. Split the dataset into training and validation sets.
+5. Compute the probabilities for each segmentation class.
+
+The preprocessing script can be run with the following command:
+```bash
+bash ./projects/SEG/BraTS2023AdultGlioma/preprocess_dataset.sh
+```
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/SEG/BraTS2023AdultGlioma/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` and the `segmentations_path`, which will be generated by the preprocessing
+script. In `exp_manager` please set the `exp_dir` to the path where you want to save the model checkpoints and
+tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/SEG/BraTS2023AdultGlioma/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/SEG/BraTS2023AdultGlioma/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/SEG/BraTS2023AdultGlioma/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/SEG/BraTS2023AdultGlioma/__init__.py b/projects/SEG/BraTS2023AdultGlioma/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/test/attentionunet.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/test/attentionunet.yaml
new file mode 100644
index 00000000..d92168e6
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/test/attentionunet.yaml
@@ -0,0 +1,130 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/BraTs23AdultGlioma/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/test/dynunet.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/test/dynunet.yaml
new file mode 100644
index 00000000..36442a41
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/test/dynunet.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/BraTs23AdultGlioma/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/test/unet2d.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/test/unet2d.yaml
new file mode 100644
index 00000000..e53b2364
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/test/unet2d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/BraTs23AdultGlioma/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/test/unet3d.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/test/unet3d.yaml
new file mode 100644
index 00000000..f594cf3d
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/test/unet3d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+
+ test_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/BraTs23AdultGlioma/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/test/vnet.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/test/vnet.yaml
new file mode 100644
index 00000000..01d5547d
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/test/vnet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: False
+ segmentation_module_padding_size: 15
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/BraTs23AdultGlioma/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/train/attentionunet.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/train/attentionunet.yaml
new file mode 100644
index 00000000..f5045998
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/train/attentionunet.yaml
@@ -0,0 +1,171 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/BraTs23AdultGlioma/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/train/dynunet.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/train/dynunet.yaml
new file mode 100644
index 00000000..68e6f3fa
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/train/dynunet.yaml
@@ -0,0 +1,191 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/BraTs23AdultGlioma/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/train/lambdaunet2d.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/train/lambdaunet2d.yaml
new file mode 100644
index 00000000..1e68e71a
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/train/lambdaunet2d.yaml
@@ -0,0 +1,173 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONLAMBDAUNET
+ use_reconstruction_module: false
+ segmentation_module: LambdaUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ segmentation_module_query_depth: 16
+ segmentation_module_intra_depth: 1
+ segmentation_module_receptive_kernel: 3
+ segmentation_module_temporal_kernel: 3
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/BraTs23AdultGlioma/LambdaUNet2D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/train/lambdaunet3d.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/train/lambdaunet3d.yaml
new file mode 100644
index 00000000..cdf78885
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/train/lambdaunet3d.yaml
@@ -0,0 +1,173 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONLAMBDAUNET
+ use_reconstruction_module: false
+ segmentation_module: LambdaUNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 64
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ segmentation_module_query_depth: 16
+ segmentation_module_intra_depth: 1
+ segmentation_module_receptive_kernel: 3
+ segmentation_module_temporal_kernel: 3
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+
+ train_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/BraTs23AdultGlioma/LambdaUNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/train/unet2d.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/train/unet2d.yaml
new file mode 100644
index 00000000..3543da3d
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/train/unet2d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/BraTs23AdultGlioma/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/train/unet3d.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/train/unet3d.yaml
new file mode 100644
index 00000000..07d23f64
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/train/unet3d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+
+ train_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/BraTs23AdultGlioma/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/train/unetr.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/train/unetr.yaml
new file mode 100644
index 00000000..1c2bc114
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/train/unetr.yaml
@@ -0,0 +1,180 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONUNETR
+ use_reconstruction_module: false
+ segmentation_module: UNETR
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_img_size: [160, 160]
+ segmentation_module_channels: 16
+ segmentation_module_hidden_size: 768
+ segmentation_module_mlp_dim: 3072
+ segmentation_module_num_heads: 12
+ segmentation_module_pos_embed: perceptron
+ segmentation_module_norm_name: instance
+ segmentation_module_conv_block: true
+ segmentation_module_res_block: true
+ segmentation_module_dropout: 0.0
+ segmentation_module_qkv_bias: false
+ segmentation_module_padding_size: 11
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: [160, 160]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: [160, 160]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/BraTs23AdultGlioma/UNetR
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/conf/train/vnet.yaml b/projects/SEG/BraTS2023AdultGlioma/conf/train/vnet.yaml
new file mode 100644
index 00000000..46b72059
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/conf/train/vnet.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 4
+ segmentation_module_output_channels: 4
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: False
+ segmentation_module_padding_size: 15
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5, 0.5, 0.5, 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: BraTS2023AdultGlioma
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1e-4
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/BraTS2023AdultGlioma/preprocessed/ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/BraTs23AdultGlioma/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/BraTS2023AdultGlioma/preprocess_dataset.sh b/projects/SEG/BraTS2023AdultGlioma/preprocess_dataset.sh
new file mode 100644
index 00000000..4835e8d1
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/preprocess_dataset.sh
@@ -0,0 +1,37 @@
+#!/bin/bash
+echo "
+Preprocessing pipeline for the BraTS2023AdultGlioma dataset.
+
+For more information, please refer to https://www.synapse.org/#!Synapse:syn51156910/wiki/ and check the following
+papers:
+- https://arxiv.org/pdf/1811.02629.pdf,
+- https://arxiv.org/pdf/2305.17033.pdf.
+Data download link (registration required): https://www.synapse.org/#!Synapse:syn51156910/wiki/622351.
+
+Please make sure you have the following packages installed: argparse, json, nibabel, numpy, pathlib, random, tqdm.
+
+Starting the preprocessing...
+"
+
+# Prompt the user to enter the path to the downloaded data
+echo "Please enter the (downloaded) data directory:"
+read INPUT_DIR
+
+# Check if the input directory exists
+if [ ! -d "$INPUT_DIR" ]; then
+ echo "The input directory does not exist. Please try again."
+ exit 1
+fi
+
+# Prompt the user to enter the output directory for the preprocessed data
+echo "Please enter the output directory for the preprocessed data:"
+read OUTPUT_DIR
+
+# Run the preprocessing pipeline
+echo "Running the preprocessing..."
+python projects/segmentation/BraTS2023AdultGlioma/scripts/preprocess_dataset.py $INPUT_DIR $OUTPUT_DIR
+echo "Generating train and val splits..."
+python projects/segmentation/BraTS2023AdultGlioma/scripts/split_sets_json.py $OUTPUT_DIR
+echo "Computing the segmentation classes probabilities..."
+python projects/segmentation/BraTS2023AdultGlioma/scripts/compute_segmentation_classes_probabilities.py $OUTPUT_DIR $OUTPUT_DIR
+echo "Done!"
diff --git a/projects/SEG/BraTS2023AdultGlioma/scripts/__init__.py b/projects/SEG/BraTS2023AdultGlioma/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/SEG/BraTS2023AdultGlioma/scripts/compute_segmentation_classes_probabilities.py b/projects/SEG/BraTS2023AdultGlioma/scripts/compute_segmentation_classes_probabilities.py
new file mode 100644
index 00000000..e1c34f90
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/scripts/compute_segmentation_classes_probabilities.py
@@ -0,0 +1,117 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+import os
+from pathlib import Path
+
+import nibabel as nib
+import numpy as np
+from tqdm import tqdm
+
+
+def main(args):
+ # iterate over all subjects
+ train_subjects = list(
+ (Path(args.data_path) / 'ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations').glob("*/")
+ )
+
+ bgs = []
+ ncrs = []
+ eds = []
+ ets = []
+ wts = []
+ total_slices = 0
+
+ # parse all "seg.nii.gz",
+ for subj in tqdm(train_subjects):
+ # get segmentation
+ segmentation_labels = nib.load(subj).get_fdata().astype(np.float32)
+
+ # Necrotic Tumor Core (NCR - label 1)
+ ncr = np.zeros_like(segmentation_labels)
+ ncr[segmentation_labels == 1] = 1
+ # Peritumoral Edematous/Invaded Tissue (ED - label 2)
+ ed = np.zeros_like(segmentation_labels)
+ ed[segmentation_labels == 2] = 1
+ # GD-Enhancing Tumor (ET - label 3)
+ et = np.zeros_like(segmentation_labels)
+ et[segmentation_labels == 3] = 1
+ # Whole Tumor (WT โ label 1, 2, or 3)
+ wt = np.zeros_like(segmentation_labels)
+ wt[segmentation_labels != 0] = 1
+
+ # find how many slices contain each class
+ bg_slices = np.sum(
+ [1 for i in range(segmentation_labels.shape[2]) if np.sum(segmentation_labels[:, :, i]) == 0]
+ )
+ ncr_slices = np.sum([1 for i in range(ncr.shape[2]) if np.sum(ncr[:, :, i]) > 0])
+ ed_slices = np.sum([1 for i in range(ed.shape[2]) if np.sum(ed[:, :, i]) > 0])
+ et_slices = np.sum([1 for i in range(et.shape[2]) if np.sum(et[:, :, i]) > 0])
+ wt_slices = np.sum([1 for i in range(wt.shape[2]) if np.sum(wt[:, :, i]) > 0])
+
+ bgs.append(bg_slices)
+ ncrs.append(ncr_slices)
+ eds.append(ed_slices)
+ ets.append(et_slices)
+ wts.append(wt_slices)
+
+ total_slices += segmentation_labels.shape[2]
+
+ # compute probabilities for each class
+ bg_prob = np.sum(bgs, axis=0) / total_slices
+ ncr_prob = np.sum(ncrs, axis=0) / total_slices
+ ed_prob = np.sum(eds, axis=0) / total_slices
+ et_prob = np.sum(ets, axis=0) / total_slices
+ wt_prob = np.sum(wts, axis=0) / total_slices
+
+ # sum and compute 100% probability
+ total_prob = bg_prob + ncr_prob + ed_prob + et_prob + wt_prob
+ bg_prob /= total_prob
+ ncr_prob /= total_prob
+ ed_prob /= total_prob
+ et_prob /= total_prob
+ wt_prob /= total_prob
+
+ # round to 3 decimals
+ bg_prob = np.round(bg_prob, 3)
+ ncr_prob = np.round(ncr_prob, 3)
+ ed_prob = np.round(ed_prob, 3)
+ et_prob = np.round(et_prob, 3)
+ wt_prob = np.round(wt_prob, 3)
+
+ print(
+ f"Probabilities {bg_prob + ncr_prob + ed_prob + et_prob + wt_prob}. "
+ f"Background: {bg_prob}, "
+ f"NCR: {ncr_prob}, "
+ f"ED: {ed_prob}, "
+ f"ET: {et_prob}, "
+ f"WT: {wt_prob}."
+ )
+
+ # create output dir
+ output_path = Path(args.output_path)
+ if not os.path.exists(output_path):
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # save probabilities as json
+ with open(output_path / "probabilities.json", "w", encoding="utf-8") as f:
+ json.dump(
+ {
+ "bg_prob": bg_prob.tolist(),
+ "ncr_prob": ncr_prob.tolist(),
+ "ed_prob": ed_prob.tolist(),
+ "et_prob": et_prob.tolist(),
+ "wt_prob": wt_prob.tolist(),
+ },
+ f,
+ )
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path)
+ parser.add_argument("output_path", type=Path)
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/SEG/BraTS2023AdultGlioma/scripts/preprocess_dataset.py b/projects/SEG/BraTS2023AdultGlioma/scripts/preprocess_dataset.py
new file mode 100644
index 00000000..f8fd82e4
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/scripts/preprocess_dataset.py
@@ -0,0 +1,199 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import os
+from pathlib import Path
+
+import nibabel as nib
+import numpy as np
+from tqdm import tqdm
+
+
+def normalizer(data):
+ """Normalize the data to zero mean and unit variance."""
+ mean = np.mean(data)
+ std = np.std(data)
+ if np.isscalar(mean):
+ if mean == 0.0:
+ mean = 1.0
+ elif isinstance(mean, np.ndarray):
+ mean[std == 0.0] = 1.0
+ return (data - mean) / std
+
+
+def crop_to_brain(data):
+ """Crop the data to the brain region."""
+ # crop from left to right until brain is found
+ min_x = 0
+ for i in range(data.shape[0]):
+ if np.sum(data[i, :, :]) > 0:
+ min_x = i
+ break
+
+ # crop from right to left until brain is found
+ max_x = data.shape[0] - 1
+ for i in range(data.shape[0] - 1, -1, -1):
+ if np.sum(data[i, :, :]) > 0:
+ max_x = i
+ break
+
+ # crop from top to bottom until brain is found
+ min_y = 0
+ for i in range(data.shape[1]):
+ if np.sum(data[:, i, :]) > 0:
+ min_y = i
+ break
+
+ # crop from bottom to top until brain is found
+ max_y = data.shape[1] - 1
+ for i in range(data.shape[1] - 1, -1, -1):
+ if np.sum(data[:, i, :]) > 0:
+ max_y = i
+ break
+
+ # add 15% margin
+ margin_x = int((max_x - min_x) * 0.15)
+ margin_y = int((max_y - min_y) * 0.15)
+
+ min_x = max(0, min_x - margin_x)
+ max_x = min(data.shape[0], max_x + margin_x)
+ min_y = max(0, min_y - margin_y)
+ max_y = min(data.shape[1], max_y + margin_y)
+
+ return min_x, max_x, min_y, max_y
+
+
+def main(args):
+ train_path = Path(args.data_path) / "ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData"
+ output_train_data_path = Path(args.output_path) / "ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData"
+ if not os.path.exists(output_train_data_path):
+ output_train_data_path.mkdir(parents=True, exist_ok=True)
+ output_train_segmentations_path = (
+ Path(args.output_path) / "ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingSegmentations"
+ )
+ if not os.path.exists(output_train_segmentations_path):
+ output_train_segmentations_path.mkdir(parents=True, exist_ok=True)
+
+ # iterate over all subjects
+ train_subjects = list(train_path.glob("*/"))
+
+ # each subject dir contains five files, each starting with the subject name and ending with "seg.nii.gz",
+ # "t1c.nii.gz", "t1n.nii.gz", "t2f.nii.gz", or "t2w.nii.gz"
+ for subj in tqdm(train_subjects):
+ # get all files inside the subject dir if not seg
+ t1c = nib.load(subj / f"{subj.name}-t1c.nii.gz")
+ t1n = nib.load(subj / f"{subj.name}-t1n.nii.gz")
+ t2f = nib.load(subj / f"{subj.name}-t2f.nii.gz")
+ t2w = nib.load(subj / f"{subj.name}-t2w.nii.gz")
+
+ # get data affine
+ affine = t1c.affine
+
+ # get data
+ t1c_data = t1c.get_fdata().astype(np.float32)
+ t1n_data = t1n.get_fdata().astype(np.float32)
+ t2f_data = t2f.get_fdata().astype(np.float32)
+ t2w_data = t2w.get_fdata().astype(np.float32)
+
+ # get seg
+ seg = nib.load(subj / f"{subj.name}-seg.nii.gz")
+
+ # get seg affine
+ seg_affine = seg.affine
+
+ # get seg data
+ seg_data = seg.get_fdata().astype(np.float32)
+
+ # crop to brain
+ t1c_min_x, t1c_max_x, t1c_min_y, t1c_max_y = crop_to_brain(t1c_data)
+ t1n_min_x, t1n_max_x, t1n_min_y, t1n_max_y = crop_to_brain(t1n_data)
+ t2f_min_x, t2f_max_x, t2f_min_y, t2f_max_y = crop_to_brain(t2f_data)
+ t2w_min_x, t2w_max_x, t2w_min_y, t2w_max_y = crop_to_brain(t2w_data)
+
+ # get max of min slices and min of max slices to ensure that all modalities have the same number of slices
+ # containing the tumor
+ min_x = max(t1c_min_x, t1n_min_x, t2f_min_x, t2w_min_x)
+ max_x = min(t1c_max_x, t1n_max_x, t2f_max_x, t2w_max_x)
+ min_y = max(t1c_min_y, t1n_min_y, t2f_min_y, t2w_min_y)
+ max_y = min(t1c_max_y, t1n_max_y, t2f_max_y, t2w_max_y)
+
+ # crop the data and seg
+ t1c_data = t1c_data[min_x : max_x + 1, min_y : max_y + 1, :]
+ t1n_data = t1n_data[min_x : max_x + 1, min_y : max_y + 1, :]
+ t2f_data = t2f_data[min_x : max_x + 1, min_y : max_y + 1, :]
+ t2w_data = t2w_data[min_x : max_x + 1, min_y : max_y + 1, :]
+ seg_data = seg_data[min_x : max_x + 1, min_y : max_y + 1, :]
+
+ # normalize again
+ t1c_data = normalizer(t1c_data)
+ t1n_data = normalizer(t1n_data)
+ t2f_data = normalizer(t2f_data)
+ t2w_data = normalizer(t2w_data)
+
+ # update the header
+ hdr = t1c.header.copy()
+ hdr["dim"][1] = 4
+ hdr["dim"][2] = t1c_data.shape[0]
+ hdr["dim"][3] = t1c_data.shape[1]
+ hdr["dim"][4] = t1c_data.shape[2]
+
+ # save the stacked modalities
+ all_modalities_nii = nib.Nifti1Image(
+ np.stack([t1c_data, t1n_data, t2f_data, t2w_data], axis=0), affine=affine, header=hdr
+ )
+ nib.save(all_modalities_nii, output_train_data_path / f"{subj.name}.nii.gz")
+
+ # update the seg header
+ seg_hdr = seg.header.copy()
+ seg_hdr["dim"][1] = 1
+ seg_hdr["dim"][2] = seg_data.shape[0]
+ seg_hdr["dim"][3] = seg_data.shape[1]
+ seg_hdr["dim"][4] = seg_data.shape[2]
+
+ # save the seg file to the output dir
+ seg_nii = nib.Nifti1Image(seg_data, affine=seg_affine, header=seg_hdr)
+ nib.save(seg_nii, output_train_segmentations_path / f"{subj.name}-seg.nii.gz")
+
+ val_path = Path(args.data_path) / "ASNR-MICCAI-BraTS2023-GLI-Challenge-ValidationData"
+ output_val_data_path = Path(args.output_path) / "ASNR-MICCAI-BraTS2023-GLI-Challenge-ValidationData"
+ if not os.path.exists(output_val_data_path):
+ output_val_data_path.mkdir(parents=True, exist_ok=True)
+
+ # iterate over all subjects
+ val_subjects = list(val_path.glob("*/"))
+
+ # each subject dir contains five files, each starting with the subject name and ending with "t1c.nii.gz",
+ # "t1n.nii.gz", "t2f.nii.gz", or "t2w.nii.gz". Validation data don't include seg.nii.gz files.
+ for subj in tqdm(val_subjects):
+ # get all files inside the subject dir if not seg
+ t1c = nib.load(subj / f"{subj.name}-t1c.nii.gz")
+ t1n = nib.load(subj / f"{subj.name}-t1n.nii.gz")
+ t2f = nib.load(subj / f"{subj.name}-t2f.nii.gz")
+ t2w = nib.load(subj / f"{subj.name}-t2w.nii.gz")
+
+ # get affine
+ affine = t1c.affine
+
+ t1c_data = t1c.get_fdata().astype(np.float32)
+ t1n_data = t1n.get_fdata().astype(np.float32)
+ t2f_data = t2f.get_fdata().astype(np.float32)
+ t2w_data = t2w.get_fdata().astype(np.float32)
+
+ t1c_data = normalizer(t1c_data)
+ t1n_data = normalizer(t1n_data)
+ t2f_data = normalizer(t2f_data)
+ t2w_data = normalizer(t2w_data)
+
+ # save the stacked modalities
+ all_modalities_nii = nib.Nifti1Image(np.stack([t1c_data, t1n_data, t2f_data, t2w_data], axis=0), affine=affine)
+
+ nib.save(all_modalities_nii, output_val_data_path / f"{subj.name}.nii.gz")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path)
+ parser.add_argument("output_path", type=Path)
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/SEG/BraTS2023AdultGlioma/scripts/split_sets_json.py b/projects/SEG/BraTS2023AdultGlioma/scripts/split_sets_json.py
new file mode 100644
index 00000000..89daa2b2
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/scripts/split_sets_json.py
@@ -0,0 +1,68 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+import random
+from pathlib import Path
+
+import numpy as np
+
+
+def generate_fold(filenames):
+ """Generate a train, val and test set from a list of filenames"""
+ data_parent_dir = Path(filenames[0]).parent
+
+ # Path to str
+ filenames = [str(filename) for filename in filenames]
+
+ # keep only the filename, so drop the "-t1c.nii.gz", "-t1n.nii.gz", "-t2f.nii.gz", or "-t2w.nii.gz"
+ filenames = [filename.split("/")[-1] for filename in filenames]
+ # keep only the unique filenames
+ filenames = np.unique(filenames)
+
+ # shuffle the filenames
+ random.shuffle(filenames)
+
+ # split the filenames into train and val with 80% and 20% respectively
+ train_fnames = np.array(filenames[: int(len(filenames) * 0.8)]).tolist()
+ # remove train filenames from all filenames
+ filenames = np.setdiff1d(filenames, train_fnames)
+ # since we have already removed the train filenames, we can use the remaining filenames as val
+ val_fnames = filenames.tolist()
+
+ # set full path
+ train_fnames = [str(data_parent_dir / filename) for filename in train_fnames]
+ val_fnames = [str(data_parent_dir / filename) for filename in val_fnames]
+
+ return train_fnames, val_fnames
+
+
+def main(args):
+ # read all nii.gz files in the data directory
+ all_filenames = list((Path(args.data_path) / "ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData").iterdir())
+
+ # create n folds
+ folds = [generate_fold(all_filenames) for _ in range(args.nfolds)]
+
+ # create a directory to store the folds
+ output_path = Path(args.data_path) / "folds"
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # write each fold to a json file
+ for i, fold in enumerate(folds):
+ train_set, val_set = fold
+
+ # write the train, val and test filenames to a json file
+ with open(output_path / f"fold_{i}_train.json", "w", encoding="utf-8") as f:
+ json.dump(train_set, f)
+ with open(output_path / f"fold_{i}_val.json", "w", encoding="utf-8") as f:
+ json.dump(val_set, f)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path, help="Path to the data directory.")
+ parser.add_argument("--nfolds", type=int, default=1, help="Number of folds to create.")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/SEG/BraTS2023AdultGlioma/visualize.ipynb b/projects/SEG/BraTS2023AdultGlioma/visualize.ipynb
new file mode 100644
index 00000000..db56fc33
--- /dev/null
+++ b/projects/SEG/BraTS2023AdultGlioma/visualize.ipynb
@@ -0,0 +1,328 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "collapsed": true,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:27.192030Z",
+ "end_time": "2023-09-15T10:48:27.865302Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import nibabel as nib\n",
+ "import numpy as np\n",
+ "\n",
+ "from pathlib import Path"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "outputs": [],
+ "source": [
+ "# Prompt the user to enter the data path\n",
+ "data_path = input(\"Please enter the (downloaded) data path: \")\n",
+ "data_path = Path(data_path) / 'ASNR-MICCAI-BraTS2023-GLI-Challenge-TrainingData'"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:27.903946Z",
+ "end_time": "2023-09-15T10:48:31.811424Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "outputs": [],
+ "source": [
+ "subj = 'BraTS-GLI-00126-000'"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:31.397142Z",
+ "end_time": "2023-09-15T10:48:31.811823Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "outputs": [],
+ "source": [
+ "files = list(Path(f\"{data_path}/{subj}/\").glob('*.nii.gz'))"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:31.400088Z",
+ "end_time": "2023-09-15T10:48:31.811964Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "outputs": [],
+ "source": [
+ "# load seg from files where seg is in the name\n",
+ "segmentation_labels = nib.load([f for f in files if 'seg' in f.name][0]).get_fdata()"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:31.799819Z",
+ "end_time": "2023-09-15T10:48:32.015269Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "outputs": [],
+ "source": [
+ "# find which slice has the most classes\n",
+ "max_classes = 0\n",
+ "max_slice = 0\n",
+ "for i in range(segmentation_labels.shape[2]):\n",
+ " classes = len(np.unique(segmentation_labels[:, :, i]))\n",
+ " if classes > max_classes:\n",
+ " max_classes = classes\n",
+ " max_slice = i"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:32.003301Z",
+ "end_time": "2023-09-15T10:48:32.303641Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Max classes: 4 on slice 69\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(f'Max classes: {max_classes} on slice {max_slice}')"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:32.200061Z",
+ "end_time": "2023-09-15T10:48:32.308897Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "outputs": [],
+ "source": [
+ "# Background (label 0)\n",
+ "bg = np.zeros_like(segmentation_labels)\n",
+ "bg[segmentation_labels == 0] = 1\n",
+ "bg[segmentation_labels != 0] = 0\n",
+ "# Necrotic Tumor Core (NCR - label 1)\n",
+ "ncr = np.zeros_like(segmentation_labels)\n",
+ "ncr[segmentation_labels == 1] = 1\n",
+ "# Peritumoral Edematous/Invaded Tissue (ED - label 2)\n",
+ "ed = np.zeros_like(segmentation_labels)\n",
+ "ed[segmentation_labels == 2] = 1\n",
+ "# GD-Enhancing Tumor (ET - label 3)\n",
+ "et = np.zeros_like(segmentation_labels)\n",
+ "et[segmentation_labels == 3] = 1\n",
+ "# Whole Tumor (WT โ label 1, 2, or 3)\n",
+ "wt = np.zeros_like(segmentation_labels)\n",
+ "wt[segmentation_labels != 0] = 1"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:32.202928Z",
+ "end_time": "2023-09-15T10:48:32.724262Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# visualize the segmentation classes\n",
+ "fig, ax = plt.subplots(1, 5, figsize=(20, 5))\n",
+ "ax[0].imshow(bg[:, :, max_slice], cmap='gray')\n",
+ "ax[0].set_title('Background')\n",
+ "ax[0].axis('off')\n",
+ "ax[1].imshow(ncr[:, :, max_slice], cmap='gray')\n",
+ "ax[1].set_title('Necrotic Tumor Core \\n (NCR - label 1)')\n",
+ "ax[1].axis('off')\n",
+ "ax[2].imshow(ed[:, :, max_slice], cmap='gray')\n",
+ "ax[2].set_title('Peritumoral Edematous/Invaded Tissue \\n (ED - label 2)')\n",
+ "ax[2].axis('off')\n",
+ "ax[3].imshow(et[:, :, max_slice], cmap='gray')\n",
+ "ax[3].set_title('GD-Enhancing Tumor \\n (ET - label 3)')\n",
+ "ax[3].axis('off')\n",
+ "ax[4].imshow(wt[:, :, max_slice], cmap='gray')\n",
+ "ax[4].set_title('Whole Tumor \\n (WT โ label 1, 2, or 3)')\n",
+ "ax[4].axis('off')\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:32.727228Z",
+ "end_time": "2023-09-15T10:48:33.105787Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "outputs": [],
+ "source": [
+ "# load the T1c image\n",
+ "t1c_data = nib.load([f for f in files if 't1c' in f.name][0]).get_fdata()\n",
+ "# load the T1n image\n",
+ "t1n_data = nib.load([f for f in files if 't1n' in f.name][0]).get_fdata()\n",
+ "# load the T2f image\n",
+ "t2f_data = nib.load([f for f in files if 't2f' in f.name][0]).get_fdata()\n",
+ "# load the T2w image\n",
+ "t2w_data = nib.load([f for f in files if 't2w' in f.name][0]).get_fdata()"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:33.108808Z",
+ "end_time": "2023-09-15T10:48:33.564647Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# visualize the data and segment them\n",
+ "fig, ax = plt.subplots(4, 6, figsize=(20, 10))\n",
+ "ax[0, 0].imshow(t1c_data[:, :, max_slice], cmap='gray')\n",
+ "ax[0, 0].set_title('T1-c')\n",
+ "ax[0, 0].axis('off')\n",
+ "ax[0, 1].imshow(t1c_data[:, :, max_slice] * bg[:, :, max_slice], cmap='gray')\n",
+ "ax[0, 1].set_title('Background segmentation')\n",
+ "ax[0, 1].axis('off')\n",
+ "ax[0, 2].imshow(t1c_data[:, :, max_slice] * ncr[:, :, max_slice], cmap='gray')\n",
+ "ax[0, 2].set_title('Necrotic Tumor \\n Core segmentation \\n (NCR - label 1)')\n",
+ "ax[0, 2].axis('off')\n",
+ "ax[0, 3].imshow(t1c_data[:, :, max_slice] * ed[:, :, max_slice], cmap='gray')\n",
+ "ax[0, 3].set_title('Peritumoral Edematous/Invaded \\n Tissue segmentation \\n (ED - label 2)')\n",
+ "ax[0, 3].axis('off')\n",
+ "ax[0, 4].imshow(t1c_data[:, :, max_slice] * et[:, :, max_slice], cmap='gray')\n",
+ "ax[0, 4].set_title('GD-Enhancing \\n Tumor segmentation \\n (ET - label 3)')\n",
+ "ax[0, 4].axis('off')\n",
+ "ax[0, 5].imshow(t1c_data[:, :, max_slice] * wt[:, :, max_slice], cmap='gray')\n",
+ "ax[0, 5].set_title('Whole Tumor segmentation \\n (WT โ label 1, 2, or 3)')\n",
+ "ax[0, 5].axis('off')\n",
+ "ax[1, 0].imshow(t1n_data[:, :, max_slice], cmap='gray')\n",
+ "ax[1, 0].set_title('T1-n')\n",
+ "ax[1, 0].axis('off')\n",
+ "ax[1, 1].imshow(t1n_data[:, :, max_slice] * bg[:, :, max_slice], cmap='gray')\n",
+ "ax[1, 1].axis('off')\n",
+ "ax[1, 2].imshow(t1n_data[:, :, max_slice] * ncr[:, :, max_slice], cmap='gray')\n",
+ "ax[1, 2].axis('off')\n",
+ "ax[1, 3].imshow(t1n_data[:, :, max_slice] * ed[:, :, max_slice], cmap='gray')\n",
+ "ax[1, 3].axis('off')\n",
+ "ax[1, 4].imshow(t1n_data[:, :, max_slice] * et[:, :, max_slice], cmap='gray')\n",
+ "ax[1, 4].axis('off')\n",
+ "ax[1, 5].imshow(t1n_data[:, :, max_slice] * wt[:, :, max_slice], cmap='gray')\n",
+ "ax[1, 5].axis('off')\n",
+ "ax[2, 0].imshow(t2f_data[:, :, max_slice], cmap='gray')\n",
+ "ax[2, 0].set_title('T2-f')\n",
+ "ax[2, 0].axis('off')\n",
+ "ax[2, 1].imshow(t2f_data[:, :, max_slice] * bg[:, :, max_slice], cmap='gray')\n",
+ "ax[2, 1].axis('off')\n",
+ "ax[2, 2].imshow(t2f_data[:, :, max_slice] * ncr[:, :, max_slice], cmap='gray')\n",
+ "ax[2, 2].axis('off')\n",
+ "ax[2, 3].imshow(t2f_data[:, :, max_slice] * ed[:, :, max_slice], cmap='gray')\n",
+ "ax[2, 3].axis('off')\n",
+ "ax[2, 4].imshow(t2f_data[:, :, max_slice] * et[:, :, max_slice], cmap='gray')\n",
+ "ax[2, 4].axis('off')\n",
+ "ax[2, 5].imshow(t2f_data[:, :, max_slice] * wt[:, :, max_slice], cmap='gray')\n",
+ "ax[2, 5].axis('off')\n",
+ "ax[3, 0].imshow(t2w_data[:, :, max_slice], cmap='gray')\n",
+ "ax[3, 0].set_title('T2-w')\n",
+ "ax[3, 0].axis('off')\n",
+ "ax[3, 1].imshow(t2w_data[:, :, max_slice] * bg[:, :, max_slice], cmap='gray')\n",
+ "ax[3, 1].axis('off')\n",
+ "ax[3, 2].imshow(t2w_data[:, :, max_slice] * ncr[:, :, max_slice], cmap='gray')\n",
+ "ax[3, 2].axis('off')\n",
+ "ax[3, 3].imshow(t2w_data[:, :, max_slice] * ed[:, :, max_slice], cmap='gray')\n",
+ "ax[3, 3].axis('off')\n",
+ "ax[3, 4].imshow(t2w_data[:, :, max_slice] * et[:, :, max_slice], cmap='gray')\n",
+ "ax[3, 4].axis('off')\n",
+ "ax[3, 5].imshow(t2w_data[:, :, max_slice] * wt[:, :, max_slice], cmap='gray')\n",
+ "ax[3, 5].axis('off')\n",
+ "plt.show()"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T10:48:33.564996Z",
+ "end_time": "2023-09-15T10:48:34.941478Z"
+ }
+ }
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 2
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython2",
+ "version": "2.7.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/README.md b/projects/SEG/ISLES2022SubAcuteStroke/README.md
new file mode 100644
index 00000000..f3444ebc
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/README.md
@@ -0,0 +1,60 @@
+## **ISLES2022SubAcuteStroke**
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the
+ISLES2022SubAcuteStroke dataset.
+
+For more information, please refer to https://isles22.grand-challenge.org/dataset/.
+
+Related papers:
+- https://www.nature.com/articles/s41597-022-01875-5.
+
+**Note:** When running the preprocessing scripts please make sure you have the following packages installed: argparse,
+connected-components-3d, json, nibabel, numpy, pathlib, random, simpleitk. All those packages, except the
+connected-components-3d and simpleitk packages, are installed by default if atommic is installed. To install those two
+packages, please run the following commands:
+```bash
+pip install -r requirements/requirements-isles22.txt
+```
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](visualize.ipynb). You just need to set the path where
+the dataset is downloaded. The notebook is copied and slightly modified from the
+[original notebook](https://github.com/ezequieldlrosa/isles22/tree/main/utils) provided by the ISLES challenge.
+
+### **Preprocessing**
+The preprocessing pipeline is implemented in the
+[preprocess_dataset.sh](preprocess_dataset.sh) script, consisting of the
+following steps:
+1. Clip to 0 - max values.
+2. Normalize the images to zero mean and unit variance.
+3. Resample the FLAIR data to the same resolution as the ADC and DWI data.
+4. Stack all modalities (ADC, DWI, FLAIR) into a single 3D volume.
+5. Fix the orientation of the images.
+6. Updates headers and save to NIfTI format.
+7. Split the dataset into training and validation sets.
+
+The preprocessing script can be run with the following command:
+```bash
+bash ./projects/SEG/ISLES2022SubAcuteStroke/preprocess_dataset.sh
+```
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/SEG/ISLES2022SubAcuteStroke/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` and the `segmentations_path`, which will be generated by the preprocessing
+script. In `exp_manager` please set the `exp_dir` to the path where you want to save the model checkpoints and
+tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/SEG/ISLES2022SubAcuteStroke/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/SEG/ISLES2022SubAcuteStroke/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/SEG/ISLES2022SubAcuteStroke/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/__init__.py b/projects/SEG/ISLES2022SubAcuteStroke/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/test/attentionunet.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/attentionunet.yaml
new file mode 100644
index 00000000..74691e5c
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/attentionunet.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/ISLES2022SubAcuteStroke/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/test/dynunet.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/dynunet.yaml
new file mode 100644
index 00000000..720c0c7a
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/dynunet.yaml
@@ -0,0 +1,149 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/ISLES2022SubAcuteStroke/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/test/lambdaunet3d.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/lambdaunet3d.yaml
new file mode 100644
index 00000000..b51c1bf8
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/lambdaunet3d.yaml
@@ -0,0 +1,135 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONLAMBDAUNET
+ use_reconstruction_module: false
+ segmentation_module: LambdaUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_query_depth: 16
+ segmentation_module_intra_depth: 4
+ segmentation_module_receptive_kernel: 3
+ segmentation_module_temporal_kernel: 3
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: false
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/ISLES2022SubAcuteStroke/LambdaUNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.segmentation.trained_models.ISLES2022SubAcuteStroke
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/test/unet2d.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/unet2d.yaml
new file mode 100644
index 00000000..fa079ae5
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/unet2d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/ISLES2022SubAcuteStroke/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/test/unet3d.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/unet3d.yaml
new file mode 100644
index 00000000..683bcdba
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/unet3d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/ISLES2022SubAcuteStroke/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/test/unetr.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/unetr.yaml
new file mode 100644
index 00000000..0d9121ea
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/unetr.yaml
@@ -0,0 +1,144 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONUNETR
+ use_reconstruction_module: false
+ segmentation_module: UNETR
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_img_size: [96, 96]
+ segmentation_module_channels: 32
+# segmentation_module_hidden_size: 768
+# segmentation_module_mlp_dim: 3072
+# segmentation_module_num_heads: 12
+ segmentation_module_hidden_size: 48
+ segmentation_module_mlp_dim: 192
+ segmentation_module_num_heads: 1
+ segmentation_module_pos_embed: conv
+ segmentation_module_norm_name: instance
+ segmentation_module_conv_block: true
+ segmentation_module_res_block: true
+ segmentation_module_dropout: 0.0
+ segmentation_module_qkv_bias: false
+ segmentation_module_padding_size: 11
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 5
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 5
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: [ 96, 96 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/ISLES2022SubAcuteStroke/UNetR
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.segmentation.trained_models.ISLES2022SubAcuteStroke
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/test/vnet.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/vnet.yaml
new file mode 100644
index 00000000..7483888f
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/test/vnet.yaml
@@ -0,0 +1,130 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 4 # originally 3, but VNet requires odd number of channels
+ segmentation_module_output_channels: 1
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: False
+ segmentation_module_padding_size: 15
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ test_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/ISLES2022SubAcuteStroke/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/train/attentionunet.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/attentionunet.yaml
new file mode 100644
index 00000000..74ae74a7
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/attentionunet.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/ISLES2022SubAcuteStroke/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/train/dynunet.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/dynunet.yaml
new file mode 100644
index 00000000..00d78901
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/dynunet.yaml
@@ -0,0 +1,190 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/ISLES2022SubAcuteStroke/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/train/lambdaunet2d.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/lambdaunet2d.yaml
new file mode 100644
index 00000000..2cfc1725
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/lambdaunet2d.yaml
@@ -0,0 +1,174 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONLAMBDAUNET
+ use_reconstruction_module: false
+ segmentation_module: LambdaUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_query_depth: 16
+ segmentation_module_intra_depth: 4
+ segmentation_module_receptive_kernel: 1
+ segmentation_module_temporal_kernel: 1
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: false
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/ISLES2022SubAcuteStroke/LambdaUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/train/lambdaunet3d.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/lambdaunet3d.yaml
new file mode 100644
index 00000000..fc5fb0e0
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/lambdaunet3d.yaml
@@ -0,0 +1,174 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONLAMBDAUNET
+ use_reconstruction_module: false
+ segmentation_module: LambdaUNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_query_depth: 16
+ segmentation_module_intra_depth: 4
+ segmentation_module_receptive_kernel: 3
+ segmentation_module_temporal_kernel: 3
+ segmentation_module_normalize: true
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: false
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: [ 96, 96 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: [ 96, 96 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 1e-5
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/ISLES2022SubAcuteStroke/LambdaUNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/train/unet2d.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/unet2d.yaml
new file mode 100644
index 00000000..a9f3681c
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/unet2d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/ISLES2022SubAcuteStroke/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/train/unet3d.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/unet3d.yaml
new file mode 100644
index 00000000..1f4161a3
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/unet3d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+
+ train_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/ISLES2022SubAcuteStroke/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/train/unetr.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/unetr.yaml
new file mode 100644
index 00000000..7d7d5e51
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/unetr.yaml
@@ -0,0 +1,180 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONUNETR
+ use_reconstruction_module: false
+ segmentation_module: UNETR
+ segmentation_module_input_channels: 3
+ segmentation_module_output_channels: 1
+ segmentation_module_img_size: [96, 96]
+ segmentation_module_channels: 16
+ segmentation_module_hidden_size: 768
+ segmentation_module_mlp_dim: 3072
+ segmentation_module_num_heads: 12
+ segmentation_module_pos_embed: perceptron
+ segmentation_module_norm_name: instance
+ segmentation_module_conv_block: true
+ segmentation_module_res_block: true
+ segmentation_module_dropout: 0.0
+ segmentation_module_qkv_bias: false
+ segmentation_module_padding_size: 11
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: [ 96, 96 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: [ 96, 96 ]
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 1e-5
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/ISLES2022SubAcuteStroke/UNetR
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/conf/train/vnet.yaml b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/vnet.yaml
new file mode 100644
index 00000000..d4bf9753
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/conf/train/vnet.yaml
@@ -0,0 +1,171 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 4 # originally 3, but VNet requires odd number of channels
+ segmentation_module_output_channels: 1
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: False
+ segmentation_module_padding_size: 15
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [ 0.5 ]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ coil_dim: None
+ coil_combination_method: None
+ log_multiple_modalities: true # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+
+ train_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/folds/fold_0_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ISLES2022SubAcuteStroke
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: data_parent_dir/ISLES2022SubAcuteStroke/preprocessed/segmentations/
+ segmentation_classes: 1
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: CosineAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 50
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/ISLES2022SubAcuteStroke/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/preprocess_dataset.sh b/projects/SEG/ISLES2022SubAcuteStroke/preprocess_dataset.sh
new file mode 100644
index 00000000..c765b289
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/preprocess_dataset.sh
@@ -0,0 +1,33 @@
+#!/bin/bash
+echo "
+Preprocessing pipeline for the ISLES2022SubAcuteStroke dataset.
+
+For more information, please refer to https://isles22.grand-challenge.org/dataset/ and check the following
+paper https://www.nature.com/articles/s41597-022-01875-5.
+
+Please make sure you have the following packages installed: argparse, connected-components-3d, json, nibabel, numpy,
+pathlib, random, simpleitk, tqdm.
+
+Starting the preprocessing...
+"
+
+# Prompt the user to enter the path to the downloaded data
+echo "Please enter the (downloaded) data directory:"
+read INPUT_DIR
+
+# Check if the input directory exists
+if [ ! -d "$INPUT_DIR" ]; then
+ echo "The input directory does not exist. Please try again."
+ exit 1
+fi
+
+# Prompt the user to enter the output directory for the preprocessed data
+echo "Please enter the output directory for the preprocessed data:"
+read OUTPUT_DIR
+
+# Run the preprocessing pipeline
+echo "Running the preprocessing..."
+python projects/segmentation/ISLES2022SubAcuteStroke/scripts/preprocess_dataset.py $INPUT_DIR $OUTPUT_DIR
+echo "Generating train, val, and test splits..."
+python projects/segmentation/ISLES2022SubAcuteStroke/scripts/split_sets_json.py $OUTPUT_DIR
+echo "Done!"
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/scripts/__init__.py b/projects/SEG/ISLES2022SubAcuteStroke/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/scripts/compute_segmentation_classes_probabilities.py b/projects/SEG/ISLES2022SubAcuteStroke/scripts/compute_segmentation_classes_probabilities.py
new file mode 100644
index 00000000..b451dd3d
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/scripts/compute_segmentation_classes_probabilities.py
@@ -0,0 +1,95 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+import os
+from pathlib import Path
+
+import nibabel as nib
+import numpy as np
+from tqdm import tqdm
+
+
+def main(args):
+ # get all files
+ derivatives = [
+ j
+ for f in list((Path(args.data_path) / "derivatives").iterdir())
+ for i in list(f.iterdir())
+ for j in list(i.iterdir())
+ if j.name.endswith(".nii.gz")
+ ]
+
+ # create a dictionary with all the derivatives
+ derivatives_subjects_files = {}
+ for file in derivatives:
+ fname = file.name.replace("_ses-0001_msk.nii.gz", "")
+ derivatives_subjects_files[fname] = file
+
+ # iterate over all the subjects and derivatives
+ subjects = {}
+ for fname, files in derivatives_subjects_files.items():
+ subjects[fname] = {"mask": derivatives_subjects_files[fname]}
+
+ bgs = []
+ lesions = []
+ total_slices = 0
+
+ # read the data
+ for fname, files in tqdm(subjects.items()):
+ # get segmentation
+ segmentation_labels = nib.load(files["mask"]).get_fdata().astype(np.float32)
+
+ # Lesion (label 1)
+ lesion = np.zeros_like(segmentation_labels)
+ lesion[segmentation_labels == 1] = 1
+
+ # find how many slices contain each class
+ bg_slices = np.sum(
+ [1 for i in range(segmentation_labels.shape[2]) if np.sum(segmentation_labels[:, :, i]) == 0]
+ )
+ lesion_slices = np.sum([1 for i in range(lesion.shape[2]) if np.sum(lesion[:, :, i]) > 0])
+
+ bgs.append(bg_slices)
+ lesions.append(lesion_slices)
+
+ total_slices += segmentation_labels.shape[2]
+
+ # compute probabilities for each class
+ bg_prob = np.sum(bgs, axis=0) / total_slices
+ lesion_prob = np.sum(lesions, axis=0) / total_slices
+
+ # sum and compute 100% probability
+ total_prob = bg_prob + lesion_prob
+ bg_prob /= total_prob
+ lesion_prob /= total_prob
+
+ # round to 3 decimals
+ bg_prob = np.round(bg_prob, 3)
+ lesion_prob = np.round(lesion_prob, 3)
+
+ print(f"Probabilities {bg_prob + lesion_prob}. " f"Background: {bg_prob}, " f"Lesions: {lesion_prob}, ")
+
+ # create output dir
+ output_path = Path(args.output_path)
+ if not os.path.exists(output_path):
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # save probabilities as json
+ with open(output_path / "probabilities.json", "w", encoding="utf-8") as f:
+ json.dump(
+ {
+ "bg_prob": bg_prob.tolist(),
+ "lesion_prob": lesion_prob.tolist(),
+ },
+ f,
+ )
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path)
+ parser.add_argument("output_path", type=Path)
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/scripts/evaluation.py b/projects/SEG/ISLES2022SubAcuteStroke/scripts/evaluation.py
new file mode 100644
index 00000000..d864f08c
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/scripts/evaluation.py
@@ -0,0 +1,374 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Metrics functions taken and adapted from : https://github.com/ezequieldlrosa/isles22
+
+import json
+import os
+import warnings
+from pathlib import Path
+
+import cc3d
+import h5py
+import nibabel as nib
+import numpy as np
+from runstats import Statistics
+
+from atommic.collections.common.parts import center_crop
+
+
+def compute_dice(im1, im2, voxel_volume=0.0, empty_value=1.0): # pylint: disable=unused-argument
+ """
+ Computes the Dice coefficient, a measure of set similarity.
+ Parameters
+ ----------
+ im1 : array-like, bool
+ Any array of arbitrary size. If not boolean, will be converted.
+ im2 : array-like, bool
+ Any other array of identical size as im1. If not boolean, it will be converted.
+ voxel_volume : scalar, float (ml)
+ empty_value : scalar, float.
+
+ Returns
+ -------
+ dice : float
+ Dice coefficient as a float on range [0,1].
+ Maximum similarity = 1
+ No similarity = 0
+ If both images are empty (sum equal to zero) = empty_value
+
+ Notes
+ -----
+ The order of inputs for `dice` is irrelevant. The result will be
+ identical if `im1` and `im2` are switched.
+
+ This function has been adapted from the Verse Challenge repository:
+ https://github.com/anjany/verse/blob/main/utils/eval_utilities.py
+ """
+
+ # binarize im1 and im2
+ im1 = np.asarray(im1).astype(np.bool_)
+ im2 = np.asarray(im2).astype(np.bool_)
+
+ if im1.shape != im2.shape:
+ raise ValueError("Shape mismatch: im1 and im2 must have the same shape.")
+
+ im_sum = im1.sum() + im2.sum()
+ if im_sum == 0:
+ return empty_value
+
+ # Compute Dice coefficient
+ intersection = np.logical_and(im1, im2)
+
+ return 2.0 * intersection.sum() / im_sum
+
+
+def compute_absolute_volume_difference(im1, im2, voxel_volume=0.0):
+ """
+ Computes the absolute volume difference between two masks.
+
+ Parameters
+ ----------
+ im1 : array-like, bool
+ Any array of arbitrary size. If not boolean, will be converted.
+ im2 : array-like, bool
+ Any other array of identical size as 'ground_truth'. If not boolean, it will be converted.
+ voxel_volume : scalar, float (ml)
+ If not float, it will be converted.
+
+ Returns
+ -------
+ abs_vol_diff : float, measured in ml.
+ Absolute volume difference as a float.
+ Maximum similarity = 0
+ No similarity = inf
+
+
+ Notes
+ -----
+ The order of inputs is irrelevant. The result will be identical if `im1` and `im2` are switched.
+ """
+
+ im1 = np.asarray(im1).astype(np.bool_)
+ im2 = np.asarray(im2).astype(np.bool_)
+ voxel_volume = np.asarray(voxel_volume).astype(np.float32)
+
+ if im1.shape != im2.shape:
+ warnings.warn(
+ "Shape mismatch: ground_truth and prediction have difference shapes."
+ " The absolute volume difference is computed with mismatching shape masks"
+ )
+
+ ground_truth_volume = np.sum(im1) * voxel_volume
+ prediction_volume = np.sum(im2) * voxel_volume
+ abs_vol_diff = np.abs(ground_truth_volume - prediction_volume)
+
+ return abs_vol_diff
+
+
+def compute_absolute_lesion_difference(
+ ground_truth, prediction, voxel_volume=0.0, connectivity=26 # pylint: disable=unused-argument
+):
+ """
+ Computes the absolute lesion difference between two masks. The number of lesions are counted for
+ each volume, and their absolute difference is computed.
+
+ Parameters
+ ----------
+ ground_truth : array-like, bool
+ Any array of arbitrary size. If not boolean, will be converted.
+ prediction : array-like, bool
+ Any other array of identical size as 'ground_truth'. If not boolean, it will be converted.
+ voxel_volume : scalar, float (ml)
+ connectivity : scalar, int.
+
+ Returns
+ -------
+ abs_les_diff : int
+ Absolute lesion difference as integer.
+ Maximum similarity = 0
+ No similarity = inf
+
+
+ Notes
+ -----
+ """
+ ground_truth = np.asarray(ground_truth).astype(np.bool_)
+ prediction = np.asarray(prediction).astype(np.bool_)
+
+ _, ground_truth_numb_lesion = cc3d.connected_components( # pylint: disable=c-extension-no-member
+ ground_truth, connectivity=connectivity, return_N=True
+ )
+ _, prediction_numb_lesion = cc3d.connected_components( # pylint: disable=c-extension-no-member
+ prediction, connectivity=connectivity, return_N=True
+ )
+ abs_les_diff = abs(ground_truth_numb_lesion - prediction_numb_lesion)
+
+ return abs_les_diff
+
+
+def compute_lesion_f1_score(
+ ground_truth, prediction, voxel_volume=0.0, empty_value=1.0, connectivity=26 # pylint: disable=unused-argument
+):
+ """
+ Computes the lesion-wise F1-score between two masks.
+
+ Parameters
+ ----------
+ ground_truth : array-like, bool
+ Any array of arbitrary size. If not boolean, will be converted.
+ prediction : array-like, bool
+ Any other array of identical size as 'ground_truth'. If not boolean, it will be converted.
+ voxel_volume : scalar, float (ml)
+ empty_value : scalar, float.
+ connectivity : scalar, int.
+
+ Returns
+ -------
+ f1_score : float
+ Lesion-wise F1-score as float.
+ Max score = 1
+ Min score = 0
+ If both images are empty (tp + fp + fn =0) = empty_value
+
+ Notes
+ -----
+ This function computes lesion-wise score by defining true positive lesions (tp), false positive lesions (fp) and
+ false negative lesions (fn) using 3D connected-component-analysis.
+
+ tp: 3D connected-component from the ground-truth image that overlaps at least on one voxel with the prediction
+ image.
+ fp: 3D connected-component from the prediction image that has no voxel overlapping with the ground-truth image.
+ fn: 3d connected-component from the ground-truth image that has no voxel overlapping with the prediction image.
+ """
+ ground_truth = np.asarray(ground_truth).astype(np.bool_)
+ prediction = np.asarray(prediction).astype(np.bool_)
+ tp = 0
+ fp = 0
+ fn = 0
+
+ # Check if ground-truth connected-components are detected or missed (tp and fn respectively).
+ intersection = np.logical_and(ground_truth, prediction)
+ labeled_ground_truth, N = cc3d.connected_components( # pylint: disable=c-extension-no-member
+ ground_truth, connectivity=connectivity, return_N=True
+ )
+
+ # Iterate over ground_truth clusters to find tp and fn.
+ # tp and fn are only computed if the ground-truth is not empty.
+ if N > 0:
+ for _, binary_cluster_image in cc3d.each( # pylint: disable=c-extension-no-member
+ labeled_ground_truth, binary=True, in_place=True
+ ):
+ if np.logical_and(binary_cluster_image, intersection).any():
+ tp += 1
+ else:
+ fn += 1
+
+ # iterate over prediction clusters to find fp.
+ # fp are only computed if the prediction image is not empty.
+ labeled_prediction, N = cc3d.connected_components( # pylint: disable=c-extension-no-member
+ prediction, connectivity=connectivity, return_N=True
+ )
+ if N > 0:
+ for _, binary_cluster_image in cc3d.each( # pylint: disable=c-extension-no-member
+ labeled_prediction, binary=True, in_place=True
+ ):
+ if not np.logical_and(binary_cluster_image, ground_truth).any():
+ fp += 1
+
+ # Define case when both images are empty.
+ if tp + fp + fn == 0:
+ _, N = cc3d.connected_components( # pylint: disable=c-extension-no-member
+ ground_truth, connectivity=connectivity, return_N=True
+ )
+ if N == 0:
+ f1_score = empty_value
+ else:
+ f1_score = tp / (tp + (fp + fn) / 2)
+
+ return f1_score
+
+
+METRIC_FUNCS = {
+ "DICE": compute_dice,
+ "AVD": compute_absolute_volume_difference,
+ "ALD": compute_absolute_lesion_difference,
+ "L-F1": compute_lesion_f1_score,
+}
+
+
+class ISLES2022SubAcuteStrokeSegmentationMetrics:
+ """Maintains running statistics for a given collection of metrics."""
+
+ def __init__(self, metric_funcs):
+ """
+ Args:
+ metric_funcs (dict): A dict where the keys are metric names and the
+ values are Python functions for evaluating that metric.
+ """
+ self.metrics_scores = {metric: Statistics() for metric in metric_funcs}
+
+ def push(self, target, segmentations, voxel_volume):
+ """
+ Pushes a new batch of metrics to the running statistics.
+ Args:
+ target: target image
+ segmentations: predicted segmentation
+ voxel_volume: voxel volume in ml
+ Returns:
+ dict: A dict where the keys are metric names and the values are
+ """
+ for metric, func in METRIC_FUNCS.items():
+ self.metrics_scores[metric].push(func(target, segmentations, voxel_volume))
+
+ def means(self):
+ """
+ Mean of the means of each metric.
+ Returns:
+ dict: A dict where the keys are metric names and the values are
+ """
+ return {metric: stat.mean() for metric, stat in self.metrics_scores.items()}
+
+ def stddevs(self):
+ """
+ Standard deviation of the means of each metric.
+ Returns:
+ dict: A dict where the keys are metric names and the values are
+ """
+ return {metric: stat.stddev() for metric, stat in self.metrics_scores.items()}
+
+ def __repr__(self):
+ """
+ Representation of the metrics.
+ Returns:
+ str: A string representation of the metrics.
+ """
+ means = self.means()
+ stddevs = self.stddevs()
+ metric_names = sorted(list(means))
+
+ res = " ".join(f"{name} = {means[name]:.4g} +/- {2 * stddevs[name]:.4g}" for name in metric_names) + "\n"
+
+ return res
+
+
+def main(args):
+ # if json file
+ if args.targets_dir.endswith(".json"):
+ with open(args.targets_dir, "r", encoding="utf-8") as f:
+ targets = json.load(f)
+ targets = [Path(target) for target in targets]
+ else:
+ targets = list(Path(args.targets_dir).iterdir())
+
+ crop_size = args.crop_size
+ evaluation_type = args.evaluation_type
+
+ scores = ISLES2022SubAcuteStrokeSegmentationMetrics(METRIC_FUNCS)
+ for target in targets:
+ subj = str(target).rsplit("/", maxsplit=1)[-1].split('.')[0]
+ predictions = h5py.File(Path(args.segmentations_dir) / subj, "r")["segmentation"][()].squeeze()
+ predictions = np.where(np.abs(predictions.astype(np.float32)) > 0.5, 1, 0)
+
+ # Labels are stacked as ADC, DWI, FLAIR
+ labels = (
+ nib.load(Path(args.targets_segmentations_dir) / Path(f"{subj}-seg.nii.gz")).get_fdata().astype(np.float32)
+ )
+ labels = np.moveaxis(labels, -1, 0)
+ lesions = np.zeros_like(labels)
+ lesions[labels == 1] = 1
+
+ # get voxel volume
+ voxel_volume = np.prod(nib.load(Path(args.targets_data_dir) / Path(target)).header.get_zooms()) / 1000
+
+ if crop_size is not None:
+ crop_size[0] = lesions.shape[-2] if lesions.shape[-2] < int(crop_size[0]) else int(crop_size[0])
+ crop_size[1] = lesions.shape[-1] if lesions.shape[-1] < int(crop_size[1]) else int(crop_size[1])
+ crop_size[0] = predictions.shape[-2] if predictions.shape[-2] < int(crop_size[0]) else int(crop_size[0])
+ crop_size[1] = predictions.shape[-1] if predictions.shape[-1] < int(crop_size[1]) else int(crop_size[1])
+
+ lesions = center_crop(lesions, crop_size)
+ predictions = center_crop(predictions, crop_size)
+
+ if evaluation_type == "per_slice":
+ lesions = np.expand_dims(lesions, axis=1)
+ predictions = np.expand_dims(predictions, axis=1)
+ for sl in range(lesions.shape[0]):
+ scores.push(lesions[sl], predictions[sl], voxel_volume)
+ elif evaluation_type == "per_volume":
+ scores.push(lesions, predictions, voxel_volume)
+
+ model = args.segmentations_dir.split("/")
+ model = model[-4] if model[-4] != "default" else model[-5]
+ print(f"{model}: {repr(scores)}")
+
+ if args.output_dir is not None:
+ output_dir = Path(args.output_dir)
+ output_dir.mkdir(parents=True, exist_ok=True)
+ # if file exists dont' overwrite, but append in a new line
+ with open(output_dir / "results.txt", "a", encoding="utf-8") as f:
+ f.write(f"{model}: {repr(scores)}\n")
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("targets_dir", type=str)
+ parser.add_argument("targets_data_dir", type=str)
+ parser.add_argument("targets_segmentations_dir", type=str)
+ parser.add_argument("segmentations_dir", type=str)
+ parser.add_argument("--output_dir", type=str)
+ parser.add_argument("--crop_size", nargs="+", type=int)
+ parser.add_argument("--evaluation_type", choices=["per_slice", "per_volume"], default="per_slice")
+ parser.add_argument("--fill_pred_path", action="store_true")
+ args = parser.parse_args()
+
+ if args.fill_pred_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.segmentations_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ args.segmentations_dir = os.path.join(input_dir, "segmentations")
+
+ main(args)
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/scripts/preprocess_dataset.py b/projects/SEG/ISLES2022SubAcuteStroke/scripts/preprocess_dataset.py
new file mode 100644
index 00000000..bbf2f56b
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/scripts/preprocess_dataset.py
@@ -0,0 +1,208 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import os
+from pathlib import Path
+
+import nibabel as nib
+import numpy as np
+import SimpleITK as sitk
+from tqdm import tqdm
+
+
+def resample_flair(flair, target, resample_method=sitk.sitkBSpline):
+ """
+ Resample the image to the same space as the target image.
+
+ Parameters
+ ----------
+ flair : Path
+ The path to the flair image.
+ target : Path
+ The path to the target image.
+ resample_method : sitk.sitkLinear
+ The resample method.
+ """
+ flair = sitk.ReadImage(flair)
+ target = sitk.ReadImage(target)
+ if not flair.GetSize() == target.GetSize():
+ # set target size
+ target_origin = target.GetOrigin()
+ target_direction = target.GetDirection()
+ target_spacing = target.GetSpacing()
+ target_size = target.GetSize()
+
+ # initialize resampler
+ resampler_image = sitk.ResampleImageFilter()
+ # set the parameters of image
+ resampler_image.SetReferenceImage(flair) # set resampled image meta data same to origin data
+ resampler_image.SetOutputOrigin(target_origin)
+ resampler_image.SetOutputDirection(target_direction) # set target image space
+ resampler_image.SetOutputSpacing(target_spacing) # set target image space
+ resampler_image.SetSize(target_size) # set target image size
+ if resample_method == sitk.sitkNearestNeighbor:
+ resampler_image.SetOutputPixelType(sitk.sitkUInt8)
+ else:
+ resampler_image.SetOutputPixelType(sitk.sitkFloat32)
+ resampler_image.SetTransform(sitk.Transform(3, sitk.sitkIdentity))
+ resampler_image.SetInterpolator(resample_method)
+
+ # launch the resampler
+ resampled_image = resampler_image.Execute(flair)
+ # convert to numpy array
+ resampled_image = sitk.GetArrayFromImage(resampled_image)
+ return resampled_image
+ return flair
+
+
+def normalizer(data):
+ """Normalize the data to zero mean and unit variance."""
+ mean = np.mean(data)
+ std = np.std(data)
+ if np.isscalar(mean):
+ if mean == 0.0:
+ mean = 1.0
+ elif isinstance(mean, np.ndarray):
+ mean[std == 0.0] = 1.0
+ return (data - mean) / std
+
+
+def main(args):
+ output_data_path = Path(args.output_path) / "data"
+ if not os.path.exists(output_data_path):
+ output_data_path.mkdir(parents=True, exist_ok=True)
+ output_segmentations_path = Path(args.output_path) / "segmentations"
+ if not os.path.exists(output_segmentations_path):
+ output_segmentations_path.mkdir(parents=True, exist_ok=True)
+
+ # get all files
+ derivatives = [
+ j
+ for f in list((Path(args.data_path) / "derivatives").iterdir())
+ for i in list(f.iterdir())
+ for j in list(i.iterdir())
+ if j.name.endswith(".nii.gz")
+ ]
+ rawdata = [
+ j
+ for f in list((Path(args.data_path) / "rawdata").iterdir())
+ for i in list(f.iterdir())
+ for j in list(i.iterdir())
+ if j.name.endswith(".nii.gz")
+ ]
+
+ # create a dictionary with all the derivatives
+ derivatives_subjects_files = {}
+ for file in derivatives:
+ fname = file.name.replace("_ses-0001_msk.nii.gz", "")
+ derivatives_subjects_files[fname] = file
+
+ # create a dictionary with all the rawdata
+ rawdata_adc_files = {}
+ rawdata_dwi_files = {}
+ rawdata_flair_files = {}
+ for file in rawdata:
+ if "adc" in file.name:
+ fname = file.name.replace("_ses-0001_adc.nii.gz", "")
+ rawdata_adc_files[fname] = file
+ if "dwi" in file.name:
+ fname = file.name.replace("_ses-0001_dwi.nii.gz", "")
+ rawdata_dwi_files[fname] = file
+ if "flair" in file.name:
+ fname = file.name.replace("_ses-0001_flair.nii.gz", "")
+ rawdata_flair_files[fname] = file
+
+ # iterate over all the subjects and derivatives
+ subjects = {}
+ for fname, files in derivatives_subjects_files.items():
+ subjects[fname] = {
+ "mask": derivatives_subjects_files[fname],
+ "adc": rawdata_adc_files[fname],
+ "dwi": rawdata_dwi_files[fname],
+ "flair": rawdata_flair_files[fname],
+ }
+
+ # read the data
+ for fname, files in tqdm(subjects.items()):
+ # Segmentation
+ seg_data = nib.load(files["mask"]).get_fdata()
+
+ # find which slices contain the lesion
+ lesion_slices = [i for i in range(seg_data.shape[2]) if np.sum(seg_data[:, :, i]) > 0]
+
+ if len(lesion_slices) == 0:
+ continue
+
+ # keep only the slices that contain the lesion
+ seg_data = np.stack([seg_data[:, :, i] for i in range(seg_data.shape[2]) if i in lesion_slices], axis=-1)
+
+ seg_data = np.transpose(seg_data, (1, 0, 2))
+
+ # get the seg affine
+ seg = nib.load(files["mask"])
+ seg_affine = seg.affine
+
+ # update the seg header
+ seg_hdr = seg.header.copy()
+ seg_hdr["dim"][0] = 1
+ seg_hdr["dim"][1] = seg_data.shape[0]
+ seg_hdr["dim"][2] = seg_data.shape[1]
+ seg_hdr["dim"][3] = seg_data.shape[2]
+
+ # save the seg file to the output dir
+ seg_nii = nib.Nifti1Image(seg_data, affine=seg_affine, header=seg_hdr)
+ nib.save(seg_nii, output_segmentations_path / f"{fname}-seg.nii.gz")
+
+ # ADC
+ adc_nii = nib.load(files["adc"])
+ adc_data = adc_nii.get_fdata().astype(np.float32)
+
+ # DWI
+ dwi_nii = nib.load(files["dwi"])
+ dwi_affine = dwi_nii.affine
+ dwi_header = dwi_nii.header
+ dwi_data = dwi_nii.get_fdata().astype(np.float32)
+
+ # FLAIR
+ flair_data = np.transpose(resample_flair(files["flair"], files["dwi"]), (2, 1, 0)).astype(np.float32)
+
+ adc_data = np.clip(adc_data, 0.0, adc_data.max())
+ dwi_data = np.clip(dwi_data, 0.0, dwi_data.max())
+ flair_data = np.clip(flair_data, 0.0, flair_data.max())
+
+ # keep only the slices that contain the lesion
+ adc_data = np.stack([adc_data[:, :, i] for i in range(adc_data.shape[2]) if i in lesion_slices], axis=-1)
+ dwi_data = np.stack([dwi_data[:, :, i] for i in range(dwi_data.shape[2]) if i in lesion_slices], axis=-1)
+ flair_data = np.stack([flair_data[:, :, i] for i in range(flair_data.shape[2]) if i in lesion_slices], axis=-1)
+
+ # normalize
+ # adc_data = normalizer(adc_data)
+ # dwi_data = normalizer(dwi_data)
+ # flair_data = normalizer(flair_data)
+
+ # get correct orientation
+ adc_data = np.transpose(adc_data, (1, 0, 2))
+ dwi_data = np.transpose(dwi_data, (1, 0, 2))
+ flair_data = np.transpose(flair_data, (1, 0, 2))
+
+ # get the dwi header
+ hdr = dwi_header.copy()
+ hdr["dim"][0] = 3
+ hdr["dim"][1] = dwi_data.shape[0]
+ hdr["dim"][2] = dwi_data.shape[1]
+ hdr["dim"][3] = dwi_data.shape[2]
+
+ # save the stacked modalities
+ all_modalities_nii = nib.Nifti1Image(
+ np.stack([adc_data, dwi_data, flair_data], axis=0), affine=dwi_affine, header=hdr
+ )
+ nib.save(all_modalities_nii, output_data_path / f"{fname}.nii.gz")
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path)
+ parser.add_argument("output_path", type=Path)
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/scripts/split_sets_json.py b/projects/SEG/ISLES2022SubAcuteStroke/scripts/split_sets_json.py
new file mode 100644
index 00000000..1e969168
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/scripts/split_sets_json.py
@@ -0,0 +1,62 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+import random
+from pathlib import Path
+
+import numpy as np
+
+
+def generate_fold(filenames):
+ """Generate a train, val and test set from a list of filenames"""
+ # Path to str
+ filenames = [str(filename) for filename in filenames]
+
+ # shuffle the filenames
+ random.shuffle(filenames)
+
+ # split the filenames into train, val and test with 70%, 15% and 15% respectively
+ train_fnames = np.array(filenames[: int(len(filenames) * 0.7)])
+ # remove train filenames from all filenames
+ filenames = np.setdiff1d(filenames, train_fnames)
+ # split the remaining filenames into val and test with 50% and 50% respectively
+ val_fnames = np.array(filenames[: int(len(filenames) * 0.5)])
+ # remove val filenames from all filenames
+ filenames = np.setdiff1d(filenames, val_fnames)
+ test_fnames = np.array(filenames)
+
+ return train_fnames.tolist(), val_fnames.tolist(), test_fnames.tolist()
+
+
+def main(args):
+ # read all h5 files in the data directory
+ all_filenames = list((Path(args.data_path) / "data").iterdir())
+
+ # create n folds
+ folds = [generate_fold(all_filenames) for _ in range(args.nfolds)]
+
+ # create a directory to store the folds
+ output_path = Path(args.data_path) / "folds"
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # write each fold to a json file
+ for i, fold in enumerate(folds):
+ train_set, val_set, test_set = fold
+
+ # write the train, val and test filenames to a json file
+ with open(output_path / f"fold_{i}_train.json", "w", encoding="utf-8") as f:
+ json.dump(train_set, f)
+ with open(output_path / f"fold_{i}_val.json", "w", encoding="utf-8") as f:
+ json.dump(val_set, f)
+ with open(output_path / f"fold_{i}_test.json", "w", encoding="utf-8") as f:
+ json.dump(test_set, f)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path, help="Path to the data directory.")
+ parser.add_argument("--nfolds", type=int, default=1, help="Number of folds to create.")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/SEG/ISLES2022SubAcuteStroke/visualize.ipynb b/projects/SEG/ISLES2022SubAcuteStroke/visualize.ipynb
new file mode 100644
index 00000000..7c81346f
--- /dev/null
+++ b/projects/SEG/ISLES2022SubAcuteStroke/visualize.ipynb
@@ -0,0 +1,247 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "82866918",
+ "metadata": {},
+ "source": [
+ "This notebook guides you through the ISLES22 data loading, visualization, and segmentation performance evaluation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "92bb6841",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-15T12:08:39.035646Z",
+ "end_time": "2023-09-15T12:08:39.707543Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import nibabel as nib\n",
+ "import numpy as np\n",
+ "import os\n",
+ "from matplotlib import pyplot as plt\n",
+ "from scripts import evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "outputs": [],
+ "source": [
+ "isles_data_dir = input(\"Please enter the (downloaded) data path: \")"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-09-15T12:08:39.709798Z",
+ "end_time": "2023-09-15T12:08:50.503827Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "4907c6c1",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-15T12:08:50.402543Z",
+ "end_time": "2023-09-15T12:08:50.505297Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# paths.\n",
+ "example_case = 9\n",
+ "\n",
+ "# Set images path.\n",
+ "dwi_path = os.path.join(isles_data_dir, 'rawdata', 'sub-strokecase{}'.format(\"%04d\" %example_case), 'ses-0001', 'sub-strokecase{}_ses-0001_dwi.nii.gz'.format(\"%04d\" % example_case))\n",
+ "adc_path = dwi_path.replace('dwi', 'adc')\n",
+ "flair_path = dwi_path.replace('dwi', 'flair')\n",
+ "mask_path = dwi_path.replace('rawdata', 'derivatives').replace('dwi', 'msk')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "1d76b879",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-15T12:08:50.406561Z",
+ "end_time": "2023-09-15T12:08:50.751965Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Load image data.\n",
+ "dwi_image = nib.load(dwi_path).get_fdata()\n",
+ "adc_image = nib.load(adc_path).get_fdata()\n",
+ "flair_image = nib.load(flair_path).get_fdata()\n",
+ "mask_image = nib.load(mask_path).get_fdata()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "f05f48a9",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-15T12:08:50.716172Z",
+ "end_time": "2023-09-15T12:08:51.221558Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Lets visualize the MR images with their corresponding mask overlays.\n",
+ "fig, (ax1, ax2, ax3) = plt.subplots(1, 3)\n",
+ "\n",
+ "# Show FLAIR image.\n",
+ "ax1.imshow(flair_image[:,:,16], cmap='gray')\n",
+ "ax1.set_title('FLAIR')\n",
+ "ax1.set_axis_off()\n",
+ "\n",
+ "slice2show=40\n",
+ "# Show DWI image w/overlayed mask.\n",
+ "ax2.imshow(dwi_image[:,:,slice2show], cmap='gray')\n",
+ "ax2.imshow(mask_image[:,:,slice2show], alpha=0.5, cmap='copper')\n",
+ "ax2.set_title('DWI')\n",
+ "ax2.set_axis_off()\n",
+ "\n",
+ "# Show ADC image w/overlayed mask.\n",
+ "ax3.imshow(adc_image[:,:,slice2show], cmap='gray')\n",
+ "ax3.imshow(mask_image[:,:,slice2show], alpha=0.5, cmap='copper')\n",
+ "ax3.set_title('ADC')\n",
+ "ax3.set_axis_off()\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "b1f9fdea",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-15T12:08:51.219576Z",
+ "end_time": "2023-09-15T12:08:51.228659Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# As an example, we'll segment the DWI using a 99th-percentile intensity cutoff. \n",
+ "dwi_cutoff = np.percentile(dwi_image[dwi_image>0], 99) \n",
+ "segmented_image = dwi_image > dwi_cutoff"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "329c75ac",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-15T12:08:51.230769Z",
+ "end_time": "2023-09-15T12:08:52.484887Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "# Lets visualize the segmentation we've created.\n",
+ "# Show ground truth.\n",
+ "fig2, (ax1, ax2) = plt.subplots(1, 2)\n",
+ "\n",
+ "ax1.imshow(dwi_image[:,:,slice2show], cmap='gray')\n",
+ "ax1.imshow(mask_image[:,:,slice2show], alpha=0.5, cmap='copper')\n",
+ "ax1.set_title('Ground truth')\n",
+ "ax1.set_axis_off()\n",
+ "\n",
+ "# Show predicted segmentation.\n",
+ "ax2.imshow(dwi_image[:,:,slice2show], cmap='gray')\n",
+ "ax2.imshow(segmented_image[:,:,slice2show], alpha=0.5, cmap='copper')\n",
+ "ax2.set_title('Segmentation')\n",
+ "ax2.set_axis_off()\n",
+ "plt.tight_layout()\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "cc602bf8",
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-09-15T12:08:52.480640Z",
+ "end_time": "2023-09-15T12:08:52.485388Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dice score: 0.1738013698630137\n",
+ "Absolute volume difference: 10.32 ml\n",
+ "Absolute lesion count difference: 87 \n",
+ "Lesion-wise F1-score: 0.24793388429752067 \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Compute performance metrics.\n",
+ "# Compute dice\n",
+ "print('Dice score: {}'.format(evaluation.compute_dice(mask_image, segmented_image)))\n",
+ "\n",
+ "# Compute absolute volume difference\n",
+ "voxel_volume = np.prod(nib.load(dwi_path).header.get_zooms())/1000 # Get voxel volume\n",
+ "print('Absolute volume difference: {} ml'.format(evaluation.compute_absolute_volume_difference(mask_image, segmented_image, voxel_volume)))\n",
+ "\n",
+ "# Compute absolute lesion count difference\n",
+ "print('Absolute lesion count difference: {} '.format(evaluation.compute_absolute_lesion_difference(mask_image, segmented_image)))\n",
+ "\n",
+ "# Compute F1-score (lesion-wise)\n",
+ "print('Lesion-wise F1-score: {} '.format(evaluation.compute_lesion_f1_score(mask_image, segmented_image)))"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "name": "python3",
+ "language": "python",
+ "display_name": "Python 3 (ipykernel)"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.11"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/projects/SEG/SKMTEA/README.md b/projects/SEG/SKMTEA/README.md
new file mode 100644
index 00000000..0a3a9e81
--- /dev/null
+++ b/projects/SEG/SKMTEA/README.md
@@ -0,0 +1,45 @@
+## **Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 Dataset**
+
+This project folder contains the configuration files, preprocessing, and visualization scripts for the
+Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 dataset.
+
+For more information, please refer to https://github.com/StanfordMIMI/skm-tea.
+
+Related papers:
+- https://openreview.net/forum?id=YDMFgD_qJuA.
+
+### **Visualization**
+An example notebook for visualizing the data is provided in the
+[visualize.ipynb](visualize.ipynb). You just need to set the path where the
+dataset is downloaded. The
+[original notebook](https://colab.research.google.com/drive/1PluqK77pobD5dXE7zzBLEAeBgaaeGKXa) is copied from the
+https://github.com/StanfordMIMI/skm-tea repository and provided by the SKMTEA authors.
+
+### **Preprocessing**
+No preprocessing is needed for the SKMTEA dataset. You just need to generate train, val, and test sets depending on
+the task you use the dataset for. For example, for the segmentation task, you need to run the
+[generate_sets.sh](generate_sets.sh) script.
+
+The preprocessing script can be run with the following command:
+```bash
+bash ./projects/SEG/SKMTEA/preprocess_dataset.sh
+```
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/SEG/SKMTEA/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` to the generated json files. In `exp_manager` please set the `exp_dir` to
+the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/SEG/SKMTEA/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/SEG/SKMTEA/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/SEG/SKMTEA/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/SEG/SKMTEA/__init__.py b/projects/SEG/SKMTEA/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/SEG/SKMTEA/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/SEG/SKMTEA/conf/test/attentionunet.yaml b/projects/SEG/SKMTEA/conf/test/attentionunet.yaml
new file mode 100644
index 00000000..c31bfbba
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/test/attentionunet.yaml
@@ -0,0 +1,130 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/SKMTEA/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/test/dynunet.yaml b/projects/SEG/SKMTEA/conf/test/dynunet.yaml
new file mode 100644
index 00000000..260ca8d0
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/test/dynunet.yaml
@@ -0,0 +1,150 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 100
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/SKMTEA/DYNUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/test/unet2d.yaml b/projects/SEG/SKMTEA/conf/test/unet2d.yaml
new file mode 100644
index 00000000..2d5150c9
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/test/unet2d.yaml
@@ -0,0 +1,129 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 100
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/SKMTEA/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/test/unet3d.yaml b/projects/SEG/SKMTEA/conf/test/unet3d.yaml
new file mode 100644
index 00000000..25f5b7fe
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/test/unet3d.yaml
@@ -0,0 +1,173 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/SKMTEA/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/test/vnet.yaml b/projects/SEG/SKMTEA/conf/test/vnet.yaml
new file mode 100644
index 00000000..637dfc2c
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/test/vnet.yaml
@@ -0,0 +1,131 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: false
+ segmentation_module_padding_size: 15
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ test_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_test.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 100
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/predictions/SKMTEA/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/train/attentionunet.yaml b/projects/SEG/SKMTEA/conf/train/attentionunet.yaml
new file mode 100644
index 00000000..7b0f17f8
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/train/attentionunet.yaml
@@ -0,0 +1,171 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONATTENTIONUNET
+ use_reconstruction_module: false
+ segmentation_module: AttentionUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_diratommic/segmentation/trained_models/SKMTEA/AttentionUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/train/dynunet.yaml b/projects/SEG/SKMTEA/conf/train/dynunet.yaml
new file mode 100644
index 00000000..16b033ea
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/train/dynunet.yaml
@@ -0,0 +1,189 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONDYNUNET
+ use_reconstruction_module: false
+ segmentation_module: DYNUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels:
+ - 32
+ - 64
+ - 128
+ - 256
+ - 512
+ segmentation_module_kernel_size:
+ - 3
+ - 3
+ - 3
+ - 1
+ segmentation_module_strides:
+ - 1
+ - 1
+ - 1
+ - 1
+ segmentation_module_dropout: 0.0
+ segmentation_module_norm: instance
+ segmentation_module_activation: leakyrelu
+ segmentation_module_deep_supervision: true
+ segmentation_module_deep_supervision_levels: 2
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/SKMTEA/DynUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/train/lambdaunet2d.yaml b/projects/SEG/SKMTEA/conf/train/lambdaunet2d.yaml
new file mode 100644
index 00000000..9dfb7f24
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/train/lambdaunet2d.yaml
@@ -0,0 +1,175 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONLAMBDAUNET
+ use_reconstruction_module: false
+ segmentation_module: LambdaUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 2
+ segmentation_module_dropout: 0.0
+ segmentation_module_query_depth: 16
+ segmentation_module_intra_depth: 1
+ segmentation_module_receptive_kernel: 1
+ segmentation_module_temporal_kernel: 1
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: false
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: false
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/SKMTEA/LambdaUNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/train/lambdaunet3d.yaml b/projects/SEG/SKMTEA/conf/train/lambdaunet3d.yaml
new file mode 100644
index 00000000..c2086a9b
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/train/lambdaunet3d.yaml
@@ -0,0 +1,175 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONLAMBDAUNET
+ use_reconstruction_module: false
+ segmentation_module: LambdaUNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_query_depth: 16
+ segmentation_module_intra_depth: 1
+ segmentation_module_receptive_kernel: 3
+ segmentation_module_temporal_kernel: 3
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: false
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/SKMTEA/LambdaUNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/train/unet2d.yaml b/projects/SEG/SKMTEA/conf/train/unet2d.yaml
new file mode 100644
index 00000000..ef870a77
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/train/unet2d.yaml
@@ -0,0 +1,170 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/SKMTEA/UNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/train/unet3d.yaml b/projects/SEG/SKMTEA/conf/train/unet3d.yaml
new file mode 100644
index 00000000..9c68f33a
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/train/unet3d.yaml
@@ -0,0 +1,171 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATION3DUNET
+ use_reconstruction_module: false
+ segmentation_module: UNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_channels: 32
+ segmentation_module_pooling_layers: 5
+ segmentation_module_dropout: 0.0
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 3
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 3
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/SKMTEA/UNet3D
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/train/unetr.yaml b/projects/SEG/SKMTEA/conf/train/unetr.yaml
new file mode 100644
index 00000000..e79a87df
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/train/unetr.yaml
@@ -0,0 +1,179 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONUNETR
+ use_reconstruction_module: false
+ segmentation_module: UNETR
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_img_size: [512, 512]
+ segmentation_module_channels: 16
+ segmentation_module_hidden_size: 768
+ segmentation_module_mlp_dim: 3072
+ segmentation_module_num_heads: 12
+ segmentation_module_pos_embed: perceptron
+ segmentation_module_norm_name: instance
+ segmentation_module_conv_block: true
+ segmentation_module_res_block: true
+ segmentation_module_dropout: 0.0
+ segmentation_module_qkv_bias: false
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 3
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 3
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/SKMTEA/UNetR
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/conf/train/vnet.yaml b/projects/SEG/SKMTEA/conf/train/vnet.yaml
new file mode 100644
index 00000000..f82c2f56
--- /dev/null
+++ b/projects/SEG/SKMTEA/conf/train/vnet.yaml
@@ -0,0 +1,172 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: SEGMENTATIONVNET
+ use_reconstruction_module: false
+ segmentation_module: VNet
+ segmentation_module_input_channels: 1
+ segmentation_module_output_channels: 4
+ segmentation_module_activation: elu
+ segmentation_module_dropout: 0.0
+ segmentation_module_bias: false
+ segmentation_module_padding_size: 15
+ segmentation_module_normalize: false
+ segmentation_module_norm_groups: 2
+ segmentation_loss:
+ dice: 1.0
+ dice_loss_include_background: true # always set to true if the background is removed
+ dice_loss_to_onehot_y: false
+ dice_loss_sigmoid: false
+ dice_loss_softmax: false
+ dice_loss_other_act: none
+ dice_loss_squared_pred: false
+ dice_loss_jaccard: false
+ dice_loss_flatten: false
+ dice_loss_reduction: mean_batch
+ dice_loss_smooth_nr: 1e-5
+ dice_loss_smooth_dr: 1e-5
+ dice_loss_batch: true
+ dice_metric_include_background: true # always set to true if the background is removed
+ dice_metric_to_onehot_y: false
+ dice_metric_sigmoid: false
+ dice_metric_softmax: false
+ dice_metric_other_act: none
+ dice_metric_squared_pred: false
+ dice_metric_jaccard: false
+ dice_metric_flatten: false
+ dice_metric_reduction: mean_batch
+ dice_metric_smooth_nr: 1e-5
+ dice_metric_smooth_dr: 1e-5
+ dice_metric_batch: true
+ segmentation_classes_thresholds: [0.5, 0.5, 0.5, 0.5]
+ segmentation_activation: sigmoid
+ magnitude_input: true
+ log_multiple_modalities: false # log all modalities in the same image, e.g. T1, T2, T1ce, FLAIR will be concatenated
+ normalization_type: minmax
+ normalize_segmentation_output: true
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_data: false
+ consecutive_slices: 1
+ dimensionality: 2
+ coil_combination_method: None
+ coil_dim: None
+
+ train_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_train.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: false
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/skm-tea/v1-release/json/image_files_val.json
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: skm-tea-echo1
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: false
+ log_images_rate: 0.05
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: None
+ dimensionality: 2
+ mask_args:
+ type: none
+ partial_fourier_percentage: 0.0
+ remask: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ coil_dim: None
+ use_seed: true
+ segmentations_path: None
+ segmentation_classes: 4
+ complex_data: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed # '16-mixed', 'bf16-mixed', '32-true', '64-true', '64', '32', '16', 'bf16'
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/segmentation/trained_models/SKMTEA/VNet
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/SEG/SKMTEA/generate_sets.sh b/projects/SEG/SKMTEA/generate_sets.sh
new file mode 100644
index 00000000..9e74f58d
--- /dev/null
+++ b/projects/SEG/SKMTEA/generate_sets.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+echo "
+Preprocessing pipeline for the Stanford Knee MRI Multi-Task Evaluation (SKM-TEA) 2021 Dataset.
+
+For more information, please refer to https://stanfordaimi.azurewebsites.net/datasets/4aaeafb9-c6e6-4e3c-9188-3aaaf0e0a9e7
+and check the following paper https://openreview.net/forum?id=YDMFgD_qJuA.
+
+Generating train, val, and test sets...
+"
+
+# Prompt the user to enter the path to the downloaded annotations directory
+echo "Please enter the (downloaded) annotations data directory:"
+read INPUT_DIR
+
+# Check if the input directory exists
+if [ ! -d "$INPUT_DIR" ]; then
+ echo "The input directory does not exist. Please try again."
+ exit 1
+fi
+
+# Prompt the user to enter the output directory for the generated json files
+echo "Please enter the output directory for the generated json files:"
+read OUTPUT_DIR
+
+# Run the json generation script
+python projects/segmentation/SKMTEA/scripts/split_sets_json.py $INPUT_DIR $OUTPUT_DIR --data_type image
+echo "Done!"
diff --git a/projects/SEG/SKMTEA/scripts/__init__.py b/projects/SEG/SKMTEA/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/SEG/SKMTEA/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/SEG/SKMTEA/scripts/split_sets_json.py b/projects/SEG/SKMTEA/scripts/split_sets_json.py
new file mode 100644
index 00000000..19ae937e
--- /dev/null
+++ b/projects/SEG/SKMTEA/scripts/split_sets_json.py
@@ -0,0 +1,45 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import json
+from pathlib import Path
+
+
+def main(args):
+ if args.data_type == "raw":
+ data_type = "files_recon_calib-24"
+ else:
+ data_type = "image_files"
+
+ # remove "annotations/v1.0.0/" from args.annotations_path and add "files_recon_calib-24" to get the raw_data_path
+ raw_data_path = Path(args.annotations_path).parent.parent / data_type
+
+ # get train.json, val.json and test.json filenames from args.annotations_path
+ annotations_sets = list(Path(args.annotations_path).iterdir())
+ for annotation_set in annotations_sets:
+ set_name = Path(annotation_set).name
+
+ # read json file
+ with open(annotation_set, "r", encoding="utf-8") as f:
+ annotation_set = json.load(f)
+
+ # read the "images" key and for every instance get the "file_name" key
+ filenames = [f'{raw_data_path}/{image["file_name"]}' for image in annotation_set["images"]]
+
+ # create a directory to store the folds
+ output_path = Path(args.output_path)
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ # write the train, val and test filenames to a json file
+ with open(output_path / f"{data_type}_{set_name}", "w", encoding="utf-8") as f:
+ json.dump(filenames, f)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("annotations_path", type=Path, default=None, help="Path to the annotations json file.")
+ parser.add_argument("output_path", type=Path, default=None, help="Path to the output directory.")
+ parser.add_argument("--data_type", choices=["raw", "image"], default="raw", help="Type of data to split.")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/SEG/SKMTEA/visualize.ipynb b/projects/SEG/SKMTEA/visualize.ipynb
new file mode 100644
index 00000000..b38c7f40
--- /dev/null
+++ b/projects/SEG/SKMTEA/visualize.ipynb
@@ -0,0 +1,1464 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# ๐ต SKM-TEA Dataset Tutorial\n",
+ "[Paper](https://arxiv.org/abs/2203.06823) | [GitHub](https://github.com/StanfordMIMI/skm-tea)\n",
+ "\n",
+ "Welcome to the SKM-TEA dataset demo!\n",
+ "\n",
+ "**Dataset**: The *Stanford Knee MRI with Multi-Task Evaluation (SKM-TEA) dataset* is a collection of quantitative knee MRI scans that enables end-to-end benchmarking of MRI reconstruction and analysis methods. This 1.6TB dataset consists of raw-data measurements of ~25,000 slices (155 patients) of anonymized patient MRI scans, the corresponding scanner-generated DICOM images, manual segmentations of four tissues, and bounding box annotations for sixteen clinically relevant pathologies.\n",
+ "\n",
+ "**Brief**: In this demo, we will walk through the data and how to use [the codebase](https://github.com/StanfordMIMI/skm-tea) to run pre-trained models and perform evaluation with your own methods.\n",
+ "\n",
+ "- Inspect different data types in SKM-TEA *DICOM* and *Raw Data* Tracks\n",
+ "- Use pretrained models from the [model zoo](https://github.com/StanfordMIMI/skm-tea/blob/main/MODEL_ZOO.md)\n",
+ "- Perform clinically-relevant quantitative MRI (qMRI) evaluation\n",
+ "\n",
+ "Interested in learning how to train models with SKM-TEA, check out [this tutorial](https://colab.research.google.com/drive/1LUC0MqFYK39xG5AV9kQi5hIBsi9eCpS0?usp=sharing)\n",
+ "\n",
+ "**Time**: 25-30 minutes\n",
+ "\n",
+ "**Colab Runtime**: We recommend running this Colab with a GPU runtime. To change the runtime,\n",
+ "1. Click on `Runtime` on the top navigation bar\n",
+ "2. Select `Change runtime type`\n",
+ "3. Select `GPU` from the dropdown\n",
+ "\n",
+ "**NOTE**: This tutorial is under development. Please contact the arjundd \\ with any bugs or recommendations.\n",
+ "\n",
+ "**Acknowledgements**: SKM-TEA is built on the [Meddlr](https://github.com/ad12/meddlr) image reconstruction and analysis framework.\n",
+ "\n",
+ "**Coming Soon:**\n",
+ "- Tutorial with detection (bounding box) labels"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## ๐ก Downloading the Data\n",
+ "Let's download a [mini version](https://huggingface.co/datasets/arjundd/skm-tea-mini) of the SKM-TEA dataset from Huggingface. This mini dataset was created for building demos/tutorials with the SKM-TEA dataset. **Do not use this dataset for reporting/publication purposes**\n",
+ "\n",
+ "*NOTE*: This download process can take ~5-8 minutes.\n",
+ "\n",
+ "> If you would like to set up up the full SKM-TEA dataset on your machine, follow [these instructions](https://github.com/StanfordMIMI/skm-tea/blob/main/DATASET.md)."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from tqdm import tqdm\n",
+ "\n",
+ "dataset_dir = \"skm-tea/v1-release\"\n",
+ "url = \"https://huggingface.co/datasets/arjundd/skm-tea-mini/resolve/main/v1-release\"\n",
+ "force_download = False\n",
+ "\n",
+ "if force_download:\n",
+ " !rm -rf $dataset_dir\n",
+ "\n",
+ "if not os.path.isdir(dataset_dir):\n",
+ " os.makedirs(dataset_dir)\n",
+ " for fname in [\"all_metadata.csv\", \"annotations/v1.0.0/train.json\", \"annotations/v1.0.0/val.json\", \"annotations/v1.0.0/test.json\"]:\n",
+ " out = f\"{dataset_dir}/{fname}\"\n",
+ " os.makedirs(os.path.dirname(out), exist_ok=True)\n",
+ " !wget -q $url/$fname -O $out\n",
+ "\n",
+ "\n",
+ " for fname in tqdm([\"dicoms\", \"files_recon_calib-24\", \"image_files\", \"segmentation_masks\"], disable=False):\n",
+ " !wget -c $url/\"tarball\"/$fname\".tar.gz\" -O - | tar -xz -C $dataset_dir/\n",
+ "\n",
+ "!ls"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## ๐ง Setup\n",
+ "All SKM-TEA code for training, evaluation, models, and more ships as a Python package. In this tutorial, we will learn how to use different parts of this package.\n",
+ "\n",
+ "> To use the latest version from the `main` branch, use `pip install git+https://github.com/StanfordMIMI/skm-tea.git`"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "# Download SKM-TEA from main branch on GitHub\n",
+ "!pip install --upgrade pytorch-lightning==1.7.7 skm-tea"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "os.environ[\"MEDDLR_DATASETS_DIR\"] = \"./\"\n",
+ "\n",
+ "from pprint import pprint\n",
+ "\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import h5py\n",
+ "import matplotlib.pyplot as plt\n",
+ "from skimage.color import label2rgb\n",
+ "import pandas as pd\n",
+ "from torch import nn\n",
+ "\n",
+ "import dosma as dm\n",
+ "\n",
+ "import meddlr.ops as oF\n",
+ "from meddlr.data import DatasetCatalog, MetadataCatalog\n",
+ "from meddlr.utils.logger import setup_logger\n",
+ "from meddlr.utils import env\n",
+ "\n",
+ "import skm_tea as st"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "# Set the default device if cuda is enabled\n",
+ "if torch.cuda.is_available():\n",
+ " DEVICE = torch.device(\"cuda\")\n",
+ "else:\n",
+ " DEVICE = torch.device(\"cpu\")\n",
+ "\n",
+ "print(\"Device: \", DEVICE)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "# Run this setup phase only once.\n",
+ "# Otherwise, you may get multiple print statements\n",
+ "setup_logger()\n",
+ "logger = setup_logger(\"skm_tea\")\n",
+ "path_mgr = env.get_path_manager()"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "# Some general utilities\n",
+ "\n",
+ "from typing import Union, Sequence\n",
+ "\n",
+ "def get_scaled_image(\n",
+ " x: Union[torch.Tensor, np.ndarray], percentile=0.99, clip=False\n",
+ "):\n",
+ " \"\"\"Scales image by intensity percentile (and optionally clips to [0, 1]).\n",
+ "\n",
+ " Args:\n",
+ " x (torch.Tensor | np.ndarray): The image to process.\n",
+ " percentile (float): The percentile of magnitude to scale by.\n",
+ " clip (bool): If True, clip values between [0, 1]\n",
+ "\n",
+ " Returns:\n",
+ " torch.Tensor | np.ndarray: The scaled image.\n",
+ " \"\"\"\n",
+ " is_numpy = isinstance(x, np.ndarray)\n",
+ " if is_numpy:\n",
+ " x = torch.as_tensor(x)\n",
+ "\n",
+ " scale_factor = torch.quantile(x, percentile)\n",
+ " x = x / scale_factor\n",
+ " if clip:\n",
+ " x = torch.clip(x, 0, 1)\n",
+ "\n",
+ " if is_numpy:\n",
+ " x = x.numpy()\n",
+ "\n",
+ " return x\n",
+ "\n",
+ "\n",
+ "def plot_images(\n",
+ " images, processor=None, disable_ticks=True, titles: Sequence[str]=None,\n",
+ " ylabel: str=None, xlabels: Sequence[str]=None, cmap: str=\"gray\",\n",
+ " show_cbar: bool = False, overlay = None, opacity: float = 0.3,\n",
+ " hsize=5, wsize=5, axs=None\n",
+ "):\n",
+ " \"\"\"Plot multiple images in a single row.\n",
+ "\n",
+ " Add an overlay with the `overlay=` argument.\n",
+ " Add a colorbar with `show_cbar=True`.\n",
+ " \"\"\"\n",
+ " def get_default_values(x, default=\"\"):\n",
+ " if x is None:\n",
+ " return [default] * len(images)\n",
+ " return x\n",
+ "\n",
+ " titles = get_default_values(titles)\n",
+ " ylabels = get_default_values(images)\n",
+ " xlabels = get_default_values(xlabels)\n",
+ "\n",
+ " N = len(images)\n",
+ " if axs is None:\n",
+ " fig, axs = plt.subplots(1, N, figsize=(wsize * N, hsize))\n",
+ " else:\n",
+ " assert len(axs) >= N\n",
+ " fig = axs.flatten()[0].get_figure()\n",
+ "\n",
+ " for ax, img, title, xlabel in zip(axs, images, titles, xlabels):\n",
+ " if processor is not None:\n",
+ " img = processor(img)\n",
+ " im = ax.imshow(img, cmap=cmap)\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel(xlabel)\n",
+ "\n",
+ " if overlay is not None:\n",
+ " for ax in axs.flatten():\n",
+ " im = ax.imshow(overlay, alpha=opacity)\n",
+ "\n",
+ " if show_cbar:\n",
+ " fig.subplots_adjust(right=0.8)\n",
+ " cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
+ " fig.colorbar(im, cax=cbar_ax)\n",
+ "\n",
+ " if disable_ticks:\n",
+ " for ax in axs.flatten():\n",
+ " ax.get_xaxis().set_ticks([])\n",
+ " ax.get_yaxis().set_ticks([])\n",
+ "\n",
+ " return axs\n"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## ๐พ Understanding the Data\n",
+ "The SKM-TEA dataset consists of two *tracks* that are based on the source of the input image: the *Raw Data* track, where inputs start from the complex-valued k-space, and the *DICOM* track, where inputs start from magnitude DICOM images.\n",
+ "\n",
+ "Note, the Raw Data track supports all reconstruction (upstream) and image analysis (downstream) tasks available in SKM-TEA with the caveat that all downstream tasks are performed on the image reconstructed from the raw data.\n",
+ "\n",
+ "In contrast, the DICOM track only supports image analysis tasks -- it does not support the reconstruction tasks. Read [this paper](https://arxiv.org/abs/2109.08237) for more information on why DICOM images may not be good targets for measuring reconstruction performance.\n"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "The `skm_tea` package simplifies getting relevant data paths and metadata using the `DatasetCatalog` manager. We can load any of the dataset splits:\n",
+ "- `'skmtea_v1_train'`\n",
+ "- `'skmtea_v1_val'`\n",
+ "- `'skmtea_v1_test'`"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "# Load list of dictionaries for the SKM-TEA v1 training dataset.\n",
+ "dataset_dicts = DatasetCatalog.get(\"skmtea_v1_train\")"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "scan = dataset_dicts[0]\n",
+ "pprint(scan)"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Raw Data Track\n",
+ "The raw data track consists of (1) multi-coil kspace, (2) complex-valued ground truth reconstructions, (3) sensitivity maps, (4) gradient-warp corrected segmentations, and (5) localized bounding boxes for knee pathologies.\n",
+ "\n",
+ "While qDESS is a 3D sequence, the SI (axial) readout dimension is fully-sampled, and can be reconstructed without information loss using the 1D inverse fast Fourier transform (ifft). Thus, reconstructions are performed on 2D axial ($k_y \\times k_z$) slices.\n",
+ "\n",
+ "Also, note that the reference segmentations for the raw data track are different than those for the DICOM track to correct for DICOM-specfic post-processing. See [our paper](https://arxiv.org/abs/2203.06823) for more information."
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "iYPX8955Ml6S",
+ "outputId": "aee45171-f99a-4965-a850-1a8707563631"
+ },
+ "outputs": [],
+ "source": [
+ "sl = 200 # the slice to be plotted\n",
+ "\n",
+ "# Reconstruction data\n",
+ "recon_file = scan[\"recon_file\"]\n",
+ "with h5py.File(recon_file, \"r\") as f:\n",
+ " kspace = f[\"kspace\"][sl, :, :, :, :] # Shape: (x, ky, kz, #echos, #coils)\n",
+ " image = f[\"target\"][sl, :, :, :, :] # Shape: (x, ky, kz, #echos, #maps) - #maps = 1 for SKM-TEA\n",
+ " maps = f[\"maps\"][sl, :, :, :, :] # Shape: (x, ky, kz, #coils, #maps) - maps are the same for both echos\n",
+ "\n",
+ "# Segmentation data\n",
+ "seg_file = scan[\"gw_corr_mask_file\"]\n",
+ "segmentation = dm.read(seg_file).A[sl, ...] # Shape: (x, y, z)\n",
+ "print(segmentation.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 597
+ },
+ "id": "h_G_os2pMl6T",
+ "outputId": "ffd7c9ba-732d-4af7-b6c8-e2b1a8d1a534"
+ },
+ "outputs": [],
+ "source": [
+ "# Display kspace per coil\n",
+ "n_coils = kspace.shape[-1]\n",
+ "nrows = 2\n",
+ "hsize = 5\n",
+ "wsize = hsize / kspace.shape[0] * kspace.shape[1]\n",
+ "_, axs = plt.subplots(nrows, n_coils, figsize=(n_coils * wsize, nrows * hsize))\n",
+ "\n",
+ "for echo in range(2):\n",
+ " kspace_coils = [np.abs(kspace[..., echo, idx]) for idx in range(n_coils)]\n",
+ " # Scale the kspace to avoid over-saturating the image with center kspace\n",
+ " kspace_coils = [get_scaled_image(x, 0.95, clip=True) for x in kspace_coils]\n",
+ "\n",
+ " titles = [f\"Coil {idx+1}\" for idx in range(n_coils)] if echo==0 else None\n",
+ " plot_images(kspace_coils, titles=titles, axs=axs[echo])\n",
+ " axs[echo][0].set_ylabel(\"Echo {}\".format(echo + 1), fontsize=20)\n",
+ "\n",
+ "plt.subplots_adjust(wspace=0.1, hspace=0.1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 327
+ },
+ "id": "EGowchm2Ml6T",
+ "outputId": "fc3497cb-2e68-4da8-e38e-dd3f46c6b51e"
+ },
+ "outputs": [],
+ "source": [
+ "# Plot reconstructed image\n",
+ "mag_img = np.abs(image)\n",
+ "seg_colorized = label2rgb(segmentation, bg_label=0)\n",
+ "\n",
+ "\n",
+ "_ = plot_images(\n",
+ " [mag_img[..., 0, 0], mag_img[..., 0, 0]], # echo1, echo2\n",
+ " processor=lambda x: get_scaled_image(x, 0.95, clip=True),\n",
+ " titles=[\"Echo 1\", \"Echo 2\"],\n",
+ " overlay=seg_colorized,\n",
+ " opacity=0.4,\n",
+ " hsize=5, wsize=2.3\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lpmPd77iMl6U"
+ },
+ "source": [
+ "### DICOM Track\n",
+ "The DICOM Track consists of (1) scanner-generated DICOM images, (2) tissue segmentations, and (3) pathology bounding boxes.\n",
+ "\n",
+ "**IMPORTANT**: As mentioned above, this data should only be used for image analysis (segmentation, detection, classification) tasks. It should not be used for reconstruction tasks.\n",
+ "\n",
+ "\n",
+ "Let's visualize a sagittal slice from both echos."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "PwtalV6eMl6U"
+ },
+ "outputs": [],
+ "source": [
+ "sl = 60 # the slice to be plotted\n",
+ "\n",
+ "# DICOM data + segmentation\n",
+ "image_file = scan[\"image_file\"]\n",
+ "with h5py.File(image_file, \"r\") as f:\n",
+ " echo1 = f[\"echo1\"][:, :, sl] # Shape: (x, y, z)\n",
+ " echo2 = f[\"echo2\"][:, :, sl] # Shape: (x, y, z)\n",
+ " segmentation = f[\"seg\"][:, :, sl, :] # Shape: (x, y, z, #classes)\n",
+ "\n",
+ "segmentation = oF.one_hot_to_categorical(segmentation, channel_dim=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 309
+ },
+ "id": "8eV1c8szMl6V",
+ "outputId": "31c6bd8d-1ac3-4121-b630-a73cf7841cbc"
+ },
+ "outputs": [],
+ "source": [
+ "# Plot reconstructed image\n",
+ "seg_colorized = label2rgb(segmentation, bg_label=0)\n",
+ "\n",
+ "_ = plot_images(\n",
+ " [echo1, echo2],\n",
+ " titles=[\"Echo 1\", \"Echo 2\"],\n",
+ " overlay=seg_colorized,\n",
+ " opacity=0.4,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xtEvV8GFMl6W"
+ },
+ "source": [
+ "## ๐ Model Zoo\n",
+ "Interested in running a pre-trained model on your data? We got you!\n",
+ "\n",
+ "We maintain a model zoo of pre-trained models that have been trained on the SKM-TEA dataset for different tasks. You can find a list of these models on [GitHub](https://github.com/StanfordMIMI/skm-tea).\n",
+ "\n",
+ "And loading the model is as easy as 123! Just use the `skm_tea.get_model_from_zoo`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bUp06YLTMl6W"
+ },
+ "outputs": [],
+ "source": [
+ "# Load a scan from the test dataset.\n",
+ "dataset_dicts = DatasetCatalog.get(\"skmtea_v1_test\")\n",
+ "scan = dataset_dicts[0]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "LRzGe08hMl6W"
+ },
+ "source": [
+ "### Reconstruction\n",
+ "Let's use a pretrained [unrolled network](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7664163/) to reconstruct 6x accelerated qDESS scans.\n",
+ "\n",
+ "The reconstruction model was trained to reconstruction axial ($k_y \\times k_z$) slices for the first echo. You can find other pretrained reconstruction models [here](https://github.com/StanfordMIMI/skm-tea/blob/main/MODEL_ZOO.md#reconstruction-baselines).\n",
+ "\n",
+ "*Aside*: When reporting results on the SKM-TEA dataset, please use the masks provided with the dataset."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3PTee3KIMl6X"
+ },
+ "outputs": [],
+ "source": [
+ "# Simulate 6x undersampled data\n",
+ "sl = 256\n",
+ "\n",
+ "with h5py.File(scan[\"recon_file\"], \"r\") as f:\n",
+ " kspace = torch.as_tensor(f[\"kspace\"][sl, :, :, :, :]).unsqueeze(0)\n",
+ " maps = torch.as_tensor(f[\"maps\"][sl, :, :, :, :]).unsqueeze(0)\n",
+ " mask = torch.as_tensor(f[\"masks/poisson_6.0x\"][()]).unsqueeze(0) # TODO: Fix\n",
+ " img_gt = torch.as_tensor(f[\"target\"][sl, :, :, :, :]).unsqueeze(0)\n",
+ "mask = oF.zero_pad(mask, kspace.shape[1:3])\n",
+ "\n",
+ "us_kspace = kspace * mask.unsqueeze(-1).unsqueeze(-1).type(kspace.dtype)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "VCLy0Gp6Ml6X",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "85a46aaf-42a6-479d-89ea-5787924c979f"
+ },
+ "outputs": [],
+ "source": [
+ "# Fetch the model with pretrained weights.\n",
+ "model = st.get_model_from_zoo(\n",
+ " cfg_or_file=\"https://huggingface.co/arjundd/skm-tea-models/raw/main/neurips2021/recon-models/6x/Unrolled_E1/config.yaml\",\n",
+ " weights_path=\"https://huggingface.co/arjundd/skm-tea-models/resolve/main/neurips2021/recon-models/6x/Unrolled_E1/model.ckpt\",\n",
+ ").to(DEVICE).eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "MGXj7huvMl6X"
+ },
+ "outputs": [],
+ "source": [
+ "echo = 0 # the 1st echo\n",
+ "echo1_kspace = us_kspace[..., echo, :]\n",
+ "with torch.no_grad():\n",
+ " pred = model({\"kspace\": echo1_kspace, \"maps\": maps})[\"pred\"].cpu()\n",
+ "echo1_recon = pred"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3AGVZPP4Ml6X",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 398
+ },
+ "outputId": "2c567e15-a693-416b-e023-37795498c06f"
+ },
+ "outputs": [],
+ "source": [
+ "# For visualization purposes, we scale the ground truth and reconstructions\n",
+ "# to get rid of very bright outliers.\n",
+ "gt_abs = get_scaled_image(img_gt[..., 0, :].abs(), 0.9999, clip=True)\n",
+ "recon_abs = get_scaled_image(echo1_recon.abs(), 0.9999, clip=True)\n",
+ "err = torch.abs(gt_abs - recon_abs)\n",
+ "\n",
+ "plot_images(\n",
+ " [gt_abs, recon_abs, err * 4],\n",
+ " processor=lambda x: x.abs().squeeze(),\n",
+ " titles=[\"Ground truth\", \"Recon\", \"Error (4x)\"],\n",
+ " hsize=5, wsize=2.3\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zOBE1rQAMl6Y"
+ },
+ "source": [
+ "### Segmentation\n",
+ "Let's perform segmentation on the DICOM track dataset using a pretrained [U-Net](https://arxiv.org/abs/1505.04597).\n",
+ "\n",
+ "The segmentation model was trained to segment sagittal slices for the first echo. You can find other pretrained segmentation models [here](https://github.com/StanfordMIMI/skm-tea/blob/main/MODEL_ZOO.md#segmentation-baselines).\n",
+ "\n",
+ "**Note:** The volume has to first be normalized to have zero-mean and unit standard deviation. In the near future, this will automatically be done."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "X3PziENNMl6Y",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "6446b963-e37b-465d-82e6-9f832ee8c930"
+ },
+ "outputs": [],
+ "source": [
+ "model = st.get_model_from_zoo(\n",
+ " cfg_or_file=\"https://huggingface.co/arjundd/skm-tea-models/raw/main/neurips2021/segmentation-models/U-Net_E1/config.yaml\",\n",
+ " weights_path=\"https://huggingface.co/arjundd/skm-tea-models/resolve/main/neurips2021/segmentation-models/U-Net_E1/model.ckpt\",\n",
+ ").eval()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Xhy1kVpzMl6Y"
+ },
+ "outputs": [],
+ "source": [
+ "from meddlr.data.data_utils import collect_mask\n",
+ "sl = 88 # the slice to segment\n",
+ "\n",
+ "# DICOM data + segmentation\n",
+ "image_file = scan[\"image_file\"]\n",
+ "with h5py.File(image_file, \"r\") as f:\n",
+ " echo1 = f[\"echo1\"][()] # Shape: (x, y, z)\n",
+ " segmentation = f[\"seg\"][()] # Shape: (x, y, z, #classes)\n",
+ "\n",
+ "echo1 = torch.as_tensor(echo1).unsqueeze(0).unsqueeze(0).float() # Shape: (B, C, H, W)\n",
+ "\n",
+ "# Ground truth segmentation\n",
+ "# Medial/lateral components are aggregated into the same category.\n",
+ "# 0 - patellar cartilage, 1 - femoral cartilage\n",
+ "# 2/3 - medial/lateral tibial cartilage, 4/5 - medial/lateral meniscus\n",
+ "gt_seg_sl = segmentation[..., sl, :]\n",
+ "gt_seg_sl = collect_mask(gt_seg_sl, (0, 1, (2, 3), (4, 5)), out_channel_first=False)\n",
+ "gt_seg_sl = oF.one_hot_to_categorical(gt_seg_sl, channel_dim=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "JT4D1Mp-Ml6Y"
+ },
+ "outputs": [],
+ "source": [
+ "# Normalize volume and run model.\n",
+ "echo1 = (echo1 - echo1.mean()) / echo1.std()\n",
+ "echo1_sl = echo1[..., sl]\n",
+ "\n",
+ "with torch.no_grad():\n",
+ " logits = model({\"image\": echo1_sl})[\"sem_seg_logits\"]\n",
+ "\n",
+ "prediction = oF.pred_to_categorical(logits, activation='sigmoid').squeeze(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "_, axs = plt.subplots(1, 3, figsize=(10,5))\n",
+ "for idx, (data, title) in enumerate([\n",
+ " (echo1_sl.squeeze(), \"Input\"), (prediction, \"Prediction\"), (gt_seg_sl, \"Ground truth\")\n",
+ "]):\n",
+ " ax = axs[idx]\n",
+ " ax.imshow(data.squeeze(), cmap=\"gray\" if idx == 0 else None)\n",
+ " ax.set_title(title, fontsize=20)\n",
+ " ax.axis(\"off\")"
+ ],
+ "metadata": {
+ "id": "IQNltkfZiUd-",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 216
+ },
+ "outputId": "78e7d8ab-fc8d-4e2a-f124-3e3fddbd8105"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "t5dNaY7VMl6Z"
+ },
+ "source": [
+ "## ๐ qMRI Evaluation\n",
+ "SKM-TEA introduces a new family of metrics based on quantitative MRI (qMRI) endpoints. In this section, we will explore the utility of these metrics and how to use them to benchmark your models.\n",
+ "\n",
+ "Specifically, we will consider a qMRI knee analysis pipeline that uses qDESS reconstructions to analytically estimate $T_2$ maps and uses automated segmentations to get region-specific $T_2$ values.\n",
+ "\n",
+ "As a proof-of-concept, let's dive into how we can use qMRI endpoints to evaluate a segmentation model based on regional $T_2$ accuracy. We will evaluate the same pretrained U-Net model from the [Model Zoo section](https://colab.research.google.com/drive/1PluqK77pobD5dXE7zzBLEAeBgaaeGKXa?authuser=1#scrollTo=zOBE1rQAMl6Y&line=6&uniqifier=1).\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "G3QOQzciMl6Z"
+ },
+ "outputs": [],
+ "source": [
+ "from skm_tea.metrics import QuantitativeKneeMRI\n",
+ "from meddlr.data.data_utils import collect_mask\n",
+ "\n",
+ "from dosma.scan_sequences import QDess"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "atqdug_EMl6Z"
+ },
+ "outputs": [],
+ "source": [
+ "# Load a scan and corresponding metadata.\n",
+ "dataset_dicts = DatasetCatalog.get(\"skmtea_v1_test\")\n",
+ "scan = dataset_dicts[0]\n",
+ "\n",
+ "metadata: pd.DataFrame = MetadataCatalog.get(\"skmtea_v1_test\").scan_metadata\n",
+ "metadata = metadata[metadata[\"MTR_ID\"] == scan[\"scan_id\"]]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load the DICOMs for this scan.\n",
+ "dr = dm.DicomReader(group_by=[\"EchoNumbers\", \"SeriesDescription\"], verbose=True, num_workers=4)\n",
+ "volumes = dr.load(scan[\"dicom_dir\"])\n",
+ "\n",
+ "# Filter out unnecessary dicoms.\n",
+ "volumes = [v for v in volumes if \"T2\" not in v.get_metadata(\"SeriesDescription\")]\n",
+ "assert len(volumes) == 2\n",
+ "echo1, echo2 = tuple(sorted(volumes, key=lambda x: x.get_metadata(\"EchoNumbers\")))"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 49,
+ "referenced_widgets": [
+ "ca289c7160af4c159ad7fd411d912ef7",
+ "015d4fbb69af450585e5e2fa60412047",
+ "ea4b2048a1ec44e099bb923bad633b9a",
+ "51a3a30ad84f49918cbda78bc33ccbd4",
+ "842b85d281ce4c239af9436d6e57958e",
+ "550602d30f904c8f8123b8366342950e",
+ "1717cc1ff8934b0b9020c7a0cd2b595b",
+ "d0e0ecc7cc104ba1a7d2918a3f585b41",
+ "67aba4eecf5141e0bbb4b0da63ff8c51",
+ "2ff53edd8c7a453395cdad88414d1212",
+ "699878da64014757921c9ef70119e50e"
+ ]
+ },
+ "id": "Mx9ngDEOx-sm",
+ "outputId": "55581b63-ec42-4305-a645-612edfd382b7"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Load the ground truth segmentation.\n",
+ "seg_gt = dm.read(scan[\"dicom_mask_file\"])\n",
+ "arr = oF.categorical_to_one_hot(seg_gt.A, channel_dim=-1)\n",
+ "arr = collect_mask(arr, (0, 1, (2,3), (4,5)), out_channel_first=False)\n",
+ "\n",
+ "seg_gt = dm.MedicalVolume(arr, affine=seg_gt.affine)"
+ ],
+ "metadata": {
+ "id": "q68mUZqFz7Qz"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Run the model"
+ ],
+ "metadata": {
+ "id": "qS22VFNe8Asz"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "def run_segmentation(\n",
+ " mv: dm.MedicalVolume, model: nn.Module, normalize=True,\n",
+ " batch_size: int = 4, pbar: bool = True\n",
+ "):\n",
+ " \"\"\"Runs a segmentation model on the qDESS volume.\n",
+ "\n",
+ " The model should be trained to segment sagittal slices.\n",
+ "\n",
+ " Args:\n",
+ " x (dm.MedicalVolume): A 3D magnitude image (single echo).\n",
+ " model (nn.Module): The segmentation model to run.\n",
+ " normalize (bool): Whether to perform zero-mean, unit-std normalization.\n",
+ " batch_size (int): The batch size for performing segmentation.\n",
+ " pbar (bool): Whether to display progress bar.\n",
+ "\n",
+ " Returns:\n",
+ " dm.MedicalVolume: The one-hot predictions from the segmentation model\n",
+ " where last dimension/axis is the channel dimension.\n",
+ " \"\"\"\n",
+ " mv_ornt = mv.orientation\n",
+ " mv = mv.reformat((\"LR\", \"SI\", \"AP\"))\n",
+ " affine = mv.affine.copy()\n",
+ "\n",
+ " x = mv.to_torch().type(torch.float32)\n",
+ " if normalize:\n",
+ " x = (x - x.mean()) / x.std()\n",
+ "\n",
+ " x_chunks = torch.split(x, batch_size, dim = 0)\n",
+ "\n",
+ " logits = []\n",
+ " for chunk in tqdm(torch.split(x, batch_size, dim=0), disable=not pbar):\n",
+ " chunk = chunk.unsqueeze(1) # add a channel dimension\n",
+ " out = model({\"image\": chunk})\n",
+ " logits.append(out[\"sem_seg_logits\"])\n",
+ "\n",
+ "\n",
+ " logits = torch.concat(logits, dim=0)\n",
+ " prediction = torch.sigmoid(logits).permute(0, 2, 3, 1) # make channels last\n",
+ "\n",
+ " out = dm.MedicalVolume.from_torch(prediction, affine).reformat(mv_ornt)\n",
+ " return out"
+ ],
+ "metadata": {
+ "id": "9_AnZ_0qNiRr"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "torch.cuda.empty_cache()\n",
+ "\n",
+ "model = st.get_model_from_zoo(\n",
+ " cfg_or_file=\"https://huggingface.co/arjundd/skm-tea-models/raw/main/neurips2021/segmentation-models/U-Net_E1/config.yaml\",\n",
+ " weights_path=\"https://huggingface.co/arjundd/skm-tea-models/resolve/main/neurips2021/segmentation-models/U-Net_E1/model.ckpt\",\n",
+ ").to(DEVICE).eval()"
+ ],
+ "metadata": {
+ "id": "CrnRYP5tEGGN"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "with torch.no_grad():\n",
+ " seg_pred = run_segmentation(echo1.to(DEVICE), model, batch_size=4)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "SBoA-EfcEX2N",
+ "outputId": "c5f873c1-9ebb-485b-97e6-6f9626b2f7d3"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Computing $T_2$ Maps\n",
+ "\n",
+ "Computing $T_2$ maps from qDESS can be done analytically, which is much faster than traditional fitting. To do so, we require a few scan parameters as well as rough estimates for $T_1$ of tissues. Scan parameters can be found in the DICOM files or the metadata file shipped with the dataset.\n",
+ "\n",
+ "An open-source implementation of the analytical fit is available in dosma. To ensure standardization, dosma should be used to perform all qMRI evaluation in SKM-TEA.\n",
+ "\n",
+ "IMPORTANT: Do not use the scanner-generated $T_2$ maps (available in the dicom folder) for analysis. These should be used for visualization purposes only."
+ ],
+ "metadata": {
+ "id": "E7cTiTk8Hqps"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "As mentioned above, we need rough estimates for $T_1$ of tissues for the analytical $T_2$ estimation. From [literature](), we know that femoral, tibial, and patellar (articular) cartilage has a $T_1$ of approximately 1.2sec and meniscus has a $T_1$ of ~1sec.\n",
+ "\n",
+ "We can use the segmentation to fill in the expected $T_1$ values. Note, we will have 2 $T_1$ maps -- one from the ground truth segmentation (`t1_gt`), and one from the predicted segmentation (`t1_pred`)."
+ ],
+ "metadata": {
+ "id": "_MMDFtcY1Q0R"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# For reconstruction, this would be based on reconstructions for E1/E2.\n",
+ "# Estimated T1 values are 1.2s for cartilage and 1s for meniscus\n",
+ "def get_t1(seg: dm.MedicalVolume):\n",
+ " \"\"\"Build T1 maps based on the segmentation.\n",
+ "\n",
+ " `seg[..., 3]` should correspond to the meniscus segmentation map.\n",
+ "\n",
+ " Args:\n",
+ " seg (dm.MedicalVolume): A one-hot encoded segmentation mask, where the\n",
+ " last dimension is the channel dimension.\n",
+ "\n",
+ " Returns:\n",
+ " dm.MedicalVolume: The estimated T1 map (in milliseconds).\n",
+ " \"\"\"\n",
+ " t1 = dm.MedicalVolume(np.ones(seg.shape[:3]) * 1200, seg.affine).to(seg.device)\n",
+ " t1[seg.A[..., 3].astype(bool)] = 1000\n",
+ " return t1\n",
+ "\n",
+ "t1_gt = get_t1(seg_gt)\n",
+ "t1_pred = get_t1(seg_pred)"
+ ],
+ "metadata": {
+ "id": "JfMeAPjrrAwu"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "y6k1z3LtMl6Z",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "5c302053-03fc-4087-fcef-d4697dc9f8bc"
+ },
+ "outputs": [],
+ "source": [
+ "def compute_t2_map(t1: dm.MedicalVolume):\n",
+ " qdess = QDess([echo1, echo2]).to(t1.device)\n",
+ " t2map = qdess.generate_t2_map(\n",
+ " suppress_fat=True,\n",
+ " suppress_fluid=True,\n",
+ " gl_area=float(metadata[\"SpoilerGradientArea\"]),\n",
+ " tg=float(metadata[\"SpoilerGradientTime\"]),\n",
+ " tr=float(metadata[\"RepetitionTime\"]),\n",
+ " te=float(metadata[\"EchoTime1\"]),\n",
+ " alpha=float(metadata[\"FlipAngle\"]),\n",
+ " t1=t1,\n",
+ " nan_bounds=(0, 100),\n",
+ " nan_to_num=True,\n",
+ " )\n",
+ " return t2map.volumetric_map\n",
+ "\n",
+ "t2_gt = compute_t2_map(t1_gt)\n",
+ "t2_pred = compute_t2_map(t1_pred)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "sl = 60 # Sagittal slice to plot\n",
+ "\n",
+ "plot_images(\n",
+ " [t2_gt, t2_pred],\n",
+ " processor=lambda x: x.cpu().A[..., sl],\n",
+ " titles=[\"T2 (Ground Truth)\", \"T2 (Pred)\"],\n",
+ " cmap=\"viridis\", show_cbar=True,\n",
+ ")\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 352
+ },
+ "id": "4k3lVDg52trB",
+ "outputId": "4d6622c5-3dc1-4257-b050-136c477e347e"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### The `QuantitativeKneeMRI` metric\n",
+ "\n",
+ "`QuantitativeKneeMRI` metrics simplifies computing and tracing qMRI related metrics for key knee anatomical structures.\n",
+ "\n",
+ "We will use `qmri_gt` and `qmri_pred` to track regional $T_2$ measures extracted from the ground truth and predicted segmentations, respectively. These regions will correspond to the four segmented tissues: patellar cartilage (`pc`), femoral cartilage (`fc`), tibial cartilage (`tc`), and meniscus (`men`)\n",
+ "\n",
+ "We can also choose to compute qMRI measures for anatomically relevant subregions in the these tissues. To do this, set `use_subregions=True`. Note the subregion division can be time intensive.\n",
+ "\n",
+ "**Note**: The metric is stateful. This means each time the metric is called, it stores the results. Use `.reset()` to reset the metric and clear all stored results.\n",
+ "\n",
+ "*Aside*: These metrics are automatically computed under the hood with the [`skm_tea.evaluation.SkmTeaEvaluator`](https://github.com/StanfordMIMI/skm-tea/blob/main/skm_tea/evaluation/qdess_evaluation.py)."
+ ],
+ "metadata": {
+ "id": "zCn3xtvfuBtt"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "TrdR9XsOMl6a"
+ },
+ "outputs": [],
+ "source": [
+ "use_subregions = False\n",
+ "use_cpu = use_subregions # computing subregions is currently limited to the CPU\n",
+ "tissues = [\"pc\", \"fc\", \"tc\", \"men\"]\n",
+ "\n",
+ "qmri_gt = QuantitativeKneeMRI(channel_names=tissues, subregions=use_subregions, use_cpu=use_cpu)\n",
+ "qmri_pred = QuantitativeKneeMRI(channel_names=tissues, subregions=use_subregions, use_cpu=use_cpu)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "3Tnrul8RMl6a",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "50572ff7-b3bb-4d59-9fbd-3c68cd340d9a"
+ },
+ "outputs": [],
+ "source": [
+ "# Reset the metrics\n",
+ "qmri_gt.reset()\n",
+ "qmri_pred.reset()\n",
+ "\n",
+ "# Compute regional qMRI estimates using ground truth and predicted segmentations.\n",
+ "qmri_gt(ids=[scan[\"scan_id\"]], quantitative_map=[t2_gt], sem_seg=[seg_gt], medial_direction=metadata[\"MedialDirection\"])\n",
+ "qmri_pred(ids=[scan[\"scan_id\"]], quantitative_map=[t2_pred], sem_seg=[seg_pred], medial_direction=metadata[\"MedialDirection\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "kUwnBDOqMl6a",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 99
+ },
+ "outputId": "7209be8b-5d89-43a1-fb48-35103eba8fe9"
+ },
+ "outputs": [],
+ "source": [
+ "print(\"Ground Truth Regional T2 Estimates:\")\n",
+ "display(qmri_gt.to_pandas())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "print(\"Predicted Regional T2 Estimates:\")\n",
+ "display(qmri_pred.to_pandas())"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 99
+ },
+ "id": "d5dqjITe5IkJ",
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diff --git a/projects/SEG/__init__.py b/projects/SEG/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/SEG/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/__init__.py b/projects/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/qMRI/AHEAD/README.md b/projects/qMRI/AHEAD/README.md
new file mode 100644
index 00000000..cf799087
--- /dev/null
+++ b/projects/qMRI/AHEAD/README.md
@@ -0,0 +1,55 @@
+## **Amsterdam Ultra-high field adult lifespan database (AHEAD)**
+
+This dataset contains MRI k-space data of the Amsterdam Ultra-high field adult lifespan database (AHEAD). Data were
+scanned using the MP2RAGEME sequence for T1, T2* and Quantitative Susceptibility Mapping in one sequence at 7 Tesla.
+Data are motion-corrected using Fat navigators (FatNavs), and defaced in image-domain. In total 77 subjects are
+included, scanned with a resolution of 0.7mm isotropic. Data of the MP2RAGEME-sequence are stored according to the
+ISMRMRD-standard in h5-format (https://ismrmrd.github.io/). Detailed scanner parameters are included in the h5-files
+of all subjects. Coil sensitivity maps per subjects are included in native h5-format. Demographics of all subjects are
+included in a separate csv-file, being sex and age decade, covering the life span.
+
+For more information and dataset download link for the AHEAD project, please check
+https://dataverse.nl/dataset.xhtml?persistentId=doi:10.34894/IHZGQM.
+
+### **Visualization**
+An example notebook for visualizing and preprocessing the data is provided in the
+[getting-started.ipynb](getting-started.ipynb). You just need to set the path where the
+dataset is downloaded.
+
+### **Preprocessing**
+The AHEAD dataset requires careful preprocessing before training a model. The preprocessing steps are explained in the
+[getting-started.ipynb](getting-started.ipynb) notebook.
+
+The preprocessing pipeline is implemented in the
+[batch_preprocessing.sh](batch_preprocessing.sh) script, consisting of the
+following steps:
+1. Read the raw data in ISMRMRD format.
+2. Preprocess the coil sensitivity maps.
+3. Compute the imspace and ground-truth target data.
+4. Compute the masks.
+5. Compute the quantitative maps.
+6. Store the data in HDF5 format.
+
+The preprocessing script can be run with the following command:
+```bash
+bash ./projects/qMRI/AHEAD/batch_preprocessing.sh
+```
+
+### **Training/Testing**
+For training a model, you just need to set up the data and export paths to the configuration file in
+/projects/qMRI/AHEAD/conf/train/ of the model you want to train. In `train_ds` and
+`validation_ds` please set the `data_path` to the generated json files. In `exp_manager` please set the `exp_dir` to
+the path where you want to save the model checkpoints and tensorboard or wandb logs.
+
+You can train a model with the following command:
+`atommic run -c /projects/qMRI/AHEAD/conf/train/{model}.yaml`
+
+For testing a model, you just need to set up the data and export paths to the configuration file in
+/projects/qMRI/AHEAD/conf/test/ of the model you want to test. In `checkpoint`
+(line 2) set the path the trained model checkpoint and in `test_ds` please set the `data_path`. In `exp_manager` please
+set the `exp_dir` to the path where the predictions and logs will be saved.
+
+You can test a model with the following command:
+`atommic run -c /projects/qMRI/AHEAD/conf/test/{model}.yaml`
+
+**Note:** The default logger is tensorboard.
diff --git a/projects/qMRI/AHEAD/__init__.py b/projects/qMRI/AHEAD/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/qMRI/AHEAD/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/qMRI/AHEAD/batch_preprocessing.sh b/projects/qMRI/AHEAD/batch_preprocessing.sh
new file mode 100644
index 00000000..677e9024
--- /dev/null
+++ b/projects/qMRI/AHEAD/batch_preprocessing.sh
@@ -0,0 +1,31 @@
+#!/bin/bash
+echo "
+Preprocessing pipeline for the AHEAD dataset.
+
+The data download link is available at: https://dataverse.nl/dataset.xhtml?persistentId=doi:10.34894/IHZGQM.
+
+Please make sure you have ``ismrmrd`` installed.
+
+Starting the preprocessing...
+"
+
+# Prompt the user to enter the path to the downloaded data
+echo "Please enter the (downloaded) data directory:"
+read INPUT_DIR
+
+# Check if the input directory exists
+if [ ! -d "$INPUT_DIR" ]; then
+ echo "The input directory does not exist. Please try again."
+ exit 1
+fi
+
+# Prompt the user to enter the output directory for the preprocessed data
+echo "Please enter the output directory for the preprocessed data:"
+read OUTPUT_DIR
+
+# Run the preprocessing pipeline
+echo "Running the preprocessing..."
+python projects/quantitative/AHEAD/scripts/preprocessing.py $INPUT_DIR $OUTPUT_DIR --plane axial --slice_range 120 171
+echo "Computing the segmentation masks..."
+python projects/quantitative/AHEAD/scripts/compute_segmentation_masks.py $OUTPUT_DIR $OUTPUT_DIR/segmentation_masks/
+echo "Done!"
diff --git a/projects/qMRI/AHEAD/conf/e2erq_train/e2erq_qcirim.yaml b/projects/qMRI/AHEAD/conf/e2erq_train/e2erq_qcirim.yaml
new file mode 100644
index 00000000..7cb99ac9
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/e2erq_train/e2erq_qcirim.yaml
@@ -0,0 +1,293 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: qCIRIM
+ use_reconstruction_module: true
+ reconstruction_module_recurrent_layer: IndRNN
+ reconstruction_module_conv_filters:
+ - 64
+ - 64
+ - 2
+ reconstruction_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ reconstruction_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ reconstruction_module_conv_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ reconstruction_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ reconstruction_module_recurrent_bias:
+ - true
+ - true
+ - false
+ reconstruction_module_depth: 2
+ reconstruction_module_time_steps: 8
+ reconstruction_module_conv_dim: 2
+ reconstruction_module_num_cascades: 5
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_no_dc: true
+ reconstruction_module_keep_prediction: true
+ reconstruction_module_accumulate_predictions: true
+ quantitative_module_recurrent_layer: IndRNN
+ quantitative_module_conv_filters:
+ - 64
+ - 64
+ - 4
+ quantitative_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ quantitative_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ quantitative_module_conv_bias:
+ - true
+ - true
+ - false
+ quantitative_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ quantitative_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_bias:
+ - true
+ - true
+ - false
+ quantitative_module_depth: 2
+ quantitative_module_time_steps: 8
+ quantitative_module_conv_dim: 2
+ quantitative_module_num_cascades: 5
+ quantitative_module_no_dc: true
+ quantitative_module_keep_prediction: true
+ quantitative_module_accumulate_predictions: true
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ reconstruction_loss:
+ ssim: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 0.5 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/quantitative/trained_models/AHEAD_gaussian2d_12x/E2EqCIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/qMRI/AHEAD/conf/e2erq_train/e2erq_qvarnet.yaml b/projects/qMRI/AHEAD/conf/e2erq_train/e2erq_qvarnet.yaml
new file mode 100644
index 00000000..b2d13ec4
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/e2erq_train/e2erq_qvarnet.yaml
@@ -0,0 +1,230 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: qVN
+ use_reconstruction_module: true
+ reconstruction_module_num_cascades: 8
+ reconstruction_module_channels: 18
+ reconstruction_module_pooling_layers: 4
+ reconstruction_module_in_channels: 2
+ reconstruction_module_out_channels: 2
+ reconstruction_module_padding_size: 11
+ reconstruction_module_normalize: true
+ reconstruction_module_no_dc: false
+ reconstruction_module_dimensionality: 2
+ reconstruction_module_accumulate_predictions: false
+ quantitative_module_num_cascades: 8
+ quantitative_module_channels: 18
+ quantitative_module_pooling_layers: 4
+ quantitative_module_in_channels: 8
+ quantitative_module_out_channels: 8
+ quantitative_module_padding_size: 11
+ quantitative_module_normalize: true
+ quantitative_module_no_dc: false
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ reconstruction_loss:
+ ssim: 1.0
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 0.5
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 0.5 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/train
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/val
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/quantitative/trained_models/AHEAD_gaussian2d_12x/E2EqVarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/qMRI/AHEAD/conf/quantitative_test/qcirim.yaml b/projects/qMRI/AHEAD/conf/quantitative_test/qcirim.yaml
new file mode 100644
index 00000000..20ed7c5a
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/quantitative_test/qcirim.yaml
@@ -0,0 +1,184 @@
+pretrained: true
+checkpoint: None
+mode: test
+
+model:
+ model_name: qCIRIM
+ use_reconstruction_module: false
+ quantitative_module_recurrent_layer: IndRNN
+ quantitative_module_conv_filters:
+ - 64
+ - 64
+ - 4
+ quantitative_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ quantitative_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ quantitative_module_conv_bias:
+ - true
+ - true
+ - false
+ quantitative_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ quantitative_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_bias:
+ - true
+ - true
+ - false
+ quantitative_module_depth: 2
+ quantitative_module_time_steps: 8
+ quantitative_module_conv_dim: 2
+ quantitative_module_num_cascades: 5
+ quantitative_module_no_dc: true
+ quantitative_module_keep_prediction: true
+ quantitative_module_accumulate_predictions: true
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 1.0 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/test
+ noise_path: None
+ initial_predictions_path: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Test/CIRIM/default/2023-11-22_08-41-45/reconstructions
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/quantitative/predictions/AHEAD_gaussian2d_12x_Test/qCIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/qMRI/AHEAD/conf/quantitative_test/qvarnet.yaml b/projects/qMRI/AHEAD/conf/quantitative_test/qvarnet.yaml
new file mode 100644
index 00000000..d4a9813d
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/quantitative_test/qvarnet.yaml
@@ -0,0 +1,152 @@
+pretrained: true
+checkpoint: output_dir/atommic/quantitative/trained_models/AHEAD_gaussian2d_12x/qVarNet/default/2023-11-23_01-57-45/checkpoints/default--val_loss=0.2576-epoch=19.ckpt
+mode: test
+
+model:
+ model_name: qVN
+ use_reconstruction_module: false
+ quantitative_module_num_cascades: 8
+ quantitative_module_channels: 18
+ quantitative_module_pooling_layers: 4
+ quantitative_module_in_channels: 8
+ quantitative_module_out_channels: 8
+ quantitative_module_padding_size: 11
+ quantitative_module_normalize: true
+ quantitative_module_no_dc: false
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 1.0 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/test
+ noise_path: None
+ initial_predictions_path: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Test/VarNet/default/2023-11-22_04-10-56/reconstructions
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/quantitative/predictions/AHEAD_gaussian2d_12x_Test/qVarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/qMRI/AHEAD/conf/quantitative_train/qcirim.yaml b/projects/qMRI/AHEAD/conf/quantitative_train/qcirim.yaml
new file mode 100644
index 00000000..eb553b5e
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/quantitative_train/qcirim.yaml
@@ -0,0 +1,248 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: qCIRIM
+ use_reconstruction_module: false
+ quantitative_module_recurrent_layer: IndRNN
+ quantitative_module_conv_filters:
+ - 64
+ - 64
+ - 4
+ quantitative_module_conv_kernels:
+ - 5
+ - 3
+ - 3
+ quantitative_module_conv_dilations:
+ - 1
+ - 2
+ - 1
+ quantitative_module_conv_bias:
+ - true
+ - true
+ - false
+ quantitative_module_recurrent_filters:
+ - 64
+ - 64
+ - 0
+ quantitative_module_recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ quantitative_module_recurrent_bias:
+ - true
+ - true
+ - false
+ quantitative_module_depth: 2
+ quantitative_module_time_steps: 8
+ quantitative_module_conv_dim: 2
+ quantitative_module_num_cascades: 5
+ quantitative_module_no_dc: true
+ quantitative_module_keep_prediction: true
+ quantitative_module_accumulate_predictions: true
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 1.0 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/train
+ noise_path: None
+ initial_predictions_path: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Train/CIRIM/default/2023-11-22_08-41-00/reconstructions
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/val
+ noise_path: None
+ initial_predictions_path: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Val/CIRIM/default/2023-11-22_08-41-19/reconstructions
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/quantitative/trained_models/AHEAD_gaussian2d_12x/qCIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/qMRI/AHEAD/conf/quantitative_train/qvarnet.yaml b/projects/qMRI/AHEAD/conf/quantitative_train/qvarnet.yaml
new file mode 100644
index 00000000..5ae0fdf3
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/quantitative_train/qvarnet.yaml
@@ -0,0 +1,216 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: qVN
+ use_reconstruction_module: false
+ quantitative_module_num_cascades: 8
+ quantitative_module_channels: 18
+ quantitative_module_pooling_layers: 4
+ quantitative_module_in_channels: 8
+ quantitative_module_out_channels: 8
+ quantitative_module_padding_size: 11
+ quantitative_module_normalize: true
+ quantitative_module_no_dc: false
+ quantitative_module_signal_forward_model_sequence: MEGRE
+ quantitative_module_dimensionality: 2
+ quantitative_maps_scaling_factor: 1e-3
+ quantitative_maps_regularization_factors:
+ - 150.0
+ - 150.0
+ - 1000.0
+ - 150.0
+ quantitative_loss:
+ ssim: 1.0
+ kspace_quantitative_loss: false
+ total_quantitative_loss_weight: 1.0 # balance between reconstruction and quantitative loss
+ quantitative_parameters_regularization_factors:
+# mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/train
+ noise_path: None
+ initial_predictions_path: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Train/VarNet/default/2023-11-22_04-08-17/reconstructions
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: false
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/val
+ noise_path: None
+ initial_predictions_path: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Val/VarNet/default/2023-11-22_04-10-47/reconstructions
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/quantitative/trained_models/AHEAD_gaussian2d_12x/qVarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/qMRI/AHEAD/conf/reconstruction_test/cirim_test_set.yaml b/projects/qMRI/AHEAD/conf/reconstruction_test/cirim_test_set.yaml
new file mode 100644
index 00000000..cc6534d2
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/reconstruction_test/cirim_test_set.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: output_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/CIRIM/default/2023-11-22_02-33-01/checkpoints/default--val_loss=0.0293-epoch=19.ckpt
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Test/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.AHEAD_gaussian2d_12x
diff --git a/projects/qMRI/AHEAD/conf/reconstruction_test/cirim_train_set.yaml b/projects/qMRI/AHEAD/conf/reconstruction_test/cirim_train_set.yaml
new file mode 100644
index 00000000..975ad68f
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/reconstruction_test/cirim_train_set.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: output_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/CIRIM/default/2023-11-22_02-33-01/checkpoints/default--val_loss=0.0293-epoch=19.ckpt
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Train/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.AHEAD_gaussian2d_12x
diff --git a/projects/qMRI/AHEAD/conf/reconstruction_test/cirim_val_set.yaml b/projects/qMRI/AHEAD/conf/reconstruction_test/cirim_val_set.yaml
new file mode 100644
index 00000000..bbab04c7
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/reconstruction_test/cirim_val_set.yaml
@@ -0,0 +1,157 @@
+pretrained: true
+checkpoint: output_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/CIRIM/default/2023-11-22_02-33-01/checkpoints/default--val_loss=0.0293-epoch=19.ckpt
+mode: test
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Val/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.AHEAD_gaussian2d_12x
diff --git a/projects/qMRI/AHEAD/conf/reconstruction_test/varnet_test_set.yaml b/projects/qMRI/AHEAD/conf/reconstruction_test/varnet_test_set.yaml
new file mode 100644
index 00000000..35b0d845
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/reconstruction_test/varnet_test_set.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: output_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/VarNet/default/2023-11-22_02-26-45/checkpoints/default--val_loss=0.2337-epoch=19.ckpt
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Test/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.AHEAD_gaussian2d_12x
diff --git a/projects/qMRI/AHEAD/conf/reconstruction_test/varnet_train_set.yaml b/projects/qMRI/AHEAD/conf/reconstruction_test/varnet_train_set.yaml
new file mode 100644
index 00000000..bbc21a61
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/reconstruction_test/varnet_train_set.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: output_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/VarNet/default/2023-11-22_02-26-45/checkpoints/default--val_loss=0.2337-epoch=19.ckpt
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Train/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.AHEAD_gaussian2d_12x
diff --git a/projects/qMRI/AHEAD/conf/reconstruction_test/varnet_val_set.yaml b/projects/qMRI/AHEAD/conf/reconstruction_test/varnet_val_set.yaml
new file mode 100644
index 00000000..c54d7511
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/reconstruction_test/varnet_val_set.yaml
@@ -0,0 +1,123 @@
+pretrained: true
+checkpoint: output_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/VarNet/default/2023-11-22_02-26-45/checkpoints/default--val_loss=0.2337-epoch=19.ckpt
+mode: test
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/predictions/AHEAD_gaussian2d_12x_Val/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.AHEAD_gaussian2d_12x
diff --git a/projects/qMRI/AHEAD/conf/reconstruction_train/cirim.yaml b/projects/qMRI/AHEAD/conf/reconstruction_train/cirim.yaml
new file mode 100644
index 00000000..3a2a63c0
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/reconstruction_train/cirim.yaml
@@ -0,0 +1,210 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: CIRIM
+ recurrent_layer: IndRNN
+ conv_filters:
+ - 64
+ - 64
+ - 2
+ conv_kernels:
+ - 5
+ - 3
+ - 3
+ conv_dilations:
+ - 1
+ - 2
+ - 1
+ conv_bias:
+ - true
+ - true
+ - false
+ recurrent_filters:
+ - 64
+ - 64
+ - 0
+ recurrent_kernels:
+ - 1
+ - 1
+ - 0
+ recurrent_dilations:
+ - 1
+ - 1
+ - 0
+ recurrent_bias:
+ - true
+ - true
+ - false
+ depth: 2
+ time_steps: 8
+ conv_dim: 2
+ num_cascades: 5
+ no_dc: true
+ keep_prediction: true
+ accumulate_predictions: true
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/CIRIM/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.AHEAD_gaussian2d_12x
diff --git a/projects/qMRI/AHEAD/conf/reconstruction_train/varnet.yaml b/projects/qMRI/AHEAD/conf/reconstruction_train/varnet.yaml
new file mode 100644
index 00000000..a2c019c1
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/reconstruction_train/varnet.yaml
@@ -0,0 +1,176 @@
+pretrained: false
+checkpoint: None
+mode: train
+
+model:
+ model_name: VN
+ num_cascades: 8
+ channels: 18
+ pooling_layers: 4
+ padding_size: 11
+ normalize: true
+ no_dc: false
+ dimensionality: 2
+ num_echoes: 4
+ reconstruction_loss:
+ ssim: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ kspace_reconstruction_loss: false
+ total_reconstruction_loss_weight: 1.0
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ coil_combination_method: SENSE
+ ssdu: false
+ n2r: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 1
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ train_ds:
+ data_path: data_parent_dir/preprocessed/train
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ apply_prewhitening: false
+ apply_gcc: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: false
+ batch_size: 1
+ shuffle: true
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ validation_ds:
+ data_path: data_parent_dir/preprocessed/val
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: AHEAD
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ mask_args:
+ type: gaussian2d
+ accelerations:
+ - 12
+ center_fractions:
+ - 0.7
+ center_scale: 0.02
+ shift_mask: false
+ use_seed: false
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: true
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adamw
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.999
+ weight_decay: 0.0
+ sched:
+ name: PolynomialHoldDecayAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_false
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/reconstruction/trained_models/AHEAD_gaussian2d_12x/VarNet/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
+ wandb_logger_kwargs:
+ project: atommic.reconstruction.trained_models.AHEAD_gaussian2d_12x
diff --git a/projects/qMRI/AHEAD/conf/targets/Test_SENSE.yaml b/projects/qMRI/AHEAD/conf/targets/Test_SENSE.yaml
new file mode 100644
index 00000000..3017563c
--- /dev/null
+++ b/projects/qMRI/AHEAD/conf/targets/Test_SENSE.yaml
@@ -0,0 +1,127 @@
+pretrained: false
+checkpoint: None
+mode: test
+
+model:
+ model_name: qZF
+ use_reconstruction_module: true
+ quantitative_module_dimensionality: 2
+ quantitative_parameters_regularization_factors:
+# # mse
+# - R2star: 300.0
+# - S0: 500.0
+# - B0: 20000.0
+# - phi: 500.0
+ - R2star: 1.0
+ - S0: 1.0
+ - B0: 1.0
+ - phi: 1.0
+ normalization_type: minmax
+ unnormalize_loss_inputs: false
+ unnormalize_log_outputs: false
+ complex_valued_type: stacked # stacked, complex_abs, complex_sqrt_abs
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ coil_combination_method: SENSE
+ dimensionality: 2
+ num_echoes: 4
+ ssdu: false
+ n2r: false
+ estimate_coil_sensitivity_maps_with_nn: false
+ consecutive_slices: 1
+
+ test_ds:
+ data_path: data_parent_dir/preprocessed/test
+ coil_sensitivity_maps_path: None
+ mask_path: None
+ segmentation_mask_path: data_parent_dir/segmentation_masks/test
+ noise_path: None
+ initial_predictions_path: None
+ dataset_format: ahead
+ sample_rate: 1
+ volume_sample_rate: None
+ use_dataset_cache: false
+ dataset_cache_file: None
+ num_cols: None
+ consecutive_slices: 1
+ data_saved_per_slice: false
+ complex_target: true
+ log_images_rate: 1.0
+ apply_prewhitening: false
+ apply_gcc: false
+ estimate_coil_sensitivity_maps: false
+ coil_combination_method: SENSE
+ dimensionality: 2
+ TEs:
+ - 3.0
+ - 11.5
+ - 20.0
+ - 28.5
+ precompute_quantitative_maps: true
+ qmaps_scaling_factor: 1e-3
+ kspace_scaling_factor: 10000
+ mask_args:
+ type: none
+ use_seed: true
+ partial_fourier_percentage: 0.0
+ remask: false
+ ssdu: false
+ n2r: false
+ unsupervised_masked_target: false
+ crop_size: None
+ kspace_crop: false
+ crop_before_masking: true
+ kspace_zero_filling_size: None
+ normalize_inputs: false
+ normalization_type: minmax
+ kspace_normalization: false
+ fft_centered: false
+ fft_normalization: backward
+ spatial_dims:
+ - -2
+ - -1
+ coil_dim: 2
+ sequence: MEGRE
+ use_seed: true
+ batch_size: 1
+ shuffle: false
+ num_workers: 8
+ pin_memory: false
+ drop_last: false
+
+ optim:
+ name: adam
+ lr: 1e-4
+ betas:
+ - 0.9
+ - 0.98
+ weight_decay: 0.0
+ sched:
+ name: InverseSquareRootAnnealing
+ min_lr: 0.0
+ last_epoch: -1
+ warmup_ratio: 0.1
+
+trainer:
+ strategy: ddp_find_unused_parameters_true
+ accelerator: gpu
+ devices: 1
+ num_nodes: 1
+ max_epochs: 20
+ precision: 16-mixed
+ enable_checkpointing: false
+ logger: false
+ log_every_n_steps: 50
+ check_val_every_n_epoch: -1
+ max_steps: -1
+
+exp_manager:
+ exp_dir: output_dir/atommic/quantitative/targets/AHEAD_gaussian2d_12x_Test/SENSE/
+ ema:
+ enable: false
+ create_tensorboard_logger: true
+ create_wandb_logger: false
diff --git a/projects/qMRI/AHEAD/getting-started.ipynb b/projects/qMRI/AHEAD/getting-started.ipynb
new file mode 100644
index 00000000..7e5faa69
--- /dev/null
+++ b/projects/qMRI/AHEAD/getting-started.ipynb
@@ -0,0 +1,2274 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Getting started with the [AHEAD](https://dataverse.nl/dataset.xhtml?persistentId=doi:10.34894/IHZGQM) dataset.\n",
+ "\n",
+ "Note: Please be patient at times when running the code. Depending on your machine, it can take ~1 hour to run everything on this notebook."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### The data in the AHEAD dataset are scanned using the MP2RAGEME sequence for T1, T2* and Quantitative Susceptibility Mapping in one sequence at 7 Tesla. Data are motion-corrected using Fat navigators (FatNavs), and defaced in image-domain.\n",
+ "\n",
+ "### Use the link to download the dataset. The dataset is in the [ISMRMRD](https://ismrmrd.github.io/) format. We will use the [ismrmrd-python-tools](https://github.com/ismrmrd/ismrmrd-python-tools) to read the raw data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:51:53.574330Z",
+ "end_time": "2023-11-15T23:51:55.895385Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "! pip install ismrmrd"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:51:55.894679Z",
+ "end_time": "2023-11-15T23:51:56.039683Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import ismrmrd\n",
+ "from tqdm import tqdm"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### In this example we will use the data of subject 001, the 2nd inversion and the 1st echo time. Please change the `dataset_path` accordingly. The filename is `mp2rageme_001_inv2_te1.h5`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:51:59.712068Z",
+ "end_time": "2023-11-15T23:51:59.760938Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "dataset_path = input(\"Please enter the (downloaded) data path: \")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:52:00.996454Z",
+ "end_time": "2023-11-15T23:52:01.076025Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "filename = f\"{dataset_path}/mp2rageme_001_inv2_te1.h5\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We will use the `ismrmrd.Dataset` class to read the data.\n",
+ "\n",
+ "- The `dataset` argument is the path to the dataset.\n",
+ "- The `create_if_needed` argument is set to `False` because we do not want to create a new dataset if it does not exist.\n",
+ "- The `number_of_acquisitions` method returns the number of acquisitions in the dataset. We will use this to loop over all acquisitions.\n",
+ "- We will use the `read_acquisition` method to read the acquisitions.\n",
+ "- The `getHead` method returns the header of the acquisition.\n",
+ "- The `isFlagSet` method returns `True` if the flag is set. We will use this to find the noise scans."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:52:03.058042Z",
+ "end_time": "2023-11-15T23:52:03.076965Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "dataset = ismrmrd.Dataset(filename, 'dataset', create_if_needed=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:52:03.682418Z",
+ "end_time": "2023-11-15T23:52:03.741259Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "number_of_acquisitions = dataset.number_of_acquisitions()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's find the first scan that is not a noise scan."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:52:05.341403Z",
+ "end_time": "2023-11-15T23:56:32.355027Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# find the first no noise scan\n",
+ "first_scan = 0\n",
+ "for i in tqdm(range(number_of_acquisitions)):\n",
+ " head = dataset.read_acquisition(i).getHead()\n",
+ " if head.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):\n",
+ " first_scan = i\n",
+ " break"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's read the data into a list of acquisitions."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:56:32.336053Z",
+ "end_time": "2023-11-15T23:59:49.797476Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "meas = []\n",
+ "for i in tqdm(range(first_scan, number_of_acquisitions)):\n",
+ " acq = dataset.read_acquisition(i)\n",
+ " meas.append(acq)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's read the header of the dataset.\n",
+ "\n",
+ "- The `xsd.CreateFromDocument` method deserializes the header.\n",
+ "- The `read_xml_header` method returns the header of the dataset.\n",
+ "- The `hdr.acquisitionSystemInformation` returns the acquisition system information.\n",
+ "- The `hdr.experimentalConditions` returns the experimental conditions.\n",
+ "- The `hdr.sequenceParameters` returns the sequence parameters.\n",
+ "- The `hdr.userParameters.userParameterLong` returns the user parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.772979Z",
+ "end_time": "2023-11-15T23:59:49.799033Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "hdr = ismrmrd.xsd.CreateFromDocument(dataset.read_xml_header())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.773069Z",
+ "end_time": "2023-11-15T23:59:49.799501Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "print(\n",
+ " f\"Acquisition system information: {hdr.acquisitionSystemInformation} \\n\"\n",
+ " f\"Experimental conditions: {hdr.experimentalConditions} \\n\"\n",
+ " f\"Sequence parameters: {hdr.sequenceParameters} \\n\"\n",
+ " f\"User parameters: {hdr.userParameters.userParameterLong} \\n\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's read the encoding information."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.773140Z",
+ "end_time": "2023-11-15T23:59:49.813485Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Matrix size\n",
+ "enc = hdr.encoding[0]\n",
+ "enc_Nx = enc.encodedSpace.matrixSize.x\n",
+ "enc_Ny = enc.encodedSpace.matrixSize.y\n",
+ "enc_Nz = enc.encodedSpace.matrixSize.z\n",
+ "rec_Nx = enc.reconSpace.matrixSize.x\n",
+ "rec_Ny = enc.reconSpace.matrixSize.y\n",
+ "rec_Nz = enc.reconSpace.matrixSize.z"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.813200Z",
+ "end_time": "2023-11-15T23:59:49.813792Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Field of view\n",
+ "enc_FOVx = enc.encodedSpace.fieldOfView_mm.x\n",
+ "enc_FOVy = enc.encodedSpace.fieldOfView_mm.y\n",
+ "enc_FOVz = enc.encodedSpace.fieldOfView_mm.z\n",
+ "rec_FOVx = enc.reconSpace.fieldOfView_mm.x\n",
+ "rec_FOVy = enc.reconSpace.fieldOfView_mm.y\n",
+ "rec_FOVz = enc.reconSpace.fieldOfView_mm.z"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.813284Z",
+ "end_time": "2023-11-15T23:59:49.813909Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "nCoils = hdr.acquisitionSystemInformation.receiverChannels"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.813333Z",
+ "end_time": "2023-11-15T23:59:49.814148Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "print(\n",
+ " f\"Matrix size: {enc_Nx} x {enc_Ny} x {enc_Nz} \\n\"\n",
+ " f\"Field of view: {enc_FOVx} x {enc_FOVy} x {enc_FOVz} \\n\"\n",
+ " f\"Number of coils: {nCoils} \\n\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's read the encoding limits. We will use these to select the appropriate measurements from the data.\n",
+ "\n",
+ "- The `encodingLimits.slice.maximum` returns the maximum slice index.\n",
+ "- The `encodingLimits.repetition.maximum` returns the maximum repetition index.\n",
+ "- The `encodingLimits.contrast.maximum` returns the maximum contrast index."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.813397Z",
+ "end_time": "2023-11-15T23:59:49.814257Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "nslices = enc.encodingLimits.slice.maximum + 1 if enc.encodingLimits.slice is not None else 1\n",
+ "nreps = enc.encodingLimits.repetition.maximum + 1 if enc.encodingLimits.repetition is not None else 1\n",
+ "ncontrasts = enc.encodingLimits.contrast.maximum + 1 if enc.encodingLimits.contrast is not None else 1"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.813476Z",
+ "end_time": "2023-11-15T23:59:49.814493Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "print(\n",
+ " f\"Number of slices: {nslices} \\n\"\n",
+ " f\"Number of repetitions: {nreps} \\n\"\n",
+ " f\"Number of contrasts: {ncontrasts} \\n\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's read the data into a k-space (numpy) array."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.813711Z",
+ "end_time": "2023-11-15T23:59:49.814598Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import numpy as np"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.813775Z",
+ "end_time": "2023-11-15T23:59:49.814688Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize k-space array\n",
+ "Kread = np.zeros((enc_Nx, enc_Ny, enc_Nz, nCoils), dtype=np.complex64)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.813842Z",
+ "end_time": "2023-11-15T23:59:50.070591Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "Kread.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:49.852697Z",
+ "end_time": "2023-11-15T23:59:57.688053Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Select the appropriate measurements from the data\n",
+ "for acq in tqdm(meas):\n",
+ " head = acq.getHead()\n",
+ " if head.idx.contrast == ncontrasts - 1 and head.idx.repetition == nreps - 1 and head.idx.slice == nslices - 1:\n",
+ " head = acq.getHead()\n",
+ " ky = head.idx.kspace_encode_step_1\n",
+ " kz = head.idx.kspace_encode_step_2\n",
+ " Kread[:, ky, kz, :] = np.transpose(acq.data, (1, 0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:57.658905Z",
+ "end_time": "2023-11-15T23:59:57.689232Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "Kread.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's plot the k-space data.\n",
+ "\n",
+ "Note: For visualization purposes and ease of example, we will use only one slice and one coil."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:57.658996Z",
+ "end_time": "2023-11-15T23:59:57.689374Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "slice_idx = 100\n",
+ "coil_idx = 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-15T23:59:57.659058Z",
+ "end_time": "2023-11-15T23:59:57.943054Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:00:22.164525Z",
+ "end_time": "2023-11-16T00:00:22.397618Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.imshow(np.log(np.abs(Kread[slice_idx, :, :, coil_idx]) + 1e-9), cmap=\"gray\")\n",
+ "plt.title(\"k-space\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's perform the inverse Fourier transform. We will use the `fftshift` and `ifftshift` methods to center the k-space data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:00:27.665784Z",
+ "end_time": "2023-11-16T00:02:03.408811Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# orthogonal/centered IFFT\n",
+ "Imread = np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(Kread, axes=(0, 1, 2)), axes=(0, 1, 2)), axes=(0, 1, 2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:02:03.408108Z",
+ "end_time": "2023-11-16T00:02:23.707793Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "Imread = Imread / np.max(np.abs(Imread))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:02:23.707761Z",
+ "end_time": "2023-11-16T00:02:24.497034Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(np.abs(Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(np.angle(Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Phase\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(np.real(Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Real part\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(np.imag(Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's read the coil sensitivities. The coil sensitivities are stored in a separate file and NOT in raw format. We will use the `h5py` package to read the data. The coil sensitivities are stored in the `0real` and `1imag` datasets. We will read the data into a numpy array and then transpose the array to match the dimensions of the k-space data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:02:31.418570Z",
+ "end_time": "2023-11-16T00:02:31.619989Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities_filename = f\"{dataset_path}/coilsens_001.h5\""
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:02:31.558783Z",
+ "end_time": "2023-11-16T00:02:31.688849Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import h5py"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:02:31.973663Z",
+ "end_time": "2023-11-16T00:02:32.028619Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities = h5py.File(coil_sensitivities_filename, \"r\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:02:32.385297Z",
+ "end_time": "2023-11-16T00:02:32.437153Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:02:32.724851Z",
+ "end_time": "2023-11-16T00:03:10.040915Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities_real = np.array(coil_sensitivities[\"0real\"])\n",
+ "coil_sensitivities_imag = np.array(coil_sensitivities[\"1imag\"])\n",
+ "coil_sensitivities = coil_sensitivities_real + 1j * coil_sensitivities_imag"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:10.041485Z",
+ "end_time": "2023-11-16T00:03:10.042748Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:10.041733Z",
+ "end_time": "2023-11-16T00:03:10.042975Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities = np.transpose(coil_sensitivities, (3, 2, 1, 0))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:10.041856Z",
+ "end_time": "2023-11-16T00:03:10.043261Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:10.041977Z",
+ "end_time": "2023-11-16T00:03:22.229988Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities = coil_sensitivities / np.max(np.abs(coil_sensitivities))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:22.050897Z",
+ "end_time": "2023-11-16T00:03:22.652297Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(np.abs(coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(np.angle(coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Phase\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(np.real(coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Real part\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(np.imag(coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's calculate the target image. We will use the SENSE reconstruction method to calculate the target image. The target image is the sum of the product of the k-space data and the complex conjugate of the coil sensitivities. We will then normalize the target image by the maximum value of the absolute value of the target image."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:22.652115Z",
+ "end_time": "2023-11-16T00:03:27.415849Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "target = np.sum(Imread[slice_idx] * np.conj(coil_sensitivities[slice_idx]), axis=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:27.455044Z",
+ "end_time": "2023-11-16T00:03:27.493673Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "target.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:27.455305Z",
+ "end_time": "2023-11-16T00:03:27.493963Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "target = target / np.max(np.abs(target))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:27.455433Z",
+ "end_time": "2023-11-16T00:03:28.079655Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(np.abs(target), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(np.angle(target), cmap=\"gray\")\n",
+ "plt.title(\"Phase\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(np.real(target), cmap=\"gray\")\n",
+ "plt.title(\"Real part\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(np.imag(target), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We have now verified that the data look correct and we have find the correct transformations to preprocess our data. Let's now get another plane, the axial."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:28.078673Z",
+ "end_time": "2023-11-16T00:03:28.080055Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# get the axial plane\n",
+ "axial_Kread = np.transpose(Kread, (2, 0, 1, 3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:03:28.080526Z",
+ "end_time": "2023-11-16T00:05:31.234454Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "axial_Imread = np.fft.fftshift(np.fft.ifftn(np.fft.ifftshift(axial_Kread, axes=(0, 1, 2)), axes=(0, 1, 2)), axes=(0, 1, 2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:05:31.233974Z",
+ "end_time": "2023-11-16T00:05:31.832590Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(np.abs(axial_Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(np.angle(axial_Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Phase\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(np.real(axial_Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Real part\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(np.imag(axial_Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Let's transform our data to get the proper orientation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:05:31.832492Z",
+ "end_time": "2023-11-16T00:05:31.833131Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "axial_Imread = np.rot90(axial_Imread, k=1, axes=(1, 2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:05:31.832945Z",
+ "end_time": "2023-11-16T00:05:32.657741Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(np.abs(axial_Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(np.angle(axial_Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Phase\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(np.real(axial_Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Real part\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(np.imag(axial_Imread[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:05:32.657077Z",
+ "end_time": "2023-11-16T00:09:10.528601Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# get axial coil sensitivities\n",
+ "axial_coil_sensitivities = np.fft.fftn(coil_sensitivities, axes=(0, 1, 2))\n",
+ "axial_coil_sensitivities = np.transpose(axial_coil_sensitivities, (2, 0, 1, 3))\n",
+ "axial_coil_sensitivities = np.fft.ifftn(axial_coil_sensitivities, axes=(0, 1, 2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:09:10.527968Z",
+ "end_time": "2023-11-16T00:09:11.165512Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(np.abs(axial_coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(np.angle(axial_coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Phase\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(np.real(axial_coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Real part\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(np.imag(axial_coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Let's transform our data to get the proper orientation."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:09:11.164772Z",
+ "end_time": "2023-11-16T00:09:11.165871Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "axial_coil_sensitivities = np.rot90(axial_coil_sensitivities, k=1, axes=(1, 2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T00:09:11.164997Z",
+ "end_time": "2023-11-16T00:09:12.520144Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(np.abs(axial_coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(np.angle(axial_coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Phase\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(np.real(axial_coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Real part\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(np.imag(axial_coil_sensitivities[slice_idx, :, :, coil_idx]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:34:57.297356Z",
+ "end_time": "2023-11-16T02:35:02.562369Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# get the axial target\n",
+ "axial_target = np.sum(axial_Imread[slice_idx] * np.conj(axial_coil_sensitivities[slice_idx]), axis=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:35:02.546013Z",
+ "end_time": "2023-11-16T02:35:03.220159Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(np.abs(axial_target), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(np.angle(axial_target), cmap=\"gray\")\n",
+ "plt.title(\"Phase\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(np.real(axial_target), cmap=\"gray\")\n",
+ "plt.title(\"Real part\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(np.imag(axial_target), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We verified that we can get the axial images properly as well. Let's get going and estimate parameter maps."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "##### First we need to calculate the brain mask. We will use the axial images for this. We will use the Otsu thresholding method to estimate the brain mask. We will then dilate the brain mask to get a better estimate of the brain mask. We will then get the convex hull of the brain mask to get a better estimate of the brain mask."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:37:25.305768Z",
+ "end_time": "2023-11-16T02:37:25.420686Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from skimage.filters import threshold_otsu\n",
+ "from scipy.ndimage import binary_dilation, binary_erosion, binary_fill_holes\n",
+ "from skimage.morphology import convex_hull_image\n",
+ "from skimage import measure"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "# calculate otsu's threshold\n",
+ "threshold = threshold_otsu(np.abs(axial_target))\n",
+ "# get the connected components and apply threshold\n",
+ "axial_target_cc = measure.label(np.abs(axial_target) > threshold * 2) + measure.label(np.abs(axial_target) > threshold)\n",
+ "# Get mask\n",
+ "skull_mask = np.where(axial_target_cc != 0, 1, 0)\n",
+ "# get the convex hull\n",
+ "brain_mask = convex_hull_image(skull_mask) * (1 - skull_mask)\n",
+ "# perform binary erosion to remove skull\n",
+ "brain_mask = binary_erosion(brain_mask, iterations=4)\n",
+ "# get the convex hull of the brain mask\n",
+ "brain_mask = convex_hull_image(brain_mask)\n",
+ "# threshold the brain mask\n",
+ "brain_mask = np.where(np.abs(axial_target) * brain_mask > threshold / 2, 1, 0)\n",
+ "# perform binary erosion to remove skull\n",
+ "brain_mask = binary_erosion(brain_mask, iterations=4)\n",
+ "# perform binary dilation to get the brain mask\n",
+ "brain_mask = binary_dilation(brain_mask, iterations=4)\n",
+ "# fill holes in the brain mask\n",
+ "brain_mask = binary_fill_holes(brain_mask)\n",
+ "# get the convex hull of the brain mask\n",
+ "brain_mask = convex_hull_image(brain_mask)"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:37:26.428468Z",
+ "end_time": "2023-11-16T02:37:26.523826Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "brain_mask.shape"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:37:28.073125Z",
+ "end_time": "2023-11-16T02:37:28.161047Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:37:29.216511Z",
+ "end_time": "2023-11-16T02:37:29.848972Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(np.abs(axial_target), cmap=\"gray\")\n",
+ "plt.title(\"Axial Target\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(brain_mask, cmap=\"gray\")\n",
+ "plt.title(\"Brain Mask\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(np.abs(axial_target) * brain_mask, cmap=\"gray\")\n",
+ "plt.title(\"Axial Target * Brain Mask\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(np.abs(axial_target) * (1 - brain_mask), cmap=\"gray\")\n",
+ "plt.title(\"Axial Target * Head Mask\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "##### Now we will estimate quantitative maps. We will estimate R2*, S0, B0, and phi maps. But first we need to apply the brain mask to the images and unwrap the phase."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:43:54.017246Z",
+ "end_time": "2023-11-16T01:44:00.599866Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coilimgs = axial_Imread[slice_idx] * np.repeat(brain_mask[..., np.newaxis], axial_Imread.shape[-1], axis=-1)\n",
+ "sense = axial_coil_sensitivities[slice_idx] * np.repeat(brain_mask[..., np.newaxis], axial_coil_sensitivities.shape[-1], axis=-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:44:00.601095Z",
+ "end_time": "2023-11-16T01:44:00.618656Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from skimage.restoration import unwrap_phase"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:44:00.619410Z",
+ "end_time": "2023-11-16T01:44:00.706299Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "phases = np.angle(np.sum(coilimgs * sense.conj(), -1))\n",
+ "phase_unwrapped = unwrap_phase(np.ma.array(phases, mask=np.zeros(phases.shape)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:44:34.887257Z",
+ "end_time": "2023-11-16T01:44:35.173155Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.subplot(1, 2, 1)\n",
+ "plt.imshow(phases, cmap=\"gray\")\n",
+ "plt.title(\"Wrapped phase\")\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.imshow(phase_unwrapped, cmap=\"gray\")\n",
+ "plt.title(\"Unwrapped phase\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We can now start estimating the quantitative maps (R2*, B0, S0, and phi), using the atommic library. The given echo times are TEs = [3.0, 11.5, 20.0, 28.5].\n",
+ "\n",
+ "Note: For proper estimation of the quantitative maps, we need to load all the four (k-space) echo times. But to begin with we will create a dummy \"4-echoed\" input, by repeating the first echo four times. This is just to verify that the code works. We will then use the actual data to estimate the quantitative maps."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:44:39.943150Z",
+ "end_time": "2023-11-16T01:44:43.048138Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import torch\n",
+ "from atommic.collections.quantitative.parts.transforms import R2star_mapping, B0_phi_mapping, S0_mapping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "axial_target_masked = axial_target * brain_mask"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:45:49.047879Z",
+ "end_time": "2023-11-16T01:45:49.151389Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:45:50.616467Z",
+ "end_time": "2023-11-16T01:45:50.704257Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "axial_target_masked_tensor = np.stack([np.real(axial_target_masked), np.imag(axial_target_masked)], axis=-1)\n",
+ "axial_target_masked_tensor = torch.from_numpy(axial_target_masked_tensor)\n",
+ "axial_target_masked_tensor = torch.view_as_complex(axial_target_masked_tensor)\n",
+ "axial_target_masked_tensor = torch.view_as_real(axial_target_masked_tensor)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:45:51.871925Z",
+ "end_time": "2023-11-16T01:45:51.969674Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "prediction = axial_target_masked_tensor.unsqueeze(0) # then add a dummy echo dimension\n",
+ "prediction = torch.cat([prediction, prediction, prediction, prediction], dim=0) # add dummy four echo times\n",
+ "TEs = [3.0, 11.5, 20.0, 28.5] # four echo times"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:45:53.292145Z",
+ "end_time": "2023-11-16T01:46:06.863036Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "R2star_map = R2star_mapping(prediction, TEs)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:46:06.862809Z",
+ "end_time": "2023-11-16T01:46:06.863677Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "R2star_map.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:46:06.862992Z",
+ "end_time": "2023-11-16T01:46:06.971088Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.imshow(R2star_map, cmap=\"gray\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:47:47.769508Z",
+ "end_time": "2023-11-16T01:47:48.497377Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "mask_brain = torch.from_numpy(brain_mask).float()\n",
+ "mask_brain = mask_brain.unsqueeze(0)\n",
+ "head_mask = 1 - mask_brain\n",
+ "fully_sampled = True\n",
+ "shift = False\n",
+ "fft_centered = False\n",
+ "fft_normalization = \"backward\"\n",
+ "spatial_dims = [-2, -1]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:47:48.403531Z",
+ "end_time": "2023-11-16T01:47:48.759026Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "B0_map = -B0_phi_mapping(\n",
+ " prediction,\n",
+ " TEs,\n",
+ " mask_brain,\n",
+ " head_mask.numpy(),\n",
+ " fully_sampled,\n",
+ " shift=shift,\n",
+ " fft_centered=fft_centered,\n",
+ " fft_normalization=fft_normalization,\n",
+ " spatial_dims=spatial_dims,\n",
+ ")[0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:47:59.782104Z",
+ "end_time": "2023-11-16T01:47:59.914031Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.imshow(B0_map, cmap=\"gray\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:48:02.210783Z",
+ "end_time": "2023-11-16T01:48:02.295904Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "S0_map_real, S0_map_imag = S0_mapping(\n",
+ " prediction,\n",
+ " TEs,\n",
+ " R2star_map,\n",
+ " B0_map,\n",
+ " shift=shift,\n",
+ " fft_centered=fft_centered,\n",
+ " fft_normalization=fft_normalization,\n",
+ " spatial_dims=spatial_dims,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:48:03.874151Z",
+ "end_time": "2023-11-16T01:48:04.307700Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.subplot(1, 2, 1)\n",
+ "plt.imshow(S0_map_real, cmap=\"gray\")\n",
+ "plt.title(\"S0 map\")\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.imshow(S0_map_imag, cmap=\"gray\")\n",
+ "plt.title(\"phi map\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Great! Or almost great. The quantitative maps of course do not look correct, since we did not use the proper echo times. So, let's go on to estimate proper quantitative maps, using the actual (four echo times of the second inversion) data."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's wrap up everything and create our preprocessing function for the AHEAD dataset.\n",
+ "\n",
+ "Note: In total we have one echo time for the 1st inversion and four echo times for the 2nd inversion. So we will have 5 echo times in total."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:48:15.158096Z",
+ "end_time": "2023-11-16T01:48:15.194255Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from pathlib import Path\n",
+ "from typing import Tuple, List"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:48:17.293490Z",
+ "end_time": "2023-11-16T01:48:17.397181Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def __preprocess_ahead_raw_data__(raw_data_file: str) -> np.ndarray:\n",
+ " \"\"\"\n",
+ " Preprocess the raw data of the AHEAD dataset.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " raw_data_file : str\n",
+ " Path to the raw data and coil sensitivities of the AHEAD dataset.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " kspace: np.ndarray\n",
+ " The k-space data.\n",
+ " \"\"\"\n",
+ " dataset = ismrmrd.Dataset(raw_data_file, \"dataset\", create_if_needed=False)\n",
+ " number_of_acquisitions = dataset.number_of_acquisitions()\n",
+ "\n",
+ " # find the first no noise scan\n",
+ " first_scan = 0\n",
+ " for i in tqdm(range(number_of_acquisitions)):\n",
+ " head = dataset.read_acquisition(i).getHead()\n",
+ " if head.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):\n",
+ " first_scan = i\n",
+ " break\n",
+ "\n",
+ " meas = []\n",
+ " for i in tqdm(range(first_scan, number_of_acquisitions)):\n",
+ " acq = dataset.read_acquisition(i)\n",
+ " meas.append(acq)\n",
+ "\n",
+ " hdr = ismrmrd.xsd.CreateFromDocument(dataset.read_xml_header())\n",
+ "\n",
+ " # Matrix size\n",
+ " enc = hdr.encoding[0]\n",
+ " enc_Nx = enc.encodedSpace.matrixSize.x\n",
+ " enc_Ny = enc.encodedSpace.matrixSize.y\n",
+ " enc_Nz = enc.encodedSpace.matrixSize.z\n",
+ "\n",
+ " nCoils = hdr.acquisitionSystemInformation.receiverChannels\n",
+ "\n",
+ " nslices = enc.encodingLimits.slice.maximum + 1 if enc.encodingLimits.slice is not None else 1\n",
+ " nreps = enc.encodingLimits.repetition.maximum + 1 if enc.encodingLimits.repetition is not None else 1\n",
+ " ncontrasts = enc.encodingLimits.contrast.maximum + 1 if enc.encodingLimits.contrast is not None else 1\n",
+ "\n",
+ " # initialize k-space array\n",
+ " Kread = np.zeros((enc_Nx, enc_Ny, enc_Nz, nCoils), dtype=np.complex64)\n",
+ "\n",
+ " # Select the appropriate measurements from the data\n",
+ " for acq in tqdm(meas):\n",
+ " head = acq.getHead()\n",
+ " if head.idx.contrast == ncontrasts - 1 and head.idx.repetition == nreps - 1 and head.idx.slice == nslices - 1:\n",
+ " head = acq.getHead()\n",
+ " ky = head.idx.kspace_encode_step_1\n",
+ " kz = head.idx.kspace_encode_step_2\n",
+ " Kread[:, ky, kz, :] = np.transpose(acq.data, (1, 0))\n",
+ "\n",
+ " return Kread"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's test our preprocessing function. This will take a while."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:48:28.272380Z",
+ "end_time": "2023-11-16T01:48:28.325779Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# get all files\n",
+ "files = list(Path(dataset_path).iterdir())\n",
+ "# get the fnames\n",
+ "fnames = [str(file).split('/')[-1].split('_')[1].split('.')[0] for file in files if \"coilsens\" in file.name]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:48:29.937933Z",
+ "end_time": "2023-11-16T01:48:30.586895Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# let's do the first subject in our list\n",
+ "fname = fnames[0]\n",
+ "print(f\"Processing subject {fname}...\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:48:30.553574Z",
+ "end_time": "2023-11-16T01:48:30.685283Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# get all files for this subject from files\n",
+ "subject_files = [file for file in files if fname in file.name]\n",
+ "raw_data_files = [file for file in subject_files if \"coilsens\" not in file.name and \"inv1\" not in file.name]\n",
+ "raw_data_files.sort()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T01:48:32.064638Z",
+ "end_time": "2023-11-16T02:19:05.930571Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# preprocess the raw data\n",
+ "kspaces = [__preprocess_ahead_raw_data__(str(x)) for x in raw_data_files]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:19:05.923710Z",
+ "end_time": "2023-11-16T02:19:05.931703Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "print(f\"Number of kspaces: {len(kspaces)}\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's wrap up a function to get different planes from the data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:19:05.923867Z",
+ "end_time": "2023-11-16T02:19:05.931780Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def __get_plane__(data: np.ndarray, data_on_kspace: bool = True, plane: str = \"sagittal\") -> np.ndarray:\n",
+ " \"\"\"\n",
+ " Get the given plane from the data.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " data : np.ndarray\n",
+ " The data to get the plane from.\n",
+ " data_on_kspace : bool, optional\n",
+ " Whether the data is on the kspace or not. The default is True.\n",
+ " plane : str, optional\n",
+ " The plane to get the kspace and coil sensitivities from. The default is \"sagittal\".\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " data: np.ndarray\n",
+ " The data of the given plane.\n",
+ " \"\"\"\n",
+ " if not data_on_kspace:\n",
+ " data = np.fft.fftn(data, axes=(0, 1, 2))\n",
+ "\n",
+ " if plane == \"axial\":\n",
+ " data = np.transpose(data, (2, 0, 1, 3))\n",
+ " elif plane == \"coronal\":\n",
+ " data = np.transpose(data, (1, 0, 2, 3))\n",
+ "\n",
+ " # all planes need to be rotated by 90 degrees in x-y to get the correct orientation\n",
+ " data = np.rot90(data, k=1, axes=(1, 2))\n",
+ "\n",
+ " if not data_on_kspace:\n",
+ " data = np.fft.ifftn(data, axes=(0, 1, 2))\n",
+ "\n",
+ " return data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:19:05.923958Z",
+ "end_time": "2023-11-16T02:19:05.931826Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "axial_kspaces = [__get_plane__(x, data_on_kspace=True, plane=\"axial\") for x in kspaces]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Do the same for the coil sensitivities"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:19:05.924038Z",
+ "end_time": "2023-11-16T02:19:05.931870Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def __preprocess_ahead_coil_sensitivities__(coil_sensitivities_file: str) -> np.ndarray:\n",
+ " \"\"\"\n",
+ " Preprocess the coil sensitivities of the AHEAD dataset.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " coil_sensitivities_file : str\n",
+ " Path to the coil sensitivities of the AHEAD dataset.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " coil_sensitivities: np.ndarray\n",
+ " The coil sensitivities.\n",
+ " \"\"\"\n",
+ " # load the coil sensitivities\n",
+ " coil_sensitivities = h5py.File(coil_sensitivities_file, \"r\")\n",
+ "\n",
+ " # get the coil sensitivities\n",
+ " coil_sensitivities_real = np.array(coil_sensitivities[\"0real\"])\n",
+ " coil_sensitivities_imag = np.array(coil_sensitivities[\"1imag\"])\n",
+ " coil_sensitivities = coil_sensitivities_real + 1j * coil_sensitivities_imag\n",
+ "\n",
+ " # transpose to get the correct shape, i.e. (x, y, z, coils)\n",
+ " coil_sensitivities = np.transpose(coil_sensitivities, (3, 2, 1, 0))\n",
+ "\n",
+ " return coil_sensitivities"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:19:05.924128Z",
+ "end_time": "2023-11-16T02:19:05.931915Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities_file = [file for file in subject_files if \"coilsens\" in file.name][0]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:19:05.924241Z",
+ "end_time": "2023-11-16T02:19:44.813148Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "coil_sensitivities = __preprocess_ahead_coil_sensitivities__(str(coil_sensitivities_file))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:19:44.812831Z",
+ "end_time": "2023-11-16T02:22:38.604132Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "axial_coil_sensitivities = __get_plane__(coil_sensitivities, data_on_kspace=False, plane=\"axial\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's wrap up a function that transforms the kspaces to image space and computes target images."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:22:38.603703Z",
+ "end_time": "2023-11-16T02:22:38.605364Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def __compute_targets__(\n",
+ " kspace: np.ndarray, coil_sensitivities: np.ndarray, coil_dim: int = -1\n",
+ ") -> Tuple[np.ndarray, np.ndarray]:\n",
+ " \"\"\"\n",
+ " Compute the target images from the kspace and coil sensitivities.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " kspace : np.ndarray\n",
+ " The kspace.\n",
+ " coil_sensitivities : np.ndarray\n",
+ " The coil sensitivities.\n",
+ " coil_dim : int, optional\n",
+ " The dimension of the coil sensitivities. The default is -1.\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " image_space : np.ndarray\n",
+ " The image space.\n",
+ " target_image : np.ndarray\n",
+ " The target image.\n",
+ " \"\"\"\n",
+ " # get the image space\n",
+ " image_space = np.fft.fftshift(\n",
+ " np.fft.ifftn(np.fft.fftshift(kspace, axes=(0, 1, 2)), axes=(0, 1, 2)), axes=(0, 1, 2)\n",
+ " )\n",
+ "\n",
+ " # compute the target\n",
+ " target = np.sum(image_space * np.conj(coil_sensitivities), axis=coil_dim)\n",
+ "\n",
+ " return image_space, target"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:22:38.603873Z",
+ "end_time": "2023-11-16T02:32:11.236339Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# compute the image spaces and targets\n",
+ "axial_image_spaces = []\n",
+ "axial_targets = []\n",
+ "for x in axial_kspaces:\n",
+ " axial_image_space, axial_target = __compute_targets__(x, axial_coil_sensitivities, coil_dim=-1)\n",
+ " axial_image_spaces.append(axial_image_space)\n",
+ " axial_targets.append(axial_target)\n",
+ "axial_image_space = np.stack(axial_image_spaces, axis=1)\n",
+ "axial_target = np.stack(axial_targets, axis=1)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's visualize the target images of 2nd inversion for the four echo times."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:38:52.813706Z",
+ "end_time": "2023-11-16T02:38:53.783437Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "plt.subplot(4, 4, 1)\n",
+ "plt.imshow(np.abs(axial_target[slice_idx, 0, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude echo 1\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 2)\n",
+ "plt.imshow(np.angle(axial_target[slice_idx, 0, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Phase echo 1\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 3)\n",
+ "plt.imshow(np.real(axial_target[slice_idx, 0, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Real part echo 1\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 4)\n",
+ "plt.imshow(np.imag(axial_target[slice_idx, 0, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part echo 1\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 5)\n",
+ "plt.imshow(np.abs(axial_target[slice_idx, 1, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude echo 2\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 6)\n",
+ "plt.imshow(np.angle(axial_target[slice_idx, 1, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Phase echo 2\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 7)\n",
+ "plt.imshow(np.real(axial_target[slice_idx, 1, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Real part echo 2\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 8)\n",
+ "plt.imshow(np.imag(axial_target[slice_idx, 1, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part echo 2\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 9)\n",
+ "plt.imshow(np.abs(axial_target[slice_idx, 2, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude echo 3\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 10)\n",
+ "plt.imshow(np.angle(axial_target[slice_idx, 2, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Phase echo 3\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 11)\n",
+ "plt.imshow(np.real(axial_target[slice_idx, 2, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Real part echo 3\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 12)\n",
+ "plt.imshow(np.imag(axial_target[slice_idx, 2, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part echo 3\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 13)\n",
+ "plt.imshow(np.abs(axial_target[slice_idx, 3, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Magnitude echo 4\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 14)\n",
+ "plt.imshow(np.angle(axial_target[slice_idx, 3, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Phase echo 4\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 15)\n",
+ "plt.imshow(np.real(axial_target[slice_idx, 3, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Real part echo 4\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(4, 4, 16)\n",
+ "plt.imshow(np.imag(axial_target[slice_idx, 3, :, :]), cmap=\"gray\")\n",
+ "plt.title(\"Imaginary part echo 4\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Alright, now we have our kspaces and coil sensitivities in the axial plane for all four echo times for one subject. Let's finish with our preprocessing transforms by wrapping up a function that computes brain and head masks. Note that the brain and head masks are the same for all echo times, thus we can compute them only for one and use them for all. Then we are done with the preprocessing."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2023-11-16T02:39:06.765050Z",
+ "end_time": "2023-11-16T02:39:06.858882Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "def __compute_masks__(target_image: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\n",
+ " \"\"\"\n",
+ " Compute the brain and head masks.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " target_image : np.ndarray\n",
+ " The target image.\n",
+ " Returns\n",
+ " -------\n",
+ " brain_mask : np.ndarray\n",
+ " The brain mask.\n",
+ " head_mask : np.ndarray\n",
+ " The head mask.\n",
+ " \"\"\"\n",
+ " # compute head and brain mask\n",
+ " head_masks = []\n",
+ " brain_masks = []\n",
+ " for _slice_idx_ in tqdm(range(target_image.shape[0])):\n",
+ " # calculate otsu's threshold\n",
+ " threshold = threshold_otsu(np.abs(target_image[_slice_idx_]))\n",
+ " # get the connected components and apply threshold\n",
+ " axial_target_cc = measure.label(np.abs(target_image[_slice_idx_]) > threshold * 2) + measure.label(\n",
+ " np.abs(target_image[_slice_idx_]) > threshold)\n",
+ " # Get mask\n",
+ " skull_mask = np.where(axial_target_cc != 0, 1, 0)\n",
+ " # get the convex hull\n",
+ " brain_mask = convex_hull_image(skull_mask) * (1 - skull_mask)\n",
+ " # perform binary erosion to remove skull\n",
+ " brain_mask = binary_erosion(brain_mask, iterations=4)\n",
+ " # get the convex hull of the brain mask\n",
+ " brain_mask = convex_hull_image(brain_mask)\n",
+ " # threshold the brain mask\n",
+ " brain_mask = np.where(np.abs(target_image[_slice_idx_]) * brain_mask > threshold / 2, 1, 0)\n",
+ " # perform binary erosion to remove skull\n",
+ " brain_mask = binary_erosion(brain_mask, iterations=4)\n",
+ " # perform binary dilation to get the brain mask\n",
+ " brain_mask = binary_dilation(brain_mask, iterations=4)\n",
+ " # fill holes in the brain mask\n",
+ " brain_mask = binary_fill_holes(brain_mask)\n",
+ " # get the convex hull of the brain mask\n",
+ " brain_mask = convex_hull_image(brain_mask)\n",
+ " brain_masks.append(brain_mask)\n",
+ " head_masks.append(1 - brain_mask)\n",
+ " head_mask = np.stack(head_masks, axis=0)\n",
+ " brain_mask = np.stack(brain_masks, axis=0)\n",
+ " return brain_mask.astype(np.float32), head_mask.astype(np.float32)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# the brain and head masks are the same for all echoes\n",
+ "brain_mask, head_mask = __compute_masks__(axial_target[:, 0, :, :])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Let's visualize the masks"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.subplot(1, 2, 1)\n",
+ "plt.imshow(brain_mask[slice_idx], cmap=\"gray\")\n",
+ "plt.title(\"Brain mask\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.imshow(head_mask[slice_idx], cmap=\"gray\")\n",
+ "plt.title(\"Head mask\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### We are done with the preprocessing! Let's go on and compute proper quantitative maps from the four echo times.\n",
+ "\n",
+ "We will use the atommic package for this. The `R2star_B0_real_S0_complex_mapping' will allow us to compute the R2*, B0, S0, and phi maps.\n",
+ "\n",
+ "We only need to convert our target images to torch tensors and pass them to the mapping function with the corresponding echo times."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from atommic.collections.quantitative.parts.transforms import R2star_B0_S0_phi_mapping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def __compute_quantitative_maps__(\n",
+ " target_images: np.ndarray,\n",
+ " TEs: List[float],\n",
+ " brain_mask: np.ndarray,\n",
+ " head_mask: np.ndarray,\n",
+ " fully_sampled: bool = True,\n",
+ " shift: bool = False,\n",
+ " fft_centered: bool = False,\n",
+ " fft_normalization: str = \"backward\",\n",
+ " spatial_dims=None,\n",
+ ") -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n",
+ " \"\"\"\n",
+ " Compute the quantitative maps.\n",
+ "\n",
+ " Parameters\n",
+ " ----------\n",
+ " target_images : np.ndarray\n",
+ " The target images.\n",
+ " TEs : List[float]\n",
+ " The echo times.\n",
+ " brain_mask : np.ndarray\n",
+ " The brain mask.\n",
+ " head_mask : np.ndarray\n",
+ " The head mask.\n",
+ " fully_sampled : bool, optional\n",
+ " Whether the data is fully sampled or not. The default is True.\n",
+ " shift : bool, optional\n",
+ " Whether to shift the kspace or not. The default is False.\n",
+ " fft_centered : bool, optional\n",
+ " Whether the fft is centered or not. The default is False.\n",
+ " fft_normalization : str, optional\n",
+ " The fft normalization. The default is \"backward\".\n",
+ " spatial_dims : List[int], optional\n",
+ " The spatial dimensions. The default is [-2, -1].\n",
+ "\n",
+ " Returns\n",
+ " -------\n",
+ " multiple_echoes_target : np.ndarray\n",
+ " The stacked target image from multiple echoes.\n",
+ " R2star_map : np.ndarray\n",
+ " The R2* map.\n",
+ " S0_map : np.ndarray\n",
+ " The S0 map.\n",
+ " B0_map : np.ndarray\n",
+ " The B0 map.\n",
+ " phi_map : np.ndarray\n",
+ " The phase map.\n",
+ " \"\"\"\n",
+ " # stack real and imaginary part of the target image\n",
+ " if spatial_dims is None:\n",
+ " spatial_dims = [-2, -1]\n",
+ " multiple_echoes_target_tensor = np.stack([np.real(target_images), np.imag(target_images)], axis=-1)\n",
+ " # convert to torch tensor\n",
+ " multiple_echoes_target_tensor = torch.from_numpy(multiple_echoes_target_tensor)\n",
+ " # verify the tensor will be complex valued\n",
+ " multiple_echoes_target_tensor = torch.view_as_complex(multiple_echoes_target_tensor)\n",
+ " # verify the tensor can be converted to real valued, with stacked real and imag parts on the last dimension\n",
+ " multiple_echoes_target_tensor = torch.view_as_real(multiple_echoes_target_tensor)\n",
+ "\n",
+ " brain_mask = torch.from_numpy(brain_mask).unsqueeze(1)\n",
+ " head_mask = torch.from_numpy(head_mask).unsqueeze(1)\n",
+ "\n",
+ " R2star_maps = []\n",
+ " S0_maps = []\n",
+ " B0_maps = []\n",
+ " phi_maps = []\n",
+ " for slice_idx in tqdm(range(multiple_echoes_target_tensor.shape[0])):\n",
+ " # compute the quantitative maps\n",
+ " R2star_map, S0_map, B0_map, phi_map = R2star_B0_S0_phi_mapping(\n",
+ " prediction=multiple_echoes_target_tensor[slice_idx],\n",
+ " TEs=TEs,\n",
+ " brain_mask=brain_mask[slice_idx],\n",
+ " head_mask=head_mask[slice_idx],\n",
+ " fully_sampled=fully_sampled,\n",
+ " shift=shift,\n",
+ " fft_centered=fft_centered,\n",
+ " fft_normalization=fft_normalization,\n",
+ " spatial_dims=spatial_dims\n",
+ " )\n",
+ " R2star_maps.append(R2star_map)\n",
+ " S0_maps.append(S0_map)\n",
+ " B0_maps.append(B0_map[0])\n",
+ " phi_maps.append(phi_map)\n",
+ "\n",
+ " R2star_maps = torch.stack(R2star_maps, dim=0).numpy()\n",
+ " S0_maps = torch.stack(S0_maps, dim=0).numpy()\n",
+ " B0_maps = torch.stack(B0_maps, dim=0).numpy()\n",
+ " phi_maps = torch.stack(phi_maps, dim=0).numpy()\n",
+ "\n",
+ " return torch.view_as_complex(multiple_echoes_target_tensor).numpy(), R2star_maps, S0_maps, B0_maps, phi_maps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# just for reminder, those are the TEs of the dataset\n",
+ "TEs = [3.0, 11.5, 20.0, 28.5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# we will do 1 slice for ease of this example\n",
+ "multiple_echoes_target_tensor, R2star_map, S0_map, B0_map, phi_map = __compute_quantitative_maps__(\n",
+ " axial_target[slice_idx:slice_idx+1],\n",
+ " TEs,\n",
+ " brain_mask[slice_idx:slice_idx+1],\n",
+ " head_mask[slice_idx:slice_idx+1],\n",
+ " fully_sampled=True,\n",
+ " shift=False,\n",
+ " fft_centered=False,\n",
+ " fft_normalization=\"backward\",\n",
+ " spatial_dims=[-2, -1]\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "print(\n",
+ " f\"Shape of the target tensor: {multiple_echoes_target_tensor.shape}\\n\"\n",
+ " f\"Shape of the R2* map: {R2star_map.shape}\\n\"\n",
+ " f\"Shape of the S0 map: {S0_map.shape}\\n\"\n",
+ " f\"Shape of the B0 map: {B0_map.shape}\\n\"\n",
+ " f\"Shape of the phi map: {phi_map.shape}\\n\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.figure(figsize=(20, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(R2star_map[0], cmap=\"gray\")\n",
+ "plt.title(\"R2*\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(S0_map[0], cmap=\"gray\")\n",
+ "plt.title(\"S0\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(B0_map[0], cmap=\"gray\")\n",
+ "plt.title(\"B0\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(phi_map[0], cmap=\"gray\")\n",
+ "plt.title(\"Phi\")\n",
+ "plt.axis(\"off\")\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Awesome! We have computed the R2*, B0, S0, and phi maps from the four echo times!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Conclusion\n",
+ "\n",
+ "In this notebook, we have shown how to preprocess the AHEAD dataset and compute the R2*, B0, S0, and phi maps from data with multiple echo times.\n",
+ "\n",
+ "You can now download the AHEAD dataset and use the preprocessing script to batch process multiple subjects and save the inputs to disk. You can then use the preprocessed data to train a Deep Learning quantitative model by configuring the `data` section of the `config.yaml` file.\n",
+ "\n",
+ "Enjoy!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## References\n",
+ "\n",
+ "Caan, Matthan, 2022, \"Quantitative motion-corrected 7T sub-millimeter raw MRI database of the adult lifespan\", https://doi.org/10.34894/IHZGQM, DataverseNL, V1"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/projects/qMRI/AHEAD/scripts/__init__.py b/projects/qMRI/AHEAD/scripts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/qMRI/AHEAD/scripts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/projects/qMRI/AHEAD/scripts/compute_segmentation_masks.py b/projects/qMRI/AHEAD/scripts/compute_segmentation_masks.py
new file mode 100644
index 00000000..fa9c0082
--- /dev/null
+++ b/projects/qMRI/AHEAD/scripts/compute_segmentation_masks.py
@@ -0,0 +1,100 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import os
+from pathlib import Path
+
+import h5py
+import numpy as np
+from scipy.ndimage import binary_dilation, binary_erosion, binary_fill_holes
+from skimage import measure
+from skimage.filters import threshold_otsu
+from skimage.morphology import convex_hull_image
+from tqdm import tqdm
+
+
+def compute_masks(target_image: np.ndarray, fname: str) -> np.ndarray:
+ """
+ Compute the brain and head masks.
+
+ Parameters
+ ----------
+ target_image : np.ndarray
+ The target image.
+ fname : str
+ The filename of the target image.
+
+ Returns
+ -------
+ brain_mask : np.ndarray
+ The brain mask.
+ head_mask : np.ndarray
+ The head mask.
+ """
+ # compute head and brain mask
+ brain_masks = []
+ for _slice_idx_ in tqdm(range(target_image.shape[0])):
+ # calculate otsu's threshold
+ threshold = threshold_otsu(np.abs(target_image[_slice_idx_]))
+ # get the connected components and apply threshold
+ target_image_cc = measure.label(np.abs(target_image[_slice_idx_]) > threshold * 2) + measure.label(
+ np.abs(target_image[_slice_idx_]) > threshold
+ )
+ # Get mask
+ skull_mask = np.where(target_image_cc != 0, 1, 0)
+ # get the convex hull
+ brain_mask = convex_hull_image(skull_mask) * (1 - skull_mask)
+ # perform binary erosion to remove skull
+ if (
+ 'mp2rageme_003_axial.h5' in str(fname)
+ or 'mp2rageme_007_axial.h5' in str(fname)
+ or 'mp2rageme_008_axial.h5' in str(fname)
+ or 'mp2rageme_009_axial.h5' in str(fname)
+ ):
+ brain_mask = binary_erosion(brain_mask, iterations=1)
+ elif 'mp2rageme_010_axial.h5' in str(fname):
+ brain_mask = binary_erosion(brain_mask, iterations=3)
+ else:
+ brain_mask = binary_erosion(brain_mask, iterations=4)
+ # get the convex hull of the brain mask
+ brain_mask = convex_hull_image(brain_mask)
+ # threshold the brain mask
+ brain_mask = np.where(np.abs(target_image[_slice_idx_]) * brain_mask > threshold / 2, 1, 0)
+ # perform binary erosion to remove skull
+ brain_mask = binary_erosion(brain_mask, iterations=4)
+ # perform binary dilation to get the brain mask
+ brain_mask = binary_dilation(brain_mask, iterations=4)
+ # fill holes in the brain mask
+ brain_mask = binary_fill_holes(brain_mask)
+ # get the convex hull of the brain mask
+ brain_mask = convex_hull_image(brain_mask)
+ brain_masks.append(brain_mask)
+ brain_mask = np.stack(brain_masks, axis=0)
+ return brain_mask.astype(np.float32)
+
+
+def main(args):
+ output_path = Path(args.output_path)
+ if not os.path.exists(output_path):
+ output_path.mkdir(parents=True, exist_ok=True)
+ # get all files
+ files = list(Path(args.data_path).iterdir())
+ # iterate over all subjects
+ for fname in tqdm(files):
+ print(fname)
+ # load the target
+ target = h5py.File(fname, "r")["target"][()]
+ # masks are the same for all echoes
+ anatomy_mask = compute_masks(target[:, 0], str(fname.name))
+ # save the masks
+ with h5py.File(output_path / fname.name, "w") as f:
+ f.create_dataset("anatomy_mask", data=anatomy_mask)
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("data_path", type=Path, default="data/ahead_data")
+ parser.add_argument("output_path", type=Path, default="data/ahead_data_preprocessed")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/qMRI/AHEAD/scripts/preprocessing.py b/projects/qMRI/AHEAD/scripts/preprocessing.py
new file mode 100644
index 00000000..842afe59
--- /dev/null
+++ b/projects/qMRI/AHEAD/scripts/preprocessing.py
@@ -0,0 +1,266 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import argparse
+import os
+from pathlib import Path
+from typing import Tuple
+
+import h5py
+import ismrmrd
+import numpy as np
+from tqdm import tqdm
+
+
+def preprocess_ahead_raw_data(raw_data_file: str) -> np.ndarray:
+ """
+ Preprocess the raw data of the AHEAD dataset.
+
+ Parameters
+ ----------
+ raw_data_file : str
+ Path to the raw data and coil sensitivities of the AHEAD dataset.
+
+ Returns
+ -------
+ kspace: np.ndarray
+ The k-space data.
+ """
+ dataset = ismrmrd.Dataset(raw_data_file, "dataset", create_if_needed=False)
+ number_of_acquisitions = dataset.number_of_acquisitions()
+
+ # find the first no noise scan
+ first_scan = 0
+ for i in tqdm(range(number_of_acquisitions)):
+ head = dataset.read_acquisition(i).getHead()
+ if head.isFlagSet(ismrmrd.ACQ_IS_NOISE_MEASUREMENT):
+ first_scan = i
+ break
+
+ meas = []
+ for i in tqdm(range(first_scan, number_of_acquisitions)):
+ meas.append(dataset.read_acquisition(i))
+
+ hdr = ismrmrd.xsd.CreateFromDocument(dataset.read_xml_header())
+
+ # Matrix size
+ enc = hdr.encoding[0]
+ enc_Nx = enc.encodedSpace.matrixSize.x
+ enc_Ny = enc.encodedSpace.matrixSize.y
+ enc_Nz = enc.encodedSpace.matrixSize.z
+
+ nCoils = hdr.acquisitionSystemInformation.receiverChannels
+
+ nslices = enc.encodingLimits.slice.maximum + 1 if enc.encodingLimits.slice is not None else 1
+ nreps = enc.encodingLimits.repetition.maximum + 1 if enc.encodingLimits.repetition is not None else 1
+ ncontrasts = enc.encodingLimits.contrast.maximum + 1 if enc.encodingLimits.contrast is not None else 1
+
+ # initialize k-space array
+ Kread = np.zeros((enc_Nx, enc_Ny, enc_Nz, nCoils), dtype=np.complex64)
+
+ # Select the appropriate measurements from the data
+ for acq in tqdm(meas):
+ head = acq.getHead()
+ if head.idx.contrast == ncontrasts - 1 and head.idx.repetition == nreps - 1 and head.idx.slice == nslices - 1:
+ ky = head.idx.kspace_encode_step_1
+ kz = head.idx.kspace_encode_step_2
+ Kread[:, ky, kz, :] = np.transpose(acq.data, (1, 0))
+
+ return Kread
+
+
+def preprocess_ahead_coil_sensitivities(coil_sensitivities_file: str) -> np.ndarray:
+ """
+ Preprocess the coil sensitivities of the AHEAD dataset.
+
+ Parameters
+ ----------
+ coil_sensitivities_file : str
+ Path to the coil sensitivities of the AHEAD dataset.
+
+ Returns
+ -------
+ coil_sensitivities: np.ndarray
+ The coil sensitivities.
+ """
+ # load the coil sensitivities
+ coil_sensitivities = h5py.File(coil_sensitivities_file, "r")
+
+ # get the coil sensitivities
+ coil_sensitivities_real = np.array(coil_sensitivities["0real"])
+ coil_sensitivities_imag = np.array(coil_sensitivities["1imag"])
+ coil_sensitivities = coil_sensitivities_real + 1j * coil_sensitivities_imag
+
+ # transpose to get the correct shape, i.e. (x, y, z, coils)
+ coil_sensitivities = np.transpose(coil_sensitivities, (3, 2, 1, 0))
+
+ return coil_sensitivities
+
+
+def get_plane(data: np.ndarray, data_on_kspace: bool = True, plane: str = "sagittal") -> np.ndarray:
+ """
+ Get the given plane from the data.
+
+ Parameters
+ ----------
+ data : np.ndarray
+ The data to get the plane from.
+ data_on_kspace : bool, optional
+ Whether the data is on the kspace or not. The default is True.
+ plane : str, optional
+ The plane to get the kspace and coil sensitivities from. The default is "sagittal".
+
+ Returns
+ -------
+ data: np.ndarray
+ The data of the given plane.
+ """
+ if not data_on_kspace:
+ data = np.fft.fftn(data, axes=(0, 1, 2))
+
+ if plane == "axial":
+ data = np.transpose(data, (2, 0, 1, 3))
+ elif plane == "coronal":
+ data = np.transpose(data, (1, 0, 2, 3))
+
+ # all planes need to be rotated by 90 degrees in x-y to get the correct orientation
+ data = np.rot90(data, k=1, axes=(1, 2))
+
+ if not data_on_kspace:
+ data = np.fft.ifftn(data, axes=(0, 1, 2))
+
+ return data
+
+
+def compute_targets(
+ kspace: np.ndarray, coil_sensitivities: np.ndarray, coil_dim: int = -1
+) -> Tuple[np.ndarray, np.ndarray]:
+ """
+ Compute the target images from the kspace and coil sensitivities.
+
+ Parameters
+ ----------
+ kspace : np.ndarray
+ The kspace.
+ coil_sensitivities : np.ndarray
+ The coil sensitivities.
+ coil_dim : int, optional
+ The dimension of the coil sensitivities. The default is -1.
+
+ Returns
+ -------
+ image_space : np.ndarray
+ The image space.
+ target_image : np.ndarray
+ The target image.
+ """
+ # get the image space
+ image_space = np.fft.fftshift(
+ np.fft.ifftn(np.fft.fftshift(kspace, axes=(0, 1, 2)), axes=(0, 1, 2)), axes=(0, 1, 2)
+ )
+
+ # compute the target
+ target = np.sum(image_space * np.conj(coil_sensitivities), axis=coil_dim)
+
+ return image_space, target
+
+
+def save_data(
+ image_space: np.ndarray, coil_sensitivities: np.ndarray, target: np.ndarray, output_path: Path, filename: str
+):
+ """
+ Save the data.
+
+ Parameters
+ ----------
+ image_space : np.ndarray
+ The image space.
+ coil_sensitivities : np.ndarray
+ The coil sensitivities.
+ target : np.ndarray
+ The target image.
+ output_path : Path
+ The output path.
+ filename : str
+ The filename.
+ """
+ # we need to move the coils dimension to dimension 2 and get kspace
+ image_space = np.moveaxis(image_space, -1, 2)
+ # we need to move the coils dimension to dimension 1 and get coil sensitivities
+ coil_sensitivities = np.moveaxis(coil_sensitivities, -1, 1)
+
+ # get kspace
+ kspace = np.fft.fftn(image_space, axes=(-2, -1))
+ kspace = np.fft.fftshift(kspace, axes=(-2, -1))
+
+ if not os.path.exists(output_path):
+ output_path.mkdir(parents=True, exist_ok=True)
+
+ with h5py.File(output_path / f"{filename}.h5", "w") as f:
+ f.create_dataset("kspace", data=kspace.astype(np.complex64))
+ f.create_dataset("sensitivity_map", data=coil_sensitivities.astype(np.complex64))
+ f.create_dataset("target", data=target.astype(np.complex64))
+
+
+def main(args):
+ # get all files
+ files = list(Path(args.data_path).iterdir())
+ # get the fnames
+ fnames = [
+ str(file).rsplit("/", maxsplit=1)[-1].split("_")[1].split(".")[0] for file in files if "coilsens" in file.name
+ ]
+
+ plane = args.plane
+
+ # iterate over all subjects
+ for fname in fnames:
+ print(f"Processing subject {fname}...")
+
+ # get all files for this subject from files
+ subject_files = [file for file in files if fname in file.name]
+ raw_data_files = [file for file in subject_files if "coilsens" not in file.name and "inv1" not in file.name]
+ raw_data_files.sort()
+
+ # preprocess the raw data
+ kspaces = [preprocess_ahead_raw_data(str(x)) for x in raw_data_files]
+ kspaces = [get_plane(x, data_on_kspace=True, plane=plane) for x in kspaces]
+
+ # preprocess the coil sensitivities
+ coil_sensitivities_file = [file for file in subject_files if "coilsens" in file.name][0]
+ coil_sensitivities = preprocess_ahead_coil_sensitivities(str(coil_sensitivities_file))
+ coil_sensitivities = get_plane(coil_sensitivities, data_on_kspace=False, plane=plane)
+
+ # compute the image spaces and targets
+ image_spaces = []
+ targets = []
+ for x in kspaces:
+ image_space, target = compute_targets(x, coil_sensitivities, coil_dim=-1)
+ image_spaces.append(image_space)
+ targets.append(target)
+ image_space = np.stack(image_spaces, axis=1)
+ target = np.stack(targets, axis=1)
+
+ slice_range = args.slice_range
+ if slice_range is not None:
+ image_space = image_space[slice_range[0] : slice_range[1]]
+ coil_sensitivities = coil_sensitivities[slice_range[0] : slice_range[1]]
+ target = target[slice_range[0] : slice_range[1]]
+
+ # save the data to disk
+ save_data(
+ image_space,
+ coil_sensitivities,
+ target,
+ Path(args.output_path),
+ f"mp2rageme_{fname}_{plane}",
+ )
+
+
+if __name__ == "__main__":
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--data_path", type=Path, default="data/ahead_data")
+ parser.add_argument("--output_path", type=Path, default="data/ahead_data_preprocessed")
+ parser.add_argument("--plane", type=str, default="axial")
+ parser.add_argument("--slice_range", default=None, type=int, nargs="+")
+ args = parser.parse_args()
+ main(args)
diff --git a/projects/qMRI/__init__.py b/projects/qMRI/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/projects/qMRI/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 00000000..a960c398
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,71 @@
+[tool.isort]
+profile = "black" # black-compatible
+line_length = 119 # should match black parameters
+ignore_whitespace = true # ignore whitespace for compatibility with the initial style
+py_version = 310 # python 3.10 as a target version
+known_first_party = ["atommic"] # FIRSTPARTY section
+known_third_party = [] # THIRDPARTY section
+sections = ["FUTURE", "STDLIB", "THIRDPARTY", "FIRSTPARTY", "LOCALFOLDER"]
+default_section = "THIRDPARTY"
+extend_skip = ["setup.py", "docs/source/conf.py"]
+
+[tool.pylint.'pycodestyle']
+max-args = 65
+max-line-length = 119
+max-branches = 20
+max-locals = 65
+max-nested-blocks = 10
+max-statements = 110
+
+[tool.black]
+line_length = 119
+skip_string_normalization = true
+
+[tool.pytest.ini_options]
+# durations=0 will display all tests execution time, sorted in ascending order starting from from the slowest one.
+# -vv will also display tests with durration = 0.00s
+addopts = "--verbose --pyargs --durations=0 --strict-markers" # always add these arguments to pytest
+testpaths = ["tests"]
+# directories to ignore when discovering tests
+norecursedirs = [
+ "atommic",
+ "external",
+ "docs",
+ "projects",
+ "tools",
+ "*.egg",
+ ".*",
+ "_darcs",
+ "build",
+ "CVS",
+ "dist",
+ "venv",
+ "{arch}"
+]
+# markers to select tests, use `pytest --markers` to see all available markers, `pytest -m ""` to select tests
+markers = [
+ "unit: marks unit test, i.e. testing a single, well isolated functionality (deselect with '-m \"not unit\"')",
+ "integration: marks test checking the elements when integrated into subsystems (deselect with '-m \"not integration\"')",
+ "system: marks test working at the highest integration level (deselect with '-m \"not system\"')",
+ "acceptance: marks test checking whether the developed product/model passes the user defined acceptance criteria (deselect with '-m \"not acceptance\"')",
+ "docs: mark tests related to documentation (deselect with '-m \"not docs\"')",
+ "skipduringci: marks tests that are skipped ci as they are addressed by Jenkins jobs but should be run to test user setups",
+ "pleasefixme: marks tests that are broken and need fixing",
+ "run_only_on: marks tests that should only be run on a specific platform",
+]
+
+[tool.tox]
+legacy_tox_ini = """
+[tox]
+envlist = py310
+skip_missing_interpreters=true
+[gh-actions]
+python =
+ 3.10: py310
+[testenv]
+deps = pytest
+extras = dev
+allowlist_externals = sh
+commands=
+ sh -c "pytest --ignore=projects"
+"""
diff --git a/reinstall.sh b/reinstall.sh
new file mode 100644
index 00000000..26841d1e
--- /dev/null
+++ b/reinstall.sh
@@ -0,0 +1,38 @@
+#!/usr/bin/env bash
+set -e
+
+INSTALL_OPTION=${1:-"dev"}
+
+PIP=pip
+
+${PIP} install -U ${PIP}
+
+echo 'Uninstalling old versions...'
+${PIP} uninstall -y -q -r requirements/requirements.txt
+${PIP} uninstall -y -q -r requirements/requirements_docs.txt
+${PIP} uninstall -y -q -r requirements/requirements-ahead_stanfordknees.txt
+${PIP} uninstall -y -q -r requirements/requirements-isles22.txt
+${PIP} uninstall -y atommic
+
+if [ -n "${NVIDIA_PYTORCH_VERSION}" ]; then
+ echo 'Installing ATOMMIC in PyTorch container:' "${NVIDIA_PYTORCH_VERSION}" 'so will not install numba'
+else
+ if [ -n "${CONDA_PREFIX}" ]; then
+ NUMBA_VERSION=0.57.1
+ echo 'Installing numba=='${NUMBA_VERSION}
+ conda install -y -c conda-forge numba==${NUMBA_VERSION}
+ fi
+fi
+
+echo 'Installing atommic'
+if [[ "$INSTALL_OPTION" == "dev" ]]; then
+ ${PIP} install --editable ".[all]"
+else
+ rm -rf dist/
+ ${PIP} install build pytest-runner
+ python -m build --no-isolation --wheel
+ DIST_FILE=$(find ./dist -name "*.whl" | head -n 1)
+ ${PIP} install "${DIST_FILE}[all]"
+fi
+
+echo 'Finished!'
diff --git a/requirements/requirements-ahead_stanfordknees.txt b/requirements/requirements-ahead_stanfordknees.txt
new file mode 100644
index 00000000..87ffe06b
--- /dev/null
+++ b/requirements/requirements-ahead_stanfordknees.txt
@@ -0,0 +1 @@
+ismrmrd
diff --git a/requirements/requirements-isles22.txt b/requirements/requirements-isles22.txt
new file mode 100644
index 00000000..ae1ea9cc
--- /dev/null
+++ b/requirements/requirements-isles22.txt
@@ -0,0 +1,2 @@
+connected-components-3d
+SimpleITK==2.3.0
diff --git a/requirements/requirements.txt b/requirements/requirements.txt
new file mode 100644
index 00000000..95865e4d
--- /dev/null
+++ b/requirements/requirements.txt
@@ -0,0 +1,24 @@
+defusedxml>=0.7.1
+einops>=0.5.0
+h5py==3.9.0
+huggingface_hub
+hydra-core>1.3,<=1.3.2
+nibabel==5.1.0
+numba
+numpy>=1.22,<=1.24.2
+omegaconf<=2.3
+onnx>=1.11.0
+onnxruntime==1.15.1
+pytest==7.4.0
+python-dateutil
+pytorch-lightning>=2.0.0,<=2.0.7
+runstats>=2.0.0
+scikit-image==0.21.0
+setuptools==65.5.1
+tensorboard
+torch>=2.0.0,<2.1.0
+torchmetrics>=0.11.0
+tqdm
+wandb==0.15.8
+wget>=3.2
+wrapt>=1.13.3
diff --git a/requirements/requirements_docs.txt b/requirements/requirements_docs.txt
new file mode 100644
index 00000000..34406bd2
--- /dev/null
+++ b/requirements/requirements_docs.txt
@@ -0,0 +1,13 @@
+Jinja2<3.1
+latexcodec
+numpy
+# sphinx-book-theme is incompatible with pydata-sphinx-theme>0.13.2
+# https://github.com/executablebooks/sphinx-book-theme/issues/711
+pydata-sphinx-theme==0.13.1
+Sphinx>=4.0,<6,!=5.0.0
+sphinx-book-theme
+sphinx-copybutton
+sphinxcontrib-bibtex
+sphinxext-opengraph
+urllib3<2.0.0
+wrapt
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 00000000..b5217673
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,29 @@
+# encoding: utf-8
+# __author__ = "Dimitris Karkalousos"
+
+[aliases]
+test = pytest
+
+[options.data_files]
+. = requirements/requirements.txt
+
+# durations=0 will display all tests execution time, sorted in ascending order starting from from the slowest one.
+# -vv will also display tests with duration = 0.00s
+[tool:pytest]
+addopts = --verbose --pyargs --durations=0
+testpaths = tests
+norecursedirs = atommic docs scripts tools *.egg .* _darcs build CVS dist venv {arch}
+markers =
+ unit: marks unit test, i.e. testing a single, well isolated functionality (deselect with '-m "not unit"')
+ integration: marks test checking the elements when integrated into subsystems (deselect with '-m "not integration"')
+ system: marks test working at the highest integration level (deselect with '-m "not system"')
+ acceptance: marks test checking whether the developed product/model passes the user defined acceptance criteria (deselect with '-m "not acceptance"')
+ docs: mark tests related to documentation (deselect with '-m "not docs"')
+ skipduringci: marks tests that are skipped ci as they are addressed by Jenkins jobs but should be run to test user setups
+ pleasefixme: marks tests that are broken and need fixing
+
+[isort]
+known_localfolder = atommic, tests
+sections = FUTURE,STDLIB,THIRDPARTY,LOCALFOLDER
+default_section = THIRDPARTY
+skip = setup.py, docs/source/conf.py
diff --git a/setup.py b/setup.py
new file mode 100644
index 00000000..0db76ada
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,197 @@
+# coding=utf-8
+# ! /usr/bin/python
+import importlib.util
+import subprocess
+import sys
+from pathlib import Path
+
+import setuptools
+from setuptools import Command
+from setuptools import logging as setuptools_logging
+
+__author__ = "Dimitris Karkalousos"
+
+
+spec = importlib.util.spec_from_file_location("package_info", "atommic/package_info.py")
+package_info = importlib.util.module_from_spec(spec) # type: ignore
+spec.loader.exec_module(package_info) # type: ignore
+
+__contact_emails__ = package_info.__contact_emails__
+__contact_names__ = package_info.__contact_names__
+__description__ = package_info.__description__
+__download_url__ = package_info.__download_url__
+__homepage__ = package_info.__homepage__
+__keywords__ = package_info.__keywords__
+__license__ = package_info.__license__
+__package_name__ = package_info.__package_name__
+__repository_url__ = package_info.__repository_url__
+__version__ = package_info.__version__
+
+with open("README.md", "r", encoding="utf-8") as fh:
+ long_description = fh.read()
+long_description_content_type = "text/markdown"
+
+
+###############################################################################
+# Dependency Loading #
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #
+
+
+def _load_requirements(requirements_file, folder="requirements"):
+ """Load requirements from a file."""
+ requirements = []
+ with open(Path(folder) / Path(requirements_file), "r") as f:
+ for line in f:
+ line = line.strip()
+ if line and not line.startswith("#"):
+ requirements.append(line)
+ return requirements
+
+
+install_requires = _load_requirements("requirements.txt")
+
+
+###############################################################################
+# Code style checkers #
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #
+
+
+class StyleCommand(Command):
+ """Run code style checkers."""
+
+ __LINE_WIDTH = 119
+ __ISORT_BASE = (
+ "isort "
+ # These two lines makes isort compatible with black.
+ "--multi-line=3 --trailing-comma --force-grid-wrap=0 "
+ f"--use-parentheses --line-width={__LINE_WIDTH} -rc -ws"
+ )
+ __BLACK_BASE = f"black --skip-string-normalization --line-length={__LINE_WIDTH}"
+ description = "Run code style checkers."
+ user_options = [
+ ("scope=", None, "Folder of file to operate within."),
+ ("fix", None, "True if tries to fix issues in-place."),
+ ]
+
+ def __call_checker(self, base_command, scope, check):
+ command = list(base_command)
+
+ command.append(scope)
+
+ if check:
+ command.extend(["--check", "--diff"])
+
+ self.announce(msg=f'Running command: {" ".join(command)}', level=setuptools_logging.INFO)
+
+ return subprocess.call(command)
+
+ def _isort(self, scope, check):
+ return self.__call_checker(base_command=self.__ISORT_BASE.split(), scope=scope, check=check)
+
+ def _black(self, scope, check):
+ return self.__call_checker(base_command=self.__BLACK_BASE.split(), scope=scope, check=check)
+
+ def _pass(self):
+ self.announce(msg="\033[32mPASS\x1b[0m", level=setuptools_logging.INFO)
+
+ def _fail(self):
+ self.announce(msg="\033[31mFAIL\x1b[0m", level=setuptools_logging.INFO)
+
+ # noinspection PyAttributeOutsideInit
+ def initialize_options(self):
+ self.scope = "."
+ self.fix = ""
+
+ def run(self):
+ scope, check = self.scope, not self.fix
+ isort_return = self._isort(scope=scope, check=check)
+ black_return = self._black(scope=scope, check=check)
+
+ if isort_return == 0 and black_return == 0:
+ self._pass()
+ else:
+ self._fail()
+ sys.exit(isort_return if isort_return != 0 else black_return)
+
+ def finalize_options(self):
+ raise NotImplementedError()
+
+
+###############################################################################
+# Setup #
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #
+
+
+setuptools.setup(
+ name=__package_name__,
+ # Versions should comply with PEP440. For a discussion on single-sourcing the version across setup.py and the
+ # project code, see https://packaging.python.org/en/latest/single_source_version.html
+ version=__version__,
+ description=__description__,
+ long_description=long_description,
+ long_description_content_type=long_description_content_type,
+ # The project's main homepage.
+ url=__repository_url__,
+ download_url=__download_url__,
+ # Author details
+ author=__contact_names__,
+ author_email=__contact_emails__,
+ # maintainer Details
+ maintainer=__contact_names__,
+ maintainer_email=__contact_emails__,
+ # The licence under which the project is released
+ license=__license__,
+ classifiers=[
+ # How mature is this project? Common values are
+ # 1 - Planning
+ # 2 - Pre-Alpha
+ # 3 - Alpha
+ # 4 - Beta
+ # 5 - Production/Stable
+ # 6 - Mature
+ # 7 - Inactive
+ "Development Status :: 5 - Production/Stable",
+ # Indicate who your project is intended for
+ "Intended Audience :: Developers",
+ "Intended Audience :: Science/Research",
+ "Intended Audience :: Information Technology",
+ # Indicate what your project relates to
+ "Topic :: Scientific/Engineering",
+ "Topic :: Scientific/Engineering :: Artificial Intelligence",
+ "Topic :: Scientific/Engineering :: Physics",
+ "Topic :: Software Development :: Libraries",
+ "Topic :: Software Development :: Libraries :: Python Modules",
+ "Topic :: Utilities",
+ # Pick your license as you wish (should match "license" above)
+ "License :: OSI Approved :: Apache Software License",
+ # Supported python versions
+ "Programming Language :: Python :: 3.10",
+ # Additional Setting
+ "Environment :: Console",
+ "Natural Language :: English",
+ "Operating System :: OS Independent",
+ ],
+ packages=setuptools.find_packages(include=["atommic", "atommic.*"]),
+ install_requires=install_requires,
+ setup_requires=["pytest-runner"],
+ python_requires=">=3.10, <=3.12",
+ # Add in any packaged data.
+ include_package_data=True,
+ exclude=["tools", "tests"],
+ package_data={"": ["*.tsv", "*.txt", "*.far", "*.fst", "*.cpp", "Makefile"]},
+ zip_safe=False,
+ # PyPI package information.
+ keywords=__keywords__,
+ # Custom commands.
+ cmdclass={"style": StyleCommand},
+ # entry points
+ entry_points={
+ "console_scripts": [
+ "atommic = atommic.cli:main",
+ ],
+ },
+)
+
+###############################################################################
+# End of File #
+# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% #
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/__init__.py b/tests/collections/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/common/__init__.py b/tests/collections/common/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/common/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/common/callbacks/__init__.py b/tests/collections/common/callbacks/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/common/callbacks/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/common/callbacks/test_callbacks.py b/tests/collections/common/callbacks/test_callbacks.py
new file mode 100644
index 00000000..2c42f6ad
--- /dev/null
+++ b/tests/collections/common/callbacks/test_callbacks.py
@@ -0,0 +1,45 @@
+# coding=utf-8
+# __author__ = "Dimitris Karkalousos"
+
+import time
+from unittest.mock import Mock
+from atommic.collections.common.callbacks import LogEpochTimeCallback
+
+
+class TestLogEpochTimeCallback:
+ # Tests that the LogEpochTimeCallback initializes without any errors
+ def test_log_epoch_time_callback_initialize(self):
+ callback = LogEpochTimeCallback()
+ assert callback is not None
+
+ # Tests that the on_train_epoch_start method of LogEpochTimeCallback is called without any errors
+ def test_log_epoch_time_callback_on_train_epoch_start(self):
+ callback = LogEpochTimeCallback()
+ trainer = None
+ pl_module = None
+ callback.on_train_epoch_start(trainer, pl_module)
+ assert True
+
+ # Tests that the on_validation_epoch_start method of LogEpochTimeCallback is called without any errors
+ def test_log_epoch_time_callback_on_validation_epoch_start(self):
+ callback = LogEpochTimeCallback()
+ trainer = None
+ pl_module = None
+ callback.on_validation_epoch_start(trainer, pl_module)
+ assert True
+
+ # Tests that the on_validation_epoch_end method of LogEpochTimeCallback is called without any errors
+ def test_log_epoch_time_callback_on_validation_epoch_end(self):
+ callback = LogEpochTimeCallback()
+ trainer = None
+ pl_module = None
+ callback.on_validation_epoch_end(trainer, pl_module)
+ assert True
+
+ # Tests that the on_test_epoch_start method of LogEpochTimeCallback is called without any errors
+ def test_log_epoch_time_callback_on_test_epoch_start(self):
+ callback = LogEpochTimeCallback()
+ trainer = None
+ pl_module = None
+ callback.on_test_epoch_start(trainer, pl_module)
+ assert True
diff --git a/tests/collections/common/callbacks/test_ema.py b/tests/collections/common/callbacks/test_ema.py
new file mode 100644
index 00000000..9ed3b197
--- /dev/null
+++ b/tests/collections/common/callbacks/test_ema.py
@@ -0,0 +1,106 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import pytorch_lightning as pl
+import torch
+
+from atommic.collections.common.callbacks import EMA
+from atommic.collections.common.callbacks.ema import EMAOptimizer
+
+
+class TestEMA:
+ # Tests that the EMA callback is initialized with the specified decay, validate_original_weights, every_n_steps,
+ # and cpu_offload parameters.
+ def test_initialize_ema_callback(self):
+ decay = 0.9
+ validate_original_weights = True
+ every_n_steps = 2
+ cpu_offload = False
+
+ ema_callback = EMA(decay, validate_original_weights, every_n_steps, cpu_offload)
+
+ assert ema_callback.decay == decay
+ assert ema_callback.validate_original_weights == validate_original_weights
+ assert ema_callback.every_n_steps == every_n_steps
+ assert ema_callback.cpu_offload == cpu_offload
+
+ # Tests that the swap_model_weights method swaps the model weights with the EMA weights.
+ def test_swap_model_weights(self):
+ ema_callback = EMA(0.9)
+ trainer = pl.Trainer()
+ trainer.optimizers = [
+ EMAOptimizer(
+ torch.optim.SGD(torch.nn.Linear(10, 10).parameters(), lr=0.1),
+ device=torch.device("cpu"),
+ decay=0.9,
+ every_n_steps=-1,
+ current_step=trainer.global_step,
+ ),
+ EMAOptimizer(
+ torch.optim.Adam(torch.nn.Linear(10, 10).parameters(), lr=0.1),
+ device=torch.device("cpu"),
+ decay=0.9,
+ every_n_steps=-1,
+ current_step=trainer.global_step,
+ ),
+ ]
+ trainer.optimizers[0].param_groups[0]["params"][0].data = torch.tensor([1.0])
+ trainer.optimizers[1].param_groups[0]["params"][0].data = torch.tensor([2.0])
+
+ ema_callback.swap_model_weights(trainer)
+
+ assert trainer.optimizers[0].param_groups[0]["params"][0].data == torch.tensor([1.0])
+ assert trainer.optimizers[1].param_groups[0]["params"][0].data == torch.tensor([2.0])
+
+ # Tests that the save_ema_model context manager swaps the model weights with the EMA weights and yields.
+ def test_save_ema_model_context_manager(self):
+ trainer = pl.Trainer()
+ trainer.optimizers = [
+ EMAOptimizer(
+ torch.optim.SGD(torch.nn.Linear(10, 10).parameters(), lr=0.1),
+ device=torch.device("cpu"),
+ decay=0.9,
+ every_n_steps=-1,
+ current_step=trainer.global_step,
+ )
+ ]
+ ema_callback = EMA(0.9)
+ trainer.callbacks.append(ema_callback)
+
+ trainer.optimizers[0].param_groups[0]["params"][0].data = torch.tensor([1.0])
+
+ with ema_callback.save_ema_model(trainer):
+ assert trainer.optimizers[0].param_groups[0]["params"][0].data == torch.tensor([1.0])
+
+ assert trainer.optimizers[0].param_groups[0]["params"][0].data == torch.tensor([1.0])
+
+ # Tests that the save_original_optimizer_state context manager sets save_original_optimizer_state to True for
+ # each optimizer and yields.
+ def test_save_original_optimizer_state_context_manager(self):
+ ema_callback = EMA(0.9)
+ trainer = pl.Trainer()
+ trainer.optimizers = [
+ EMAOptimizer(
+ torch.optim.SGD(torch.nn.Linear(10, 10).parameters(), lr=0.1),
+ device=torch.device("cpu"),
+ decay=0.9,
+ every_n_steps=-1,
+ current_step=trainer.global_step,
+ ),
+ EMAOptimizer(
+ torch.optim.Adam(torch.nn.Linear(10, 10).parameters(), lr=0.1),
+ device=torch.device("cpu"),
+ decay=0.9,
+ every_n_steps=-1,
+ current_step=trainer.global_step,
+ ),
+ ]
+
+ with ema_callback.save_original_optimizer_state(trainer):
+ assert trainer.optimizers[0].save_original_optimizer_state is True
+ assert trainer.optimizers[1].save_original_optimizer_state is True
+
+ assert trainer.optimizers[0].save_original_optimizer_state is False
+ assert trainer.optimizers[1].save_original_optimizer_state is False
diff --git a/tests/collections/common/data/__init__.py b/tests/collections/common/data/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/common/data/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/common/data/test_compute_coil_sensitivity_maps.py b/tests/collections/common/data/test_compute_coil_sensitivity_maps.py
new file mode 100644
index 00000000..3284bd90
--- /dev/null
+++ b/tests/collections/common/data/test_compute_coil_sensitivity_maps.py
@@ -0,0 +1,24 @@
+# coding=utf-8
+# Copyright (c) DIRECT Contributors
+
+import pytest
+import torch
+
+from atommic.collections.common.parts.coil_sensitivity_maps import MaximumEigenvaluePowerMethod
+
+
+@pytest.mark.parametrize("size", [20, 30])
+def test_power_method(size):
+ mat = torch.rand((size, size)) + torch.rand((size, size)) * 1j
+ x0 = torch.ones(size) + 0 * 1j
+
+ def A(x):
+ return mat @ x
+
+ algo = MaximumEigenvaluePowerMethod(A)
+ algo.fit(x0)
+
+ all_eigenvalues = torch.linalg.eig(mat).eigenvalues
+ max_eig_torch = all_eigenvalues[all_eigenvalues.abs().argmax()]
+
+ assert torch.allclose(algo.max_eig, max_eig_torch, 0.001)
diff --git a/tests/collections/common/data/test_subsample.py b/tests/collections/common/data/test_subsample.py
new file mode 100644
index 00000000..124908ef
--- /dev/null
+++ b/tests/collections/common/data/test_subsample.py
@@ -0,0 +1,412 @@
+# coding=utf-8
+# Generated by CodiumAI
+import numpy as np
+import pytest
+import torch
+
+from atommic.collections.common.data.subsample import (
+ Equispaced1DMaskFunc,
+ Equispaced2DMaskFunc,
+ Gaussian1DMaskFunc,
+ Gaussian2DMaskFunc,
+ Poisson2DMaskFunc,
+ Random1DMaskFunc,
+ create_masker,
+)
+
+
+class TestCreateMasker:
+ # Tests that the function returns an Equispaced1DMaskFunc object for valid input parameters.
+ def test_returns_equispaced1d_mask_func(self):
+ mask_type_str = "equispaced1d"
+ center_fractions = [0.3, 0.7]
+ accelerations = [8, 6]
+
+ mask_func = create_masker(mask_type_str, center_fractions, accelerations)
+
+ assert isinstance(mask_func, Equispaced1DMaskFunc)
+
+ # Tests that the function returns an Equispaced2DMaskFunc object for valid input parameters.
+ def test_returns_equispaced2d_mask_func(self):
+ mask_type_str = "equispaced2d"
+ center_fractions = [0.3, 0.7]
+ accelerations = [8, 6]
+
+ mask_func = create_masker(mask_type_str, center_fractions, accelerations)
+
+ assert isinstance(mask_func, Equispaced2DMaskFunc)
+
+ # Tests that the function returns a Gaussian1DMaskFunc object for valid input parameters.
+ def test_returns_gaussian1d_mask_func(self):
+ mask_type_str = "gaussian1d"
+ center_fractions = [0.3, 0.7]
+ accelerations = [8, 6]
+
+ mask_func = create_masker(mask_type_str, center_fractions, accelerations)
+
+ assert isinstance(mask_func, Gaussian1DMaskFunc)
+
+ # Tests that the function returns a Gaussian2DMaskFunc object for valid input parameters.
+ def test_returns_gaussian2d_mask_func(self):
+ mask_type_str = "gaussian2d"
+ center_fractions = [0.3, 0.7]
+ accelerations = [8, 6]
+
+ mask_func = create_masker(mask_type_str, center_fractions, accelerations)
+
+ assert isinstance(mask_func, Gaussian2DMaskFunc)
+
+ # Tests that the function returns a Poisson2DMaskFunc object for valid input parameters.
+ def test_returns_poisson2d_mask_func(self):
+ mask_type_str = "poisson2d"
+ center_fractions = [0.3, 0.7]
+ accelerations = [8.0, 6.0]
+
+ mask_func = create_masker(mask_type_str, center_fractions, accelerations)
+
+ assert isinstance(mask_func, Poisson2DMaskFunc)
+
+ # Tests that the function returns a Random1DMaskFunc object for valid input parameters.
+ def test_returns_random_mask_func(self):
+ mask_type_str = "random1d"
+ center_fractions = [0.5]
+ accelerations = [4]
+
+ mask_func = create_masker(mask_type_str, center_fractions, accelerations)
+
+ assert isinstance(mask_func, Random1DMaskFunc)
+
+ # Tests that the function raises a NotImplementedError for an unsupported mask type.
+ def test_raises_not_implemented_error(self):
+ mask_type_str = "unsupported"
+ center_fractions = [0.3, 0.7]
+ accelerations = [8, 6]
+
+ with pytest.raises(NotImplementedError):
+ create_masker(mask_type_str, center_fractions, accelerations)
+
+
+class TestEquispaced1DMaskFunc:
+ # Tests that the code correctly generates a sub-sampling mask of the given shape.
+ def test_generate_sub_sampling_mask(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.08]
+ mask_func = Equispaced1DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[1] == shape[1]
+ assert acceleration == accelerations[0]
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code correctly generates a sub-sampling mask of the given shape for multiple accelerations.
+ def test_generate_sub_sampling_mask_mul_acc(self):
+ shape = (1, 10, 10)
+ accelerations = [4, 8]
+ center_fractions = [0.08, 0.04]
+ mask_func = Equispaced1DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[1] == shape[1]
+ assert acceleration in accelerations
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code is generated with partial Fourier.
+ def test_generate_equispaced_mask_with_partial_fourier(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.08]
+ partial_fourier_percentage = 0.5
+ mask_func = Equispaced1DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, partial_fourier_percentage=partial_fourier_percentage)
+ assert torch.sum(mask) > 0
+
+ # Tests that the code selects the correct number of low-frequency columns based on the center fraction.
+ def test_select_low_frequency_columns(self):
+ shape = (1, 10, 10)
+ accelerations = [6]
+ center_fractions = [0.03]
+ mask_func = Equispaced1DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape)
+ num_cols = shape[-2]
+ center_fraction = mask_func.center_fractions[0]
+ num_low_freqs = int(round(num_cols * center_fraction))
+ assert torch.sum(mask[:, :num_low_freqs]) == num_low_freqs
+
+
+class TestEquispaced2DMaskFunc:
+ # Tests that the code correctly generates a sub-sampling mask of the given shape.
+ def test_generate_sub_sampling_mask(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.08]
+ mask_func = Equispaced2DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[:1] == shape[:1]
+ assert acceleration == accelerations[0]
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code correctly generates a sub-sampling mask of the given shape for multiple accelerations.
+ def test_generate_sub_sampling_mask_mul_acc(self):
+ shape = (1, 10, 10)
+ accelerations = [4, 8]
+ center_fractions = [0.08, 0.04]
+ mask_func = Equispaced2DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[:1] == shape[:1]
+ assert acceleration in accelerations
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code is generated with partial Fourier.
+ def test_generate_equispaced_mask_with_partial_fourier(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.08]
+ partial_fourier_percentage = 0.5
+ mask_func = Equispaced2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, partial_fourier_percentage=partial_fourier_percentage)
+ assert torch.sum(mask) > 0
+
+ # Tests that the code selects the correct number of low-frequency columns based on the center fraction.
+ def test_select_low_frequency_columns(self):
+ shape = (1, 10, 10)
+ accelerations = [6]
+ center_fractions = [0.03]
+ mask_func = Equispaced2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape)
+ num_cols = shape[-2]
+ center_fraction = mask_func.center_fractions[0]
+ num_low_freqs = int(round(num_cols * center_fraction))
+ assert torch.sum(mask[:, :num_low_freqs]) == num_low_freqs
+
+
+class TestGaussian1DMaskFunc:
+ # Tests that the code correctly generates a sub-sampling mask of the given shape.
+ def test_generate_sub_sampling_mask(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.7]
+ mask_func = Gaussian1DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[1] == shape[1]
+ assert acceleration == accelerations[0]
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code correctly generates a sub-sampling mask of the given shape for multiple accelerations.
+ def test_generate_sub_sampling_mask_mul_acc(self):
+ shape = (1, 10, 10)
+ accelerations = [4, 8]
+ center_fractions = [0.7, 0.7]
+ mask_func = Gaussian1DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[1] == shape[1]
+ assert acceleration in accelerations
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code is generated with partial Fourier.
+ def test_generate_gaussian_mask_with_partial_fourier(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.7]
+ partial_fourier_percentage = 0.2
+ mask_func = Gaussian1DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, partial_fourier_percentage=partial_fourier_percentage)
+ assert torch.sum(mask) > 0
+
+ # Tests that the code defines the center scale.
+ def test_define_center_scale(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.7]
+ mask_func = Gaussian1DMaskFunc(center_fractions, accelerations)
+ scale = 0.5
+ mask, _ = mask_func(shape, scale=scale)
+ assert torch.sum(mask) > 0
+ scale = 0.01
+ mask, _ = mask_func(shape, scale=scale)
+ assert torch.sum(mask) > 0
+
+
+class TestGaussian2DMaskFunc:
+ # Tests that the code correctly generates a sub-sampling mask of the given shape.
+ def test_generate_sub_sampling_mask(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.7]
+ mask_func = Gaussian2DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[:1] == shape[:1]
+ assert acceleration == accelerations[0]
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code correctly generates a sub-sampling mask of the given shape for multiple accelerations.
+ def test_generate_sub_sampling_mask_mul_acc(self):
+ shape = (1, 10, 10)
+ accelerations = [4, 8]
+ center_fractions = [0.7, 0.7]
+ mask_func = Gaussian2DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[:1] == shape[:1]
+ assert acceleration in accelerations
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the Gaussian mask is generated with partial Fourier.
+ def test_generate_gaussian_mask_with_partial_fourier(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.7]
+ partial_fourier_percentage = 0.5
+ mask_func = Gaussian2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, partial_fourier_percentage=partial_fourier_percentage)
+ assert torch.sum(mask) > 0
+
+ # Tests that the code defines the center scale.
+ def test_define_center_scale(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.7]
+ mask_func = Gaussian2DMaskFunc(center_fractions, accelerations)
+ scale = 0.5
+ mask, _ = mask_func(shape, scale=scale)
+ assert torch.sum(mask) > 0
+ scale = 0.01
+ mask, _ = mask_func(shape, scale=scale)
+ assert torch.sum(mask) > 0
+
+
+class TestPoisson2DMaskFunc:
+ # Tests that the code correctly generates a sub-sampling mask of the given shape.
+ def test_generate_sub_sampling_mask(self):
+ shape = (10, 10, 2)
+ accelerations = [4]
+ center_fractions = [0.7]
+ tolerance = 1.0
+ mask_func = Poisson2DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape, tol=tolerance)
+ assert mask.shape[1:-1] == shape[:-1]
+ assert acceleration == accelerations[0]
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code correctly generates a sub-sampling mask of the given shape for multiple accelerations.
+ def test_generate_sub_sampling_mask_mul_acc(self):
+ shape = (10, 10, 2)
+ accelerations = [4, 10]
+ center_fractions = [0.7, 0.7]
+ tolerance = 1.0
+ mask_func = Poisson2DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape, tol=tolerance)
+ assert mask.shape[1:-1] == shape[:-1]
+ assert acceleration in accelerations
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the Poisson mask is generated with partial Fourier.
+ def test_generate_poisson_mask_with_partial_fourier(self):
+ shape = (10, 10, 2)
+ accelerations = [4]
+ center_fractions = [0.7]
+ partial_fourier_percentage = 0.5
+ tolerance = 1.0
+ mask_func = Poisson2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, partial_fourier_percentage=partial_fourier_percentage, tol=tolerance)
+ assert torch.sum(mask) > 0
+
+ # Tests that the Poisson mask is generated with calibration.
+ def test_generate_poisson_mask_with_calibration(self):
+ shape = (10, 10, 2)
+ accelerations = [4]
+ center_fractions = [0.7]
+ calibration_percentage = 0.5
+ tolerance = 1.0
+ mask_func = Poisson2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, calib=(calibration_percentage, calibration_percentage), tol=tolerance)
+ assert torch.sum(mask) > 0
+
+ # Tests that the Poisson mask is generated without cropped corner.
+ def test_generate_poisson_mask_without_cropped_corner(self):
+ shape = (10, 10, 2)
+ accelerations = [4]
+ center_fractions = [0.7]
+ tolerance = 1.0
+ mask_func = Poisson2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, crop_corner=False, tol=tolerance)
+ assert torch.sum(mask) > 0
+
+ # Tests that the Poisson mask is generated with the specified tolerance value.
+ def test_generate_poisson_mask_with_tolerance_value(self):
+ shape = (10, 10, 2)
+ accelerations = [4]
+ center_fractions = [0.7]
+ tolerance = 1.0
+ mask_func = Poisson2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, tol=tolerance)
+ assert torch.sum(mask) > 0
+
+ # Tests that the Poisson mask is generated with maximum attempts.
+ def test_generate_poisson_mask_with_max_attempts(self):
+ shape = (10, 10, 2)
+ accelerations = [3]
+ center_fractions = [0.7]
+ max_attempts = 1
+ tolerance = 1.0
+ mask_func = Poisson2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, max_attempts=max_attempts, tol=tolerance)
+ assert torch.sum(mask) > 0
+
+ # Tests that the code defines the center scale.
+ def test_define_center_scale(self):
+ shape = (10, 10, 2)
+ accelerations = [4]
+ center_fractions = [0.7]
+ mask_func = Poisson2DMaskFunc(center_fractions, accelerations)
+ tolerance = 1.0
+ scale = 0.5
+ mask, _ = mask_func(shape, scale=scale, tol=tolerance)
+ assert torch.sum(mask) > 0
+ scale = 0.01
+ mask, _ = mask_func(shape, scale=scale, tol=tolerance)
+ assert torch.sum(mask) > 0
+
+
+class TestRandom1DMaskFunc:
+ """Tests that the code correctly generates a Random sub-sampling mask of the given shape."""
+
+ def test_generate_sub_sampling_mask(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.08]
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[1] == shape[1]
+ assert acceleration == accelerations[0]
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code correctly generates a sub-sampling mask of the given shape for multiple accelerations.
+ def test_generate_sub_sampling_mask_mul_acc(self):
+ shape = (1, 10, 10)
+ accelerations = [4, 8]
+ center_fractions = [0.08, 0.04]
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ mask, acceleration = mask_func(shape)
+ assert mask.shape[1] == shape[1]
+ assert acceleration in accelerations
+ assert isinstance(mask, torch.Tensor)
+
+ # Tests that the code is generated with partial Fourier.
+ def test_generate_random_mask_with_partial_fourier(self):
+ shape = (1, 10, 10)
+ accelerations = [4]
+ center_fractions = [0.08]
+ partial_fourier_percentage = 0.5
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape, partial_fourier_percentage=partial_fourier_percentage)
+ assert torch.sum(mask) > 0
+
+ # Tests that the code selects the correct number of low-frequency columns based on the center fraction.
+ def test_select_low_frequency_columns(self):
+ shape = (1, 10, 10)
+ accelerations = [6]
+ center_fractions = [0.03]
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(shape)
+ num_cols = shape[-2]
+ center_fraction = mask_func.center_fractions[0]
+ num_low_freqs = int(round(num_cols * center_fraction))
+ assert torch.sum(mask[:, :num_low_freqs]) == num_low_freqs
diff --git a/tests/collections/common/losses/__init__.py b/tests/collections/common/losses/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/common/losses/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/common/losses/test_aggregator.py b/tests/collections/common/losses/test_aggregator.py
new file mode 100644
index 00000000..2df8a0bd
--- /dev/null
+++ b/tests/collections/common/losses/test_aggregator.py
@@ -0,0 +1,73 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.losses.aggregator import AggregatorLoss
+from atommic.core.neural_types.elements import LossType
+from atommic.core.neural_types.neural_type import NeuralType
+
+
+class TestAggregatorLoss:
+ # Tests that the forward method returns the correct sum of losses when given valid inputs.
+ def test_forward_method_returns_correct_sum_of_losses(self):
+ # Create an instance of AggregatorLoss
+ aggregator_loss = AggregatorLoss(num_inputs=3)
+
+ # Create input losses
+ loss1 = torch.tensor(2.0)
+ loss2 = torch.tensor(3.0)
+ loss3 = torch.tensor(4.0)
+
+ # Call the forward method
+ output_loss = aggregator_loss.forward(loss_1=loss1, loss_2=loss2, loss_3=loss3)
+
+ # Check that the output loss is the correct sum of the input losses
+ assert output_loss == torch.tensor(9.0)
+
+ # Tests that an error is raised when the number of weights is not equal to the number of inputs.
+ def test_error_raised_when_number_of_weights_not_equal_to_number_of_inputs(self):
+ # Create an instance of AggregatorLoss with 3 inputs and 2 weights
+ with pytest.raises(ValueError):
+ aggregator_loss = AggregatorLoss(num_inputs=3, weights=[0.5, 0.5])
+
+ # Tests that an error is raised when the input types are not correctly defined.
+ def test_error_raised_when_input_types_not_correctly_defined(self):
+ # Create an instance of AggregatorLoss with incorrect input types
+ class IncorrectAggregatorLoss(AggregatorLoss):
+ @property
+ def input_types(self):
+ return {"loss_1": NeuralType(elements_type=LossType())}
+
+ # Tests that the forward method returns zero when all inputs are zero.
+ def test_forward_method_returns_zero_when_all_inputs_are_zero(self):
+ # Create an instance of AggregatorLoss
+ aggregator_loss = AggregatorLoss(num_inputs=2)
+
+ # Create input losses
+ loss1 = torch.tensor(0.0)
+ loss2 = torch.tensor(0.0)
+
+ # Call the forward method
+ output_loss = aggregator_loss.forward(loss_1=loss1, loss_2=loss2)
+
+ # Check that the output loss is zero
+ assert output_loss == torch.tensor(0.0)
+
+ # Tests that the forward method returns the correct weighted sum of losses when given valid weights.
+ def test_forward_method_returns_correct_weighted_sum_of_losses(self):
+ # Create an instance of AggregatorLoss with 3 inputs and weights
+ aggregator_loss = AggregatorLoss(num_inputs=3, weights=[0.5, 0.3, 0.2])
+
+ # Create input losses
+ loss1 = torch.tensor(2.0)
+ loss2 = torch.tensor(3.0)
+ loss3 = torch.tensor(4.0)
+
+ # Call the forward method
+ output_loss = aggregator_loss.forward(loss_1=loss1, loss_2=loss2, loss_3=loss3)
+
+ # Check that the output loss is the weighted sum of the input losses
+ assert output_loss == torch.tensor(2.7)
diff --git a/tests/collections/common/losses/test_wasserstein.py b/tests/collections/common/losses/test_wasserstein.py
new file mode 100644
index 00000000..27b5aac7
--- /dev/null
+++ b/tests/collections/common/losses/test_wasserstein.py
@@ -0,0 +1,43 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.losses.wasserstein import SinkhornDistance
+
+
+class TestSinkhornDistance:
+ # Tests that the SinkhornDistance class works correctly when given two point clouds of the same size and
+ # dimensionality.
+ def test_same_size_and_dimensionality(self):
+ x = torch.tensor([[1, 2], [3, 4], [5, 6]])
+ y = torch.tensor([[7, 8], [9, 10], [11, 12]])
+ sinkhorn = SinkhornDistance()
+ result = sinkhorn(x, y)
+ assert round(result.item()) == 52
+
+ # Tests that the SinkhornDistance class handles correctly when the eps parameter is set to 0.
+ def test_eps_zero(self):
+ x = torch.tensor([[1, 2], [3, 4], [5, 6]])
+ y = torch.tensor([[7, 8], [9, 10], [11, 12]])
+ sinkhorn = SinkhornDistance(eps=0.1)
+ result = sinkhorn(x, y)
+ assert round(result.item()) == 52
+
+ # Tests that the SinkhornDistance class handles correctly when the max_iter parameter is set to 0.
+ def test_max_iter_zero(self):
+ x = torch.tensor([[1, 2], [3, 4], [5, 6]])
+ y = torch.tensor([[7, 8], [9, 10], [11, 12]])
+ sinkhorn = SinkhornDistance(max_iter=0)
+ result = sinkhorn(x, y)
+ assert result.item() == pytest.approx(0.0)
+
+ # Tests that the SinkhornDistance class handles correctly when the max_iter parameter is set to 100.
+ def test_max_iter_hundred(self):
+ x = torch.tensor([[1, 2], [3, 4], [5, 6]])
+ y = torch.tensor([[7, 8], [9, 10], [11, 12]])
+ sinkhorn = SinkhornDistance(max_iter=100)
+ result = sinkhorn(x, y)
+ assert round(result.item()) == 52
diff --git a/tests/collections/common/metrics/__init__.py b/tests/collections/common/metrics/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/common/metrics/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/common/metrics/test_global_average_loss_metric.py b/tests/collections/common/metrics/test_global_average_loss_metric.py
new file mode 100644
index 00000000..dd6a1fe5
--- /dev/null
+++ b/tests/collections/common/metrics/test_global_average_loss_metric.py
@@ -0,0 +1,49 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.metrics.global_average_loss_metric import GlobalAverageLossMetric
+
+
+class TestGlobalAverageLossMetric:
+ # Tests that the class can be instantiated without any errors
+ def test_instantiation(self):
+ metric = GlobalAverageLossMetric()
+ assert metric is not None
+
+ # Tests that the update method updates the loss_sum and num_measurements attributes correctly
+ def test_update_method(self):
+ metric = GlobalAverageLossMetric()
+ metric.update(torch.tensor(1.0), torch.tensor(1))
+ assert metric.loss_sum == torch.tensor(1.0)
+ assert metric.num_measurements == torch.tensor(1)
+ metric.update(torch.tensor(2.0), torch.tensor(1))
+ assert metric.loss_sum == torch.tensor(3.0)
+ assert metric.num_measurements == torch.tensor(2)
+
+ # Tests that the compute method returns the correct mean loss when called after update method
+ def test_compute_method_mean_loss(self):
+ metric = GlobalAverageLossMetric()
+ metric.update(torch.tensor(1.0), torch.tensor(1))
+ metric.update(torch.tensor(2.0), torch.tensor(1))
+ mean_loss = metric.compute()
+ assert mean_loss == torch.tensor(1.5)
+
+ # Tests that the compute method returns NaN when num_measurements is zero
+ def test_compute_method_nan(self):
+ metric = GlobalAverageLossMetric()
+ mean_loss = metric.compute()
+ assert torch.isnan(mean_loss)
+
+ # Tests that the update method works correctly when take_avg_loss is False
+ def test_update_method_take_avg_loss_false(self):
+ metric = GlobalAverageLossMetric(take_avg_loss=False)
+ metric.update(torch.tensor(1.0), torch.tensor(1))
+ assert metric.loss_sum == torch.tensor(1.0)
+ assert metric.num_measurements == torch.tensor(1)
+ metric.update(torch.tensor(2.0), torch.tensor(1))
+ assert metric.loss_sum == torch.tensor(3.0)
+ assert metric.num_measurements == torch.tensor(2)
diff --git a/tests/collections/common/nn/__init__.py b/tests/collections/common/nn/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/common/nn/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/common/nn/test_base.py b/tests/collections/common/nn/test_base.py
new file mode 100644
index 00000000..16176021
--- /dev/null
+++ b/tests/collections/common/nn/test_base.py
@@ -0,0 +1,132 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+import os
+import pytest
+import torch
+from omegaconf import DictConfig
+from pytorch_lightning import Trainer
+
+from atommic.collections.reconstruction.nn.base import BaseMRIModel, BaseSensitivityModel, DistributedMetricSum
+
+
+class TestDistributedMetricSum:
+ # Tests that the initial value of the metric is 0.0 when no initial value is provided
+ def test_default_initial_value(self):
+ metric = DistributedMetricSum()
+ assert metric.compute() == torch.tensor(0.0)
+
+ # Tests that the initial value of the metric is set to the provided custom initial value
+ def test_custom_initial_value(self):
+ initial_value = torch.tensor(5.0)
+ metric = DistributedMetricSum()
+ metric.quantity = initial_value
+ assert metric.compute() == initial_value
+
+ # Tests that the update method correctly adds the tensor argument to the metric quantity
+ def test_update_method_with_tensor_argument(self):
+ metric = DistributedMetricSum()
+ tensor = torch.tensor(2.0)
+ metric.update(tensor)
+ assert metric.compute() == tensor
+
+ # Tests that the compute method returns the initial value of the metric when called without calling the update
+ # method
+ def test_compute_method_without_calling_update(self):
+ metric = DistributedMetricSum()
+ assert metric.compute() == torch.tensor(0.0)
+
+ # Tests that the compute method returns the correct sum of all tensor values passed to the update method
+ def test_compute_method_after_multiple_update_calls(self):
+ metric = DistributedMetricSum()
+ tensor1 = torch.tensor(2.0)
+ tensor2 = torch.tensor(3.0)
+ metric.update(tensor1)
+ metric.update(tensor2)
+ assert metric.compute() == tensor1 + tensor2
+
+ # Tests that the metric state is synchronized across processes when dist_sync_on_step is True
+ def test_dist_sync_on_step(self):
+ metric = DistributedMetricSum(dist_sync_on_step=True)
+ tensor = torch.tensor(2.0)
+ metric.update(tensor)
+ assert metric.compute() == tensor
+
+
+class TestBaseMRIModel:
+ # Tests that the BaseMRIModel can be initialized with a configuration and trainer objects.
+ def test_initialize_with_configuration_and_trainer(self):
+ cfg = DictConfig({})
+ trainer = Trainer()
+ model = BaseMRIModel(cfg, trainer)
+ assert isinstance(model, BaseMRIModel)
+ assert model.trainer == trainer
+
+ # Tests that the training_step method raises a NotImplementedError.
+ def test_training_step_not_implemented(self):
+ model = BaseMRIModel(DictConfig({}))
+ with pytest.raises(NotImplementedError):
+ model.training_step({}, 0)
+
+ # Tests that the validation_step method raises a NotImplementedError.
+ def test_validation_step_not_implemented(self):
+ model = BaseMRIModel(DictConfig({}))
+ with pytest.raises(NotImplementedError):
+ model.validation_step({}, 0)
+
+ # Tests that the test_step method raises a NotImplementedError.
+ def test_test_step_not_implemented(self):
+ model = BaseMRIModel(DictConfig({}))
+ with pytest.raises(NotImplementedError):
+ model.test_step({}, 0)
+
+
+class TestBaseSensitivityModel:
+ """Tests for the BaseSensitivityModel class."""
+
+ # Tests that the model can be instantiated with default parameters.
+ def test_instantiation_with_default_parameters(self):
+ model = BaseSensitivityModel()
+ assert isinstance(model, BaseSensitivityModel)
+
+ # Tests that the forward method can be called with valid input.
+ def test_forward_method_with_valid_input(self):
+ model = BaseSensitivityModel()
+ masked_kspace = torch.randn([1, 8, 320, 320, 2], dtype=torch.float32)
+ mask = torch.randn([1, 1, 320, 320, 1], dtype=torch.float32)
+ result = model.forward(masked_kspace, mask, torch.ones_like(masked_kspace))
+ assert isinstance(result, torch.Tensor)
+
+ # Tests that the output of the forward method has the expected shape.
+ def test_output_shape_of_forward_method(self):
+ model = BaseSensitivityModel()
+ masked_kspace = torch.randn([1, 8, 320, 320, 2], dtype=torch.float32)
+ mask = torch.randn([1, 1, 320, 320, 1], dtype=torch.float32)
+ result = model.forward(masked_kspace, mask, torch.ones_like(masked_kspace))
+ assert result.shape == (1, 8, 320, 320, 2)
+
+ # Tests that the model can be trained on a small dataset.
+ def test_training_on_small_dataset(self):
+ model = BaseSensitivityModel()
+ optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
+ criterion = torch.nn.MSELoss()
+ dataset = torch.randn([10, 8, 320, 320, 2], dtype=torch.float32)
+ dataloader = torch.utils.data.DataLoader(dataset, batch_size=2)
+ for data in dataloader:
+ optimizer.zero_grad()
+ masked_kspace = data[:, :, :, :, :2]
+ mask = torch.randn([2, 1, 320, 320, 1], dtype=torch.float32)
+ outputs = model.forward(masked_kspace, mask, torch.ones_like(masked_kspace))
+ loss = criterion(outputs, data)
+ loss.backward()
+ optimizer.step()
+ assert True
+
+ # Tests that the model can be saved and loaded successfully.
+ def test_saving_and_loading_model(self):
+ model = BaseSensitivityModel()
+ torch.save(model.state_dict(), "model.pth")
+ loaded_model = BaseSensitivityModel()
+ loaded_model.load_state_dict(torch.load("model.pth"))
+ assert isinstance(loaded_model, BaseSensitivityModel)
+ os.remove("model.pth")
diff --git a/tests/collections/common/parts/__init__.py b/tests/collections/common/parts/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/common/parts/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/common/parts/test_fft.py b/tests/collections/common/parts/test_fft.py
new file mode 100644
index 00000000..b8f94d43
--- /dev/null
+++ b/tests/collections/common/parts/test_fft.py
@@ -0,0 +1,128 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.parts.fft import fft2, fftshift, ifft2, ifftshift, roll, roll_one_dim
+
+
+class TestFFT2:
+ # Tests that the function works correctly with a random input tensor of shape (2, 3, 4, 5, 2)
+ def test_fft2_random_input_tensor(self):
+ input_tensor = torch.randn(2, 3, 4, 5, 2)
+ output_tensor = fft2(input_tensor)
+ assert output_tensor.shape == (2, 3, 4, 5, 2)
+
+ # Tests that the function works correctly with centered=True and normalization="ortho"
+ def test_fft2_centered_true_normalization_ortho(self):
+ input_tensor = torch.randn(2, 3, 4, 5, 2)
+ output_tensor = fft2(input_tensor, centered=True, normalization="ortho")
+ assert output_tensor.shape == (2, 3, 4, 5, 2)
+
+ # Tests that the function works correctly with spatial_dims=[-3, -2]
+ def test_fft2_spatial_dims(self):
+ input_tensor = torch.randn(2, 3, 4, 5, 2)
+ output_tensor = fft2(input_tensor, spatial_dims=[-3, -2])
+ assert output_tensor.shape == (2, 3, 4, 5, 2)
+
+
+class TestIFFT2:
+ # Tests that the function works correctly with a random input tensor of shape (2, 3, 4, 5, 2)
+ def test_ifft2_random_input_tensor(self):
+ input_tensor = torch.randn(2, 3, 4, 5, 2)
+ output_tensor = ifft2(input_tensor)
+ assert output_tensor.shape == (2, 3, 4, 5, 2)
+
+ # Tests that the function works correctly with centered=True and normalization="ortho"
+ def test_ifft2_centered_true_normalization_ortho(self):
+ input_tensor = torch.randn(2, 3, 4, 5, 2)
+ output_tensor = ifft2(input_tensor, centered=True, normalization="ortho")
+ assert output_tensor.shape == (2, 3, 4, 5, 2)
+
+ # Tests that the function works correctly with spatial_dims=[-3, -2]
+ def test_ifft2_spatial_dims(self):
+ input_tensor = torch.randn(2, 3, 4, 5, 2)
+ output_tensor = ifft2(input_tensor, spatial_dims=[-3, -2])
+ assert output_tensor.shape == (2, 3, 4, 5, 2)
+
+
+class TestRollOneDim:
+ # Tests that the function correctly rolls the tensor along the specified dimension by the specified shift amount
+ def test_roll_one_dim(self):
+ data = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+ expected_output = torch.tensor([[7, 8, 9], [1, 2, 3], [4, 5, 6]])
+ assert torch.allclose(roll_one_dim(data, 1, 0), expected_output)
+
+ # Tests that the function returns the input tensor as is when the shift amount is 0
+ def test_edge_case(self):
+ data = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+ assert torch.allclose(roll_one_dim(data, 0, 0), data)
+
+ # Tests that the function correctly rolls the tensor along the specified dimension by the specified shift amount
+ def test_other_case(self):
+ data = torch.tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
+ expected_output = torch.tensor([[3, 1, 2], [6, 4, 5], [9, 7, 8]])
+ assert torch.allclose(roll_one_dim(data, -2, 1), expected_output)
+
+
+class TestRoll:
+ # Tests that the function correctly rolls a tensor with shape (2, 3, 4, 5) by shift=[1, 2] and dim=[0, 1]
+ def test_roll(self):
+ data = torch.randn(2, 3, 4, 5)
+ result = roll(data, [1, 2], [0, 1])
+ assert result.shape == torch.Size([2, 3, 4, 5])
+
+ # Tests that the function correctly handles a shift larger than the tensor size. Tensor shape is (2, 3, 4, 5),
+ # shift=[6, 2], dim=[0, 1]
+ def test_edge_case_1(self):
+ data = torch.randn(2, 3, 4, 5)
+ result = roll(data, [6, 2], [0, 1])
+ assert result.shape == torch.Size([2, 3, 4, 5])
+
+ # Tests that the function correctly handles different lengths of shift and dim. Tensor shape is (2, 3, 4, 5),
+ # shift=[1, 2, 3], dim=[0, 1]
+ def test_edge_case_2(self):
+ data = torch.randn(2, 3, 4, 5)
+ with pytest.raises(ValueError):
+ roll(data, [1, 2, 3], [0, 1])
+
+ # Tests that the function correctly rolls a tensor with shape (1, 1) by shift=[1] and dim=[0] (tensor with only
+ # one element)
+ def test_other_case_1(self):
+ data = torch.randn(1, 1)
+ result = roll(data, [1], [0])
+ assert result.shape == torch.Size([1, 1])
+
+
+class TestFFTShiftIFFTShift:
+ # Tests that fftshift function works correctly with a random input tensor of shape (2, 3, 4, 5).
+ def test_fftshift(self):
+ data = torch.randn(2, 3, 4, 5)
+ shifted_data = fftshift(data)
+ assert shifted_data.shape == data.shape
+ assert not torch.allclose(shifted_data[0, 0, 0, 0], data[0, 0, 0, 0])
+
+ # Tests that ifftshift function works correctly with a random input tensor of shape (2, 3, 4, 5).
+ def test_ifftshift(self):
+ data = torch.randn(2, 3, 4, 5)
+ shifted_data = ifftshift(data)
+ assert shifted_data.shape == data.shape
+ assert not torch.allclose(shifted_data[0, 0, 0, 0], data[0, 0, 0, 0])
+
+ # Tests that fftshift function works correctly and unshifts the result.
+ def test_fftshift_ifftshift(self):
+ data = torch.randn(2, 3, 4, 5)
+ shifted_data = fftshift(data)
+ unshifted_data = ifftshift(shifted_data)
+ assert unshifted_data.shape == data.shape
+ assert torch.allclose(unshifted_data, data)
+
+ # Tests that ifftshift function works correctly and unshifts the result.
+ def test_ifftshift_fftshift(self):
+ data = torch.randn(2, 3, 4, 5)
+ shifted_data = ifftshift(data)
+ unshifted_data = fftshift(shifted_data)
+ assert unshifted_data.shape == data.shape
+ assert torch.allclose(unshifted_data, data)
diff --git a/tests/collections/common/parts/test_utils.py b/tests/collections/common/parts/test_utils.py
new file mode 100644
index 00000000..4e2eb2b3
--- /dev/null
+++ b/tests/collections/common/parts/test_utils.py
@@ -0,0 +1,626 @@
+# coding=utf-8
+import tempfile
+from pathlib import Path
+
+import h5py
+import numpy as np
+
+import torch
+
+from atommic.collections.common.data.subsample import Gaussian2DMaskFunc
+from atommic.collections.common.parts import (
+ add_coil_dim_if_singlecoil,
+ apply_mask,
+ batched_mask_center,
+ center_crop,
+ center_crop_to_smallest,
+ check_stacked_complex,
+ coil_combination_method,
+ complex_abs,
+ complex_abs_sq,
+ complex_center_crop,
+ complex_conj,
+ complex_mul,
+ crop_to_acs,
+ expand_op,
+ is_none,
+ mask_center,
+ normalize_inplace,
+ parse_list_and_keep_last,
+ reshape_fortran,
+ rnn_weights_init,
+ save_predictions,
+ to_tensor,
+ unnormalize,
+ zero_nan_inf,
+)
+
+
+class TestAddCoilDimIfSinglecoil:
+ # Tests that the function correctly adds a coil dimension to a tensor of shape (1, 2, 3) when dim=0
+ def test_add_coil_dim_if_singlecoil_1(self):
+ # Create input tensor
+ data = torch.randn(320, 320, 2)
+
+ # Call the function
+ output_tensor = add_coil_dim_if_singlecoil(data, dim=0)
+
+ # Check the output tensor shape
+ assert output_tensor.shape == (1, 320, 320, 2)
+
+ # Tests that the function correctly adds a coil dimension to a tensor of shape (2, 3, 1) when dim=-1
+ def test_add_coil_dim_if_singlecoil_2(self):
+ # Create input tensor
+ data = torch.randn(32, 320, 320, 2)
+
+ # Call the function
+ output_tensor = add_coil_dim_if_singlecoil(data, dim=0)
+
+ # Check the output tensor shape
+ assert output_tensor.shape == (32, 320, 320, 2)
+
+
+class TestApplyMask:
+ # Tests that the function applies the mask to k-space data with default parameters
+ def test_apply_mask(self):
+ data = torch.randn(1, 32, 320, 320, 2)
+ accelerations = [4, 8]
+ center_fractions = [0.7, 0.7]
+ mask_func = Gaussian2DMaskFunc(center_fractions, accelerations)
+
+ # Call the function
+ masked_data, subsampling_mask, acceleration_factor = apply_mask(data, mask_func)
+
+ # Check the output
+ assert len(masked_data) == 1
+ assert len(subsampling_mask) == 1
+ assert masked_data.shape == data.shape
+ assert subsampling_mask.shape == torch.Size([1, 1, 320, 320, 1])
+ assert acceleration_factor in accelerations
+
+
+class TestBatchedMaskCenter:
+ # Tests that the function correctly applies a 2D mask to a single batch of images
+ def test_single_batch_2D_mask(self):
+ data = torch.randn(1, 32, 320, 320)
+ mask_from = torch.tensor([140])
+ mask_to = torch.tensor([180])
+ expected_output = torch.zeros_like(data)
+
+ result = batched_mask_center(data, mask_from, mask_to)
+
+ assert not torch.allclose(result, expected_output)
+
+ # Tests that the function correctly applies a 2D mask to multiple batches of images
+ def test_multiple_batches_2D_mask(self):
+ data = torch.randn(16, 32, 320, 320)
+ mask_from = torch.tensor([140] * data.shape[0])
+ mask_to = torch.tensor([180] * data.shape[0])
+ expected_output = torch.zeros_like(data)
+
+ result = batched_mask_center(data, mask_from, mask_to)
+
+ assert not torch.allclose(result, expected_output)
+
+
+class TestCenterCrop:
+ # Tests that the function correctly applies a center crop to the input tensor along the last two dimensions
+ def test_center_crop(self):
+ # Arrange
+ data = torch.randn(1, 32, 320, 320)
+
+ # Act
+ result = center_crop(data, (160, 160))
+
+ # Assert
+ assert result.shape != data.shape
+ assert result.shape[-2] == (data.shape[-2] // 2)
+ assert result.shape[-1] == (data.shape[-1] // 2)
+
+ # Tests that the function handles edge cases correctly when the output shape is smaller than the corresponding
+ # dimensions of the input tensor
+ def test_edge_case(self):
+ # Arrange
+ data = torch.randn(1, 32, 320, 320)
+ expected_output = torch.zeros(1, 32, 1, 1)
+
+ # Act
+ result = center_crop(data, (1, 1))
+
+ # Assert
+ assert result.shape != data.shape
+ assert result.shape[-2] == 1
+ assert result.shape[-1] == 1
+
+
+class TestCenterCropToSmallest:
+ # Tests that the function correctly applies a center crop to the input tensor along the last two dimensions
+ def test_center_crop_to_smallest_1(self):
+ # Arrange
+ data1 = torch.randn(1, 32, 320, 320)
+ data2 = torch.randn(1, 32, 160, 160)
+
+ # Act
+ result1, result2 = center_crop_to_smallest(data1, data2)
+
+ # Assert
+ assert result1.shape == data2.shape
+ assert result2.shape == data2.shape
+
+ # Tests that the function handles edge cases correctly when the output shape is smaller than the corresponding
+ # dimensions of the input tensor
+ def test_center_crop_to_smallest_2(self):
+ # Arrange
+ data1 = torch.randn(1, 32, 160, 160)
+ data2 = torch.randn(1, 32, 320, 320)
+
+ # Act
+ result1, result2 = center_crop_to_smallest(data1, data2)
+
+ # Assert
+ assert result1.shape == data1.shape
+ assert result2.shape == data1.shape
+
+
+class TestCheckStackedComplex:
+ # Tests that the function correctly converts a complex tensor with shape (n,) to a combined complex tensor
+ def test_stacked_complex_tensor_1(self):
+ # Create a complex tensor with shape (n,)
+ data = torch.randn(1, 32, 320, 320, 2)
+
+ # Call the function under test
+ result = check_stacked_complex(data)
+
+ assert result.shape == data[..., 0].shape
+
+ # Tests that the function returns the input tensor unchanged when it has shape (0,)
+ def test_stacked_complex_tensor_2(self):
+ # Create a complex tensor with shape (n,)
+ data = torch.randn(1, 32, 320, 320)
+
+ # Call the function under test
+ result = check_stacked_complex(data)
+
+ assert result.shape == data.shape
+
+
+class TestCoilCombinationMethod:
+ # Tests that the SENSE method works correctly with valid input data and sensitivity maps
+ def test_sense_coil_combination_method(self):
+ data = torch.randn(1, 32, 320, 320, 2)
+ coil_sensitivity_maps = torch.randn(1, 32, 320, 320, 2)
+
+ result = coil_combination_method(data, coil_sensitivity_maps, method="SENSE", dim=1)
+
+ assert result.shape == data.sum(dim=1).shape
+
+ def test_rss_coil_combination_method(self):
+ data = torch.randn(1, 32, 320, 320, 2)
+ coil_sensitivity_maps = torch.randn(1, 32, 320, 320, 2)
+
+ result = coil_combination_method(data, coil_sensitivity_maps, method="RSS", dim=1)
+
+ assert result.shape == data.sum(dim=1).shape
+
+ def test_rss_complex_coil_combination_method(self):
+ data = torch.randn(1, 32, 320, 320, 2)
+ coil_sensitivity_maps = torch.randn(1, 32, 320, 320, 2)
+
+ result = coil_combination_method(data, coil_sensitivity_maps, method="RSS_COMPLEX", dim=1)
+
+ assert result.shape == data.sum(dim=(1, -1)).shape
+
+
+class TestComplexAbs:
+ # Tests that the function correctly computes the absolute value of a tensor of complex numbers with positive
+ # real and imaginary parts.
+ def test_complex_abs(self):
+ data = torch.randn(1, 32, 320, 320, 2) + 1.0j
+
+ result = complex_abs(data)
+
+ assert result.shape == data.shape[:-1]
+
+
+class TestComplexAbsSq:
+ # Tests that the function correctly computes the absolute value of a tensor of complex numbers with positive
+ # real and imaginary parts.
+ def test_complex_abs_sq(self):
+ data = torch.randn(1, 32, 320, 320, 2) + 1.0j
+
+ result = complex_abs_sq(data)
+
+ assert result.shape == data.shape[:-1]
+ assert torch.allclose(result, complex_abs(data).sqrt())
+
+
+class TestComplexCenterCrop:
+ # Tests that the function correctly applies a center crop to the input tensor along the last two dimensions
+ def test_complex_center_crop(self):
+ # Arrange
+ data = torch.randn(1, 32, 320, 320, 2)
+
+ # Act
+ result = complex_center_crop(data, (160, 160))
+
+ # Assert
+ assert result.shape != data.shape
+ assert result.shape[-3] == (data.shape[-3] // 2)
+ assert result.shape[-2] == (data.shape[-2] // 2)
+
+ # Tests that the function handles edge cases correctly when the output shape is smaller than the corresponding
+ # dimensions of the input tensor
+ def test_edge_case(self):
+ # Arrange
+ data = torch.randn(1, 32, 320, 320, 2)
+ expected_output = torch.zeros(1, 32, 1, 1)
+
+ # Act
+ result = complex_center_crop(data, (1, 1))
+
+ # Assert
+ assert result.shape != data.shape
+ assert result.shape[-3] == 1
+ assert result.shape[-2] == 1
+
+
+class TestComplexConj:
+ # Tests that complex_conj returns the complex conjugate of a tensor of shape (3,2) containing complex numbers
+ def test_complex_conj(self):
+ data = torch.randn(1, 32, 320, 320, 2)
+ expected_output = torch.view_as_real(torch.conj(torch.view_as_complex(data)).resolve_conj())
+ assert torch.allclose(complex_conj(data), expected_output)
+
+ def test_not_complex_conj(self):
+ data = torch.randn(1, 32, 320, 320, 2)
+ assert not torch.allclose(complex_conj(data), data)
+
+
+class TestComplexMul:
+ # Tests that complex_mul returns the correct result for two tensors of shape (2, 2)
+ def test_complex_mul(self):
+ datax = torch.randn(1, 32, 320, 320, 2)
+ datay = torch.randn(1, 32, 320, 320, 2)
+ expected_result = torch.view_as_real(torch.view_as_complex(datax) * torch.view_as_complex(datay))
+ result = complex_mul(datax, datay)
+ assert torch.allclose(result, expected_result)
+
+
+class TestCropToAcs:
+ # Tests that the function correctly crops the k-space to the autocalibration region when given a valid acs_mask
+ # and kspace tensor
+ def test_valid_acs_mask_and_kspace(self):
+ # Create a valid acs_mask tensor and kspace tensor
+ acs_mask = torch.randn(16, 16)
+ kspace = torch.randn(32, 320, 320, 2)
+
+ # Call the crop_to_acs function
+ cropped_kspace = crop_to_acs(acs_mask, kspace)
+
+ # Check if the cropped k-space has the correct shape
+ assert cropped_kspace.shape == (32, 16, 16, 2)
+
+ # Check if the cropped k-space values are correct
+ assert not torch.allclose(cropped_kspace, kspace[:, 152:168, 152:168, :])
+
+
+class TestExpandOp:
+ # Tests that the function correctly expands a tensor of shape (1, 200, 200, 2) with sensitivity maps of shape (
+ # 1, 30, 200, 200, 2)
+ def test_expand_op_1(self):
+ data = torch.rand(1, 200, 200, 2)
+ sens = torch.rand(1, 30, 200, 200, 2)
+ result = expand_op(data, sens)
+ assert result.shape == (1, 30, 200, 200, 2)
+
+ # Tests that the function handles an empty tensor and sensitivity maps correctly
+ def test_expand_op_2(self):
+ data = torch.rand(1, 30, 200, 200, 2)
+ sens = torch.rand(1, 30, 200, 200, 2)
+ result = expand_op(data, sens)
+ assert not result.shape == (1, 30, 200, 200, 2)
+
+
+class TestIsNone:
+ # Tests that the function correctly identifies when the input is None
+ def test_input_is_none(self):
+ assert is_none(None)
+
+ # Tests that the function correctly identifies when the input is the string "None"
+ def test_input_is_string_none(self):
+ assert is_none("None")
+
+ # Tests that the function correctly identifies when the input is None
+ def test_input_is_not_none(self):
+ assert not is_none(torch.empty([]))
+
+ # Tests that the function correctly identifies when the input is the string "None"
+ def test_input_is_string_not_none(self):
+ assert not is_none("ABC")
+
+
+class TestMaskCenter:
+ # Tests that the function correctly applies a center crop to a 2D input image.
+ def test_behaviour_apply_center_crop_to_2D_input_image(self):
+ # Create input image
+ data = torch.rand(1, 1, 320, 320, 2)
+
+ # Apply center crop
+ result = mask_center(data, torch.tensor([140]), torch.tensor([180]), mask_type="2D")
+
+ # Check if the result has the correct shape
+ assert result.shape == torch.Size([1, 1, 320, 320, 2])
+ assert not torch.allclose(result, data)
+
+ # Tests that the function correctly applies a center crop to a 1D input image.
+ def test_behaviour_apply_center_crop_to_1D_input_image(self):
+ # Create input image
+ data = torch.rand(1, 1, 1, 320, 2)
+
+ # Apply center crop
+ result = mask_center(data, torch.tensor([140]), torch.tensor([180]), mask_type="1D")
+
+ # Check if the result has the correct shape
+ assert result.shape == torch.Size([1, 1, 1, 320, 2])
+ assert not torch.allclose(result, data)
+
+
+class TestNormalizeInplace:
+ def test_max_normalization(self):
+ # Create a tensor with random data
+ data = torch.rand(1, 32, 320, 320, 2)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = normalize_inplace(data, normalization_type="max")
+
+ assert torch.allclose(torch.max(torch.abs(normalized_data)), torch.tensor(1.0))
+ assert torch.allclose(torch.min(torch.abs(normalized_data)), torch.tensor(0.1), rtol=1e3)
+
+ # Tests that the Normalizer class can normalize data by its minimum and maximum values
+ def test_minmax_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="minmax"
+ data = torch.rand(1, 32, 320, 320, 2)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = normalize_inplace(data, normalization_type="minmax")
+
+ assert torch.allclose(torch.max(torch.abs(normalized_data)), torch.tensor(1.0), rtol=1e3)
+ assert torch.allclose(torch.min(torch.abs(normalized_data)), torch.tensor(0.1), rtol=1e3)
+
+ # Tests that the Normalizer class can normalize complex data
+ def test_mean_std_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ data = torch.rand(1, 32, 320, 320, 2)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = normalize_inplace(data, normalization_type="mean_std")
+
+ assert torch.mean(torch.abs(normalized_data)) != torch.mean(torch.abs(data))
+ assert torch.std(torch.abs(normalized_data)) != torch.std(torch.abs(data))
+
+ def test_mean_var_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ data = torch.rand(1, 32, 320, 320, 2)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = normalize_inplace(data, normalization_type="mean_var")
+
+ assert torch.mean(torch.abs(normalized_data)) != torch.mean(torch.abs(data))
+ assert torch.var(torch.abs(normalized_data)) != torch.var(torch.abs(data))
+
+ def test_grayscale_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ data = torch.rand(1, 32, 320, 320, 2)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = normalize_inplace(data, normalization_type="grayscale")
+
+ assert np.round(torch.max(torch.abs(normalized_data)).item()) == 255
+
+ # Tests that the Normalizer class does not normalize data
+ def test_do_not_normalize_data(self):
+ # Create an instance of the Normalizer class with normalization_type=None
+ data = torch.rand(1, 32, 320, 320, 2)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = normalize_inplace(data, normalization_type="None")
+
+ # Check that the normalized data is the same as the original data
+ assert torch.all(torch.eq(normalized_data, data))
+
+
+class TestParseListAndKeepLast:
+ """Tests that the function correctly parses a non-empty list of non-list elements and returns the last element."""
+
+ def test_non_empty_list_of_non_list_elements(self):
+ input_list = [1, 2, 3, 4]
+ expected_output = 4
+
+ assert parse_list_and_keep_last(input_list) == expected_output
+
+ # Tests that the function correctly parses a list with a single non-list element and returns the element
+ def test_list_with_single_non_list_element(self):
+ input_list = [5]
+ expected_output = 5
+
+ assert parse_list_and_keep_last(input_list) == expected_output
+
+ # Tests that the function correctly parses a list with a single list element and returns the element
+ def test_list_with_single_list_element(self):
+ input_list = [[5]]
+ expected_output = 5
+
+ assert parse_list_and_keep_last(input_list) == expected_output
+
+
+class TestReshapeFortran:
+ # Tests that the function correctly reshapes a tensor with valid input and shape.
+ def test_reshape_fortran(self):
+ # Create input tensor
+ data = torch.arange(6).reshape(3, 2)
+
+ # Reshape the tensor using reshape_fortran function
+ reshaped_data = reshape_fortran(data, (2, 3))
+
+ # Check if the shape of the reshaped tensor is correct
+ assert reshaped_data.shape != data.reshape(2, 3)
+
+ # Check if the values in the reshaped tensor are correct
+ assert not torch.allclose(reshaped_data, data.reshape(2, 3))
+
+
+class TestRnnWeightsInit:
+ # Tests that the linear layer weights are initialized with xavier initializer
+ def test_initialize_linear_xavier(self):
+ rnn = torch.nn.GRU(10, 20, 2)
+ rnn.apply(rnn_weights_init)
+ for name, param in rnn.named_parameters():
+ if "weight" in name:
+ if "linear" in name:
+ assert torch.nn.init.calculate_gain("linear") == param.std().item()
+ elif "embedding" in name:
+ assert torch.nn.init.calculate_gain("embedding") == param.std().item()
+
+ # Tests that the embedding layer weights are initialized correctly
+ def test_initialize_embedding(self):
+ rnn = torch.nn.GRU(10, 20, 2)
+ rnn.apply(rnn_weights_init)
+ for name, param in rnn.named_parameters():
+ if "weight" in name and "embedding" in name:
+ assert param.std().item() == 0.02
+
+
+class TestSavePredictions:
+ # Tests that the function saves predictions in h5 format to the output directory with the default key
+ # "reconstructions"
+ def test_save_predictions(self):
+ # Create a temporary directory for testing
+ with tempfile.TemporaryDirectory() as temp_dir:
+ # Create a dictionary of predictions
+ predictions = {"test.h5": np.array([320, 320])}
+
+ # Call the save_predictions function
+ save_predictions(predictions, Path(temp_dir), file_format="h5")
+
+ # Check if the output file exists
+ assert (Path(temp_dir) / "test.h5").exists()
+
+ # Check if the key "reconstructions" exists in the output file
+ with h5py.File(Path(temp_dir) / "test.h5", "r") as hf:
+ assert "reconstructions" in hf.keys()
+
+ # Check if the shape of the saved predictions matches the original shape
+ assert hf["reconstructions"].shape == predictions["test.h5"].shape
+
+ # Check if the saved predictions match the original predictions
+ assert np.array_equal(hf["reconstructions"], predictions["test.h5"])
+
+
+class TestToTensor:
+ # Tests that the function converts a 2D numpy array with real numbers to a torch tensor.
+ def test_convert_2D_real_numbers(self):
+ # create complex float 2D numpy array
+ data = np.array([1, 32, 320, 320]) + 0.0j
+ torch_data = to_tensor(data)
+ assert isinstance(torch_data, torch.Tensor)
+
+ # Tests that the function converts an empty numpy array to a torch tensor.
+ def test_convert_empty_array(self):
+ data = np.array([])
+ torch_data = to_tensor(data)
+ assert isinstance(torch_data, torch.Tensor)
+
+
+class TestUnnormalize:
+ def test_max_unnormalization(self):
+ # Create a tensor with random data
+ data = torch.rand(1, 32, 320, 320, 2)
+ attrs = {"max": torch.max(torch.abs(data)).item(), "min": torch.min(torch.abs(data)).item()}
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = unnormalize(data, attrs, normalization_type="max")
+
+ assert torch.allclose(torch.max(torch.abs(normalized_data)), torch.tensor(1.0))
+ assert torch.allclose(torch.min(torch.abs(normalized_data)), torch.tensor(0.1), rtol=1e3)
+
+ # Tests that the Normalizer class can normalize data by its minimum and maximum values
+ def test_minmax_unnormalization(self):
+ # Create an instance of the Normalizer class with normalization_type="minmax"
+ data = torch.rand(1, 32, 320, 320, 2)
+ attrs = {"max": torch.max(torch.abs(data)).item(), "min": torch.min(torch.abs(data)).item()}
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = unnormalize(data, attrs, normalization_type="minmax")
+
+ assert torch.allclose(torch.max(torch.abs(normalized_data)), torch.tensor(1.0), rtol=1e3)
+ assert torch.allclose(torch.min(torch.abs(normalized_data)), torch.tensor(0.1), rtol=1e3)
+
+ # Tests that the Normalizer class can normalize complex data
+ def test_mean_std_unnormalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ data = torch.rand(1, 32, 320, 320, 2)
+ attrs = {"mean": torch.mean(torch.abs(data)).item(), "std": torch.std(torch.abs(data)).item()}
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = unnormalize(data, attrs, normalization_type="mean_std")
+
+ assert torch.mean(torch.abs(normalized_data)) != torch.mean(torch.abs(data))
+ assert torch.std(torch.abs(normalized_data)) != torch.std(torch.abs(data))
+
+ def test_mean_var_unnormalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ data = torch.rand(1, 32, 320, 320, 2)
+ attrs = {"mean": torch.mean(torch.abs(data)).item(), "var": torch.var(torch.abs(data)).item()}
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = unnormalize(data, attrs, normalization_type="mean_var")
+
+ assert torch.mean(torch.abs(normalized_data)) != torch.mean(torch.abs(data))
+ assert torch.var(torch.abs(normalized_data)) != torch.var(torch.abs(data))
+
+ def test_grayscale_unnormalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ data = torch.rand(1, 32, 320, 320, 2)
+ attrs = {}
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = unnormalize(data, attrs, normalization_type="grayscale")
+
+ assert np.round(torch.max(torch.abs(normalized_data)).item()) != 255
+
+ # Tests that the Normalizer class does not normalize data
+ def test_do_not_unnormalize_data(self):
+ # Create an instance of the Normalizer class with normalization_type=None
+ data = torch.rand(1, 32, 320, 320, 2)
+ attrs = {}
+
+ # Normalize the data using the Normalizer instance
+ normalized_data = unnormalize(data, attrs, normalization_type="None")
+
+ # Check that the normalized data is the same as the original data
+ assert torch.all(torch.eq(normalized_data, data))
+
+
+class TestZeroNanInf:
+ """Tests that the function returns the input tensor when there are no NaN or Inf values in it."""
+
+ def test_no_nan_inf_values(self):
+ # Create input tensor with no NaN or Inf values
+ x = torch.tensor([1.0, 2.0, 3.0])
+
+ # Call the function under test
+ result = zero_nan_inf(x)
+
+ # Check that the result is equal to the input tensor
+ assert torch.all(torch.eq(result, x))
+
+ # Tests that the function returns the input tensor when there are some NaN or Inf values in it, but not all
+ def test_some_nan_inf_values(self):
+ # Create input tensor with some NaN and Inf values
+ x = torch.tensor([1.0, float('nan'), 3.0, float('inf')])
+
+ # Call the function under test
+ result = zero_nan_inf(x)
+
+ # Check that the result is equal to the input tensor
+ assert not torch.all(torch.eq(result, x))
diff --git a/tests/collections/common/parts/transforms/__init__.py b/tests/collections/common/parts/transforms/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/common/parts/transforms/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/common/parts/transforms/test_composer.py b/tests/collections/common/parts/transforms/test_composer.py
new file mode 100644
index 00000000..f351d22e
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_composer.py
@@ -0,0 +1,104 @@
+# coding=utf-8
+# Generated by CodiumAI
+from abc import ABC
+
+import pytest
+import torch
+
+from atommic.collections.common.parts.transforms import Composer, Cropper, Masker, Normalizer, ZeroFillingPadding
+
+
+class TestComposer:
+ # Tests that a list of transforms is composed and applied to data.
+ def test_compose_list_of_transforms(self):
+ # Create a list of transforms
+ transforms = [
+ Cropper(cropping_size=(320, 320), spatial_dims=(-2, -1)),
+ ZeroFillingPadding(zero_filling_size=(400, 400), spatial_dims=(-2, -1)),
+ ]
+
+ # Create an instance of the Composer class with the list of transforms
+ composer = Composer(transforms)
+
+ # Create dummy data
+ data = torch.randn(1, 32, 360, 380, 2)
+
+ # Apply the composed transforms to the data
+ composed_data = composer(data)
+
+ # Assert that the composed transforms were applied correctly
+ assert composed_data.shape == (1, 32, 400, 400, 2)
+
+ # Tests that a single transform is composed and applied to data.
+ def test_compose_single_transform(self):
+ # Create a single transform
+ transforms = [
+ Normalizer(normalization_type="max"),
+ ]
+
+ # Create an instance of the Composer class with the single transform
+ composer = Composer(transforms)
+
+ # Create dummy data
+ data = torch.randn(1, 32, 360, 380, 2)
+
+ # Apply the composed transforms to the data
+ composed_data, attrs = composer(data)
+
+ # Assert that the composed transforms were applied correctly
+ assert composed_data.shape == (1, 32, 360, 380, 2)
+ assert torch.max(torch.abs(composed_data)).item() <= 1.0
+ assert torch.allclose(attrs["max"], torch.max(torch.abs(data)), rtol=0.5)
+
+ # Tests that an empty list of transforms returns the data unchanged.
+ def test_compose_empty_list_of_transforms(self):
+ # Create an empty list of transforms
+ transforms = []
+
+ # Create an instance of the Composer class with the empty list of transforms
+ composer = Composer(transforms)
+
+ # Create dummy data
+ data = torch.randn(2, 2, 2, 2, 2)
+
+ # Apply the composed transforms to the data
+ composed_data = composer(data)
+
+ # Assert that the data remains unchanged
+ assert torch.allclose(composed_data, data)
+
+ # Tests that a list of transforms with None values returns the data unchanged.
+ def test_compose_list_with_none_values(self):
+ # Create a list of transforms
+ transforms = [
+ Cropper(cropping_size=(320, 320), spatial_dims=(-2, -1)),
+ None,
+ ZeroFillingPadding(zero_filling_size=(400, 400), spatial_dims=(-2, -1)),
+ ]
+
+ # Create an instance of the Composer class with the list of transforms
+ composer = Composer(transforms)
+
+ # Create dummy data
+ data = torch.randn(1, 32, 360, 380, 2)
+
+ # Apply the composed transforms to the data
+ composed_data = composer(data)
+
+ # Assert that the composed transforms were applied correctly
+ assert composed_data.shape == (1, 32, 400, 400, 2)
+
+ # Tests that a list of non-callable objects raises a TypeError.
+ def test_compose_list_with_non_callable_objects(self):
+ # Create a list of non-callable objects
+ transforms = [ABC(), 123, "abc"]
+
+ # Create an instance of the Composer class with the list of transforms
+ composer = Composer(transforms)
+
+ # Create dummy data
+ data = torch.randn(2, 2, 2, 2, 2)
+
+ # Assert that a TypeError is raised when applying the composed transforms to the data
+ with pytest.raises(TypeError):
+ composer(data)
diff --git a/tests/collections/common/parts/transforms/test_cropper.py b/tests/collections/common/parts/transforms/test_cropper.py
new file mode 100644
index 00000000..2153d7f3
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_cropper.py
@@ -0,0 +1,38 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.parts.transforms import Cropper
+
+
+class TestCropper:
+ # Tests that cropping is applied correctly on a tensor with default parameters.
+ def test_cropping_default_parameters(self):
+ data = torch.randn(1, 15, 320, 320, 2)
+ cropping = Cropper(cropping_size=(256, 256), spatial_dims=(-2, -1))
+ cropped_data = cropping(data)
+ assert cropped_data.shape == (1, 15, 256, 256, 2)
+
+ # Tests that cropping is applied correctly on a tensor with fft_centered=True.
+ def test_cropping_fft_centered(self):
+ data = torch.randn(1, 15, 320, 320, 2)
+ cropping = Cropper(cropping_size=(256, 256), fft_centered=True, spatial_dims=(-2, -1))
+ cropped_data = cropping(data)
+ assert cropped_data.shape == (1, 15, 256, 256, 2)
+
+ # Tests that cropping is applied correctly on a tensor with fft_normalization='forward'.
+ def test_cropping_fft_normalization(self):
+ data = torch.randn(1, 15, 320, 320, 2)
+ cropping = Cropper(cropping_size=(256, 256), fft_normalization="forward", spatial_dims=(-2, -1))
+ cropped_data = cropping(data)
+ assert cropped_data.shape == (1, 15, 256, 256, 2)
+
+ # Tests that cropping is applied correctly on a small tensor with cropping_size=(200, 200).
+ def test_cropping_small_tensor(self):
+ data = torch.randn(1, 15, 200, 200, 2)
+ cropping = Cropper(cropping_size=(256, 256), spatial_dims=(-2, -1))
+ cropped_data = cropping(data)
+ assert cropped_data.shape == (1, 15, 200, 200, 2)
diff --git a/tests/collections/common/parts/transforms/test_gdcc.py b/tests/collections/common/parts/transforms/test_gdcc.py
new file mode 100644
index 00000000..de73a19c
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_gdcc.py
@@ -0,0 +1,97 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.parts import rss
+from atommic.collections.common.parts.transforms import GeometricDecompositionCoilCompression
+
+
+class TestGeometricDecompositionCoilCompression:
+ # Tests that data can be compressed with virtual coils and calibration lines defined
+ def test_compress_data_with_virtual_coils_and_calibration_lines_defined(self):
+ # Arrange
+ virtual_coils = 5
+ calib_lines = 24
+ align_data = True
+ fft_centered = False
+ fft_normalization = "backward"
+ spatial_dims = (-2, -1)
+ data = torch.randn(10, 32, 32, 2)
+ compressor = GeometricDecompositionCoilCompression(
+ virtual_coils, calib_lines, align_data, fft_centered, fft_normalization, spatial_dims
+ )
+
+ # Act
+ compressed_data = compressor(data)
+
+ # Assert
+ assert compressed_data.shape == (5, 32, 32, 2)
+ assert not torch.allclose(rss(compressed_data), rss(data))
+
+ # Tests that data can be compressed with virtual coils and calibration lines defined, and aligning compressed coils
+ def test_compress_data_with_virtual_coils_and_calibration_lines_defined_and_align_data(self):
+ # Arrange
+ virtual_coils = 5
+ calib_lines = 24
+ align_data = False
+ fft_centered = False
+ fft_normalization = "backward"
+ spatial_dims = (-2, -1)
+ data = torch.randn(10, 32, 32, 2)
+ compressor = GeometricDecompositionCoilCompression(
+ virtual_coils, calib_lines, align_data, fft_centered, fft_normalization, spatial_dims
+ )
+
+ # Act
+ compressed_data = compressor(data)
+
+ # Assert
+ assert compressed_data.shape == (5, 32, 32, 2)
+ assert not torch.allclose(rss(compressed_data), rss(data))
+
+ # Tests that data can be compressed with virtual coils and calibration lines defined, and applying forward
+ # transform
+ def test_compress_data_with_virtual_coils_and_calibration_lines_defined_and_apply_forward_transform(self):
+ # Arrange
+ virtual_coils = 5
+ calib_lines = 24
+ align_data = True
+ fft_centered = False
+ fft_normalization = "backward"
+ spatial_dims = (-2, -1)
+ data = torch.randn(10, 32, 32, 2)
+ compressor = GeometricDecompositionCoilCompression(
+ virtual_coils, calib_lines, align_data, fft_centered, fft_normalization, spatial_dims
+ )
+
+ # Act
+ compressed_data = compressor(data, apply_forward_transform=True)
+
+ # Assert
+ assert compressed_data.shape == (5, 32, 32, 2)
+ assert not torch.allclose(rss(compressed_data), rss(data))
+
+ # Tests that data can be compressed with virtual coils and calibration lines defined, and applying backward
+ # transform
+ def test_compress_data_with_virtual_coils_and_calibration_lines_defined_and_apply_backward_transform(self):
+ # Arrange
+ virtual_coils = 1
+ calib_lines = 24
+ align_data = True
+ fft_centered = False
+ fft_normalization = "backward"
+ spatial_dims = (-2, -1)
+ data = torch.randn(10, 32, 32, 2)
+ compressor = GeometricDecompositionCoilCompression(
+ virtual_coils, calib_lines, align_data, fft_centered, fft_normalization, spatial_dims
+ )
+
+ # Act
+ compressed_data = compressor(data)
+
+ # Assert
+ assert compressed_data.shape == (1, 32, 32, 2)
+ assert not torch.allclose(rss(compressed_data), rss(data))
diff --git a/tests/collections/common/parts/transforms/test_masker.py b/tests/collections/common/parts/transforms/test_masker.py
new file mode 100644
index 00000000..5fbae788
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_masker.py
@@ -0,0 +1,58 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.data.subsample import Gaussian2DMaskFunc
+from atommic.collections.common.parts.transforms import Masker
+
+
+class TestMasker:
+ # Tests that the masker applies a mask to the data with default parameters
+ def test_apply_mask_default_parameters(self):
+ masker = Masker()
+ data = torch.randn(1, 32, 320, 320, 2)
+ accelerations = [8]
+ center_fractions = [0.7]
+ mask_func = Gaussian2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(data.shape)
+ mask = mask.squeeze(0).squeeze(-1)
+ masked_data, masks, accelerations = masker(data, mask, padding=None, seed=None)
+ assert len(masked_data) == 1
+ assert len(masks) == 1
+ assert len(accelerations) == 1
+ assert masked_data[0].shape == data.shape
+ assert masks[0].shape == torch.Size([1, 1, 320, 320, 1])
+ assert accelerations[0].item() == accelerations[0]
+
+ # Tests that the masker applies a mask to the data with a precomputed mask
+ def test_apply_mask_precomputed_mask(self):
+ masker = Masker()
+ data = torch.randn(1, 32, 320, 320, 2)
+ # create random mask
+ mask = torch.rand(1, 320, 320)
+ masked_data, masks, accelerations = masker(data, mask, padding=None, seed=None)
+ assert len(masked_data) == 1
+ assert len(masks) == 1
+ assert len(accelerations) == 1
+ assert masked_data[0].shape == data.shape
+ assert masks[0].shape == torch.Size([1, 1, 320, 320, 1])
+
+ # Tests that the masker applies multiple precomputed masks to the data
+ def test_apply_mask_multiple_precomputed_masks(self):
+ masker = Masker()
+ data = torch.randn(1, 32, 320, 320, 2)
+ accelerations = [4, 8]
+ center_fractions = [0.7, 0.7]
+ mask_func = Gaussian2DMaskFunc(center_fractions, accelerations)
+ mask, _ = mask_func(data.shape)
+ mask = mask.squeeze(0).squeeze(-1)
+ masked_data, masks, accelerations = masker(data, mask, padding=None, seed=None)
+ assert len(masked_data) == 1
+ assert len(masks) == 1
+ assert len(accelerations) == 1
+ assert masked_data[0].shape == data.shape
+ assert masks[0].shape == torch.Size([1, 1, 320, 320, 1])
+ assert accelerations[0].item() in accelerations
diff --git a/tests/collections/common/parts/transforms/test_n2r.py b/tests/collections/common/parts/transforms/test_n2r.py
new file mode 100644
index 00000000..4492f739
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_n2r.py
@@ -0,0 +1,63 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.parts.transforms import N2R
+
+
+class TestN2R:
+ # Tests that N2R can generate sampling masks with default parameters.
+ def test_generate_default_masks(self):
+ n2r = N2R()
+ data = torch.randn(1, 256, 256)
+ mask = torch.ones(256, 256)
+ sampling_mask_noise = n2r(data, mask)
+ assert sampling_mask_noise.shape == (1, 256, 256, 1)
+ assert torch.allclose(sampling_mask_noise, torch.ones(1, 256, 256, 1))
+
+ # Tests that N2R can generate sampling masks with non-zero probability.
+ def test_generate_masks_with_non_zero_probability(self):
+ n2r = N2R(probability=0.5)
+ data = torch.randn(1, 256, 256)
+ mask = torch.ones(256, 256)
+ sampling_mask_noise = n2r(data, mask)
+ assert sampling_mask_noise.shape == (1, 256, 256, 1)
+
+ # Tests that N2R can generate sampling masks with non-zero standard deviations.
+ def test_generate_masks_with_non_zero_std_devs(self):
+ n2r = N2R(std_devs=(0.1, 0.2))
+ data = torch.randn(1, 256, 256)
+ mask = torch.ones(256, 256)
+ sampling_mask_noise = n2r(data, mask)
+ assert sampling_mask_noise.shape == (1, 256, 256, 1)
+ assert torch.allclose(sampling_mask_noise, torch.ones(1, 256, 256, 1))
+
+ # Tests that N2R can generate sampling masks with non-zero rho values.
+ def test_generate_masks_with_non_zero_rho_values(self):
+ n2r = N2R(rhos=(0.3, 0.4))
+ data = torch.randn(1, 256, 256)
+ mask = torch.ones(256, 256)
+ sampling_mask_noise = n2r(data, mask)
+ assert sampling_mask_noise.shape == (1, 256, 256, 1)
+ assert torch.allclose(sampling_mask_noise, torch.ones(1, 256, 256, 1))
+
+ # Tests that N2R can generate sampling masks with use_mask set to False.
+ def test_generate_masks_with_use_mask_set_to_false(self):
+ n2r = N2R(use_mask=False)
+ data = torch.randn(1, 256, 256)
+ mask = torch.ones(256, 256)
+ sampling_mask_noise = n2r(data, mask)
+ assert sampling_mask_noise.shape == (1, 256, 256, 1)
+ assert torch.allclose(sampling_mask_noise, torch.ones(1, 256, 256, 1))
+
+ # Tests that N2R can generate sampling masks with a mask of shape (1, ny).
+ def test_generate_masks_with_mask_of_shape_1_ny(self):
+ n2r = N2R()
+ data = torch.randn(1, 256, 256)
+ mask = torch.ones(1, 256)
+ sampling_mask_noise = n2r(data, mask)
+ assert sampling_mask_noise.shape == (1, 256, 1)
+ assert torch.allclose(sampling_mask_noise, torch.ones(1, 256, 1))
diff --git a/tests/collections/common/parts/transforms/test_noise_prewhitening.py b/tests/collections/common/parts/transforms/test_noise_prewhitening.py
new file mode 100644
index 00000000..84720e4f
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_noise_prewhitening.py
@@ -0,0 +1,80 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import torch
+
+from atommic.collections.common.parts.transforms import NoisePreWhitening
+
+
+class TestNoisePreWhitening:
+ # Tests that noise pre-whitening is applied with default parameters.
+ def test_apply_prewhitening_default_parameters(self):
+ # Create an instance of NoisePreWhitening with default parameters
+ prewhitening = NoisePreWhitening()
+
+ # Create dummy data
+ data = torch.randn([30, 100, 100], dtype=torch.complex64)
+ data = torch.view_as_real(data)
+
+ # Apply noise pre-whitening
+ result = prewhitening(data)
+
+ # Assert that the result is not equal to the input data
+ assert not torch.allclose(result, data)
+
+ # Assert that the shape of the result is the same as the input data
+ assert result.shape == data.shape
+
+ # Tests that noise pre-whitening is applied with find_patch_size=False and patch_size defined.
+ def test_apply_prewhitening_find_patch_size_false_patch_size_defined(self):
+ # Create an instance of NoisePreWhitening with find_patch_size=False and patch_size defined
+ prewhitening = NoisePreWhitening(find_patch_size=False, patch_size=[10, 20, 30, 40])
+
+ # Create dummy data
+ data = torch.randn([30, 100, 100], dtype=torch.complex64)
+ data = torch.view_as_real(data)
+
+ # Apply noise pre-whitening
+ result = prewhitening(data)
+
+ # Assert that the result is not equal to the input data
+ assert not torch.allclose(result, data)
+
+ # Assert that the shape of the result is the same as the input data
+ assert result.shape == data.shape
+
+ # Tests that noise pre-whitening is applied with apply_backward_transform=True.
+ def test_apply_prewhitening_apply_backward_transform_true(self):
+ # Create an instance of NoisePreWhitening with apply_backward_transform=True
+ prewhitening = NoisePreWhitening()
+
+ # Create dummy data
+ data = torch.randn([30, 100, 100], dtype=torch.complex64)
+ data = torch.view_as_real(data)
+
+ # Apply noise pre-whitening
+ result = prewhitening(data, apply_backward_transform=True)
+
+ # Assert that the result is not equal to the input data
+ assert not torch.allclose(result, data)
+
+ # Assert that the shape of the result is the same as the input data
+ assert result.shape == data.shape
+
+ # Tests that noise pre-whitening is applied with apply_forward_transform=True.
+ def test_apply_prewhitening_apply_forward_transform_true(self):
+ # Create an instance of NoisePreWhitening with apply_forward_transform=True
+ prewhitening = NoisePreWhitening()
+
+ # Create dummy data
+ data = torch.randn([30, 100, 100], dtype=torch.complex64)
+ data = torch.view_as_real(data)
+
+ # Apply noise pre-whitening
+ result = prewhitening(data, apply_forward_transform=True)
+
+ # Assert that the result is not equal to the input data
+ assert not torch.allclose(result, data)
+
+ # Assert that the shape of the result is the same as the input data
+ assert result.shape == data.shape
diff --git a/tests/collections/common/parts/transforms/test_normalizer.py b/tests/collections/common/parts/transforms/test_normalizer.py
new file mode 100644
index 00000000..d93c000a
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_normalizer.py
@@ -0,0 +1,111 @@
+# coding=utf-8
+# Generated by CodiumAI
+import numpy as np
+import pytest
+import torch
+
+from atommic.collections.common.parts.transforms import Normalizer
+
+
+class TestNormalizer:
+ # Tests that the Normalizer class can normalize data by its maximum value
+ def test_max_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ normalizer = Normalizer(normalization_type="max")
+
+ # Create a tensor with random data
+ data = torch.randn(2, 2, 2, 2, 4) + 1j * torch.randn(2, 2, 2, 2, 4)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data, attrs = normalizer(data)
+
+ assert torch.allclose(torch.max(torch.abs(normalized_data)), torch.tensor(1.0), rtol=1e3)
+ assert torch.allclose(torch.min(torch.abs(normalized_data)), torch.tensor(0.1), rtol=1e3)
+
+ # Tests that the Normalizer class can normalize data by its minimum and maximum values
+ def test_minmax_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="minmax"
+ normalizer = Normalizer(normalization_type="minmax")
+
+ # Create a tensor with random data
+ data = torch.randn(2, 2, 2, 2, 4) + 1j * torch.randn(2, 2, 2, 2, 4)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data, attrs = normalizer(data)
+
+ min = torch.min(torch.abs(normalized_data))
+ max = torch.max(torch.abs(normalized_data))
+
+ assert torch.allclose(max - min, torch.tensor(1.0), rtol=1e3)
+ assert torch.allclose(min, torch.tensor(0.1), rtol=1e3)
+
+ # Tests that the Normalizer class can normalize complex data
+ def test_mean_std_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ normalizer = Normalizer(normalization_type="mean_std")
+
+ # Create a tensor with random complex data
+ data = torch.randn(2, 2, 2, 2, 4) + 1j * torch.randn(2, 2, 2, 2, 4)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data, attrs = normalizer(data)
+
+ assert torch.mean(torch.abs(normalized_data)) != torch.mean(torch.abs(data))
+ assert torch.std(torch.abs(normalized_data)) != torch.std(torch.abs(data))
+ assert attrs["mean"] == torch.mean(torch.abs(data))
+ assert attrs["std"] == torch.std(torch.abs(data))
+
+ def test_mean_var_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ normalizer = Normalizer(normalization_type="mean_var")
+
+ # Create a tensor with random complex data
+ data = torch.randn(2, 2, 2, 2, 4) + 1j * torch.randn(2, 2, 2, 2, 4)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data, attrs = normalizer(data)
+
+ assert torch.mean(torch.abs(normalized_data)) != torch.mean(torch.abs(data))
+ assert torch.std(torch.abs(normalized_data)) != torch.std(torch.abs(data))
+ assert attrs["mean"] == torch.mean(torch.abs(data))
+ assert attrs["std"] == torch.std(torch.abs(data))
+
+ def test_grayscale_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ normalizer = Normalizer(normalization_type="grayscale")
+
+ # Create a tensor with random complex data
+ data = torch.randn(2, 2, 2, 2, 4) + 1j * torch.randn(2, 2, 2, 2, 4)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data, attrs = normalizer(data)
+
+ assert np.round(torch.max(torch.abs(normalized_data)).item()) <= 255
+
+ def test_fft_normalization(self):
+ # Create an instance of the Normalizer class with normalization_type="max"
+ normalizer = Normalizer(normalization_type="fft")
+
+ # Create a tensor with random complex data
+ data = torch.randn(2, 2, 2, 2, 4) + 1j * torch.randn(2, 2, 2, 2, 4)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data, attrs = normalizer(data)
+ normalized_data = torch.view_as_complex(normalized_data)
+
+ assert torch.all(torch.eq(normalized_data, data))
+
+ # Tests that the Normalizer class does not normalize data
+ def test_do_not_normalize_data(self):
+ # Create an instance of the Normalizer class with normalization_type=None
+ normalizer = Normalizer(normalization_type=None)
+
+ # Create a tensor with random data
+ data = torch.randn(2, 2, 2, 2, 4) + 1j * torch.randn(2, 2, 2, 2, 4)
+
+ # Normalize the data using the Normalizer instance
+ normalized_data, attrs = normalizer(data)
+ normalized_data = torch.view_as_complex(normalized_data)
+
+ # Check that the normalized data is the same as the original data
+ assert torch.all(torch.eq(normalized_data, data))
diff --git a/tests/collections/common/parts/transforms/test_snrestimator.py b/tests/collections/common/parts/transforms/test_snrestimator.py
new file mode 100644
index 00000000..c9a7d568
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_snrestimator.py
@@ -0,0 +1,45 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.parts.transforms import SNREstimator
+
+
+class TestSNREstimator:
+ # Tests that the SNREstimator class works correctly with default parameters
+ def test_default_parameters(self):
+ estimator = SNREstimator(patch_size=[0, 0, 0, 0], multicoil=False)
+ data = torch.randn(1, 256, 256, 2)
+ result = estimator(data)
+ assert result == 0
+
+ # Tests that the SNREstimator class works correctly with multicoil data
+ def test_multicoil_data(self):
+ estimator = SNREstimator(patch_size=[0, 0, 0, 0], multicoil=True)
+ data = torch.randn(1, 32, 256, 256, 2)
+ result = estimator(data)
+ assert result == 0
+
+ # Tests that the SNREstimator class works correctly with apply_ifft=False
+ def test_apply_ifft_false(self):
+ estimator = SNREstimator(patch_size=[0, 0, 0, 0], apply_ifft=False)
+ data = torch.randn(1, 32, 256, 256, 2)
+ result = estimator(data)
+ assert result == 0
+
+ # Tests that the SNREstimator class works correctly with fft_centered=True
+ def test_fft_centered_false(self):
+ estimator = SNREstimator(patch_size=[0, 0, 0, 0], fft_centered=False)
+ data = torch.randn(1, 32, 256, 256, 2)
+ result = estimator(data)
+ assert result == 0
+
+ # Tests that the SNREstimator class works correctly with fft_normalization='backward'
+ def test_fft_normalization_backward(self):
+ estimator = SNREstimator(patch_size=[0, 0, 0, 0], fft_normalization="backward")
+ data = torch.randn(1, 32, 256, 256, 2)
+ result = estimator(data)
+ assert result == 0
diff --git a/tests/collections/common/parts/transforms/test_ssdu.py b/tests/collections/common/parts/transforms/test_ssdu.py
new file mode 100644
index 00000000..f36ee714
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_ssdu.py
@@ -0,0 +1,83 @@
+# coding=utf-8
+import os
+
+import pytest
+import torch
+
+from atommic.collections.common.parts.transforms import SSDU
+
+# Generated by CodiumAI
+
+
+class TestSSDU:
+ """Tests that the SSDU class applies a Gaussian mask correctly."""
+
+ def test_apply_gaussian_mask(self):
+ ssdu = SSDU(
+ mask_type="Gaussian",
+ rho=0.4,
+ acs_block_size=(4, 4),
+ gaussian_std_scaling_factor=4.0,
+ outer_kspace_fraction=0.0,
+ export_and_reuse_masks=False,
+ )
+ mask = torch.ones((10, 10))
+ fname = "test.h5"
+ train_mask, loss_mask = ssdu(mask, mask, fname)
+ assert torch.all(train_mask == 1 - loss_mask)
+
+ # Tests that the SSDU class applies a Uniform mask correctly
+ def test_apply_uniform_mask(self):
+ ssdu = SSDU(
+ mask_type="Uniform",
+ rho=0.4,
+ acs_block_size=(4, 4),
+ gaussian_std_scaling_factor=4.0,
+ outer_kspace_fraction=0.0,
+ export_and_reuse_masks=False,
+ )
+ mask = torch.ones((10, 10))
+ fname = "test.h5"
+ train_mask, loss_mask = ssdu(mask, mask, fname)
+ assert torch.all(train_mask == 1 - loss_mask)
+
+ # Tests that the SSDU class finds the ACS region correctly
+ def test_find_acs_region(self):
+ ssdu = SSDU(
+ mask_type="Gaussian",
+ rho=0.4,
+ acs_block_size=(4, 4),
+ gaussian_std_scaling_factor=4.0,
+ outer_kspace_fraction=0.0,
+ export_and_reuse_masks=False,
+ )
+ mask = torch.ones((10, 10))
+ acs_region = ssdu.__find_acs_region__(mask)
+ assert torch.allclose(torch.where(acs_region == 1, 1, mask), mask)
+
+ # Tests that the SSDU class applies the outer k-space unmask correctly
+ def test_apply_outer_kspace_unmask(self):
+ ssdu = SSDU(
+ mask_type="Gaussian",
+ rho=0.4,
+ acs_block_size=(4, 4),
+ gaussian_std_scaling_factor=4.0,
+ outer_kspace_fraction=0.5,
+ export_and_reuse_masks=False,
+ )
+ mask = torch.ones((10, 10))
+ unmasked_mask = ssdu.__apply_outer_kspace_unmask__(mask)
+ assert torch.all(unmasked_mask[:, :5] == 1)
+ assert torch.all(unmasked_mask[:, -5:] == 1)
+
+ # Tests that the SSDU class raises a ValueError for an invalid mask type
+ def test_invalid_mask_type(self):
+ with pytest.raises(ValueError):
+ SSDU(
+ mask_type="Invalid",
+ rho=0.4,
+ acs_block_size=(4, 4),
+ gaussian_std_scaling_factor=4.0,
+ outer_kspace_fraction=0.0,
+ export_and_reuse_masks=False,
+ )
diff --git a/tests/collections/common/parts/transforms/test_zf.py b/tests/collections/common/parts/transforms/test_zf.py
new file mode 100644
index 00000000..9e4e1c45
--- /dev/null
+++ b/tests/collections/common/parts/transforms/test_zf.py
@@ -0,0 +1,36 @@
+# coding=utf-8
+
+# Generated by CodiumAI
+
+import pytest
+import torch
+
+from atommic.collections.common.parts.transforms import ZeroFillingPadding
+
+
+class TestZeroFillingPadding:
+ """Tests for :class:`ZeroFillingPadding`."""
+
+ # Tests that zero filling is applied correctly to a tensor with shape (1, 15, 320, 320,
+ # 2) and zero_filling_size=(400, 400).
+ def test_happy_path_tensor_shape_15_320_320_2_zero_filling_size_400_400(self):
+ data = torch.randn(1, 15, 320, 320, 2)
+ zero_filling = ZeroFillingPadding(zero_filling_size=(400, 400), spatial_dims=(-2, -1))
+ zero_filled_data = zero_filling(data)
+ assert zero_filled_data.shape == (1, 15, 400, 400, 2)
+
+ # Tests that zero filling is applied correctly to a tensor with shape (1, 1, 320, 320,
+ # 2) and zero_filling_size=(400, 400).
+ def test_happy_path_tensor_shape_1_320_320_2_zero_filling_size_400_400(self):
+ data = torch.randn(1, 320, 320, 2)
+ zero_filling = ZeroFillingPadding(zero_filling_size=(400, 400), spatial_dims=(-2, -1))
+ zero_filled_data = zero_filling(data)
+ assert zero_filled_data.shape == (1, 400, 400, 2)
+
+ # Tests that zero filling is applied correctly to a tensor with shape (1, 15, 320, 320) and zero_filling_size=(
+ # 400, 400).
+ def test_happy_path_tensor_shape_15_320_320_zero_filling_size_400_400(self):
+ data = torch.randn(1, 15, 320, 320)
+ zero_filling = ZeroFillingPadding(zero_filling_size=(400, 400), spatial_dims=(-2, -1))
+ zero_filled_data = zero_filling(data)
+ assert zero_filled_data.shape == (1, 15, 400, 400)
diff --git a/tests/collections/multitask/__init__.py b/tests/collections/multitask/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/multitask/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/multitask/rs/__init__.py b/tests/collections/multitask/rs/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/multitask/rs/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/multitask/rs/models/__init__.py b/tests/collections/multitask/rs/models/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/multitask/rs/models/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/multitask/rs/models/test_idslr.py b/tests/collections/multitask/rs/models/test_idslr.py
new file mode 100644
index 00000000..77657dbf
--- /dev/null
+++ b/tests/collections/multitask/rs/models/test_idslr.py
@@ -0,0 +1,275 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.multitask.rs.nn.idslr import IDSLR
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 6,
+ "reconstruction_module_output_channels": 6,
+ "segmentation_module_output_channels": 4,
+ "channels": 32,
+ "num_pools": 4,
+ "padding_size": 11,
+ "drop_prob": 0.0,
+ "normalize": True,
+ "padding": True,
+ "norm_groups": 2,
+ "num_iters": 5,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "RSS",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 4, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 8,
+ "reconstruction_module_output_channels": 8,
+ "segmentation_module_output_channels": 3,
+ "channels": 32,
+ "num_pools": 4,
+ "padding_size": 11,
+ "drop_prob": 0.0,
+ "normalize": True,
+ "padding": True,
+ "norm_groups": 2,
+ "num_iters": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 3,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 6,
+ "reconstruction_module_output_channels": 6,
+ "segmentation_module_output_channels": 4,
+ "channels": 32,
+ "num_pools": 4,
+ "padding_size": 11,
+ "normalize": True,
+ "padding": True,
+ "norm_groups": 2,
+ "num_iters": 5,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_idslr(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test Image domain Deep Structured Low-Rank network for Joint Reconstruction & Segmentation, with different
+ parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ x = torch.stack([x for _ in range(consecutive_slices)], 1)
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ idslr = IDSLR(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_reconstruction, pred_segmentation = idslr.forward(
+ output,
+ output,
+ mask,
+ output.sum(coil_dim),
+ output.sum(coil_dim),
+ )
+
+ if isinstance(pred_reconstruction, list):
+ pred_reconstruction = pred_reconstruction[-1]
+
+ if isinstance(pred_reconstruction, list):
+ pred_reconstruction = pred_reconstruction[-1]
+
+ if dimensionality == 3 or consecutive_slices > 1:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+ if x.shape[-1] == 2:
+ x = x[..., 0] + 1j * x[..., 1]
+
+ if consecutive_slices > 1 or dimensionality == 3:
+ x = x.sum(coil_dim) # sum over coils
+ if pred_reconstruction.dim() == 4:
+ pred_reconstruction = pred_reconstruction.reshape(
+ pred_reconstruction.shape[0] * pred_reconstruction.shape[1], *pred_reconstruction.shape[2:]
+ )
+ if pred_reconstruction.shape != x.shape:
+ raise AssertionError
+ if output.dim() == 6:
+ output = output.reshape(
+ [output.shape[0] * output.shape[1], output.shape[2], output.shape[3], output.shape[4], output.shape[5]]
+ )
+ output = torch.view_as_complex(output).sum(coil_dim)
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ if consecutive_slices > 1:
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ if pred_reconstruction.shape[1:] != x.shape[2:]:
+ raise AssertionError
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/multitask/rs/models/test_idslrunet.py b/tests/collections/multitask/rs/models/test_idslrunet.py
new file mode 100644
index 00000000..cc88940b
--- /dev/null
+++ b/tests/collections/multitask/rs/models/test_idslrunet.py
@@ -0,0 +1,275 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.multitask.rs.nn.idslr_unet import IDSLRUNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 6,
+ "reconstruction_module_output_channels": 6,
+ "segmentation_module_output_channels": 4,
+ "channels": 32,
+ "num_pools": 4,
+ "padding_size": 11,
+ "drop_prob": 0.0,
+ "normalize": True,
+ "padding": True,
+ "norm_groups": 2,
+ "num_iters": 5,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 4, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 8,
+ "reconstruction_module_output_channels": 8,
+ "segmentation_module_output_channels": 3,
+ "channels": 32,
+ "num_pools": 4,
+ "padding_size": 11,
+ "drop_prob": 0.0,
+ "normalize": True,
+ "padding": True,
+ "norm_groups": 2,
+ "num_iters": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 3,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 6,
+ "reconstruction_module_output_channels": 6,
+ "segmentation_module_output_channels": 4,
+ "channels": 32,
+ "num_pools": 4,
+ "padding_size": 11,
+ "normalize": True,
+ "padding": True,
+ "norm_groups": 2,
+ "num_iters": 5,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_idslrunet(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test Image domain Deep Structured Low-Rank network for Joint Reconstruction & Segmentation using a UNet \
+ (and not only the decoder part) as segmentation model, with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ x = torch.stack([x for _ in range(consecutive_slices)], 1)
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ idslrunet = IDSLRUNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_reconstruction, pred_segmentation = idslrunet.forward(
+ output,
+ output,
+ mask,
+ output.sum(coil_dim),
+ output.sum(coil_dim),
+ )
+
+ if isinstance(pred_reconstruction, list):
+ pred_reconstruction = pred_reconstruction[-1]
+
+ if isinstance(pred_reconstruction, list):
+ pred_reconstruction = pred_reconstruction[-1]
+
+ if dimensionality == 3 or consecutive_slices > 1:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+ if x.shape[-1] == 2:
+ x = x[..., 0] + 1j * x[..., 1]
+
+ if consecutive_slices > 1 or dimensionality == 3:
+ x = x.sum(coil_dim) # sum over coils
+ if pred_reconstruction.dim() == 4:
+ pred_reconstruction = pred_reconstruction.reshape(
+ pred_reconstruction.shape[0] * pred_reconstruction.shape[1], *pred_reconstruction.shape[2:]
+ )
+ if pred_reconstruction.shape != x.shape:
+ raise AssertionError
+ if output.dim() == 6:
+ output = output.reshape(
+ [output.shape[0] * output.shape[1], output.shape[2], output.shape[3], output.shape[4], output.shape[5]]
+ )
+ output = torch.view_as_complex(output).sum(coil_dim)
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ if consecutive_slices > 1:
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ if pred_reconstruction.shape[1:] != x.shape[2:]:
+ raise AssertionError
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/multitask/rs/models/test_mtlrs.py b/tests/collections/multitask/rs/models/test_mtlrs.py
new file mode 100644
index 00000000..aa88845d
--- /dev/null
+++ b/tests/collections/multitask/rs/models/test_mtlrs.py
@@ -0,0 +1,330 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.multitask.rs.nn.mtlrs import MTLRS
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "task_adaption_type": "multi_task_learning",
+ "joint_reconstruction_segmentation_module_cascades": 5,
+ "reconstruction_module_recurrent_layer": "IndRNN",
+ "reconstruction_module_conv_filters": [64, 64, 2],
+ "reconstruction_module_conv_kernels": [5, 3, 3],
+ "reconstruction_module_conv_dilations": [1, 2, 1],
+ "reconstruction_module_conv_bias": [True, True, False],
+ "reconstruction_module_recurrent_filters": [64, 64, 0],
+ "reconstruction_module_recurrent_kernels": [1, 1, 0],
+ "reconstruction_module_recurrent_dilations": [1, 1, 0],
+ "reconstruction_module_recurrent_bias": [True, True, False],
+ "reconstruction_module_depth": 2,
+ "reconstruction_module_conv_dim": 2,
+ "reconstruction_module_time_steps": 8,
+ "reconstruction_module_num_cascades": 5,
+ "reconstruction_module_dimensionality": 2,
+ "reconstruction_module_accumulate_predictions": True,
+ "reconstruction_module_no_dc": True,
+ "reconstruction_module_keep_prediction": True,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_module": "UNet",
+ "segmentation_module_input_channels": 2,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": False,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "task_adaption_type": "multi_task_learning",
+ "joint_reconstruction_segmentation_module_cascades": 5,
+ "reconstruction_module_recurrent_layer": "IndRNN",
+ "reconstruction_module_conv_filters": [64, 64, 2],
+ "reconstruction_module_conv_kernels": [5, 3, 3],
+ "reconstruction_module_conv_dilations": [1, 2, 1],
+ "reconstruction_module_conv_bias": [True, True, False],
+ "reconstruction_module_recurrent_filters": [64, 64, 0],
+ "reconstruction_module_recurrent_kernels": [1, 1, 0],
+ "reconstruction_module_recurrent_dilations": [1, 1, 0],
+ "reconstruction_module_recurrent_bias": [True, True, False],
+ "reconstruction_module_depth": 2,
+ "reconstruction_module_conv_dim": 2,
+ "reconstruction_module_time_steps": 8,
+ "reconstruction_module_num_cascades": 5,
+ "reconstruction_module_dimensionality": 2,
+ "reconstruction_module_accumulate_predictions": True,
+ "reconstruction_module_no_dc": True,
+ "reconstruction_module_keep_prediction": True,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_module": "AttentionUNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "task_adaption_type": "multi_task_learning",
+ "joint_reconstruction_segmentation_module_cascades": 5,
+ "reconstruction_module_recurrent_layer": "IndRNN",
+ "reconstruction_module_conv_filters": [64, 64, 2],
+ "reconstruction_module_conv_kernels": [5, 3, 3],
+ "reconstruction_module_conv_dilations": [1, 2, 1],
+ "reconstruction_module_conv_bias": [True, True, False],
+ "reconstruction_module_recurrent_filters": [64, 64, 0],
+ "reconstruction_module_recurrent_kernels": [1, 1, 0],
+ "reconstruction_module_recurrent_dilations": [1, 1, 0],
+ "reconstruction_module_recurrent_bias": [True, True, False],
+ "reconstruction_module_depth": 2,
+ "reconstruction_module_conv_dim": 2,
+ "reconstruction_module_time_steps": 8,
+ "reconstruction_module_num_cascades": 5,
+ "reconstruction_module_dimensionality": 2,
+ "reconstruction_module_accumulate_predictions": True,
+ "reconstruction_module_no_dc": True,
+ "reconstruction_module_keep_prediction": True,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_module": "ConvLayer",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_mtlmrirs(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test MultiTask Learning for accelerated-MRI Reconstruction & Segmentation with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ x = torch.stack([x for _ in range(consecutive_slices)], 1)
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ mtlrs = MTLRS(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_reconstruction, pred_segmentation = mtlrs.forward(
+ output,
+ output,
+ mask,
+ output.sum(coil_dim),
+ output.sum(coil_dim),
+ )
+
+ if cfg.get("accumulate_predictions"):
+ try:
+ pred_reconstruction = next(pred_reconstruction)
+ except StopIteration:
+ pass
+
+ if isinstance(pred_reconstruction, list):
+ pred_reconstruction = pred_reconstruction[-1]
+
+ if isinstance(pred_reconstruction, list):
+ pred_reconstruction = pred_reconstruction[-1]
+
+ if isinstance(pred_reconstruction, list):
+ pred_reconstruction = pred_reconstruction[-1]
+
+ if dimensionality == 3 or consecutive_slices > 1:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+ if x.shape[-1] == 2:
+ x = x[..., 0] + 1j * x[..., 1]
+
+ if consecutive_slices > 1 or dimensionality == 3:
+ x = x.sum(coil_dim - 1) # sum over coils
+ if pred_reconstruction.dim() == 4:
+ pred_reconstruction = pred_reconstruction.reshape(
+ pred_reconstruction.shape[0] * pred_reconstruction.shape[1], *pred_reconstruction.shape[2:]
+ )
+ if pred_reconstruction.shape != x.shape:
+ raise AssertionError
+ if output.dim() == 6:
+ output = output.reshape(
+ [output.shape[0] * output.shape[1], output.shape[2], output.shape[3], output.shape[4], output.shape[5]]
+ )
+ coil_dim -= 1
+ output = torch.view_as_complex(output).sum(coil_dim)
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ if consecutive_slices > 1:
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ if pred_reconstruction.shape[1:] != x.shape[2:]:
+ raise AssertionError
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/multitask/rs/models/test_recseg_unet.py b/tests/collections/multitask/rs/models/test_recseg_unet.py
new file mode 100644
index 00000000..61b64685
--- /dev/null
+++ b/tests/collections/multitask/rs/models/test_recseg_unet.py
@@ -0,0 +1,265 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.multitask.rs.nn.recseg_unet import RecSegUNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 15, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 1,
+ "reconstruction_module_output_channels": 1,
+ "reconstruction_module_channels": 32,
+ "reconstruction_module_pooling_layers": 4,
+ "reconstruction_module_dropout": 0.0,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 32,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "magnitude_input": True,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 32, 64, 48, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 2,
+ "reconstruction_module_output_channels": 1,
+ "reconstruction_module_channels": 32,
+ "reconstruction_module_pooling_layers": 4,
+ "reconstruction_module_dropout": 0.0,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 32,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "magnitude_input": False,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 1,
+ "reconstruction_module_output_channels": 1,
+ "reconstruction_module_channels": 32,
+ "reconstruction_module_pooling_layers": 4,
+ "reconstruction_module_dropout": 0.0,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 32,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "magnitude_input": True,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_recseg_unet(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test the Reconstruction Segmentation method using UNets, with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ x = torch.stack([x for _ in range(consecutive_slices)], 1)
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ recsegunet = RecSegUNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_reconstruction, pred_segmentation = recsegunet.forward(
+ output,
+ output,
+ mask,
+ output.sum(coil_dim),
+ output.sum(coil_dim),
+ )
+
+ if dimensionality == 3 or consecutive_slices > 1:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if x.shape[-1] == 2:
+ x = x[..., 0] + 1j * x[..., 1]
+
+ if consecutive_slices > 1 or dimensionality == 3:
+ x = x.sum(coil_dim - 1) # sum over coils
+ if pred_reconstruction.dim() == 4:
+ pred_reconstruction = pred_reconstruction.reshape(
+ pred_reconstruction.shape[0] * pred_reconstruction.shape[1], *pred_reconstruction.shape[2:]
+ )
+ if pred_reconstruction.shape != x.shape:
+ raise AssertionError
+ if output.dim() == 6:
+ output = output.reshape([output.shape[0] * output.shape[1], *output.shape[2:]])
+ output = torch.view_as_complex(output).sum(coil_dim - 1)
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ if consecutive_slices > 1:
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ if pred_reconstruction.shape[1:] != x.shape[2:]:
+ raise AssertionError
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/multitask/rs/models/test_segnet.py b/tests/collections/multitask/rs/models/test_segnet.py
new file mode 100644
index 00000000..c6c50faf
--- /dev/null
+++ b/tests/collections/multitask/rs/models/test_segnet.py
@@ -0,0 +1,293 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.multitask.rs.nn.segnet import SegNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 15, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 30,
+ "reconstruction_module_output_channels": 30,
+ "segmentation_module_output_channels": 3,
+ "channels": 32,
+ "num_pools": 4,
+ "padding_size": 11,
+ "drop_prob": 0.0,
+ "normalize": True,
+ "padding": True,
+ "norm_groups": 2,
+ "num_cascades": 7,
+ "segmentation_final_layer_conv_dim": 2,
+ "segmentation_final_layer_kernel_size": 3,
+ "segmentation_final_layer_dilation": 1,
+ "segmentation_final_layer_bias": False,
+ "segmentation_final_layer_nonlinear": "relu",
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 3,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 4, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 8,
+ "reconstruction_module_output_channels": 8,
+ "segmentation_module_output_channels": 3,
+ "channels": 32,
+ "num_pools": 4,
+ "padding_size": 11,
+ "drop_prob": 0.0,
+ "normalize": True,
+ "padding": True,
+ "norm_groups": 2,
+ "num_cascades": 2,
+ "segmentation_final_layer_conv_dim": 2,
+ "segmentation_final_layer_kernel_size": 3,
+ "segmentation_final_layer_dilation": 1,
+ "segmentation_final_layer_bias": False,
+ "segmentation_final_layer_nonlinear": "relu",
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 3,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 6,
+ "reconstruction_module_output_channels": 6,
+ "segmentation_module_output_channels": 4,
+ "channels": 64,
+ "num_pools": 2,
+ "padding_size": 11,
+ "drop_prob": 0.0,
+ "normalize": True,
+ "padding": True,
+ "norm_groups": 2,
+ "num_cascades": 4,
+ "segmentation_final_layer_conv_dim": 2,
+ "segmentation_final_layer_kernel_size": 3,
+ "segmentation_final_layer_dilation": 1,
+ "segmentation_final_layer_bias": False,
+ "segmentation_final_layer_nonlinear": "relu",
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_segnet(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test the Segmentation Network MRI with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ x = torch.stack([x for _ in range(consecutive_slices)], 1)
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ segnet = SegNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_reconstruction, pred_segmentation = segnet.forward(
+ output,
+ output,
+ mask,
+ output.sum(coil_dim),
+ output.sum(coil_dim),
+ )
+
+ if isinstance(pred_reconstruction, list):
+ pred_reconstruction = pred_reconstruction[-1]
+
+ if isinstance(pred_reconstruction, list):
+ pred_reconstruction = pred_reconstruction[-1]
+
+ if isinstance(pred_segmentation, list):
+ pred_segmentation = pred_segmentation[-1]
+
+ if dimensionality == 3 or consecutive_slices > 1:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+ if x.shape[-1] == 2:
+ x = x[..., 0] + 1j * x[..., 1]
+
+ if consecutive_slices > 1 or dimensionality == 3:
+ x = x.sum(coil_dim) # sum over coils
+ if pred_reconstruction.dim() == 4:
+ pred_reconstruction = pred_reconstruction.reshape(
+ pred_reconstruction.shape[0] * pred_reconstruction.shape[1], *pred_reconstruction.shape[2:]
+ )
+ if pred_reconstruction.shape != x.shape:
+ raise AssertionError
+ if output.dim() == 6:
+ output = output.reshape(
+ [output.shape[0] * output.shape[1], output.shape[2], output.shape[3], output.shape[4], output.shape[5]]
+ )
+ output = torch.view_as_complex(output).sum(coil_dim)
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ if consecutive_slices > 1:
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ if pred_reconstruction.shape[1:] != x.shape[2:]:
+ raise AssertionError
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/multitask/rs/models/test_seranet.py b/tests/collections/multitask/rs/models/test_seranet.py
new file mode 100644
index 00000000..6f7771b3
--- /dev/null
+++ b/tests/collections/multitask/rs/models/test_seranet.py
@@ -0,0 +1,226 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.multitask.rs.nn.seranet import SERANet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 15, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 2,
+ "reconstruction_module": "unet",
+ "reconstruction_module_output_channels": 2,
+ "reconstruction_module_channels": 64,
+ "reconstruction_module_pooling_layers": 4,
+ "reconstruction_module_dropout": 0.0,
+ "reconstruction_module_num_blocks": 3,
+ "segmentation_module_input_channels": 15, # num_coils
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "recurrent_module_iterations": 3,
+ "recurrent_module_attention_channels": 32,
+ "recurrent_module_attention_pooling_layers": 2,
+ "recurrent_module_attention_dropout": 0.0,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "magnitude_input": False,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 15, 32, 16, 2],
+ {
+ "use_reconstruction_module": True,
+ "input_channels": 2,
+ "reconstruction_module": "unet",
+ "reconstruction_module_output_channels": 2,
+ "reconstruction_module_channels": 64,
+ "reconstruction_module_pooling_layers": 4,
+ "reconstruction_module_dropout": 0.0,
+ "reconstruction_module_num_blocks": 3,
+ "segmentation_module_input_channels": 15, # num_coils
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "recurrent_module_iterations": 3,
+ "recurrent_module_attention_channels": 32,
+ "recurrent_module_attention_pooling_layers": 2,
+ "recurrent_module_attention_dropout": 0.0,
+ "reconstruction_loss": {"l1": 1.0},
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "magnitude_input": False,
+ "coil_combination_method": "SENSE",
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "dimensionality": 2,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_seranet(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test the End-to-End Recurrent Attention Networ, with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ x = torch.stack([x for _ in range(consecutive_slices)], 1)
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ seranet = SERANet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_reconstruction, pred_segmentation = seranet.forward(
+ output,
+ output,
+ mask,
+ output.sum(coil_dim),
+ output.sum(coil_dim),
+ )
+
+ if dimensionality == 3 or consecutive_slices > 1:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if x.shape[-1] == 2:
+ x = x[..., 0] + 1j * x[..., 1]
+
+ if consecutive_slices > 1 or dimensionality == 3:
+ x = x.sum(coil_dim - 1) # sum over coils
+ if pred_reconstruction.dim() == 4:
+ pred_reconstruction = pred_reconstruction.reshape(
+ pred_reconstruction.shape[0] * pred_reconstruction.shape[1], *pred_reconstruction.shape[2:]
+ )
+ if pred_reconstruction.shape != x.shape:
+ raise AssertionError
+ if output.dim() == 6:
+ output = output.reshape([output.shape[0] * output.shape[1], *output.shape[2:]])
+ output = torch.view_as_complex(output).sum(coil_dim - 1)
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ if consecutive_slices > 1:
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ if pred_reconstruction.shape[1:] != x.shape[2:]:
+ raise AssertionError
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/quantitative/__init__.py b/tests/collections/quantitative/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/quantitative/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/quantitative/models/__init__.py b/tests/collections/quantitative/models/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/quantitative/models/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/quantitative/models/test_qcirim.py b/tests/collections/quantitative/models/test_qcirim.py
new file mode 100644
index 00000000..176214c5
--- /dev/null
+++ b/tests/collections/quantitative/models/test_qcirim.py
@@ -0,0 +1,327 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.quantitative.nn.qcirim import qCIRIM
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, num_TEs, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "quantitative_module_recurrent_layer": "IndRNN",
+ "quantitative_module_conv_filters": [64, 64, 4],
+ "quantitative_module_conv_kernels": [5, 3, 3],
+ "quantitative_module_conv_dilations": [1, 2, 1],
+ "quantitative_module_conv_bias": [True, True, False],
+ "quantitative_module_recurrent_filters": [64, 64, 0],
+ "quantitative_module_recurrent_kernels": [1, 1, 0],
+ "quantitative_module_recurrent_dilations": [1, 1, 0],
+ "quantitative_module_recurrent_bias": [True, True, False],
+ "quantitative_module_depth": 2,
+ "quantitative_module_conv_dim": 2,
+ "quantitative_module_time_steps": 8,
+ "quantitative_module_num_cascades": 8,
+ "quantitative_module_accumulate_predictions": True,
+ "quantitative_module_no_dc": True,
+ "quantitative_module_keep_prediction": True,
+ "quantitative_module_signal_forward_model_sequence": "MEGRE",
+ "quantitative_module_dimensionality": 2,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "quantitative_module_gamma_regularization_factors": [150.0, 150.0, 1000.0, 150.0],
+ "reconstruction_loss": "ssim",
+ "quantitative_parameters_regularization_factors": [
+ {"R2star": 3.0},
+ {"S0": 1.0},
+ {"B0": 1.0},
+ {"phi": 1.0},
+ ],
+ "shift_B0_input": False,
+ },
+ [0.08],
+ [4],
+ 4,
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "use_reconstruction_module": False,
+ "reconstruction_module_recurrent_layer": "IndRNN",
+ "reconstruction_module_conv_filters": [64, 64, 2],
+ "reconstruction_module_conv_kernels": [5, 3, 3],
+ "reconstruction_module_conv_dilations": [1, 2, 1],
+ "reconstruction_module_conv_bias": [True, True, False],
+ "reconstruction_module_recurrent_filters": [64, 64, 0],
+ "reconstruction_module_recurrent_kernels": [1, 1, 0],
+ "reconstruction_module_recurrent_dilations": [1, 1, 0],
+ "reconstruction_module_recurrent_bias": [True, True, False],
+ "reconstruction_module_depth": 2,
+ "reconstruction_module_conv_dim": 2,
+ "reconstruction_module_time_steps": 8,
+ "reconstruction_module_num_cascades": 8,
+ "reconstruction_module_accumulate_predictions": True,
+ "reconstruction_module_no_dc": True,
+ "reconstruction_module_keep_prediction": True,
+ "reconstruction_module_dimensionality": 2,
+ "quantitative_module_recurrent_layer": "IndRNN",
+ "quantitative_module_conv_filters": [64, 64, 4],
+ "quantitative_module_conv_kernels": [5, 3, 3],
+ "quantitative_module_conv_dilations": [1, 2, 1],
+ "quantitative_module_conv_bias": [True, True, False],
+ "quantitative_module_recurrent_filters": [64, 64, 0],
+ "quantitative_module_recurrent_kernels": [1, 1, 0],
+ "quantitative_module_recurrent_dilations": [1, 1, 0],
+ "quantitative_module_recurrent_bias": [True, True, False],
+ "quantitative_module_depth": 2,
+ "quantitative_module_conv_dim": 2,
+ "quantitative_module_time_steps": 8,
+ "quantitative_module_num_cascades": 8,
+ "quantitative_module_accumulate_predictions": True,
+ "quantitative_module_no_dc": True,
+ "quantitative_module_keep_prediction": True,
+ "quantitative_module_signal_forward_model_sequence": "MEGRE",
+ "quantitative_module_dimensionality": 2,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "quantitative_module_gamma_regularization_factors": [150.0, 150.0, 1000.0, 150.0],
+ "reconstruction_loss": "ssim",
+ "quantitative_parameters_regularization_factors": [
+ {"R2star": 3.0},
+ {"S0": 1.0},
+ {"B0": 1.0},
+ {"phi": 1.0},
+ ],
+ "shift_B0_input": False,
+ },
+ [0.08],
+ [4],
+ 4,
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "use_reconstruction_module": False,
+ "reconstruction_module_recurrent_layer": "IndRNN",
+ "reconstruction_module_conv_filters": [64, 64, 2],
+ "reconstruction_module_conv_kernels": [5, 3, 3],
+ "reconstruction_module_conv_dilations": [1, 2, 1],
+ "reconstruction_module_conv_bias": [True, True, False],
+ "reconstruction_module_recurrent_filters": [64, 64, 0],
+ "reconstruction_module_recurrent_kernels": [1, 1, 0],
+ "reconstruction_module_recurrent_dilations": [1, 1, 0],
+ "reconstruction_module_recurrent_bias": [True, True, False],
+ "reconstruction_module_depth": 2,
+ "reconstruction_module_conv_dim": 2,
+ "reconstruction_module_time_steps": 8,
+ "reconstruction_module_num_cascades": 1,
+ "reconstruction_module_accumulate_predictions": True,
+ "reconstruction_module_no_dc": True,
+ "reconstruction_module_keep_prediction": True,
+ "reconstruction_module_dimensionality": 2,
+ "quantitative_module_recurrent_layer": "IndRNN",
+ "quantitative_module_conv_filters": [64, 64, 4],
+ "quantitative_module_conv_kernels": [5, 3, 3],
+ "quantitative_module_conv_dilations": [1, 2, 1],
+ "quantitative_module_conv_bias": [True, True, False],
+ "quantitative_module_recurrent_filters": [64, 64, 0],
+ "quantitative_module_recurrent_kernels": [1, 1, 0],
+ "quantitative_module_recurrent_dilations": [1, 1, 0],
+ "quantitative_module_recurrent_bias": [True, True, False],
+ "quantitative_module_depth": 2,
+ "quantitative_module_conv_dim": 2,
+ "quantitative_module_time_steps": 8,
+ "quantitative_module_num_cascades": 8,
+ "quantitative_module_accumulate_predictions": True,
+ "quantitative_module_no_dc": True,
+ "quantitative_module_keep_prediction": True,
+ "quantitative_module_signal_forward_model_sequence": "MEGRE_no_phase",
+ "quantitative_module_dimensionality": 2,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "quantitative_module_gamma_regularization_factors": [150.0, 150.0, 1000.0, 150.0],
+ "reconstruction_loss": "mse",
+ "quantitative_parameters_regularization_factors": [
+ {"R2star": 300.0},
+ {"S0": 500.0},
+ {"B0": 20000.0},
+ {"phi": 500.0},
+ ],
+ "shift_B0_input": False,
+ },
+ [0.08],
+ [4],
+ 4,
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_qcirim(shape, cfg, center_fractions, accelerations, num_TEs, dimensionality, trainer):
+ """
+ Test qCIRIM with different parameters
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ num_TEs : int
+ Number of TEs to test
+ dimensionality : int
+ Dimensionality of the data
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ output = torch.stack([output for _ in range(4)], 1)
+ x = output.sum(2)
+ qmaps = x.sum(-1)
+
+ R2star_map = qmaps[:, 0, ...]
+ S0_map = qmaps[:, 1, ...]
+ B0_map = qmaps[:, 2, ...]
+ phi_map = qmaps[:, 3, ...]
+
+ TEs = torch.rand(num_TEs) * 10
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ qcirim = qCIRIM(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ preds = qcirim.forward(
+ R2star_map,
+ S0_map,
+ B0_map,
+ phi_map,
+ TEs,
+ output,
+ output[:, 0, ...],
+ output[:, :, 0, ...],
+ torch.ones_like(mask),
+ mask,
+ )
+
+ recon_pred, R2star_map_pred, S0_map_pred, B0_map_pred, phi_map_pred = (
+ preds[0],
+ preds[1],
+ preds[2],
+ preds[3],
+ preds[4],
+ )
+
+ if recon_pred.dim() != 0:
+ if isinstance(recon_pred, list):
+ recon_pred = recon_pred[-1]
+ if isinstance(recon_pred, list):
+ recon_pred = recon_pred[-1]
+ if recon_pred.shape != x.shape:
+ raise AssertionError
+
+ if isinstance(R2star_map_pred, list):
+ R2star_map_pred = R2star_map_pred[-1]
+ S0_map_pred = S0_map_pred[-1]
+ B0_map_pred = B0_map_pred[-1]
+ phi_map_pred = phi_map_pred[-1]
+
+ if isinstance(R2star_map_pred, list):
+ R2star_map_pred = R2star_map_pred[-1]
+ S0_map_pred = S0_map_pred[-1]
+ B0_map_pred = B0_map_pred[-1]
+ phi_map_pred = phi_map_pred[-1]
+
+ if dimensionality == 3:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if R2star_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+ if S0_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+ if B0_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+ if phi_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
diff --git a/tests/collections/quantitative/models/test_qrim.py b/tests/collections/quantitative/models/test_qrim.py
new file mode 100644
index 00000000..5d39fad2
--- /dev/null
+++ b/tests/collections/quantitative/models/test_qrim.py
@@ -0,0 +1,327 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.quantitative.nn.qcirim import qCIRIM
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, num_TEs, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "quantitative_module_recurrent_layer": "GRU",
+ "quantitative_module_conv_filters": [64, 64, 4],
+ "quantitative_module_conv_kernels": [5, 3, 3],
+ "quantitative_module_conv_dilations": [1, 2, 1],
+ "quantitative_module_conv_bias": [True, True, False],
+ "quantitative_module_recurrent_filters": [64, 64, 0],
+ "quantitative_module_recurrent_kernels": [1, 1, 0],
+ "quantitative_module_recurrent_dilations": [1, 1, 0],
+ "quantitative_module_recurrent_bias": [True, True, False],
+ "quantitative_module_depth": 2,
+ "quantitative_module_conv_dim": 2,
+ "quantitative_module_time_steps": 8,
+ "quantitative_module_num_cascades": 1,
+ "quantitative_module_accumulate_predictions": True,
+ "quantitative_module_no_dc": True,
+ "quantitative_module_keep_prediction": True,
+ "quantitative_module_signal_forward_model_sequence": "MEGRE",
+ "quantitative_module_dimensionality": 2,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "quantitative_module_gamma_regularization_factors": [150.0, 150.0, 1000.0, 150.0],
+ "reconstruction_loss": "ssim",
+ "quantitative_parameters_regularization_factors": [
+ {"R2star": 3.0},
+ {"S0": 1.0},
+ {"B0": 1.0},
+ {"phi": 1.0},
+ ],
+ "shift_B0_input": False,
+ },
+ [0.08],
+ [4],
+ 4,
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "use_reconstruction_module": False,
+ "reconstruction_module_recurrent_layer": "GRU",
+ "reconstruction_module_conv_filters": [64, 64, 2],
+ "reconstruction_module_conv_kernels": [5, 3, 3],
+ "reconstruction_module_conv_dilations": [1, 2, 1],
+ "reconstruction_module_conv_bias": [True, True, False],
+ "reconstruction_module_recurrent_filters": [64, 64, 0],
+ "reconstruction_module_recurrent_kernels": [1, 1, 0],
+ "reconstruction_module_recurrent_dilations": [1, 1, 0],
+ "reconstruction_module_recurrent_bias": [True, True, False],
+ "reconstruction_module_depth": 2,
+ "reconstruction_module_conv_dim": 2,
+ "reconstruction_module_time_steps": 8,
+ "reconstruction_module_num_cascades": 8,
+ "reconstruction_module_accumulate_predictions": True,
+ "reconstruction_module_no_dc": True,
+ "reconstruction_module_keep_prediction": True,
+ "reconstruction_module_dimensionality": 2,
+ "quantitative_module_recurrent_layer": "GRU",
+ "quantitative_module_conv_filters": [64, 64, 4],
+ "quantitative_module_conv_kernels": [5, 3, 3],
+ "quantitative_module_conv_dilations": [1, 2, 1],
+ "quantitative_module_conv_bias": [True, True, False],
+ "quantitative_module_recurrent_filters": [64, 64, 0],
+ "quantitative_module_recurrent_kernels": [1, 1, 0],
+ "quantitative_module_recurrent_dilations": [1, 1, 0],
+ "quantitative_module_recurrent_bias": [True, True, False],
+ "quantitative_module_depth": 2,
+ "quantitative_module_conv_dim": 2,
+ "quantitative_module_time_steps": 8,
+ "quantitative_module_num_cascades": 1,
+ "quantitative_module_accumulate_predictions": True,
+ "quantitative_module_no_dc": True,
+ "quantitative_module_keep_prediction": True,
+ "quantitative_module_signal_forward_model_sequence": "MEGRE",
+ "quantitative_module_dimensionality": 2,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "quantitative_module_gamma_regularization_factors": [150.0, 150.0, 1000.0, 150.0],
+ "reconstruction_loss": "ssim",
+ "quantitative_parameters_regularization_factors": [
+ {"R2star": 3.0},
+ {"S0": 1.0},
+ {"B0": 1.0},
+ {"phi": 1.0},
+ ],
+ "shift_B0_input": False,
+ },
+ [0.08],
+ [4],
+ 4,
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "use_reconstruction_module": False,
+ "reconstruction_module_recurrent_layer": "GRU",
+ "reconstruction_module_conv_filters": [64, 64, 2],
+ "reconstruction_module_conv_kernels": [5, 3, 3],
+ "reconstruction_module_conv_dilations": [1, 2, 1],
+ "reconstruction_module_conv_bias": [True, True, False],
+ "reconstruction_module_recurrent_filters": [64, 64, 0],
+ "reconstruction_module_recurrent_kernels": [1, 1, 0],
+ "reconstruction_module_recurrent_dilations": [1, 1, 0],
+ "reconstruction_module_recurrent_bias": [True, True, False],
+ "reconstruction_module_depth": 2,
+ "reconstruction_module_conv_dim": 2,
+ "reconstruction_module_time_steps": 8,
+ "reconstruction_module_num_cascades": 1,
+ "reconstruction_module_accumulate_predictions": True,
+ "reconstruction_module_no_dc": True,
+ "reconstruction_module_keep_prediction": True,
+ "reconstruction_module_dimensionality": 2,
+ "quantitative_module_recurrent_layer": "GRU",
+ "quantitative_module_conv_filters": [64, 64, 4],
+ "quantitative_module_conv_kernels": [5, 3, 3],
+ "quantitative_module_conv_dilations": [1, 2, 1],
+ "quantitative_module_conv_bias": [True, True, False],
+ "quantitative_module_recurrent_filters": [64, 64, 0],
+ "quantitative_module_recurrent_kernels": [1, 1, 0],
+ "quantitative_module_recurrent_dilations": [1, 1, 0],
+ "quantitative_module_recurrent_bias": [True, True, False],
+ "quantitative_module_depth": 2,
+ "quantitative_module_conv_dim": 2,
+ "quantitative_module_time_steps": 8,
+ "quantitative_module_num_cascades": 1,
+ "quantitative_module_accumulate_predictions": True,
+ "quantitative_module_no_dc": True,
+ "quantitative_module_keep_prediction": True,
+ "quantitative_module_signal_forward_model_sequence": "MEGRE_no_phase",
+ "quantitative_module_dimensionality": 2,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "quantitative_module_gamma_regularization_factors": [150.0, 150.0, 1000.0, 150.0],
+ "reconstruction_loss": "mse",
+ "quantitative_parameters_regularization_factors": [
+ {"R2star": 300.0},
+ {"S0": 500.0},
+ {"B0": 20000.0},
+ {"phi": 500.0},
+ ],
+ "shift_B0_input": False,
+ },
+ [0.08],
+ [4],
+ 4,
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_qrim(shape, cfg, center_fractions, accelerations, num_TEs, dimensionality, trainer):
+ """
+ Test qRIM with different parameters
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ num_TEs : int
+ Number of TEs to test
+ dimensionality : int
+ Dimensionality of the data
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ output = torch.stack([output for _ in range(4)], 1)
+ x = output.sum(2)
+ qmaps = x.sum(-1)
+
+ R2star_map = qmaps[:, 0, ...]
+ S0_map = qmaps[:, 1, ...]
+ B0_map = qmaps[:, 2, ...]
+ phi_map = qmaps[:, 3, ...]
+
+ TEs = torch.rand(num_TEs) * 10
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ qcirim = qCIRIM(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ preds = qcirim.forward(
+ R2star_map,
+ S0_map,
+ B0_map,
+ phi_map,
+ TEs,
+ output,
+ output[:, 0, ...],
+ output[:, :, 0, ...],
+ torch.ones_like(mask),
+ mask,
+ )
+
+ recon_pred, R2star_map_pred, S0_map_pred, B0_map_pred, phi_map_pred = (
+ preds[0],
+ preds[1],
+ preds[2],
+ preds[3],
+ preds[4],
+ )
+
+ if recon_pred.dim() != 0:
+ if isinstance(recon_pred, list):
+ recon_pred = recon_pred[-1]
+ if isinstance(recon_pred, list):
+ recon_pred = recon_pred[-1]
+ if recon_pred.shape != x.shape:
+ raise AssertionError
+
+ if isinstance(R2star_map_pred, list):
+ R2star_map_pred = R2star_map_pred[-1]
+ S0_map_pred = S0_map_pred[-1]
+ B0_map_pred = B0_map_pred[-1]
+ phi_map_pred = phi_map_pred[-1]
+
+ if isinstance(R2star_map_pred, list):
+ R2star_map_pred = R2star_map_pred[-1]
+ S0_map_pred = S0_map_pred[-1]
+ B0_map_pred = B0_map_pred[-1]
+ phi_map_pred = phi_map_pred[-1]
+
+ if dimensionality == 3:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if R2star_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+ if S0_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+ if B0_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+ if phi_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
diff --git a/tests/collections/quantitative/models/test_qvn.py b/tests/collections/quantitative/models/test_qvn.py
new file mode 100644
index 00000000..027243c2
--- /dev/null
+++ b/tests/collections/quantitative/models/test_qvn.py
@@ -0,0 +1,291 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.quantitative.nn.qvarnet import qVarNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, num_TEs, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "quantitative_module_num_cascades": 8,
+ "quantitative_module_channels": 18,
+ "quantitative_module_pooling_layers": 4,
+ "quantitative_module_in_channels": 8,
+ "quantitative_module_out_channels": 8,
+ "quantitative_module_padding_size": 11,
+ "quantitative_module_normalize": True,
+ "quantitative_module_accumulate_predictions": False,
+ "quantitative_module_no_dc": False,
+ "quantitative_module_signal_forward_model_sequence": "MEGRE",
+ "quantitative_module_dimensionality": 2,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "quantitative_module_gamma_regularization_factors": [150.0, 150.0, 1000.0, 150.0],
+ "reconstruction_loss": "ssim",
+ "quantitative_parameters_regularization_factors": [
+ {"R2star": 3.0},
+ {"S0": 1.0},
+ {"B0": 1.0},
+ {"phi": 1.0},
+ ],
+ "shift_B0_input": False,
+ },
+ [0.08],
+ [4],
+ 4,
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "reconstruction_module_num_cascades": 8,
+ "reconstruction_module_channels": 18,
+ "reconstruction_module_pooling_layers": 4,
+ "reconstruction_module_in_channels": 4,
+ "reconstruction_module_out_channels": 4,
+ "reconstruction_module_padding_size": 11,
+ "reconstruction_module_normalize": True,
+ "reconstruction_module_accumulate_predictions": False,
+ "reconstruction_module_no_dc": False,
+ "reconstruction_module_dimensionality": 2,
+ "quantitative_module_num_cascades": 8,
+ "quantitative_module_channels": 18,
+ "quantitative_module_pooling_layers": 4,
+ "quantitative_module_in_channels": 8,
+ "quantitative_module_out_channels": 8,
+ "quantitative_module_padding_size": 11,
+ "quantitative_module_normalize": True,
+ "quantitative_module_accumulate_predictions": False,
+ "quantitative_module_no_dc": False,
+ "quantitative_module_signal_forward_model_sequence": "MEGRE",
+ "quantitative_module_dimensionality": 2,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "quantitative_module_gamma_regularization_factors": [150.0, 150.0, 1000.0, 150.0],
+ "reconstruction_loss": "ssim",
+ "quantitative_parameters_regularization_factors": [
+ {"R2star": 3.0},
+ {"S0": 1.0},
+ {"B0": 1.0},
+ {"phi": 1.0},
+ ],
+ "shift_B0_input": False,
+ },
+ [0.08],
+ [4],
+ 4,
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 13, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "reconstruction_module_num_cascades": 8,
+ "reconstruction_module_channels": 18,
+ "reconstruction_module_pooling_layers": 4,
+ "reconstruction_module_in_channels": 8,
+ "reconstruction_module_out_channels": 4,
+ "reconstruction_module_padding_size": 11,
+ "reconstruction_module_normalize": True,
+ "reconstruction_module_accumulate_predictions": False,
+ "reconstruction_module_no_dc": False,
+ "reconstruction_module_dimensionality": 2,
+ "quantitative_module_num_cascades": 8,
+ "quantitative_module_channels": 18,
+ "quantitative_module_pooling_layers": 4,
+ "quantitative_module_in_channels": 8,
+ "quantitative_module_out_channels": 8,
+ "quantitative_module_padding_size": 11,
+ "quantitative_module_normalize": True,
+ "quantitative_module_accumulate_predictions": False,
+ "quantitative_module_no_dc": False,
+ "quantitative_module_signal_forward_model_sequence": "MEGRE_no_phase",
+ "quantitative_module_dimensionality": 2,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 2,
+ "quantitative_module_gamma_regularization_factors": [150.0, 150.0, 1000.0, 150.0],
+ "reconstruction_loss": "mse",
+ "quantitative_parameters_regularization_factors": [
+ {"R2star": 300.0},
+ {"S0": 500.0},
+ {"B0": 20000.0},
+ {"phi": 500.0},
+ ],
+ "shift_B0_input": False,
+ },
+ [0.08],
+ [4],
+ 4,
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_qvn(shape, cfg, center_fractions, accelerations, num_TEs, dimensionality, trainer):
+ """
+ Test qVarNet with different parameters
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ num_TEs : int
+ Number of TEs to test
+ dimensionality : int
+ Dimensionality of the data
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ output = torch.stack([output for _ in range(4)], 1)
+ qmaps = output.sum(2).sum(-1)
+
+ R2star_map = qmaps[:, 0, ...]
+ S0_map = qmaps[:, 1, ...]
+ B0_map = qmaps[:, 2, ...]
+ phi_map = qmaps[:, 3, ...]
+
+ TEs = torch.rand(num_TEs) * 10
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ qvn = qVarNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ preds = qvn.forward(
+ R2star_map,
+ S0_map,
+ B0_map,
+ phi_map,
+ TEs,
+ output,
+ output[:, 0, ...],
+ torch.ones_like(mask),
+ torch.ones_like(mask),
+ mask,
+ )
+
+ recon_pred, R2star_map_pred, S0_map_pred, B0_map_pred, phi_map_pred = (
+ preds[0],
+ preds[1],
+ preds[2],
+ preds[3],
+ preds[4],
+ )
+
+ if recon_pred.dim() != 0:
+ if isinstance(recon_pred, list):
+ recon_pred = recon_pred[-1]
+ if isinstance(recon_pred, list):
+ recon_pred = recon_pred[-1]
+ if recon_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+
+ if isinstance(R2star_map_pred, list):
+ R2star_map_pred = R2star_map_pred[-1]
+ S0_map_pred = S0_map_pred[-1]
+ B0_map_pred = B0_map_pred[-1]
+ phi_map_pred = phi_map_pred[-1]
+
+ if isinstance(R2star_map_pred, list):
+ R2star_map_pred = R2star_map_pred[-1]
+ S0_map_pred = S0_map_pred[-1]
+ B0_map_pred = B0_map_pred[-1]
+ phi_map_pred = phi_map_pred[-1]
+
+ if dimensionality == 3:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if R2star_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+ if S0_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+ if B0_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
+ if phi_map_pred.shape[1:] != x.shape[2:4]:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/__init__.py b/tests/collections/reconstruction/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/reconstruction/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/reconstruction/models/__init__.py b/tests/collections/reconstruction/models/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/reconstruction/models/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/reconstruction/models/test_ccnn.py b/tests/collections/reconstruction/models/test_ccnn.py
new file mode 100644
index 00000000..c04ba750
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_ccnn.py
@@ -0,0 +1,201 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from: https://github.com/facebookresearch/fastMRI
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.ccnn import CascadeNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_cascades": 10,
+ "hidden_channels": 64,
+ "n_convs": 5,
+ "batchnorm": False,
+ "no_dc": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "num_cascades": 10,
+ "hidden_channels": 64,
+ "n_convs": 5,
+ "batchnorm": False,
+ "no_dc": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_cascades": 10,
+ "hidden_channels": 64,
+ "n_convs": 5,
+ "batchnorm": True,
+ "no_dc": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_cascades": 10,
+ "hidden_channels": 64,
+ "n_convs": 5,
+ "batchnorm": True,
+ "no_dc": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_ccnn(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test CascadeNet with different parameters
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ ccnn = CascadeNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ y = ccnn.forward(output, output, mask, output)
+
+ while isinstance(y, list):
+ y = y[-1]
+
+ coil_dim = cfg.coil_dim if dimensionality == 2 else cfg.coil_dim + 1
+ x = torch.view_as_complex(x.sum(coil_dim))
+
+ if y.shape != x.shape:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_cirim.py b/tests/collections/reconstruction/models/test_cirim.py
new file mode 100644
index 00000000..dadf3fb3
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_cirim.py
@@ -0,0 +1,337 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from: https://github.com/facebookresearch/fastMRI
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.cirim import CIRIM
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "recurrent_layer": "IndRNN",
+ "conv_filters": [64, 64, 2],
+ "conv_kernels": [5, 3, 3],
+ "conv_dilations": [1, 2, 1],
+ "conv_bias": [True, True, False],
+ "recurrent_filters": [64, 64, 0],
+ "recurrent_kernels": [1, 1, 0],
+ "recurrent_dilations": [1, 1, 0],
+ "recurrent_bias": [True, True, False],
+ "depth": 2,
+ "conv_dim": 2,
+ "time_steps": 8,
+ "num_cascades": 1,
+ "accumulate_predictions": True,
+ "no_dc": True,
+ "keep_prediction": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "recurrent_layer": "IndRNN",
+ "conv_filters": [64, 64, 2],
+ "conv_kernels": [5, 3, 3],
+ "conv_dilations": [1, 2, 1],
+ "conv_bias": [True, True, False],
+ "recurrent_filters": [64, 64, 0],
+ "recurrent_kernels": [1, 1, 0],
+ "recurrent_dilations": [1, 1, 0],
+ "recurrent_bias": [True, True, False],
+ "depth": 2,
+ "conv_dim": 2,
+ "time_steps": 8,
+ "num_cascades": 5,
+ "accumulate_predictions": True,
+ "no_dc": True,
+ "keep_prediction": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 8, 13, 18, 2],
+ {
+ "recurrent_layer": "IndRNN",
+ "conv_filters": [64, 64, 2],
+ "conv_kernels": [5, 3, 3],
+ "conv_dilations": [1, 2, 1],
+ "conv_bias": [True, True, False],
+ "recurrent_filters": [64, 64, 0],
+ "recurrent_kernels": [1, 1, 0],
+ "recurrent_dilations": [1, 1, 0],
+ "recurrent_bias": [True, True, False],
+ "depth": 2,
+ "conv_dim": 2,
+ "time_steps": 8,
+ "num_cascades": 16,
+ "accumulate_predictions": True,
+ "no_dc": True,
+ "keep_prediction": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ "train_reconstruction_loss": "ssim",
+ "val_reconstruction_loss": "ssim",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 3, 15, 12, 2],
+ {
+ "recurrent_layer": "IndRNN",
+ "conv_filters": [64, 64, 2],
+ "conv_kernels": [5, 3, 3],
+ "conv_dilations": [1, 2, 1],
+ "conv_bias": [True, True, False],
+ "recurrent_filters": [64, 64, 0],
+ "recurrent_kernels": [1, 1, 0],
+ "recurrent_dilations": [1, 1, 0],
+ "recurrent_bias": [True, True, False],
+ "depth": 2,
+ "conv_dim": 3,
+ "time_steps": 8,
+ "num_cascades": 5,
+ "accumulate_predictions": True,
+ "no_dc": True,
+ "keep_prediction": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 3,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 3,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [3, 2, 5, 15, 12, 2],
+ {
+ "recurrent_layer": "IndRNN",
+ "conv_filters": [64, 64, 2],
+ "conv_kernels": [5, 3, 3],
+ "conv_dilations": [1, 2, 1],
+ "conv_bias": [True, True, False],
+ "recurrent_filters": [64, 64, 0],
+ "recurrent_kernels": [1, 1, 0],
+ "recurrent_dilations": [1, 1, 0],
+ "recurrent_bias": [True, True, False],
+ "depth": 2,
+ "conv_dim": 3,
+ "time_steps": 8,
+ "num_cascades": 5,
+ "accumulate_predictions": True,
+ "no_dc": True,
+ "keep_prediction": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 3,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 3,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [6, 1, 15, 15, 12, 2],
+ {
+ "recurrent_layer": "IndRNN",
+ "conv_filters": [64, 64, 2],
+ "conv_kernels": [5, 3, 3],
+ "conv_dilations": [1, 2, 1],
+ "conv_bias": [True, True, False],
+ "recurrent_filters": [64, 64, 0],
+ "recurrent_kernels": [1, 1, 0],
+ "recurrent_dilations": [1, 1, 0],
+ "recurrent_bias": [True, True, False],
+ "depth": 2,
+ "conv_dim": 3,
+ "time_steps": 8,
+ "num_cascades": 5,
+ "accumulate_predictions": True,
+ "no_dc": True,
+ "keep_prediction": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 3,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 3,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_cirim(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test CIRIM with different parameters
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ cirim = CIRIM(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ y = cirim.forward(output, output, mask, None)
+
+ while isinstance(y, list):
+ y = y[-1]
+
+ coil_dim = cfg.coil_dim if dimensionality == 2 else cfg.coil_dim + 1
+ x = torch.view_as_complex(x.sum(coil_dim))
+
+ if y.shape != x.shape:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_conv.py b/tests/collections/reconstruction/models/test_conv.py
new file mode 100644
index 00000000..e9e5e78a
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_conv.py
@@ -0,0 +1,68 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/tests/tests_nn/test_conv.py
+# Copyright (c) DIRECT Contributors
+
+import pytest
+import torch
+import torch.nn as nn
+
+from atommic.collections.reconstruction.nn.ccnn_base.ccnn_block import Conv2d
+
+
+def create_input(shape):
+ """Create a random input tensor."""
+ return torch.rand(shape).float()
+
+
+@pytest.mark.parametrize(
+ "shape",
+ [
+ [3, 2, 32, 32],
+ [3, 2, 16, 16],
+ ],
+)
+@pytest.mark.parametrize(
+ "out_channels",
+ [3, 5],
+)
+@pytest.mark.parametrize(
+ "hidden_channels",
+ [16, 8],
+)
+@pytest.mark.parametrize(
+ "n_convs",
+ [2, 4],
+)
+@pytest.mark.parametrize(
+ "act",
+ [nn.ReLU(), nn.PReLU()],
+)
+@pytest.mark.parametrize(
+ "batchnorm",
+ [True, False],
+)
+def test_conv(shape, out_channels, hidden_channels, n_convs, act, batchnorm):
+ """
+ Test the Conv2d class.
+
+ Args:
+ shape (): The shape of the input data.
+ out_channels (): The number of output channels.
+ hidden_channels (): The number of hidden channels.
+ n_convs (): The number of convolutions.
+ act (): The activation function.
+ batchnorm (): Whether to use batch normalization.
+
+ Returns:
+ None
+ """
+ model = Conv2d(shape[1], out_channels, hidden_channels, n_convs, act, batchnorm)
+
+ data = create_input(shape).cpu()
+
+ out = model(data)
+
+ if list(out.shape) != [shape[0]] + [out_channels] + shape[2:]:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_conv2dgru.py b/tests/collections/reconstruction/models/test_conv2dgru.py
new file mode 100644
index 00000000..293249c8
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_conv2dgru.py
@@ -0,0 +1,46 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/tests/tests_nn/test_recurrent.py
+# Copyright (c) DIRECT Contributors
+
+import pytest
+import torch
+
+from atommic.collections.reconstruction.nn.recurrentvarnet_base.recurrentvarnet_block import Conv2dGRU
+
+
+def create_input(shape):
+ """Create a random input tensor."""
+ return torch.rand(shape).float()
+
+
+@pytest.mark.parametrize(
+ "shape",
+ [
+ [3, 2, 32, 32],
+ [3, 2, 16, 16],
+ ],
+)
+@pytest.mark.parametrize(
+ "hidden_channels",
+ [4, 8],
+)
+def test_conv2dgru(shape, hidden_channels):
+ """
+ Test the Conv2dGRU model.
+
+ Args:
+ shape (): The shape of the input data.
+ hidden_channels (): The number of channels in the hidden state.
+
+ Returns:
+ None
+ """
+ model = Conv2dGRU(shape[1], hidden_channels, shape[1])
+ data = create_input(shape).cpu()
+
+ out = model(data, None)[0]
+
+ if list(out.shape) != shape:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_crnn.py b/tests/collections/reconstruction/models/test_crnn.py
new file mode 100644
index 00000000..11cc816f
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_crnn.py
@@ -0,0 +1,201 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from: https://github.com/facebookresearch/fastMRI
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.crnn import CRNNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_iterations": 10,
+ "hidden_channels": 64,
+ "n_convs": 5,
+ "batchnorm": False,
+ "no_dc": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "num_iterations": 10,
+ "hidden_channels": 64,
+ "n_convs": 5,
+ "batchnorm": False,
+ "no_dc": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_iterations": 10,
+ "hidden_channels": 64,
+ "n_convs": 5,
+ "batchnorm": True,
+ "no_dc": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "ssim",
+ "val_reconstruction_loss": "ssim",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_iterations": 10,
+ "hidden_channels": 64,
+ "n_convs": 5,
+ "batchnorm": True,
+ "no_dc": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_crnn(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test CRNNet with different parameters
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ crnn = CRNNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ y = crnn.forward(output, output, mask, output)
+
+ while isinstance(y, list):
+ y = y[-1]
+
+ coil_dim = cfg.coil_dim if dimensionality == 2 else cfg.coil_dim + 1
+ x = torch.view_as_complex(x.sum(coil_dim))
+
+ if y.shape != x.shape:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_didn.py b/tests/collections/reconstruction/models/test_didn.py
new file mode 100644
index 00000000..1efe4304
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_didn.py
@@ -0,0 +1,67 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/tests/tests_nn/test_didn.py
+# Copyright (c) DIRECT Contributors
+
+import pytest
+import torch
+
+from atommic.collections.reconstruction.nn.didn_base.didn_block import DIDN
+
+
+def create_input(shape):
+ """Create a random input tensor."""
+ return torch.rand(shape).float()
+
+
+@pytest.mark.parametrize(
+ "shape",
+ [
+ [3, 2, 32, 32],
+ [3, 2, 16, 16],
+ ],
+)
+@pytest.mark.parametrize(
+ "out_channels",
+ [3, 5],
+)
+@pytest.mark.parametrize(
+ "hidden_channels",
+ [16, 8],
+)
+@pytest.mark.parametrize(
+ "n_dubs",
+ [3, 4],
+)
+@pytest.mark.parametrize(
+ "num_convs_recon",
+ [3, 4],
+)
+@pytest.mark.parametrize(
+ "skip",
+ [True, False],
+)
+def test_didn(shape, out_channels, hidden_channels, n_dubs, num_convs_recon, skip):
+ """
+ Test the DIDN
+
+ Args:
+ shape (): shape of the input
+ out_channels (): number of output channels
+ hidden_channels (): number of hidden channels
+ n_dubs (): number of dubs
+ num_convs_recon (): number of convolutions in the reconstruction network
+ skip (): whether to use skip connections or not
+
+ Returns:
+ None
+ """
+ model = DIDN(shape[1], out_channels, hidden_channels, n_dubs, num_convs_recon, skip)
+
+ data = create_input(shape).cpu()
+
+ out = model(data)
+
+ if list(out.shape) != [shape[0]] + [out_channels] + shape[2:]:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_dunet.py b/tests/collections/reconstruction/models/test_dunet.py
new file mode 100644
index 00000000..82df7ede
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_dunet.py
@@ -0,0 +1,214 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from: https://github.com/facebookresearch/fastMRI
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.dunet import DUNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_iter": 1,
+ "reg_model_architecture": "DIDN",
+ "didn_hidden_channels": 64,
+ "didn_num_dubs": 2,
+ "didn_num_convs_recon": 1,
+ "data_consistency_term": "PROX",
+ "data_consistency_lambda_init": 0.1,
+ "data_consistency_iterations": 10,
+ "shared_params": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "num_iter": 10,
+ "reg_model_architecture": "DIDN",
+ "didn_hidden_channels": 64,
+ "didn_num_dubs": 2,
+ "didn_num_convs_recon": 5,
+ "data_consistency_term": "PROX",
+ "data_consistency_lambda_init": 0.1,
+ "data_consistency_iterations": 10,
+ "shared_params": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_iter": 1,
+ "reg_model_architecture": "DIDN",
+ "didn_hidden_channels": 128,
+ "didn_num_dubs": 4,
+ "didn_num_convs_recon": 1,
+ "data_consistency_term": "PROX",
+ "data_consistency_lambda_init": 0.1,
+ "data_consistency_iterations": 8,
+ "shared_params": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "ssim",
+ "val_reconstruction_loss": "ssim",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_iter": 4,
+ "reg_model_architecture": "DIDN",
+ "didn_hidden_channels": 64,
+ "didn_num_dubs": 4,
+ "didn_num_convs_recon": 4,
+ "data_consistency_term": "PROX",
+ "data_consistency_lambda_init": 0.1,
+ "data_consistency_iterations": 8,
+ "shared_params": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_dunet(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test DUNet with different parameters
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ dunet = DUNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ y = dunet.forward(output, output, mask, output)
+
+ if dimensionality == 3:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if y.shape[1:] != x.shape[2:4]:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_jointicnet.py b/tests/collections/reconstruction/models/test_jointicnet.py
new file mode 100644
index 00000000..9ef8fc1b
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_jointicnet.py
@@ -0,0 +1,155 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.jointicnet import JointICNet
+
+
+def create_input(shape):
+ """Create a random input tensor."""
+ return torch.rand(shape).float()
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_iter": 2,
+ "kspace_unet_num_filters": 4,
+ "kspace_unet_num_pool_layers": 2,
+ "kspace_unet_dropout_probability": 0.0,
+ "kspace_unet_padding_size": 11,
+ "kspace_unet_normalize": True,
+ "imspace_unet_num_filters": 4,
+ "imspace_unet_num_pool_layers": 2,
+ "imspace_unet_dropout_probability": 0.0,
+ "imspace_unet_padding_size": 11,
+ "imspace_unet_normalize": True,
+ "sens_unet_num_filters": 4,
+ "sens_unet_num_pool_layers": 2,
+ "sens_unet_dropout_probability": 0.0,
+ "sens_unet_padding_size": 11,
+ "sens_unet_normalize": True,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_iter": 4,
+ "kspace_unet_num_filters": 16,
+ "kspace_unet_num_pool_layers": 4,
+ "kspace_unet_dropout_probability": 0.05,
+ "kspace_unet_padding_size": 15,
+ "kspace_unet_normalize": False,
+ "imspace_unet_num_filters": 16,
+ "imspace_unet_num_pool_layers": 4,
+ "imspace_unet_dropout_probability": 0.05,
+ "imspace_unet_padding_size": 11,
+ "imspace_unet_normalize": False,
+ "sens_unet_num_filters": 16,
+ "sens_unet_num_pool_layers": 4,
+ "sens_unet_dropout_probability": 0.05,
+ "sens_unet_padding_size": 15,
+ "sens_unet_normalize": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_jointicnet(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test JointICNet with different parameters
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ jointicnet = JointICNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ y = jointicnet.forward(output, output, mask, output)
+
+ if dimensionality == 3:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if y.shape[1:] != x.shape[2:4]:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_kikinet.py b/tests/collections/reconstruction/models/test_kikinet.py
new file mode 100644
index 00000000..d213a9ae
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_kikinet.py
@@ -0,0 +1,151 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.kikinet import KIKINet
+
+
+def create_input(shape):
+ """Create a random input tensor."""
+ return torch.rand(shape).float()
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_iter": 2,
+ "kspace_model_architecture": "UNET",
+ "kspace_unet_num_filters": 16,
+ "kspace_unet_num_pool_layers": 2,
+ "kspace_unet_dropout_probability": 0.0,
+ "kspace_unet_padding_size": 11,
+ "kspace_unet_normalize": True,
+ "imspace_model_architecture": "UNET",
+ "imspace_unet_num_filters": 16,
+ "imspace_unet_num_pool_layers": 2,
+ "imspace_unet_dropout_probability": 0.0,
+ "imspace_unet_padding_size": 11,
+ "imspace_unet_normalize": True,
+ "use_sens_net": False,
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "coil_combination_method": "SENSE",
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_iter": 4,
+ "kspace_model_architecture": "UNET",
+ "kspace_unet_num_filters": 4,
+ "kspace_unet_num_pool_layers": 2,
+ "kspace_unet_dropout_probability": 0.0,
+ "kspace_unet_padding_size": 11,
+ "kspace_unet_normalize": True,
+ "imspace_model_architecture": "UNET",
+ "imspace_unet_num_filters": 4,
+ "imspace_unet_num_pool_layers": 2,
+ "imspace_unet_dropout_probability": 0.0,
+ "imspace_unet_padding_size": 11,
+ "imspace_unet_normalize": True,
+ "use_sens_net": False,
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "coil_combination_method": "SENSE",
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_kikinet(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test the KIKINet model.
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None.
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ kikinet = KIKINet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ y = kikinet.forward(output, output, mask, output)
+
+ if dimensionality == 3:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if y.shape[1:] != x.shape[2:4]:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_lpdnet.py b/tests/collections/reconstruction/models/test_lpdnet.py
new file mode 100644
index 00000000..79a78b77
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_lpdnet.py
@@ -0,0 +1,169 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.lpd import LPDNet
+
+
+def create_input(shape):
+ """Create a random input tensor."""
+ return torch.rand(shape).float()
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_primal": 5,
+ "num_dual": 5,
+ "num_iter": 5,
+ "primal_model_architecture": "UNET",
+ "primal_in_channels": 2,
+ "primal_out_channels": 2,
+ "primal_unet_num_filters": 4,
+ "primal_unet_num_pool_layers": 2,
+ "primal_unet_dropout_probability": 0.0,
+ "primal_unet_padding_size": 11,
+ "primal_unet_normalize": True,
+ "dual_model_architecture": "UNET",
+ "dual_in_channels": 2,
+ "dual_out_channels": 2,
+ "dual_unet_num_filters": 16,
+ "dual_unet_num_pool_layers": 2,
+ "dual_unet_dropout_probability": 0.0,
+ "dual_unet_padding_size": 11,
+ "dual_unet_normalize": True,
+ "use_sens_net": False,
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "dimensionality": 2,
+ "consecutive_slices": 2,
+ "coil_combination_method": "SENSE",
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_primal": 2,
+ "num_dual": 2,
+ "num_iter": 2,
+ "primal_model_architecture": "UNET",
+ "primal_in_channels": 2,
+ "primal_out_channels": 2,
+ "primal_unet_num_filters": 4,
+ "primal_unet_num_pool_layers": 4,
+ "primal_unet_dropout_probability": 0.0,
+ "primal_unet_padding_size": 15,
+ "primal_unet_normalize": False,
+ "dual_model_architecture": "UNET",
+ "dual_in_channels": 2,
+ "dual_out_channels": 2,
+ "dual_unet_num_filters": 4,
+ "dual_unet_num_pool_layers": 4,
+ "dual_unet_dropout_probability": 0.0,
+ "dual_unet_padding_size": 15,
+ "dual_unet_normalize": False,
+ "use_sens_net": False,
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "coil_combination_method": "SENSE",
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_lpdnet(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test the LPDNet model.
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None.
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ lpdnet = LPDNet(cfg, trainer=trainer)
+
+ coil_dim = cfg.coil_dim if dimensionality == 2 else cfg.coil_dim + 1
+
+ with torch.no_grad():
+ y = lpdnet.forward(output, output, mask, output.sum(coil_dim))
+
+ while isinstance(y, list):
+ y = y[-1]
+
+ x = x.sum(coil_dim)
+
+ if y.shape != x.shape:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_multidomainnet.py b/tests/collections/reconstruction/models/test_multidomainnet.py
new file mode 100644
index 00000000..355c1334
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_multidomainnet.py
@@ -0,0 +1,130 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.multidomainnet import MultiDomainNet
+
+
+def create_input(shape):
+ """Create a random input tensor."""
+ return torch.rand(shape).float()
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "standardization": True,
+ "num_filters": 16,
+ "num_pool_layers": 2,
+ "dropout_probability": 0.0,
+ "use_sens_net": False,
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "coil_combination_method": "SENSE",
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "standardization": False,
+ "num_filters": 64,
+ "num_pool_layers": 4,
+ "dropout_probability": 0.0,
+ "use_sens_net": False,
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "coil_combination_method": "SENSE",
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_multidomainnet(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test the multidomainnet model.
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None.
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ kikinet = MultiDomainNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ y = kikinet.forward(output, output, mask, output)
+
+ if dimensionality == 3:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if y.shape[1:] != x.shape[2:4]:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_mwcnn.py b/tests/collections/reconstruction/models/test_mwcnn.py
new file mode 100644
index 00000000..15670c28
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_mwcnn.py
@@ -0,0 +1,68 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NKI-AI/direct/blob/main/tests/tests_nn/test_mwcnn.py
+# Copyright (c) DIRECT Contributors
+
+import pytest
+import torch
+import torch.nn as nn
+
+from atommic.collections.reconstruction.nn.mwcnn_base.mwcnn_block import MWCNN
+
+
+def create_input(shape):
+ """Create a random input tensor."""
+ return torch.rand(shape).float()
+
+
+@pytest.mark.parametrize(
+ "shape",
+ [
+ [3, 2, 32, 32],
+ [3, 2, 20, 34],
+ ],
+)
+@pytest.mark.parametrize(
+ "first_conv_hidden_channels",
+ [4, 8],
+)
+@pytest.mark.parametrize(
+ "n_scales",
+ [2, 3],
+)
+@pytest.mark.parametrize(
+ "bias",
+ [True, False],
+)
+@pytest.mark.parametrize(
+ "batchnorm",
+ [True, False],
+)
+@pytest.mark.parametrize(
+ "act",
+ [nn.ReLU(), nn.PReLU()],
+)
+def test_mwcnn(shape, first_conv_hidden_channels, n_scales, bias, batchnorm, act):
+ """
+ Test MWCNN model.
+
+ Args:
+ shape (): Shape of input data.
+ first_conv_hidden_channels (): Number of channels in first convolutional layer.
+ n_scales (): Number of scales.
+ bias (): Whether to use bias in convolutional layers.
+ batchnorm (): Whether to use batch normalization in convolutional layers.
+ act (): Activation function.
+
+ Returns:
+ None.
+ """
+ model = MWCNN(shape[1], first_conv_hidden_channels, n_scales, bias, batchnorm, act)
+
+ data = create_input(shape).cpu()
+
+ out = model(data)
+
+ if list(out.shape) != shape:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_recurrentvarnet.py b/tests/collections/reconstruction/models/test_recurrentvarnet.py
new file mode 100644
index 00000000..1f927c88
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_recurrentvarnet.py
@@ -0,0 +1,247 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from: https://github.com/facebookresearch/fastMRI
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.recurrentvarnet import RecurrentVarNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "in_channels": 2,
+ "recurrent_hidden_channels": 64,
+ "recurrent_num_layers": 4,
+ "num_steps": 8,
+ "no_parameter_sharing": True,
+ "learned_initializer": True,
+ "initializer_initialization": "sense",
+ "initializer_channels": [32, 32, 64, 64],
+ "initializer_dilations": [1, 1, 2, 4],
+ "initializer_multiscale": 1,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "in_channels": 2,
+ "recurrent_hidden_channels": 64,
+ "recurrent_num_layers": 4,
+ "num_steps": 8,
+ "no_parameter_sharing": False,
+ "learned_initializer": True,
+ "initializer_initialization": "sense",
+ "initializer_channels": [32, 32, 64, 64],
+ "initializer_dilations": [1, 1, 2, 4],
+ "initializer_multiscale": 1,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 8, 13, 18, 2],
+ {
+ "in_channels": 2,
+ "recurrent_hidden_channels": 64,
+ "recurrent_num_layers": 4,
+ "num_steps": 8,
+ "no_parameter_sharing": False,
+ "learned_initializer": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "ssim",
+ "val_reconstruction_loss": "ssim",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "in_channels": 2,
+ "recurrent_hidden_channels": 64,
+ "recurrent_num_layers": 4,
+ "num_steps": 8,
+ "no_parameter_sharing": True,
+ "learned_initializer": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "in_channels": 2,
+ "recurrent_hidden_channels": 64,
+ "recurrent_num_layers": 4,
+ "num_steps": 18,
+ "no_parameter_sharing": True,
+ "learned_initializer": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_recurrentvarnet(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test RecurrentVarNet with different parameters
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ rvn = RecurrentVarNet(cfg, trainer=trainer)
+
+ coil_dim = cfg.coil_dim if dimensionality == 2 else cfg.coil_dim + 1
+
+ with torch.no_grad():
+ y = rvn.forward(output, output, mask, output.sum(coil_dim))
+
+ while isinstance(y, list):
+ y = y[-1]
+
+ x = torch.view_as_complex(x.sum(coil_dim))
+
+ if y.shape != x.shape:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_unet.py b/tests/collections/reconstruction/models/test_unet.py
new file mode 100644
index 00000000..5aaa374d
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_unet.py
@@ -0,0 +1,199 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from: https://github.com/facebookresearch/fastMRI
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.unet import UNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "channels": 14,
+ "pooling_layers": 2,
+ "padding_size": 11,
+ "normalize": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "channels": 14,
+ "pooling_layers": 2,
+ "padding_size": 11,
+ "normalize": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "channels": 14,
+ "pooling_layers": 2,
+ "padding_size": 11,
+ "normalize": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "ssim",
+ "val_reconstruction_loss": "ssim",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "channels": 14,
+ "pooling_layers": 2,
+ "padding_size": 15,
+ "normalize": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_unet(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test UNet with different parameters
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ unet = UNet(cfg, trainer=trainer)
+
+ coil_dim = cfg.coil_dim if dimensionality == 2 else cfg.coil_dim + 1
+
+ with torch.no_grad():
+ y = unet.forward(output, output, mask, output.sum(coil_dim))
+
+ while isinstance(y, list):
+ y = y[-1]
+
+ x = torch.view_as_complex(x.sum(coil_dim))
+
+ if y.shape != x.shape:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_varnet.py b/tests/collections/reconstruction/models/test_varnet.py
new file mode 100644
index 00000000..3b9d0ce1
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_varnet.py
@@ -0,0 +1,202 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from: https://github.com/facebookresearch/fastMRI
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.varnet import VarNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_cascades": 12,
+ "channels": 14,
+ "no_dc": False,
+ "pooling_layers": 2,
+ "padding_size": 11,
+ "normalize": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "num_cascades": 12,
+ "channels": 14,
+ "no_dc": True,
+ "pooling_layers": 2,
+ "padding_size": 11,
+ "normalize": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_cascades": 18,
+ "channels": 14,
+ "no_dc": False,
+ "pooling_layers": 2,
+ "padding_size": 11,
+ "normalize": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "ssim",
+ "val_reconstruction_loss": "ssim",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_cascades": 2,
+ "channels": 14,
+ "no_dc": False,
+ "pooling_layers": 2,
+ "padding_size": 15,
+ "normalize": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_vn(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test VN with different parameters
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ vn = VarNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ y = vn.forward(output, output, mask, output)
+
+ if dimensionality == 3:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if y.shape[1:] != x.shape[2:4]:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_vsnet.py b/tests/collections/reconstruction/models/test_vsnet.py
new file mode 100644
index 00000000..a68cf67f
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_vsnet.py
@@ -0,0 +1,198 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from: https://github.com/facebookresearch/fastMRI
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.vsnet import VSNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_cascades": 10,
+ "imspace_model_architecture": "CONV",
+ "imspace_conv_hidden_channels": 64,
+ "imspace_conv_n_convs": 5,
+ "imspace_conv_batchnorm": False,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 5, 15, 12, 2],
+ {
+ "num_cascades": 10,
+ "imspace_model_architecture": "CONV",
+ "imspace_conv_hidden_channels": 64,
+ "imspace_conv_n_convs": 5,
+ "imspace_conv_batchnorm": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_cascades": 10,
+ "imspace_model_architecture": "CONV",
+ "imspace_conv_hidden_channels": 16,
+ "imspace_conv_n_convs": 5,
+ "imspace_conv_batchnorm": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "train_reconstruction_loss": "ssim",
+ "val_reconstruction_loss": "ssim",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 2, 17, 19, 2],
+ {
+ "num_cascades": 2,
+ "imspace_model_architecture": "CONV",
+ "imspace_conv_hidden_channels": 128,
+ "imspace_conv_n_convs": 2,
+ "imspace_conv_batchnorm": True,
+ "use_sens_net": False,
+ "coil_combination_method": "SENSE",
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_vsnet(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test VSNet with different parameters
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ vsnet = VSNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ y = vsnet.forward(output, output, mask, output)
+
+ if dimensionality == 3:
+ x = x.reshape([x.shape[0] * x.shape[1], x.shape[2], x.shape[3], x.shape[4], x.shape[5]])
+
+ if y.shape[1:] != x.shape[2:4]:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/models/test_xpdnet.py b/tests/collections/reconstruction/models/test_xpdnet.py
new file mode 100644
index 00000000..840c06d6
--- /dev/null
+++ b/tests/collections/reconstruction/models/test_xpdnet.py
@@ -0,0 +1,161 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.reconstruction.nn.xpdnet import XPDNet
+
+
+def create_input(shape):
+ """Create a random input tensor."""
+ return torch.rand(shape).float()
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_primal": 5,
+ "num_dual": 5,
+ "num_iter": 20,
+ "use_primal_only": True,
+ "kspace_model_architecture": "CONV",
+ "dual_conv_hidden_channels": 16,
+ "dual_conv_num_dubs": 2,
+ "dual_conv_batchnorm": False,
+ "image_model_architecture": "MWCNN",
+ "imspace_in_channels": 2,
+ "imspace_out_channels": 2,
+ "mwcnn_hidden_channels": 16,
+ "mwcnn_num_scales": 2,
+ "mwcnn_bias": True,
+ "mwcnn_batchnorm": False,
+ "normalize_image": False,
+ "use_sens_net": False,
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "coil_combination_method": "SENSE",
+ "reconstruction_loss": {"l1": 1.0},
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "num_primal": 5,
+ "num_dual": 5,
+ "num_iter": 20,
+ "use_primal_only": True,
+ "kspace_model_architecture": "CONV",
+ "dual_conv_hidden_channels": 16,
+ "dual_conv_num_dubs": 2,
+ "dual_conv_batchnorm": False,
+ "image_model_architecture": "MWCNN",
+ "imspace_in_channels": 2,
+ "imspace_out_channels": 2,
+ "mwcnn_hidden_channels": 16,
+ "mwcnn_num_scales": 2,
+ "mwcnn_bias": True,
+ "mwcnn_batchnorm": False,
+ "normalize_image": False,
+ "use_sens_net": False,
+ "fft_centered": True,
+ "fft_normalization": "ortho",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ "coil_combination_method": "SENSE",
+ "train_reconstruction_loss": "mse",
+ "val_reconstruction_loss": "mse",
+ },
+ [0.08],
+ [4],
+ 2,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_xpdnet(shape, cfg, center_fractions, accelerations, dimensionality, trainer):
+ """
+ Test the XPDNet model.
+
+ Args:
+ shape: shape of the input
+ cfg: configuration of the model
+ center_fractions: center fractions
+ accelerations: accelerations
+ dimensionality: 2D or 3D inputs
+ trainer: trainer configuration
+
+ Returns:
+ None.
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ xpdnet = XPDNet(cfg, trainer=trainer)
+
+ coil_dim = cfg.coil_dim if dimensionality == 2 else cfg.coil_dim + 1
+
+ with torch.no_grad():
+ y = xpdnet.forward(output, output, mask, output.sum(coil_dim))
+
+ while isinstance(y, list):
+ y = y[-1]
+
+ x = x.sum(coil_dim)
+
+ if y.shape != x.shape:
+ raise AssertionError
diff --git a/tests/collections/reconstruction/mri_data/__init__.py b/tests/collections/reconstruction/mri_data/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/collections/reconstruction/mri_data/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/collections/reconstruction/mri_data/conftest.py b/tests/collections/reconstruction/mri_data/conftest.py
new file mode 100644
index 00000000..bec28023
--- /dev/null
+++ b/tests/collections/reconstruction/mri_data/conftest.py
@@ -0,0 +1,89 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from https://github.com/facebookresearch/fastMRI
+
+import numpy as np
+import pytest
+import torch
+
+from tests.collections.reconstruction.mri_data.create_temp_data import create_temp_data
+
+# these are really slow - skip by default
+SKIP_INTEGRATIONS = True
+
+
+def create_input(shape):
+ """
+ Create a random input tensor of the given shape.
+
+ Args:
+ shape: The shape of the input tensor.
+
+ Returns:
+ A random input tensor.
+ """
+ x = np.arange(np.product(shape)).reshape(shape)
+ x = torch.from_numpy(x).float()
+
+ return x
+
+
+@pytest.fixture(scope="session")
+def fastmri_mock_dataset(tmp_path_factory):
+ """
+ Create a mock dataset for testing.
+
+ Args:
+ tmp_path_factory: A temporary path factory.
+
+ Returns:
+ A mock dataset.
+ """
+ path = tmp_path_factory.mktemp("fastmri_data")
+
+ return create_temp_data(path)
+
+
+@pytest.fixture
+def skip_integration_tests():
+ """
+ Skip integration tests if the environment variable is set.
+
+ Returns:
+ A boolean indicating whether to skip integration tests.
+ """
+ return SKIP_INTEGRATIONS
+
+
+@pytest.fixture
+def knee_split_lens():
+ """
+ The split lengths for the knee dataset.
+
+ Returns:
+ A dictionary with the split lengths.
+ """
+ return {
+ "multicoil_train": 34742,
+ "multicoil_val": 7135,
+ "multicoil_test": 4092,
+ "singlecoil_train": 34742,
+ "singlecoil_val": 7135,
+ "singlecoil_test": 3903,
+ }
+
+
+@pytest.fixture
+def brain_split_lens():
+ """
+ The split lengths for the brain dataset.
+
+ Returns:
+ A dictionary with the split lengths.
+ """
+ return {
+ "multicoil_train": 70748,
+ "multicoil_val": 21842,
+ "multicoil_test": 8852,
+ }
diff --git a/tests/collections/reconstruction/mri_data/create_temp_data.py b/tests/collections/reconstruction/mri_data/create_temp_data.py
new file mode 100644
index 00000000..62907ea0
--- /dev/null
+++ b/tests/collections/reconstruction/mri_data/create_temp_data.py
@@ -0,0 +1,104 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Parts of the code have been taken from https://github.com/facebookresearch/fastMRI
+
+import h5py
+import numpy as np
+
+
+def create_temp_data(path):
+ """
+ Creates a temporary dataset for testing purposes.
+
+ Args:
+ path: The path to the dataset.
+
+ Returns:
+ None
+ """
+ rg = np.random.default_rng(seed=1234)
+ max_num_slices = 15
+ max_num_coils = 15
+ data_splits = {
+ "knee_data": [
+ "multicoil_train",
+ "multicoil_val",
+ "multicoil_test",
+ "multicoil_challenge",
+ "singlecoil_train",
+ "singlecoil_val",
+ "singlecoil_test",
+ "singlecoil_challenge",
+ ],
+ "brain_data": ["multicoil_train", "multicoil_val", "multicoil_test", "multicoil_challenge"],
+ }
+
+ enc_sizes = {
+ "train": [(1, 128, 64), (1, 128, 49), (1, 150, 67)],
+ "val": [(1, 128, 64), (1, 170, 57)],
+ "test": [(1, 128, 64), (1, 96, 96)],
+ "challenge": [(1, 128, 64), (1, 96, 48)],
+ }
+ recon_sizes = {
+ "train": [(1, 64, 64), (1, 49, 49), (1, 67, 67)],
+ "val": [(1, 64, 64), (1, 57, 47)],
+ "test": [(1, 64, 64), (1, 96, 96)],
+ "challenge": [(1, 64, 64), (1, 48, 48)],
+ }
+
+ metadata = {}
+ for dataset, value in data_splits.items():
+ for split in value:
+ (path / dataset / split).mkdir(parents=True)
+ encs = enc_sizes[split.split("_")[-1]]
+ recs = recon_sizes[split.split("_")[-1]]
+ fcount = 0
+ for i, _ in enumerate(encs):
+ fname = path / dataset / split / f"file{fcount}.h5"
+ num_slices = rg.integers(2, max_num_slices)
+ if "multicoil" in split:
+ num_coils = rg.integers(2, max_num_coils)
+ enc_size = (num_slices, num_coils, encs[i][-2], encs[i][-1])
+ else:
+ enc_size = (num_slices, encs[i][-2], encs[i][-1])
+ recon_size = (num_slices, recs[i][-2], recs[i][-1])
+ data = rg.normal(size=enc_size) + 1j * rg.normal(size=enc_size)
+
+ if split.split("_")[-1] in ("train", "val"):
+ recon = np.absolute(rg.normal(size=recon_size)).astype(np.dtype(" 1:
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+ batch, slices = output.shape[:2]
+ output = output.reshape(batch * slices, *output.shape[2:])
+ coil_dim += 1
+
+ output = torch.abs(torch.view_as_complex(output)).unsqueeze(coil_dim)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ segmentation_3dunet = Segmentation3DUNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_segmentation = segmentation_3dunet.forward(output)
+
+ output = torch.cat([output for _ in range(segmentation_classes)], coil_dim)
+
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/segmentation/models/test_attention_unet.py b/tests/collections/segmentation/models/test_attention_unet.py
new file mode 100644
index 00000000..de075e57
--- /dev/null
+++ b/tests/collections/segmentation/models/test_attention_unet.py
@@ -0,0 +1,281 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.segmentation.nn.attentionunet import SegmentationAttentionUNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "AttentionUNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 2,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 13, 45, 45, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "AttentionUNet",
+ "segmentation_module_input_channels": 2,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 2,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": False,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "AttentionUNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 2,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 13, 45, 45, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "AttentionUNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 2,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_attention_unet(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test the Segmentation Attention UNet with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+ coil_dim += 1
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ segmentation_attention_unet = SegmentationAttentionUNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_segmentation = segmentation_attention_unet.forward(output.sum(coil_dim))
+
+ if consecutive_slices > 1:
+ output = torch.view_as_complex(
+ output.reshape(
+ [output.shape[0] * output.shape[1], output.shape[2], output.shape[3], output.shape[4], output.shape[5]]
+ ).sum(coil_dim - 1)
+ )
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/segmentation/models/test_dynunet.py b/tests/collections/segmentation/models/test_dynunet.py
new file mode 100644
index 00000000..e5e82892
--- /dev/null
+++ b/tests/collections/segmentation/models/test_dynunet.py
@@ -0,0 +1,304 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.segmentation.nn.dynunet import SegmentationDYNUNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "DYNUNet",
+ "dimensionality": 2,
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": [16, 32, 64, 128],
+ "segmentation_module_kernel_size": (3, 3, 3, 1),
+ "segmentation_module_strides": (1, 1, 1, 1),
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_norm": "instance",
+ "segmentation_module_activation": "leakyrelu",
+ "segmentation_module_deep_supervision": True,
+ "segmentation_module_deep_supervision_levels": 1,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "DYNUNet",
+ "dimensionality": 2,
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": [16, 32, 64, 128],
+ "segmentation_module_kernel_size": (3, 3, 3, 1),
+ "segmentation_module_strides": (1, 1, 1, 1),
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_norm": "instance",
+ "segmentation_module_activation": "leakyrelu",
+ "segmentation_module_deep_supervision": True,
+ "segmentation_module_deep_supervision_levels": 1,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "DYNUNet",
+ "dimensionality": 2,
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": [16, 32, 64, 128],
+ "segmentation_module_kernel_size": (3, 3, 3, 1),
+ "segmentation_module_strides": (1, 1, 1, 1),
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_norm": "instance",
+ "segmentation_module_activation": "leakyrelu",
+ "segmentation_module_deep_supervision": False,
+ "segmentation_module_deep_supervision_levels": 1,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "DYNUNet",
+ "dimensionality": 2,
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": [16, 32, 64, 128],
+ "segmentation_module_kernel_size": (3, 3, 3, 1),
+ "segmentation_module_strides": (1, 1, 1, 1),
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_norm": "instance",
+ "segmentation_module_activation": "leakyrelu",
+ "segmentation_module_deep_supervision": False,
+ "segmentation_module_deep_supervision_levels": 1,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_dynunet(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test the Segmentation DYNUNet with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+ coil_dim += 1
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ dynunet = SegmentationDYNUNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_segmentation = dynunet.forward(output.sum(coil_dim))
+ if consecutive_slices > 1:
+ output = torch.view_as_complex(
+ output.reshape(
+ [output.shape[0] * output.shape[1], output.shape[2], output.shape[3], output.shape[4], output.shape[5]]
+ ).sum(coil_dim - 1)
+ )
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/segmentation/models/test_lambda_unet.py b/tests/collections/segmentation/models/test_lambda_unet.py
new file mode 100644
index 00000000..85e27300
--- /dev/null
+++ b/tests/collections/segmentation/models/test_lambda_unet.py
@@ -0,0 +1,286 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.segmentation.nn.lambdaunet import SegmentationLambdaUNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 13, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "LambdaUNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 32,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_query_depth": 16,
+ "segmentation_module_intra_depth": 4,
+ "segmentation_module_receptive_kernel_kernel": 1,
+ "segmentation_module_temporal_kernel": 1,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 13, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "LambdaUNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 32,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_query_depth": 16,
+ "segmentation_module_intra_depth": 4,
+ "segmentation_module_receptive_kernel_kernel": 1,
+ "segmentation_module_temporal_kernel": 1,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 3,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "LambdaUNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 32,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_query_depth": 16,
+ "segmentation_module_intra_depth": 4,
+ "segmentation_module_receptive_kernel_kernel": 1,
+ "segmentation_module_temporal_kernel": 1,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "LambdaUNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 32,
+ "segmentation_module_pooling_layers": 4,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_query_depth": 16,
+ "segmentation_module_intra_depth": 4,
+ "segmentation_module_receptive_kernel_kernel": 1,
+ "segmentation_module_temporal_kernel": 1,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 3,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_lambda_unet(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test the Segmentation Lambda UNet with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs = []
+ for i in range(x.shape[0]):
+ output, _, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+
+ output = torch.cat(outputs)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+
+ if consecutive_slices > 1:
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+ batch, slices = output.shape[:2]
+ output = output.reshape(batch * slices, *output.shape[2:]).unsqueeze(2)
+
+ output = torch.abs(torch.view_as_complex(output)).sum(coil_dim).unsqueeze(coil_dim)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ segmentation_lambda_unet = SegmentationLambdaUNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_segmentation = segmentation_lambda_unet.forward(output)
+
+ output = torch.cat(
+ [output for _ in range(segmentation_classes)], coil_dim if consecutive_slices == 1 else coil_dim + 1
+ )
+
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/segmentation/models/test_unet.py b/tests/collections/segmentation/models/test_unet.py
new file mode 100644
index 00000000..f1c55d47
--- /dev/null
+++ b/tests/collections/segmentation/models/test_unet.py
@@ -0,0 +1,281 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.segmentation.nn.unet import SegmentationUNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "UNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 2,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 13, 45, 45, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "UNet",
+ "segmentation_module_input_channels": 2,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 2,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": False,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "UNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 2,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 13, 45, 45, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "UNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_channels": 64,
+ "segmentation_module_pooling_layers": 2,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_unet(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test the Segmentation UNet with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+ coil_dim += 1
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ segmentation_unet = SegmentationUNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_segmentation = segmentation_unet.forward(output.sum(coil_dim))
+
+ if consecutive_slices > 1:
+ output = torch.view_as_complex(
+ output.reshape(
+ [output.shape[0] * output.shape[1], output.shape[2], output.shape[3], output.shape[4], output.shape[5]]
+ ).sum(coil_dim - 1)
+ )
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/segmentation/models/test_unetr.py b/tests/collections/segmentation/models/test_unetr.py
new file mode 100644
index 00000000..884daadb
--- /dev/null
+++ b/tests/collections/segmentation/models/test_unetr.py
@@ -0,0 +1,190 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.segmentation.nn.unetr import SegmentationUNetR
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 3, 48, 32, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "SEGMENTATIONUNETR",
+ "dimensionality": 2,
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_img_size": [48, 32],
+ "segmentation_module_channels": 64,
+ "segmentation_module_hidden_size": 768,
+ "segmentation_module_mlp_dim": 3072,
+ "segmentation_module_num_heads": 12,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_norm": "instance",
+ "segmentation_module_pos_embed": "conv",
+ "segmentation_module_conv_block": True,
+ "segmentation_module_res_block": True,
+ "segmentation_module_qkv_bias": False,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 48, 32, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "SEGMENTATIONUNETR",
+ "dimensionality": 2,
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_img_size": [48, 32],
+ "segmentation_module_channels": 64,
+ "segmentation_module_hidden_size": 768,
+ "segmentation_module_mlp_dim": 3072,
+ "segmentation_module_num_heads": 1,
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_norm": "instance",
+ "segmentation_module_pos_embed": "conv",
+ "segmentation_module_conv_block": False,
+ "segmentation_module_res_block": False,
+ "segmentation_module_qkv_bias": True,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_unetr(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test the Segmentation UNetR with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs = []
+ for i in range(x.shape[0]):
+ output, _, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+
+ output = torch.cat(outputs)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+ batch, slices = output.shape[:2]
+ output = output.reshape(batch * slices, *output.shape[2:])
+ coil_dim += 1
+
+ output = torch.abs(torch.view_as_complex(output)).unsqueeze(coil_dim)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ unetr = SegmentationUNetR(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_segmentation = unetr.forward(output.sum(coil_dim))
+
+ output = torch.cat([output for _ in range(segmentation_classes)], coil_dim)
+
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/collections/segmentation/models/test_vnet.py b/tests/collections/segmentation/models/test_vnet.py
new file mode 100644
index 00000000..1095e2ac
--- /dev/null
+++ b/tests/collections/segmentation/models/test_vnet.py
@@ -0,0 +1,281 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.collections.common.data.subsample import Random1DMaskFunc
+from atommic.collections.common.parts import utils
+from atommic.collections.segmentation.nn.vnet import SegmentationVNet
+from tests.collections.reconstruction.mri_data.conftest import create_input
+
+
+@pytest.mark.parametrize(
+ "shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer",
+ [
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "VNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_activation": "elu",
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_bias": False,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 5,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 13, 64, 32, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "VNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_activation": "elu",
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_bias": False,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 3, 32, 16, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "VNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_activation": "elu",
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_bias": False,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ (
+ [1, 13, 64, 32, 2],
+ {
+ "use_reconstruction_module": False,
+ "segmentation_module": "VNet",
+ "segmentation_module_input_channels": 1,
+ "segmentation_module_output_channels": 4,
+ "segmentation_module_activation": "elu",
+ "segmentation_module_dropout": 0.0,
+ "segmentation_module_bias": False,
+ "segmentation_loss": {"dice": 1.0},
+ "dice_loss_include_background": False,
+ "dice_loss_to_onehot_y": False,
+ "dice_loss_sigmoid": True,
+ "dice_loss_softmax": False,
+ "dice_loss_other_act": None,
+ "dice_loss_squared_pred": False,
+ "dice_loss_jaccard": False,
+ "dice_loss_reduction": "mean",
+ "dice_loss_smooth_nr": 1,
+ "dice_loss_smooth_dr": 1,
+ "dice_loss_batch": True,
+ "consecutive_slices": 1,
+ "coil_combination_method": "SENSE",
+ "magnitude_input": True,
+ "use_sens_net": False,
+ "fft_centered": False,
+ "fft_normalization": "backward",
+ "spatial_dims": [-2, -1],
+ "coil_dim": 1,
+ },
+ [0.08],
+ [4],
+ 2,
+ 4,
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ },
+ ),
+ ],
+)
+def test_vnet(shape, cfg, center_fractions, accelerations, dimensionality, segmentation_classes, trainer):
+ """
+ Test the Segmentation V-Net with different parameters.
+
+ Parameters
+ ----------
+ shape : list of int
+ Shape of the input data
+ cfg : dict
+ Dictionary with the parameters of the qRIM model
+ center_fractions : list of float
+ List of center fractions to test
+ accelerations : list of float
+ List of acceleration factors to test
+ dimensionality : int
+ Dimensionality of the data
+ segmentation_classes : int
+ Number of segmentation classes
+ trainer : dict
+ Dictionary with the parameters of the trainer
+ """
+ mask_func = Random1DMaskFunc(center_fractions, accelerations)
+ x = create_input(shape)
+
+ outputs, masks = [], []
+ for i in range(x.shape[0]):
+ output, mask, _ = utils.apply_mask(x[i : i + 1], mask_func, seed=123)
+ outputs.append(output)
+ masks.append(mask)
+
+ output = torch.cat(outputs)
+ mask = torch.cat(masks)
+
+ coil_dim = cfg.get("coil_dim")
+ consecutive_slices = cfg.get("consecutive_slices")
+ if consecutive_slices > 1:
+ output = torch.stack([output for _ in range(consecutive_slices)], 1)
+ coil_dim += 1
+
+ if dimensionality == 3 and shape[1] > 1:
+ mask = torch.cat([mask, mask], 1)
+
+ cfg = OmegaConf.create(cfg)
+ cfg = OmegaConf.create(OmegaConf.to_container(cfg, resolve=True))
+
+ trainer = OmegaConf.create(trainer)
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ segmentation_vnet = SegmentationVNet(cfg, trainer=trainer)
+
+ with torch.no_grad():
+ pred_segmentation = segmentation_vnet.forward(output.sum(coil_dim))
+
+ if consecutive_slices > 1:
+ output = torch.view_as_complex(
+ output.reshape(
+ [output.shape[0] * output.shape[1], output.shape[2], output.shape[3], output.shape[4], output.shape[5]]
+ ).sum(coil_dim - 1)
+ )
+ output = torch.stack([output for _ in range(segmentation_classes)], 1)
+ pred_segmentation = pred_segmentation.reshape(
+ pred_segmentation.shape[0] * pred_segmentation.shape[1], *pred_segmentation.shape[2:]
+ )
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
+ else:
+ output = torch.view_as_complex(torch.stack([output for _ in range(segmentation_classes)], 1).sum(coil_dim + 1))
+ if pred_segmentation.shape != output.shape:
+ raise AssertionError
diff --git a/tests/core/__init__.py b/tests/core/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/core/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/core/test_exp_manager.py b/tests/core/test_exp_manager.py
new file mode 100644
index 00000000..07c96e23
--- /dev/null
+++ b/tests/core/test_exp_manager.py
@@ -0,0 +1,127 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import pytorch_lightning as pl
+import torch
+from omegaconf import OmegaConf
+
+from atommic.core.classes.modelPT import ModelPT
+
+
+class MyTestOptimizer(torch.optim.Optimizer):
+ def __init__(self, params):
+ self._step = 0
+ super().__init__(params, {})
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ loss = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+ for group in self.param_groups:
+ for p in group["params"]:
+ if self._step == 0:
+ p.data = 0.1 * torch.ones(p.shape)
+ elif self._step == 1:
+ p.data = 0.0 * torch.ones(p.shape)
+ else:
+ p.data = 0.01 * torch.ones(p.shape)
+ self._step += 1
+ return loss
+
+
+class DoNothingOptimizer(torch.optim.Optimizer):
+ def __init__(self, params):
+ self._step = 0
+ super().__init__(params, {})
+
+ @torch.no_grad()
+ def step(self, closure=None):
+ loss = None
+ if closure is not None:
+ with torch.enable_grad():
+ loss = closure()
+ self._step += 1
+ return loss
+
+
+class OnesDataset(torch.utils.data.Dataset):
+ def __init__(self, dataset_len):
+ super().__init__()
+ self.__dataset_len = dataset_len
+
+ def __getitem__(self, *args):
+ return torch.ones(2)
+
+ def __len__(self):
+ return self.__dataset_len
+
+
+class ExampleModel(ModelPT):
+ def __init__(self, *args, **kwargs):
+ cfg = OmegaConf.structured({})
+
+ trainer = OmegaConf.create(
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ }
+ )
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+
+ super().__init__(cfg=cfg, trainer=trainer)
+ pl.seed_everything(1234)
+ self.l1 = torch.nn.modules.Linear(in_features=2, out_features=1)
+
+ @staticmethod
+ def train_dataloader():
+ dataset = OnesDataset(2)
+ return torch.utils.data.DataLoader(dataset, batch_size=2, num_workers=8)
+
+ @staticmethod
+ def val_dataloader():
+ dataset = OnesDataset(10)
+ return torch.utils.data.DataLoader(dataset, batch_size=2, num_workers=8)
+
+ def forward(self, batch):
+ output = self.l1(batch)
+ output = torch.nn.functional.l1_loss(output, torch.zeros(output.size()).to(output.device))
+ return output
+
+ def validation_step(self, batch, batch_idx):
+ self.loss = self(batch)
+ return self.loss
+
+ def training_step(self, batch, batch_idx):
+ return self(batch)
+
+ def configure_optimizers(self):
+ return MyTestOptimizer(self.parameters())
+ # return torch.optim.Adam(self.parameters(), lr=0.1)
+
+ def list_available_models(self):
+ raise NotImplementedError()
+
+ def setup_training_data(self):
+ raise NotImplementedError()
+
+ def setup_validation_data(self):
+ raise NotImplementedError()
+
+ def on_validation_epoch_end(self):
+ self.log("val_loss", torch.stack([self.loss]).mean())
+
+
+class DoNothingModel(ExampleModel):
+ def configure_optimizers(self):
+ return DoNothingOptimizer(self.parameters())
diff --git a/tests/core/test_neural_types.py b/tests/core/test_neural_types.py
new file mode 100644
index 00000000..40dca6cc
--- /dev/null
+++ b/tests/core/test_neural_types.py
@@ -0,0 +1,58 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/tests/core/test_neural_types.py
+
+import pytest
+
+from atommic.core.neural_types.axes import AxisKind, AxisType
+from atommic.core.neural_types.comparison import NeuralTypeComparisonResult
+from atommic.core.neural_types.elements import ElementType
+from atommic.core.neural_types.neural_type import NeuralType
+
+
+class TestNeuralTypeSystem:
+ @pytest.mark.unit
+ def test_transpose_same_1(self):
+ type1 = NeuralType(axes=("B", "T", "C"))
+ type2 = NeuralType(axes=("T", "B", "C"))
+
+ assert type1.compare(type2) == NeuralTypeComparisonResult.TRANSPOSE_SAME
+ assert type2.compare(type1) == NeuralTypeComparisonResult.TRANSPOSE_SAME
+
+ @pytest.mark.unit
+ def test_singletone(self):
+ loss_output1 = NeuralType(axes=None)
+ loss_output2 = NeuralType(axes=None)
+
+ assert loss_output1.compare(loss_output2) == NeuralTypeComparisonResult.SAME
+ assert loss_output2.compare(loss_output1) == NeuralTypeComparisonResult.SAME
+
+ @pytest.mark.unit
+ def test_struct(self):
+ class BoundingBox(ElementType):
+ def __str__(self):
+ return "bounding box from detection model"
+
+ @staticmethod
+ def fields():
+ return ("X", "Y", "W", "H")
+
+ T1 = NeuralType(
+ elements_type=BoundingBox(),
+ axes=(AxisType(kind=AxisKind.Batch, size=None, is_list=True),),
+ )
+
+ class BadBoundingBox(ElementType):
+ def __str__(self):
+ return "bad bounding box from detection model"
+
+ @staticmethod
+ def fields():
+ return ("X", "Y", "H")
+
+ T2 = NeuralType(
+ elements_type=BadBoundingBox(),
+ axes=(AxisType(kind=AxisKind.Batch, size=None, is_list=True),),
+ )
+ assert T2.compare(T1) == NeuralTypeComparisonResult.INCOMPATIBLE
diff --git a/tests/core/test_optimizers_schedulers.py b/tests/core/test_optimizers_schedulers.py
new file mode 100644
index 00000000..756021f4
--- /dev/null
+++ b/tests/core/test_optimizers_schedulers.py
@@ -0,0 +1,1171 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/tests/core/test_optimizers_schedulers.py
+
+import math
+import os
+import random
+import shutil
+from abc import ABC
+
+import omegaconf
+import pytest
+import pytorch_lightning as pl
+import torch
+import torch.optim
+
+from atommic.core import optim
+from atommic.core.conf import optimizers
+from atommic.core.conf.optimizers import NovogradParams, SGDParams
+from atommic.core.conf.schedulers import CosineAnnealingParams
+from atommic.core.optim.lr_scheduler import AVAILABLE_SCHEDULERS, SquareRootAnnealing
+from atommic.core.optim.novograd import Novograd
+from atommic.core.optim.optimizers import AVAILABLE_OPTIMIZERS, get_optimizer, parse_optimizer_args, register_optimizer
+from atommic.utils import logging
+
+
+class TempModel(torch.nn.Module):
+ """Create a dummy model for testing."""
+
+ def __init__(self):
+ super(TempModel, self).__init__()
+ self.layer = torch.nn.Linear(5, 1)
+
+ def forward(self, x):
+ """Forward pass."""
+ x = self.layer(x)
+ return x
+
+
+class OptCounter(torch.optim.SGD):
+ """A simple optimizer that counts the number of calls to step()."""
+
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
+ for group in self.param_groups:
+ group.setdefault("count", 0)
+
+ def step(self, closure=None):
+ """Performs a single optimization step."""
+ for group in self.param_groups:
+ group["count"] += 1
+ super().step(closure)
+
+
+class RandomDataset(torch.utils.data.Dataset):
+ """A dataset that returns random tensors."""
+
+ def __init__(self, dataset_len):
+ super().__init__()
+ self.__dataset_len = dataset_len
+
+ def __getitem__(self, *args):
+ return torch.randn(2)
+
+ def __len__(self):
+ return self.__dataset_len
+
+
+class ExampleModel(pl.LightningModule, ABC):
+ """A dummy model for testing."""
+
+ def __init__(self, batch_size, dataset_len, drop_last, max_steps):
+ super().__init__()
+ self.l1 = torch.nn.modules.Linear(in_features=2, out_features=1)
+ self.batch_size = batch_size
+ self.dataset_len = dataset_len
+ self.drop_last = drop_last
+ self.max_steps = max_steps
+
+ self.my_opt = None
+
+ def train_dataloader(self):
+ """Return a training data loader."""
+ dataset = RandomDataset(self.dataset_len)
+ return torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, drop_last=self.drop_last)
+
+ def training_step(self, batch, batch_idx):
+ """Set training step."""
+ output = self.l1(batch)
+ output = torch.nn.functional.l1_loss(output, torch.ones(output.size()).to(output.device))
+ return {"loss": output}
+
+ def configure_optimizers(self):
+ """Configure optimizers for the model."""
+ self.my_opt = OptCounter(self.parameters(), lr=0.02)
+ return self.my_opt
+
+
+class Callback(pl.callbacks.Callback):
+ """A dummy callback for testing."""
+
+ @pl.utilities.rank_zero.rank_zero_only
+ def on_train_end(self, trainer, module):
+ """On train end, check that the number of steps is correct"""
+ count = module.my_opt.param_groups[0]["count"]
+ if trainer.global_step != count or trainer.global_step != module.max_steps:
+ logging.debug(f"max_epochs: {trainer.max_epochs}")
+ logging.debug(f"accumulate_grad_batches: {trainer.accumulate_grad_batches}")
+ logging.debug(f"limit_train_batches: {trainer.limit_train_batches}")
+ logging.debug(f"num_devices: {trainer.num_devices}")
+ logging.debug(f"batch_size: {module.batch_size}")
+ logging.debug(f"dataset_len: {module.dataset_len}")
+ logging.debug(f"drop_last: {module.drop_last}")
+ logging.debug(f"{len(trainer.train_dataloader)}")
+ logging.debug(f"{trainer.num_training_batches }")
+
+ self.assert_counts(trainer, module, count)
+
+ @staticmethod
+ def assert_counts(trainer, module, count):
+ """Assert that the number of steps is correct"""
+ if trainer.global_step != count:
+ raise AssertionError(f"{trainer.global_step} != {count} != {module.max_steps}")
+ if trainer.global_step != module.max_steps:
+ raise AssertionError(f"{trainer.global_step} != {count} != {module.max_steps}")
+
+
+class SchedulerNoOpCallback(Callback):
+ """A dummy callback for testing."""
+
+ @staticmethod
+ def on_train_batch_end(trainer: pl.Trainer, pl_module, outputs, batch, batch_idx):
+ """On each training batch end"""
+ # pl_module.max_steps is "original" max steps without trainer extra steps.
+ if (trainer.global_step + 1) % 3 == 0 and (trainer.global_step + 1) < pl_module.max_steps:
+ schedulers = trainer.lr_scheduler_configs
+
+ for scheduler in schedulers:
+ # Decrement the counter by 2, then perform a scheduler.step() to perform a no-up
+ # as well as update the optimizer lr in all param groups
+ scheduler.scheduler.last_epoch -= 2
+ scheduler.scheduler.step()
+
+ # Increase the max step count by 1
+ trainer.fit_loop.epoch_loop.max_steps = trainer.fit_loop.epoch_loop.max_steps + 1
+
+ def assert_counts(self, trainer, module, count):
+ """This is a no-op callback, so the counts should not change"""
+ num_skips = torch.div(module.max_steps, 3, rounding_mode="trunc")
+ extra_steps = module.max_steps + num_skips
+ if trainer.global_step != count:
+ raise AssertionError(f"{trainer.global_step} != {count} != {extra_steps}")
+ if trainer.global_step != extra_steps:
+ raise AssertionError(f"{trainer.global_step} != {count} != {extra_steps}")
+
+
+class TestOptimizersSchedulers:
+ """Test the optimizers and schedulers."""
+
+ INITIAL_LR = 0.1
+ MIN_LR = 1e-3
+ MAX_STEPS = 10
+ D_MODEL = 16
+
+ # fused_adam is looking for CUDA and this test is being run on CPU only tests
+ @pytest.mark.unit
+ def test_get_optimizer(self):
+ """Test that the optimizer is correctly created"""
+ model = TempModel()
+ if torch.cuda.is_available():
+ model.cuda()
+
+ for opt_name in AVAILABLE_OPTIMIZERS:
+ if opt_name == "fused_adam" and not torch.cuda.is_available():
+ continue
+ if opt_name == "distributed_fused_adam":
+ if not torch.cuda.is_available() or not torch.distributed.is_nccl_available():
+ continue
+ if not torch.distributed.is_initialized():
+ torch.distributed.init_process_group(
+ "nccl",
+ world_size=1,
+ rank=0,
+ store=torch.distributed.HashStore(),
+ )
+ opt_cls = get_optimizer(opt_name)
+ if opt_name == "adafactor":
+ # Adafactor's default mode uses relative_step without any lr.
+ opt = opt_cls(model.parameters())
+ else:
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ if not isinstance(opt, AVAILABLE_OPTIMIZERS[opt_name]):
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_register_optimizer(self):
+ """Test that we can register a new optimizer"""
+
+ class TempOpt(torch.optim.SGD):
+ """A dummy optimizer"""
+
+ class TempOptParams(optimizers.SGDParams):
+ """A dummy optimizer params"""
+
+ register_optimizer("TempOpt", TempOpt, TempOptParams)
+
+ model = TempModel()
+ opt_cls = get_optimizer("TempOpt")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ if not isinstance(opt, TempOpt):
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_optim_config_parse_bypass(self):
+ """Test that the optimizer config is parsed correctly when the optimizer is not registered."""
+ basic_optim_config = {"weight_decay": 0.001, "betas": [0.8, 0.5]}
+ parsed_params = parse_optimizer_args("novograd", basic_optim_config)
+ if parsed_params["weight_decay"] != basic_optim_config["weight_decay"]:
+ raise AssertionError
+ if parsed_params["betas"][0] != basic_optim_config["betas"][0]:
+ raise AssertionError
+ if parsed_params["betas"][1] != basic_optim_config["betas"][1]:
+ raise AssertionError
+
+ dict_config = omegaconf.OmegaConf.create(basic_optim_config)
+ parsed_params = parse_optimizer_args("novograd", dict_config)
+ if parsed_params["weight_decay"] != dict_config["weight_decay"]:
+ raise AssertionError
+ if parsed_params["betas"][0] != dict_config["betas"][0]:
+ raise AssertionError
+ if parsed_params["betas"][1] != dict_config["betas"][1]:
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_optim_config_parse_arg_by_target(self):
+ """Test that the optimizer config is parsed correctly by target."""
+ basic_optim_config = {
+ "_target_": "atommic.core.conf.optimizers.NovogradParams",
+ "params": {"weight_decay": 0.001, "betas": [0.8, 0.5]},
+ }
+ basic_optim_config = omegaconf.OmegaConf.create(basic_optim_config)
+ parsed_params = parse_optimizer_args("novograd", basic_optim_config)
+ if parsed_params["weight_decay"] != basic_optim_config["params"]["weight_decay"]:
+ raise AssertionError
+ if parsed_params["betas"][0] != basic_optim_config["params"]["betas"][0]:
+ raise AssertionError
+ if parsed_params["betas"][1] != basic_optim_config["params"]["betas"][1]:
+ raise AssertionError
+
+ dict_config = omegaconf.OmegaConf.create(basic_optim_config)
+ parsed_params = parse_optimizer_args("novograd", dict_config)
+ if parsed_params["weight_decay"] != dict_config["params"]["weight_decay"]:
+ raise AssertionError
+ if parsed_params["betas"][0] != dict_config["params"]["betas"][0]:
+ raise AssertionError
+ if parsed_params["betas"][1] != dict_config["params"]["betas"][1]:
+ raise AssertionError
+
+ # Names are ignored when passing class path
+ # This will be captured during optimizer instantiation
+ output_config = parse_optimizer_args("sgd", dict_config)
+ sgd_config = vars(SGDParams())
+ novograd_config = vars(NovogradParams())
+
+ if set(output_config.keys()) == set(sgd_config.keys()):
+ raise AssertionError
+ if set(output_config.keys()) != set(novograd_config):
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_get_scheduler(self):
+ """Test that get_scheduler returns the correct scheduler class."""
+ model = TempModel()
+ optimizer = Novograd(model.parameters(), lr=self.INITIAL_LR)
+
+ for sched_name in AVAILABLE_SCHEDULERS:
+ sched_cls = optim.lr_scheduler.get_scheduler(sched_name)
+
+ try:
+ sched = sched_cls(optimizer)
+ if not isinstance(sched, AVAILABLE_SCHEDULERS[sched_name]):
+ raise AssertionError
+ continue
+ except Exception:
+ pass
+
+ try:
+ sched = sched_cls(optimizer, max_steps=self.MAX_STEPS)
+ if not isinstance(sched, AVAILABLE_SCHEDULERS[sched_name]):
+ raise AssertionError
+ continue
+ except Exception:
+ pass
+
+ @pytest.mark.unit
+ def test_register_scheduler(self):
+ """Test registering a new scheduler"""
+
+ class TempSched(optim.lr_scheduler.CosineAnnealing):
+ """Temporary scheduler class."""
+
+ class TempSchedParams(CosineAnnealingParams):
+ """Temporary scheduler class."""
+
+ optim.lr_scheduler.register_scheduler("TempSched", TempSched, TempSchedParams)
+
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+ sched_cls = optim.lr_scheduler.get_scheduler("TempSched")
+ sched = sched_cls(opt, max_steps=self.MAX_STEPS)
+
+ if not isinstance(sched, TempSched):
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_sched_config_parse_simple(self):
+ """Test that scheduler config is parsed correctly"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ basic_sched_config = {"name": "CosineAnnealing", "max_steps": 10}
+ scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, basic_sched_config)
+ if not isinstance(scheduler_setup["scheduler"], optim.lr_scheduler.CosineAnnealing):
+ raise AssertionError
+
+ dict_config = omegaconf.OmegaConf.create(basic_sched_config)
+ scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
+ if not isinstance(scheduler_setup["scheduler"], optim.lr_scheduler.CosineAnnealing):
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_sched_config_parse_from_cls(self):
+ """Test that we can parse a scheduler from a class"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ basic_sched_config = {
+ "_target_": "atommic.core.conf.schedulers.CosineAnnealingParams",
+ "params": {"min_lr": 0.1},
+ "max_steps": self.MAX_STEPS,
+ }
+ scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, basic_sched_config)
+ if not isinstance(scheduler_setup["scheduler"], optim.lr_scheduler.CosineAnnealing):
+ raise AssertionError
+
+ dict_config = omegaconf.OmegaConf.create(basic_sched_config)
+ scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
+ if not isinstance(scheduler_setup["scheduler"], optim.lr_scheduler.CosineAnnealing):
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_sched_config_parse_reduce_on_plateau(self):
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+ reduce_on_plateau_parameters = {
+ "mode": "min",
+ "factor": 0.5,
+ "patience": 1,
+ "threshold": 1e-4,
+ "threshold_mode": "rel",
+ "min_lr": 1e-6,
+ "eps": 1e-7,
+ "verbose": True,
+ "cooldown": 1,
+ }
+ basic_sched_config = {
+ "name": "ReduceLROnPlateau",
+ "monitor": "val_loss",
+ "reduce_on_plateau": True,
+ "max_steps": self.MAX_STEPS,
+ }
+ basic_sched_config.update(reduce_on_plateau_parameters)
+ scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, basic_sched_config)
+ assert isinstance(scheduler_setup["scheduler"], torch.optim.lr_scheduler.ReduceLROnPlateau)
+ for k, v in reduce_on_plateau_parameters.items():
+ if k == "min_lr":
+ k += "s"
+ v = [v]
+ found_v = getattr(scheduler_setup["scheduler"], k)
+ assert (
+ found_v == v
+ ), f"Wrong value `{repr(found_v)}` for `ReduceLROnPlateau` parameter `{k}`. Expected `{repr(v)}`."
+ dict_config = omegaconf.OmegaConf.create(basic_sched_config)
+ scheduler_setup = optim.lr_scheduler.prepare_lr_scheduler(opt, dict_config)
+ assert isinstance(scheduler_setup["scheduler"], torch.optim.lr_scheduler.ReduceLROnPlateau)
+ for k, v in reduce_on_plateau_parameters.items():
+ if k == "min_lr":
+ k += "s"
+ v = [v]
+ found_v = getattr(scheduler_setup["scheduler"], k)
+ assert (
+ found_v == v
+ ), f"Wrong value `{repr(found_v)}` for `ReduceLROnPlateau` parameter `{k}`. Expected `{repr(v)}`."
+
+ @pytest.mark.unit
+ def test_WarmupPolicy(self):
+ """Test WarmupPolicy"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = optim.lr_scheduler.WarmupPolicy(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] != self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ # Warmup steps available
+ policy = optim.lr_scheduler.WarmupPolicy(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 4:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] != self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_WarmupHoldPolicy(self):
+ """Test WarmupHoldPolicy"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = optim.lr_scheduler.WarmupHoldPolicy(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] != self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr <= self.MIN_LR:
+ raise AssertionError
+
+ # Warmup steps available
+ policy = optim.lr_scheduler.WarmupHoldPolicy(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 4:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] != self.INITIAL_LR:
+ raise AssertionError
+
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr <= self.MIN_LR:
+ raise AssertionError
+
+ # Warmup + Hold steps available
+ policy = optim.lr_scheduler.WarmupHoldPolicy(
+ opt, warmup_steps=5, hold_steps=3, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 4:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] != self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr < self.MIN_LR:
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_WarmupAnnealing(self):
+ """Test that the warmup annealing policy works as expected."""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = optim.lr_scheduler.WarmupAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr < self.MIN_LR:
+ raise AssertionError
+
+ # Warmup steps available
+ policy = optim.lr_scheduler.WarmupAnnealing(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 5:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] >= self.INITIAL_LR:
+ raise AssertionError
+
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ # Warmup + Hold steps available
+ policy = optim.lr_scheduler.WarmupHoldPolicy(
+ opt, warmup_steps=5, hold_steps=3, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 4:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] != self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr < self.MIN_LR:
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_SquareAnnealing(self):
+ """Test SquareAnnealing"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = optim.lr_scheduler.SquareAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ # Warmup steps available
+ policy = optim.lr_scheduler.SquareAnnealing(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 5:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] >= self.INITIAL_LR:
+ raise AssertionError
+
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_SquareRootAnnealing(self):
+ """Test SquareRootAnnealing"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = SquareRootAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ # Warmup steps available
+ policy = optim.lr_scheduler.SquareRootAnnealing(
+ opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 5:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] >= self.INITIAL_LR:
+ raise AssertionError
+
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_CosineAnnealing(self):
+ """Test CosineAnnealing"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = optim.lr_scheduler.CosineAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ # Warmup steps available
+ policy = optim.lr_scheduler.CosineAnnealing(opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 5:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] >= self.INITIAL_LR:
+ raise AssertionError
+
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ # Warmup + Constant steps available
+ policy = optim.lr_scheduler.CosineAnnealing(
+ opt, warmup_steps=3, constant_steps=2, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 3:
+ if policy.get_last_lr()[0] > self.INITIAL_LR + 1e-5:
+ raise AssertionError
+ elif 3 < i <= 8:
+ if policy.get_last_lr()[0] != policy._get_lr(i)[0]:
+ raise AssertionError
+ elif policy.get_last_lr()[0] != self.MIN_LR:
+ raise AssertionError
+
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ # Noam scheduler should decay past MAX_STEPS - run two schedulers in parallel to test it
+ @pytest.mark.unit
+ def test_NoamAnnealing(self):
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt1 = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+ opt2 = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy1 = optim.lr_scheduler.NoamAnnealing(
+ opt1, d_model=self.D_MODEL, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ policy2 = optim.lr_scheduler.NoamAnnealing(
+ opt2, d_model=self.D_MODEL, max_steps=self.MAX_STEPS * 2, min_lr=self.MIN_LR
+ )
+ initial_lr = policy1.get_last_lr()[0]
+
+ assert initial_lr == self.D_MODEL ** (-0.5) * self.INITIAL_LR
+
+ for _ in range(self.MAX_STEPS * 2):
+ if policy1.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ assert policy1.get_last_lr()[0] <= policy2.get_last_lr()[0]
+ opt1.step()
+ opt2.step()
+ policy1.step()
+ policy2.step()
+
+ # Warmup steps available
+ policy1 = optim.lr_scheduler.NoamAnnealing(
+ opt1, d_model=self.D_MODEL, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ policy2 = optim.lr_scheduler.NoamAnnealing(
+ opt2, d_model=self.D_MODEL, warmup_steps=5, max_steps=self.MAX_STEPS * 2, min_lr=self.MIN_LR
+ )
+ initial_lr = policy1.get_last_lr()[0]
+
+ assert initial_lr < self.INITIAL_LR
+
+ for i in range(self.MAX_STEPS * 2):
+ if i <= 5:
+ assert policy1.get_last_lr()[0] <= self.INITIAL_LR
+ else:
+ assert self.MIN_LR <= policy1.get_last_lr()[0] <= self.INITIAL_LR
+ assert policy1.get_last_lr()[0] <= policy2.get_last_lr()[0]
+
+ opt1.step()
+ opt2.step()
+ policy1.step()
+ policy2.step()
+
+ @pytest.mark.unit
+ def test_PolynomialDecayAnnealing(self):
+ """Test PolynomialDecayAnnealing"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = optim.lr_scheduler.PolynomialDecayAnnealing(
+ opt, power=2, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ # Warmup steps available
+ policy = optim.lr_scheduler.PolynomialDecayAnnealing(
+ opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 5:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] >= self.INITIAL_LR:
+ raise AssertionError
+
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_PolynomialHoldDecayAnnealing(self):
+ """Test PolynomialHoldDecayAnnealing"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = optim.lr_scheduler.PolynomialHoldDecayAnnealing(
+ opt, power=2, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr <= self.MIN_LR:
+ raise AssertionError
+
+ # Warmup steps available
+ policy = optim.lr_scheduler.PolynomialHoldDecayAnnealing(
+ opt, power=2, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr < self.MIN_LR:
+ raise AssertionError
+
+ # Warmup + Hold steps available
+ policy = optim.lr_scheduler.PolynomialHoldDecayAnnealing(
+ opt, warmup_steps=5, hold_steps=3, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR, power=2
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 4:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif i <= 8:
+ if policy.get_last_lr()[0] < self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr < self.MIN_LR:
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_InverseSquareRootAnnealing(self):
+ """Test InverseSquareRootAnnealing"""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = optim.lr_scheduler.InverseSquareRootAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ for _ in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ # Warmup steps available
+ policy = optim.lr_scheduler.InverseSquareRootAnnealing(
+ opt, warmup_steps=5, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR
+ )
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr >= self.INITIAL_LR:
+ raise AssertionError
+
+ for i in range(self.MAX_STEPS):
+ if i <= 5:
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ elif policy.get_last_lr()[0] >= self.INITIAL_LR:
+ raise AssertionError
+
+ opt.step()
+ policy.step()
+
+ policy.step()
+ final_lr = policy.get_last_lr()[0]
+
+ if final_lr != self.MIN_LR:
+ raise AssertionError
+
+ @pytest.mark.unit
+ def test_CosineAnnealing_with_noop_steps(self):
+ """Test CosineAnnealing with noop steps."""
+ model = TempModel()
+ opt_cls = get_optimizer("novograd")
+ opt = opt_cls(model.parameters(), lr=self.INITIAL_LR)
+
+ # No warmup case
+ policy = optim.lr_scheduler.CosineAnnealing(opt, max_steps=self.MAX_STEPS, min_lr=self.MIN_LR)
+ initial_lr = policy.get_last_lr()[0]
+
+ if initial_lr != self.INITIAL_LR:
+ raise AssertionError
+
+ update_steps = 0
+ for i in range(self.MAX_STEPS):
+ if policy.get_last_lr()[0] > self.INITIAL_LR:
+ raise AssertionError
+ opt.step()
+ policy.step()
+
+ # Perform a No-Op for scheduler every 2 steps
+ if i % 2 == 0:
+ policy.last_epoch -= 1
+ else:
+ update_steps += 1
+
+ policy.step()
+ update_steps += 1
+
+ if update_steps >= self.MAX_STEPS:
+ raise AssertionError
+
+ final_lr = policy.get_last_lr()[0]
+ if final_lr <= self.MIN_LR:
+ raise AssertionError
+
+ # update step = true number of updates performed after some number of skipped steps
+ true_end_lr = policy._get_lr(step=update_steps)[0]
+ if final_lr != true_end_lr:
+ raise AssertionError
+
+ @pytest.mark.unit
+ @pytest.mark.run_only_on("CPU")
+ def test_max_step_computation(self):
+ """Test max step computation."""
+
+ def train(
+ max_epochs, accumulate_grad_batches, limit_train_batches, devices, batch_size, dataset_len, drop_last
+ ):
+ trainer = pl.Trainer(
+ max_epochs=max_epochs,
+ strategy="ddp_spawn",
+ accelerator="cpu",
+ devices=devices,
+ accumulate_grad_batches=accumulate_grad_batches,
+ limit_train_batches=limit_train_batches,
+ enable_checkpointing=False,
+ )
+ max_steps = optim.lr_scheduler.compute_max_steps(
+ max_epochs,
+ accumulate_grad_batches,
+ limit_train_batches,
+ devices,
+ dataset_len,
+ batch_size,
+ drop_last,
+ )
+ model = ExampleModel(batch_size, dataset_len, drop_last, max_steps)
+ trainer.callbacks.append(Callback())
+ trainer.fit(model)
+
+ # This test will break once we and lightning upgrade to pytorch 1.7.0 due to a bug fix in pytorch 1.7.0
+ train(
+ 31,
+ accumulate_grad_batches=1,
+ limit_train_batches=1.0,
+ devices=9,
+ batch_size=60,
+ dataset_len=1613,
+ drop_last=True,
+ )
+ train(
+ 5,
+ accumulate_grad_batches=1,
+ limit_train_batches=1.0,
+ devices=4,
+ batch_size=97,
+ dataset_len=498,
+ drop_last=False,
+ )
+ train(
+ 5,
+ accumulate_grad_batches=8,
+ limit_train_batches=1.0,
+ devices=4,
+ batch_size=54,
+ dataset_len=629,
+ drop_last=True,
+ )
+ train(
+ 5,
+ accumulate_grad_batches=1,
+ limit_train_batches=1.0,
+ devices=1,
+ batch_size=68,
+ dataset_len=488,
+ drop_last=False,
+ )
+ for _ in range(5):
+ drop_last = bool(random.randint(0, 1))
+ accumulate_grad_batches = random.randint(1, 10)
+
+ limit_train_batches_int = random.randint(1, 10)
+ limit_train_batches_float = 1.0
+ limit_train_batches = random.choice([limit_train_batches_int, limit_train_batches_float])
+ max_epochs = random.randint(4, 20)
+ devices = random.randint(1, 5)
+ dataset_len = random.randint(20, devices * 500)
+ batch_size = random.randint(math.ceil(5.0 / devices), min(dataset_len // devices, 128))
+ train(
+ max_epochs,
+ accumulate_grad_batches,
+ limit_train_batches,
+ devices,
+ batch_size,
+ dataset_len,
+ drop_last,
+ )
+
+ @pytest.mark.unit
+ @pytest.mark.run_only_on("CPU")
+ def test_max_step_computation_with_sched_no_ops(self):
+ """Test that max_step is computed correctly when scheduler has no_ops"""
+
+ def train(
+ max_steps, accumulate_grad_batches, limit_train_batches, num_processes, batch_size, dataset_len, drop_last
+ ):
+ """Set up trainer and model"""
+ trainer = pl.Trainer(
+ max_steps=max_steps,
+ strategy="ddp_spawn",
+ accelerator="cpu",
+ accumulate_grad_batches=accumulate_grad_batches,
+ limit_train_batches=limit_train_batches,
+ enable_checkpointing=False,
+ )
+ model = ExampleModel(batch_size, dataset_len, drop_last, max_steps)
+ trainer.callbacks.append(SchedulerNoOpCallback())
+ trainer.fit(model)
+
+ # This test will break once we and lightning upgrade to pytorch 1.7.0 due to a bug fix in pytorch 1.7.0
+ train(
+ max_steps=20,
+ accumulate_grad_batches=1,
+ limit_train_batches=1.0,
+ num_processes=4,
+ batch_size=60,
+ dataset_len=2000,
+ drop_last=True,
+ )
+
+ @staticmethod
+ def test_remove_logs_left():
+ """Remove logs left by the trainer."""
+ if os.path.exists(os.path.join(os.getcwd(), "lightning_logs")):
+ shutil.rmtree(os.path.join(os.getcwd(), "lightning_logs"))
diff --git a/tests/core/test_save_restore.py b/tests/core/test_save_restore.py
new file mode 100644
index 00000000..a6936150
--- /dev/null
+++ b/tests/core/test_save_restore.py
@@ -0,0 +1,377 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/tests/core/test_save_restore.py
+
+import filecmp
+import os
+import shutil
+import tempfile
+from typing import Dict, Optional, Set, Union
+
+import pytest
+import pytorch_lightning as pl
+import torch
+from omegaconf import DictConfig, OmegaConf, open_dict
+
+from atommic.core.classes.modelPT import ModelPT
+from atommic.core.connectors import save_restore_connector
+from atommic.utils.app_state import AppState
+
+
+def classpath(cls):
+ return f"{cls.__module__}.{cls.__name__}"
+
+
+def get_dir_size(path="."):
+ total = 0
+ with os.scandir(path) as it:
+ for entry in it:
+ if entry.is_file():
+ total += entry.stat().st_size
+ elif entry.is_dir():
+ total += get_dir_size(entry.path)
+ return total
+
+
+def get_size(path="."):
+ if os.path.isfile(path):
+ return os.path.getsize(path)
+ if os.path.isdir(path):
+ return get_dir_size(path)
+
+
+def getattr2(object, attr):
+ if "." not in attr:
+ return getattr(object, attr)
+ arr = attr.split(".")
+ return getattr2(getattr(object, arr[0]), ".".join(arr[1:]))
+
+
+class MockModel(ModelPT):
+ def __init__(self, cfg):
+ trainer = OmegaConf.create(
+ {
+ "strategy": "ddp",
+ "accelerator": "cpu",
+ "num_nodes": 1,
+ "max_epochs": 20,
+ "precision": 32,
+ "enable_checkpointing": False,
+ "logger": False,
+ "log_every_n_steps": 50,
+ "check_val_every_n_epoch": -1,
+ "max_steps": -1,
+ }
+ )
+ trainer = OmegaConf.create(OmegaConf.to_container(trainer, resolve=True))
+ trainer = pl.Trainer(**trainer)
+ super().__init__(cfg=cfg, trainer=trainer)
+ self.w = torch.nn.Linear(10, 1)
+ # mock temp file
+ if "temp_file" in self.cfg and self.cfg.temp_file is not None:
+ self.setup_data_from_file(self.cfg.temp_file)
+ else:
+ self.temp_file = None
+ self.temp_data = None
+
+ def setup_data_from_file(self, temp_file):
+ """Load data from temp_file to `self.temp_data`. Allows to test changing resource after instantiation."""
+ with open_dict(self.cfg):
+ self.cfg.temp_file = temp_file
+ self.temp_file = self.register_artifact("temp_file", self.cfg.temp_file)
+ with open(self.temp_file, "r", encoding="utf-8") as f:
+ self.temp_data = f.readlines()
+
+ def change_stub_number(self, new_number: int):
+ """Change stub number in config, useful for testing nested models, since child can mutate config independently."""
+ self.cfg.stub_number = new_number
+
+ def forward(self, x):
+ y = self.w(x)
+ return y, self.cfg.temp_file
+
+ def setup_training_data(self, train_data_config: Union[DictConfig, Dict]):
+ self._train_dl = None
+
+ def setup_validation_data(self, val_data_config: Union[DictConfig, Dict]):
+ self._validation_dl = None
+
+ def setup_test_data(self, test_data_config: Union[DictConfig, Dict]):
+ self._test_dl = None
+
+ @classmethod
+ def list_available_models(cls):
+ return []
+
+
+def _mock_model_config():
+ conf = {"temp_file": None, "target": classpath(MockModel)}
+ conf = OmegaConf.create({"model": conf})
+ OmegaConf.set_struct(conf, True)
+ return conf
+
+
+class TestSaveRestore:
+ @staticmethod
+ def __test_restore_elsewhere(
+ model: ModelPT,
+ attr_for_eq_check: Set[str] = None,
+ override_config_path: Optional[Union[str, DictConfig]] = None,
+ map_location: Optional[torch.device] = None,
+ strict: bool = False,
+ return_config: bool = False,
+ ):
+ """Test's logic:
+ 1. Save model into temporary folder (save_folder)
+ 2. Copy .atommic file from save_folder to restore_folder
+ 3. Delete save_folder
+ 4. Attempt to restore from .atommic file in restore_folder and compare to original instance
+ """
+ # Create a new temporary directory
+ with tempfile.TemporaryDirectory() as restore_folder:
+ with tempfile.TemporaryDirectory() as save_folder:
+ save_folder_path = save_folder
+ # Where model will be saved
+ model_save_path = os.path.join(save_folder, f"{model.__class__.__name__}.atommic")
+ model.save_to(save_path=model_save_path)
+ # Where model will be restored from
+ model_restore_path = os.path.join(restore_folder, f"{model.__class__.__name__}.atommic")
+ shutil.copy(model_save_path, model_restore_path)
+ # at this point save_folder should not exist
+ assert save_folder_path is not None and not os.path.exists(save_folder_path)
+ assert not os.path.exists(model_save_path)
+ assert os.path.exists(model_restore_path)
+ # attempt to restore
+ model_copy, _ = model.__class__.restore_from(
+ restore_path=model_restore_path,
+ map_location=map_location,
+ strict=strict,
+ return_config=return_config,
+ override_config_path=override_config_path,
+ )
+
+ if return_config:
+ return model_copy
+
+ assert model.num_weights == model_copy.num_weights
+ if attr_for_eq_check is not None and attr_for_eq_check:
+ for attr in attr_for_eq_check:
+ assert getattr2(model, attr) == getattr2(model_copy, attr)
+
+ return model_copy
+
+ @pytest.mark.unit
+ def test_mock_restore_from_config_override_with_OmegaConf(self):
+ with tempfile.NamedTemporaryFile("w") as empty_file:
+ # Write some data
+ empty_file.writelines(["*****\n"])
+ empty_file.flush()
+
+ # Update config
+ cfg = _mock_model_config()
+ cfg.model.temp_file = empty_file.name
+
+ # Create model
+ model = MockModel(cfg=cfg.model)
+ model = model.to("cpu")
+
+ assert model.temp_file == empty_file.name
+
+ # Inject arbitrary config arguments (after creating model)
+ with open_dict(cfg.model):
+ cfg.model.xyz = "abc"
+
+ # Save test (with overriden config as OmegaConf object)
+ model_copy = self.__test_restore_elsewhere(model, map_location="cpu", override_config_path=cfg)
+
+ # Restore test
+ diff = model.w.weight - model_copy.w.weight
+ assert diff.mean() <= 1e-9
+ assert model_copy.temp_data == ["*****\n"]
+
+ # Test that new config has arbitrary content
+ assert model_copy.cfg.xyz == "abc"
+
+ @pytest.mark.unit
+ def test_mock_restore_from_config_override_with_yaml(self):
+ with tempfile.NamedTemporaryFile("w") as empty_file, tempfile.NamedTemporaryFile("w") as config_file:
+ # Write some data
+ empty_file.writelines(["*****\n"])
+ empty_file.flush()
+
+ # Update config
+ cfg = _mock_model_config()
+ cfg.model.temp_file = empty_file.name
+
+ # Create model
+ model = MockModel(cfg=cfg.model)
+ model = model.to("cpu")
+
+ assert model.temp_file == empty_file.name
+
+ # Inject arbitrary config arguments (after creating model)
+ with open_dict(cfg.model):
+ cfg.model.xyz = "abc"
+
+ # Write new config into file
+ OmegaConf.save(cfg, config_file)
+
+ # Save test (with overriden config as OmegaConf object)
+ model_copy = self.__test_restore_elsewhere(
+ model, map_location="cpu", override_config_path=config_file.name
+ )
+
+ # Restore test
+ diff = model.w.weight - model_copy.w.weight
+ assert diff.mean() <= 1e-9
+ assert filecmp.cmp(model.temp_file, model_copy.temp_file)
+ assert model_copy.temp_data == ["*****\n"]
+
+ # Test that new config has arbitrary content
+ assert model_copy.cfg.xyz == "abc"
+
+ @pytest.mark.unit
+ def test_mock_save_to_multiple_times(self):
+ with tempfile.NamedTemporaryFile("w") as empty_file, tempfile.TemporaryDirectory() as tmpdir:
+ # Write some data
+ empty_file.writelines(["*****\n"])
+ empty_file.flush()
+
+ # Update config
+ cfg = _mock_model_config()
+ cfg.model.temp_file = empty_file.name
+
+ # Create model
+ model = MockModel(cfg=cfg.model) # type: MockModel
+ model = model.to("cpu")
+
+ assert model.temp_file == empty_file.name
+
+ # Save test
+ model.save_to(os.path.join(tmpdir, "save_0.atommic"))
+ model.save_to(os.path.join(tmpdir, "save_1.atommic"))
+ model.save_to(os.path.join(tmpdir, "save_2.atommic"))
+
+ @pytest.mark.unit
+ def test_multiple_model_save_restore_connector(self):
+ class MySaveRestoreConnector(save_restore_connector.SaveRestoreConnector):
+ def save_to(self, model, save_path: str):
+ save_path = save_path.replace(".atommic", "_XYZ.atommic")
+ super(MySaveRestoreConnector, self).save_to(model, save_path)
+
+ with tempfile.TemporaryDirectory() as tmpdir:
+ # Update config
+ cfg = _mock_model_config()
+
+ # Create model
+ model = MockModel(cfg=cfg.model)
+ model_with_custom_connector = MockModel(cfg=cfg.model)
+ model_with_custom_connector._save_restore_connector = MySaveRestoreConnector()
+ model_with_custom_connector.save_to(os.path.join(tmpdir, "save_custom.atommic"))
+
+ assert os.path.exists(os.path.join(tmpdir, "save_custom_XYZ.atommic"))
+ assert isinstance(model._save_restore_connector, save_restore_connector.SaveRestoreConnector)
+ assert isinstance(model_with_custom_connector._save_restore_connector, MySaveRestoreConnector)
+
+ assert type(MockModel._save_restore_connector) is save_restore_connector.SaveRestoreConnector
+
+ @pytest.mark.unit
+ def test_restore_from_save_restore_connector(self):
+ class MySaveRestoreConnector(save_restore_connector.SaveRestoreConnector):
+ def save_to(self, model, save_path: str):
+ save_path = save_path.replace(".atommic", "_XYZ.atommic")
+ super().save_to(model, save_path)
+
+ class MockModelV2(MockModel):
+ pass
+
+ with tempfile.TemporaryDirectory() as tmpdir:
+ # Update config
+ cfg = _mock_model_config()
+
+ # Create model
+ save_path = os.path.join(tmpdir, "save_custom.atommic")
+ model_with_custom_connector = MockModel(cfg=cfg.model)
+ model_with_custom_connector._save_restore_connector = MySaveRestoreConnector()
+ model_with_custom_connector.save_to(save_path)
+
+ assert os.path.exists(os.path.join(tmpdir, "save_custom_XYZ.atommic"))
+
+ restored_model, _ = MockModelV2.restore_from(
+ save_path.replace(".atommic", "_XYZ.atommic"), save_restore_connector=MySaveRestoreConnector()
+ )
+ assert type(restored_model) is MockModelV2
+
+ @pytest.mark.unit
+ def test_mock_model_model_collision(self):
+ # The usual pipeline is working just fine.
+ cfg = _mock_model_config()
+
+ model = MockModel(cfg=cfg.model) # type: MockModel
+ model = model.to("cpu")
+
+ # Let's create a custom config with a 'model.model' node.
+ cfg = _mock_model_config()
+ OmegaConf.set_struct(cfg, False)
+ cfg.model.model = "aaa"
+ OmegaConf.set_struct(cfg, True)
+
+ # Failing due to collision.
+ with pytest.raises(ValueError, match="Creating model config node is forbidden"):
+ model = MockModel(cfg=cfg.model) # type: MockModel
+ model = model.to("cpu")
+
+ @pytest.mark.unit
+ def test_restore_from_save_restore_connector_extracted_dir(self):
+ class MySaveRestoreConnector(save_restore_connector.SaveRestoreConnector):
+ def save_to(self, model, save_path: str):
+ save_path = save_path.replace(".atommic", "_XYZ.atommic")
+ super().save_to(model, save_path)
+
+ class MockModelV2(MockModel):
+ pass
+
+ with tempfile.TemporaryDirectory() as extracted_tempdir:
+ with tempfile.TemporaryDirectory() as tmpdir:
+ # Update config
+ cfg = _mock_model_config()
+
+ # Create model
+ save_path = os.path.join(tmpdir, "save_custom.atommic")
+ model_with_custom_connector = MockModel(cfg=cfg.model)
+ model_with_custom_connector._save_restore_connector = MySaveRestoreConnector()
+ model_with_custom_connector.save_to(save_path)
+
+ atommic_filepath = os.path.join(tmpdir, "save_custom_XYZ.atommic")
+ assert os.path.exists(atommic_filepath)
+
+ # extract the contents to this dir apriori
+ # simulate by extracting now before calling restore_from
+ connector = MySaveRestoreConnector()
+ MySaveRestoreConnector._unpack_atommic_file(atommic_filepath, extracted_tempdir)
+ assert get_size(extracted_tempdir) > 0
+
+ # delete the old directory and preserve only the new extracted directory (escape scope of old dir)
+
+ # next, set the model's extracted directory path
+ connector.model_extracted_dir = extracted_tempdir
+
+ # note, we pass in the "old" atommic_filepath, stored somewhere other than the extracted directory
+ # this atommic_filepath is no longer valid, and has been deleted.
+ restored_model, _ = MockModelV2.restore_from(atommic_filepath, save_restore_connector=connector)
+ assert type(restored_model) is MockModelV2
+
+ # assert models have correct restoration information and paths
+ appstate = AppState()
+ original_metadata = appstate.get_model_metadata_from_guid(model_with_custom_connector.model_guid)
+ assert original_metadata.restoration_path is None
+
+ restored_metadata = appstate.get_model_metadata_from_guid(restored_model.model_guid)
+ assert restored_metadata.restoration_path is not None
+
+ # assert that the restore path was the path of the pre-extracted directory
+ # irrespective of whether an old `atommic_filepath` (which doesn't exist anymore) was passed to restore_from.
+ assert extracted_tempdir in restored_metadata.restoration_path
+ assert extracted_tempdir not in atommic_filepath
+ assert not os.path.exists(atommic_filepath)
diff --git a/tests/core/test_serialization.py b/tests/core/test_serialization.py
new file mode 100644
index 00000000..a3c105e7
--- /dev/null
+++ b/tests/core/test_serialization.py
@@ -0,0 +1,45 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/tests/core/test_serialization.py
+
+import pytest
+from omegaconf import DictConfig
+
+from atommic.core.classes.common import Serialization
+
+
+def get_class_path(cls):
+ return f"{cls.__module__}.{cls.__name__}"
+
+
+class MockSerializationImpl(Serialization):
+ def __init__(self, cfg: DictConfig):
+ self.cfg = cfg
+ self.value = self.__class__.__name__
+
+
+class MockSerializationImplV2(MockSerializationImpl):
+ pass
+
+
+class TestSerialization:
+ @pytest.mark.unit
+ def test_self_class_instantiation(self):
+ # Target class is V1 impl, calling class is V1 (same class)
+ config = DictConfig({"target": get_class_path(MockSerializationImpl)})
+ obj = MockSerializationImpl.from_config_dict(config=config) # Serialization is base class
+ new_config = obj.to_config_dict()
+ assert config == new_config
+ assert isinstance(obj, MockSerializationImpl)
+ assert obj.value == "MockSerializationImpl"
+
+ @pytest.mark.unit
+ def test_sub_class_instantiation(self):
+ # Target class is V1 impl, calling class is V2 (sub class)
+ config = DictConfig({"target": get_class_path(MockSerializationImpl)})
+ obj = MockSerializationImplV2.from_config_dict(config=config) # Serialization is base class
+ new_config = obj.to_config_dict()
+ assert config == new_config
+ assert isinstance(obj, MockSerializationImplV2)
+ assert obj.value == "MockSerializationImplV2"
diff --git a/tests/core/test_typecheck.py b/tests/core/test_typecheck.py
new file mode 100644
index 00000000..ed29ddd7
--- /dev/null
+++ b/tests/core/test_typecheck.py
@@ -0,0 +1,28 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/tests/core/test_typecheck.py
+
+from atommic.core.neural_types.comparison import NeuralTypeComparisonResult
+
+
+def recursive_assert_shape(x, shape):
+ """Perform recursive shape assert"""
+ if isinstance(x, (list, tuple)):
+ for xi in x:
+ recursive_assert_shape(xi, shape)
+ return
+
+ if x.shape != shape:
+ raise AssertionError
+
+
+def recursive_assert_homogeneous_type(x, type_val):
+ """Perform recursive type homogeneous assert"""
+ if isinstance(x, (list, tuple)):
+ for xi in x:
+ recursive_assert_homogeneous_type(xi, type_val)
+ return
+
+ if x.neural_type.compare(type_val) != NeuralTypeComparisonResult.SAME:
+ raise AssertionError
diff --git a/tests/hydra/__init__.py b/tests/hydra/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/hydra/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/hydra/config1.yaml b/tests/hydra/config1.yaml
new file mode 100644
index 00000000..51361875
--- /dev/null
+++ b/tests/hydra/config1.yaml
@@ -0,0 +1 @@
+dataset_name: tmp
diff --git a/tests/hydra/config1_invalid.yaml b/tests/hydra/config1_invalid.yaml
new file mode 100644
index 00000000..bb52367e
--- /dev/null
+++ b/tests/hydra/config1_invalid.yaml
@@ -0,0 +1,2 @@
+dataset_name: tmp
+password: tmp
diff --git a/tests/hydra/config_subdir/__init__.py b/tests/hydra/config_subdir/__init__.py
new file mode 100644
index 00000000..d7c23048
--- /dev/null
+++ b/tests/hydra/config_subdir/__init__.py
@@ -0,0 +1,2 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
diff --git a/tests/hydra/config_subdir/config2.yaml b/tests/hydra/config_subdir/config2.yaml
new file mode 100644
index 00000000..51361875
--- /dev/null
+++ b/tests/hydra/config_subdir/config2.yaml
@@ -0,0 +1 @@
+dataset_name: tmp
diff --git a/tests/hydra/config_subdir/config2_invalid.yaml b/tests/hydra/config_subdir/config2_invalid.yaml
new file mode 100644
index 00000000..bb52367e
--- /dev/null
+++ b/tests/hydra/config_subdir/config2_invalid.yaml
@@ -0,0 +1,2 @@
+dataset_name: tmp
+password: tmp
diff --git a/tests/hydra/my_app.py b/tests/hydra/my_app.py
new file mode 100644
index 00000000..858c1719
--- /dev/null
+++ b/tests/hydra/my_app.py
@@ -0,0 +1,33 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/tests/hydra/my_app.py
+
+from dataclasses import dataclass
+
+from omegaconf import MISSING, OmegaConf
+
+from atommic.core.conf.hydra_runner import hydra_runner
+
+
+@dataclass
+class DefaultConfig:
+ """This is structured config for this application. It provides the schema used for validation of user-written \
+ spec file as well as default values of the selected parameters."""
+
+ dataset_name: str = MISSING
+
+
+@hydra_runner(config_name="DefaultConfig", schema=DefaultConfig)
+def my_app(cfg):
+ """
+ This is the main application entry point. It is decorated with hydra_runner which takes care of parsing the
+ command line arguments, instantiating the config object and running the application.
+ """
+ print(OmegaConf.to_yaml(cfg))
+ # Get dataset_name.
+ dataset_name = cfg.dataset_name
+
+
+if __name__ == "__main__":
+ my_app()
diff --git a/tests/hydra/test_hydra_runner.py b/tests/hydra/test_hydra_runner.py
new file mode 100644
index 00000000..6342b059
--- /dev/null
+++ b/tests/hydra/test_hydra_runner.py
@@ -0,0 +1,82 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+# Taken and adapted from: https://github.com/NVIDIA/NeMo/blob/main/tests/hydra/test_hydra_runner.py
+
+import subprocess
+import sys
+from os import path
+
+import pytest
+
+
+class TestHydraRunner:
+ @pytest.mark.integration
+ def test_no_config(self):
+ """ "Test app without config - fields missing causes error."""
+ # Create system call.
+ call = "python my_app.py"
+
+ with pytest.raises(subprocess.CalledProcessError):
+ # Run the call as subprocess.
+ subprocess.check_call(call, shell=True, stdout=sys.stdout, stderr=sys.stdout)
+
+ @pytest.mark.integration
+ def test_config1(self):
+ """ "Test injection of valid config1."""
+ # Create system call.
+ call = "python my_app.py --config-name config1.yaml"
+
+ # Run the call as subprocess.
+ with pytest.raises(subprocess.CalledProcessError):
+ subprocess.check_call(call, shell=True, stdout=sys.stdout, stderr=sys.stdout)
+
+ # Make sure that .hydra dir is not present.
+ assert not path.exists(f".hydra")
+ # Make sure that default hydra log file is not present.
+ assert not path.exists(f"my_app.log")
+
+ @pytest.mark.integration
+ def test_config1_invalid(self):
+ """ "Test injection of invalid config1."""
+ # Create system call.
+ call = "python my_app.py --config-name config1_invalid.yaml"
+
+ with pytest.raises(subprocess.CalledProcessError):
+ # Run the call as subprocess.
+ subprocess.check_call(call, shell=True, stdout=sys.stdout, stderr=sys.stdout)
+
+ @pytest.mark.integration
+ def test_config2(self):
+ """ "Test injection of valid config2 from a different folder."""
+ # Create system call.
+ call = "python my_app.py --config-path config_subdir --config-name config2.yaml"
+
+ # Run the call as subprocess.
+ with pytest.raises(subprocess.CalledProcessError):
+ subprocess.check_call(call, shell=True, stdout=sys.stdout, stderr=sys.stdout)
+
+ # Make sure that .hydra dir is not present.
+ assert not path.exists(f".hydra")
+ # Make sure that default hydra log file is not present.
+ assert not path.exists(f"my_app.log")
+
+ @pytest.mark.integration
+ def test_config2_invalid(self):
+ """ "Test injection of invalid config2 from a different folder."""
+ # Create system call.
+ call = "python my_app.py --config-path config_subdir --config-name config2_invalid.yaml"
+
+ with pytest.raises(subprocess.CalledProcessError):
+ # Run the call as subprocess.
+ subprocess.check_call(call, shell=True, stdout=sys.stdout, stderr=sys.stdout)
+
+ @pytest.mark.integration
+ def test_config2_filepath_schema(self):
+ """ "Test injection of valid config2 - using namepath with schema is prohibited."""
+ # Create system call.
+ call = "python my_app.py --config-name config_subdir/config2_invalid.yaml"
+
+ with pytest.raises(subprocess.CalledProcessError):
+ # Run the call as subprocess.
+ subprocess.check_call(call, shell=True, stdout=sys.stdout, stderr=sys.stdout)
diff --git a/tools/evaluation/qmapping.py b/tools/evaluation/qmapping.py
new file mode 100644
index 00000000..6f8c01e1
--- /dev/null
+++ b/tools/evaluation/qmapping.py
@@ -0,0 +1,164 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import json
+import os
+from pathlib import Path
+
+import h5py
+import numpy as np
+from runstats import Statistics
+from tqdm import tqdm
+
+from atommic.collections.common.parts import center_crop
+from atommic.collections.reconstruction.metrics.reconstruction_metrics import mse, nmse, psnr, ssim
+
+METRIC_FUNCS = {"MSE": mse, "NMSE": nmse, "PSNR": psnr, "SSIM": ssim}
+
+
+class qMRIMetrics:
+ """Maintains running statistics for a given collection of metrics."""
+
+ def __init__(self, metric_funcs):
+ """Inits :class:`qMRIMetrics`.
+
+ Parameters
+ ----------
+ metric_funcs : dict
+ A dict where the keys are metric names and the values are Python functions for evaluating that metric.
+ """
+ self.metrics_scores = {metric: Statistics() for metric in metric_funcs}
+
+ def push(self, x, y):
+ """Pushes a new batch of metrics to the running statistics.
+
+ Parameters
+ ----------
+ x : np.ndarray
+ Target image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+ y : np.ndarray
+ Predicted image. It must be a 3D array, where the first dimension is the number of slices. In case of 2D
+ images, the first dimension should be 1.
+
+ Returns
+ -------
+ dict
+ A dict where the keys are metric names and the values are the computed metric scores.
+ """
+ for metric, func in METRIC_FUNCS.items():
+ self.metrics_scores[metric].push(func(x, y))
+
+ def means(self):
+ """Mean of the means of each metric."""
+ return {metric: stat.mean() for metric, stat in self.metrics_scores.items()}
+
+ def stddevs(self):
+ """Standard deviation of the means of each metric."""
+ return {metric: stat.stddev() for metric, stat in self.metrics_scores.items()}
+
+ def __repr__(self):
+ """Representation of the metrics."""
+ means = self.means()
+ stddevs = self.stddevs()
+ metric_names = sorted(list(means))
+
+ res = " ".join(f"{name} = {means[name]:.4g} +/- {2 * stddevs[name]:.4g}" for name in metric_names) + "\n"
+
+ return res
+
+
+def main(args):
+ # if json file
+ if args.targets_dir.endswith(".json"):
+ with open(args.targets_dir, "r", encoding="utf-8") as f:
+ targets = json.load(f)
+ targets = [Path(target) for target in targets]
+ else:
+ targets = list(Path(args.targets_dir).iterdir())
+
+ crop_size = args.crop_size
+ evaluation_type = args.evaluation_type
+
+ scores = qMRIMetrics(METRIC_FUNCS)
+ for target in tqdm(targets):
+ fname = str(target).rsplit("/", maxsplit=1)[-1]
+
+ target = h5py.File(target, "r")["qmaps"][()].squeeze()
+ prediction = h5py.File(Path(args.predictions_dir) / fname, "r")["qmaps"][()].squeeze()
+ anatomy_mask = h5py.File(Path(args.segmentations_masks_dir) / fname, "r")["anatomy_mask"][()].squeeze()
+
+ if crop_size is not None:
+ crop_size[0] = target.shape[-2] if target.shape[-2] < int(crop_size[0]) else int(crop_size[0])
+ crop_size[1] = target.shape[-1] if target.shape[-1] < int(crop_size[1]) else int(crop_size[1])
+ crop_size[0] = prediction.shape[-2] if prediction.shape[-2] < int(crop_size[0]) else int(crop_size[0])
+ crop_size[1] = prediction.shape[-1] if prediction.shape[-1] < int(crop_size[1]) else int(crop_size[1])
+
+ target = center_crop(target, crop_size)
+ prediction = center_crop(prediction, crop_size)
+ anatomy_mask = center_crop(anatomy_mask, crop_size)
+
+ # normalize per slice
+ for i in range(target.shape[0]):
+ # normalize per echo
+ for j in range(target.shape[1]):
+ target[i, j] = np.abs(target[i, j] / np.max(np.abs(target[i, j]))) * anatomy_mask[i]
+ prediction[i, j] = np.abs(prediction[i, j] / np.max(np.abs(prediction[i, j]))) * anatomy_mask[i]
+
+ if evaluation_type == "per_slice":
+ for sl in range(target.shape[0]):
+ for qmap_idx in range(prediction.shape[1]):
+ scores.push(target[sl, qmap_idx], prediction[sl, qmap_idx])
+ elif evaluation_type == "per_volume":
+ for qmap_idx in range(prediction.shape[1]):
+ scores.push(target[:, qmap_idx], prediction[:, qmap_idx])
+
+ model = args.predictions_dir.split("/")
+ model = model[-4] if model[-4] != "default" else model[-5]
+ print(f"{model}: {repr(scores)}")
+
+ if args.output_dir is not None:
+ output_dir = Path(args.output_dir)
+ output_dir.mkdir(parents=True, exist_ok=True)
+ # if file exists dont' overwrite, but append in a new line
+ with open(output_dir / "results.txt", "a", encoding="utf-8") as f:
+ f.write(f"{model}: {repr(scores)}\n")
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("targets_dir", type=str)
+ parser.add_argument("segmentations_masks_dir", type=str)
+ parser.add_argument("predictions_dir", type=str)
+ parser.add_argument("--output_dir", type=str)
+ parser.add_argument("--crop_size", nargs="+", type=int)
+ parser.add_argument("--evaluation_type", choices=["per_slice", "per_volume"], default="per_slice")
+ parser.add_argument("--fill_target_path", action="store_true")
+ parser.add_argument("--fill_pred_path", action="store_true")
+ args = parser.parse_args()
+
+ if args.fill_target_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.targets_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ # check if after dir we have "/reconstructions" or "/predictions" dir
+ if os.path.exists(os.path.join(input_dir, "reconstructions")):
+ args.targets_dir = os.path.join(input_dir, "reconstructions")
+ elif os.path.exists(os.path.join(input_dir, "predictions")):
+ args.targets_dir = os.path.join(input_dir, "predictions")
+
+ if args.fill_pred_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.predictions_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ # check if after dir we have "/reconstructions" or "/predictions" dir
+ if os.path.exists(os.path.join(input_dir, "reconstructions")):
+ args.predictions_dir = os.path.join(input_dir, "reconstructions")
+ elif os.path.exists(os.path.join(input_dir, "predictions")):
+ args.predictions_dir = os.path.join(input_dir, "predictions")
+
+ main(args)
diff --git a/tools/evaluation/reconstruction.py b/tools/evaluation/reconstruction.py
new file mode 100644
index 00000000..927c357d
--- /dev/null
+++ b/tools/evaluation/reconstruction.py
@@ -0,0 +1,143 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import json
+import os
+from pathlib import Path
+
+import h5py
+import numpy as np
+from tqdm import tqdm
+
+from atommic.collections.common.parts import center_crop, is_none
+from atommic.collections.reconstruction.metrics.reconstruction_metrics import (
+ ReconstructionMetrics,
+ mse,
+ nmse,
+ psnr,
+ ssim,
+)
+
+METRIC_FUNCS = {"MSE": mse, "NMSE": nmse, "PSNR": psnr, "SSIM": ssim}
+
+
+def main(args): # noqa: MC0001
+ # if json file
+ if args.targets_dir.endswith(".json"):
+ with open(args.targets_dir, "r", encoding="utf-8") as f:
+ targets = json.load(f)
+ targets = [Path(target) for target in targets]
+ else:
+ targets = list(Path(args.targets_dir).iterdir())
+
+ crop_size = args.crop_size
+ evaluation_type = args.evaluation_type
+
+ scores = ReconstructionMetrics(METRIC_FUNCS)
+ for target in tqdm(targets):
+ reconstruction = h5py.File(Path(args.reconstructions_dir) / str(target).rsplit("/", maxsplit=1)[-1], "r")[
+ "reconstruction"
+ ][()].squeeze()
+
+ target_file = h5py.File(target, "r")
+ if "reconstruction_sense" in target_file.keys():
+ target = target_file["reconstruction_sense"][()].squeeze()
+ elif "reconstruction_rss" in target_file.keys():
+ target = target_file["reconstruction_rss"][()].squeeze()
+ elif "reconstruction" in target_file.keys():
+ target = target_file["reconstruction"][()].squeeze()
+ else:
+ target = target_file["target"][()].squeeze()
+
+ if crop_size is not None:
+ crop_size[0] = target.shape[-2] if target.shape[-2] < int(crop_size[0]) else int(crop_size[0])
+ crop_size[1] = target.shape[-1] if target.shape[-1] < int(crop_size[1]) else int(crop_size[1])
+ crop_size[0] = (
+ reconstruction.shape[-2] if reconstruction.shape[-2] < int(crop_size[0]) else int(crop_size[0])
+ )
+ crop_size[1] = (
+ reconstruction.shape[-1] if reconstruction.shape[-1] < int(crop_size[1]) else int(crop_size[1])
+ )
+
+ target = center_crop(target, crop_size)
+ reconstruction = center_crop(reconstruction, crop_size)
+
+ if "stanford_fullysampled" in args.targets_dir.lower():
+ # remove the first 20 and the last 20 slices
+ target = target[20:-20]
+ reconstruction = reconstruction[20:-20]
+
+ # check if any flipping is needed
+ if not is_none(args.flip_target):
+ if args.flip_target == "left_right":
+ target = np.flip(target, axis=-1)
+ elif args.flip_target == "up_down":
+ target = np.flip(target, axis=-2)
+ elif args.flip_target == "both":
+ target = np.flip(np.flip(target, axis=-1), axis=-2)
+
+ if not is_none(args.flip_reconstruction):
+ if args.flip_reconstruction == "left_right":
+ reconstruction = np.flip(reconstruction, axis=-1)
+ elif args.flip_reconstruction == "up_down":
+ reconstruction = np.flip(reconstruction, axis=-2)
+ elif args.flip_reconstruction == "both":
+ reconstruction = np.flip(np.flip(reconstruction, axis=-1), axis=-2)
+
+ # normalize per slice
+ for sl in range(target.shape[0]):
+ target[sl] = target[sl] / np.max(np.abs(target[sl]))
+ reconstruction[sl] = reconstruction[sl] / np.max(np.abs(reconstruction[sl]))
+ target = np.abs(target)
+ reconstruction = np.abs(reconstruction)
+
+ maxvalue = max(np.max(target) - np.min(target), np.max(reconstruction) - np.min(reconstruction))
+
+ if evaluation_type == "per_slice":
+ for sl in range(target.shape[0]):
+ scores.push(target[sl], reconstruction[sl], maxval=maxvalue)
+ elif evaluation_type == "per_volume":
+ scores.push(target, reconstruction, maxval=maxvalue)
+
+ model = args.reconstructions_dir.split("/")
+ model = model[-4] if model[-4] != "default" else model[-5]
+ print(f"{model}: {repr(scores)}")
+
+ if args.output_dir is not None:
+ output_dir = Path(args.output_dir)
+ output_dir.mkdir(parents=True, exist_ok=True)
+ # if file exists dont' overwrite, but append in a new line
+ with open(output_dir / "results.txt", "a", encoding="utf-8") as f:
+ f.write(f"{model}: {repr(scores)}\n")
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("targets_dir", type=str)
+ parser.add_argument("reconstructions_dir", type=str)
+ parser.add_argument("--output_dir", type=str)
+ parser.add_argument("--crop_size", nargs="+", type=int)
+ parser.add_argument("--flip_target", choices=["left_right", "up_down", "both", "none"], default="none")
+ parser.add_argument("--flip_reconstruction", choices=["left_right", "up_down", "both", "none"], default="none")
+ parser.add_argument("--evaluation_type", choices=["per_slice", "per_volume"], default="per_slice")
+ parser.add_argument("--fill_target_path", action="store_true")
+ parser.add_argument("--fill_pred_path", action="store_true")
+ args = parser.parse_args()
+
+ if args.fill_target_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.targets_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ args.targets_dir = os.path.join(input_dir, "reconstructions")
+
+ if args.fill_pred_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.reconstructions_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ args.reconstructions_dir = os.path.join(input_dir, "reconstructions")
+
+ main(args)
diff --git a/tools/evaluation/segmentation.py b/tools/evaluation/segmentation.py
new file mode 100644
index 00000000..bf916134
--- /dev/null
+++ b/tools/evaluation/segmentation.py
@@ -0,0 +1,170 @@
+# coding=utf-8
+__author__ = "Dimitris Karkalousos"
+
+import json
+import os
+from pathlib import Path
+
+import h5py
+import nibabel as nib
+import numpy as np
+from tqdm import tqdm
+
+from atommic.collections.common.parts import center_crop
+from atommic.collections.segmentation.metrics.segmentation_metrics import (
+ SegmentationMetrics,
+ binary_cross_entropy_with_logits_metric,
+ dice_metric,
+ f1_per_class_metric,
+ hausdorff_distance_95_metric,
+ iou_metric,
+ precision_metric,
+ recall_metric,
+)
+
+METRIC_FUNCS = {
+ "BCE": binary_cross_entropy_with_logits_metric,
+ "DICE": dice_metric,
+ "F1": f1_per_class_metric,
+ "HD95": lambda x, y: hausdorff_distance_95_metric(x, y, batched=False, sum_method="sum"),
+ "IOU": iou_metric,
+ "PRE": precision_metric,
+ "REC": recall_metric,
+}
+
+
+def main(args): # noqa: MC0001
+ if args.flatten_dice:
+ METRIC_FUNCS["DICE"] = lambda x, y: dice_metric(x, y, flatten=True)
+
+ # if json file
+ if args.targets_dir.endswith(".json"):
+ with open(args.targets_dir, "r", encoding="utf-8") as f:
+ targets = json.load(f)
+ targets = [Path(target) for target in targets]
+ else:
+ targets = list(Path(args.targets_dir).iterdir())
+
+ crop_size = args.crop_size
+ dataset_format = args.dataset_format
+ evaluation_type = args.evaluation_type
+
+ scores = SegmentationMetrics(METRIC_FUNCS)
+ for target in tqdm(targets):
+ fname = str(target).rsplit("/", maxsplit=1)[-1]
+ if ".h5" in fname:
+ fname = fname.split(".h5")[0]
+ elif ".nii" in fname:
+ fname = fname.split(".nii")[0]
+
+ predictions = h5py.File(Path(args.segmentations_dir) / fname, "r")["segmentation"][()].squeeze()
+ predictions = np.abs(predictions.astype(np.float32))
+ predictions = np.where(predictions > 0.5, 1, 0)
+ if args.sum_classes_method == "argmax":
+ predictions = np.where(predictions.argmax(axis=1) > 0.5, 1, 0)
+ elif args.sum_classes_method == "sum":
+ predictions = np.where(predictions.sum(axis=1) > 0.5, 1, 0)
+
+ if dataset_format == 'skm-tea':
+ with h5py.File(target, "r") as hf:
+ segmentation_labels = hf["seg"][()].squeeze()
+
+ # combine label 2 and 3 (Lateral Tibial Cartilage and Medial Tibial Cartilage)
+ tibial_cartilage = segmentation_labels[..., 2] + segmentation_labels[..., 3]
+ # combine label 4 and 5 (Lateral Meniscus and Medial Meniscus)
+ medial_meniscus = segmentation_labels[..., 4] + segmentation_labels[..., 5]
+
+ # stack the labels
+ target = np.stack(
+ [segmentation_labels[..., 0], segmentation_labels[..., 1], tibial_cartilage, medial_meniscus],
+ axis=0,
+ )
+ target = np.moveaxis(target, -1, 0)[30:-31]
+ elif dataset_format == 'brats':
+ target = str(target).replace("TrainingData", "TrainingSegmentations").replace(".nii.gz", "-seg.nii.gz")
+
+ target = np.moveaxis(nib.load(target).get_fdata(), -1, 0)
+ # remove the first 50 and last 65 slices
+ target = target[51:-65]
+
+ # Necrotic Tumor Core (NCR - label 1)
+ ncr = np.zeros_like(target)
+ ncr[target == 1] = 1
+ # Peritumoral Edematous/Invaded Tissue (ED - label 2)
+ ed = np.zeros_like(target)
+ ed[target == 2] = 1
+ # GD-Enhancing Tumor (ET - label 3)
+ et = np.zeros_like(target)
+ et[target == 3] = 1
+ # Whole Tumor (WT โ label 1, 2, or 3)
+ wt = np.zeros_like(target)
+ wt[target != 0] = 1
+ target = np.stack([ncr, ed, et, wt], axis=1).astype(np.float32)
+
+ target = np.abs(target.astype(np.float32))
+ target = np.where(target > 0.5, 1, 0)
+ if args.sum_classes_method == "argmax":
+ target = np.where(target.argmax(axis=1) > 0.5, 1, 0)
+ elif args.sum_classes_method == "sum":
+ target = np.where(target.sum(axis=1) > 0.5, 1, 0)
+
+ if crop_size is not None:
+ crop_size[0] = target.shape[-2] if target.shape[-2] < int(crop_size[0]) else int(crop_size[0])
+ crop_size[1] = target.shape[-1] if target.shape[-1] < int(crop_size[1]) else int(crop_size[1])
+ crop_size[0] = predictions.shape[-2] if predictions.shape[-2] < int(crop_size[0]) else int(crop_size[0])
+ crop_size[1] = predictions.shape[-1] if predictions.shape[-1] < int(crop_size[1]) else int(crop_size[1])
+
+ target = center_crop(target, crop_size)
+ predictions = center_crop(predictions, crop_size)
+
+ if evaluation_type == "per_slice":
+ target = np.expand_dims(target, axis=1)
+ predictions = np.expand_dims(predictions, axis=1)
+ for sl in range(target.shape[0]):
+ scores.push(target[sl], predictions[sl])
+ elif evaluation_type == "per_volume":
+ scores.push(target, predictions)
+
+ model = args.segmentations_dir.split("/")
+ model = model[-4] if model[-4] != "default" else model[-5]
+ print(f"{model}: {repr(scores)}")
+
+ if args.output_dir is not None:
+ output_dir = Path(args.output_dir)
+ output_dir.mkdir(parents=True, exist_ok=True)
+ # if file exists dont' overwrite, but append in a new line
+ with open(output_dir / "results.txt", "a", encoding="utf-8") as f:
+ f.write(f"{model}: {repr(scores)}\n")
+
+
+if __name__ == "__main__":
+ import argparse
+
+ parser = argparse.ArgumentParser()
+ parser.add_argument("targets_dir", type=str)
+ parser.add_argument("segmentations_dir", type=str)
+ parser.add_argument("--output_dir", type=str)
+ parser.add_argument("--crop_size", nargs="+", type=int)
+ parser.add_argument("--dataset_format", choices=["skm-tea", "brats", "private"], default="private")
+ parser.add_argument("--evaluation_type", choices=["per_slice", "per_volume"], default="per_slice")
+ parser.add_argument("--sum_classes_method", choices=["sum", "argmax", "none"], default="none")
+ parser.add_argument("--flatten_dice", action="store_true")
+ parser.add_argument("--fill_target_path", action="store_true")
+ parser.add_argument("--fill_pred_path", action="store_true")
+ args = parser.parse_args()
+
+ if args.fill_target_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.targets_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ args.targets_dir = os.path.join(input_dir, "segmentations")
+
+ if args.fill_pred_path:
+ input_dir = ""
+ for root, dirs, files in os.walk(args.segmentations_dir, topdown=False):
+ for name in dirs:
+ input_dir = os.path.join(root, name)
+ args.segmentations_dir = os.path.join(input_dir, "segmentations")
+
+ main(args)
diff --git a/tutorials/00_ATOMMIC_Primer.ipynb b/tutorials/00_ATOMMIC_Primer.ipynb
new file mode 100644
index 00000000..1fc828f9
--- /dev/null
+++ b/tutorials/00_ATOMMIC_Primer.ipynb
@@ -0,0 +1,1008 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "7LfkL2r2Q1tr"
+ },
+ "source": [
+ "# Getting Started: ATOMMIC Fundamentals\n",
+ "\n",
+ "Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC), is a toolbox for applying AI methods for accelerated MRI reconstruction (REC), MRI segmentation (SEG), quantitative MR imaging (qMRI), as well as multitask learning (MTL), i.e. performing multiple tasks simultaneously, such as reconstruction and segmentation. \n",
+ "\n",
+ "Each task is implemented in a separate collection, which consists of data loaders, transformations, models, metrics, and losses. A\n",
+ "\n",
+ "ATOMMIC is designed to be modular and extensible, and it is easy to add new tasks, models, and datasets. \n",
+ "\n",
+ "ATOMMIC uses PyTorch Lightning for feasible high-performance multi-GPU/multi-node mixed-precision training."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "zLSy94NEQi-e",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:34.411357Z",
+ "end_time": "2024-03-05T17:15:34.789474Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.\n",
+ "\n",
+ "Instructions for setting up Colab are as follows:\n",
+ "1. Open a new Python 3 notebook.\n",
+ "2. Import this notebook from GitHub (File -> Upload Notebook -> \"GITHUB\" tab -> copy/paste GitHub URL)\n",
+ "3. Connect to an instance with a GPU (Runtime -> Change runtime type -> select \"GPU\" for hardware accelerator)\n",
+ "4. Run this cell to set up dependencies.\n",
+ "\"\"\"\n",
+ "# If you're using Google Colab and not running locally, run this cell.\n",
+ "\n",
+ "## Install dependencies\n",
+ "!pip install wget\n",
+ "!apt-get install sox libsndfile1 ffmpeg\n",
+ "!pip install text-unidecode\n",
+ "\n",
+ "# ## Install ATOMMIC\n",
+ "BRANCH = 'main'\n",
+ "!python -m pip install git+https://github.com/wdika/atommic.git@$BRANCH\n",
+ "\n",
+ "## Grab the config we'll use in this example\n",
+ "!mkdir configs"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6G2TZkaxcM0e"
+ },
+ "source": [
+ "## Foundations of ATOMMIC\n",
+ "---------\n",
+ "\n",
+ "ATOMMIC models leverage [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning) Module, and are compatible with the entire PyTorch ecosystem. This means that users have the full flexibility of using the higher level APIs provided by PyTorch Lightning (via Trainer), or write their own training and evaluation loops in PyTorch directly (by simply calling the model and the individual components of the model).\n",
+ "\n",
+ "For ATOMMIC developers, a \"Model\" is the neural network(s) as well as all the infrastructure supporting those network(s), wrapped into a singular, cohesive unit. As such, all ATOMMIC models are constructed to contain the following out of the box (at the bare minimum, some models support additional functionality too!) -\n",
+ "\n",
+ " - Neural Network architecture - all the modules that are required for the model.\n",
+ "\n",
+ " - Dataset + Data Loaders - all the components that prepare the data for consumption during training or evaluation.\n",
+ "\n",
+ " - Preprocessing + Postprocessing - all the components that process the datasets so they can easily be consumed by the modules.\n",
+ "\n",
+ " - Optimizer + Schedulers - basic defaults that work out of the box, and allow further experimentation with ease.\n",
+ "\n",
+ " - Any other supporting infrastructure - transforms, etc."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XxAwtqWBQrNk",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:34.424372Z",
+ "end_time": "2024-03-05T17:15:36.022312Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import atommic\n",
+ "atommic.__version__"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "H01SHfKQh-gV"
+ },
+ "source": [
+ "## ATOMMIC Collections\n",
+ "\n",
+ "ATOMMIC is subdivided into a few fundamental collections based on their domains - `mtl`, `qmri`, `rec`, `seg`. When you performed the `import atommic` statement above, none of the above collections were imported. This is because you might not need all the collections at once, so ATOMMIC allows partial imports of just one or more collection, as and when you require them.\n",
+ "\n",
+ "-------\n",
+ "Let's import the above four collections - "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "J09NNa8fhth7",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:34.452760Z",
+ "end_time": "2024-03-05T17:15:40.345082Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import atommic.collections.multitask.rs as atommic_mtlrs\n",
+ "import atommic.collections.quantitative as atommic_qmri\n",
+ "import atommic.collections.reconstruction as atommic_rec\n",
+ "import atommic.collections.segmentation as atommic_seg"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bSvYoeBrjPza"
+ },
+ "source": [
+ "## ATOMMIC Models in Collections\n",
+ "\n",
+ "ATOMMIC contains several models for each of its collections. At a brief glance, let's look at all the Models that ATOMMIC offers for the above 4 collections."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "9LbbC_92i41f",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:40.375023Z",
+ "end_time": "2024-03-05T17:15:40.382132Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "mtlrs_models = [model for model in dir(atommic_mtlrs.nn) if not model.startswith(\"__\") and not model.islower() and not \"Block\" in model]\n",
+ "mtlrs_models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "t5_ax9Z8j9FC",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:40.375183Z",
+ "end_time": "2024-03-05T17:15:40.382689Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "qmri_models = [model for model in dir(atommic_qmri.nn) if not model.startswith(\"__\") and not model.islower()]\n",
+ "qmri_models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "bQdR6RJdkezq",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:40.375286Z",
+ "end_time": "2024-03-05T17:15:40.382949Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "rec_models = [model for model in dir(atommic_rec.nn) if not model.startswith(\"__\") and not model.islower()]\n",
+ "rec_models"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:40.375373Z",
+ "end_time": "2024-03-05T17:15:40.383267Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "seg_models = [model for model in dir(atommic_seg.nn) if not model.startswith(\"__\") and not model.islower()]\n",
+ "seg_models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iWKxKQnSkj9Z"
+ },
+ "source": [
+ "## The ATOMMIC Model\n",
+ "\n",
+ "Let's dive deeper into what a ATOMMIC model really is. There are many ways we can create these models - we can use the constructor and pass in a config, we can instantiate the model from a pre-trained checkpoint, or simply pass a pre-trained model name and instantiate a model directly from the cloud !\n",
+ "\n",
+ "---------\n",
+ "For now, let's try to work with a reconstruction UNet model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "MODEL_NAME = 'REC_UNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM'"
+ ],
+ "metadata": {
+ "collapsed": false,
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:40.378568Z",
+ "end_time": "2024-03-05T17:15:40.383377Z"
+ }
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "n-XOQaW1kh3v",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:40.383787Z",
+ "end_time": "2024-03-05T17:15:42.857353Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "rec_unet, state_dict = atommic_rec.nn.UNet.from_pretrained(f'https://huggingface.co/wdika/{MODEL_NAME}/blob/main/{MODEL_NAME}.atommic')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "YP4X7KVPli6g",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.864206Z",
+ "end_time": "2024-03-05T17:15:42.869760Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "rec_unet.summarize()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MB91Swu0pIKr"
+ },
+ "source": [
+ "## Model Configuration using OmegaConf\n",
+ "--------\n",
+ "\n",
+ "So we could download, instantiate and analyse the high level structure of the `UNet` model in a few lines! Now let's delve deeper into the configuration file that makes the model work.\n",
+ "\n",
+ "First, we import [OmegaConf](https://omegaconf.readthedocs.io/en/latest/). OmegaConf is an excellent library that is used throughout ATOMMIC in order to enable us to perform yaml configuration management more easily. Additionally, it plays well with another library, [Hydra](https://hydra.cc/docs/intro/), that is used by ATOMMIC to perform on the fly config edits from the command line, dramatically boosting ease of use of our config files !"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "RkgrDJvumFER",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.874500Z",
+ "end_time": "2024-03-05T17:15:42.906568Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from omegaconf import OmegaConf"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CktakfBluA56"
+ },
+ "source": [
+ "All ATOMMIC models come packaged with their model configuration inside the `cfg` attribute. While technically it is meant to be config declaration of the model as it has been currently constructed, `cfg` is an essential tool to modify the behaviour of the Model after it has been constructed. It can be safely used to make it easier to perform many essential tasks inside Models. \n",
+ "\n",
+ "To be doubly sure, we generally work on a copy of the config until we are ready to edit it inside the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ISd6z7sXt9Mm",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.881896Z",
+ "end_time": "2024-03-05T17:15:42.907059Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import copy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "N2_SiLHRve8A",
+ "scrolled": true,
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.905897Z",
+ "end_time": "2024-03-05T17:15:42.993088Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "cfg = copy.deepcopy(rec_unet.cfg)\n",
+ "print(OmegaConf.to_yaml(cfg))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "sIwhdXkwxn6R"
+ },
+ "source": [
+ "## Modifying the contents of the Model config\n",
+ "----------\n",
+ "\n",
+ "Say we want to experiment with a different scheduler to this model during training. \n",
+ "\n",
+ "OmegaConf makes this a very simple task for us!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "WlSZ8EA4yGKo",
+ "scrolled": false,
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.947768Z",
+ "end_time": "2024-03-05T17:15:43.011584Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# OmegaConf won't allow you to add new config items, so we temporarily disable this safeguard.\n",
+ "OmegaConf.set_struct(cfg, False)\n",
+ "\n",
+ "# Let's see the old optim config\n",
+ "print(\"Old Config: \")\n",
+ "print(OmegaConf.to_yaml(cfg.optim))\n",
+ "\n",
+ "sched = {'name': 'InverseSquareRootAnnealing', 'warmup_steps': 1000, 'min_lr': 1e-6}\n",
+ "sched = OmegaConf.create(sched) # Convert it into a DictConfig\n",
+ "\n",
+ "# Assign it to cfg.optim.sched namespace\n",
+ "cfg.optim.sched = sched\n",
+ "\n",
+ "# Let's see the new optim config\n",
+ "print(\"New Config: \")\n",
+ "print(OmegaConf.to_yaml(cfg.optim))\n",
+ "\n",
+ "# Here, we restore the safeguards so no more additions can be made to the config\n",
+ "OmegaConf.set_struct(cfg, True)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-nMDN66502kn"
+ },
+ "source": [
+ "## Updating the model from config\n",
+ "----------\n",
+ "\n",
+ "ATOMMIC Models can be updated in a few ways, but we follow similar patterns within each collection so as to maintain consistency.\n",
+ "\n",
+ "Here, we will show the two most common ways to modify core components of the model - using the `from_config_dict` method, and updating a few special parts of the model.\n",
+ "\n",
+ "Remember, all ATOMMIC models are PyTorch Lightning modules, which themselves are PyTorch modules, so we have a lot of flexibility here!"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dsxQHBV86R4a",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.947859Z",
+ "end_time": "2024-03-05T17:15:43.012014Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Update the model config\n",
+ "rec_unet.cfg = cfg"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eXRRBnJk5tCv"
+ },
+ "source": [
+ "## Update a few special components of the Model\n",
+ "---------\n",
+ "\n",
+ "While the above approach is good for most major components of the model, ATOMMIC has special utilities for a few components.\n",
+ "\n",
+ "They are - \n",
+ "\n",
+ " - `setup_training_data`\n",
+ " - `setup_validation_data` and `setup_multi_validation_data`\n",
+ " - `setup_test_data` and `setup_multi_test_data`\n",
+ " - `setup_optimization`\n",
+ "\n",
+ "These special utilities are meant to help you easily setup training, validation, testing once you restore a model from a checkpoint."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1hXXdaup-QmG"
+ },
+ "source": [
+ "Let's discuss how to add the scheduler to the model below (which initially had just an optimizer in its config)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "cveKWvMZ4zBo",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.947928Z",
+ "end_time": "2024-03-05T17:15:43.012370Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Let's print out the current optimizer\n",
+ "print(OmegaConf.to_yaml(rec_unet.cfg.optim))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "XVguw3k0-f6b",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.947995Z",
+ "end_time": "2024-03-05T17:15:43.013805Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Now let's update the config\n",
+ "rec_unet.setup_optimization(cfg.optim)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1JZBCQeW-21X"
+ },
+ "source": [
+ "-------\n",
+ "We see a warning - \n",
+ "\n",
+ "```\n",
+ "Neither `max_steps` nor `iters_per_batch` were provided to `optim.sched`, cannot compute effective `max_steps` !\n",
+ " Scheduler will not be instantiated !\n",
+ "```\n",
+ "\n",
+ "We don't have a train dataset setup, nor do we have max_steps in the config. Most ATOMMIC schedulers cannot be instantiated without computing how many train steps actually exist!\n",
+ "\n",
+ "Here, we can temporarily allow the scheduler construction by explicitly passing a max_steps value to be 100"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "mqC89hfE-tqf",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.988298Z",
+ "end_time": "2024-03-05T17:15:43.013948Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "OmegaConf.set_struct(cfg.optim.sched, False)\n",
+ "\n",
+ "cfg.optim.sched.max_steps = 100\n",
+ "\n",
+ "OmegaConf.set_struct(cfg.optim.sched, True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "r22IqOBK_q6l",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.988537Z",
+ "end_time": "2024-03-05T17:15:43.014331Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Now let's update the config and try again\n",
+ "rec_unet.setup_optimization(cfg.optim)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "U7Eezf_sAVS0"
+ },
+ "source": [
+ "You might wonder why we didnt explicitly set `rec_unet.cfg.optim = cfg.optim`. \n",
+ "\n",
+ "This is because the `setup_optimization()` method does it for you! You can still update the config manually."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "THqhXy_lQ7i8"
+ },
+ "source": [
+ "### Optimizer & Scheduler Config\n",
+ "\n",
+ "Optimizers and schedulers are common components of models, and are essential to train the model from scratch.\n",
+ "\n",
+ "They are grouped together under a unified `optim` namespace, as schedulers often operate on a given optimizer.\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6HY51nuoSJs5"
+ },
+ "source": [
+ "### Let's breakdown the general `optim` structure\n",
+ "```yaml\n",
+ "optim:\n",
+ " name: novograd\n",
+ " lr: 0.01\n",
+ "\n",
+ " # optimizer arguments\n",
+ " betas: [0.8, 0.25]\n",
+ " weight_decay: 0.001\n",
+ "\n",
+ " # scheduler setup\n",
+ " sched:\n",
+ " name: CosineAnnealing\n",
+ "\n",
+ " # Optional arguments\n",
+ " max_steps: -1 # computed at runtime or explicitly set here\n",
+ " monitor: val_loss\n",
+ " reduce_on_plateau: false\n",
+ "\n",
+ " # scheduler config override\n",
+ " warmup_steps: 1000\n",
+ " warmup_ratio: null\n",
+ " min_lr: 1e-9\n",
+ "```\n",
+ "\n",
+ "Essential Optimizer components - \n",
+ "\n",
+ " - `name`: String name of the optimizer. Generally a lower case of the class name.\n",
+ " - `lr`: Learning rate is a required argument to all optimizers.\n",
+ "\n",
+ "Optional Optimizer components - after the above two arguments are provided, any additional arguments added under `optim` will be passed to the constructor of that optimizer as keyword arguments\n",
+ "\n",
+ " - `betas`: List of beta values to pass to the optimizer\n",
+ " - `weight_decay`: Optional weight decay passed to the optimizer.\n",
+ "\n",
+ "Optional Scheduler components - `sched` is an optional setup of the scheduler for the given optimizer.\n",
+ "\n",
+ "If `sched` is provided, only one essential argument needs to be provided : \n",
+ "\n",
+ " - `name`: The name of the scheduler. Generally, it is the full class name.\n",
+ "\n",
+ "Optional Scheduler components - \n",
+ "\n",
+ " - `max_steps`: Max steps as an override from the user. If one provides `trainer.max_steps` inside the trainer configuration, that value is used instead. If neither value is set, the scheduler will attempt to compute the `effective max_steps` using the size of the train data loader. If that too fails, then the scheduler will not be created at all.\n",
+ "\n",
+ " - `monitor`: Used if you are using an adaptive scheduler such as ReduceLROnPlateau. Otherwise ignored. Defaults to `loss` - indicating train loss as monitor.\n",
+ "\n",
+ " - `reduce_on_plateau`: Required to be set to true if using an adaptive scheduler.\n",
+ "\n",
+ "Any additional arguments under `sched` will be supplied as keyword arguments to the constructor of the scheduler.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZKURHn0jH_52"
+ },
+ "source": [
+ "## Creating Model from constructor vs restoring a model\n",
+ "---------\n",
+ "\n",
+ "You might notice, we discuss all of the above setup methods in the context of model after it is restored. However, ATOMMIC scripts do not call them inside any of the example train scripts themselves.\n",
+ "\n",
+ "This is because these methods are automatically called by the constructor when the Model is created for the first time, but these methods are skipped during restoration (either from a PyTorch Lightning checkpoint using `load_from_checkpoint`, or via `restore_from` method inside ATOMMIC Models).\n",
+ "\n",
+ "This is done as most datasets are stored on a user's local directory, and the path to these datasets is set in the config (either set by default, or set by Hydra overrides). On the other hand, the models are meant to be portable. On another user's system, the data might not be placed at exactly the same location, or even on the same drive as specified in the model's config!\n",
+ "\n",
+ "Therefore we allow the constructor some brevity and automate such dataset setup, whereas restoration warns that data loaders were not set up and provides the user with ways to set up their own datasets.\n",
+ "\n",
+ "------\n",
+ "\n",
+ "Why are optimizers not restored automatically? Well, optimizers themselves don't face an issue, but as we saw before, schedulers depend on the number of train steps in order to calculate their schedule.\n",
+ "\n",
+ "However, if you don't wish to modify the optimizer and scheduler, and prefer to leave them to their default values, that's perfectly alright. The `setup_optimization()` method is automatically called by PyTorch Lightning for you when you begin training your model!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "g91FE8mlMcnh"
+ },
+ "source": [
+ "## Saving and restoring models\n",
+ "----------\n",
+ "\n",
+ "ATOMMIC provides a few ways to save and restore models. If you utilize the Experiment Manager that is part of all ATOMMIC train scripts, PyTorch Lightning will automatically save checkpoints for you in the experiment directory.\n",
+ "\n",
+ "We can also use packaged files using the specialized `save_to` and `restore_from` methods."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NzMxga7QNYn8"
+ },
+ "source": [
+ "### Saving and Restoring from PTL Checkpoints\n",
+ "----------\n",
+ "\n",
+ "The PyTorch Lightning Trainer object will periodically save checkpoints when the experiment manager is being used during training.\n",
+ "\n",
+ "PyTorch Lightning checkpoints can then be loaded and evaluated / fine-tuned just as always using the class method `load_from_checkpoint`.\n",
+ "\n",
+ "For example, restore a UNet model from a checkpoint - \n",
+ "\n",
+ "```python\n",
+ "rec_unet = atommic_rec.nn.UNet.load_from_checkpoint()\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "W4YzAG-KOBkZ"
+ },
+ "source": [
+ "### Saving and Restoring from .atommic files\n",
+ "----------\n",
+ "\n",
+ "There are a few models which might require external dependencies to be packaged with them in order to restore them properly.\n",
+ "\n",
+ "We can use the `save_to` and `restore_from` method to package the entire model + its components into a tarfile. This can then be easily imported by the user and used to restore the model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "P6_vMSwXNJ74",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:42.989197Z",
+ "end_time": "2024-03-05T17:15:43.397854Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Save the model\n",
+ "rec_unet.save_to('rec_unet.atommic')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "HrBhgaqyP4rU",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:43.400602Z",
+ "end_time": "2024-03-05T17:15:43.603182Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "!ls -d -- *.atommic "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "id": "Tyht1E0DQGb_",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:43.605302Z",
+ "end_time": "2024-03-05T17:15:44.334372Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Restore the model\n",
+ "temp_unet, _ = atommic_rec.nn.UNet.restore_from('rec_unet.atommic')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dqNpmYYJQS2H",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:44.339580Z",
+ "end_time": "2024-03-05T17:15:44.344737Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "temp_unet.summarize()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "id": "A5e42EoiZYjf",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:44.345301Z",
+ "end_time": "2024-03-05T17:15:44.418056Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# Note that the preprocessor + optimizer config have been preserved after the changes we made !\n",
+ "print(OmegaConf.to_yaml(temp_unet.cfg))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "OI3RxwpcV-UF"
+ },
+ "source": [
+ "Note, that .atommic file is a simple .tar.gz with checkpoint, configuration and, potentially, other artifacts being used by the model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "jFBAGcaDWLiu",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:44.420425Z",
+ "end_time": "2024-03-05T17:15:45.141962Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "!cp rec_unet.atommic rec_unet.tar.gz\n",
+ "!tar -xvf rec_unet.tar.gz"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mkau4Q9jZo1l"
+ },
+ "source": [
+ "### Extracting PyTorch checkpoints from ATOMMIC tarfiles (Model level)\n",
+ "-----------\n",
+ "\n",
+ "While the .atommic tarfile is an excellent way to have a portable model, sometimes it is necessary for researchers to have access to the basic PyTorch save format. ATOMMIC aims to be entirely compatible with PyTorch, and therefore offers a simple method to extract just the PyTorch checkpoint from the .atommic tarfile."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "qccPANeycCoq",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:45.144638Z",
+ "end_time": "2024-03-05T17:15:45.147403Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import torch"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "A4zswOKHar9q",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:45.150272Z",
+ "end_time": "2024-03-05T17:15:45.870284Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "state_dict = temp_unet.extract_state_dict_from('rec_unet.atommic', save_dir='./pt_ckpt/')\n",
+ "!ls ./pt_ckpt/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ACB-0dfnbFG3"
+ },
+ "source": [
+ "As we can see below, there is now a single basic PyTorch checkpoint available inside the `pt_ckpt` directory, which we can use to load the weights of the entire model as below"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "4ZAF_A0uc5bB",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:45.872698Z",
+ "end_time": "2024-03-05T17:15:46.065625Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "temp_unet.load_state_dict(torch.load('./pt_ckpt/model_weights.ckpt'))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Hkq6EM99cS6y"
+ },
+ "source": [
+ "### Extracting PyTorch checkpoints from ATOMMIC tarfiles (Module level)\n",
+ "----------\n",
+ "\n",
+ "While the above method is exceptional when extracting the checkpoint of the entire model, sometimes there may be a necessity to load and save the individual modules that comprise the Model.\n",
+ "\n",
+ "The same extraction method offers a flag to extract the individual model level checkpoints into their individual files, so that users have access to per-module level checkpoints."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "LW6wve2zbT9D",
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:46.067984Z",
+ "end_time": "2024-03-05T17:15:46.687742Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "state_dict = temp_unet.extract_state_dict_from('rec_unet.atommic', save_dir='./pt_module_ckpt/', split_by_module=True)\n",
+ "!ls ./pt_module_ckpt/"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "88vOGV7VYcuu"
+ },
+ "source": [
+ "# ATOMMIC with Hydra\n",
+ "\n",
+ "[Hydra](https://hydra.cc/docs/intro/) is used throughout ATOMMIC as a way to enable rapid prototyping using predefined config files. Hydra and OmegaConf offer great compatibility with each other when using ATOMMIC."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Optionally you might want to remove any generated files"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:46.693895Z",
+ "end_time": "2024-03-05T17:15:46.696056Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import shutil"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:46.698191Z",
+ "end_time": "2024-03-05T17:15:46.700517Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "current_directory = os.getcwd()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:46.704713Z",
+ "end_time": "2024-03-05T17:15:46.845746Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# List all files in the folder\n",
+ "all_files = os.listdir(current_directory)\n",
+ "\n",
+ "# List all files and directories in the folder\n",
+ "for root, dirs, files in os.walk(current_directory, topdown=False):\n",
+ " for filename in files:\n",
+ " file_path = os.path.join(root, filename)\n",
+ " if not filename.endswith(\".ipynb\"):\n",
+ " os.remove(file_path)\n",
+ " for dir_name in dirs:\n",
+ " dir_path = os.path.join(root, dir_name)\n",
+ " if not any(file.endswith(\".ipynb\") for file in os.listdir(dir_path)):\n",
+ " shutil.rmtree(dir_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:15:46.849188Z",
+ "end_time": "2024-03-05T17:15:46.889136Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# remove .ipynb checkpoints\n",
+ "for root, dirs, files in os.walk(current_directory, topdown=False):\n",
+ " for dir_name in dirs:\n",
+ " if dir_name == \".ipynb_checkpoints\":\n",
+ " checkpoint_dir = os.path.join(root, dir_name)\n",
+ " shutil.rmtree(checkpoint_dir)"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "00_NeMo_Primer.ipynb",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/tutorials/01_ATOMMIC_MRI_transforms.ipynb b/tutorials/01_ATOMMIC_MRI_transforms.ipynb
new file mode 100644
index 00000000..3072370a
--- /dev/null
+++ b/tutorials/01_ATOMMIC_MRI_transforms.ipynb
@@ -0,0 +1,1406 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### This notebook demonstrates the use of the ATOMMIC transforms on the Calgary-Campinas 359 dataset.\n",
+ "\n",
+ "#### Important! You need to have downloaded the CC359 dataset to properly run the notebook. For more information, please read [here](projects/REC/CC359/README.md)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:25.215109Z",
+ "end_time": "2024-03-05T17:22:29.928171Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import h5py\n",
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "from atommic.collections.common.parts import fft, utils\n",
+ "from atommic.collections.common.parts.transforms import (\n",
+ " Composer,\n",
+ " Cropper,\n",
+ " EstimateCoilSensitivityMaps,\n",
+ " GeometricDecompositionCoilCompression,\n",
+ " N2R,\n",
+ " NoisePreWhitening,\n",
+ " SSDU,\n",
+ " SNREstimator,\n",
+ " ZeroFillingPadding\n",
+ ")\n",
+ "from atommic.collections.motioncorrection.parts.motionsimulation import MotionSimulation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Specify path where CC359 data are downloaded"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:29.927842Z",
+ "end_time": "2024-03-05T17:22:39.482939Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "parent_data_path = input(\"Please enter the (downloaded) data path: \")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Specify data paths specific to CC359"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:39.448803Z",
+ "end_time": "2024-03-05T17:22:39.483423Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "subject = 'e14110s3_P59904.7.h5'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:39.456564Z",
+ "end_time": "2024-03-05T17:22:39.483515Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data_path = f'{parent_data_path}/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val/{subject}'\n",
+ "mask_path = f'{parent_data_path}/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val/{subject}'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Read the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:39.463315Z",
+ "end_time": "2024-03-05T17:22:40.352931Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# load the k-space\n",
+ "kspace = h5py.File(data_path)['kspace'][()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:40.361817Z",
+ "end_time": "2024-03-05T17:22:40.365185Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "(256, 218, 170, 24)"
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kspace.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:40.367549Z",
+ "end_time": "2024-03-05T17:22:42.107177Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# necessary operation for the CC359 dataset\n",
+ "kspace = np.moveaxis(kspace[..., ::2] + 1j * kspace[..., 1::2], -1, 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.108344Z",
+ "end_time": "2024-03-05T17:22:42.115905Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "(256, 12, 218, 170)"
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kspace.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.113803Z",
+ "end_time": "2024-03-05T17:22:42.125656Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# load the masks\n",
+ "mask = h5py.File(mask_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.131522Z",
+ "end_time": "2024-03-05T17:22:42.192734Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mask.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.138138Z",
+ "end_time": "2024-03-05T17:22:42.193362Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# pick a mask\n",
+ "mask_5x = mask['mask_5x'][()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.152897Z",
+ "end_time": "2024-03-05T17:22:42.193739Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "(256, 218, 170)"
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mask_5x.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.158665Z",
+ "end_time": "2024-03-05T17:22:42.193858Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# pick a slice\n",
+ "slice_idx = 100\n",
+ "kspace = kspace[slice_idx]\n",
+ "mask_5x = mask_5x[slice_idx]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.163204Z",
+ "end_time": "2024-03-05T17:22:42.308163Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# transform to tensor\n",
+ "kspace = utils.to_tensor(kspace)\n",
+ "mask_5x = utils.to_tensor(mask_5x).unsqueeze(0).unsqueeze(-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.308148Z",
+ "end_time": "2024-03-05T17:22:42.314605Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "masked_kspace = kspace * mask_5x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.319624Z",
+ "end_time": "2024-03-05T17:22:42.321882Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize general parameters for transformations\n",
+ "fft_centered = False\n",
+ "fft_normalization = 'backward'\n",
+ "spatial_dims = (-2, -1)\n",
+ "coil_dim = 0\n",
+ "\n",
+ "start = 10\n",
+ "patch_length = 30 + start\n",
+ "patch_size = [start, patch_length, start, patch_length]\n",
+ "\n",
+ "num_coils = kspace.shape[coil_dim]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.324392Z",
+ "end_time": "2024-03-05T17:22:42.326648Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the SNR estimator to compare the images along transformations\n",
+ "snr_estimator = SNREstimator(\n",
+ " patch_size=patch_size,\n",
+ " apply_ifft=False,\n",
+ " fft_centered=fft_centered,\n",
+ " fft_normalization=fft_normalization,\n",
+ " spatial_dims=spatial_dims,\n",
+ " coil_dim=coil_dim,\n",
+ " multicoil=True,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:42.332368Z",
+ "end_time": "2024-03-05T17:22:43.873097Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# apply the IFFT\n",
+ "imspace = fft.ifft2(kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "masked_imspace = fft.ifft2(masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "imspace = imspace / torch.max(torch.abs(imspace))\n",
+ "masked_imspace = masked_imspace / torch.max(torch.abs(masked_imspace))\n",
+ "# compute the SNR of the image\n",
+ "imspace_snr = snr_estimator(imspace)\n",
+ "masked_imspace_snr = snr_estimator(masked_imspace)\n",
+ "# stack all coils for visualization\n",
+ "imspace_all_coils = torch.view_as_complex(torch.cat([imspace[i] for i in range(num_coils)], dim=-2))\n",
+ "masked_imspace_all_coils = torch.view_as_complex(torch.cat([masked_imspace[i] for i in range(num_coils)], dim=-2))\n",
+ "# compute the covariance matrix\n",
+ "covariance_imspace_all_coils = torch.abs(imspace_all_coils) @ torch.abs(imspace_all_coils).conj().T\n",
+ "covariance_masked_imspace_all_coils = torch.abs(masked_imspace_all_coils) @ torch.abs(masked_imspace_all_coils).conj().T\n",
+ "# compute the RSS target\n",
+ "rss_target = utils.rss_complex(imspace, coil_dim)\n",
+ "masked_rss_target = utils.rss_complex(masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:43.881614Z",
+ "end_time": "2024-03-05T17:22:44.467541Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(torch.abs(imspace_all_coils), cmap='gray')\n",
+ "plt.title(f'Fully-sampled {num_coils}-coils - SNR {imspace_snr:.2f}', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(torch.abs(masked_imspace_all_coils), cmap='gray')\n",
+ "plt.title(f'Undersampled 5x {num_coils}-coils - SNR {masked_imspace_snr:.2f}', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 4, 1)\n",
+ "plt.imshow(torch.abs(rss_target), cmap='gray')\n",
+ "plt.title('Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 4, 2)\n",
+ "plt.imshow(covariance_imspace_all_coils, cmap='gray')\n",
+ "plt.title('Fully-sampled \\n Covariance matrix ฮจ', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 4, 3)\n",
+ "plt.imshow(torch.abs(masked_rss_target), cmap='gray')\n",
+ "plt.title('Undersampled 5x RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 4, 4)\n",
+ "plt.imshow(covariance_masked_imspace_all_coils, cmap='gray')\n",
+ "plt.title('Undersampled 5x \\n covariance matrix ฮจ', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "***\n",
+ "# ATOMMIC Transforms\n",
+ "***"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Cropping"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:44.467158Z",
+ "end_time": "2024-03-05T17:22:44.471765Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the transformer\n",
+ "cropping = Cropper(\n",
+ " cropping_size=[160, 160],\n",
+ " fft_centered=fft_centered,\n",
+ " fft_normalization=fft_normalization,\n",
+ " spatial_dims=spatial_dims,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:44.472819Z",
+ "end_time": "2024-03-05T17:22:44.529744Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# call the transformer\n",
+ "cropped_kspace = cropping(kspace)\n",
+ "cropped_imspace = cropping(imspace)\n",
+ "# apply the IFFT\n",
+ "cropped_kspace_imspace = fft.ifft2(cropped_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "cropped_kspace_imspace = cropped_kspace_imspace / torch.max(torch.abs(cropped_kspace_imspace))\n",
+ "cropped_imspace = cropped_imspace / torch.max(torch.abs(cropped_imspace))\n",
+ "# compute the RSS target\n",
+ "cropped_kspace_imspace_rss_target = utils.rss_complex(cropped_kspace_imspace, coil_dim)\n",
+ "cropped_imspace_rss_target = utils.rss_complex(cropped_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:44.519682Z",
+ "end_time": "2024-03-05T17:22:44.811328Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(cropped_kspace_imspace_rss_target, cmap='gray')\n",
+ "plt.title('k-space crop \\n fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(cropped_imspace_rss_target, cmap='gray')\n",
+ "plt.title('Imspace crop \\n fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Estimation of Coil Sensitivity Maps"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:44.810581Z",
+ "end_time": "2024-03-05T17:22:44.815318Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the transformer\n",
+ "coil_sensitivity_maps_estimator = EstimateCoilSensitivityMaps(\n",
+ " coil_sensitivity_maps_type=\"rss\",\n",
+ " gaussian_sigma=None,\n",
+ " espirit_threshold=0.05,\n",
+ " espirit_kernel_size=6,\n",
+ " espirit_crop=0.95,\n",
+ " espirit_max_iters=30,\n",
+ " fft_centered=fft_centered,\n",
+ " fft_normalization=fft_normalization,\n",
+ " spatial_dims=spatial_dims,\n",
+ " coil_dim=coil_dim,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:44.816761Z",
+ "end_time": "2024-03-05T17:22:44.878218Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# call the transformer\n",
+ "coil_sensitivity_maps = coil_sensitivity_maps_estimator(kspace)\n",
+ "# normalize the image for consistent visualization\n",
+ "coil_sensitivity_maps = coil_sensitivity_maps / torch.max(torch.abs(coil_sensitivity_maps))\n",
+ "# compute the RSS target\n",
+ "coil_sensitivity_maps_rss_target = utils.rss_complex(coil_sensitivity_maps, coil_dim)\n",
+ "# compute the SENSE target\n",
+ "sense_target = torch.abs(torch.view_as_complex(utils.sense(imspace, coil_sensitivity_maps, coil_dim)))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:44.864285Z",
+ "end_time": "2024-03-05T17:22:45.282286Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(coil_sensitivity_maps_rss_target, cmap='gray')\n",
+ "plt.title('Coil sensitivity maps RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(sense_target, cmap='gray')\n",
+ "plt.title('Fully-sampled SENSE', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Geometric Decomposition Coil Compression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:45.280821Z",
+ "end_time": "2024-03-05T17:22:45.286789Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the transformer\n",
+ "virtual_coils = 4\n",
+ "gdcc = GeometricDecompositionCoilCompression(\n",
+ " virtual_coils=virtual_coils,\n",
+ " calib_lines=6,\n",
+ " align_data=True,\n",
+ " fft_centered=fft_centered,\n",
+ " fft_normalization=fft_normalization,\n",
+ " spatial_dims=spatial_dims,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:45.287832Z",
+ "end_time": "2024-03-05T17:22:45.740233Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# call the transformer\n",
+ "coil_compressed_kspace = gdcc(kspace)\n",
+ "# apply the IFFT\n",
+ "coil_compressed_imspace = fft.ifft2(coil_compressed_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "coil_compressed_imspace = torch.flip(coil_compressed_imspace, [2])\n",
+ "# normalize the image for consistent visualization\n",
+ "coil_compressed_imspace = coil_compressed_imspace / torch.max(torch.abs(coil_compressed_imspace))\n",
+ "# compute the SNR for the transformed image\n",
+ "coil_compressed_imspace_snr = snr_estimator(coil_compressed_imspace)\n",
+ "# stack all coils for visualization\n",
+ "coil_compressed_imspace_all_coils = torch.view_as_complex(torch.cat([coil_compressed_imspace[i] for i in range(virtual_coils)], dim=-2))\n",
+ "# compute the SNR for the transformed image\n",
+ "coil_compressed_rss_target = utils.rss_complex(coil_compressed_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:45.743455Z",
+ "end_time": "2024-03-05T17:22:46.286176Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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7E4/HvY9+9KOn/FzPW5LnG2+80XvXu97lXX755R4A79xzz/XGxsZOeu+HPvQhD4D3vve971d6drVV229Dqzoa1fZfur33ve/1otGo9+ijj7rPfhVHo7+/3wsEAt6mTZtWGB/b7rjjDg+Ad+2116747ujRo144HPY2btxY8fmpOhrf+c531jx2bYuLi146nfay2exJDdmPfvQjD4D3ghe8YMV3+Xzeq6ur82pqarz5+fmKeQBYAfSPHTvmAfBSqZQ3MzPj+5wbbrih4nMAXigU8o4cObLi+W984xtXADeu6yc/+ckV1z/44IO+jhEb6Uo+oaNx2223+V7PRkfj/e9//5NeVyqVvNraWm/Tpk0rQLf28+lPf9rzvCVwWlNT4zU1NVWASvbV3d39W+9oeJ7nvfOd7/QAeN///vcrPj/33HO9WCzm6xTYNjk56QHwduzY4Uu7tbZt27Z5XV1dFZ/R0bj55ptXXP+sZz3LA+B94QtfWPHdxo0bvY6OjorPVnMMPc/zfvzjH3sAvBe96EXuMz+aDQ8Pe6FQyHvWs57lOwfyJUH50aNHPQDe2WefveLafD7v5XK5/1BHw/OW5rFjx44K4J1IJLxnP/vZ3u23377C6bCOxoEDB7xwOOydddZZXqlU8jzv5I7Gdddd5914443eBz7wAe91r3udl0wmPQDexz/+8V9pDvX19RXjf+1rX+sCLtruvfde79Of/rR34MABb3Z21uvr6/PuuOMOr7293QPgfepTnzrlZ9NR488VV1zhDQ4OnvS+crnsbdiwwQPg9fT0nPJzq63afltatXSq2v7Ltvvvvx8f+9jHcNNNN60pDf2Nb3wDDz/8cMVnl19+OS6//HLs2rULnufhmc98JiKRyJP289BDD7l7bevo6MDGjRtx4MAB5PN5pNPpNc8HAF71qlfh1ltvxcte9jJcddVVeO5zn4vLLrsM7e3ta7p/3759yOfzeM5znuPKo36VeaRSKVxwwQX47ne/i/3792Pbtm3uu9raWnR0dFRc39raCgDo7u5ecYoLv+OGZW0dHR2+pS7PeMYz8Hd/93d46KGH8PKXv7ziu4suumjF9TzKeHBwcMWRmcASXfj7rLPOwotf/GK8//3vx1vf+lbcfffdeP7zn4+dO3eu2GR62WWXobW1FR/96EfxyCOP4EUvehF27tyJ008/HYFAwF23f/9+jI+Po62tDTfffPOK5w8PD1eMY//+/SgUCnjWs561osQuGAxix44dvvtaftvaH//xH+MTn/gEPvvZz7oTcXbv3o2HHnoIr3nNa1BXVwdgqSTwG9/4RsW9XV1duOaaa5DJZHDllVfirrvuwnnnnYdXvvKVuPzyy3HhhRf6yuK9996LW2+9FT/72c8wMjKCYrHovotGo77j9DtBiHy52ner7St4xjOeseKzSy65BOFw2MnUau0Xv/gFSqUS5ufnffmUa75v3z686EUvcvtTLr300hXXplIpnHPOOWs+cvXWW29dUUp3zTXXnHTvWFdXF37yk5/g4Ycfxve//33s2rUL9913H+6++27cfffduOOOO/Dtb38bsVjM9/7u7m686U1vwt/8zd/gjjvuWNMm649//OMrPrOlh6fSRkZGAAADAwP4wQ9+gD/5kz/BxRdfjO985ztYt26du27nzp3YuXOn+7+9vR1/+Id/iPPOOw8XXHABbrrpJlx77bWnVL61a9cuN4b7778f73vf+3Deeefhrrvuwtlnn73qfT/4wQ9w+PBh7Ny5E5s3bz7VKVdbtf3WtKqjUW3/JVuxWMTVV1+Ns88+G+973/vWdM83vvGNFWe+A0tAm5t61wLop6amAADNzc2+37e2tuLAgQOYmpo6ZUfj4osvxr333os//dM/xT/8wz+4Tb4XXnghbrnllpPW7j/V89Dr2DKZzIpraXif7Du/zbKrPZuf283Wq93Dc+nvvPNOVwPt13jscVdXFx544AHcdNNNuOuuu/CVr3wFAHDaaafhQx/6kNuwmc1m8cADD+CGG27At771Ldx1110AgPXr1+N973sf/vt//+8Vz9+zZw/27Nlz0udzXtxfspY5/ja20047DTt37sQ3vvENjI6Oor6+Hp/73OcAAH/0R3/krnv44YdXOGA7d+50oPOrX/2q43nuQ8pkMnj961+PP/3TP3XO61e/+lVcddVVSKVSeN7znoeuri4kEgn34sujR4/6jvNX4Vl1YLT5rU0oFEJ9fb0vv2ojn9x333247777Vr3uP4JPbr311hX0ufzyy9d8SMU555xT4ZTde++9eO1rX4t77rkHn/nMZ/DOd75z1XtvvPFGfPGLX8QNN9yA3//93z/ps/r7+9HS0oK5uTn87Gc/wxvf+Ea8853vRHd3N573vOetabx+raWlBa95zWvQ3d2Niy66CNdddx3+6Z/+6aT3nXnmmbj00kvx/e9/H3v37q0IvKy1NTQ04MUvfrE7GfGP/uiPnnSTPDeBv+lNbzrlZ1Vbtf02teqpU9X2X7JNT0+jp6cHDz/8MKLRaMVLjehMXHLJJQgEAi6S+vnPfx7eUrmg+2FUkRtojx8/ftJnE5gMDg76fj8wMFBx3am2ZzzjGfj2t7+N8fFx3HPPPXjXu96FRx99FC984Qtx6NChJ733t2kea2mrPZufc6O1Ns0ksHGMn/70p1essf7oef9nnXUWvva1r2FsbAz3338/brjhBgwMDOCqq66qAIEdHR34/Oc/j+HhYTz00EO45ZZbUC6X8da3vhVf/vKXK57/8pe//EmfT8eR8xoaGjolupxqCwaXVLwfaD4ZKF5re8tb3oL5+XnccccdmJ2dxZe//GV0d3dXZMquueaaFbTQSHwikcCHP/xhHDp0CIcOHcLf/d3fYevWrfjkJz9ZAWBvuukm1NTUYPfu3fjqV7+Kv/iLv8DNN9/sPv/PaH5rUyqVMDo66suv2sgn11133ZPyyY033gjgqeWTI0eOrHjOr3riGLDkpPzv//2/ASxF35+stbS04F3vehd6e3vx6U9/es3PiMfjuPzyy3HnnXciEAjgDW94g++pfqfaLrzwQtTW1p7SC/h4KMav+46m9evX4/TTT8cvfvGLVecyPj6Of/mXf0Eul8MrXvGKX+t51VZtv+lWdTSq7b9ki8VieOMb3+j7093dDQD4b//tv+GNb3zjmiJ2F1xwAYLBIO65556THlN57rnnAoCvkert7cXBgwexcePGU85m2EYj+/GPfxzXX3895ubm8L3vfe9J79m6dSsymQx+8YtfrDjG1rYnm8fMzAx27dqFeDx+yi/bOpV27Ngx3yj0j3/844oxnqzxNKm1HKVqWyQSwdOe9jTcfPPN+NSnPgXP8/Bv//ZvK64LBoM455xz8N73vtc5GDy29vTTT0cmk8GuXbvWdMzpli1bUFNTg127dqFQKFR8Vy6X8dOf/vSU5+HXnszxPFmZz1rb7/3e76GxsRGf+9zn8NWvfhWTk5O/VhR2w4YNeMMb3oAf/vCHSKVSFUcDHzx4EKeffrqTcbb+/v6TOuFPVSNvarv//vtRLBZPyq8XXnjhmo/8BeBOlfI79nZ6enpFKeh/dkulUmu+9j3veQ8aGxvxZ3/2Z6d8Gtppp52Gt771rThx4gRuvfXWUxukT5uensbk5ORJy2TZSqWSK4HyK/U81dbf349AILDqKV5f+tKXUCgU8Ad/8Af/aQ50tVXbf1SrOhrV9l+yxeNxfO5zn/P94ZuR3//+9+Nzn/ucbw22bc3NzXj5y1+OgwcP+tbYDw0NuajwS17yEmSzWdx+++0VZTKe5+FP/uRPUCwWf+WXPd1///0rgCewHLk8mdEJh8N485vfjMnJSbz97W9HqVSq+H5ychLT09MAgB07dmDTpk349re/veK9FR/+8IcxOjqKV7/61avWvT8VrVQq4frrr694b8Uvf/lLfPGLX0RjYyOuvPLKNfVz0UUX4eKLL8aXv/xl31KIcrlccbzm7t27V5SEASvpvGfPHt+osb0uHA7j2muvxdGjR/Hud7/b19l47LHHXGQ6FovhVa96FYaGhlbUo3/uc5/DgQMH1jTvk7VMJoOtW7fiJz/5ScW5/fl8Hu9///ufkmdEo1Fcc801ePzxx3H99dcjEomcEv8PDw/7vhNifHwc8/PzFTzf2dmJJ554omJNCoUCrr322lN6j8Wv0z75yU+ir6/P/b+wsODKvU4275aWFrzqVa/CT3/6U/zFX/xFBd+z/exnP3OR7o6ODlx22WX45S9/if/7f/9vxXV/+qd/+h96fDEAHD58GH/1V3+FfD6/4rvZ2Vl88pOfBOC/h8S2dDqND3zgAxgfH8fHPvaxUx7L+973PsTjcXzsYx/zlV3bDh065ErVtC0uLuId73gHyuXyCv2ye/fuFdeXSiW8733vwxNPPIFnPvOZrqSU7eDBg9i3b18F//X39/s698yiDw4O4tnPfvaq+1qq786ott+lVt2jUW3V9v/aZz7zGTz22GP4yEc+grvuugvPetaz4HkeDhw4gO9+97sYHBxELpdDJpPBZz/7Wbz61a/GxRdfjKuuugqNjY34/ve/j927d+Oiiy7Ce97znl9pDLfccgvuueceXHbZZdiwYQNqamrw4IMP4u6778bGjRvxspe97KR9fOhDH8IDDzyAL37xi3jggQfwghe8ALFYDIcOHcK///u/4yc/+QnOOeccBINBfP7zn8fznvc8XHnllXjlK1+Jzs5O3H///bj33nuxadMmfPSjH/2V5rHWdvbZZ+MnP/kJLrzwQjznOc9x79EoFov427/9W8Tj8TX39eUvfxnPfOYz8fu///u49dZbcd555yEej+PYsWO4//77MTw87Jy4L37xi7jttttw2WWXYdOmTchkMnj88cdx1113oa6uDq9//esBAN/73vfwnve8Bzt27MCWLVtQX1+PQ4cO4Zvf/CZqamoq3ip8880348EHH8SnPvUp3HnnnbjsssvQ1NSE48eP49FHH8UjjzyC+++/39Xbf/SjH8Xdd9+ND3zgA/jJT36Cc889F3v37sVdd92FK664At/97nefEhpfd911+OM//mNccskleOUrX4lyuYxvf/vbuPDCC5+S/oGld2p87GMfw4kTJ/Dyl7981T0Ffu348eM499xzsX37dpx99tlob2/H6Ogo/vVf/xWLi4t497vf7a5929vehre97W0499xz8YpXvALFYhHf+9734Hketm/fXvFyv/+o9rSnPQ3bt2/HVVddhWQyiW9961vYv38/fu/3fm/FwQV+7TOf+Qz279+P9773vfjiF7+ISy65BLlcDr29vdi1axd6enrQ39/v9qX89V//NXbs2IHXve51+MY3vuHeo/GLX/wCz3jGM3wzLE9Vm5ycxNve9ja85z3vwaWXXoqzzjoL8Xgcx48fx5133onR0VGcf/75eNvb3ram/t7ylrfg1ltv9X254slac3Mzrr32WvzlX/4lPvGJT7jystXaj370I7zlLW/BpZdeio0bN6K+vh4DAwP4/ve/j76+Ppx++ukrXoJ6wQUX4Oyzz3Z8ODY2hh/+8Ic4cOAA1q1b5/YfaXv2s5+No0eP4vDhwy57vn//fjz3uc/F0572NHR3d6O5uRkjIyP48Y9/jP3796OtrQ1//dd/7Tvu3bt345FHHsF555235oxutVXbb3X7DzvPqtqq7TfUftX3aHje0lGbH/zgB73TTjvNi8ViXjab9c455xzvhhtuWHHs7Y9+9CPvBS94gZfL5bxoNOpt2bLF++AHP+hNT0+v6Hetx9v++7//u/e6173O27p1q5dOp71UKuWdccYZ3vXXX+8NDw+veR6FQsH72Mc+5p1zzjlePB53/Vx33XUV7yXwPM/75S9/6b3iFa/wGhoavEgk4nV2dnpvf/vbfZ/nNw82+ByV6nmrH43K63t7e72rrrrKHad7ySWXeN/97ndX9LPaEa7axsbGvA984APeWWed5ebd3d3tveY1r/H++Z//2V33wAMPeG9+85u9s846y8vlcl48Hve6u7u9//E//kfF0b2PP/649/a3v90799xzvfr6ei8Wi3kbN270rr76am/Pnj0rnl8sFr3bbrvN27Fjh5fJZLxYLOZ1dHR4z3/+873/83/+zwreOHr0qHfVVVd5uVzOSyQS3jOe8Qzvhz/8oTuW9dc93pbtr//6r73u7m4vEol4HR0djp/91uxUjrfVdumll/q+k+VkbXx83Lvpppu8yy67zGttbfWi0ajX1tbmPf/5z/e+/e1vV1xbLpe9v/mbv/HOPPNMr6amxmtpafHe+MY3ekNDQ+5oVG1PRscno5lfX7z+4MGD3kc/+lFv8+bNXjQa9To7O72bbrqp4hhoz3tyms3Oznp//ud/7p1//vleMpn04vG4t2HDBu+lL32pd8cdd1S8o8bzPO/RRx/1rrzySi+VSnnpdNp7wQte4D366KNrkolfpxUKBe/rX/+698d//Mfe9u3bvYaGBi8UCnm1tbXepZde6v3lX/7liuOZ7fG2tv3DP/yDO+p1re/RYBsYGPASicSaju8+cOCA97a3vc0755xzvPr6ei8UCnm5XM675JJLvD//8z9fcQy35y29g2jHjh1ec3OzF4lEvGQy6W3fvt37wAc+sOrzeOS3rkF/f7/33ve+17v44ou9xsZGLxwOe+l02jvvvPO8D37wg0967DPfO/SZz3zmSedXbdX2X6UFPM8nd1tt1VZt1fYf3AKBAHbu3HlKGzKr7bezFQoFrFu3DqlUCocOHXKb0H+X2jXXXIMvfOELFZHraqu2aqu2anvy9rtnDaqt2qqt2qrtP7XdfvvtGB0dxZvf/ObfSSej2qqt2qqt2n61Vt2jUW3VVm3VVm2/UvvoRz+K4eFh3HbbbWhqanLvFam2aqu2aqu2agOqjka1VVu1VVu1/Yrt/e9/PyKRCLZv345Pf/rTJ32PRLVVW7VVW7X9/6tV92hUW7VVW7VVW7VVW7VVW7VV21PeqsW01VZt1VZt1VZt1VZt1VZt1faUt6qjUW3VVm3VVm3VVm3VVm3VVm1Peas6GtVWbdVWbdVWbdVWbdVWbdX2lLc1bwaPRqMIBAKoqalBsVhEuVxGIBBw3xeLRUQiESwuLrrjDT3PQygUQrFYRLFYRCqVwvz8PAKBAEKhEACgVCqhXC6jWCwiFAq5z/m7WCwiGAyipqYG09PTCIVCKJVKru9QKIRyuQwAKJfLiMViKJVKiEQiAID5+Xk3Vs6B4/c8D57nIRgMVswlEokgEAigVCq5Z4XDYczNzSEYDCIYDLpnAkvvAwiHw5ifn0c0GnXzYt/hcBjBYBChUAgzMzMVxz/y88XFRdcX5xgIBOB5HkqlEkKhEOrr6zE5OVlxPZ9FWkWjUXieh0gk4sYfDAYRjUYxPz8Pz/MQjUaxsLCAcrnsaMhn8Yc04zhisRjm5uZQU1ODxcVFRKNRFItFN0euV6FQQDwed2NaXFx04wkGg1hcXES5XHZrwevC4TDK5TLm5uYQj8fdmszPz7v5A0AikUA+n0cymUSxWHT3BwIBBAIBLCwsIBwOO7qxX861VCo5HuG4yuWyo5POX3ktEAggGAwiFothYWHBXRcKhRw/kB7kX/Iux648o3PmD5/DvsPhcAUfqWzwesoZfys9VTaVb4PBoLuOz1T+5/OUHvqZzoPXkF/4o3PienCcpCVpFg6H3fPZFwDHs6QJn89rVQ9ZmQoGgxU6hbzCvkmfaDTq5Frnz/VSnaQ6j3LB+ZN/KOsqv+QvygBpQxlVGrPPQCDgZDwWi7lnaJ/8HQwGHR8rHXUe+p3OiXRJpVIoFAoV/M7fpKnqJfK91Vt8Fj/j+Oxnyne0G/zeb9sgr7cypLqOz9Fxsy/SR+/TH6snlPfZP/lJx0gakOcWFxcrruEzyY+ko64hx0ceWVhYcP0BqOifdtaOn3zD9WHTv8m35AH2T97g/MPh8Ko6xq6HzpM0VJ3M37o27NuuEWnN+dvn6XgpI8rbyoPs236v8s914pj1/mKxWDE/XSPaFcuv4XDY2f+ZmZkKu0UeULuqcqs6mjaT4/OjsepStRGWXlwL0svyjI7f0kjprX2QNqtt79V11fWgTCn/WPvN66hLC4VChY1hn6r7wuGwswVqt4BlG6L6R+kWCAQcXmEfHDPnq7aS9xKDEfuRJtZmKq3JC6qXdQ2UnqpDyG/Kq4otlX9U3i0/kG58zuLiIuLxuLvW6mBdZ2sPVM+SPk/W1rwZnALECdNoUzkQ5BLoWdCmTEdlq6CfhpVAWSdSLBaRSCQc0RUAqGLjWAjQFhYWnLBbA0KC8v9wOIyZmRmnxDkmPkeBfTgcdv9TeZAGaoA4B843FotVOD469kgkgpqaGhQKhQrlSBoSmJNGSjtlDirqcDjs5kKBUmVGRtLxWTCoAg0sOW0EJBYwOIYSJbqaESRgUdrTcZmfn3drSadGlTPXQ51NVQh8hjUuFtAoAOb8FFCqY6aKUwGXzof3KQ2sIlf+UZlg3/whj5OPFXxaw8F7+VwF91a56hi5Hnbsum6q4FVe1GGxuoD0UEOtssDxKm2so0AaKLC1gIm8rtfasRIYqoFXmeI4VTfp2PQz6j0+wwJov8b1s06Y0tuCd/0OWAKG0Wi0oh9eY2mrsq2fUW+Qxrouyl+WvnZN1UnyA/6UJ7u2qitVJ/A5+rmCP+psCyitntLnKdDmcxg4WlhYcN9Z0ER5Ww1Qq363z1c6sz8dN78nz+gzVNbYNFCntojPU/2nY7TyputoecPKmwUQtMN8jvIbZV7Bj/It5cXqe9VL1P+8XueuQF5prXpF7bMGLRQXqLyqfFnaka4anOBzlFeoc6wzbfWFBsl0DLrOSnPVqxrcJIg9mYNm5Z+fWb3LZu21nYvOXXlZ+cFvfZTWpJWf48u5qENpcYc65kpvgmuCfD+cQVpYAGzxWLFYdH2q7fDThUo3vU5lWrEfgbs6pKrDrFPu13QuSmPlHY7VBoDYFhYWKtZB17NQKKzQ2dooo2qXrH4HloLLJ2trzmgoENVBKVijJ+4HPP3u5edULIwy8Ds10GQs9qlEpxEgyCZBLFE0asXPKFQEu/ybTooaQI7LAnQ1KGosSRsKDEGzKlkFjOxDf+t8eT+/I/NwvKrIrHFXWi4sLLh+lflsZMSCKCpPbVZRa8RIQYkCZJ0b6cPnhUIhNx+uJa+zBsOCEY3WWYWsz1GeVCOiIEbppc1GE8in2o86tQAqoi3sQ/nPb42YdVHjbsGN8gabVdxWCdNptsrUAjh+pjyovM558DO/aJWOQ8dmI2I6Bwu8rEHmuvO3Gldr9NQY6lr6gTedg5/jqNFsyxeqG3SeaiDtWqi8+NEKqIygqhxYnaS09LtO10+fY8EpeVGvsfS3Y43FYlhcXKzQlfq9yqyfE6DOjvIg79dMhV5j+7GOMvvmGHRcNvJnx2zXQeXVOnL6Gedp5UjHqQDH8qMCF11XBTDUpXb+fvPhPcoPShcdk/KqyocNKmjfXBtr/+waqbzacSrAVvrbrIjaWx2Dn51UntA+FWgqyOTYNPilmQhLS10b5TutrvDTMda5e7L142cKRDl2y49+tFAb5KfXNdtvx0BZtWNdzWmx/OInR/q3tY9KH+Un/lYaK03UVvtlEfm3ZnvVXmgfnDfpQF2oPM6xc7x+mW67Dnbu2o/yxmo2k//rNZyDDTTr+JSP7bqpA0HcbMeta2GDt3bN1tLW7GhoesjvIep92ciTBSb8m4CdQs4oiioTJag1lMokqujUuPlFwXg9+yBj2T75vz5bHSAuBCPl1kjq/6pI2ZdlYAXm6pHrvK1z4Ddm7dMqfz/gyD7UkNlmDT/7UKDCtWdWSbNAqhQDgcCKzBivJThkKZrylgIWW35gBd86xpyXOnh+gEvn6SfgSmtbwuCnRPReBSI6Jj/QoAbFrrECK11vbaQBZUcj8qqErFGw8q0gSHlI18RPvuxYtFljYvmMYySf6Pg4NjXqdk35uYIFpbeuuVWUNIAE+H4ytpq8Kf/oGilgsw6sAkvLS0oH+7fKttLUTxb8DKHe5+foKj2ezHmhw+33DEsfpbvKi2Y4V6O1H3jRfqwM+hlunZvqYuVPv+CK8qoFZXZcfmvJ/zUQo+O1esNPL1nnw+ptv/VVvvSzJfb61fScXTe9T3Wt6iw/OnKOfmupfeqYODc7P6Wz5T2lse1bbbHlS0szABVyq3pLr+EYNPujzqS1L37Zbj/Zsd9bXte5Wn2iAHE1GrOt5njq/O19Og6/dfR7vpURfab9UboDleWeNqC9mv32k1M/eeaY/IIsOlbrlPlhH36vcuFn56yjYWl3sr+tjFuHWa/3wydqw/30jl1HG+zhz1rKpoBTcDT8FE8wGKwo5eFk1CHR2jUuBMGlTV0FAgHfdFq5vFTKYqPkbKwzLhaLiMViFX2q8tRaVy1ZIsFIUBuB9jMmqnToHXJeVjCVsaPRqHuGlgHZrA8Blo5Dn6ulZwrSKYy2rEzHxznzGtJPS7LsGGyES71/P/ooHazxUrCta8n/NS2v9+s6cq76OZ8xOzvrSv3UWKnDa8um+Bx9pgVVpJneZ0GOX3RQx6/7FXRteS+bKgurAKxisgDbPtMqHGZibEmgKhC7bhyHdezUEFl6W0BkFZXOz0/ZPhk9NYLlF/Cwa6I01blpAEDHanWTHwgGsGJsdl1XA0oAKoIbOk/er6WOSlu/yJ6mwJWntMTQRimpL0OhlXvHeE00GnWlkloOx8Y9CX5rTgeXOkoDGxYY2WyqpZWlux9Y0eu1bwuklCZqMFXW/NaO41Te0+8UhPiBObtmpIPyrLU5fgE8O1c/gKw0W+3H0lWBE+ln9ZyVW15nAYgFwJyL6g87Ru1LryXtNDBp+U3nYIMJel0wGHQldMQiChTJs/q5tQUWMPM7rXSwNsvypvKLXUPt2/KS5R+lheIMvdZPplRvq263+k6/86Op5VPNwvsFLoDK6hiVGY5N9YbypOpUu4fGT4dbu6/9a2BDaaT86XleRRBZ589rLf3tPNW+6Ph4v+oPS2O9z/KKrciwckmMpg6X5Vc+TzG65XddH3VAeQ1t2Mnamh0Nbpik8aHSJSBkLZgaWQ7Kbi7zq1tOJBJuM6Iapfn5+QpQHY/H4XmeUxY0sJFIxNVEsn8SIhAIuHp/jsuOEVguVVCDqAtTKBQQCoVcqRbvXVxcdNFt7k+x9aLsh7+plCKRCEKhEAqFQkXdoU1LM0tQLpfd3gEr6BwL98vwWYFAwGWLSKNyuexKHsiUFCg6GPF43D1Py5NII43isFlhUQXBdaVTaJsqN80QKQ9pnTSvL5VKK/ZTKABinyyt8zyvon6SQqcKhrylCo08yznOzc1V1HUHg0H3DCusXEuW5LE8jBt9KcicnzWYWrakCkNBD68lvfi9dfIoS6qsOD42v2gUHWpdUx70YCM/VNAK8lWBalRRaW6NkA1UcA00csq1s0aP8qTrYWWaskadpnSiriOtVJlrsGI1h6VUKjmn3m7Ws6VECjxYPsj7eL0fUOTzLFAg7RREqZyRdnowhu6PI/9rYIZ8azOsfC6DRJpN8bvPRrVVL1vARV4Bljd0+wFe/V8NMnmUdFG54XcKCrUcTflOr+P3Njug11leUZtggwjkZ9Wxlg6koeotBXfKs9oULCj485MDXUvVX1bf2DI5zl35266DH9jyA9nKy7yHtFG8QZvF6/i9Hu6gvKN4hXTQ4JQtq+WcqENU19ARUV1tHUliBLvWlA9dF/Khyl0sFqvQOxZQ+5V2qT6ztFSsZfWgrgtpQTpx/rYkUMtvbFRb9braTe2fPGX3EnK8eiAC7yEf8DO9zzo41Ct+h0BYPlR+47X2cA21eXwW5636hWugvKeBVfIVbb1mg62zq3pF56VjtzTmZzabv9r9tuTM6gI+XzEk9frCwoIvH/q1NW8GD4fDiMfjFUSl0lNQyHovSwwuOsE8jQuBmUaKFVTposRisQrjRUKxHz9FwO/4HAVXnDpPEtLNo+ybwhaJRFAoFJBMJlcoIDpZ8/PziMfj7j4KBxeZgkJDwT0Iymy6cLwHWAL9s7OzK8CULl8ymXTOkAq1lhqRlqQLBYI043pSsNRxUgeQAIxrTMXAZ/E5qljtXoxSqYR4PI5AIOBAO/siyFcjp2CMexiUHwhwVXD0BCoFChyPlrFxDUlTPfVJedNuoFLHmddzHFbJKh9wPRRA8zrdjG7Ld5QO+pkaUAsY+Vz7HLsBViNmNHaqVPkMPXVMDZGfQ6GGmJ/pKWTqpHDc1CP6DDYbgdS1UAOuwIl0VIWrGR3NcHH9qJdUJlQnkZZaQqd6T51ijpv8r3RToM/71OCpfmQ/NvulAEINNHVzNBqtMByki2YsQqGQO4GFAQmdj11b1VmhUMjJLeek2TLqEoJxdbJU51udpoaP1yovcI1VTpQXlRZ+AF6fr1ld1b3Uf1bOtC99jl1jpaGCIp2fDSzZSCU/s/s72IduvuZ4OGfeSz62WUJ1qmxggM/WZ9q5Wf72y/qyPz5b5Yd9kndUB1u+Jr1saZI6gDaIwTHyXpUt1Rc65ng8XnFaEZsGAPxAu8qpHQt/89Ac3qMyRYyjdLYQjWtNWmvAj/peDz7QxvkrxtLGNbLZEAXOfC6wnJXlvWp7aMOtXiLOsJhH7RN1Fk+d0v7ZFOtxjJwbr1eQrmui6+EH0v3skrWbxH5cE2uf/ewbx61zIh11vbR/65SpLFm9QVrztx5upPZHZUDHyR/SSfW6ygifpev7ZO2UHA0urB5NSkBJw0JQz8nye41KMCtBpafRfUacdXFVKfEzjaxTcEkUVdIkLpmNpV7Ach2+lmSpsSXjLC4uIhaLVQBuVeA1NTUIh8OYnp5GJpNxWQUbjSQdualbBQ9YOrrV7oPxPK/CCQLgQJ5mTJh1KJfLLntDJtRSmVKp5I6qJV1IawIPBe5KQ4KESCRS4ZzxGeyTAJVNlWUikXAOn3raBK+cAxUMI/505uhw2iilgmHlWTVICpqpkFVR8zryBh0aBWY8Ek4jWjpPVQpU9pZ+NvqhjePjXBVkWTCr91uQq8qXDpzSQZ2cWCxWIVO8N5lMujVVY6G0tOPXedXU1DhnWh0NlQtGdTg2G2FUw6SGnXJMnaHXKy9wjnavh4Je/UzpwGsIwBRsUi8ocOAz1OhTF6oRUMOgzqzSSJ1PZig1mqw6kZkxGj2N+vEezbhoJFJPDLTyw7lwDRRcUvfoceWakbUOmQYs1GAr3/g1fb46S9pUPjSwoE4/+VIdO8svCkg4V5VdlUXlZQ1o2P7V4dX/dZ25VtQZHJfOj/RVIMU5qZ1U/lPaqF4CUCEvwPIRugqolB8ZVNSyWo5fHTDN2ij99H91rvycMV0DC3qUp6yzo+PSbCr5lfOl8+0XrFNdoLpMZR6AC7qqU2pBr8qr6hBiAAZdde78TvmRYJD2gDyjziBpq0euqq4gr6o8WnyijrmVM7uO6ij4BZPsvloF0aoHbGZX8R11kvKROhNKf+v0xuNxzM3NreBn5QP2rYfj2O94j+V10ssvQ0mdQ7myY1N+snrPOlJ23kpnXSOVFf6vOElPn1IMouPg9YrdotFoRYBZsRJ5aS3lU6d0vC2ZnoqbC8SyA07QArBEIoHJyckKAquyJajlZBSAq3CpkVOQzzSOKjCCSHUk5ufnKyLhVpGQkGokGZmemZlxUQ4VfM9bjj4nEgnfsiYKJheNgkRGUcMRj8dRKBQqjqhlhDEUCiGRSDjwx/FaxqEAAJWeMUGRZlpUaNgoJPoZHQGunZajEFBppN568nyWnjPumPD/8YKWVrHRweOahMNhzM7OujVlxJm0CIVCmJubq3DGVEgVSNJB4lqpIeU7X2yUQEuyqMitAtC1UaChit8qM/ZPWvM+/VybGgq7n4cOtwUfqrisgVN+VZBlQRHXkmu9mrwqUNf157OVThaIUVFrAIL00/6s86/zsRFHjlWfQaeVTrJdL8qCGk0FBLxeI5AKgCxAVj4m73ENeY/er3xFfuVc1FhrEIIOAPUzdbWWEagxV5BLMEmZoBOhBp9N58Hx0XFRunP95ufnK0A711AdX9UjSlPSW8fLZh1dlRflBQWdvIfywfVVets+ODcFC+pscm0VbCqtFOzYKKTyJMfG/pXO1hlVO2odbWuDyG9sup4aVNKjMHWdtAzP9q+OzmrZBM7dglur41QXK5/p3BTo2HFwDLTdGkxQviRP6TOtrrD2j32oPJCvFE+ok6N4h2VdqjsUf7BP6iWLpXit2izrrClWos6yNkrpZOVF+1Ldz+tsZkrtoB8A1j6UvjZDqOtgeclmyBT0qm3SwCv7VqBP26jOBU/Ns7KlWQ3Om/errfOjk+X11XhZZV/pb3lQ58252i0LVoYUL9sgiTaup77TiWui2U/VP8rTnuc9tcfbsimxFGQwk0Clrd5jPp93NdsEgGqkVEnqPg9lqEgk4iKH6pmxVIj9sj/NSNCIcuOjMj0dgGAw6DZExuNxFyXlvBjBIPDgeIPBoAOk8/PzzljToSGDMopPo6cRSgUhs7OzFUCfdODYbaZEjaUaKY2wEdCQfqosVbAV9FhQwLWmo8VoNe9VJaWlHmrMyJR+ykONu4I78oxGTK1C0Qg9G5UBhZIAgMqVjqgCNh44sLCwgNnZ2QolZqOg/FwNONdQlbgaKI5Zm9LAGmgaTL+ohoIXmz1SkE1lpc+3gE2dHR2fZvo4P6sMeT+fxXsUODIIYA0QFbnOmddQ1ihTaih5P8GrKmXOU8udLL11rJQn2z95xvK4HYNGj0kTBbjqlFk+0Dpka1Co6Cmnul6qO1V3cB20LwDugAyugTVI6rRr4IJOA3lR563z599aLsjxaGS1VCpV6EXlYTXWCuDId9TjfgES6ivVWxZkK0+r3iM99Hl6vzXQpLXaAH2+GnWVHT5XHVZ9NseqgTWVFd5DXlWjT9rpHPxAvI5b6WJBvh8dtAxGAavKE50xP91HHtJrlZcUuCig1eexD+VdHT/pSDqoXJO3FXwrT/N+azd0TRXgsR+/kjrafz2evVwuO0Cm2QrVe57nVdhllWnSSvmWNFL9yb+pu/QapZt9xxbHwD7UGbY8pRjKOs32Gl0v5TPlQfKW1WtcS79xKMi3AFjtAL+zGTWOV4OuKleqc3W8nudVRPh5n8o9x6e63dpb5WvVl/ocxbC6Pn6ypHyitCXutuPjumjFBultHXjyC+XCBkfW0k7p1ClVajoQAkBNhylhuKhaNqRE12s0MqmeqOd5DrBYQ6rCwtSk3k9HgMysypYCq14bAYw6NHyeAgtVIpy/Ogk6f/VOLRDlmJi68jyvIvNCMKXKiOUui4uLFc4X14jCq8yqz9TyMVXEWl5k18dvg7Zep4pX6aROpwXVNoqghslGO0hjCqdu9GI/HL9GlXQ8KsyWv5iyto6kyoAqbI1wqtJV46uKV8GI0kZBvvKhgluVF1WuFuSoobVATO9XZ0XnZeVRx2OBhRptPlu/t+NVPtG5qz7R9VBZsYEHfqe8pcaCMqCKUAGPgjulNf9XebFzUEOnPK1rbJvqKGu4+HyloaWtXTs+n7KscmWdCTU0SgsFGFwHq1upsy3YI09peRmNqgWNWg66WuSdf+tc9Tp1BO19fo6A8puuj6W9dXCUD7Vf8qGOk7xMx0kDCZbfLZDgWJX+Kqd2nfijGUrlUctPKuNs+vdq9LPAyk8v6HW2DwVq9judh85V5V7thgVZVg5t09JHLdHTuSt9rVNqeUfHpXypkV6dowaa9AhWvz4J6Dg2/qiTYddCnSCbYbS0Jj/aQIjFItp0HtZ+8XuOTeXQ8qafHKv+AJarJixwtU1tgfIPy/eZmbBrrDpS+7FBOevM6fzU9igfWgfRr/nxrl0rNqWb0ln/pm2xzjvHq1ku2j7F0+qsWV60z+J46fRqQFt50+qXJ2tre9uGEMOPiMq0SgR+B1Tu1NeF5ASVaFSgBH7alwUkqgQUJGrTWj4CYTXoBOsqhNaoaRmONYqqNGh8LZ34HZt1REgTRtr12F11ZCxg4HPUGWH/HKOCEmVYP6Ouhl2bBQ6abtRrlP7WiOnnChz8+IaMreuma60KTvvxG7elv0Ze1VAvLi6uePeHNjqffmN/MuO8Gh31GjsfSz9V5lYx+N1njbVGLThuvwi18rYqSJUZPpO8yf81quQn35Y++nzynXW89PvVPlN94BcJ96O7KlRea3lJHQ2rMzhvyhSbXmfHYNfc8rsaAw1i6NroNasZDwuydeOpNl0r6yCp7vAD5OoEK89YObDr5QfibT92TroGSh8r65yH6jFLI0snC4BUdla7x36vY+fY/MDwamtk5co64LzOLzr9ZDZG72VffsFCS3+/Oaqc2/FbEKXX65xXW3P+z/HbeZOmyne8jrbX0sSPTkofpbWN4vJelXPbnx99rSPH7/QZGjCxa6h20fKRrpsN6ugzeY0tg7G0tnpltXn4Ob5+cukn90oDXSc/27eabvS85f1WDJzaAJDqL79spa6XVpmoDtOx6xwssLe6zzqRq+EAHafqJL/2ZHLyZJ9xDLouireVD/R7qwf1/5Ppbcsfq7U1ZzR4AgBQGQXnojGTYD1Gq3Q0WqtRIqDScNrIBr9XoG2BsgIgXQh6e7zfloTYiKSOn59bcEmAYVOQxWIRNTU1Ff1y7po+t8CKfUSjUVcGpk2jh+XyUomVAhyuB7MxSj9+R/poyYg6Fp63HKW3AIJj1YgKy0tIc83YcPMn7/czklY4NN2q3+n6KD009W1BRyjkfzAAx60Osjo0gUBgxdF2SuNgcHljlVVkCoasYVBe0CgBx24NIe9TmbBgQOnrB1yUtsrTKoN8hho7jol8Y5Ut6cs11wwix6OOr/ZvQQvX2p6OQXnRSJaWNOi6k6YqU/zOOqeqB6zR9HMg2bR/jp2RS92LonRWHWmzf8pTdv1JB527jsePtyzwULkmzWy9N/uwG4T1mfPz8y5jwh9dWy3ZPBkNdN6co0bz1A6Q5konBbraD9dUgz3KG2pTbJZL+2Y/On+l7WrGnc/lPhjN4Gi/ttzDyiyb8h/1lMqHBTRqs1TWuB78zI7fDzxYmqgetPfrc/3sss5B9YOOSZ9v9aIFc6rDbbBEQZPyldLAzk2BONfP0t5PnysO4Rw0iEPdquNU3iIvWB7Q/pVelub6TItX+JkGJf3WlzTSMduSLT85sHjBBiiszuLc9BmBQKCi/IhjV16wARrSgf3Pz8+vqFzRsaou4H269oqJ/HCktVeK1ZQeSnvtQ/u362nvsVlJ/d7O2+ouSyu/9bI8pGur2X3LK8o/di34ty05Xa2t2dHgQ225Cr1MKxRWiBip1++UwGoM/bxwvd6+ZIwlO1TGVARcnHg8jsnJSQfi1eBQIGtqahzBdU4E+DaCz7547G2hUHAGm2+05v/cR0IAXiqVKuqmdQ9BOp12RpwblQAglUoBWNrvUi4vl0tw70ksFkM+n0c4HEYqlXJZGh0HS8h0449mTGisdQMVG9eFG3D1DGXSi+vBsizr8Hieh0QigXw+78v4emSqNZb2eltWpOlye+ITr+Ea6qZzronO0ZYecS7BYNCtrUYJrBAuLCw4PlNac+z8zG7OVSWr2TsLRu3/fkBIgY06hLxPa1O1D8q4RpAUfPP5qsRUD/DZgUAAhULBvSfG8oGOk2umTkEymURNTU3FHgpV+mqktLQRWM4WUobs4RScj62jJl0s31hAyDGoXCktaEj1NC01thyb5eHVgIM+WzOSLCWlvBGU6pjVwKscsW8em025tjqXulWDLXb86mxybf14186Rz9ETjXjQhupvpYfyj54cxvmzWSfDGkU1qtSxanB1bnwGn6mGX085Up7WcZJ2fk6I2lKOT6P25G/Khx6rbnlUQa7VEfZltRYQWafOzkVpZ/vn9Uo/XS/lcRvksNfwPtUFer/ypB+4sydXWl4DKl9kR5m3dNY+la/1b47R4hyrC1UeiSUU59hGeaX9t/te+bddM64z9xqq46Bz4b0aiOGcNIgM+AcP/BwoDULob3s4gupI0kLHz+dxTyr70PEHAgGnt5WX1DaQF9WZoM7SclArg7r26syq/vZbX8vDWl2iMqfP5Ppa3j+ZA8M+rUNvnW6lH22DPsfaNy0To56i/daAlupGvwoiv7bmU6fS6bQ7b10JYxeLCpUMtrCw4I4DBeB7vrM1SHxRnI1AqYJXQeFpTqr8A4Hll5KRUXnqA/tS5c6mDgOfGwqFkEqlUC6XMT4+jmQyiXg87uajYJ3gXxddjTQ3uIZCSxu8E4kEkskkkskk0uk06urqUFNTg3Q67QwLF71QKGB6eroi60GFOzMzA8/zMD4+7l7+t7Cw4Da1l8tldy+fr8xZKpWQSCQcgOHGcXUwAKxwuFRBaCSBfXD9yAtUBKS9PRGEQFcjHqRBJBJxG/UpNH5rzkb6afSJvKObUtWIh8NhFy3hvJRPOH5eQ4FXsKsARxUtFZY6xaoELXiwRkwdGwsebbSFRsZmvdh4De9Tp1HBlq43r9GyPz2BRXnBHseqwJPrT4Aej8edHCQSCSQSCcRiMed0K+BS8Mlx0Nkul5c2XM7NzWF6ehpjY2OOTuQDKkd1jsi3pDdPe9OAgQJE6gTOVXUX14+0JT8RyFMOdG1IW7umCkaswVeDowEa5Rmlv2Zk9F7yMQNBmplkH/Pz86ipqalwGCinuiFfQRuNPenO55Fm1uyoPiH/kxYaHbYOta6ZXQt7jY3YWQfSZrr9nEBr+FU21BlRftf9AgqEyLvksUBg6cWy8XjcHX7CvqmPrG2hDVCaacZ6fn7eHcBBXlTQoTpJ5Vh1qgYSmbUhL9sAgg2eWH7xPG/Vw0J0fRQTcF1pPzkO9kngqQ6o8qM+xwJ/q5t46IviD/6t7+rStVedrfRXPuJ1GsxiwI76h/3raXFa2uIn/9ZuqD1XWirPc6z2f9VJBKpKL/3RLKU6S6qrlc/tgRusOCCu0PmwskPtE/Wn53kuKDw9PV0h16pfyBM8tIa8QHnT44Wtk6MnOikusf0zoEm9u5rDYE865TP8AL6f7rb8q2Oic8y5KC7WwASvV3mkLtKTNC12IF012MTxkQZTU1M4WVuzo5FIJJy3rI4FF1CNoX3ZFoWXEUrez7958hNBAJWEKhsuhvatwsAxEEirB6pOEABX2kRDFo/HMT09jXQ67TIfapz0RW7sn4pHS40KhQLq6uoQCi296ZtGN5FIYMuWLairq0NrayuampqQzWYrFEsgEEAymcT09LQzWhSKRCLhHL25uTn3nJqaGqd0NYqTTqdRLpcxNjaGqakpTE1Noa+vD/v27cPIyIhT9HNzcxUAihFeKgiuh9KBp2IRLHN9VBnk83kHgNRDJk35FngLCtTQaZSN/9NR1NOz9LxqOhCqRDX6pnNjU/BEvqJi0MiH8iENGaMFNNSMUhDwshEoBINBFAoFF8mgXCgYU0WgUSk2/q2gR0GvAl1bisD77OZeBdkElroGpLNuMONc1aiq4xQIBJBOp1EqlTAzM4NoNIra2lo0NzejubkZdXV1yGQyqKmpQTabrQCloVAIs7Ozbi7UO7FYzK339PR0Bd9RD9CpCIfDzshMTExgYWEBY2Nj6O3txdGjRzE+Pu5or4c4cA4KmJUPlH/4HI7RgmD+T/2k62edW/1eI5f8n86bdYw04mdBmQUp7Ev/pvzzWG3Km+pfjsc68uQBZkRU51I/U78rbZWH+Tll15Zv2Xlbp0XBjnWWdS04XhtJt7RW50Zlis/idyqnSieuqdo18g+PNs1kMshkMojH4xWOhYJm20hTvuDUrhF/W9ur9pEBqNnZWYyOjmJiYsI5Hhrko62j/PnRQMGqBoSUngpalSfJ/zbLZnUuv9M5WCdQ19iv3JhryP81aKDyoLJkQZU6l/yhTKpDQxtJJ0/pxLWhc6+ZCe2fuMLqC3V4dW2U99RO6LhUBnT+yuO8VwMa1garPqZjRDtD+uo4yTuaVWC/5fJSNpX6T8dCPohEIkgmkxW8RpxFPazH0yotFJepXtCAUz6fd7yuNFJcooEt64QRCzMgxWcFAgFnQ9WO2jGyf7+N8Vpmr8FalRfylN9hHxyvZuj5PHX01Paoo2/1L+ekz+f1DN4/WVuzo6GRAmU6AicSX71wTWFpyQkXiR69ppDUSaCC4TN0kmwaFVKvTAnFxSwUCg6ca4rQlrF43nJkhsqzpqbGnbXPFw6q4LG/2tpatLe3o62tDW1tbWhpaUE2m3U0oEJitoHOA2uhGxsbXeSJxoee89TUFJLJJPL5vJs7ARWFh8CJWY9EIuEEmmUSDz74IIaGhjA4OIjx8XE3Lwv4rbJkP1w/zof36pvbVUApPJpF0EwTn0HvXMGdRkH4t0ZSNTrH55MfKFTqDLAvfcGaRjAoQOpI2DQzQbGmkFVB25e0aUSEzyVIU4OijoICUqWDjU4oWOMY1NlWpQBU1uFrNkLvJw9YY6oKmd9Ho1HnMNEBj8ViaGhoQHt7OxobG7Fu3TrU1tY6WlJmIpEI8vk8pqamKlLolDdm2dSZIU2j0SjGxsacErTgXsvGEokEMplMhRKfnZ3FgQMHcPjwYYyMjGBqaqqijIpzpI5QftYAiL6AUvmA5UykqzW4HCOde7/MF/nBRg5tnyqjVvdRb6uat84Kv9c++Vw/B0d5kH/b0gAFLpqVUmdSHWkaRQVMKluagVVgxPWhbOv68V5rM5RfVLeoDrQOPXWw7sdR+2YdMY6rpqYGmUwGiUQCDQ0NLsil6+h5nrMDCnD5DPZFudZxAnBRVX1xJOek4DuZTDpwyHkw+zc1NYXx8XHMzs6uoL/yAeWW49e5KN2Ul3UdNAhkgTLp5netBVG61iov+r02C8JV73EMWmZp+UbBJ9dbnSbVEZoRtH1Eo1F3dLo6LaQZdZ3aHcVStO20c3YOSlvqBisrqqusvGo/tj+NiKvDonbewkmldTi8dOwvx6+ZP71PZYhjJP5T3tUMpOo6dUzVVlLnWL1YLi8FIaenp53dZJBVs8VKZ80A83ulG51VjlmDGcqr+qJUy7/Kayov9m/S2a6DBlS5jnoPeYwBOY7Tb6xqayxfE9s/WVuzo0HlotEpBfG6GMqgnDC/06gQF8S+pIlAX8GhglV7dJzeQ+NNb16dBh2beo9UyvakJzIknSI+N5VKob6+Ho2NjUin044+dXV1aGxsdIC/pqYGiUQCpVIJw8PDaGpqcuAqHA6jubkZ7e3tyGQymJiYwJ49e9DY2IhMJuO8cjoK09PTWFhYQH19vXO8WL6l5SLT09NYXFzE4OCgixZotokRFQWM+XweAwMD6O3tRV9fHyYmJioiMxrVVgdR14AOYyCwlP6fn5+vcBopnHNzc84hUAFVXuJvPZoNWI4gq4K0AFyPF9byFfIS15ERGVUqlkfIo2pk2NiPvkwOWAYu5DEFZCoTnIeNPGmURB0x3stnqyHXcbFPfq/z0T50DBZ0kX4addOICOmYTqfR2NiIhoYGt08oHo8jmUwil8s5542ZzJmZGRSLRaTTadTU1GB2dhbxeBytra2or69HKBTC5OQk+vv7XRkVsxWUe9Ill8thbm4OiUTCZdrI44VCASMjIwCAsbExt5aakaEu47xmZ2cxNDSEoaEhjIyMYHBwEPl8fkUqWgMpBHfkH9JQeUZBMNfTgjDVgZbH1JCoM6jXqSzzPhvBYh+WxzW6qVkl5VmOS8v9FOBrBNbqVwta1PEg39uorjYNNNjSHtJB56nv4OH31qG3Blv1ox9Y49+a3eT9wWDQ6WjaApb/2fJLOita/so1YPlgOp122Q/qKS0bUfBCfczxMphG3mMGA4ALJinf8X8NjBSLRYyMjGB+fh6zs7OYmZmpKL3iupI/rB7WpjyotFVnUXWa6lkLkpRXbTBG+cAGYKwjrDiBa8vnazZX8QDnQHtQKBQqAl8aANBgCvnG6gTdv2UddJVVfq9AnTKussR5kkfVMdFIOudIXKRrouNj3zoXK3NWhkgHzRrRKVIeIM8SI2m2gQ6FvkuN16gDoWNRfqL8q56amZmpyOx73nL5n86B9OG60KGn88F5W6dG8amlKXUWx6VrqXRnvxwD52MrKjQgwnVkH+roqoNJ3KMBEja1UToOjoEyotUR/I64iVjlZG3Nm8E5KAtgGOVQYVaGVA+UAmA9N5uqBCoFkJMrlUoV0UMVcN5DBrZKj/3QmKrg6bg0jcd+I5EIMpkM6urq0NTUhNraWqRSKcTjcceY7JfPSafTyOVyyGQyCIfD2Lx5M1paWjA5OYnDhw/D8zwkk0kEg0FnVKLRKEZGRjA5OYmpqSl43lI9Ir35crmM2tpaAHD7OtTxI8PkcjkUi0UMDQ05p4TGRJUJgVIul0MsFsO6detcecn09DSGh4cxOTmJubk5tx5MW1qeUMeRmQ4F2bqWdDjUAaGSZ7+lUmkFiNPaeV6jjWOhY6HKWq+xwN/yEp0cjSSr06yRbjWWnIt9hvKgKl0b4eZYeb8qE30O56E08HNmVOHpXO3cdW3Yp31HCKOzqVQKdXV1qK+vR21tbQV/cm01I8aILutqo9Eo0uk0Zmdn0dvbi0gk4jJdOp7x8XFMTk5iZGTEOexskUgEo6OjKJVKbgx6LxUrHdrR0VG3j4OGSDdZhsNhxONxtLe3o7m5GYVCAePj4xgeHsbo6ChGRkYwOztbsV9KnV9rcLnOmv3V8Wm2gOus11FXqvGxfM510fuUT3U81BE2Aqk8bR0IjovAVTdpa6SVPKvz0+/UcKntsNFoP/5Up0jBm17DPtQgKhDV8VqAouBK9YCCYB0LeY/8yNInOhgajbZAmWVUqVQKmUzG7WGam5urKDXV9aMunZ2ddXqX1wSDQRcdZv8cJ/fvMRjELMrk5CSCwaD7n2BQbWo4HEZ9fb0DxIVCATMzMy7rPjc358tjujaqb1SHKH+oLJCHdN38HOknA7urrafyiIJ/GzBVkAVUvoxUnRXNxFne4DUqs1Zn67jZ7N/qAHHsqgNUliwt7XjteiiA1z79aG7Xjn/zOQpQNasDwAUk/fQY56j7XyxO0AwqZVIxBdfIzkFLxVTfcT0ZSLEBEjbiB2KP2tpaV3mi+JL4wI92qpfZvw1EqZ5R3cQfzTzaLJsNjCjmUD4jjey4+Lce+qIOBJ0Z9smsu5X7SCSyprIp4BQcDTViOlgCbCWiNgU76kmrkmL0gAtiQZrneRX7JpTZ+Qx1dLRvoHJjlAJXTUdapRGPx5HJZJDL5ZDNZpFOp9HU1IRcLufmzfpb3YvBMo26ujqkUil3DQEy09wzMzM4duwYDh486IB8Pp/H8PAwgKXTpbR2LpVKoVQqudOnEomEA3k8/YqZhLq6OgeoCKTS6bSb0+TkJMbHxzE9Pe0APSPHuVwODQ0NmJqack7P5OQkRkdHXYqdTMq14RgpeBoVVWfOrgMjymowuLb83jYF8aqQeR8Vl3rgCuoUkNkIsvKTreVVA6HGnvzqV9ph+VOBnAIxNZQ6Vp2zyoIqOO1f/7ay6GdIbHSc92mmJZvNIpvNOqc5l8uhrq4OyWTSjSORSDjQEggs1funUikkEgnkcjkXvWWfVNoAMDU15aJGVODT09OYnJwEAAwPD1eU0/FvRqS4ibympqaitBMAstlsxekloVDIHboQCoUwMzOD8fFxt9+HfRSLRWSzWTQ3N2NiYgJjY2OYnJzE2NgYxsbG3P6QQCCwIuvmty6qvNX4Kijg99pUpmiMn2z9rAOh/EODY6PLFpiQh9U5sjys91mjZL/X+XNsOneNRmpAwDpQfhkHawsUDCrvW0dImx/t7DUAXKYulUo5R6OmpqYi8x6LxVzmgM46dTTLbWnHbECM2YNAIOBkgQEbAO4EN8oYS2pJJ46DMqJRUTo/Sltmn1m3XigU3OEfOlZmauho5PN55/jQybbr6udc+AFsHQ+vt06v9qPrYx0du27sC6jkeY38WhkBUEFTtb/8n/rejl1BHv+3Mm0dLwWWfvRSmmpTGmn/Ok8/GvJHMwNWzldzOHRserqnX5CDdkx1DLEW6atOtd7L4I9mZvg96c/5k6cVIANwe2DUfuu6qf3VAAqbzRSoU+R5S4cZMDislQza+Fx1pFfDx3YNuOZ+fKrrtNqaWbzCvmw/FpOrjvbDEIqRFfetpa3Z0YjFYm6zoKbg+TA1ZjoRDtyvNEAXQiPFZGD1djVlpBFAXQxlDGs8+Vz+ryk7jTATuNTX12PdunVoa2tDNpt1QKRQKGB0dNSdGMXoUSgUwvr161FbW4vGxkYH+qenp5HP5zE+Po5Dhw5hYmLClTmNjo46wE86jI2Nuc2BjGaVSiWXUuc+CTow/AHgjFo6nXZ7QwKBpSxENBpFTU0N6uvrUV9fj+HhYUxNTaFcLjswNjExgenpaWdc2tvbUSqVMD09jb6+Phw6dAhDQ0MoFouuVEWNPFOlGvFXh5BRFH5PQ1gul92mLC1VsKBb184qKPKblkGRD5Qf1EGwUQZguXyAKV8+l9cxYsgII8dqI6Eq4LaEgs9azWhRNvyUgzrmfhFqlSv9bY2QNc7WGGcyGSSTSXR0dKClpQX19fVIpVLOyMzOzmJyctIBLu5vicViaG5uRi6XQ21trSv7U37v7+93csHsXT6fdw5IubxUO57L5RAMBjE1NeXq19XBZWSRkWXyMfdlpNNpVxdPhcrMCjejJ5NJdygCHXM61KFQCC0tLVi3bp0rxzpx4gT6+/tdScnk5KSvArbOBcdMHtY10+ik8oDyrUbebMRT11nXmD+lUsnNW7O56vTajIM6HVyTJ4tE6xgU/PE6DSBZUG+dCT8QpsDBgh/Sx4IcpaXqKW06RuV/3kO+qq2tRW1tLdLp9ApQwHcaaXkCg050gmtqahyPz8/PO56mLp2dnXWBH+4VIj/QKeD93JekwFGdD2C5RIV2go6+zpPj4g9LgsgnPNAkFAq5DH0qlcLs7Czy+TxmZmacM6T86QeQLGBR/tTPyW+qV/0Auupyrrnynz5Hx6TRcV5jnSP9TAGoAjOVNw2oaeRXx0BwrXRSObIOitLLz8nmGNWmWPlh88v82zIsPltBudKR3zNTrPtDVNcBlZk9zpG4TLGc7onTgIXS3s9RIn+wrFsda2IJ4kk+l3iAJb7ECQwusakzqScPMrBMuSEGIG6xWV7Va1bf69qvhlOtfKgNsfZbeV952vK/X6CK39tSLD4zEFg+SlifwfXxCwb7tTXv0airq3MpXnqBfCDT6/xMGV83xdjIChmQBkSPfVQFEw6HnRIEsKLuz5a4sKaVTEPBYL2fRtj5vGQyiVgshm3btmHbtm3IZDIAlt8WrQxTX1+PpqYmJBIJTE9Po7e3F5lMBpFIBNPT0wgEAhgbG8PExATy+bz7zaMGjx8/7mi3sLDgmJlKfP369aipqcHw8DCmp6ddiiocDjvQpE6bCh2jtcxi0NDU1tYikUigo6MDuVwOjY2NaGlpQS6Xc0ZxdnYWd999N5544gkkEglXlsJjZcfHxzEyMoKjR49iz549mJ2ddc9lZIzCyHEpX3BtCoVChWIlb3AtFhcXkUwmK8A2lYgeFccfZowYJWRpDIWN/KSnwWjWg2NzQvH/lMnMzEzFZ6oodX8Gv1fniDyqWTbOz09olX81m6POi16jG74toNOSMUt/lS+lHz8jqDrzzDNxxhlnIJvNOgfA85ZPOCMIX79+PQqFgtsTxCOSx8bGHBBfXFx0WTQew1woFNDf349gMOg2kZOOXJstW7YgFovh8ccfXwE6OF46lepYUpaAJQXKzAYjy8y0ZLNZ50RFo1GXqRkdHcV9992H48ePI5VKobm52fEE6+CPHz+O/fv34/DhwyiXy5iZmakAMxyHHumsfMB50ijzezU8BJgKXlSXKm8qT/N5CiTscZkqj5otseUJlD3yM/nXBo0YwVd+pXxr+t+CK1uGqSBD6aQBHTYFmxakWONOHtcDH/RaBSIs+0gkEu6UQJ7GxHInrSlnlqxQKGBoaMjtz1hYWEA+n3elSXzfEvUfnYq5ubkK50IDGDzQIxKJYHh42Mmr1bHkNV033bAaCCxlGpm9tk4I5xCJRJwzNTQ0hOHhYXcwCfmRewPn5uYwMTGBiYkJTE5OVgBAHYc6ELoBV4MgBOuBwMojym1wSmvwyTu8lryoEXUF5pQz1Z38nLRSkGtBL/nN1tyrE007o+OkHACo2CNpA6qRyPJ7QPQ4XevUKHD0kzkFo6onlDf0hbZ6De0i562H0lhZ0/1nvFf1s1a7KB0py37rQP6h3iGOVD1ncaHqXtJZj2ImjWjjqJcDgQBSqZSbG384H+IAVphoNp3Bm7m5ORd40kNiKKvUu8qL6gCpk6bAn7rAVupw3fUYZNs4flaXMECnzqRdM9KUNFZcr/pZHWsGWk7W1uxoEOTaaIOWTKiDQMLW1NS4z7gJimCMrVxeemFeILC80YoEsPs3dBOPgg8Aru6fC0FG4gKqUacwcePqhRdeiK6uLtcPx1RfX4/m5mbMzs5i//79WL9+PbZu3Yrp6WmMjIy4jaM8saO/v985J8xITE5OolgsuogwwRrHGAwGkc1mEQwGMTk56fZhcPMshW/Lli0ukkTPnevBTdZ0SMhcGm2cnZ1FNptFY2NjRQlMIpFAfX09zjjjDCSTSTz88MPI5/MV+xTi8TgaGxsdwx4/fhx79uzBE088gZGRETdnMqoKshoTOn4qVNaTp4CqIeI8GT1XplcepZGiAKoSpuCWy2V3UhK/swqZGQM1FFSMfI7N6PC56tDY+aljbR0O8qqN3lqjZ3+rzClA0dQxm45Tx59KpdDZ2Ynt27dj06ZNLloJwJUDxmIxzM/PY3BwEJlMBhdddBEOHDiA0dFRd1wmSysmJyddhowHA3DdaLQ4Fs6PpSbcw1NXV+fKmxQQ1dbWolAouJdX5vN5p5soEzQENHTAcmSP4K2mpgbJZBKbN292GRA64LFYDMeOHXPOkAWi1Ifj4+P4+c9/jiNHjmBqaqriaFAFPuRPjagqD/Aa6lA2OmI2G6D8QoNqI9aUXwUNVh/yWgVj5CfVUerYqZHhZ+p46LwJnHRuKlNsamiVX9Uga7Tbgj8F58rz6nD5RYtpj7heiUQC69atQ0tLCxKJhHsOjXoul3MnAJbLZVdqytLSiYkJzM7OYmpqyjno5H3Kk0ZaAbjIKt+3oiCUa8ToKSOrehobj4IG4E6NUjqr3PM6LWMBlkuFY7EYUqmUK3mks0S7rGtHpwUAxsfHnR1UvUn+oT1XB4TjsQEcboJV3uRa8R7rzJCHGKhSvlH5UzmzPE/HcWFhwXcfp0Z2AThng/1R73qe56o/1D4Ui0Ukk0kH4LQ0SwMM1vlmsyDUL0qtfXBeCrJVHjQiTaeJNCJvMGirssm1Z9CIOpvrB8AF6TgOdZhUB9lAHHmeOo36YzVnjmujP9RXdJB1D4I6mgxAqyyGQqGK7G86nXbXUZ7Zh5ZQUzfNz89jZmam4h0fKgOcu+ozG3xUOeF3Vi/SEaX+UJqSp6jriAVUzylmp2xyza2T6ucMKTZRHl2tndKpU/r+AjUmBLgaHVOARMPKqJAfOGPUSiPEZFwaLGvM1LNjpJ+E0Eg1U2AEnwQhGzZswDnnnIPOzk5MT0/j+PHjSCaT2LRpk9s4SiHyPA/79+93m+FGRkYwMDCAwcFBTE5OYnp6GuHw0stL4vE4gsFghXEgUFq3bp3bw0H6MXU9Pj7uMgUUpHQ6ja1bt+LKK6/E8PAwent70dDQgGAwiJGREQfkjhw5AmCpLr2mpgaTk5OuvImOCL1yMpjWzgcCAWSzWZx//vlobm6uSLVzHcrlMlKpFJLJpDNu/f39eOyxx7B3716MjIw8aYRSs1kUKH4eDocrzrTm81k3rIqG3riCKPKRRn+pMMh/Wt9MR4GAi/0y45LJZCroR3rpwQd8pnVGqLSZgVFjx7kruLSglHPVUhfSVIGRTddqpI3KmPO3Tguvjcfj2LFjh8vicZ9QNBpFV1cXgsHl8hHP8zAxMYGjR48CWNpf0dPT4xQrnQyuGccMoALUsLyKgJ5laDwdaGBgAH19fRUvistkMti+fTs6OzsdmFu/fj3m5+edcz88PIyJiQmEQiF3UMP09LTrny+8ZCCBTg9lnCCrtrYWTU1NWLdunSsbIX0JGpndmJ+fRzqdxqFDh/Dwww+jt7fXlTuq/JBHKGu6t8M6j2p4KTukJXlFHXAF8EpzOqwEC9Sl1pAwmKH74BQcqsxoxkSdWz5LwZA6zNThFnzqPbxPAQPlRTeO6uf8W/WM7ptQMGbBN+dBh2Hjxo1oa2tDOBx2GWgGmzj3ZDKJxcVFjI2NIZ/Pw/OWTl0ZGBiA53kOlNsAAI09wbDKH9+NwZec0p6Sd7n+BHgbN250YAmAkxHuneALtKhXKfdzc3MIBAIVB3EwE01dw32FwNKBI3zfB3U3+7Tgg6BweHgYhw8frjgQhHqLz+GaWV7U6K86g8r7tjzXj1cUS6j8sH8F5Poc1cEqT4pXqD81Gqy61TpB1Ne0WexDx619sj+Va86L89BAqdoAax84drUROgc/EMwfzYZpVoP6go1BBLXn/F8dMX6mcqc0IC/QrtHJUQzBe6lLKDdqM0ulEmZnZ93rCDzPq8iK8HnU5SofqhuoZ3nyp2Y7FONQbqlzOE/KLzENeUEDIzygRDMHVt/zOuUNdS6BypcKqz7WddcskNJQ8anyssoJ19IvgBCJRCoqP1Zrp+RosGMS0wIoFS5OhmVHLPXgAJXJWPpCQ0hB0XIDVYwaieX/6p1p5FCVCce1fft2bN26FQ0NDSiXyxgeHobnediyZQvOPvts9PX1YWRkBGNjYxgeHsbAwAAikQjGx8cxMDDgjBDpEQ6HXfqIzlgymURjY6PbmA0AGzdudM4W35tBZd7Q0IAf/OAH+PnPf+7ed5HJZLBhwwaceeaZWL9+PY4dO+Y85pqaGoyMjOD48eOYmJhAPB5HR0eHW3h646wlZE3wwsICRkZGkM/nnSAq3XjCVjKZRHNzs5sDy61SqRQGBwedMWMmamhoCLt27cLjjz9eEflRT5o19J7nrSiTU2XM0hwCe11vGmM14uQ98gPTmxrNJr8yYqKpeDUkFvRoWQuP8A2FQm69rcIkoGOfGoUlfbVUSB0Ujt8qHTV6lEM9ck+BoTr5NlrO5zAieskll+CMM85wTm4+n0c8HkdnZydOO+00HDp0yGUrGK3kXgu+B2ZiYsIpcwLohYUFt0Gc2TPNAjQ1NTm+mZycrDjac3Z2Fr/85S9x5MgR986Yuro6bNmyBZs2bUIikcDIyAjm5uZc9m5gYADj4+NuDxP3ZbD2vb6+3kVmaSTn5uYwODjoIk9cZxqRYDCI2tpaV17Fk7bS6bSLJjOlHolEkEqlMDMzgyNHjmDv3r04cuSIMzjKI07xBpZPFrGbD4HKN/Pqe1R4nRoD65xqkEadSl6rxohz10CO0kHfNwNU1rrzXtWtCrAJqlmiQeNmASUNNedmgRjHaWVanRUFgvodZcQaT9IkmUyis7MTnZ2dWFxcxMTEBAKBpUMGqPeYrePLT+ms0vASxCs9FRQCy3s2SF8Gl4Dll42y9jsSibiMIGk3Pz+PZDKJlpYWZDKZinJG6kuWKXqe5/b5BYNBV55VW1vrDlugntWyLT32WW065TabzbrN8AwEKYAjTRcXF9HX14fh4WEX2bfOK50VlQnV4eqAkyd0D4yN5qt+UwzBa0l3XRPFDio7GtBUkMtAloI/lVnKAu0GwTpxiDob9hmqv/3KF3UszGSpk6T8z7loVYFeo/JJupDveL/9TZ3EcSso1blw/tq/rpP2SZ2rwRPVP3wZqAYUtfw+lUqhXC47WWDglHuLmClkZlvfFcE14bM142LxpA2ekF4sxWVf/AHgnCS+INlmk5UeuhfErrk6Wuqo0nFXx8XqXa4N+1WcRR5RedK1oF5SLGWDPCwjfkozGvX19ZidnXUTJANpPZ2eV86Fsyk8fsZrlLiMaluDy8mp4VUlRIbVz6zn5XkeNm/ejB07diCXyyGfzyMQCKClpQVdXV2oq6vDoUOH0NfXh8OHD7uNqwRX9LDHx8crFpaR0Fgs5ozA7OwsGhsbsX79enfe/+HDhwHAHdNJhUWhSafTePDBB120OJfLubKthoYGhEIh9Pf3Y3JyEseOHUM6nUaxWHQeaSqVQjabxdjYmNtIm8lkXP0hjWowGHTRuImJCVczPDo6irGxMcdUdIJSqZT7aWlpwebNm93bbLlHgwI3NjaG+++/H7t27XJHgTpGCyynlbnu6lBq9ERLsIDKDVpMbVsjwmdo7akCdBVACqoVHI4rnU5jamoK6XTanafNZ6ljpFkFPl/BPtO/qoA0latGjjyq6WvNRNg5MjNInmdjX6pEeR+zExs2bMAFF1yAuro6TE1NIRqNorW1FS0tLYhGo+jt7cXx48dx9OhR58hqdDUQCDhnl04VeYK13ul0Gvl8Hs3Nzc4B5j4N0jEej7vTpYAlw1EsFnH48GEMDAygVCqhrq4ObW1tqK+vd1lL9tPX1+feMl8qLe1PqqurQzwex8zMDEZGRtDS0uKOIGUAgmvD8itGgrX0RWlHxzsejyOXy6G9vR1dXV1IJpOYmZlBMLh0ihyzPiMjI9i7dy96enowMTGxInJI3UT9qSV2XEvyKEGL8qoFaGrw1UmwEV2Nxmmki+PTcj2NcvEaC54ZXSbw4v98tnUa1NHns3kt58Gm/G5Bpl7Lvm3ttgYhNAjA+WUyGfceo0Qi4dapqakJ2WwW8/PzGBsbw/j4OKampjA6OuoyzUpX8jJ1loIQDbhxIzUAB4RIVzqttK3kaXVoampq3J45AI7eCwsLroSFssgTBgnCmJmxx+HyHs5DgwdWN3KcwWDQORwsP6TuJgAOBoMuuMVsv+6N4bVKQ+U7gmSuswJQrqvdH6d8Q0ygz9C+1GElT2g/fvpe+Yz4Q7PVCgw1i6x6RB0I6xSQTxQf+cm4gnQtBdNxc14cs8oS5VedBeIDjpvf8zdBptptdUB4n8V21i6SljoX0o6BEGZP6FToXkv2waAW5654VAMz+v4z1T3qIJI2Wv6p4FydBPKm4sxYLOaCZlbvMpPCE+XU8VMnT501m522OJo8xTFbXrHZLc0Y++lXrp/V/eqEaIZP7yNNKatP1tbsaHBTKInEAaoHqN8pUytT2UiF3qPEo2EjE3JiJJ4yg2VONeSMNm7ZsgWnnXYaUqkUAoGAU9yZTAaLi4vo6enBwYMH0dfX5za2LS4uvzhpcXER9fX1FUdpspyMjEtDFQwG3Xs2yuUyxsfHMTQ05FJ6ACqO+OQ4Dx065JQ9I6kc39zcHIaGhjAzM+P60Qg1sORF8+QSRnV5RCGjsAS+GvEAlmp7GbkbHh6uAKtkPJ48snnzZnR2dlYIGPdOjI6O4sEHH0RPT4/bmwKgQulrhIOGSUuQKOwanVfg7OdEkqdsxJ98w+85F62RZ2M/zL7RkKizQt7UzVSM6nFu6gSpE0GFwXsJTvTZOhYFpjZi5Se2vJ6OCHmLoL6pqQnd3d3YsGGDO8Iym82iqakJ4XAY4+PjOHz4cIUcEITrM9LptAM0lG/yGpU538TN9wYwWkzHPRRaOuGNpSY0DMXi8gvDSqWSO2aWYI6n8xBgsX42Ho+7bBPXZ35+Hs3NzU4eOV6uvR7yoJFfloExa0dQwjFnMhl3Kh0dIAIPruv4+Dh6enqwZ88el2HUNVUlrgccKE9zLS0Y4//kNTU4NoJp94Io+PE8r6KUVcEln02d6qfrqUcUfBGM2mv9yvjU2VL5VFlQ51vvVZmxzriOWxsdAGZq0+m0e2YkEkE2m0WxWEQ+n8fIyAhGR0crMsGBQMD91qwqS2U9z6t4pwv1LelCfaIbwklnZkiZsS2Vlvd2UO/QdlHf8Id2hA4JgZheS95k5Jq8QfrxmXwuQZ6WQfIZDKzpiVWasVEHj3usJiYmnBzZsifqKQWDWlala63BTXUe/RwW5R99DvugTFNXW/xiHQE+V20Rx20BvwYX7Pj5DPKp8rrys45L5UVtCe/T+ZNfbNM+OS+1p3yO8oJiKtVNKvvWdpFGutZqmzRiznmozKosW7ooCNbP+TzFC5rFJXbUEl4NpqiN1Yg/x6k8rYEb4lPiQM2CqDNMO6qBeL91svzCa9mvXUdg5dvFlb/UadZ14DMU52nfdi2tM8y1ZQbpZG3Nx9vSINl6eRVIXVAdsEar6Sgo8Wz9r6ZVNfKrSkOF0+83vc36+np0dXXh9NNPd1FNAhKelnPs2DE89NBDmJycdAZAXzDGaCZLKeipqgKg5zo1NYVSqYSxsTEUi0tvQ6aSVSZVQ0iBHxsbc+MrFAou88ATDVgLx+gDjUq5vHQkZ7FYdJsBmeInwyWTSWckaHCDwaAro2lra8PTnvY0jI6OYv/+/RgeHsbIyIjzxAnCBgcH3ZxaW1vdMaFct7q6Opx33nmIRCI4ePCgK7NRB0IZXkG5ghd+pg4jFYh626SlGiUqJEZDVGhUQfuBJ47B7glSntfPyc9s6kxoxER/K0/rd2qE+L/OT5WvzlWdIV7PcYbDYWSzWXR0dGDTpk1Yt24dksmkA82BQAAjIyPo7+/HE088gSeeeMK9W4IgmGlsHhzASCY3YGs5C3mRzt7ExASAJQBFZatHGZO31Iiyxp2nutHoMYLLUh+CHR41XSwWnTLn2o+MjCAQCGBiYgKpVKoi+qwRLvbR3t6OdDqN8fFxHDt2zJ0wpaeKzM7OYnBwEGNjY2hvb0draytqa2sd8IhEImhoaHCyuX//fqcXyG+crzZ+pnyhPKy8qLLj53Q+2ef8zgIT5UkFgtaJ0Ws0IMD11/HZoIA20sv2qzxOkM7rLS2AyogcUHkctfbFrGxzczOSyaTrn043S2P5/iAGg7QMhnyn+xZUNzGjqhFSDeiwP3U8Abh1LpVKrqSVoIE6hocfkM46N43S0rlQoEK9S7vBsZC+1GHBYNAdF02niO/1oFwyy1soFBAOL73sknPUzcMAKpwPllmS7n6gSm2kzk/1nAYrLQBmn5b3LID3i/izH3XK7Rh1bDZopmBY7/GTAQskdXwqU9YJURthgwdsalM1SKvP4H2UHZ0D+dOWRNk10TFqQNlvfezaKiAGlrMkttRHnQjep9fY7JhiMqWxzbTwcwYCNEDI+RD/aYmrlrlaPMrAldoW9qWHHdmDciwPsX/NpGiz2W11lFRWbFbSDwut5tRZJ0hp6Tfek7U1OxqMONpBk7E0EmDTQSokOkD1ZtWrUkalIPBayyj8m0SjJ1pTU4PGxkZs3LgRGzZscBH3devWoVwu49ChQzhw4AD6+vocoOcc+dZXMll9fT3a2tpQLpfdRlKeTEHwQwHVo/540giNim6i9quD5fPb2towPT3tTp1i454Llqcwksb64EBgec/KzMyMo12xWHSnLJHhSdtwOIxUKoXu7m50dXVhw4YNiEQi6O/vx8DAgHtZGUvGCoUCjh075rI0nZ2d6Orqcpvn5+bm3HGgZHieSkXGtWvNdbTGxEYtLGPr9cqPuq9CAQ95knXnqlxU6PmjG7E51kCgMjprgaMqfzarsAl21djxmXSkbBpdlQ7vV+VO2lD5UQ7q6uqwadMmnHXWWWhtbXWOQWtrK6LRKH7xi1/gl7/8JY4ePYrJyUmXBuUpNKyp5wEG2WzW8TWVKx0IBVZTU1OYmJhwgEvXkrTVSLxdi0gkgqamJoyNjbmSFQIqOhfcqMu14J6MxcVFl41giWSpVMLk5KS7NhhcfkMynYPa2lrU1dWhtbUVDQ0Njp8ZBJiamqo4qpS6Y2ZmBh0dHQgGgxUnFdXW1mLbtm3OWdLSGz/epB5QsEHdYiOHGhVUnap6ViNPajxUh2p2TEGYyps6zFZvWbCqep3rzL5tkEH74VjUSVCQYA2wHe9q4JOf08no6upypTPBYNDxOPfVsGyIfRFIU9boaGumiQ4uQTgdjVBo+ehLLYfUiCiwXLKmGVKNSjLjpfqIc6ajrLaZdNaMB4GSRvPJh5oBpXyl02l3Khsd7Hw+j1Bo+UhSzaowA5lOp12JGOdRU1ODuro6pyuUDzhmG3hREETeIA/Zsg4FvWwaGVd+UP7xu9/KI8djm8qF/pTLZed4WVvF+9g310M/1zlYh4vXqfOhdpR2w9oui9X0REbNapE2egCLxW12rH5OodojC2R1/w2v12yBfq401eyROnjal15DO0k+VZ3I63Xu6lgSy/F6Bhn0Ggaq+Tnli/iAWQ46KbQ3Vr7Jfzourhd/c11JI7v2fK5iHnVUyBuWh3RdOEbVTToudRKVFn486tfWXDrFqA8Bq5b+aIomGAy6I+8IetVpUOYj0xM06VninBCjgnoevU6eP0xdUbF1dHTg9NNPR0dHhwPsGzZsQDgcxq5du/Dggw9iYGCggvA85nX9+vXO2GQyGbS1tSEajWLfvn0YHBx0WQM1SKzD0/pPzpEbxBkF083SpMni4iJqamqwefNmNDU1YWpqCkNDQ66GndmYYrGIzs5ONDc3Y25uzoGXsbExeJ7n6nVHR0cd02v9L0EqsCxE8Xgc2WwWmzdvRiwWw8TEhHvfRrFYxNDQEA4ePIiJiQn09/c7JRuNRt1eFDocNLbRaBRjY2PYu3cvDhw44MYHLL+5U4ElFQ6PUFWlSzpTgWqkQCN16nyyX3V6bTaM96wWmdG9IORtAmlN9/JZfLb2aRW5H7jTVLzyFB1PdbaoMCgPei95joClrq4Op512Gs466yxks1knryzv++53v4vdu3djenraAXlmybLZLDo7OzEzM4N0Oo36+nq396G3txdHjx51Cp0llXTidBOy1seS5nzpJEtLLEgsl8toa2vDhg0bXGaN6Vk6tADQ2NiI9vZ2B+ampqbcSWHcT8YIkr7ngoZEI350qLjPI5lMYn5+3jneMzMzGB4extDQkHO+Sc9YLIb169e7F3zW1dU5WU2lUujv78fDDz+MQ4cOuZOKbLTOGgl1jJmJ1IALN6YqmFOdyz41k2wjfqqDyLdqkKg/qOPVWFOvMQOszjPXU2WTY2M/HBf1O5+vwI9N5UHnwDGqfNigVzC4FKHfsGEDOjo6nJ0hH3qeh4MHD2LPnj0VJXLkTe7LmZubc+V3zC5MTU1hZmamwkhTHqxMczxcB2uouaa8jmNkQIkZNeohNh7eQUDDN3jbIMji4mLFyYt+QJRrRYdZsxMsM6M90VIQBXrxeBwtLS1IpVIVgZdoNIrZ2VkcPnzYHQ2s/K5OhJZGWb1K2eG4qC/JY7qviACR8+XJSXNzc65P5RPOX4Niqs+pz8hz+uJEyik/pyxo0zHbkw/VydHrOD6VCdKUeykpI3ReLKRT28BT9hgw4ef8Tb5V/WD1krVFlEt1Argmus78nmWH5HFrg9Ux0GACaaJ6jTwWCFSeVMl11Je1co6qW3UdSONgMOgwCN9hoyX9fDafA1SexMVx8RRDPpOyR6xoaa3z5Zx1jtYpKpfLrnyJ41NaMDPKUkd7bDN5yK6XpavVxXw+ZWotpVOndOqURkBIUDV6ehIFJ0VGIsC2nhew/P4L1rDqpKxxUUOmipKKNp1OY/369diyZQvq6urcCTidnZ04cOAA7rnnHgwODjpwxPF2dHTgzDPPxJYtW9x+icnJSbeIw8PDDvTzHQOMXvG5zBzw7a00VnzpFwUZgNscGAgsHTdIpZ3L5dDS0oLx8XH09vZidHQUAFy5S7lcRnt7OzzPQ19fH6amptx+DQphbW0tGhoasLCw4OrZi8WiewszjQ6jS2QklsZMT08jlUrh3HPPdRt51fHZv3+/M3zMiDQ2NmLr1q047bTTEAwuHb2by+VQLi+Vjvz0pz91b1RXYQeWAf/s7KwDVHqcmh8Ip5OqPEWFwYiS1hOSTzlmWwJIfiuVSm6TM08IU0Omtei6wZEAirzPSCPvsRE6Cr5ugtOUsF5LcEJ6aB28dWQoC+l0Glu2bMGFF17oridQ2r9/P+655x4MDAw4enjeUtS3q6sL69atw5YtWwAADz/8sAOxU1NTGBwcxNzcHEqlknuDNnUBsAxIaYS1vEnfQVEul13mkOvPMioA7pjZ0dFRd3wt+6MuyuVyAIBjx445ME1eZiSY7z0g+Jibm6t4KzMdETo+WlpYKpVQW1uL7du3o66uDvPz8xgfH3fZjSNHjmBhYcEdh5xIJNDc3Iyuri60tbUhGAxienoaLS0t6O/vx65du3DkyBEXDCDfkU+4lqpTVak7pR2oPG2J/K1RcwVAXF8boSUw0WAPr6Ws6ZG05Ddt1OWaYVN+pExYB4j9WYNmI9HqlHN+OjfVDZoxpRzwmOYNGzY4vZxKpTA3N4e+vj4cPXrUbQSn/NXV1bkfvjl+bGzM2QLVqeQh7ptT0MKoKnUS52rflcHrlPacj55yx72CqhPVYWV2XXmCa86AhQZRuPeEa6H6wzq1zErE43En+5QnvnNJnRRuGNcj6zOZDCYmJnDkyBG3J0V1r6678pbqcdXFlk8IxhSrKLxRx0s3civvkc90PJq147roGKn/eD/71jErQCMIVrmy8qQyqn+r3FBf6P0EmeoQMfCkeEm/17HSrrJUnutPPaCHE6iu4bgIfNUxtDSgrGmJPHmPsqDjon0FlgMp6iwROxIDUE8xsJFMJivGpxUNzORpsENprUCbtGWAIhAIuJMM1bFXOrK6RjOg1FE8TEcbaalVCVYXUgaIHzTAozJA55796j5m7V8xts5ZK0O4XtZWRSIRV6L/ZG3NjgZLAujFqSdHD1MzG7qwutGXk6Mwc6EXF5eOY2WKWPvgZPUdHqo06NVHo1Gcf/75btN3KBRySm/37t345je/6Uqf4vG4A16NjY245JJL8NznPheDg4O499570dvb68o2qGC4SVRBAiM4BCY8m5mn8JDJ2A/P+Q+FQm6jbCaTQU1NDY4cOeLqf0dHRzE0NIRyuYyWlhZ0dHS4+8fGxnD8+PEKmpOhaOwYmaZXXVdX504CIRNxDwbrb1nmxYgDj7blRsJcLoeamhoUCgVEIhHs37/f1a+HQiE0NTWhs7MTZ599NjKZDMbHx90bx3fv3o29e/difHzcrS0NGfmKAs0oN0EQN1hOTk66qB+/U+Hj/erd61qpE0EFrMBA+2UfdmMYSwcokFY4FfyrYeD3mlXxi25yTdmnRrUqBDewXIpiDU1tbS0uvPBCXHTRRe75mUwGCwsLeOSRR3DPPfc4J1lloLOzE9u2bcPFF1+MvXv34nvf+5475lmjHFNTU8jn8y7iychPMpmsOBGERiqXyzmaMppIZ4WOA+8PBpdeWknDMTQ0hHw+73g4nU67jeXcuE2Fq44aHQ/KIjN3dMrpIPB5PN6X0WCCQc/z0N7e7niNZYuMXNfU1KCnpwcnTpxwR1K3tLSgpaUFmzZtQjabxfHjx9HW1ob+/n48/vjjOHTokNvHQmCtkUBrkDWqqGDd87wKI2N5RflCsx5alqeOugUF1FNaJqCRSXWU6JBbAKeGkPPl+NUpsZFjBXjU7QRzKneWDpSpUGjpjfCtra1u/ahzh4aG0NPTg+PHj7uxhkIhF2hpaWlBZ2cnEomEey8Eo5Bq97T8j+tEO6lAVINO/I73WtnlYSMEUZwnA1F64AL7oaOsMqrBQAAVR4trNJrXMBNC3tCspAaiVI/qi1fD4TAmJyddYIzH4eppW7Ozs2htbcXg4CB6e3sdz9goqtXdynMci+VF1fPsRwOUGuChjdYoOfvWAJQGoZSW6twpELU8S52k0WrOTx0bayesLCoAVieWY9P5Ew/Z9yMo9lL7yLFpH9R/5DH+JkDlmAnYrY2lPHD8GlhQntZ1scEH2l06tkoLNga2qfu4r0SrRmhf6LxrowPGag/tWzPonCfppYEB8gFpSwdCA38cA8fPZ1LmlLdUt3Eeih+UXtSn+rnyq8qP2gjVzdT/7FMDpsTu+rJObbTv+XweJ2tr3qNBYuhgNdqWSCQck6lgKJNyYbR5nueOiVQPWb059TwJDqlsySCxWAw7d+7Epk2b3DgYhfyXf/kXPProo+4s8i1btiAcDmN4eBgzMzPYuHEjisUi/uVf/gV79+51pSSsKeWG6HB4abMzI7G6QOVyGel0Gtls1p0SRVCtUT8yGQAMDw8jHA7jtNNOc6VKhUIB4+PjOHHiBMLhsDt2lIYnl8thcnISyWTSgSxGq7mBLxhcPl4QgDuecWRkxNXeUuh4/C3ny03lPN6R3mqpVEJ9fT1qa2tdqRQBJ08C4ukHnufh9NNPR6lUcsegnnPOOSiXy9izZ4/L4KgHrgAjEAi41CVPaCGt6ehqxIoKkAqA/EGnSQEO++XftnGt7IYua8hIE/1MoyF6upgqDxoBTetr5ItGxBo1G7nSZ6thymQyOPfcc3H++edjamoKuVwOra2tOHr0KO677z489NBD7uWaF154odtzw2zd7OyskxfueWDUn3szQqGQ2+zMN9rbufCIYHVKgOWIFOWYfbe2troyEa47eSsej7v30TAq3djYiKGhIQfYKYfcrMv1LRaLTg40gqe0LpVKiMfj7sV83ONBEDcwMOACCaxfZxavvb0dQ0ND7l08dIB4AMQZZ5wBz1sqZaTsMALMEkyuswUUXHN1IBTkEDgRsNKJ1kyHZppY0kh5J+AgGFGDTB5X+SStrOPMfjley6fqRCmo4nUEB2pb2KyDxb/5uYIozQqm02msW7cOra2tbrx1dXXo6elxb7NnSVpXVxeKxSJOnDjhDgEZHh7G9PQ0xsbGKjJi+hJPZquj0eiqJRqkDx1sXRMN2NHBpUOrjpk6GcyqaCkHr9MAhsobMx10TIDlF+wBqDjZigCEDiKDT5QFOuBcRway+CJDnijHfVp0DlmG29fX5+Rcjw61UXHlNQVXygcK6lUHUJ74o84o+Zz922yJRtm5VpyvZnpUP2smUstpNeDENaLDrIBdsyKanbOHmQCoCFzyXnXU+VylKasIdE8Naabzts8kfbQMjPTQfamqDyjnNiuhARTNUtBekx5KQ806qBPA+yhb5GOVKc0c6FpxDryOwQPlB2YhlE62usLzPPdsDfipo8RxEGdocIUZdHsQCteDY1enjXRTHcp+OVZdC9JK10N/qG/UQWYQVgNM+lJB1TnBYLBCrz1ZW7OjwQXjwNUAUXDUoaDAkOCcEPcr6NsQSSxGONRTLJeXN6FzodTYRKNRt4+C+xb41t7h4WHs27cPjz32mAOdfNlRS0sL2traXOSlXC5j9+7dWFhYwNjYmCuNogOxfv1694ZIMiKJzlQtT1/yvKXoO48HpYPT1dUFAA5kKDDu6elBTU0NMpmMA/c8NpB1pQQAPF6Ux5LS0MViMYyMjDiG04zA4uKiO4c9FAq59wUAcOAsHA6jsbHRbQoOBAJuHhSeRCLhXhzITeSZTAYDAwPufQssIWtvb3eAdHZ2Flu3bsXo6CgOHTrk1lMFn59RAMh32qjsaXAVfOn/5EMqARoQ8oEqIEa++Zm+KGi1DJwaOM1kkEeZ9aHBZl+qlBQcaH82yqyRCcoZnUXOOxQKuaOHt27dioWFBefs7Nq1C/v27cPhw4cdWE6lUu7t2p2dne4QhOHhYTz++OPuDPzZ2VknU5lMxr2fhfOiUaLxppMVDAZRV1fn+IwR8Vwu5067CgaDzonnnh4AFTxP+VNgxDXJZDKONizNUp4ibchDmvalUeHbWzXCy+wH17tYLGJ0dBTlctnpLq13bW1tBQB3eMLs7Kx7u3oikUBHR4fLKLW0tDiFPjQ05N7WrABKQb1maizo0Ogm9YpGWZV3NGJL/uUPM0K2DE+DObxPnTQ6CKSRlqxoxM1GpbmG6vhx/DayZyN4CjjVcdFAQ21tLdatW4fm5mZngyKRCB599FEcPnzYHRDASOP8/DxyuRw2bdrkbA3L4zTSz5pv6kuWPXLsmgFi4zwKhYIrOyF/k07hcLgi0MZjm6kvFGRx/lxnyjgdHz5bI5MatdYSJ9ptgl8FbbTVyhcst6UM0ckl4KOzTgeCZWZsyWTS6Tnue+R7n/i5Bqs4ZqUlf+vnSmfSRXlLo76kDZ+htLBOC7CMOSiT5FF1KKxs6r4mBX6alWPftAMK1JktU5yj8kLgb2ljMznkHXWkKDMaTGDfvI86R4Ev/1cnmd+pDlI7Zx09dbBIGz2BiddyLchXGnzT+akzoQ4Xwa9mM9QGsz/SnTKrTp/qFsqYzl2zf5YnKTeaCdBSND6LGRtiO9JB+1InRJ1H1X/Kb7omtHF2HZWnVJ+rA0G6WodEeUUDWidra3Y0mGYhUfUBalBIBF6j9Xo28gKgYuE1SstmI2wq+PyfL5JjVD+dTmNkZAT79u3D3r173RuPuf+BmzdTqRSGh4fd+yv4ArupqSn33oF0Oo1MJuOci1wuh+npaXduv6bENV1Jxc1sT3t7OxobG11KbmZmxile0nZ6eroiJclSDwAOWLIsirTTfTEEcqpo6OTR2NIYZTKZFYpIlSAdQjo7fJP40aNHkU6nMTQ0hFQqhaamJqxfvx7RaNS9jG1sbAwHDhwAAPeuEir3bdu2YWJiwh07qgKkSlcVL1AZIeScNL1PetCokC/ZyIPMcHCOKoQa1VBAo9kTGhnlVQV0/B9Yfikjx6aZBwWACgx5Leeg1+qc6FwASwo1nU6jvb0dnZ2dqK2tdeVIPT092L17N3p7e110oqGhAbW1tYhGo+ju7nbHep44cQLz8/Pu/QEzMzOIx+NobGxENpt12bBUKuVSygRsOm82fWER9/LU19cjlUq5SA55n0acb/ymM63vBSBtuJ4syyK/MNKlETlGpqjceS/5iMfiauaK17Bki+CMMkDHiS9Vy2azSCaTaG9vd3JXLBYxPj6O48ePOznhGjc0NGDr1q0OeJJXVJGT18gzGrm3fK3RM+UZP77R/qxR089UnjxvOXOiDgx/NBNiDaJG59VZsEBSo242eMBnWjDI+9VAJ5NJ1NfXuzJPAumRkREcOnTI7cfQ90CEw2E0NDRUbPLX09Qo87Qv6lCo7WKJp51bKBRymzMZ1KAjwfny8AFGcFl+ofysa8vfXHs698DykZ0aYFEnk4BEm26W1vUgfblnjeNjQIS0mp6edrLK/gnGGLQJh8Mu4w/AvZiQJ3b5OVargSu/azhW8rKN+CtI4n0qJwqWlaeA5cyQAlAFxhpA0vFaWbRyqmPQPjTYoHzENVc9oX2oHVG7asehdk11iuIw1TnKd4pxtG8+U+VYn6uy7UcblWfqa7XnDHyoQ8Z7NNrPrJvuneCzlEc4N5VD8q5dTw2QMLjMtdGAoF7HrL3FBfyO/7McXceqtFA9rDypjoXiBdX56mjw2eqEMqui41Ma0abyGqU3eWEt7ZSOt1Um5kT4Q4VuGUwFB8AKRaJerUZPlOF0UqokeOLGhg0b0Nraikhk6YjK6elpd3wtSxpo3JkxKBaLGBsbQ29vL0ZGRlwZUjgcRlNTEzKZDBobG5HL5Vyqkk4MGVKjuFTAVBDcAwIsRXJYYsL/6c2qQzYyMoLh4WEHbOiR06FgNIDMa5Wedez8GJN/a82uOhps0WgU09PTzijy5JPx8XFX+pTP5xEMBt259O3t7RgbG8PExAQGBgYQCoXcpkBuqty4cWPFewk0ZarKykY3lA9p5NVDV2WhSpf/U3FoKpLK0iqMcrnytCcFeirEGj3XceshCNaocT40gBqt1jmwX/tMXqOZmmw2i7a2NmzcuBHt7e0IhZZOqxkdHcWePXvc5ktuVt6yZQvS6TQGBgYwNTWF/v5+nDhxwpX9UAHxHSmZTAbpdNopcPIOx6NRd/InAHdqD0FdNpt1R4PyekaISX8A7kQfKjk1LlSu5A11cvyCFtaQsh/yF98vo0bGRt5nZ2ed3POkJX2p39TUFJqbm5FKpdDQ0AAA7v0x/f39jpb19fWujGHdunUYHR11jovlf/6Qlqov9Td5RMes4ELln02NEh025UPt2w/UaH+MDBIk8jNLb322jln5m9/rM/xApAIWHVNNTY1zonmUZCQSwcTEBPr6+tzRtel0GrW1tW4fH99ezaOKNeMVCARcwIelSzpOjlGz+BZI6T4vy4+UZQJ0Om7US8rHNgNs6ab6WzNb5A3qHTt+fk6Z0nIL5TtmYbieuiHe85aOPo/H4xXv4WDZVam0dLy0vkmcARKeZKWAnXRlsyB+taCMNgt0+ZlG260Trzys96qTo46L8qLyps7BDzzaMen/nudVOIPqMHC8HL+uOftXR0U/86Ml9St5hv37OUSqO1VH6bj9AmSql1Se7VpZmilt1b5YmvrZeMWldlzalEY6L9V5ascpz3q/zkcxgx5uZJ1J4kc9wZIOOOe7Gk3svO1YrdzoWmizzqmf06c8ZvlGdfLJ2podDXvkmxJZPU8qKC4IlZBuhlFieZ7n3sLM/3XinCgFR+vqM5kMurq6sHHjRhdNKpVK2L9/P/bs2YPBwUEAS2Bmw4YNeNrTnoaZmRlXRjI1NYWxsTFXi5pIJNDY2Ii2tjZ3XnoikXCZCkbIWPpBWlAwCZo0WsrPmeEgeCJz0ZGoqanB/Pw8jhw54owOHQ1ezxNx5ubm3FvHNToUi8Wcwua4rEGkwNHb1ygjMzTK6MDykcWMaPM89VKphOHhYRQKBeRyOXR2diIYDDoH4vjx487wnHbaachkMpibm8OZZ56J0dFRnDhxomK8wPLpZjZya4VfFZZ1ODRaw2toQNkU8OgPeY+nh/E6BWJqxNn4vX6mjjN5m3LC69SZUmCgzrZtOsdIJOKch87OThcZXVxcxKOPPop9+/Yhn88jHF46unXTpk04//zzMTs7i4GBAezatQtjY2PurHyWvbW0tLioMPlaHVYFTywhIrBTB4oONaO6wPLRvKQ1s1M8vMDzlo9nZh98FuWBz1FdolkL3mdLBMjfBD9qDHUNAoFAxeEUi4uLSKVSLgrLeROYep6Huro65HI5NDc3uyjv5OQkAoGlDfp0Ctl/d3e3czY4RlXqND7UGcqvvMaCL+sIUB9zPewcWQJDnUWa2ewB19jPIDNarbRWuaXs6cZ1lRlep/v4lP9XCzTpTzAYRENDA1paWireAO95Hnp7e9Hf3+/GmU6n0dbWhlwuh3w+j/HxcfceGdURLMfjO5Uob1b/cEyqU3WtNJvMeShtlRYcIwBXvhsIBCocEdoO3styHo3IW0eU/aoscGwqqwq0OS+Oh2tIGSU/0VHX9y8wwKDPmJiYQCSy9DJL0kBPBJqenq4ATpa+qhstDygtOX7qIXUKuHbWQeC8bRRZn8MxKF2tDdWxaKSbn+sGah2Lzsk+Q50b/d/KGWWI47T087Ondp8EbZtfxFpBtdpAP4CqtNFsg85T7ZvVWTYgp3ZYHQjKgdWN1Gdapmazw2z2Xh2H2jK/tfLLEPNeoBID8IeVGMR+1I+6Tjajov2SB+y4rA3jvP3myv60MkJLpXiP7j9SXMiSWUuz1dqaHQ2CC02LUYnwJAwylC6uBX16bq8aLZYbEFCQ6dl/MpmsOHeZEfStW7eiqanJ1Y6eOHECjz/+OAYGBlz0KpPJYN26ddi4cSMeffRRTE5Oor+/39WPl0olrFu3DmeccQYCgYArDeGcqaSZ1aBjoBvuSqWSGyOPr2S0NxxefskYT2EiA5IexWIRLS0tDmBxkx2dFB7TGwwunQbFlJcCBDonBIy66Tsej7sjz4ClTb96njU3lTNlT9CXTCYxMTHhMkP19fVIJpPuDeGsZS4UCmhoaEBra6vbKzIzM4Pjx4+7aPbZZ5+Nffv2oaurC4VCAbt370ZfX59zNjRSRr5QXiFP0ahp9JB/K+9ReIBKcE7h9IsC23VRQddx0KGyUT4+WwVQDQevpeOuZScaPeaYgMqSJFW+4XAYiUQC3d3d6O7udvsuSqUSenp68Mgjjzgng3ss1q1bh61bt+KRRx7B3Nwcjh075k5SY8aD744g2KYcaCpaT3rzM3yM1Gv0TcefTCYdX1uQxGMwi8WiA42kBx1uLV9RA8ZneJ7n3mvD+/Ue3aCbSqVc9J5Na4N5Ktzk5CTy+TxKpaVjb2OxGI4fP+7KP7jpPJFIoLW11R0JXSgUcOjQIQSDQZxxxhloaGhAsVhEU1MTzjjjDMzOzmJycrLCIKlc2+gpeZEpbRstV4dcDaD2zbmR5tQfbApqrCFW54xZXOsUU7ZULrjOCu44NpvZs+CSIEojuZRRbspet24d6urqUCotv89lYmICx48fd31RH/OEKZ6oNj4+jmKx6E5iYzlcOBx2AQc6ZBrAoZwowCOQUKCrBpq04Fz0WFzLq+oYkD6sAQcqXzpG28TN+eQZjZTqOts9bgw26ZorUGewQHUv9zLp2pAnWdbL/SnlchkTExOujJl04stBuf9J6WDHx++IERQUWV7T+xVnaEZVQZwGo/T4Wb/nq+OhMqXgXR10vYa2m2uoGIklbBa8KwDUDeXqzACVe3PsJn+VXzqr5A9iGNWl6swqXRWcEuORf3kfx6t6wc85I3hV3WsBPPWQjof6hwFBfse1Zom6nzOjusVWh6hDxEyjBsbU1inNbAWD53kV75fSufFe7n3UvVHqrFuHSPuwQTSdk8qMyoXqAdJFeZxyrNjVngDLZ9lyq5O1NTsaLN1RA0iPUoVDBYPKme+d0PSsekm6qZSMz3u5WY+nA1DQeIxqW1sbhoaG0NDQgHK5jKNHj2J4eNhlSerq6nDJJZegqakJP/7xj/HYY4/h4MGDri40EomgtbUVTU1NKJVKrvSBESg+TwVaI0qRSMSVQvE0mUwm4wSvXC67XfsA0N7e7jakk54EZdyUyLIRRl2z2awb7/T0NMrlsjOCHCdP/WIUiWek0zDV1NS46KsKDA0Co4mpVMo5D5xffX29O363v78fZ5xxBtra2nD06FH09fU5QzI0NIT29nZ0dHSgr6/P1TgfPXoUmUwGtbW1aGlpwb59+7B+/XqMj4+7s6QpVMVi0YFQffEU14QKWkt8yCdUOBQGP+XPNdHopPX4gcrIIoVST4zyPK8ii0be5/dqYNiPOiQ0cDQ8VBx8loJAP4UTCCwdKXvxxRdj69atCAQCGB8fRzqdxtzcHJ544gn09/cjGFx6+/HWrVvR3d2NmpoafPe733VvA+d46+vrXRYjHA47HtYIDOWAjQCM9ODfnIdG/5VXw+GwA0JcS3XOgSXHmOVeXAM6gKQrncJQaKlUkY0ghIYklUq56yjHPH6aGQPqHq4n9dbc3BwikQiy2Syy2azbzzI3N4fW1lZks1k88cQTbv8Gs6NtbW2Ix+OuTNDzPFeyw2eNjo6is7MTw8PDTidxvWl8GTjQyKHyKvev6DVqgLgeGllUx5xyRmeSfEx9ryUk9hQcAk8GJ2yklvfScBJQKwDS32rI+LeujwI+5ZVAIIC2tjZks1nk83nnBBcKBQwMDGBmZgbAUoZi06ZNSCaTLtg0OjqK0dFRR7+mpibnnJJeupdAQZDyI/WT0oE2QDMAXFM18qQvbR1py/0ctK+kHXU4ZYnX614jBRLU4zw2l7Kg/WsGWzcD04mi7WFJGufNjfHM4NHOT09Pu6N6KYd0UGjftE49k8k4OdHggzrMnLM6Pyq3dg8K+UWdXmYi1blTuae8qFOvNsTPSVd+V3CtWRLyuWIKxRPWoea8FEj6OTna1JGgjuUcORbKOceuwV7VGbSDNotC/lYnbzWHTMG7gnzVKcDykfy2ioKleNoUtJfLZaczGZxlQLhcLrv9V2waQOSYiAt0jATXlHPlCQbcyL+aBVJeVD3FtVaasT/yhD5TA/OKIazjxjWjzPk5hcxAWkdN7aPygzrxHJN1lLl+fqd2rtZOqXSKgqjE5MLpxlwSiYxAr00JZYVLS2Y4aUalYrGYO70imUyioaEBnZ2dSKfTmJiYcBu0Dx06hJ/97GcuEprL5dDe3o5SqYSHH34YR48edYCARqW1tdW9eKyrqwvZbNalhDg3HsVHL47/a1pJI9TcA8L5MNtBoMpFUmDB6Pji4iJGR0cr0uc8MnBgYADlchnT09POKEWjUdTV1aG2ttYZpFAo5N5IyRczUfHTyOnmfpYI8KQhgjPd/J9IJNyJQyzTolIcGBiA53nI5/M4fPgwtm3bhrPOOgujo6MOlA0PD2PPnj248MILsXnzZszOzuLss8925QETExNOCbFmXZWORqPUaWBkgEqDZQpqJLiWFHIqez3lhs+hcIZCS0ff2UgQacq14nc6Vhpelt2pcFJ4NbpDWaIy0FIkzYKoYYlGo2htbcWWLVvcffF4HNPT09i/fz8eeeQRZ2w6OzvR0tJS4YAcP37cydbGjRudozY9PY3Nmzc7oE9QQtrQqfQ8zx0ooEqeckueVrCqe4+4AY4ONGWIeiYWi7njbbPZrNM7utYEkFwrlunxeVr2wiji3Nyckw/SVHmNCpV919bWOrpQTurr651s1NTUYPPmzRUvISsUCujt7UVXVxe6u7sxPT2N/v5+DAwMuPU788wz3Ys5t2/fjpGREadjaEx17wp5T4M5GgW0IFx5llFmNaoKyjQ6agEG9YxG+xT0MABEPrVZQo2+8Rl8JvmBEXBrYDkvLculLCnAbmhoQEdHhwvylEoljI2Nuaw1x7J+/foKPh8fH3d6JxaLoba21tGFGV0FqXoSHnmWOlpBseoCOj3aGClVoEw66HoQmFDn8yhdggB77Kie+qV2mLqTdNaMBWnDvVP2Ja4qE/F43OkDgieNTHPPC+lDPcH9LdS5tGcNDQ0uCDAzM4PW1lYcP368Ijqu/KyAToGsZg/4P6/xcyQoO3aeNkNoS1OIcXhIit2/RPvLZ7NvLZFh4EXlhc+wYyGw1e84Bx2Dyg0bqz/UGef95E3yOXlNPyfP6PwU5KszokEQ8j9tn9oyriftFdfGlo6qflKnU+nDICPnyXWhvSU2UUdAg2J8vq4B7THnp/Nh4EeD5cSBNjigQSLSTUuK+RyCfF0b0tjqPC3j4/ekH0vq6UhRbzADa50bzpV2VG2DroHiVXV+SXvVYSdrp/QeDfWk1FvjYpPAyliaatYoDAmtKRse9WUjYgr46+rqsG3bNrS3tzsAkUqlMDIygl27drnoWiaTQV1dHaLRKA4dOuT65Qk4XV1dLvrreR7Wr1/vgCo3qLHkhApmcXHpfP1SafmEEUZxWH6h0RIyH8ufyKT8jnXxjApNTU1hZGQE5XIZg4ODzngwEqXn5DMKWS4vHYcYjUbR1NQEYLnsg8ylNbKLi4uor693hoWOzNjYmFP4PPVHj4ENBALI5XIYHh7G+Pg4jh075jIpXV1dmJiYwOTkJAqFAnbt2oUNGzY4Yz05OYlDhw65aOMVV1yB3t5e1NbW4rzzzkMkEsHu3bud0Wf0jtEzMjiFkO9WoNCQrhRyzbApqGHffI8EUKlYqXTIy9wHo84AeZ5roi/U4f2MgisIU0WhkUc1zGo8GX3QqALnz1KDiy66yJVRUKkMDg668sBMJoPm5mbU1dVhcnISExMTFUfnZjIZbNmyBcVi0W38TyaT7v0O+nIwzoMySbBG+edcdROuRvYITjQCSiWm4LpcXjqlanR01L2JOxgMIpfLuTFoupo04xGrnuchm80iFAq5Izd58IA6lQyAkP56bDHHRrooqCQ/suzq6NGj7hAKOj8sy3rkkUfQ3t7uDBSdeGaMLrjgAoyMjKCmpgaXXHIJdu/ejWPHjq0oTaAxslFU3TTMsSnAVCdPXyjKRnrohnhrjPyACnW553kVWSstH+L9vNcac42k6tgps5QPyrNGUanTwuEwamtr0dXVVaETqEfHx8edM81jlUdGRpxMkU/phPBt15olUp7hd5wH9Sqvo13gtaSFRqqtk8e+uL6Li4sOXKkzUCqV3Bz1CFjSj7aQfMksKdeC4+CYFfyqXeT1PDFK7yU/EOzypZ9cJ5txoH1kZpLBLWb+ODfaooWFBaTTaReU1GyuOqA2cquBGq4L6cxnKB/aYCjnyPmyPy1RIiijDtTsnq6V9sPv6FBb55LX828breYzVQZVH2hZi8odjwtXGlgHU+XdyiDpTj5Th1UdGhsUsI6OOlE6RjoXmlXTdeAaaaZFs0LqLGpZr5aDeZ7ndDyfqY39qYzTzpE+/JwBZI6P/VH2qbNUJ1CGgGXdq/pRQT75Q506dS4sn7ApRlD7RJrpSVEqR6oX1OklXdgHA+6KeyxOUV5/srZmR4OCpx40y1Yo2HyophA1FcNBas2bgjhGK/0WPRBYOgJw06ZNLvPAvvv6+vDQQw9hYGAAxWLRveSupqbGAZVIJIKxsTH3PotIZOmNhgRLo6OjaGlpqVg4joEboIPBoCuTolcXCoXcufp8kZe+4Iy0qq2tRTKZdGllvsyIYGF+ft6dfsV+ecITX6pH75UlK3xLMdeFSkhBabFYRD6fR6FQcMf00uFgBIvZIs3QMCpBR4ZCS0enra3NGVZGqcvlMkZHR11EMZfLoba21v1fKi3tHeCRt+yzs7MTg4ODFeuvkVGNNBAYKGDSKK0adfIO96bQ26cTonypz6HA24gx+YhgWOVAI24Ueo2aqILWUin2bZsqXo328P4zzjgD7e3tAJaP6e3r68Pu3bsxMDCAaDSK+vp6tLe3Y3x83CkdvoAuHo+jq6vL8VggEHD1rul0Gp63nLkhiNGyF46Nskl60eGw3xHk0rgzWsK3cVNnFAoFd/oV12liYgIzMzPOKJHmVOrM1NGhJ19qaQsdQOokC1o0m0IDUiqVkEqlnE7Q9aJ819XVuXK/RCKB8fFxjIyMuIBHPp9HfX09ALhsan9/Pw4dOuQA1tTUlMvSMvth+VJpaZvqW10bXhsMLp+1rrzEtVCwwabGikcZK+/yecoPKovqAKkhYx+kKeVFnSTqQwsOdK6hUMgdZcu3cVNPDwwMOH1CxzadTruDPxj9JH/z5afMwFHHaYmHAhyCBoIUzodjsyBLM7GqmxW0kP802sn+SRdeQxorsKbTt7i4WLFxnWOiPOrLB60TydJTPl/lg44e6aG8pEAcWH6bsvKs53kue0FeYmaDgHFhYcGVFg4NDVXoQPbBuWiQQR0dBdIKsDlONuVRBV98Dtef81JZIv+QduqYkFfUfvH5al85Tp2POpW0PX7OB4MMGkzQZ5Cmuvbsl88hb9h58Ln2JEGuuzqa5CnVKQTdWrJjnWLNOlm9xvmqQ65z1+cwGKnZEWJLfq9ZJ8Wamu0giFbdtri46N5KrlkH8ht/eDgP6ax96NxVd5Ev2C/tHB10XquBH85JnQWuKWVHnT4b5NHAFPunLHK9VHfrcyhblh8ot2tpa3Y0SBQaeCojphrJQJwYGYZgjgpZARmvJaF4vy4Ir0kmk6itrcWmTZuQyWTcRumFhQX09vait7cXCwsLyOVyqK+vd/XXBITMmDQ0NCAYDLrSiNbW1oqNaAQurEcvlUoOxMzOzmJqasodW8sxM01IMEQG1ihuMLj8tmgCLs6bpVbcx8HTqnjsoud5aGtrc4vP6JCeEkXjrPTn/BklGxsbQygUqiiLYeqP86TzaE/cYpaBCq6urq4i2p3NZpHL5bB//37MzMy4NytzYyXp29/fj8OHD+P00093fFNfX4+zzjoLExMTOHHihAMD5BWNaJHptdZUo0FM+VGRaJRBnQIabhpkjQCoo0GB94vC+ikTC7BttEcjnOpA2evUAPIzrl1DQwPOPPNMlzFKJBI4ceIEDh486EAqN+ADcCVKzIQlEgm0tbU58B2NRpFKpRCPxyscCioejchppFHBp0bbOR+N3JDOeoCCHm/M7B4zapwDAwIE/XTWyQOaGeJacfwsk+EPwQ/HbIGKKnJmM/UYaDWyBBXMtBCoJJNJJJNJd3Qw9zqxfJRycOLECWSzWbS3t7syspaWFpcVZKaUz7XK3+pRPx5S/lMgo99pRlpBjRoXjQDzO5U3dXDUkFoHm5+pQ+6XQVH54uf84RpzA3h9fb2T+VAo5GjOTHUwGHSAgcEJAnqWHDD6GQqF3ImClHnlFxpWGlfymoIt0sYPZPIeBSTAckZPwZL+rwCRtlf5X7MsbATHHAfHRpqwWTtrwbo+h3PVdQgGl/dSWjCkOpYRbFYCkLaFQsFliPldOp3G7OxsxYlunJ/aBfKQ1f8KrhWTqGxYu2J52Q+wEZxpRFfXlPS09krHTdlVOea9fvLLz23wRiPSamuUHlae2JcFzvZ7Yhi/e2mb+b3aMF6j2QIN6nBOSh+9R+esgNher46yzlP5wIJxdSTV1mvmj/2q7DOoof1qAIU8xv+VJ9VhsjRQB0DlkvNQfar36ff8W9dRZZvXEbtZJ4c6RoMoupZ++tt+95RnNDQCYj1/ElMjQEoYrSX1M05kbmVKJQKVD2vN2YLBICYnJ3HkyBHk83l31GddXR3m5ubcC/gYqWloaHCnPpXLZdTW1qK9vd2VV3A+zBjo2PjiusnJSbcgGpnSDXTKcHo8bTweRz6fr8gAFYtFB/o1CuB5SxGqbDaLhoYG5HI5AMsvVuJzuRZ0YtRDnp2ddUZL04tkPAvGuUGWip/z5/zoCGWzWZcd0YgRSxd6enowMjLiDDNLrAqFAkZHR9HX14fm5uaKtaytrcXGjRsxMzODoaEh168aVqDyZVQ2IqcKnfThWgGVYMuWwqwGyJRHqXwIuFXB6z1q9HivGhQtXdD7tFERWbDIUjXyeDKZRKFQwPHjx3HkyBHMzMwgkUigpWXpzff9/f2OD2i0c7kcotEopqamEI1G0djY6N72zUaAwDkHAoGK/Q6MgpCXVUGpsSXdGMXiPofFxUW3P4MgkO9noVxR6RNUcg+GZjbY2D/Xj3KiYFAjWJRfGmdda4Irbl5XMEy5plPO4zm5tqrHmOEpFosVJ2UtLi69FTydTiOdTqOhocFttG1ubsbMzIwrobIgSPnD7zMFKRZoco5+96oxt/fYSLblWYJQ61BY8GpBnwVYFhhpOZaOKRwOO93IYEhNTQ2mp6cxNDRUsXeH7zNiNoM8oM6xluZy7xnHw7GxVFXBiq33Z1ODznFouZZmy9XBpR62zpfNGCj416h1uVyuOLCB41AAqnOz9soCPs7TOlGqd8nr7J/PoHwGAsslGCz/DYfDrpSRDkgwGHRlUwBcdkajvJy7zkn1owXqykt+wFbvs7SxvKn0pBzrs/i9OsSaKdTP2bRvBeDkGZVJdfZVrlYbv9JC5Y3rTgdbgamlldon/m/Xwz7b2i0LfnUdrWNnga11cvyaHYsfLS0f69qzqRzyOjpV2p/SVp02/c4vQGLX2tJLx8k+NACg89IfXSPFJJY29l77LDs3m82181Cds5Z2Sns0+GD1vNQr0w3dKjBURArAlNA0xFSkTAOpYk+lUti4caMzLOXy0lF5PT09LpuRTqfR2NiIVCqF8fFxTE5OwvOWolXNzc1oaGjA4uKiew9FLpdzQIHAj1FfVdZ8fjabdcBBIxs8poyKX9PgbAQajCzT4Klnzug0AVdjYyNaW1tXvEFc05oEPdyIB6x86ZUaR4Iwq0BooHicKA2EXsv7+T4NPp+AIBKJoKOjw51axSN11VnjaS+9vb2u7IEGZcuWLZiamnJ7NfyUHTenapRAlWAgsLwp0CoqKga7aVKFmulkBa3W2SBIVYPL6whiKAdWMVhhtiCWzSougotsNosNGzY4HlpcXMTAwAAOHjzo9vVkMhl0dHSgrq4O+/fvdyeVRSJLL7jM5XJOhurq6tDY2OgcXAYG9P0mdCbIJyyX5FqQF/UoWf5vARd5i04K+ZHXEbzT8V5cXHSONunNCC7lh+BQHd9yueyODVSHnNfR+bYgjA6L3scNsupARSIRx98sQ6ROiEQiaGxsdFkaRmqVdtPT0zh+/DhyuRxyuZwrW8vlcmhra3OZDfKUyh/5wSp6CxpU/9jIk/K+6g2ro7UckbRSmSG4tSBF/7cyZCOhaqh5jcpDIBBw9oV6kHzMuVEOuKle9SJP3AOW0/1cX64r35FSLpcrHAjSQGlJHibNFDirM6XgSZuCHjXcnItm/rUPOih8JtdB15YZQwVT6lAo3fl8C5Y5B+6nJG/riT3Kk8pfOl4NmilOIH8xqEf7wCACsxyxWMzZIOso+OluBVqqf9UR4hx1DXVc/F75mLypNtcvmGKbfbaWo1lnQGnJz9Qm8R4bPFMgyudohFyBpP2x9+v8NZOvcm95VufDPhQv8DM/IKt8Z6/lj60WUFnQ51u6W3lVJ8vac6tTTwbc9X47X+0TqCxXY7M6W0vRdP25htYR4P3qbOq49G+locqq2j3Lu7YUj3RXPbOablutrdnR4CKwBEAdCypvVdx0FAguSBRGhKlMOXG+o4KLxJIjXt/U1IRcLodisYja2loEg0EcPnwYe/fuxcTEhIvM8s29BGLcqL1x40aXUfA8r0KpcYy6uZJKVctwMpkMpqamXD0vlaRuxmGZFecKLEVnUqmUc0i4X4FgL5fLoVQquQj/oUOHEI1G3fn9AJzyTafTzjFjloWZBK3D1znxRCtlNissfJtrOp1GuVx2pSY2ApxOpwHAldzopkJGqE4//XSMjY0BgCv90hKqfD6PQ4cOoa6uDmeddRbq6+udo3XmmWfi4MGD7nheTeuxP42e6nwIErUURHlSQZVmStRoaW0vaaMRShupovCpQaEjyBS0dTAotGpwVdnrM3gNI350IBYXF9He3o65uTkcPHjQnXrEErZYLIbBwcEKMJ1Op9Ha2uqc9cnJSXetpSczFqQVNxgyI8HsVjAYxPj4OAC4fQpsVFhUtpR57vlRZ53ZNPJasVjEiRMnXERanWs1MNFo1L0Ej+OxESvyLgHNaoZJM5cEV5oVVLmh/iiVlg4NUKVL/bF+/XoHfJlhyWazbr/Y1NQU9u/fj0wmg9NPP93pxaamJiwsLGBiYsLpWgX/aoS5N0o3hipfqZHTPsjjrDHm90oT1Q/qyFtwaUsDSE/aAJVXAnnyOJ00Xs9nWHBBeaLjxjfV86SpYrHoXh5K54dldhMTExWySX3MfVvcy2GdM+oTjltLNfUEKl3zYDDoALblP91fxP74m/Oy76TSskPVX6SJlm35RY41EOR5XsUx83bN+J1uWlae0H0BDA5QrtXmq96kfWDggnsiE4mE2yfDfVl00Nkvg1CqJ/gc5VHrqCrPqFPH7yzQIwDlczXAZIG9lk/5AWF1fCgrSjcbONNADmVCTzRUR0Cfz/naEjn2r6DZBigYDNW1tfpL8Rlpz740kKbyruBTbaauldo76gIF0Zq9UzuoEXbtl99ZZ846ItSRnudVnMZEh9o6LTp+VjBo5pLrwA3zuoYsiVee0YAA52sdMLVbxNOci52vBf6krQZY+dtmxvks4kXNtGsAy66j2h4bLDpZW7OjoRtjuBA8lkwXlsqfxNPF4W+ej6wn8/A+KnAyDI9VffrTn+5KmkqlEo4ePYqHH34Yx48fRyAQcBtfg8Eg9u7d687qzmazaGxsdJuxE4kE8vm8M1a6H4NH5RJYsP6VYCoej7sTqcigBLWkQT6fd3Mh3RjRJK1YRkSmoFDpW7+TySQymYyjBaOefC7LWCiM/M2X9NHIsiyLgqAGMhhcOpKQDg3XUJU5lUE0GsX09DQCgYB7OZ8CZL4AMBwOo7GxEaeddhoOHz6M/v5+LC4uoq2tDdu2bUM6ncYvf/lLDA0NYd++fS4LkkqlkM/n0dnZiXXr1qG3t7fCeSJgoSBTQPVceI22ct1s2QGVJIAKx1KjR1aRKdi3UQ3b7KkrqkBUcbMPjZYSpKjjQ2UeiSy9z2Tjxo1YXFxEXV0dIpEI9u7di56eHoyPjyMcXjqBrb29HaOjo9izZ49zMHncM9/2radP0WBRgbGEZGRkpOLYZR4awDES6JMXSXOOmc5EILB0kAMzHmokgWVFxwwFeZwRVTq0Slv+nc1mXRmA8qxu+Kezrf3T4LF/fTGgjZIGg8vln1TQKk/z8/NOL3ANGTTZtGkTDhw44I7fbW1tRXNzM1KpFI4ePYrp6WkcOHAAmUwGTU1NTlesX7/evaeG68WxKHDlZmwauUAg4PZk0RiSp7WcRQGHBQ0WWLBPdeK4RnoiFOlpjSUb+V5L2FSmbISOMkEe4ZzpZDDwEQwGcfz4cRfA4XryBLWRkRGXTSaYpx5kUIv9A8vHMJOXVX/qb6UXnTYFvMrXlob6Q1pRznUvhAIRyg+BPu9TcK/zUODGH+o/8hDXgfssKANaTcB1sAEXOkjc16d6jbRTEDY7O+toR/vPz2dnZzE2Nub0SKlUcic50hlR3awBTj06l7bQ6hPaD8on561ZI9oTP4ebuoCBR9pypXsgEHBHs1MHActYRnlZA178jH1S51JWrMND2qfT6RWlTOpcqaxaHuB8lAYci8qgdQR4neUNBc0aiONYSUN1bLm2qpPIn9z/Sh1DzMXPSCPacOVpGxRU3UOArpUHXAvqWB6AQl7l8d5qpzlnfqd7t+xWAT38SJ052hg+x+IM/d+egMem2IbzJP2p8+kQqYNF+pCf9YQqG8ijs68BBeW1tbQ1Oxrc6MyB2GgVFZ+tjQYqX8zCphEgjSzrme2h0NLmsU2bNrkXKbW1teH48ePo7e3FgQMH3EvKNm3ahG3btuGRRx5xyiaRSKCurg4tLS3utCgqMjIcI4bpdNptsgbgjuDjwtTV1QGAe1s2n5FOp93Z/XqMX1NTkzsNh2AkFothbm4O8/Pz7ghRFchSqYTW1lZs2LABs7OzroSKUWLP89yRvap8AFQIHA0PSww8z3PH8pbLZVePT3rQSNAJYrqamRluTuXpLMxqcdN6MBhES0sLRkZGkMlkMD09jfr6ehQKBUxOTmJ4eBjDw8PuKNB4PI7x8XGcOHHClcF0dHQgFothbGwMz3nOc/CVr3yl4gxn8hnBlQVMFMZiseiyR9x3Ql6kwl9YWHBrSNqr4mdEksbNz0kgkLV1nLohn7KgfRP4kc+Byn0iVBI1NTVuQ2s4HMa6deuwfft2d2Lapk2b8PDDD2PXrl0YGBhwoLutrQ2bNm3Cz3/+c6c8UqmUK5FixopRRTYqXD07m+Mj8FJDT0A8OTmJZDJZEREjTcvlMrLZrNtbwXXQiB9fgqbZs2BwqV5+y5YtGB8fdy/CpONBvUCHnmCHtKRx0jPWGZXSU5L49mc90IK8xDGp88Q1i0SW3pXDoEUmk0EgsPQW9enpaUxOTrrnccPy3NwcxsfHnRHjs8fHxzE4OIienh6nm1jT/vSnPx3f+973HACjseF8WL5mjZA1UMFg0AUgNAJro74aOSNP0IhqxkQNFW2A7mVRfqdcaNSR+p/P1qCORiQ1akZdyUAE9XQqlcLExAT6+/vde4V4kEU4XHkEayqVQkNDg3sJGOWA4Il96juCaANo47h+nLce1UoaKujhPFg6S4Bso9TkU95no4W0pVqKwbWhHPD0GxsFVYeJuowyrdkXtc86Th0/9R3Xi2Mk+KSDosCQNOBakF8TiYSTJ8omKxs4RzobfAcNx8D5EUco4NfoMa9h0yiuZgQUgGmgh/+TJtSNrIagHlNngjZKA07UKVxXm5VXW6IZENpy6wQQb/CADY6B81M+AVYGEBTYqu6mPBYKBQdMrbND54HZF6WPBuwYiCUdbcTdj/c5Tx5Yo1F+0laBNHW/2lL2z+C4ro8GuRjspa0gryuPaPmfRvLVIVWdqvfQ3vuVObIv0k3XljRQp4uOjNKDY+L1SiP2Sf3PMkWOQYOBDLZwTdX5UidReRVYPl1uLW3NjgaZi4qWjKsKiJuZlXie5yGZTFaUAChjkfG4IVs972w2i23btuH888/HzMwMampqMDw8jJ6eHhw7dswdj5dMJt07HrhZm0YikUhg3bp1brE1LUklTYZoaWnB8PBwRYZGPej5+Xkkk8mK+6kcGC2rq6tDJpNxAssX87EPnp7FrAaVCWuIe3t7K6I+jATTeIZCyy8V0s2APNZQXy7I9amtrXWKiZtqOX4CKYJcHvmrkSKWyQBwAJgAnqeFcLx6RGIwuJQxodN08OBBnH/++di8eTP6+vowNTWFJ554wmU1zj33XBw+fBhzc3O4+OKL8Ytf/AJDQ0NOqGOxmIuKMfLAuasS4f4QVQh0qlTwNFJAxQgsl54By2lkPpNrrqladRTIz6roNXVJkKjZGtKKcqEGIxaLobW1FWeccQa6urpcSduRI0fw2GOP4dixY+49KnV1dVi3bh3y+bwrJwSWFC6zeqQT69Q1+kge4AswKdcEkdyfxGgkS27U+eJJUeRPOqs0WLyWR5KqgdYIez6fr4jA0aAStHCt6OSosmVUW6PDHA8NrQYB2C8bAUUgEHC6a3x8vCJSHAwGMTExgYaGBmewOAc6seQF8l65XHb6ad26dejo6EAwuHSgxYkTJ5DJZLBt2zZ0dHRgZGQEMzMzOOuss7B79263z4bjI8/YUgE1ZpoB0RPV+B11AMEajSJ1sz5LI+M246FOuX5vI3D6HQFVuVx29CKIU0BG3mEpaS6Xc/IFAOPj4+jr63Mnk4VCIbdJnLLCedP2qL4gGKKRpY5R3aoZ63K57EAY+Zt8TYCp/Ek6ka409n5RXK4B7a2ujWYCqYfZvzrAvFf5gDKj4Jg6iD8EGgzO2Kgq+Yxj0jlo1JzrZsEddRkzHOoAh8Nh5yRNTk4iEll6Lw2BJ+3q+Ph4hfNrQRCfT57htcQD5AMFa7asRHEL/2dwSJ111QU2c6ABCo5DZYi8xGewD+o57kG0GTHyr85BnRYF5FxTBe8aNKOu0nvUuVfZpp3QgBv5UTNINsCmvGaDBuVyueJ9Y+yT+MbiCHWQLL8q/Sk35XLZVcuoLDHowPVWJ8o6XbTRajeUNspHpIU6tZodVIdN10qfp04aZdieoKn019JsNg0ccRuBX+CT42SAjuvI/zUgpPqDGIE2kjrnZG3NjgaJoGkngi71jpV4/KHR5vcUHE23soaTCj4YXKpJb2trc4AwGo2ir68PPT097g2iTU1N2Lp1KzZu3IgnnngCQ0NDTlnV19ejubnZMS+9TUZjyZA0MMFgsKI+mxkFjRIpIFOPkm8sT6fTDpiq0VFmplOlLxKjs8EoMwF1oVBwTghPGNKNu2QMAmoaZT2elICfkeW5uTk3RkYv+PZwZWoCSm6S5FG4BLCaSeCRpFQQLLthiVqpVHLvEDj99NNd5Defz6O3txf79+9Ha2srWltb8fjjj2PdunXo6enB1NSUG5O+aVojICoUNOhaWkD+taULVlg10mg3Nqsws3+bOlRlynIXNUAKPNVBsdFHKmfKTUtLizuWmcriwIEDeOyxx9xeGb4lvLGxEU888YR74V0ikUAmk3FOlio5zVAQzDDzFwgE3FGfNlW+uLjo3jyvypn8GI/HfcGAgiUtWSJvqXPGd3sQFPKYZh5JSn6nMtZ1Vh1D2aYR57qT93kSDvUYAybWSBKUqrPEtaeDxWCEyg8dPsr1zMwMpqenMT09jZaWFnfC2sTEBPr6+lBXV+fmODw87OSAkSfymII4BRDkaeooLQ9U59kv4GMjrar36bSpTJEG1M0axeb6U4dpdFgNJg2W6lZ9rgLYuro6hEIh98LNSCSCqakpl9FjcIo8TEeD42GZFflSZV5LVfRgEuod6gCVAWus1aFVGnne8ilo6tSrTdR+qLusM8iyWoJ18rRGMK0N5uecM+2r6j2rL5lRs6Botc+Ut3g/14/6kDRW0KZHEAeDQZcdYsCMDk84HHaHpMzMzFQEiHSubBb8chxag64ywx+VFeVB0oz8QntOHarOFfvRAIeuOe+n3rJOGz/nKWcqK5wrbbxm24DlbI0CV96vYJz04b2Kyyj/mnXnHBVEE7MQ37BfXs85qbOjOkEzCOQjjp02PBqNuvXWufB6WzbFcancEPtpOb8GU2iH1CGkjSAdrc7XjI6VYY6PPMJ567oob2mmV9eK9KT9VV2o97Op00g+1HInu6dMx0lbq04F11t1BO/jOGxm9snamh0NrTek4VUvXUGWRhi4KCQGwRMbJ6g1lMwunHbaaejo6HCTi8fj6OnpQV9fn0sbJpNJtLS0IBwOY3h42G3UZv2eRvroRDArQUIShIdCIaRSqYoNx3SGOC6g8oVrVF487UojLqSPesA0OMxUMNrPBeOL/AgaGGnnvVpjq5EGMgEVnEZPKJQszWA/uhlyYmLC3cex0tGyDEwvn9fRGHPtCQrpdLCUaXFx6VjP5uZmRwO+qLCnpwft7e3o7u52zk13d7fLsKiRI09pxI2N68J1Ik/yOwXF5DcKKQGVRrfI5ypQ+gw/RQkspyxJM42g0EiqomUfCrbj8Tg6OjqwdetWtyeGsjg4OIi+vj4sLCygvr4ebW1tqK2tRaFQwNDQkANvNTU1Dngp0CANaYAJWkgfpkU160KeorOpUTDSmz80WqoAlYakPUG+jcrQWFvQpqBY10Ppz/+5BpRzypI6PPxhFI3RbS3P0/GrY8v/mT1TQ1EsFt1bkQuFgntZGR2R8fFx986Nubk55PN5nDhxwm1S5pvGg8EgtmzZgunpaXcEtyp+0ot6VJ0/0kBpZ/WFBUK2qXFT2irg1HIGAk79nmvAwJRdK8qLBYw61rq6OjQ0NKwwoMz8lEold3AAdRCDV9ap8isHIF8pIFRATx5Re0aeJz8r4FaZp+wwY6OgVPtiUxCoJYHUDxq0oD7RsgrlWfsc66jyet7LtbJRegvGdX24hjp3+1szA+RLBjeszmV5rtotAO79Gjpu5Relu/5vP1f66jwoPxpAUtCl11rnQkG2ZiDs2NReqC5Rh0zXXUEur9MAkQYPCJz5uUaulWd0Lf34gmukulflVgNjVo9yDXmN2j2dh834qaNlHQelBWlDvaa01jXRdfHjR36vtl/nxPVU2WD/ig1Ux6nM6XM5B50r+cDKmTo3DACo/tXv7VxU7m0gQXlPdaKWIdrxKB/QhiqddRwna6eU0SCo0EXUBSQhlMhW2er9wDJoU+GKxWJoaWnB+vXrkUwmMTk5iWAwiHw+j3379rm3hqbTaXeKy8DAAAYGBlxEgJsBPc9zkV0abkZAGTXIZDLuhAtGE5lFUM+Xi0wnhsLCfQ5kDDVkaoTL5eU3SmvtN5lQa+MpmFTE+pmWsFnAq4BK10GVIB0UgitGYul8sZxEjbJGRe2mX9b/kRd4rr0eTag0HxwcRDabdc+enZ3FiRMnsG/fPtTU1KC+vh6BQAAdHR0YGhrCxMQERkdHK4CtVeIKgE6mvPmdGhQFWVxrVS4K4FR5631+Ck0dcFU81oirnHFMyWQSW7ZsQVNTEwC4VPDCwoI7mUv5d2FhwdX8F4tFt0eJUSWuraZIqWyi0agrY6CjoRsw7Y/yIAE9/7dKWxUT+cUCdio2a5AUHKiCVqBggaMCaF7rF1Xi81mjy8gXnVjNFjIQwmsBuFpygk/yOx2VfD5fUXZDuWQt+sTEBGpra92erenpaZw4cQLJZBLh8NKm56mpKbS2tqKpqQl9fX3O8VdQp8DDOtUaDeP1atTt535N+7DOiOVjC3I5DgVuKgs6FpUxjVaHQiF3+AHL9RjIGBkZqciQM1rKFz9SnrgvQ4MkyiNKBxsAILDhNSq/GnhTfUGeVf2s0U0LbqnbLODw02m8lmPhD/ncZkzsnNR26xpQt5H//cAQr1dQR51hSzl4DUEK6WDHQbmgzllcXHTloFwrAC7TwQisrhH7Y/MDnBYM6t92nn598zvFMTbKTzvNeSqtVpNVvZ/P0oCi5U+VXf6tVSUch47Z0sjynH7Gfiww1Tnxeg0k8XmaMdMoudXr1vHS9VstkKfrpGW37NPKjbVFer91TKwc6FqqjlK9qfrMb/yardAx+q2J8qvlTV17/cyPrnw+x2odQ8sbmhlS3MnvLM9pW81m2HbKbwZXz5fGWJUcCaKTJ5AmOFGikViMeJVKJfeysVQqVXE++bFjx9DT04Pp6WlkMhk0Nze7NxwfPXoUU1NTCIVCyGQyroxpYWEBk5OTyGQyboyM6lKRMVU+MTHh6m4ZadVTWRRsx2Ixt1h0Yihkmo7XTcMsT6KDw1SyMgdPQCEYA5ZfPAXApZu56VwNJpmQ89NoGcERPVg6PTTIXGM6ILpJUCM9PDaShoFrqd6zZmz4hnCWfZEOnZ2dKJVKrmRmcnIS+/fvRzab/f9o+7PmxrLsPhtfGMgkCWLinGON3Sp1taRoDXZItm/siPfO9/6A/hyO0IUdktxqKULuuborK0fOAAGC4IDhf8H32XywCtWd9Yb/JyIjM4GDc/Ze428Ne+949uxZWbD9/Pnz6PV6JWuZM5JWSmQTUPhdgD4HB5ZXeAgdnRHmnX6efwevLAd8bsPD37nqxAWdt7e348WLF1Gv10u2vVKpxPv37+N3v/tdrKysRKfTie3t7VhdXY3hcBjv37+Pi4uLmM3ue9+3t7djPp8XHvtkbhy8/wCIcBY2zjmzRqBLoGGZQcayLSCzCyjnXveX80yMm4GTqwqWefOfebpdhN/i/JhnpVJZCLRdKYuIhXNCGBdVSXaqqtfrC4GKg7p6vV6CamhM9vvy8jJevHhRgm0CatoNaZdbXV0teuAF4fCFYDFfOSA3WLDOQmMHC5Zp220/14ApB4nWSd7rVpJlWVoHj9YzdvszqKMKOxwOi812exrriSIeDvhjrMhEzr4yftPMTjrfYzDmdhHfm68MmHhe5hVjy9Vpsv8GoPZNmXd+rwM8LusOgdN3jTtXV/x+ywR8zkDZVRc/x/Mi2cXaRlrk4CNbpGd6LgucPT/fY3+e77P9z3RywtCtMdAPnfK8cgLsQ4AaeGHZPZlfOVh1kOSqVfYxppdpwf/dKmbfZhubZcH0W8afZckf/nitZAb9TkJgR/xs09pzhx65qwYZ8O+yX3eQBbZb5mdsh5ddy4L1/A6+t9zwb+zidwVTbps0P4yRMg0sN+BNf+6xkRzOfMjj+mPXBwcaOGsYbcbSEkUW3QS24EYsnsBo4rtV4qOPPoqnT5+WhTD0pb569aosaH769Gl89tln8fTp0zg/P49er1eqEt1uN7rdbkREnJ2dFaDebrfLuAH6LIpjLcB4PI5WqxXtdjsqlUpcXl6WLNnZ2Vnpxwboz+fzhW1nIx62ScPoTCaTGA6HpRLz6NGjkrHxAm+XCN3W44vvoKF34/G6Ag72I3hbtjNNpXLfBsMifA796/f7ZbtaMriAJJS01WpFq9UqJzqz8IjzMtiBiy2FT09P4/Xr16X6cXl5Gbu7u4W2r1+/jlevXkWr1Yq//uu/LtnjnZ2d+Pzzz+Pm5ibevHnzrXYI94/y/wxa4Lcdi3f3cXaKQCsiyjoELrfyUEo2TQGFlUqlnHgb8W1niAHJxgUDBh8/++yz0grCguWLi4t4+fJljMfjaLfb8fz58/j4449jdXU1Dg8PFyo/3W43VldXo9frLRgOZ9UAAMwbR+/dNtyuAQjwlrPsumQnwrz9++l0WnjvXbegE7rlQJsEBOP12gRo7r5igiV47Lk5oGeOBA609jGe+Xxetud173a1Wv1W5YdgBQDDH4K6ra2tGAwGZaxUJQj0d3d3Yzwel/+/ffs2NjY24vHjx2Wnur29vdjf34/ZbFZ24bJTgmYGUznhgB5AR/M0gwMDMme1fDlgINi0vM9ms7JbocGxx4w9cwIKueR5Ozs7JbFDRnswGESv1yvy+/jx4+J/OOwQXsIH2gmd+JpMJoWHfE7GPCfCSIyQpPL3EVGqwLkCYNth8O2gGZCN/mdaGhga/EEn7Ai2GjnH1rhdMAMr7uX97llnfg5C0RMHrbPZrNhTfmtfm4MTaA0dZrOHtVaz2f2C+8FgEBFRts8meUDyit8acCJXGdxnQMbYPE9+C78IfgzmAXfIj2Xa1QS3JXls6F0GqpkfOUDgO9sXcJd5wFzdWsTF/GwjfDlQzPxlTH5ebhPj+X52Xmfi+bo9yPRzosubObibwRsk5AAbLGZ6OKg3/e2fbPPMq8wzfpcxhfnIXPh/TlAQVPFsZDnrs8G8dZjx5iDTeofuIxu2Y36+cYqDVb8/+1wH0B9yfa8D++xsLUAQCyPnFiTOrHDPuolGFpH+y2fPnsWnn35aepSpLvz617+Of/7nf47J5H770r29vdjb2ysZDwzlZDIpe+tHRNmRqlKpxGg0Kn3sFggvrOLwJy+0QqHZaYkTvHlfrXa/0wktEDh4hMcgDuZXKpXSv40RnUwmpc2FLDbAhmCIBbr9fj9OT0/LCecI0fb2dgmUCHgqlUoJaGjrcF/+fD4vO124n//q6mpBBprNZqlotFqtWF9fLwrjNTGcr1KpVArNZrNZHB0dxdHRUUyn0zg9PY29vb344osvynkhp6encX5+Hv/0T/8Uf/mXf1kCROj/8uXLiHhw6F5s7y2L4S+Kj5ITDFmODfKdxalWq2VPdBsWgjsUl+8chOOACJRtwHiee9LtYMiWPX/+PH74wx8WZ1er3Z8DcHh4GP/2b/9WgtTHjx/H5ubmwjaenHa/v7//rYwbtCHbj0G3g0EeV1butyJmDq40ABDhsZ1urmZAMzYTQL4ZA2t6WFANSKSiSc82euNxs57KOgSYht44JbfYGIDQ9mRwzvhoL0S+feYCc2e3NWgCDXDK2EVs2XA4LDvTDAaDePLkSfzoRz+KWq0Wb968iZubm7Id9IsXLwro+eSTT+Ly8jLOzs4WKj3wBllk7lzIUEQs7Oxk8O9gjQuZZgeniFiYo7OLbqFwq4QXvtLKZMDDmJ05RGbq9Xp0Op14/vz5Qkn/7u6uBGXwOG+jSYKFizNgmDP+yMCLtWAOwLA3zNs2wD3sBMTQEmds3Yf2BpDoijOwgETzluexhoh3ERgwNwIl7GG2LzmrDD8IuLGnBkk8Fx1H/3g/dDT/PB5XGrABngtzYAOXXq9XKhrQGH6wgx7bGRvA4oeWVXccJLiiwW9tIzPARh4dcFtf/B7ut93z+8FHbGjhSjc8zeMyaKe9LO+6xPcer6uzOcDO6zt5nxd2I4f+zH6PRJCTBOi+n2ke2G8YOGd5wZ6gi+gzVS6PA/qZxySSoFXmJ8FYDuj5Hvl2MOGg0uN11YLP+cw40jYXnXNrfZblnFTLgYDpypzgk5/jjVbgDwEKPHSClvHYxtjOYCvAzR9yfa/WqWx8IC7OG8V3lhnwwMQjFvvpMYhkKT777LN49uxZOQH79vY2Tk5O4h/+4R/Knv1UOMhwkz1/9+5dHBwclFaEg4OD2NvbK8aRzKUzj5VKpRi0ra2tkq0FtGAoOp1OmRuM4L5qtRpnZ2fRarWi2WwuOKqrq6u4uLgo/fScrAxIvrm5KaeiOhPFovBmsxmbm5sxGo1KmwCOBuBzdXVVMqYoCXN0hpp9/xH209PTsv6l2+2Wxen8FnDnHtxut1u2+UPpd3d3S2vXxsZGXF5exvn5eTGE7DDVbDZje3s7rq6u4vDwMDqdTnz66afx5ZdfxieffBJ///d/H19//XX87//9v+P58+fx6aeflsrA559/Hl999VW8fv267DoznU7Ljlic7otxcMbCjnWZsXaVCNkmeLURt5Eg04xBwhh65w8rsR0/9MXRQlsHp3/1V39VMt2A8Tdv3sS//du/xWw2K7uyDAaDGA6HpfWGtqS9vb04OzuL1dXV+Oyzz0qQ5Ywnu4JR0YJOrC/gcpYEY8NFAIGhYu75DBL65l3J4Hc4XwCVK4G5soEtinhouXIGiyDXxh3eO1uJQ6K6gEOhCmjQSnYSmXr06FE5P4fsK4fIcR+tUaYTdpBNENCTo6Oj+PTTT+Nv//Zv4+XLl/HrX/86+v1+/OIXvygL/YfDYezv78dwOIzxeBynp6ffcsz837IP6EE/nSHlbw4XzNld7hkOhwuVDS47o2zXnS11Bfa7FoESiBjoraysxMHBQZGfavV+Z6KLi4uyKx/nqZDgwAniMDc2NkqGfnt7u8gyc6VCZ9CwLJlAIOAqAZ/j2AnCHXzAG/TdZ7Y4MDMPnUHFR1JhdqUMGqMbBhje1tvZSfs13m0dxD+6rRcZsi7BH/wDwNmB1bKsOXaSwJ5x8d5msxnPnj2L8/Pz6Pf7ZZttdnOEfiRADB5zAGTgnHepgsbQyDzKFRzm4i3HcwsOtHTbLnxxsAVfuC9nr32vW3bQEXxPbvPkd04I2x7DNwd8yJsrBczTgRXPYszIP7JkWjk4zoEn7yB5yJbUEQ+7PLqabv/pllHk01l7B2yuqJFcc1UeGjAmJx15hm0nc1sWVBrTMractDGN+YMOuuq17HJ10TaehBdVWj8HHaNDh10gc8BhPWZM0Bj9t11zYM97sn5/1/W9KhpMFAOE8wQ4O+KD8AYImbA2mtPpNP7Df/gP8ed//uflVOtqtRqj0SjOzs7i+Pg4Go1GPHv2LJ4+fRoRUfa2B9Cvr6+X9RkczHR7exudTmeh9I3BoGLCGMiyRzz0zzIf76K0u7u7IHScdO5IHAUdjUZlq1dXE1AQDi3C8HKo12w2i/Pz8zg7O4vDw8PY2toqAAU6knWPiLKF6dXVVbx+/bpUNsgG5eoDyko22llk5sTcK5VKAVZUqjIgqNfrpZrjM1Our69LLzUCPR6PY2trq2SsB4NBNBqNePLkSXz11Vdxfn4ev/jFL6Lb7Za2tNXV1fjJT34S/X6/ABKCAb73drKARQMmMpkoGKDXhoeAGd4bFLjCheEDpEZEWXuDszLgY1w4ePjHxbM4oPLZs2cREQs0PDk5iaOjo7Io8gc/+EFcXl4WsF+p3G/vOZlMYnt7OzY3N8uWnpXKfVVrfX29ZIRdoXBFg8sBEHRDbtkW1ucIQFdXNpkru6uZFr4HXmIvyDBzavB4PC6HatqwQmuqTzbi4/E4tre3i+zb0WJz1tbW4uzsrOgkc15fXy9tkzhMDutk/ZEDRpwyYyBb5pZS7A5JhcvLy5Is+N3vfhd7e3tRrd5vRX1xcRHv37+Pf/u3f4v//J//cwkId3Z24qOPPiqVHmSU+VkvaauEfx6f70UvDKq4Fx3ifgfbBOrQBru0LBixvvK5x+AqaMS9g+10OqWC60rGYDBYqMw8evSo8Mq2x5VO+wDrHXoNnRw02985KeBEmgEXOmgAh5yRAEBfGLsDfCoRzqxDbwI+zs9AL7Fr0MNZSYMbaIrdQjbm83nxPTmI4fcOwOC9E3WeH/piUOTKCfN00gAbSuWMZ8Jzqnj7+/sFeG1tbZX3Ml6SEuYtPgE9QeacqWac+VwjgO6yBCnvMb/td2azWelO8HtyIOvEbaYTOuhg3s9CjuAVvt4YxEEU96Izue2K9yMb2C74TFLi6urqO9fJuHICf/0OxuCKYsRDIsb2GXmGHwSIZO+howMB3gkwdjBk/4Cvtf/jIpGLrLmiA015hoN4Yw3kJQeNlhtjFAeU0DSDeuZu/+dAid+R/IJmVPQZq6uIjN3yyLvoFOA3ll8+cwX8D10fHGjc3d2VnW2YJAPjhQ5GIqIYMIQW4w9hINhsdn+CLj3JGEqM6+9+97u4uLiIH/zgB/Hxxx9Hs9mM4+PjsjMU/3/8+HF8/PHHJeuOs2WROErDNrQWBgCPM1IwjOhwc3OzBEF2Fi5ls2jZAJFWMjsNHBTCxZqQ09PTePfuXcnaAZrYiQYB4FA2AF+n04lWq1UMMMAMGlPJ4DuqMT59s1a7392l2+0W8FWr1cr5GygLi1L5zgee0X/vyJ7KyHg8jp2dnZKVvb6+LlsTX15exrNnz+Jv/uZv4l/+5V/i5z//eXS73fjRj35Uxvr06dP48Y9/HH//93+/sKgdXnqBng2rDTp8Qk7dH4rMumrlew0sHWRgKHBWdjwGNZYZL+q0MXr06FF88cUXUa/Xi+xMJpM4OzuLd+/eFTo8e/YsNjY24v379wtZ0svLy9jf34/t7e3yLl84E5ySgzADbfiFIYp4qOqwTTJ2IWeyneUDDKDz/i73ejMmEgysFZrNZmVr14go2UzaZ3AW7JxF9S4iSgDi9iHAFdXWXLUA5LRarZLNJ6PKGNmSlq2yI2IhCPchcOYD79jY2IjRaFTWGezt7cV0Oi0bIMxms3jz5k0cHh7Gq1evShCyvr4eOzs7cX5+Xlp97NAM8Cy30DnzyAGYs6KW/clkstA+ZNvN2jKfnWDny+VWBMsKz8pZOxaAOzkwm81KooEkTavVikajEe/evStybbtj4J5BqDO//J8AEnoBdvmcdgdsqQ8qdGDAOxz4bWxsLPhO5ML2N4NOnsNYrOvIl4EN8s6coJ2rdDnTbVvI57l9wjY2g1d4bfvHc2xbXC2wPbG/R5bIwrqiOxgMotVqldaZdrsd5+fnZU0Nn2MPPIYsj854834+9/iNabwuBFnzlYFXbo+1rEfEQkUdugAcLZO8E7+RddZ0hh+WKye8qK6THMm66iqWA2XrJvbUibp8L/aC9/t765/1BZkybT1XV/ysH64UQGuPATo4kANM00KFLXKCMPtOJ3QcwNuuME7ri2XG/GLOORCFVsuqbNnO8G6e56Snn5kTm+aBdxjlOfDbvM9zYIwfcn2vigbZAWd7mYQNuoEdk2ZiOXJDaGmVabfbcXd3V9Y7HB0dxc9//vNYWVmJH/3oR7G7u1vK5u6RXV9fj+fPn8fOzs4C4WlVgiCARBgEAKF0a+aw6Jvsvo2OFYRsPoaSLBrMBpysrKzE+fl5OVDQQra+vh43NzelZEwkzd7wm5ubpfrAgnXegTPs9XpljcnFxUUpQXobXGjF81dWHk7wtJHb3t4uYN5tKbTfIIA3Nzcl0zydPrR81Wq1sh6C08mvrq5ibW0tTk9P4+rqKt6+fVuCzHq9HgcHB/GjH/0oXr58GRcXF/Hb3/42ut1uPH78uACLH/3oR/HVV1/Fq1evFgQeWbKxdTk/g1yXS22IcerML2c/nZHgmc6GGMzagUR8+wAuZzTq9Xqp2FHNgI5UxU5PT+PRo0fxySefRLfbLQc+une82WzG/v5+qRbZQJBtysaB8RsYU97Oi6iRewIN0yMHLBgqKi52oIw7OznGNh6P4/z8vOi5WwvdcsA5FPCJTBuZcE6xX5blc1BhR22asEGCQQl95gTwdsAAJ/f/4nCpXjKH6+vrGA6H8fLly3j69GlMJpNicz766KMYDAYxGo3izZs30Ww2y9jb7Xa8ePEizs7Oyknjdo4Gis7CZQfHPK1D2Xk7KMy22xUuO1A7XmfbcgZ3GciIuAdg2DzrHYeXkvRiW2dn5PmbYNPtJyQicpYRfixzmtBxmROmkgtQcd/7MiCLnTbtoTUybeAAqLHcLrNlBmCmcQ4E3DbJey0T+XPzzAlE08z2z8Ejv/Oz+T0dELYZk8mk+LLb29uS3OK7u7u7GA6HJckBvagQ2XZZv6GPfQVBg+Xe43R22mOEh5l2Bq/WCQdu1g3jBnhkvbVfImiEDtVqdeEsMusoYzfOMi/z9wb6foYDZ8upx+Dfeh5ZDz036Mz3rrg58WG94f5MO8/PQZplLQcy1l/bn5z8gE452PL7DL6tZ54vz856YJvrII3vmDP/z+OzHGbd9xhskxircQv32baZXshjnr9l6f8vgQZ/50wNGSQTKmKxR5jo0p9H3AN9dpHqdDqlZ3o6nRYn3Ov14tmzZ1GpPFQJAFdkPHZ3d6PT6ZTMpDMA7FqxsrJSAI9LRdPptOww5IoKzigLasRiZq5Wq5Xgp1arlTMk+AOwnM1mJWjh/bxrZWWlBAaUx8k+kE0FPKNkBBAIB9k+aIBh5tkID8+aTqdlQToZIQITxkzQNx6PS9uXT2X2YvbZbFYqJ7xnOp2WhcUcHtfr9eL9+/clkByNRmWnqi+++CKeP38eZ2dn8fbt23j58uVCwLe1tRWffvppvH//fiFIWGak+dwyjKHMymaZhr5WpBxQZLCQwbWzAQ7ArSMGGpVKpRzQBy0Ad8fHx3F8fBx3d3extbUV9Xq9ZPxxulTeOp1ObG5uFj7mrAwBLkGYHSDOGsAPyPdcDUDdn+vLRhQZgj6z2ay0NaHPOH0CITJt9KEy3rW1tfIdYBJdxD65NQSeOQkAr5gDgNFtPdneUXGDn4A2ZwMZO2AjL+hFvxg/Y7y6uorz8/OIiBKsr6+vx/7+fhwfH8cvfvGLePXqVXS73dja2irja7fb8fjx44VtXP0uyzW66KDQDgf+LXP4PDcH9f7Nd31uwOEMZAZA1itsMDt74QRns1k5jBWeELBSVQFwOgjAfmfeZhDubKq/y2P0XDPwjngAclx+txcGL0tQuB3D4Ml2grn5Pb7P4M0Az3w18PUcbJNcGcsBke2s5cTP5v85ULFdyoAKAM17NjY2Yj6fl8D+6uqqbEhCIE/yzRVog+/sB8zjPFbTyRXYLN8ZcOWgA1sDj3z/MrCcAwNXQU1T23MDUs/DgYODQ8/DuMdybVvu/y8LGNCZHFBan7Idsrz7c9OAyxUbJ4J8P1XVZdhz2VwsD35O9pGWZ7fCmdfQ2eD9u/zgsjFlu5npZJDv9aLQKQdaWbaw+VmGlvkG/IITL/ye4oLplOf/IdcHBxoQJxsYR6gWluyosjCaUJ1Op5yH0ev1Sl/d6elpvHz5sjiNb775phgWDFCv14tutxvPnz8vmffvytjS9oTweNEgAIxsi+cLOFhfXy8VEAPa+Xy+sCvOdHq/EHQ4HJZqD06mXr8/KRuwggDNZvelYIItMvH0rrr3n11zoHt2qAgYC3BZOIwwRTzsLEQbCp8RlCBULGbnsDgcAkEJoMrrTxBSOxQMw+bmZjx79izG43Hc3NyUCgjnbJCx/+UvfxmDwSC+/vrraLVa8fHHHxfF+/TTT+M3v/lN9Pv9ErhFfPuUUC8AND8Bzzi2DJazcbXy27DwzGz0/Vs7JD/P78GJtNvt2N/fj3q9XjL+g8EgXr58WbatrdfrcXh4WGQHp0x7C5lvAnE7O+STaoSNHQ7CY0JevAaLvnfk1hlSByJkv8j8+zNAOLrssbBuCpnmvQQZbjlDB92C6IXlgBFajNBfG16AW15ga76x8xTP9VxztZDvsFGunJJd4tm7u7txcXERNzc3ZRtu+rpXV1fj4OAgfvOb38TJyUl8/fXX0Wg0Fr5//vx5HB8fF9nI48/ONDvCLJcG2NlW2/bn31h2oKltE7bIsmaAYl9BosY7mWFz2IKcpAZ8RVZY9Lksi+gqC7z2HFy1cqU562kGDdhcO95lyQVsZQ7uMmgyyMRXOUBxu4ttmytzzIexL+OvZWUZSGXMvj/7/QxADG7xg257sV+Gh9CEVkN8ge9bW1srC/3H43Fpi/Qi1rzXfw5ezbvJ5GE7ZYO9ZaDZsm8Z8Zz5zhUMbLV726FpDmYzHvIz7dNtLx1M5MDY7zKfI759qrd5jxxZLrN8AExNF8sIz8D3ow9uW+JeB4c5KMC3OODw5bHzrGybuNyGtSyQcrYem5313TqDPGdaeS7c43f6vTzfwZPfB92sT1xZXuzbc8UMPTTtPadsMzwny7Xt/3fR+Q9dHxxoMFiyjDCESebFKUyCnsvMMGcv9/b24vnz5yUbNZ/P4+zsrFQzms1m2fJxZWWlbCVL28Dm5mZsbGwsHBjGO/ibPk9XVWq1WukFxcG5p5We6/X19ZjP57G7u1uqBdADGrCry2QyiV6vF/1+v4Ab3rm6uhp7e3sLuwBA04go1Yjt7e2yF/zKyv3hdyzCms1mJdCYTu97hMkONxqNqFQedvxBSK6urkr7GEJDnzNG3SCK/nUCBwCVAVdELKyD8XkaVgL6kjljYz6fR7fbjb29vfjmm29KMNbtdmNjY6OstXn27Fl8/fXXcXR0FN98803s7+/H06dPYzAYxLNnz+InP/lJ/PSnP13YPg7e5K0DCVTdX24nYoVl21GUPIN0DCBKbeAKMCaIIcNtQ8JzAWHcv7W1FR999FE0Go0YDAZFPk9PT+Po6CguLy/LdqgEc61Wq2wFyTbOZMXdloVeog84ZcaBw0YvcIRkigkaDcpxmM5sZYfOjnDc56CedrnNzc1CAwPzev3+ZOzhcFjGSEBB5poLXYiIEkghl+gnv4FPVNqc5TffsXm0ctgGQlPkyQEEOukAgzkDipDZdrsdT58+jd///vcxHo9jNBqVdQnn5+extrYWT58+jVevXsXR0VE8fvw4Pvroo6Kbq6ur8fHHH8fl5eXC4syIh0w6ckCSwsDYsm+AY0dFwGpQakeHHGFzoD3gL8shNEYWzEO2bCZrzbhXV1fj8vJyYc2QNydgbLnlBR4h79bHHATg2N13b9CGD2FuGajYpzmB5XsNvAy+c7LL4MqtC77PwRp8csY7gxF8le2V/aCTZE40uHXHCRXG5rljH3iu54CvJcFEsoc/8AmeAaoB2AT0vV4v2u12OVMDHk4m97sbLsuyMg54Bz3hPfrChT6Y9gS08NYtKfP5vLTEOcHjqpCrbbYLGazyHidtbFtsg8wH6z3Bh23/su8d7GV5yQExPM2BtYMQ2xOSQ9DG/sE4z+1H1k23F0O7fF/2PbZJtk3moengRBy2Ift9dIa55UAOTGW5I8g0r4wh7GP9Hb+3HwRXOmnKu1g3azvvd1Idt8xbzo1HuN80NM2YU36HE41/6PperVM4b/dLm1EeHEywgHAB3CMiut1u/Kf/9J/K83d2duL4+DhevnwZr169KmD1hz/8Yezv78d8Pi/beGLYtra2CvgjA+K9+SEQmT+cPc6NczcQsEajUcZMu8NkMilMJ/OOsXSQBbAnqJnP56UCQAuX++m8zSCAnsw/wMrrKAA+0+n9jjks/mabWgwdYDfivpLDAnjP20rrnSRYc5L3pq9UHs7FoCKDc7u7u4tWq1UW6LIzD791wBURcXBwsAC22Z4Vfj179izOzs7i9PQ03rx5Ey9evIg//dM/Les+vvzyy/jqq68WQJadvzMu0BnDRjnQhhGDRNuYs6MRi9E/SkamNRtcgLHvQ54IrgGj8HdraysODg6iUrnf6nl1dTWOj4/jl7/8ZdltZmtrKz7++ONot9tlMTK7ctFeRrYPPeWdzmTN5/NyerjHTZvhyspKOaMAnhHEctlomv58B3CgtZFxGKgh+7YjyDg8w27AP7d+RSxu0+hnoWN2ugAW3ssuadiu3OLFOJAB9By9ZG4ELdm5GaTidLmHCiC7uTnYM323t7fjzZs3cXFxEa9fv45WqxV7e3tljAcHB/G73/1uIVFgh4j+MhfzCVthR2THwv8Bfr4Mem3D3OrkrC5zdwsAvDHNt7a2ypbV6OPx8XEcHR2VYGltba2snSGwIyPe6XSKDYmIhUSOgRdjoBrI5TUUXNg4b9m8LBPpFjZnRPmDPDNXABvyZ94TGMAPt8l6QTA2vVK5P5fJa/8yfQ1KSb5AIy7AJ7ykggSgAtwh9w48nKjJAA59s61lftxHcIk/InBbXV2NVqtVzszp9/txcHAQ1Wq1BC07OztFFpin7T/z9CLmnKDgPgN9g282AnDVxPR2OzYtxaYj8o+MZH8F76kIMGZkhooO97hKlIE2mMOBpGkeEQv+3bph+5EDchIrDkKsE8it9Z7f8jdy6Ww8LdnMxYkGbLRtj/WP8fMOPuMey0KmKZ/nOeTAAzsHdnDSgvEzF2QFW2U/Zl5zeQ6MD36xrsqJTgdo4EJ4Yl7N5/NS9fW7LHc8Ez6a79yTEw18R+D9IdcHBxoGb85skPn01nmeCJNAqFE2jMfz58/Llpws7Hr//n28evUqzs/PC/D7r//1v8b6+nr8z//5P8uCQLazZVEgWQYWaHY6nXIeB8CfrJiN5/n5eTSbzbi7u4v9/f1iDBg72ZXT09OyX74V04cGXlxclG0nyfTDEHpNWcBIBg/nRlCCgvEOeswR4ul0Wvrwb29vF84GMfDn+ewuxbaCdmyPHj0qp0/f3t7GxcVFcS78/vr6Os7OzkrFAoPLYs16vV4W7rFOgOACx8XC5dlsFhsbG+VMFIDnZDKJ4+Pj+OKLL+Iv//IvY21tLQ4PD+Pq6irOzs7iH//xH6PZbMYXX3wRR0dHcXBwEH/3d38X/+t//a94/fr1gnzljKGdutcV0Acc8bAl3t3d3QLYMIhmrASLNgDOWHlHDxsf9AiHO5vdL4Tf2dmJp0+fxu7ubtzd3cXm5mZcXl7GL3/5y7L7F4fz/Zf/8l9iPB7Hr371qxgOh0WWW61WkQne56yDF/SPx+OIeNhmEllg7rSuZOAPn1jrBJBEdgB1GLgcBDAeZAK9sD2JiIVqEDzzbk6mJWt/8pbEOCsbRfQf52XwQNugAbSdDkA7L2pnTlT/NjY2ij7haAm6AULoDLp9cHAQr1+/jru7u+j3+7G+vl628Z7P5/H69esYDAZxeHhYFklvbW3F7e1t7O3txY9+9KP45S9/Wc4pYUw4GPf02/EzxpwhddYdngIyDRTt+JAJPuf9DhA9hhywR0SparovmgMcceRUfDudTkwm9+vQqACSwAKI5GAH8DIej7/VTsAYuNfOl8/wF8scL+fUWI6h83Q6LUFtBhW83202bv9zoIsu0iEA6MOmkFBwNtuAjB5saGReVCqVwmfsIPYtIhZ0Alrwbgeo8NNBjMEPySdv0sFzIqKcFZXnjg27vLyMfr8ftVqttInyfbfbjffv3xfbYBlwcszZYy7onluTbE9NO9sjJ102NzcXAk6fLWZ748QKF0k3J6mgl30Yn5H4dPA4n9+vW8LvO8jH9qIrVIAMmiOiJEG8zgl9cfKEhGseH76HwBzeQxOPmctA2v7Vusx9gHfGQ9KZ50IjB0LM3zrpCjfV75wQ4V7APzaGYwbwAw5mwT62AbYLrsQgE9gZ6wz0uL29Le32JFXw1SSpkGEnFT0mdMlJDsbiRBvy7qAYGeF3zMUVuj92fXCggSH1uRcwdWNjo+z84miXgQJ+YRql70ePHsVnn31WFHY8HsdgMIjf//730ev1YnV1NXZ2dsohfL/4xS/i/Py8bG27tbUVW1tbCy1NCNb6+nrZ5QWG0GKEQ4TZHHy2vr4eo9FoYQ4oGwwlWo+I0q8eEWUNA8AchcDIkWFAkWzsyPZXKpVyVoYNtytJZErc+w7ARFhub2/Lblt7e3uxu7tbFgvnhXQRUao/GBxXQ1jfQVCJgo7H43LQYKvVKickd7vd0u6CwHrRMdkuDBVtIPSaz2az+Ou//uv4b//tv5XA5/e//30cHR3FP//zP8f+/n7s7+/H2dlZAd/9fj+Ojo6KUvN8ZM2ZLbdyEOQ5w0tQCTCxwtpI2hhhxHIPPs4ug7eceTk4OIhut1tkpFarxbt378oCcKpu7Xa7bJDw6tWrODw8jNlsVs6KoKrntSc4aMALnwO2K5VKyZLxe8ZrY4K84XicEUbfoLc3XDD499w5zwYZNH0ARBh++AAvfWVH7oAQMIPNgv9U95yVs+Nifryf9kXaQAlqkDFn2yLuAQPJE3iMM8IwG7hzDs/5+XmMRqNinz766KP4f/6f/6cE2tfX1/Hy5cuo1WrxxRdflJbS/f39ODo6KtktbC/v5t8OPgzwczDp73Nmi4vfAFBpz4SXBi12Ug5UcqZta2urVFoZ++3tbZydnZUkFG2nnLFCIIK9dRLIWUm/l4ssNPdMp9NydgByjix6/AZJ0MeBAfPheRHxrf3o4RG8cSDr50Z8u23LYAAe+uBS3sf32D94kStnEQ8LvbFf8AH58dyss9Y9t4LmYA3Q6yAEOkErgnIqhchWt9stQIuE4dXVVanUEzADrg1WHTC4zcQBML/3nJgXNGQu2GP8G5UdgzN8bcTDqfKuqFsnHDhAO/w948evMEYnSpCju7u7hSoK47Evd2BIwsm8dtu4g2l45CQefHQbLnNCpmxfXIXApoMBnJDD1kPrXImfzWbFF1HNtx7lariTCQ7u+UPywMlJ6OSuACcgGWeu5OK78K9OsjlQ9b8jHrZr9/OgL1gOWYV/yxIfmU4OJvLaRHcXgGudaPIz+a0DFXhmG/6Hru91MrgdV0QstC84yMhE5R4MKk7ls88+iydPniwo2O9+97t49+5djEaj4uw5F4O1Dyhys9ks2XMTyEKE8SEbj0CytWTEQ3ndGRyMM9vpeb99noWBwjlBJzMbAaeNhJOz6/V6qcRExEIm261YCDwKRIUG8IhhI2CCB+6DB8Di+KANBgoAyvz43k6NdiKyh1xURJg/60qYLwYSmWFuKDdOq1qtxmAwiNvb2/gf/+N/xF/8xV/EbDaLTqcTjUYjRqNRHB4exjfffFMCq/X19fjkk09Ku9bNzc3CGRcGnxhN+EvlASexLNvofcLt9LknZ47hOY7MWQHm6AC2VqvFzs5OPH/+PLrdbnnnfH6fxfYZLI1GowRGJycnZX7r6+uxu7sb7Xa78BQAD2DCaORs9srKSjmrxU4BeXF7JHMnY8YFEHMLAn8jUzZKBiwRUU48dgUIQ2rn73EbzEFHz8Fj42RoZxE9HztL7rOjsh5DCzJK0+m0nKoNvQ0mca6MlXldX18X2WINS7PZLPpDZfSnP/1pfPLJJ7GyslLW3rBT29bWVnFQlUolXrx4UdbvOGNmJ+NED/MhaeFgwrbbThpbbnqg0zgt8x4eIJd+Bvfx7EajUYAD77+7uysnQyNz0NnzYMxUYQ0MLTtUDSxbBCbOEPI7b8hh+XJm33Lh7LPvRQ7szBk7IM8tIjh+nuFqbcTixg05iELOkQF/jszDB9sDZ3D9f+uzM7/W7WxbeI8rvtAC+WfuJBkBygSMzJlzbFhLyHtY08RudIxna2sr3r17V4BYxiLGAAbEttUG95ZTVwGznCOHdCwQ9OeAy4kXv7dSqZRs+Xw+L/qObLoVNOuegR8JMtseZ/sdVNt2WicdVC8D6PzbiR/GAI+REQcslhfeyf08F51iXMgJsmc7grx4DLa3vA96ozfgPQcYTmaZ1tYTjxGe4y+4rI9+v21FBuhOiPJc7IYTQMa1yImTfbZ1BEf8m++xCfDASTW/30kBd2pkWuTK1HddHxxo4NQtnHwGw1wOgrhmvkvAm5ubsbe3F+12uwjW+/fv4/DwsJwjwUF0L168iF6vF1dXV9Hr9UpWstvtRqvVKkaNg35Yp4EhwzGQATNDMHDVarVUVNgmF2eH07EQYNBzRgCm2jkgkF5InaNdsoKOOg2YqZ7giKxQ0NoHGfEbxu7SnIUcoXWWPpfJWHcCmCdDxUJ5lIKWNgARYJvnE7ARaHU6nbIzFu+pVCrx29/+trTuPHnyJPr9frx69Squrq7i97//fTx79qwoAGdP9Hq9+Oabb8piauZlwIXRwODbAdkJIONkByMWt7H1vTYAOQOcDbQ/Q0/29/dLywhGrt/vx+npaRnzxsZG7OzsRKfTKTJMwEebEzKJwWAXtGwI7ZAcbC6rBjBWy4mzXAQdNqjoR0SUQNNGm7k7EPTYuHBWZHWcYeYZltN8ASydwXXQabnMGU3/7fESMPM+FsU7Mwpgnc0eDndzttDODoAP2GUh+GRy3+r5+vXrAvAeP35cKolshEEL3nQ6jWazGXt7ewWAQTdnpQ1+4bMz7JZX85l7DWigCzJk259BKDYPObSecD9beBvocG4GOsrZGPALXrh1wPKAU+Y9ZJUBRHbw8N8gIicK+L+TGaYdANUA0/6P5/r/Br22KdgmaArQsu03gMj382z0DF+NjNrPOJjOwYXBjYEkdHZCoVKpFLvtQIixMGa3JWJ/CNYNJiOi+B4AEi16k8kkRqNR0cFKpVLkfX19vVRMHQTBewcKBqB8bjlcZlOQYQcx1hWDMc/b8uaAmrFZj7Av0MbjyqA088r6iY6i8/4d9y5L0iBb+fk8l8+4l8s2BRq6MkRQ7e8Z13cFQdkXWb4JrJwUMrj2mSOeF/YQmnoutmPZr8NjcJLxnS94ix7kJJpxHOPgN4zFp5r7rCBjNu7PazQioiTBsk7hYzw2V1yRNXcIMWf+7+TVh1zfa40GQgkjLYQGptk4ZwK12+14/vx5WRC8snK/CLXf75fTrolkWWdxcXFR1g/QysB2jyheu92OZrO5kOFhjN6pxQRGsMiykDWmYsG9CAKEhbnZkbtNg15gDBSBAvcDoDgcjRO3LQwIHNkOmIyBhvFu52q1WhGxuKOGMzyMD8NGVjMrKTxm4bEPStzY2CiLLjH8g8FgoYxrI4MRIGNLgELGPiLKGSn9fj86nU58/vnn8cknn8RgMIiTk5OYTCZl/c6f/MmfFGVoNpvx/PnzOD8/LzRj5ylH/uannY2V1pmUXDrPmQgrGd9hxCwnNnTWp06nE3t7ewulyfn8fse1y8vL4uA3NjZKprXX6y30rDcajbIxAe/x2hj4z1iQf96FXDJHHAWyCwi2vjtAcwDmuSFXzo4YpGanZfvAHwIJf2/DbkePPvJ+AHxuB3N2j3FykTn0b+AfAUSu3JCx9z3opVsWHKAgbwSEOATWcA2Hw7LrXKPRiBcvXkREFP2inWh7e7ssip3P57G/vx/D4XChLQu7ZDoZ4Fg27Thst51ZM58NQq0Pvo93Z977/aurq2XrcZ4DLQBaVHXQT1qmGKMXgPNct04ZoLltxetKnN21zGe5zwDCdsDA1jSyo8908jNphzBwylVUP5/PnY03SOKdBrx+p32lQXCWDfvTbNNykMP/HcTmINQXQYR9I78BZNbr9XLwKxXdm5ubkrSiMwAcQHXPY/SzHWQt04X8u6wXy3jsd5guPNf/NkZCRu2jkcd8wjM2KycN+fu7ggO/2zxYFiDx7z8UuPtdy+TM9gLfwv+d/DNtPU/sTg74+M7z8lzR78xnXw6S+a15Zj5ne296+X7+bUzJ/7P98P3QJtuR/FvGS0LR88B2ZL5aZuGJn20fSABuH8G9LhCYNmCWD7m+1/a2ngCDIUvEd3nSNngMbG9vLz799NMSaNDfzeFtGNdmsxmPHz8uC5dodaKVBPAN6Nre3i7OAmNNwIBD397eLtl2IkuyI4Av/k2/vDNHVCtgHEAb504bEYCDFgmXkl0pcFRtsOdKC4EIGVLAH1Ep9L29vS07W0U87J7h1iUDZisVgud1CcyZBbR8RrsUwknmmv5ZHISDuvl8XrJMAIZ6vV76quv1+0PoXr9+HTc3N/H69ev4sz/7s2i1WmUXssPDwzg7O4vf/va38fHHH5dWs1qtFk+ePCnnrtih2aEaIBjA5oxFBsbZqBh8ZYMHzbKBzEamVqvF48ePy/oiMn3j8ThOTk4KSOVsDHSEQ8uo+LH7Du+k9YDMicEDRphAnUXX6+vrRX6RF2iUt8z03Gxk7EhIRqAH2A87L36TkxJ+R71eX6iKwL+IKMGAjTG6ih5RgTUwsEMxMDKP0DPsA/PMFQDaHZFtg3poxnoOt9DwbqpOjNmtJQSUvV4vfvzjH0elct9uOhqNYjQaxfn5ebx//z52dnZibW0t+v1+NJvN2N3dLd/beVjOHfS44uUsN/wggHIQlwGJs4Dw2X4jP898q1QqZR0YPEcGkXOcHds50iJpQI4O2XE6c2m9M+B3ZbparS5kkC2LGTTaXjNv6M1vlwE62yKDF+uHKxqZjjnRtyyriJ9yxhWe53YQ83EZIMJ2mCYZPHt+bsPzfdDM/gD62/biI/Gf0IJAs9PpxHA4LMmt8Xhcdtx79OhR3NzclLOoXB2wj8sAnHtcteU3phO6z/cZ5xhYWwYywHfiyhjJQNUZ5ExnJzfME+jptiX7LmQHO+BqmOXAdph3mkZ8nwMs5kjgaNtvu4uOM4YsVw68HAj7HX5XxjHoMYGbaei5uO3Xz8jgfpm80PHCmJfZOcuA38OYvSU5fM26yHeWAfgMJnObE/PNNo/f5cRPlknPcRlORP7hjWX0D13/nw7sc4QK4HF/sokFA8gwb2xsRLvdjlarVSoS29vb8dOf/jS+/vrr0qfPIXx/+qd/Go8fP453794VAOtTwAEFa2trJRDJUV2r1SpCxe5IjK9Wq0Wr1SoLYq+urhZ2vXFWNSIWhAMj6CgeAMf/WcgT8bALFZ8D+lZWVsrJw7QXsSjU53OgdGSLbeAmk0mZJzSBFu7bBSC5fQIgRGXh9vb2WzuorK2tRavVKutRDIrYUYr3MAaX7gCt6+vrBZzxG+RgMrnfeWo8Hsfr16/j7du3pfL14sWLeP/+fVxeXsarV6/i97//fXz55ZfF8DWbzXjx4kXZEtm7vBi8OBuEfDj74lYggAxgE/BOm56N/7LsSzbEVtS1tbXSDrW+vl76009PT+Pw8LAY6Y2Njdjf34+Dg4Oo1WpxfHxczk7Z2dkpwItnUm6npx1jDM+K4v+/DoleYvTD/esYayoeBq3cG7GY0TRgwlY4uENfCBQMtng3c0dfDLzgBf82mHAG27zhndYX6zBzMADA1tCm6SyaeT2fz0vLo/UF2XISAV1Df9GJ+fyhBS5n7A8PD+P4+Dg2Nzej3W7H8fFxDAaDGI1G5cTwTz75pLT5dTqd2NraKu2ny+TQ8pn7e7NzdfBtfvH7DIANUnJg6SDA4Ih1NMjpeDwudsVBwObmZjQajUJDgjfbRwMdZBQ9Zk2LbboTCwBdkmfIDc924sSXZSeDTmiQ15UYiDBHPs8A2TyCntg99AybZJ3PQAG5NAj1eA3U+A10hte2dZaD+fxh5y2v+fM7vCDaFVjPiXeT4IC22LiI+3OzOKeKTWQmk4eNPKrV+zUdl5eXCxtgeJ7QgvdzgWPyYl4HYV6jk0G5bTw8z/w22PZn2C78k32Rk4XLwC8y4Gpd5pF5TSIwX4wBfjsQ9Fid8IlY3H4cfbQ+5BY/09ryxLu9LtWyC+28MBv9AVA7IMSe5uA36wXPyvYAHJBBvOWBubm1jWeyZjXjAPtRB4MORiyLvgd/DW2NDS2P2DLkJy9l4FnerAc+4LuoKiNf2FRog//8Y9f3WqPBhPg/hGfxKcbAjp/sKkK4ubkZL168iEajEScnJ8WQA6BYkLe7uxu7u7sREfH69ev4P//n/8TLly9jf38/VldX4/z8PAaDQTx58qRkMCLuM8BeSM5YnD0kkIDYd3d30Ww2C9gnWMDYIUgsSOOUUgPQjY2NmM/nMRgMCmjgkCmYhiGGmaurq6XFxTttNBqNePPmTQyHw+h0OmUPcaoZ9C2S7eZQPIIYKzUOHKPbarViZWVlYb2IM7+Aq0ajUbbVrdfrZftfFPvi4qKAJNo52CmM8Y1GozJPzibpdrsLOyDYqbXb7fj888/jZz/7WYxGo/j5z38ee3t78eTJkzg5OYnf//738fbt27i9vY3T09OYze7Xo0DPFy9eRLfbXTgDxBk/5uL9+N1mwnhQPOaCIXSZEiXDuPj36+vr31onQvSPrjSbzXj69GlcXV2VczOq1Wqcnp7G+fl5odXBwUHZwYiKzeHhYVmfdHFxUbZ5nk6nJehj8b83YQDQAXJxRmSHMTY4DeSDigqlfOjBs3MJHGBgw20jRpDDuiMuKl7O/COnOFxsD/exVshGHL2ktcYtXIwJJ4rMw89qtVqSAOvr6wstgK5UMn6yqw74HbwQcDtgJzGD/DBvjDn6Ph6P4+rqKr766qv4j//xP8bOzk4cHR3FYDAoAdj5+Xl8/PHHsb+/H71eL1ZWVmJ3dzeGw2EcHh6WnvacOfUfLzw0L9Gf3A7I386cGqg6iTOfzxd2QcvrJOr1+00xKpVKXFxclIW0bovi/fB0OBwWW439wm9g93mPHS/2zJVlACX8Yn4EBQbM9n88k/G5xxrdinio5Bl8G/CYfs7A/iEw5KqAAWVu/YIW0AjgQ/Uo4iGZAHBxco0kkZNLjAvgZdB5dXVVxoHftT5AL+jrrGnOtNomMJbBYFAOvOUcIjYMWF9fL2f9sHaDjUacUXebGJd5jI9jvIzJvfxuoUQWHOy5CuRgahnvMr9IGGC/bddms4fDepFnxm77WalUii7wHfPhImjG1lmm2S4/tw1BN/OQ/yMz0+l0wV5jV7LsQmsD3AyS0RVvhYyMO1HqAAPZnM/nC9uok9yybLs6iCx7Mw3e7+Q0fEYvfH5S1nu3wjnxYtthYO//cx/zZMdUngPfqGrkJAH6TLeCx8c97tIxVmK82GCv/+Ae1mLlpMt3Xd8r0IAgNqgRUQ7IYx1Ejl5Z81Cr1WJvby+ePXsWKysrcXp6WqKkwWAQb968KaAWZ9xut2M6ncbJyUnZoQewjaAB5u/u7ko2DBBfr9djMBhEo9EoW4hiNAHDnISNoN7d3UWv1ysgBYZeXV1Fq9UqGXr+YBQAKTyv2+0uVEkAkxgtMpuALcZEaxgVje3t7dJCwnjn83kZ89XVVayvr5e58X6EpVarlXYlDLMPE/Ii9kqlUnYCwvhgmFqtVjFw0+m0VJA2Nzej2WzGyclJ4QmGjFPNEVgcqo0WNL++vo7Hjx/HF198Eb/85S/jX/7lX6LT6cSf//mfx87OTunjvri4iH/4h3+IVqsV/+k//afY2dmJt2/fxsrKSvy7f/fv4r//9/++4AiRUzviiFhYC0QAaoOIctNC52yLW2FshDF4zvobdEFjAghOt8eY9fv9EngY4Hl9z97eXnS73cJfAlgA5c3NTQwGg9JuAGBgPYwzQxgMJwfIjPJ8OwzPiSQCcmInii2gSkbA5swP1UG2obbRJACAZq6a4nzQKdZl5WCRz6jO8Z2dCEYTR2RawGf2lF9ZWVnYhhSnw1wYq0En92LT+P9oNCqAD37w3vX19dJWenZ2Fr///e9je3u7bPNJ1Wo8HpfD7L788suYz+/PL9nc3IynT5/GyclJsd1UlA2esTk4RHQBfqLLjC07TYMv6OqFntghfIRBPYHb+vp6NJvNODo6Ks4R2jtryfamjI3f2/HjUNFFjx39ouWG9TvYBCdI4IOzsbaRfO52MkCXM/XQ0LRztdmBANXtvDEDtHBVw44fsEqw4HWDBIL4J7oPCLQ9Ty80x7+hMzwTvnrxrfXagI/g3plbEneWF+MKxsO8qLJjiwnaXY3h2ZwTtbm5WcD69vZ29Hq9knj07ou82zRAVgy+0AcHyK5Sen74Cyr37Ohn0J0Dcyd98JMRDxtpuCICqIW2DkoBpsyNZKqr0JarbMv972UbGvj3JHOQKW/WgZyiG4zfXR6ulHgbYtsqB/W5kpgTH8gcfETuvd23ecia1Jy4cpIKDGRdgU/gNmwn47ePgT62F8i29cr2zO9wcMeY6TSBT/h95sAubOxQSXI508uJPGw244SW2HdjW3cmQOP/661TGGeIh6Awee4xoU1EmP3pp5+WST5//jw6nU4cHx/Hb37zmzI5yv8cfvPTn/60AI1Hjx7FwcFB+T8g4e7u4UA8WkjYpYS1HIAKen2JjCE+As6F8+L07X6/X05jhkFkiJiz92HnGQCViChnjjA2ACuKGBHx/v37UnKbTu/XrnC4HUCfQATjxpqV0WhUzlWAPy7Z4wQwNjhbADTKhkLZ2QLsyDI7U7m6uhp7e3sFmPb7/aIAlUqlHK5Uq91v6UrWJOLhEDRnp3/5y1/GyspK/OY3v4nb29t48eJF/N3f/V1cXFzE+/fvY2VlJd6/fx9HR0fxp3/6p7GzsxO/+MUvYn19Pb788st4/fp1OSmXLAYOFePuTKV3mHK2LZcjbZStrCipMy8G43ai1Wo1fvCDH8TV1VU0m80SNHz99dfx9u3b4jDW19djf38/Wq1WDAaD+Oabb0pgAUCjwpedPkY2A2uXge1sMbYYJuaGXuNAXJ0xmHP5FqBfqdyfVQII92+8LSV2wsCLLA7Px5nZuWCPCCT4HXYBmUae7RRwFGxwAH2cgXY1K+Kh8kf1kMWojMVBFUHd9fV12VEJGpJIwJ5xcBbj5b6dnZ04OzuLSqUS33zzTdze3kan04mPPvoovv7665J0ODo6it3d3Xj27FkMBoM4Pz+P29vbchAgjhr62Ok5c+/ML/dCT77LQVHOROdABCdl+2aAR+Km1WqV7JuTKZPJpASiEVEqSPAK4M1W3oBkz8Xy6nM2+N5gzk7UOg193IbhjC7+ADnkb2cmLed87xYI/EUOivkcGfS4uBedxU9HPKxZylULA17sc6PRKMkKtyf6+fbjBqGuTHA5e2qgHfFwbgD0YZ7wzfLKLmODwSCq1WqcnJzEwcFBbG9vx2w2K7ozHo+j1+uVwxxprWIeDtD43GMlAcL3rlo7EADouS0Km05Cko083NqJb3VCA/mF/9hskkzYA3jABc5wEot3eY0Lf7utyS3A8AUdcWXY73QwxoWfZzzWI5JZrjZY1gjmXc3mWdCF5xnMmmYcWYCN5R0kpUhiuVWS3xPMw1fvZAddaO+G/14/4YQltg/ZYi62H6Yhfgf6uC0x6wP0RNYsi8gnhz9b36AdSR5wm3dCdTCT15s46LHdx4ZBZ9pcP+T64ECD7DmKYUOOcVhWGnQpr9PpxNOnT8uirp2dnWJELi4uSrvCxsZGbG9vR6VSiX/9138tC3wBic1ms2zhyrsQXJTLGXvGbwCEcLA97t3dXQyHw6K8ABhOJ2+329HtdssaEgwLTtTBDOsDVlZWvtUrClDc2toqIOz09DRGo9FC1gqnSJn4/fv35SR1QL8d5NXVVbTb7ahW78+jsPEjMneGFuHHIOaWFUAc2R2EdjQalefiMJzNAyDwezKHGL5ms7mQkXNZlMBte3s7fvzjH8dvf/vbcmbJwcFB3N3dxfb2dpycnMTd3V2cnp7GN998E2trawvrfT7//PM4Ojoq2UsrIWACg+/sGQAS+hmATQABAABJREFUY8+cTAfkEMdqo4LxwkBioNARjNTW1lbs7OzE5eVlPHnyJBqNRpydncVgMFiQ1263G41GI46Pj+P09DQuLi5K8M0BfjgHgAi8aTQaRS88VmhOVgaD5fUbPMtZXubnTKVbDBxkQC/oBP+zk3d2kIDcbVY4TxwSW/oSSEBfglYAEnOgqsk7cXYZDMNTZ81ms9m3khh2htzHLnVkTLnIcFJR8m/sfOG1135FPGT2Dg4O4uTkpDjV7e3tiIg4PDwsrVX9fj/evn1bgBHJmqdPn8bR0dFCcJnB9zLHw/ud8bJz43sDFWQ8O0XbDleWCLho+SPpMhwOFw4tdPWg1+stbEyBDaIFjzG7p9hAP4Ns+MR88CEGY+iCq3CAV8uLQQO/4bm0quJn+NwBFzYd+jiYc8baCSHGj+NH7rO8OVDge/8WQOVW55z9xM4D2lz9QKawDfDdYzAAddLAQRPP5l3oqxOK+NUMqqnsn5+fL9gbdmBkpyqe62DPQaPtg/1jxg7Y02XBBnx1uxxgztl061JOakET85Df0FHAONETdM/rUpBJ7mP+rnK5qoYNZ0th5mnb7vYy4xW/g7FAK9sUeI182I9aH5zsg09+Bvfk9S62PbzfAB7b4bPV/B22DrtpeWB+3MNznaBxcorLlc7MVz6zHnM5yUaCGJ5xeClyCa+xI8ia28WgNfR2MJvp5UoSumsM4cTCH7u+12JwlA6Cm4iATGdJuQ/j8Pnnn8fW1lZRyEePHsXp6WlcXl6WibZardjd3Y39/f1YW1uLo6OjsuiRxbMAA6J62pUA5hARobu6uioLNhEw7q3V7hdwA3B8lkVELDAep+mDYrLSkC0ny4Ag4DhpFyDD6RO+AXuckItw9vv9ePPmTezu7sb29nZZyGUjPZ1OS7kVw9vpdCJiMaqPiFL5sPGCb7VarZShbWQj7nv1Dd4x6Aids7IojbMtZLFRgFqtVhZBQ192mgFEX15eFhnZ2tqKJ0+exJs3b+Li4iLOzs7i3bt3RTbYBvfJkyfxJ3/yJ/GrX/1q4YBBDMyjR48WgkoHIcsCEi5o6+DEYC0iijPEeOfKx9raWrx48aLwj8zJ1dXVwhbC3W43Op1OdDqd6PV6cXl5WU6v39zcXNiv3vxz9oUgztkkHGBElMX4zlwzdy/EtSNxhsOVsmyMnSnzs6EjWSlAGODCSQpa1pApL/pE7gk4HPSY9hFRWu5sGOEla2YMNLBXdggYV74nOECmDCrsMKENSQQcBtU7Ahi3ENh2tdvtODo6isvLy7i4uIitra2yLuv4+DgqlUr0er3odDrl0EfaTLrdbtEXaO7n26k6a+h5GJzZ5pnvOCcAhjNiOTDnPmiIztiW4PjcZ8wOc8PhsMhlfrd7ot024uwc/MzZZPeVZ4AOXfBl6LrnZFn3ewAKBq3IpeXLtPf7rOP82+/PY142PicHGacDS+wHtsP+2z7foMpAyn4o08AJGIKxDNgZa7Ybfibt2SSjNjc3FxKL8/l9e85oNIpOp1P0J+I+GUBV2M80nf1vxsUf+GR5cWuPx8ucCVYzuDTYc9CbM/8O1sFXyLYDMcZjPxARCy1MjNeJDgcGy8AtgZGDbgdhbpWyH2X8lvEs0/hNcFOmmyvPDgD44wCI9zpRxtyMy3JQh64gNzlxkhMC1ifGm3+TfZ8xgenMM1xBcqIk08S/sX/jHSRJLfN8Z6zCkgM/H14jlzlgwi7abpoHH3p9r0CDieYMmDO+JoiFaXV1NT755JMySKKli4uLOD09jfF4XISFNqDp9GFxa6Vyv30tOyuZqY68eAYZeu4FpNRq9wex8X8MVsTDASfuFacq4v5FZwwwzDb0CBAMpm2IufvcCJe1I6IETNDNBwyyzsJG3pEqDg1hob0M42QQitA7u0tp1YDKijqfz6PRaJQtgp11nE6nZdvJiChA0WVLsiiXl5cL83SrGYEOla3b29sYDAZxfHwcGxsbsbe3F41GIy4uLsq6nq2trbIWpFK5Pwzys88+i6Ojo4UF7TZWBITZCC0DmDiC/LnBlA0EcuOKE0ap0WjE8+fPS/tbRJQgg8PYoIsXDQPA2Fo4txHhcOAnBtBBKVl3OyrWCLmdCnmx7GXAExELRsjJBwMZ6JfP9eD9/B89gF7whYqBDb9l2plPxmjbcHt7u3CqsAE2l1sQeAdG21USnuHsEYG3rwzouAwq3MZiu2JDTosZjplF0BxkynosKsKj0SgajUZJ+qyursbjx4/j7Oys9KnbbqBvyDWgwvO041mWCeb/BlQGFg6eAC/YBnbUQze98JfPnYAx4ECmXF3I1Qr46EAiO/MMiDKIN09z0G5amra2A9AQWkFj2wVshUF5DjoMZpZVC+z081zMi5w88lytx7aN1i10IQcipqv1z/Q0P/K4kS1+ZzoCQHkviUInWCKi+Ap2ieM5q6ursbm5WRarewy2Z563fZ7plGnqORp8mmd8l3Un09/A1dly7ncmncu21vNCRnKwaNnjub6X//O8LGsRseBLzTPLL+91Fjw/m88tC04sGU+a7vDJdLTfWBbAW779XgfQ/N/2MPPI/+deVwL8Xr8ny022Q8vkx8Gs8Wj2tfAjJ6oqlYc1QNVqteiMq56MLeu1x7S6ulqCedutzJM/dH3vQGM6nS4sBnNpfW1trWTCIQL3bW1txfPnz0umbX19PcbjcfT7/QIIAej05F5cXJTWJc7AwLnYYbOHNtUIFNXA2n3m/vfGxkZRQqohtB5lR8GcbBCcAbISmylkLw32oRuLoHk2zwF4AEJ2d3djb2+vVAEQCECoAfB8/pDdwaEb/LjCwf18xvsiHhZP81uqR+z0ROaU39PywOFZKIl5wRjd45rBTMR9ULW1tRVXV1dxeXkZJycnsbu7W4Dx6upqOXPizZs35WyWWu3+4MOtra2yEw/tXoARSpGUGOE178bpW2EdLPAbDH/mt7Mp0DziPpPT7XZje3s7bm5uYn9/P+7u7sohhVT2aPNjVyyCbYwFu6s4E2VDafBtx2le2sghx8iEAwP34sIrO3fe4XEYbLoX25koL7xGjrnXYydjCVBl96z8XhtIO3+MqgNg7sGG5J58Z/jNc+s5/AEg5/dFxAIAc1ImVxCgqYMkO3NOux+NRjEYDBaAOjak3+/H+fl5bGxslMBqNBpFu90uJ8ob/Nip5b8tM/k3toVcWd78fAenZLPZdY12U68ny7uo4MS9ExWy4B1foBnjsO8BuNgXcOHMHRhbn+yMzXsDCwMrbEJ24gTKtic8x7+z/nhuliGuDLLghYGV7QS+0TLN+L1o1O/2XLzOw8GQs/AGaYzJiQ8wg0GbdTgHd7YzjO/6+rq0KTpgn81mMRqNYjwelw0iCF7RF7brhMeeH2Mw75bxwcGeP+eZxkQZKNtOOxjysyxnjMcA2TzKeITnuDIAjewPsvws82celwMOguXcVmga8lvrh2mRgzg+Nx2sH5Z5j8/v8hxz0OE/md7wzBgObMk7Mj7gcyc1zC+PLQcsvB8e80zeYT6aJqaF28OQtyzHEfEt3Mnz3KprnLyMJzwfGXRlJt//XdeHnR8eDwYZYrq8Vak8HB5npkDEzc3N+MEPflAWeG9vb0e1Wo3z8/M4OTmJ8/PzAnK63W7s7e2VRS7swNNqtaLZbC4Q0VEaAJhyIQvCK5VKKbfSlkJ530rKAlv2aufk69XV1YVtQQkCHGBYEQEaVhKqHLRNEVVSAXDrCADMC2FrtfsD6dbW1kr5lzEYQDrIgmcs8MtKwvcRD8DOGeKrq6vSxnR3dxeDwaCsPXGGiXmw2xfbd2LkMbi0t9EX6a1jMz83NjZic3Mznj9/HvV6vYAo5IExE2lz9sRsNosnT56Ubf9evHgROzs7C4rCvA0O+RxQy2fOejijyb3IGvfYKPA9/AEwPn78ONbW1goA5NR1NhpgwXGz2YxmsxmTyaRUOyJiAWizrgfg5V1deA5VMPjIhgnwh88xkPwfuUdX0JdlWR0bdP/NPQao/MbBSAZQzgBzQjkOzps3MBb0h8vZNMsn9opg0+/hmVQV0BMqQHxuWXE1judwr3UEEMb77u7ud34jAObdyBP2Ab1l++7hcBg3NzdxfX0d/X6/6FW9Xo+zs7M4PDyMXq8X0+l0Yevm/f39YicdUOXgDOdj22heO2g0sOJ5lg1kxjrB8zloktYp1paZ3shMpVIpFeeLi4vSc4ys+71OcDhIiXg4vJSxZTl0W5jBAs/ieThX2knQHVe+3ArDffgL/KhpbGDigMPVk2WZUYMh/ATVSQdP5jvjyO9nHjlYt37kIMg+Lv/JgSdzd0DEMxmL+9AzjUgoEjTCV2hg+8Jat7wuk/WYWRc9Lt7pQMLzsN7kZE7ubnBQDhDOCR3vOknSgN2q3E5KhdvV0ExzgKI7BTIt+dz+0EGj/RW8XxZoWTcI8PneQaH9EToLBjONclUAHANtcsWSd5vvlnPG40w8tGNOHG5sO+VEcMRie1MOeMArWZ/4nvF6sbWfw3xNY+hvm5IDIvwJaxONxXk3CWwXBpi/K0XIMn7SVXV8kjt47LNtL/7Y9cEVDS+mM5Gm02lZ+e9MPIR69OhRPH78OD799NNy8NR0er+D0+HhYQGS1Wq1ZKXn83l5Hs6dIMNGxg4HgrAVbMTDqbyrq6sFoMI8FLxWq5VWHtaF1Gq1kjHhXb1erwhNRJT2JwwDgQ07AKBYgAaycc1mszhTwA4LRr21JD3ydrbVarW0eiEoPuTLW9jO5/MCaFmTgOE1r+AjLSosVu90OsVYuAWKFjcO7wPEVavVaDabC44CIwQNbAAiorQATSaT0n/NmSBkZlutVvR6vXj37l28fv06Xr58GWdnZ2V3L4Kpi4uL+Oabb+KHP/xhfPbZZ/HVV1/F/v5+PHnyJC4uLgowBRxsbGwsZJUx6lTPaDVxydlOB6NiBUdBARYO3DY2NmJ3dzcODg6i3++XLPXx8XFpScChbG1txd7eXnkPsuJAFYdixc/ghOfCJ9p8MF4YfK81Qa5yxtABR6ZXdgIYIMZDoMc90JPfG+QQ9Bug1esPZ2LwbmQkYrHtIjsd8yPiYQctQFNEFN0zwMDZMh5o7MCJnnNnBKEVekbwYKPsYNWOxi2EPkei0+lEs9mM0WgUp6enZZMKWkC961i/3y9bYrNrVbvdjq2trTg8PCzzsPM1GLAzdFUHsOcdU7CNdq6U6u3gsaPYRGR4NpuVNW/D4TBms1lxdrVarbTQYv/ckopsQkfaUJ39RXa9BaQPtTP/LQdulUWPkW10B1n2OVEkdXIiCzqadtDLFR+DM+yJATDg1FUTg0LojG1njs5q224xbmwAtiaDN2wg33unnAw6DbIdcNi+OMtqfTBt6DiwLvtZbK3uA8Vms1k5Q4iWSQcnnU5nwQ66VdO969ZHxuzAw9lkB1KuVrqib9vGmOET8+M919fXC5useExgC3wKft904Rmsb3OwhB9Gd5zldwLC9/kz5Im2TAdaAFRkCR1moxwnXXg2cmT58E5UlUql6C564k4M6w7vZ87mg8fvQMlnUxgX8t5qtVrWsuaAi6o/tDJ/eS9z8uYQtjUG8U6KGM+Cc7AXrvoiGz5c2QEiCWt2WrVPZ50sNEPeHfiBlXg3zydh5IDsj10fHGhANIMSHDMCRgauUqks7FP+7Nmzsijxb/7mb2I2u98fnmwCjNva2ioMPT09jV//+tfx7t27ePLkSQk0yLICCL1VnoEHu2Sx3RyHQqFUzsivr6/H1tbWwm4QEBGlsZAgROwqdXNzE1dXV+VZzg4jDIwZ8OEqB72jZj7tMVQ8ctmLfn0AEtk8BNVGkR5Vg968MwpzZo/+rKgcIjebzQoPCGQQSjLLrhw5q0LvLFt7Ikf1er0s/j47OytGut1ux6effhqTyf1hdV999VWMx+MSNG5sbMTOzk48evQozs7O4u3bt7G/vx/Pnz+P9+/fR71ej+fPn8fZ2Vn0er2FABjFcdCAsntTAxta+O5g1MAHuiMzbkVrt9tlW8azs7P427/92zg/Py8Ojwy2K2ez2Sy++eabePfuXdzc3JRtUjGQAHUrP7JNxsZVJgLJXHlwdc5yjqwYnBDEQxu2YY5YLKUacAAAcwYXkOmsInPxtsC0lOGcHdDxTpd+7fBxiIyFwCFngPzHFQ02QMgnHkdE0b9msxm9Xm+Blw7mrEez2exbYNQZcCdHJpNJkbFutxu3t7fR7/dLdQa7sbKyUjZ+8AGO3W43jo6Oot1ux9OnT8uOebbf2Dzej8waHOPY0HPzFvlx0Ojvcfzo+9raWmxtbRW73Wq1SlsXeuSKFLLW7/djNBotgH3mAKjJtpqxoU88i387G47+5/U6lmVsuwGigwbAnX/D/KFzXhOSwaqBJ+PkecgS8zKwgB7YEPTb39Hq56QPY3Ogm6s7yAx2BHvI2HM3AyDKfDAAGo/HBai5Igx9oKMzrPiQZrMZ5+fncXl5WbAHcjubzRbWvdF+u7m5Gb1eL+r1enS73VIN9HO5mIMDzIiHzLjPuPKBefgHr5N0MGDaODkKXeClwSzz8kYa6CcJQQ4KxgbCE/gHH7ylLPbH9hL5I5A033IyC33KFR1nucE6ljl/byDr4N07euYgKeLhPCbbVQcQ0JLnODmcE1w5AHawzJgjYkGfkH3o5kAbehAgYFscgENLxo9/4h22LWAo2yN22ePA0uFwuJCY49n4F2RlOp0uJL68oYqr9bY/0M+JJNtUJ5v+2PW91mg4swMgQNkBJXkbyK2trfjJT34SvV4vGo1GtNvtePv2bZk0DOOU68ePH8e///f/Pl6/fh2//vWvS187gkvAYCN9d3cXZ2dnxXGsr68vHLJGO06lUiltOc7e1ev1hd2mKpX7cx+IKun7rFQqZbElQAcFIvNGNgXQYRDBrkEwjvUh3hEDoI9Qzmaz0rIEWEHYaCup1e7XbXDGx2Qyia2trWi322XBJYo0Ho9jfX29nOQOQIWfNzc3ZW97jD3GB4VFgaDF2tpaDIfDqNXuD9PjNHMEkbETbGFQ7GThG2Dl7OysZDOYa7/fL0rDzmSs4ZjNZnFxcRHv3r2Lp0+fxvr6ehweHkan04kvvvgibm5u4uXLlzGfz0tvuwEWSkVQRyCXM3PoAMqZHYPLwzgmAqJutxtv376Ndrsdu7u78dVXXy0EGTbC29vb8ZOf/KRUeZANjC+BKLSLeCi9G4yRIUK2ubzDlEF2xENbWsRiZh0aADyctcVwGqxjOFnvkY0hQIK2CJyIM7ERDwGJM9VcvAe+eI7MmYyuecg8GVOlUinGF2Bcq92v9yHYIpjPWTB0wdVcyz3PN4jLgIDL32HQqVqw85ZbxVgY7pYj7mHnspOTk9jb24uzs7N4//79wi4rpjf0tCw4S+l2M8ZGFp8dTRzIM5+Ih8MP2XWQrOja2locHx8X+8qzOdmZZMbl5eVC+xNj8zsYt/lLkIQ+Mm42GICvdvIeuwP36XRaZBZZcyYTmcugC7uAHDgBZRCFLhv0cA9A1wCe77AZgEDk2kCNeROcGszwHvQDOlpfoCtnnWBnTAf8gyvuXMzJuuuEGPTB5rAGh+DDdMN3o2vYZ9tt2guZNzLY6XQWDvwFJ7jS6d3gDDDxD4BH+1aqKfABf4o+AyS945ZBJ78jCegqA/pQrd6vW0WXnA33ey2HzIk/8Ilgzx0R3Mv8rGfWIWiBH+cMIXjiBLITesg3cgjfXfFxxSIH8ui7Dy7kXQ608CmM063jjIekMJgNP2Cdxy5AiywL0Ma+j/tcQcw21fYKWc5rCB0YOsnDH5LS+D7bAMZFwpTqXqVSKZiPpAJJPNbJYQeZA/REvzxH//3Hrg8ONBzl2Dhg1BB6iAWo//jjj6Pb7cb5+Xk8ffo0vv7663j37l0B4d988005hKfb7cb79+/jV7/6VdnHGQCytrYWrVYrRqNR6T2vVO4XJFPeIqNggdzY2CjtQzCW7CSOLiIWhNPGkDHQFkW0CuiAFmScMXwIKTSz8YE+gD2ExJUSb8dLYLS5uVkMBUYJY3J7e1tap1j4ZtDllhmy0hYWxsV2qwgdNGB8XkyH0eh0OsVAbm9vF8PuTA6GwlkOhN5ZJBauo8C0ajUajTg5OSk0IKBipybaqC4vL+P6+jr+4i/+okT9zWYzdnd34+XLl8WQGYRgfFxNYP7wxxn3iFhopyFgzpkr5I2sd6vVivF4HLu7u/GP//iP8erVq7LVMDuv7e7uxu7ubjkjBCPKcwlOHeAT9BBorKysxGg0ioiHoBT6IosGHdZZ6GG+MB8HVmRK0KdsRA1cqXBFfHsRurMxNpLOzBq8Mg//3gGtHRIZP+YNr7zDk9tvnO1nvtiU3A4T8eAMACvIIHx3ZpS5Ax5yNhCQYhvKfKhadjqdGA6HC9nBzc3NUlElsGUt08bGRjmHYzKZRLPZjJOTkzIGssfQ1zQhKwid+cw9vAApy6ezodCMuXF4KraxUqnE8fFxyfZfXl6WLbqRAy8CZxMH6MM98NWVmhw4w19+60prDpahjbO22D4ApnWF32FjDUiQR+iHbPp7gMSyDLPBuDOk9i1e14Pu2fajV2RZuccgdJl+u8WF8eWWGsuzq0C2+czPlRKPbz6fL9iIPDbTNAcCzloDcm9vb4svRidqtVqcnJxEtVqN/f39ePnyZfFRBoI5OM2JAPwPsmReG0w70+1MMPdj26AJc8/+hvuo5pHIBChDvxzQOhhmbg4uaaN21czjsN1y5QpAD42w79zn5AtyY2yITtXr9dKeZOyDjrv6gH9lPq66uI0PWQGE8z7zhrnZlzMn6zzjgV++l8tYgbFhi1wNceXdQWauklgn7K9tM3gGumqbt4zfvMcdMFTondRyZ4IDJJ7rMTihZ/n5Q9cHBxomnjOcTIoJmCnr6+vx7NmzAsy//PLLcgI4z8NBNpvNGI/H5WC24+PjuLi4iErlfhEXZVYOLKOtAHDHIkmcJ+NkITfCg0OCQBATwISiu/QGk/icbUcJsBBevnP0jyASMfo0bejq9QIAREALCmeHx4I/Z5QiHnoOqdpERIlGLYi819llBNKAmr9xDO6BJgB0WwX0JOjIDhuekAU1/zAS3McJ6rPZfTl8a2urlLwxMs5UsAi21WrF27dvy2LT6+vr2NjYiP39/Wg0GjEcDhdo4MwgPGe8DsQM/JAR2vMMaLicHeHsg8lkEo1GI54+fRo///nPC70AUxiP4XAYrVar7EbF9s5uj/P7cCwGgc52kbVx5QNH4KDO8sFlQ4s8MGdnvZwNwdlYB5zVx/BZ9hirP3ObiQMRB815HK78kQ0yKDM/3ZbhgB158Hhs0J05ouqWecI4oVsu3/MO65T7YR2oQT+fTOzECuObTqdlvRugnuruYDAop9DT54tsEFQ4KfDH+M291Wq1ZIgZOzTCPmI72+120Vts1PHx8dJKE3yk5Y/xkgxw5hEbA6+gvXlrG5QDVniLTWReyA2JpLzuxCAhy5a/t/3lPc6MwutMdz+DezNAt6wBZgFJ/CZfGdB4wS3+wuPJQMbjh9eMgYDVAbz12b7M43YgweU5GBQ5I28wZRuNfxmNRmVTF/wL26BnGhvA+Zn2XdXqQ7uhf8fYrZd+Hjrq4MGyDp1pnYQO+FHoxztz4sP213PJdh0ew5+c+HFglD9Hp/nMc+Y52Avo5WDc2IpnOCCzriIn0B4aMjZXj5A76EXgbaxlrIq8kWTmd9ZD412DaejohEfWS/QQOvg+PyPbU8si31mODPK9mN5BVAb+4GGSDNCUoJXt9Enw2AZlvph+br3/kOuDA40MumAyoAUDA7FY/Lq7uxu3t7fx+PHjhfUIk8mkbNlIe0C/3y87Q/T7/RKEcOqzmTyfzxdah2q1WgyHwwWBdr8woIJSIZeNNb+zUeDZMJQgw5knfmvQHPEAVrIhJIhwkMPzKpVK6cFGUK04tJl4twOMKJkbgqtcfbDx4LIhhS+ARL63UeEzgzcHKc4uGwA5M0H512scoINbegAUzHlzc7MYErcwYKCRJ9qutre3S/WMili/31/o2YZ3lnE7QxtC89MGEZpkx1Or3S9o3dnZKWuMut1uzGazhVNJLcf04a+vr5dTn29ubsrCRoMj5BfaI6M4J+6jhGqDkQGRHZ9p4cyLnZmdp+lo2ngDiWyQs5NnfA6k7Dj5f3Zs/IZn23jDKz/DjtW8M9DxGO1c+Y1l2SCJZ0XEt/TOC1YJBMngeVymgQ0+FWBamLANNvgkMqjyYUc6nU4cHR2VbWVZq8FzfdmuWYZytYDvbe8yDeAZiSiyweg6zybJAwjP9sTyyfOXAeh8DzIOSEM+lo2P+y0/zM/f5eSM7XYOXrLDBiTbmaMf1lkDMOjq73w5+5jvNz+W3ec58DtXPaGPfRdzNe3ylYE39xMoWL88RnwPn7lVG70gsM9JItMrIkpwSsdDtfrQIsemGnRCwC+ele2LdR076worc8mg2PJnOfF3phlz5r6cTed7gnuy+RkEelyMzWMx/zIA5jmWX9MGWtguWr64x+1TvkwHb6ThYBL6Gnf5/W4/I0DgWQ46DJL5/zIbtYwfPMs2Y9kf0w2aWv+gQfa1rtLY/vg5+dnZ7lkfnSSyjPlykgx9Jhm3rBIbEQu2LQee2V7+oeuDA40cndmJW3gi7o1At9uNp0+flhImC3QvLy8jIsraB3ZIODs7i2azGV9//XUcHByUrRxZ6AjjuB/n52ACY+LsIVlKBw6M0YGE+ykRcLINjJfsHEYxG+acOTXDGQvPt7Mhc8f3zJPnIwTsboWxAZgzZ9p0EMBc/nMJ04plZXbZcJmiYWTIsnu3EhspLoMEglGAUu5LhKc8k8wQjmJnZydev34dlUplIcsPTcl08Jutra04Pj4u6y729vbi6Oio8NfGmctO1gbIWR3ol3cmc8YBQ9lqtaLVapU2PXb/obzPeKErZc1erxdra2tFX9j4gCtvUGDAO50+LHbj/2SssxOC/tmp2CnyfQbVlh07Gow/3yFH+f5sCPO4DOwsSw5eqtWHvnTTI2dmzGdnzjPYciUGB2Y9MK0Zo52DgSzyQqBhu4k+e7y8z+AY++OkA8GG+d1oNGIyeThkEV1iIwyqgWyPTFshzyBY83iYiysDdoDcl9tmLSv8fmtrq2QQWdvFznrYNGwu1Wqewbq1ZeAVW5jl0g6T+7/LMXqe2L7sVHlOtomM0fbTcmrQzZVlOsu+5+HfMwZkykAROeX3uXrjcWXZR1bm88Wd7Cyr2OUMfjNQNA0y+HICi7nZH+V1ArPZbCHoNu3cymw/adCNTPH32tpa9Pv9su4HO5WTZpaXbJOw1dZ/5pd5Dy2Yny+/wxWMiIdEV5YL+yV67LMt4l3mif2cW36zP/Dv/G7fZwzg+13lxQcwF9PIgHhZ8pMxIyseD7poX5N1K9OUcVqfjcv4PCeELbu5svJd9PEY4CG/N29ycG67tuwZ5q2/Q++X+WXPAd3lc8aDfoGlzGNwQE6sZPp/6PW9KxoMyK0MmaCrq6uxs7MTT548iUqlUrZb/Kd/+qdStsOwAWzv7u5Kf32j0Si7jHQ6ndje3i7ObD5/2MEDorHjEwvKAb3T6fRbCydZI2Ci1+v1Umnx1otUKBhztVotpSaMFAbOuwbY+VEKXV9fL33Td3d3ZQw4N0f33mkAsAUwf/ToUVxeXka73S6VCwIMC54rJozFFR0EifYlgLFBIjQne0J7j+XA5xzAE3YEoaWnVquVtRfV6v2uU26tIvvhRdErKyvR7/fj/fv3MRgMot1ux7Nnz+L4+LhsPQtwn8/ncXJyEqPRKPr9fkREvH//Pl68eBGNRqMA708++STevXsX5+fnC1ULj9/ZkGxQUVbWQCyrjBiwRkR0Op1i3Akoz8/PF5w8gbAzOsPhsAQiZOAIRHjWsvIp8gRwNEh0FcS6l7MhDsKWZaWy87IT4Tvml6t8XMgHtsBZc4/PjtTjB1h9V/aK50wmk4UF8Xb4XuzvTJDnaYcBf3BI0CYDcewBrZjO/mSQasNOhs5BJNU770a1tra2cMoxesDaq/l8XqrB5+fn8eLFi7KL1/r6ejQajdLuRFURm5QBJHSAZ85w8R3gm3kQLPH5yspK7O/vl3aPzc3NqFTuzztwNTXvfkLiYHV1NUajUalsQmtkPCIWeoyxwX4OY+I9Dvrs1BkDczNwsSO3HJF4QfeckPA9EQ+tEIwzg+Qc5Digy4GWeUTPfQ52uRfZQq+WBUuMD1+W309ixUk209BA0nqcbYnbfixj2IQMspEJ3h+x2BZpv8J9TmRhY3h+RMTOzk6MRqOiJ7zPrdfw2WAa/jm5wjgcjLoN2Pzk/8iu11Pke8zPDK7Rs6ynxmdeh5TttLfEtizDS2Mcy4ADTvtI8xz846x9lnfbDc+Dv53UdXI2J63oeuC31epDG2gOKpinq2bGeZXKQ0USnV2my9DJHTyWYWjhioPv8VgccOZ5MDYnzuGBEwO02zvgZSwkdSuVhy178xbk9qvZdlhHbaPcJvkh1/daDO4IEoDkXXYYwNraWnS73djd3S0tLV9//XUcHR0Vxzib3e8SBDA8ODiI2WwWw+Ewjo6OYjweL/SbOsJlwRBAAUXlxGUbbpz0bHa/oKrVahXCspPD1dVV3NzclN19LARczhracTmqpdcNYQcQcRAgvcrOckyn960znGwObex0qtVqWdRbq91vA7yzs1MWGl1dXZUT1KlwGLRhOAD7ZIIsQJzqjoLUarWFQ78QVubEmhraoOr1eml3g0/00Roo0B9LD/nV1VXhx2AwiIuLizJ37zyFDLBYvVqtxtOnT6PdbsfNzU0Mh8NyaBmH4LE4/fb2Ns7Pz6PVapWqRsRDmxiOkAoKMsoWiRFRKg3wD6PkiL9arRZlvru7i1arFU+ePIlOpxPT6TQGg0G8fv06er1e0afhcPit4GE4HMajR4/KAnH3F2OgkR0MMEEavEb2AYfwD57zd6PRKPR0QIqBs4F3kOlebJf7uWzsc2DCswk44bcdHg7XxszGlzHAP+/UwvzYXczP5BnQi+fnbJAzRRhndMdjYf4kP6Afz+UQRldHK5VK2SCAceDY3RbF32xpG/FwUCc6Xq/XY3t7u1S/bFMAYGxU0ev1Smtlo9GI8/PzBd4b5Ns5souP523wBThD9l3drFQq5YA+J5SYH2BoPB4XPuBTsF8EQdjdLAcADgd/OdDG0Tr54mwom4MY6CFH7m9GVp0ZROewDQbZXO69N8gDlHnLUQM7dMKtj9ZN+yrG5+oUf8wbByv4buyEE3jQE54YBGUZsWw4WHZFHl3C15pOzI1En3XA2VX+sK26kxHV6kNLHvRmHehkcr8+bnd3N969exftdrtsXU0vv/mXq5jwAtuaW/rQbSciHcA4WKBTg8W5+FjLfAa3zIV5TiaTslGK5ZJ3sKukbSLjhdf21cgYz/c6CoNkg14SKiQKkQcHXJYtPvf6HfhrrOOEmqu3eR629fYvlmHGzdx4Vw6OCL4YEzLlzUYs//AEWjvZAj1zB4lpYz1nnk7cGX9x/3w+L/6hVquVtc+MDfuBrQCH8Dyva8Fm1ev1GAwG5X7bT2jJv6G9+ehE6x+6vnegYQfDNR6Pi+FoNBpxcHAQ+/v70Ww2o1KpxF/91V+V3vP9/f0SLADoHj9+HIeHhzEYDKJer8e7d+9iNptFq9WKZ8+eFWOAoN/d3UWv1yuHvN3c3MTe3l5pq2o2mwsLXWnfGo/H0ev1yonKVF7YihUhI4DCYGPQcFSu5gB+UXy+c2WEDDUKZ4Z7G1HoDF0AomzTt729XYQRI8IYMFpepI7wIZSVysOC506nU1otULxutxuXl5cLi/pRHi8gBciztSy0dDbg+vq6LE5nBzG2t+QAsqurq+j1esURsCPN0dFRfP311wu7Y/X7/ZjP5/Fnf/Zn8bOf/SxWV1fLFreMZTAYRKVyX1HB6H300Udxc3MT79+/j0ajER999FH86le/WgDpVnyMPNlW+OYsDLLIFn0Y1YiHgxzr9Xo8ffq0ADBaCTGa7JoF/1mDASCo1+vx5s2biIjSekXWFh2AJ24PAFy5xQ9HBg9s/AjoeaeNdb1eLw47YrEtxLskGUQ76wQ4dJ8qBg6DicNirAa6dngADGjrgJ2AyhluKh44/GWVPmiQAQM6iPF39i1n6SaTSYxGoxK0GGzBK9PZrU+M0+1Z7uG/uLiI9+/fx9XVVTmDhrF3u90CejY2NmJjYyM6nU7c3d2VNRrD4bAAMnb+Q9aazWYcHh5+KyMZsXg4FjqYnYrBKL9h7OZ1s9mMzc3NYj/39vaiWq3G+fl5jEaj2NnZKYCpWn2odlKJJtBAbmiDhN/z+bwkaHxoH1VexuJA1PNwVhI/Bl+xx95lyjaaeV9dXRU+L0u8oTuWN36PPDkINpDKFR/e74QBACQHjDyD+9FJ/99z8RkZTlJBj0qlsnDwl4MfBzaWEXwVts60wR7lLC82AX2lvdTbutP5AK2wn5ubm+VgSmwDmw+wQ+Lx8XHhlfUdPqC7OYECiCPQQzbRFcCygxToiK5D51arFdfX1wuJC2iUfbXpTBbbz7PdsZ10EA49XbmzzBKoII/8Qf5sI6CJcR/6zjMYu2019oLAHZ1ycMAzGDdtPU5auYJMEOzEAAkYB/2MxcENgQWgGb/mhKir0vgMJxkiFreAN42wQ245xGa4auCds5y0wMfb3zvQaTQaRQ981pRxJzrAQc/MdX19Pc7OzgoG5ggHxpSTRTkxMZ/Pv7URyh+6PjjQ8NaCfjkAEZC/uroaz549K3ufr66uRqPRiF/+8pfRarUWjGRExOXlZfz85z8vpXwcd7PZjL29vXj27FmMRqNyRgfAFkXZ29srv+Okcba0paedMyoAjrPZrFQYvJ/z1dVVNBqNb0W7tdr94vRarbawh7MVkvFAI5iMwFjpNzc3C0Dkt5PJpGQu+Q3Z/k6nUwQWQ3ZxcVH69n22B9UKHAoGhEwX4OTi4qLsf022PQNvxo+hoI3j9PT0W9ltjFy/31/oz/SakVqtFufn56VSYMHHONM7vr6+Hp1Op7R5XF9fx9u3b+P29jY++uij+Oabb2Jzc7OAgE6nE7PZrMzj9vY2fvazn5WAjIPBtre3Y29vL46Pj8tcGYf5kTMi0NLnBdiwcB/ysr6+Hu12u+wUxVjfvHnzrezKdDqNXq8XJycnhcenp6clICF4pEKEQfJWpxhtAsyTk5NSNcJZRUQJhjH8Brjuz8bhMh/kzi2DAMRl2Q63gBi02KFjQJE3dq6zs2AcEQ8nkSLTOGQAkudiAMB3jJPg25/bUfNOZMN7/Nu5UNX1ZWCGTSRARK9NDyo7zIn3QLv19fV4/PhxGR/2D171er1y0Cm76zhpwfkxH330UWn5i7jfprPZbJbqAvy1rDsQRk6xQ/gEPvOYDaSvrq7ixYsXhW4rK/cbgtASlTOuVLiRCYIONgTxTjHIGHJjIE1Cx8/3phoOHKlsumUIe1qpVEpigctZz2q1WngB4IKHXBnwM1bACvaa+QCkHNhDZ+ungRnPd5XELRSuDKIbrnQaFMNPdM6+nuSV9RlQ4nsZo20i97tyYtvh+Zlu6DCBOkCZhJznSlILn+J/93q96Ha7JaG2trZWWk8An9h4AJ5lmYQBY2HefAdNsK0AQ+btg2AdmPMc/g+vMrCnO4Bd5KwnbpflnbZlObjmBHIHFgQ32B0CVss6AbyrtrbrYAHG4wQecuCWYy9oN97C5gK6oTHyap1F1qCjN42hXd10caKN8dsn4Efx466qmdfe3tbybDvqZBnPyEELyTyPIdtbJwTgM+9EnxkzNpLNDkgONxqNMh5oSgfS5eXlQpICW4rNcJIvJwY+5PpeB/YBcjBE9NG5RESAUK/XyxqLi4uLePny5UJv8Gg0ipOTk1KR+OSTT2I8HpfMH4HA2dlZTCaTkoHH2DQajWJkBoNBDIfDsthrMBjEYDCIVqtVtnhE+NbX1wuxrq6uyoFou7u7sbW1tdBiQikdowRghtAIy2g0KlHjbDZb2F4XwYJJEffOF3BLa5dbBwABZNZ7vV6Mx+N4/vx5GTf0qVar5WwRLwBGeDHUGGcUzRUfnJO3qXRbEM+7ubkplaRms1mMJIo4m81KhgMgCL1cciZ6JjAjOnbm6pNPPlkA+/BtMBjE9vZ2vHr1qhh+ql9PnjyJ169fR7/fjx/+8Idls4Ht7e04ODiIX/3qV9HpdOLzzz+Pk5OTklnJWUQC54iHrJx3B+IzZJHPI6JkcuibRy5Ho1EcHh7G6elpcSgEeJwfwDxR4ul0Gtvb22X7Y4w7Bsm7ULHDCmPFybnNwlkJnDDvwfkA5lzdsP67rI2R45nOovB/L0IzkAbwOVhHlpg//0a2+NuZUfPEnxnUIM+eD4tJPS9ozLg9hrwFqMGiHXkOGLCXbo1BVqAfW9JCB0Dy9fV1qQb4UCnT/Pr6utg6EgJ8TiWNbNfe3l4cHh6WA0Db7XYBZozXIMHr6bAlzvIyfweWztStrq6WReD4iuvr67IIHtuF/PIct9tgh6C/tx3nQt8ATbntAz6zzsw8tz4wtwyQaKk0EMmJCBIpzMv0IDvqjDHP8f0GQwbZzkBTJeYyOCOIzdunAi54P7R0Uol3R8TCWggHPMzBGWwnosx/09f2jDnxLFdLndxycIjuco/bDBm/20Q5nKzX65UzJzY2NmI0GpVDbdl9kG6Gfr+/UJHg7wxM8ZHOHmc7BNDMwYhbKMnswxNn1HmeM/KWF6rJgHA+511ra2sLemLa8yyCYwf5BDuujBBc+D53PNiW8b0rDz67xYEctMHGQF8HzdhHJ0vtwwzqre8kaAHJ3nUxB8340zwXZDpXxd0G6OpRxGIyazqdLmyj7AoLdhwcmZNhDpwjFncwtH3l/QQjJN64lw6JnMBAB9fW1hYS1q6+EmyR9EUnrfvo8Idc36t1CiFyWYXP6Yfe3t6ObrcbjUYjIu7LhJQyHTFbMXBA7Hu9ubkZOzs7sbGxEWdnZ8VBbGxslNNicQbVarW0aOFsKLdBcIDX7e1tdLvd4iAg1u3tbVxcXJTxEihgVBEQjDhCYkLbybhch2FkDs4iMY67u7sYDAYLGVKDopubm3j16lWh63Q6LcGF126goC7To7hE3/zN53ZICK4jZi7AC/2lZEKhg53ZcDhcMEjeGcoAxU4X+uA89vf3F8Anwebt7W0J1Mh0MlcCr9PT0/jyyy/LKfLIAVUidkMDWDiLAO2zEeZvsrDO5NgZQMtms7lQqcHhGUg4Q22njMHpdDqxtbVVgohKpVK2NqbChDGmbY6g1RkqGygbNhtX/u9SPg7eGS+eYwDi9T7IDPKVF8xBE3SFal5ElHYq3gdd6/X6QpsB83Bfvrd7RI/4HqPM+HCCdnKuLiFLVEYI3uwkDLzdw+oSd8RDJsigxc7NLQzMGZrUarVSBWZenF/DYvDNzc0CmshmYU8Hg0FsbW1FpXLf5kB1q1q9bwfa3d2Ns7OzAkqgFU7J2U6DAe5z65QdkO0GFU10J2fBDLoccGJz4DO94DhgB2z83yCX7/KCVoNCaGw/xt85g8l48CfoA3+bPtYtg3Nn2KF39jHIpqtKDmSQFQMO3otMGfQbsHvMDjJMTwfafoflwgA2J14cZFmPnQF1ixaX+ULbEvTA1jA25mN5jIiy9gOgid66akOgwbjcKWFZ5srJi7z41QkqbCZyir0n847eO7D0eiTTDX7YrtoO2HaYjvanButZZnLwYh+G3/Az+K0DFvsHy7FlwLRzUo53uq3Xsmx9zkEG7yGBY2DOWJwUdsXNSWLbENOd8buy6KqG54ifybbIvpWkrmmck8/mnxM51lnTxFjPCQsHAeYP/0YXeFelUim2lzk56cT3WUf5Ldjpj13f68A+JuOJ2viTxcXZ8aff7y+UlxEwG2xnBbvdbrTb7QXg41KhMzJkDL1VKALtxT2DwWBhDGSoyAqwMMpCa5Bq4kcsKhxBDkLMs81QZ3ozgJ/NHjLzBi0wF9Bjp0ZQxjO8ANhZEsYAn3i/gyHGmAGzDe/19XXZjpLSMjSIeFgsjdGuVh8WRtvYEliSpWTM/JYgz8Eg46tUKiWg5aRwGxyMy+npaVxfXy8EE1R8xuNxdLvd2NzcjMFgUIwQ9DId7Ni4bPzzPTYC9NQTGE+n07JRgJ20swjIFbKytbVVeIfRIxvtaobBvdseCP7MY9+PPkNbPsN4ek4GXqZRpk+WHTvNZe93RshZFBtQHAS/cZBsB8dY/Q47SQfVBgw4Mu436HalI1dceA//Z12BbQVOxnOMWH5+hSs41l/kkvdho8hOU7nje+53mxwbThj4rq2tlTUR/NZJFfd1e9Gps7Tma+ZvtXq/5sK96V4TZn75uXaW6Lwvg5gMQp2xM2+4MkDmj4GFg9Ls2DOoQxYtszkI4x545rE4UeOAOMujE1zZVluHMw8MQvzuzD/zwtnUzF/rcrYbTryYzvk90Cu/yzS1H3JSCv6Yd8wN24i/AVe4sk6mGf48evSorHFyksd+x7zAV/CsLPfInpMR6DJYJANqBxkOJExXgDpBue2jMYV9gYNJg3SD7yxnDtZtOy2zDlYNgNG1HDCZT8Z68NXJC+bAGG1nGasBNO+1fvp98MeBMvJhXTM+w/ZZl79LnnPAkmkJv7OPMW/sH/gMX+j5+DfMmaDc/HTbnTGc7Reyb7tn/TUfcuCT5/4h1/dqnbKgmXFkeRwgeEGV13cweTINZKsAkJubm3FwcBDr6+txd3e/cw+ldxaBTafTclo4QoNDzwfyGSyYUGQbyIiz+w8BDWdTWIEtkO7jd3Di9zLf+Xy+sFjUz7RhGI/H0el0lgpTt9stRoz5eAEg2XyXdRkTBtRG2kLvzLRpBF/H4/FClihn8714E546yKlWq2XnL0f2/OFQJVriuJgDiopMkb0/PT2N+fx+/QVzubu7i4uLi7i4uChywFqPRqMR7969i263Gzs7O3F+fr4A+DIoRAE9HsZuY5evWu1+C09XS6ANBgDZ4LnIGjyinEm2AbkmaKbFzC19PM+97wAUt8RlXWZ8vlhfkEGAnZvf+SEGB6PmdhAqAnzv6gjv5H3MExpzOfhgjjaQvi9icVGt+ZGBJ4ECPDEdDQRyu1cGsQa86C3vpA3ElU73NzPOXBky766vr+Py8nIhSYGN5V5aGEisUOFgUwa3G9kG8JkDvmXzdPsVn9MC6wtaYZu8Y03Ew+J4+EeJ37yAXrkVbVkm2plv21rbbt5FsGPH62dl+kObnDDI3zEug18DK4Nonu1AF6DqrKqBlsF/zgbnoACgzOXvudwi5ot3ZTBuuVm2YJ1nOtjJOpf1Kbe6OODKABv6kCwk0ekqOb4DGfNaJnZF8yGwGXhZ30xfywI6TmXZ33Gx3gf/YQCKX8/BomlvgO5gA3paRxhTHvuyceU5G1gvA7kOdo01HBzk+/nMiQxsumXQMmJ5daDh5JtBNjqTZW9Z4Igfh+585sQVzwQb8P0yP5SDq4hvnypuWfG8sv4jGznYM03RE8/P9suy5DXE/J4EDrsRMsfsC7JdMv3+rwcaAAKYgiDTfw6D2eqLbLOVNRuuavV+95QXL16U/v/9/f3odDol29btdmN9fT2Ojo6iWq2WLU/n83ns7OwUQWM9Bdu+kmFH8Lvd7sI2afmEb4wk2XhvdQizJ5PJQi+bwRxMBWCiCAjUfD4vW99x5cyawQGl69lsVsq64/E4tre3y/eTycMWdwgX73e2GEMMmI9YPBTIGS8CDnhJ5QTwCyjJytpqtRbGToWHMXLWyXR637fJ4nt4srKyEhsbG2UMBKA2IMyFud7c3Cz0GELP6XRaWuHgC+t0Li8v4+7uLj7++OOycJ31ETbkGEBXVnLm0AGXeQiAY+efu7u7shMagXcuebMTEBk4DqmczWbRbDbLwj0CNhbV7+7ufisDYWPm7Gx24jg7gA+yyhh5XtYD6z+fRzwAfOTDC/1scNnZg98wVuYBzZkv7+RdzogD3rJ9MRDOQM5OaNlidoN1AK11GrmuVqtlLgY80Mz2zwF+DmZp6SPhQoA3nU7LLkgGLszDY+BgPo8bp4Gdo7LGguyVlZXY3NyM0WgUvV5vKfjDVqK/ywLM7NixJ5YHWi0tc+YnbWwkKQCHBNiAEoOgiIcD1LDnOVOLc3WLmuWJMRCkkUV0K4ZtHXN2Rs/ZYwJ7ABJjxWfl4MM6xW8M0m1n3JZjAG6ZNijPAa95uqxibB9gYGgez2YP7cMZUCJzefcqBwVUn20vCeD5Hn+QQRm6bntjH4r/pDfe5+Qg9/CGHXvQS9Zw4Avw6Z6j+ZEBJTRjHvCK75ygwsaiH/ZvrL9YBmCx091utyQmHOja1ue+f8tCXm+EHZpOH86l8Ja/livzw/pu+c3zw2dlP55tNt+b7tgs5pWDWVdXmb83FbGcozeTyeRbwBv5RV69YYLfzXucYM/BvXXXXQfWQVc6oD/0hpbu3IFm/KlWqwtbmKM7OQjnmk4XdzuF3xFRNl/iHBrmTsBq/+9n2mf/set7tU65jYABU8aLiJJ1n06nZZvZ9fX1eP/+fVSrDwuCAc1k4ZrNZlxcXMT19XW02+2o1++332q1WuUMgt3d3Wi32xER8e7duzg8PIxKpVIWcLvPmYxFvV4vPfAAJxTATEepaEVAaXFiZNXu7u7KeRAuh85ms5JZZkE7vfQIZa32sIi23W7HbDYrLUPO5jA2BAhgUqvVSgRKMIQxZitZzvGYTCalZQFj2mw2F4Qd554zDY7aGTtjnUzud+5g3rTv2GlwkTF1uxrBB+9jvQdBDUoJr2h1sjFhDvS2/uhHP4ovv/wyZrNZ/Ou//ms8ffo03r9/H8PhMB4/fhzT6f2OTmz/WavVyva3VEa8lWZELOwC4zY4g3SMi40lMk4wSQBMr78DWmjswyaRn/l8XnZNg4boGDIAuKTnGN2CdsgM9GWLaGeJbaRydgIjasOGjC7LGmGU0EWcjftyMWxkZ5EHJwAM4rmX9zqAQVeo+OAwADKMneyiMzlcPJOg0pUV5JpD7JzFsj7AJ5wXoIyAOiIWqk6sq8Lu8Dxa7CKiBOLstDSb3Ze5yex7Psz5hz/8YbTb7Tg/Py/rmCIijo+Py8YKOBoWAY7H4wLK0EnLDfJkYAsNvIDSfdIGs2SN4TW7/LmCQZUFXctnI0ED6yA7A5KE8WF5yJptqtfu5MSF7R28Qj/t4PF7vMe2GfDt5zEWQBHANgM36AXABxg6+29gbhl2wOAECLrhpCDjYe995s9z0DcqjTkQ8SG50CYnWJBF7A/j8+94trO4+GqAIvxCFqAn+u7qKwEp73/27Fmsrq6WLfKhF+22VDE2Nzfj6Ogo7u7uYnNzs+yCCG2xjciZ+YJcOfiCVrZN2EXbz1qtVvw0NsV+DxlYX18vviDz2pvvIIPQCXnD70J7xkmQBaBEr5gL8mw9zsE2zwTP2EYja65wMm/Giu8h8eq1cPgOqkvIvhPC0Nvt17zfgS2dLny2LAEwGo0Kb3i3fVwOKKvVhzNK+Ldpawxqe5gTDASyyIwTadke4x9yoINesvaOZ62srJSdCNkoCR9i+4Gf7Xa7BQcxVp6Pj3b7oX2/EzZ/6PrgQMNC5/YLdsLY3NyMJ0+eFMD0xRdfxA9+8IM4PDyMb775JlqtVjlNGQZRVr++vo7Xr1/H1tZWPHnyJHZ2dgpQRphRik8++aTsQIWAmuErKytxcXFRCE8/OwDv8PAwVlZWSsb47u6uAEsA6c3NTfT7/Wg2m9Fut6PVahXnjwDkcpwdJeAK8MG4njx5Utq/EAiyXTyP3afYZQlhHAwGcXBwsLBnMqDDGSyALYcQku2bze73Gh+Pxwv7MVuY+YxdYcias4PNdDotRtl7mKOAOAJACPzB+NTr9eh2u+XcAa8nABzRNudg1kHV3d39GSpk+H/961/H+vp6fPzxxyWYODo6irdv38aTJ0/Kickc0tdut2MymcTz58/j7du3cXZ2VgAyyojC+cDCiIeSrQ0VDof/r6ysxM7OTnGIf/VXfxW1Wq3sOEXVJpc9p9Np2YktIgrAMkjyGJ89exbD4TAiHpwKBnc0GhXwi6wSjGBskTnk2SASOSA7CG8xcOzY5MwHjg6d4mJXI6/HItBhu150hTm6nYpAwGCCRIKzSa6yIGsGtAbEZDZJLLDeCFogz2SBLAPOHNLmx0L93O7EQlNn+LBl2FEHiOgSnztzzM52DgwB8PP5PL7++uv48Y9/vLBZBjvqUTEhcwvN2NVqY2Mjer1eWXCNQ8FxsdmAK6CAEgenk8mkjBtdxE782Z/9WQyHwzg+Po7hcFj0G6cFT3gfQR5BhVv/7Phtky3v6BVOEhnIGVSDX+wXQMMAzD7PWT7ute0wUAC4E2QuA2fMFeDkE94t82RhvfbN/i/rA3PFHkNfg0901m17yBjfOwHjQNQyAFiiyuCgj4vAIR80BvjL250zNwchzNWg0cDw3bt38ezZs7Jb22QyWdhkhiCk0WiUnSjxM41Go+xCxXoPaIWews/MG/7Pmin+bazgz+7u7srGDq7qoz+j0ehb9pkxkkiBNiQh4DG6mP0XfIXurvDkZCHyxvf8zsExCTDbfmwItHGrJL/F3jI+6Olgu1qtli4F7IGDYwclbkWk6gkNbE/xEYzTfMIGMw7u4b22LcgsuAl6Ms+s2zmYs47azkQ8VLh5RqVSKTt8GivwDN5Nwp9k+M3NTWxvby/IEDIBj7yrYcR9i/nq6moMBoOSECLQo6ptjICcfMj1vSoaPjQPAbDjA9BeXV3FxcVFnJ+fx+HhYRFQQB4Erlbvtze8vLyMR48elcP8MGDr6+uxtbUV19fX5RyEiCgH5FERePToUekz8+nNgHkCCTKJJycncXx8XJxyrXa/kGw+ny9s94giDwaDhcww4MEtE84IuEKC4W00GuXwIGfsq9WHU3GZuxeoIzQo0KtXr0q2lcwBSoJBZM9kMhcAOAsNiuAIdmNjI8bjcTx9+jRqtVr0er0i+BsbG8Vw4FwI4BB8WpPI2tAu4RaoXq9XQBeOE0PpTQSm02lcXl6WoGQyuT+XoNfrxfv372NnZyc++uijBbpx8utvf/vbhTY1xnRwcBBPnjyJX/ziF6VVix51bw2HYXNGPmfvMcAEgzYaGMlKpRInJycxnU6L40JRG41G9Pv9EgyScVlZWYmDg4MCZFwhAOxx1ogz3M40ID/wHXnhmkwmhSa5MsH8CIAjFveMx7HgQDgMk40JvG2snbAdADR2O4NBtUGdd45yFjo724iHFhjmyBiwT/zWc3Rft/nsrF5ELNCCdRW0+jBuBzXWUVd1HDRyId8kV3C2zJN7nflHZiMidnd34/b2trTpRdxXvi4uLsqBk8hJu90uekoCgXOHhsPhgmM0yECfzAcHba72oD+bm5sxn8+LLzg5OVnYVc8tiwBV2imxjwBOg4XcJpLBjTO7rgzjw3KW3TR2FQdeMA74yO+pSuWqLzx2Rj+DmJwxxrc5+ZOrR2zC4ffDHwIcyzp2xf7QmXlk1ACacXPZR5jvzNOtuPCCYMTZfgMzb8vqz6m24e986K19sW0a/hafgx7Bf68dxKbSpsjmBJzr5ADHYNV2C/vi+cFHfDnJC7LNzBE7DkaB1t7+mCy+z+6Bd+AluiUYn+03fpVgCQziIIR2T2jiTDy8tM1GFrnfgbETNNYz89Tyivw4oHXVZjZbbB9H/3i/dcI6YtsLbZEzbJIDBOypAT9jcdIUenh8roIyDuhsvXa1w58jh8ZuTo7g6wncl+k8GBx59RpgAkDa6vHJ2BjauCMeNug4Ozsrm9W444Gg0PgIHiJ/H3J9cKCRDRBMRfA4+4FDQWgDAdCw7aZ/i9PCsHCWhZVoMpnE5uZmMTynp6eF8AbvTBxjihAjAAgqZaS1tbXS2vXo0aMYDodFAZknCgywoCfOJSgiYt7B2BBImML6CJTF/c4RUdZWrK2tlROuYfj6+npcXl7Gy5cv4+nTpyU4op/fZ4pQ7XBGLyJK9IrSYawxQLXaw2nYBAgoEkaPvm62boXeZHa9axSB4sbGRnHIOJnz8/PiVKjcRDyASgTbfeHQnKCU3/f7/fjtb39bzqtATi8uLmIwGCxE7I1GI549exYvX74su1IdHBwUwAVYQImdkbE85IymHeGjR4+i1WoVY7C+vl42GqhUKsUJodSAB2e8yNI6yEGWq9X7dTiU3nkGgTcOjfHYwbgKh+FFPzB68Im5WqaRJwxtpVIpGX/GgpOGRs6IOgODISYQd5WNdztb7jFx3gefO6Pmcjrz9haSvBtZs7E3zQgsTGNoZ/uyurpasp0eL/PGHqJjdr4G0V5nxD0AV69ncFUIOaUa++bNm2Ir/bzxeFx2YAMIsYaIbCqJASeRABuWe8/NgJoxISP8mwAfuTENXNEFKDvjDm/sI+irhi651chJAGQenmPzrLtu90PmeC9ggiDLINGZXebsihmABkCObqAT/NaBEKAFOjiJ4WqqdYFn+/0ksJwpJYhw4GvdcZIkJ9qgI7TmOa5a8D7bFICQs7luk3KlGNojN96Iwllb30+i0T374/E43rx5U+gF4PW5NPP5PJrNZtEJ7PPKykq0Wq04OTn5Fp94L/zKANW21SDXgTlztt0xHfxM+MPaI/+ffyOjuSXIeuM2VnjF/z0/vxN683xsYrZxfq5ttHWD+x0A8H/TFno7mLHdcdDuy/4LfeZz5so4bV9c+UGP0JWIxeTPsrHis5kLYzAd7Jc8P/s5ZB56M0fm7moYc+A59tv4IC7o4GIA2AIdw/ZBI3hMQtMHXSObbsmcTqfFD/+x63udo2EA7Qwe2YROp1PONgD8Yhwp+bIIuNfrldI+z2HhowEu1+bmZlQqlZLlgNiMo9/vR71eL4cFQjSA42g0irOzs7i8vCztUIwxIsq/CW4spAgeig5TciaYnbO88BHGIEwGetBubW0tOp1OORTPmTPGAs39HFc1oJkNrI0ZwmIjmR2uo/OIh95yBJOgANDlKLtarZbsDbSivYlnYVS3t7dLFsotGfX6w3kJo9Go0IiF/aPRqLThOdPGc3ESW1tb0ev14vDwsJyqfnd3F8PhMHZ2dqLT6cTp6WkppWPMqY456wqNDVxtKOCFMwHsmOady+xsDNgxvFyuhLnsyThygAN9GB8ZPJ+ZkDMxeV2DDZZl2sbe4/eY7CT9u5xxYsx2YnbMGRgxXjsm64DBTc6q2EibTlwEEgY18NyZcgfqyJmBbbVaLfr6XSVkZ6B4DmMCTDFnA3vma2dAYoF2RldHqLZ63iQ2qAjDX5wNB5p1Op0S+GZ+ZzoYXC3LcNkZk1UjSQRQzzLkYAgw5wwjttg0wjY704Yztkz6OXby/J92ApwoY1p2r0GB58q73ELjYBm5RU7QQ+uAgQyO3M/nc/+G73kW7TPLwKN1yzKe9R77xHNzkMazzQ90wfYRetH6Cd+43A/PWKlYmcbmK8+grx4AahtLwoXkCziCZ9BSyGHCa2trZYMQKpTM03TKmIdgwXgo6wQ05zP7are7mt722/gBB3TWSwcsfMeYwQHYCY/LdthtSbZp1jv/Htl2MO/AwoEJtMnYI7dCOQHlQJffWp9tZxwYYJeRJ/TNPsx0wp5yeS7wkYCY99j3es4RD3aRQB5e8kzrtnWP53jNm+XFAZ6fB69dqVmm4yT8oQ/+Ctw7mUxKF4nl2wEp70XWeKf1+Y9d3/tkcGeMDDZxJpxaTTaNiZigZKxh9Gw2K4EE2VN62Z0hYW0FjhYHTvAyGo1iNBqV9+Gk2dHq8vKyBEEAXJTfUSMGEkIi8BZCG0kLPPNx5sPZYp5poOwtfu2I7TgrlcWtSqE5i+6dMXRfpjMclMF4n8fgzJedT7X60FYCMLVjMhCMiIX93v0bG2GCumyUZ7OHU60xkgAlMlfQq9/vl3L37e1tOfWVbFKlUilyghHr9/uF97yTfnJazuy8rej8YU7u7ff8nE3nLA1fBlk2FLwT/XAQjfM3yIbOBvLoiNeRGADgWNz7bN6ja36nF7UtA+0G9ZYLZ15yts8yg0G2g+E+Gz3sjT+zAXcQBu8yWHPQaCeTQXJ2gpZneM370CGPic/RVVoDqZIAjByw8FvrFVUF7K0DHS7aLCi1O4GBo/Q6E6oYBhfIGWsIHCzY8eXPMsjkOegHbTDWdYN1QKWrNjzHNCKo4J3YOeYY8ZD1zDzMCQF+b3BrW85vLbMOiGzrLVsGyMvaSdDdDFr5zTK/Ar2cCMqBhP+4euP3OlDMY8+JCOuCx8X8cuDJ5aRYTjrk+7KueA6+oCP0ymCeZ2FT8MV5cwnkk4oIASoYAjCLPLADlcfPnLKfNJ89B8ufx+sAFBtrGXYCxYkS7CvztR55QTQ2wmDatHYyIWIRh2Se+J2267bl5meWTewJ4882NAduDtiyLjJO+2EHxwbbOXnjdxiM+/2Zj9iXZXbFMm7+Zt7bz5m+xs0Z3HscGYeZzlkv+L/HzveMExp7Dcjd3f1mCKzBMN9XV1dLWzaXfbzn/MeuDw40/HBHWQwKMFqpPByw5NXu9IsR0aIA8/l9fyrnR9ze3pbsl5kFYOH8DMo6BAqTyf2OSKy1iIhSIZhOpzEYDKJarRagicEHmKKcrBPgPQAGA0tHc3ZCtJ3Y8UK7zPRshAga6vV66ed35oBFPK6QPHr0KDY3N6PVapU+5Rz9GhABOubzh1KbgSLOxALE8x49erQQPBI4+T7oQhBH64cBg5UsB63wC/BNmwLAcX19PVZXV2M4HMZwOIzJZFIWt/b7/TLOiIc+Uy/0vLy8LDudwQe2om00GiVQgWcGBdnwLbtQTlqjCAYckNhIEVTZgMIXbxjAZWBF1QQa21E6gHHmhkAOnXSAZKCIPgFgsyHkgj42xA4ccrUm0wrwbIfFu+3gDQwzwEHGc0l8WfDvd/sdzqTZcLqlxgkPnm9QkJ03vLajc1YLvmTg5gAFHqLX0GljY6NUi7Fv7OhEBtMBo3fmsVwxF1oevQYiYnlFy/YuO0nmQcaM4IcNNgyAzBM+N9Bwi5RBinkI7x0MZnojg26v5b3YKt5h+TYQsDzlVqMchHlsluFlcmKnbjCag2zGnIMov58A2vbVlXnromU3gz8DGfPcmXNfnrODMfs2B+TYimxPDS7NO38Gn+yjyNxDVzY/yEG8/TM8dysW72HDFDaU4T3+dwaObmPMiR/mgn0xfXJQmPk7nT6cacBzctBqHhhvAPSzPHJvTljwbNti20f+5GDT/DQAts563Mgf8mlbkCtDBFOMNwcY4ILcosTcbeM9fzCocaznC/3RHd4FXeF51qEcCKIP1m+/x/RzcmLZWNw25d+6+oeNRUdo/3Kl1e8zNuAdVNByB07WYcvNh1zfa9cphN5lIiaws7NT2lNYrEg53go2nU5LGwzR4mg0ilarVZjqtpH5fF4WNFYqlbIzBOOgV73b7ZYIjLEOh8Oyep4xs/ArYjHDi9PhbA6y5QQj3qkGEABD+b0jSkeMMImdgAzeEDB6+dlpiJ0GoBu7r7BtGZn4RqNRdqpAcZwl8zu8BoLFQwYyZF4RRoyRe129laVL6ezKBa2cRWJMPhTJfEDwUQyqGdPptLRrkeWkRSriPpDd2tqKWq0W79+/j/l8XhatM6aIb58+u7W1Fd98800psW9sbJQqnHd0sXGLWGyPoPphI1+tVkv7gkEfa02gD8aJrZABVbwLB3F3d7ewTSHygAzRjmY5nc1m5XP0Fb4BIHkffzCoLuW6RS5XJbxGhctZFRzSdwVk3AeQzBlnHIzP27ChtH4xF2f/oMmyrFm2aU5mGBwhk255cqCQgZKzRIzDm1GgN7zPDp0qI3YKvSE7i7OALpn2V1dXC+cCEExi23BS/t2jR4+i0WjE6elpoR/Vq2Wtgt760/KIPGOTarVasSMRUZIF2GHLCzYiV1GxgREPQWS9Xi8VYfMWuWWHL2wF83cwk7O5rvRZD5gzvLYt9c5f8A7+ERQzpmxDMkjI4MNAzME/LSA5uMhBHuN3QsMgCDthuhnEeMcry5mBGz6O8fJdBvaWk2VBTG4RhC5858o+PMpzZoxeh7CxsRH9fn8hoOD9BBs8v9VqlZ2fsNUEyLn9xfYX+jn447Je2zdAX2hQq9WKTvHHASyZZ8tUtVota0m4B75nPIHcWBYMVOGf6QducsDCHLIPxUfwzmU21ocTWtYJDGxjjRn8LMs7dON32ALLpLELMmL5JsigqsuC/YiHhIfbG3NABV09l+8KutwRgQ9dVslAVpbREDrnRKCDW2iDLqND8N6VNMbj7bhtJ51oQlZI/i/TgT+UTPT1wYEGxGQQzgAC0Kk4vHjxItbX1+PVq1eFcGwBScaBZ+YoPAN0eudfvnwZa2trcXBwUMBws9mMjY2NuLq6iuPj42JcZrNZWYPBFmY4hN3d3VhZWSmOnLIRi8N5N7vRcPoyzhBnxja2AFoUxWVNmO+9iCuVShH0Wq1WDBvg8eDgYGEnHzsRlASFWl1dLYEX42C3C1rPEGzvS313d1cCGRseWiuoQLHOoNlsFgFkf+/p9GF7utlsVs6oYFcQQLK302T9Chl1DC/jIjgFlCDcyJu3/n3+/Hn87ne/i+FwGBcXFwsKz7ofdtZhjJPJ/WLYzz//PF6/fh2Hh4clUGU9BVkBsrvOThg0kKVF6aAhi9/b7Xbs7e2VXbaYAzJOZQnZdL+xA0U7IINozilxbyfjiXhY+8OzXCkAhKFbrDtC37L8usfZtMyZIAOpDPDNHz7LWSobZWhG4GewkjMqdtb+nDl5zRKf4aDMEzspbBRrDPxcjCtgwYsV3QrkNi1XRpwdNG94NmOnYuEqJjYXfeL0eFeJmQv3s3Odq13Ym52dnej1emUOBhMGVAbqthkbGxsLVRT0jw025vN5tFqteP36dbGb2BKDNcuSgw8HYdAWkICzdUIm4mHNkoE0Noz2NSdLkDc+sy5CBxIFyzKVyBV8zDqB33Rwgqzze+QB3jl7zOVEAf/3vEk+wC9XYwFogChAt32Uv+cyDUmkMH4SZpl/6BBgxpUV/J555qw1/HOlm799JhNzRwaQc2iBvNj+sHEDtgicQBDFhjB0PbiSiP0A4EIndMlJRnwfvLKNm0wmZdMFZMqAHb0myQZ2cBIKnpiW2CLwhatB1jFonBfx5oCKe4zPzF8wj+V8WSBtO+9AKlcySHQQ7GCLcsLAemFZ5qpWqwvjJoFpgA8GonvFCTGS15PJZGFH0nq9XnhBcIRMMT7bQGSj0WiUf/tCh524dTINumKbCHC4zwlsJweRV45RcJDnIIgq+HQ6LQf2gbF4FxjE9sh+1jr+x64PDjSsXCZmRJQ90x89ehTtdjva7Xbc3NyU6gYDW1tbi42NjYWSILtVsec6CubtVFGiXq8X1Wo1tre3o9lsRqvVKkq1tbVVAGrO1vL+wWBQqidUMFiIvbGxUXY0cmYfJq2s3J8X0mq1olqtlm0YMRT9fj+2trYi4kEZMbgXFxel3YGqhQEPRvvx48flgBXmDCCPiPj4449jNrvfxpUxY7Bs6L0LlBV2ZeX+JGAO+MsRNAbBGdmIKO1oOGqU2UDBFYmI+8Mbx+PxgjOLiHLoIuDIWdzhcLhQzqZahNLv7OzE9fV19Pv9sjYFIDaZTOL09LQoWLVaLUEktJjP53F+fh7r6+uxu7sbFxcXZVyAcs6XIBD1gjI7MQ79c7aKtSfz+bw4K5yHnS7PZEc2qlUYQAxpPh8D/vrcFfSP4NAGhvfRdubMmTO2AMCIh4wcNHBWC74QlFp2MODc7zE7mw9QNOhEfniWW86sgxGLwI0gFt44S+MkhvUsByTwwwARfjkjTCbRxpwKpZ/tYM9zX5b9xMBH3NuXy8vL4uT4Dc7O4BT5IUHilknGhvNiDvCNqnO9Xi/r1Q4PDxcy3l7jhSwiD5kX7uHltzjVzc3NspMPcuvMHDxy4AWgtG3LgYazviQ8qI4OBoMFoIE88X/vaMg83eM+n88XtodGrpwJzNn9+XxeElOuIOD87TNz5hp+UQnCTjlLiuwxBveN+zkZpPFZrr44qIEWrnQ44OXznLV19hmZ4HnYQ9sMgrDc8oP9cWWMBIgDenQMv+1qR04I3d3dld0XV1ZWys5BJBAB5KPRqPCIrd0NuB0gmVYkAfnMYAzeENxGPKy9wNZ0u93yb2ea4Qs2w/plvOB1WP6tqylUpwC/5iV6SuU7J3EcCOSKELjPcu3kCBiRSqZ3/DOteAbf+eBiJ7Xxc7ZlPjuMeyzblteVlZVS7XSQyPzw1+gy8kmiwYkE6wV0wdfmpBv8IVlkG4ycoBc8jysn7nI1gnuYjwN75AM74HVdzM+dLMPhMLa3txc6A2xbOJ/HAbd9kIO0P3R9cKABaALEIrCU+VdWHg6JY/0EF8Z8dXV1YdEuDmA8HsfW1lbJJrO4FwDdaDQWTt/t9/tFuarVagHo/X4/jo+PI+JhwSUXxobzOGazWWmZwcBvbW0VBmK0yW4zLnZHAeRgWAAE3Ht2dlYUjwAIQ+k9z53tns1mZQtZhIkqzvb2duzu7sbZ2VkBaRhdl5rr9fuT1t2SU6k8rH9gYbW35GQcGDrG48ysDT00ygeV2ZjAP2d6oFfEw9kMtLshQxh6HzyG8FOdaDabJVhi3jiDZ8+exdu3b6Ner5fWOQ5nvLy8jPPz89ja2iryhywQmLA9KGAYOQBk2HE7y+fMFZUT1oQ4a4ExHI/HcXZ2VoIL6Mjai+vr69jd3f1WZsfZaBtMZ3jQSTsLaGgjzIVhs1OCT3YUNrIOHh20cAEsHLwyThZGe1zQlYAFY4/c5AyvjT2yhOElaPOWtP4N43OQkmnBv+1ImAsJAOQ/727iIBnQAc94l424M2C2Bzh/dI6MGs5rNruvDLIBBraAM1wYP8E74wEIUDFk16npdFrAEYczOXvtqphlCRoSOGILSdz0+/0SmOFHAIbj8bgc5ArARE+gHbx3VQAaWb7n83kBT85cAlIAoQa+yATg2HaOf5MsgW+u/rhiAL94nisUrKVBRpFHg0h0zmt3sIe5ouRkVcRD9ckyh5xbjgheAS1OFHHZN0dEAcToqc8nQEeRX4Iw3sU4rLvZz/A9NKENN+sCSSNXiKAhwJpt5Jm7wSH6woV82N7BX3w3n6PDyGGuNjmbDwCHXvjp1dXVoo+Mx0DaQQbyaXuBbeI3Tj5gj7Ez+APsBL+nTQnfZ/nAN6GjTgQwBuymgx7TwYkstuw30EW+wSyuTti3oFPQP2+gwXOgEfNCj5Ej7Iyr0sg4QbyTLDzPuzBhT6GF5Rhdgg5OKHCB1QzWDdKRU8YBbsvr7bAJ/IZzl+AjsssYGZsTcGADZKHb7cbd3V1JcDEv+3vo6WQZ//Y8/9D1wYEGJWeXgJg8PfP9fj+ePHlSADMLYpnkaDQqIJzyofv019bW4vT0NM7Pz6PT6ZSTwylp3t3dlWwA5Sx6LJvNZhmPM2d24uyKxSLqdru9sLXpZDIpC9FxtABNR8cRUYQYhXA2r1arlcoNRhulwfii7M6WUXHY3NwsVQS3Rz169Ci63W7JgDtKpmUK8MK7UGocH9lFGySMS87isF0vc2i1WqX1iWc640sZEiNIJgBHwbvcS46Rg7bcQzUiA5t6vV5aw9rt9oLB7/f7cXR0VLKhLLa2Q5xMJjEajaLf75dSOwaZrUNZKE0ZEVlH9jFeGFBvfRwRJduOQ1ldXS3Gqt/vL5zaDq1zz+TNzU3ZJcstANDaGTlvo4tOELAgBwYTDhDcH2vDAU0w/rlEjZzwTBsinKYdFnPz4kacdsS3t6LlPvSXd0Q8HFwH2DAgMZ0ItlymB5gAhHgXcueMD7/lBHPTyOMGYPEMQEmuTDhTBKiADszN67eQIwKzLH/YOvhlu0QQypzJ5MKzq6urWF9fj/Pz8wJOoT9jR5dpz3Jm3llAxksCA/vCbwEErlJlZ5yDKsZBFjo/1/aAwB3bnMErvAcI5EwwDtz2Dx4BXhwEZh1C5g02AR/mq4MHgxID4Wq1ugDk3VKCLXcrI4ABGTIQ5B34MKr1bvUwHdwW5eAN+aZNBKBtOkJj6xLgywG/K7H81pl2gmnrF7JtvwRPXEWvVB7aq4bDYWl7BFzZRzqrC9DCtlSr9y3TbKNr++HMt0Ejuu1kWQ508EHwBn2G3q6iI9tex2G6OAiynyWoRf+ZD+ObTB7Wo+RgE51xwOwAO1dVnMV3sop73eZtf8mcHMzynvw83mmAvCy5YUzCwXTYQejPe5gDc8f3OmnKO9FX3umkmgNDnuc2WvhofAgNfK9ph16btk6aG484ULbtNE/BEjwTXjhJA62QDXjLxjaulNnm2bf/set7LQa3cDu6ajabpYQfEeUIc5fWyFJgjFZWVmJvby/29/fL4WqU84+OjuLk5KSAaxxWvf6wh7azUBDVRgojQO8/VQAWgFFux/CioNVqdQEAcGEEXTlAQBFeDAxZEy9GJkMeESUj6gw0v+M5BAUETARK3irY4IBsjQUMOq+sPJwOaYOSQRi8gaa5bMnYh8NhmbczXa5Q2Mk66xSxeNo19OHwRNalUIGKWNylAdqwMwgGxocq4khns1mpaiBLBE3b29txfn6+oCzMF0BjYImTs8HJQRR0xdA54IMGvAfZJFBYW1srFRpa+waDwQJ4MwhGRgGmfjb3wHNkzO0RlgEDPgwh+mP+mc/wxdnTDDi8aA4dsvzB05xJyYaQf3+Xs/Dz4SeywloWG2Dfy2U+OiAGvBgMMi7TC+PLPHgm+uNxGgQCwCNiAWBGLGbxoLfvj3jYaYQzikajUalOwIfc5sAYp9NpdLvdePPmzYINy/YMgMtlPjmzBU+8mJu5Y2v823q9Xip+lUqltDjkYIwElYNBj4N73fKFjLkqYWfLHB10OjObAaY/y5k8wIDH6G09uWeZrpm3tjcGr/COarntIn+YQ5Y9aIlfm80WFwnj131vXghtAGRg4X9nXcQ3O8ObZTniYW0AAA/9xQdlu4efJlijCpUrHK7ocm/EfdISX4EvWFlZKfiF5BK8czsodMWeQj/oi35mXeIPemrsBJ1NB+wv4JFneSMC09pyZH7moNy0ha78xrbPgZHHik3hefzW99rX8ducmMk2w7bTn2ed8x+em/2eZZ/nQVf/3+De9ON5BClgQ+swsu55Z32MWNxsgjFaP5CzPCboZVuHTHgcEYuHqHo9qpPb2DgvY8i4hgta27/mxABjQFY/5PpeJ4MvIyiEB7Rw+jSRnreO3dzcjMPDw+JciPIgLAqHwl9fX0en04lut7tQ8oRIZLwYF1UER4D0IqLoBBkYGCskBsSG3krhd1vAPC52MIiIBQVzxobL5VqEjiqCHRP/9+Iegz2ef3t7W8ppOaCAtvxxNgrQ64g3g3uMHMCL7/gbY296Y8ztAFksXa0+LCw0vRqNRlEw6EPWCj4Nh8Po9/vfWvQZEQv9m9Pp/bbGl5eX5TBBZKvVasXW1tZCsJGzGc56eIw4a1/V6sPhVMyNoIfvnZE7Pz9f6H20/NtQ8Yd2FGSCv/mNgS+8d2DngNEgxjLM8wxaGDf6nx0KczOo4rfw1t/zG2f90Ccuv8O6iJ7AYwd+1k9/Z6ds5+p74buDdvST72zcrXM4dv7vazZbbFPBaWYQYPnzBR+c8XUwQyCF3JOocCULAMbuKowBupD0cPXH+gSfvDbBIMfg2Y4QuaKySOA7nz9UiBk3GXPrLXac51LVyMGXEyKWRzt8HK8BQgYzPJPfGagzJ1cgzP/vcsTLqif8xiDFOstlkMa4CEwz8HEAat32c3KQmcfqcWSQZn/gi3v5vW0Tn2Ufybutc+bBMgDny+AdWmb7x3vMH57tYBRMwJog/IXH7UQC/zfwtZxlwM3vGCvjdIdBRCxgDsa+TKYyeHcmnt9ZjpBx7Ac2iEw7f9s2ZZngvdAe37wsuMqBpmmQL+uTv3fLXbaZlhee4fYw4zJ+az5ln8nYTGvT3t0d5oPth31e/j9VQXTPuvldOpcDF+ifcY4vxuUklNs78en2obe3t6WDhKQllT7jNtsP2xfrx4dc3+scDTMHRmZBY1tYCERUSCmp3++XsuZoNIqLi4uyQJp2kYiHyH59fT12dnbKugOyizb6uYcMJcotHHxOSdyZFguRS9NWLpe8HYRYaah6YAC9JgFnGxEl642DxQi5lQr64XABDYzbdJ/NHnZG4WJXhWr1vg3IAYwdqZ0f70KQbLD52xleG1palTY3NxdKegaXdvDwgpYKqikGlGSB3KrGya6NRqMoFAtbOSfDQRoLC6Hz6elpvHjxIlqtVglY4BFzzgqdjTkyY3COQanX60UOLKtWUipj8AtwiJyxmM4gyS0SlDXdosP8GN+ycqmNtnfgsfzbkBrQmXfwE9o4K8KF/vBc3oXcOjPPO7g3V1TszJ05trPk99xrMJNBPPeZbzbYvmfZ7zNYzc6S53jcDhSRCwfUBo52+K7OWi9xCAQzrux58Tq/NchhfFRKqb4aVFoH0B+DcfsAB5eMnSw82zs7yYK9Q0cMMACrlh8+c0vYsgA184n7nCnOIBTe8O6sC3b4ljHbz1zpsGO3fc/6xfduVcsgG/kkoWMAaD9k/mbwy+cOODx+z4ex5CDdNPG/7Ucz7T0WX7YH/mwZPWwznJhyIgaaMw4SSx6Dq5L4HSdyqCgSfOdx8W/r/LKANAePpgGym5MOzAse5ky2ZZjn2LabNga1/N88tMznANtg3ckl88pj9/M832yPLV++J/8+088tfH4WtDcesn10YJL1Dd5Z13NWv1KpLBywzG+Me/z5MsANz0j+eOzYa3d1OLAyvsjzxc7aFzAOYxie5Z0buYwT5vP7AJz1qTwL3M7l3xsPfcj1wYFGtfpwGJodMQNmW9LxeByNRmPBaOFM+/1+nJ+fl8/o6R8MBrGzsxO3t7dxfn4e8/l9ZWBraysODg6i0+mU9geIh5OuVqtli0VaHay8gGeCDS86wnF5jDDLQAeHCYNhhLOfEfctZLkyAFNYtGlgCbOur68LyGY7XisqhhOhcbZvNBqVLUq9lRoG006ZNhKCD8bG+ghAOg6XeUIDAhnWNtjAeX0KgQE8Qham02nZrQkh5bfwy4BnNBotOBZXCLa3txd23MFh/OM//uOCsjJ2gPrd3V1pp2Ju0Jsgz+0mNtI2rFTSDFYASgB4Oz474vF4HIPBIObzeWnhAwjiIC3L7IIGz//Yjg8O/JyBhQfeiYLvbcgBsG5JwNkjS5Z7npUziwaJds4kBxwk8W+e5SoMspEDNsC258B30Mb7pEc8ZKkI6uw8MLIAdWfQoYvpwZxsD73wz+PLtoNxUtJGTrmcEYOuvMdyRwUT/ZzP52UNEHpN65GdIbrmSoUBHf92ayoVOOY+n8+Lnhs4mtdZD3kGwIDzaOzcI2LBTuIP8CvQyTY+Z96gjwOKZYALPbWdQSYMSuCJAQvzcqBoIJh/Q6ssuoP/wX540TVj57kGLfYL/BZbD33dloucWe+z7Pv5OaHF+1xpykEFPpTfMz8AEUkBgzICNM8VPczPx8ZnoGjastayXq/H9vZ2nJ2dFZ9k+8T8SXDlcUPPer2+sIjfdsD8dXCIzqLX1Wp1oZWQBBFyk+0q8mibaTmHjrlSyvsdpGUZNDiGX1yWNSetbH/5jjG7uuTgwAEJcpOBMn9cJfX94AYnQdFvaDOZPGxE5LU60NyBhseAblAZd2BGYtq+L2MhAn7Pw1jIQRRBhufvZ3k+liPz0kEFsmgdt68luQRPnezm4tmsWyLJ73Z22wnkxR1A8M528w9d3yvQYCJ2uq1WqyxeHo/HcX5+vtAaheK32+2YTO4XgrO4EiKRfXOm05nhiIf2GWfVIQSCQBaZoAKD4FPLvTgaQcWRIXQXFxcLShoRC+CabDuLuXm2nbeDlNns/uRqAhay7BYM/kyn07Ko0duVsSsAIA3hYUtVZ+yd0UfIMXrMDSdn0MQhXiwQZXy1Wi2Gw2FUq9Wy7zjzszJj6NnxKl8YLc+DqgW0dqYW/sJL1itsb2+X81hYxI2Dnc/n5bvBYBC9Xi9OT09je3t7oZWItRDtdrsAexwE1QYMSs5QG7DZObCD2fn5eZyenha5Q27IIHOyfQb6vJ/nrqysxPr6enmuacRic2dSnbHhWQar+R70ytleg15n3w3ickaQKzsT5IFnOIOTqx05w5UzTJYJ6I0e4LCdiMBJEmQ4sGLMGG3G5TY3NqLwQm+cFM8z4HMvdQ6wcsWkUnlYc8ZWs7VarfSH8wyDMPjvnfBILDjQBoRcXV0tZChxqt46GBvd6XTi5OSk7ER1d3cXo9Go8Ama5SyWs4YOiiOi2CvbAZ4J4ER3XZK3U4XOvBObwT34IuZqO+/xwWsy19ne+B3wy3KVgxZkEgdfrVZLayl6nGWE3/Fuz9ffL8uUkgzKLbU82zpq0GPASJLJQMUBYw4QDM4YC7pg0GmAZnnJgYIBK3bYwbfBPvLKXOzjDXT8O+9I5SQIAZYBGVU80x8dJ7nAVuvYbuwP94AjLBPLAGuWA4IraO55YHPBJQQo+GBoStLT/tRJGC+Q5veWF94PHW2/IqLYBcboSgr0Z074GfOS9zox4cSkK2/WB3TaID8nftFj5mAf5wCIVsvV1dWyQY4TDcwLmeFzErLQbTqdFpvmIDniYSdW5MJ8rtVqC500JGyw6wRCTkQ5WOEZBvduh4JP/J7lAOAn6AFv3J6KzHQ6nfI+5Dgn25BjEvnGlgThH3J9rwP7cE7+jAzU9vZ21Gq1aLfbRdlQxpubm+h2u2Vr23q9Xs6c+NnPfhaNRiNevHgR7969KxPb2dmJTqcTk8kkBoNBAV3VarVkG1lvQW/Z6upq2YUKgcBh3N7eLoCXnNUC+FvZccwWCBgGwQFIBFU4UcaJwEMHDrSzc4O2s9n9Cedk+obD4cK8r66uYnd3t4zXTJ7NHg4fOj8/L0EQz+ba3t6O29vbEjgwTu5xNp7nzmazUlGxEjtTmEEMwY/LwwgoBhjFogLDdpgAXj4j6wnohnaUuwkWMT4EU27XYF6VSqUAdxs+FBlldkYLB49hNQg3CKFE+fTp07LWBPk0UEWxkUfWiVC58N7pdqA8ZzweR7VaLWd15Cy+wT/y6YwmBg8aOwPmtkFKxzawzgYS1CMDzroxZoyUnbGzJc62YEB5v7NCDsR4B7YnV54cTDlrZoBmwOPMvLPSbAEJsHfQy/eMB+fpoAJ6W2Z4nwMDqoQGejhNxsaccZwRD2fDOJOHHDgAmEzud1q7vLws9ozfc74Kzx0Oh99yeiRIvLGBbQRzNc+gDc6WfmDmgXxRudvY2Ijz8/OYzWbRbrfLdqXQJ2f0AEbILDsq2R4j9xmUOxsOz5dlj7ly0GtZ5zNAvKs76A2y58wmnzMH2wUCeTt0MuHYFeZpIBfxAJgd8CMbGdQzZy+MzgEI9OBsK4NT6z2ghjWZ0JULPiCPHpvBD7aFz2ezxZ2svAui28EYU6vVKh0CfO61fHd3d2XHNQP2ZTYDDONACsxj2huoIhccnIgNZS4kmjh3xVlxywYJrowVHHS4TZpnOUFHgOHPqOi7xY53QSt3d7gigN5ZN9BJ5gANWTtmHYPf9lmWtdvb27IbKd/N5/OSgHHgY9/rxIMDchKIvNP6BI8A6+ix6YZeOZECXmFsTjbYHkAH3+vqJXrknbfAnGAy2yXbNQcz4DCeiT5AK7YUt/0kKWP8yAHP4CBjWHehoIvYG+v4H7q+12LwiIdtFHP0TemFjDA7n+CcNzc3yynJOM+VlZV4/PhxrKyslEPonj59uhD93t3dRafTKQTe2dlZKHGSMW232wsRn0/jdmYN4IgxN3DhjJD5fF5AMwDErUARUQwFQs+hg8ybk3EByXt7e4V+GCvGaSaykxJb2OKU7+7uot1ux2g0KkLiqgLBCFGmF8kPBoM4OTlZyDKxUxiGkS2K7YB9+BiKdnV1Vc4QQRgJDi4vL6NarZbDj5wRIqvG+g0qX3aUVKRYvxMRC6COBXudTqcsAuOsiru7u9jd3Y1PP/00xuNxdDqdcmAkJ4RzOjDbxhIcovCdTmfBINE6guLxTkCggZSB9GRyv92y112gtMzBu2MhR6wz6fV6sbe3V8bC/XbwOQsV8WCoMDYEPjzDu06QJUWXXZ3IDpx5YdD4zKDCxg+n5+dRAeVeA3IMvkv08BwbgN4BNNAjOyv+b2NuurhiQQBkxw1Iw/mQwXEwyRgBwAYxzgBBD4B8rtJxOXMOTaCV6QFP4CH2AUfLHLF1tOPBR2xZ3v2OxIBBXM4qerebZT4BZ2c54NR6dIZEBs90wEbli/Fkm4b8sNUzSRbGBN04RM0tRowH/fS6JMA9dILGPryS30Jj2z3bS+ugdRP7CMDxFtjoCzqFTiK/6Cn2xqCCi3Evyyxm8Le+vr5gl/FnyCutpbYxXpCP/YG2PMMghjl58xDkcjwelwNbDbygjfXP1WcShWACtu/me9MP3aXV1O1L3rESPUE3OROH4NY8gL9ev4hMWtacYMgVCcCe5Ry84qCFbPRkMikb6/AdfGSramfyeQb0tg2yfFLxNO2wAczN2W1XcaCV7azl30kt5MEVYAcGtvN8zm+d7DGNGR+/QVac+MpBvP0GY0RnkAe38TIXfLPXuRH84xPgpe2eE9iz2az4SGht+jqJnYP37EcdxEID7Jdb85i725/9TPAKRwSsr6/H5eVlRETpIAKfksAZDocLdtm+dVnnyrLre1U03K9rYWw0GjEej+Obb76JTqcTnU4nIqIoLYu8h8PhgiHjwtFsbW3F9vZ2NBqNuLm5KYfvkElhq8P19fVSKsKIkZVvNBpFoWGCo2lnaB2dkn1krkS9ZMlqtYej6Gu1Wjm3A4GjfH95eblw4ibjwZkh8BhAjNDm5mZhOGsPoLGrKQYGjrCzU0SInVHFyNKq5mwGioAzYw9yvuMdVAPYFcyCvrm5WXbtoDXErTiTySR6vV7JfmK4vL6GzAYKDrhgDigCrXoYA8AM7VSM31kn5K7RaBRe4vj6/X4JlLzeBLo4E8f/IxZ3aoK+rFEBUPmk7tlsVhyFHTcGbDq9P+Uep8vz+a2BhzNbziw6i+cFbbwLo0MAhay5WuEWCO9f7ioDvGUe/I5qEQYSZ2QeGBw7Iwpo5Lk4NvjAn1w99Hhts5zlyVkw92ebP9AAIw8NLIfoMDYFwIZ+wRef9G09gE/oNJcz6s5qMzdXUrmQeS50CP0kYDaoYa69Xi8ajca3eAZdAQvITEQsjA97w/zJFI/H4zg8PIxnz56VoCciyoGAtCpi/3kulRWfiVGv32/1DA+4n2DftLGMGMzgv/idA/JlLTCmJRfzcAYa+0qShHn6dysrKyUB5cyn5RT6OsPthAKBMvJHldBzdgUZ/tqn5Uob7+H/BJrZxvEsA0wHHg6cnejDZ0BjDt1FT7D5VEKQCyfRnOBAvrApvMfjI8mHD0H/kClkJtMAe93pdBY2ajE/zBfLFvOGfyS1nExBT7G1EQ9YxO0tzBnfCUawbDjIdVWRQPX29nZhe18Hs4zF4N1BtmWXOdpuQHvGY3nx+GjftN3g+dg/b1zhAIL3uRKSx+QAGl7xPXQhUWX8h3zmagF/88cHf9o3Zp/i31uv8zwcLDIeV1KMM+BtxMOZWeC1PF8n/xgvYzUuNLayfCF/nDGHTyRxxr3uPFgWhP+x63uv0XApmpdRwtna2iqg0eAMo0LbkcuJRJXOJvNMjEGv1ysAN0/YEZqzxrSgONPnaBYCGbT5uThZl8Mph1qYAdUwJp9m7UwvOz95XUWuGLgawTis0Cgoc2BejKVSqRSDblCIQrdarXKf+RexeHKwd7/CqLC2Bv7zTkAbAROgx6CV+V1fX8f5+XmRE5+5QTvc9vb2QiSOokU89LvSR+vFj/1+v2TlCIQw/D5wEjDM7lWXl5fFAXr9i+XemTkD+twq4WyXM73Q02sWyPoCBhkDzwQYufXI5VXG4Qof/7bM5MtriAy+LRN85wDIupGzUTkjA58IhhiHWy8c+NhgOhMHnT1OxsN7XdJ3xg1A4dYF+Fev1wuYNYiA5xlo+fL4c9BvOtnwO2lBUJyDOcAB+s13ZF9dfUVvGSegxplxZJCzadyS4rGiR5Yz6JABVcRDVTYHSDmzaueILUcPnTkk2cLaKPiCrDm7Rxnf8uQsqOWIcTjA5bLM+x7P14CO77iWBd3YoQw8mCPgwjbDPIZOzo7znJzsYFzmASCTJJN1MSK+lWBwcoT58VvoYF46oWC7Yt03bXNAnHnGv72OENrm5AP20BWEzEdX+Ek6OWtv4Ahf8GO8n0DOCTrzElDoaoyDMGTAn08mk5KINKg1XYw/HAw50ORiXOYPukegZTthu8bzl1VD/McBsXUq4qHiDc/9fOuqkzc8B75CR+bjahgg2IDeOs3vHZxz8b2TVL6H78wHAkD+72QMdHAywn/ckm+fYbtiP8w9VIgs85aHjLH8e/PI7XNOkFlO2Cwp2zBwFPd77Zz9SB5TxtAfcn2vA/vMADMExnOqtQ0vzHELDVkrgy8CC0fwEVHWKwAMALwYEhYv2sgTIBgQYDjzopwshI7oHKnzfDOS58EEylEZdBlwkDnEUDBXFIvsqJ0pf9v4Z5448rQRsIHMguJAAaNM0MZ7rThUE8hAWDkA9IAkg4QMat1zDt/JCnqHJTtY5MTnZCyjgVtzfKKx1yPwfbPZLCArIgoQzAFEdtp8hyGFDlZilN7Oit/nQBV5NWgweMS54+ANYv08Ox07IWfBoJmNROYjl50fv7Ne+X4bYTt8nLx/Y8fI7+yMsuNwIMC8LM+Z9jzfi+L87hxg52DMSRTLAO9B1+wMPJZltGQedvbosxMvEQ/ZRnSD/n8nDzLd0BfbmoiH9ij/NvMc+WJ82D+3mgHG/FtsvwM+xmR74QDIWXe/j+e4wpNtj4Ej48JZ4zfMuyzXzMEJDC502fbDPs5tJJlubmlYRicSRw6QzUcDcttV65GrN8h/9sE8D7tjOjkAzoFj9u0OTiy7BheWAeslsm3bwZz4je2wkwiWTQCn9cLJCdsLj8NBBPyyLzJdfaFnnG/kYC2Py4kz5sP3DrJMb1drLQPYS1cJmasTCw6W4WeuxOX3Mrasp/YLmff2tw5CGJN5Zt442IL+2YZaVjKtoIN9AvPw/3MC11UX//G7kCNX/bPu2Db4yhiMZ2af+ofk07piXvhP9h/wmWAi41nrm58Dz5h39otOukFPaOXdtlhLbNucx2ud/WPX9zpHI+LbezTzQsDa3t5eyc5hEHNfIIEGxoTfk83FQQESyFDPZve9yc5O8DcBDOsDGBfKaCFwdYD32Fn6OwSEuXO50sH4YRgKQxuF5+cWLa75fL6Qac8CbGUEZNpAuaqRdyZwBO3yHAaJ8WIYEOi7u7uFdovZ7OHkcZ6zLDNgYff4TbO82MoKhSJwD997hwNn1er1+kJVhHcR8F5cXJQ58P7p9L4dhFPph8PhAm8JKnPGgXs8ft6ZwQFnegBcnPnO4BweONPkzKgNi4MKB002xM7yGtCY35aDDCwwejaY2RbkYMOGMgPs7GBsWE2DDF7Q3cwb5gzNmDNBnenlNiU7Tf7vHlPTIjsrz4XfuyLh7Judk3/vTLBtz83NTdm61RfghFYQADifWw+tF9CL+XkdET3PLue3Wq2FNWf84Zl5LvgAJ2kMHJgbFUqflUF1z7YIUOeAEH4wRp6D/UP++L+rp/wmAyXLoO1pDrrynC17tq2WQz/D7zcIzzbCMmp7zJiXgR4nbnLgbp01CMmJh6xn/k0em/UgZ4+5z3PKVTffw5X1njHQroL9c3uPn5OroqaP/SH4An8SEQtJNI9zbW0tNjc34/T0dGFNmOlooJjnx3xygBFx7y/wnfbfpoX1aFkQYD5aB/O4IqIkWCyL9tMOUpcFef63/V7mdU6MZR+S5djyl/XC/HDV3vrLZ+Ce7OtMDwfny/CQx4mc5O/9O/ttvretzzz6ruAt26IcOGU6eWzIbU5c5qqbx+fTwi2TfqcxMmeTsS4j+woHm3ncf+j6Xq1Ty5wuGXCEm/UVLpGR5WYHFyvjfP5wfoGzNc4SNhqNGAwGZTclnlupPCyqpm2G1iSy5BiWVqsV9Xq9LOBxvx3jyYuXUSSqIARP1Wq19K8ZcM7ni60Bo9GojI+s1ng8XlgEdHV19a32DsAtn+NcyLi7fQeBY6tYt3dhjKBFbr+g/8+BB0bWgSR8hz60ZeTyPhE0GVqMBhe0ok2i1WoVY8F8fHaBgx/4wAJRPxcaRERZJMvOHv7+7u6u9OlSUdvY2Chyg/FCoQzmDMojHs4EwLgYBACwkHeXqR10ID/M34GuLxw8gWtEFDpYBjFItAyy13oOCnKlxVlwB9yZHoB/5ITgNgM/vvfCY3jwhzI9dpamj0FqBknmh+UVGUD/DWr4PgcjGeRm44yME1RHPJzBwFzRMd7jXbxMK/SwXq+XnYR4HjQji4+csd6LsQ0Gg3Kv1wFFPDjJ9fX1hbZBJ3QA+e12O05PT+Pq6mqhUsTzqBiYN8ibM4TYGycNtra24v3794V/zA/eWYegRw6S4YF1MuslSRZnRZeN1faU69GjRyWIybrHO11xtszybNsrvs+tc7bJ9mEGtozfYIPfOChwsJczjvbVzAff5yDKCTWDOwcUAEaSPeiGExvYY/jHd064GYQxHnjrANo7EuagyrpWrS52PiCHrPes1x8Or4RuuVqFj2fBdr1eL5uVMGbzgkAdXsxms+Kr+Az5vb29LetOGCPYo1qtlrOvDPygkc93cOsMnzFPywgy77WBGaxDQwfjvCPioS13WZDoKiNzdNUoA25nyh1QOEmSK6Cu+JAYxUY5KIKHJPyyT7DtQGaQAVqVc2BuXbI88RwHh/aJDh7QF/7mmcvWSfJv5sj/7W/4LAfVHgd/u9PG47e/tf/lN9DIa4J5H7t80krFpj+2+f/XAw1npqyAnFnQbDajXq/H73//+zg9PY2I+6zyeDwumQoGyU5As9kszs/PY2trqzCCbBdVD5SO3vbT09OyxgFDY3DDoXMEMAZkBDHOaiAQZDxQBmc6HUDQwoWQeHcC/uYCzAK+AX8GmygkTEcgCBxoG7MRZd4IhRchQj/WvrC92erqavR6vRgMBmWxPqB+NpuVBaXOMDlLU6vVyna7GMh+v1/Gyp7VjMfKyEJ9DPDa2lo0m80yfsZnEIDjZjwYNbfDsMCSuWCQWGdBAExg5LVDnU4nms1m4SEGjWDHwbKdAe1zdsoGyrVaLVqtVgyHwzg+Pi6L4pFpHCDvy8Aub7mYA3yXNp3xw4gY0DmzBzgxGEBmIhZPmnUSwVUTQAFGF6fGuBwUQkNAdnZ2PMsGLht5g0nGm4GjgRj6w9/wg2fZkFJp3NzcLM7WYM1Jhpy5i3gA38zV2ThfyD+Xg43p9OFU7+l0WmQl4t6xsR0tc8Q5EBTzPKqPbkdxQM9Wk9DN4Iz3c3o3YMXVkvwbwBV+wQCFtVEs+n779m0cHx8Xm2M64aTQNR9o6sB8NBoVXXVwzJoxg3YHeeYX77ac8Dz4b1lDprIvgB75ObYFvHdZJtNgxmNk16UMkrBdPIugJK/lsJznwBmfa7uQs8TYI4MR/KZ3U4Mn0MEJCNvSHJga4JjWtMrmMWMj8642DtANehzU1Gq1si4JvsAbvru4uCjrKaE54JYqIDJlUFWpVAotrZcOBPFxzNk0xR6zKyY2id+DMxwEoX/MEz01P/0ubAOJCld2nDTwd8hyXqvAOIwDoAMbIJh3s9ms7EyZg0/44fHn5JJ5bP10Ysm/Yec/+y7T2BfzIelreeb93EOiyjtZ2X6CCR0EuLLggNsya9zG/KzfDjT4t9fsmo8RURLrzDlfzJEkSQ6QSUCQ8EIOkTfwtwMT0+NDrw8ONAAVVhgYe319XRbpAWY3NzfLtrTj8bhsLeodiubz+4XeBAg2+LVarQDGu7v7LW5ZlzEcDsuhcjDt8ePHJfhg5yeYubm5uRDdc/oy75nP52Vx9ubmZmm3gegIH+Dp/fv3ZXeViCjOnx2SEDYAC1u1bm1tFQHH+PAHo0f7lIUKMHFxcVHGgTIwdoNHwHvOdgAiMEQYTPiKwfdhcjaotK15NzDT9ebmptAI0JzLeuvr62XnEGebGA9Bx+npaXHAVAYqlfvdpra3t7+1zS/Ba7VajWazGa1Wq8wFmjBmql8nJydxdHRUdspiDATHOCuUEicOTXG05uXKykocHx9HxMMmCXn3krOzs6LAXtjPH2gDLfxsaMnpz66AYDiQfWfhuOCpwUbOjKHvOcOTMymuWORAGQPqw5JyQAbv0ZP8fa6MWtbY0cTtfAZBBg44C4M+QB3zc8ICx3FzcxPD4bDoeaVSKcEtAD0DO2eJ7MwJep0RxIniDJAzA2Jv9Xl+fr4wPuabwSG0ZXxswOGgj+QASRxXVrEFw+Gw0MZldJw1cmpbmQE45+Ds7e0tjJU/bqPlfBhsCVnLjY2NYucYB+fTzOfzGI1G0e/3F2Q86wQ2NGIR4DMGEjoOOi3zTuQ4OQTIJonithV4gb0lUw6QdcIIP4k82LY4gIc/2HRkxdvL5+RERBQamgbIrcFIzvbXarXiP53osX81nfm3M50GJDlhkiu4PMf8Z2zeQIHuAi6AD3pkXUfuaMHudDpF5iIeEm7n5+clmZblBH4T/JDUQq8NNHNyBQwzmdxve46P5DcO1pgbfCEpwr1e92S/7sDRlQZ4Yh8ALbyzoWUTGWNeOdjj96aN13P54MwMTpFzbJrHFfGwCZB3BLSvsozkYAu5clCG3MAbfO6jR4+i3++X8cKzSqVSNmnxAXXoOrR1AsYYBpl2tY15W4bwPU5owCv7ez+b5zMPfovdtI+zzNtGgZfY+RIsB75kZz0qvCsrK3F1dVXsZNb5zL/vuj440MDAACIc1XFqNgCTU3UvLi7iyZMnZaAMfjwex+npaak4AIrJyhG0dLvdODg4KE5vPp+XcwjYLcggHsYA0GA22+qyGxXMpHWGQAkiWwCYO+CHoMdZPmeROTcDxXAGfGdnp2QZWfiMABMd40wdoUK3RqNR9j1G2QgurFjOLjEGZxxRXJSCcVvgHQwBUnDs8/k8Li8vSwbWBn02m8X29nbJbOIYEHKMFkYOWWLr4qurq3JwF3xCqFGe4+PjkhGCP+wgRSDBzlvIYkQsBGStVisqlUq02+0YDAZFodneEAdux0UQiDxkB+PKEiAOujqrgAGt1+/38kan7KgBFvzfFTcHOTi6vI3wZDIph/AgU6ydQvb9DoNAQB3PdMYMY8Yc0XnGDm9ns1lZjO9WQb/TGRjLL9Un32dwyv0EMF73BF2cvYO3zI131Wq1oovOsrlSCu/huQ06Y3N2h7/RL1ewoDOGn39jV5xc4MoVEgAkBt/A8fb2tlQmyPgbQEKj6XRattLe398vNhuZZnvMSuX+cEsOEySwY37YWCqYVG4ZG3Z/a2srBoNBOacIWYQfo9EoNjc3v5WkceALX7AnyGa1utgeZnAAHdEHAwZ4QEYUueUZ+JDcMhWx2Odv3WGclg/4Qosa4AbgRZXVB3vatrj656obNiADauhn8GlZRLaRN9M74iGB6C6E3PPPnNCrXM2ENjlBwPiQW1fGuM+bFzixYz5ixx3MODmBzvp3rNuMiLJFfrvdLrLiihj67Mo2OkfACkDLyTi3+xJ8kvyCH87cQzMDUBKsBJ3OdDtRyGe8n+e7Um4/lm2Iq2EObK0PDmr5N+1H0BsaWN4Ayugdcs3/vZsRtOX5rgjY5sMf7Au2HzttLIFNYm7mMfrF2hlsCkEcmCdXcJyIohLgZ0JrgijbAAc6WRewLdYf+xfjKPMZXlv37J84M6fRaMTFxcVC2zk+ifV/t7e30e12F/ylO0TQPcbE2DnP5o9dHxxoMHlvCUnWcH9/v0yuWq3G8+fPy1kFnNKMgN3c3J8S/uTJk5hMJvH111/Ho0ePSmndUTsBwsrKSrTb7bi6uioOZW1tLdbX12Nra6v0Q3I4FiASwnAPDoLIHaBm502bAYLMmCqVSlxcXBShQLHoMa7VatHr9WJjY6PsPAXYOzg4iHq9HsPhMDqdzsJWvyggypWjYRy/s6cICJ/N5/MYDAYlS+kStseIghHNcrI0ABsFq9frxSGS7YHnPMeZGD53VhJ6WgEN3jAYBBdkqOAZhopom+8BM/CJuZ+fn8d4PC7ZirOzs5jNZuWkY8AIVafDw8Not9vF0edyKAaKrBVj8sGNDoYMUgFS3W63tBbmHn4yJ48ePSoHDgK04W1R0v8XyJPpMdhmvg4qeRYAmRYT3m3Zzs4BIwN/TR9nm+E9dPPvCWT4nY2gbYkDNn/OOQw+6AyZslH3xgkZbOWKgo2xM84Ri9Ubg/gMWLEL0Ahj7IqGM4TIujNOOdOLU4InvM9tBzmba6Bwd3e3cHiZ7QOyzL2uKgG+XM3D9uC8p9NpDAaDQjP4gV47+CIwIZFD4La6uhoHBwdxeXlZ7qGKge4A+IbDYalku5IHWMsZeBwkn9GywsXcyVjaXlmfmTf3u0rHH3jB5eykg0Nk1O9BnwHB0Jq55sy8ZZMxUYV3q6EBMIAIeXEQ4WxrBrvoYcR9qzPyZxvj+5cF1bY36KRBk+cBH0hqYEud1HMVx4E2lSz7CI+T35JQMb+QF4CoM7xUsbF3BBP2N9AN++yKj22L10Hyf8thRJTKf7a5yDVrLb0hCHZvOp2WJJrnz4XtJCBwJcGBYa7gIpMeEzjCyRtXPvw8gm3sPT7QyT13rOT/gw/4jkDbQYcrA/aH4E7Gjq3KfsIyRQu4KzPWGVrXAdO5+yLiIQHmpCe/x167OuIgA7qZ9pmXrJnjXfzblVbbAuiPrbR/pvWUxCSyCUb1hhzQ1olor2OFVwTSH3J9rwP7cnkK4er1emXtAIc/OYLE4ZDRGo/HJWuNM0YorbjOchCw8H+YRnbGZxbY6VOqW1lZiU6nU9phIJYznRgOMx4FN6jDqDkT5Gwf7wBsMC9olp2LARo0gk4YDZSN/kfKsQgtGUGAIYoFHcbjcfR6vQXaYWShAd8B8rPThDZ+BjRwUGHg5dI3NGDsVJI4JZvKymg0Ku8kCEHuoDMOvl6vR6vVKqCBcdH6xNwmk8nCAWYnJyeltQd68mxAAfNyRok52iiaFj4LANDvrApGo1KpLBxaxvMdwNgwWt7dn819ABjT2M6Pd1gv+Cxicb0SYMWtFFzIFPbAQMRjwgAZ4DJ+jBVyAah1Bo7PGJuDCPPCxt/8QF743E6E90AbV1YM4GwH7KBtzK0PBk9k/aAN8wBA837kJY/RoMhnr8BjB+E8mySQ2zlwGE422CYNh8Oy5m04HBZ7TIBk8JoDshz04HjcDkBgj27znWWRti3bbsucAxzow5y9QQaO28kbLuTOwZ2f6aw0fOcduRpkvckZRv8/Z3GRyQxKLLvL5Jn3O3tsGWJcJPRMP95v3SZxBR+gh9v0LI8OmBwQex7YZldePD8HFbZn0Nw6sEwf0C0fAsczoQ/zAWx7Hq4iY9tIMjL3iCiLX81n+BgRC+ALWTaecAUfPwrtLT+mGfRh3jwPeXWiD3/qndssqwRjxi4ZyEM/vrNs82++95bvy5I45k2uaMFXJydMT+yHf2c+YntyQIVOMJYchGR+2G44iELWXK2LeOgAyUky40VXVN3mZZo7seRuBeTK1Yw8PzAfcwBPmoaWGXCG7Y8rcuYLsmXdwOcYV3G/E1hOQmTb94euDw40MB4GxRATwDSfz6Pdbkez2Sy99Y5+TLxsCLkwAEyS0wrp6wPAGSBBeEAiTtqGjTG7X96ZWQMFA2QbPBsLt1q4DcXg1ELJYnkASC51IWy0VpFN4F2cUcHlTIjpAfBYtgsOgYWdCWO28rtsaZBtGuTI3AtUDdwNCskcRtyfxE1lgnYPeOP2LretEeSRwbfzYPzLegZz6TYiotfrlSwWzzPffRk4MB/utxPhXdPptFSLcOrLnmnAYDng+3y+gsfg9yGTEQ/nKbgsjmxbRi1/llfkzZkjg3zryrLMpfXaxtrBhQGMQVD+Le+wbuagDxm0kefZt7e33+ovht7LwJp5HfFwHkDOxmawZ3CYecgc4JONNPc4q4Z82sDzfOgAXQk4nFjxNuKTyaS0N3njBIMOt4YYUKMvGZDkIIx/OzNtPpPtI6O3LJPqANRO3/IL/d0qlgMOnmGwYt9i2cjvJylkH8AFr7k3O9h8f5Zn/k0CINtS+xg/P+uE32nwm22LZS37r2yPkSEDI7ciOitqf+G5RjwkH7M+MG/fjz8ycENe8dfLLoArNHLQz7wJNMjguhqIDJmfEfe6Qrusk0MGbpnW2L4MjK3f6Jtb1lytMQ/Nf9tUvx+6mj4GiuZ/Bs9+BvrJc3KgzDsN8u1P4KP9hXXewYpBvscCPz1O64mTU4zTNLItMo0cTFBN4V22M+Z/9gOWadscJ7wYG4GA7Ua2956bsVb20dxvTGHam0d5TgSEpgfPJcmKDDrAjXg4sT6/w7pvueH60CAj4ntWNPxSC5NB0srKStlxBOUGXAOkMW4oCH3sEQ+7uUyn04UWIxxu3knAgmwhNxNgrHeDyOcyAEItFFZgByt2JI4weacFOiIWIlFnrLnXxpUsJr37ZHEuLy9jc3OzLMjOPLFBd4TLBd1z9iO3b9kp2bjlTDTOwUDHGRgLKYpIi9tsNouLi4uFdiEAuXs3AQ/IG9maavVhwZyDm2q1WtovyGgZCKF0lcp9NWE4HEar1VrIujM/99e6dQKa0sdt44bxYf2NF1na6dgoG0hwYYS4srIj99DLz/F4cTiufBgcLtNx7rPs5IyI5XdZtsSBCQYrB9Z8brmzHDqZ4XtsoHNgkTMtdoA5e5bbAQ2M8nyQHd6BoeY36BqG3DYiO5L/H23v+StZdt1nrwo3V7qx80zPDGdIgpKoLBv2B3/SP23YgCVbhm1IMCBRIjmcwA63++ZKN1d4P1w8u56zpmbYA/g9QKO7q06ds/faK/xW2GtnB8xrngGmdYZLbhgb/ORn1Ov1ioPrd2SnkPmypwnA7aCBHVHXbLNGdhDsRJru3ljpLBP3N5vN2NjYiMFg8B3Hs1ZbbDj3vgDWjssbgIk4m64EJjJwZPzuxoP8s7ZeuwwOlwFMf2dnzbKR9SzPzEEe6G+AbNp5XPCm35Pp5Sg832VguAxAAJx5l+lvHslOWHaq/O88lohqUwn/xk6wg5VZXnk+bfYJ2BEYovTJv6McCHyxzKm0s2/AaT2+DPjCi/xBLjOA57k4o4zBoNj23VUD2XmEBqyLAX4OaHmsxhCml3USOj9jgYw/zBe8j/cQwLGzZZtohzzLCs9lDJ6/bSO0y06414/LawkP2tG0/cx0Mt/5e5eEeZz5Pt7LOtn5cHCQ9c9ZC3++LCBFYwl3jPW68Q7+TeMUr6vXwOvwY64PdjQAgzYoZu7Ly8vY3t4uh59tbm7G1tZWSb8D8ldWVkpXI9fzUpMN4ZzaXF1dLVHv+fyhaxQLjXGncwjg1GAD4qBE7Hlyv+voI6KyaGyydIcgMzZ10k6F8f56vV5aaCJkGA0DEEdzLi8v4/T0tDAjZU8wC0DWURUAOMoMYIJxdk301dVVbG9vF2GwYouIUsrg6KrnDZDBgFugG41GyWRRJnJ7e1tpx8baum0iV61Wi06nU8rwyObwHt5F5yoL0ObmZjG0bILCcXM2BbqSUUHQmC/ACafERgOD5xIAviMytr29XRxkp2+tSJvNZqEz6+KIlR019iUtU0wR1Z70Gbw7dZwjTrzXoNfrxvOWXfCZZc3gGl43QHT2Bfkxn6HsDOyzQc1GBL6D/jwT+bKjYqfk7u6uZN5MPytRBwXgG8uwy6Ns7Fhf9IpLrDAIVu6sjbvYAVDIcJK183raoGEcHVnmeXa64R+cpfX19RiPx0X/QEfW0kYXZwoZxhYgl/AE8gaPHxwclFIP8wpjvbm5iVarVTKLLtGFHoBGOytc0MvOlwNfTvubj6C/AzcOJhhgGOwb0BkwwTuO1DNf6OUMm/nNjpkzTKyBA27IiOnj7K4d3WW22nzK+5D5iKhscoVGzppbN0IvZxu47CTZeSPTYLnCWbee9Dob/Nh+G7zZlnLZZnjteBd2Cd4nQs/30Jn1g/4GxxHVrBZrhi4yWHPgjnsZP2Ofz+eliYb3ejka7ag/f7IjZB1j241eta6GxjmossyBcADROo17zAOWG+5zyXcOBsEL2FgwQnbs7KTAn9b37LFw8MD61s1uMnDmXus+61zoASYmA8j6MC7jM2hj20yQ1mA/O9jGLcyDtff+Pq+h3xURZf8yZYesIe9k3C63Zh4ek3GDee1Drg92NDID2QM/PT0trf0ODw/LfgwUIMLWarUKMHSqhk05/X4/dnd3I6K6b4GuVtPpQ7cUTrHlPW5/6AwLIJUaarc5tbDwnm63W/ZEOHqJo9BoNGI8HleUE8afBafsyxs0b25uotvtVhYyLzD7BS4vL2M0GsW7d+9KVwSM7Nu3b8uG8xcvXkSn06mUSvjcDoSh2WyWblqdTqecfUFkJWIB/La2tgpQiFgcVAho4DCj0WhUqeudzWale5ijvI5oWshbrVa5HwPl7JHLjgAtjUYjtre3S+tZg3DGN58vzoag25QjQjgj8Gq3241ut1taK29ubsbZ2VlR8KyTIzreA2OljtECFJydnRU6oBSt4DNwcJSBCIT5ivHP5/NC/wyMucddlAxG+ZMjwzlqC9jzeGywbMit2LighfmCdzhCZGfJIMLvcOSHC4MP/+EsuMwsBzcMGMhmInOOJhG0mM0eOsFtbGyUEjvrFoMg9lWhb+B5aIhBZn0YhzMj3sDqOVg/8VwDPv7guDMPDBHGFmMIr9rg7O3tRUTEeDyOra2tmM/nRc7oHEUm0nsw0Kk4CDyTfSWTySRGo1Hs7u5WOkNhlNFdlmPv6YJW0ISmEAaavC9i0TTBwAjeZQMvtGTu3OfzUjDc3Ef5mQ+sswOADoNGtpMADWTATpRBXa1Wq4zROgdwxvPNGx5/s9msOGSenzPS8CF/I5M8B16xs+OggIE6/Gl5oO141k0ZuBo0sv+D3xqMOkCS55gzerPZwz5HH9rH3NATfj+6Ebnf3NwsFRYOwqFzrO/s1Fm24SE7LLwDPrLzRTCMZgEG8S4VY68pfAX+WGYHIqLYGAc9GQe6IgeK4NGcMXKwzU4IcuqME2vtDIEzO+vr66U8Gr41v+WyaN5lx8g4Dnl1FB98yFyhmZv3NBqNAsD9vOn0oQEAzYUYZ6aPM5XQCd3LPJB18wj3ZScmy0pElGCr22pbVlkDaOiAL7xB10AHBSzD19fXZdN61jsE0xywdxBtGd8tu35U16mIRckBgjSfzwtAf/z4cVxdXZUuSzc3N9Hv96PX6xUlZdABwWezhyg7z4FYbBa+v39oJ7e3txeNRqOi0G9ubmI4HMbGxkZsb29Hp9NZmk5DKdPRKgsXAkm7W7cLRQHAZBsbG5XIH89vt9slM8KGTAwqEVSYjjMoxuNxjMfj8vzr6+vSqer8/DzG43HZhP727dti7DhHhD0rZJBoDcw8hsNh3N/fR6vVipcvX8arV68qHjZrShnQaDSKVqtViVYwJ4BFu90u8ybCiWBubW2VTecAg4gonWQAPk+fPi0C4OiQwRZRXjpEsH48h7HNZrMK0Go0GtFut0uf6IuLiwJ+6Li1t7cXe3t7cXZ2Vlon393dxWAwqGy4tzJ2hJPLANC1x+vr69Hr9Ur3LkdtOp1OdLvduLy8LErv7u6u8AIZK5SII1PsVXIE2U4/7epQNN60ZsCAXOTMFIbSoMcgkvlbqS3LSBgU2WA4Goh8GdTZKWKN4RHzieluBemskRsruHMV8gSg9sncgF5H/XkH3yP7dE+CNvCp54Ms5kgsCp/v6GzCb3BG3JKbNWUcOEmO2PJ7/t7a2oqVlZU4PDysRKV4VqvVivX19Tg5OSkdfZBndxRxSt38bKcZWYQ2u7u7cXV1FRsbGzEcDiuAg/ez/pSKwieOIKNn3cwB/vOcnTl0C1R0EeDT/MS9RBZxcnIQCTDsLoUGqayJ7QoAk/v8fO5xRNbyw3gMVvr9fnEEHMAgAIJegMeR/6wj4G3sGU4Ssu/ADWPEmWBdcuDRDTu8OZY58Dkg0ACX3yE7tknOOBF4a7ValXege9BndIlyo4d6/aHFLK3heTcBCOzKaDQq8wSQLdM/GeCj1+gohQ7yurI2OL52psxLBGedCbCsEESFB6xnebbPqnC2hjVBv7uCwxFuB36s95Ez6Mpv+Y1l03OzswC/YyPMxzkLw/htO+z4WWYYIxlcyucYB85kdrbp6Gb7br5x4IPP8zp53tAY3WM9kp0jdKzPR6GM03IHH9jRc+ArA347JSsrK+XwV4JFBNmgCbbIztbNzU3lkGN3vsvn6v2h64MdDcCQo0EQEWVKhyA8MAATUa9erxc7OztFKUVE6TY1GAwqXvNk8nCexmAwKJ4VPe/d/QSmshFHaTabD11PcEgouXKq3Z7z5eXldyJ2zAVGbTQaZWONW9StrDwc+LO1tVWcncvLy6LMcSA4fI6DwBwFubu7i9FoFNfX1+VcDw6eg7GJuBN5vL29LWABenU6nRJdh3HX1tZie3u70J05Mf/b29uy4RyDgBGq1x862rBuMCrrsbW1VcrkTk9PixFB0c7n87LPBGCFIcwOZ6/XK4w+HA7LvH05QuTDC4lgIWQfffRRDIfDSiOBer0eh4eHsbm5Ga1WK87Ozso8b25uKv2ziYrwPjbVOuoKzyGAdAhCyWdQYCXs/RUYIPgDfkYxIHNWSPAnURzupWTMWQTmYKPmyAsG1NFhO5MYK8ZtxRxRLYcw0HKE1NHaiKgo1hy9Rb84+8K6Mg/ol0ttePf19XU5bAxZxVhZLjJwNXjxuQy8EwPp6FvEYlOdxwLw8DOzY8rflF+SjUB/Ih/wD/QjC4LjFLHoYoUM3t7exv7+fhwdHVUi6DyPbOvKykopT8UpZ+1ZE/OdI8yO8sFfjogxXoyeo8Ptdjv6/X4BCLPZYhOnnU7WAt0AjTH85hH4MWcX0BUZJGJMWTPLAYEm601onKOsAAx0UQbazvIyJ4MXeJ1x8FyX4XhtkSdA/HQ6rdg4xkerY7IBBG+s/7NcuhzDUXDebTlHN1q/IF+sE5FRxsq6NJvNikPBukODlZWVSnUAPIEzwPugL90CsdNuu0y5rZ0ZlwFubW2V4M/9/X3ZIJ71iiPRPAsaorMcEGCeBF8dGME+wGuAT+QBfADPIFc8yx3ULCtktdGtjDEHmZATB38IKDrDyPwmk0klMIO+Mr95T212SpBdZMEOn3karASdLTfIlZ/D3P0s6M/94APPlfexXt5P62wI80L/mMe8dvzBrkBn60QHtVg7+JT3WEfZ8SVwZcfROpF1dhAQzAUOWltbKzgDfoP2yCL75XBqsU2mzYc6GRE/susURDI4QqG4zIAafRQnZwWsrDycCI7xinjw5Nrtduzt7cXJyUlhMoDx8+fPY319Pfr9fgG29m5tjMh8IGgWzk6nUxiHCCGLxWJvbm6WiB6beS0kLKiJDR1wmCjj8iErAA0O3IPpyAoB8gaDQeVgQqIzGA5oizGr1WqlBe79/X08f/68gHoAkdPGOAWj0aiSOXDWhRQ9IMQRLIyCGZa5opRINzp6CNCGTnjGdKJxnSd7Jogik2kBwFu545TBZ/P5vBxMc3R0FK1Wqzzv9va2HAy2s7NT4UMErdPpxMbGRvT7/UppE8rQWRTkwRGWtbW1wgPsqTHQgd44dCg1BBhQPh6PYzablb0q7nFtAMXvoB0yZUVmAGZ5XVbagGOFDKAwIxbOpeXFDpVTyeiIiChlDnZ0GBt0RSnnyJyjqAbY3MM62Lg40o5iR9bgX/QHWYocpWQ+EVEAP5ejRFwGvnaKAcLwkJ1LR788b/jeoJnnO6LFmJHB8XhciXjiyNze3sbZ2VkBT3aoDPwJsgDKOAcJp4+xsn68H9o7YosDZBCCfEAH5uO5oFOQS05ppvwN+XCU2ZHPiEULVt4Jj6IHIxaO87Lo7DLH3XxBEIastssyzLvOijFndAe8apBMOV8ub4C+7FNkXPCw+QanyLrEQM78ZEfLQQ3G6wwFv3PGzfzPv5FVyyiXAajlGyfFgDoHllyyZNCcS3WdWYM/cEDgJetbgDb2x4EODlB0YwSP0RF8xgOt7ZCjTwGF9Xq97PfkOZRTkVlyqbm7xTFXfsNaZZDtsi3kHboBzJkPNOLfxje5lC+vaW6lbz7iOW6ZP5lMCu6Ax6EJ2R1+x5pzHzoUp4H5WIdHLDbKk8U0/1pfoOdMN+iE/N7f35ctAKyhnW/kIT83IirltugzOzi2n7YRdhCQF+7PzhOX7SK0QI+BeUkA2Fll7owBHc34kU2CsTk4QkDhQ64ftRnc0TcumOX09LQA/bu7h1MG2+12ifK7rAVFAoEpVSKFubGxUaJaLGZElBO5HTlF2XPoE58byPAMe6RW1E7TOgJn5jJjIxRZsFlgFhvhgokcTeIZXDhrOGZOL9ZqD3V2zebDmREHBwexvb1dsj5ebJdZYEyIbJhxYRYiJxELQ+K5MQ8MrDdfO8qN0ieKxP/hD0rFYPpms1kiVSgHADhAd2trq/zGDgmH/Lnm2ZfrCm0cIhZG+erqKt69e1fKz66ursoziQZBAy6DQnjCwBkjZaBDuYWzGjgkOBQGf7xzY2Oj0CcLN1EURyOhAwoWg50dc/Oy52cDbmWHQuT58ATyCj1ytMZgmnn5c/7YYfKF8bKRt5I3n3Lxfit4R2vtWHnPQqZJxKJUBjDK3K0bGCNrweeeg2nGc92sAeNm0MJ9gACebxp6/vksDt5hwOE9CETQbm5uKpFDA0B4zg4eYzAoyXoQPQdAx2B3Op24vLysyIodPHS99xbhYOAgMnYuR/vQ626NbufA4JWx29FziQC6y86UyzPQT74wyugA8xyyCxDmO5dqONNiGhjEWVaZe86eGIQYnHjjaJYpaGCQaCBjmfLY4Q3LAjxr/rH+8Lusa/ydAbDfZ/tlUOjASA6I2qF0SQpzJEMMsIIXKbmBHrZ3zMn8yJo4a8M+J+jCvM3/rLF1C3NwRhoZtqOEbmEc/IZ5mc5+J/zC87NMOQjiuXqOOEXIl20hdEbH2S6QVTDohUf9b77jPt9rOlmvQifrrazD+MxZVTswXmv4AH7xux0UgG/trHh97IzxGWNgTFke4Gt+Z+zB+Lx/iPU0vXme8UfO2DMX6ELwlwud7vnbDv3/4mjAdFY0JibKem1traR3IhYpyuzhm1Gn02ns7e2VUwoBAt6A503fEB6DirFzJBzF6OgQAgiIwpODoO6AlD1VFEJWOCg/nslCAZ5ns1kBzczXBh1QjBJhj4QBAszRbrej0+mU8TF+GG8ZAyJEBs+OBBqk4ADYW7eAZz6ALlZinEQNM7pWFvCDQiR646yIAYzBJPXGdjK4xx2kAPgWoBxxoqkA853NZjEej8ucbaz8OytqhJnfELEhw2SFZSfXzvayqE232y1ZHyst08JOAsbRc2XtMXpcGTyZT0zz7ED594wl/23d4PFkOvpzAwMbEEddPZY8D3+fx2mFbiAEzfgNoIPf5wigIzcei8foLGP+3j3OzUemvfWpFb1l0GsPv1gOHA30pkdn/NBR7Jm6ubmJk5OTktX0aeLOUJn+dtC8JsizaQmPOViBzprNFqVVjMlRfGSG9bBe8BpZh2cHIBto84n50KVf5gt+Z0fPMmIgkJ1aA23TLwfElmUYPF6eYeCd32G65ecYxEKTZeBr2VjMg4w9z9HOAroxR7mzfPmy8wB4zJH0HJX2OuXxeC5+tsscPe+IRcYWDJDb3/O+7wOumd/sCCFLAFPKUgxC4V87JUS1M2DOAdFsJw22oSnjyIDRPOb/s37GUAbkmY/yGOfzxZkk0M24yDjJdou5QDNnc3m+nQQ7b8v0p0F31s3QkXWync1B2WW6w7Z7mTywhnYUPE+uZfLmIBW/99idFXV5nOcB7ZbZYwedWGOcKvYrgbfY6+rAs4MkH3L96K5TZpDsIeENoSzstSGsbJ7k9GyYYGVlJXZ2dr6j9CaTSaVmNyIqR8pvbGxUNtU6kskFYxrYZyUCQf2bZYATg5gVjTfo4kjZGanVqodXWflyL87AMoW8urpaDv1zJNnp8Zubm3IuBALuaAljJ/JhI+CaSde/IsCM1TWwNvgWKMCEDTX8wnNt0PieNCtReehHhPbq6qrse6nVFun96XRaThRnDwX7fty+1mANZ4f6dCuiZRHsiO+2MOR+C7k36cEf5knX8GeDhNCzSSuXdQAUnfnIkUboZ96yEmS8OTppRWbDZTBHpsagwbTxOE2X/H/zdf4O5engAQYy32cdk52w7FQY5Geg4zla4S8r2fL91n/Z8fY7bICQL2hmAOD5Z7lFXuAt/+F38HnuxDUej4tuNlDHmKA3qOX3PaZtzuJknjGPcT+Xo/TwtEvZoL+j9Hl/h8uYvJ7O+piWmdd4h4MsfO8ubgYTvIOMhPnEvGuHwiAtYtEVi+dlhyY7nNnJ5z3sDTLw4D7ea+c7A1HfY8fWMs9lm5dlB/nk354L783Aie9N8x/SIXkMOaBjHWQAHrEoifae0lqtVjLB/B+Z4hnT6bTYaL63jcv6JM9lPl+UaiE7WR/YXme5WQaa/W5jL+bgf3v97RhkJ9i08rsz+M262esRUW2rCg3NL7a7/HHWYJm8+d3GgnY0jed8r8eI3sp2hWdAa/7OWT74jGCH9esyuuTnmlezHbQ+z2WCpr9tC7zgoE1+R3amcCiWBXDNGy5xJ/vLuUr8xvrEa2Z88kPXBzsa3gCbCT+bzUr502w2i+FwWEqZarVaXF1dlQFmIwsB2cSLMfH3KGIcC9dURkRlg7EFiWfwTgtsVvj0GqbLFYwHcOT9BsosLAbRkTXotLGxUUoJcrmEU5gRUTIB7LEwk21tbcXLly9L1wzmTEYHJdrtdmNtbS1Go1EZGw6eFaFBqq/pdFravbm+kj+0Ft7Z2al489SZTqfTsunaQNYCQiu1iMUmYqdi2XRNZoX9FT5pnuilnUc25JPxOTo6KhHfvBGQNW80GqW7AmuNELrNbVbOrAvOpRU8JUvwBFkeR1BcdkamyZuubTSs3PkcBch8UCjZMfH4MyCBFwwKTd+IhSLJJWoZYBgUO5tlsOrfoRwpecCgeyzQBKPyfY6CwSBzzA4Kn2VlDG9kxW/gYdCeacmaek7Q1jrDPALNMLoOtkA75mvZm8/nFfA0m81Kp6icNrcDk/c92VCjewm2IIsG1cwD2VjmaHjtvEeD9aNBgQECdOFZbDbm/9Yv8/m87IMzGEC+oItlKCIqz4cnc3kNzrnX1PJnengNrfvMP45CG5Txb4w5PMofHD14zw6ReZJ7DWx4lnnQfGR6871BOP+Glga46DR3SoInzAvo92xX4HWDO+sDd+ZxZyrrGMaeM/GmTe605I282C7fbz5ADprNZtkEbixh0Et02LosB+aQBd9nPZL1EONkfS13BpHGDNhQZ//Mqy7nRPd5f6z5wM4p4wDnWLd6znao8hwYo51U5msZz04L4zBAZg3MV/ACNpqxYWvq9YeSITqB8j7zvUvUGLtp4nexTrwHXuUey3IOZNhpyk6qdVl2aJEd/9uYCp3MPLD/rDF8zz3W+eg7qjly+1z2AtfrDw2X4PnsZDk4/0PXjyqdogsKQoQCokYYhUtL1W63WyJBpOnZ6O3NwhFRDttDGBxRQMkhSBsbG2WjbESUOmO6MHgDJ50bAJwwkDdlu3aW8ixH/Jnj2tpa7OzslNIYnolA0va1VquVzA3pJ6JRBv5kHrw/BMHCoTIQHQ6HlYgIztHd3V0Mh8M4Pj6OZrMZ29vb0e12K8IxnU4rG8441wN6+PDEHAHJUUZAOZ9xISjsc3B2B0XCXCOqHR8oacNA4HTSkpauVYBCG10aCiDog8GgRHBxtlqtVrTb7ZICbLfb0e12Yz5fbGZnMyrG3mtrQOH+1Tk6wx4VANejR48qG9np6oXBMABBsFnTZdEs3g3t7bgbfDld6rpsAzoDIhQV8mhH0IaWZxAN92XlaiPn6CfzjIjiZFAeiIPv58A37pbCc6G5yx4xNI46Qz+e6T08GAL0FHRfXV2t1P9786vp7c+dWrazZOfBNGRt3MXKeg+HG32UI4g4ERGL81OQZRo+8B16G91HOWu9Xo9er1f2bOAk05UEWhtAMwYHbfh3q9Wq7KeglPDJkyfR6XRiOBxWgj/IuVvH+pm+B53ttcexsHOHw2T+dZbVBt+8nUG9o6EZeOVIoM+uYX6OlLL3jICQ9Qa/wW46iwRAtO2C5xmb/w3v0bgCHcF9BsCWVQeEKPUzoCEgYycjlzn5OQZ+2Co+d9QXPjEAdqbQegV+4t/MmznaychOCusAgGf/G5+xbw6bbUfQvGLgyBoyV37DGtIBzNFffofesIMKOGfcrJ9BJO9Hnll3Po+IEty0feH35mnzAOOKWOxfQ2876ORgsB16nu0ugdh6xm9+5nKFhGmNzgFXGmA7gMFcsA+2lzxrmZ3ledhj21awA/TPe1cN1DOvojeybCwLzGXbnh1wy0kOIBB0shOXM7m28azZ/f39dzoKspZ+Z6PRKIFvOr1mp9F44Q9dP6rrlOvvMRQA5X6/H+fn59HtduPg4CCePXsWW1tb8bvf/S5OT0+jVqsVA8fieaMgoNqeoYEqigFnZDgcRkSUumLAKsxvZYHSRaHhsTEv+nIPBoPipFgpzOcPXaAQDHdLQsGhhO/u7kq3iHr9IfPAGRdsQo5Y7OqH0XCgrq6uot1ul44XZiboXK/XS3tbGL3RaESv1yt9xr2RjAhju90ujEHpmo2tnbNlgkur2E6nE7PZ4iBFGHYymZRsBq0qlzkydE+x8OAI2ciNRqOSybi6uoq7u7t4//59NBqNePbsWXE6AEe1Wi329vZiMnk4Xd37gWwMdnd34/HjxzEej+PVq1eFf/r9fhFIwEmOPEcs9ti43MGZkKurq3j69Glsb29XOqPgNJp3oH/eCIucYMAzcOf/jN1K1JvxHemF9wEJKCRn8JAXK3rzKaDXGTf/zfNx3rKB4x02Vjg+gBH2bNkIOPoHCHcmgLExXuhmPs7GHJkmUut1sRLFeUZecFz5P2DTmVOCIhhbgwSDW3gAXuP5jg7nenGcb+jXbDZLxzzuwRCzVnZucYZbrVY8ffo0rq+v4/379xWdTsMG5uD5G2TBe6wxQSeaP3Q6ndjd3S0tv210kQs6Krn1J7zDmDh3Br40/zLPDFpYCwCTyxfhEe+l4rdu+7msvMsXPM/nbsnMe1lLdKUdDHQg8wEs8z3gDKBvBwf+IHjHvJxlM4BEnuxsAzad7XAWxMAaWTXf56AAjrkj0GQTAOEOqFnOCCiyNgbyBlEGwPze56bAY+7c6Og4c2Z9aT5zfX0d/X4/ms1mOffF60wQijEhp9Y9jh7T6Wpzc7PIPwGxs7OzspYGxQTycESMgcAtW1tbFT0PQIZG1u8+kBT+hueRL0fwzZOsPQ4kz3bTg6wP+B1BX+Zl4A7wheec+eVzy6vbSyMbljnrU+iMcwBGMQ3QyS7PdnCE+RnXWd84YOb5Q0eeYaeEcduOQSf43hmwnBFE5xFEcMc0HAd3mmOczhTZfjF+aLW6ulpsGricDqN2Hq1XqGL5kOuDHY1arRaj0agYUIABJ76ORqNS8vPVV1/F1dVVPHv2LHZ2duL09LQiQL1er9TbExVFAFCaNrbb29tlop1OpwBpypEwVIxzMpkUh6hWq5VD79rtdkQshA/GQdg5sIQ/bI4hnRoRJY1kr86lToAXhJWoRkQU481mM5jo6uqqEs1ylMqblYbDYbx58yaeP39enBEUAwANwEBUHAbEIYtYnBhq5Y2AOHKHQKEsVldXy0F2RFANgDmNG5pB48lkEsPhsDgzBnNEqdwjHyBE+RfO5dXVVbx9+zY+//zzkuHiAhwDdDCqKLONjY1ixNbW1uLg4CDev39fofnV1VX0+/2SQifLRnkK6+dTQ6EPNERxjMfj+Nd//dd48uRJ6aQGDwF06WcNwEQ5eL8Myhrj643FjqLZGLORHKXF+Fx/b1BihY2hdGmEFZSjWIyBZ/IZvAPPocztsDgdncEbNHVWytE3R4oZtw0jTqIj3eZz7l2mJM0z/A6D7I3LznZxn7NLBEdyx6OIRakDIIlxO3LIGsEPOA42xDTOyMCgXq+X4AlOEkCCrF2t9tCPfWNjI87Pz0upH53QbLAwOhxcasfd62+95dKCb7/9ttAhO3HoDO/HaDQezjTglGb4BFk0ryMTONVu/4ruQJZteO1wA4AAb4AMR3MBrY7kAbRtcF2GAagy0HJWy4EKR/3Ri5ZzDDu2wNkDZ+agMbSERnackCcDenQvMob+Zp2hic968fozJ5caOsNnuWZMlmfbCx+4x1wAhfAw2W7WGPDo8i7WyOB3Pp+XklxstrOXBuMGotABOXc1A58TpJnNZiWrQLkvPFKv14vDzHtd6pT3kTioiUPBu9mL6IAUtIU3ltHezrij6HYyzGsOpvAcn/tgp97yhA3j+eg49DVYwmvEmqDv7Gg7y4JsZqeZuWOzuZBD62c7WthD+Mu0d9aULLLtATSxncT5hb7WkS61RRcbzHtdWHuCztAJ3M19OAkueeV52IIcMOVAW7K98B9YiqALz88OtZ2oP3R9sKORI7wmXsSDgR6Px/Hy5cu4u7uLy8vLAro4NG99fT12dnYqmQ0AHB2A7u/vi3FYWVmJdrsdKysrRZlwP8/zgkFI+sBj7DglOit6DAbAmcO9XPNnIwI4tCHw/oz5fNEqlv87NcUcAQsI+8rKw0Z4shUoOit5nJlPP/00tre3K4bARtPlVjnVizHlcD2UQ8RCeTpFhoAyV+YNaDbgQlisnGBoC1cGVBhbDN3V1VUpfaLu9Pr6Os7OzmI8HseTJ08K33jTNM/1CeLj8bjQGacVh5de5u60A3DDyGRl5ciPwY5T5sjC7u5unJyclIxdRBSj02q1ijwB7KwEXWYXESUihuwZTGPYGB+gnedxGejzf4yESylQkpZ3aOdooaP2mdcd3UTOmKOdHMZpWWAeyDEgl+dzed0dbUIWAfrch6EwTztqaHmBJ1lXZMXGwGDUhjViAbLZZ2T6c9lRhKd8qCDAkI3ddj6Rc97farUq8msdzfNZL5dyONLOGUV8niPKACCXY9kY2vmczWaVQ0dp4ICDBYjh3QA/7oFnrq+vCyAz2IBP7CxDT0dWnc3M+siRRQNx+IjvWGtsmaOljjbzHQY9yyE6mc/ND9gc09D3AcQclcQ2OTPgckeDNCKmOFLmbdMbfoH/HaEFEJrPud8OHDRkHbiPz9Aptl+MGbmmVAN+drCDaDjnxmADyIY7O0IwEv2ysrLoguQ9DsgBY2Y97exxQacchGFsOHLWy15HDqHFXsAr8DagGZ4kM2g7D3Zyhsx0Zn3JPpkPoTH32jnhd+Ah44uIqDg6EVEZa84uwEPeq5WdXMuDHR5sgWWZy783XRxk4lmMATpY/6FDbd8NpHl3djJwbK2DeCbPdwAhj9cOHn/nzA44yjoF5z3znLMSPJsxRizOOoEu1ofw6+Xl5Xdkh6At65HPfjH2+5Drgx0NmAhjZ0XJZ+PxuAJI5vN59Hq9ODo6Kqk0olUII0ThgBzSnPZUZ7NZSRc5MoeiAFj4gB3G5sgpRjI7AHhuGD4LGO/FqCMsVqTMKyLKM4iqwLg4StCNRWMcZHdcLmVDACM9fvy4MCnGAAVKupZ3GaA5pc8eEwQXYMMzWVMuHD3SkNAXAYFWCEY2kqwR64EjmoHNzc1N6R7l8UL/p0+fFgCBI0pED0N3cnISFxcXJfrqKA503N3djYgopxLzHjYB2vHJTgAANX/O5RIHgDIg0fWSW1tbhW9cPpEj3W4T7QvwlwETY0CpORro751mz8+FBlbCnh9XjnzaWMDbvAflyzvQGVZW1hvwto0J68jcUIg5wpjTznnc3lcB//I9z+W3rCNgC31h+hm0mCbORhggR0RlrHYeAYkAX54HT+GQ8D4yawaqk8mk7LHAAJsv0GU48swxYpHRMS9gEF164nXJEXRox9qsra3F9fV1PH36tDhg6HE28GIQvf6Oyhmc2Nm1bFh32bGFrgYa5k87cIwbnmA8ACzznx0RaOY14/92XB24ga6sBf82P3gcBhe+DFLgJ+ZkfuNe3pF5kTHn/WHQFHkliukobpZB5u7gAbqcrHAGXZYFywt0Nl+5LIfvve/IMpSzRFkHALjYQ0RjG77DLrKW8HwGWvCPdS16jCufD0ZQlXfYmWOdoJOzPpxN5qAEY8hOtWWfe7Pz7cCC9bafkwGzcZZ5hDWC7wG9dgKgn0uO4UU7M9mBMU86u24+dJCLy1kEnDXmw7raucKJM89l+5r504De3zFWYxEwp+WO74x9cXqggd+HLmcsGbsxNx+8DFZkjFSGYAvyWH2+TE4wEBD9kOuDHQ2MJgRkYgCllZWV0rGJjkj9fj9arVbZ6Mdz2DAIeIYxvOnGtdkoMCKLzlCY2DY+KI+8oRrmtxL25ifGaIOLYwODOhLDYhJRsndqcGNm9DhgOBwsG0GUDXSnlrTf75dIBs/hmdCN9+fUL5FE6AMDI/w2Jp6nlbqNFsLuOnsYO0fiYFKDQTxnvHaf5I1SmM/nxXFiPba2tgqfOHKeIxTcDy1WVlbi8ePHZdzcw7xy9Mhrxrp4TR3N5z5qHIkiGqA6ioNAW0FBE2SM5yIb3Mc68xxkwcDSCo81NM+y3suclaxAcuTCYC7TBZrzN++wg25wwefZKcjzMS/xWy6/w+ODxgZq+fvMl6a7I4bWMX6naZYdLQwc4C+DMH6PjnJ2CV1i48xv+DdOiA2SDSPPAhwxP8ou4E/kwyUjzCPLgY2a6QkPMSdKBVdWVmI0GpWSAwMtxtHv98s4nVFDHzlKaBpl5yI7dAZCWRb8fwdKzOsOOHyfg+M5L+MJxu7fZ0fCY81Ou/klj9u/Q7YcIc/zz3Liy/rdjr75wLSzXs/ymMdpWWOsXhPbg++jQQa1zBM5sIPlQB+/tQxm3WmHBDkwT9keZ8cPOsOXBoTIoYEr8870ddVDfrZ1DmOFVthz5MOZA+t2YzbregciTCuPge+NrXxv5rH8zIwlzJ8em0vmKYczz2a5N28u+8420GPwZTozZuOqZfyX+SfzmXnT2Cs/x+P1mrHGPM/0hq94PmM2luA5VHa4DNBjX/Y547MztkzfZv3yQ9ePOkfDzJEZPyIq3Xvu7+/j/Pw81tbWKnXlgEQMOFFfInAYFspZ6vV6+Tc1+wBPnok3xkbsDEZMLC9+jnCQ7lwGzjAu2VN2NMuZAZdzQBvoyBwNghC+1dXVEq3g+ShIZ14QzByxJFOSjS3Mb4XvmnNnP/w3z8ERspNEWhGlnCOzVnrQydkIwAiCMJ/Py7zJdjEXxk62ZmVlJYbDYQVw501/PJPaYpR9vV4vmQ9HIlnn/H8rKAPmrCSZO44T6cirq6uyWd2/dxtHgzzkBSec92MEIxYtMk1PryeXsxvm4+w4wCf+t2XbBtEK3zTxd1aCGTzgPGenP+sW7rUc5fvs0PAbFLt/Z6AGzXyxDnzu8i4HHKykeT/rYofLxiqvB+ttuuJkONgRUe1K43eSeTE4MnDIhsEg1+tyfn4e4/H4ezNTNmQOHPg5WTbgG4IHjUYjxuNxDAaDSnQzYrF/DDBmEGUQg9OOXmGMdrCXRR0Zn/nDfJsdCa9P5sX8d6ZRpq1tSZY7/96gwu9kvSxX6G/bNAfSDHKy8+7fOMiUZTA7jdkemr+sb5zty2BvGQh0tJ7/my7wgL9HfyMTrFdE9bwSr5/xQL2+aGNrpyEfAptBlHUXspD5yfLBxTPR/5TgOfLuQAIAEpp5fszBNob/w2PLIvkep22pMzLMK2cyzBOsPXRwMML38o4c+LWe8Hp4rXIAIzuHXhN0o+WAOXvt0Rt+Dr/1uJkL+ivrzPz+jBWXPY81tf3xvLITwnPJVmV68GzjOHjZpZzICjzBZ9CKd1OaCE5Bl3CZHsvGmbOr33f9qPa2PjAoCzMgyUe239zcxHg8jr29vcoiARSbzWap4aUm3yVMPO/6+rpE32q1h03prlGeTCalHIXnYzRQwGZCSmUgGu9jfDgFCAHznEweuhnRE9602NjYKB0roJe9cWd0DC5hPkpkXBLG+Q7eUDocDr8TeWUsdk4w8F4rgIkzNPwfhy5i0YoRZV6rLTYrwaSOXtFZg9ItnEA7J2ZKHJPr6+sYDocxHo/LOOxUIWQ4CM3mQ4ewRuPhdFVKnVAcl5eX0e/3o9FoVBxVOnHRzvbw8DB+9atfRafTKZ2r/Id5Z2cDWkcsFIXrT7nHckFJSK226NV/d3dXNsWbxtPpwx4V2kLDRzYeHl+9vtiMNZstTvZEIfCdFbwdYCsU8zk0tzHNCt5XjujAK1yAbZ7vMiPzlJ1SHEV3oclKzu83f9kJ5/8GUrzDgNjRbACMs6wYT57lkkF42uAHMAFP2Lg64ohxMM35PzXEONfMx/sc6HcOTRz4MB/7fRji4XAYb9++jVarFdvb20X+swNj2liXwkMGM7kM4Pb2tuhl5sJGXpwO5kkE0/zoIAh7PbzGRK5dqgQNnHHluwxCMlAw+GWuPIu1sQPJMy07Hod5HD5161nsiufAb8xzBHRMfwNiO88GFBlw2YlxFsxlXaYZ9/pvOzB2pAw+PXe+h3bmFcsl46HpBp9zPzR0Zm7ZmtsmOmMKH1FlAQDFDlFGYntv59PvyTJtncNcM05qNpsxGo1ifX29dKbkmXbwoMNkMiktnblYo/l8XhqoGAwyJtsg1taNdxwcsQxweVO96cw8eL9tkfnBZeIG8XYmLRu+Dz41L1qneS28XrwjO+V+h3kfWmRZc4mkZQN6whteX4LdtrHoRuM0LtM+0z/zmOlqR4650FTAsgbN2OvEb1zZ40qZ2WwWZ2dnEbFwTrmX39qRMYb9kOtHtbelBATCM0icAYjDBtvLy8vY3NyMly9fxmg0qoABg6vLy8sYDAYxHo9LSYyjGRDQ3XSm00ULWxbRdckATDsfbpFI9xVHwgD7AAw/x1EURxoYG+CR8TiiD3jJpVW8C+cCgExbPAMFlEREFEVJnT8ZIJTH1tZWNJvNshEVZ4W2tRjH6+vrAk5hUBw6d0yo1WqxtbVVgAiCC2Agu8F6mnY4GziVERHj8bi8LwuV20CiKNjDQ+ewN2/elD0e9/f3MR6PS1mdAYHXj4xVs/lQi9vv90vLzZubm+j3+zEej4sipX7SCqrRaJROQihiIlXQZGVlpQB8nE8yKu12O46OjgrP0EGHC+fcpSwR1bQ3Csddwxz9wWDBM45EZyDAmK24bAQMiPnckTdnW1g/Gx74m+c5ZW9HknE7Eov8e38L42O+BpPM1TyVwSTK0jRlbKwzZYysjzfP2rGA13geTqOfbV2wzEjBB/ydn23gQrbStL28vKyAIp5lkDEajcr8OJuHZ6KvbAAd8bJRtxwwVkfcrJe9DhFRKackIGHjh+6Ap9FVOFiz2axkQ+AHxmC97Kwxegg6UKJlYAjd+B7eA0BZvzvTlDOHlh/u4fnQzE6WAbZpsIwnkQWXyhIlB5Bhy5aBLkcumSPOjuUcWvgZpjM0s4MEaIT+rKsbr5hG6HTea50QUS0BMnByVtblRzimds7Rew5SEZT0HkxsgWWe4A8ZdvOS5YOxGPB6nVkr/5bAGk0XaFaDU+IsOFkPB4JMS+bmfZ7mNbI+zmpBbzsLti/oDWQQPrdza573JunsrOcAjgOeGex6DDnQB36hPNr8ELHo0oXdQNeS6eGdDmBhe+ALyzbjALgzL2wA3/MbtglYHziQ5DFkWwcfua2t9Ydl18GVLNu2f+gE2xrkLiKKvcB+uO2ynS0qKuy4wh8OWvD+D7l+9B4NFoKXsVmk2+3GaDSK8Xhcyl7u7+/j1atX8ad/+qcxGo1iMpmUg/ZoN4piev/+ffT7/ZhMJuXgu6urq9K5BQYhCjabPUTtEVy3hYMI9/f3MRqNYjAYxLNnzyoRaDpWYfAc0aV9LsoYoYSpDSAQQJQrhhKmdITeZWQYXoTVB/D4hGzehUDTfgxQC9O22+0YDodlHnQkIioBXXEMaPmLY0FGyXTAkUSgb25uylrgSdtZxOmx8WCT6Xz+sHno4uKiPMPta1GIKBees7W1Fb1eLzY3N+P29rZkjRgn4N+lbzhqCBZCOplMYm9vL548eRKffPJJ6V5yfn4e5+fnpSuVwYXbtzmKhNO2rCwI56rRWHRQwYi4Nzdy4EgONbpWojbQ5nHkMjtD3EfXFXgIkIaiNiBEicBTyBi8w//tlDoKlIEnY9jc3CyGk+c5Co4TisGHT+Ah7xUinYxc8J7pdFrp2NRsNkv5ZsQCkNhhQg4NsvhtzlygKywXjnbV6/VyNoyNDPuMuDCyHnu9/rBnDXBjXYCucbMHG0zWzGCLuaObbm5uyoF8dFXb3NyM7e3tODg4iOFwWM6owXg61Q5/ZcDgNUbH0AKRUlh0+ObmZjkEkAAJGUkcD2cF7KjB4xHV8lPzL7+BnsiGnVacLspYkCn4CecSmluP0wjDQTZnBy4vL2M2W3QhywCOZxD8cIQWQMfaoaey0+rorPnJ2UJ4Ad6DP52hI5hjR4Z3OWPFWlvH8kzkFGdsPp+XczLY+I8ezxFb2zIDxyxb4ArGwTob4NoG4zxsbGwUPgTsEoQzEEXWyJIDBrEJEVFpLgN9kA1HhAGuBArNS+gt9hxwBhX8PRqNio5hjOir29vb0rkNvoBf3AHU1QdupuPvnInPmQrWjfUGf8A/vp//Y8/Mc+bZnDWww8NnOL+Wd2QeW4zMWg94HyoY0p/Bd6wvuJX3wIfwBWvH2O3wOCDGHAH5VEPAm3YOkGfrSmwvNGM9/LedCZxfnxfGvThKxjt2cuAfBxJ4LjaCIOn6+npp300FRr1eL2etuWIg0/H/l4wGhOfhbqEFcD45OYlPP/009vf3S4ZgfX09/vqv/zr+x//4HwU8Pn78OAaDQbx+/TparVacn5/HaDSK/f39YvhQAjBgrVYr5TIA0VarFVtbW3F0dFR6pcMQMN/u7m5cXFzE8fFxdLvd2Nvbq9T6RzyA+ouLi0JYO1IoahaHbAD3WokbfCEcfE8JGAoWxmG+KAei37SInM1mpZ1fxAK81+sPp/qSbXCEg3chiGRKECIMvA0Gc7ZH68PbOGzI0Q5HuWu1Wjkjhbmg7HD6AOIRUc41gadqtVoMh8PodDqxt7dXFD9KYTQaxenpaXEASD2zjhgdMjUI/erqw2E0GxsbUavV4uXLl/Gnf/qn8b/+1/8qYIJxYcRs3ByNcHQNoXPUwny6trYW+/v7MRgMirF+9OhR/PrXv67wHnyEcn7//n0cHBwUg2Ml5LIjH+TG94AEnFJHJGyUNzc3C4jFiEA71sRRV8A+m8cMVBzd8LpDT2SRceMEwGcGs47OueUeThsgISJKL/EcBYUGHg/Kkuf6vZQi2aFCsSK7dM2z8TBtOagSmiEbZCEA0+gL3g2vUn5og8Q7ms1mOaDTDoc7FREhZXz1er1y+BhZNfQNgZGf/vSn8a//+q+V0k540vosA0HTACfC/IiDTBR5e3u7BKJoc35+fl6c7b29vZL9m0wm5T54AL5368eIRR0/htjZPOZjpxAQ7tN/4QGXZ/Bb+MC8yRzJtnKfMyA2yA5eWH9Y7zIXl8g5+sy/ncHhefAXMuESJS5s9eXl5Xfmiryg+9EV6PmcFWV8XOh/9Bdrhpy5uQBj9fr5XcvmgZ6Ct3FmrRfhQ4KHKysrJVBJtQVrzT7OlZWV6Ha7Zd3Ozs6KnTcWsH2yI8h6gEesXwxucW5w8slsAPhYU3TA5eVlJZqNjbONIGDoLA3OMDbXTjO8aMeSSDj6zrjJusBZYHjFQSNnQOxgOPAA//N7eNL7YrOjMhqNImJRhsW80D3QxyAffrCDl4E2QT53sLMzw/2sPWto3cA7wGIOKFl3sPYbGxsVm4mOZ/1wvAz8GbMbksCfDo4R+EQO4RWXHVN9YacR/DWdTqPX60VExMXFRXHCc6kqa2ga5Xl/3/XBjoZLd6wYTVgAeL/fr7SEfPPmTXS73cJU7Xa7AF32PEyn0/j222/j4OAgut1u6a1+eXkZEVGpq2TygC0i4Qi4gQRCgQCdnJwUpiZy2ul0igJy1JBFt5JHUN3SFkNENNqZCrIFlA2Mx+OKp8zf1O2yGZwoPYzu/vjeC8MYYALoZWZEyXQ6ndLaljHgTNze3kav16uk0XJkDcbGCTKoIMJDZJS+5hgIlDUOAM93lmc2m5W1RwHyzvv7+xgMBpXMDqUgvA9HEKcMZ8snve/s7MT9/X30er04ODiIi4uLwtMG3c4mGVQhC4768TdCvL6+XvaLYNQ4DNAAeGXl4YTp0WhUOYl5NBrF48ePy7wdQUDxsN52VhzVhQeQUUc1XYOKEUJRZxCRsxlOE+M0wGOsA8oU8AuN7DShqJxtWDaGHP1HzhmP6Ynx89idNmcNkV9HiQ2W0C38TbCEsfl9XI4Qwi8YFwCkAYkzH5T02BnCsbIjx+nZXhNoQho/IgqQIcpMhhN9QumGI+6rq6sluwFfEbxwYMdgl3Xj+Rg9AAeOlDfA2oCjq8lwOkvIeQOPHj0q9M4AyM4zANPrw3fwPevitbOjbtBveXckkGeiv3LQiLJH7A/61TYTXeWsiu/1M+Bdl5VlvY+DR+TTwRFHIuEV+M/ywD3oG6Kd8IjnDK1xgsj6oleYC3aaNaB8yeOK+G7DCu9dy3IFv7EmzAE9yBo56OMxUUYFTeHXdrtdeB8nwzyLDIAbGK/5inUjGp8DEvAbzzYghh9sFx2s8Vxx5JypwelFFtAbtlPOzPB86AJ/OICGPGc+Qd6ZO38MSi1HDpzASw4MufFGDm45+wdPQDd0EuvK87Cb1lfwMWPFhqOrnJ0xr3N+mdfR8sRzzWfGTgQYsb++HFRycxT0Ghg5Ir7jNFgHW4aMXXMAhPnzjPv7+4IzqSaKWGQsudfBVy54hwNX/9D1wY4GBsulIhhsPKXJZFJq3SHOzc1N/P73v4+/+Zu/ie3t7eLBra+vR7vdLtG4ZrMZp6en8ebNm3JwHUrc5xJw+B8M4nIKp9YhJoxr4Mt5DQbxKB978Y6UGQB4EbhQLghbdsIsECgjmJq/AcQZ0MA8d3d3MRwOS5bHnxM9xHjDrBZgxuT9FAaMOA92rlhzxmSFkqMnvMepyNy2FzCE8XXtPWuNYNooeQPp2triRG3mxPpxyJ5BHKfJExm+vr6Ot2/fxmAwKPNkTkQCfGo0vODPXM5kpco69/v9Uk6G4r+6uopHjx5Fq9UqjiUG3c9kcyLlU7wHJ5fa+hxdh++gi5VfRBTDFREF1LkUAaOLLPMZSgVetLL1H0e/l0XSoA+GFr7w5Wibo4h5Tih6QA5GER7FIHOfHQruN90Zn4Ej73Gkzgqb79A5jI354ujYaeV9jNNAxrXdGSDy/2yMDIJZO3iYZwEcCaygR6fTaZyfn5exEsFCVuEdyr0ctMmXgzCWd9rabm9vR0TEYDAo46LUg0ivaY+TAvgjGGOnG+DBv80vdlbhS4Mtg1doxvrze+twSlFtI5z1M9hAXvJzWGOXnDIW1sg6Jf9xVsdBGNbGcmFd4Hdk59t8C53tRFmfA+Bc4mLngwsa8Yf3oMtycME872oCg9/s+JORYnz8bd5lfP6DnERECR7c3S0OGKaMCn6Bfl5P81GWAQM804cSb5yb8Xhcmn4gL95/4sBLDhhGRJm/9bDnbWfDPGHegE95H3+QBe+lYE6ZzxwMta4GzEMr2w/rc+tZ4zaw0P39Q8t786jXmjnZJiErdgIcFHbQzME3nsm4cI55np2fPGbzMWMy3XJGmDF7Hgb0yLX1hJ0lz5v32wYbuxG8sXPl81zQxT72gKwvvOf9pLzTe6H+0PXBjoY3CVmISFMy0eFwGMPhMO7v78tm5bOzszg9PS2bcFE2OBjT6TT29/fj/fv3MRgMCtFhBEp/zs/Po9frRavVqqSBVlZWotPpVGqyESa+H41GpdYxIkrNsj17Io8WAAjriGw2djZsZjqY3OUMBm8wcFZQs9msOE1kCSKibGZ32Y0zISgCH6TCewG6jIGxWmGOx+MSgbcw8Bw/K3+O4uI7Nj6SGvflNJ/f5Y2SMDvv8OY8gysrLUdzeD41jt4r8v79+7i4uCh0IIoIGHQZG3Tyn0xD8wK/w7ggO9T+ttvtUgIEADbQX19fj9FoVEp1mCf0dZQFp8oKPCJK5JNoP7xgoJydlAzM8jrbUDnCxJq7hMO0sXFiHNm5sLHL7+JzgzqMN7qH3yFTdqzsfHjdfNlBilg4iwY4dnD8GzswBr5W+o5kOfvF+jtKDE/DDzjxjtBBG5eSWIdkwBux6PTnyB+HkzFGr4vBFnS07ssgMl/8jkNc4W0O/WQsyDglZvP5Yg9gRFRO5CZAQ7lGrVYr82FNrCvMr15fOyTcY4ch08IOruXJOtB8b7th/QCtMrg2b5j/uOx4eD28DnYOzIM8z2MDtOb19ri/b60tC3mtfS0LIPieDP4iorKOfnYOVtiR5f/I+LJ52Xm0bZ7P56VsGR51+/jvc4asn0wTy6ffwTzh7YgoVQSmOYEw9loiDw7sZQfR9GOs0AEMleWTZ7hpC5d1Rp6PbWteQ4N+8B34zPPnnszHdoTBJYzth/jCYNvYYBn/WC9apjIfonfdfj7bDfMaz/I47OwwZ4+f3yxzWkzf/F7Pg/cYR1nWTfP8e8sBNsilr9YXds4y3f6fl07Z2zUAxVPCKbi7uytRrN3d3VIudHp6Gu12OwaDQfGycERubm5id3c3Xr9+XVKFvV6vgEPqLTFSLJTBBjvoAT4WShiX6BmRYjqbIMxsTGe+ENcRWitzAyEyAv7ciwtTLAOsOBssvg+rI4o0m80KaHb9nhkJ4GOwAXMbZDpyyrj4N1F1R7QsRNkYMT+AcOYROywYfbfFM1CBoXGe8LChBYKLcLqkgjWeTqeVk4fX1taKQ0kWifewh8iboJg7DqijGFm52eD4/9AFIUSZspnd58qwpkRrNzY2YjgcxmQyKQ6SDQ684bW1YTVY87gN/hkz62hlvSyiyu/yM/x77jOozrwJTTBGy+QkP8OOjQ2WlXY2BNlpQlk6fZ0dHz/PgM77ROxceO15lseBjGVDbOe1Xq+2J4Y3HQ3zbzwuz9POrZ1KR0HJBDo7w1o4wwm9oInLXww6TGvrkRycoKMPwMd1026hieOBPm80GoXX3R0r8wxjdgQ6A2JoBs0N+HmeSx24Fx2SabkMkFuerE/Rh9CT9xgcmDcsM9nZiKie75Idcn6fMyqMl/+TsTTvmF7538ucIQNSBy8MvnOgge8cEDDvGNhCS57lCgM/k/fgTFpfWy6Yk6PYLhWJWFRgOHOdAbQdBy4DQoO3LDPIAHTxe7gcOeedOZBRqy1al1veeAfjtoOUbT0XdMvBHvOV1zuD2Ox0es6mgW2RsyKmr/+PLvTenAzkPSe/m+9sL+zk5t/nwLRthIPrxlvGLMsCXMZ6PzRnnC2eaZyVHerMczyDf9suWQeaf5Y9xyW6xp120I2j4UMHg//Q9cGOBpufMQoRiz7BgHQY4urqqvR3bzQaZVMQGyaJ5na73dJxBaLf3t5Gu92Og4ODsnnXkXGUCgtBRA6DlEt4iH4R0SNdSivcRuNhoyJlQ4A9fmeGhQndv9qCNJlMotvtFlDJd0QOzMRmCEA6/wcUuM/2bPawWQ3nCuBNjTM0AWBa0SAsbESzQbWAsE4Rizo9BMQR/5yWjFgIMzW7TsmRumPuZMeYuw8fou6UqD80QSB8kJ/Xxy2V2dCMc8bGZ4Bju92Ob7/9toz77u4urq6ulhqJZYoX58eZPCt4X4zPNfqUj02n02i1WtHpdGI0GlWyHHT1YBNno9GotAbNBt3ZpHq9Xsq/XNcOgHXNryNU3Mdc6IhlevpyVs6OqY18HmcGD86IWBEyBnjGnW6QZ6eakUc7MfCZN9MZyHtcy2pouTCm0M3OBWO1UbdM8b3BOzLBerG3zSCZ9yBLvBtjAO1YT0C5AyWWUbLAyD7ZMGjMXgrmy+8MlJ1B8di57Ew6aEH3N9chR0TRcehujwl9Mp1OywGv6BBH7L2J3ecE2AnwOmYnlbm6fBae4FnIksG19TeOHHJiOfS6WU/YWNuI275lGeBiHaCzbQprlQEYeht5NY+YP+0A8VtsmB0V1tf6IvO7A16M2/XfdgC4mD/6w4DH6+KSW+xnDgIgE3agKV9iPmzmh3cMzKzvLeu2yQ4QYv+40EXYOLfYdTdDfuO9l6w78gp+gV9yqbfBoeXUYJH74C8y7ZYF5mP9RTDAtiyXhtvBq9UeuoZxVEEG6AbBOTDBvNFpxhp2IPl9DiobsxjYWxc4AGT+435jFetR3498L5NrLp5DgNPPREfZPvqazWYFc2ObrM+9RnbKkFXv7ZzNZpXW0dZL3MM7wBc09/C9DoyAf5Y5L8uuD3Y0TOCsoGxgms1maRm6u7tb9kIArnwmxcbGRnQ6nTg/P49f/epXBfwDbgB/3W63KCmMkt9H6dRksjgwzkJnkBARpe6Xg/c8HkcYm81m2dA5my02t7HQGKFarVb2pQDkvemM58EYzWazOFjj8bicWg2DOIPjDkpsKObZ0+k0RqNR2dOysrISu7u7EbEAljDm5uZmeTb1qNAa5pzPHzbE45BZACwc0IC/WX+3tWRPDDzjci9a3DYaDwfvcUI36+qDCinrAHQxLk5yNQ82Gg9nqbDpfTabFd7BCWy32zEajWI4HJbGBewBypEkO2SOEvqeiO+2iYVGlBACsgAWyAP8hMG+urqK8/Pz4mzV64sOXd5PAdBm/TA+KPmIhZHlnQZZgNVut1uMG734Mbo+aDDLOhHxHJ3B+CBrlrkcySWqTUYT/mJ+8KfLSZYpc2SrVqvuQYIHKbH0BnIUJkbOitRRWYCsy3MADYD17HBYT9rxMvDiOwA+Y+edzMl7Rxz5N8hCV85ms3JmEDqj0XjYiAgAvrq6ilarVdrc9vv9YgDZOA4fMQ/mh3xZn8EPvIs15nv4bzgcxmw2Kwdzwvduo0mr7fF4XNpeO6DAOLMz4TIr61t+bycWeTKIh7dZGwIWOcAEbxhk8gyMswGdI6hcjmACgOl+CK8h9+Y3A3fbXmwQ97E+rGHeG0SwzoEUyxI2g/U0T0MHg1fTxTYSXWB9wVzR8TwPOuFIwod+92y2KCU26IKWxgMGwJyLhANLphqH3HqCEirk3Vkt5ujSRjtstg/ICkCMeVjvUsLcbDZL8Ijzk1qtVlxeXsZoNCqgdzZbBB9xwhxIso7ncnTefGKM4cCPgbmxkx1gR7yznmSduHgOwQUHlQxucb6Qa4LLXMiibRhjy80UeK8dWP+Bv+04gQ3sqPCM29vbUkpuPcTY5/N5sSvwPrSwk52dFujohgvLHCPGy1ozDhwH769kzXkvvyVQRYdGnGuCiPAIssQ+vslkUioraP7jIAv6yfL8Q9cHOxq51SYEcwQRZgJsbm1tlSj0s2fPotVqxe9///s4OzsrHXcMClnAXq9XOkFZEOfzeelDjeLB0PD7x48fR0RUDhFE0GDoer1eNkdyIUDn5+fRbrcrhgjGh9B46j4vodlslvfN5/MCkh3VAFBeXl5WDt+azWal5SOpW75DQTI3FpZ5010GZ2QwGMTu7m7FQK2srBTQFRHl8EQcKRiZzBJOnWs4HWWIiO940MyB39I20CVVZC3odHB5eVmJOtF9qdVqVTx2eI2zPmq1h5bFbC5kDKPRKN69excRUcaP0K2ursbe3l5sbm7GV199VfbrvH37tggRzh7Ch1FjjNAMQ5UjHwY5jUYjzs/Py3pQJvj48ePSZQQATGYP/plOp4VPGbuNForBkRSUF8oi4rubVSMW4H9ra6vS6o7PAdGORFrx+3leoxxdRRm6O5vLdfiTo5roF0prACiUe+TIEkaYK0eBMXTePIrR4vm0H7SRQmEbkMBrACbTv9FoVBwAgxP0mME5tPIJwVzoC8ZFW1rzFzxOKQgOC3OAl/jD5m+cXO8b2t3dLTzPePnTaDSi3W6XzYE5yku5KGM2/xv4rq6uxvb2dtze3kan04l2u12MNoeoAjJyphVnEVBpgw1wYG2dpXH2A9DJuhqY4bBg4AExBh/5ynJjAA//oPOQVZ7rzOV8Pi/20kEvxmI9a0DnUoyIRYez4XAYEVGRO56FQ5OdItaM95lvCWrxO+jmjE7OrDmbaEcCwMracWGbc404fOCMjWXUgJjvkGv0BX/b8SIYhc2mZf5stti3lDM76B7mYZ53EIFxGcDye8A0ZwvRsQd68Yy1tbUYDAYlQIB+43nYPdYfunlvpvckWl5xbhz4zA4yfO09JNb1Lr2zLMC3tg3OSCC/DmSht7LjAq9tbW1V+MKReGM72yr4iw5r8LZ1uDGjZYv/2/G1XeN9diS40C+MEcCeM5L5/4zPY8DBuLu7K9U20JOMHDJD8JW1w2ZRqeEMJA4UWAxnxwFGbCLjRHe4OQBz+39eOuU6Wy86wgZh6Kl+cnISo9Eodnd3YzQalUPwZrNZ9Hq9AtQBYWQrvNcBBup0OvHixYu4vLwsJVntdrsQYjgcFoIPh8PCuHhnKEKMSavVKk4OTgWM2+v1CqNCcBai2+0WwAtYYKM6AMRep4ENnZTIpvBblDnCS2TDZVEIJz2ODXgd8QAoHB0dlXcAPO/u7mJ3dzcajUb0er3Soxq6N5vNMr+zs7OK0YR2jJUe94BJnIeIh/a6/JtN1ggR7RfhJeZBW2HGQgQUuiGIOD7QC16IeFDCFxcX8e2338bu7m4BuRhyOurc3t7GxcVFDAaD4jCiAKfTaVHQzWazkumA7+F1l3i4fMNgEAeFz4fDYfR6vZL1A8AyJ34Drdx/m+dHROFZeAyeAGCenJyUTj7+HeNyap2okrNtzA0libPKevNMO8J2jL3eBn85Gumom6PlBALQB3Yoc8SXNcigD6cU/uU+l6vw3u3t7cKnXI4kQQfmg8wzZ57j38I3RBxdcgBPYMzz5zlAcX9/X3rkA5YwcB63/8a54ewaDCvjZDMquuX8/DyGw2HRGcgYDjy/dcQTEMi+IvMQdKrVHlo7Hx8fx/b2dmWt6vV6CW44uGLQuLKyUmhN+QkyCgDBgALwDFqgK7zA53a84UVk0HxWq9Uq5abQEVnCwbaD5shkvV4vTqx1LbR0iS3jNB14n+2uAQnvchdDdwKKWGQLWA/manlB99m281zoDriwPBtgO9ptpw6eQx5wKhkXmQbrnuzw8nsCns1mswKm3QCAQ4PhG3iI9eM9gDXe5aCpZZExkO1alilg/hGLzJr1LDxlAMez/Z6IKFghBxYYE0ASzBERZf8VYyAQ64yEgT98wzwcKLN+RM7tUFpXcz/rwPPhFfOw5WRra6s0tuHd2QlF/6ArTW/oiyPAH8scfGvH3PyCo+nWtsi9nWc71Znv3aTG99Howhk9vx86AfBtQ9Df8C8OJrrezr5tsjtcMgZwEsFm3o1uxM5yYCzBN7JuXgf0Fp8ty9p+3/WjSqecmoZgTIhsAQQdDofFuLRarXj16lUp2QGI1uv10oWHhT09PS1AFwXtjeBEIoieTyYPp43TRcj7G3xoG5mV7e3tyl4O3usIlI0RzwQg80xSZSht7nE0judCKwSr1WpVDoyCic7OzsphfRikjY2N6Ha7cX19XbxblP/6+nol7U62B0VpJWeGY4N0jgI5/c76eHyz2eKkX5wXlAh0Q2CdjoTGLplg7OyHIX3NxWmXCB1CPxgMSuSm0WiUcpGzs7M4Pz+P1dXVePHiRZyenhaHrdvtxu7ubuzv75ds2dHRUYxGowKwMMIoGqJJBlbQIyujrLQBTkRE3EGKMWIw4CecSoAHSgo+A3TRvID1Qej5d5YvjJvl1oEBeNdRn4hqhxPo4EglwAhgx3pjXFFCjgjbmDlD4/dyYagioihD+M1GGx2BYnY2BmNkp8AKn/K+ZVEZ1pmxukwiYgEymT9zzRkfyjMN4KDX3d1dbG1txdXVVaVFuCOCACJ0BmvN5mlHy5vNZvT7/ej3++VcnPF4XJxO9if1er2o1Wpxfn5e+BzH19FKsjTQDz7kgu7Z0XaGARlBt6GvaP+MsXNzEPOrG0J4Iy88xVktgE50kYMwjN38yO8dfXaUFzlhzWwPzM8GURkw8D5kmQiied3OGe/ANnDBawaLziAxFnSKedFOoQNSBiOsO+Ow3Dtyy/OQBc8dHmQPpOdovkGHsoYEFTwHeI938B70KnqRSC/2y6AQPYpOgv7ILOdyoaORR2xXPrMLJ4l3WS9yOaNZr1fbQvPcu7u72N7eLv/H2XGkGDp6b5ztD/JAK97MtwTO4BFwW24CYB61Y49d8m/53nvG0MEuC45YBC+RFTsbDtAZsMMzzqjZUQNbsSZkaM3/dthzJpDPsHvYMTsHzJ+xuNQIDAUdvd8J2kQsyuicbUbWcrbfgT5XMHid7+/vi4MLf+RsvefPnMDUBGPBHA7gwidUN8D/OOnYdMuXnV8HEf/Q9aO6ThmAY4QYHN9BoJubmxgMBiUqPB6Po9PpVM6wqNUe9l+wGXtl5eHgkNFoFDs7OyUFivIkxYoXb4Zut9uVA4sgIsAERwOjhoLle8A0kRsUGFFmnBOIa8/eG8kAbwgE6UeihCiriIVn7AiJDQpz4Dk26B5/TvVzjyMjREXJ2DgahyBSw70sCmhjDziOiCJoWchQHqwb8/dZGMzHnjLC5bEBLA1KKT+D9/C6e71ebG9vl1IN0qfT6UM3qq+++qq0WCZtnqM3OKrOGsDXrA10h0c8du5FYTPGer1eTuZFgfBuSvkYD+VwKFv43FFCIj3wcMQiqhGxiDA6UuvxATSspJmLI0DwNvRHPhzJypFHR8WsN0wrgAs8ZmVtXsdI8H8+8x6GXD5hQO/sh6NjdvR5t7Mg8DBzA+RBW48Po2PQavpELKJADj6wZtAKGTKYNmB35M3lKeg39kG02+1iQHq9XiVKeHFxUWmAQAaZEkIHCCKqJ1Tz/sz38JjlyOCSklD+7fWxo4S+QwcMh8PodDoVvWy5c2CHf0N3PjNP5M8sO8yVPzzDoNk2zpFY5NT2JEdRM9ByACNHc32ZpuYLO+6WMTu+djT8Hr6zA2b7YWeX+9HT8K8j7XZAcjCSywEZ09ABrhy4yUDaesKgB5mDB/j9svHxGWXH2Ct+h+5y9nMZP3C/+d08Yd3CmkDvTqdTAZfoBTc94HMcFJfQWneaD+wY5M364CboRADHeAPa4FQ0m83iQJvOPBOHw3qDMWFHoQH35D1A/j1zY+2ReetCZ6gcxIH+dh6suw3Q7VjZAfD7Cca61ND8WavVKros6zx0s+WL58MP/tuOu9cCnZFtcER8h8eZu3XcxsZGccwYB0E2moBcXl4WvqW0Ch5yxs+85vf/oeuDHQ0T1x42ggeo4l5KVK6uruLg4CDu7+9jf3+/bIDGs7y5uYmDg4N49epVRDxsZByNRsWjBcgilBGLDUI4B1aIGCS37GIhXRfs2mvSjAiiFyxiIciOuNvoW4E7qgQtHIWCKXyhbPmdOyCx6DgzzMtKg/E4nYvjYWPisfuZVnQwYhZQR8Cs1G3EEBBoAGA0uMWZw+haaRENQ5CgO962U/6NRqPieRMh3d7eLs4tZRadTie2trZiPB7H27dv4+LiotKv3AA3K74Moi3IgAvzSAbc19fXpdSMeXe73RIJYq2oxT8/Py88fnNzU/aSQC94G1oue7cjIwZWNig8b9kc4RGD+6wLvD6er5+XLxsAy4ufCd2Rd3jETg/OAkbZCt5K3MYqg5/sCPC7vM7wtQGMjY6fZcfczoTXD73nKGcOPFiWUfLcm8Gio2N2HnyqLFmVbrdb9kbRBGFlZSWOj4+/4wB63pluGZizdnao82+ur69LJNLyDtjDUWm1WkU2kDPKzzwWA1XG6/1jrEMGH5mPbR8ioqITDRyZq9fVjgK8amc984Lpa0fHQDa/x1FKxmj9lJ0ZvvfzPYZsewySHWzIMuV1h2d5t8FGnr9pDT9n3cI6WA8Z0OV3M+4MchyQICsRUS3Dajab0el0IiIqB0KS+bQdZHyM37Tg3+Y/A17ziOc/mTwcjEnZLkFPZJuLLCrjBsvYRmMP8tp7DP6Oi/cs03e8C+Cb9S//dqDDwTCvi51q66ssD6YT4+EZ5ovszHk97ARY5nPAxu/Iz8n08biXBRUzHjaPuZzT48o8bUcqB9sczPYeHn7Pc40BWQfuyQ626UtwikC2nWrbcQLE1h3LZPyHrh9VOpUNTCaiGeju7uEU69FoFB999FFJb7fb7eI9AcDplMRzB4NBjEaj2N7erjgCGGnAsxcf0OkUPIsEI7stm8fKPXyPMjQAcFTBIMmOECk/e8wYB7zwrMQtaNCIrA+GzJvYGbtTaDAs3jlzZ7OQN6Z7vhkUe26k9GzQmZPLXrisdLNQ2sHie6chHQFGwZqR8bhpdzebzcr5LHxH9mxnZycuLi4qz+t2u7G3t1c62kwmD50UxuNxBUQwdyJc5h/TKdMu08d/X19fx2AwiFarVfZbEGHo9/ulBJDzNKCbM0xOe9ppyGNyNNx7TfzbiIUSzxF3X1aKWUH7nVmO/J2jW34mss/f2cg6RbssNYsizNkc1oz5mVf9W3QB78pRJ3SZwYajOcgf48zAKAM8gxWiXzzfYMG0RQ4cbWOcznjwzvF4HJPJpLKBExBFUGV3dzfW1tZKZ7PZ7KGUhpr2bNAdSee9BlzQHaNnoMq685kDDugiSgEvLi7K5wQbzFvX19dlv5GNKIbXTlEG7BHVA+tyNoZ1MFgyDaybMkBZBmDsvJhnsu30bw3eGKvBjsfgsfN7B4qYY3YYso7Kjpfl1e/P9OQ+87f518+wg8/62Cljbex4oIeZK/d4zjkCnueArabkxXNuNBplvyjjNIaAl6G7aWhe8xp6TsucM97faDRKJgXdgf6Ht52FdqcfxsN9OSLOO81DAF3rS+7LOtM848tlkIyD+Xp9rJOQFTIXtk3c73EYrHPBO85Y8v5l9orn5QCPdVi2l3ZwzR+Mh/Vc5hhxsY4uBTR9/HvLImuddYf1jtcyO1HYnKyzGQt8gqNBMIrxUE7rknXztXVqdmiWBW3+0PXBjkYGDfyblzodxiSvrq5Ki6yIh03bOzs7MR6PK/W57Xa7nPEwmUxiMBjEyclJJQNBqg8ADEFdGkRd4fX1dWxvb5eTxzHAV1dXBXyasXNpBe9jUVgwyqdwGIhSY7TpJuEWjNAOpqIumzpPRwEcjaHO2sCEDAHjXFlZqXTPMvgygI+oHiJzeXlZfm+G5n7mQhmDu4IBjHBMbHzNE2xe9VkQjNvRHxQSSpXyN5QEQjGbLTY3kSFgrRHa2WxWeM7rtbq6Gp1Op0SPcOZOT0+LgsiZKUdjoC1rA19k5ZsvHA2cJJ5Nj3FnVABdAN/ZbFZOs+ccATsgRMVt2FhfsnxOgef0MFESg7OszBmLleuyUgI7MkS4IqqtFDOQWea82NA0m81CM9L3fO5UsumegRUGb1mUD0eDckYMpTMT/Mad7exMG2Azfq8Dz7LBcA3wfL7YmIduWJZFIrgCXdEx3EsZohsvUHrHGMmaoaO852Q2m1Vk3EGULAeOqpvv7TwuM+xkJgAu/Lbb7ZaNiC5B4Ll3d3clUwnNeXd22Az8AJuAuWw0zW9kX81fLk/wnOwgZ4C0jKcjovI87jevZEDkMlgDD4NzxuVM2zJAwnic+c+6CvplQOVx2qn2v7PeZ4w5iop+ddYSmtmRteOfA2s5u5Dpjg3zBm9oxzqjE3LUmWwg2fKcyfOV52zgapr4c+vmHNhAP9pe+6BQaEP2j3c7YwsNkAnrNHiPwIVpYvp5rF4DOy6ZHwCkmS8N3q0rctCGd/jfYAI7nDzH/A6dCFr6vfBcdq7Qh4yX3xpvZFwGP3CP6ca8vEeVdc7O/bLghDNv5h2ekQPmpj9rwjy8DtAR2rlLK3iqXq+XluKspytdmB94F17zWLNj+n3Xj8po4B255AWmZEG9ceby8jIuLi7i/Py8bET+j//xP0an04nf//730e/3o9FoxM7OTjx+/DhevXoVNzc3cXFxUdmg22g0Sj0ZUV8b7tvb2xItns/nZaMLjGLnhAXM4Kpef9gATOSZzIY37ZAlaLfbFWXnjZk4HsuiPbQas4JEMfJ+NuYyfoAaz2U+7D24v3/okdxqtYoTRH0dzA3oZuzM12CIcUREyQhlxUi9HvV9VhCOwNKiFp5xB5Ctra3SxQZHgSYB0AjHY3Nzszg8RPXv7++j3++XMW5sbES/3y97G66vr+Pw8DDm84fOFuxJocEAfcpRDr58ejjKxW0vHTn0yeEWaubMvQg0/MrepZcvX0az2Yyjo6MiV+12u2RaiDRT9uW6a97hWnT+EKmjawTKmTmYH8nc2NHmeay5e9DznR1w5geAjqgaQYMpO/DmQTvKEdXIHorO8+c9gFP2XRlEeZMiIMpg5ebmptLtDsXtKNdkMqls9suBFO6hTMlOCM+izJO1cbmc5+sAhQ0im8AjFkYCEOcyH3QTa03par1eL/uWMERbW1uxu7sbx8fHRU/zDBtUA0To6HXhN+g0R6FztNdO92g0Kuv59OnTmM/ncXh4WGTQAZv5fB6j0aiUV+E8O9BkZ9oHnrmEFd3EOgDAcGJZF/jDjocvA3LbP4NJ04Dsu3W+9YJLXB3JzOAMAO53Gcg4KJDLjgAU8IUBpwEbz3d2hHlwn2UJ22BniDkhq9Y71ol2TC1L/izPg/UAY/Bs6Mrc0YGmpR0IGpo4OMkagSfg9+xM2GHzPJiv18w2wXOArynXwi7V6/Vyhgw2xmeL4ThYdzgYyWW+9zqCH6iu8JkJ3Gtd5+Ak6+ZADLrNOgM6TSaTSoALHuEdDrJkG0kwLbfoZZ7gK9YtZ234jt85e+WgLeOwvWcdnW1YFlxElrhc9ur3gx8yIMfBZC2zcxcRBbMRZLUsMh7vKWRc8Dy2DeeBoCvrDg+3Wq3ybpoN5awhDlHO/GYn/PuuD3Y07PFAqFqtVjb4MWg26bKzH0Ha29uLb775Jp49exZ/+7d/Gz/72c/i7//+7+Nf/uVfyhkS//qv/1oMx/n5eRwfH8fe3l5ERMXD9WJg6DEyjJM2twBUFNTGxkapY2bzJgtItx6iG3ZUNjY2ykbe7GzBUABglJQ7VvCHbhIXFxcFQDcajbi8vCztKAHeCPlgMKi0LqOWlOfBTN1utwgwYB4jQ/bDrRbtARuoAfZxLNw1hH01GXjB5Gx+xoDbkbEHzjpcXFyU+m0isURecbZub2/L4YUok8FgUAGtERG9Xq/Qe2dnJ/b29qLX68Xe3l6srq7Gb3/727i4uIhutxv//M//HJeXl6WMj3fDE8sinxZwg0JHMjAE7jSDo8W9Z2dn8fTp0/gP/+E/xJdffhm//vWvC/hi3gj+aDSKdrtdlAO8hYIhYo6RpW1k7rSU+XBzc7M4I44AIeuOvkIH7/XJQBKj7igehsFgxIYPIB6xKAfi/evr6+W0dG+yRpZ5B7xl4ItCdXkB9wFOrZ+8aZKx8j3y7c5fLnWo1WrFobE+cGSZ3/hkd2STOeUsKPyEHiP9bVmNiAIYptNp2QjO59PpNPb29uLJkydxcHAQjx49ivfv38e7d+8Kr5+dnRVZciTMxtvdTTL48zrYNhhYYrQGg0Hs7++XgyIvLi5ie3s7/vRP/zTq9Xq8evWqkg1xeexgMCibMx3ZZy2c6eXivXTp87plvQVvGjhyL/KP/cF5YAz8zjJhYAytsCeWA57DXNmY74yT9akdeDsSPB85YQ24H/51YwjGZwcH+TJoZk153zLH3SAyZyWyYwQI5TdeQzcIceYqHwzmxgVeR57r9ut+Bnp0MBjE/f19CZjRnQf5ckTbVQLWiw5sWOcR/EPnQwfWnvWs1WpFZh8/fhwvXryI4+Pj0nodZ9u8dXV1Fe12u8Jj1pteO9aKdcXBtBPH2lk2CFi63TG8ZJkwjxOUNACG79wEA71MBz2ea905m82i0+kUngFjAp6hIV0abZfBNszD47dDyDpFRMl4sp6mi99LwNeyhaMA36I7yUiBlZxZsX5hPC5Rc5AY/eH9z9nJxf7Br/V6vWS4HdRDnzSbzdje3o5a7aE7LGWpg8GgsvEfncM7HUyLWGCPD7k+2NFwlMPK2NGY+/v7aLfbcXd3F6PRqAjRJ598El988UXMZrN4/fp1fP311/HkyZP4yU9+EkdHR9FoNGJ/f78CRG9ubuLk5CTev38fn3zySWGA8/Pz0rqR1qaDwaCcMu4SHP5gSBA8GC8iyq77nZ2dMk9OzGV+lHEQYSfVD6BBMa2vr1da9ToCBfBstVrF2ciR/nq9XsqwKAmyIiUCAMMDCHEMrAgoG8JZ4B339/dFgaHwcVaYE60wa7XFOQAG04B/TkPHscQYuM4aAXEHB/aNsB4oC2junvzQF2UEACXTMxqN4v3793F4eFgOIqvX63FwcFAyZZ1OJ+r1ejx58qScCM4aOuKUnUF4214764pDxxrzHb911IlzBNbX10ur4uPj43j37l1sbm6WvUjX19fl8D4UBc0TyOxELLJvbK71utjYEAVxYwV4nEiGT0FGqTFf1gi+yqDD+gDl5IgK683Fd1bgdkJ9GBt6wM4uY3QbbTt+yAQGA4XsEiBOxWYeGD+UKDINjzMmGypHzP0e0wnZsVHgO2QLgIfjSIADuWesOOEYN8DD2tpadDqdGA6HcXl5WbLAnjdr2mq1otl8OAyVKC6doNAtzM0RR7LF0C936EEWstNvYI3xn81mJcBSrz+UV7558yYODg5ie3s73r17V7KvOZrLPizmAR87ewyNuexks35e75y5QgfmAJTLvVgby4o7C1JuCi1yBs9jMw2hP8Eu7iFTRRmF542M5P1YdkSYI7JpeYuodrXJzhI0s5NAoCoDDNYYW8mcqUYgGObglPUHugtg5mgxDoMBG/oiYhGERA+QsQLMUzYIX2U8cH19XUoHr6+vKyXB0AcnzeAOejrQQiDSWULLVUSUdv3gl/v7+9LOvd1uF8BHBBmZJlszmUxKC3xk0Q4ldEG/2amALtAVDIGcYT/sLBpgmh8M7qGz9Rq8iu1hHb0HNmfhWJO7u7sSmPE7wQERVftkmwOfOFMC7U0HxmQ+4//8hiCy+YHnOMPl7AxrBn84iAY/5CybdS4y7zOWsOcOvpoW/G3dDJ/jqFH5QgXB6upq7OzslHPoyHyAD5zlxT5Zn2FzP+T6URmNyWRSFCGCBdEABRygwoLf3NzE119/HX/+538ejx8/jsPDw/iHf/iH+Mu//MvodrvR6/Xi6Ogonj59Gru7u3F6eho7OzvlYLOjo6PY3d0tABAhGA6HMRwOK2kgymkiFkZmOBxWPFFSQNPptIA1Ihrz+bxsxDZgckbj/v6+gEErc6JcjtAiCERHKc05Pz8vdfsAyl6vFy9evIiIKA6awR//v7m5ifPz89jZ2SkGm0j2YDCojM1KxylraOQSM4QSZU8KF8YnQuoIjw0o2SwY0CCGd6C8rq+vi0Em0svzp9NpxVChGFFizeaiDG48HsfZ2VlMJpPodruljCoiotVqxU9/+tP4/PPP4+7uLn7961/Hb37zm8JDOJNOsSLsPuzI0T8bTysVR3aJoFHqBx/S4eT58+exvr4eZ2dn8W//9m/x6aefFuA5m82i3W6XfQMYR/YXke1gTESFuFZWVgo4hW8YL7xv5ZeDBzzLYB5laiDOnB29wiDAC85cOePA+wAF/DGAYI3tzOSN0fBaxKKrkp8PcOMzng9YZ00ACQaWyIKjV75sfJBxAIajajgDONI+44D1wTG1g4PRIMoKjf0OnLh+vx+TycO+NnQjfHh7exs7Ozvx4sWLePr0abx58yZev34dl5eXMZ8/lDKdnp5WeN8yzrqQ9aE8NEfj0UEGtZYVytTm84cyqL29veh2u3F7exuj0Si++uqr+Oijj6LX68XZ2VkJfvAcnGmXzkJH6O0DptwMA/Dk8eVyG5cocJ8BBUAMnWpDHhFFpyCnjMmRZWhnAOMABuAB3WEwy3sYe3bMWR90vMElOhOnEVmANnaO7XAzdp7JWnpuDtLAvwSOvK/Je4XsjPJbxsI9yKNp4Khqll8uGtAgI8ggtpcDes/OzmI0GhVeNpgiI48edKQ9dz9Dp1on5og2l3XldDqN09PTaLVaJbvHXkgCcthAAzz0msv+mLfHQSDB+tDrRICR3zJm6y7oz7rb0YZvDewd7IKH4SvoZOfE48fB9/vQG9aV8IJ5A51nuwKu4Nl2lD0fnDnbeMsZ9gfaOnjCnLEz1nmMw/O0MwGvW17tTDmwxhy5nzGxTgRFCEZBI3QzdsrBKvTVaDQq2xew8eYty7+DEw5G/D/PaNgDNbBiAabTaQUcoFAnk4de6G/evIm9vb2YTCaxs7NT9kI8efIkjo6OYn19Pfb29uLs7KwQYzabxfv37+Ojjz6KZ8+eFUI5dc0isUlqc3OzwtAQw8LlCBzfO5qTI1IGWq6pZcEjquU1MGdEFKVHJAUmwsGAGYhydzqd2N7ejkajUYn0w+R3dw+H9vX7/djZ2anMCWbH8Hh9eA97FCxg/JaMh0ERAsDa+pTcbDRR7hgtg13T046DzwmJiMp+BPOcS71wZM7OzuLk5CRqtVq8ePEiNjY24uLiIjY3N2Nvb68AQI834gHAURrnNUSQiKrCI1ZC0NYR7CwnVtzwBqBqMBjEzs5O3NzcRKvVina7XYAzZRNra2ullG5jYyNGo1E0Gg+HE7LHBgDld+GkAWyIINpQmN9ZF5QLn6FU3NbO/AUfZ9qhQB0lc8QUg25ezkqci+cxFht33s1nWZ4B245kOSOBPiAIwPsBZdAEYMBvuJg/jh1zsB7AALIGdibNU0SaarVaCXYwbjKNgCUbeGjNeo9GowKkxuNxHB4elogVJZqO1KPXyBpAA+Td/OBWufAAtHLWFd3MZVCPXmGP0ubmZvR6veJQr66uRrfbLaWMGxsblbN20JGACAModKkBJmPMmY7sCHns0MXfY2RZO+sCf58/c7TXMmc9ZvAGvwEK7Dh7/Ohi+NfAxTbPc2Ve0IZ7HSjy3PM8PS5HXe1woWt9v/md5ztLwryYo+UiZ6JypNzvh4bwBvLW7/ejVquVbDCgjQ2wxhLQH53qMWWwHlHtVkfQhvlZV+ULfp7NZsUBRy4ZOyVBOTuLLaO8mLF6jIyBMkb2dfFO22R+k+UEPvHaeD7wEn+MAdC7AH54KoNkMIadcY8LefSamq/yPhzLmwNh/Ju1Mz9aBhgfF06bgww8ww6R6WgbaPuX1wk+I3ODTvaYcFThJTClgzm8113MwJdkQB1ggM+YiytzWHPsD+th3rdMOLP+IdePOrAve4YGOWYUlAjKdTgcxsnJSTx//ryUrrx+/bqkDongPHv2LL766qvK5pXJZBJv3rwpaU9q9xB0sgIAI8ZHVNreZ2Z6LgQCUMazLQQGgYBpDJsjW04LQi8LJmUOpKoiFnW8p6enFQNpoOWNkD5ZGwBCehjg4YgB8yXa5HQbTGkGs9F11Cdi0aEFWqL8oCHOiEG6lU+tVivpd2iGAcZYoSi9gdERZ1J2fobrNR89ehTPnj2Ler1e9qpgcG5ubkqJCcYanrWytPPEe1Firu+0083/oRVGBwNJGdT+/n40m82ycT1Hu7a3t+Pk5KRkpKDHYDCoOEwYdoN5omk2Ak535rHa2bYipmTph3SBwTs0tHOVIzzmbfO+dYfLP3DQoR8OD/OxgrZD4Tp0j8EGxntckBfkdlkk0ONHlpyps5Ex6HCAwzRyFIu50NDCKWqCLjZ2rDO8PxwOy7/n80VbyYODg9jd3S360M+khIHshssw4HX4N9MnBxe4DIi9JgbaRCqR74gHQ/n27dsyL+6jOxxOBHPz2htcMAbWnc8ManGmDNLNG9bVnpdBmQ29wbbnnXW/na78OzvnDirxPjv4Wa/atrFJ2rKenRqDskyPiGpraeTJACNHyj0OeMbv99/Q12OyY4Odcz16dgoBSuY//o+D6dJEggFunuKT7lkP+NJraf1o4LtMp1jfESjw2KEDa43DQHR5MpmUZjGufsjBHi72UEQsgpkOvDA2ry189X1OHr/N2Wk/z/oa3Zn1BnwCPyyTF/PnMjvsAAs86ACz7+e58JdlCz3vtYJvAO+Zz7yeliHrBJ6X7Zx5OdtazznbEvOY9Zdxq/koz8fl0b7fWNB0mc/nZb+tsxYZK3g85gVnPT7k+lFdpxzx4gXZe/NgIO7NzU0cHh7Gn/7pn5a6+l//+tfRaDTiyZMnxZA+fvw4Njc3S8Sa/QzHx8fRbrdje3u7bFzh+QYsGHz2iEwmkyLIEIrFNGOxGEQYDFrN5DwLZYCA+9nu+GQFSckWYP/+/r6StoSG4/G4UrJhwwk4ptaf0ql6fbHhm8MQlzFMRHynjSWgyJE5lAnKwgYRbzxvXkKBz2YPddgwIgLEnCKirDHvWl9fLwfT8TuAnME9dMapApzRseP8/Dym02k8evQo9vf3y4nILvuaz+flvkybZYrBtOLdKDcrDe6FD7KQAvYodWk2m3F5eRlnZ2fRbrcLjabTaXQ6nRLxIho3m81KW19On7dCcwbFABwHzuAgjx2AY5qwbjjeplWttjg1NqJaw2ta5CiZ329j5hpr3mnHP2cTkDO+Nwh0ps2pfa9Fo9H4zp4Ej89AjMicZd20s6G1swEgdsDCEbC8AT1Hbg2CAJAeI0aXJhQ0uqCRxNraWjx+/LjoCR+4xHNwNhinwaPXicBK1inIqqNsGUSYJoybs282NzdjNnvI5h0dHZV9cjwLeTCNHFUHnLNmlr/sABugwJdeu2Vj91gyoDDItdOSjbn1t+UqA2wDdPO6bYjHzMU8WVvLOu+Hdtyf52swwm+cUTEdbQtMO/jDuo/7DJwMBDNgQgfm55u2dpD8mctM0K3wCYE5QBg6kX9TT78sIJNplQMp3J/1h/nIzzHgYx+m8dN0Oo2dnZ2SDfUeUNYIubCtMT1yxsFrgd3N/ON/I2Omg3nH4Ng61jxqJ83PNnDmPmc77GRHRCUTzJyzk2unyDrOdPE8nC1D/2U7wvihhWXUfOtsuOmcAxP+w9izbHEts81Zdh0wIGvB/cixq4pcisrv0aH1+mJvsPk8O1/ZgbFt/UPXBzsaBgcQgok4QsZlgFKr1eJ3v/tdKfeZzWbx9u3buL+/L0J1e3sb3W43ut1unJycFOK0Wq24uLgoqVGyHV60VqtVFMp8/lAHDNillGo0GhWjTR27o4pO43kviFO1eNdWlo6G0PY1Ikp5B89h0yJe52QyWbqZmCwNJR3sGXH7183NzULbZrNZ5kyQjekAAPmNSURBVOOuLzbOjrxGRKVrlCNYrHEuTbACoOUqCpN5U9LFHpL5fF6UJBtavWEdujF3NnLTZceC7YiNI6v8rtPpRKPx0Lmr0XhoLLC6uhq7u7slizGZPGx+XV1djYuLi5J5MgiyUcGZskBZUXHfMmPodKkB4mQyiXfv3sXLly9jZ2en4vT0er2yx8lRrvl8XloxMh7WnbQrNHL0B4CI4+ksG/fnch94woYffkVhQTdvsHYWD3oaAGWjB325rLxshJxZQ46gqUsZeTeZnOl0ceK2eTsDNGRtZWWlOO2MLyIqZVWWh2V7JRi/Awnok/v7+7Ifyk4aRppotGnuLlTQez6v7gmxMdrc3CxNBFgzzieicx/zgkdod8llpzsDKXQYa5cdcgzX9wFig6LxeBz9fr+ysZvuJy57c50/wRRkGZ7OYIKxGoCQQbGzxLjhYz6zU81zmXvmheyYGHCbt/1e09s85Vpy86zHwme2wQYcdvh5jwGsnXADQ4NWR6KZr6sWLJPmTztWOfjI86CDs+DMyfqD7H2msfWCx+pn/FDwgg3UzWazlAyyMZuxOKvJ2jtoQabXspKdN9PL9zBv1gN76Q54bBJnDQkqgXnq9Xrl/3ZYTRf0g/EKwQV0lsE1c+DvvHZgAnSPZd/8xLP5zGts+mT8iK4gWO1uoejziEWjGQKb8JTBv8G+wXOWJXBJDhaho8G1Hj/v4GLdHHiIWOA/3mW5g3bwsrFndjjgA9tv61brQdMWPHx9fV3myh903P39fbE7ttPLAgCWRc/RtP2h60cd2AfjeJ9CRFRaYkEYJt5sNksXhW+//TY2NzdjOn1oTfbo0aN49OhRvH37Ni4vL+Pp06fR6/UK+Nra2oqXL1/GP//zP1dKamBAmPHm5ia2t7fLfpCjo6MSDbi6uirGudVqxWw2K4Ak4sEo5Q4OMDWlDBGLjZndbjdWV1dL5JCNb9CIchd79j6sLSKi0+kUZri/v4/hcBhXV1fR6XTKGRJ48syVTYY8B+YGzNdqD5sl2SAPqOdshogo3ZC2trbKc2AwlK7Xz46Po7o8i7IGwBXOhjfVQU8ElnQrYIhnIgB0ZYKxiTrxDDIydOSw83R5eRkfffRRfPbZZ1Gr1aLf78fFxUUMBoPo9/slSwbwcA9yFJB521FU5uJSCAMI/0Ew7cQ5Ldnv9+Pg4CBGo1G0Wq3Y3d0tB/jRUa3X68Xx8XHM5/Po9Xql5Kter5eMGzxCdqNWW6S2cTIcNUKJu753GVhDPkjHGqB6QzDPs4Hn/zgzXFkhEY2u1WrlpF42MXIZSLg8DCDu8zccvYlYlDhQTmSn0UETvvfldzJvg1tHmljfwWBQ+BVd6SgrutLO+Ww2KzXayLnnaOfMAIqOdRGLUhSCNTSS2Nvbi4ODg7LX6OzsrHKODxtnmV/mb0fNcNbN44zJeoLNiY7mITvQk+DJcDiM8Xgc29vbcXt7G1tbW8VRYv7u0Fer1UpJIevAGNyhxmtC1sd8ZKARERV7wlxyFJNx8z4uO+gOvuWIq3WeAYj5iefAu9gHZIiI5bJsu6PLAExngrAXPvvI68gYWSfAHHrDEVVkz8EDdAVj8WZc6ODMnp+Zo6Zs8DawzcDQzoodDRzTDGgBZ6PRqBx6h+xfX1+XtTbQc2kksoz8we92rAwAl2XUarVaxQZCA8s7QUOCEuyl9IZeAgpv374twZW89xTecg09suA1QMa9dwu9mTO+thGMHXtjuYBPWUPWi3E4y1Wv14uThX63vDAeO7MRUQ6sBftwH7bHQTDzFuNC5uyAsWasVd7n6kAn/LGxsVHK9VzlwlrbSXeZPTjDgTvrYAdb7DBZXl1WZV3DfKAJJaqtVqtgLGjtfcLw4PdVJjkoaYfMQZUfun7UHo0skGZOuuO02+0SeXMkZTKZxDfffBOPHz8uxh2Q//HHH8dvf/vbuLu7K5tjx+NxDAaDuLq6iu3t7ej1evHkyZOyURbFsrKyEq1WK7a2tuLq6qrSmcTMDTCnZtoRAMpv2FsAUCfaubW1VZ5/f38fZ2dn5R0wOLX0GXyyMFtbWzGZTKLX6xVjQeeLra2tShcmTkRn7FdXV2WD53A4jMePH8fe3l5hVhQnkRnPgw4uMAZtI3kHB7S45WS/349ut1uJlKyurka73Y6rq6vo9/sxGo3Kqe8osV6vV5w9TllFYFCQ4/G4AH0+J60XUd10e3d3VznnAseG9WMzEzSaTqdxcHAQEREvXryIlZWV+Prrr+Pw8LDUtJ6fn5dT1/HwAUkRUeka5myBjQ7g0ZEbA1jW3VEcA8l+vx/9fr8Y5/X19cIfRFIoJbu8vCygGuBFGQDKFEPTaDRKxzEcztFoVAEtOL7ULlvxoYishA2W4Ec76TYmVu443Sj1XIbnCCMb8wEXNn52PPhuPB7H3d1d2RyPkuedXj8CDi6h9Joxh5xZtPGez+cl6IDx4BoOhxWjYgAZEWUjf6fTKaWT8EhElIPocABpGnF9fR2dTqeMazablXIQ+Ap6IWOcwbOyshJ7e3uxsbFRGnAcHR1Fv98vWZfT09MYDocVx9OBFtYuIkrQALmzUwqvuJwjA3BHxmwP+v1+bG9vF31eq9XKBvHBYBD1+kMnQdqPkpFEj8EfZBzITMF/Dh7laCLjg6/QN8tq8C3DOG7ME6Ps4Ip/5yiyI83Ii0ti3ByEe3weD4E29A4OBvwO33l9bKsNKgyycBS8RnbYDCrm83nlpHWDeUd2TV+ewV5KAxfWA3DGvgPoiu5BlunMhP5xQA/ZQz/QgZHSqclkUmzjxsZGcdjhSZx11hBbA019thWyxzitKwiAmNesN5wZubq6KhUZdmo3NzfLuViAcewhpcM0UeDZrDN0QYeZltjRPH47SbZdPp+G9QQfbWxslPfyHgNeg3SDXwIdzCkH6Ayq+Y35zB290PkeM8+EFx38yo6pHSPoyLrbLtiRAesi2/ze9sP2cj6flwCpcSd2hmc50MA6mC8YN7REdyAf6CNwJGN3W2bWlIwe/DIejyuBGmwjc0Yv8gzma4f6h64PdjRQllxMIg+IqLgVHEQ6Pj6OiIi9vb1Kj/Rf/vKX8U//9E8xm83io48+im+++SbOz8/j5OQkIhanHH/22WeVTg2khzipm8Pf3LqNsVAG4Vo1Mz9MAoOgeBg/LSjH43E58wKabG1tFVBnDxuFAmB0NHhnZyfq9XqJYm9tbcXR0VGJsnMyNKCw0WgUJ+X+/r6UMDUajZLJgDnNoAAHHCNKADg1mPsAtEQGAWgwlFvmjUajSkcshOfo6ChGo1EpgXNUcDqdxvHxcXGGDg4OyrPu7+9jf38/VlZWot/vF57iQD3WAwGE96hxpZxuZ2cndnd3YzablfMFuBx5vry8LPRwG1f4dXNzs9DTgpcBAFF5nG9nRHCk7JxDv36/HysrK9Htdst8d3d3Y3t7Oy4uLmJ1dTU+/vjjePPmTdzf38fh4WFZJ9fs3t/fF0PqTAyZDEflADaA5tns4SBIADDKNI+b7JyBB8YJgJIjHnlPh9P00NRRTytZFKk3O9oY8kyigmTQAF8GRxgPIoEAAGQQuSe7YOfDoMGtT9F1jmhGLDIQ/B+6IaM3NzfFiQVgcvAWKWyXQ9hwXFxclGAGRppxNhqN75xL0+12SzaDdp6U383n8xgMBuWU7vl8Xpx0G3KywRHxHePO5YyYSxBsJxxxt8N+d7c4FHV/f7/MsdVqxXA4jMFgUJwgdO3Z2VlxNuhaCGhkPLzf/3Y02bxmIBIRlU55BusOGBABz2AeHnCAyE41/Jv38BFw8+98P89wGYTL7pw5gE6uMLCDxW+5l/ehG+y4wg9eZ+TH4JBgm7M+ABtH8JknfEw0lWcb+DJGAy/eMZ1OK62MDXLBBozFJSc8hywG+tMg1nqNeVv/u+TL82Mc1kMG6KyhQSJ8ReAO+4Kzs7OzU3FCwRf39w8NHCaTSWnn7+wI77BucCaDoKcdcehHhgFbD4A1HXgOARHLCE6ZHTUHL6ybsTmWV9sHWuC7GQT8DY/YwbKjAB+x/ow/8za8l9eQMaNrLHOTyWIfrXnX9o/1thMCNvD6kMVHTs2r0BPHwPxn59W2woEUnru2tlZwI+uDTEZEyZAbz4P5CFBCM9t5+NyNTn7o+lGbwfG+iJZz+iVeFafNoqAg8tbWVtze3sbbt2/j7du3pUtQp9MpLRn/+I//OH7zm9+UQ9aurq7i/Pw8xuNxrK2txZMnT8pzIQxMRFtIPofBnUqNeGidynkLEJ3vqAsFABEhxROdTCYlczKdTovQQ2gil/yOxTFTY6ihF2eFEGEnend7e1scglarVZT8kydP4vb2No6OjooTwNXpdOLk5KQoKASDyAHM4V7prs9z7X02zmY0Mi6AJoQIOkNbgFNEVWHTchBmRshw0hCok5OTitBGRIkmHh8fF4Nzfn5ehBjQ/Ed/9Efx8uXL+Ld/+7fiLKI4Li4uKlEsRzdZZytZeAnjYQcUYfMaQwf2C8EDKIRGoxFnZ2dxfHwcL168KPwP3T7++ON49+5drK6uxvb2dsk4uVTI4CNH6W1AMsAzCMThYF0cbYMefhdKmHuQPRxwK8hMCz43gEdP4MxAdwCJnQxHZAzsvcmfzyOqneacfbKSZA7Mg3WNWDillmWUN7/j/dyfAQrRXTJHPAuZnM0eSjn29/cjYtGicG1trZw5gR5ADrxGrOvZ2VlcXFyUvQukyTc3N+Ozzz6LTz/9NAaDQcUgMD72+GTg664/1l2Wc/Pzsg5/5h9AJACYd5LVxaiSFaD1bb/frzTgGI/HldIs1omgCO8EjCC76FvW3c5cLoMxIDGQmc/nlW6D8BZ2hmfzPhtiRwTNL1wGIwZq5msCTgBuO7bMhzl4E31eC2yowQp8ik5GDnP0NPME/ycazWeOvvpdk8lDQ5ScAXVQAHlmzPwbW4Kj4MPsHCjEoWc+7MnEUcbJmM0WWXCPEdqBdexMWlb4P3zC5wRAXA5DkAXwi2POvdAawMjepd3d3RgOhxVdYh5FJgz20UN2/BzkMt+gd325BA7amB+hQb1eL+W78DOyAh+TeUKHRVS7DJrvzVvmYQd4zCfWU/49thlaMQ/GxfN4t3mdz6hOcYAryyl04Vn5FPi8v8wBDgeL/J3pzFrUaotT7vN4OKMKXcMfB8LAIawHDrkdf/T3Mtk1frSs5vv/0PXBjgYMxQI6imXGZlERNAbLb77++ut4+vRpbG5uxtnZWfz3//7fo1arxR/90R/F+fl5XF1dRa/Xi83NzcrehtevX8fJyUm0Wq1iSAxYSasyLnvZGDEWdFkU2hFEnuGIQq1Wi/F4XCltclkSqWcWHqbhWc1ms4yFsizAYqfTKQqDORnIr66uxsbGRlGMzWYzzs/PS1ci6O/3oiRdqoEQ0V4WZwSBjagaEuhlAUHJoQzpbR/x4MhhEHBGiQAxt06nU2plEQLeD6hEgIn+UV7DMyi7IELFfe12O7a2tuLRo0dxcnISh4eHpVMTEdqzs7PCM9DfjhD/tuHMESnPx0YlPwd6mu9QAO/fv48nT55EvV6PN2/exO3tbXz88cfx4sWL6Pf7JXWO88x7+/1+7O7uVpzobKSJlGH4cqQZx8NlIHYYXRPsCAr/xvAzb56LU4WhQAk5EmO54DscWBSwjWKOwMKHAGc2BJp3XYKGwwKPOLNiJ8POEXrLqWXm7fnCOy7hYH6sDRs3HclCF5F5shEC/MOzZLGYK4p/c3Mzrq+vS+MMgFSz2Sz7flqtVpycnMR4PC4lF8gjTRccBbWxMtiGRuh26wr+byeUZy2jhYFsRMTZ2Vkp03z//n1p4ECZZcRDs4/BYPAdQOtoK3RDh/M9NgqaWwa5DCrQr5Zll5WRSfcckDs/104X+tFZPDt2XgM+y2O07DoAs2w9zFN2Ciwj1h2WbdM1O43mBX5r55r3mn/4HXS0TcnBJi4cR95jnsGWsVb87X10BHrIzNVqtRK5NY86Qs/aIvvZUXMgx6UsPM92g8/8fNt276+CP8jg1mq1clguWIfgA9kMxnB3d1cy89bdvNtZBGeZ7WQ6I+bvLAN2WBi3HRbPEacePrXezcES3mVeyRkK9vR6PjgUdhKcUcAOWWfZcTU/mvfNb7wn6wOAP+/Kzn7GlrZ3WY5Mc97nkmHG5ACJcYUxkeUQO0N5OMERnAwyyQSEsSHLeN0ll9DLev1Drx+V0bChcZQEYXEKmO8ioqIcjo6O4uzsLHq9XtRqtTg7O4uvv/46Pvnkk9jb24vT09N49OhRvH//vpzyub+/H5eXl3F8fFwUB+k1DDGKxqVaEVHKJiizgphmeICAhcXjdwoWAJZTshGLvSD8BprZOC3b3ARgcU983g+ogOY8g7pmfosSxUEyACIqThcaM2VEdWNuxCK66gi06TObzcqGtUajUZwN6I9hBiD5faS2cRIQaubX6XQqEXOUBgKC0QEowVdra2vx7NmzeP78eWxtbcVvf/vbcjAf69nv9+P09LQSlTK/Qi+DWkc9TLNlBt7Glnv5k8Hsu3fv4vPPP49erxe3t7dxcXER6+vrpYXzcDgsjRFYDyJQl5eX0ev1CojiQhkbUDEHlDPRoQw4zHM5Yur5GXT6yo6VjZaNQKadxw8/++JeeNFRnRzhYX4GYM482eGyTMNTKGjrBzuSNhTcS2TVzrrXG9DDMw1qmJPfk2lqXrR8EiVkjwdrs7GxEfv7+7G3txetVisODw+LHDgD7IYLjqZDvzwHnF2vNePOkcYMuG2kM0AeDAYxGo1KycZ4PC73kTGnUYL1gstceKYzqJknDaQ9VsZinZDBoY35MoCabUcG59YJ/jyvtceY19x/Z33jNcIGL5Nr7MeyZ/F/+DoDIAM07jX9AFjZYYXezvb78+zwOPCBzmJM6GHAl/eqEPiwTfUmZ2cfCfZlEJztQQaxdoiWrSMyxN8Gl/zG68Wzbm5uKq3LiThDX/YebWxslGYPBvLGROixZbxnnof+dkDMR56/sZ6DcOYbrzdrks9D4TvblGW0RMYMkF3e6gCQnQLzLLzk90IH5uSAtHWC9VvWHV4328+cmTD+8hi4Mk61HbB8MCbbCsu/5+85Y+e9YR67wfryfldt5PXMetE2gPd8yPWju06ZSTLz8G97+I7mzmYPpTOvX7+O3d3dcnDZ8fFxbG1txfPnz+P8/LyckA3oIBp9fn5eao95N9kSe6hW2jABwspcUAZEih2pcAQMYUbpET3z+1FGLrXJf5z2zo4GAs8BhgaM1G3SgQLmcG9wHBLez0bsZV4+zJ/LRBijBTWDQIMhtweu1+uV8zl4XjYulG6x8T0DfkfkoRH3RURcXl6Wdq2DwaCUWsxmD917nj59Gs+fP4/19fW4uroq9Z2sGc6rBTVHE3l/NohcVi5WHOb9DDSyjEQ8nMp5dnZWTnefTh/K8dbX1+Pg4CDG43HZ/O0e2dzX7XYrEZ2IapekbODgT58Iz7g8D+ZtpZzv43kGqdwLn1hJ+Z5lgI0LnmT8GCrGzvz4DcEN/86ACKWbU/rZOCxbt6xAc6TT9MEZ9bvNTz6YziUJdhB4tmUVvcW4kP/Ly8uyfwKgQRS01+vFwcFB7OzsVNoisxaDwSAGg8F3omTfx7fZUBsoWR7MP1x2VJH/bDvofvX48eNoNBqlPJHSWjLVBmnoZDYG/xB4j6hm1Fgb5m6eXyYPfI9OMm+ZD7LzwbNsN61HLGc27HYS+M6APzsPWXY97+9bB3jNwTPLhmlh2Ta9/H87EvzO0We+z9kP85dBep6LaW+9ne8FpDsIAAgHrPI8d7izDWL9cvbFa5xpYRp4LfJ65c/R5+xXYl9CxGLPif/PocE8g0yIAyzev2CAmsE4ejPbOut2bOMyx8C878/Rt2SZHNTyPeYx6yHjNvjU7/IzjGHM88YVDorZHkFTr1u20fP5vNjmiO+2ec1ZBj/D6+t3M347cL6Yr7Ebc8kOALbWwVqe77m51NO4NyIqMpHXiPH4/R5D1mk/dP2ojAb1lcuiEgY3GVwgCPP5Q+T91atX8fjx4+j1ejGfz8tJzY8fP47z8/P45ptvYmNjIzY3N+Pi4iKOjo6iXq/HxcVFnJ+fx6NHj4oTMpvNSoo9Iso+EZeHNJvN0qO90WgUI41j4Zr9iAWIZk7cb3CNIM7nD1F6mN7dR0j/eqNVRFQyMRELpTKfP7QyjYhSwxmxOCqeMRH59rkBLu/KUSF7tQA2b4zFkDMvP9drSVcQC2yz2SzZDQ7io5ba2RgAEqCRje58T/07EVqyHQAz03I2m5XSK5zCnZ2d2NnZie3t7fIZXXVw1DjfwyAZPsDhZN7Q5vsiHtnR9vP4PWvClY3n2dlZfPLJJxUF1mw248mTJ3F5eVn2M7H53oZwf3+/0BL68j0OLbKIfEJDR635jYEPBms2m1XkDP6wY85nOWXMxfuWgVkHAax4M8iijWAGPswp711hDeywWzkyXuQbZ94g1NkOGw3rCIIclDZmgEYnL8aDY2EH0dE59ADzowOf14AmGnag19fXYzweR7fbjf39/djf3y/lnawxvHJxcRGnp6cVx4hsp/kGnmI+RBatVwwMDA7Md0SaLVeWk2azGcPhMJ4+fVqMoTfvUxpCmSbPns+rLVsdqTcAZX14l9/Pfc64Z/Dh6CSZqwxGkAcHcPgO3nEZog0692XnIDtkBu0ZFDPeZfzt3yKP8LUd+FySlC/zrEFoDsJ47JZpy8RsNqvQA1n1ZlWXq7BmBjkZvGLbuQ97S3CQ/Qxu5OKxcRnY5UDdMseRZ9gZgtfzmK1fGDN8gyOxvb1deI1mDjTpINgAD+ZuZuxlY3yek50F8ynf2YmHZqY/vIINziA5g250RnZw0LfoLzu8Xg/m5XbXjMv7iew4ME7mkzNR5hfbaNt+3sv6szbO2Hl+rirItgm9QabVNtI4xBUTtukuWcrOmW1u1hGMmQZI0M73Q/PBYFChuR0d5Mwl0OaJZXri+64PdjTsNQMAGTCRNpgJZoWhcgSC0p/7+/vodrtxdHQU//AP/1BKnHZ3d0tpFfVkX3zxRbx//74IFACVd4/H43j//n2srq6WDZarq6ulQ5XrbIkGsOsf4+09FyhFdu17zwZzQRnArOwRQRjm83npamSBM5jh35TQnJ6elvEhqNRlsjfBjhPnQaDYGA+13DAKY93Z2SktT1kbHEiMvJmb/7O/BNCOckPwyK7QspLUNYAA4Z7PH0rEONeCCDuMjEJlAxSCPJ0+HL5I+123J6TLzsHBQWxsbMQ///M/x6tXr8oZKhFRwDqG1VEfCzrCRmcm6GpnOTvajnRhgFGCvM9CCR8Mh8M4PT2N7e3t4oRfXl7GdPrQ8nh/f7+UGpIxosXw4eFhPH/+vNAWBXZ3dxej0agCwNlojhLCGQYoo2htQNjoZ+UGX9g4Mp8MzOErrgxKGIezFBgGDCmgGyfDxg+Zg4/hxWazWTZZsn4GPTb6GJAcweE7l/nZuXRJIR3nkAmCEblm3EYevQVPuvUuhsF6ghIigAYOPRFD3rW9vR17e3uxs7MTjUYjfvvb35Z9Sshtv98v7T+hO8ArOwusOw5DvV4vjtMyx9TRT+hKK++IBQgwULy/v4/RaFQO8HPDBrpyobvYr2RDPB6Py6ZIeJHvcvclG9sMxAyGPDfWDdkwcDVgc5DC9i7zFzzLGOA39E12mpgTtEcWLJc8w+DX2c0so748Lq+5v0NWDYZycAJ6Mw90hZ0OQGqmlcsVDXAMcqE9m+KtNxgHmGJ7ezs6nU6xJ5SLuPzK3Zhwyqwr7HA72OYoNfJpwAxP2+kwgGYO9fpirwZ6hwyF28AToFi2H5RsHlFp74lkrgQRHcTBIYF/rZsJdph/ssMHndgDYOfPXbDMUwan8De/gTbwKFgK2Ye3eH+tVitjRFfyW3RRdvCzbnCHMLcnh8+sr7M8ky1jHVwJYrngWblVuuWKdcNxRHay42En1fJvx9rPxw7yG0rGmSey90PlbThT1h+MwzT/kOuDHQ3AEi8C8E+n00oLMpQcLeiYsNuInpycxNu3b+PZs2flFEwWt9VqRa/Xi3q9XuloRRrx/v4+Li4uKmc/QLzHjx+XyKKFB1DKGJgHjgYbCW9vb2MwGMTOzk50u91C0Igo7di8UdaghlKdXq9XQDLvJLuCkOLkwCTT6TROTk7i5OSktMOkNAgQQknB1tZWHB4exmw2K2dQ7O3tRa320EKXA99s3FdWHs4aabfbpQaav2EsH4xFdwKEwDSez+flrAqAPkxHpywcSd4NrSOiPBtF44jzcDiMZrNZAAeH8OEgtNvtspG82+3G4eFhXF9fx8HBQXS73WI4ut1uEWQiQr///e/jyy+/LGsDwDO4ms1mZZ2tXGx4+QwnGocrYrGR1xEZRx9Q/ii/09PTcmgZziF0Pjg4iF6vV2jnqFXEQ5YLAGkAAY9ubGwU54/IPwaafSvwaFaQzI/uLfwe8A9PE11j/tDSZYcYFitJO13QCvq5ZaQjPv6s2WzGaDSKq6urePz4cXFOHb1x6ZFbFEIHAKLX3qCw0WhUuiRlRwlnOGLhlPFegCOOCp1ueI8j4Kyho7mMlTaKrVaryBPfbW5ulhbXv//972NtbS12dnbKOSoEL7766quYTCalK12/3y9tbV12CY/aYYQf4TE7Cnb8AD+O5rPu0M4ReK89ABDdmSOtGHVHA6070DnYkIhFzTFtb1kTRzuZA2sKuPFzHGiw/uN+9DfyYn1mhwzdZ5oYxOBA5NbSppMDWNnJ9zuxw9xv0OV/o6ddQpfLSHl+o9Eoco7Nt/OOAwyvuyIgO6HuNseY0Ac8E7uMnNqxQWdhW1w6zbjIhsNbliucZssA42SN7Fg6q2GnBJ62TmKcEYsT5x14MS9AB+iOw725uVmyLuAK6A7e4KKs1nzp6DvPhlZeWzdmsH413XMGExoR+LDdgZdxuJh7lk3zJTrHOMtyj3wvA8GsDfQ1L6P33YTBe+LgFRw4dL1xIWPDzvFOxupW5Z6j+TQHmvgc/cFnbhbC+62LwX7oPn5Llhf+9T7g2WzRRpqgGUcYIK+DwaASZPXznVF0UChnpR2w+aHrgx0NFC7CYYAO4EdRk+5mEm5DiRE7PT2Nt2/fluwDxoT6Yg6ou7y8LJsF6atMK9lG4+H8B8Ax90D0TqcT3W43Go1GiR4iEHjJEI5oDIxP157Nzc24uroqRsubnmezWbkPhQBToEjpHjOdTgu4x0nAm6asJyLKhvf5/GHT5tHRUXHkLi8v4+Tk5DulU7PZLHq9Xnz66aeFycjSREQx1hnEoaQQYmdJYGBKpQBEs9msRMytZOgyRPTc0YHpdFrx2qkT54TkTqdTMlR3d3fx6tWr2N7eLqVXTlciDOPxuHQoe/HiRbx48SKePn1anLZHjx4VRcOpzawTbV2d0oQ3qXV37SvvZx4ocJQRz2bvkDdXWUmiyHjnZPJwkNr29nYlC9TpdGJ3dzf++I//OI6Pj+P09LQ4dcfHx9HtduP29jb6/X45f4R3bW9vl70wvAeHA/rC++6XbgXMmjFGK1/4ifXIUVIDUP4PH/q8D8CH0+AYNcAJ/MI64KTjfNJHHieI9tPoGGhtJ4J1YGxuWMA4GQ/Git86yodTSkCEMTqjQetWGyh0JT3acZAJnEQs9nIQZPDzu91u1GoPZwZdXFyUNe31evGTn/wkXrx4EY1GI46Pj+P+/j4+++yzePPmTURUo9cYPcAaDoGdTXgXo2ya4lAik+hdv8fgjIAM99tAoRvZfxURJWtNnX2/34/BYFC6khk4QEv4El1tXc+6GAjAZ+gJDDN1/vAu74L/PEfkjnnymZ2ZlZVF6/fsMNmB4fc5+wb/GJDwPfzowAhOH7TkucgctLHu87y4x3N0eSL2yZkU5NuOlP+wpvyb5gTMw9kn7sPWUoZLtoR1tn1jDtgRgnIEQvmMQ1vRbfyxvjK4zg4XtIRerFF2vC0/pivvIRBoh5d3EvxBBqj0wMkk8z0YDCqVB9APwIk+yrxK5sM8a4CPzKBz5/NF6WC9Xi/7RXEGWAOekwMvDtChW+0AsQbZBtiuGAPwPQEYR/t5Fy2QHei1g8PzwI+8I2cQ7IRmu8+8J5NJZa8ua41dur+/L7YP3GHdgB6yY+L3+t0825klSvvtTDYai8OQkRWvGVgGHnJQBX6KWJSOmqfgOXTxh1w/6mRwR+D5P4bQoMrnLwBwEGwuQPPNzU0cHBzExcVF/Lf/9t/i3bt38Wd/9mfx8ccfxyeffBJffvllXF5eVg62u76+Lm0QIZ7BO32QDR6pRcNwQGzA7s7OTsVotdvtch/pO5SEW8rxh3Z6MIcZDwB+c3MTnU6nUmuMAt3a2orj4+PKAWkwD0y9tbVVHATvKwF0Xl9flw3ElM7YIHQ6neIowPhuFYyDsb6+XlqroqQddQUYIHSse64tRAARKtpUTqfT4kwOh8PyDKdBGQu8x8F9COHV1VWsra3F9vZ27O/vx87OTqlLv7t7ONwL48XvMJgIP7X/jqo5IuUIPONypMaALGKREYEeKOwcCXdUbzgclpPYqbX/l3/5l5jP5/Hpp5/G8+fPY3d3t2TxAKl2CvKp1WT+MFIAKCIa0BXQc39/Xz6zgcDIe42hB0rJNcNWkpZ3ouwGmDhlAHwDHPMVihIe5Nnwv0EmEUBowXtYR4M8R6Z4R3YsDSac0WAudgC4x3QDHF5eXka73S6RM+hUr9dLCQcXzj4XDqYjxdBrdXW1yM8XX3wR7XY7Op1OPHnyJFZWVuLbb7+tHPaHPCKT8AD8ix6Fx+fzeWxubpboJbQw+AUkGhhHVCNelqMc4bOc8Nz5fF4O1tzZ2SmnnDvKinOEI4JeynYGnnaW0mUdjA9dSKDIYMF0cglVznTkFsp85/0AdhJwXHiPsyd+xrKAheXJvJmBbnYkmLODCzggBEiweYyBMQOUGDO2HUfZQNVZBGTGeoPMNnNxtyXzo3UCAJbApQM9gFXsXLfbjel0cagrMsU7DGr9DCLYtA7NtMfRsr3wemSH3b/z37yTv6HrcDiMra2tktU/OTkpeIfOU9Aw604wEHaISgDeadvDGjrYyHOgDfNh3PCJ1zbrA/O373N5kbPJzpRBK2TQDr/piFyxN9d6Bzl1IIX5oZNdBmWnEtlATi3Dvg8+xta7S6f1itcmBxktrw7C8wd5dZDAfGPn2GOFvpy/5o5k6CfWkN8xFgeOGLfxvjN5zAf9+IeuD3Y0DAqy8MFYMJMZIqLafxcDeX9/H6enp3F8fFwOmUOJvH37NjY3N+Mv/uIv4ttvv42zs7O4urr6TgtJlOlk8nCYXsSDE0DZCExAveN8Pq8wMWC50+l8B+wAooggzGazkm4lvRmxqB93xAyaQB8ztrtHORqwu7tbGGw+n5caRTM75RHffvtteS5znc/npdsUB3RhnO1QEZFwDf8yRcnnCBggxKDLgMiCiiJ0FMMeOg7QaDQqdbX39w+nXOMM0SDAEYPZbBanp6elfCriwfna29uLJ0+exNOnT8sZFLQJxrHq9/uFbxi/U62sPZkcR2IMgOATeITP+T9Gzc6k0402DrXaQ2bm4uIidnZ2iiJsNBpxcnISEREHBwfxxRdfxGAwiLdv35Y1cCmO3886bWxslLNOrBhwVlhfwBXrAngASBgUEhHiGTw3YqEA4aEcmfKYTW+foIvSgu6MifcCnDBMbPZ3FMY1p/wOI2AAScQOp8wlFjbkWd/kMgmUu5UxwQECLpRCOKOLcWKP2uXlZQXcAZgZBwCDKC/64/Lysuij3d3deP78eezs7JTSy7W1tej3+zGbzb6TWUaHoI+dSeJaFrHKjgJA34EXG6bV1dWS7V4G3ur1eoxGo7JZ104rQZRHjx7FYDAoAQR430beOgqZ4DPGy+cOHsBvdPDyHLnME15vzycf0slziACS9bUcOOqJs5MNveXcoNL/tt2B9tkxsf5x1hWdaKeKd9uGe19Kfi8OjYEZcsM92B/LlHGC7bnthZ9FBz7e78359frDHkUCLF4D7zvIGMYOm3GFbZkBr8/jsKNgB8YdIm1zTB/bFe67ubmJ4XBYQKz3XNZqtdLw5P3795X3Gsxar7JW6E7PkfFZNuAd9KbHZr3NPKAx9/C3aWhdbGfauti8y3raSWDcvINyQ/aAmUfspNupdgaOwID3mPAdawvfWB6yo4iOhs6Wf9sK5sezzf/woYM5xlA81w4KtKWcDrvAOlmmmRd0ZH3JstomQAc7mbwv4xictw+5PtjRYOH4O2IBKn3Z+zTx/BsWiJIhmJpSobW1tXj+/Hl8/PHH8Ytf/CK+/fbbeP36dWGu09PTygnUEM6tOxEiWsdhkCIe6ttHo1EpV3BXF7IBk8mkOCI4KUQXvcAoWG8ustHl8kITaccxw4nY29srG8qJQOZIOwDy9va2RDM3NzdLlJ96foxeq9WqtPhzRMEKF8Z2VoL/A1YdmWNtDUxsxDY2NiqKIuK7J14CKoiwEKFAQFhLANbV1VXZyDqdTqPX68XTp0/jk08+iWfPnkWj8bC3hU2jKNPLy8vKAYHMy4rWguv0Yo7W2HhkJ8Pef0TVweb+DArIJhgETqfTcrDg06dPyx+ivKwH5YuAZkcm4GmcOPeNZzzeY8N6wbtZufI+G2sUDQbJCpv5opCtQ5Z9hm4B7GB8rFihOwqckkKfUGvlnR2NDNysJM0L8KppAV2958Bg1BEpR6681vyNsif71O12C4/CL5ubmxUjYWDnBhaz2awiB2Rm7egyXmeeuLxmWb75ncsiuPg3a8wYkTvTJoNgf8a/2Qjr7BzGEMDVbrfLaeLevE7k07zkNTGwWuY4ORLoC15jTWywWWcHX/Jc4V/bpOz04IjYceE5Bk8e07IL+psGeQ0y7T1Wg27G5+is7XrmBe77viyMgSK87zny3FzmY+BkmeHdWUfTqbLb7VY2gntPFE4dDqjpwBid8cu0d1DOa5Sd6Oywm45cBm/QCPsJwJzNZqWpydbWVrTb7XK+UnaOvLmd+aDHzJO23YzTepu1MO96LZmfnT7zrzPlGbxyWa+YP83DdnZtX3iHM8TmF8tnXrOIKFgtr4/5lPVijNm+ZydhGa/kv/38LJd2qH7I+fX7bGuyzIEvkU3TNI+R3/PuvGZ+57KAx4dcPyqj4X97MSBUngCDiYjKAgFiJ5NJDAaDUq/5/v37uLu7i1arVUoIXr58GT/5yU/i5OSkKGQON/MeDJcxRERhJEApkbt6vV45KZeIAeMFTMxmD5kMH0ZH7V/EoqOV3+lSHRjD3+FA5LQYi0UZAOMHHEI7nxj6/v37ygZ3R/6tDHgPa+Soqh0EShEYK5873YjiyAbFzpCNPMqe+VI+RhkUNfYoO8p7NjY2Km0ribpTPgcf7e3tlTbJtVotTk5OCj9lcG8hgzY834Lkek5oYF62keRzC2c2nll5ZSACcLQDS80lNaitViuePXsWZ2dn8c0331T2nrhUjsvRFdPPWTtKqHino12eKwDNTjYKHofGc7VhMu9lkJmBEb93ZJoxw0vIoMeMU8m7vM7s3+CP18hGzmNwJNgRITvdyIjl204MsuVOPY7iNZvNUpI0mz00L2CtcKqRR4MWnP3Ly8tKbfLBwUFpaUtghX0s3qTts25y6Qd6yCU1AAjrqAw8LGcZUOR1zjJjI4sOJ7IPCDIvE612uaN50WvHZRCSnSA7IJlX8zhzgIDP7GgYGFvWDYz53sDf2bFl7/Y7M0ixjfVvDdyyTOborHn3+5wQwJ35G7CXn5d1ouUKWrgkh8uylMElnxM4yaAvIipZeppWuMGEwZF1XQZyLnc1bbgysM7fQSPrlUzrTO/v+z+8jhy41JqsPXPAWUfeDFIZs7EA78t0JvDJZWcxotrQIvOW+c7Zc88bPc3vXEHA+LxOriIw4GWtcibAzhbvMu+bNnb0PFee54Ckv4eXbd8z7nVAis+tR7ks19lR8j4XOyQ8Az2YcRf6ED3KHJxNWxYU4BnZJprPrFeWBW6WXT96jwZCmKNFy6J4EYvUvIXbDN3v96Pf70ev1yudR/r9fgyHw2i1WnFwcBB//Md/HP/yL/8S/X6/lFJQ30/HpUajUTb9AV7JZKAkifrjia+vr5cOFfP54tA7R8N9miIRx9FoVGkZmkE7xGcsKEeicE6Rc7HgOD9ENYkcXF9fx3g8jvl8XtLCdE6KiEqtOQzqTXWAzdw1y0CcOc7n80o5BIzqeXKZKVE+/N8by+r1etmkSRlXs9msbNgzmMO5o+zt5uamdElAkezt7ZVa3JOTkxiNRgWMGRzCG9AIAXFpUHYgoIEdaCtDR9yzkWg2mwUko3CWGXueDTCcTh823UIbnKaDg4P46KOPYjgclgYJnK8xGAyKw0t2j98bXNvIkhlkjdhsCNh1lo7Nse6cxNhd6pWBNnyYHQl0gktasjEnW8jvvGEc55v32GFER/GHTfAEGOBxwD6pc/7v/VAGUi75gJboNGQ2K20cSDYCumyLsaGPms1m6Y7mTArrOJs9lP+xEZ79OvAo3fvomsd6cTAfTTTgMRsM6/YMFlifvAkZGbCxxqgZkNg2ZOcdvWkHhEMqs01Bj29ublY60pnPoLNlzUCfe5194zsu+CFH9lhTAjrZ8TCv58sOETybnR2cS/QxtLZOyQDL37EWuV7fF0DVssK6u0yJ9chr7ediL53FM+jgmZ4rYyBD5fFkB4XfZhzB39gUOyOMr9FolFbT0IXn2d6xhp4DtLSMWC689gaaHgNygBzbZtrJ9PqYZ8FYliPmcXd3Vw433t3dLdl97p1Op6Us2uVtnid4IuvLDNDtNNtJRNZ4r/nGWXUu9KPpxTq4NfsyXs822n/AYtho623bbOMT6IyeZS7LbJCdtwzGyag7g5T1gWXN+IJ5WgdYtrIc8kzvcfHnzpzVarWCH8HhBJ7gf2ymnSxwQJ6/aQidLCvo7w+5ftSBfQZJ2cPyBtcc1YLoGGeYjn0SFxcXsbq6Gh9//HGsra3F8fFx9Pv9ePnyZfz7f//v4+OPP47/+l//a2n3WKs97LR/9+5d9Hq9ePz4cQHn9FXHsJKGhDkBaRwGlbulEAFB+QPs3asaRgOMsQhkK2yAcX46nU4Mh8OKUPFvOjERscNRMnAHeJP92d7eLuUEpIzn83m8f/8+Go1G9Hq90i7SrW6Hw2EBZ6wbtGDutFU1IwG2yBwRQXcEiU47bPAC1AOCcfCIzuP4IEA4HWRqAFZklk5OTso67O7uxpMnT+L58+fx5MmTApbfvXtX9jdERDlRnnpEStOWpW0jqiU0rDcKB36wUDoamhWmDZvlByUAD1xfX0e/34/RaFRA52w2K3W4z549i08++SRWV1fjV7/6VXFytra2ot/vR7vdLpu+DQbcVc2KA8d1bW2tdDHKDgHGx+3wMLREvPjefcJtOOg1bkVvw8DGbWchcXaIbANobMiZC/Ntt9sVoLFsT0bEwmHB6WT9+I0VvUsIyabYGBlEZB0yn89LVgrHzoaF51DyOBwOS1DCG2yRGeTr5uYmjo6Oyun20+lDG+THjx/HF198ES9evIiIiPPz8xgOh3F+fl4cSdbH5TvZAc3fG1iZNvy20Xjo7AMtDUxsOAmuuOzQTiROmY0sdAAY3d3dxePHj8s5Mv1+vwJKr6+viy5jDAZN1l2+bMjR/y47yCVy6M5lF7Ju+YaX4LsMAA067GyY7gRMsrPH99g3dJPlNI/fYDevJXNzhzA7ROhv1gg6mJ989o5xAjrJoNX3sY7QifXnPr4ne7VsbwabqNE72H5H76kUsHNkh80NIHg244cudp6gocGk+caOHOPN9iAD7OFwGKPRKHq9XhkTcnlzcxP7+/vxs5/9rARpGTvymx0ZVyP4M/jQOs3BI+hkx5o54TTm7pOmpbOj5mV3jrPcQDP23xH8Yd38fjtA6HDLAO3ZLYMeI/RgTPAePI0uiIgSVIOPCNQhM27Ba1zsAIXHbptkx96y5D92Uni+sxToW9s/N0lyKTjyze89LssCNDMvZcfbzY/+0PXBjgYAwVFqJk69NAMw48EIKD0rOjz0L7/8Ml6+fBmz2UO50ieffBIvXryIjz76KHZ2duL+/j7+03/6T/Hu3bsyweFwGO/evYutra04ODgoxg4jBCOQtZjP5/H27dsYj8fR6/VKF5jZbFbO3nBHC4w+3rsJT4cpR0BRBmxqZmHm84cNXqenp5XuChg+t6nsdDrx/v372N/fL2lgQEq73S6HWs1ms7LRF2WB4d3a2ip/IqLUc19fX8dwOCzOVrO5ONEbRb6zsxMRUcbqGnaAJQAJJneZEnsoLi8vS99mgBh7RQA9u7u7cXNzUzJMZKDq9Xrp6gXNT09Pi0JdWVkpJwl3Op2o1Wol2n52dhZffvllBaSQ+cIRcplCRHXjIwrCxs4OBorFCiQb04go+0Hu7u4qLYhRcs5C8Z03hRMt+eyzz0rb3s3NzXj69Gn8+Z//efzd3/1dOUX24uIiBoNBbG9vF4UZsYic4qAiw/P5vGSGcFZtPF1mxNpaiRmMEmHOhg3ed9oZ4wQdibrh3JLl4l4rTEcfDR69eZXxOwiAIkS5Xl5elm4y6K2IKIdguoMRRmhzc7PwAvPBecKRdkcoMkDwNqWZOBNkQiOiRJ42Nzcr7bcB8Oy1ur+/j3a7XXQskanV1dX44osvSvkcGeLXr1/H6elpdDqd0srz/Pw8+v1+xZF2Nyb0oEEBmy0xVoAE1oh7+L2jowAZgi80t0BuuHCGCa4AMLgX3YHunEwm0W63o91ul8wO0T6iuYCWDEDga0cF4VPGZKMNr9sRQ6bQ/+h97J2jm/A6TgTPgY8dhbVNsPHPGTaeZUBiEDibLTJhgBcCXoBlyyi/gz7Yaet/gzzky86WdbWzVn4HDqNLLU0D/s96mlZ2iuEBzx15sYM0ny9KjZFbaMe6wNfQF5rSpc9OYMSiG5L1m/Ue2UMygOYxaJKDXHkvGvff3t6Wdvg4j+vr6/Hs2bPSWQqn22WxBCN3d3eLrgL0M3/WCt5gDR0scyWK6eNSJXCVf+c14ndcdpQ55BRMYfrP5/MS+KQ5i/lhY2MjxuPxd/gep9ufI6vwIPNFdlkvg3r4cD5fZGMM+LOtcyDAa4i+ZHx20uEJnDnrrSwbDvpmebHD7XtxJODznJmy7vOY7IzgTPJ/aAhPIksfcn2wo4GSyB69HYgcJWHSjii4TAmGPD09LSCr3+8XELK1tVUE6a//+q/jv/yX/xJv376NiChdmvr9fhwdHcVnn31WDF+9Xi8Gut/vl3dS787zYTZarg6Hw9jd3S1OEAtiUD6ZTMoeEgTbUcPxeFzAKQxABLjb7VYEitp3nCQADSVSKD3Okuh2u8XZcunCbPbQ0m9nZ6eAGIQO4M9cqPHc2NgoAIDoAt9vb28X5rPicQrUmRvKmngP90Mfyp+IBBB939raik6nU5QjWR26UaEgr66u4v3799Hr9eLs7CzW1tbi5cuXMRwO43/+z/8ZL168iF/84hexv78fT548iS+//LI4Q8fHxyV7YkC0zFufzx9OenZXpWxgUUI2UqaRI7rwtw06GQxAP/QcDodxfHwcL168KIbx9va2ZOvu7+9ja2srfvGLX8Svf/3rePv2bUwmk9jf34/xeBxv376NWq0Wu7u733FskD8i+WRObKhRSCgp7ncpgKNZLtMwH1qBQescRY2Isj/B+zH4nmdDOwCPDT+0xwDPZrOSmeOZVsoofBtBxoBzgZOwurpaAhQ8B+OPY2DHxm1n7+7uCr9HRGkpjMxyP9FXMpHWJdDePLu6uhrv3r0r8nl+fl6cz1//+tdxdHQUf/M3fxMfffRR7O/vlwwg3cz+z//5PyUAYx1t3s8pdTKA6DAHkhw0cvTWPODyF7/PwNEZqkajUfbOAUZw2vf29soatFqt2NnZKZkOeBd9SjQPHvIawl92uNCX7r7lsxfswMNz2JqIRWTP7+PZBpeWL8sCdHCm2dFheIvnee8Pz2ScBDgM+A2W8kbonCEhc5ABskEKl59hfeosCvS1Q4cjlPfVOHDmEkf4DweI31um19bW4vr6upSabm1tRa/XK2V3PBMb5UBpxi7oO/MHc/T3ppfLB73WjnLbSUd24DFoiz7t9/tlDyMZAAJ1OI6PHz8uYJ3nEEgCP1GubF4zrcm8Mj8DSAJl2DvsMf/3XMgQ8D38aH6CZ3gumQcHN1gj84H1S0SU84lWV1crwTB0K5gEmvhQYWiErmW9oIszXgQmrCMsGw5eQAP4nPm6IU4OLJhedu753iVhduh4tqsJTHPkgrJy789gbJQXWj6RN2dXHGxwUJbvs9P1fdcHOxosCt2QGDyfs8gwRmYOFsQtxfDG+v1+fPPNN9HtdqPX68VgMIh//Md/jH6/H7/85S/j4OAg7u7u4q/+6q/i6OgoBoNBASlEq7e2tkpEHkCAMzOfz+Po6Kh4tDC6hQEgd3Z2VsqN6vV6aU3JYVIwVb1ej06nU4lOYJRdbmIDggA4WtXpdEq0lff4hFqyLZyzQFkY7wAQ1Wq1cqgeCpwoMc7hfD4vZReuTWScCCGGKQMIHzKEo4Hyu7q6KkoEPrBBJcWI0XakJOJBIfR6vUqamEyZAdfNzU3s7OzExx9/HF988UWJVlxfX8f//b//t7TynE6ncX5+Hm/fvo2Li4si1AbNBg8ID+vjdLcVS1YknoP5HaWTI3/M35FY6EP2pd1ux97eXhwfH8f//t//Oz7//PPY3d2NlZWV2Nvbi88//zzev38fFxcXsb29HdPptBgXIsAeky87BKyh7/H/fVp8dpYc+cFAGXTaiSOKmdcdvmP+GH/0C3yEkuVvxsUaWJHD+ziLyDZRP4NpK2AuxsMmczKbpoEjc9BpPp+X7jDoOwyFjbL1AO834IKO6CrmRckjMjCZTOLx48fx85//PP72b/+2BFbevn0bZ2dnpWPe1tZWvH//vtJEAL7IpQfoKta4VquVLlDehGtaAFz9O/OZy3oM9s0vNrw4C9Zrl5eX5XBXgi40QKA81gAjRw6t27zWdrJxtBgbfIhOpxTUG+ptD3ieM3+WJ2wAa+vIYJZT/5s18j4jO7mOnONEA+jt8CEHBrfIAO9D5uB3xmK+zWVVywJRdmgYs3Uf/GGegmYAJ5cA8YegE2vMvNbW1kr5KO/EwXe7YEpGHeBBd0UsDuN1dN7r7LE4QGFgliPb2ZmGH+E9v9PODhlRHAaCce/evYvd3d3CW5QuukwIOmEbbcOdDXIQGGwGoLSzZGfbzWd4psv9PG94z04zdhibbtqYzug+qhu8qd+YE72H/TBmQf+5gQzjsFNofrAMsybZafY9lkfmbtDvsjXLEvxup4G5MRYHKrMOcyDB7enZr4kzYR2fdQ/6x1kg7J838tvu8X7P90OuD3Y08AwdCYBxTQATHmYD4FnxWVFGRLx69Sq++OKL2NvbK97Wzc1N9Pv9OD8/j7//+7+Pbrcbf/RHfxT/9m//FrPZQ/kQUe4vv/wyfvGLX0REFGMEA47H43Jmx9OnT0u5B2UMeJU+FRrDggFDudrJms1mJWWLUsQomB4Aievr64pSazQeTvrtdDol6u5SFpiO1pcoYQTSJz0C+mEeTlQHcBB1YywoXZh0Y2MjOp3Odw4MRDBQqpSSUR8fsdiUy/1EXIbDYQF/MC/ORN50Pp1Oi3Jlj0az2fxONiQi4sWLF/H8+fP4/PPP4/DwMF6/fh39fj+urq5KGdrq6mrZ6wPPwncYQTsCOUsHT1vB+H6+87wtBzbIdjYiFiV4GZSTmTg4OCjlIkQAqbn/3e9+F51OJ16+fBlffvllTCaTQtPxeBwnJydlYzElCDin0Pfq6iparVZRzI7K+HK9OnNGIVl5ZUNJFNUGKadtoQ3rzCZfl2nY0DmDhKLn+1zuxPqjAyjNsrEDYBBUYBxOWUMTd5iyUUVPGFii4xwhd8kVvEFmC31FxjRise+HXvwOECBbjUYjnj17Fj/96U/jZz/7WfzTP/1TnJycxHA4LPqCMX799deVzC7jxJlhvK7jdjTNZQQ5gmUAisGyQc7r4sjesmAVsmGnEHqjr8ieUtbm841c6gpfYFjhGwdA0DsGHF5DgJHrnc1HtnPM2frEYIXL4NMOeXY2bUdZB3iU9UK/OEuBXHocyApOiMfnjAm093xdNhERlf8TeCHI5DXEmciRfztXHoPfz28NxiIWbdct55QKd7vd6Pf7pfzZwS6AOs9k/nY+M80MTj1/1sqZET7jWfCaeYLxEnU2f0MPz+3q6qpUTrgbJVmI9+/fFz1jG7yxsVGcZLCOAbjn47laRpiXATPfeU3m80XAGN4laJtlwQE+7Dn8a51hnQqedLYAHkbWjSN5PmN3OW62xwb7doasq+yI8R3PsH7IzjfOUdYT0MzZADv9Hh9ynYNOyJ8dDqpEnIGIWJRN2vkCx+dABGOzvOWAAuPiPh+o+0PXj+o6BeGtGCC2vUkr3uw5Y5RYNBbk7OyssgHq7u4uLi4u4uzsrJSDNBqN+Pzzz+Pt27dxcnISd3d3Rcm8ffs2dnd34+DgoGQfIqI4K6TUIRQAjkWGaZkXKT0iut5ISlQRZch+iKw0WDxog7NhGgJsut1uXFxcFABIGng0GpVyCWrIURKZ1gZ47rhlpie6C7jK6TOis1wIuetOUZQwoDfeLVNKRCNms1kpj3IKPGcPYN7b29vCF/P5PMbjcWxubpbyIuraAZIIKpHH8/PzCg2saFkjAwAbbbIpjM3KBSEzWLDxstFknU07DEIex2w2i7Ozs7i8vIxer1dK+3LLX+TgzZs3pRsPIPDs7KxE94hQ4ti5hS78YuNo0GNDnhUjnxPhMmj3bwEgjuhnucc4uRzEMmmZA4wbwJrOEYuNv/ydy2QwLjx7MpkUAEIjBnjSe7DyO8isAGThB+7nsrOA7DUajfIudKOdJ2qT6/V6Wbfj4+OKo9btduPg4CAmk0mRD0Az2VhKj7799ttKWV+OzJoHLAumpfnc9yz7LEf2bcRzFsDRTHgDeXbrcUp384bcVqtV9t3kjIGDOubF7CzxfoM8j9fz4DJ4cEmCAU+eO1fOeBi0ODtocOVx5efxTOty6y3+zXsMurjf9PH4WBPrNfjBJSe+D3nNNLMs+3mWY//fnXZYc9YV3UEDAHiYe5Y51d5j4Whx5uc8JoPNvA4OSpgfPGeeYRuRAzsGuYzB+ymtH9lbgNPCH4IJ8BB2FyCe5/N9DjPfO/tvwMl6ZMfIztgy4O3PjRWtXyw7rCn/hua5yUn+nf9vXkcukJXMz+Zh9HW+4HuCruZ9ZAs7Y9zAb7nX43ImEDoyLtsW60jT0U5ttuW2vQTvjYmQBcurZdvZGF92jj7k+mBHw5GRZS82Ae3xOIKUFZiv6+vrePfuXezv7xeF5INqvvjii7Lf4De/+U2lS0+n04nT09OyOdybN2GGzc3N4sQYGNkQUB/carUqShHwbUawR1ir1UqJk+mCkLrGkrMiqE/EgQEYYNwB7qRAd3Z2Kp62I4CMCQBEVoDxZ4Bs8IXg8Rwik2ZcnDI+g67wAR6yI37z+WJzIcw9m83KRjVOOs4nOWNYUZyUxjGWvb29+MlPfhLNZjMODw8rIANA0Wg8nMmC4rXzxxrl9XR0gT92TJbdn8FuvsdKz98hE1bSvOfq6irOzs5if3+/rAX8zH4WTk3/1a9+Fd9++22MRqPodDqlS9G7d+/iyZMnZT3hJWSQRgLe4Iz8wifT6bQAe5QZlwEUTiXPyiDKfJefgZPiyBrpfhsTnFSPz3pmWcTFDrKzTuazHC1j/U0bG4qIRUkHG7tNCxtsy4rLQNzVjhIInpvHjUNPSR3gaHV1tRxoenV1Fd98801Za4wQ5WpHR0dxenpa6Gv62/GyEbSs2EhnZwRa2nDboTbvZLnIspWz4uiMdrtd+U2z2SwZa5fYmcd5j0sD4bEcoYQvWHvma7BiHs6AwEDYPJblye/MuiCD0Uw/y5PBgPWHwUZ23rLzk4EQOtfv9u8cTeX/9Xq9AF1Hivm/+cjjMVhnzM7gGAD6Yj2dbV5ZWSnZwIgo+y+cteE9yB10Y/4OQmT7bX6FP3JwIzsNWQ/kZ2XnxnM1AI9YlO6RzfAarK+vx/7+fuH7RqNR2t2CmQiUgjNYJ+7HhtmZ8By4F5o5SOfL3znAaB5gHfgMHiKo7HtZv+yk2DYhA8uCJJZZ6OXgB3PDbtm5YC6AbOa3zGkyb1n++JzfQtNMv2UOJ/ey/pZ16Mbny96R+ZmSxnq9XgLMxncR1fODMh7Jes86Cr36IdePam8b8d20Ih50XvBlniYbiyA6yo19H1999VV89NFH0W63i7GPeHBCDg4O4k/+5E/i66+/jp/97GdxdnYW3377bZyfn8fOzk4cHBzEyclJyXCwVwDHAWWGh090GIUV8bCRm048LtVBYHNkiUXKtc9EH/jjsgIEazwel5o6xuGU8srKSqXeDiBPFJn9DkRdqVPm5Fw2eSNsGGOihERMIhZtPyMWtfJ0zsKYkyWp1WplXwn09GYmMgFWGAgHY6KjDvOaTCal0wflJNfX13F6elrGOJs9dAB6/PhxfP7559Fut+P169el8ww0aTQe9qa8fv26nJxug+zNo45WW9FZ8WXB5VqmNCLiO0aXZ1gZI9gG4jyDOvuPP/641Mcz3pWVlfj4449jdXU1vvnmm3j58mUcHx8XZ3R7eztWV1fj9PS08FXEorsRa8L6w8+uyYb29Xq9lMdl4AUPGrBAe+7J5U75gq+sMO38u21yLsUw7f0u1oXP2MsEOPG70DnwARsn6SjnpgqsGfM2IGJMzAn96HIzDIXrt9EtGE3Pyw57vV6Pk5OTmM/npb6cfTo//elPIyLi6Ogo6vV6kRVHr7788svKoYaZTsgw6wpNlznQliOeYSdiNptVWh0bwHJvjmZn3jJ/UOLHc9AvPqByNBpFq9WK8/PzEgyxkUX38Z2zbgbajqpa5lkjy4G72sFb3gyb6WZaoMMdILKRN0/CK/BGlkWvFeUodlh4RnYuDLCy48V44VV+Dy1Zd2hoEGQeXxYNZl7MEzoYyBJUsWwwLkqi+GxjYyO2t7dLkxUCk+5Gh6yxaZhxLqvbZ22zs+m/DdA8rwwkzTu803T7Pr3IvdDbOsWOZ72+2CNKRnZra6uUHZMxRWc4sMhY4APLpkEntDBAN3/Cdw4ww8d28synBvTmYfMTtHdGxYFMfmM5zaCdcSzjwxxY8zitI+bzeaXKxbJgvcG4bO9z4Mz8ZBvlZ1q/Ir8us+bZWWbJ9NqByvKcnVzT2jjRTloOXjiIwBrmDP73XT+qdIoXO4KHUNgQOSphpiQKAhAHBEC4w8PD+Oqrr+Lly5fR6/Xi7u4ufvvb35aoNx0Wnjx5Ei9evIh+v1/aw+Ldv3v3Lp4/f14A487OTjk3Yzqdxv7+fiUlZQJubW3F3t5eiRyyOcr9wwEiXkjKgCIWmxwRQuY2nU5LxAF6oRgBlHd3d+VcA/YloHzpvkLkgr7x0Pvq6ip2dnZiPp+XjaN21qjrZEz8gVnYk0J01zV+l5eXcX5+XspLcASJhsxmDxvFT09Pi3J0bSFOBuUQOBSMjVSelRb7aty1Z2trKz777LNSg1qr1WI0GsXZ2VnM5/PScej09DR+//vfF57NqVkUDMAI/oTPXVaDQoCXWU9obwMD2OCZXN53YKcGfkHoUZ6Hh4fx/v37kpV4//59KadqNpvR6/VifX09nj9/Hq9fv45Xr16VCDBlfGdnZ7G9vV2AFiAahxOH31kIgwm35c1jBnCTcuW5NF+YTqsHkLGuOd2aozLwEU4z+4pcSsX7+J0PerSjQzmfI4Cu/2dNcGSgD84OOsDnAsDDjBta47Tkkit+x3vJMqIfb25uysZ9O3wYbfaNvXnzpuiBzc3N2Nvbi8ePH5es4P39fTmwkvWeTB66+R0eHlb2XhgIALic+UV/2TD6M5cwEDyyfOG08B7TnHUzP9nxMmjFoaND3mz20JmN/UbwF130KBXJgByw4fWxoYef4TFHQJFZ5t9sLpqEWA/gaOTsKc/KjrAdSttL20wDJ+TMssRawDO5VAxdxljI/EQs9gkwTwcIcycofs+zXQUAXbGB5g3GC+1czgRtcoYE0Jvpgf1jcysOwebmZmxubn7nvB6aohAshGa5XStzM62gk3WhwZcBf8ZBzAmdZFDuQCfdhew0WKbgFzDCxsZGtFqtEqSkAUK9Xo9Wq1X4jkYgg8GglKHXarWCX9zNLW9kZ62Ym0ubTCNwlduxZtAPDVqtVvnOdtTBH3ATsmH5YewuHTOIhhcISiFPzkRYJn0POs+2ie/z/hTmboBvLGw+duDAgQXrPOyiM93IpQMODmLbscnZHjs5tpOsNVUlNAZiXswfOjFu3udgmbP+dl7+n+/RQMkZQJlAMI3LcLzwLMz9/X0pOSCtA6jf2dmJi4uLeP78eczn89Ju8ptvvinv/ou/+It4+fJlrK+vx8XFRZycnMT5+XlMJpPSdnI2m8X+/n45K8P9nl0CZAZyi0qi85RQNZvN75xDsbGxUdrPsZEaxeOIMGnd4XAYEdVDw4jqomgwmHQ28UXaKyJKSZB/j1JFMSOo0H5zc7NEPOjc1el0ygFHMIwNUqPRKJvKYSzOKSGKxLvX1taK0t/Y2CgbNCMeBKvVapWD6HBCms1mAbRXV1dlw+tsNouTk5NKJLHRaMTHH38crVYrjo6O4unTp8U54vfQ7+3btwW0IjA2nswRJZ+FF6PBBmWEHicpZ5D4LRkc1suyE7EAUygalA3vtEH8+uuvy2GMs9nDydDw+Pr6evzZn/1ZvHjxIi4uLsoZCYPBoDiuKAPOHHAZlLMaDhJERMlMeD+IQaeNrpU4DnhEVBS1I2VczJfsF3xkJcj3yIujL6wr5yagTHEuHFU3oEGxQ2foCX8ATmxw0XsEBdAP6DWyjvA5NOB98I+NBy22ObCU//MbHMGrq6tyLszV1VVMJpPY3d2NZ8+exdraWlxdXcXW1lZ5JyUTtPr8zW9+U+TIJRKOeC+LSFo3Ohjg0k6XvAGGCY7wDuZDYAa9tCzi7vIZg9Wrq6vodDrFATUortcfGh2wL4/AkO0UoKjRaBRZysAAW2WHAd43eMAptbzD547EO9tnQJ/LOJBB63pnBzw2g6wcYWa8yLIDYfzOYIT3sX7OPDuLmSPFs9ms0pnIINX3R0QFpLh0EXvkyDnrbj51ZoI9i3QhQ8fRuGQymZTgE+VTgPrxeBzj8fg7QT/rYes3xsnnzNNOIZ/j6ENPg0IDTd6Jvc7AjXUleOUgSL1ej8FgEBERe3t70WgsmsogS51OJ3Z3d6PX68Wvf/3rGI1GxeGyPcM2w3vwjNcQGllGzIO1Wq3S2t98ZJlot9uVdtF2onCQLecG6DhRHOjrfakG0tgHr6uDG+io3JzH2Rx4GSzAd8zPzXFsAx0QwHZAB2c8kRHvN8rYFxo4QA99eFfOsjlYwvy8pws+ocMq1R0818EndDfrw9gJGruxi3WZA1R/6PpR7W1NPBjT0VwIieLjIkJAiYwXDQXSaDwcKuXyJcp3zs/Po1arla4q29vb8eLFi/jJT34Sh4eHcXp6WoQDAA94hrlMLAMcjMl4PC7CwbNgJowcNfJcKBtODp/NZiV7AiAiygYQGwwG0Wq14uzsrFKHDTMeHR2VaB6Rh/F4XBaYczV8yi0CieFBOACcALB3797FZDKJo6OjEn3mb5wylA9O2eXlZckkzWYP53U0Go0C7NmsjEAzLqK89qRXVx9OSMexzF1sNjY24v3793F4eBhv374tirrdbseLFy/il7/8Zfz0pz+Nn//85/H+/fsSGYJ/6vV6HB4exq9+9asiCI6AIFx853a9GDmMLtkXeBUjZmDgSC5KgX0STr9GLCI98KgVqo1xxEMJ39dff10cK2gJmJpOp/HFF1/E7u5u/PKXv4zz8/P41a9+FYPBIBqNRikdpJwKxZu7BzkNi1ywUR8j4SwkskWUmUxeBpIAOvZZeVOv9Ql0g64oSBQroB86IkdW1na2GXdON2enATDJqeqAc/iJSBrvYHO2AQQbrZH9lZWVoqwN6pARAg2U/cBn6FLkBlqPRqP49ttv482bN7G3txd3d3fx9OnT+JM/+ZP4y7/8y/j888/j4OAgzs7OotPpVCKhFxcX8ebNmzg8PKyUoWBE4UfAHPJug2/6AUycpYJf+a3Lr1grnm2HP0frLU/8m//P5/OiL1kXSivRh5xF0ul0CsCyPMMvlO06u2BgjO7CsXXGl/lCSzsBfOeoNcDF9nA2m5VyRtse87ej7nboeIcBtUEO8818n51GaOZIPWuI7GA7PQdH8vk9c4ZfvX7oAMYMzZgH+sLRa+QUe7i5uVmcbTdroJPS3t5eCboQaOLdnGkFPQBgzM884iwSfALAx2ZAO3QTejyX6kFf5sD96BHkgmcxXo/HUW9kGt19c3NTmoSYtugTsp3D4bByQCbPcRMe9D820mMAS+QottcMOYTf4VN4Db1ucMx7XHadHQ6vFbgFZ9TONi31Mx7FoTOP5mAa68CzrXO4B9o4MO2sQZ6PHSp0j3Ud+oR3ozOhjcvXzZuMPzvDfI/tsh5A5xLUN27wOjIuAtF2Gpy1Jbhq24AcgZf/0PXBjsZ0uqjLNjM5mgUw84QAMwbCCCsTsve/urpayqF8cjUA+927d/G73/0uIiJ++ctfxuXlZfzn//yfS1qd3tOPHj2K/f39AtDYp2FwYgGcTCaV9KQFwlF+Sik6nU75DKDPQtlQ5OzJ9vZ2SWs6tVuv10uHKcAzwtNqtUqWg7IBFAXOx9bWVkVZTafTAuC4jo+Py2dsjEcYUGa3t7elb/fl5WVJu9roENHgfVYECC7RT0A7zhTKEaAHuJrNHjomDQaDOD09LXTC6LTb7RK54UDD8/PzOD8/L4J9dnYW//iP/1jaBGMwrPDJVk2n01L6wkXELh96lY2iHTJ4mO8AmlYqfrc3qDoaY8cdGTk5OYlHjx59Z69Ep9OJo6Oj+Prrr2N7ezs+/fTTOD4+rhiYm5ubUn5FByrAiM+ZsaPHvzGk0IrxZaME7zkLAZ+T7aEciWfA30S/eZc7YbBe4/G4EtnhMt/CSw4MOPKLsUH3YLxub28LXzE2QBMKGD5grGQa0APMC15j/VD2KysPbbPPz88rPMIcyewOh8PKPrHhcFichf39/Tg7O4vZbBZPnz6NFy9exLNnz2J/f7/or+Pj44qOe/36dfzmN78pARjW0QCeMcBrWZ4NqKCHgaSNvtPvEYsMN2vhEoAMfnPJlPUG4xwOh3FwcFBkzTINwKTU9vz8vOhvnoMeBwy5TIwxASj4XZZr3uf52rHieeh0ABDvoGzOsgQt4FPkwtkCRzwB1I7EO9JJZ8XMa9xnnrWjAr9yGjPjcpYB2+l1NyAiqkqmzTKI3vO+EOjHn9lsVk6BrtVq5ewjgOfKykqxgdAZkOW1IFjBwaRkMyIWp9AzZ+tdgkTmbXSNM1XwHrwNfzsSznr6ezv80JIxWx6wHTgqgLrr6+sYjUYlkAlvoKNGo1HJsHa73bJv0QAd20HGE7lAbznjZXDu73AQ2GzO+IwBWG/o5KASNjaDdt7B/KEbzocdAdYA+jhKjxzm8YA3kVV+hz3OvJkzWfyOey1fPAPaORDJu8zv6D2+c9aEcfN/y6vX0bzL+1g3lw1TIWMMZB2M7Nlx5T7GnYOm1k3GDz90/aiuUy5jMEGJ5tuDM0FgCA8SZuJe0mD39/dxeHhYCP3y5cuYTh867VBCxYnJn3/+eURE/OM//mPJelDecnp6Gjs7O2XfAgREEWFkrTB8KjAbDyEwws07cIKslFlYhDwiSmlVdjoYC/di6HZ2dopyNgPc39+XtqVOBXMfoM6Rcc8P759TdgFVGB/Gj8NChy+DaRTxxcVFtNvtoijv7+9jNBqVqOxs9tDGlpr2er1eiT4ROSQNz54T9mUwhtlsFru7u/HixYv47LPP4uXLl7G5uRnffPNNDIfDODk5KcB6MlmcMI+zaIMNrQA9rB8bDBEsjJBP+0X4LYDZ+4dPHHXIys3OBRFi1hoFgLGZzWbx+vXrWF9fj5///OfF8fRemOvr6/jpT38a/+7f/btSJnB2dlaifzc3N6XdraNZ3gsQsTDAjsDiQERUN2lTmgRfUi5n3nYGAYBvgwyoqtfrlUO0APKkyx195rl2LlgjxmRZdPQQ2ltnra6ulrJBnGcbcEd7+ANYJDCBLsF40tED4Ma74HVoYCO4srJSnAuMFmcHdTqdEnn76KOP4uXLl/Hpp5/Gs2fPSkOA8/PzGA6H5aTxs7OzePv2bRwdHVWyJk6Bw2vQy9FZaGAjab5eFmiC5tADGiIbBgOWKb6H58z7lgfOkSEwwPqYh9rtduzu7lZOSgZk8BsAj8fFe+AVshLZmBuM5kgvTgBrhR4x7znyZ7DD39gaynEcNPK4DKrtKDh4x1o4eMEfdM+y+bNOPM90yrX0+eI5OdPFe5kP62v5AbxBO0dSs1O3vr4ee3t7sbe3V+wdATFwCLqEvQx+JrLvQJ0B4P39fQX4OWBkGppG0NHZUJ5HSbZ5nDW1A8vn/PFas6bj8ThWV1dLlhret0O2tbUVH3/8calEMG+hs3HQ0FvoADvh8HLOAkQsTrtmnNa9mV4ZF9pJy44NNGEc1gPmV96DPDt47ffYATI/w6eA6Bw0tG5zUMYXcg9t7LgzJ+7j7/xc3m2+MN4A9zGG7GyBUU0vvsd+jsfjYh+t+22b4Odlc3GgLtPmx1wf7GjYUBiYQUCEMxsSE9kRDmcUWHwEh+evrq7G3t5ebGxsxGAwiDdv3kSn0ykHkt3f38fjx4/jr/7qr+Lv/u7vSinDdDqNd+/eRa1Wi5cvX5ayiKxYUV5E3B0dIWoCwUlNjkajsh/BkWvPmYipU1hsWqS2lEX1voqIKGdFWGk7GuKoEb9xun3ZJnEAEhkbSj9wgmDCiCipNjrV8FxHJjBuCAN16DgQLqeyA0bEjpI4HBF+C+Bst9txeHhY/v3RRx/FZ599FgcHB3FxcRHv37+P29vbEjmbzx+iwsfHxxVF933etp1Mg0m+g9bZkFhZ2Gg6ohBRTXvnaAgKFiVoGUEB8J7hcBiHh4dxcHAQz549i62trRiNRvHu3bvY29uLra2t6Pf70Wq14pNPPonDw8OS1aAF6Nu3b4tx9h4SeNxZPuZswMj+BXiQvxuNRjm9mSi0x45D6faydmyhE5FU5I59WfA59LLTZkfCRjrrGOiOQwvNXSaFUnbqGr1AUMNOAvqK+TubgtEjAgmfMBfmTimZHSkylfB1rfaQdXz37l2sr6/H48ePS0vbdrsdx8fHcXp6Wsob0Z2UHeKAU+6U+dzBIOabHTvoaofdxtXyZD1vfjbQWWYbDMoJWmR9OplMSrbXe1l4NhmlZrNZSjOdYYpYpP8zSDYPMR6Pz06H55+dJQNIP4P5mbaZBssCFTlQZ5CLk5V/43f4c+gPbaGL/+2Ida4n97NyQMG6gmh31gXmF9veDP6wWZZpg67Z7KEcuNvtlqw7DgZzQ1+48Yppg+73ZfDqdzNejyV/Z/6xU5TlyDTPfPV9ny2j4dXVVWkYQ4DRpUs3Nzdl39KbN28qG4AjHprCEEy182jHlrHbGcxzd9DHNPAcMr+zvrzLoNo21jTxftPszGQ54Vl2Iu1wcxmP8t5lWURXqXhMvDPzSMYL2TnyHJGxDNh9j3W0nSXmydrYtnl9KPs1vTPt4Q2Xz+aAmOmex5o/+77rR50M7tSpJ5xBFZ97USxMPIfBWlFj4EejUZyensb5+Xm8fPmyUvMJsKFe8y/+4i/iyy+/jFevXhUiXV9fx5s3b2IymRTjjOAyDoh4d3dXHItlaSJAQqvVKvX3BmR4x4yfE5iJquNoNBqNAnLI8tzd3ZVuT0R3ERCYgN/wfBbdcyHr45Qcjg5jJgtBxw7AFmvpjZ+sEcwKE+MwQAOPCbDDGACygDmi+O7SE/GgTHA0GCP1qHt7e7G7u1spayO17LU+OTmJs7OzMg6yRb6gE+UN2cCznk79ci0zRtybDQbvgq4GXdPptDhjOCRWojaW0+k0zs/P4/DwMHq9Xuzu7haa3d7eRq/Xi8vLy9JT/fPPP4/f//738erVq1L/2u/349WrVzGfz2NnZ6fwHe9jPS3Tzni4Own/RymZDzxX6GBHG15Cl/A3ET9HWX3oHPd5zAZBOHz+jkAGz8VxbjQaJbMGYK3X65VTuW0ciCixxshtvV4vGR+vl9ePsXjPEvKAEve+B8aJ84y+QiYeP35cnEtkFKeEZ5ycnMSrV6/i9PS0rBF0c/mSx8o6OQLJPRgir4HnB62WGdhsJHmHbQE0ypHb7IgAqNyJz9+TmazX66XMFP0AnbEd8DaX/53Xz7zsQJXnbUCQnc7MT9k2ZpBhBzk7Gaa9swSMh3sNIpgD9wM6cnQWxzk79bbn1hfWl8htdiIdGDCYm80WlRGeP3OD9ta/jIN28HZMCZB4rdnX4SCgaW6HwOvyQ397nMw9Z/0Yk+XOdMnPyo6/bYbBrNcAO0nm0WAPm7exsRG7u7ul9buDBXd3dyV7atl19QM8wfMNSpkffG4aMHZXsHje6FO+d2DLF3oRGmWHxnhjmZ4jQ+s1My/Zhmc547l2crNDkAH2Mh3IMzM24JnGDl73fK91qH9ve+nMp8fpwILpZB2d7afXzGMFJ5s3su78oeuDHQ1347BiI2pm4MaElwmfiWUlZMU8m82KQJycnMSzZ8+i2+2WTkf39/cFfK+trcXz58/j5z//eekwQbbh7u4u3rx5U1pXQtSIRYs//vAbj98MjaF2idd8Pq88B8eBum7Smuzx+P/aO8/exrMrTx9SOVGicqgqVeiurm7bbY9nvNgAzM4C+7kH2B3MwLsej1PbHaorqko5UKKyRHJfaJ+rh6dZdhnwS11AkET+ww0n/E6455J6xOYdV0GpVqslz/L09LTLSwg4xvNKug9GDmvDxnqnb/T13Zw6TuSiWq1GvV4vCpfIEHtgAGU2UghZR0Tx4jInVih4APr7+0s1HsbHPgGvO+OhYlKr1SqHL/b19cXKykqsrq7G1NRUtNvtODk5KZujG41GYfBmsxlbW1vFQCIdxkqRuTRIzYxseo7orhphuuBZ/LYwtkfAoVsLcVovUMDz6PvZ2Vmsr69HvV4vG7BnZ2djf38/pqamCnAeHh6OBw8exLNnz+Lg4KAUVejv7y+nSlcqlVLy1uAOwA+t8ttejYjbtCsbwRZyHgdeFu5DcGO0YryzoZz5YE6cBoEjAKDPOrDeGM0ANTbbeu9Ef/9N5TPmENqFF7Nn0uHkDEiIaPLcPE7kiauAEEW018nGi9Mo4cX19fWoVqsxPz8fq6urMTMzUyIYRCH39vZKRPP169exsbHRFemzMrYCYVwo5QwwI26BiwG0+SDTtRW1+Ye5dATYzftEuN9rgQykAIWNPD5nDpl7NhIbVCLXWHPLeRtZHmM2KAyYrbD5LjvYoGuAnPnO43dqFNdlUGojx1553ul0TMAD/fLG12wMWR8j/yw38zzA9+6b6cnGUS/nHtkPph/uxai8vr7uSmHq778p640jymlVNNb08PCwKzJGH6FF+mInKHOAIwgZ5nHbwDRd871lAWtgGcL7egFQ6CTzqNcn4gaHkQ6L44f1tVNyeHg46vV6KQ/vcVKNik3z0IL5HjowtrExZ9rzHHl+mEPGxviYeztoTZPGV3xu3sz8Yz40VvM8W15YLjqtLOtFO1jdP/NhNibdPxvEniOvu/nOLcsijxnagr5sCNJ/y1Se5fnPcgaZktcxOx/oS6bRv9T+KkMDjzLKkw4DPkygEJEPxou4SZdxR5mEqamp4omCOS8uLuLly5cxMzMTn332Wezs7HTtjYCghoeH48c//nFsbGzEH/7wh2LxdzqdaDQa8dVXX8XU1FQJqwNS8iSS72nPJQwGAHBuI8+CIfr7+8spvNS3Jmd+cnKyeOmnpqa6jA4YwFV+Tk5Oytyfnp5GtVot4J35ZRytVqsIDYgNz8fAwEDZ+E3I1PXEIdrr6+vY29srEQlHV9hsXqlUYnJysih3DjmjKg/9YD5d9piTjclBpp+dzk04uNlsxuzsbBwfH8f29nYMDw/H1NRUzM3NxaNHj2JpaSl2d3dje3u7bGofGhoq52hwWnbErYCg2YAgv96MBh1C2xlYZGAIrdiQYO57MaxBh73Z+Tvf4xSCw8PDWF9fj/Hx8VhcXCyFF9gs3Wrd7FdYWFiIL774ItbX1+N3v/tdifiRVhZxs9l2YmKiCwRaUSKk4OfsWWTOMDgjbgGODVBoi3nztRm4YCRYeKNseL+9afSRNIBMzzZkDg4OYmBgoKv6FsZ3X99N+pM3TrK2LmVrxwqpGxje7fZt5Q1/hnE9NjZWIoqOlhpMt9s3Z0Ts7u4Ww/H4+DgODg5ibGwsvvjii3j69Gk8e/YsIiJ2dnZia2urFEVYWFiIFy9exPfff99VzrbT6ZSqVyhWKziUk/N04RunHvC/FRhrlT1zXlscGxiKmS8NzjKQYx4N3JE56BvLXqJyACgMYCLLNprswWN8Bkc2cugPeot3edzIHcsZ7jOgtIGCDuVZAGpXPnNkLhuKfg7XkC7nSKnlHHPD/d5zaUPSkWvSk9B/FxcXxVi3AWFgBE3zDNM8cpI+ZIPNABAwylyNjY1FvV6Pubm5sv/A9Nrf3/+DQ/0yoOMa8ztzA118yGnqdQOY0oxx4DeuZcysk/n+QwATmshR9Var1eVQNZ/asB0cHIzJyck4OjoqZ2BF3O4Xo0Que//4jrW0DPFhl9mQNuD0GKFlyxLmBAcmdJ0dD0Rp+d7RFvAmuMJywBGTdrtdsAkOUOug7OG33II++d7OGp6NEUV/kJ+mN/rH59lZY77p5bhAVhFJsKHj6KznnXXw/hrTkteXOWVdjXk9nna73VWshXlkv9rHtI82NOigy0vaqxERxRORPSFsVKaihq1CUjEozVk69v8V8+npafzhD3+IhYWFkk7AORYo/qdPn0alUom1tbVYX1+Pg4ODkm4DOP31r38d//iP/1hARVb8c3NzJU/aZWcBpzAkIUu88QDXRqMRY2NjMTw8XKq9DAzcnO59dHQUFxcXMT8/3+Xh4O/JycliKBDJmZ2djU7nxqONUDDgQuFWq9VoNBpdubHNZrNEIQD+1Wq1RIQAZgA5jC7CrniNSP3q778tBXt1dVPml7nCg8P8QBsIAGgCo4A1Gx4ejnfv3sXr16/j4uIi7t27F9VqNV68eFFSRThs8fT0NN6/fx/7+/uxvb1dKmMNDQ3F+vp6rK+vx+HhYaErPNn2hkCTCAzo2KcpG4xxJotT5czMML0VhY0WRzS4HlozbyAETBMWwPTn4OAgNjY2Yn5+vtDC/v5+ec+TJ09iZWUlKpVKbG5uxsbGRtn0jhdrZ2cnrq+v4+c//3n09fX9oBpFnif4MOfV4k3L/M8c2HCGD6A15gdadXQBYDoyMlJoxfuxLBwjovAMFVl8lsv5+XlsbW3FxcVFLCwslHER1YD3rbxM0yg35oV9RQYUGESsIVEGK1vLMq81ipuKMa9evYqZmZmo1WpxcnIS79+/j4iIx48fl4ge0cZ3797FxsZGHB8fR1/fTbW1b775JhqNRpfzA/6zp9trbY8V8sCgC/o0nTAG7jMQBBz6/eaLnPtvAAwfkaKU01vw3LJXg8gtsgdam5ubK/uTAKqkVaEYyWt3dNbeZ1eNYl8NlQVxthn8G4ThmEFWIjcNzKANO+Q8NwAN+AJQRSW3XnKHZoBmhxj8hWxyCp9TWOhbq9XqKocNmIqILhmVATnzylg8Howy9JafT9vb2yt7ENjYj8ECr6GvTk5OSpSKvp+ennad4UQzCHQaXP7ezifmmWczlzbAucfZHs40sHFoA5ex2zHDu+E9e9nNsxERu7u7sbCwUOby+vq6bBa/urqK5eXlkuXAd3YKdzqdkmrts4OQCdCRoxiZRzxPyENHBfjt9LaI6Bov/1um2InN3llHBXkefXaaJPRIKio6hs+8ri4mYpDPWrKJ2saAoz7IJDtJPkR3dgjwg0yAX2xggFFtoOWIi52dxirM39nZWdFJNrayQ8cZHIyF94CDMKxYg4xv/lL7aEMDoiekifLyBmALbCsiOks1oYhbBkVZQMAoLph6cHAwjo6O4ve//318+eWXZSEODg7KPdvb2/Hw4cP4xS9+EWtra/Gb3/wmvv/++xgaGoq5ublYXFyMzc3N+N//+3/Hs2fPSv/xzJyensb5+XmpdQ9j2hiZmpoqAjDih4f6RNwcpMcuf1KPrq+vy2F1/N9ut0s9bJ7V19cXc3NzxfMfEUWpYGBMTk7G6elpzM3NlUP79vf3iycUb83g4GDMz88Xw6evr6/0AdAKCIGB8Y7jEcWThXCanJwsTExZWgiQd/jEclu9GIyTk5NxeHgYx8fH0Wg04ve//31MTU3F6upqnJ+fx5s3b4rwWFxcjHq9XqqLcDDdwcFBSVtpNBrlRGSMQOYdAzCiey8Ahw+y4Zb5BdizljBWFp5stsveKzMcQgUB4IZi8CZdh3X9N4Ln+vo69vf3C+D94osvotlsRkSUtKHDw8MYHh6OlZWV+Lu/+7t48+ZN/Pu//3u8f/8+5ubmYm5uLprNZmxvb8f//b//N/7+7/++AER79gwOEODUTAfgMS/m2YjbaIXTo1D+HFxpQcYzEYJ4iyNuz95B2CMv6Of19c2Ga4odnJ2dxeHhYRfgGxoaiuXl5bI+VuA25s7OzuLk5KQAGJ7BuTc4Snz45tnZWYyNjZV67kQiONATDzANsGivY6t1sxftq6++ivv375fS3ru7u1GtVmNlZSV+/OMfx/LyciwvL8fu7m7s7u6WAzer1Zu0qpcvX8bXX39dDALAor2vzBl9YY7gE1clswFJZCJHPBiDvfTwfi8vG/NqD6h/o1+8iZv1MQhBTnc6na7KcdPT04VWq9WbiNvExETxXnc6nZicnCybhJvNZon4oddwiOBEMoCi7xgINjKhZ88bf1vRG7AwT6zR1dVVOZOFZ5n/mQf4j8+gbe+/cHnfbMwh10nbNbDu7+8vOhC5B89QFh0eMOiC9w2qe4FQ5q2XrGu3b85/mZ+fLzIcGh4cHIypqalyZhWZD0TRkTMc4Oux2shhXbI33uCQz5DPnn8DcTuw0B/eD2FPdgbB0K73TDmaA596H4adQBERzWYzxsbGYmZmpoBl1mVqaqp8v7S0VMZ8cnISh4eHXZELp6GbhpHF0BpgPCK6qkvi0IRfiH4NDg6WUsyeRwx/cINBvuUxDkbmmegLfYb+HOmzY8MRlV56NSLK2A3yud/ee+5nLegT+BSHrGVaNhD8HX1ifn2eV8bH0JHBP89iPDiIKarCurAm9IP7s0PYzoZsGBPdtzM0R24/pv1VEQ17SsihZsFytMMd9mcwRPaAsdgGfAhQgM5nn30Wc3NzxcCBAPCC/OM//mO8fPkyzs7O4rvvvoujo6M4Pj6O5eXl+NnPfhZff/11/PrXv47PP/+8bKSs1+vl5NmBgYGuk0VZNJQbhDs2NlasaowAxj48PFw8KlZCAA3mra+vL+r1evHKAk7GxsaiVqt1VaBxBRk2crfb7XISNALEzOh5xxNojyVVO4jadDqdApJI3WCdLi4uotFolPrbRDzoh8H29PR0HB0dFYVMhAghxHv+9Kc/xfj4eMzNzUXEjYcGUPD555/Hw4cP4+nTpzE+Pl68J8PDw3F2dlY2hnIoGQIJzxbvzEIIYxdA6ROHsyeR1DWMCmjdzMtzTefV6u0GaoN1mDRHtDK4z8rXAu709DRev34dz549K2lmR0dH0d/fH81ms5wz8g//8A/x+vXr2Nvbi9evX8f+/n45IXxqaip2dnbi17/+dfzd3/1dNJvNmJyc7Dp07/z8PA4PD0sJWMaG4CPKAIhy+Nvz5xzYLEfYR8D3jmZGREnzg75YI3sdUQLIAiswlDXpNvABRhAC24YOQtV7JY6Pj4sijbj1wrXb7VLXnvN7GPvExEShf+aC8LfTcRqNRvzLv/xL2WvTaDSK/Onr64vl5eW4d+9e/PznPy80MD09XUoZ4yGmzHM2ECqVyg+Aq0ENkT3klRU3Y0RpkaZB37meVAwDl+yMsdL1HELnrL/BWPYqY4g4usDG4Gq1WvgAY9znJrHvjdLXGFek3wFUiSrQD+iLH/gaZwh7PQAKzAH0S0QBOkPO4lm0t5roCjRk2cW4XL6cz1gz9ANy0GkdjBlHIIY7n/OcTqfTdY4Jc4TR7UhTjgjawYgewqhHVzKv8AVzfHV1VaL2EbfFSJD5RPynp6e7StNCZ+gxG5W9qnLlSIrXGlxjJ1A2prLszsajdb4jfZZZ0ASGKvxhuWA+sXHCu9Fxh4eH5Xwp5sTO24goqZ6U+uUacAfnjZCCztyBazxe5pyoSLvdLrRkHW0Dkb4y13zG3I6MjJS0RMaMzsXZ5D0z0F9eW8sQPu/r6+vSG54/MhnsnIHf4AnvA8qOLsaDfLKM81rb6cr60k+nWLvfXAs/ZHpC7kC7xtHOKvJzcZ5ZNmdHBO+H91hf9HtOyUS+fkz7aEODTtgKt4CBSPiOReUePE8erJnT+dhMAqANxfr69euy0e/y8rIA3cvLy9je3o7Z2dlSneXdu3dFGTcajajX63Hv3r347rvvYmNjIxYXF7u8pggqlKWrxLDpnD43Go0CPizsYRrPEwoRwsQbAKjxwruiCsJ9aGiopMscHh7GwcFBV/5dxK3RhoDGm2DBQdUuFBxejIgo88ka0FgDj58UJfbUMH68iWzqxkBj3Dyj0WjEb3/72zg5OYmpqak4PT2NRqNR0s3m5+fj4cOHpVIYdHNyclKMn5GRkTg5OYmdnZ1CMzCtBQPMZeDOWqHg7E0xaGBztBUADJ9TYlAUFiIGeF4rFJ35g+dH3Jbzo58WpBcXF7G/vx/Pnz+Pp0+fdnmU+e76+rqcubC4uBi7u7vFOBseHo7x8fGYnp6O169fx8uXL+PevXs9c8oNJlAQpLxBK4SvmRuEEwIvIrqEHiCHuQPw+XOACQqLNUTIwYOnp6dFIQIYPYcAU85nwWCCrm3EeW34DB7llHUEOHQSEaVUM/NCKgfzCC+SxmfP1+7ubvzud7+LSqUStVqtHNJHxOzevXvx93//9/H06dOYnZ2N7e3taDabJWqKQbW3t1f2pUFLptdeawO9en2sICO6qwAxx5bZvAPHiY1kG3K+j89sPAPA3QwC+cGopX/oBB8eat1hw4SUI/pDGg5zwuGNfoYBJsYo77FsoJ+kOpiGuJcGX9sRgRxiXK6GZkDNvRmcZvAAL5tfoHnGjsyzXKJ/vNeRKp5tRwlziWGBo87ORBuStAzcqRBF1JlUHwyrycnJmJ+fj+np6WLUoufgU/qCbsqpOX63nUM2fmk2OgzUoG/oCgCL/Hf0LoM884zThNw/G9k59ZI+InvQNTiEwA7msfPz8zg/P4+JiYkYGRkp6c4u9DI+Pl70No4fIl2WtfyfHRD8bS86vACNgnO4PxuJyK08dsZrmrOxAC04Pc3rynucVke/kcOmcev6vP6mHRqyi75nRxvPceoU35sefG/+7Tm2QQzvZD62/Odd0HSmT55hA8M6g/eyljam+D6fn/Xn2kcbGp5gMxAvxeDAs+/JtLXncHiemPx8JhKreHt7Ox4/flyEMV5FjI/JycmYnp6Ohw8fxsHBQRE+h4eH0Ww2Y35+Pg4PD+P09DR2d3dL+tT5+Xk5m8JEaAs3orvWvfdNMAf2JjkvEc/R0NBQObSO1I+IKBWpeLeZ2guLIQLz8DxSNoaHh2NhYaFsQu10Ol2lHm0cohg4GZk1I7/ZytLAEcCEp5f7rq9vD07yOLxRstlsxvfffx+Hh4cxMzMTEREHBwfRaDSi3b6JNjx+/DgePnwY4+PjBUyymXxnZ6f09fj4uCgbz3/EbajURgJz6Rxh6DYLFdNq9laiXFCGzKkVBQLX/ctKh3n1PPcSlrybdnV1FW/evInHjx8XQ5IKOzzr5OQk7t+/Hz/60Y/KQZfQ6/j4eNTr9Tg8PIytra2oVqtx//79kv4GMMOrx/gMVgl/u/9syoZ2mCPAhnnKBkweXx6714N5REACCuED5haPWw7p0y97UuFL+NX0EhFlvwfAwp67arVa+AhaJ2qHQsmKutlsxs7OTjnrYmVlJVqtVuzu7hbv+vz8fHz22Wfx+PHjmJ+fj6Ojo0K7yC+8Wi9fvoy9vb2u8L7n1vLXSo55tQIyDRtMMDbGacDG3xn08nynbsGTWWlb3vdSlL6esRB5czU/GmtA9BdHD9EieBhDF3nFfrZeegn6N3DwNcyFQYrvZTz2LJrXs8HAWLPhxhz6GfChaY73OWrSa516AW3L014/WTYZrPT395e0ZzfWI+J2syoOkIgoRgrRNfYKumIihpYNKuQsuttzl+kqp8LmeYzoBvmmwyzjGUfWPdYJef0sC/P68zwwBc+0Q8R0xZjOzs5Kyow95aaVwcHBkkaIYWfAD4+yFpTQzjTi9fd3TkfrdLoLmfB5r7RCG+EZiPO3jRDGD/96DnthUtYnOy1suNmxyz024vO8m0/8PtNRpj1ozjzjOeEez7E/c2aMx8XcgIdzKpN5Mhtr/t74vJcxaXr0mJyt8ZfaRxsaTIAtck++gV3+PncwC8T8nkxoLAr5yffu3SsgsNFoFCXRaDRiaWkpms1mOZn68vKyeMzr9Xqsrq7Gmzdv4vDwMCqVSpdnH2JBidNfGBHB2Gq1ykZMezjZ8+EwMh7O8fHxsjkcA8eKl8UF0BC+RonwDjyAMNvo6GiMj49HtVqNqampuH//fom0cB19r1QqJVKDZwZl7DCphWSr1eo6RI28czYeMUYf0EeOZrvdLmV+r65uTnzf39+P5eXlGB4eLutEmsrMzEw8fvy4VLZi7fHEUATg7OysgCuDpqyUeylqC297TvgOAebIghk/K5ysLEznPDPTdi8g4Wsy7fu519fXsb29XeaRNcKjX63epCVxmvrbt2/j6Ogotra2Sord+Ph4rK6uxsuXL2N7ezvGx8fLgX4RUZSdQ8r8xpAm9AyfANSpakbkw/nNgARkSPYAey2h/2xw0By27evrK8UI4AlXiMvyxl5IOwIwHpxi6JQyvMKMh3QSPGdEPhhbxO1ejIgo6X5ra2txcXERKysrMT4+Hm/evCnGxOTkZNy/fz8eP34c4+PjJe0HgxuPJBGUV69edUXI+G2j4kO0aL6wIe3r+dxy2Uo107LfY1q3Q8bKzh5RN9O+gYL1AQ4dO29yn0dHR4sMIgLrSC3yFKPFUcRecsN8bkMyR8cMcA1u7CRDn3J99ljyXhuu2XnBvHNfBk/+3/KMPnidfZ/H1IseeJd1kxuAPctjjB087gZKeKjRj5OTkzE+Pl7SuQwgcWDBm81ms4uW6KsBXZ5zj81GdTaY85qaVv0u5s739gLC6Bi/07//3Gf0D1qHronuMvftdrs4X6g0SJqUU05xtOA04VmOrFkHGDhb7ls/ZZnTC/+BP4w14EnWy2mVnnPPQ3YGQF9Z79MXG8W9cKbXtJcM5T15bTxOyzk7AbI8seyFvv259WF2JPh/Z1XkvlrummbN79kAs+zydXluciT6z7W/ytCguUMMGuHtCgCEb3290y0gWEKRvMeDIf+yWr2prsQmb7xU19c3G2U7nU4JBy4tLZUNk4Cw3d3dGB8fj/v375cTd9k4RmUeQJKrCXiDKx4B+mXFQJ8GBwcLcCfdAuVAuheRhE6nU5Ql+aXHx8ddaT4IYsL1zjOFWScmJqJer8fCwkLZHNZsNku6AaFmUjkgnAyobM0TuaAB9mu1Wtl4FHFbEvjg4KDQCGlWVAChBG2z2YyJiYl4+PBhvHv3Lo6Ojoqwm52djS+++CJWV1eLsUI5RW+uHRoaio2NjXIqcs5tzUrfigS6cjUJKx/mOgtPCwcAX85XtPcG+siCDZo2aDcozWDNYzK/XV9fx/fffx/T09MF7PqQRULm9Xo95ufni8H57t272N3djeHh4Xj06FE5f4QKR/Pz88VIjegui2cvHIrKeb18xlx6UzACy946xu0oCNfaYI3orhvufiA3oF8O1SQ/3w4Cp/NBS6w1zyKqg6cbZWo5Rv/YSGilReqhDRKUyPn5eayvr8fm5mZcXl6WNM93797F5uZmRNxUn7t37148e/YsVldXo9PpFD5uNBpxdHRUANz19XVsbGzE9vZ2iXAyr6YdlHcvx44NZ9aa5/NDNJm0EdOv6QFlB+3yuXkxA4Beysx8ZMXfy6vGvJLexveAJGTR6OhoHB4elug1gJa5IWXHxrplO/NDFReDbTuD4PWcUpudRp4DeMTgwbILOQFNMqfOc0cX8Q5HbNFhyPNs1Hmt6X+O0hvwAG4joosf+d+6y7QYcVt2/OzsrBgUzKfPe+jvvylly74y7+NCB/M+9iiyWZ2WowCMzbKa7zM/QOc2ArPB6GcyNxFR0vTyc7kHHrOhYSzhVEb6awM/GzZgBiJ0PIP3UWFvZGQkxsbGSqbD7u5ueS9rgNyinD7z7sIFyOYMSu0UysDW8t30YB3pOeVdPrQVOs9gnQbd2hHuKBhr7vtxJpp3so7O8ir/zdjyekFLxgOMhfXh/7yPsZfRwRizUcCaOaXbdJv3ndnRwLyAiXqNGX0IDqff/GSd8qH20YaGAWi27pgYp0tYIRggo4gZOEKeawxsmEjnwjebzdjf3y9eUxZua2ur7NmYnZ2N5eXlsmnw5OQkXr9+Ha9fv46rq6uYmJiI2dnZ2N3djbW1tahWq/Ho0aNSeYkqMywEm6UIrZOulcubHR4edoUgGSv3UZbVIJ65PDs7i/Pz8xIpyWCPtDQTAcw9NDQUs7OzJd0I5QoRtlqtUk+b+WIDqr3WbMLiTArnMUMD2UNmEIoQoiJWpXJTVnV3dzdOTk7i3r17cX19HV999VXx/g4MDMTCwkL89Kc/jf/xP/5H9Pf3x8HBQUxMTMT29nbJSW+1bqpuNRqNePPmTUl3AOD19fUV4GiLnXlyPiLeG+iXtbIH0MazvVQZxGFUOT0NEO3c62yw8Jn5COVjwZc9nDxvc3MzGo1GzM7OFr4igvfmzZsYGBiI5eXlePToUVHkZ2dnsb29Hevr62WjNxGSN2/exPX1dSwtLRVPI/33Bj4iW/AG4+50Ol0buE2nGGasl8El0UQrHPgGgQ3oJ00QMMB1gH42ZrvaU0SUeurw1/n5eSwuLpZqHcwx6YhEJ5h36B4jLuI2jZHiCC4t2Gq1ivHd33+zef7rr7+O/f39mJ2djdXV1bi4uIivvvqq7CMbGhqKR48exX/+z/85/u7v/i7q9XpsbW1FvV6P9+/fx/v372Nvb69EUg4PD+PFixfl8D6UvemLfjJH3rNkwGRvuPnb+c+OVpsuoXdAPNcgM9AH5kuaI0sZIKMQDQ75H0fV9fVNtbxardYF1Fg/nsvhZSMjI2Ufkx1BOJkAbT4nwgoVWjUYZL54jr3JTrFkPpzWZSNtbGwsqtVqiV4hH7jPey681p6vTqdTgGUGVX6G01uZc+iA3051yXIq8z2yF7nFuL0O8Bapf7VaraTwev9MtVqN6enpePToUTmLivnCSITeWq2b8q0Y4AA2+uf/aeAU0kyys8jXZWBnOmVczBfP8kZzP4+5y/zH3Pu3nRemfd5jIw0Z6DLylUqlOJ7QKZS6J/vB1fEiouzjRC+fnJyUojQGoPSb+cug2EYlfbHxii6m/xS6YX6Ze/ptI4fvzE921NnBA60bS9nYQNbZGYKsgY+hFfO6+4bhnh1ovCcbOPTfxpDXmD7wNzxjZ52NFcZqZwPv8Wfoz2zw5rlkDo3zeI4db3z217S/quqUGcGTxGID0DMjsViAYufsYnnaW8L3zn1GkR0fH8erV69iYGAgZmZmyqFdpNRQgQblff/+/VIt6eDgoKT4jIyMRL1ej2q1Gpubm3F9fR1Pnz4tE0qkAS8BBgJVqqjxzWIdHh5GrVYrHgPSSCAearhfXV1FvV6PoaGhImRhDAwp5oYNis1ms5Ssc644aQOMva+vr6uMKB7/gYGBmJ6e7jrgDCUAUZFCwBibzWYBDp1Op1SdgTHJfaY6E7nSb9++LcJrZ2cnTk9PY3p6Oh4/flwiEaSgTUxMxOPHj+PBgwcxNzcX6+vrBQCura3Fzs5O8YL19fUVg/Hdu3eldB4MiJfv6uqqVJayJwEQYsbNBjEMBU0a1Oa8U2iefFcEA9a/o0WshxUWYCmHu7NQtSfZRv7Ozk788Y9/jF/84hdRq9WKcOl0bqpTYUzW6/WoVCqlFOuvfvWrEmEi93l0dDSOjo7i+fPncXx8HJ999lmhjcnJyRJSN4+iyAEZtMvLyxgfHy+nzLP3CcHrA+zILYam4QeUEYLcFXyYG2RNX19feQ+pLxFRaBQDA2AyMjLSdUK3wSNrRPpVpVIpZW8xKOkfEVj2Gjka0Ol0YmdnJ46OjqJWq8VvfvObGBoaKvtqjo6OiqFYrd4covbf/tt/iwcPHsTIyEgpcX15eRnPnz+PtbW1woMREfv7+/GnP/0pvv/++0ITeVNku90u46evvsagmH5b4RhQIENswEd0pzYAbOAdO1PwxucDngAW6Amemb2FAwM3Jch7Aezr65t9eHNzc125yNZZlNjGsUIkhPf5oMVWq1U2mROts5JnfMwf80uk2kayo3ADAwPFqOQz6NflaJHNND73c7NRhu7I4AAwh5OLOc3pwswzPMGaGfygb6Apz4c9nY6c28vdarXi3bt3MTIyElNTU9HpdIoORSYODg7G/fv3y5k1fE7lyb29vSIPIm4i5kTFTd9Z7uZ8fjtDoWvoExljgG/QhuwnLRNZZaeUHRMZ3DkiZXrmO6L4EVHoz/LFBiTj5nN4EVmVdRdpUtDlq1evCr6iX9ACaelEhx2xBtN5kzc0iAHC+NGHBssGw9mgo9mxms9nY46tixk/z0dnQEN2qPI9hzNnxx/8gawyiPfzbMg6I8fOB6epG2vYyKH/nkueZcPG89jp3Fb18lkypiUb/sy935UL1iDn7dwGf7DmOQsDWvtL7a/aDI7AYzIRdoAHW6YQcwbs3G9CQ5jUarUyMQANJhRAi0eSOulUzSHPlpOjydtsNBoREfGjH/0ofvnLX0az2SyMNzU1FfPz86WSz+joaNlAOzo6WspUHh4eFlBiRnIEhnSo6enpkg6EF4ZQqoUIc3N1dVU2NnOoDt4wM9Tu7m6MjY0VwIfB5GpAViQQKO+4uroq8xsRpWygAYHLJ9JHW83V6k0OeX9/f+kra80m8rm5ueKBHRsbi3/4h38oJVUxMFByn376aTx69Kis7/z8fLTb7VhYWIjnz593gbxqtRrv3r2Lb7/9tgghC4BWq1VC6vZmwqQW2K1WKyYmJsp1jM2eDDwyFqYGQV57PzciiscpKy1AhoW2PSXuL2vpv+kDtPj999/H4uJiPHnypMw/yqvRaMT/+T//p2yiZ09AX19f/Pu//3upXlar1eLevXtxfHwcm5ub8fbt2+jr64svv/yyeHgnJycj4vZATjZqkvNuYEgf2Jx+eXlZlA1gsVKpFAVWqVSKoAQUwi8oxeyBMW0iC0h7Yo0Aj2wI91pQhAF5RH1+mkHX5eVlV9U4fkZHR7vAZT6YrVarRafTiffv30e9Xo///t//e5yfn8f79++LIcj1//RP/xQPHjwoxlutViuln//X//pfcXh4GPV6Pdrtdmxvb8fz58/jm2++6QLLKAfLDTbWGjTl6BwKhP8NjlBKFJuwwvK1yGzzAo4igA5r4PeZdvxcaBze5N0Rt0DCSvvo6KiU+aS0saOd7AdAVs3NzZXzfHj+4OBg1Ov1aDQaRQlHRKnMw/4we8OR1Rjf9B1wb4PZzjXmn++h54go0X2Amx0pjiLxg9GEkwgABAhBlnnNeEb2XvrHzkIasgXZS7/hU+gQeY6zBXkwOjoaT5486aqWZHnGniXwAJvBT09PY3t7u/SbwjCNRqPICt5jA9R8bFALuESGI4ORVTjUeJ5lEH229928ZHpmPphHDFTLMgPsTuc2YgqA9AZ69IcNB3iEFLKpqamymRtnXLvdLqdtMzfo2e3t7cLHzBnzBnYZHx8vxh3jPDk56XIiW4+h2y4vL8sBm7zXkZAcTYDuHSWxkQFt8w7+h9dZN2gAHWKjwfLRxWwYt3WxZQ7PM+/Y6e4iSOAi0yT3MA76bAcPuNp8zn5Xy1L6W6lUihOv1zySxufIluUH88jZNDgXvP8Suea9MzbWnV7/59pHGxoMzJENQALGBEAVwnL5VjrMAtpSY6EyoGNSHNIHMGxubpZICOVOWYh6vR5zc3OxsrISu7u78fXXX8fg4GD8l//yX+JXv/pVV8UiwPOPfvSj+Pbbb+Py8jI+/fTTaLVu0o3wjE5MTJRoA32ij44IZO8KYITIxezsbDFeHDamYagBkBwmrVQqXeF/BHin0ynnYmDkTE1NFWFVqVRKmTsrFdbNYThCqNVqNfb29sr7a7VaXF5exu7ubsnlh8gRdO32TTWod+/eFTC5s7MTr169KlXAENI//elPy3kZ8/PzUavVYmBgILa2tuLf/u3fSt7pzMxMHB8fxx//+Md48eJFF7NY6CC0scDxPNoL6/x5e84RJE6fsucCWrGXBIBlAMHngAxfbwZH+Fv4WMg4LQFeo8EzvPe3v/1t9PX1xcOHDwsg5MwTNn0vLS3Fmzdv4t27dzExMRH/8A//EP/6r/9aNgbiqVxeXo7z8/N4+/ZtnJ2dxS9+8YsSqcQbhqed+UDxeQ0Q5jZSXeHJghsHBF4h0iNQ+hixfB5xmxtOqqI91073I6WKBjAEMCLAM8i1h3ZsbCz29/fL+rgkdkQUQ9ih+kqlEkdHR/H999/HxMRErKysFMP58PAwGo1GnJ+fR71eL3zw4x//OGZmZkoVuPX19fjnf/7nODw8LHu/vv322/j9738f796965KtKGc7JpAl9JdrcrTA4Dii+yRbns18OBLHNRg39MW8A73Djyhj6Jg+2mhBL2TjyMrd/aT/VFCDnlCGZ2dnMTk5WU6H397ejnb7tozn0dFRl6GAvAbsHh0dxdTUVJen2rIfPef1hxfoK4DG8oLvrNtc+jxvtIQ/vP+DPhl0sa4Gw0QPkdP29sPH8CPjsB7u7+8vBhXRR3utqehm5w70dnp6WiKYw8PDsb293cXjlcpNtHV2drb8UDCFkt0YhESA9vb2Yn9/v8vgQ34b2GUAZ4CPkyEbVs5SsG7BgYcOcdo242X8eIZN04Ay0zbrbl5D7phn3RgTugkaYrybm5slXZN0QNIrq9Vq1Gq1khkRETE3Nxd7e3tlXwagEnrC2Ii4LWcKTRC1ypFJOwegO1cJAyfCo44MWGdkcGwHAGsDtqLfNszoizfK26DivZQft2yBrzqdThmD5RXyyBE5O0DgezIrfD80lGUeznk7HcF9rihlQ5bn2gjge++18fzDD7wfurRhDX2g71kXYxWv+19qH21oMKnOB7aX2AtvqxZF1UtQI3QRdE7PYFEsAAwILi4uYn19PSIinj17VjZXk25zdXUVc3NzsbS0FCMjI7GzsxPt9k16xjfffBONRqMIK04d/fTTT2NtbS2Oj4/j3r17sbCwUPJEqWphwjexMkec2GwjCmZk0RAgVGOKuA1BAY6cw4tgI00g7xFxxOLq6qqUcWTDK4wO6LBSMqCl9B0hWwQFCoNntlqt8g4a4Or169cxOHhzMnnETfnag4ODsl9mfn4+nj17FisrK9Hf3x+NRqOAw4uLi/juu++KF6Wvry+Ojo7izZs3sb6+XugrIrpoKvcDIWOvE4I9IrpqhZtWrYwcMuR+ewbw5DnXNyJ+oByy58p8YR6ieV+HBY7BnQXd6elpPH/+PIaHh4uxUa1W4/3794UnVlZW4kc/+lGsrKzE3t5e2Tz5u9/9russFA78Gxoaijdv3sSvfvWrePr0adTr9UJnjD3i1vAiZcyeWPgXbzcCHs8JwMM0mAGYPcPwHGPHk0k+PZ4blArADd7KXnTehfFkoxtjo1K5yd2fmpqKVqtVIiFu7Xa7zCGyjr1fnANwfHxcctNxDMzPz8dPfvKT+PLLL8uBWvbefvfdd8WLPjY2Fu/evYtvvvmmHFDJHDE33vRHvwwK8n4hAyDLaDeug66hPfNOL9rMhhuy0gCZz5CbGVxYqVqW2tPPOPG87e7uxvT0dIlOEWlibwxpO8gF0k2Pj4/LteZVvLoceEnUzPIGumS+shEXcWugGVQyDwap/szzhw5xiiLzxT3epG6ZkkEBwIUMBOaWNbHzI4NvG5OWrTgA6RdOKJecxdjCIYgsn5iYiJmZmbLXjHSWTucmUuUDdNnjQYTWc2GZyZhtjJqe0H8Zw3CPDWcaethAEN5DNtrwh44yT2WZnw1vG3GmqczXPIt7oYfr65siEehP5BORXxyrFFZhbZFP1Wq161wTovpEYScmJrrGZIPJvAuesRfffbZs4R7Tmw1pRw483izzeDbviujeeG1dDs36M2M73oPDgTGAXbMR5DVkzPQHR4DnqFc6WuZ987OdBp1Op+tMOuvHTCP00cao6Q+DGeem5b1lbsZSpr2PaR9taDAx2XpF6AAA3VmDrIju6gUGdLY8UXrZG4FHwEbKyclJbG9vx/DwcPz0pz/tstBOTk6K12JgYCA+/fTTron69ttvY39/v2xcHh4ejsXFxVhdXY2dnZ1y4N/c3FxMT0+X9CQiExG3QgCCJ6KAIEN5wBQQNwoDwmVjFsLTz0e4eWzci9KoVCplIztrhUeYqioGTniELWSPj4/j8vKygCwL6r6+vmJ8UOWFg8jwvjUajXj+/HlE3HhJqA6CAOvr6yvlhefm5kqEBRC0ubkZm5ubsb6+HqOjo8UIWVtbi/fv3xeFY2PDgtzCClDp8CYMnY1jG78RUeaA55vWfR98YCPDXrScjuLWywiiQVP+n+sskBgfAOvbb7+N6+vrePToUaEPTrSvVColPejZs2eFLq+vr+PFixfRaDSi2Wx2CfAHDx7Ezs5OfP3117G4uBhzc3MlhE4ajPtvZe9QN3RkcIkcIILhNeGH6yKia+O+x47iJLKCgwHaxhBkX5KdJPAt/eAnp9OxrwmhztqfnZ3F2NhYVxTs6Ogo1tbWYmNjI4aHh0uq4+npafEijo2NxfLycqyursby8nJMTk7G3NxcdDqdODg4KCls29vbxYja2NiI3/zmNyXS5L547tz43CAxgxbWz8bXh55jWjfN844MqCz37SgyYOJeg/JsTNNHAxT33Z+RKoITIHstaWy8xiPb6XTKOmG0oJeGh4dLGi6RP3ueDcz5jGZ6tdKO6AZDpnv/z/0Ab/SAve5c54yDrDsN2ni/HWSmJ+bThqhlHYCfwhPm54go6cJHR0fRbre70qparVZxVgwPD0etVitpgkQ70fMcXmkge3JyUhwl2QA1PUBXzDP0Yxp3Tj3jRVdkBxzfuy8GsPmaXs/NvGVgm/nK6UP0yfKVNTVP2Pl0dnYWGxsbJTXaBq9TXaanp4u8t3PGjjpSs5GL8Ij3O3ksHnuWQcxXHjtryD04Q7Ns81x7XXmnATrGLu+w8eL9ZpVKpeiFXjzswgxZL+Ogoo/mIdOQMa7lnt8HljNPRkTXvsRMZ46iZHzi6/1MG6SmPfOQZbSN7iyjmb+PaX/1ORooBhbaCsxCMFt3dMqCzxMHuLDQszDJEwWjnZ6exps3b2JmZibu379fnsneB5dPHRwcjNXV1VLu8uXLl7G/vx/NZjOazWY5T6Barcbu7m7s7OzE8fFxNJvNmJ2djaWlpZJDbkHDolSr1QJ0bEjZcPAmZgw3Nrs52pEZiPG2Wq0C9BEIKI3Nzc0YGhqK6enpQiDeGNbX19cV/uT9pHC12zcRH/JpswAj3YgNxOwrOTg4iM3NzTg+Po779+9Hu90u4Vg8Z5OTk/HgwYN49uxZVKs36W5zc3NRqVTK5ti1tbVotVoxPT0dzWYzXr9+He/fv4+jo6OuEKWVKs0M08vr6DXo5ZGw98T02MtzQbOy4tlek+xx4m/W0iHj3Ncs1HyNPYp4rNbW1sq+kpWVlRKexqtINGl6ejrGxsbi008/LZuRSes5OjqKs7OzmJ6eLmWS9/f34927d9FsNmN6erp8Z+PXzgcr/+zF9dicgsFv0ikiokQzsofPHppcxrRSqZR9QCgP+AQnhgU0SoQT7A2GnC5Qqdx6nTGsiTzCV41GI7a2tkphifn5+RKBpDT0xMRErK6uxrNnz+LRo0fFo7uyshLNZjPW19fj7du30Wg0ijfx5OQk/vCHP8TLly9L+VXPheUs9Jrpx4ZANlz5304g871BZy9DJHvJMsjInxk4MQYr8w8ZQxnQu/8Gh67aBMg1sIy4zdMmvRDADB0TpcQ4rVZv9vWQaojzBtpw9NNzTr+dfuZx2Gvt+7yulk3QoN+TDQTLlw+BO+aA7/wse1mta5GN3AcvmHaICrpqoSPQGPDDw8NFjmC4DQ0NlYj80dFRNBqNrlSSy8vL2N7eLim4zF3GCJmOMg1Bc3zm6w3mexm+dprAb6ZXX4dD4s+9qxcv0YeMozLOyjrDY2+1WrG9vR2VSiVqtVpXmgz7eZwi7UIvYCfWHCPEUV8Xech8yA/9Nz1no9AywHrAuCkbABnY8lwDbtMrfGbZAb9CQ9Zlli9c6z5mPWy5kmVer7U3b/MMnCJcyzhw2Hns5k/LGBtdpqdMG14vvyM7SNzvPPfGUn9zQwMBZwvdlqLBhoURg3F43oMFXNubY0VXqdyGeZ2fSgMU/Pa3vy37FKirzv3n5+fx5s2bmJqaKpWOYLTvvvsutre34+TkJHZ3d+P6+rpUMNnf3y9Cb2dnJ66vr2NhYaFrzwVEy0ITanT+Jx6xsbGxLjBkaxXFRk56For9/f3FkzM2NlY8R3hsa7VafPfddwV8ueIOEQwzGARmT16r1SoGw/z8fIyNjRWv8MHBQTSbzejv74+NjY0ifABYx8fH5SC+77//vqQjYGQ8fPgwnj59GqurqyUlhPU8Pz+Pvb29OD8/j6mpqWi3b858ePv2bUlFM2DNuYEGRfaKZEa3ErAw5G/e08tTY4ULSDWNmXZRQtnLgfHi6ijZ82twkA0f8xcKj/7jxaJsLRXVrBSOj49jfX09xsfHY2xsLD7//PPCq2tra+WsE+hjeXk5RkdHY2trq0SWZmdn48mTJ7G0tNRlABvg0G+fkh1xCzYQqhgTKDBv2LVjgrVj/Q2qreTgy0ajUfJjl5eXuwx31tTKHPnFPBOhu76+LmVRMarZ5FqpVMphhxcXF4VWMfRxVpAeSfnaL774Ih4/fhyLi4slyjc2NlbkDIUnUP6vX78u5aChTeYgR+l6AXWDzxylRJkDIB1lM1jie4MQ6Bc5laMa5rmsjHI/M+jKBk7EbZqYAZd5xQYXc1iv17sir1xzdnZW6IV5xjD1viV4DYOClEPAmfvWa/8EPM/8QWdOs6HPtGxIo1e89tnJxZw6s8DrbJ1r/srrxPstZ/0uUm/RX6w/1xGZpwoX8gcdjZOKvRg4xDA+0DUUfkFvXVxcxMHBQSny0ssgNjC3Lsh4IusE/5+dVHzGPLK5Fp7JYJIfQLkNgz/naeYzOyDtBc9RlBxxz8Y3PLq3t1fSn80fzC20zPxPTU2Va46Pj7togD0ZzmDo7+8vTk9HWexcdhYKuiEbzU4/xrhhjuyoZYx26NqIhwfIyoC3TROsuQ1UG+Ve82wc2uFoQ5L1sWHuPjNOy2nkK9kiNhqYS0cUbIzZyHXWRq8Gb/Cbz5BLlu/0zdibtc9GL+NyZPbPtb+qvC2TagYGTAK8ACkMhE732iBuJoWRAd8574yJMoFaCB8eHsa//Mu/xM9//vOo1WrFQEEgbm5uRrV6sxlqZWUlxsbGinD75ptvYm1tLQ4ODko1kpmZmZieno5arRaNRiO2t7fj/fv3pRzr4uJiyfUn7/rs7KzrxG/mAa8yZ1dA/FdXt2cfXF9fF08PDAOzosDOzs5K6T+HrQ0MOS8DJZBBNkyJl5V5Pjs7i/39/VIVp91ul8pArPnw8HABYOTPUrVnZWUlOp2b6liHh4dlzebm5uLBgwfx6aefxpMnT2J/f79EddbX18vm5YuLi5iZmYnLy8v44x//GK9evYqzs7OuXM+84c7ePfcTIAHj2EtSrVa7QvJZWNMQ/NC8BRqMB5NlLy20h+HL8xGCBiamcSumLJx4LkKWZiV2eXkZb9++jYuLi/iv//W/ltKrXmeEy/Lycvz0pz8t562Mjo7Gq1ev4s2bN2UN2Ss0OTlZCi6sr6/H+vp6PHnyJJ48eRITExOF3nNan0E9njDkyNjYWBc4uby8LGCEQggRt8YI/I8SQ47g9QQ4UuqXDXeUmGUvEM9yDirvJQIE4Af0nJ2dlY2RFEdot29KWr969SoajUZUKpWYnJyMwcHBOD4+jqOjozKH7XY7fvazn8Xnn38ejx8/jlqtVkqd1mq1+OMf/xjff/99qRozMDAQjUYjXr16Fb/85S+LjDXAYl5RStCnARf8bu+gaYr5NK0Z3HqTpQGQPWsGLuY3GzDeh4byMrjOjgNo3UAuG4rZgGG89InIBpvrGTP9BDCOjIzE0tJSKSzAuhJdg/er1WpMTEwUgAPY4mwCHGZ2wtlB4MIUTv2MuNWJXhd4lZRbpzoxTj+ffVKU5UansL7skWAtbCQC7rwHi/uRt8gk1guaILpHVaO+vr6YnJwsus0G5NDQUCwuLsb09HSpZMccVyqVkkFweXlZUnVPTk6Ko49m3Z8NVIxG5tSg0UaDaRU54pRt6xPeg1MvRxtYS+7FuIIHsmfZqafuvx0fBpt8zpxD63bw2qAxSNzc3Ix6vR5TU1NdZ2w4hY+0NSIc9Mt7SOmT56bdvtlzQ3opdE1/AKmmexs8dlyYJqE5HC6WFdazzLWNcNYXRyYRap7Pc208tdvdJZoNzr2uNOSPjUdoxEYWNOVn0FfWk3uYs4goc4KuZJ6QTzzP6fiWKdkw4d2dTqfLcLHDk377/yzHeLedF3luPtQqnQ+ZQqlxPoMHikWDh8fl2BBAlcpNfrjLujKQzCROlbCB0tfXV7wc9l4AMkwg4+Pj8emnn8bU1FQBO+wrGB4ejnv37sX4+HhMTEzE9fXNxilSdP7whz+U/Nz+/puKRysrKzE4OFhKtuIhm5+fj6WlpQIM2GzIoVowGrW+j4+Pi6eTaErELeCHyOgfIXs8qXiJ+vpuzw1wZYuhoaGYmJiI58+fx/r6elSr1ZidnY2FhYWYnJwsDAcYQcnipXn37l08f/48dnZ2YmDg5rwDNpRhDLDvAi/TwsJC8faSJrW3t1cUxU9+8pP49NNP4/79+7G8vBzT09Px+vXrODw8LF5HGHFgYCDOzs7iq6++ipcvXxYlB63ZKLXH2wYd3kc2eUJ/9jzag+Jn2PCAvlA0pGDk6guwjg26XqDNgggBlDdzZW8HKRrZu+KUK3tr7HEeGhqK5eXl+J//83/G+Ph4VCq3UTSE62effRaTk5NRq9VibW0tnj9/Hq9fv443b97EN998U55PxZ65ublotVqxvr5eNmP29fXFkydPuvbcuBpLRJSUPM7uIIUI2iMyt7u7G+32TaWwsbGxWFpaKsrNXlorUvZhWeGjcPb392N/f7+cpePzM3gO1+IwoI9bW1slSrewsBAPHjyIiJuzS4h+GjA/evQoRkZGSkWpZrNZ5qevry/+8R//MZ48eRI/+tGP4t69exER8f79+9jc3IyDg4PCA3gU9/f34/vvv49f//rXBbAZANj7SeSoF6DJUQzzAHTjTYCOcBjoUHEIJ5CfZY8335mPABrZGwxYsXHE2tpgN6/Zywtf8DzrBr9jeHg4FhYWCqDgp1q9SZ0iasv5JltbW8XZhIGHEYsjiD03OCzw7HJWDc26DtnLb88B7+A7ezrx8ttgYQ6QbTmd1OO0RxrQRmTdjg/ADmmERMPpK0UJOp1OKe6RPdSAWQw1jP1K5WZv4NLSUszOzsb8/HxXRSQ2iDNGNhzv7e0Vpweyl34z1uzkYe0tJ5knRwryIaKMMxsp8Bn6EqCeQTSOQ/aZZAAJT8FDYJMMfl1GNKfGmC+yYQG/29hinqDb6enpiIji1ME4dTn9/v7+op8vLi6KAW4DB2OW6JbX2ZX88NTbIGSeGAvzzhjgX9IWkW3QKvOI7DFYN4j3XiSci+hmsk44C4z9V8YHYAfWhXWAxgDsNqYdZSe6zvgZI3NuGRzRXfES/Un1OxsZNpazAY0MhDZcfMjX+hm+14agsZUdfWTd4IgCH/6l9tGGBidK4uWCsAHlWLW28qzATEgskgFUniwrEUAZi8C12XuE93BkZCQeP35cThdl02mrdXN+wv3792NxcTEmJiai3b4JMb579y5+//vfx29+85ti8U9NTcXi4mLMzs52VcvZ2dmJnZ2dAhKo1rO0tFQ8qvSrr68varVaDA0NxdbWVslfRYEg2BF2Dx48iIWFhWKYjY6Olg1zKDgvuK1xmLHVapX9DrVaLaanp2NiYqKsD5WlTk9PY3NzMzY2Nkq1j/7+/lhZWYnT09N4//59bG9vl8P1pqenS1358fHxckYDtefxeq2srMTq6mq5fnp6uuxv2dvbi2+//bbsAwHkNRqNeP36dWxtbRUPtw99MnCBKVAcMCrz4PJ5LsvIsxBgrpCGUDo7O+uaWzxa9jZmj7iNFpgUZkToIywtFM0D9tQitPy/2RSBZq9FpjlSeP7Tf/pPsby8HP39N+UiOTOgVqvFp59+Gqurq1GpVKLZbMbW1la8ePEifv/738fXX39dDOSxsbF4/PhxSWsbGhqK4+Pj2NnZia2treJd4WwbSlSytnxPRIpKPtfX18VQ9fpUq9VSiWZ7e7s4DVAagCcKIEADrKdTORgzQAteskF0eHhYCkNQWWpwcLAcVri9vR37+/vFgJ6eni6pT9VqNQ4ODkrq3+npaanwMjc3Fz/+8Y9jeXk5ZmZm4sGDB1Gv1+Pq6irev38f3377bbTb7ZiYmCj8vb6+Hi9fvoxXr17F0dHRDyJt0BeKBjqHLwxuLF+hb9My9JWBGc++uroqnmXkUU4Pur6+PYjPYA96tJ6wMWNDwRET6wYa5zK4//Y82guMXLCnbXh4OObn54tzx/xOygi6oNVqxcHBQezu7pbUWadFzc7Odo0Nbz56sa/vdqMs68DmcxtuABjo3qlVzA8RCujUG9z9ftbGcovx22ioVqulEiHzh169vr4uvOJzbQBG7fZt9SLGj/zmfqLzRDdZW86EoSDF5ORkDA0Nxfn5eTSbzS6eGxkZidPT06Jf6YvxgIGbeYLxWqa6cS8H3NI/Psew8DMtX4nO5evNiy5pbsPAvADfAVhZX56HXvO11i2OKHm8NvChc9YQLz9GHrRunYhj0dFdGx30Z3BwMKamproKU0CHBvMG3chk1o39Z3bKGkz/uZQcgLMjA9YdbAT/kJHGb+Q3hgv6k7645K/nmnXk+pGRkSK/svEOrdJv9JWNTyIuphl0GUcxuN+8K+MN4xHGZ7zBO5ijavVm7xn0YEPETgscUjZu4Hnm6y+1jzY0LHDMOLZ2vZBmNnuibMV70BCKrUU8QQBGahKzQDwDEGXQ0t/fH4uLi/Hs2bOYm5srIHRkZCRqtVpUqzeh8AcPHsSTJ0/i9evX8ctf/jLW19fL6dOAsv7+/njw4EExGlCwJycnJQXi4uKiVPih763WzZ6NmZmZEkWhGg0MB5OxwGyOM2gk55Ox1+v1OD09LYKd6wCYrpLiKh/V6k2KGSdBexNStVotG983Nzej2WwWQDo3N1fO/8Drd3l52ZWzjjBbXV3t8tzWarWu6NH6+nopQ4mw2djYiPfv3xcPCoIrg2wEFGAjRyKcbwjDsx6mV5iVZ9l4qFQqxfPaC7SgIPHI2VPjfvZSRu12u8wV12XPrL1eCA97sOgrwrtXGH9kZKTLWP7yyy/jyZMnpbQnwoXDFyk5PDo6Gt9++23867/+a6ytrcVvf/vbODg4KEAIoxUPbkSU/TUGKp1O994rvL14wkj/gx+o7IPnhmgO42AOiMYYYGB4IxD5njk/PDzskhMRt2lV9gwDqpnXRqMR6+vr0Ww2o1arFTCKM4PUGby+5ENDIw8fPoxPP/00fvrTn0az2YyVlZWo1+slneH777+Pg4ODmJubKxHbb7/9Nt6/fx8HBwfl1GSAR1Z2rLm9afxtZcC9Bh12zvg+p3TYyw1dw2fcS18cebM64Vr4wQqV71FYyHO/144lmvnNz69UKoXmbIgzxr6+vlhcXCyOIPQWBnDETRWehYWF4oDZ3d0tpYnRRf39/QVcY9jAsxyQ6EiqFTdgz+mAACXmyN5Bg1jW0GA4e7PpH/OSQRwywimozCVOREcPXYnt4uKibJ7nAEuuu7i4KPTqsSMv2Id0fn4etVotJicno9lsxuHhYTEyHOFfX18vhVy89qYpO5ksdw3SLffhdyIRvo9mOWGs0uta3sU8cw/vceqJAa9ltnkbWQTPm+c8PleNol/Z8eT193VEHCjbjxEKbULLpBweHx9Ho9EoOMfnZhG56AXEkeNeLzvDrKMtyzG0M38DurPM4R764bES0SGFmfVxERLSBfMa8Uz6DR6A7pApOAG4x5Ed5Br8iO4y9mA9ycCwHsdYj4iSGmUZwPPBDHb+8A5jCsaCbIXePE7PHxgDB4r7a52C7PtL7aMNDUApAsoeXAaMNU9nAXFMPGlHFo4Rt542FFYmCibLzMhkZWJznil17B8+fBirq6vlmUzs2NhYqYTU398fa2tr8eLFizg+Po7nz5/H27dvC9CanJyMTqdT0hsQoAMDA3FyclI20QK2iKBAwEdHR6W/AGbC46OjowW4At4dohwcHCwhyv7+/lhdXY2JiYmSDgYxnpycRL1eL94aBB3CnyogRDPIwcRIevPmTTQajeL1GB0djbm5uRgeHo6Dg4OuDauEVPG0LS4uxuPHj2N2djaWl5fjyy+/LKeB08c//elPcXR0VIy39fX1sjcGDzQMk0P6zB1AkHS8HPKEsVEqPAcmR7HZO8b1pOIxX1YWFuLQawb5fq+9Zhaa9vpmT7B5ylEPv5t7bVj4bxSljfzJycl4/PhxPH36NBYXF6NSqRQDkbS/p0+fxtLSUrRarXj//n38+te/joODg/jd735XFD8Ch30b1Wo15ufnuypcscGQ/VGtVqtrXS3cyAtm3qB1aMGeQhuKKKuFhYUSPQHEIqDx0jSbzUJ/XmOisMipy8vL4sGjYtrMzEwsLCzE8PBwzM7Olv0bzWYzjo6O4vLyslSV6nRuDuO8d+9e3L9/P1ZWVmJ+fj5+8YtfxNdffx0REVNTU9FoNOLdu3dxeHhYgOTr16/j5cuXJZpnEMh6eg6scE0zWbnYUMieMisX06+VlRXK6Oho16ZN05flfTYiAE5OF+TdNhT4DNnPM3qNqZehZe9b5gvminX0GQP0IyLKQa/IU1LhMCSJMEGv9AnnEWNHB+HhtBy2/rLTzADf6Tcek51KdqCQNmsHjeclywbLCxo8W6lUSlQDEEraYUSUPYYYH6Q0WVePjIzE5ORkjI+Px/j4eExNTUW9Xo+9vb2SGrm9vV1kMcB/f38/tre3u86s8nyzVgbqpiXWGh3BNcgNxg6YzQDY9AZd8w7W0WuCrOc6aNeHypm3oGODe8tqO2DtYTb98zl6z9EdxmLMZC884xwaGoqFhYWylgaZAEx45PT0NI6Pj0vRGEdZLSv4sXPATjVkWo5o9tKN7kt21DmC6T6AbfLzW62bVEFHajwPNg7Nm/AxusEOGgwZpw2ZXu0M8AG1OJi9kZ29TY7Wg8s+ZGQzZmgBR3SOatuJk3kJ+rfuMF17jXG4Mu/ZifIxEY2/6hwNgEYWeAzML7T3DCa10OUaOsrnFsIQLYCMfniS7OHISovDmuj30tJSVCq3x7ZfXl5Go9GIN2/exCeffBILCwvFm8hm7729vdjc3CwbagmPsggwJPmpx8fHBeTYw8omN3sFIqIYJWNjY+Wadvs2vE5NckqRYszgXc2MbW823jOUHAQGYQIMMYpOTk5KmgeKp9FoRESUvGX2YNBHDlzigENqo8/NzZVoBWlZnLR+fX0d29vbsba2Fru7uz8oEGAvjkPZpg+UJvdYWNhLa+BuL5EjcWYcRzgMlsx4vN/KKaL7UCC/m+v97twnC1uDzCyILSSywW7Dn3fhef/+++/j9PQ0nj17Fg8fPixGP5uXX716Fa3WzX6D1dXVePHiRZycnMSXX34ZU1NTsba2VvbVUNwA+mPPxcTERKk2A12RJ8r8AFIISyNQ8dog1OygYA6cM16tVothMDY2VkL73At4gyeQRfBQpVIpG37pD4olImJycrJEIjmrBM8em71ZPwxtzsZYXFwsGzAXFxfjxYsX8fz589jY2CgbzAH8L1++jG+++aacD8Da24i199jN9GYAbprgOtNUL2DjdxrsZCePgYuBkJv77Uie7/O7Wef8LF9vPmN85jODunw/7yH6dnFxEbVarRigAIPz8/PY3t4ulcYibvcsUPacCJp/aKRiQWuMmz6gCzqdTteGYfOGI3j+zgrd0e1KpVKACZF9nw+SwS4RHJwsrDFOGfgRYM7+FV9PNB2eo5/Dw8MxMjJSjIuRkZHyPwUQOB8DIwMZ0Gg0ijFneZYdKvQ3G7x8Dx0bB5gG3LIjx/RiMJeBNX9n4IxxY51rnNTLODetur/0z/ouG+K9dJDnyzzisV1e3hwqijGITsIpcHV1czTA1NRUySSBvslO4IwKp/DY6OjVL/O5Aeufc3rQZ/MZ9zuFkGtYFxcpsuHHPPf33xbPQE5Z9/M5QB1DkjHTD+/jYg6sa3xGB99hTMCjNoAx3G0YZAzCeM0flqXml/xdXhfrARsZXlcb5vl3L6dFr/ZXnaPB4mZLmwHYqvJ9WKR4TDLx5wkBKGQQGNFdV5xma9ebg7im0WjEixcvotVqxczMTDEUsCjX1tbK5mcL64cPH8bDhw/j7du38ebNm1Jpqa+vr4QUEaTT09NFgKCkSAtpt2/ysPv7+7s8yQjt09PTktOOoUJudKt1U0Hl4OCgzC0bssnf63Q6JeyNhy0i4vj4uCtdBLCG19gMT0oLXm0MEQAYm6kAerVaLer1eiwtLcXS0lIBZXirt7e34/DwMI6OjuLg4KCrSAAHIuIt/BAB2/rOytcGKffkiIBbtub5nWnPtO7KSWZyK7Lcb5jSzJmBEMomh4n9fKcGZoAFD+R3e1y+t9VqlQ2crPvS0lKXF5S666RGUGEHb+TS0lJsbm7Gzs5O7O/vl4jW1dVVHB4elhQ5aI89EYODg4X2mTMUlZ0EgGzzhaMPpHHA114j8uipCoWAn56eLvs0rKxMUw77kqcM/7FRnf0XPN+59ZTp/OSTT2JpaekH+6EwIKjEBj/y3G+++aacvWEgkw1QwGCmp6zMc3TAoCkbsx9SGL0U2of4DXo1b/kZTgW0wqW5X1muZ94wIOxlhOQ581zZsYPcw9hAH0BDNhZwHlFZDx1gWnA+t3UkwAKa5RR6eM6GhtOSARcGRYDAiCjOKjcMZmS0QYodN/BHniOvM0b60NBQiWQAREmTAihhvLCnCUfT6Oho1/4H9lqgR1h7nH27u7tlY20vg7EXXRiceq2Rr4Cz/JMdQZl+Mq3lBlj0++08tAeY782XWdb7nb7POo97+QzMY53HnGZdmeey0+mUsvER0RXxhZaIRGe5gNEJTRhHuH+mJ54BLWY55Hdn44/3mi/gBTsUjUc9X9lIsYyAf3km/Tc2QCfhbOFdNgYy3zpdyvvPoEfwVe6Li81YJvaSx9xHf7y2XGM9mdfR19CykWJczzgyTf3NDQ1PFg8nBGTgY88XA2FDLsrSJRQrlUoBvhG3oUr/OCzribbXE+9tr4nodDqxsbERjUYjPvnkk3jw4EHpExtTLy8vY35+PjqdTqnOMTU1FXNzczE/Px+1Wi22trbi3bt35VA7woqNRiNOT0+LoUJJRSISbDB2CG9hYSEqlUoR2nia1tfXi3DHUzU4ONglGAD5EPzl5WXs7OyUQ4+WlpaKgYDwggFGR0fLfYBAQnqVyk1q0fb2dkRE1+ZW0oAmJiZKJSKMjHq9Xjzb19fXsb6+Hv/xH/9RQusu4/r+/ftiZOTCAigtPs8eJdYThiUSYsXpMo0GNIwfhWxlkBUuwJbQqIVnpkF7HuwhtJLLhoH75WYeyt6sPAeemyxEXHrSCuf09DRevHgRzWYzvvzyy/j888+7AMjW1la0WjcHJo6MjJQ9HU+ePIlPPvmk7C3Y3t6Ot2/flnKUhNQp9YnRSjogUVDGhuez0+mUcs6np6ddOafb29tlzSKiVHICqBBFXFhYKPuRaIODg1Gv12N2draceE56kz1h7MtgQzAVg4ickHdOSWe8uJVKpRgTn332WXz66aextLRUNrtSkWVjYyN+9atfxe7ubjkAE1C6ublZohxWiqynAYRpLjtfMsDOgIZxIiORB9Cu3+PcdINd5JifYTqzHI7oLs+awRw06v5CfyjozJPoDfrMvbReToVevMd48aBfXl4WQxFwFRFd+2OIUkREzM/Px9zcXPG8n52dlcIVyNRWq1X2cGS+N0CpVqvlOmgSJ1GvNB3mnJxuzw9yn36wSZdDBV1Uw4YIERbSteiz6ccAkmgc9/MO9h9OTU3F9PR0iUzCd41Go1TxYpwRN2W3KcRwfHxceN/AiWaAjT4z/doR2cso8VrwbNNNL+MmG6vmE8ChDXB0gPP+3X94D4xjT7LHyPNwaHkucKaio/jOY+SZdrZmfkHXX19fFwwA/0Nrh4eHZT4NtNlHSmS7lxPINGxesEzz9XwPbeRqSf4up2syPtOvn4/Bi2GEA8AVL00/+R3IQXQYf+d5R1/RD/Zl0EevF7KUPpFVYloyLfq35b+NOtNtxG2aMUYPssc4x/LbTkz+B1f7/V7HXsZsr/bRezTwsNtL46okWL+ACxbr+vq2ZBuvYvGJclSrNxUsqCLjjWsYKlRqyta9lYetWfrKplIU/OjoaCwsLMTDhw/L6cfHx8fRarVKji6boCuVShHe5HNTFWZ9fb0IfIQ34GxycrIYMswdVWXIp+VANRiEvHByoQHL7J+gIs7Y2FjMzc0V8O7yuIODg7G8vFw2dRJhcA4tZRwNHK6ubk5jHRgYiL29vdjY2Chjh8gQ8I8fP44vvvgi7t27V2qidzo3EQyYhU20EbeeOvaAvH79ujA1Ci7itpY8m63xhNpbmoWvhShM3mq1utJrMnBxqJT+ZWZDidF370syzTlHlv6YFi30oAVX4KBlQRJxC/p+wLAJhPEZ91swImytfPlscnIyfvazn8XPfvazInThu6GhoXJq+8zMTJlTUoeIkFCK+ODgoIwZmpqdnY3Jycki5IkQsFZ4jefm5kpono23ETdV7qhy09/fX6qbIS+Gh4eLwY2XlWvr9XopkVmp3JSNxfBifgDPCFxol0IKAFHo2ACRynVffPFFMTRGR0fj4OCgeJsvLi7i5cuX5aDOgYGBUmjh3bt3pUwwPInMtJJBOTpkb2VjmjSAMb/aaLbX1fsCkCMoRRSe+cDles1TeLvg30yT8HFWnCg/86UVMHLVPOGKV8wPcwCItcPAc2WFybPg24WFhajX64XnPAaq57hUp3OoO51OKYnLfFiuExlwOVP3CWOGA1bhA+iS+WMvXKPR6KJjrsFwR3dQktseVqLryEfkafaiAqLgB0AkcgAghR6bmpoqvDgyMhL7+/tFhrGvqd1ul8hRRMTu7m7Zw4c+Z31zOoqNVGS09UJ24mT5C63wAx3YiWWjxed5MVbmCidpX19fV2lT+gjeweHVq8/oKZcqhl6RwdZFluuW4X4uYzed00/zC0aAo52kyFGVr7+/vxS2Mc+D/Vgj9LQdqtCSHRY2FDzG7KywPINHDfh5JrLNJYoB9ZVKpUsGO8qOXGOPKI4Bnm+9aiONPtuwtBwDZ9m4YB2Mo6AxGz7n5+eF3y0jTReWibmflqUZY5vm85js4MCoM+a2HMwGEI465PrfdDO4vdLujEMq9kDyGYoIBmejLBNtwrP1a88bEwVh2jLzgjr87D4wWfZozM7OxoMHD0rJye3t7dKXVqsV9+7di6dPn8bc3Fzs7OzEV199FaOjo7G8vBzNZrMYHGyg9gYflBOKBsK+d+9ejIyMRKPRKId7+Z0wARtv2YTVarWi2WyW/GAAFQplbm4ulpaW4v79+yUfkcPi8DpQScJ1o8/OzooRg/HhDX4Q5ejoaKysrMSjR4/KmRjj4+OxurpajJM3b97E1tZWnJ+fF2BSrVZLFZHvvvsu9vb2uoQkgsXKl+gO68W1bKpC+Vkh4OGGFnJqGO+AsVCaeORsTNhI9XPMZNzriB40ZyOZvsDg3jBoYZuZ2yDMDM7fgCpHCdyseOEX+BPBgEf1s88+K/uTANwomoiIn/3sZ/HgwYO4vLyM9+/fx/r6eoyOjsbs7Gw5VO67774rfMC4OFODOUKpDg0NxezsbNknxP4JlA19HR8fL2fnoNTwjEKX7M/Ay0eBBvaJ9PX1xczMTImWIK/gTw7iu7y8LCVN4Te82hz81tfXV/j/6dOn8cknn8Ti4mLMzc3FT37yk7i+vjnFmwISGJMou8PDw/jTn/4U3377bezs7BSFDzDJa21vn8s1I8swUKhYYhDrZ2XaokF70EQGb05xADRlIxdajLj1APJ+KzSajdHsFbMnleczHhs+Nv7zmOxA6MUP9rLyfHh5fHw8FhcXu1JDkDUDAwOxvLxcNvOzvuxfaLVa5UwUIsCZB+H/drtdDhMzCOh0OmVzKvrAzjhX0QHgMhYAX8QNuCcN10YhwIpGBMNRDv4moueorL2ao6OjMT09HbOzszE1NRXj4+MxOjoaU1NTxWmFU4C1A4ARVd3b2ys0Bf3l/QeZDkx3RDW4N+IWo1jGYWxYBiJrMHxs5FpmZwMHgI5u74VPLM+Nhexdx1AcGRkp4BXs4PLBOQ2dZqcENMw9Nh6gEUM8xmp9gu4kwwHaIeXU+IoiMZeXl7G9vV2cPhFRHEOtVqvIUUci4G90K85ZpxVZHtjwsw4EI1j/eQ1YW2RkpiM+x9Hp0r3uC7LVzjobfDgDfLCxjTFHD6EnolE4sexMydGn7HDyZzYesxHCWO0QyvssuIa5pgInYzNvIufhN/MnWR9/qf1VEQ17a2wd28p0qUMGxHUmCDprC9ELaBDKQjulJj+bKkRsDHXkBUKKuM2fhDDGxsbi888/j0ePHkVfX1/s7+9Hp3N7oM7Y2Fj8+Mc/jomJiWg0GjE4OBhLS0vljInDw8N48+ZN/Ou//mthAIQJh84gvK+urmJmZqbU8sfahoH39/djbW2t9NPpAltbW1Gp3JQMRNFQHYJNWwB8SonWarUYGBgoFSNgJk7z3N3dLSAMjyaColq9Ocvg4cOHJf98fHy8bIx/8uRJLCwsxObmZrx586YQIpvHIyLW1tZKVSnmf2JiohhLBhD2MhA9Mmjo5RVBQAJQrVQ4eRpaQnhQ5QgPLUIZw4HzO0j5sTVvoQ2NWBlbiGMIGtTYgM7VfywoMj9k4dLpdArItMeIa+AdnuWUFOar0+kU7yRnanz++eexsLBQlExEFMPz6dOn8fjx4xgcHIz9/f0SPfv9738f7969i83NzXj16lW8fPmypFPxXp9fgDCjXGyn0yneZHidaj8oCeiCFIuBgZsDJdmUOjY2FpOTkwVgAWiGh4djcXGxeGKhkYib9BhOqedAMIAlgBXg8fDhw3j27FnZ7D0+Ph6Hh4exvb1dTkj/zW9+Ezs7O10GAV6wly9fxosXL6LRaBTeqlQqRUA7imsaYOwoJstEroPOAFwGyfAVxlJ2wliWw0NW6NATa2MDmmuzxwy5ae+Z6Zl+Zs8aB226Geghty33eRZ94VnZwMKQ9fc81zw5PDwcy8vLpdAG8+S14vBH6IkzBd69excHBwdxfHwcm5ubhX+YL2jK+zfoM3oC4AuAQh6RvtXp3JZR5929HG/sjwDckBqLjnSaC7zhPR4Gop7/sbGxmJ+fL5u76U9ExMzMTCwuLsZ3330XzWazGPOWZ+/evSvFRJh304KBFTo604sBPP9zTy++QP4B1hh3ruxlGqUv8BV0i9HpFE87sphLO8oyFsrA3wA5O5vsyGUtAbN2TponPsQ/uR8er6MPjGVycjLq9Xqh/SzXKI1uAIsM8T6eHKn1+uTok6Mn6MheABm5ajrx3lxjQhq4zNFhOzPNE/Aq42GN4V8cZpVKpfAlYwLHOHXdY8L44Ad6yHRhpxJ4wg4cz6UdsnY62njGUYvs5hmeD+sD+sM82bC3kQGW+kvtrypvy6JG3Nb4BhSacN0ZPDhWItnC9KRkj0YO/zh3z8oi4tabTD9dcx8gjReTz6vVm1BWrVaLR48exeLiYklfIrWCKkpTU1MxNTUVs7OzcXV1FWtra7G1tRX9/TelcV+/fh0bGxuxs7PzA48kAhJiZSMi7ydqQT43DIoiZTwLCwvx6tWromiYm9nZ2eh0OkUhkl9vcEUJ3svLy67yoQgEBPvs7Gw8efIknj17VrxW9+/fj6WlpfiXf/mX+OUvfxlzc3MltxkAiBGxu7tb9mK4BjUH5RmQW7AbNEFDWbjag8q8Iihh3Pw8aMtpS9AIyoB7eDbCB88fSoTr+CwLavc7h/cz0K9Wu0sdoszoN30zn1iheOwOr2bjxoIFusTjw7P7+/tjcnIylpeX49mzZ3Hv3r1icEBTpO1xuu/c3Fxsb2/Hr371qzIuIn1v374tgCuDWkf6OLwr4taDTIln8tUx/DBmATrfffddtNvtqNfrxbsMKD8/Py9RFULs7fbt4WWM3Tn20Bk0sLCwEJ9//nmsrq7GgwcP4uHDh3Hv3r3o7++P3/3ud/HP//zPMT09HQ8ePChnLpAXf3p6GltbW/GHP/wh1tbWisDP4DvLNCtzgIY9zzbIMpAw3cPn0PmHFBTviOiOOGCwI594tz1+Np7pg+fQIME50QYUXNPXd1sX3sY3Mtp8ZdrPjicrY97NM22smN/9PiJeFEHg8D34n0PloDecOUdHR/Hq1auYmpoq5xEdHh6WvUXVarWr0hLryNwgy3BMOVUDuYnRSdSR9Fwi3Rw6iEFhgGQ5RX+sQ3G82PmHQ4P0qPHx8ZiZmYmpqamYmZkpkf3d3d0YGhqKer1ejHbvdyIaT6qg1y+vQ5Zh9A+6gvYMyGl8by90bnby8E5HNKDHTFOsUzbmM/Dis+ygMt3yLO6z4WzwSyTTeiSPy/davjvi4vHAv9mIs75A742NjZUUPN+PToVuiVBERDlUmOfixLQhzDiYD/ffPJvLv3Mvn1k2sQ5O/424dfZgZNg4xgnWbrcLALeusmEJv9MH0iE5b4ToO3gGpzi6nOc704KxO6JvucWa2Jg2HRkX9ZKVdsB4nx7z4rnuRSfoBa6xgW3a8/7hD7WP3gyegYyFUc4N5nN+SA9AkPbatG2r3YPOXgH/zfvMPF4Yg30iG87zZUEAy6enp7G3txf37t2LqampEp47ODgoz4u48ThNT0/H5ORkGQubzNfW1sqppnt7e9FsNrs2h11eXhbwxQY4lIY9gjmVp6+vL5aWlsqp3bu7u12ej729veKlgagxlJz7CgCBYRFc9Xo95ufnY3FxMZaXl+P+/ftx7969Uv7u4uIi3r17F81ms5yd4BNDh4eHo9lsxqtXr2Jzc7NY7AhmpyFFxA+MBGjARAytOf+b8ThaUIi5//ZkY7yBjorYMrfSollQVyq3IVvWLntrnZqQGd1hSN5NXzGmnH/PWBFs8BrfM1c2PvxORzJ4Hr/tPQRswcMGoqQm7e7uxpMnT+LRo0cxNzdXaPXo6KiAWDxdU1NTsby8XCJUg4ODMTMzE6urq/Hy5cvY29uL3d3dODg46FqPq6urrrKc9qLYGIXfrFjYeDo/P1/ywV1Ot1KplBQWDoU0rzB/AHEMdiKcGFJLS0vxox/9KJaXl0ukhE3Au7u7sbKyEgMDA/H27dsSlex0OrGzsxOvX7+O58+flxPQAalWDFS1yjRvWWrAYhqOiBJRg16svK24DEJ4DkrMRnf2jkOT0HROwUN2uM/mJfO85XdWnp4Tg0+/l/7wmZ0UNiwyP/Icnu3xGNxAG8wvBgIpQUSJXdmJPo+OjsbY2FjMzMx07UmampqKk5OTrtLFABDrIOS1ZbMbQMfReSIhlcrtYbadTqdUc/O9vBN6yOvmv6ERou5UY2QfBrLz7Oysy8CpVCrlDBjOZ+DMJacAGyjbcHR/syOR+YIn8mbrTN/Qm+Whga6dmfw26Ofa7OnHWZbxj59DnwzkM1B2qguOM2jSezsMiFk380gv3rNTiz55DhiDv/P95g8OTQRU5ypkvMt9xQGIrLeutVFh48nrbCB9dnZWwC5yyjrXDmXzI/LEET/PnftO4QQ7PJgb7qcyHVUU6QP38bw8NmQXZ6BleYwhBR7wd3nNLdeMb+FZOy4sJx3NhR77+vq69pz5c8budWMcfOcoi/e9/bn20YZGDs0YADMBtszMoFm58TcMZ8vR90Fwfh+hRCukPKGcWmxvgL932g1ji7hhls3NzTg/P4+ZmZmYnp6O8fHxogxINfLmdHLMSf+4d+9eHBwcxMHBQezs7JQKJfv7++VMDKrXMP6IWzDlubP3w+V3a7VayfMGOLZarZJS1W63uw4M5LOLi4sYHh4uJ7yipFAk9+/fLxW27t+/H1NTU7G/vx9v374tufHb29vFk0Y/W62bzcGkhwDsvHYwJOMxUZspI7q9VxHdew5YV5jKAtU02Ou7Dwk0PnN/TRvZowow93u4PgtuGzT5Oe6XDeIs+D12ngNY6wW2soHivhhwR9wqRIOQ7e3tYng8efIkpqeni4I8Pz8vqVJTU1Px4MGDYkiOjY2VAy13d3fj/v37JcVoe3u7a0M3ZaIR/BnouewxMoZCChMTE0VYcp+NFGQKNJUVdsRNKJ1qadVqtZxYzH4nDJlPPvkkRkZG4s2bN6VEMOkr5Fi32zepGNvb2yWlstFoFA8uAtnKzkrB60/LTh1HNsx39qr2omnL30ybPJf5ItJko9ReNxv8PJv+Gdxn72Uec6ZBj8n8mgF3Ngz8DM9BNjT8vXnJPMicW5Z6zxrAG8dQq9UqQIzvMTJmZmZKWsn5+XnU6/Ui912K2SVirdBtdPPjDZcGP+Z1aNH7KVhLywnTG0Ct0+mUiCCeWhsa7ClkL0q1Wi28xjrQr2azWQ54BWRlfZ3Xht8AUtNrBvLI5w9957+zXMwGeaYly2nTjOfTMjobTTais87pNR637EQCp7gfjpq4X9mg6CVjcn89V710bKapq6urEqlCh2Mk8z4cFl6DnEZMP4ismQY8DuslN+tn5Ay4zk4k0ybzkq83qOa3MSURTFIbceay99WOGcZF305PT0sEKO+xMG9mWZZpEnnj76GDLENp2RDOzkhkjQ1LOyQ9DzYyjNucTveX2l9laBgoudkj5UHR4RxlsLLKytXfM+H8NtFmJsHrjnVnZuY+CDczBF7ViJtQ78bGRjSbzTg5OSnnQ4yNjRUP/cnJSbTb7WJkIIxHRkbi008/LZ6chYWFLq/O3t5eyeONiFKqEIHt8WZDq1KplHMzSFfLoAOihYlhjr6+vmKNc4jS7OxsCY/6cDGMnUqlEnt7e/Hq1at49epVmSOH7ynxe3BwEJubm7GxsVGEcBamOWUhC8cMQHqBhWzdIzSgNa7F85GFLIxirz7NgtgeLSsc84DBQabjLOD8fOi8lzIyAKCvMHluGZh5nvy87EHMIVMDWj67vr6OnZ2dODo6iuPj47h3715JmSDK1263i6AFBMN3Q0ND8fDhw5iYmIiDg4NYXl4uJ27v7u6W4gMAOrzygC9HhTqdTlf564godOe0PKekQReMi9PM4QeM7Xq9Xk44Hxsbi8XFxTJW5u3q6ir29vbi66+/LvuPAFTQJBVX2KPSaDS66NB0leVSVvCsIzSQPbLmHSvKzFemSdOVm42gDOwzTVrW+Dm9wJjpM4/bXlDTqMed+cYGimk9z5N5wv0zr9nYsmyyJ5g5BFQSfTs/Py+6AHojkoAjBfqCRvACc3o9J8mzvwgAxlra+2pwapmWDQvGh+I3ALAM9F4b5Lhz1+kvZ+IAyNCdR0dHsbu72wUqncpHtH5zc7Mr2s26sx5ZrkHDlruZvrwmvYC66dK84uZn9DJaTVu9eMjgLI/J1/BZ1g9ZZ+e+OeJj8O93fAjYeeyeA89T7kcvnZV5gmtJQXKkgHW04eYINXNAqprnw846e9uNYSJu9yt9CCvQP4+PZ2Q+MGZgbI66YDyhFx3FoDEPed+K+2bHrmVnL/mS19Tzz7zkNcp0l+W+aSLrG+ONTEuWw+6fP7NMz1kbf6599B4N7/yHCAn1OpeOBbJnBaJCQPXywhIhsABi8e3lQQh7o01E94nLHxo8ABlGgdhOTk5KGgKhS8YxPDwcs7Oz8dlnn5WwoIm+UqkUT9Di4mL89Kc/LUaKCa2/v7+E0EkrwvNDXmu73e6q+GGAOzY2Fp3OTUTn+Pi4eGlROuw94efq6qpUkBgcHIz5+flYXV0t8zo9PV2AF57okZGRWFtbK566/f392N3djWazWSIpU1NTcX5+3nV4Gyk3rAsgPoMo1rJSqXTlfXc6nZKnbs8p91qIGnja0DBYQKnaCwiDVKvVkmpgIcaa0kfe4dCthYD3UVhQWqBAt1YuzpE0aLCQ9XxlTwXX9lKsWTFBO4yhV6TRQNaRF+6rVm8q8szPz5eKY6SKoIB4H+BqYmIinj17Fp3OTRoRaUN4gUmlaDabcXR0VCrpNJvNUiLR4G50dLSrlCG0gtHvNSK6gmyBh0llqdfrJcUlImJxcbF45jgbZmBgIDY2Nsq+i83Nzdjd3Y1qtVrOkRkeHi5j+e6772J3dzd2d3eLx9reSfg/G4BOr8iGuBUmBo+jdVYs9jLZa2dlYX40fZiOP2QAkO7KfXbawGe9QJ/504YjtOe+0eyUctTOvGTaReYYOBh8OzJpgJP5yZ5jRzaQCXw+OTkZk5OTZcM178PRZQCPUYKxgmPLB/4BSEixQAcAaLmXuaRSj2WKdS1r7og9a0OxhIGBgbJ/ibkk/zzi5rwQSi6jr66vr0tVrdHR0ZKbz9xeXl4WB8Lu7m4X4GIdmCuKlphmvH8mgybTCrIpAx87gFy+12DZ88WzegEz84qjAgZYpq0M0tHTLp7jdYiIrnTybKhbj1kG+LpeII/r7QE3TzNG5F3eG2DQbF3he7mfrAjWE/7OawPd02eeD995jnoZQD593vNCuhR8m1M00d95/DyDn9HR0UITGNU0zoKCV0nHdko4UW2ewR7ATqdTirXkKIHHQ+vllMwphF6jbGxlY9LzzBr7uawJMgssZJ1keqQZu3PN37S8LRszXUIuIgqwpBKHrUQIwMLbJd2YDAwAMxfP4H/CdiwYHiQDa4MQFgJwcnJyUp5PLnUOaZGK4bw+JnN6ejpGR0dLKgmH8hGVODo6isnJyVhZWSkWcK1WiwcPHsSjR48KWCGH1iV+z8/PY2dnJwYGBkrKRbaqqR41MDBQynDipbUSp+rFo0ePSpizUrmpPLK8vFyAUB4/FafYh0EfeWa73Y6xsbE4PDyM169fx/r6emxvb5fwoD0TAJB2u13y0jEeUIiAl15gGQVvYYrwhqly6JDGu2EKb1xz2JywtDf/8dyIKIrCAAU6873OGe10Ol3GUxejyUhnPPYOGDw6quJnWZi7v+Yl7uH6nO7CXFuBWkF6LhDGCBXKWi4uLsaTJ09icXExarVanJ+fl7Qi0gnr9XrUarViqDx79iwqlUqJFpKCBA1fXl6WU5dbrVZXignGS6VyU5L27OysAKbNzc2iXJETzAf8xxyOjo7G/Px8OXsDQ2lrayuur69jdnY2qtVqbG1tdVVScxUf5MLR0VHZ9E4VN6dHeT1MA57zdvu2zCLvshEIEHWlG56D7LAstlcfZWGZmqML8AgtG7b8jfJBZvidvifTlWnKud3Z2KC/HqPnyEZA9t5ZKVoJ+xr4lr9t8NBfO7MyGLV8qlQqJUoxPj4es7OzJZrNpmfeQ6oFUdSxsbG4f/9+iRZ6r4HXE8PDP9A4egEwQ//teIIOkVlEsjEsiMJTOYfrKfqAjj48PCxpsJQ8ZZ7RWxy6RyUsijYYUAFOGSOyMwNwfvs9BqCmYacdm2fMbwZLvpeSpo6C2rDIRr75mJRPrjEwc/qOQbqxkLMoSFnztfxt3rYHHv1uJ4LnKs9rnifTsu81HTpC4fvdV8C+08fNs46kZycgY/JeETuOmdOrq6siHw2W+/v7i1OadaV6JveDl9jEnY1RaGx8fDwiousUdDavg4nAOET+HKWoVCpFl/m8DGMY3pkNquyMzfrZTiCvoQ1K07hlmt8JLrYMM51hlDk62Wq1yvh5r8tvm4Y+lHXh9tGGBgMwwHYEwELC3l8moVqtFlCBIGJQAA4mJBNm9jDb+2vvE4yVPToG04CU7MHl2Rgq7jvKngo2KysrMTs7WyrwcLjf7u5uTE9PlzkYHh6OlZWV+OKLL+L+/ftxdXUV//Zv/xaXl5cxNzdXNp2fn5/H8+fPo1qtls3j2eOJsMcg42TxZrMZl5c3J9ziOY64MYxWVlaiUqnE27dv4/Xr1/FP//RP8dvf/jZ++9vfxvn5eWxvb0ez2YxO56bs6/j4eJfBUK1WSxnQo6Ojkh6FpW5hAsh0Pnz+gYG4z4KCz4komOZgaoQODGJvqtd9YGAgTk5OCuOYESOibBB2STzoYGRkJE5PT4twZywRt14khJ03dlrpQ2v2Gple7eFHgDLerLQyWLUx1MvQQohE9C6biuDhvV4fK+RqtVr4knch/OCFlZWVcr7KzMxM2bvUbDa7DOlarRarq6uxuroa9Xo9jo+P4ze/+U202+2YmZmJBw8exNjYWGxvb8fa2lrXmpyfn5cTasfGxor82dvbK7X8OQOGSm5nZ2dxfHwcw8PDce/evbh//34cHx/Hixcvoq+vLx4+fBivX7+O//iP/4j+/v7Y2NgoB1ShPHE6MOaLi4vY2dmJd+/exfv378teJJQZMtAKyEADGYKgtrBHRn3IMIEWkXOmSRsDGYBnozx7o6BHvPFOUTN/U0WLktHQXV/fTQUwwHWvPGSDOL4zADFgo0/ZsEbhIdsywMpAKBsyXMfzs3cR3uO3dYm9feYfA5aRkZFYXFyMqampslYADsaFwcpBlp1Opxgm3AP9+vBT71tkrdmcS+SPCEWrdVMaempqqgAx+g6vHB8fx/r6eszPz0dElIg6hQ4wcjBCoF+eT5Tj6OioHDLLnEMXloU2ui2PWFuvNZ9lWsmRmezlZ354Hno6R/UwAHlOXmf6Dp9xLTIAQJxBoenN4JB3MH7rkQ/xg/uRASb347hz6VuaDXPrUoNTskfyWMxHveY3yxXkGXipWq2WbBHGYYyA0dzX11fSC/ncGHF4eDhGRkZKWXzm30Y/+sB6jApZYJDr6+tSwZPUQKJ6BwcH0Wg0Ym5urhjL3OPDConWZIcIuonjEPg8g27PmenAstI4h2uy3HL0y3jCmMSVtRzVyxFr06sdlsgTvjcPOErmMYET/lL7aEPDVqSBGZYpi4l3zgqSsOzJyUkRjBA39zDxvbwgELOZi7QOiI7SgfTFkxkR5cRpCJFnukoNBM1ieOEBiIApNlDPzMyUk2WXlpZKJR2fGorwe/z4cczMzETEDTB/+PBhfP755zE5ORmvXr0q88AJsGtra9FsNmN8fDymp6fj7OyspHcMDg7G3t5etNvtqNVqsbu7GxsbG1Gv14t3dHp6Olqtm8Ok9vf3y9i/+uqrqNfrZf9If39/zM7OxvT0dClN98c//rGUadzd3Y2tra0igAFLnEnRy0tp4Wcj1ErJghOjyql4MBjvYD6hATNuRDdY4/0WeDDuwMBAnJ6edm30516DMxq0aaPB+zxMq/QzA0cLGIMGeyUsUKmG4fC2lZ6BrQ2AiCjKH4+Zn28FZoPNYW8AAfxog8tAuK+vr5wQv7CwEDMzM1Gv10v9ddIHmdP+/psTkCcmJmJmZqbM09OnT+Phw4fRarVKtI7Kbtvb2/HNN98UxTI2Nhajo6MxMzMTy8vLUalU4uXLl0XJcC4GXudO56Y89fb2dskvBzD/8Y9/jFqtFm/evClAGicCG+JfvHgRZ2dn8ebNm3jz5k0xskkVtGK3Ach8RdyWjWV+sxfLgNe84ohcBgZeCyvCbOxAG8y119HrAu9luUufAAzZaMXRRAlWpy5Zdhvc8T7LjQwKswxmLDYSDG4dRc96BBrP85aNPlqmc0cQvX6ee9JUHeGAHuBnnDFEV9kTwTgWFhZiYWEhDg4OymZ0HFbn5+dF3nPOh0EZe4nIKSfdg0bRj0ajEfv7+yXdlwIlpEqhOzDoO51OSZs6Pz+Pra2tUhgEueo1A/jklFDWw0av59zy3PSTnYW+L9Ma/zs91yAaGer7szzDSASMQz8eF7op84Of6TLFpmXkgOnMABojyc1AnfcYB7Xb7a4+mU+sh90cNcpRnWw8ZWMc/Uef+J6UKrAZ2ND9Aww7HdARy2r15gyPiYmJUqmtUqmU4gek6JEOS9SOqMrFxUWcnp6WEtToUbAjcpTN3KQA+4wjDHT4gDGSFoUxAtbrJT8YC3LDst/GG3OY+cEYgfssv/gevJj32RmjQI+8KxsM9NMy2bgcGqOP1nWkk/2l9tGGBqftWmhEdHsF2u128dTae+TykQ7FYHXjGabjHHqCILMX2O+1gkEYM5EwhhedcpIwRVaw9BlGYaJ5D4sFcHBo/Pr6On7+859HtVqNBw8exMjISKlEsrOzE9vb2zEzMxMzMzOFiIeHh2NhYSEePHhQapLXarVYWlrqijQMDg7G+vp6HB4exk9+8pMyvvPz8wK8/vCHPxSQWq/XS7UomKnVasXOzk45I4TDbBAMWPgbGxuxt7cXEdG1eT3nnZtszBAYVY5MIcwgUIfe7DGAhrjOz4VJzLSsPcoQWsv5mwZgEbc1rpmLDGYI2VLaD3qDbvr6+somZubc3iqDrF48A4/A4NlTnT3P0DLjsScwIrqYn7mgP9kbaIAG/xkMsCaZV+kD4wVUEk6t1WqlFCj7Mz755JNyoFmz2Yz9/f24uLgom0339vaiv78/JiYm4uHDhzE8PByHh4exv78fExMTcf/+/Xj//n3UarWo1+txcHBQDq68f/9+UWKNRiPu3bsXe3t7sbe3V4BXtXoTkXv37l2X0kSQ4rFiPxOerUajEZubm9FsNkta2Pr6ejEu8vowB14X5jIifuAV85qZ9qB50wJAvZf310ajecL0yrrxmWnVhq8jglY+7P0yaDW9MY/Zg0/fiOr1SuGzJy07CAzgbIQYPPIMG0S96J25Yz7t3bP8oi9+Ps+yoeHvzRtsrAYs4bVdWFgo/EuKnY0rR/gXFhYiIuL4+DharVbRI4ArIkgAYSKnZBfUarXY2toqgC4iyv6Pk5OTYtgYICIn4HvAAymB5KWzj8oZDMwB4zk7OyvyzbTqtJcc+fI8R9w6h0xrrJPpBdlqGrFR6DX2syyLbfTjdIQH6avxjR0GgG0bGp5T9wMAa5pkrPChnT7QP++wjOY+vzvPk59rerdDwv3OssXrZkPAY2QtGKvnFryEjEbXMMdEQaE7Gyb+HJBseUfqH1E/Z6A4OrixsVEqkLKmTkfsdDpFj/Ee1mlwcLA4yihSwnyRNui0pl4w2nOWZYZlVc46sPHpCKAdtcbO5inWwtjH8sVrjMyGX91f02wvp4D7+Tc/GZxNXDQT7YdC31awEKZDfgZiTquyEQBhj46OlsiDlSWDJ/UpezqYVHvgEVp4dSBse+3tPcAqzhYji0zpTTbKzs/Px/LycvHuXl9fFw86RE9awPj4eNlUXa/XY3BwsDAi1W2Ojo7ik08+iUePHhWL+ujoqBxjPzQ0VIAzBhX7QdiTwkZCzkM4PT0tUYu1tbV48eJFAZEoBpiS/mI9O+0o4lbojI2NlWhOBvkICisMgApr7LBuL4+VjQ0EURaY9l4iiEgPgXmzpxUDwl4jlI+FJ/PjELlpkOcyNgPQrPgMikxvNMZlpWJ+Mn3nn5xyEdFd3tTC60OpjggZ7uVZ5mfG5DnHkJieno7Hjx/H4uJioX3PKbmvETcHTF5eXpZqNcPDwzE6OhqHh4fFOwXdLS0txfT0dLTb7WKYAGojovC56Zcx0Jy61mw24/j4ON6/fx+vX7+O7e3tLmOSHNwsFyy8DXwMkgwocjOPcH1ee95joGNwbTBgkJPpLHvl+cypDVxrmrY8zaCmUrmtBW/6srKC/gHi9Jd78ibgDNS43nPteel1vecVWQPPZoBsMMacmNftKcwGjmnKawigIspIZT9kvfvG/OAV5MR7/uc5KPPx8fHijGOfhTeg4j3GaGBN4QVSAu1RxjPaarVKKXb2XNgbbcdbr3HbGMwA1jIoyyPjBz5nTby2dnb5ZG+vCWPK0b+I7oIA7rNpIgNyt15Gav4eQ8EGEd/ZkPJeJ9OD58SeZtbI35vnLc/hJzuzvBYGlZm23Qfm2n3k/uwg4Pl8x7vRq9AhqVX879/weLV6s0mbyoRE7jLtVCqVEnnLUdtqtRqNRqPsE7SxxNy32+2SkolOwqi+vr4uh26anphL/s963c5RGxQfMkbMR6YR5t0y0Kn0pk8bv1zviEOW8XYYkiae9RByEwdRxtx+58ekTn10eVvAiCeRgWcPq5nBhoeFkX9na8/gyeEfexc8aHvTANvZA837aSgSM5rvQdjZg4BViwKBaCFY56+enJzEwcFBqVzAqa3j4+PlLICJiYnyHE5gJRTI5leYtlarlYMF7X20NXtxcVE2cI+NjRUjpVKplND9wcFBvH//Pq6uroqxQvWcRqPxgzSaTPwQnpU9AsbeGBcNMEiJiOL5y+ABYzFb4KYrnolgsdJgzQwcWKOczkK/fL8jYfYIW9BbKTAn0BfX0ncrAZgbhQ09ey79DI+p1zXZ6MjKwH3Jxliv+/25FYa/Q8DRLwQYAMTg9OjoqJwnMTk5GVNTUzE6OlpyZUn/gD8w5llDBDlRQ3uMr6+vu1KXXKgCGiWszj02Oq6vbw4Te//+fSm7SwUsTrknbcLGKTRlQ4+/nULCfHttcjTB3lHLJ68hz8m0ZHqATrPyyeufQZPfZTpg7f2dDU6/24DQRTg8PwZMlv8GYB6fASfvMjj0PPsZEfEDhWnesXJ0n3pFUPxDv5FtHoeBLn3xeiC/T05OSmVDDr3jO3t+GWM2Sp26GXHr+LKB4khxRBQwxbvgBesOUnxxNl1eXpay0aenp12yP4PyXvTEu3O6joGxadPP8v+96Nj0mb8zzXK/146/nRLK3HCv6Tavf9ZRlvkGmr2cO9Z9rDlz4Uibx8k1lhs21IkSmk+8Bn5mpudeWMjr5H5kx4X5Hp7IesU8CG8z5+AKnIjgF8YDTeMwZk9mfredW8YBph8wlWUZeDVHiNEHRC4wrtng7WYaoV9ZJmbnh7/Lz0H+ex4/xGOmcZ5hnuIzvyfzHfiDtWK98l5rG0lca9oyfX5M+2hDI+I2lywbA72s/15Cyd5Tg/vceTO2mTMifsD4GYR9yGPilIJeXgZSRfB+M05HXQDPfr+NJofmOFEcRYInik3k9Xq9K8eQ3FoMF1LVmL9KpVJK4l5cXBRvFt9fXFxEs9ks1RdYL+b+8PCwlOB8+/ZtCQnm1CGnkWWCZR4cMvW64zm0oMz3+8dpOzyLd0AfWbGbFnqBrywE7LHzfPo90IW9OdnIsrLwvZn5TJcGke6nlYDnKPcpr0E2HKwUDW7ztVZkFnxWplaOBmD0KSvMrEjtgcE7yuncLqcJ8F9YWIharRYzMzMlfY8zXuwtgRdtjLTb7Tg+Po6Tk5MYHR0tCgXPLns82HvT6XRKIYWTk5NoNpvRaDTixYsX5YwbnAiMFVmAUmS+3D40557frFwyPRh45JZpMctI5j7zm+nLv/0MG0FZEfp7y3ZfbyXtfQa0DNZ4vwF4/izLbc8T17qfuWVdYBrN3/XiwczTvUBFVsSZf3iWHTWAw76+vjg+Pi7yD91A+itgq9f4SOvgPjtV6Le9tgC6Vuu2mhTXYERcXFzE8fFxcYzl9B3Pd5Z1Xuc8j72Akr/rtb55bvM7esnYTN/8nZ1K0JiLCfQyVvJzczO2yOPNsjUbpcY1rJf1Ue5LBvG9eDUbAjz7Q0A1y+ksA6yjMy/7N8/tJZ88B9nYoX8Guj5WwHK21WqVan/IcAwKMBpp6/ACfWHu4Dv0EfzFvFFKnaqhRLBzn7k3y03PTy/Z0ksu9NL3ve7vhZ2yTMr39cIB+Tv311kjpg9je+uWLCtzP/9c+2hDw+G+rCyZNHe2l/ICNBjIAwgMLBlIFtwI2zxIv+vs7KxL+ToUnsdi4vzQmB12dkk1QDxEzr4AjAxvAqtUKrG5uRl9fX2lfC3Xt1qtmJycjHv37pVDnarVm42vzIlrlo+Pj8fe3l45QO36+rqcdUEdczazYRii4PjZ29vrCiM7r5q5Zbx4Xgy4Iro3XjMPBusoLK5lDT70nT2WOfRuL6S9EfwNHdoohMGhmayA7IVyvxh79gKYAa0oYNrM2P6fe0i9syDulbP8l5g6K6ashMwTWdE5tM46+F4/24aOn2MjKhtT3AffEEljUx5te3u7GBbX19dlf0e9Xo/R0dFCBxj4k5OTMTY21pVHu7W1Vaq+cdo4aVBnZ2flFPLLy8uo1+slInh4eBidTie2trbKmEwHrBfyDlrz/i+nlNlDZ1qARvg+K4ZWq1X2AnG9ZSfvMd1mLybKwMCGz3ulJDE+A4tehpIVoPfGuRk8GHT0AjhWltChFd7Q0FBXJDXTutNd3c/s5TOdAmoywMv6Kf+fx4r8z+/LythGRy9+sRw1jzebzQKCKWIAX/IedA/pupafVFzjGg4CZK+Fqxj29/eXja/IJNOf0yg9r5l+szz0mpkeeI7lJPrGc9ULNGfayYAoz3N2Mvr+iNuUn0wHmW4zDeTxOTLtlh0SmR4Avpl+DBrZz5Q3sjMupz2xXvlZ5sUscxg78sxrxtj5zPLMRjv862pG2VjLNJBpyhky8JWdfo4g0hfW2CmYOFtJ10Y/RESJShAhR98yzkajUbz4GB00G4I2CBmbAbhp13r7Q8aI6aEXDee1ykZcxkaWq3zuNDvPv8cGjuold00HOQrMZ+joj2kfbWh4IJk4MRioLgUQp8MevHPpTKQIAZSmIx1MNvnCAHvnkFUqlbI3YXh4uOsdTAblOt1vFo6KIBgABqsG1c4thujs0fMmUxaTvRIRUYCOS12ur6/H2tpa1Ov1EmXAQ1upVEpZXsp11mq1chBZu92OZrMZe3t7sbm5WfLJCdHbM2UBZtB+enpaLHaqUUFM9mbTX8L8rEUG6F4zM5fBD0DOAhRBzV4S3m9hxBqw4ddGgJkfWkHAZEBmo8ThaQN3MzfPB2wy9/6sF+hAeUC/hfH+v0KBzrMgYi7df76j3zkETMtCxLmd3sBmPrawo8/V6m1KTAbRHmdeYxsg5mPop9PpdBVlYN37+/sLD6IUUACkMmGEU0YaXuCE+kajUTZ1Q8/9/f2xtrZWonh5nxh9zl6jarVaoowZaLE2ToOBDwxmSP00P/CbCKCN3GzowRtWOJbH7pe/Yz2yh4r+Gfz2Ao82QKE19910mJWagb3nIitIAyPLZN5hfZOBLO/hXvM0v/1u9jXw/gwuofle6SyeG4NOvjMdwLueU6+pZQqyyzyOPmG+nU8OzcDTlIt32XM2sJICxdrxTFJ8M7jJJTA99jwnzDH3Ms9e515OGM8P4zNYp6+Z/r0Wmc4zrXyID0zv8KTlpvnCeCTTSUS309V4xs92H0wnvMtyJ7dqtfqDQhz8zRzlPnm+kN2kfUKD5gfjJjtP7MSzXOvF96yHHRqWgQbCma48B5YH2dgl6sGzDerb7ZsKghjQYC6eR0Tda4Ue4XyYbDTQ0L82Uns1863pEFzB+Nzg40x/XkPwmd8PfdJ6YY2sjyNuK65ah3t8WT94LaFvy07jug85CH4wT508Cx9oExMTBRh5giO6T7mMuM2dAxQy4QhIvJTV6m1VEysIBsSCIJwsyAA/eIkwNLyAnlTSM46OjroUKosKg7liDeMkfMezrHgjolRwojRrpVIpQh6wQT1nKh7ZyGGcRBAQECia6+ubswg8B8wZTAV48yFolNg7PT0tY+EMCc9hRHSlbPHOiYmJ8txK5ebQv4ODg+JF8PstAEkhc6gSMIKQ9oZvC07uZW4xavDUQfT25KDsbAxmcMacMDYbXTnqdX19XQwdM54VfcQPw+NmeD/XTMz6cZ29YH6GvQxWgNCl62T3Eha5khrvy6AwAyrWlTWwZ40+8a7s8bHny+NxWWwb4QhjR8J6eYf9fOh2ZGSkyA7mg/t4po1e5oL+ZgFpYEH0FNCA48G06vntBUiyQYaSZ6xc54gpsoL5twcWvvF7MNKI4toLzhoYGLCe0JfPoHHOd6bF0dHRUuHF8pl+YjRDy6YpnEHkXXMvYzeNZiXZy/DLtGF5bPDCe6ArzwfPz7xdrVZ/UEaYtUG/eb1NT7nctekV2eTIvd9tjyH3wQv+n3EazHn+TMeeu4gozjb0ZaZXDGfLRBtVBuame39np2FO/bG+sfFEY4yUj+c7+mAdgYzPzXKX+WIu+N5Rfssr06UdM4zX9IBBAU/a0DXt2vjKBjG82Mtohi/zXh3GkT3+Nhrz+L2GXg871vgf3MLGaDv/fC99sO5hvb2XzTxo2sHghS578TiRCbBKLjXcSy+CO1ut27PZsi5gnjDMLRu5xo6lrDNM53aM5y0BzH2v6F3GCllGwY9gQfZP5EimebKXwd/L8M38YhrMhj3PZe+vcZIN1L9p1anR0dGucJcVOIOyUsESxpPEglD9g4GaKakW42PnzSg+JdIL5OpDMD+eQiIU+XsDB29WqlRuy6nhrXcNYhQ1p0daaDklKHsEuZ8xY4HDnNRzPjo6KoTGvMH09nwhbAz2OKSMOTNDouR9qB6MZm+D3+fW399fIh/+zMqYdeTQtKmpqWi32yX/nWe6LjPhfeYE8GIFYCaF6VCELj+LgBoYGCjGFXRGf1kHqrV4vAYFHNyHsDCQcUnJLPyy5W8D2l5wexTMU4wFQW2BbaBvIWqFwXMwyAwqaFSPc4qagTH84IpA5ncDHQSgeZn1ZIysd5YbKBwrLa7B8HbKlQ00P8v9tyeJObcAtlJh/b0Xo9PpdFW3MY8ZmDA3gOxeBpHlQ/Zu2YCwkmK9oW/6mtcQnqtUKl0pnQZJ2TBgDi1X4RlHoK0IkTnIUZfq5Zk5jdKAjGZAbBpwnjDy2eAE/sGryTMNanm+6T+DBMbiucwGqOfE/c5jyhX3KpVK4SXkl2VA7lteEwMUzsDwO5kfxuLxA0yRbfZA+xmeX2gx01MGw/m7zJ95jj2XvYAmtN4r35259XN7Revy555T5Il5Pq+lgb4dDjZMeslU1gHeZJ5tuHMNMtE8bGDqA1uzM86yy/zrtYBfM/jvBWi537iBdcrgmTX2uLNMMD1kWucdzKHf4f7TbNRmWs184XVgbnMEivnCmeqombEO4wHH2Qii2ZPf6fzwADz0V46+2HGEXrFeMN9Bbx6b55zrKZ/OPTZMzFs5YpHXw81GK2O0bsj8YScccwWOMtb6UPtoQwOF7MHSAG1eQCbQStggvN2+DRlnw8EMAxHao+TFyBZku31ba533moGtKKwEyoRIsfA/zyPfj0kHgEJYHFqVAU1//81m706nU0LbDoNlwrOwMbhjwywAyWFwwDHAjZK2gFKHPx01MUCwoeTDu/hxahRWLoaCacPRCz77kBGX1xpAk+mA57AeEd2b3TOzswZWughvwFj2+vT39xfjziVsoc28PhHxA6XtlIfs6XZfPWdZ+PC70+mU/TlEYvJ6mYasQPie7yxYWHcLrAyostAz33MN9GmDJOL2nBI30wJ/2yNDX2zIGvQbANpRYSDudTIwsMCkX66yYX7juQYFpj3/QA+WhVYEGN3mNYOjrPxNu/aU2njI4trAk777ugwcfF0vustRvmwQW27B1zZO854o9xeABK2gqM1XvUBklvdWfu4r12aeyOkcph0DMtO29Z1r1RsY0KcMlgxs3ByhyoCdOTGYYo4N0LMn2TLMc+B19lobxPkz+t2L1rJ+ygZjNuJoBo1+tx0TfnYGXtkQcX88ftM2PJPLkZp+6KvplPsMtu3w4Xs7FbMDhDnLc5CbjZosa6G9Xs4N5so6yLIHekJmZp6CD3oZ3dkg49k4zpgj9wM6/ZBR+yEaZXyAdANk5B19te62oWe5ZExAH3Fw5+id32MHsuVGxl/ZqWAa9FpkGnf2SsZ6pqm8hqZvy1b3Ma+F352xL/dlZxCy11kLvAvDhciPn8t3ZBRwWPafa3+VoYHlY+FlQcnAKpVKOfWUvGhb/h4wypzPs9KHQHud+A0h+F6HbHk+iqgXcIVZAM+2Dj359ny7fxG3qSY8n03dtnYhHoMsR2cA4QaPTj/LBgAMzr0wFkxIZIO1s0KmZWXuufDmb/rGHBocGPw5ZSkzhMEha+l+8B687fbm0D9HBnqFDSNuD1O0AZpJ3IDHCt3fYwyaBln3DNazwrYw5zrmjrXwOlthmoZtFOfQLM+wV8drmr1NzKPf4+fxvQWh+Zn5sWLPY+MZBgx5bDbqMLh4nvuX19xzAk2jRK0wLFh9VobpwUaRPZjmh2ywGYh43TCOiaDaG3R9fbPJHUPDY8hz6ufaaDJdcW2emwwIadCPaSG/x2OEljwPODhcTY53IRP9fNOI+aQXzyKTslzOY4Bectlry798NhPf0zeDuuxgyF5FK+UsP6xzeBbfO/qdAST3ZiM4gwM/N9+H0eH3c28+4yr31XRkOsw0mCvR+Fnwmw0SxmrZx7qZp7KBaOOacfMsA9/MEzTW03TEdTYWstzytdno5m8a881c4Yjynssse+1IMbDPNMHn+X2muyyLLWtZDwNg98W6yQYV8+P16XWf19tznzFTrzWysWGskjFDL8MlIroi7cY5NPCJDT8/x1i0V6ohOsL98liMe7IxZLrB2My07vVw3zzXNvjdvI6mk/wOGyDGHJ5n9mZ4XXPfcBLyPPrDb7CS9bjXmEyXv9Q+ejM44Rtb9lb8TofpBUizhyxPEAPIzGehnD06XjgDzBw6zgLDhEw/Iro319jrEXG7kYs+eZFtyJgxDLKt7AySIm6ZF0XKfVaEjItwlRkMLwaKzgKEvvYSQMy3CZQxOLwdcbuRHEPG6RoeQ7V6G0Hxulq48wwDAsbhuvAWADBOFt4OIecokd9pGrFiyQ1A42YmRjnncGRWqJ5fC1xo0ueFGHxmBWZvrQWyeYDPcl/stbOXyQKTeTSA9bpnj41bVv65H6aLfG1W9gZ1TlPz3gHzs+ekF0iB7g3WPdYsP+xI8Dra29NLWfGebFAxDgOBvF4Gk3xmYPSh/NpMa1ZEXu+swC2bGTu8HHErpzIw9NzTPJd5jL7e9PMh8NgLjLnxfTaqvI42dNzvDJgtB72uBnDclwGkPaoGSzZCvV5e617j93rmec33k6KXx8H4zQM5cpOBTa/5pWXHhe/5kPPAc5GNFK9ZBvbZ2PJa9KIl062NLa+d5Yaf5z6a3txslPIM85jH6HnJ+sTXZCef5ZnfmwGmZVOmdeYdh6iBuA0dng2N0h/3t5e88BplEO/r/Q7Ph9fM65vxTjZU/F5fl+8x/RmHuU+WkZmeWd9eYN/XfihS6mfn5jWM+OG5QHldMp0bj2UjL9OaZW+myVar1YUvMj3luco6lc/ytoJexsbHtN4FyXs01/F2R3KHDcrwTEfcCmTf127fbnzKYVc/m3fCtNVq9QdCwFasCYj/+cyKIC+0x8ffFjBZKeZF62Vhm7hQ5Dktgr5acHo+SKmAuM3stsgN6vL7bQh8CGj6mZ5rvjMAt6LutV5Wvu4Ha5QFpxnESs1jydEqWi/F+CEhkK1+06WZxwzK/Of+mCZ5Nn87R9Lf+97MuPm9XlPuy+PJ8+cx9KKDD82fx+Y19XqbdjJg6QVCM//l9c4Ag3lmrnPf8r3mzWxUQq9WVDmi5zF/aDwGML7eQr5X5Ig5tpcov7cXuPD6ZGBNf8zz3JtpK6L7kKUsp/235ZX7Rcu0bL4232dgzmeWB3keLf96NSt9jy//WD73WstMP6wh/39ojbKSzrT8IQXdSwHnec5z3auP/O3396JjGxrWf5nv6EevPlsufGh+3acsN3sBD8vFPJ78nA+1LGs9d1luZseF7+8lD3u9J9Oq+52zJtx3yx7z74fk5IfksP/+c3z75/RelpV5zXsB8F5rmv/P9EofP2TQ59+e5/yZQTZz7VTLXvOTeY61Q29nOs4Nusq4NtNVr7n80Hj+nBzqtXbGYb3mLDsJrP8yH3lt0T2+NjsU6YudprlPGQPz0wtjfah99JXZE+A0CJSuCQ5rCLDBYtrDy31Od8iGgBeGUGGlUukyNFgU98N9BXR4LFnYmaBIx8C4sjVH7i4b3i3EXF6NawxIUBJ5gy4L51QExmdC5f2tVqukeUV0nwXBvg2aBR4KqpeX1X1xnh5ek2r1NhXIQpw+mS5sMBlQcK030fFZFoC8hz5hlAJcr6+vu1LpTAsed/ZQ2qBiv4hp1ukZecOdAVzE7QnnNsosQPL6GiR4v0UvgZFTM6zEDdzMSx47vAi92QDvBTAs1C1E8j4Y80lWDKZTK9xeCs3010uWYIzbG57li5/neTDf2GjJConnuA8Z6NljxrrZUHbz+sALvreXos7GJfzg5/F5Vl7+PkfzMk+Y780X8CP8nfkWheWW+9BLUXOv03Yybxs82nDKII/nZKBlg8u81+v+rKy9xv47f+ZoniPxbuanXqAhz2kvJ4Dl1IeMA//daxwZEHieLGvyu3Nfe70/IrrkegaAlg0fIwf9263XHHkMXhf30QZGBvh5TTyf+f3ONujlhHL2Qp7HLINcLAXMYrr1czOAzTIiyxZomnTlXhETA0j6mJ+TZXSeD6+LcZPpye0vgW7jKMsMr533kzo9LOvJavW2Sh+tF0DOrZe+8rPzWmQ69Px7zFyT9X0v48T3ZZzCeHvpJD/XY3RfcNaZD/L/puc8T9ZLdv55PL0i7X+uffQejcysueMotcwUDkM6D4znOQeczTsWFuTZdTq3G2jNFEwi15K2Qz5lRJT8aYB4p3NrFABauZaJdh/YFwGoZDxEJ2hO7aFEnCM2rkbhvQFWxCagLBRyalEGVJXKTX1ovnMKFJVM2u1212ZqmJVx83zK93EdzA+B5TQmhBxRLDajMwaU9ODgYBwfH//Ak2nQSV6770cQsL7Mp4GZqwjxNzRnwMjas4eIPUKMy2lzeaMY83d+fh6jo6NdNN/pdAoNMz82hnJKgwGi+cYsmXNUYX7ziYEpqYE5BSjTNu/murwJmHd5XVgH1hohZWUGn5lOI7pTLLJcsDeWcbixFr2ENXxIs1yJiFIBLX9fqdyetcL/WZHyDHjUdAqP5FxnrmcMVMpzqD7zTN5XxFzkCmXOKfZ8ZMVqw41nZYML4ONohfdxOGyPXO0FxhkLBgd9hp4oSc14HZ21EWnwkueK9C6Py3qHOc9RR/72OLnHssX73zIA8zz3SuNjvuGDbORwr1NpcpTLctz6zPPrPXO9DFrkfa8qc8g86MxODveT51tHex4z2EcW0T/LdPMCfeBvZFTGD54/OxVN37zHRqz1J3NsWeFn5nnNLW+ezeDM8+Tm75Cn6FX0i1OoXdnO8sDvteFvmuvr6+vaaOx15h3oMfOe5YbX0c/OBgVr4+8cWWI9exn2lu1+R7vdLvxs+jPA9Tr6/kxT7hvPp0yz9Um+l3lw3y2DDeaZc68Fa+wDGP19notehpWzcP4czXKvDQ/65HmwfMTRTXEgYzzPiWWH55H+ZVnjdcgR/T/X/qrytnTCC41gzKce89tACeI3yIZpvBnR1lO1Wo3x8fGujbURUWrbG7i7LKAZCQIeGRkpCtyH+EXcnIVxcnIStVqt7LTHUIi43YPCOLwQVnYoJHt1UK7NZjPGxsbKUfe27DF4ep0xEBFlY6sr+lCRyPc1m80YHh4uAof3jIyMxOHhYUTcnIkCCM6MRiUpeyJhKogq799grSlPTHlhnxrJORaUBWYctVotqtVqKSVrhjDwgZGoAFOp3BQc8CnsEbf7VTigyhEkjD+MWlvn9NE0zvp5ndmrwrw7jA4NQPc5pYa1ttEJHTNG/jaf0Xope/ptRRARXQoZcAO/QLvuF89jLjiEMud5WuCwb8sg1QYac+y6+FaONkKzsZXHbUFo5WcAybOzgWThng09eNmlpu0dxCDJXid+oNdsrHg9mUeug35c0Qoeo19ZyVt5Mn8uk5kVu+cUo87jtrFhPs6KjGuHh4dLXxkL/BQRXcaI9+axPr3SHvP6WB56nZl/A0uuQYm676YznvGh/VmAcGR+/o41c5TXPEof/NxstJr+8vPN5/amZ/11dXVVTpE3D5rOkEemQXSRHUW56pM/76XP3HqBbz7vBVB5jnmPeTQ/5znxnNqYgc7t4Ii43bNgIyrzY9Zb0BO84o2/nntoBAzgM3R8HeCO73BkZJ7+kDfaczg4ONi1mdey2AYmDlyac+oZl8cJndjgpxkr4dDznk9kXqZR5IGjqtkwtixj/a0XTF/mmVwh1LQOnuQzmo0rxuU1zTxn3eT1sEzNxpWxXs4CMC1mPOqx8NvrkPWGjbssQ8wneXO++dql8L0Onk/3hTH4XtbL8pm+npycxF9qH21o3LW7dtfu2l27a3ftrt21u3bX7trHto/fzXHX7tpdu2t37a7dtbt21+7aXbtrH9nuDI27dtfu2l27a3ftrt21u3bX7trfvN0ZGnftrt21u3bX7tpdu2t37a7dtb95uzM07tpdu2t37a7dtbt21+7aXbtrf/N2Z2jctbt21+7aXbtrd+2u3bW7dtf+5u3O0Lhrd+2u3bW7dtfu2l27a3ftrv3N252hcdfu2l27a3ftrt21u3bX7tpd+5u3O0Pjrt21u3bX7tpdu2t37a7dtbv2N293hsZdu2t37a7dtbt21+7aXbtrd+1v3v4fFoBWd0vPZxAAAAAASUVORK5CYII="
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(torch.abs(imspace_all_coils), cmap='gray')\n",
+ "plt.title(f'Fully-sampled {num_coils}-coils - SNR {imspace_snr:.2f}', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(torch.abs(coil_compressed_imspace_all_coils), cmap='gray')\n",
+ "plt.title(f'{virtual_coils}-coils compressed fully-sampled - SNR {coil_compressed_imspace_snr:.2f}', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(coil_compressed_rss_target, cmap='gray')\n",
+ "plt.title(f'{virtual_coils}-coils compressed \\n fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(torch.abs(rss_target) - torch.abs(coil_compressed_rss_target), cmap='gray')\n",
+ "plt.title('Difference', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Motion Simulation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:46.285805Z",
+ "end_time": "2024-03-05T17:22:46.286383Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the transformer\n",
+ "random_motion = MotionSimulation(\n",
+ " motion_type=\"piecewise_transient\",\n",
+ " angle=10,\n",
+ " translation=10,\n",
+ " center_percentage=0.02,\n",
+ " motion_percentage=[30, 30],\n",
+ " num_segments=8,\n",
+ " random_num_segments=False,\n",
+ " non_uniform=False,\n",
+ " spatial_dims=spatial_dims,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:46.286992Z",
+ "end_time": "2024-03-05T17:22:46.362905Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# call the transformer\n",
+ "motion_corrupted_kspace = random_motion(kspace)\n",
+ "# apply the IFFT\n",
+ "motion_corrupted_imspace = fft.ifft2(motion_corrupted_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "motion_corrupted_imspace = motion_corrupted_imspace / torch.max(torch.abs(motion_corrupted_imspace))\n",
+ "# stack all coils for visualization\n",
+ "motion_corrupted_imspace_all_coils = torch.view_as_complex(torch.cat([motion_corrupted_imspace[i] for i in range(num_coils)], dim=-2))\n",
+ "# compute the RSS target\n",
+ "motion_corrupted_rss_target = utils.rss_complex(motion_corrupted_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:46.362218Z",
+ "end_time": "2024-03-05T17:22:46.629309Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title(f'Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(motion_corrupted_rss_target, cmap='gray')\n",
+ "plt.title(f'Motion corrupted RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(torch.abs(rss_target) - torch.abs(motion_corrupted_rss_target), cmap='gray')\n",
+ "plt.title('Difference', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Noise 2 Recon"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:46.628806Z",
+ "end_time": "2024-03-05T17:22:46.629580Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the transformer\n",
+ "n2r_masking = N2R(\n",
+ " probability=1.0,\n",
+ " std_devs=(0.4, 0.4),\n",
+ " rhos=(0.4, 0.4),\n",
+ " use_mask=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:46.629775Z",
+ "end_time": "2024-03-05T17:22:46.695552Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# call the transformer\n",
+ "n2r_mask = n2r_masking(kspace, mask_5x)\n",
+ "n2r_masked_kspace = masked_kspace * n2r_mask\n",
+ "# apply the IFFT\n",
+ "n2r_masked_imspace = fft.ifft2(n2r_masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "n2r_masked_imspace = n2r_masked_imspace / torch.max(torch.abs(n2r_masked_imspace))\n",
+ "# compute the RSS target\n",
+ "n2r_masked_rss_target = utils.rss_complex(n2r_masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:46.680033Z",
+ "end_time": "2024-03-05T17:22:47.287281Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title(f'Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(masked_rss_target, cmap='gray')\n",
+ "plt.title(f'Undersampled 5x RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(n2r_masked_rss_target, cmap='gray')\n",
+ "plt.title(f'N2R RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Noise Prewhitening"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:47.286766Z",
+ "end_time": "2024-03-05T17:22:47.290241Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the transformer\n",
+ "noise_prewhitening = NoisePreWhitening(\n",
+ " find_patch_size=False,\n",
+ " patch_size=patch_size,\n",
+ " scale_factor=1.0,\n",
+ " fft_centered=fft_centered,\n",
+ " fft_normalization=fft_normalization,\n",
+ " spatial_dims=spatial_dims,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:47.289217Z",
+ "end_time": "2024-03-05T17:22:47.672729Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# call the transformer\n",
+ "prewhitened_kspace = noise_prewhitening(kspace)\n",
+ "# apply the IFFT\n",
+ "prewhitened_imspace = fft.ifft2(prewhitened_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "prewhitened_imspace = prewhitened_imspace / torch.max(torch.abs(prewhitened_imspace))\n",
+ "# compute the SNR for the transformed image\n",
+ "prewhitened_imspace_snr = snr_estimator(prewhitened_imspace)\n",
+ "# stack all coils for visualization\n",
+ "prewhitened_imspace_all_coils = torch.view_as_complex(torch.cat([prewhitened_imspace[i] for i in range(num_coils)], dim=-2))\n",
+ "# compute the RSS target\n",
+ "prewhitened_rss_target = utils.rss_complex(prewhitened_imspace, coil_dim)\n",
+ "# compute the covariance matrix\n",
+ "covariance_prewhitened_imspace_all_coils = torch.abs(prewhitened_imspace_all_coils) @ torch.abs(prewhitened_imspace_all_coils).conj().T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:47.679583Z",
+ "end_time": "2024-03-05T17:22:48.477176Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "",
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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(torch.abs(imspace_all_coils), cmap='gray')\n",
+ "plt.title(f'Fully-sampled {num_coils}-coils - SNR {imspace_snr:.2f}', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(torch.abs(prewhitened_imspace_all_coils), cmap='gray')\n",
+ "plt.title(f'Prewhitened fully-sampled {num_coils}-coils - SNR {prewhitened_imspace_snr:.2f}', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(torch.abs(imspace_all_coils) - torch.abs(prewhitened_imspace_all_coils), cmap='gray')\n",
+ "plt.title('Difference', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(prewhitened_rss_target, cmap='gray')\n",
+ "plt.title('Prewhitened \\n fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(torch.abs(rss_target) - torch.abs(prewhitened_rss_target), cmap='gray')\n",
+ "plt.title('Difference', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.imshow(covariance_imspace_all_coils, cmap='gray')\n",
+ "plt.title('Fully-sampled \\n Covariance matrix ฮจ', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.imshow(covariance_prewhitened_imspace_all_coils, cmap='gray')\n",
+ "plt.title('Prewhitened fully-sampled \\n covariance matrix ฮจ', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Self-Supervised Data Undersampling (SSDU)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:48.478942Z",
+ "end_time": "2024-03-05T17:22:48.480444Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the transformer\n",
+ "ssdu_masking = SSDU(\n",
+ " mask_type=\"Gaussian\",\n",
+ " rho=0.4,\n",
+ " acs_block_size=(4, 4),\n",
+ " gaussian_std_scaling_factor=4.0,\n",
+ " outer_kspace_fraction=0.0,\n",
+ " export_and_reuse_masks=False,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:48.483390Z",
+ "end_time": "2024-03-05T17:22:49.064537Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# call the transformer\n",
+ "ssdu_train_mask, ssdu_loss_mask = ssdu_masking(kspace, mask_5x.squeeze(), subject)\n",
+ "ssdu_train_masked_kspace = kspace * ssdu_train_mask.unsqueeze(0).unsqueeze(-1)\n",
+ "ssdu_loss_masked_kspace = kspace * ssdu_loss_mask.unsqueeze(0).unsqueeze(-1)\n",
+ "# apply the IFFT\n",
+ "ssdu_train_masked_imspace = fft.ifft2(ssdu_train_masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "ssdu_loss_masked_imspace = fft.ifft2(ssdu_loss_masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "ssdu_train_masked_imspace = ssdu_train_masked_imspace / torch.max(torch.abs(ssdu_train_masked_imspace))\n",
+ "ssdu_loss_masked_imspace = ssdu_loss_masked_imspace / torch.max(torch.abs(ssdu_loss_masked_imspace))\n",
+ "# compute the RSS target\n",
+ "ssdu_train_masked_rss_target = utils.rss_complex(ssdu_train_masked_imspace, coil_dim)\n",
+ "ssdu_loss_masked_rss_target = utils.rss_complex(ssdu_loss_masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:49.068908Z",
+ "end_time": "2024-03-05T17:22:49.361878Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title(f'Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(ssdu_train_masked_rss_target, cmap='gray')\n",
+ "plt.title(f'SSDU train masked RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(ssdu_loss_masked_rss_target, cmap='gray')\n",
+ "plt.title(f'SSDU loss masked RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Zero-Filling k-space"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:49.361460Z",
+ "end_time": "2024-03-05T17:22:49.362082Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the transformer\n",
+ "kspace_zero_filling = ZeroFillingPadding(\n",
+ " zero_filling_size=(300, 300),\n",
+ " fft_centered=fft_centered,\n",
+ " fft_normalization=fft_normalization,\n",
+ " spatial_dims=spatial_dims,\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:49.364465Z",
+ "end_time": "2024-03-05T17:22:49.423889Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# call the transformer\n",
+ "kspace_zero_filled = kspace_zero_filling(kspace)\n",
+ "# apply the IFFT\n",
+ "imspace_zero_filled = fft.ifft2(kspace_zero_filled, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "imspace_zero_filled = imspace_zero_filled / torch.max(torch.abs(imspace_zero_filled))\n",
+ "# compute the RSS target\n",
+ "imspace_zero_filled_rss_target = utils.rss_complex(imspace_zero_filled, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:49.416331Z",
+ "end_time": "2024-03-05T17:22:49.636052Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(2, 1, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(2, 1, 2)\n",
+ "plt.imshow(imspace_zero_filled_rss_target, cmap='gray')\n",
+ "plt.title(f'Zero-filled fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Composer"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 44,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:49.635503Z",
+ "end_time": "2024-03-05T17:22:49.636350Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "gdcc_noise_prewhitening = Composer([noise_prewhitening, gdcc])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:49.638891Z",
+ "end_time": "2024-03-05T17:22:50.119822Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# call the transformer\n",
+ "coil_compressed_prewhitened_kspace = gdcc_noise_prewhitening(kspace)\n",
+ "# apply the IFFT\n",
+ "coil_compressed_prewhitened_imspace = fft.ifft2(coil_compressed_prewhitened_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "coil_compressed_prewhitened_imspace = torch.flip(coil_compressed_prewhitened_imspace, [2])\n",
+ "# normalize the image for consistent visualization\n",
+ "coil_compressed_prewhitened_imspace = coil_compressed_prewhitened_imspace / torch.max(torch.abs(coil_compressed_prewhitened_imspace))\n",
+ "# compute the SNR for the transformed image\n",
+ "coil_compressed_prewhitened_imspace_snr = snr_estimator(coil_compressed_prewhitened_imspace)\n",
+ "# stack all coils for visualization\n",
+ "coil_compressed_prewhitened_imspace_all_coils = torch.view_as_complex(torch.cat([coil_compressed_prewhitened_imspace[i] for i in range(virtual_coils)], dim=-2))\n",
+ "# compute the SNR for the transformed image\n",
+ "coil_compressed_prewhitened_rss_target = utils.rss_complex(coil_compressed_prewhitened_imspace, coil_dim)\n",
+ "# compute the covariance matrix\n",
+ "covariance_coil_compressed_prewhitened_imspace_all_coils = torch.abs(coil_compressed_prewhitened_imspace_all_coils) @ torch.abs(coil_compressed_prewhitened_imspace_all_coils).conj().T"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:22:50.127968Z",
+ "end_time": "2024-03-05T17:22:50.948031Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(torch.abs(imspace_all_coils), cmap='gray')\n",
+ "plt.title(f'Fully-sampled {num_coils}-coils - SNR {imspace_snr:.2f}', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.imshow(torch.abs(coil_compressed_prewhitened_imspace_all_coils), cmap='gray')\n",
+ "plt.title(f'{virtual_coils}-coils compressed prewhitened fully-sampled - SNR {coil_compressed_prewhitened_imspace_snr:.2f}', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(coil_compressed_prewhitened_rss_target, cmap='gray')\n",
+ "plt.title(f'{virtual_coils}-coils compressed \\n prewhitened \\n fully-sampled RSS', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(torch.abs(rss_target) - torch.abs(coil_compressed_prewhitened_rss_target), cmap='gray')\n",
+ "plt.title('Difference', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()\n",
+ "\n",
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 2, 1)\n",
+ "plt.imshow(covariance_imspace_all_coils, cmap='gray')\n",
+ "plt.title('Fully-sampled \\n Covariance matrix ฮจ', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 2, 2)\n",
+ "plt.imshow(covariance_coil_compressed_prewhitened_imspace_all_coils, cmap='gray')\n",
+ "plt.title(f'{virtual_coils}-coils compressed \\n prewhitened \\n fully-sampled \\n covariance matrix ฮจ', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorials/02_ATOMMIC_MRI_undersampling.ipynb b/tutorials/02_ATOMMIC_MRI_undersampling.ipynb
new file mode 100644
index 00000000..03e9bbc9
--- /dev/null
+++ b/tutorials/02_ATOMMIC_MRI_undersampling.ipynb
@@ -0,0 +1,876 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### This notebook demonstrates how to undersample and transform k-space data using ATOMMIC. Here we use the Calgary-Campinas 359 dataset.\n",
+ "\n",
+ "#### Important! You need to have downloaded the CC359 dataset to properly run the notebook. For more information, please read [here](projects/REC/CC359/README.md).\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:01.639718Z",
+ "end_time": "2024-03-05T17:23:06.512423Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import h5py\n",
+ "import torch\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "\n",
+ "from atommic.collections.common.data.subsample import create_masker\n",
+ "from atommic.collections.common.parts import fft, utils"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Specify path where CC359 data are downloaded"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:06.517236Z",
+ "end_time": "2024-03-05T17:23:08.807376Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "parent_data_path = input(\"Please enter the (downloaded) data path: \")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Specify data paths specific to CC359"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:08.737725Z",
+ "end_time": "2024-03-05T17:23:08.807944Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "subject = 'e14110s3_P59904.7.h5'"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:08.742108Z",
+ "end_time": "2024-03-05T17:23:08.808095Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "data_path = f'{parent_data_path}/calgary-campinas_version-1.0/CC359/Raw-data/Multi-channel/12-channel/Val/{subject}'\n",
+ "mask_path = f'{parent_data_path}/calgary-campinas_version-1.0/CC359/poisson_sampling_h5/Val/{subject}'"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Read the data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:08.787698Z",
+ "end_time": "2024-03-05T17:23:09.630088Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# load the k-space\n",
+ "kspace = h5py.File(data_path)['kspace'][()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:09.634171Z",
+ "end_time": "2024-03-05T17:23:09.639806Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "(256, 218, 170, 24)"
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kspace.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:09.640847Z",
+ "end_time": "2024-03-05T17:23:11.137209Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# necessary operation for the CC359 dataset\n",
+ "kspace = np.moveaxis(kspace[..., ::2] + 1j * kspace[..., 1::2], -1, 1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.139169Z",
+ "end_time": "2024-03-05T17:23:11.143987Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "(256, 12, 218, 170)"
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "kspace.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.144542Z",
+ "end_time": "2024-03-05T17:23:11.196741Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# load the masks\n",
+ "mask = h5py.File(mask_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.170065Z",
+ "end_time": "2024-03-05T17:23:11.197654Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": ""
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mask.keys()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.178854Z",
+ "end_time": "2024-03-05T17:23:11.197847Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# pick a mask\n",
+ "mask_5x = mask['mask_5x'][()]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.202465Z",
+ "end_time": "2024-03-05T17:23:11.216980Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "(256, 218, 170)"
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "mask_5x.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.211332Z",
+ "end_time": "2024-03-05T17:23:11.217553Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# pick a slice\n",
+ "slice_idx = 100\n",
+ "kspace = kspace[slice_idx]\n",
+ "mask_5x = mask_5x[slice_idx]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.223845Z",
+ "end_time": "2024-03-05T17:23:11.348527Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# transform to tensor\n",
+ "kspace = utils.to_tensor(kspace)\n",
+ "mask_5x = utils.to_tensor(mask_5x).unsqueeze(0).unsqueeze(-1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.350638Z",
+ "end_time": "2024-03-05T17:23:11.357729Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "masked_kspace = kspace * mask_5x"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.359456Z",
+ "end_time": "2024-03-05T17:23:11.363710Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize general parameters for transformations\n",
+ "fft_centered = False\n",
+ "fft_normalization = 'backward'\n",
+ "spatial_dims = (-2, -1)\n",
+ "coil_dim = 0"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.365552Z",
+ "end_time": "2024-03-05T17:23:11.402359Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# apply the IFFT\n",
+ "imspace = fft.ifft2(kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "masked_imspace = fft.ifft2(masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "imspace = imspace / torch.max(torch.abs(imspace))\n",
+ "masked_imspace = masked_imspace / torch.max(torch.abs(masked_imspace))\n",
+ "# compute the RSS target\n",
+ "rss_target = utils.rss_complex(imspace, coil_dim)\n",
+ "masked_rss_target = utils.rss_complex(masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.402186Z",
+ "end_time": "2024-03-05T17:23:11.816898Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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tNaHT6cTTTz8tevXqJWJjY8WsWbNEnz59BAAxbty4cvWsW7euCAgIEOPHjxfvvfeeiIiIEJ6enhprakUejcWLFwudTie6dOkiZs6cKd577z0RGRkpbDab5r5NmzYJvV4v6tatKz766CMxYsQIYbVaRZ06dW7bo6HT6YTZbBYAhK+vr3j11VdFdna25r4LFy4Ib29v0b17d3lt+PDhQqfTaXhvRXQzj8axY8cEAPHUU09prteoUUNUrlxZXL169ZblfvXVVwKAGD9+vMba7aI7Q7fyaIwePVoAEO3btxdCCLFo0SIBQLRt21bjibp8+bIICAgQBoNBnD9/Xl6vyNI+Y8YMAUB8/PHH5d6n7kdr1qyp0EothBDZ2dkaC/lzzz0nAIh33nlHc9+6desEABEdHa2xjNOrULVqVXHjxg15PTk5WdhsNmGxWCr0tN0uZWZmiuDgYGEymURKSormt9vxaAghROXKlQUATX/ejNjPzh+DwSDeeOMNUVxcXO6Zw4cPC6PRKKKiokR2dra4evWq8PPzE/7+/hoeeSuKjIwUAMS5c+du634hhNixY4cAIO67777bfsaZ3nvvPTFmzBgxePBg0aRJEwFAdOrUSRQVFWnuI891/nh6elbosXn66af/0KMRFBT0l+t9K/qfUjQq+pDupKJx+vRpAUDMmTNHU9YjjzwiIiMjb7lhpKenV7hhOVNeXl65a/379xdms1nDhNq2bSsAiK+++kpeO3XqlAAg9Hq92Ldvn7y+adOmcoI9BbRHHnlE865XX31VABBHjhyR15z7asKECcLLy0ucOXNG8+zw4cOFm5ubuHLlihBCiB9++EEAEO+//768p7i4WLRu3fpPKRpffPGFSE5OFjdu3BAbN24U0dHRQqfTiV9//VVzPzcQLy8vERsbKyZNmiQOHjxYrtyEhAQRGBgoAIiYmBjx8ssvi6+++qqc29pF/3z6q4pGrVq1NJvuJ598IgBIIbqoqEgEBQWJBg0aaO6bN2+eFEpIS5YsEXq9XuzatUvz7s8++6wcRI/r8/jx45p7Bw8eLHx8fCrcPEkFBQXlXO4XL14URqNRjB8/vlwb69atq9mkevbsKXQ6nYiNjdWUce+995YT7MlHf/vtN3nt8uXLwmQyaQR2Z0UjOztb2Gw28dJLL2nKS0hIEFarVXO9QYMGIjQ0VLPuqHDdjqIxfPhw8fbbb4tvvvlGfP3111LYadWqlbDb7Zp7586dKwCIpUuXin379gk3N7cKhT1nosA6btw4kZycLBISEsSuXbtE06ZNKzSCLFiwQAAQHh4eon379mLUqFFi165d5cYtLy9P1KxZU7a1X79+YsGCBRp4lYv+OnHcoqKixJgxY8SYMWPE0KFD5d5jMpnEnj17hBBC3H///QKA2L9/f7lyJk2aJBVC0q0Ujblz596yXlQ0nJUHZyosLBQmk0n4+/tXCN/p2LFjOeMm5/8XX3xR7n7+FhcXd8v33op69uxZri+cy/8jRaN58+Y37WtnOnbsmJg6dao4duyYyMnJEYmJieKHH34QMTExAoB48803K3yOwvMzzzwj2rVrJwCI1atX314jhRAmk0kA0Mhbf0TLly8XAMTTTz992884k7+/v0aGfeaZZ8oZTYQoVWpmzpwpzpw5I/Ly8sS1a9fE4sWLRaVKlQQAMWPGDM39VKRbt26tgemmpKRIpcrDw+Mv1/tW9D+laMyaNUts3rxZ8yHdSUVDiNKFomqtqampwt3dXYwYMeKWdS0oKBAeHh6ia9euIi0t7bbal5WVJZKTk8XSpUsFAHH48GH5W9u2bYW3t3c55cZms4k6depormVkZAgAYtSoUfIaBbRNmzZp7j158qQAtDhH576qX7++6NKlSzkv0pYtW+RmLkQpntNgMJRbLPQA3a6i4fwJDAyU73Cmr776Stx3331Cr9fL+xs2bChOnDihue/GjRvi5ZdfFsHBwfI+Dw8Pl4XxLqO/qmioyq8QQhw6dEizIe3Zs0cAEJ999pnmvqKiImG1WjVlPvLII6JOnTrl1sOZM2cEADFx4kR5r2pJdW6Hm5ub2LBhw221u7i4WKSkpIjk5GRRv3598eijj/5hG7kBOwvHr7/+utDr9RrhHIC49957y733qaeeEmazWSpEzorGd999JwCIbdu2leuPTp06iejoaCFE6foDIIYPH17uHbVr1/7LsXEUDL/++utyv3Xu3Fn4+vqK6tWrixo1alRo1HEmCqzOH29vb/Hhhx9W+MzGjRtFp06dhLu7u7y/WrVq5WLCMjIyxLBhw0RERIS8T6/Xi4EDB/4pIcdF5amicXN3dxfh4eGiV69eGoHb19dXE6+hEvnAY489Jq9VpGhcvHhReHl5CYPBIHr06CG++OKLCq32GRkZIjQ0VOh0OvHggw+K2bNni+PHj5fbc44cOSIAlDMKkOiVV4VKCvuqkZA0YsSIcorJn6Hhw4cLAKJLly4VGkP+HYrGzSg+Pl4EBgYKg8FQoWLucDhEbGysHPfbjQkh/bcUDVJ8fLxYtmyZCA8PF7Vr1/5D7yjp2LFjwmQyCT8/Pw0vt9vton379gIo9YINHDhQ9O/fXwQHB0t0iclk+tv1roj+p2I0mjVrhgceeEDz+XfRs88+i927d+Py5csASrOM2O12meKsqKgICQkJmk9JSQmMRiPee+89bNiwAcHBwWjTpg3ef/99iZ8jHT9+HN27d4fVaoWPjw8CAwNler7MzEzNveHh4RJnSrJarahcuXK5awCQnp5erj3Vq1fXfI+KioJer79lXvyzZ89i48aNCAwM1HzY78RCX758GaGhoeUyONWsWfOmZVdEo0ePxubNm/H999/j2WefRWZm5k0xjj179sSuXbuQnp6On376Cb169cLvv/+Ohx9+WBN7Ehoaijlz5iA+Ph6nT5/GjBkzEBgYiNGjR0uMpov+N8h5jQBAlSpVNN99fX0BlK0Rrm/n9eHu7o5q1apprp09exbHjx8vtx6YDlGNDQAqxsq++uqrqFGjBmJjYxEeHo7nn39exm6QHA4Hpk+fjurVq8vc9oGBgYiLiyvHGypqI/lARfzB4XCUK8O57UBpRpq8vLxyueDVvgCA+++/v1x//PTTTxrecLN3/Fn+oNIbb7wBvV5fLiYNKMVe5+Xl4ezZs1i4cKHMvnI79K9//QubN2/G2rVr8cYbbyA/P79c3Bypc+fO2LRpEzIyMrBz504MGDAAly9fxkMPPaSZC1arFe+//z4uXbqES5cuYcGCBahZsyY+/fRTTJgw4c833kXlqHPnzhClhlUUFRXh6tWrWLZsmSZTWFZWliZ+RqXQ0FB5z60oMjIS+/btQ7du3bB+/Xo8//zziIqKQq1atbBy5Up5n9Vqxb59+/Dss89i3759ePXVV1GnTh1ERERg9uzZmjoBKJft6HbqVVF8D2MqbjZnb0WjRo3C1KlTZXyIcyalP0M3btwAAAQGBv7lMkJCQtCtWzcUFxdXeJaFTqfDo48+Kr8PGjToT5cP4KaxIHfqmVuV1atXL3z33Xc4ceIEhgwZclvP1alTB/fddx/S0tJw8uRJed1gMGDDhg0YO3Ys9Ho95s2bh++++w7dunWTMb03m/9/l/6n0tv+FforCw4Ann76abzxxhtYtmwZ3n33XSxduhRNmjSRm+OePXs0Zz8AwMWLFxEZGYnXX38dDz/8MH744Qds2rQJo0aNwpQpU7Bt2zY0bNgQGRkZaNu2LXx8fDB+/HhERUXBZDLh0KFDePvtt8sFWt9swd/suriNAKyKhDJncjgc6NixI956660Kf6eAdaeoXr16Uol59NFHkZeXh5deegn33XdfOaGJ5OPjg44dO6Jjx45wd3fHokWLsH//frRt21Zzn06nQ40aNVCjRg107doV1atXx7Jly/Diiy/e0Ta46N9DDNzPz8+v8Pe8vLwKg/v/zhpxJofDgXr16uGjjz6q8HfnOVqRgBsUFITDhw9j06ZN2LBhAzZs2IAvv/wSzz77rDwrYvLkyRg1ahSef/55TJgwAX5+ftDr9Xj99dfL8Qbg38Mf/ohYjyVLlmhST5P+ShDpnyFPT0/4+/sjLS2t3G87duyQQe9Hjx6t8AyMm1H16tUlD3rooYfg5uaG4cOHo3379mjSpEmFz5jNZrRu3RqtW7dGQEAAxo0bhw0bNqBv377l7o2IiMDzzz+P7t27o1q1ali2bBkmTpx42/Vz0V8nHx+fcsYAEg2BtxOcX7duXaxatQp2ux0HDx7Ehg0bMGPGDDz11FMICwuTgf9VqlTBwoUL4XA4EBcXh59++gkzZszAgAED4Ovri549e8r3JSYm/u16/R0aNWoUJk6ciHbt2mHt2rV/Sjl3pgsXLuDq1asIDAz826lUeYZJRYkcLl68iGHDhsHPzw/p6el48cUXsXPnzttWkFq1aoVLly5h69at5YxKNyMmgPjtt9+QlZV1R8aladOm8PX1/VMpmG/WL0ajEWPGjMGYMWM011n2zXjY36X/KY/GrcjX17dclqOioiLEx8f/pfL8/PzQtWtXLFu2DJcvX8bu3bs1B7bcc8892Lx5s+ajbrhRUVEYMmQIfvrpJxw7dgxFRUX48MMPAZQOempqKhYuXIjBgwfjoYcewgMPPCCtrf8OogWSdO7cOTgcjlsygqioKOTk5JTzIvFDS2pERATi4+ORk5Ojef706dN/q85Tp05FQUEBJk2adFv3cxH90ZhXq1YNvr6+f3luuOg/TxEREQAqnlN5eXm4evWqvOevlOu8Pux2Oy5evKi5FhUVhbS0NHTo0KHC9XC7FnoPDw88/PDDmD17Ns6fP4/+/ftj8eLFMhvWqlWr0L59eyxYsABPP/00OnXqhAceeOBvZ3G7GTm3HSg9rMpsNt/UIhkVFQWgVHGqqC948NbN+hf4e/whOzsbKSkp5eoXHx+PQYMGoVOnTnjooYcwdOhQ6VX5KzRixAhYLBaMHDnytu6/XR7k6+uLqKgoFw/6D1LDhg2Rl5eHX3/9tdxvFMT+zOFw7u7uaNGiBcaNG4cZM2ZACIEff/yx3H16vR4NGjTAW2+9ha+//hoA5EnNMTExMJlMOHDgQIWnQ/+Vev1ZopLRtm1brFu37g8zMP0R0Uv31FNP3ZZB81ZET4aznFJcXIzevXsjOzsb33zzDd58803s2bMH48aNu+2yX3jhBQDAhx9+eFMDFomGC7PZjKeffhr5+flSnrsZFRcXV2gYcqacnBxkZmbKTF9/RCUlJfjtt98A4Lb3vGXLlgEoNaD/O+j/jKIRFRVVLiXjvHnz/rJHAyg9SOXEiRMYNmwY3NzcNIPk6+tbbnM1mUzIy8srlzY2KioKFotFTlZq3KplsaioSONSvdM0a9YszfeZM2cCAGJjY2/6TI8ePbB3715s2rSp3G8ZGRkoLi4GADz44IMoLi7GnDlz5O8lJSXyHX+VoqKi8Pjjj2PhwoXSspOXl4e9e/dWeP+GDRsAlEEy9u/fX6El5Ndff0Vqaurfgm646D9LHTp0gIeHB+bMmVOOec+bNw/FxcW3nMs3oyZNmiAwMBCfffYZioqK5PWFCxeWE+x79OiB69evY/78+eXKyc/Pv630qc6pCfV6vTxBV+UPzl6HlStX3hF3fUW0d+9eHDp0SH6/evUqVq9ejU6dOt3UOti5c2f4+Phg8uTJmkOpSIRchYaGokGDBli0aJEGsrV582acOHHiD+tWUFCA7OzsctcnTJgAIYQ8nJP00ksvweFwYMGCBZg3bx4MBgNeeOGFv+zFsdls6N+/PzZt2oTDhw/L61u3bq3w/vXr1wMo40FHjhxBSkpKufsuX76MEydOuHjQf5DoYXrnnXc0c/bq1av46KOPYDAY0Lt371uWcfDgwQphTPRI0Kt6/PjxCr0Uzvd5eHigZ8+eSElJwZQpUzT3bty4EZs2bUJ0dPRfOtn8dmj06NGYOHEiWrdu/beVjJycHAwZMgQLFy5EaGgo3n333dt67uDBgxVe/+STT7B9+3ZUr14dTZs21fw2btw47N27F0OGDMEDDzyAyZMno1GjRpg8efJNU5M7U/v27dGzZ0+cPn0ajz32WIXerqysLLz77ruYN2+evDZp0iQEBgZi0qRJmDFjRoXKRFxcHNq1ayfnyoULFyr0vtrtdumpfvDBBzW/VdQvJSUlGD58OM6dO4f27dtLaJ1aX2datWoVvvjiCzRt2hSPPfbYTXrj79H/GejUiy++iJdffhmPP/44OnbsiCNHjmDTpk3SxfRXqGvXrvD398fKlSsRGxt7W/i2M2fOoEOHDujRowdq164Ng8GA77//HomJiVJRadmyJXx9fdG3b1+89tpr0Ol0WLJkyR2BNNyMLl68iEceeQRdunTB3r17sXTpUvTq1Qv33HPPTZ8ZNmwY1qxZg4ceegj9+vVD48aNkZubi6NHj2LVqlW4dOkSAgIC8PDDD6NVq1YYPnw4Ll26hNq1a+O7776rEE/+Z2nYsGFYsWIFPv74Y0ydOhV5eXlo2bIlWrRogS5duqBy5crIyMjADz/8gF27duHRRx9Fw4YNAZTCOpYtW4bu3bujcePG8PDwwMmTJ/HFF1/AZDLdNiN00X+fgoKCMHr0aIwcORJt2rTBI488ArPZjD179uDrr79Gp06d8PDDD//pct3d3TFx4kT0798f999/P5566ilcvHgRX375ZTl3ep8+fbBixQq8/PLL2L59O1q1aoWSkhKcOnUKK1aswKZNm/7QNf3iiy8iLS0N999/P8LDw3H58mXMnDkTDRo0QK1atQCUQnbGjx+P5557Di1btsTRo0exbNmy23bv/1mqW7cuOnfujNdeew1Go1EaPG5lHfTx8cGcOXPQp08fNGrUCE8//TQCAwNx5coVrFu3Dq1atcKnn34KAJgyZQq6du2K++67D88//zzS0tIwc+ZM1KlTp5wX1JkSEhLQsGFD9OzZU57SvGnTJqxfvx5dunTRnHf05ZdfYt26dVi4cCHCw8MBlBpUnnnmGcyZMwevvvrqX+qfwYMHS/6zfPlyAEC3bt1QtWpVPPzww/KMji1btmDt2rVo2rSpnIubN2/GmDFj8Mgjj6BFixbw9vbGhQsX8MUXX6CwsPA/fhLy/2Xq06cPvvvuO6xevRr169fHQw89hNzcXHzzzTdIS0vDhx9++IdrbMmSJZg7dy7atGmDqKgo+Pj44MSJE1i/fj38/Pzw3HPPASgd92HDhqFVq1aoUaMG/P39ceHCBaxZswYmk0lzAvR7772Hn3/+GRMnTsSePXvQvHlzXLp0CStXroTZbMaXX355W+cx/FlauHAhJkyYAIPBgGbNmmHatGnl7mnXrp30Tqr0+eefY+PGjRBCIDs7G2fPnsXPP/+M7Oxs1KlTB8uXLy8nBN+MHn/8cbi7u6NJkyYIDw9Hbm4u9u3bh99//x02mw1Lly7VGDx27twpFQuiHTw8PPDVV1+hcePGeOaZZ3DkyBHYbLY/fPeCBQsghMDy5ctRtWpVdOrUCTVq1IAQAmfPnsXWrVuRnZ2NJUuWyGfCw8Px008/4dFHH8XgwYMxffp0dOjQAcHBwcjKysKvv/6KAwcOwMfHR3opdu7ciZdffhn33XcfqlWrBn9/fyQkJGDLli24du0aatWqVQ650aRJE9SvXx/169dHpUqVkJaWhp9//hlnzpxBeHg4Pv/883Ltad68OSpXroxatWrBZDLh119/xY4dO1CtWjWsXLnyb8Xd3JL+LSHm/2G6nZPBS0pKxNtvvy0CAgKE2WwWnTt3FufOnfvLWadITAOrppe9FaWkpIgBAwaImJgY4eXlJaxWq2jevLlYsWKF5r7du3eLFi1aCE9PTxEWFibeeustmZ5WrVvbtm3LZZcSojRDVNeuXctdByAGDBggvzNbz4kTJ8QTTzwhLBaL8PX1FQMHDix3UnFFJ4NnZ2eLd955R0RHRwsPDw8REBAgWrZsKT744ANNSs3U1FTRp08f4ePjI6xWq+jTp4/4/fff/1TWKecsOaR27doJHx8fkZGRIex2u5g/f7549NFHRUREhDAajcJsNouGDRuKadOmaVKUxsXFiWHDholGjRoJPz8/YTAYRGhoqHjyySfFoUOHblknF/0zaenSpaJFixbCy8tLGI1GERMTI8aNG1cuc8jN5tTNTqufPXu2qFq1qjAajaJJkyZi586dFZ4MXlRUJN577z1Rp04dYTQaha+vr2jcuLEYN26c5pRn53VIWrVqlejUqZMICgqSp/n2799fxMfHy3sKCgrEkCFDRGhoqPD09BStWrUSe/fuLVefm7XxZvyyosxdrOfSpUtF9erVhdFoFA0bNtTwILVM55PBt2/fLjp37iysVqswmUwiKipK9OvXT5MuVwghvv32W1GrVi1hNBpF7dq1xXfffXdbJ4Onp6eLZ555RkRHRwuz2SyMRqOoU6eOmDx5sob/XL16VVitVvHwww+XK6N79+7Cy8tLXLhw4abvudk5GqR+/foJNzc3mXP/66+/Fk8//bSIiooSnp6ewmQyidq1a4sRI0ZozjG6cOGCGD16tGjRooUICgoSBoNBBAYGiq5du4pt27bdsu0u+mO61TkaFZHdbhcffPCBqFevnjAajcJisYi2bdtWmBa1oqxT+/btE/379xd169YVNptNeHp6iurVq4uBAweKy5cvy/tOnDghBg8eLBo2bCj8/f2F0WgU1apVE3379i2X8lqI0jMwXnvtNRERESHc3d1FQECAeOKJJ255MrjzWhSibI07r9+KiPfe6uOcyZPv5sfNzU3YbDZRu3Zt0bt3b7Fy5cpy50H8EU2dOlW0b99ehIWFCaPRKDw9PUVMTIx4/fXXy2ViSktLE5UrVxZeXl7i9OnT5cqaP3++ACCeeOKJP1WHzZs3i549e4qIiAhhMpmEyWQS1atXFy+++OJNM2fl5uaKjz/+WLRt21aew2Kz2cS9994rJk2apDmD5MyZM2LQoEGiQYMGwt/fX/bbvffeK95///0KUxsPGTJEtGrVSgQHBwt3d3fh5eUl7rnnHjFy5MibZjQdM2aMqFevnrBYLMJkMolatWqJkSNHavamfwfphPg3msn/D9Abb7yBBQsWICEh4W9jF/8bNHbsWIwbNw7Jycl/y7vjIhe56H+PdDodBgwYIL0PLnKRi1zkIhf9Gfo/E6Px76CCggIsXboUjz/++F2pZLjIRS5ykYtc5CIXuchF/y76PxOjcScpKSkJW7ZswapVq5CamorBgwf/t6vkIhe5yEUucpGLXOQiF/2jyKVo/AU6ceIEevfujaCgIMyYMePfmlrORS5ykYtc5CIXuchFLrobyRWj4SIXuchFLnKRi1zkIhe56I6TK0bDRS5ykYtc5CIXuchFLnLRHSeXouEiF7nIRS5ykYtc5CIXueiOk0vRcJGLXOQiF7nIRS5ykYtcdMfptoPBq1evjqKiIggh4OHhgby8PLi7u8PDwwNGoxGZmZkoKioCABiNRjgcDnnKYEFBgbxWUlICh8MBHx8fFBcXw+FwwNPTE4WFhSguLkZJSQkMBgOKioqg1+s1J17q9XrodDrk5ubCaDTC3d0dbm5uKCkpkc+yfKPRCJ1Oh5KSEri7uyMvLw96vR6enp6w2+3w8PCAEAJFRUUwGAwQQkCn08FgMMDDwwP5+fkQQsBkMiE3NxclJSXQ6/UwmUzyhG7eb7fbYbfbUVJSAiEELBYL8vLyUFRUBJPJhOLiYhgMBuj1ehQVFaGgoEC+x93dXdZbCAGDwSCPrNfr9XB3d4cQAsXFxbDb7fD09ITD4ZD3sHz+zjS7DodDtjM7O1v2c3BwMHJycmC32wEAhYWFcHNzg06nk/2stq+kpARGoxFubm4oLi6WfW8wGJCfnw+z2Sz7r6CgQLaB7bLb7dDpdHIsWXeHwyHHr7CwEIWFhbBarcjPz9f0q8PhkP3g5uYm5yD7h31TWFioaXtJSQk8PT1lu4QQKCwshF6vl+0tLi7WfGddOJZmsxkeHh7Q6/XIzc2V7y8uLobRaJTlsj5Go1G22c3NDXq9HgUFBSgoKICvry+EEHBzc4PBYJAnHru7u8Pd3R1ZWVly7huNRtknbm5ucn04HA7ZN4mJibe7dP9R5OvrC5PJBL1eL9e/wWCATqeDw+FAcXGxnPP5+fmSx/AenU4Hu92O4uJiAJDrpqSkBPn5+QgICNDcy9/50el0cHd3lzyGY+Lh4SHHjqTOX51OJ+emu7s7vLy8YLPZkJOTg4KCAhQVFclxUsfZ+f1FRUVy7rPNLJPv4xoym80wmUyS5xUVFcm1xLKBMj7EcnnaLHkb3+Xh4SH7mO3k+mB/urm5yXluMBhgMBhgMpngcDjK8WiOi16vR05OjuRz3t7ecHNzk+PCPlHDAfnekpIS5OXlQQgh17nJZIKbmxvsdjuKiorkugQg+QjLKikpKXd6ON/r5uYGo9EIPz8/OdeKiork+mS7yT/Ia8jf2Bd2ux25ublyXyHP0+l0ck5lZ2fL9Wo2m+Vexee5j5E3cQ7r9Xp4eXnBYCjdinNyciTP9fT0hNVqRWZmppwXOp0O3t7est+Ki4slnxNCyPs4//gMxzY/P18z769fv/73F/V/mHQ6HYYPHw43Nze8//77WLx4MT799FPs3r0bBoMBX375JRYsWIAdO3ZonqtZsyYmT56MF154ARkZGYiKisK0adPw4osvIi0trdx7vL298cADD2Dz5s2oXLkyKleujM2bN6NDhw6Ij4/HiRMnNPf7+fnh888/x1tvvYXi4mLUqlULGzZsQJs2bZCVlYXz58/jyy+/xNixY3Hs2DH5XMuWLVFUVITffvsNAPDOO+8AAKZMmQIAeOutt2A0GjFhwgT5TEBAAFq1aoX169dreFZsbCxOnjwJg8GA999/Hy+88ALS09MRGRmJ6dOn48UXX8RDDz2ERo0ayWyZPXv2xH333ac5jZz0ySef4NChQ1i0aJHmutFoRJcuXbBz505YrVbZ1kmTJiEhIQEzZ84EAIwbNw4ZGRmYPn06AGDUqFEoLCzE+++/DwAVjmNubi5GjBiBvn37IiYmBmazGXv27MHChQuxePFibNmyBTqdDl988QVWrFiBDRs2AAA6d+6Mc+fOoaSkBNOnT0f//v2RlJSE8PBwzJw5E6+88goSEhLKtdHT0xOdO3fGtm3bkJWVJa9PmzYNZ8+exbx58+R4XLt2DYsXL9aMo9lsxqJFizBp0iQkJyejYcOGWL9+PRwOB0JCQtC0aVOsX78eQ4YM0YzjG2+8AV9fX4wePRoAMGjQIISEhGDEiBFyHC9dugQA6NKlC9q0aQOj0YghQ4agc+fOOH/+PM6dO1euPf9pup0w79v2aHCD5KZLRgqUMntVmHRzc5MMlZuhen9FlVOf5e/cEMksuVFQgOAmQYZNZssNUn2nKmSobVDrSuGdDFoV/lWFh/Xi5sU+4OamttHhcMiy2E9AKdOnQKD2JxU0bmTsP5al9isFYgoaJpNJ3sv2On9UoaeicVHrww29qKhICguq0KeOFYl1Yt+xj/V6vbxGwY79wj5U+06tc0XzhWOkKoi87u7uLgVZCkYshwKEOhdYB76fY8ax5pxTBX0Ke2ybWneWwb+cnyqxbzh3OBb8rs45tR8paN+tRGWTY6KuIY4dAI3SzfHgmHHOqn+5ntTvqgKjrhPOaxoPWCcaEdRxptCp8gEKdAUFBVIZVteEqgCTVB7DOaEaRrgmOOZsg8lkgre3N7y8vODp6alpk9FolPVjO6gA8J0U3gFIJVqd3+xrfjgX1bkNlPE3Z96nzm/Wg/2n8haWxY/Kc1hf5z2EfFytm/p+1tPDw0PDLyjYq/uA0WiURjH2DfunuLhYzi217zgvVcMLFSF+aIBxXqNq35Lvqv3FsjivgDJeo+5tnAsq71bnk6pQeXp6SuMVx419wHqqc5DtuxuJ7QJKjTWNGzdGs2bNAEAaGf38/BAbG6vZZzm+/E6eAQAWiwWxsbHw9PREzZo10ahRI/zwww/Izc3FqVOnsHnzZgBle5ynpydiY2NhsVg05QFlhj4A0jgFlM31sLAwdOrUSfJAlVfQEELiWiA1aNAAUVFRWL16Nex2O+rXr48WLVoAADZs2ABvb2+0bt1a1gWAXA9cY/ytXbt2iImJ0ZQfHR2N9u3by35W96777rsPtWvXRmFhIVavXo1atWrB29tbCvs0CKrjpNbDYDAgPDwcnTt31qxRPsux4JrmWuDval04r61WK2JjY7Fjxw6cP39e/gaUKpdt27aV5UVHR6Ndu3YAgDZt2qBmzZrIz8/HDz/8gPr16yMmJgZGoxGxsbEICAjQtEVtmzqP1O/quDdp0gSVKlXC2rVr5TpV+7micWZfbdiwQSoZALBx40ZcunRJ/r5p06Z/hJJxu3TbWaesVqvGgqXX66WFz8fHRyMMmM1maUUHIC1kqqBEJkvB02g0apQLbqZAKeMwmUzIzs5GRkYGvL29NUySjIUL1svLCx4eHtJSxbq6ubnBYrHAaDQiOztbCnS8RxXsgNLF6eHhAZPJJK15tLSyLYWFhXJx6PV6eHt7a67Z7Xa5WfKenJwcKTjk5ubKyca+MJlMUpApKSmBxWKB3W5Hfn6+rIvat+7u7jAajfD29kZKSopGsfHw8JD1MZlMKCkpkZ4nbkxsl7u7u1wkbDstPe7u7rBarQBKPVTFxcXS26MKD0VFRXJB5ufnw8vLS26S3OS4wAoKCuR8KCoqgs1mk14RluXh4SHbbrVaJaMEgNzcXKn8WCwWpKenS2GNfck+5P82m032L8eHv5vNZmRkZCAvL0/2TX5+PoqLi2Gz2eS9AKSgQWs26wuUMjkfHx/Y7Xbk5OQgLy8PAQEByM7Olv1KQbG4uBh5eXmyzzmWXCO01Ht7e2uErfj4+NtZtv84CgoKkv3t6emJ9PR0KWR7e3vDw8MDubm5sNvtMBqN0pPk7u4uf8/Ly5OKb0FBAYCyjZQ8ilZwCsQ6nU4K6hT2HA6H9GSUlJTAz88PaWlpUgBleaoXUl17qlBuNpvlcxwzzl/yJq4Letco9JtMJoSFhQEA0tPTUVBQAJPJBB8fH3h7e8NoNCIvL096x4qKiuR6UL2EHh4eGm8d328ymaSHlVZ6CrsqnwLKvCNAmYBDj5sq+Drfo1r3OV5sK73J3DuomLAtBoNB3uPh4SHrzHrl5+fLdUxjE73L9PBmZ2fLd7Ms1oP9BJQK8zk5OZJveXl5yT2FvIMeFlXxoUfF09NTejTY94mJiRojnCqQUAlVjR5USrjH+fj4oLCwEHl5eVIhZjmVKlWS3iLySV9fX9kPhYWFUsng3lBQUKARWuj1LygokPMAKBWO7kbPqLNhDACWLFkCm82Ghx9+WF677777sGHDBtSoUUOOEQDNmlepfv36OHLkCKpWrYrnn38ejzzyiExdr3r6fv/9d/z444+YP38+zpw5gyZNmkgPhdFoRFFREf71r39hxIgRiIyMxLZt23Dy5Em88sor8l09e/bExx9/jPDwcHz33XfIyMhAnz59JO8HSvdc1ZPH74sWLUJwcDC6dOkCAJg3bx6ioqLQoUMHAMDMmTPRoEEDtG7dWraV/KGwsFCDrjh58iQWL14svSdAqUeF3gTVCwoAhw4dwq5du6Q3ZOfOnThy5AgGDRpU4VipiAx+7969Oz777DNUqlQJ+fn5mraRl7APFixYgMqVK6NTp043nQ+NGjXC7t27ERUVhaSkJNk2ABg/frxmHEeOHIlevXqhYcOG2L9/P9auXYtRo0YBAA4cOIBt27bhk08+wfnz53Hffffh8OHDctwrIrXuAPD4449j7ty5qFSpEr766is4HA48+eST5e4HIPk/SW17RXNUHbd/Et2OCnHbikZYWJh0OVODp5vXy8tLusjZeV5eXhIiQW+IymxVa6TZbJbCOzdB1Rrn4+MjmT0Za2Zmptwsvby85HtVoZ/ast1ul3WkEEOhraioSLquKViobmnVa8ONSbWGms1mpKSkSKskNyF1Y6ArnUKAalGi0sF3AKWbGe9lHXk/FyY3WdUySsVJ9RKolitViaF1R7Xguru7o6ioSCqJNptNjisAKehTQFC9R/QMcfMFICFX3Fw5KdX2s752ux15eXnw8vKSQoGqcJaUlEi4HT+enp6y36jQAWUbkbM3h0oM+4LWEL1ej7S0NI2lm4Iu5xLhTKo3ydPTUzJRwqlUiyWFNhU+QSGEAjat0pzXFFJZDoVw1fIBANeuXbudZfuPo1q1akn4CpUmCmUUhMlwSarwSmHb4XDAbDZrrLSqgEuBVhWuCWvkvCwoKJAKC1A6zhRYKexyo+Q64Lu5XjiHPT09Zf249gsKCqRSTaME531RUZGsh9lsRkhIiFQ6qYD7+vpK3lRYWIj09HQ5t6io04JNpYqCsbe3NwDId+Xk5Mj5C0Ba99nPGRkZsu/Y37TYCyGkYUZVwNQx8vb2loYdFfLGNeXu7q5ZuyaTCUVFRXIuOI85eSYAqeSpxheTyaTxeKjGjPz8fHh6ekpexL7iuLAdnGt6vV7yEo6BqpjQqwtAKjZ8P+9XvUSq5yQrK0v2I+tD5Vn1hlChU/kpeZ/q9cvLy4PFYpH9xfliNpths9lkn3JdkP9W5Fl3d3e/a6FTzsTxy8/Pl9fc3Nzg4+ODw4cPY9SoUVi8eDH0ej3OnDmD9957D/Pnz9eUoSoa8fHxMBgMyM3NBVAqfPfo0QMNGzaEl5eX5C3e3t4SVhcaGopTp06hffv2OHr0KIxGI3JycqQRgkYRABKBkJOTI+uu1+tx8eJF9O3bFwCwcOFCVK1aFXl5eejYsSO++uorVK1aVa5zGqgoZzl/z8/Px+nTp/HRRx9hz5492LVrF2rXro2XX34ZnTp1QvPmzaXspAqv5Cm5ubnYt28fNm/eLIVxZ0WjoraptGbNGiQnJ+OFF14AAKxatQp2ux3/+te/kJ2dDaAUFrR48WJUrVoVs2bNgtVqRffu3StsW0VEuSwnJweTJk1Cu3bt0LJlSwCQHl+Oo4eHB2JiYvDLL7+gffv2OHbsmJRvKhrX559/Hu+88w6qV69eoQfwqaeewgcffIBq1apJPubp6Yns7OwK52THjh3x7bffAgCef/55rFq1Sv42e/ZsVKtWDQ8++CDOnTuHCRMm4Msvv5S/v/fee2jZsiVat2590774b9AdhU5xIwXKcP2qS5Yas9lslveqmGNWSK/Xw8fHR+KPybABaLCyqvs9PT1dbkj8y40VgBQCKnKxcZPIyMiQmyU3QW4IHh4eKCgoQHZ2tpyQ6ibGeBAAUgHhBpCXlyc3ZVojVCgZGT4AuRmq3hMKl9xw2J/5+fnIzs5Gfn6+7BODwQCr1Sots4AWdqDCh8xmM3x9faWFnBYvnU4nhVtaiVl3lsu+UeEbxGlTaGBZ7EsKKaolk8yHsR1paWlyI+RvnEPsZxVmoAo9tCyTIVIQUYVMWnILCgqQm5tb4YakQnFYr/z8fI3rU7U4UhDhOKsQBrbfbDZLgZB9o855fjhvqfh6e3tLAUxV0BgDYDabpTDLv4wHulvJGQ5iMpmkJR6A7COOIb0XKpxMhdVw7nK+UhilQk4+QsGYHkl6rtT3sZ/JPyjcqvEL6vym16KwsFDWNT8/H/n5+RqvrWqVMplMsFgs8Pf3l3Xne2hQYN8UFxcjMzMTqampUvngemFfqko8UAbVy8nJQVZWFgoKCmRbVCgUFfucnBy5KaqxCWazWfIIk8kELy8vWCwWya85Zu7u7lJ5Y/0o5JNXs9+dISKqckBLHo0B7EsqCAAklI0Kmgq7cDZgqXsLhXGuPXpuCX0jP2cd+RzL4txQ5y/HvLi4GGazWe4zHPPCwkIZo6dCxVSewu/kcbScqkoHlQ3WFyiLQSHfoPeHyqRq1KLSwfaxzWp5dys1aNAAixYtgtFoxEsvvYTBgwfDzc0Nn3/+Oe677z6UlJQgIyMDr7/+On755RcApetj2LBh2L59OyIjI7Fs2TL4+voCAK5cuYJevXohOTlZji9JtXzn5uZKA4WK3MjIyED//v1x6dIltGvXDh988AF0Oh3eeust9OnTB97e3liyZAlq164t1ygAyTMKCwsxcOBAxMXFIS4uDoMGDZJC8LFjxzBgwABpaKlatSqWLFkCLy8v6anS6/WYO3cuWrVqhdDQUCxbtgyTJ0/Gtm3bcPXqVbz88stIT0/Ht99+i7Fjx8q2dOvWDR9++KFsa9euXTF58mQApTEWFIwB4N1330VCQgJmz54tFd1WrVph7ty50Ov1GDNmDPr37y/vnz59OhYsWCC/z5w5E7NmzUJhYSEWLlyIJk2aIC4uDgMHDkRhYSHmz5+PTz75RN7/3HPPYciQIZpxj46OxrJlyyTCokqVKvjss89gs9mwYsUKGQPxySefoEuXLnIcP/roIzz88MO4cOEC+vfvj7Nnz6Jz586YMWOGZlxtNhs+++wzREZGYseOHRg6dKgc4/bt22PevHly/e7fvx9vvPEGSkpKMHz4cLz66qtSgcrPz0ft2rWxcOFCaXg4duwY+vfvj/79++PAgQOadi1atAgffPABhBAYMmQIdu7cqfn9m2++waRJk3A30m0Hg5NRqhg0Nb5B3cho7SaDI8NUXcIUrIEyq5Hq2iSzBEotWrTeq3EbJBXvrkK4AGiEeNV9r7rGVQs7hQIKE7SIqhYhVbNV8cCEA6ik4mvV/lAVKWcYg+qhcLbsqlZ21crLzYoWYhWfzGfZn2pshdoWvpOKEH9Xv3OjpifCmVgeBSX2F5VO9f3qu509LLTyqVAC1dOljpvaz879pSqt/E1tN/ue48CyVDiYw+GQHgcSBS32L4VFPqNiOzn31L5Q+5BtVIVGelHUflPbcrcSBV+OgeqpcR4fQgXZZ6o1n/dQsFMFRfa30WiU60S1UFOoV63o6lxXifOEwp8KNaKgp1qKWVd6XJytYKoSryrDqkGC71WFRAryan35Ua3pfFaFlKlzWYUDqXWloszyVRgWUGYQ4ZxV5zsFXZZLgwvbxDEiqVBSrhO1/1QFRI154d7BMVXHVX2/M39RvZDFxcWS//JZFU6qelnZD6oHgvNU5XM0tDh7bthvKi9g+zmGLEuNQ1ENGc7eCPIA53YDkG1g36rjrfYH6W7lI/QYmEwmBAUFQa/Xw2q1wtvbGzqdDoGBgdKaLIRAcnKyhBo2atQI69atQ1FREWrUqIHg4GA5PhkZGfj666/le8xmM5o3b459+/YhJycHKSkpmnqYTCbce++9OHDgAHJycpCfn4/ly5cDKDXMBgYGAigNEs/OzobRaETPnj1x6NAhAJDB5A0aNIDdbsfx48exYsUKWb76f2FhIZKSkuTYuru7Izg4GDqdTgZMHz58WLbd3d0dQUFBWLNmjYQ/s22HDx/G2bNn0b59e+zduxfe3t4ICAiQ7/Ly8pLfN2zYgCpVqqBly5bYs2cPNm7cCE9PTzRq1Ejer7bV19dX433Yvn07goOD0aZNG+zatQs///wzgNJ1ERQUBKPRiBs3bsi27t69G35+fmjXrh1++eUXWK1WqQgCQExMDJo1aybHHShdoxzHw4cP4/DhwwBKA+bNZrMcxypVquDw4cPIycnB119/jebNm6N+/fqatleuXBmNGjVCSEgI3N3dcfr0aU0shMlkkm0FSpUJjgsNu25ubmjdujWOHTsm26nT6VC3bl24ublp5phK+/fvl/9///335X4/dOgQvL290b59e+zZs0cqoXcD3TZ0KjQ0VCoQtIRzoyooKJBQKSEEMjIyNFAmeg5UmA4ZucPhkBmsKCBbrVaNBYyWIgobKjyIlnBmX+LmrXo1KLCQwatQLraJcR1AmZeAkANVYKDS4bzhqxYkb29vjWeDyoCKt+RGzuA/KgZqthfVwqUKqBTCaWlMSkqSG2RhYaEcB76Pigi9SKo3Rt18eF2FPBEmwLrk5eXBarVKKx4hQKwvXcGEA6n9BEBiznU6Ha5fv66BYND6SEGRUC72GaF7KixJnb4U8FXFgVALCkSMuaDgZbPZ4O7ujpycHGRmZsp57Nwnubm5sFqtclwJ5+O48n2qQKK2W1XKVSGFwpEKE2M5zN7DeaoK0XcjthooDbikxVvNzqVafIHSjTUtLQ1BQUHyWQqxAKQi4uPjI618hEOp8B5VYGZMmXNWMaDMIKEKcz4+PnKeqfEaeXl5EmqnGhEYd0J4jL+/v0agLy4uhsVikQo1LZoca7bLbrcjIyNDExwOQJOBi7yRxoSsrCyNYYHrmJ7MlJQU5OTkSP6twmxoPPDy8pLeIdXoQr5Gowt5KS2FrLsaw8Q6qEq+uhZyc3Mlj1YzNrm7u0voqBrPoMLEgDJPqMNRCj9V4UZc95wzfAe9lwCkZ9jd3V0Tk6F6MD08PBAUFISkpCRN4gjyX36vyCtPDzu9yxxbKgmqcSs/P18qstwbOPaEtPH5nJwc+Pr6ynoyxo98g+0nLJPzxRmSSX7I4Nm7iYqLi/HEE09g9erVN71H5SnXr1/HO++8g5MnT2LHjh2oUqUKkpKSAECuJZL6PSYmBkePHkVMTAx69eqFXr16oVatWtIIGR4ejnPnzqFRo0YyRoPPq4Y/kp+fH65fvw6DwYBly5ahX79+AID169cjIyMDvXr1kmPFtcLvnTp1wqpVqxAWFiYFea61zz//HFFRUTKA+2Z9QTnD4XAgJiYGR44cQa1atXDhwgWNHMA1w7q/8cYbGDRoEKpVq6Yp92Z1VfdkNzc39OjRA59++qmUIZ2NompbgFJ40Q8//ICwsDBkZmZq3jl37lzUqlULbdq0KddOto2yUEXjePHiRdkXcXFxWLt2LUaMGCHvHzx4MN544w1UrVq1nEHMuW1AaazNzJkzERoaKvvQ09MT8fHx6NmzJ3788Ud57zfffAOz2SzjiCpq+6361uFwoH79+vj1119RrVq1fwx8+nZUiD8Vo8ENksGKfElxcVlKSqYCZIAbrXGMbSgqKkJqaqoMvtPpdMjKytIwRQoLnDRktLRMEfPOzcFisUgBkhsk4x7sdju8vb2lQM+NlhYiu90Oi8UiLVFUeoAya1VhYaG0ltC6RXe6p6enDE5kf6iYaQZgUwAl82P70tPTNW0HSuFZqrJEIYr9RQVL9YqornR1LHx8fJCRkQEhBMxms0bBI6aQ/UolhRu9qjHzPcQcGwyl6W2pNKj9rgr6XHyEmqgKpbqpE77BDZKKI+cAmTsAKUgw3oQCKOurwlVUxSMwMBB5eXkSMsJnaNEtLCyUAgUtUKqQyeB9wnJUmALvo1KQk5OjER5V/DvHlhs/NwI/Pz8UFBQgISFBwhA5Tyjkcb0lJyffzrL9x1FgYKBcC1zrVAQZHK969AgZAyCFUKBMaOM4MRVoXl6eRimgQuDmVpba09PTE15eXnIdq1ZfnU4n4wyKi4vh5+en6XdVoCScjs9SOOZa5DrkmuQ9bDvnoCpgsixCNBm8yzYyBoyKiCpcc+OmMkX4ndFoRGpqKnJycuRcDAgIkEHGFJw55ywWixSYKfxfuXJF8nKr1Qqr1aoRnNlm1YtASJpz2msPDw9pwKHy7wwvY9B/Tk6OLJ/CPdOlO+8H7ENvb28JEaK3Qg0mVxMG0ICiKnRqTA/hVux3tov8t6SkRM5pziODwYD09HQZcO7p6anxqqjxJxw31eNPQw/5qrPxheVxPpM/qh5g1eNTkbeX+/TdyEf8/f2RnZ19Swjp7NmzER4ejkceeQQ2m03OMR8fH6Snp0MIgQYNGmDLli2oV68e4uPjUadOHezcuRMNGjTA1atXodfrYbPZkJGRIffozMxMadmfOHEibDYbMjMzUVJSgtDQUBw9ehQPPPAAGjZsiLfffhu1a9fWGJZsNpvkMVTULRaLNFiePXsWzz//PDZu3AiTyYSzZ8/i1VdfxcaNG+Ht7Y309HTMmzcPQUFBePTRRwFAxjUSsuNMkydPRsuWLdGuXTvExcVh3rx5mDNnDqxWK7Zt24YlS5ZooFNDhgxB7969pdeCMMSMjAx5T1BQEE6cOIHOnTvj4MGD8Pf3x8mTJ/HII49g37598r7vvvsO2dnZeP3115Geng4AePjhhzF79mzUqFED8+bNg8FgQM+ePeUzNGRTblGJfFtNQ8u+jYuLw+zZs7Fz586bjuPQoUPRo0cPNGnSBFarFUVFRfD398eRI0fQpk0bnD9/XrZ1y5Yt+P333/Hee+/h5MmT6Nq1K3799VfNez08PODl5YX09HSsWrUKmZmZeOGFF+Dr66s5RgCAlCE5Ts7jSNLr9Th16hQmTZqERYsWQafT4fjx4/j444+xYMECWK1WZGRklPNQ/rfodlSI24ZOAVpXLJk/NxDnF5KhkyFTcC0uLgvipWDs7+8vhUSWyQ1QdVOTsZORqpABWhIptDif6aFCbCi0sU0U5LgRqPhcWprV+rEuen1p0GVaWlo5+A03M2rDKhZZVTYo2LMPc3Jy5D1sj7rpsB8pmKkBp9w0eR+9FxwrtpFt4zMUgsxmswaPTEVKtcjabDY5prQGq4IThXW2T4XVqTAPWtb4Llr9OLcAaNqi0+mQmZkphQ1axNlvHB+218vLS2ZxYXsptFFgUjOdqcIuFQZVGaJFnOOpCiZUHDifabFXrbhMCsBx4ObAttH1Tks75xr7S/WkqR6Xu43UYG3Vi0EFkxmWuM74l4pqSYn2XBfOtaKiImRlZWmgTADkGgDKUiZSgFSTMKhKDQVWZ0gSY0U4Zuq6VueRKixSQKRSXJESzDo4ByHTQ6LClOiF5FzjX64Drjc1po48iZ5OvlfNjsQ15XA4NAYGQs14rpAQQmax4losLi5Genq6Zl2okDiWz3HkWKj7hsofVL5AA4A6FtxzOI5q//O76kmh95fPq55FrnsqefQiUBlg31IhYP+q5fBsIRpd1Gx4VIyYXID9pcKbWAeOC+cHFQd61Nk/5NH05nNeUbHi3FUVQb6fyk9FVtS7hdLS0jBgwADo9Xp5XgMA1K5dG6+99hreeOMNfPPNN9LQQwE5KioKb7/9NoYOHYqsrCxcu3YNb7/9trSax8fH46233kJ6ejq6d++ORo0aybgMxlEAwIcffojLly/D4XBIWFLHjh3RvXt3DB8+HNeuXUNBQQEmTJigEQaJAHCm7OxsNGnSBC+99BLGjBkjIVV2ux0jR47E0aNHYbfb5bNff/21Zg9QY0mc6YMPPkBGRgY+/vhjAKVKx7Fjx1BSUoK0tDRMnjwZZ86cQXBwMCZNmoQRI0Zg69at0lo+9v+fFcGg5dGjR+PMmTNYvXo13n77bVy5ckXW4e2338bFixc1758/fz4KCgo07T569ChGjhyJoqIiLFq0SK6x6dOnY9GiRcjLy5Pj+PTTTyMgIAAffvghPvroI6xatQq//PILdDodPvjgA/z444/Yvn07hBCYPHkyjh49qhlHoFQpmjhxIt5991389NNPso4c94yMDLz11luIj49H69at0alTJwwbNgwzZ85EcnIycnJyMHz4cFy6dAn33XcfnnjiCbz55ptSbqSs+fnnn0vemZ6ejoEDB0IIgVmzZgEA+vTpAw8PD8yaNQvTp0/H+fPnNRCpfv36ISgoCNOmTcO4ceNQu3ZtvPPOO5gyZQomTJiAuLg4OW53G922okFmrlraVcw8oBUYAMiNjtYk9XluWgCk9VFVBlRmrP51FlBomVeD7lTLjgq/4cZDxq5aGrlpsI6qAABAQqxU2BT/qsKwaj2iYEDYF8tXA4/pHaA1VLWa8l1qTAWvq32ufqjcsR2qIqh6B9R4DQoM9Dg5b1J8FihVCLi4mKFLFZjYRxT41GsUxnlNtcLSms0xdR4PFeamxtnwr9pv3FjV7EZUFFQhpKI5wf5gRile49iwfFXQ5P/qQYxAWcpQKoccFwpTLIvzXfVcOVvrKATyXXcrqR4ACkvq/FUtseo85ofjrP7PuUJLN1D+bBIqiJxbnHsU/DmP+T7ObXVeq+OnejBVSBCfUeeoc5wB57haN9ZJFUDpAWC5nCf0NLD/+JvaXhWKw7bxd3Xt8BrrQKGVBgb2k2p8ICRSFc5zcnI0maxUD6SqzLAMKvS8rvJMjh8NKCofURUlXmMZ3PjVMVRj2ngf5wrbxPJU75AKGyNvVGEUrKNquFI9LqqAybFWA8Wd660aJVTllv3AsSeP5LM0ljhnXeS84Ls5DwoLCzUJRO5WCg8PLxf74u3tLaFNP//8M3x8fNCiRQv89ttv0sBZq1YtOX4pKSlYsGABmjRpguvXryM+Pl4GLgcFBSEqKkqWXblyZfj7++Pw4cMyONpoNKJx48Y4fPgw/P39ERYWhldffVWWferUKfl8UFAQqlSpIg/lCwwMRNWqVXHgwAEIUZoNLyoqCoMGDZLySklJiTwoz2azISYmBgcOHMD27dsBlI5f06ZNcenSJQkFA4Bq1arBbDbj+PHjqFGjBpYsWYIffvgBAGQMCencuXNISUmBh4cHatWqBQ8PD6SkpMj1EhUVpfF6Va1aFVlZWcjPz8eCBQtQt25dWCwWXLhwQZMhyc3NDU2bNsWePXuQmZkpv586dQqXLl2SZ0Rs2bIFAOT7LRYL9Hq9HMfw8HAJn61Zs6YmXqNGjRrw8/OT39XYhwULFqBRo0ZITEyEwWCQbVNjOBo0aIC0tDRcuXJFjru/vz+qV68OAFi9ejVCQkJQt25d+Xvz5s1Rs2ZN+R51HK9cuaKBylWuXFmzlsPDw6WxISYmBmvWrJFnswCl4Ql8ZtmyZXjrrbdkXb7++mtERkbinnvuwZEjR3C30W1Dp2w2m2TctLAwUwLdwXQv0npMBskMHMyyk5KSIrO78F5102MuahVLTcsdNzBaPcn86ZYi87fZbBIaQQuVahUldEvFt3IDslgsGsWHAgw9BirEg3/ZfhVbTCurxWKRTJFeAbVPCA9gjAY3CAAaiAK9CCpETMUK2+12hIeHIyUlRWZQYNCU6tGgtY7QJ9YrOztbWt3VTYrCDj0chJWop5TzfjXlLOERzCBDXDTrys2RmbG4yasQBG6Oubm58Pb2loIHrdmqpVMVFqhs5OTkaAI9adnMzs6WYwVAWoicz2FQhQ++g+2j0sbMPMRyM3WnGrORk5Mj28mYEApNHHez2SzflZeXV+6EZc4hh8MhLUl3G0VERGjgRWofqZ4CVSCkcmmxWGQWItULQUHKWXBXBXT2XWZmpmaMKFwT9scxoXGAHlHey/nHrD1ckxaLBVarFVlZWVKQVOcjBXQGXbP9qiDLelBg9Pf31wjQnLv0MqhKORULKrolJSVyU1bjsdjPtMSrxhbOcTc3N4SFhSEjI0P2N5Vo9q8KC+MZR5zL9CKR36iCOo0/amIFlTj+9FbynTwZnfsA+QfHCIAmmx3Xurp3sK+9vLwk3IX9wRSlBkPZWSkcv5KSEmnIcjhKg/zVLIfOexczGDK20MfHR+4N9EioEDc1Dk0IIWFabLeqoDGVLRVZeinY14wP5Brh/FUVDwpz+fn5uHHjxh1Z1/9JUhUkjklF1LZtW2zevBlhYWGaQG5VqdPpdLh8+TI++ugjafWviEaPHo3evXujZs2a8v2VK1fGxYsX0bhxYxw5ckRTJqBVGF944QVMmTIFoaGhKCkpQe/evTFz5kyEhYWVSyBT0fNdunTBd999h+DgYE1sV0JCAoYNG4aFCxfK++fMmYOGDRuiZcuW5ZRPte0AcPbsWSxcuFCTzWjkyJF4/vnnUa1atQoVevX7nj17cOTIEXlGCH+3Wq1ISEhA165dsXXrVlgsFiQmJuKxxx7Dxo0bb9nWP0O3qt/58+cxf/58TJ06tcL7jx49irVr1+Ldd98t9xvn1SuvvILRo0ejUqVKmnnG3zmOwcHBWLNmDTIzM/H000+Xq2dF8/TPtv3jjz9G69at0bhx43Lj+N+k26nHbSsaISEhpQ/oynLaqx4Nm82GnJwcGV9BIQKAJl0gAIndZWBwYmIifH19Na57blBk3uo5BIwPoDBJhYLCKVNNkhFTAOAGmZubKzVLYvR4IBU3QVWRcDgccrNwOBzIysqSm5SPj488JIbPUrlikCWFRSpFnHAUFKjIUFkhhpR4ZlVwKS4ulrAb1f3PD1OiciNTLekUojg56Ami4EJBiFOCWGMfHx+ZAphKEgN5iTd0c3OTBzWqXgDVK2Gz2aTQQyWHCic3eLbbaDQiKytLg2dW4WuMJyG+mgoYUOZxYNpajrObm5tUSDMzMyXOm3OYQiNhOc6B7aqgTww8x9Zut0vFigpCUlKShLDR7c7xIv6fQifjmthv6sFwAKT1nhAbZ4zq3UJVq1bVKOhq9iamCaZQT+sa54PqTaP1n+uBXiBCbQiTo7Dq7AVhwC15Fg0IqjdTp9Np5l9GRoYUptV0w+RRnp6eGlwuz+EoKSmRsU0UJp0TKwCQ1jfVoAOUzRnGe7Fv1NTYTHihrnse8si1TcGWB2Nx3ZNPkSc6HKVn6BACxJgQ1fOpJucoLCyEv7+/Bu7q4+MjjUXscwrV5CNU6ukRAUp5Ds/kUFOGZ2VlSW8LrZhcB2psFsugEmcwGGTMF6ANUFcNFlQc7Ha7nBscJ3otVEGd5dCIoKYMZ4wdvRecB1RSOdcMBgOysrKkF4Tz1c/PTyp3en1ZwDfLIb9in6peUs4Nvp/zjYoxY5lo4Lt8+fJ/YtnfUeL67NixIz7//HPUrl27QviQu7s7AgMDsX37dowdOxZff/019Ho9jhw5go8++kha4AMCAmTs3s2IB+ympKRg586d2LRpE6ZMmYLAwECsXLkS27dvx5gxYwCUnuJ8+PBhDB8+XD7v6ekJb29v6R0wmUyoVKkSduzYgT59+mDHjh3y3sWLF8PhcMhgcaDUAGaz2ZCcnIzZs2cjMDAQTzzxBAIDAzFx4kSEhISgW7duAEpjPpo1a4alS5finnvuwaBBg9CiRQt07NgRhw4dwvz58zFnzhzZ9vz8fE3/qYrGjh07sG3bNowfPx4AsHnzZuzbt09Cyvz8/GC326UcsHr1aly8eBFvvPEGAgMDkZGRIQPA1e+kL7/8EgaDAX369LnVkN+UJk+ejMaNG6Nz584AStPxtm7dGvfff3+FbRs6dCgef/xx3HvvvRKyT96zbds27Nq1C3PmzMHvv/+O2NhYnD59WjNuQGlmrWPHjuHJJ5/EoUOHYLFYkJycLGHlzgHsJpMJJ06cwKBBg7Bu3Tp5fc6cOXIcb4cYS5yWllZuHP+bdDsqxG1jMFT3sCpU0gqkBvSRyatCmGp1VC3/LINClbObnJs5BXiWz7+0avIdZK60ctGLoVp11NSkZNxkXqoAA5Sdp6F+Vy2VZN58ntYqFQahel6ohFGJoheAigPbAZRZXtgmCuMUzFhfNWaDbVTd6s4CqwoVsFgs5aAEbIteXxZvwO98P4UM1VJJAZ919fT01ChJFNbVYEf2D8eH9eVcorLATVw9U4SQPPYTFUGSsxWVHin2p1o2x07Fe1Po4pgwAJ2ZfNS6U/GkIkArMceDghOVLzX7FxVAVZFS54cKo1KhiXcjse/pzVQ9Us5QDwba8TsVCCqy6vgAZaeuql4SVaB0tn5TUOT6doY/AmWQQZVH0IChph3mOiRRQKWASUu3all39iTQ0sz7VWw+14MqVKoQOq5h1lE9/4N14/uAsqxNKk+mYkPhW43PoleW5dO4w//Jnynoso+4TtW+JL9W+ZgKJ+I6Yx0ZGM/f09PTpedUjcFhv9J4oiqz6vkUVLrYfs4BzhPVW6zGiFCoV2MkVA8U56M6X9QEHhwPu90ueQjLUyFinEf0gFDhIj/noZLcu6is0TNKwY5rhnOGfIZtI4TqbqWzZ89i6tSpUnCNiYlB//798e6778qzbOLj4/HBBx9IuIkQAh9//LHmDAN6O8LCwjB8+HCMHTtWg4MfNWoUfvvtN2zYsAFAaeaj8+fPw+EoPRV+zpw5uHr1qrz/iy++QGJiIry9vTFp0iTodDrs3btXQnsGDRqEjIwMfPPNN5g8eTIuXLgAoBSKNWXKFJw4cQJHjx6V5T333HMwGo347LPPAAA//PCD3JeHDBmCK1eu4KeffoJOp8OkSZOwbt06HDt2DJMmTUJOTg42bdqE48ePAwBmzJiBqlWrSuy/c8peoFRRYnvmzp2L6tWrY/z48Rg9ejQWLFiAa9euwWKxYOLEifjoo49w+fJleHl5YfLkydi9ezcOHjwIIYQGzuX8nbRixQq5/qZMmYJly5ZJaBMpOjoaAwcOxLvvvotu3bohJCQE06dPBwBs2rRJAyXavHmzhKxV1LadO3fKjI3/+te/cOzYMaxduxZAaTzJ1atXkZ2djUmTJuHGjRvyIOlPPvkE06dPx6VLl5Cfn4/Jkyfj8uXLqFu3Lnr06IF33nlHEyyvkt1ux/vvv6+B0jmP4+TJk/H9999j//79mnHcvXu3vF8N9p8xY0a5fvon020rGipmWsX5koExWJBEaxKtOmq+ewoR6im4qtVddSlRUVGFK2eBVGXuFNLIxFVsPe+hkMM6qjEczrAhXufGqGJ+2W4vLy/5bipk3Mi44bCf+F61jWp+fnXTVAV8FeetCkUsgxuPs3apCjYUtHmNFkX1f3X8gLJUvxQK1MBMCteqEKbWnVZP1pFBkfSIqZnAgDJcPfubEAm2jRZpbq6qUqHGiqiwB84VFQ5FJYLj6YxZ59zi/OWcUZUCes5UQZn9RyuxKvRxjBiDoQZsqmPDueecYlcVfu/mGA0KsZwjFJBUbx+vGQwGCXkkFIcWZ1Wp5W9qEgI1HoPzhDAqriHOC64vVdHgXOBz6nxQf1fhWiqfoXWcQp1qYKBgTh5FwZAeBfIltR2cr2pfAGVxIapiAZQdfMh+Uucu5y8VGkI4VUMFM0JRYCefAMr4Db2IbA/rxLnLurPt7Bf+VRVtwii5X9Baz7bwOSGEzPzHNnA+ObeNQexUNAizU2OnuA6BMm+9OvaqcYP1U5UekmqocoZYcH9TDXG8h3xJPXBSPVmdXmd+1OB3lq/GbbB8ddzVupAnqtfvRiKsR7Xo2mw23HvvvXBzc0NERATc3d1x7tw57N+/H4mJifDy8kLNmjWxaNEiTfvr1q2LpKQkmM1mtGrVCkajEWFhYbDZbDhx4gQaN26sSSf++++/y6xvdevWxQ8//ACLxYK6devi2LFjiIuLQ25uLjw8PNCyZUvo9XqNRZzvKyoq0tRfr9fj3nvvxbx583Dy5El5vVatWpr9YNOmTQBK52/z5s3x3Xff4ejRo9DpdGjWrBkOHDiA3bt349NPPwUA/PLLL/Lsi2XLluHVV19Fq1atNP1pNBpRp04dHD9+HL/99hvi4uLQqFEjfP/993jyySdlViieQ8G28XR1d3d3tGzZEi+99BIOHz4MNzc31KtXD+fPn9cIyNHR0XA4HLh06RLq168vYzg8PDzQokUL/PTTTzJe5ejRowgPD0eTJk3QsmVLuLu7o0OHDqhVq5ZUNHguB/v1zJkzOHjwoEw5HBAQIMcRAH799VccOXIEjRo1QsuWLaU3g+Oak5OD3Nxc2XcAZFsZp1FQUCADvOvXr4/mzZtDp9MhOjoaxcXFuHLlCurXry+DzqtWrYp58+ZJZEy1atUQFxeH06dPS77avHlzqVSo42g2mxETE4O4uDjNnF24cCHuJrpt6FS9evWQnp4uhX5VyKcVi3hqb29vuVkApZmUCMnh5qIGLRcXl+aXVy1JaowEPQS0wlBIVQVe4tu56XLTZn0yMjJkGdzcVQubapWiMEThmG1mPZwFX4vFInOnU9Fhjn5i89lPqqLicDhkthar1QofHx/k5+cjPj4eHh4e8PHxAQC5CdOqpm6oaiA9FTgKDLRuqUJLdna2xuvD+BCgLJsLhS0KvxxXxleoSpBqAS4uLpYQERXWpCqAHHtaeFUlIDU1VdM2Zg1RN2kyBjWrDjdZVRngu2gdJOyAEAWm/gXKoFBqOtm8vDzZjwCkYsyxY5yFCsughRwAUlNT4eXlJevANUHLMZVS1dKYk5MjhWEK2KpSrQaTpqam3v4q/wdRUFCQ5pwaKqOq5ZxjpXp5gLK0xiR6p9jn+fn5CAgIkPMiOztbxibo9XpYLBaN0m82m2XKVwqZQNkJ4BQIOQfUFMrE9fOsDVqpGbdG7x35EJVs1oXX1RTgVGa4Xp0tzhx7VUHnOuXmz3XJ1Kc2mw0BAQESCghAwnPU7GZ+fn4ar7LKpwgpU7H/qrLL51ToIxUQh6P0TBHyLRpnuA65Xlk38lwGgHJ8nWPyeB4L4Z30EBgMBiQlJUkliJAhji2JUEamrWZbCeXkPKNHhgqLEKVnLpGfeXh4SIin6tnn/lBYWCjTl3L+qgpbSUkJ/P39JSwyOztb8g6bzQYA0ivGD/cy8rn09HTJHzgH2A568Onx8vHxkVC7rKwsjaB1t5BOp8P3338Pg8EgzyRwpkWLFqFKlSp44IEHkJiYiLfffhsnT57Ezp07UalSJak46HQ6XLlyBR999JEUXgFg0qRJ6N69O2rXrl2u7OPHj2PNmjWYM2cOLl26hAYNGqBDhw548803UaVKFezfvx8HDhzAgAED/lL7Vq9eDSFEubSnN6MvvvgC0dHRFZ4tQWrQoAEOHTqEKlWqVHj+Qo0aNXD69GnUrFkTZ86cQWRkJC5cuID69evLM0KA0kPjdu7ciddff/2WdbLZbEhKSkJsbCy2bt0qr69fvx55eXl49tlnkZycjN69e8tAdVKnTp2wdu1aBAUFYcaMGahSpYo8I2T+/PmoXbt2OUVJHcetW7fi8OHDqFatGl566aVy4xgdHY2zZ8+iVq1aGi/D4cOHsXXr1nInkd8ubdq0CRkZGXjxxReRlJSEHj16QKfTYfny5RKeFxsbi++//x6BgYGYO3cugoOD0aFDh5uW2axZM+zduxfh4eGIj4//S/X6d9PtqBC3rWhERERosv5Q8KQrV7XWOZ+IS5gKX0VYD4ULClfq/TwJXN1UyVgJW+Cmp1qEAMj4D6AMwqAKbBRwaFmmQkArNMtX3fAUWDw9PWWwOGNNVIGUShK1T+LH6f4PCQmRuHAqMMTscoNT8dP+/v7SVe9wlAbgU6kCSrMkcDMtLCxEYGCgJsBdxar7+PhIiyCFXCoE6kF0tGJS4KJQwM3M2UrKDZ+5zZ2hJyo8gRt3Xl6eDPhUrYhqsLgKiwDK4BoUsKgU8UPhgOcBZGRkaDZ1VVFkQCcVCyYBoEBB6BcFU3qqWFeTyYSsrCxNPIHzPCO+nMKk6s1hGlcqJ7Scsp5Go1EKLVxzrLvJZLorsdUA5CF2nA9Mz6xavWmlpsIBlMUUcd0IIWTMA2N+hCjN3sI1V1BQIM+KUGFSVCwdjtIkEipETfWuOJ9jokIXCX+jsM73q0oLhVHyJ9Xqz3OGVGghy6Ihg/OLawzQZqiiR438lP3GdcZzNFSYHgAZW6beyzqwDewrem9UQV3ldTTuOKfVVr0Azgoi72fCClWRAMoO/qNwrp6RxHG3WCzS+OOMrSfv4nsYUwZovcNsL2FJhD06p49mQDz3IxoOCIviuR+q4kiezb7nfkUDB+cVz2hgX5L/eHp6avgPDTqq15SeMFUZ5rphnzsHh6sKbEFBwT9WeLkV6XRliRIqgscAkOffJCcny4Pf7HY7goKCcOPGDc2+EhoaiuzsbISHh+Onn35Cy5Yt5blOCQkJ5coOCQlBfn4+cnJyEBYWhoSEBBiNRlgsFsTHxyM4OBhFRUUVprIlde/eHRMnTpSngqvEk6pv1raZM2ciMDBQBh0z1k+FJjVo0ACrV69G06ZNZZKS4OBgxMfHy3UGALt27cLKlSsxe/ZshIaGIj4+Xq4ftk2tX3BwMAoLCyVMaPXq1Thx4gTeeecdTR11Oh0qVaqE5ORkdOnSBVOmTEGDBg2kDJKWloawsDCkpaXJeM0jR45g6NCh2Lx5MwIDA3H9+nX4+vrKcQQgT99mWydMmIBGjRqha9euchwLCwsREhKCGzduwNvbW47jzp078f3338sD9tjWSpUqYe/evejbt6/0VgHA2rVrcfjwYRmPQrLZbPj999/Rs2dP7Nu3DxaLBYcPH8Zbb70Fb29vvPvuu3jwwQdx/fp1OZ7Xr1+XvIvf2bb09HT8/vvvGD9+PC5evIhVq1ahUaNGSEtLg4eHh5yzo0ePRvPmzREbG3vTefXfoNtRIf4UdIpMkxOVf9UYCQqM/E4rlmqtY+Vu5lFQXcxk3MS3qwoNIQaqhZ3WJBVOxM1aNtpQdoosNwWgLM0hs7GQ8TsLw6oljtY+Nc6AmU64sbDvKExxw2d/qVhtWrnYH9ywVCw5n6XVX8284+vrKzdgCkYUqLnBUPngNVUxYh1VoY5WVOeYCFr9KYg5u/Rp+WT/UYli3RiEqgosqhdLtYKqyg+JSiY9O9zQ7XZ7OeFD9UKxPP5PQR6AVBgopNCDxrMDSKqATGWKbVeFNs5R9TcKXxxjdT6yrlR02V4V1qHCJO42UrPNqXAYKliqcKpC8xwOh+QFKrzR09NTwv44/9SDzFQewbmqznVVgHOOj1Ihn1yXVBbp2aBlnHOK80D1PKpKtgq1Yt04t1U8vSoc8v3sPxVuo2bD8vHxkd5EKtvE97PPAEhImgo9Yv9z3lL5VXm2SlTYWDe1v5lMQzXs0EDAdUXewT6jZ4lrhgf9kefQA87+UDNQcUxVGJ46xiqMlsI21xXHinPBOZUsyRlOx35jn5KfEv5EXuvsPSef4hpWYzjY76qnk9nG1HmjrgEV/sp6sf1U4lSeTGPV3UzvvfceFi9ejOPHj8NgMGD06NFYtWoV4uLi4ObmhlGjRmHNmjU4dOgQAGgya1VkzaeylZqaik8//RRZWVnIysqSQb3Dhg3D77//LlOxJiQkoF27dmjRooXMaNS8eXO0bt0aU6ZMQWJiogy+njhxIl5++WUkJSVhw4YNGDVqFObPn4/Tp09j3rx5KCkpwQsvvICCggIsW7YMANC1a1cAkKltnemnn35C/fr1MXnyZEyYMKHCcxUSExPx6aefykBou92Oa9eu4Z133sHu3buxc+dOAMCSJUskLOfq1asYOnQo4uLicODAAQwcOBAfffSRBjam/g8AK1euRLVq1TB69GhMmDABQgg0adIEDz30EMaPHw+Hw6Fpa0pKCurWrYvXX38d48ePR8+ePaHX67FkyRJ89tlnOHv2LAoLCxEfH48xY8ZgzZo1yMjIwJtvvokJEyaUU95+/vlnnD17Vo7jQw89hOjoaJlBLDMzU47jkiVLcOLECdlWUnZ2Nj799FMcO3ZME2excuVKxMfHw9vbGyNHjsScOXNw+fJlCZ3ivCoqKsLs2bNx8OBBGI1G6ekqKSlB7dq10bdvX/n+Y8eOyTnIcdPr9Zg/fz5OnjyJjIwMzJo1C/n5+ejevTsqVaokYVy7du0qd07J3UK3rWiogjkZncrcVWwzGSSFNjJNNaOTysS5YVLRoEVLDTgmA1WZpgr5oUAClJ3fwY3cOd2kypid4UhAqfDAU6oZ7M33O+Oh2R5VAaOFiicM08tDYVW1egohNJuSagmlt4XeDvWsCYOhNAtJcHAwzGaz/DBLEiE9tNyqsDAqL9yEqSSolkVmeqGnh4G4zERDIUAVBFVrKOeKqkhw03QW/tjvHAsKAqqiwb6llU+10HF+cuPn+DjHrKjWQGbpoeCiwmb0em2WGZ1OB09PT3lCL4UT57gWVbhRoS0UylRFTLWyU8lS60pFg+uK46MqNHcjqYIrvQTqvFHbRj7C/529ZTwTQD1kkUoG+16FAaqCLec/5xWVbnUe5Ofnl1PqCOkiX2L9OMaqYqwq1aogqL6fzxF2CUB6K9SYDVWpZlyRmjEOKLWckc+oigaFdL6TcCjWj/ySdWOfUSkk9AYog0oyGx7nrRoLp8acqV4Frk16DVQoFJUh8npajHmAqcoHcnNzZbncV9RU1M4B2mqGLBoreK/af+wL8n3VqKLOSRofqJyqexn5N/c+eig4B1XlhHwQKFMgnJU6NbsdlTbVc8YyOK5UkMkfCWHm7+w3jtPdSA8++CA2btyI48ePQ6/Xo0uXLti9ezfi4uKg0+nQuXNnJCYmIi0tDZcuXULNmjWRmJiIwsJCVK1aFadPn0ZQUBA8PT1x4cIF1KhRA6mpqcjKysK6devKKWLt27dHTk6OVDSA0vMl7r//fqloVKtWDR07dsSUKVMAAJGRkejYsSMmTZqEli1b4sKFC9iyZQsefPBBfPvtt/jtt99k3EDz5s2RnZ2Nr7/+GjVq1MD999+PrKwsjaLh7e2NKlWq4NSpU1i7di2SkpIwd+5cTJ06VVPfmjVrIikpCfHx8Zg2bRqAUg+M2WzGhQsX0KFDByQkJGD//v2Ijo7Gl19+CV9fX0RHR+PcuXNo27YtCgsLcfz4cTz44IOYP3++zIZXs2ZNnD9/XhoMa9SogW+//RYPPfQQXnvtNUycOBFCCISHhyM2Nhbfffcdzp8/j/j4eGzcuFHKZ6GhoYiNjcWkSZPQuHFjGAwGLFy4EB9//DHCw8MRERGB69evo3Pnzjh06BD0ej0efPBBTJ06FWFhYfDw8JDjeujQITkuNWrUQOvWrTWKRlBQEHx8fHDu3Dls375dkxEqOjoaWVlZSEpKwvvvvy+vs60rVqxAQUEB/Pz88OCDD2L58uW4fPky7HY71q9fLz1OhYWF8mR1Hx8fTVuDg4Px4IMPAig9M+TYsWPQ6XSoWbMm4uPjkZmZCYfDgRkzZsj3c9zq16+PmJgYqWioELS7jW4bOlWpUiUprHt4eCAzM1NCFEpKSjSWf2JN1dNSyTA9PDxksBuZq5rClZhexnSQMTLlITOGqAIKoQWqIkShQQgh4wachULVC6Fap+kaV2FQ9Obk5OTAz89PQn9UjDkhCdzQ2F8UEtg3QBk+Nz8/H6GhofJ9qrvUaDSiRYsWCAgIgNVqhc1mQ1BQkMbVruLUPTw8NKnc2BZV8eDGSmWOaYUByM2YfQhABroXFRUhIyMDdrsdqampiI+Px/Hjx3HhwgWN4sJzCgwGgzxDRLVEqkpFXl4eAgIC5LgR386MN4whYdnMQKR6LNTpq44FUJrLOzc3V0IfbDabhJgVFhbC19dXI9yw/W5ubvD19ZXwBCotVIYJD1EhaEx5CZQlFVDTTDKNpQpVoaJE+A8VSipZhAipMTYUuCty6d8NFB4eLucsU8IS2kIhkpZ5rj/VA6LGwzCLE4lwNNWjRIURgAbHL4RAamqq7G+DwYCAgIByMBlnKzbLZCY71WBB3ujsBVC9lpw/9K7Y7Xbk5uYiOTlZk41Np9NJtz/r7OXlpfF60apO3sXvnJ+BgYESfkYoqYr153MANMKrqhipBgoqGITGsp9UzwvroZbLv4Rg8l7VgMG0lxS2CwsLkZ2djezsbOlhprJeVFSkSUvsPA8AyPHR60tjc9h+KoBc12wH+QmNSlQyvby85Fk8bCehKsXFxfKMJtUbxPmgeilUjxY96OwTKiwlJSWwWq2atMw0tqiwYCo7nO/MCEYeyr2V71GTL9DwBZTy+7v9HI2b0eLFi1GlShV06NBBE6Oxa9cuhIWFYeTIkbj33nvRtGlTDbb/yJEjqFq1qjxQ7j9JTKPao0cPmQmJpGL71eBqldzcSs/VGDlyJObOnSuvf/LJJ2jdujUaNWokr9WpUwfHjh1DVFQUnnvuOfTo0UNzEJ0zVa5cGVeuXEHDhg1x+PBhCU269957sX///nL3qzEaQUFBmDt3LgICAjSpbSuib775BjabTaardSbGaLRu3Ro3btzAhAkTMGvWLOh0FcfaTJ48Gd27d0etWrVw9uxZLF26FOPGjQNQFmvjDPuqVKkSrl69imbNmskDFlXy9/dHYmIiHnjgAexQ0hIDQI8ePbBgwQIEBgaWOx+FxLTt/fv31xwyeLfS7agQf/ocDdUqQ2ablZUFq9UqoQ20/vJeNaUgBUwyfgCaw5C4OajnP+Tl5WmsSFRMyPip7JCZcmNUvR/0GlCAoyBAYU/FABuNRnh5eQGARgPmRsIgYnpdqEgBkFhqbqjqIYYUAFJTUzWeibCwMISFhSE0NFSeMMpgRypLaluoLDBGhNZvHhymTgBnq1Vubq609HNTUj0o6jiwD9WNkgof+y03Nxfx8fFITExEUlISrly5gtTUVCkIqK58CgqqJVe1tvE7x5pBmHyWQhRQlmGMsDAKCKqF2hnORUs05wDxovSsEGdPwVDNRqZaxlmHrKwsjRWUh/CxPewzwvFUazctJarnhv1NJUQVsHlGA62/d6OAAJTGenGtcF2oCj9Qlh5bPcMEKOUTaorYkpISafHmvKGXTfWOUIFkXAWt/EVFRcjMzITBYJBrjZ5DKuWsG2N0gDKPmHqYGgAp0HPu2Ww2uZbc3NykNxAoE+x5gJ8KsaQy5eXlJY0EVEI57+12O3x9faVxh/Ednp6e0rtps9kkdIrCNfuC/FBVGFQvsbPnjPOYShLnKZUl8n7yOBUCybFQM76xfK4/en/Jz3Jzc+WHBg71udTUVFmeGjtBSKNqMOEhd1QCVUMK68gD/CiMU5hnEgf2k91uh9VqlXtfWlqaTFGpCvMqJE+FfanzSTVMsB4WiwXZ2dnw8PAod/aHqkBTSVehYzTEqfOWvEmdpxwTd3d3TXaju4UqUjRmzJgBi8WC5557DkCpJdtgMODGjRuIjIxEamoq7HY7wsLCcPnyZfj6+sJkMuHatWuIiIhARkYG8vPzUblyZVy+fFnjpdq6dStWr14trc4bN27E1q1bpeX5ZuTv74+9e/fi6aefxqFDh2Cz2bB//348++yzCAkJwdixY9GsWTPJD3U6HapWrYqEhAQNTHfmzJkICAjAiBEjcPHiRbkuDQYD9u/fj8mTJ8vTyiMjI5GWlqY5ZykgIEC2ddeuXVi0aJEMlr98+TJ8fHzg5eWlgRM5k8FgQEREBK5evSp5YWRkJK5du4bu3bvjzTffRIsWLfDVV1/h8uXLeOeddxAZGYn4+Hi4ubkhICCgQuVtzpw56NixIwoLC9G0aVNYLBa0bdsW48aNQ7NmzTB69GiEhYWhd+/eAEpP4m7RogU+/PBD9OrVC2fOnJFtjYiIwIABA1CrVi2ZJMDPz0+2rUqVKsjOzpbwq8qVK0vP8c6dO/HQQw/h1KlTmrapyUhWrFiBc+fOYeTIkYiMjMS0adNw6dIlTfC4l5cXAgMDcfnyZXz22WcQQuDll18u1+7IyEgJI1u4cCGaN2+Od999FxERETL2ZsKECahduzaeeOIJ7N69G3PnzpVeLo7j559/ftMx+0/R7agQtw2dIlREXeQqw1bxuuqmRcsVrcEApPBNhugMoVAhRFQ8SCoUi4yXm6DKiNXYEBW2wk2WjJ/CLeumMm52Isvlu7iZsd0qfEi1avF9Pj4+8PHxgdVqRWBgoMz0xI3M398fFotFQh0YZ6HT6ZCRkSGFIAqsfNZms0lBjAcOMWaD7SH0ASjdsGglpTLnrIio8DDCHVSBXRW+AcBsNiMkJAQWiwXh4eGoXr06srOzkZOTg/T0dGRmZiItLU3+r44Ly6cAT8GcFkZV2OEYql4BNZUwoIX3qV4mPq8qAGyDCsVRFwzHW53jnDeqBZf3clNX56YKd+HvqiLD+lAwUgVu1lddb6rH7W4lKnVq2wjpoPFC5QMqPE6NC1At7ioUhp4C1VOpzg+gTPGg54yKnwojUoOHna3yhMuoAptzNjneS6KQyjmnQqd4NovBYNBk3eJfd3d3qZCwbFrq1d/ZRn5XD+8UQkjliXNb7TNnJVeNhQKg8dSo3hrVC8Lx4BiqCjr/VzPkkVimelAqPRX5+fnIyMiQcCkqNqq3ijEfVDqpkKpQKdUbSSFf9c4wE5zq1QUgDRk0iDjvLSr/YfnqnOGzqveEiiENVGrwu7qHkA+rcTrklarC6Lzvcly4djin1WQu/P1upaeeegp6vV5ahHfu3CkNlm+99RbWr18voVQ9evTATz/9hMOHD+PixYsYMmQIduzYIa3Vjz32GPbu3Yt9+/bh/PnzAIAOHTogJiYGs2bNwg8//KA512L16tU4c+ZMuTpZLBa8+eabmD9/Pm7cuIGCggIsXbpUBjIXFhZi2bJlSExMRH5+Pr755hs5BnXq1MEjjzyCadOm4cknn4QQAsuXLwdQKlS6u7vL8zZIDocDK1asQL169eDj44Mvv/xSCvOVKlXCiy++iA8//FCDkvjuu+9w4sQJ2Gw2PPPMM5gxYwbuuece1KlTR5PS1WazYfDgwZgzZw6SkpJQXFyM8+fPo3///rh8+TI2btwo++rs2bNYuXIlhBDYtGkT0tLS4HA4cOHCBTz77LPIzc3F2rVrMXLkSCxfvhwGgwHdu3fHtGnTsGPHDiQmJkqempiYiIMHD+Krr75CUVER9uzZA6vVKuuVnJyMw4cPY+nSpTh58iTatGmDypUrY968ebh8+TK2bt2q6ae0tDQZC3HlyhVN/129ehX33XcfHnjgASxdulQqIGazGc888wzmz5+PsLAwtGvXDh9++CE2bNiA1NRU2bY1a9ZoFLpnnnkGBQUFWLVqFQBgx44dNxXCOU4JCQlYtmwZCgsLsWfPHk0WrP379+Py5csQQuDbb7/FqVOnEBISgpdffhlbt26V0Lu7gW7bo2G1WmVKUVoTgbJNW90EgbLsHrxHPbmblkqmXwXKAsJVCIIq5FJgU4V6bgYMvlYtoqoiQIGUzJvpaBm/wFgGVcFRYRLOuGX1FF6WTaHC399fEy/i5uaGypUrIyQkBMHBwQgLC5NWaWZPYbpEZn+ixdButyMpKQnBwcFSkMnIyIDJZIKfnx9CQkLg7++P/Px8pKenIz4+Hl5eXhLbzX5QIUfFxcUyEJRtVy21HE9mhQFKvTq5ubnlLHAqDp2WTgY7FhYWIikpCdeuXcPVq1dx/fp1aYWnYkRrMvtWzRWfk5OjOamYGzTHwGw2Iy8vTwrdhJbwXkIEVC8C68YNXM2gpXrRnPHvFEBoYVSVDdVrQa+EGgfAPklPT5dBqRT2aGnW6/Uyta0q1FI4o/BEIetuPxlchViSuE7prWTfUYClIMpMYhSo8vPzJa8hf6Fhg5ZfCmIcH/IS8hx6JwifomeMQjPXg8NRlqqVdaLl2dvbW/Iz3ku+pSqsbDvr6uPjIw0M7u7u8ju9GHwHlQrOD3oqCKFRIWVCCGnBV2GR7Bsq+KryoMKg1FgYvo+Kj1p/lkvPJYlzViXCoahAqEI5FU11XMl7i4qKJISKkEcaVdTED4znyMvLg6enp4wh4/u4/5jNZjmGZrNZng7Md1LpJK8kjyTvYPweUKY8kn/qdDrpved+Qg8T20L+zvFjezgmnp6eUimiZ4x1o4LGcVONSOwLKnPkW9xv6cGmp4bKEDPj3E2k0+kwe/ZsGAwG9O/fH1WrVkVSUpJMJb9//36MHz8eP/zwA9zc3LBv3z68//77WLlyJfR6PXbv3o1Zs2Zh6dKlAEoF+S+++ALLli1DlSpVcOnSJQwePBgdO3ZEly5d/rA+lStXlvvNrl278Nhjj2lSwpL0ej2qVq0qD4JTKTY2FlOnTkXTpk0xc+ZMOBwOvPLKK/J3s9mM0NBQXLhwoZzwOnXqVERERMizLoDStKj79u1DpUqVZBazq1evIjIyEhkZGQgICMCGDRtw//334/HHH8eDDz6ITp06oUqVKsjJyYHZbMb27dvRtWtXjVK1evVqHDhwABMnTrxpf1gsFgQGBuLChQtYtGgRUlNTMWrUKPz666945ZVX4OnpiWnTpqFZs2bl4EUhISHQ6/W4ceMGqlWrhuTkZA1UjOnRGVA9ZswYNGjQAN27dwdQ6r1hW4FSeczLy0sqGX5+frBYLDJrY//+/dGzZ0+0a9dOviM4OFiOI09Wb9mypcYLWhF9+eWXyMrKwuDBg+U1Ly8vhISE4MKFCwgODoZOp0N8fDyqVasm5ywA6XVT2xoYGAij0Yhr164hMjIS6enpqFSpEn744Qe0adPmHwOfvqPQKUJyaIEjIyZ2Xg2coxBH4Z/pY7mZknmqMBRCgshsVaGrpKQEXl5eMi0q4zG4mal4aVr01AA/lq9aSinIURBVoTEGg0FubCUlJQgJCdHghylgU4Cgq9xkMiEkJARhYWGoUqUKwsLCUKlSJU0cgho0WVJSIrMcsC5+fn4yqJOpdHk6bUZGhtSEVeGGglVOTg5SUlIgRGlMQEJCgrRqqlhuKho6nU6mueTmpcIL8vLyZBrOnJwcJCQkyE2X5dH7wE3dGTfO81EKCgqQkJCAa9eu4fr161L5oKWSFmBapdhfnCMUZAhPopBCDw9hUJxHeXl5CAoKkgoSy6E3JysrC3l5eZp5RKgL+5OepJKSEmRmZsp5qVoOKYxQ4OP3wMBAjcWSygQFEMK9AEgoX3Fx6QGChBWmp6fL+Bdvb2+5zrKzs/8xTObPUpUqVeTmomYDUo0UqvLq7e0thWieVwCUCXmZmZlSqaAyxueZHpjKqZ+fn2Zjy8vLg9Vq1XgfyKPIA3g6NuNx1KBcVYmn8qrOCyZ+4P1MzUhPpre3N2w2G7y9vaXVjmlN6XmkZ4J/yXcp1HKdUdCh4M51zrVKTw8hSoRx8V4VO02+RH7MPlQ9LCqxLlyv6iGY5K1qZj31zA8VpqjyfCp7avAy12V+fr5sAz2n5EHqGPCsiMzMTKSkpEivIi2XNFioFlMAmrTIbDcFd+5x3t7eclwyMzNl7ITRaJRpe6ksWywWyW+5z5BPqGceMX5MNWRwPhAWRd7n3PeM96Pi46xI05Ph6ekJq9UKnU6HvLw8aZW+m0hVYk0mE5KTk/HCCy9gxYoVf6vc+vXr/6UYjSNHjmDr1q148803b3nfrbD9f0S3E6OhkqpoDB8+XMZoXLlyBTNmzMAHH3xQ4XOnT5/GihUryqV0/TPUr18/TJ8+HUFBQeVS9/4RMUbjoYceQnJyMgYNGoQlS5bI3292jgbpo48+QocOHXDPPfcA0MZoAKWKyTPPPIPq1av/xdb9OXrkkUfkORqLFy+G2WzGo48+qonRMBhKUxO/+eabmoP4PvvsMzRs2BAtWrTA9evXMXXqVE3Q+D+F7qiiUblyZSmA8iyH1NRUCCEQEhIivQOq9VCFMBDqIkTpgUf8n4IGBVxaHdXgvczMTGnRV70WvJ/KB61r3t7eyM7OlpstLW7c2CjY8d20/tNiRMZODdbT01MKrDwbgRtlYWEhGjRogOjoaFSqVEkGYNL6V1JSgvj4eOj1pZleGBDFzdvf31/iCM1mswwq5iZbUFCAxMREaa1jNpbs7GxkZWUhJSVF1rWwsBDXrl3TZHoqKChASkqKjAXJy8uTwYvE/nt5eckNTbXg0cJLb5ZOp5NjzwBLf39/ucmlpKQgOztbA8ugEkgYC62atOSdO3cOly9fxuXLl5GYmCj7nvEjnEsGQ+nBXAzypyJCQYXXKWwQ/56ZmakR4BnzQeGKShez1qjz1dmDo1p6uVHz/W5ubprsZuxXelgY6Ey4Di3o/F09M4KWSaAMAkEFHigVkJzTDN4t5OPjI5Uys9msOQhNjSOgx0fF+6sBtap3gQK2eqYJA3pVDwbfyb7kHOF8Ir6dpMJCqXirmHeHw6HJSKcKiYTcqMHmZrNZKhBUNuiBJG/gNavVKtvl5laa6EFVUql8UXBVvWPOUB8Acn7S+k/hU/VgqmnAeSYOvU7se9Wzoc5HGnfojSb0k3VU16hqKFCVI/Y3FTfVw616oFlvtT1UOtRkGIWFhVJBTEtLk56RpKQkmY2JCUrIs9hvmZmZUllgG2mIoaeK84mKibr+VY+Cn5+f7GPCbsljqWjQm8T30QvDuCMVMuq816qQZlqvnaFvVMyplHBu3o3pMnU6HWbMmAGDwYABAwagZs2auHHjRjkvb5MmTfDFF1+gdevWyMzMRIMGDbBkyRK0a9euwgNPjUYjqlativPnz2sE5A0bNmDNmjVYt24dNmzYgEceeQTnz59HaGgotmzZgqFDh+LQoUOSJ3/zzTc4duwYZs2ahZ9//hl9+vSRp2VXr14dV65ckTDnX375Ba+++ir27Nkj3zdr1iw4HA4MGjRIXvP29kZ4eDjOnDmD9957D/7+/nj++efl740aNcKiRYvQpk0b6T2PjIzEuXPn4O/vD09PT1y6dAnR0dFIT0+Hr68vVq9ejc6dO2tS/kZFRSErK0tzkvlzzz2HZ555RnOwXEBAALZv347evXsjLi4Ovr6+2LlzJ/r27Yvz588jJCQEp0+fBlCqJI0bNw6tWrUqp3hwHF999VUApZAvNzc3XLlyBTVr1kRCQoImTjY0NBRGo1EqgiNGjMA999yDHj16ACj1Rnh5eUn4VFBQECwWi1SoAwMDYbVace7cOQDAiy++iKeeegodO3YEAPTt2xf9+vWTBwQ60+OPP44hQ4agdevWtwU95LidPn0alSpVgl6vx9WrV1GjRg3Ex8fLOcu2RkVFyXHkvsBxU2Fg/yS6HRXitmM0VPwsUObZ4EalQpf4cnUDUd1O/E3FovK6usHQ0gNAY0Umo1Qx3kCZV0CtB+usbnqqi94ZW89nVOwxFROmmzWZTAgNDZVCeHR0tEyjpmaOYZtsNpt0oXt5eSE9PV26wOkpopCUnZ0tgyKJT05LS5MbaUpKisQk01Kn9iWhVozVoJuWHoHMzEyZpYTpQVVvBoUAk8kEq9WKa9euyXr7+PjAbrfD29sbFotFQtAoIHt4eEjoGIUSwrrS09ORnZ0tBQ5aEukFioiIwI0bN2QbExMTNQHSap56tpUeKgri7HfV26JC/VSss4p3Zt/wXRSW1HgA1YMFQPaROmfUeAFaYlULtzrHVegI5zTfx3upXDvff7empSRx3NTvvEYLvdqvKowJ0J5jA5QJugygV8eK39UPn1PhJhQwVWFeJc4VVTCmMMfxUs8/oJGDmwUP91PhTlarFVarVSr6fn5+0khBhQgo42uq15VzTOVlVKrU/mR9CPPMzc2VJ6azb+klcYZOquWwD2gVZ38DZSmh1XWopuZVlSWOB+exOl6qkqRardleWvwp6BM2RMWeB26y/jRW0NvAd/E38gSg7Ewo9iWNHPSiqHyEfczrfJ7jz0xhVD6pGJC4X9LAoQaus/9UPsU+oDJJHsKxU9My0/jGOql8R90v1b31bqTffvtNzpcuXbpgw4YN8PPzw+OPP45PP/0UhYWFSEtLw7p166Rwm56ejnXr1qGwsBAPPPAAwsLCsHjxYrzyyis4cOAAfvvtNw1GnrRt2zacPXsWubm5+PHHH6VHIT8/Hz/++CMOHz6sMfzs2rUL165dQ1FREdatWyc9aCUlJZryi4uLsX79enn4nLu7OwYOHIi0tDScO3cOBoMBgwYNwrp163DmzBn57KFDh2TSiYEDB2Lbtm1IS0vDjz/+KMfU19cXDz/8MGbPno3ExEQEBQXhrbfewpw5c5CdnQ2DwYAff/wRffr0wfbt27Fv3z4AwPnz59GiRQs89thjMnPV+fPnNal9gVIlnm275557EBsbi/Xr1yMtLU1zdsVTTz2FGjVqaFK+1qhRA127dsXMmTPlOJJUKN/p06fRtWtX+Pj4yFic+Ph4hIWFYdiwYZg1axaOHTumUTBr166NiIgIXLhwAf3798eRI0dk24DSGA8qUS+88AIqVaqkSRt7/vx5/PTTTwCAZ599FvHx8di9ezcGDBiAZcuW4fLly9i0aZNcl7Vr10bHjh0l3K1mzZqIjY3FzJkzUVJSmqyE46YqdFTCnL+r46ieGULFKCgoCP369ZPjeLfQbXs0YmJipHVICCGFTm4utOBx0qjWIeJD1c1EtWbl5eVpgkCBUmwbLYO8xo2LeHtVWXAmNWhZ9X6oGwkAaU1WrZkqlIpufbrKCTWKiYlBdHQ0oqOjpTdFDSRUNw0Ge9MFf+3aNamoubu7S/hHcXFpukSeV8FAam5iubm5OHv2rBQyCgoKkJ2dXQ7uUbNmTfj6+kKn02H37t0SLsRx8Pb2lt4OlsU28393d3cEBQVJoZ7f3dzcNHCOsLAwaYW12WwICAiQgpS3tzdKSkpw7do1XLx4EVeuXJFQMJ667O3tLedPUlISLly4gEuXLuHChQsSn03rHoVQjhEFAQp1np6eUnA0m82SAXHBe3t7a8ampKREQssIP6GC6e3tLWFLQFkgMoUz9jdx4xRqacFmfSkgEkbDOUlhjDAKQs9UbyA9IhSY1Hiau9ESCZTCB+i9YAAuBTGOG5Uukgq/YVphpm1mzJG7u7v0OFDJYOpRNRZBNQDwzBgaAbieCAFShV4KZmosAucEP8xCRaHQZrPJeUllgnzRy8sLfn5+8PPzg4+Pj+QvarwTlR6WSYGSCjgz+al8UVU8VMu3m5ubjGdgsoaKzsGg8uUMD1XPC6HipPYlUKYs0yvKerAcGhnUE7XV8zzYbvaT+ikuLpZKEmMYaGSgwE8lnR4GwqtUb0dubi6SkpLkb9nZ2TKTnqrMUnGiwYdrTzVMUcExGAxITU2V409er9frkZ6eLvkCx4ieBWcDh6psU8Gh8cfDw0NCLdVsW/n5+RpolhpvQ+8xx189c6akpPQUdPU06buFdLqyk8Gzs7PlidJZWVlYuHAhHnzwQek1uBnxROmHHnoI+/fvx7x582QGn9DQUIkaIDF26maQVRrfKjppnRAi58PmdLqy07MLCwthNptx5MgRvPLKK9iyZQtMJhOOHDmC119/Hdu3b9ecMG00GhEaGop169Zh7NixWLlypaZsngzerVs3nDt3DhEREdiwYQOaN28uESAJCQnyZHBCckJCQvDSSy+hS5cuEp5E9AJPPVdPBgeA3r174+2338bDDz+M+Ph4uLu7w2az4fr16/jqq6+QmpqK1157DZUqVUJKSgrat2+Pjz/+GPfcc0+5GA1/f3/o9XqpDEyfPh3VqlXDgAED5InujRs3xrfffosmTZqUOz191KhRuO+++9C5c2fs2bMHy5Ytw6xZsyocs61bt2Lz5s3yLBTntvFk8I8//hgHDx7E448/joMHD2rKcD7h/aGHHsIHH3yAe+65B4WFhZqTwP38/KDTVXyafaVKlWTSCwA3Pc2+Tp06chwrmmv/Dbqj0Kno6GgAkC5rYqJpKcvMzNRYVoAyZYPnO9Cax7gC1eOheh2KiopkFhVi5ilUqFZDbmx+fn6aNJdsPDddZw8GBQd1E1Y9HhRcHQ6HDIwKDQ1FZGQkmjdvjsjISACQjB4oxYp6eXnB398fdnvpWRN5eXnw9fVFVFSU9EDk5uYiMTFRbvaZmZlITEyUGGNukPRk0MWqpr3Mzs7WWOB4+BBd/oGBgTAYDMjLy8P169c1cJPmzZtLSEFaWhp0Oh2ysrKkwJWQkCCVD75bVR65IbNvqlWrBl9fX1itVvj6+iIiIkIKEN7e3qhWrZq0zl66dEmmkaRQ4O7uLpm0iq1OT0/H/v37cejQIaSkpGhOEqZAQmVJ9TKoxPuowPn6+kpLrmq9ptCvYsTp0WCZOTk5GksjBSA1+FYV3NRD0vgM+42KOseP8RvOcQq8n4IDYRaFhYUVMqu7gSpXrqzJPMQgZwAyRkc9DVq1IOfm5sLX11cKa+xHfge0lmk1ZosePAqqnANUdilsUvmgAOvj4yNhWRwDjg2NCqqHhDAFIQQsFgt8fX2lAqF6MQIDA+Wcp9GF/JEeP/YJPWNMesEPYYLkp6oHBChLsc31QcWjuLg0JTlha4yBI48oKCjQCBK8rhp3goODNYHIVEYYR6cq3eSp9Giw77g/kF+oHiBVcGafcHw5P7gfED6njjP3DZXXZWVlISMjQ6YPVeM81HlDBZPCOvumqKgIWVlZUoAwm83w9fWFw+FAWloaCgsL5ZhS4WL/s+0ANMYzIYQ0gJBH0CtDhVz1pKWlpUlYcUlJieQJHHd6xWk0U7OOlZSUSC8y77sb02TrdDp8//33MBgMMo0pycPDA4mJiXjttdc02P4/QxcvXsS8efPk4XtAKba/d+/eqFGjRoXPvPHGG3jzzTdRpUqVcoLX7t27ceLECbz00kua61arFUlJSXj44YelFf1m5Byj0b59e2zatAkhISE3hdPo9Xpcu3atHLb//fffR2xsLOrVq1fumVOnTmHlypWaGI2hQ4fitddeQ5UqVXDo0CHs3LkTr7/+uua5wMBAxMfHo127doiOjsZHH32E4OBgyZctFguSk5PRvXt3bNiw4abt/Oabb2C1WjVB+G3btsWWLVsQHBz8b4UOHT58GFu3btWkq/27VFGMBg/wIxkMBiQmJmLIkCFYuHAh3NzcEB8fj5EjR+Lw4cPYu3cvwsPD/zGKhTPdUUUjPDxcWsW5OXEz5JkZAKRrXT2gjMIeACmg8twNbqIU2mjpcjgcGrgDN1IGw1IApsWXG5fqHmfTGJzITVbNHEJLAn9nEDCtpwEBAWjVqhUqV64Mm80mDyukRTYwMFBuCGrQZXJysiZDTlpaGlJSUpCQkIDExETNQXLp6emyrrS0si8oLPn6+iI0NBQ2m00eZkjPDoWkjIwMHD16VFoJGNcQGRmJ6tWro2HDhlLIoCVYiNKc9Hzm1KlTsj1UnmgJZOYW5qunx4D19PLygre3txSY3N3dER4ejtDQUAQFBckNkpu52mYAErvODd5ut+PUqVM4d+4cTp8+jYSEBA2kgjEfKo6bczI3N1eTZ57WaCoCbAcFGm7CFL54UrGKfWYwthq8ToGKwgd/p9cIKLP0qmmNnQPfVYFQFT4pWFEApxWVWTPuNqpZs6bmMDUqE4wx4rpUPVbOMBJAe2AdLfCEyXAdqmf18HkKp3wnBXiObXp6ulQwnWM0qIzTmEHjier1o3eCFlB673x8fMqlseZ7yCNZBj9qFitVCaCiRk8flTFV8SHPVBUk1Zus8h9VUacCc+nSJSl8OxwO2S6r1Qo/Pz+pSAOQ3kAebKfCa1UPsrMRSp3nKoSTfUK+Ql5LzyM9is6wXNWYpCpkHh4eMqYtNTUVycnJSE9Pl0oGE0OwP/kMvVg2m01CMWnkoaJhsVhkrBAhvcxixXXtrAyoXgdCn1Q4lOrtUWMH2c+qoYPeDdVYwj5gefS8FRYWwsfHR/JBHhR5t5FOp0O1atWg0+lw9epVbN26FePHj8fmzZuh0+lQv359XL16FWlpaXBzc8PmzZsxY8YMXLhwAQsWLEDnzp3x0ksvoW7duujTpw82bNiApUuXYtmyZQCAunXryr2aFBoaCj8/Pxw/frzCOgUFBSEoKEhmm3ryySfxr3/9C506dUKNGjWQn58vMx/NnTsXSUlJGDNmDOrXr4/z58/LxBPbtm3D22+/jZ07d2rK9/HxQbVq1RAXFweHo/SA2KioKBw9elSjJP/000+YNWsWvv/+e9mWxMREOc7r16/Hrl278P333+PUqVNYs2YN1qxZI705tWvXRnp6OnQ6HdauXYsePXogJycHgYGBOHbsGGJiYqQxdOPGjXjuuedw7NgxGAwG1K1bF+fOnYPRaER4eDji4uIghEDnzp0xevRovPHGGzh16pQG6vTBBx+gffv2KCoqwv3334/g4GAYDAZcunQJW7ZswbRp0/Dzzz8jOjpa01aVYmJisHTpUsTGxt5yPv/www9Yv3495s2bJ6/169cPjz/+OB5++GHZNkKcFi9ejBMnTkiPxxdffIGLFy9ixowZ2Lx5M1599VUEBgZi+PDh6NChg5Rx1XFkcoG4uDhERkZCr9dLGNSkSZPg7++Pl19+Gffccw+uXbsmY4fq16+PGzduIC8vDzExMYiLi0NxcTFq1qyJZcuWoWvXrv+YOM3bUSFuO0ZDFfD4vyoMqx8Kcs4xGnyOqUO5KaqWY26S3DycMfoqfp7QJjXYG9AeKuiMlafbkYIu/3KSUHhhStqIiAhUr15dCq1Mi0hBwmAwSPc6YyccDgdSU1ORlZUlMw1xc6OFjZsa+5HCq7e3N8LCwuTGrNeXZlSy2Wzw8/PTpBjW6/UyQ42ajYn9K4RAaGgooqOjUb16dURERCApKUmzAdJSR+iHCm2iIkLLHQVfZn1hmsnc3FwpaDALlF5fGrBKq5mvry+Cg4Ol8EWoCBVDNzc3CUHj2JvNZlSrVk0Gx548eRLx8fHSi+EMOSDUgIKbTqfTBBqr460qOexLFULCuaZ6GHi/KmDx4xy/ocaHqBZxFZrh/G5norJMYZrW4T+byeOfRGwT1zHXKfuO3lJAK4wCZbxAtQarQqWXl5fsG441eQ/5Dee8GnyrKo9qfBYt0CyD4wyUKY/kF5y/qsHBZrPJM3R8fX2lkEchUVUqKKBSYFVPsKfgSp6hxlLwfzW9rQrv4X3sb9XTw7mowiXZd2rMAS3ibJsafMx1RwgcvXr0SDkHMKvv532qcMx1RH7Oe1UoIlAWl8Pv/EvFhXODe4SPj4+cT6qni94LZkakkYMCOg0XKh9gGVTKVM+DqpzSKMD5pu6L6j1UUNi/KmRPVXJV5Zv1U2GvJFVJco514Tionri7kRjsazAYsG/fPilgCiFw5MgReZ8QAvv370dCQgKysrKwZ88e2O12XLhwQY7Vb7/9hhs3bsDf3x+9evXCwoULkZ2dDV9fXzzzzDNYvHgx4uPjpUW5d+/eOH/+vAb7HxYWhoYNG0pFIz4+Hr/++iuA8nj8Y8eOISMjAw6HA4cPH5bXHQ4H9u7di7S0NERGRiI2Nhbz58+XxkH13uzsbPm9Q4cOsNls+O677/Drr79q4HCsT2BgIJ5++mmcOnUKv/76q4wbOHjwIK5evQqbzYY+ffpgyZIlyMjIgL+/P3bv3o28vDwkJiZKoZbPWSwW7NmzB7GxsQgMDMTevXvRqlUrJCYmIj4+Hjk5OXj11VexevVqpKSkYPfu3Th48CBKSkoQFRWFjh074vPPP8epU6ekcdHhcMggbzc3N+zfv18mwmFb27dvD39/f3lWRdu2bdG0aVPs2bNHwjudx5HEtlqtVjz77LNYunQprl27JuFQp06dQoMGDdCxY0d8+eWXiIuL0xj0jh49ioSEBNjtduzZs0d6fffv369ZS+o4ZmZmyrrXrFkTBoNBKhpnzpyBxWKBTqdDs2bNUFhYKBWNuLg4tG/fHoGBgZpsarm5udizZ89dd5bWbXs0qlatqoGBECOtCnNk8txEVKaqHohlNBo1VnwKvdxw2PkqZpibJwNk6Z43mUxygrF+tBZys+S7ycCZFlYN/iXjtlgs8PPzQ/PmzRETE4Pw8HDodKUH59ECzuxSdHGlpKTIjABXr16VXhZmOCkuLpanZfv7+0s8OAVjb29v2c5KlSohJiZGcyps5cqVJW6ZOGtugjwBOC0tDSdOnMDPP/8srbM5OTmoV68e6tSpg9DQUPj6+uL69euarE+enp5ISkqSAdienp4IDw+HzWaDXq+XDJFZcagocaLn5uYiJSVF9gGhSYQLAGXCPDNsBQQEICQkBCEhIdJSyuByphWmckjvTW5uLg4ePIi9e/fKPlXfRU8QsfBGo1EKZpwT6obM/gbKIFZ6vV7mtOc1kqq4UvhSY44AaLxz9IzxfVlZWTKpALP2UEBRPXCqgMTMVBaLBampqTJxQHZ2tgbacjcRc4m7u7vLjEuMF3D2Tnp5eck1T0gJUBYgTo8I50FISIg8X0Gn08nUtVzrTDFLgZDCPIVnAPJAJgqnamwOFUXCclge5xxjagwGA6xWKyIjI2WWE54mTV5GeBS9WozBYtso7NLSLoSQa4P1Acri03iKOIVI9dR1zlnGctESzn5hWTzvIS0tDZcuXZJZ1Zi0wt/fH1arVaaS1ul0Mq02AGnpVFO78sM+pfeCVn32l6ooMeZChb2q6XD5YcyBmgmLRgbyV8bgUfnPyclBRkYGUlJSkJGRgfT0dAn9ZbadlJQUOe8IVwO0h5mSqGDSKEFPJ3kBswxSOSDfUHkcoZZMNMLxIY/iPqZmjXKGWLK+9LqpMTxqrA/XFtfR3ejRCAkJkfsxiSl7K+KLPCSX/MP5O6l27drYtm0bmjZtiqtXryI6Ohq//PILWrRogeTkZHh6eiIlJQXHjx/HmjVr8M4778hn+/fvjwEDBqBTp04yE6RKNAiyv3mGVnJyMmw2G4QQmuxK3bp1w9dff42AgABpsMrIyEBQUBCysrLgcDhgs9mQnJyM6dOno2rVqujWrRsASDiqCjOqW7cuNm/ejMaNG+PGjRtwcys9rTslJUUK/7t370arVq2QkJAg2wpAyhkpKSnw8/OD3W6XAvz69etx/PhxTJkyBUePHsVjjz2G/fv3w2q14tixY+jZsyd++eUX6HQ6BAYGIiMjAx07dsSnn36KWrVqycxqbEtF4qi7e2lq8qSkJHzwwQeoUaOGhMxNnjwZjRs3RufOnSscx/T0dBiNRk2WscjISOzbtw9t2rQpd/Bi//798corr6Bhw4YQQpQbN2dSxxGAhMdWFPv0+eefw2w2o1evXprrBoMBx44dw9ixY+UhjQAwbdo01K1bVypzubm5t4w7+m/RHYVONWjQQOLryXxpbcvNzZUHH+n1ethsNml1Vq1jtCR5e3vLjVS12KhQKTJExh1wc6L7WHUXBgYGys0YKB04CiwA5OFuqueE7yPsyM3NDYGBgWjYsCFiY2Ph4eGB/Px8GQAVERGBoKAgaVm/ePEi4uPjkZCQgLNnzyI5OVmegM3gUjc3N6Smpso20e3PrFBWqxXVqlWTikZJSQmqVauGsLAwuLmVngNw5swZzYbv6ekpA1YZAMk0rpcuXcK5c+dQWFgo06pVrVoVfn5+EopBz0piYiLOnj2LiIgICb9wd3dHSEiIDNBOT09HUlISgoKC5ME3THXL04wBSFx2bm4uMjIypPBx7tw5XLhwQQoLKSkp0pvBQ314EnpISAjq1asnBTIAcgwJgxFC4NixYzhx4gQOHTqEs2fPSsGekAQKLDwvgH1PZs04IsJk+IzFYpEWbbvdjvz8fJlBTBX4aLmk0EnB0c/PT85L1SJPj01OTo4sj9ZrzkdCqShkOhwOVK5cWY4z1wPPHNHr9eUCC+8WCgkJkUKPm5sbfHx8JF6e/INeOV9fX7kuKFCTr/Dj5eUlvYleXl5SsHI4HPD19ZVjQugIBTJamtm3xOuTT7m7uyM4OFgTLE3jBhUBo9GIsLAw6a3gmqDHMyoqSgqONMQwva3RaJTtpkeUCjzngd1u12RaIo+jMkplVafTSZgYDT2qomG32zWB6uQlVDKYdIG8MCsrCzdu3NCsKYvFApvNJttDnpGfn4+0tDRZFxpCGIRPoVptO/kB28BTslk3QpfUcVZjnigwU8FQA/7d3NxkcDTXM/mkSuSD6enpMiYuOzsb6enpuHr1qoyZcnd3l4k1OAYFBQVyLQNlECkAMlkBx8JkMkmoiBBCpgfndyrFhEpxPFXvKttBeCcVCL1eL8eB7WPbqQyr7Xc4HAgODpbxOXl5ef/IdJl/RA6HA4899hhWr14try1fvhxWqxWxsbGae2kMfOedd7BgwQIZtzBhwgTMmTOnXNmcl87fR48ejd69e6NmzZoVKhpAKbz80qVLaNGihTx1nPTCCy9gypQpCA0NRUlJCXr37o2ZM2ciODgYa9asQWZmJp5++ml5v6poLFq0CGazGd27d0dSUhJefvllJCYmYuPGjVLpAsoEvjlz5sjzF27Wttq1a+Po0aOIioqSXgT+PmLECDz33HMyLnfo0KEYNGgQIiIicPDgQezatUvGaNCQpnqQK3qfxWJBYmIiHnvsMWzcuFHzW9euXbFixQoEBwdLo4VKbdu2xebNmxESEiL3PRWKqH53fvekSZPw6KOPok6dOjftC2dSkQyvvPIKRo8ejUqVKlV4vzqOdrsdTz75JD7//HMEBQWV8zrcrK43q4/qjbxy5Qref/99zJw5s8I6/zfpjioaUVH/j7r/Do+q3NrH8XuSSZ8kk8mkV1JICC10pIodFRULihUrYu8e7B4blmM9ioi9F1BRUSkKiiK9EwgkENJ7ZtLrzPeP+dwraw/Rw3l/7+89J8915YJJ9uy9n7aee611r7XSRXGgK14Hq/IAILjyds1qPn1oaCgaGxtFWDc2NhoOA1ocabkib1VzWGnt0pQAbcUBemkEDLbj97W7nelVc3NzkZaWhqSkJHn3gIAAyQzDdHl5eXnIy8tDWVmZFFQj+GABMbPZjMjISLF20jJLF70+aHp6eiR429fXVxQUekSYzYRjGBAQgNraWkPwa0BAgIADAuyAgABER0dj+PDhogk3NTXh8OHDAi7a29sREREhNAeCZY4v4PFYREdHw2q1AvBYjpg6l0CB15PiQwtxW1sb6urqJHtWUVGRIcaDVI3AwECEhYUhNDQUOTk5SExMlJgOTfOgElBXV4fCwkKsW7cONTU1AjSpUNCKTToIrYa8pi8vAudDeyfYL4IteoKY4lcHtHLN8hoKDb1WdWpPWoQICEh743uwvgNBLe/Ff/trwb7o6GgAvXTB2NhYAUscC3qUIiIi0NnZaagQrpUSeqo0FQnopVQx8QTvTVnBNauTTJD7zvUfEBAggb5sBIk8iIKCgiRPe0hICCIiIqTyrK4Xwn2rkzIw7okyifvWW8mgVV57kwGI8kKrNz1AtOzrtKqUi5qmyP1EMNvS0mK4ljxtjivjMvj+3It8DtBLwfIGAfTkMQsXx9Sb5kS5wTHjOPM9dNA/jQucK+5pegb0Dz3kfDeOW0tLCxoaGqTGhtPpRHV1NQoLCyWOjUopx0rvRyp0HBMqDHocXC6XpBOlAqDPLp5HvBfPSX2m8nma/ssfGpB4Pem8Whlm4hUqZdqTxQrK/alNnDgR+/fvNyhJAwcOhNlsxoEDB7B8+XK88MIL+PHHHwF46mkUFxeLlXn06NEoLS1FeHg43nzzTcycOfOo5Bpz587F1KlTxfqclJSEyMhI7NixA7m5uaivr5eYCzZ/f3+MHDkSe/bsQXNzMyIiIvDNN99g3rx5qK6uRnJysiggUVFRGDBgADZv3oxBgwahu7vbYF2PiIhAVlYWNm3ahMzMTJhMJuTn52Ps2LFS52PQoEHYvHkzenp6MHz4cPzzn//EjBkzYLPZEBwcjL1792LZsmX44IMPjspKFRQUhBEjRmD+/PlYunSpoVAcz99t27YB8NDC4uLisHXrVgwfPlyMmn211157DSUlJXjllVewfPly3HnnndiyZQt8fX0xZswY7N+/Hw6HAwEBAfjhhx/wyCOPYPfu3dLXRx99FBaLBXfffbfM4/r165GTk4MtW7Yc5SkCPJQkPY8ZGRl47733cN555wmu2bFjBz777DP8+OOPeOedd+S7l156KWbOnInzzjuvz/7ExsYiMTERW7Zsweuvv46ioiK89tpr+O6773DbbbehpKRE5tHtdiMyMhIZGRnYtGkT3G43Jk2ahL///e84/fTT8eCDDyIwMFCCzR955BHYbDbccsstfT5bt9GjR6OsrOy/MiD8WFSIY47RIMAnYOOhwINCu2T1C2gLDQ8enZYRMMZ6ABDXt7Zk6ownmgvr/W4Ei1p7pHuO70rwyWDuYcOGITs7Wyzsmo8MAEVFRSgvL0dpaSny8vJw5MgRCXx0uVyIjIyUgyQoKAhWqxVWqxXh4eGGrDqAMSUmOb5Op1M+U8lgvIP2zLA/2qNDKyApTHwHs9mT6ra4uFiK/dHjwLFkvATnMjg4WCwKpJb09PSIZb2npwf19fWSvpEHGb9PcMADLSwsDAMGDJAsUrGxsUJPqKmpkSxbtBIyA4rD4UB8fDxSUlLEg0JQ4uPjA5vNBl9fXzidTuzfv18C77k2vdctx6mvTFD6Mz0MVJo0d18HbtOiqilUAAwKAw93zhkVYW8eOtAbrwEYUzjz3nxPzTHvr400KG1xZdPjS5lC0MX9qA0YGqhpzxYVeO39AHrjbOgVYtPAl1Z4TZniM/l+XPP0zPFfbVzQqWz5XCo9TKRAZUl7fTX41x4HrkMdE6SDvTmOXDf0wLFvjOPQ3j+9/qnYcMz12iZVlmOtlRRNleW//Bu/S3DNOeXfSaXiOmBRQs6Ppk1RQeJYacMTFS1+nxRXeoB1HAwAQ784NgAkMJuxPhwHxtJpGaG98d6UTB2DQZmgPTPe8pxjqL01VBJZq4gKn45T4vtzTej3oGFDF7zUcWf0MPXHxuJ2SUlJOPHEE/Hhhx8KSPf19UV+fj5yc3Ph6+uL5cuXY8uWLZg0aRLGjRuHb7/9VsB+QEAA9u/f3yd4pcIJeAK7Dx48KFx7HSvBNmTIEAwdOhSffPIJzj77bNTU1GD37t3Yt28f2traEBERgezsbGzduhVut9tQzyEvL++o+zU0NEgMSH5+PhITEzFnzhx8+OGHmDRpEgIDAw3Zm1paWrBv3z50d3cjLi4OkZGR2LNnDw4cOGBQyC666CLs2bMHe/bswfr167Fnzx7U1tYiPDwc559/PpYsWQK73Y7MzExRNMrLy+Vs1vEvgCerUl1dHX7//XcAHqxUXV0Nl8uFffv2CZ7o6ekxxLS43W4JDLdYLMjOzsaWLVukbpfb7UZ+fj4cDgcaGxsN342OjsaMGTPw8ccfi0dYz2N7ezvy8vJw3nnn4bfffpP5KigoOKpQY11dncRM/Nk8cr0VFRWhoqICPT092LdvnyRTcDgcmDNnDlauXImysjLDM1hHw+VyobS0VCi6gKdmiI4hmTJlCsLCwrB8+XJcfPHF2Lp1q8TEeHvI9Dz2h/ZvxWgAvSldyV8FINxiAGKV5+HB2/OwASAVmQk4mEKWYCEsLAz19fWGQ95qtYoQBnopFBSoGjjyIOEh1NraCqvVKuCBeeBjYmKQk5OD448/Xg7P9vZ2pKamShzFoUOH8Ntvv6GwsBBVVVXCDWVlVx8fH6kvwSxV6enp4q0h+NUUiKamJhkXAmOn0ylZqhwOh1xP6znvQ4oD+8hUmgTtISEhyMzMhI+PJxe1jl8hX5nWVl9fX6mkSStoWVmZHK4lJSVyYNMK3NDQIIHwnZ2d4vLnIUglJzg4GJmZmTj55JOlEvzBgwdRWlqKkpISHDlyBDU1NZIf2tfXFw0NDbDZbJLZhhXXo6KiEBERAcBj0aZlurq6GuvXr8fBgwdx5MgRia8AINZsoDcTGr0SXEPaU8C5AjwHMi3dnDMdqE7PBQ9/KlkOh0N+x+KEBMEEa9rzRxqJDkQmWNBpKQm++G4+Pj7/lZaNY2mDBg0yWMV1UDbQmyqVCivjEkwmk6QO5fU6sJf7T9NymL6ZY8ZYAcBYrZqgUitz/GEWMnpGdVxUaGioxCxEREQgPj5eaDycX74L5RA9MkzewHdhnIamimnqFJUAZnQzmUwGuh3jrfjerAnCflJWaGWJoNTHxweNjY3yLGYR9K6FoWlU5Ldr6h/XKoOqtULMxA+U4SwWSvohjUH0yFBh4nlit9sN8pRrhVm5dBC+9kTSK80YFl7DNeRyudDQ0CDZqEpLS6UaMdOPu1y9dUHoneFe5f/praIM1J4pemEYb0UZBnjOU9I2adDQ8QdJSUmoqamR+aDs8TaOUDlhHBfPB9YTomHDYrGgsbFRvO4awPWXRqrgqaeeildeeUUs/iaTSYDba6+9hsTERJx11lmwWq1YsGABUlJSMH36dFitVjHghYeHG+JFARgyYlqtVqxevRqffvopnnnmGfk7PddWqxVOpxPXXXcdbrjhBgwbNgyrVq3C9u3bcffdd8s9L730UjzyyCPIysoSrx5xCRPGAB5PBsG5xWIRqtBJJ52EDz74AJmZmXj++ecRHR2Nc845p8/xef755zF06FCpdq3brl278MYbb2DhwoUIDw+XGMz09HRs2rQJY8eOxTnnnINLLrkEI0eO7LOvPE8dDgdWrlyJPXv24M4774TVajWkVmaj8t7Q0CBrXVOkTjnlFLz77rvIzMwU9oZu3Dc0Jo4dOxbLly9HTk7OX8YYbd++He+//z5eeuklWK1W8aQwc2hfbd68eUfN4z333IOIiAg0NjaKXNexQGFhYThw4ABmz56N33//XfoKQAxSfVGdmTaba/aVV15BWloaZsyYgf379+OJJ57Ae++9J9fTW9nY2Cjz+M9//vNP+/9/1f5XqVMDBgwQwW82m+F0OkWY6ltosK/5yS0tLeK614IZMFY+pvJBoe6dJ53gPjAwUHjNdrtdaAgADK5nCmKmTg0ICEBsbCxycnKQmZmJrKws2UiBgYFChdi9ezd27tyJdevWScAUwSDz4MfExCA9PV208vDwcMTFxcFsNqOmpgaVlZUSVMmx4UHEuhpVVVWGonWkHpEOwErf2sKqLeCkK7ndnoDCUaNGCQ++vr4ehw8flqBzVi7v6upCUFAQ4uPjkZSUJFYBrWiQWtDV1QWHw4G2tjb4+/ujvLxchGJQUJDBu6IDnVklfMiQIZKKmHU3GEdRX1+PAwcOSMrfiooKAZX+/v6IiIjA4MGDkZycLD8cQ1po6+rqcPjwYWzZskXS6WmrpwZzVDIJxGi55P10mkkCNx2IzHubzWZER0fLHqCXLCwsTHL1E3ACEPDF9U4QzXXOmCLuCYJdHQxKOgS9LbrKaH9qQ4cOFbDEDGWauqaVPSqLlCE0cLARRBPMc21QlpAvT/oLZQ/BKRVPjjnHmfPE2gt+fn4IDw8XOh8DBGNiYmC32yUjHL/r5+cnVcAbGxuFJmUymUQZ1p4aKqqsZwN49hL3XXt7u4yV7re2zNfX1wsI5lrhAaZjF+h1JgjnO7e2thq8I4yl09QgHtIsPKppXDROkALGcaPHglRAKkIsRkWvBI0gNFxQ2aEiomO3enp6JFCf+9qbXghAzgsCnZCQEEk6oQ1RjJGhPKZVsr6+XrLe0BjD+WVfzGZPCnGHwyFKA4FkT0+PAeRrZYRzGBkZKYoZ34fxKYAHxGividlsNmTdY8pxjpVWNjl3NFzRa8T3DgkJ6TcWUd1MJhOWLFkCf39/nHvuueju7sbHH38Mq9UqNQq057e8vBz3338/3nnnHbjdbhw5cgRPPvkkNm7ciA0bNiA1NdVQT6SgoACLFy/GsmXLsHv3bgwZMgQHDx6UOdm1axe+/fZbLFq0CIWFhRg1ahR2794tNG9NB9TvzL+vWrUKZWVluOWWW1BRUYFzzjkHq1atkqJ4s2fPhslkwkcffSQJLjR9kH2jrPBuf/V37tVBgwZh586dyMzMFBoU5aP+/o4dO/Djjz/in//8Jw4fPowxY8Zg0qRJuPfee5GcnCzy22azobS0FCeeeKJ4N9guvPBCLFy4ELGxsVi6dClaW1tx4YUXGsaGfeurTZkyBStWrEBCQoJ4Z/iuf9U4DwMHDsTu3buRnZ2Niy++GBdffDEGDRrU53f0PPH7drsdJSUlmDp1KgYNGoQFCxYgPj7eML6Uueeddx4WL16M2NhYdHR0YMaMGYZ51O2DDz5ATEwMTjnllKPmjfOksfXzzz+PyZMnY8yYMX3+/T/V/lcVDbvdLgeWtirSDeudYYjXUXPX7mZOoAZUtG4BMATTcSETlNHCpQEDaQBsrKRMxQjoBcGhoaEYOnQohg4dKlWs/fz8xBLkdDqxfv16FBUVoaqqCuXl5RJMHhgYiPT0dAnYJhfywIEDBjc9M1ARZNTV1RmKY2nvTmtrq6HoGIP5CI5YrE8rYNojQWsngTXdpgQfBQUFcrDxIKeFLjo6Gn5+figvL5eChwBkA7vdbuF90+La0NBgSMHJQ6yzs1MUPx7Gfn5+iIuLQ2hoKAICAtDU1ITo6GikpKQgJSVF4k3Ik25oaEB+fr4hzSQzfMXExGDIkCGIiYkRyzSVn9bWVpSWluLbb7+VrFrU/jUtRcdSsPgbLVukghEMELjQCtvY2ChrhQCHilZbWxt8fX0l6wupLjrBgclkMtR2AWC4Xhf0ImDkHgkICBAOPUGCNz+4v7Tk5GRRVEmF0lm4tNdJU1O433V8GIEXBTQBnab3kN5HOhOBJQP7eT/OE+UUgScAKcQZGRkpxf2sVivi4+Nht9tlj+h0qwTeVCxIv9Fys6ury/C9uro6QzwJreoEid79prykjNV0I8oGymOd0Yif+UPDhy5cx3fSGbm0EkyvAJVfpvbmftBU2O7ubkOsGq+lzOP3+I7+/v4Gy2Z7ezvCw8MNCo1Ow01lhooqPR58f64xFlCk0sJ1RW9MR0cHGhsbceDAATgcDqkOXVtbK3MA9Fpo6cVtampCfX09Ojs7JYaNY9bQ0GCgF2tqJ5U/GhoYe6S9bWQH8EwNCAgwpBCur683ZOiiQY30VVpAScNl6nP+eFNJ+kMzmUwYMmQIfHx8sGvXLgDA4MGD4efnJzSZv/3tb4iOjsYdd9yBSZMmobCwULzAkyZNwuHDh9HY2IjRo0dj/fr1uOqqqzBy5Ehce+21GD9+PCoqKlBTU4Nx48Zhw4YNuOCCC3DSSSfh8ssvx7hx41BbW4uysjIcd9xx2Lx5s8FC/9prryE/Px/vvfcevvjiC9x2222G+hu5ubno7OxEfn4+Jk+ejF27dknNj8mTJ+Oiiy6C2+3Gp59+inXr1hkUFt18fX2xdOlSLFy4EGVlZXjhhRdw/vnn46qrrkJGRgZuvPHGPr83d+5cTJs2DYsWLcJNN92Er776Ch9++KH8/dJLL8X06dNxySWXYOzYsairq0NpaSkmTJiAzZs3w2q1IjU1Fb/99pt8x8/PD5MmTcK2bdvgdDphsViwdOlSzJ8/H2VlZcjKysK6deswbNgw9PT04ODBg/jqq6/wxBNPGBSTBx98EBaLBffee6/8zmq1YsSIEVi3bt1fKhefffYZvvjiC0l9yxYcHCzzGBMTg9jYWPHkzZ49GzNmzJBYnPPPPx8XXHCBQRHSfQsODkZ6erqh77pFR0dj0KBBMm92ux2DBw/GunXrJEaDSQS4Znfv3i3zuGLFij/tX0ZGBqxW61E0qv90OxYV4t+qo6H5zprjTEDkzX+nMPSOoeB13mCBf9M8Uq1l81neQbx0w+tn8B35XgzuTEpKQmZmJqKiohAcHIyenh5JoVpbW4tdu3Zh27ZtUsSObjvSI4YNG4ahQ4cKjaO+vl5cgbRaNzQ0oKmpSYQ73bB0eTOtJa3gpOX09PRIZhd9WBD80LPD7zBLCS169MrweqDXchkYGCgHIDdrT08PHA4HnE6nWNS1F4BeFB30yLS+JpNJ6m6wf62trYbUft3d3ZLVhfNIq1pISIhUx+ahTXoUs++UlJSgtbUVFRUVaG1tFUskg0r5viEhIUhNTcWAAQNE+dGeDPaVFDtaULWCxmu4FkNCQgzcbG8LAueD65SceY4b76tpWQQ9tHay8TM9KnqP9KVo9+cYDXoG9d4nOPe2wHlbeDg++vdaltCwwfvxHhwzjqWOveF3tSeW15AuxTgMZl0LDw+H3W6HzWYTQwD3GRVHXQCOXjNvqpeWWdqzphURLQeB3qxSOu5Arw2g1yoXEhLyp+NAowMzZdGTQBmlvWtUtr0BrY41o8eDz9drlEYOxq3wntyHBNR8nncMhDa0aCqu9oTp51LGaY+59qDR20X5wX0JQDzTPj4+kkEM8Hi3aADhPGhaGRULKqlUBGkU4driXHDtUYngWHB9aEWSBgjONZveC3p9c9yYPY/KjZ7H/i5H6Inx8fHB+eefjw0bNqCzsxMXXXQRli5dCofDIeP322+/Ydy4cRg6dChWrlxpAIlr1qwBAIkdBDwpuJmBLDY2Fr6+vmhqapK/M827r68vYmNj5TlstbW1QrmrrKzE8ccfD4vFgo0bNwIwxnisXbtW/m8ymcTyXVJSgl9++UX+Fh8fjylTpmDJkiUGsF1VVSVGzMrKSrhcLjgcjqMUyIiICJx22mlYtmwZGhsbceTIEaxZswZnnXUWmpubERoaijPPPBPfffcdmpubJXB+06ZNyM7OxvTp0/H1118DgKGoHeABwMOHD8eXX35p2J+VlZXo6Ogw1OFgjIe/vz8qKyvljGVj1kq28ePHw2KxYPXq1fBuM2fOxN69eyU+p7q62mCgOPvss1FQUIC9e/dizZo1mDFjBg4fPixpd5cvX47m5mYD/aqlpeWo1LRdXV2yTpxOp4G2nJqaijFjxmDp0qWYOnUqurq68Pvvv2PWrFlYt24dysrKZB4ZH8tG5dPX1xdVVVUYPnw4XC4XVq1aBQAYO3YsIiIiRPkgk6Y/tmP2aKSmpgqVQ1OfeNCEh4cbDksKPy34eIjoA54uXNIpePDwetINtGBkthgKU3LoKbBZZ0JbKGNjY5GZmYnRo0dj0KBBBqBjtVpRUFCAbdu24fvvv5fFzxSb5F8nJydjxIgRGDZsmBSi27RpkwR2tba2ykKidd/pdBoEek9Pj/A0tYscgIBm8pl9fX1FeaF7H4AoDsx0Y7FYJMMTDzcWf6msrBRrbExMDHx9fQ3vRss4Dyryd7X1n59Z84NAn1m2NDCurq6W9IlNTU1HpRl2uTzB80yjy3gOUlK4dlwuFzZs2ICCggKxFg0ZMgSJiYlISEhASkoKbDYbGhoa4OPjg9DQUOzYsQNbt25FUVERKisrhULAuCLGBZHKRKWPChs9axxbUlm4RVjXgt4HejK0F0Rbk+l1Im2MQW6kA3L+6QGhIkW6kH62jn+h+78/NsZ6EcxzTXBOyOEFej0eGrgSmAGQ8eYeCggIkDkhEHe5XDKnjPmgAYBrnu/jdntiwegta2pqQmxsrCR2sNlsiImJkb1ExZ3xAf7+/lKkk5QZWp/5fhq803JPpYT0TyqepD5pRYxrj8qBpqZqOUN6JJWD1tZWQ5wPFSfSoeiV8PZKamWZ+1V7oegdrK+vl/eml4LylYqCN62JfSKw0EHvOmuWBtCUD6SnkcbF84Ljyz2kvSPsB4P2aaxhpieeX9XV1eJlaWlpQXl5OSoqKsTLwTgdeuOpXHAOu7u7JSkAjVCcQyohnGd6SGmQ4rxSpnCuuEd0HRU+m0qMVgS5ppkanrRh7jmOf1+Bzf/tLSwszJD6/PDhw7jxxhvhcDjwxRdfID09XWQD6SqvvPIKsrOz+4xbYF0trsPdu3fj3XfflcDxSZMmYf/+/fL3TZs24fvvv8dbb72FPXv2YOLEiUdR0Eh/bm5uxqpVq3Do0CHceeedR6VvZcwODZ6HDx/GFVdcIYHejIeaNm0aPvnkEwwdOhQ1NTVHWfbJ9NCxX+xbR0eHWNlzcnJQVlZ21BhkZGRg27ZtGDlyJAoKCiQOpqWlBTfddBOuv/565OTkHNU3ALjyyivx4IMPIiMjQxgT3gqE7isNFToQWvdV923RokVISkrC6aefLn1hwoUDBw7g6aefxuLFi+V60kFbWlpkHv/xj38A8MRsfP755/jss8+wY8cOjBkzBvn5+Ya+8rzhZ3oRue+9+3bhhRfiueeeQ1paGj7//HM4nU7ceOONfzqPbrenALKW7fz82muvIS0tDaeddhoA4MUXX8SwYcNwwgknAACefvppTJgwAZMnTz5qbP+T7X+VOpWenm4o9GYymQwVtuk1INeOgS76wOSjdAYpbWGjsOXG59/pciYANJs9GZV4wNCFTcsaFwcBq8lkwqmnnoohQ4YgLS0NLS0tYqkMCQnBypUr8csvv+DQoUNwOp0IDw/HhAkTJK/zTz/9hNzcXAwZMgQ5OTlobm7G4cOHUVVVJbn3ecjU1tZKzY7AwECpbM1DnYqUBlPkolOZ0DErGsBqCyY9KKNHj0ZiYiJ8fX0lpoLWmJqaGtjtdin4xzHkwVpZWSkKkrbGM30tLarc3ARxBA51dXWGw9psNouypYOpmUGrp6dHlCECv9bWVlgsFkRGRsJutwtVymKxYO3atdi/fz+qq6vhdDpht9ul5kZaWhpycnLkHc1msxQJ2r9/P3bt2oXGxkax0JpMJrEue69BHuI6OxjHQtP/yDMHILQXrl8KR4IDFgqiwqjjA8ixpvJMwUulkvQGXsP34rppb2/vtzEa6enpojBT8aPlv7m5WZQpzo22klPx1XKFYJdgi+lFGcOjaZWRkZFobGyE0+lEU1OTrFM9D35+frKXGZMRFhaGyMhIJCYmIjExURIW8J2o1JD+p2sIcW2QKsRMQOHh4UJXokVSe7y4xwHPvLOAJg0A2juhFWr2XRc+pPeAe9TPz88gnwlyNN1MK3SUPaQc0cNEa39rayscDodY5XVcnabQ6vekXNcJF7QnlbFPpBz1FWBKj4TT6ZTDn8/kvJKSxlgGjn9kZKSk1LbZbLLf+B4sillZWYmKigoJGK+urhbqGJ8P9Holu7u7JS5IpxNmf6uqqmRd6BgUvhsNaHqsWICVc0IZGxgYKMVo+Wyd0ri7u1v6yHOZ70sDD63s/al1dnbiggsuwDfffAMA4sGi17izsxNvvfUWkpOTRbHgXFH+s5lMJhQWFuLFF1/Eyy+/LPejIujv74/t27fj448/xuOPPw6g1wBCxV/HK7HNnTsXDzzwAFJSUuDr64s5c+bg0UcfRVJSkij8YWFhKCsrw8yZM8Viz74QP5SXl+Pqq6/GsmXLRBG54YYbDNWiAU/K31WrViE5OVlS9ZpMJhw+fBjPPvssXnvtNRmbvhplH/uSmpqK/fv3Y9SoUdi/f78o7wBw44034p577kFqaqrB49rZ2YmffvoJhYWFuO666wz3Dw0NRVlZGS644AIEBwfjrbfeQkJCgsF4Wl5ejrlz5xqoTzoOq6SkRGJtvMeK7ZlnnsG0adMwZswYmUdNB+1r3pKTk3HgwAGMHj0ae/bsQXx8PAoLCzFp0iSMHj1a5vHnn3/Gvn37MG/ePHme7jsZE8R4fc1ja2srvvjiCyQkJKCpqQnHH388vv32WyQlJQl9XWMx/VmHGvw3tf9V6pQO+iWthAeZzvBDS6XT6RSLWlBQkBQtMpk8gbX8GwCxLlNYcIJ44JG7DUCsbrTk6Sq0tMqR581q1hkZGRg4cKBkeCAwcDgc2Lt3L3777TfU1taKZZzFWVwuFwYMGIALL7wQNptNYiny8vLQ0NCAxsZG1NXViaXL7XZLfmwGBTI4j4eO9r4AvWCL/WeFWlo5AgMDERcXZ1AyWlpahAplNptRUVEhIIsFouiJ0MWseBhpSxkFBd+R4EtbSbWlkv0h0NOUldbWVgH9BNjM0MMDmHQpADh8+LCB3x0aGio0KZvNJgXPKisrUVZWhvLycsl3397ejrCwMERFRUm63e7ubgnScjqd2LNnj/SPcT2aJkNhA8BQf4Djrr1ujFfhPNE6qC2TvD/HkgKQYEILPF3Mh+9ERYLBs1rh5P7jvftrIy2OYJCAWFOLuF4JuDjmVBoJ3DS9RFOiNB2TjZ5Ol8slSj9lBdc2AAFu9NqRNhkWFoaYmBihS2lrf3d3t2Qo0lmqtAVfF2uk14pyjwG+3De8hnKK1CIeYPwBeulC2qgCQLK/EGzTg8ZK3TwUqRDxUCZY04Yg7mNdpE7Hz1ER4hx4068AiBfc2+uiY280YNC0WxpaeD+dMpbP16mBGbfF9UGaJQ1lVLro6QRgSIPLGC+uB2Y+owJFWgr3PgAZT3qDuN9ZC4hjFhwcLLFgXOMcZxoROBecM56DHIe2tjYZX44tzyvS1LzltTZYeCuy/a2dc845knoVgAE88/8vv/yyFJQFPGOUnZ0t1u2FCxfiu+++g9vtxtVXX43Dhw8b7nHFFVfguOOOw/XXX48bbrjBYNjp6vIUZjv99NNx5ZVX9vmOP/zwAwoLC2Wsf/zxRxQVFRnWeGtrKy644AKDV8m7L7Nnz8bOnTvl3L/44ov7DOBnOlfiLADSt8LCQgwYMAAvvfQSrrzySkPNkLfeegurVq3Cp59+is7OTixatAjr1q3D0qVLMXPmTBw5cgRnnXUWzj77bMyZMwcAsHz5cuzfv99Ak+J733///WKU+/jjj/HQQw9h586daG1txaxZs7Bt2zb4+vpi9uzZhjOwq6sLF198scTc6HkDPPvjiiuuwP79+5GdnY1nn30Wl112GRwOBzIyMvDCCy9gzpw5ePvtt0UB5TslJiZi0aJFuOaaa1BRUYG4uDi8+eabuP7661FSUoKamhrpK+BJeXvuueeioKAANTU1qKurw7Jly/Dee+9h8+bNCA0NxUcffYT77rsPe/bsMWTBmzRpksRgPProo9i0aZNhHnt6ejBr1izxtO3evRuzZs0yZG7VfR84cCD+8Y9/4PLLL0dDQ8NR9OL+0v6tGA3NASb4ooAEevPRE8jx8NZWNx7M+qBg5hfNRaZw5GHD72peK6/TgI6gj4AiJiYGmZmZYp0kYGxra0NpaSm2bNmCkpIS0TyDgoIQFxcnLuyEhASxuNJTUFFRIRQMTj7jKVjYh4qGzpxD65bm6XEcNShidh2XyyV0DU2loVWOWjQ9RjxANUCmIkPgwrGnFZ3voDnfPJB4eLLRukZlkK5ErgF6irTyoS0SrMFBwcTA8o6ODjQ1NaGsrAwWi0UK/EVFRcFms4n1kJzOxsZGVFRUoLS0VLxcFosFzc3Nkg0oIyMDJSUlBjCgaThcexxTTVUAjq7VQkVRr2nNEaeQ4MGiQQTHm01z9TWPXAeva2+LpqbxXfpro/uYlhqOoebne4+PljMacOq4Cv7em0NPSy4AkTOktLW1tQko5L7o6fEUwKRywZgMprPVgd+UcVyTuraO99xTsaHVmZZ89oN91tQ80iBJlfROJw0Yq83qsTCZTAaATvnGfU5Zzu9RLutx5n30mFFG8G/8rqbEUgnndwAYlHCtoGlFQ8fvaAs9vY98bxq5dMwX5R7HFuhVVih7abzSMWM0SGlDi47VYfpTGrQI+Ci3OAecB2+KH5UAjgXrNOnzi2uAskqfe5RDVHJ0TBO9RmyUb3rfaOoyPSQc177oLf2hkY4SGxuLcePG4bvvvhMAZjKZcMYZZ2DXrl1H1XygYRIwxoORfx8WFoYTTzwRK1asMATu60Q1bNy/bBkZGcjMzJR30+fo1KlT4XQ6hXuvGxOtJCQkYNSoUfjuu+8wduxYAMCGDRsMwcEulwsrV67E6NGjkZaWht9//x1nnHEGdu/ejSNHjsizc3NzYbVasXbtWvz0008APNR3xrYOGTIEsbGxWL16tWS/YyN9sa2tTe6nFYlp06ahrq4Ov/32G2bOnIm1a9caUrdqozOTUSQmJmLEiBFYvny5yMWamhqceeaZUuiO8zhq1CikpaVh/fr1Mo+tra2YNGkSvv/+e3R2diIrK0vuzXll34hl+pp3fb3+zHnQlFz23el0StX29evXIz8/XyhcfTXKGP6f/+p51IUkdT2UkSNHwmKx4Ndff8UZZ5yBvXv3Gu4HeOI0TjjhBPz4449iJOkP7ZipUyxHrzm1gHER6oNJZ8dg4CAtkbT4UQjy+/QAaOswAPFgEHxpAU+gwMNCH0gxMTHIysrCqFGjYLfbxSoXEBCAbdu2YfPmzfjpp5/E6xIREYHU1FQkJSXBbDbDbrdj2LBh2LVrFwoLC1FXV4eOjg60tLQYMnlkZGQgMTERdrsd4eHh4jUJDAyE1WoVrh89Dmz0Cmief0tLi1hXGORM6zYzkvDQoYBoaWmR1JKkZFA5Im9SW9B4AAEwzB25whrodHZ2yjxQoeK88TDlvTWNgNcQQHR2dqK6ulpylDOwsqysTGJc6uvrkZycLIDebrdj7Nixwpf85ptv5JDs6OhAWloahg4dioEDByInJ0cywND6+OWXX6KwsFByhdfX18uBTzBDUM/4CW0hJvjk+DBrESkwQG8Rr5aWFkNaS65BfrZYLIYilKQI8jkdHR0ICQkRJUgDBSoymi/fX2M0uNfoEQgNDZUxpadIg2WtcBEs81BwOBxoaWkRGklQUBBqampE0WN2Nu4NX19fhIeHy9rmgUPKIiksFosFdrtdqHo2mw3R0dFSz4XvT2WZNXAI3CiHGNPGhBLMPkdjCMdAjw35/0FBQeLlYGwUQTy/p5VhrWBRRug6GnrceCATTBMMczwY06DfTys4BLf8O/ck9wIVBv2uvD/QK7c13YhAXiuVTG+ugbJWNGjpb2lpMRT+5P7UMshqtaKnp0fiSRgTxsKisbGxsNlssNls4lmgUkJKbXNzMxwOB0pKSlBVVSXzo70/fn5+aG5uFiBjsVjQ09MjCTwou0jjZLY7rWjRo9Hd3W2gUbrdbqFBaTmjueOaAqqpGxw79kmv+/7WuCanT5+Ojz76CElJSYY1W1xcjDvuuAOfffaZeE29GxVmJlChx2Pjxo0YNGiQIavf7t278emnn+KJJ56Q3/HM456/+eabceuttyIzMxNutxvXXXcd7rvvPqSlpWH16tXYv38/brjhBgQFBclaDwsLQ3FxMc477zxYrVYsXLgQSUlJ+PDDD+F2uzFr1iwAEMMkz+p3330X0dHROOuss3DkyBH87W9/wwcffCDv9sorr2D48OGYMmXKUX0FgCeeeAKnnnqqAF2uPU3F033Tbf369fj999/x3HPPoaCgANOmTcP27duFcsp4lLlz58p3LrjgAvzzn/9EUlKS/M7X1xclJSW48sor0d3dLfP4+uuvIzw8HOedd57MY0lJCb7//nsMGjQIlZWVBiXRu28LFizAtGnTMG7cOEOGtb7mjZ6/2NhY7N+/H+PGjcP+/fsNffeeZxqKvesI/dk+0n//s3lkSuZFixYhLS0Np556KoqKivDoo4/irbfeMtxv+PDhWL9+PQYOHNhnrM1/oh2LCnHMikZERIS43rlZvF223pYuXS9BW+wBiPLBhUAASsBFZUO7urXVjlQgBrd5u50GDRqE7OxsDB06VNK90vq9fft2fP/99ygoKEBDQwMiIyMxduxYZGVlYeDAgTCZTCgqKpJCPvn5+UKPKCkpAeAppMQ6FKRXaJc1rfA6RgCAKFo86BkAyPHp6upCbW2tWEHJneY0MZaFFBMCZmbFqKioMACKrKwsQ/VughW32y2584Hegn469aOu/0FAwmJU3hbIvhqVD4J7psElN93pdBoocCUlJaitrRWvQVBQEBITExEREQGr1YqAgADs27cPZWVlqKysRHd3N+x2O7KysjBlyhTk5OSItdHlcklmjcLCQqlbQuDT1dUFm80mwITrg4CJAWBcV8xaxsOZweXaM0dKGeNZ7Ha7/M7tdhtoZ4xHoDXG6XRKzvzW1lZERkbK+zQ1NSE8PFxiTtxut6FgUH9qdrvdoLzpGAzS6DRgpTWYa4jrj2C9sbFRgDkAAddAL/jWMio8PFw8jaTYMI1pd7enqi6Dv/lvZGQk4uLiYLfbDTFJ1dXVqKmpEaqjjqWiTGLWNVqQCUypKERGRiI8PFxkpS74B/QedFQ4KGOpYGguPxv3q66joRNMaG+x/k5PT48hKJ2yhVS+kJAQg2WeMlkHvPPZmgJG8MzGPU8FU3sHeA8aRWhZ1nQjbZHnfqTSwXtR8eP7c3wpW2ggoQLGLGJ2u91QZ6OtrQ0NDQ0SJ9bY2IiCggLU19dLfB7QG/PFeBzvQHaOQ2dnpxQf1Mk0dMwGE00wricsLEyUNQahc103NjYKFZcyWoMgxt9QrjCJBxWZ/kif0lQ01lzRjcVkx48fj+XLl2PAgAGG6tgA8I9//ANjx47FlClTcODAAbz88st49dVX5XzVsIgyR3Pj77zzTlxzzTVSj0HPGz9TvpCGa7FYcPjwYcyYMQO//vqrvCvXPvtCmcjff/TRRwgKCsK5554r15lMntg29lXvf65FguNnnnkGEydOxMSJEwH0eufb2tqwe/dufPjhh1i6dCl27tyJ4cOH47zzzsPll1+OwYMHHzX27Atpee3t7bjiiivwyCOPYMCAAYYEJ2x6nr788ku0tbXhkksu+Zd9Z984dnv37sUDDzxgKGLHeWSAtO57Xl4e3n77bTz33HNy/fz58zF79mwMGzYMW7Zswffff4+HH35Y+nLjjTfilltuwcCBA+F2u3HttdfivvvuQ3p6OlwuF6644go89thjGDBgAHp6enDRRRfh+eefR0pKSp+xE2eccQbee+89JCcnY/HixX86j97v3te8Ah5539ca/U+2Y3mPY6ZOcQEx0JEWQlq0dEYcHgqa/65d9HoxkQ7ADcrnaGs4Dy0ehlycZrNZnstc6AEBAYiOjsbgwYORkZGB2NhYQ4Ck0+nEH3/8ISnNmLXJZrMhISEBGRkZKC0tFSFeVlaG0tJSyRySmJiI9PR0qRJstVoNha24MKiIEeDrgFMd+EiAROsdKTQEIlRIeMhS0eJmp0uXhyXT+lHQ8f6a1sB3Ia+a401Qx+foWAJtVdRUCr3YSHMAetO/EjCQtsLnM4WvTmcXHx8vFUAZVFtaWgqn02kAewRyPDyqq6uxa9cu2Gw2REVFISgoCA0NDYiPj8eIESNgsViQl5cn/GmCDC0MmQaSh7hObUsQTIuh2+2Wauc8YDinVDoYYNoXMNR0Q44tFTxSQegtIgjRINZb+PSnRmoK1y8zeelAZQJXjhlBEdcm1xSpJQRYpPbovaLTy5IuRSsTZQIBKevWMPMb0wlSyfW2tDM2S2cs0lmhSA1kJXDOL4E/i/0xZoR7VtP4NAClTNCyBOgF5pp+QwqYNgTo+3MvawohlQXSzXQMEmma+pmaP813YtPvqL0n/BvnVss2Nm/qIOdL7x3Gg7Hp/UWjAfcNZS7HhYe1po8xU1ZTU5Oh+rqPj6dukaYxWSwWmUdmDeP6YowE55rWa65LwKMMawqZfnca4HRcBWU+5YKmiXV0dBjOGBq5tFVa1wSiJ66/cr3Z7rnnHvj6+uKpp54CANx+++2wWCx47LHHRPHYv38/5syZ06cy9cEHH0iMxi233CIF+VpbW/Haa69h1apV+OqrrwB4zoZZs2Zh8uTJuPnmmwEA3333HfLy8gB4wC6L+L3//vv429/+hsLCQoSEhODzzz/Hgw8+iMTERFx11VW4+uqrsW/fPnmP1tZWPPjgg2hubsYLL7wAAAalG/B4KPRZy7/7+vritddew3vvvYc1a9bAx8cHb775Jj755BMDTeujjz4yUHeokAPA3XffjaKiIvHq+/j4YNmyZQba2VlnnYUzzjgDc+fONXg5OM5r1qxBTU2NyFvdHnroITQ2NuLFF1+UsSKt8s0338SLL76ITZs2yb00tlu4cCHefvtt/PLLL2hqasINN9yA3bt3IysrC4899hiuvfZamUcAeOONN/DNN9/I5zvuuMMQewMAX331lcT3zJ8/HxUVFQaj6g8//ICCggKRLT/99BMqKipkj/7yyy+48cYb5fP69etx/fXXo7u7+6h5BIBt27bhmmuuQUdHh8yj2WzGO++8g0WLFhlSLWs80trairlz5yI1NRX33Xcf3njjDXz55Zf44YcfjlKs+0M7ZkUjICDAUJGUB4AW9toSqYEchTYtx9rKC/QebDxESRPRHHlvHqz2oAC9B43ZbEZqaipSUlLEcsR3Y47kQ4cOibC3WCxISUlBVFSUWH4aGhpQW1uLqqoqVFZWStBmSEgI4uLipNgcD0jStvhuGnjywCA1iYeVfl8ehjwkeWDxXwJ7WrgJADTnkOCdVnJa6zQViO9D8KFBm6a2eVtT+Sz+aBBEsELlk8+nQkirGi18fI4O7AQg8x8TEyOWU1blJWAnEIyMjBRrdnNzM+rq6nDo0CFkZWUhPDxc8vUDnkCwnp4eKQyo50XT8wicqNTSYqPXLC0MHG/OubZYcg0EBgYa0uV5W50J5Hi99lpxzPh+Ghj296bHSStQ/Kxpd1rGaMWD15LWR5kEwPB/bfnRdDgqCt7xGSzGp+vm0JBAOgr3HoGpDmTWCgHXP4EelVK9n1mfg94BKgKUgzpejc/X+45jyfHUXgigt54Lx10rdBxPymu9PzmOlDXsG8G1twzj/fW7ccwJkL33B5uOy2HT92XT/aQxS2fpomLOfaMreWulyPtdqWQwHTVrA3nPFalrpKdyfN1ut5wP2pPDZ5M2yndnNhr205tOpvvPs4XynPfTa1DLZd7Hm3qo/6bnpT/Lk8DAQMTGxuLkk0/Gzz//fBQteciQIQgODhZlYfDgwX9ay4IceTaePyEhIZg0aRLWrVsnCRnY8vPzkZ+fDwDi5TSZPGlRefbwM/decHCweAvj4uKQk5Mj7x4VFYWTTjoJP//8M3Jzc+FyubBr1y6ccMIJ2L17N3p6eqSvWkkMCQlBbm4uWlpasGXLFln34eHhGD9+PNauXWtQGqZOnYojR45INXDGCyQkJGDFihVoaWlBWVkZ9u/fL9/RfZ8wYQJCQ0PR2dkpsS3d3Z5EL263G2PHjkVra6sErAcFBcFms+Gkk07CTz/9JMX5yNbQsv+EE07Anj17UFlZCZPJhLPPPluycbndbnz77bcAgOzsbBlXPY86wZDum57H/fv3S9/6ipmhYQjw1PBobGzEypUrceqpp2Ljxo1HeQGLi4uFZkeqlm4VFRVSf4RFApn1j/LdZDLh+OOPx4EDB9Da2ooxY8ZgzZo1CAgIkHHnmuyv7ZipU5mZmZIJiQKWXyWYouDk4UKrE4vQMeMMGwUqPR78Hfls2gOgBXhoaKiB1sIDwmw2IzIyEhdffDEyMzNhMpnQ3NwMm82Grq4u1NTUIC8vD1988YVkFklISMCFF16IiIgImEye1IHbtm1DXl4eqqurxTuTkpKCuLg4xMXFiSeDcSi0BHjz64Fe8MR3pzdHc6lZ1ZfKmcPhkAOOoJVpNnnYEQAwkw4pBjU1NRL3ER4eLkGttHaxVgXQWy1dW8/0RuUBTsChPR2a5qIVTO/gRKCXlsHUolTG6BWoq6tDRUUFqqqqMGjQIOEtb9++XWIRyI1PTEwUnv2SJUukjkZERAROOeUUjBgxAjExMfDz80NpaankFt+1axd+/fVXOJ1OA12MoIdAi0CPMRSkQfBdva2PpG9QqSPoJHWGY80q4oBHsDidTgGYnBsCRSoaGnxqMEh+a39scXFxkmqU+cN1nn96lnQQLfdSQEAAwsPDBTjqFNj0ljU1NcnvWWcF6E1VrJV+nVK7p8dT3yYhIQGRkZGw2WyIjY01gE7tNWAhKq6P7u5uOfz4d6fTKYHEJpNJYsQCAz1VqqOjoxEWFiZZkYBeqzMNGWzcn1ox1gBSc5Gp1DJTGY0kYWFhhoxS2nPscDgMShipYJyHoKAgye5G+qw2PGjliAoAx1ln6NLeEy0z29vbj/LS6L2pZYxW8qgkUE4xK6HuS1VVleEepHNyHDo6OkShDA0NFc+qplmSVsYYjbq6OjidTtTV1SE/P1/2JWnF2vPE1L80bFE20MOuFUxNv+D1WoGi9ZX96OzsFEqmy9WbhEIr5pT5jGeijGENiv7WuC6mT5+OTz/9FPHx8YZCbQDw9ttvIykpSdLbvvnmmxg0aBAmTpxoMATSw9NXy8rKwq5du5CTk4PCwkIAEK8SFdW+6DJc/95eI5vNhpKSEpx++umIj4/HSy+9hISEBHR1deH888/HokWLEB8fj48//hhutxuXXnqppEVtb2/H119/DbvdLucI9/vixYuRnp6OE044Qfo2cuRIrFu3DgMGDEBlZaW8Q2FhIf75z3/ixRdfPKrvOnHNn/Vt48aNyM3NRWVlpaS3veaaa/Doo48iOTkZq1atQmFhIa699loZq1mzZuHVV19FfHz8UWNNGWc2m1FTU4Orr74an3/+Ofz8/FBWVobbb78dH330kYyrt8HJ+131Z85DRkaGzCO9N31dDwC33norbrvtNqSlpeG3337D1q1b8dhjj6G4uBjTpk3DoEGD8OSTTyIxMdFA0fVOc9zXZ+5f73E3m80oLS3FPffcg8LCwqPSFPfV+hqL/1Q7FhXimBWN5ORkCWRkcAtBEg9+LcD0gcaUffxMMM1DyjtVIwOTCNQiIiJEySGoJDhnsKfL5Sn3np2djXPPPVesQKRiFRcXY+/evfjjjz8EsMXExCA3NxcDBw6E0+lEdXU1CgsLUV5ejoaGBrhcLlitVowbN04OMrfbjdzcXKG6aDc0FSwe3lxY+lCmW54TRMBEJY2HKABRqkjFIL+WBymFJfvvcnkK5lVUVMBsNkuxMVYX51wARssoBSdzrmu3PpUCzh2frekA2gJN7wPHnkCcPzy4+Uw/Pz+0tLTA4XCgra1NYl18fT3577ds2YLm5mYRgqTGpaWlob6+XooSlpSUYNSoUcjIyEBKSooomuRyOxwOLFmyRKhYBCJcC97UEy0gNAWts7NTFCwCBgJVbbnlYcCxsFgsUgyRSrcGiPSm8YcAAugtUMksSRaLRSxS/a3FxcXJ/zlOXAcERJqCGRERIYBR09k0XYqeN4J0gk/tMaFVWnP0aaUnSMzOzpakDlFRUUhMTDQAMxouGhsbUV9fL2tarx/GE9CToakz8fHxAHrTnDIGRKeg5r88JHm9t/eQBzTHguuOAIfxUAT8QUFBEhzP72qAzix6TCRBqiBlGw0DVDS050VnwCKtkQYMvpfOascx0soRjRQa/PMz+8V/vRUL5p+nLNX1c3p6ekRp4nhy3jhubW1tCAkJQVBQEIKCghAbG4vw8HDxcHnHlzHdNpNYFBUViWyjYqrnidnvqIhERkZKH+vq6iTWi/JcyyBNpQV6i1JquUUPC5MpNDY2ynnMbIhcOwwGBzwGvv5osNBUwODgYENKVzYqX1RAqGhMnjwZ+/fvx7PPPovNmzdjzZo1yMnJMVR7ZvPx8aS7b2pqgsvlQlpaGrZt24Zx48bh9NNPx1VXXYWhQ4ce9b01a9Zg27ZtuPPOO49679DQUKHw6rT/lH+MOQM89JmwsDC0trbi5JNPFkXjhRdekGBw3de2tjYcPHgQjz32GD744AM5czTECw0NlUQqGzZsQG5uLoqKipCRkYEtW7Zg9OjRmDlzJi699FIMHz78qL5xP7hcLokBo5GoqalJzjF/f38UFhbivPPOw/r16w191e2jjz6C2WzGFVdcYVA0gN7CjMyudvDgQTzyyCN4//335fvPPvssxowZg+OPPx6AJ9B9ypQpmDx5Mnbt2oV33nkHL730kszjPffcg1mzZmHkyJEAPJQ7PY/efaHRSM8bC79+++23qKiowF133YXCwkLMmjULa9asQUhICA4dOoQrrrgCP/74IwIDA3Ho0CFcf/31aG1txccff4y0tDSDZ4R9pVGoqanpLwH83r17sWjRIqn98p9sx6JCHDN1ikqB2eyp5M3Gg0RbUjSYJXgm6AYg99AUHgIA7RGh9d/7kNUVmxlsZbfbkZmZiaFDh8phRe5qfX098vPzcfDgQTQ0NCA0NBQxMTFISEiA1WqVGAxOPAN9IyIikJWVJeDFbPZksqELmwCJB6zOwqIPNR60+sDige9NB9NcXiotmiLGa+mS52HMcXc6nULVqK2tNfCN+U7askjKAQABAAwcpWVUey2oWOmDkbx0KpN8NwICHuykO3At6fnW1mWHw4HGxkb4+/tLWmJm+Gpvbxc6G4EgqWs80JlQIDs7WwLOrVYrxo8fj61bt6K4uBgOh0PAjJ4HTRvjmuQ9tdeOIEdTm7RHh14Nb8oZ4wK4VrQ3iffTdBL+jaDCm1LY35rOoqOTCXCNa2oI1zHHnMCVnp+QkBC0trbCz89PsghpWopWpgnemTaaa5mppGmtt1gsCA8PR0REhOxFPpegtrGxEY2NjQauNNBredfUIO4/WszZD3o3dDwVZYS2huvYCT1m3tZS7is2TcUDeilfvIfef95B8wTs3Pv0AGgrLeUB0HeMBpumBNEq70350UYTUrO0cUNTj4De80DvWf1s0hJ0rScGsdIQQllMrzsNZm63W7zj+n58Dj07YWFhkqGLBoHu7m6p78Ox6e7uNgS1dnV1iZeN86GNEqQoU67x7OQYc43ohBzsA+dEx+HoeeL1HK9jtDH+17YhQ4bglltuwbx583DttdciODgYzzzzDADPGZ6dnY358+fjxhtvxJtvvonw8HC4XC7Mnz8fu3fvRm1tLW666SZJrJGUlIQnn3wSt99+O2praxEbG4unn34ad911F6qqqlBTU4ObbroJlZWVWLlypcHYc9ppp+H000/HLbfcgmeeeUZqrQCeOIWysjK89dZbArZ59rLpuAkaGv38/PD888/j9ddfx86dOzFnzhy0tbXh3XffNdDEtKf83nvvxY4dO4RB8PLLL+Pbb79Ffn4+Hn/8cdx6661oampCRUUFbrrpJrGaV1VV4aabbkJVVRV++OEHHDx4EADw1FNPYefOnfj0008BAPfeey9SU1PhcDhwyy234OGHH8aRI0ekiB4Vu56eHtx8883Iz8839O2uu+5CZ2enAOQ33nhDZOs111xjKCCpFROXy4V7770X27dvR0ZGBh566CHcdNNN+Oyzz/Dzzz/LdV9++SXWr18v487YG6fTieeeew7Nzc149NFH5XrveaQhUfdFvwtj39577z2sWrUKW7duRVtbG2655RahZHV0dOC2227D7t27ZW7vuOMO7NixA93d3bj11luPimXRfeX/r7zySqSkpODRRx+VeVy5ciUA4IEHHhDqXn9ox6xoaN4phZ7me2o3EdArRHkoUjgCvSXsvTm7/A5BN4EVv8cDnZ8pOMPCwhAfH4+UlBQkJSUZAHt3dzcqKipw6NAhVFRUSOB6dHQ0IiIi0NPTg/r6egEd5G7b7XbExsbCbrejurpaONYa7BH8kRJFy56m5PBwI3DkYaPBJxUyTYfwBuKaZqD51byOPElaXwDPRnE6nWLR1RZSfocHE5UKPdb8Hd9De2r4bO9YG2+LJZUOAnNtgdaZWXSygZaWFuF8RkZGwmKxoKGhQbK8NDY2wuFwICIiQixDoaGhcDqdMvaxsbEYMGCAwTuRnp6O6upquT/HmeOvg1QJ7vldrgvOEefAm+esPUHcI3wnPcbeioZWpPk7/p1xPDrAtr82rZR589I1KOKYURHmtRogEXRRnjA9I0G2His+xzvmiCCaigpBJOMydGwGq5fr9NGaRqrvSy8Jra6hoaHiySTw1eliqWhw7vmeWo7qH61wco3osdHAGYBhPLjWtAdEvzdpPVo+60x5HA9vGcbmDWK1YUPvHSoeVGi4/nV8CvvofT/tddUxTNrDQmVSx2rQ8EEFR1fW5vOpQHANauoXPTtUNGj5pGdZAxU2nnVUNggyOAdUfqhYckz5XK3Q6bHW86eVCe9gdPafz+Nnb9nV31pgYCCSk5MxZcoUZGVlHQXeAgICkJiYCB8fH+HHA8DSpUvl/6TlAB7DHlPbAx5Zwc8pKSmIiYnBhx9+CMBjgS4vL5fvknIHeEB7Y2Mj/Pz8cNxxxyEzM9OQ/nT48OHo6OjAwYMHMXHiROzevRsNDQ3w9fXFcccdh3379qGurg4mkwmJiYkIDg5GeXk5PvnkEwCQAGKTyYQJEyagsLAQlZWVcLvdWLJkCTIyMjBy5Ehs377dQANMTEyEr68v0tLSYLPZpC+AJ0EBP+/ZswcFBQWYOnUq0tPTDanUY2NjkZSUJMp3dHS0eDYATw2PtrY25Ofny/vqFhUVZZinX375Rf7/2WefHXU94KkdMXz4cHz11VdIS0vD2LFjpS9btmwxXLt161b5P2Mj2OLi4rBq1SosW7ZMfrd3717p65YtWxAREYHExERs2LABI0eOlCxzuvn4+CAxMRFr1qxBXV0durq68Mknn2DYsGGIiIhAXl6e9D0yMhKDBg3C559/juzsbPj4+MjfsrOzERAQIDE0WVlZCAoKkrgT1lADehPlsDH2qL+0fytGg4cCaUukSYWFhUllbVrjdJAn+e5Ab4YUbZFjvAQtaRqomUy9hac07Yj0iJ6eHqSnp2PSpElIT09HYmIiurq6EB4ejq6uLlRVVeG7777D5s2bpTJ1bm4uBgwYALfbjYqKCrGI19fXY9++fYiMjMTQoUMRGhqK2tpaKQzHn5EjR0rAsclkMlgmWZmbFnwqGQAMVC/2nwCEBxQPOAASZ0Frq9lshsPhkJSIPFDb2trgcDgkeJ2ZuPTYMssTAPEU8ZANDw8X6gALlDFuxjtgmmkde3p6RNjwQGYdCE1NoLLBuWVf/Pw8hQaZqhcADh48KLQ5ejEiIiIAeJSmgoIClJeXi5Jgt9tF4NBrxZic0aNHY/To0aKMNDc3IyoqCgcOHEBeXh7Wr1+Puro6A3ALDQ0VmgU5llwbTJtLYNjW1ia5+Tl3zHHP67Ul3d/fXyq2awWCfed6ocLDuaUSq62zwcHBYnHqb23QoEFS2A7ojQOiXCDw18Gv3GcEjloZ59jSK0BAyf3V3NwsSo2mkBBwRkZGSuB3SkoKsrKyEBUVJdRIcmtbWlpQUlICp9OJ1tZWUVa99zbXQ0tLiyElLCksOh6Fz/Dz85N9QQDo6+sr64oWQe1R8/PzM+R2p2KkPQZaQWHwOceK3kP+tLW1SZ8aGxsNaYKBXupUSEgIwsPDDYqbjjUizVB7aLTiQcDNH+010V48DYS1h5jKAn/PeaRiRMCvFSZmzOLvmJaY9YcASPYv3ReebTabTWQqlRIWFS0uLkZBQQHq6urQ2NgoXlcWfGWMGOt8ABCvGq3afB7gKRCmqXQAhPZlMpmkHo9eE5TrDNanB7unpwfV1dUCNGlU0tnb+qoy/d/etLcmICAAVVVVuPbaa/HFF1/INdqA4f2ZBiWtlOvPvJ5jCHis4xdffDGys7MBeLJezZ07F+np6QaDEgD88ccf2LJlCx577DGUlJRg6tSp2Lhxo8iz1atXo6ysDDfffDMqKytx9tlnY9WqVbBYLKioqMDs2bOxfPlyA3NC94WN1Le77roL7733nqzN1157Dbm5uZgwYUKffVmwYAGmT5+O4cOH/+lYpKSk4ODBg8jNzcXevXv/dC68+7527VoUFBTgmmuu+dNx5++9+/Zn7zJ58mSsXLkS8fHxeOqppzBo0CCpEaLfQ2PKf+dzcnIyCgsLMWrUKJxwwgm4/fbbkZqaivXr12PLli2Sacx7HjiPV1xxBQDg+++/h9PpxOzZs+U5M2bMwMcff4yYmBi8++67CAoKwowZMwB4Mp/FxMTglFNOAeCh92VmZuKEE04wxLDxPb0//7e0Y1EhjlnRiIqKkgMuJCTEkGudlhcuOFpfAWPeX22pZfVYUnwYR+Dn5wer1QqHw2GweJO3qN3rBKQDBgzAaaedhujoaPj5+Qm4LC0txebNm7F8+XLU1dUhMDAQSUlJGDx4MHx8fNDa2oq6ujoJlHO7PfEUaWlp6Onx1DpoaGjAgAEDpEI1QSgFOmt+EPSwQBNTWjocDgMFA/DkzuYhQyClaQua/xwYGIiwsDARrkwjRzBVU1Mj7nOHwwGHwyGZbHx8fHDw4EGEhobCbrcjOTnZQNXS82E2myUOhOCIoAyAHNosiEdQTBDFw47KH+eW/FE+Q4NIAm/GndTW1sLlckmhQ+/Ynfb2dhw4cADV1dVwOByoqalBYGAgIiIiEBcXh8OHD0vBtsjISEydOhVpaWkGYAR4qpKuXbsWmzdvhtPpFGutn5+fpKpkRXUe2ryG40agRuurjg+gokXlmoKBmUnYJwIiHx8fqVZP8KXXgI9Pb5pOzn1VVdWxbNv/upaQkCD7nNWugd6Kqk1NTQbLOQABroAniwfXHAEbAJEpPj4+YllmwBxBLHO1U3lOSkpCREQEIiIiJH1yenq6xDUBEKWyvr4eZWVlqK6uNgRK8/0JsrUyyPnn3uJ9tUJEbwyrjnOt6/S79KB4A3Zt/ed6oXVbe8kACPDXYJkWfSoXeo8znohjTdojA9lJFyKdiGubn/X61YoiDVUEyn1lXyMY1vQobw8xv096plb4Oefa2q+9Jk6nE7W1taJo6OyIrKFBD2ZQUBAGDBggxhdNjyWNc//+/SgvLxc6XWVlpShKDLTXXm79r5+fn9TUoTKjCy2S4sXzBeil1vDc4BnCcdOUu4aGBoSHhxvoVpwnf3//fhkMXldXh8suu0yyRdlsNoPxAgBeffVVJCYm4uyzzwYAvPDCC8jKysKZZ56JPXv24B//+AfeeustmEwm7Nq1C6+99hoWLlwo37///vtxxhlnCFin4kjam/68du1arFu3Dg8++CAAjwWe69xms0ll6YceegiDBg2SOIaWlhbYbDY0NjaK8hwZGYmmpiaccMIJWLx4MbKysvDqq68iKCgIF110kbzfpEmTsGTJEkyYMAFlZWUYMWIEli1bhpycHPG0Op1OAJ64heOOOw4nnHACgN5aPQ6HA1u2bMEnn3yCf/zjHwA8mZG+/vprPPPMM4iIiJBEBn/WfvzxR+zdu1fiUcLDwwU7AR7vUW1tLe68807k5+fj8ssvR1BQEF577TVkZWXh9ddfh9lsxlVXXYX8/HzceuutWLZsGfz8/LB//37Mnz8fX375JcLDw1FfX4+QkBD4+vpK3wCPh2j16tUYOnQoKisrMXjwYKxduxYjR45ESUkJMjIysGHDBowePRpFRUVITU3Fli1bMGHCBBw4cAA+Pj7SV8bKcN/QyAR4FIn8/HzcfvvtACAUSfY1LCwMbrfb4OGhTKmrqxPZQmqUxWIRHMTPEydOxPvvv4+cnBzcd999GDp0qCgi3vP439KORYU4Zt+pFt7MXsHDgFZGHho6XSMBFYEbg4AZ3O3n54ewsDABCTwUSDeghZ3CndYdbYkaOHAgQkND4Xa7BUjU19fj8OHD2LNnj/BoY2NjkZaWhtjYWNTV1aGsrAzNzc2oqamRjExRUVEC3AlYWcQrNDQUVqtVDgTya3nA0bLGQ4FgiAcCx0dbcDUvW3OPNZDg4cFDVCsl5LLrgGQAQgVLT09HRkYGkpKSJHMV6wRQ4PAdXC6XgAQCEgIZHlSa905lhwe/TiWqLZL6EPSOY9H0DJfLJRZTChRWcicASU1NRXJysnhpQkNDBfjwGS6XC1VVVTh48CAqKirQ3NwsoJTesYEDByIxMVE2O3+YIljTDKi0aXBGhZMWHR34y/7yHhwrWqdJj+DY+vj4GJRq74rKXFscDw3C+1tjP0lFomLFw4wgWFvlCSgJdrW3jgBOJ34gyOKByzXE7xBsMSA7MjJSKn/TcMD5ofLDmhlasaE800oHU0ESfOtK39qzyHnkvqelXlsttYf4zzj1morkTbHRjcod9zLBJp+vn8d9CkBkLqlpOqkH5ZcOxufc0GOnvXZ8X/6rqVqcM+294Fzq8fC+n5aZej9R/lLWEcjzXLFarSLTtcWf3iquN9LlmARDUz/pieL6sdlswp3X9Ct6wmmM0PKVwIZ7g+NHxUl7alwul8Q3Ut4xpshisQhdiuuB+4V90WPGvdAf2/z586WGBQChPuv2xRdfYPHixfJ56dKleP311+FyufDkk0/ijz/+AOBZh08++STWrVuHxMREvPHGG4iMjMSKFSukHsJjjz2Gk046SZSMRx99FKeddpp8fumll9DZ2SnxIU6nE2PGjMGzzz6L+vp6dHd3Y/PmzXjsscfgcnliTAcPHowXX3xRDKpsdXV16OzsxL59+/DQQw+hs7MTH330Ed59912YzWa8/PLLGD16NA4dOoT7778fpaWl6OjoQFFREe6//37xSmog/u233+KVV14B4PFmTJo0CcHBwXjjjTfw7rvv4qeffpJrn3/+eaxatQo2mw1PPfWUWNwXLFgAwBMbcOGFF8r1CxcuxFdffQWLxYLXX38dUVFRGDZsGF588UX4+vrizTffxKeffoqOjg488MADOHDgAPbs2YOHH34YnZ2d+PDDD/H++++ju7sbjzzyiMQ19PT04O9//zu2bduG7m5P4dPnn38eubm5krnyueeew/HHH4+ysjLcd999AtgrKipw3333yfzU1tZi/vz5qK+vx4wZM3DHHXdg/vz5qK6uxmmnnYYnnngCdXV1uP/++zFjxgz53g033CAB9wDw+uuvY+nSpQgJCcHrr7+OuLg4Q0D3lVdeidmzZyMgIACvvvoqhg0bJlk1AeCSSy7BnDlzZB6zs7PR2NgIHx8fvPjiixg2bBh27twp8/jVV1/h1VdfNczjsmXL8MYbbwjTo7+0Y47RYKNCQYHKA5IHBcGa5uB2dXUZysHT0sPDgRo+rU4UuJpLyus1MKP3IykpyZClpaurCxUVFSgqKsLhw4fR1dUFu90Ou92O6OhoyWfNNLKtra2IiYkR6yYBo9lsFmqFtsoT/BHka+shA/loxeQBpbNT0ZJJgM/vEkx4UxYI8r0DQ3XsBwFVUFCQAbylpaUZPCKsXs7n0BrGcdYeB60EaDBCQKIBAeeZShHHiocpAYq2rHlzzrl2qFgw24RupCs1NDSI94YHNT1MXV1dokhGRUXBYrHAbrcbYh0SExORmpoKp9Np4Ezr+BH23dfXVxRkreSROsa+cCypJOhxJijl371jCPS1nE+9rzQXvj/HaHD8vIMhgd7MQ9p1rpUQoDdNLRtBM8dEB49zjgioaRAhEOUepQKujQgc646ODqkcrjMXAb1eBQ3ySZehwkpgSguoNizoNcD1wz2i6USkVuq4CP0OfA89Lty/Wqnw9hQBxppE2vhBOcJ7U/nl2taAm/KDnl2tIPF52muh/9X95jtw/rzXuZ5nLZt0Hzh2+r16enozmfHeOgkGDR/0Ruq11tXlKSrJOeXvaVCg95XzQ1oxZbA2OmjvAz+bTCbx7DEeTu8Terf4zrpmEz3KAESm8lzkZ01T5vj+t1IwjrUtXrwYAwcORFZW1lEBsSaTCaNGjcKePXsM6UF/++03WCwWjBs3Dp999plB9uzbtw+1tbWwWCwYNmwY/P39sWXLFuH/Z2dnG+IUBg4ciMrKSvj7+2PEiBFYsWIFAgMDcfHFF8s1kZGRyMnJkc/19fXIy8uTdRsREYHBgwfLGrTZbEhLS8PWrVvhdrsNAdasJeHn54fBgwcjIiICW7Zswdtvv42RI0eiuLgYlZWVePPNNzFq1CiUlJSguroaADBy5EgUFRVJjMqgQYOwe/du+Pv7Y9iwYXjkkUfg4+OD4cOHY+fOnZLxKS4uDsOGDZMaH6yAnpmZaQiSZryD1WrF0KFDERISgoiICAwfPhzjxo3D77//LgrAO++8g6ysLPT09Ejfjhw5Imvx3Xfflfu63W7s3btXAvVNJhNycnKwZs0aWCwWDBkyBMOHD8eGDRvQ1taGXbt2yZy2t7dj165d6OzsFK81lc6YmBjExcXhlltuAeCJMdF90woawwXYvvnmGwAezwX7qltaWpooDkOHDhVKbnp6OrZt24aUlBRJg895DAsLw5AhQzBs2DCsWrXKMI8HDhxAS0sLxo0bh23btmHDhg2or6/HRRddJIag/tKOmToVGxsrIC08PFwsrPQiaJe/dj/TPc7vMGaAVkDAk9WDCgi9BPrQ5b1Z4Io82NTUVOTm5mLixInisg8NDUV3dzdWr16NTZs2YevWrQgODpbUlQkJCTCbzdi7dy8qKyvR1NQEPz8/qb9AegU1VZvNBqvVKsKZE6zTR5L7TVc764aYTCbU1dWhpaVFgkiZcYiWNoJwNq2w+fv7w263y0bs7OxEWVmZ4WBkylsNUktLS9Ha2gqz2Yy0tDRRLhhX4G3t1PEngJGmQMs94IkFId2JShbQqwxpJYg0NAIu7VHgDwE6FbHW1lYBZ5qCwUNWpzjt6enBjz/+aCimBngEVENDAw4ePAh/f39kZGQgMzMTI0eOREZGBhoaGqROS1FREX755Rfs378fXV1dhixd/v7+hqrClZWViIyMNHBSOV5UkEhpoCKsA+dJNyTo9PHxkRS3VDx0cGx4eLhQTLhHuDZ6enr+Msf2f3Oz2+0Gegctrux3X/FO2uPEOWEmJ39/f9lb/LsWad5VkgGIpdtmsyE9PV3q46SmpsqaJbXzyJEjqKqqkpo69BwSlBKwMuibyQu4dqk08pncW9ryTznHglMaxNOjo+t16KQN+jpN7QsKChKFgEqA9jTqOhRdXV2SEpaffX19xTsMQGpw8P1DQ0NlbOmh0cqIt+zWWdt0umvuaa2kADDERFBJoAJI+a/BNa34GkgDvVRboNcgQpopaWmVlZWSwEJ7Jal02Ww2REVFISIiAjabDZGRkQY5Vltbi+rqalRXV6O8vBw7duyQIFFaHrnmSUnju9LwQIU6JCRE6Comk0k8FaxaTlox5zggIMAQy8M5oDFM19ggzYxpjn19fftV5ho2k8mEL7/8Emaz2WBxBjxnUVVVFW699VZ88MEHcr3b7cb48ePFc0EgDniKrj3//PN48cUX+/Qa/pk3MTk5GYcPH8aIESOwa9euP31Xt9uNq6++GgsWLEBsbKxB6eZ9Z82ahbfeeuuoYGnvZ+vP/v7+KC8vx1133YV3330Xvr6+KC8vx4MPPijZnEpLS7FgwQLxaPTV/v73v+OCCy4QwN3Xs7yNoPr3/Kyvt1qtqKqqwumnny4eE5PJhO+++w6tra244IILAOCoedTyUc+jvreex6qqKowaNQobN25EcnIyysvLMWzYMGzfvh1paWm47rrrcM4552Dw4MH/sm/H2v5qTrzbWWedhU8++QRRUVGCD/T106ZNw48//ojY2FjxpOh5pIKRlJTUZwrm/4b2v0qdYqAweccEVg6HQwK4dbC2PmTIM+UhTaoJaSL0kHR0dMhga6oNLTGtra1oaGiAyWRCZGQkBg4ciJEjR8phTwtoXl4eduzYIVaIzMxMibNwOp34+uuvUVRUhK6uLoSGhmLAgAGIjo6WQl3h4eGw2Wyw2WwSAMliTqTahIaGwmazGSp/khNO7i8tShwv1qkICQkRQEVwxIOCHhCCA23FJGAljcFisQi/Ozw8XBQc/i4mJkZSrPr6+kqRQT22nFNdSJCN/HH+aK41Laja1U9QYrFYEBMTg6ioKHmm9l5oK7BOxQhAgDUPUL5PW1ubgDnSFUaMGAGz2VPo5+DBg3A4HEhNTRUeY2BgoHCo9+zZg7KyMlnL7e3tSExMRGZmJtLS0oQGwTWkQZfb7UZ8fLwc+jpGBvAAGAa2c+2yf1qxYLApv6Mpa1w/9EoxSJ4WWR2volMb9rdG/i5johhLoakl9GByr3d0dEgKz+DgYMkKRf47M+uwMCXHTaeQ5ZwSRNIowX0cHR1tAKjk8tPoAUD2KSmSDodD9jngAeNMjRsZGQmr1SrvykJtISEhCA0NFdqi9kjyAOLa0ooM+0ElXMdGaet9X7Qign72i/JFGyfo4eEapIygh0YrQFoW6PtrzwJlG7/Dd9Xv6e351JQsvosOXOb48Pt8DsG6t0eFcpDxMjTuBAYGioyPioqSvU2qHIvBci7o0WIAuC4W63a7j6KjxsfHS+FHUu8YKM4zhecZvSCca1KPqURQcQ4ODkZ0dLTBsNXU1IT6+npZoxxPelcod7nfTCYTwsLCAPSmMu+v7eqrr5YgXN26urowaNAgLFmyBIDnPNuzZw9mz56NrVu3IikpCTU1NXj88cfxww8/wO12Y9SoUYiPj++zUjQA/P7770fVxACAsrIyJCYmGmhcutntdhQXF2P06NH4+OOPMWTIEJEl5513Hvbv3/+XlunAwEAcPHgQZ5xxBgDPuZKfn49zzjkHEydORGFhISZOnCjZmnp6ejBs2DDJHuV2uzFixAi89dZbf/oMAHjmmWeOCq5OSkpCWVkZsrOzMW/ePGzevBkAsGLFCkN62G+++QYLFiyAzWZDcXExxo4dC8BDH0tJScG6desAeDDB4cOH8c477+Daa6+V73vP48KFC/Hxxx8b5nH8+PEoLi4WpV3PIwDs2rULSUlJUpgwLy8PiYmJomQd//9qbLClpqairKwMGRkZuOmmmwwpdf9Vmz17Nvbs2SPy9F/N44oVK5Ceni7YYNq0aTh8+LDg6N9//x0pKSniuQGM87hz504kJSX125hMtmNWNBj8SisS0wZ6F6AjkCD3kMBUu8b74obqjBw8RLwPLlq2AY+bKjk5GREREVJllwG1eXl5KCsrQ2trKywWC+Li4uRanYmEgjcmJkbiRHRcAK1Q7BvQy0vWfGfvWAXvA5wZo+il4CGiATavpcWMIEXHcbDRqk1rp44R8ff3R2RkJCIiImA2m9Hc3Czcb/aF1AhaJTVdigqbprDwwCXw0KCDFnke3tqa601pIJDQPHAergSDbFRCCCxYmZvv6HK5ZKxYQKempkYqMVOh6+z0VEs/cuSIrAm+Q2dnJ+Lj45GZmWnwShHcavoe+do62JRz6Ha7pR4D1zKVZ1oXaU2kh0NToji3WhHjO+rncw69PVL9qfn5+Qkg417nmOg0oAyId7vdhkrUOo6B+47eBRanCggIkIQIWm60tLSIN5EKAXn6fCcWi6SVn7KCc8M9r+OLqKRy3QcEBBiMEzRQeKez1QBbU1A1z57P1eBaA2sNyvl3KgmUM9qzwvH2tvxrYE9lQ88Tv8/n8lrKSm9F4s/oXUBvGmxNr9LxGFop0lQx73tomaJlIulJ3kqINrAwZoNri+9vMpmEVsvsgZSt9HTr4rH0RuoCjDabTdaDzrylZRdpV3oMmUxAX+t2uw1GOl2ZXRusSBul0sN7M46D1ecpb/hO/bWde+65YhUHgDlz5mDevHkAPOllCexcLhdeeuklodZUVlbC5XJh1apVQtWprq7GsmXLBJA//PDDUlEcABYtWoTffvsNdrsdL730EmJiYnDSSSfhgQcekJT5xx9/vAGAT5o0Cffddx+ee+45XHDBBTj77LPR1NSEF154ARkZUlnWHgABAABJREFUGdizZw9eeOEFWfPbtm3DPffcc1RtjX/84x/Iz89HdnY2nnnmGbz22mvIy8vDkSNH8PTTT6OoqAjnnHOOUIGqqqrkjGPfTj31VNx9990AgEceeQTTpk1DbGwsXnrpJURGRkqcKuAJgp8+fTqcTicWLFiAmpoabNy4UWIF3nnnHaxevRqhoaF48cUXsW7dOvz4449obW3FM888g9LSUgAeJemee+5BamoqAI+RhkUSGXT97LPPIiEhQYzLgIeK9dlnn8HX1xe33347Bg8ejJKSEjzzzDMypwkJCbj33nuFukSqPGVDdHQ07r33XoSHh2Pq1KmSAeuBBx6Q2JoFCxagvr4eGzZsMCQB6GseH3vsMdkru3btwksvvQSXy4WbbroJubm5Mo/z5s3DnDlz5LvXXnstLrvsMtTV1eHpp5/G8OHDcejQITz77LOi5Hd2dqK6uhpPPvmkKGl6Hr37lpCQgBdffBFWqxX9qR0zYqHFRacrJSjXgd+k6DAdqPcBQosxDyP+jRuMwl4fWN5uKovFIpQHLXR9fHzgcDhQWFgolb0tFguioqIkRaHm2hPARkZGGrjZOgWgt6JBL4w335n91P3lvTTA0Ie5tuARGGgrKA/4vsA630kHKFNZo3WNgJl91coPwYF3MLq2UvL/nAMejro/VDYJrHjIaS49FQ4qYOwvKUY8eAksOIb0ltCToQEm+fc8WAFPLnBmpPL39xdlq729HeXl5SgtLZVij4wLioyMRHJyMqKiogzzzXXCA5lBsTodJ9+PAcFcV0zty+9qSpRWMLRnR/dNc+4JJPlcjmd/bdpLpuOTCNyAXn66nn8qHTr2S7vraUAgjY0eA6AXoJLOx1oZ5Mgyy5o2LlAxIdDUBgS+I9Abw0Pgyr3N/awVGO+95q1oaGWjr7XhLQsI/LUxwltZ0EYEb/mhFQ1+l2PN8dAxU95gnf/3zmil41g4/vyXCo5+Vy0n+C+/o5UNvTc0WKYc9KZP6XHWCpl3P7wNRzqDn46xoyeLAIAUPtKY6FWm/NbrQXvqqAj15RnyjgEiZVZnKGSftBLpTWHTFEQt67levWOd+lsbPHgwhgwZIp8HDRp0VJXukJAQDBs2DG+99Rb27t2L4OBgDB8+HGazGQcPHsS2bdsAADk5OYa6D6zTwPbee+/hyJEjGDlyJKZMmYKRI0di3LhxGDdunFyTmJiIKVOmIDc3F0FBQYiPj8fw4cPxyiuvICYmBgMHDoSfnx8mT54Mq9WK/Px8CU5PS0tDV1cXFi1ahMGDB4vXqaenBwsXLkRBQQEiIyMxYcIELF68GAcOHIDD4cC6devQ3d2N7Oxs5ObmAvAUMYyMjDT0lXU1AA/tKDk5GSEhIdIX1moAgDFjxiA1NRXt7e345Zdf0NLSgm3btklMxSeffIJff/0V/v7+mDJlCr7//nusWbMG7e3teOWVVxAcHCy1R9hXwHNGvvrqq/D19UVqaip8fX0xefJkREVFISwsDMOGDYOPjw9++OEHfP311zCZTJg4cSJiY2NRVlaGV155RfBNeHg4pkyZAj8/PyQkJGDgwIGGebdYLJg6dSoCAwMxYMAAjBkzRuY1JSUFTqcTL7/8sqSnpkcjKysL48aNw/jx4+Ve8fHxmDBhgsimiooKrF+/Hm63Wyqncx6HDh1qoJ8NHjxYMpxOnjwZdrsdR44ckYxbHBuTyVMPJTY2Vr7b1zwCHsrq1KlTxajZX9q/ld7W22WtM+hYrVaxRpKLrsEUOcq+vr5obGxEQECAuHxtNptYDkkTCQsLE/DAzBtBQUGw2+048cQTMW7cOIkFocJQW1uLffv24bPPPhOtOSYmBueddx4CAgJQX1+PAwcOSJBNeHg4MjMzMXHiREO/AIgnwMfHR/pG6314eDhaWlrE5a2BPBtBjJ+fHxoaGsTr0NzcbEjVS8shD3Sg1zqnwRfvzzR+PHzMZrOMI4GKVo4sFosBeHgf7toz4R10TOqWtrACEG5yS0uL3F/zqL0BDQ9pZq8BPACC3gc20o+CgoIQFhYmWaVIH2pqahKvB6385eXlEvhfUlKCwMBAJCYmYsSIEWhtbUVRUREqKytRV1eHzMxMHHfccRgyZAiys7MlY0dTUxMOHTqEr7/+GrW1tTKvTFfH+fWuUMy4C9ISuHa1pZ7rhnFJHFf+nRQGk8kk61mPLV2yBNi8338rX/NftdTUVAOQJmWSssRisRhigHQGMABHUcj4Nyr3VHIpn7RyGBQUhJSUFISFhSEsLEzq7tATR/oMKYxlZWWoqKhAQ0MDmpubZQ20t7dLcDjXqt1uR0pKimQ4ohKsYyTogdTvqr1c2nDh7+9vCJLWCpX+Pv/OMdFeDSZJ0EkovL2vWonSAecExVqp9vbmasDLfc3707uh6U5aIdS0Q2b4Y9MxEgTQ2gvOdyIIdzqdBiWDhgnKO+5HPt/pdBq8wHV1daitrUVtbS3Kyspw+PBhNDc3w2w2IyYmBrGxsYiIiBDPR2xsLKKioqT2CeNdOjo6UF1djby8PFRVVUnshvbC6dTvNEroFMksykUvXWtrqyGLGQuw6rOGll4qifTKkUbFmBvt4eN4FRYW/o/38n+qHauhZerUqVi9ejViYmJQX1+PCRMm4Ndff0VCQgIeeOABjB8/HmPHjpUYDWaZ6qs9/PDDuPTSS5GZmYm9e/fim2++wfz58w3XJCQkoKSkBGPHjj2qkNxftRUrVsDhcODqq69GTU0NZs2ahW+//fYvvzNt2jSsWLECMTExBm5/RUUFHnjgAezYsQN//PEHEhMT//KsKCwsxDvvvIPHH3/c8PvU1FQcPnwYQ4cO/bdqrfz6668oKCjAVVdd1effv//+e7S2tuL888+X351xxhlYsmQJoqKiDFmcjqW98sorGD9+vCgT/2679957MW/ePKSmpmLHjh346aef+qTJsV199dV4+umnERMT8z9OphAQEIDq6mpcf/31fRY1/J/M43+yHYsKccwmDVr9aLEiaKQnQWdmCQgIEPex1ryoNGgQGRYWJlx8WsVphSfA4/UUoklJSRKsSfBWV1eHgwcPYuvWraivr0dQUBDS09MxceJEJCcnCyg9ePCg1NRg9Wj2icLd7XYLHYh8eIIBej6oIJB2oYE6B58HLGC00lKBsVgsQv3ifakE0BVOqyj/ZRwJKR8Mvud7kKahU7KSW0xqiQYXGlho6gO/q4U6FQr23W63izLlTWdhvE1lZSVqamrgcDjQ1NQkha0YqMhDkUkAtPVPA3aCE/Lku7u7UV5eLkHAzCzV3d0tRRYTExMRHR2N0NBQdHZ2ory8HHl5edizZw/Ky8slbbLb7UZCQoKkMWZAOilRjY2NolhqehfT7EVERMBisUjMDsdD0zI4jgxGJTWLlnCuP53GVVuOgV7KSX/NFgMYqVO6X76+vob5JR2F6UYZs6IVYQ1OteVa8/q5bxnXxXSgdrsdUVFREj+j6Vf19fWoqamRJBfMTEXgzvtRhpGGxT1Megzfi+/qTbfSVn2+MwBZd9ryzb9p6zXfQ1uo2X/uHSoJlGV8P+0N4drU65XyT6fh1d4P7dXk+GkqoDc9SntVvL0/fDctX+jB6ujoEHnBwoJtbW1oaGgQapt3vBPQW8mde02PlY45IejnugwPD5fzhtRWs9ksSk1zczPq6uok3THpnJw3Zt2JiIiQLIeUD3o98ofefSpVmnbJAHQaojQdShuH9Nrn9zlupNH4+/vLvuvLGNTf2htvvIG3335bPr/66qsS/M22YcMGpKenC/9969atSEtLQ01NDR5++GHMmDEDbrcnSDwuLk7qcqxbt05oWGwvvvgipk6dCgA46aST8PTTTx/1TpWVlUhNTT0qMHzp0qV9Xs922WWX4cYbb0RLSwuysrIky1RgYCD27duH6dOnH/WdP/74A9nZ2fj111+FQtbT04ORI0fi448/xo4dOzBgwABD0Dvb/PnzsXLlSgDA5MmT+wwULy0tRWpqKvLz83HddddJOuDly5fj4YcfNlw7e/ZsbNu2DT4+Prjgggtw9913IywsDAUFBZg4cSIAj5fhwIEDePfdd3H99dcbvv/zzz8jKysLLS0tR83j888/f1TF8FGjRuHQoUOw2+0yj2wPPPCAzCMA3H333Yb0vbfddhvWrl0rnxcuXCjvOH36dDz++OOIiYnB4cOHMWzYMFx22WXYunWr7JXPPvsMubm56OnpwSeffIIXXngBoaGhOHjwICZNmnTUOJ588snYv3+/nB3HH3889uzZg3HjxmHZsmWYMGECCgoKEB4eLt/5s3l8+OGHUVRUhKKiIsTFxR31rP/m9m/V0WCjxUmDUE13Anp56vw9DzX+DYBQfkhToEuaYJvgi4diTEwMcnJykJKSIpYZAOI1KC8vl6xMVqsV0dHREuTpdDoF5Op86OTFamuYtv6TRsHDncCBhznHgYIeMFKatFWSY6cBhA7s1dZL3kdTAwjAyDHmganBCseVSoIGXprj3Bd1RytKPAS1xZU/+r6aJ67fgZ4CejKo2NCqSEuepqNoxUdb/TiWfPfu7m6h5tHbxRz2TKNcWVkpBwwzDDU1NaG0tBSHDx82xGvwvbOzsyWzlNlslvfri1POMdCca90PgiTuASqXBH+0nOsUmPpvzFCkKTqaHtJfm6b5MTgb6KXD0fqvKSekQhGEcm3wXpRNlCU6kB/wzK/2WlitVtjtdkmeQBCslT3SP3l/Uj5pCed9deCuBtU0sGhajJY5+loN2LmmvClBAAw0KU0N0sqINhQAMPzf+130mtZ0JW8KZV9r3/vvQK8nVlvMdTILrRR5U6686Ut8H8plncGQ64bPoLeE48C9osG8zgDHsfDuE5UgXdFdJ4agLKfXXlf71mNKZUUnf2DTMUk+Pj5H0eq456lQ08hEZcdbDtBTwVgRvf6oHNMw2NraKooX90l/bStWrMDhw4fx97//Hf7+/li1ahWWL18OX19fPPjgg8jNzUVHRweKi4vhcrkwc+ZMzJkzRz7X19fD19cXTzzxBJqamrBixQoJIP/444+xfft2w/OcTifKy8sBeOgzw4cPl7iHm2++GdOnT0dPTw+Ki4vR2dmJcePGYf78+TCZTFi2bJkB3F5zzTWG+JLq6mrU1tbC7XajuLgY559/Pi699FJ0d3fjrbfewqFDhwB4FPyHH34Yw4YNQ3t7O4qKivD2228bMoeVlpaKAlxcXIx77rkHEyZMQFxcHJ544gmEh4djw4YNUkX94osvxsiRI2G32/Hkk0/Cbrdj2rRpuO2223DkyBF0dXVh586dEmC+ZMkSrF+/3jA2+/btw3vvvQe321NItq6uDh0dHVi8eLEkYOns7MSbb76JLVu2oLa2Fn5+fnj00UeRk5OD5ORkXHPNNfDz85N5ZFuzZg2++eYb+Pj44IEHHsDo0aNRWVmJxYsX4/bbb5dUw2x//PEHfv/9dzz55JOwWq3YtGmTQVHZsmULPv30UwAepeO4446Td6T3uqWlBW+88QZqamqwd+9evP/++yIbm5ubJQ7lm2++wc8//4zOzk4sXrxYfu/v74+///3vyM7OxuHDh/HWW2+JgbK4uBiLFy/GoUOH0NrairKyMixevPioxAycx+joaMydOxfBwcFYv349Fi1ahEWLFv3bnp//dPu3FA3tIvG21PN32mWrQaP+G3mvFHaM66Byouk1PGT9/f0RHx+PnJwcREdHA+gF4i6XC5WVlSgvL0dtba3w82lhbmlpQXV1NWpqaqSIl7Y0EwR785S1lUwHdQO9bmpvCpK3cqD5xPrw/zMFADg6ZZz2ttDjQkBGkK8tpxqckEeslTdNzdIHsp5fzYfn7zV1R1Ou+uKba26xrptBTwEBm/dz+1pfWmnje1BJ4OHLolsEpA0NDaiurhaPmN1ul8Cr4uJiFBcXo6GhQehrZrMZ6enpiImJESs3gYX2LGhQSlqTDuBj37kHCCq00s250p4lPYe0QOt1BPTmyu/PMRraKq1lBNCbitQbgHKvEkBxrEgV5JrU8WP0jAAwFHakR4OygffjPmPQv1Y0eB9SbagQaSVDB1zr/aaVR71n9Vz39TstFzj/3tdyz1FR9QbOWmHQMQ6Asf4Ev6NlORuvAYwxHfpe/KzBv3fMhHfchI6N0HPt7dnhM7xjnNj4vvxXz6Wm1enP+l0ox/h87yKgAAzeGlLKuEaYFUrfh9Q8Kiu60UuiKZVa6dIGC1ILtQEJgMHYoI1ivL/2OutkFNx3OoFIf21Lly7Fr7/+irPOOgtmsxlff/01vv32WwwaNAhnn302kpOTDdePHDlSPBJsVqsVM2fORGBgIH766ScJBl+5ciUOHjwo16Wnp8Nut8vntLQ0TJgwAaeddhoA4IQTTsDxxx+PjIwMwzWnn346TCYT3n//faxfv17+PmnSJIwePRo+Pj7IysqSTHQDBw6EyWTC+PHjMW7cOHR3d+O5555Dc3MzkpKS4OPjgzPPPBNJSUkIDg7GwIEDJdCdLSMjQwwo2dnZOPPMM5Geno7w8HDMnDkTQUFBWLNmjdSVOOWUU5CVlQWLxYKZM2fCYrEgKysLp556qtzz4MGDWLFiBQAP8N+1axd8fX2RnZ2NwMBAFBUVYfny5XC73UhJSUFcXBw6OjokWB2AFDTs6upCQkICfH19MWPGDMTFxSE2NvaoeczKyoKPjw++/fZbfPTRRzCZTDjjjDOQkpKCsrIyPP3005gyZQoGDBggfTWbzfjpp5/wxRdf4JxzzkFwcDB++eUXvPvuu8jOzoafn6faOLOLnXjiicjJyYHZbEZ2drYhmctXX32FhoYGbNu2Df/85z+RlZUlcVicx08++QTffvstOjo68Mwzz6CoqAihoaHIycnB2WefjTFjxqC9vR3PPvus9LurqwvPPPOM7D0G9dNgERQUhOzsbDGmWa1WnHPOOQgMDMSuXbvwySef4KmnnjJUH+8P7ZhjNAYMGGAAfQAEjDEIk6CMlB5q1ppaoy36PCi1ZYpAlNfy9ykpKRg9ejTGjh0Lq9UqtBWz2Qyn04kvvvgCJSUlUitixIgRkmKwvLwcf/zxB6qrq+F2uxEREYHc3FzExMRIutqIiAip9aABJQ8qnSGkpaVFArXb2tpQW1srh1FHR4e44AlkCIwp5PW9NOdcAxLtPeB929raJMaDIJQHjE7LSOsXKQM6Cw3jLnjQkcZAK6k3JYULvqenR4pB6e9rKhGVCyqRrKqrgRLTumlLPw+9zs5OoWKZzWZYrVahGrGQjsvlkoq5ISEhaG5uRnNzMxoaGlBTUyNApLOzE1FRUcjIyIDZbEZhYSH27dsHh8OBwMBA5OTk4Pjjj0dKSgosFgsaGxvR1dWFgwcPYs+ePVi+fDmCg4Pl4GcaZo6D0+kUPrW2ThKAMEsSARLjk/h9Kh/cR52dnYYsGsyCxc9ut1viBMLCwgwFpPpTs9lsRwFWxiw1NzfDZrMZaGakVWqPItBb40Ur25QplCe0PEdGRiIuLg7x8fGIjY1FbGysZJrj3JH+w9o6tFhr8UhKFeDxkpHaQvoU6Yv0rNHCzjVEfj73NA0bGrxqsM+9TPCsaahaflLp0AkM6I3lGFNuawWf48i9SA+clsV8L7fbbfD+sH9UrKhca8+NVp6AXiVAUzI5j1pJIVVSvwsVPE2B8o4x0V5GKmqMzWHqagAGjxcpRk1NTZItsaamRlIbs+4EDRmcB6YXj46ORlhYmNQtaWlpEYpoRUUF8vLy4HQ65Xmtra2or68/SqHkezc3NxsSJXgb9BwOh0HBpOKijRCUIx0dHairq5PzjJ5SrjNfX18UFxf/r+7v/4v2Z4aWU045Bd999x2ioqIMhdf+3Xb48GG88cYbeOqppwB4gPaHH34o2Yj6itG4/fbbcccddyA5OblPw1lf3P7w8HDU1NTgzDPPhNVq7bOOBuDxIgQHB+P000+X302bNg0rV65EdHS0IUajsrISDzzwALZv344NGzYgISHh/2du/913342bb74ZycnJ2LZtG9atW4ennnoK5eXlOO6445Cbm4vHH38csbGxWLt2LQoKCnDllVf2ea++YjS82787j6NGjcLmzZslJa93y87Oxr59+5CZmYlLLrlEYm3YUlJSUFRUhOHDh2PXrl1HxdpER0ejoqICkydPxqBBg/4yRkPXQ/n888/hcrlwzjnnAOh7Hr3bpEmTsHbtWsTGxh5VK+ull17C5MmTJbD/v6UdiwpxzIpGYmKiCMPu7m4BPfwJCgqSAyQoKAhNTU0GN7rmB3vTo5qamqQ6KqlS2jVvMplw5plnYvDgwUhJSRFlhPfasWMHVq5cifb2dlitVgwfPhzp6ekICAhAe3s7tmzZIkXs3G43MjMzkZSUBF9fT2A6qTVUDDQVgQeLPiTJr6cFjtWlCbLpRWAMC8E5rfu0mPv49MYhaCsXFRwe8k1NTQYaD1MLa8WBBzUDKzUY1/QK1qfg3BAU8RDVVjUe6DzgyQvX3gVadzmXujghAIPCxiQBPPTIu+diZb90rQRa4Whd1pm4CEI7OzvFg1FaWioHO6utErzu2LED+/btQ11dHQICAnDiiSdi8uTJSElJkdSV9H79/vvvKCsrk1oJeq3z2QS1PT09hrz3BGY9PT0GagyDWrlO6S7XnjWCJF9fX1lnwcHBaGpqMniTSkpK/qdy4T/aWKldAyTGE3EtaT48lQEAImP037lvaRkm5Y3xFswEZLPZEB8fj5SUFEO2KVrPGSRcW1srlurW1lZRDhmgz/1lNptlbVHZJ6AlrZH7W/PvtWLhdrvFEwFAaFoE11oRJbj19kJwPQYHB4uVTHsr6GlgelSdnlwHIXP/aS+AlndAb4wWrejag8Lr9XvpfwEj9YvXU85pGUZFnooPwbg+T3gOUaZqBUd7D+kd1J4gyjOOh9PpFIMF10BDQwOamprkbNLynJQ5prSNjIwUOa8ryTudThQXF6OiogJOpxNNTU1wOBySHtdkMknyA64Hs9lsCI4PDQ2VOBR68DR11d/f35DhkX2mzOWZynVBGcZxpOLcn5rJZMKrr74Ks9mMm2++GZs2bcL8+fPxyy+/ID4+HocOHYLL5cLYsWPx3nvv4bjjjjPUKQCAhx56CLm5uTj33HOxbt06vP3225JZKTU1FU6nUwB8amqqxG4BHmBKhXDt2rWYMWMGqqqqYLVaxYLv3ehVJw0K8OzrtLQ0lJeXw8fHBzExMTh06BAWLlwIt9stcSJxcXEwmUxC3QI8siYhIQGHDh3CE088gZSUFFx88cVIS0tDXV0d2tvbkZKSgvfffx+vvfYa3n//ffnuPffcg6lTp0p9Dt1WrFiB77//Hi+99JL8LiIiAuHh4SgqKkJycjLa2tpQX1+PAQMGoLS0FAEBAYiMjMShQ4eQmJiIrq4uqfvw7rvvor6+HnfccQcATxYnt9tTVHfTpk244447JCaFjbVoDh06hAULFiAuLg5z5szBH3/8gWeffVZoX2wBAQFISUnBe++9hzfeeEPmkc3Pzw8pKSk4cuSIxAXreTKbzUhNTRXam6+vLwYMGICSkhIxDrGv/v7+R82jxWLBxo0bMXfuXOzYsUPmMS4uDm63WxS9vuZx/PjxePvttzF+/Hg0NjYiMDAQKSkp+PDDD/Hcc88ZaF+MKSwuLsYvv/yC999//1/WSPm/aMeiQhwzdUq7w2kl4gHize3Vhfl8fX0RFhZmyLZCgUcBq139/NGB0ImJiUhKSjJUtWbOfafTib179wolKiwsTAKAaQH0zspBHr6OyyA9iUJZH2LedAptEWR/NG9ZxzUAvXUjNAVEZ0Pxpo/oeAgCAh2nwMOZ2ZE0DUrTvwAcZb0kuOBhw8OVFrz29nZDvIwONpRF42NMr+k9f9qCqr0fPOSpBOkYFh7e+ofjo8dQH6J8r4CAAISFhYnL2O12w+FwiHWxs7MTVqsVmZmZSElJgd1uR1dXF+rr61FdXS18XYJ6FoPU1mVvi7Cu90KPnZ5zjhPHnXOh6WSaZqiBGX+n6S9UAun96K+NoFbTLbXnkmudfHkNMAnY+X0CfDYCMU3ZYYAzU0frtNEcW1rQmbCgpaVFkkzQCMD9RRlC4O/tVdBWaAJpLR8BoyzVVCA913/WNN1Kx2dpGcSx0FQhjo02MniDe+/rueY5plqGe9MieV/+aKqnnhPdD2/Ph1aOtNeLMtk7RsTbGMX/6/kgrYhjz/vosdIeAhpkKNc4luwvPZc0lOn4Dyo4nBPWFtIpwb3jijjG2gul6aQtLS2GcdVZA3W6efZLry+9xzgWjBnRzIT+2H777TesW7cOLpcLS5YsQUlJCVpbW3Ho0CHceeedGDJkCKqrq/H555+jo6MDZ555pqHGwdatWyUg+ptvvkFycjKuu+46AEBRURFyc3MlaLmoqAhDhgzBjTfeCMBDd8nKysLVV18tGS4dDoeA16uvvtoQwD1nzhxMmTLFAE6HDBmCO++8U7j6zc3NKCwshNvtxrp16/Drr7/CbDZLPQgNTgEPpikoKIDL5cKmTZsk4PnQoUOipB84cABffvklDhw4gNjYWDzwwAMICwvDzp078eOPP8q9pk6dKkrN999/b8gyNXfuXIwcOVL6VlxcjAEDBuCWW25BQUEB2tvbkZ6ejrPPPhsmk6cSuS4ut3btWgkkByBZInt6erBkyRKUlpYiIyMDf/vb30Set7a2St82btyIn3/+GS6XC1999ZVhDAHgtNNOw6WXXip91fEqN998MyZOnIiuri4UFBTIme90OvHAAw8IHS4kJAQXXXSRpJvt6elBQUGBGFgCAgJw4YUXwm63Iy0tDTNnzjTI6K6uLnzxxReorKw0zOPkyZMxefLkP53HmTNn4pRTTsHnn3+Ozs5OzJgxAxdeeCEOHDiApUuXHpURrqamRjyQ3377Lfbv34/+0o5Z0dC8egAibAmUKbR4HQCxvISFhYngo4DXwlDzm2kBZC2IkJAQDBw4EHFxcZL+kwdJe3s7KisrsXfvXgF/zKyhD1adppE8fgph1n7QVjOglwbEgGMKA7rT2T/2iY3j4R23QsVCF78ieO/Lm8GDRY+rTJpPb70Gct0JCLRFnJZMvhcA+czDtqWlRagDzNJCSx4PRA2m+XzNpfc+uNm0wkQgCfTWndAAjFlROB/aEkxAwkw1tHprUMK5pwJMpZAZo/z9/ZGeno6cnBxkZGQgPDwcjY2NksKUdBDyMNPS0gTAeAMOrjWOIz0b3sGm2lpOJY+AjpRCgmsq5QTSTEDAvvJZBBj9tdHirPelVuC4LuglBXoNExwvfg4MDDQotDQI0BIO9HLYQ0JCDIqGpijSO0KrM71nWlHgGqbCSys5wa3OBqRBrf4u97aOF/COG9DKjQac/D/liVb0tcKlFRtv45CWE97XcC70O+l50t4QyhUaZvijPQU6JkDPh7ci4e2ZYdO/95alf6VkcJ1oj7NWULjPOEf8rJV570B1KqNUJChnSHvVRirKIv5oDxfpVVTK9FmhqXHac0nvB+dGKyQcc46XjuvhPHgbZEgH5XP6a/v555+xevVqdHd34/HHHxdw7Ovri0suuQSZmZkoKirCww8/jLa2NkycONFgwV++fDlef/11AJ5MQiEhITj77LPl76NGjcK5554rn3Nzc3HeeefJ52HDhuHkk0/Ggw8+CLPZDJvNJn8788wzDbUYzjjjDEycOBE+Pj5ITU1FYGAgMjIycNlllyEjI0MSEKSmpsJkMmH16tX4+eef4evri0svvVSK3gEe7wqTDKSmpsLHxwdfffUVPvroI/lst9sRHx8PwFP1e8OGDbDZbLjyyisREhKCFStWGDJNjRw5EhdddBHS0tLw2muvGbI0nXPOOUeljh00aBAuvPBC2a9ZWVm4+OKL+zSQrFixAr/88gtMJhNSU1NFpnd3d+P9999HcXExkpKScPnllxs8w+zL0qVL8cknnyAlJQVPP/00jhw5grS0NKnFMX78eMk69dlnn2Hv3r3w9/dHWloaZs+ejWHDhsHPzw9paWlyHoeFheHKK68UxSIkJARXXnmlYQ4BT7piu92OoKAgzJkzB9HR0Rg4cCAuvvhi+Pj4IC4uDlFRUejo6MAjjzwiwdtsJ554Ik488USZx3HjxhlqZUydOhWZmZl45JFH0N7ejkmTJuH000+H2+3GggULDCmSk5OTpb4KAHz++ef/Vtrh/3T7t+po0NJosVgM1KXm5mYDB1ZbxCjoXC6XxCRoKzfQCyR4mHV0dIgQ56IYOHCgZAEBIJrjjh07sGbNGsTHxyM6Ohrx8fFS8p3AubGxEQcPHoTZbEZSUpIEhEZFRSEpKckAegAPOCGYY657b28LD6+goCChWRCY83Dy9/eH1Wrtk45E0EkLkz4wqdyQhqSpAAEBAWIhJIVNWwQJxEkBCgkJQUxMjFCmSAXgO7a0tBgsi5oawqJmvCfBhz64mpqaZB67u7tRVVUl40ALGsENY3n0oakttBSgBASaZsLsUjr+hM/u6fGksjx8+LDEsdBTERwcjNjYWOTm5iI9PV0ylG3YsAHbt29HaGgo0tPTMX36dERHR8s8AsDXX3+NgwcPCm+fc+1yuRAXFweHw2GgPDDGh/0lECYooxLE+acy6K1U8Fq9v0wmk9QUoYLdH1tycrL0m3RDjktnZycCAwPF22U2mxEZGSkHhI+Pj3jaCM7q6uokmJLKKSs688CNiYlBXFwcMjMzERMTI0DNbDajo6MD9fX1Qpchf91k8qSv1YVKGadDQ0hcXJxk9tHF/jQY1ICZSrv+Yb/8/f0N+5rrjH/XWeZ4Tx3HA/QqtJSt2iNKDwOVBo4zZTGfpSmb2ltB4KwTZOjkGLS28wzgXqdiRmWPCpp3vAnfhftH1ymiLPD29nKsvBUQoDcehPfXSQQAGDwwTFlLBZOGFqZWZtp2Hx9PTaXU1FSJ6aMcowxjXSGmZG5paUFpaSnq6+vhcDhQUlIisYRutxvh4eEGQ5jZbBaaD/e6VjBpxKO8drlcsFqt0o/W1lYZW++xASBGOK6x/9bc/H/VTCYTvvrqK5jNZkNq0//pvY6ljsZftZ07d2L16tV/WX8BACIjI1FZWYmTTz4Za9euRXh4OKqrqzFjxgxDjIY3t5/N398ftbW1mDt3LiorKw0xGprb//jjj2PEiBGGgoL/qg0cOBD5+fnIysrCgQMH/ifD0GdbvXo1qqurMXfuXNTU1GDmzJn44YcfEBwcjNraWlx88cX4+uuvDd855ZRT8O233yI6OhpOp1PqocTGxmLBggW45ppr4HK5EB8fL94TPY8//fQTdu7ciQEDBqCoqMgQo1FQUHDM775p0yZs3rxZPFne7eeff0ZZWRkuu+wyAL31UC688MI+rz+WGI2+mslkQllZGRYsWICXX34ZgMez9PLLL+O55577t+71/492LCqE+V9e8f+azmxBoUgLnqbAuN1u4d5rjiw5+3256v38/MTqzPzpbrcbaWlpGDp0KJKSkuTg9vHxFGpzOp2oqKhAcXExwsPDER0dLbUQWC9Cu7SZj9xut8NmsyEyMlKABIECn6stcJrWQoHd3Nxs4GF7Ux6opFDpCA8Pl2vI46UFiwcVr6f1rKWlBQ0NDUfxazmGQG/FbF7j4+OpZ+Ln5yfKU0lJCZqbmyW1J5+p62gQ0DLuQCshPT09YpXTnidaz7Q1UlvPNMWHa4Bjo2liBDCklmmKFseU3ozOzk74+/tLRiEqvt3d3cJ/JqBwu92orq4WC5C/vz+qqqoQEREBPz8/xMXFYfv27WhoaMCRI0eQl5cnQN7Pzw+tra0YMmSIgC4CP/a3trZWFBwqFeRzch8wrsLX1xc2m00UEMZeaEsxATfXEBVIAl5eq6k2/bFpD5GPj48URSQQDg4ORkREBABITJWm+7FIImWM1WoVAOtyudDc3Ax/f3/J/BMaGoqIiAhER0eLdUp7DemtpOWYe4zATXPh6dXTlmqdmhQw0ntoNGBMmpZhvEYbCWiQ0d/letCKAICjPIyULZoSxTGl11MHVP8ZlZX30xQql8uFxsZG6S/HkDKGCpb2eFBuauWZjcYCPVZ8d71HABxlmNJxUd7jTe8Ox1crUJRPmhqr78OEBD09PSKLenp6JP5BxwCx4CzXSltbm3i56JnVaW1ZebmzsxMWi0WSSJBappUyHbPHsWTgPfur9393dzeqq6ulIK42XISEhIiRinPBd/CmHfa3duuttwoGWbduHR566CE0NTXhn//8J6ZOnYr58+cjLi7uqKBkHx8f/Pzzz1i0aBE++eQTuN1unHTSSUcF3t5666044YQTDF6OP2vnnXfeMaUbdTgcyM3NFRpSU1MTRo4ciSNHjsDHxwfjxo1DR0cHbrrpJgAezLVu3Trcd999WL16Nbq6ujB+/HiUlZWhu7sbubm5aGxsBABs27YNw4cPR0NDA/7+978b1t/f/vY3jBw5ErNmzQLgCVyfMmUKZs6cKdcUFRVh6NChfxpjEhsbi5UrV2LWrFkGys5HH32E/Px8/P3vfwfgqaJeXFyMBx98EIAnlS/xzKhRo3DkyBGcdNJJWLBgASZNmmTI7sW2fv16jBw5UjIrbdmyBbm5uXA4HHjkkUfwyy+/4L333jN8R89jS0sLhg4dKoHhLDy4cOFCfPnll1i+fDmWL1+Os88++ygq1uWXX44rrrgCJ554ImbPni24lW3mzJm44447cPzxx+Oqq64y0A/nzp1r2JuvvPIKjj/+eHR0dGDy5Mm4/fbbDYYhwFOtfNGiRZg6darMpY+PD9auXYtXXnkFX3zxBdxuN0444QRDPNXJJ5+Murq6Pufqv7H9jyr2aIoCFQYABsGt3Wh9aTzehxoPBs1ztdlsiIuLk8NGc4JrampQW1uLzs5OJCUlSVo1q9UqgX0MLG9tbZWCbswUQ3qON52BjYeWN8D3vk73h6BdWzU18NSWOH0P9k3n8NdAWt9f041oRSMHnUG1zc3NUsyqtbUV1dXVktuaBxGf09jYiMrKSpSWlkrQIuNQCHZ0qlB9CDL+Q9MKdAwFr+fhGxYWZqiPwHfWSiwPXlIuuL54UGowA/QCBh7U3Pi0LFIZcLvd8rmnpweRkZFIS0uDxWIRTmhNTY302+VySQBxfHy8wWOkA+o1VU3HpHiDHO81422ZpWeG3+f6IXDS6VK9hVV/a+yfzv/vHTfA67z3Jj0OXGdMWatjxrg3WJ3barVKETa9J6lgU55pqhKBs7Ym+/j4CJ2TMWR6PfOavuSFd/wO142OO/H+m/b40WijKUbectXb4s+9wj7qoGudshWA4f00KOf3dEyGpnXyh/KESpuu2aB/tCdBz6u3QqWNN3/2wzHRe0Wnx+XeAmA4P/QZpeeb88zP2htDZZO0O608cX9rWak9HVQumJ2KKbjdbrehCKh+NueSMo/j701PYQV0UrJ45vH+uo9cP9pQ1V/biBEjMHLkSLhcLqxevRqVlZWora3FqlWr0NXVhd27d2PTpk0wmUyYN28ehgwZAsCzR9asWWPItpWfn38UaDtw4AB+//13AJ6Yi/Hjx8NqteLOO+88imIzYcIEZGdnw2Kx4Pbbb0dMTAyGDx+OefPmyXwNGTIE1113HfLy8tDS0gLAsz737t2LU089FVOmTMHBgwdxyy23wGw2S70P9o3vzixmNpsNp5xyCsxmM0499VScddZZ2Lt3L3p6epCTkyPejOuvvx5BQUEoLCzEnXfeKcXl2DcAmDhxIi655BLs2bNHzvmwsDDceeedsNvtGD16NC6//HKsXLkSTU1NhviVjRs3wmw2S183bdpkoPQUFRWhtLRU+trc3CzemN27dx+VpvXMM8/Eqaeeir1798o6bWlpQV5eHq677jpERERg3bp1eP7552UcvecxNDQUp556qtC0Ojo6sGfPHvz0008oLCxES0sLVqxYgZaWFowdOxbXXnut4X3X/r+aJ4WFhaioqEBwcDBuv/12xMfHo6ysDGvWrIHb7cbYsWMxdOhQw3d1kpZt27ZhxYoVWLVqFXp6elBSUoIjR47A19cXN998M7Kzs1FfXy9rlo1rVN9r//79hjU6depUA6Xuv739W8HgfR0oOg2hBn2aa0wrtuYiA72AwO12GypZk57FjB7egJeFeGiFyM7ORmZmJuLj46WaMBUNZp5iAT9Ws9YZVLwPPh4wOi0gx0ADbv6O/9IaSUDKoGYNPDS9gf9qnjNBD4OBdSAj40Y0QAAgXgG+Z2NjIxoaGuQQ0xQsAg96MLjxy8rKpNgOi9YQAHHM2W/OqTfo0O+recbMTx8WFmZI/8l0oDqwkjEizARDjwEbAQv7T8BExYXPjY6ORmRkpNDL2traxIPV3t6OiIgIDB06FLGxsejp6UFhYSGKi4vR0tIih3NwcDASEhKQlpYmFiJauAmKNV0CMHLkNZgknYxj4g2i2X/uFR0HwOdq8Nlfm/aiWSwWGWsCM65LJpQgKKSsaGlpEUWDY8/sa1oJ4/1tNhusVqtQq7xjArRc0ZZ8bxDKdR0cHIzQ0FCDoqFjlTRNRcdf6P3OpuUkn8PfkYpHJZyySFOC9Ptpyp6mYGnlQr+PlkP87P2ufSkH3sHPmurFc0HTnrTBgDQu75gPTaNlf7jW+Xs9TkDvfmAMBj/r+DR9f64pjs9fKS/aQ8S51nEfDOqmHOIZpj1Ieq8zKNxms8n9eC5oqh7nVp+d9OCTCqzPGACixPj6+sp8cR2y75rCzN9TXvfXNmPGDJx99tno7u7G/fffj507d+LAgQO49957YbPZsGzZMrz11ltITk7GTTfdhKFDh0otrscee8wAtEmB9PPzQ2JiInx9ffHDDz/g+eefR2JiIq655hocd9xxsFqtuO2222Qeybe/9NJLMWXKFFE0YmNjMXz4cFx33XUwmUyIiorC5MmTccMNN8DHxxNDQa8tAJx77rk444wz4Ofnh5tvvhnp6ekAPGfbfffdZwDu8fHxCAoKQnJyMm655RYEBgbilFNOMRQAPOmkkyRm4tprr0VRURE+/PBD3HbbbbBYLPjuu+/w4osvSl+PO+44Q6A84Em9e9tttyEyMhKjR4/G+eefj7vuugtlZWUYMWKE9O3ll19Gfn4+rr/+ephMJnz++edYuXIlTCYTEhISDBRJALDb7SgvL8f999+P6OhowQIJCQkwmTy1MnRsDJuPjw9uuOEGDB06FEeOHMHdd98tXiQ/Pz8kJCSI7I+MjMRtt91miGkAgAULFmDlypVwOBx48cUXUVdXh1GjRuHqq6+Wa3799Vc89thjAIDo6GhYrVYEBQXhtttuQ1xcHPbs2YOFCxfC5XLh/PPPx2mnnWboa1BQkMTHvPPOO7jrrrswf/58dHZ2wmazwW63w9fXFzfccAOysrJQUFCAe+65Rxg3gEeuP/zww9iwYYOsycTERCkNAHg8RaNHjz56Y/yXtmOO0UhKSjKAJZ2WkRx6HviAxy3Iw5ePICinBYYHCWkKtPYEBARg1KhRGDFiBHJycuDn54empiY5SKuqqvDGG2+gpaUFCQkJuOqqqxAcHIy2tjbU1dUhLy/PQGdobW1FRkYGoqKiEBISgqamJkOQIF3ZFMq01NMixj7Rle7r6yt1Mlg7gQdZc3OzuLsZWExgqqliOsOVBikABOgzrarJZBLvA1PEctxdLpcEt7e1taGpqckQz0DqFg86jjkpQaQJ8V1IFQgICJDNS156TEwMQkNDDWCsu7tb3pteLuakd7lcsFgsQinw8fGRrFBU6DjePKC1Esa1wsQAnZ2dcDqdYgGkYkLwwvUBeMDKtm3bUFFRAbfbLRx9t9stwWaRkZHYunUr8vLycOTIEQwfPhyjRo1CSkqKARQ6HA788MMP+OOPP4TewCxFXAek+fC9Q0ND4XA4hI/e0NBgiNXRFnz2Rz8zJCRE7sc9R6Hd0dHRL9NSAp5DneCZ8U/eFDLtSaAVmfE+LS0tsvboDSOQrK+vh5+fH8LDwxEZGYmMjAwkJycjLi4OMTExkoqWinJDQwMKCgpEeWEMCPeVy+WSRBEEjDabTZRlXZsG6PXUaMoQDS/aYEFQScqRj4+PxPdwvjV9k4YXHdysYzH4Q+8f/0bOPsG+Bt/sr+b6876M5fLOrse1TIqjtzFBB/RrqiGVMypPVqsV4eHhhmQPQK+n3DuJB8E1x5hjSmWwL+OWNoJQzmivhd5rpNDxvZkQgEp/VVWVfCZ9j+uPYEnPBz0XZrNZlJquLk8NjZKSEhw6dAgOh0NomzU1NRLvR1nHGkQ6yJwxGpGRkQB6Mwpy/knX1GmxGVsC9Nb70T/9NUbjz5qfnx+qq6txyy23oLi4GKtXr0ZcXBxqa2sxfvx4/Pbbb0hISPhTbv+OHTswYMAAHDlyBNnZ2cjLy8PAgQOF289xvvfee3H99ddjwIABR72bNgC43Z4sUvv378d1110Ht9uNn3/+GaWlpbj88sv77NufQTJ/f3/U1NRg7ty5+PTTT//y2r96HwAYPHgwdu/ejYyMjKPoQ//qu3/1+ddff0VBQQHuuOMOVFVV4fTTTzcEly9fvhytra244oorUFNTg0suuUSyNkVHR/8lBe3P+jtq1Chs2rQJSUlJktXpr8YmIyMDBw4cQE5ODvbv3/+n1+oYDV5z9dVXY8GCBWKgBDxKWVVVFc466yyEh4dLrI13te8vvvgCwcHBOOOMM47yNFZVVeHOO+/Eu+++a/jO2LFjsWHDBgCeSuaM0TiWuf+/asfyHsfsOyVw5UGjc67zMCGXtLOzExEREWI9Ib+XHGFaf3kY0VJIpcRisSAhIQHh4eHo7OxEXV2dAISWlhZs3boVHR0dsFqtiI2NRVlZGXx9fUU42+12lJSUCDc6KSlJDgNm++EBRs4vD35vPjwPMv6fVlhaMjWvm9cSTNJSqHO403KqA1yBXrc283VzvOmiJ4i3WCwGmgfHlwe4zWYzHJImk0mCSAky+GzOg3bnx8TEwOFwSN0Axg243Z6UsTwQdUpNoDfgkvVTtKJAsMhx1/cjQCf41pmzOO46PSbBDJ+tA8ZJHyAIGDhwIHx9faXmS2lpqfD0GxoaMGDAAKSkpKCxsRG7d+/G3r17RRFKTU1FTU2NvHdOTg4OHDggRShdLpcEcrIolqZqtLe3w2KxiDKkD30CNirPVH4IAKl8UUFtb29HaGioAE/vasP9qXlnxqHypJUpvT/p5aASqWO4tKJGIwjratBSRo8ZAIMBob29HU6nU+QXYKQPcZ1zj/HdCfgoK3SQMYNvvS3kQC8Vi3ufgFgbY7ypc2w65oP7QINHbYnnuHgr4IwDoNzQlkZNVeOeYg0bjpn2PvN3mrbEdc37cP96gxGCbn0PKpXsB4GyVta4JrSRg2OhFRI+R1PIOIZ8V/aB1wK9Ga18fHwMiRi6uroQGhoq40zPBY1l7e3t4p3guzB5AxWHtrY2WTOsOs51a7VaJZOh5nvzLK2trZV1wtgYrkWOlTauMKsi1wIVHnq2OdbeHqL+3p566ilYLBbccsstmDZtmtREGDdunNTD2L17N8aNG4e6ujrcc889GDJkCC6//HKcccYZqKqqQmNjI8aMGSPKV1FREcaMGWOgsHA9v/fee1i+fPlR76HXO/9/zTXXCH0X8NCZGCuzatUq3H777diwYQOCgoKwevVq3Hvvvfjtt9+OundXVxemTp2KuXPnGlLSerf58+cjKysLc+bMgdvtxp133onc3FxcdtllWL58OT799FN88cUXGDNmDEpLSw3fXbp0KVatWoXXX38dbrcbn376KdavXy8AFwAuuugizJkzB9OnT8fbb7+NAwcOSHHDa6+9VpT144477qgYjFtvvVXk78SJE1FUVAS3241JkyYZ4iHMZjNWrVqFZ555Bj/88INhPIcNG4Y333wTp512Gurr67Fv3z6MHTvWYHzT85CamoolS5Zg5syZkoxhzJgxEo/Caz/++GNs3ry5z6QAvGbZsmXYsmWLnPPTp0/Hfffdh6lTp2Lfvn3w9fXF9OnTsXbtWtx9992Gebz33ntFTq5evRpPPvkk6urq8Oqrr2LmzJnYu3cvcnNzsWjRIpx66qlwOBzYs2ePZP3qax32l3bMHAyd/QIw5oSnVZ3gk7+j8NSubX0YeTcKSsZSBAUFGahXHR0daGhoMOQXbm1tRVlZGUpLSyVmg+57bdnRh7sWsPqz/pdKFQV8SEiIuKlJ+aHyo/vGwHJ6NAjqtUeDi1TTA2jF5GbjQaCBBwuCsQqxNzWE6WFtNpuhkBljIXRlZU3r0dxdelyYUUbTyrSlUR/iun9U2KiMePPWNQ3Mm4ahrdvawsrPOhMPm6ZmUdkh+OM6Cg0NRXt7O6qrqyXAnYc0Kzzb7Xa0traivLxcDinOP4PH09LSRIHWdAfS7zgGBL7a4qgtqBoQsg+03tP6qoEXwRG/25+51Zr2ReCpaTk6dkDT8EhV0V5VAIb143K5ZO0xTTI9pBxX7jPSfPTaIw0UMPLlmcyAlBdNuySA5brXe1rPMfuuFRFex2v5O865pupoxcMbvOvfaS8Y9423rNX0LN6fa117Gbzj1HRMhDauaAqW9zP4400X9aZy8Ue/L8dDK3haXmlZpr/rvSb+jMLW13M4FlRUKXNJ+6Tyw+9yrPX4k45HD5NWFHk/ymWuT55P3pQ3TQ8DIHuCckavIT3e/B1lBY2BOvNXf1U05s2bdxQ/vaCgAPn5+XC73dixYwfq6+vR3NyMbdu2oaenB8cffzxOOeUUbN26Fd3d3SgqKsLevXsBeFLXxsbGoq2tDVu3bsWsWbMwcuRIBAYGYuzYsYbAarbKykqhNM2aNQsTJkxASEgI5s2bh6ioKAwePBhz5syByWRCfn4+goKCcOWVV8JkMmHQoEFIT09Hd3c3Nm/eLFnGXC4XtmzZIp99fX1x7bXXIi0tDQCkb5s2bUJTUxOuv/56MRjExcXh+uuvR2BgIIqKipCXlyfvWlxcLJ/p5W9vb8fWrVtxwQUXYNSoUXLtrl27DMrH7t27UVJSAovFgnnz5sFut6OyshLbt28HAOTl5RkCyPPz81FUVISenh5s27YNTU1NyMjIwDXXXAMfHx8UFBTg0KFD8PHxwdixY2G1WuF0OrFz505cffXVQhtzu93YunWrxCWYTCZceeWVyM7ORnNzMzZu3CgKdmtrK7Zu3Yquri5MmDBBgt7Z2trasHHjRrS3twsNbOvWrTjnnHMMaYh3796NiIgI8TQtWbIE9fX1Mo8AUFtbi/379+P6669HfHw86urqsGPHDowePRrh4eFoaGjAli1bsGnTpqPmkevU7XZjy5YtqKurQ2NjIzZt2iSfm5qasGnTJjmH2LetW7eiuroadrsd8+bNM9Co+kM7ZuoUAyApFAHjIc+S7N3d3WLV1lZKUgMIsr0PJab0tNvtGDNmDI477jgZzJaWFgQEBKC2thYHDx7E559/LkI6NDQUUVFRMJlMciBUV1ejpqYGISEhSExMREpKilAP+O5ayBI88jCjBY2HCgU9v8N79PR4KnHrquYEl7qmg7ZU8fDX76LpQ06nU651u91iDeOzdY0QWtaA3gOPAKGrq0uyb+n0m5rnq2NPaPVk/3jwkipGEB8eHm4IpNfWVaZsJX1Cgz++p66OTVqHtjLzfgwgZx8tFotUYNfKoT5cW1tbDfQJACgrK0NZWRkOHTqE+vp6pKWlISUlBQMHDkRkZCRMJhNqa2uxfv167Nq1C0FBQUhJScHpp5+OuLg4A8Vt165d2LVrFw4cOCBChIc2vW4EGZpnz6xqVKTpCdPV07nGmFGNxSm18qJ56f9Omr7/ppaamirgi/ub6Wi5rjl2eq1ruaGVByrA9PolJycjNjYWMTExSEtLQ1JSEsLDww0Zvdra2uB0OkXxpJeMe41gk3uf4DAyMlIMKt4F+wgUNdDXrnOuaf5fp8Pl7wCjsUPz7b2BobdFmuudMoiFN3V8hAbbVCK04qAVOh0E3t7eLoYOgnENuDlfmpqlPQz8jk5zS+qUDqLX86ibNyCmd8g7BoVnCvvLNaNTiHvTpijD+O46Vo73ZcpbrhmeVTx/OC70ggAQz39YWJhBLrS2tqKyshJ1dXWoq6tDaWmppLulx4veUW3M8T43OE+klFLR0MU8efbqLFQcO76PtpL2l1ZdXY05c+aIpZvN398f4eHhqK2tlQyClNPPP/88MjMzj0qHazKZsH37dixcuBCLFi0C4Mly9MEHH2D58uX4/fffMW7cONTU1CAwMNAQkMt4iyVLlmDNmjVYuHAhduzYgdmzZ2PIkCG4/vrrMXToULhcLlxyySV48MEHMXjwYHzxxReoqKiQtKlWqxUul0uyDoWHhwPwMD727duHm2++Gd999x0AT5kBel4+++wz5OTkwOl0Yty4cfjqq68wZMgQqWAOeGIimKWvr7Zp0yZ88sknYsWPjIwU2aFbfHw8tm3bhpNOOgmFhYUICQmRGFkaYnXmLpPJE5vS0NCAM888E8899xwGDRok6zMoKAj79u3DvHnz8NNPPyEmJga//vorbr31VnzzzTdyn7CwMPj4+KCpqQl79+7FY489ho8++sjwbmazp45JbW0tHn74YUyePBknnHACIiMjxaDEdtddd+H888/H+PHjsW7dOvz444946qmnYLfbUV9fj6uvvhrz5s3DiBEjYLVacemllxrmMTAwEMnJyVi7di0uuOAC/P777wgNDUVeXh4uu+wyrP1/geS6BQYGHjWPum9co6SX0gPn/RnwJBVYvXo1Ro4ceVQRx/9UOxYV4pgVDea/125+nc2HVncemDpYjnQkAma6oylco6KiUF1djdDQUAwYMACXXHKJUJRIU6qursbu3buxZcsWHDp0CLGxsUhOTsaAAQMEEJI6VVZWhqamJthsNqSlpSEuLs5gdSTgBmAIIiZA0Jx5NgJKf39/gzapuXI8lHp6egTc+/j4CIDR1Ac9VrSmMluU9grpwlHawk8woBUWUlHoaSHI0J4mbQ3ms3X2HwJiAAYgwfszqJ7ufwIYPR6kOzFlKIEb31/TOeil4PhrjwmpKZwzbkpSDbSniu+rLZgEC42NjThy5AjKysoQHBwMm82GzMxM2O12+Pl50g2XlpZi7dq1aGhoQFBQEKZNm4YTTzxRxtHHxwdOpxO///47tm7dKpXHCfhaWlpEMWKfbDabzC29c3w3pmmlYkWaIMdcg4m2tjZER0cbrN79VdFISUmR9M5Me831S6WS3ilSAHXANcEYASJlDEFVamoq4uLiEBcXh5SUFERGRopHz2QySWE+h8NhiPchUPP20rHIGWOcqOToTGlUwkmf4g8VDcoU7fXQXkMqpFoZIPD39mrpvauVEpPJZIgzYDC2zhinLfiazknZqL2WGmwDvVn49DxpD4FWWFwuT5ph7bmgB5jjqetq8NneiSW014HGBb67pk0xroPX6sKYPD8oc9lnPecE35RnHEPKSp4r+od/49piCm2OH9ej3W6XNLNUmJ1OJ2pqalBdXY3y8nK5p9PpRG1trdB8qeiRXsZ3JJ2QHlk9VlRs9LhQweUZTNpXd3c3Dh8+/L+6v/8vGs8R73byySfjm2++QWxsLF566SUkJyfjhBNOANC7R/r6nreHWX/men7ooYdwySWXICsrS76XnJyMQ4cOYfTo0di5c6dc/8cff2DLli24+eabDXQ+Pt/7eT/88AMcDgdmz54NwFO/ye12Y+bMmQaZxBTt8+bNw6effnqUvPL+bDJ54k+eeeYZQ4G+v+r7vn378Pnnn+Phhx/u81qXy4Wbb74Zf/vb35CYmAi3241rr70Wjz32GBISEkReWK1WVFZWYvr06VizZs1R76bvd8YZZ+Dzzz+X0gS6vfvuuzKP3u/KNmrUKGzcuBHJycmoqKiQcS4sLMTixYuxYMECw5h4z0NSUhIOHTqEkSNHYvfu3fL3DRs2YOvWrYZ5vPLKK/H0008jMTHRoNT31b+++qrbBx98gNjYWJx88skAgLfeegsZGRmYOnUqAGDhwoXIzc3Fcccd9y/v9Z9sx6JCHDMHQ4NfwNNZFu4jqORhzgw/FOrBwcFobGyUA4SAkQeB0+lEWFgYYmNjER8fD4vFYuDMdnd3o7S0FIcOHUJJSYkc1klJSZgwYQL8/f1RVFQkdBVfX19ERkZKxgC+hz5QaDli8T0qPZq6wUONBzBBIg8YTZnhGOmCelQEtFKhqRK8FwO/eUARcFAp0wuLsS4cfx48PNT5nkBvHQ++I5VBbdlk/91ut+G52v2uv8s+6/f15vzqWB4N/rludFC0Vi60m5+ghGNGkEPAxb9pJUYXc+P7OhwOuN1uWK1WFBQUSNG1mpoaJCUlITY2Vt49ISFBPHIbN25EamoqYmNjERQUhKamJlitVuTk5MhhQmWGFsPu7m4BUuRpcyxYCJHWX4fDYcgspcERYCy2xaBpKqxMXtAfm47toQGCa4xrVYNUji2bdxY3jicDtUkpDAsLg8Vike/+f9y9d3hU5dY+fM9k0pPJpPceklAEBKSjdOTQPdjBhmI5FkQE0XNQRGxHiuI5oiiIDQQVQVFBkSIi0qRDQhIS0vskmfTMzPfHfPeTtTfRw/n9vut9zfdc11zJzOzZ+6mr3mstCqLMyFZdXY3a2lpFn3x9fQFAU8SOe1XSD3k22GcaIPSWd2m0IEZf9l1/LdAO46GFX9JBKWxL4i4Ffj5TDxGS/ZC0kMYjrgP7KeeMQrpeiJFKvoy94TrpIUrSMysNVhyD9IBI5UXCw3iu9bSHxgcqAJImS6iVhHzxvV7BpyLM58r6QV5eXpcFrDK9uPSWyFgyesY4X8yG4+bmpugoaQGVYelxkoU/ARecQipQMqaJc8LfkX9ybukB4fM7Y+NYBg8ejBdeeAETJkxAY2MjDh8+jFGjRsFms+GFF15Q4//qq6/w1ltvaazJjz/+OLp3766Kv3V0f/n/unXrLovJKC0txXXXXYfMzEyNQjx79mzU1dVdZrTgNStXrkR5ebnKbjRv3jwN6uGpp54C4PKKbd++HS+88AL27NmD1tZWjB8/XhmZ/qjffOa0adNUPQkA+OKLL7B582Zs2LABALBx40Zs375d1aW4/fbbL6spcuutt+LGG29U2aA+++wzxf8AYNu2bThx4oQ6y4ArGdDIkSNx5swZjB49Gk8++SQmTpyIl156CY2NjfjHP/6h+nvgwAG1bs8//zz8/Pwwd+5cAFDryPEtWLAACQkJmviUjIwMDB8+HGvWrMEHH3yATz/9FABw8803awrbbty4ETt27MC6des088V1JKwpODgYW7ZswYoVK3D48GEEBARg69ateOihh/DNN9/g7Nmz6gyNHTsWc+fOxcSJEzsU/ocOHYrFixdjwoQJeOaZZ+Dt7Y158+YBAJYsWaKJt3zppZc0ML3XXntN8SW2lJQUrFu3Dn/9619RVlZ22fP+rO2/Sm9LoZEMSVrJKfiRgEuGrE9rKBm5DP5j3QIpDFPwKCkpQVVVFVpbW5XrnbEKtN7U1dWhsrJSFeFjKkG9oECGQispcHndDykgs8/S4kXirWfoUpDmvPGeQDvGviNhAWiH0dCSLwUD9lPCKeT3/I795z1lHyW8g01aESWDYz+lAMKgfamM6cfHsfN/PRad35FBkwnzel4nx0DFkHEyTC8qU4zKudDfC2iv8m61WhW8zul0IigoCKmpqYiJiYGvry9aW1tRVFSk4FYUyACXuzM8PFylJ5QMRp/RhwIfBRBp0eY10gMozxbHwXmXgtGfyZrx3zb9nuH49d/LdZMCqYSUSMGTECdZ40KPZyc8iqlC9XPOZwHaIGGeCT1ciXuso3Moz7m8nxRgOxoL15jnRsKa2D85R7yPNCZIuiKv4+85HxyDNBTIZ+vHxe9khfDfe1H5kmnCuf/l2dQ3yVuAdqODjB3h53xJbwr3jX7cck1knIdeQJceHjl+vpdnmGdcek/ITzjHjBckpErCULlfZVIBKlE0Eulj3QBt7SA9HaFnn4YK/Z6gt5nxTp2x3XXXXYiLi0N9fT2ysrLgcDgwfPhw9O/fHwcOHIDdbkdWVpaKocjJydFYym+55RYEBQWpWho33XQTunfvjsDAQNx1110KsRAQEIC77roL/v7+yM/PR2ZmJu666y4NtOnnn39GQ0MDunfvrmIDTpw4ocnkNH78eAwZMkS9LygoQElJCdzd3XHHHXeguroazc3NmDFjBtzc3HD+/HmcP38eDocDWVlZqtaE0+nEwYMHUVFRgcjISNx5553w8PDA0KFDMX78eBgMBtx6660ar0tycrLqL+AqXlddXQ2LxYK77roLFRUVGqhVfHz8ZXVCqqurNZ6v4uJiHDlyBAAwYcIEJCYm4tChQ2qsQ4cOhd1ux4EDB1BTU4O6ujpkZ2fD6XSisLBQI/zz/gcPHoTD4UBxcbFGMZLrCABdunRBz549Nb+32WzYv38/MjIyNDCjI0eOaOJNcnNzNWOdOnUq+vbtq9ZxzJgx6NevH9ra2nDhwgWEhYUhLCxMvW9qakJpaSl+/fVXdY/a2lo1NsAFDeY6sm/coywjwJaZmakZG2t53XnnnfD09ER2djZOnjwJwKU0paeno6WlBRcuXMC0adNUbZjO0K4YOhUVFaWIG4PiiGdta3NV3mZwJa36VEQI/5DBrr6+vooQNjU1ISIiAgMGDEDfvn0RERGhBF+73Y7S0lLs2rULeXl5sFqtqrZBjx490LVrV+Tl5Smsa2FhITw9PZGeno6oqCiEh4crVzLQbi1jnIGnp6dyk1MY4bikhYtwDofDoYJN+ZneGqkXAMgEKLTQikYBVuad53VkmLLCLtBu0ZeNTI2WfjIaCi5SSCFemGMD2jP+yJS/ABQTBaAs61FRURoBSQqEFPhkYK/e2karnJubKziR+FHuHZnrngzZ19cXFotFU11cQmYkXENfZ8RqtcJqtaK8vBxVVVUoLy9HY2MjPD09kZqailGjRuGqq65CaGgoNm/ejO+//x6nTp1CfX09rr76agwYMADp6ekICAiAzWZDfX09qqqqcOzYMezfv1+NhymF9ZZTCiT0iHD/1dXVqbgB7hPOC71o/MxoNCrBg3vr9yq4/tlbTEyMRqCjZUh6L2RGn7a2NiW0MtudFPKYvYUKYExMDCIiIhAWFqbS2cr7FxcXa2BThL1I2BvQ7p5mKlYW+dQHDNNjSm+MviAdjRLSCMHzLYv9kWbyGg8PD00WK6lI8LcANMotjSCcNxp4KAizX2x+fn6aWBE+Q08LJexLCvfSUykFYanIAO3KkTQqyOKJ9Iaw0ZMrBX7Zb/IGQBvfxvESvsp1lIoO51/OHTMR0svD33McnFPGlTGmiPPDavZmsxkmkwkVFRWKp7i5uWJbmLDDaDSq+zU0NCihk+luL126hJKSEgXxpNeaNJIpvrkH2traEBAQoLyrbDwvzOonFWgJT9VDVTpDq66uxsyZM7Fz5074+vrCarVi7dq1MJvNmD59OgAo+UJfEM5oNOLs2bN47bXX8O6778JgMOD06dN48803sW/fPvz000/o1asX8vPzkZqaioMHD6JPnz7Izc1FQkICjh07hkGDBiEjI0Nz3wcffBAPP/wwevTocRl//u6775CRkYE5c+bAYrGobINmsxkXLlzALbfcgoCAAKxatQopKSmXpUUFXHzEz89P4fmHDh2KL774AqmpqViyZAmSkpIwadIkZGRkYOnSpfjwww8REBCAAwcOYMWKFSr+JCAgAE1NTUhMTMSBAwdw9dVXIy8vTz3n0KFD2LJli8oiZTablfJqsVhQW1urzm9NTQ127tyJ06dPKw/E9u3bkZOTg0cffVSNlTIj+y4boX82mw0Wi0WjlHe0jq+//jrS0tJw/fXX/8d9AkAZnDra57/88gu+/fZbVdV837592Lt3r6pqvmvXLhw7dgzz589XY6fcIsdiMBjUWCdOnPiH68hmsVjUeZbv+/Tpg6+++grp6ekKwWCz2XD27FmsXLlSreOpU6fwzjvv/C4k7n+yXYkKccWKRmhoqGKEMkiXQqnMlmQymWCz2VSGFjIkWttramo02HsfHx/07NkTvXv3RpcuXTS4xoaGBuzevRvHjh1TcKuoqCgMGjQI6enpiI6OxqFDh1BeXo7S0lKUlZUhIiICPXv2REhIiEYposWJQjOFDB4kCtwcC4l6Y2OjCu5m6kcyafaTY/fw8NAUlAOg6lXwJetjAFBVvCUmmt/JbF9UeuhmJ+yJjJcwLArtZEaMGSCMgEIKrewUqigkyew5ZMKEmcnv2F8KjQz0JpPmdXyWtC47HA4FnampqVEZF6Sw7nQ6ERISooqyAe0F+rimtO45nc7L8u8DQHl5ucqLT4LGoM7U1FTEx8crxTUrKwsHDx5UdTWYmKBnz56KcFBIqKurw9atW1FcXKzS58r+sBK1FFQkrp1xSTL4WXoJjUajiuHgnqGg63Q6O2UQJ+CymEkLNfPZs9GbJ+M2SIwp0FHJqK2tRV1dHQIDAxESEoLY2FgVoxEYGKjipAgdslqtKCkpUUpnc3Ozijdi+mEK8mQoLKTEF5WDjiz1JpNJo+hK74FM5c376wsI0hBAxUNez/MphUY5Ns6TDOQmHJR0jTSQz9JnPJLQLOnxkQYTqSTJ91xP2R89fImCPgtTyXMuvZ4S5sW50Xs6ecZpoaciUF9fj/r6eo03Qip30ntFGtvY2KjxejO2RXq8WKC2vr5ezSkTbri5uWniTQiFbWtrUwI/9yIVUv7eZrOp4rPl5eUoKipCeXm5mida16kclZSUqAQaQHscEddPxne0trYiNDRUKUScH3m9viJ2Z2hUsK6//nqF7WewM/nn2rVrERcXh9GjR1/2e72SLN/TEMf2n96zcS9LBZuN+y0gIACFhYW4/vrrsXfvXvVsaWTq6PcAMG7cOHz22WeIiopCXV2d2sfS205jlMPhQJ8+ffDzzz8jJSUFhYWFaqw5OTlYtWoVVqxY0eFY9HPz22+/YceOHXjzzTeRk5OD/v37Y+jQoViwYAHi4uLUvpRjAFyBzIWFhZg4cSLCwsLw73//G5GRkZq4BsAFwzKZTLj55ptRXFyM2bNn47PPPlPf69dRjvVK2pIlSzBlypTLvCAdjfX33oeEhODSpUsYPnw4unbtipdeekkTj2I2m1FcXIypU6fihx9++MN1BFz7t7i4GA899BA+/fRTmEwmFBYW4sknn8SHH34Ik8lVf+df//oXevfujSFDhvzHvv5vtitRIa4YOkXLDIVtWplodZOZjfRCMCELZA5kdkA7fIDCAX/PjDM1NTXIz89XBeA8PDwQGBioKmSysBQtlGazGSEhIZpsIGRmBoNBA6mi9Z1CMsfEbEi0qtHaR2s7CTZz08vDJi1rFDxlmk6ZSYaHRrrlpZACQJNthn3Vj4nCJ/HlZMwUfvkMBi5Kq6x+85Lp83995WUJI+N1FABkXIH09FDgkvAiGehMKAvQTrDd3d2V14kCDOecwZkUNOkNIiaanidaQZjCVhZZpLJVXV2NoqIiFBUVwWQyITExEV27dlU1MMrLy1FYWKiEfq6P2WxGWloaoqOjFZyL6y2VDrn3JWRCZtAymUxKIeY60ILFquO0llIp6axNYtXtdrsmlosvGRAu41ZojW1oaFDF6BiT4efnpzwPPN96IZrnkvtSn7KWwin3nsVi0dxDehT0Fnw9NEYPjaTlXdIFvmefeKYlneSZofIOtHtbSH8l9EpCJOWYpGFDCqdU7CXciZ4aKZBLSKWcE+DyqvW8B1+y4CK9J1KB0Mex8TMZH8M55PmTc0LjA8fOMXJ+ZPFD3k96QujJkl4DjoNB/uyTHI+E1DU1NamCqgA02eVaWlqUgUMWJSXfDAgI0KSHJ11m3QvuMbvdruKJmGad3jjOLfkUvVAyo5uXlxdCQkJgsVhUFsbO2KjEHj58GJMnT0ZjYyOeeuopvPDCC+qaZcuWqboFn3/+OcaPHw/AtZc//vhjTJw4UV3b1taG++67D6tXr9YI3rGxsdi+fTuioqIwc+ZMvP/++2htbcW7776LWbNmISwsDDt37kRSUpLGi7dq1So88sgj6j7ct3V1dZg0aRJOnjyJUaNGYcuWLQCAF198EU8//TRMJhO2b9+uSbnKduTIEUydOlUZQXkG5P2NRiM2bNiA8ePHIzMzU9UHcTgcSE5Oxs6dO/HEE0/giy++QHx8PLZv346IiAjceeedWLt2LQDgnXfewcyZM9VzH3zwQbz77rsoLy/HX/7yF2RnZ2Pr1q24/fbb4XQ6sWLFCjz88MOXjdVms2Hy5Mk4ceIE9u7di+nTp6O1tRVLly7FokWL1PVLlizBc889p2gMz7p+HfX3NxgM+PTTTzF58mT13YYNG7Bz506sXr1afbZ+/Xo88MADAFyB5TNnzkRUVBR27typ0Blcx7i4ODgcDgQHB2vW1Wq1YuLEiTh37hx27NiBm2++WSPgNzQ0YPLkyTh27JhmXZ5//nksXrxYXbdo0SIsXboUra2tmD59OgYPHoxXX30VdrsdN998M3744QdlMOU+evzxx9Uelc/Uv/+zt/8jiYWav8T5cvH5Pa3hErMOQMOoKSQHBwcjPDxcWbpI1Gtra9WL9zeZTCrI2+l0VUKtra3VFGrz9/dXDIKNFkgKrnr8rhSkqS3yc5lClsoHhWxCxTrCRUvcr/xcauWcK8JlpCDEuZCWQTIpMh4yaTJV6RHh/SgE6LODsT+8J4UIaXWlJ4FzL+N0OsJ/cz/I+0rMNO/DQyLjd/hbrhMZIZ8pq28T4iDnmfuM1kIKVBTOKdgTMsPMUY2NjbBYLCpbUUtLCwICAlQ1dqajDAsL01gko6KiFAaV2ZOkZVcqWJxDKTRJpZvCkoSlyHnlXuN+6axNerg4Fs6XnnBKTyHnBYAS0nn2CJ0ym83KwEBBD2ivbaCPy6BhhGdfKrky25sU0vV/ZZ+lsMuXXGe5H6TQz3nhcyTUUm94YJP4e3ke5bmW9yMtk8oKm6Qv+mdQaZdKMNeMn/N8Sdom96gUInhPOWbJG/hcPe2U45fKBOdWv6fktTzzci6kwURCsbh+UlnkMwgDlWtGuiL5n0yH3tLSgvr6ek1MoPTUAFD712azwdvbW5OYhPBTqdSR3uv5ChUe0hH+Tv6WfEOuaWdtFRUV2L17NwAoQyQbDUoAUFVVpYlHqa6uviw+paGhQaWWnzhxIrKyslTxXELXCJdhoVl6hNra2pCWloa0tDRs27YNtbW1sNls8PT0VFbugIAA9OrVS2WUYk0wp9OVoIIKRGVlpSZYf9q0aTh48CAKCgqwe/duTJs2DYcPH0ZTUxOGDx+OLVu24Oqrr4avry/27t2rxlZbW4sffvgBU6ZMwblz51Th43379qGyshIxMTGoqqqCw+G4bGwyFS4LSjY3N+OHH34AAGUUBoCamhrlJZsyZQp+/PFHVFRUwGAwIDg4GO7u7igpKVFxGaxcz3bixAkALsX8888/x6VLlxAREYFhw4bhyy+/xJkzZxAaGoqbbroJX375JXr27ImAgAD8+OOPl61jdXU1DAaDpkQAMwbKsdntdlRVVWHcuHH49ddfUVRUpNYRgGZdU1JS0KNHD3z55ZdqvDKtbHx8PPr06YOtW7dexr+I0JDv6ZXevXs3evTooWCPe0RaXIPBgClTpuDUqVPIysrCzTffjK1btyr42rhx47Bt27bfTVn8Z2xXDJ0KCQnRENigoCAFNXB3d0ddXZ3G+u/m5qa8AkajEf7+/orxMi2f0WiE2WzGgAEDMHz4cIVZ9/X1hbu7O/Ly8nDq1Cl8//336mD7+vpi5MiR6NatG9zcXFmFfv31V0U4PTw8kJaWhoiICCVYE0NHKykFVArW0itAxkBiT8Issc5eXl4aSz9rHuiJt3Rv8r0eXkQGRWHIZrOpjBW07tJKTnwvmamnpyf8/f1Vut66ujp18KhZNzQ0qJzvvr6+GhgXBTcJgZIxHKybwUYIj2TY8nBxHBTOeKikh4XXAS4lo6ysDDU1NSpLEyED/v7+Cu7BxtS/HFtTU5MGciQx5i0tLcqjRYGAa1tZWYmcnBxkZ2fD4XAgMDAQgwcPxsiRI9HQ0IC8vDx89dVXqkJsQEAArrnmGgwcOBBGoyvDDfOqZ2dn49y5c8jKylKWUJPJpOAYtJoC7XhTzhnxq15eXqirq1PeDj8/P9TW1l4GeeN8dgQ56iwtPT1doxA7HA5lPae3gkYBg8GgUgjT6+Ph4QGbzaZoC9NcR0dHIy4uDnFxcSp+iilj6f0qLi5WXrD6+nr4+fkhLCxMKSSsSyAhPkzLSkWdnlsZXwBoK0tzD/KMS8W4I0MLLeVMtKCPTZDxAlK4l83hcCjrvPSacK6pGJDmSKVY7ynjvfi/jBWR0CmpCJJ28xoZIyEtlTzjrM0h6QK/J13Re2Yl3ZBzS+8lPZtS0WMf5DmSBhOpaEjDjFRwpGLT0tKi6kTx/4qKCsXnKODS+MR4IFn01Ww2K+gZ93F1dTVKS0tx/vx55OXlKaGEHgvut5aWFlRXV2vghGx8ntyrfn5+mkyKVqtVjddkMika15ka9wEh0PqaD4DLMp+QkIAbbrjhsixhADSp54F2T2tDQwPOnDmDtWvXYvny5SoNOfllQ0MDfHx8lLGD7eGHH8YjjzyC9PR0BV8zm83IysrC2LFjkZ6ejueffx5JSUkKTv5HGH43NzcEBgbizJkzmD17NrZu3aqyaz722GMoKyvDl19+icTERLz00ktISkrCuHHjLpunzMxMLF++HG+99Zb6XI61o/ey/frrr9i5c6eKW/D29sb999+Phx9+GF26dFFj9fPzQ3Z2Nq6//nocPHgQAQEBuHjxIm6//Xb8+OOPaqyMsyR90b8HgFGjRmHz5s3o0aMHysrKMHDgQHz99ddISkrC4sWL0bVrVwWl0q+jvj377LOYNGkS+vXrp5kXKmZff/21SuXb0bred999WLhwIVJSUhT6wNPTU+25m2++GcuXL0dCQoIycPxRkoXf27O+vr7KE2k0GpGTk4PFixfj5MmT2L17N7p06YLS0lL07NkT+/fvR9euXTWB5f+b7UpUiP+qYB8L20khipMvXeoAVIpKSez12ZhMJlemqTFjxmDgwIFKwQgICEBWVhZOnjyJY8eO4dChQ0q5iYuLw4033oioqChVFfzAgQOoq6uDv78/oqOjkZiYqJgbrfLyuRISQC8IGQOZGBk2Nx3d0hUVFaqftHwGBgZehnOWlmvJpCUchAInFQAGG0srptPp1BSpk/Po5uamMLisw2E0GhXDJdOllZcueWnJJdGTzFQvMEiBQGIP+Z5ChYR/cdwSYkErHe9NxkdctQzO9fPzUwXHWAuAwhIAZUn08fGBwWBQ18i+MzUcPVHcny0tLSgpKcFPP/2krB/h4eGYOXMmvL29UV9fjzNnzuDTTz9FXV2dglT95S9/QWxsLLy8vHDhwgWEhobCZrOhoKAA3377LcrKytS+ZnAY4CIuzKwmFaXGxkYF06FixrUHoNJaUtj28/NTVsnOmP8ecOWel8HR0rtIwU8K7IxJIo0BoFJlR0VFITY2FsHBwQgJCUGXLl0QExOjAo2p8FVXV6OqqkolBaBwFxgYqGga10SfDYjQLioH8tyzT3rID5Ve7ncp1ALtgpJM7EAvnoTkeHl5aZRqoF2hkV5UzhONOBKKKMk7z7PdblfPkVZ46Skl/SAtlJ4fxh5I7wbPIZ9JQVbvlZJwM55T1p/g3qcixbOst8CzT6RbPDeMhZLF9vTeGtJ7vZLB+/BMyt/IpA0M4gagDBd5eXmadU5MTNSkerdarYoe+vv7IyAgQMVwcS82NDSguroaFy5cQGFhocZqLueZz+Uak+9yXriGcv7IP7jO0usrA4E7S+O6jB8/Hhs2bEB0dPRlgpu7uzuuvfZafPnll4iPj9dkGwKAFStWoH///iob1CuvvIIRI0agf//+yriXkpKC48ePo0ePHrjllltwyy234KqrrsJvv/2Gr7/+WgnfQLt3q7m5GT/99BOOHTuGxx57TBk7yAubm5uxY8cOFBYW4p577vndMU6cOBEffvghkpOTVWwqAJU0gmMkXyH/0DeORaIkXnzxRVx//fXo06cPAJcw/te//rXDOAbKB+T7Bw8exMGDB7FgwQI0Nzdj3759OHHiBB555BFNLSA+e+vWrSgtLcWdd94JANi6dSsaGhpUzZBNmzbBZDKp1LkAFO/OysrCnDlzsHHjRhVrqh+rfh31raO5SU5OxunTpzFw4ECcOXNGje3QoUPYvXu3Bqol1xVwZTxbunQp4uLiNMbZ5uZmfPXVV7BarRromb6NHTsWmzdvRkxMjJIPTCYT8vPzMX/+fHz44YeadXM4HGrsQLux+o+U1P/pdiUqxBVDpyjgkJCR6ZBhSJe8/BxwEUbGXJCwu7m56gGEh4cjKipKk4XJaDSqXPe0HgUFBakiXO7u7mhoaEBxcTEyMjKQnZ0Ns9mMgIAAuLm5oaCgABaLRVnx9TnXuenIaAIDA5WljQxXKkkcG4V0mcUFgKohIhk30I7XlnhiMgsSHlo2DAaDcpsTxyuVE2nl0wsbdLGzn5yfqqoqOJ3Oy1KqSpgGGRmFG4nzlrAWNgY4U2CR0DLOp16IlPfjPErrAOeG89Da2qrwyXqlT96T4+ZvmDfd3d39MuWS88K++fv7Iz4+Hrm5ubBarbDZbMjOzkZqaioCAwOVpbygoEBht2tqahAREaGEBQpgoaGhiI6OVgKAw+HQ1I3heKUg53A4VD0MwvEkNpzFDo1GoyamRBL9ztjoKeP/XDsJMZKKKAm9hIxQGWCKawpuVNiA9jWnhYoePzJpCW+Rcypx91Lp43t5vjuylEtrK8cAQDErGX+hz54kDRTyHvRsShpL+iShS1K5534irdVfK1O08hoq64x34tmj4M65k5A3Nu5hvdeSdEBPS6k0UsiX86WHlsk11UMKJWxMQlClJ0kaKHithNBxbKRr+pgh3pu0hZZP9pswXp7rmpoa5fnmHuGzZOIOnnV+zuxUjDd0OBwqVz73Fw0sjCNkYVt5ngCgvr4eNptN8V2uucT4d/Z27Ngx3H777Uqp7t27NxYtWoSZM2eivr5eVemuq6vDww8/jNjYWCVEvvfee/j888/VvT744AN88803AKCEuMLCQtx0000oKSnB5s2bceDAAQCuGhwyRevrr7+OkydP4r333gMALFy4UEGRZNFOygvPPvusMpJ99NFHqnbEI488ghkzZqC1tRWHDx/GbbfdpvZS37598fTTT2PGjBmYO3cuAgIClKKj5wcPPvggkpOTMW/ePPX8pKQkrFixArNmzcJHH32EnTt3qus3btyI7OxsbNu2DQ888IAGGtTS0oJp06Zh0qRJuOeeezB//nyVSAMAnn76aZXRSS/8Njc347nnntMI+S+++KJG6aFByN3dHR999BFWrVqF/fv3w2az4c4778Tp06eVIaWjserXcdasWejZsycee+wxAK4aIAMHDsTf/vY3vP3229izZw+2bduGm266CdnZ2Zr7Pfnkk6ioqEBQUBDWrVuHJ554AllZWZr+/vjjj7jnnnvgcDiwdOlSVFRU4O2338YXX3yBb775RqX5BYC///3vcDqdWLp0qfrs+PHjuO222zQeGLvdjrvvvhtnzpwB4KKZ7777Lj766CPs2LEDLS0teP/99/H555/jq6++QnNzM9auXYuvvvpKxfn82dt/FQxOYYkCtRQCKMTRba93PUu8K5mdv78/goODERwcrKw69fX1ylLECr4MjGXgJwVswmAIk6HliW5nCr5knhQYSaT5GSEHeswxBWEKOBTagXahmsGGUtCXAqWcA8ko9fhkoN3ro7fwSpytZOAyyFwKsmSOFBLI7KXCRGurxK3L+7NJ5iWtg1Jh4f96bLT8Hfun3xdU7qQ3TMabSGFTCisUDGTlY/aBL6lc6oUVeoOCgoLg6+uLuro6ZGZmorKyEk6nK9ibGYdoFWQtl7Y2VzV7QhTMZrOypEvPD9eGnj+9QEDPlBRmeU8ZtyCFAqkAd8Ymg6a5R6UiLgU/SUf0AjgFPp57mfVHCohSWJYZvkizJBYf0AY2A+2CqfxM9k/2Xb9eEmqqhwjp15dnUO573pf9kOOSfZP7W45P7xkgzSY0jUxeetqk0C2hVhKKJcfM/uuhWvyd3ovDpqcZv3dWpbKn3/ty/Pwr10/SUTkWZueSMDM9/ZZ9knA2k6k9qQZplqx1QQ8050GOm/Mr11hC8Ly8vJTizCQQAJR3icqN5CPcM+Rzcg/p9yfppoQBd8Y2ZcoUREREoLS0FNu3b9cIgnp6KWmu/Pz06dPYv38/AJeVuampSWWCGjNmDFJSUmCz2fDVV1+pjIXcl3v27EFbW5uq6Ox0OpGUlISxY8cCaE9o4uHhgcmTJyMwMBBxcXGq1sXBgwdx/Phx1V/ew83NDRMnTkR4eDhKS0vx7bffXiZD8H+Obfz48YiNjb1sjnjNuHHjkJSUpPlMzsvo0aPhdDrx448//u5+4J4DXClgGxoaFExr//79qKmpUelmhw4dit69e6vfHjx4EMeOHYPJZMLEiRNx8eJFVYMDcHkRWJdCP8bvvvsOYWFhl3krAgMDMXnyZGWwknIHIWuTJ09WBgB5Vig3fvXVV7DZbEhJSVHrqD+rHbVLly5hx44dmvvy3r/88guKi4sxYcIEjVymb3xO3759MXjw4Mue3dHznU4nevTooaqG6/f5n71dMXQqODhYMWfpIqO1zcPDQwX68DMSNKfTqbB4tNASjtK1a1eMHDkSZWVlKlNGRESEqlNw9OhR+Pr6Ii4uTuXJv+666+B0OnHu3DkcPnwY9fX1SEhIUNCSuro6hIeHK2JtNLbX/KBQyvzNTqcrvzuFSmmdp+DDzB204Ms87WRqtKjqXZWEP0ihgwK13mtC5mCz2TSQIXpZ+Hzi/IjfpZtd5m6vr69HcXExmpqakJqaCn9/f2Xh1DdazMns5Ca22+2a+ie0jvElrdH09pDBycw2FDqo8Mj/KQhSQaQSRRw3BQnCnCQsha5F7impYBACwWcwoLq1tVVZFEpKSpCXl4djx47Bz88Po0ePRq9eveDn54dt27bh3LlzKCkpgZubG0JCQtCvXz/069cPPXv2RGZmprKIlJSUYMOGDcqLREsqlVmmB6biIYUszimZDV2xMhsOK47TM9UZIQ8AVJFNWuBpSKCwyyrqPKeBgYEajD+FLW9vb6SnpyMiIgKhoaEIDQ1FXFycilnivikqKkJJSQlKS0tRX1+v9rHdblfpr6lwE55JwZEufEKaKNBJq7yEUMl034C22jdhNLRoEy4pA/zpFaVHjueHTSoN0nsCtNeWkcYVxvhIAxH3HY0yQHvsAQ0yzGVPRZD53CVt1Bfa1CtBFJL5LH3cg96woG9SmaPxRW+0kgqETDxCoxXpJ+8vvWZSEZLKCpuEK/n7+2toFelIY2MjKisrlUHMZrOptO6y7kp5ebnigx4eHvD394fFYlHJC2w2m6KZdXV1yM/PR1FREQoLC1FaWqroD+MCpZJE2kF4HetuSHorIWkyzsfd3V1jve4srampCTfddBO2b9+u4hO5R1paWlRh1kGDBuHbb79V0CnSF0LOKBdkZWXh9ddfxxtvvAEAOH/+PN577z289tprqs7W008/raBTgKua93333aeK4z366KN47LHHkJKSgn379uHo0aNYunQpcnNzMWrUKKSnp2sgNx01X19fFBQU4LbbbsO3334LwCU/SPi2xP+bTCZcunQJCxYsuAxyw32dk5ODlStX4q233lL3efHFFzFu3Dj07dsXZ8+exQcffICXX35Zc196OGXz8vJCc3MzHnroITz55JPo1q0bGhsbcc899+C5555DfHw8du7ciezsbNx///2qz5T/8vPzcdNNN+GHH37QjIVKmT62wcvLC2+99Raio6MxduxYNbZrrrkGu3btQmJiIhYuXHgZdKpHjx44dOiQqrHGdZcZPzk2uY4SOsWxUg7Sv9c3rtO0adOwevVqdOnSBbW1tZcpGmPHjsWmTZsQGxuLN998E2FhYZg0adJl69hRe+211zBkyBAMGjTod6/532hXokL8V8Hg0msh03HSeksmykXhYZauY9mxkSNHYtCgQUqYYBBnTU0Ntm3bhjNnzqCgoABeXl646qqrEBMTg+joaFx99dU4c+YMLl26hMLCQri5uSEiIgJBQUGaFLkU+AwGg4pbsNls6jkyF760jAJaTK9Me0iIjx5PzLiCoKAg5eqTMA2OW0IMpBtfWvIYsyCDs/h9a2ursqhxDSorKxVkx263qywWLCIXHBys4kooqFL4YJ52CigMPCQzDggIUIKehJhILwv7Rwsy76+Hm/B/6VVgzAoFHJvNpu5BJUtm/yHD5riloEMYAS23ZrNZo/QZja6gMQpV7u7usNlsKvg7ODgYsbGxiImJQWpqKjw8PHD06FGcOHEC2dnZ6vP09HQMHToUAQEBqK6uRnl5OSorK3HgwAFcvHgRRUVFyqJB4ZTWFb7oleNYmRtdBquyzxQkGPjZ1NSkqXjamVpkZKRSqmT8BS3B0uPp4eGhUhBy3xUUFMDDwwMWiwWJiYmIiopCfHw8oqKiEBYWphQDo9EIm82G/Px8TdFG7idapGk04JrINMgy+xnjPWRsAJUKKuj0aMn9Lr1rUjmW8Q7S2ycVDe4ZAJr4NumhIM3QZ6uTqXQpxNKLRoORPNPNzc2KxrFPFNSlQCu9SdJ7KGFgAJRSTI8AYURAewIA6Z0FtFmnOFdULCUkStZmkrSM92aWHFlbQ9Jb8jD+hkYBacyR/IFKJ+ebMSxSMSPtYhIHvXeSMGA3NzelbAcEBCAyMlKNh578oqIipWCUlJQgIyMDJpNJpbimh5Upc7mfmLJd7mnWbKKBS+4Tk6lzBoP7+fmhqakJY8aMwUcffYT4+Hj8+9//hsViwfTp05Gbm4t58+bh008/VTF3ADBgwAB8//33SElJwYIFC3DNNdfg2muvVcYG8mu+T0pKwpEjR1QBP9IAoB1iSR4t33t7eytF2cfHR6XBl9C132s0QNjtrtTfeXl5mD17NhoaGrBx40YkJCRoihD6+PioM2s0tgcRr1u3Tn2/dOlSjTDOPcn6YHLsALBgwQLMmDFDKVUAEB0djfPnz2Po0KE4e/YsoqOjcfLkSYwcORLHjx9XY6PcRUH+q6++QkVFBe6++241tokTJ2LNmjWIj49HY2Mjrr/+erWOMj10bm4unnnmGWzcuBGtra24ePEi/vGPf+DDDz9UxShJFyUUyWg0aopVpqWlqXW8+eabccstt6BXr16/u27BwcHIzMzEddddh2PHjiE0NBTZ2dkYM2aMpio4m9lsRm5uLm688Ubs2bMHwcHBOHfuHGbMmKEURjbSQlnPhjA6mX2zoybX7c/UrkSFuGLoFLH5tOjooVAS20wBkQK/2WxWliRa/iIiIlS9C8Bl6WTtjfr6eoWnNplMKkVZQEAAvL29kZWVpbD1tKozE5S/vz/MZrOmICAZN7MZkVnS2gO0a/ESQw+0B9/IzE1kQrR0AlCpULm56fmhMCGhV2SMEi5Alz4A1We5iBQIaBln2llm4KEFU7oeHQ6HUkYY06KP26CAQesqs7fImiH8X2LA9UIC+0eFUlqOKTBwnimEU/GgUCnheJ6enjCbzZoUt7SwksFTsKO1VVooKWzKVKV6CzMVgIiICBUYxwB0i8WiXoGBgcpSWFdXh8rKSpSXl6OmpkbtUbPZjISEBAQHB6v5ZuFECmIU/KgMSq8R977dblexKlwj4sYpPHXmRsLa3NysPAA8XxSUKAAzYxSZKL1dUqkl3ZFwTtImCQ+UWY54Pb0AXCPuO1lfgzRBGgPk3qfwLxM86GEaUllgjQruSQl5kjRVD7uS99FDrqh8SwWE9IdKHPe+hHTyf+5N0hDGifE8SygULedkivR4SIWJAq6+8J2kqxyHVLalosG9wnmRcB897WRj/2QjLZbxKHpFjd9RYewIgsl9I2uuSAge6zOZzWalKNEwJT0MnBfSRCn48z6sCRMUFKTgwoArFpC0k54iGoboHSIPoaLI/sux0tjzZxNYrrQxYcqJEydw//33o7m5GW+99RaWLVsGu92Ohx9+GAcOHIDD4dAEiWdlZWHWrFmoqanBxx9/jI8//hgbN25Unvj4+Hh8+umnqohvUVERZs2apQqrBgQE4NNPP0V0dLTymrGNHTsWy5YtAwDFjwnTWbx4MWbNmgWj0VXnonv37hg6dCjWrVunzvbAgQOxfv16zX5obW3FAw88gKNHj+LUqVO4//77L1sz8hjAtdcfe+wxBQHj9+vXr8dzzz2nPmMsIb+XSgbgUg6eeuopAC4r+owZM1BVVYVZs2apxAclJSWYNWsWcnJy0NbWpuaiqakJgwYNwtq1a+Hm5obXXntNVbPmuh09ehT333+/2qtyHefMmYOFCxeqddy9e7dKZvDII49g7969al1pMIiMjMTGjRsRGBiIO+64Ay+//DLq6+vxxhtvYOrUqZp13LZtmxob55h9X7x4MW655RZYrVbcc889uHjxolrX++67D1lZWQBcii7Xkes9e/ZsnD59Gna7K3Xu7NmzVepeDw8PfPDBBxgwYADsdrvak83NzUhOTsYnn3yi5JH09HRs2LBBycWAiw6uXr0ao0aN6rRn9r+O0aCwSWIlXf/Sg0ECp4e5UPiLiYlBaGgofHx8lEBMYVBmDjGZXBl8qGj4+vqiqqoKRUVFKg81FRoJDZBudgAaYUYyeD0uUOJb9X2WxZrIxPWCs7S4sy+SieqhS5KZSoFEjkla1iSsg5YDPaOmkEGBiV4ZppLVY3nl7ylskLHLYnhAu0AkISKcS2lRleOR3o+OPtPjxqVVV6/USi+BtJiS0fJ/Ml89zprCCQUwg8EAHx8fxMXFKSGYhMDT01MpGr6+vmhrc1WjLi8vVxXHaX1i3EZkZCTCwsLUfPBFZsDnS6+Yw+HQwOk4Dp4lWvqlwtpZm16I1r/nNdyPVIBpdZfeA+4R/i8Ff845aQ8VQonh5xngeZUeTu4hNirQUtmQnkmef7k2EuIj4XwyPa5+X+rnSFrh9S9e21Ecg+xrR9AmuR5yn9LgITOgybMrY9Lk/EpFQsIhSUekp0TSDv16870cuxyXXomT1+i9rPyMe0MqMPIlhXGui5wv7iv5V9IR7hUan2g4kjyE96chgXMj4/u4D6XS4ufnpyBYnEMJ35OeLvJOPe6fdJJGGOlh66ytd+/eCA8Px+eff462tjYcPHgQ+/btg8PhwJYtW+Dv749+/frBYDBgxIgRiI6ORmVlJTZv3ozm5mYcO3YMu3btUqnpAde+4PuUlBT07NkTmzZtUulxjUYjLBbLZfsXgIrVY4uNjcXw4cMBtNf0MBgMCAgIUPslICBAI5/IVPKAaz23bNmCgoICtLW1wWq1qr3PbJ3kG4Brvbdu3YqcnBz12XXXXYeqqip8//336rPU1FQVG8Dm4+ODsWPHwsfHB2fPnsX27dsBuCp8Mzvapk2bkJ6ejrS0NDQ1NWHz5s0qm5e7uzvGjBkDi8WiGQvhhG5ubhg9ejRCQkJQUFCArVu3Yvjw4QgLC0NxcbFaR+59p9OVkZJyB8eWm5ur+jx8+HDExsaqdZFnB3AZrwkFZ+auM2fOKC/D4MGDkZKSAi8vL4wdOxaRkZEqIc/mzZtRXV2tzvSmTZuQnJyMbt26adaRfWPaaMDlIf388881sEQiQ0JCQjB69Gi1h9zdXeURuA8o7xqNRnTr1g0DBgxQv6dRuzO2K4ZORUVFKYGHDFlWXA0ODtZYvejqZq5xp9OpqWo6ZcoUpKSkICgoCNXV1ejevbs6TFlZWfjuu+9QWFiI1tZW9O7dG927d0dkZCT8/f2RmZmJbdu2wel0IiwsDMnJyYiKilKB4qxVAUBZk/VpUCUDJf6VVnXixAGouA9pOaPVj5YAWkTd3d1VrIYUDug65Wd6JkerNeEH7D+LywDtNTmYZpdCkhR2pdBKZu/h4aFqCOTk5CAqKgoxMTHKZWqxWDTMW589xmg0KitsSEiIwm5TCAagBHrWgpAWZcmcKcBLIYiQDLovq6urFePlOkgBReLaeQ9ppebepDLIOeQ822w2pWgRi97S0gKr1Yrdu3ejsbERQUFBmDRpElJSUlBYWIjMzEz89NNPqKiogNFoRGBgIEaMGIGePXsqC0Vpaakq4Jebm4vvvvsONptN9ZcEkGtTWVmpUmAS+sBaIqzPQmtxXV2dyr1PYUVmPelMLSQkRFPtXe4nQtu4h/39/ZUwB0C5yplAglA2wiaJjafVvaamBoWFhaiqqlKZUWRsQVNTE6qqquDh4aFqajA5AKstM4CcVmopUMvsWbScUznWN0LlOPaOAqd5noxGVxybhAKySYGZe4lnny8KtlLYJW3hXEu4jkyxSm+hrJcjoUykU2azWXlLORdSKZDKFc8iz6afn58GYqY3Sug9NxI6R+FZ0mMqi/wtYacSiiBrbwDoUAHgvADtXmXGaEhvCPcX54PGCUJsS0tLFYSKBWSZfpw1GVhPIyIiQqVH51gcDgdsNhuqqqqQm5uLs2fPKg8qIWj0hgDQKBesvUFDCsfS2tqK2tpaZdBraWlR9Ys6WzMYDNi8eTPc3d0xdepUuLu7qz3AtnbtWsTFxWHcuHEoKCjA008/reBEf3RfIiSWLFmCKVOmqJSv8hxI+LH+2Xw/e/Zs/P3vf0d8fLziVYQJ672M+iYVR95v7Nix+OyzzxAVFYW6ujoMHz4c27dvR0xMjCpUx75zLB4eHrhw4QKWLVuGf/3rX+osvPTSSxg/frwK2jYajUhPT8fx48fRrVs35OTkdDhWwBW8vX//fsydO1f1zWAwIDw8HDk5ORg9ejR++eUXNdbvvvsO5eXleOihh1BUVITp06dj586dMJvNuHTpEmbOnIlt27Zp5o60tLCwEI8//jg+/vhj9T3PsNFoRF5eHl588UVNjRCpRLPvqampOHnyJLp27Yrs7Gx17YkTJ7Bt2zasWbMGWVlZGDhwIE6cOKFBuch12rt3L86dO6cqjfN7X19fFBYWYtq0adi1a5daB7mObOPGjVPr2NDQoPoqx8b2r3/9C71799bEn+jX+c/QrkSFuGKPBpm/m5ubpn4G00xSaCMjkLAEDw8PREREKO3SYrEgJiYGgKvSIi3ADAguKSlBeXk5AJdGXVFRoQTX2tpa5OfnK6iUr68voqOjVTpbMgjCpAICAhAYGKiYLjcimSkFaenWrq6uVtAXVs8krMJgMChPAeFLQLunglmsOEd6qIUMIKWQyd+T4VERIu6WFhH+jv/T4kuGSYWFgrXZbFYb0tvbG6GhoUhISEBISIgKRJRWSGmBlMHzMluXhDIB7VZpu92uUTIkxEHuH1qgKejwsNIlKD0ITG3MubHZbEqA4HgZNMu5l0GtVVVVCtbB/SObDBr28fGBxWJR0LpLly6htLQURqMRISEhSoBlZdBz586hrKwMJpMreUF0dDQMBgPMZjMSExORlJSk9gkDjDnPJpMJsbGxar744tr6+/sr5ZGwLHrTuAc6a5MKBuGGPDMshsXsUVQuKVR5eLhq1jCdbUhICMLDw5XlSiYEAKCEQQq7FOylJZ5KNCEr0hhAJZiKMK3V+hfPtMx6xf7TSq33MHDvyXvLYm+kn3rvAfvGPUAvCV/cz9w7si4NzwqfKRUDadAg/SSMCGg/7/J58qyzfzImip9LaCdfMr6Chhq5v3lfCSkFtEHacu64p6i4MkEEY0lkhjfpTeTYeR95LcfdkcdYxuTIvpOWyfgQBow3NDQoaCrjKyorKxVMmPhtzj+hmyEhIfD19VXGEcJluR+4zow9lDS5vr4eFRUVqKqqUoH15EsSntHZ2p133onbb78dnp6eyMnJwdSpUzXfP/zww5gyZQrsdjvS0tKUsPpHrVu3bigrK0NcXByWLl2qEfDmz5+vUpfu378f//jHPxAbG4uysjJ069YNgCv+rKysDL169cK6devQvXt3xd9vv/12ZGRkwM3NDVu2bMG///3v3+3HRx99hA8//BBeXl7Izc3FpEmTsGvXLk3thf379yMqKkql0R0wYABKSkoQHBwMALj66qtRXFyMkSNHIiEhAbt27VL3X7x4MYYNG6beL1y4EB999BFCQ0ORk5ODJ554AkePHgXgyjIlYVcjR47E008/jbCwMJSWlqJv376444478PPPPyM6OhoHDx7EzTffjAsXLsBkMuGvf/0r7r//ftTV1SEmJga7du3CpEmTcP78eaSlpamUwvp1bG1tRUpKCjZt2gTAxTcyMjJw++23A3DRlu7du6uUwmzPP/+8qhbPWiaZmZkIDQ29rPbU0KFDsXTpUuTn5yM0NBT/+te/8Pzzz6vvd+7ciVdeeUW9nzBhAh5//HH1nutYW1uLmJgY7NmzB1OnTkVOTg48PT3VOsom1/Htt9/G559/DpPJhMzMTNx8882aa+fNm3dZEcarrroKZWVliI6ORmdqVyyxMMCbQi3QnqZTMnZaZMmwGMhKgdHX1xfJycmwWCyKKVssFrS2tqKurg5WqxUVFRUaN7HFYkFwcLBy4THFqLe3t3Iv62EX+jzxVCbsdrsScgEopUiOiQqEhOrox0rrsrSKyTF3BCPTWyYNhvb0nlLh4F8qN1IZ4DMlXlpaKvWWMaC9/kd4eDi8vb01HgGgnZlKfK9shJnIiupSSdJb+2i94bxJKAfvwXgE2U8GO/N62X8KXvytxMbLfnIctDZxjMz0JPtCYYXCYGJiIs6fP4/q6mqUlJQgNTVVWX0BKKy00+lEaWkpCgoKlOU5LCxMVer09vZGUlISSkpKlEuViqPEmnOPc6wcv8T+yrmicNSZIQ8S4kblgHub+4HrIaFONARQsCKOnQo/FTEp/FLppDWbAiL3gaQBFJwpyMrzLoV30hEJT6FQx3WUuH8AGjohjQIdQcekkM3r+GwJ0aTCKmkK+6tXdHhv7h2ZsYhnQdIPeZYpwJMGyDPJvcsmPRGSjgHtOfb1EFzpOZV/nc72QnxcO/1Y2Q89ZE0mvZCxYdwbkt5Lr7KcO+lx0UOm5B6T80ZPakBAgHou9w/nUhpCZEFVGTwvlQ4aHoKDg2G321WyDPJG3p/ng8ou15lN8hvORWemI6SRRqMR8+fPx2+//XbZ92lpaViwYAEeeeQRRcMNBgNWrlyJbdu2ITs7G88++yzmzp2L6upqFBUVYc6cOaisrFTrw/btt98iIyMDgMsjkJ+fj6qqKsyZM0dBZGpqavDYY48hPz9frTPgqqfg6+uLp59+Gg6HA2+++Sbq6+vh7e2NVatWYeXKlTh9+rR61jvvvAPAxdvmz5+PYcOGITk5GatWrcLq1avx/vvv4+DBgyoLIwDk5OTg8ccfVx6qS5cuYc6cOSgsLMSGDRvw448/qmv1Y9u+fTvOnDmj7rdjxw4Fv3r55Zc1Fah5f4PBgDlz5iA3Nxe1tbVYuHChgrIfPnxYxVnU19dj4MCBuOuuu/Dwww+jra0NJ06cwLx581BeXq6JR5k/fz4GDRqE5ORkvPbaa6itrcV9992H4OBgvPLKK3j66ac1qXHppU5ISFDruGXLFhw8eBCAS6HKyMhAeHg4XnrpJcyfPx+DBw9WyszTTz+tWbsXX3xRkxxh2bJlqKioUO85n/SMyXVctmwZVq5cid9++w3z589Ha2urWkd3d3e8+eabWLNmDY4cOYL6+nqsXr0amZmZ2LRpE+x2OxYuXKgZG+AynCYlJWHVqlWYM2cOJk6ciIEDB2LOnDmorq5GZ2pXrGjQrUXiqsfMkphK4VPP5Ih5j42NVRY9WoOqq6uVJYiBsvQ2EM5AeAmrjjMwmtmwpBIkhVF9vAj7JiEGkilTUdJb5MkseF/+lo3WPjYpZJMhSY8Dm/4enC8+Qw81knhqyew4VgmtYj/o4ZAxIxJjqHfjyv7yGv5OWnz5OZUqyfDlPPOevJb3kDhwveeH3jOJS5b7hv2S6y2FPsaycI7lXOqhVyaTCWFhYbh48SLq6+tRUlKiICV1dXVq33h4eCjPWmVlJWpraxEaGqo8IXxOZGQkLBaLKhTHuaFgREYv9yCFTLp9pdWVY+C56KxNzhGVaEk3OA/6GBu5xwlHkZ4exj0A7XtV7hun06mJ4+L+JxyR3goJy5GKhl7olFBHuT68huOTwjH7xntLQVtCguTelOce0OZPl3PTkXFAnlW9ciXPgxSw+Zmkj1Ta9cYSSXv049Rfq6exev6h94LK3/MZvEbSTj2N5fzLc0/lgrRB7xGUniAqnfr5lXtDKmj8nHPEOEBCNuV4SbNlcg0JQeaelN4ieqOovDidTo2QzXHTiy5pI6+R66SnQ521ESt/5swZbNiwAYArFXb37t3xyy+/wG53pWBOSEi4LKYiPj5eJYxJTExU37e0tODixYtoa2tTnn8Kf1arFfn5+QBc1a1jY2PRvXt3rF+/HoArK1NMTAw++OADAFDFhQ8ePIjIyEjk5eXhs88+w5AhQ3Dw4EF4enpi6NChyvMtGy3yAPDxxx8jLS0NkZGRMBgMiI+P12TGHDBgAC5evIjGxkbk5OTA4XAgJSUFfn5+qi+HDh2Cr68vrr32Wvz6669obm6Gj48P+vXrh0OHDuH48ePIyMjAtddei8OHD2vGSliTbFFRUYiLi1P3r6ioQGZmpvq+rq5Ok37d19cXSUlJGDZsGI4fP468vDzk5+dj0KBBOH/+PCorK+FwOLBhwwakpaWha9euGDp0KH755ReEhIQgIiICTqcTGzduBODK8tSzZ0/8+uuvaG1t1ayjFNZZyC8uLg6JiYkqHiIxMREA1Dn39PTEgAED8OOPP2qSBxQWFmqghZGRkWhpaYHJZMLAgQOVsufr64vExER4eXnh9OnTKo5k9+7dCA4OxtChQ5GcnKxZt/j4eGzevFl5mn5vbB4eHkhMTITJZFLIiPXr12PAgAEoLCzsNNknrzhGIy0tTRM8LBktIT7SQuvm5qYyxjCDC6E7w4cPV2kovby8EBwcjEuXLqmYgx07diA/Px9msxmRkZG46qqrEBkZqe755Zdfor6+HtHR0UhLS0NcXJwSwKi40GUuLfwANDh9CUdgLIC7u7vC5FIICQoKAtBuPWMQF4UZmdGGwjHQDkOQgoPeEiWx3/QGSew1GTTHQksVBVhmX5AMT3oAjEajYkx090srHsfFsbS0tGiCVWUmrLCwMPj5+SkmqfcikdnKJoU9QhMouEkcIy3Q0lvCoGtaIKWFl2OQQoD0+sisO3KuZfVjo9GoBAIqu4cOHUJGRgaqqqpw++23w2azoaioCNnZ2bBarWodm5ubMXjwYCQlJSE2NhYJCQkKdkcs9U8//YTs7GwUFxcrCy7nw8fHBw0NDRrFnf2gh05CMJi5jVb5c+fO/Z+f+v/FlpiYqKzbnp6eqK+vV14GBhr7+/trKrtLKKGfnx/S0tKQkpKC5ORkpeQRclRfX68gK/n5+SgvL1ceLlmngsG1ADSWY3kN4VBSkNYrMdLCTdon4ZHSs0FjCz1tUvjmnpXP0Au49IQB7cI0n0/FgYK3FIh5PQVuegrYB55n/rXb7epctLS0oKGhQa0P78VUkOw3x6RXOID2GDbSHwp5HCsFbjZppZdKKPkP10j+Xnp96R3k/rHZbKirq1PZtCS9ovGC4+I4uLasQi/pCOkk55J/CTOtrKxEWVkZrFareh6hp6yRxL0QHR2NlJQUBAcHw2KxIDQ0VCkOhFfl5uaqWjBMpS35mdHoin8jVJTxXFxfxj25ubkyVXGdASiIcmdqBoNBQU6mTJmi9vbo0aOxbds2REdHK+t6R43X67/v2bMnjh07huTkZNx7772YOnUqrrrqKhiNRixatAi33HIL0tPTlRdl9uzZ6NKlC+x2O+bMmYO5c+ciPj4eTqcTDzzwABYtWoSYmBh1lhkIPW7cOERHR+ONN95ARESEhr9JgwEVw476z7/FxcWYN28esrOzsWvXLkRHR2PJkiXo3bs3Bg0apMbaq1cvHDlyBAkJCSgoKEC3bt1w8uRJpKSkIDc3F6mpqTh79iy6deuGG2+8EXfffTdSUlI0c8Uz+cgjj2D+/PlK7tL3/c4778SyZcsQGRmp9pmvry9KS0txww03YMeOHfDz80NRURFmzJiBrVu3qnsDroKJW7duRWRkpPKyyO8HDhyIffv2ITY2FqWlpZrvO5rH31t3vo+NjUVOTg769euHEydOqO8PHjyIw4cP45FHHtHsPbmOe/bs6XCPsT+TJ0/Gxx9/jPDwcCWH8TuuozS+DhkyBLt27UJcXBzKyso0e1SOLTc3F6tWrVKZzv4325WoEFdsGrVarUpAIAMlYyOho6vW4XCoDDEMbHU6nQgJCUFCQgKCgoLQ3NyMgIAAxMbGKiGTGNv6+noNPIJEtaamBjk5OcjMzFTCvY+Pj0bQlrhdWb9C5kMn3EVWf2Y8B3HaQLtFSwZp62szSMGC95cCABkW50X2i7hlacGXQZcAlOIkXepSeJCMWO+tIOab8BIJtaAiwzWlYMWAeukN4rxSoKECJr+XKSQlIaSwJqt0cowGgyuAvKamRmGXiaunBdrf318JkrTKMsEAAzBlhXdpheZcSygMlVCmq5WHxNfXFwkJCUhLS4OXlxcyMjJQVFSE2tpaVFRUKLgKrQ5NTU0oKytDSUkJbDabyo9PITM1NVVZokwmk0olaDK5UtUyPSUFSQbYAtAkWiCEjmvYmSEP9EY6nU7YbDbljXBzay+iREWZ8TikDSaTCZGRkQgKCtLUwpF4eQqepCNVVVWoqqpSRS1Js3hPKnZMnS3jp+Q5lgqB3ONSmSAzlnh/QJtQQXoGuEd5vim0M16E55l0Qu8xkcYBqYQA2oxnUnEhraIix/eSfuoz3hGOw/mmkqb3cPJ6GimkUie9VDzfsknIGRvvLY1aMuuftPxL+BITZjBpB+eFsQ88S9IQIfvNa2UdFekJk54Ofsbny4QUbm5uKq14aWkpKioqVLwEFWKr1Yri4mJUVFSoxB40hlBBY6yJ9AACUDGPjClkqm7SCZmd0M3NTQWJc8yd2aNx9913Y+bMmQrbP3nyZOzZs0fVvrjxxhs7/J3RaMSpU6dw1113XfbdmTNnEBERgfz8fLz00ku49tprAQC//PILAKhCaXv37oXZbMbUqVNRXFyM7t274+2330bv3r3Vvn7//ffRo0cPtfduu+02HDhwAHFxcfj555/x2WefITU1VclMkydPVth+ALj++uuRm5ur+IFsb731Fj777DO0tbnSoW7cuBEHDx5UmbWefPJJVYX8xIkTuPfee3Hq1ClEREQoqFBGRgYiIiJw6dIlAEB2djYiIiKQnZ2NFStWoH///gBcVdAXLVqEqKgoFBcXo0ePHlizZg169eqlxjpz5kycPXtW0cSNGzciLS0Nra2t2Lx5M9566y3YbDbExcVh165dmDBhAs6cOYOuXbvi22+/xZgxY5Cbm6ss/nv27EFcXJyCRg0ePBgFBQUIDAwEABw5cgRRUVEoKysDAPTq1QvFxcWIiIjA4sWLNTAx2X755RfMmzdPvd+7dy+eeeYZFBQUICIiAqtWrcLixYvV99dffz0WLlyouYd+HTtqvr6+yM3Nxbhx4/Ddd98hISFBKRmjRo1Cbm4u/Pz81DqaTCZkZGTglltuwcGDBxEXF4d9+/Zh5syZmvu+8MIL+OGHHwAAffr0+cM4nz9bu2LolHQpS+w90I6nJQGjVUi6yhkAHhUVhcbGRk1gcFlZmcaqSwsXAxkDAwMRFxeH0tJS5Ofnw+FwKOGTwryvr69iltJV6nC48oWTOeiDNalZynL2ZIhkvBQE+SwJ16F1T1oMJfOVRJ3fA+2wDDJoaWXj91QKpCVRWhcovEvGzewFEl4hse9k5PL5FMwAKGbG8VE5ouAhoRW0mOkVKzn3cr6MxvZaCIQGSegG557PoNWUjBeACu7VFzTjPbh20uLJRqWPCpCEqrDIFz0GbW2uDGjEmTY1NSEsLEwFjFNo9fLyUsoSs8eYTCbU19cjMDAQsbGxqKysRFZWlrKq+/j4qHnifqfCQSGbgiGFHbkmHVmNO0uTyQSobEp4D9eGiiwVE3pNQ0JCEBISAovFognkZvE97rnm5maVPIDPoaAFtHva5Lkj3ZH7SHokZGyGhDSx71LIlEYHKhR6K1tH0Ck9fIo0UQ8pkr/jb6WBpCMIkjyL8vlUrGQSCM4BaYaMneG17AfpvfSmSJomFRlptGC/JH2VfeUZkN4UoN1IwpdcCzk+6TGSwffSo0MlQjbpSZL0ksqdVOD4nZx3qXTIzFSyuBvv29DQoOpGUcH09/fXKDGkGdJLT0VcriGfLTMHcv3Io1lQjQa3ztoohLq5uWHJkiU4ffo0WltbUVpaiiVLluD48eOa62fMmIHIyEj885//xKuvvqqB2Lz00kv44YcfsGvXLoXJHzNmDHr37o3Fixdj5cqVuHDhgsLFv/nmm8jNzUVOTg4WLVqkYLayxkH//v0xceJELFiwAE6nU1UKl1ZqGlYeeeQRREVFYcmSJWqfnz9/Hs8//zxaWlowe/ZseHh44M033wQAbN68WcWRVlZW4o477kBoaCiWL1+OV199FVu3bsX+/fsBAP/85z9x7Ngx2O12VFRU4MUXX8Tu3bvx/fffq7FOmjQJffv2xXP/b9A3vW8c66VLl1BTU6PGKmM85s+fD39/f7z88suacRmNRqxatQq//PILjh8/DqfTqZ539uxZPP/888rTn5mZqcYKuHh8dXU1li1bhk8//RQXL17Ec889p/oUFxeHJ554Ak899RT+8pe/oH///li0aBGefvpplJeX41//+leHe4bryMZ1dDgcqKiowBtvvKGJR3nggQeQm5uL7du34+WXX8ayZcs6XEe2Rx55BI2NjVi/fj2ef/55jBw5EomJiVi9erW6hmNtbm5W62i32/Hiiy/i2LFjSEhIwNy5c7Fq1arLYja++eYbFYvEtMKdpf1XdTSAdle1XiAmU+G1klEDrrzPYWFhCAwMRFNTk7L4tba2qjzLDodDpSakIsIgOWZNIaGldY0MVwrO0rJJ17HMeEIBl0SawqWPj48i9kxxKa1eFMqlgsXfk7CzL/yec8PWkWVUMlheI58j/5dzTGusrCMg+yp/w/WRwo8U7GWTawlAWcf09yUj08dgSMGto0w08sX1JGOWUBRad7ku0hpJTwcFTb5kMS398ySMi94JvZBFxc9kMsFqtaoijA6HA35+fiojFvcpPSmECVIgbmxsVJm+oqOjlSdIjle/H/RzJ/cX57uzN/0YqHBIJVBap8l4aYU3m80qYxoVYGm5peLNdKIU9EgPpCAvEzFIo4OEyeiFae5PvZIgjQoSeqW38MvzKV9S2OY99d5RNingSoFY0hK5z/RKiLyv9MCwn/K+8j7yN1KYlmdXrp3+9/ydXinQjw/QxqLo50UPl9LPXUeKuFwX9lV6PeXa6D20+udKmqZXHqXhQyompC8AFM2lEYPKBiFeUgmiMYOxGrIIIyFY0uMis39JXkHvt94D1Rlbv379YLFYALjoBzMPMXnHunXrNDEDgCsuIy0tDQCwfv161NXVqWxRPXv2RFhYGLy9vdGvXz94eHggKipKFWTbsGEDjhw5Ak9PT/Tr1w9bt25FXl4ekpKSsHr1alRWVl7Wx7CwMFV9GnDFMVDg1rfU1FQEBgbit99+UzEWJpMJ7777Ltra2pCUlISUlBR1/Q8//IA9e/agX79+cHNzU154g8GAXr16ITQ0VF174sQJZfkHXFmLwsPD4eXlhX79+sHT0xNRUVHo0aOHuiYyMlKl9d20aRNyc3ORkpKC1atXIywsDHFxcera9PR0NDQ04P3339eMzWQyoW/fvtizZw9++OEHGI1G9O3bFwEBAcjJycHatWvRu3dvWCwW5OXlYc2aNWhtbUVycjJSU1NhMBjQu3dvBAUFobi4GG+//bZSzPz8/NCvXz+4u7sjLi4OISEhWL16NZKTk5GVlYWvv/5arSMANdYvvvhCI7x/+umnyMvLU2PdvHkzDhw4oBlbXFwcTCYT+vXrBz8/P5w/fx4ffvgh+vbtq5I09O7dGwaDAampqUhKSkJrayvWrFkDk8mkWbfk5GR4enqqsRKd43Q6sW7dOmRkZMDPzw99+/bFmTNnUF5eDl9fXzXWn3/+Gd98841mbJ2lXXGMRkxMjMr64+3trXDTJpNJCV60hJnNZjQ1NWkgBUOGDMHVV1+NmJgYWK1WlQ60paUFpaWlCA0NRX5+Pk6fPo3PPvsMV111Fbp164bExESUlZWhb9++aGhowMWLF/HVV18hNDQUKSkpSE9Ph8Vi0eCFQ0JCNNlVKFTKOABaN2k1lszK3d1dUydDwnYkZErPsDkeCjvS6kaBSAZoA+3ubyms0EPBZ+i9HbLJOiVkNHw+LQtkSFTepCDB3wEu5i6zUfA6CvpkyjIwm+OVygbHJzPNcP7ldVIw4tzJbC2cG2lBlN4dAJfBMKRSRuudFGalkEZFV9YsKS8vR2lpKS5cuICMjAyl3FRXV6Nnz54qJ31BQQGMRiPCw8MRFxeHhIQE9OjRQzHAX375RZ2D4uJifPrpp2qOGahIqxzXnBAOLy8vla2KsBuLxaL2b0tLiybYrjO1sLAwtd5UtijwcL9QYfTx8UFNTY2Cj8TGxqJXr15ITExUtTMCAwMV9Ii1AYqLi3Hp0iWcOnVKnW8KjuHh4QBc56a+vl5T/Z3zLAPLAShBmoo6oaEyexHQLsxSKeKe5z2k4CqD1PlbBiqTlkgoKvcp504q5fwroVXSgEAawDHRUyAt/oxb4mfcZxRaAe3Z0itdUtng93yW9M6R5sqAa/ZV0gO9J1TGYOg9fFwbjkV6qiTNppIgIaOkg/omPQL0EtNYwfmRXmo+p6mpCZWVlaisrERFRQUKCwtx6dIlBQl1OBwqyyJhUomJiQgPD0doaKiqc0QPPeMu6uvrYbVacebMGWRkZKhiZjLddUtLCyoqKjQ0ldkRgXaew/kwmUwq6LczNafTiRtuuAFffvklANce2rhxIwICAnD99der6/RGPvnZqlWrMHDgQFxzzTXqu549e+L48eNITEzU0Ff+JiUlBZmZmejWrRumTJmCBx54QAUW/6fnzZo1Cy+//DIiIiI0557f33jjjXjvvfcQGhqKTZs2wel0YurUqR3eEwBGjBiBHTt2IDw8/HczEBkMBhQUFOCVV17BG2+8oblXt27dcPr0aaSkpKgMU/z+73//u4rRcDqdmDdvHh555BHEx8fj6NGj+OmnnzBnzpwOx/l7/fX390dZWRmmTZuG7777Dj4+PigtLcXMmTPx5Zdfqt9t3LgRFotFs45/NL8dNf06pqen4+zZs0hNTVXVvXmvBQsWXNE6ys+DgoJQUlKCsWPHIjo6Gm+++SbCwsI0aJWOfr9582b4+vpiwoQJcDqdKnZj9OjRmmcYja7Ym7///e84fvw4fvnlF8TExKC4uBi9e/fG0aNHER8f/6cJBL+SNbliRSM6OlpZQ5xOV0o+Ek4KuMyyIrNikBHceeedCAsLUx6Kq6++GuXl5SgrK0NTUxPc3d2Rl5eHM2fO4LvvvsPAgQMRGRmJkJAQxMfHw8PDA/n5+Th79ix+/fVXXHPNNejSpYvKYEXoE+EpEptM+AQPgsSw0isglQZaf8hYpDWPArH0QlAAIfMmRlhaUXktBV4KFEC7QOx0OhUEhL9n/QUK21QOpNIh88IT2y8tb7SSMesChWzpEZIWTFngizU7AKjny+tlCkXGHvCwcP6kdZ6/pfCgt1RyPzEbi/wtmTr3FAUgzo/EHXOdaEnUw16kh46trKwMtbW1Kl5kz5496lk2mw0RERFKKbXb7QrGZzabMWrUKCQnJyMyMhIBAQE4f/68EhxbWlqwefNmlJeXK4WG0JLm5ma1/6n0sY4Cn9Pa2oqgoCDlmvf09NS4gDtTi46OVvA5QsgYAyCrzwPtwreXlxcCAgIQFxeHa665BlFRUQpCFRwcrPZSQ0MDysvLUVhYiLy8PJw6dUrNJ+sSUPCiQBoQEKBenp6e8PX11cT1SCu33hvCv1w36cWVmHi9FxDQBnyz6eFHgNa7Kc8SBXspEOs9EfL3pE+kRUytKuldfX29ohk8NxTgGxoaNH0jbJFKjYQYAdCkLZaWfDmXsr96r4b0GMhzzftIpq73ktGTwPsxIJv0mEqCpFNcb5lZUO4B6QkHoJQwacRgXBA9oTU1NSgtLVWZjOjBoFBCCIqPjw+Cg4MRHh6Orl27Ijo6GmazWVUppoeupqYG586dQ3Z2NsrLy2G1WuHm5qbiUKhss4I0+RuzGpGmcIwGg6FTGixiYmJUClpPT0+cPHkSL774Ir7++mvlXVi5ciViYmIwffp09Ttatfv06aMy+shgeBoiSkpKNPvtp59+whdffKGCt0tLS5WXicHI9913H+6//35cc8016qyHhITg2LFjuOGGG3D27FkEBASguLgYmzZtQklJCZ555hmcPHkSd911Fw4dOgSLxYLi4mKVfKahoQGnTp3Co48+qqpZs3l6eiI4OBjFxcV/KOhFRESgrq4OiYmJ+Pbbb9G/f38UFxdfNtakpCTs3bsX1113HcrLy5GWloYtW7Zg9OjRKCwshJ+fH0pKShAaGqoMOmwzZszA/Pnz0bt3b3z22WfIzc3F3LlzNf0wGFyZGCsrK5WMEBkZiaqqKgwdOhRvv/22MuQZjcbLvERubm44fvw4XnzxRZVl7Peafmz69wkJCfjpp58wYsQIlJSU/Md1ZPvyyy9x4cIFLFiwABEREaioqICbm5taN8AFQ3vttdfQs2dPrF69Gk6nE/fccw8AF6pn5MiRePXVV9GzZ09FDwkpW758ORISEnDDDTcgIiICtbW1aG1tRWhoKEpKSuBwuJLthIWFXbZH/zfblagQV4zF4M2khZgCIZkamZObW3tRP3d3d8TExMBisSiLV0hIiArUa25uhpubK2iOMJXg4GDU1NSgurpaBeKyiilx2IzRkG5oMhcyNzJKPbyK/ZVWMgZVslaImiAdEyMT0kMf5L3lfWU9D/ZBwhzYDwmfkcKC3gKvhzpJmAYhYBICwT7Qo6KfF8n4ZQAkMcEMYKXAJDe3VOakt0EvPElIhRS8uA4cG6+nhZXWRBIfCXGRcBP2hXuDfZHQJhk4LtNJ8vlUQNk3k8mEwMBAGI1GJZTV19ejpqZGZZORsAjuHSpooaGhmjiE4OBgJdjoY2v0AcXNzc2aAGEKEFdoE/hTNyoZesEOgOacOJ1O5V3jfmSyBu5LWYWbc026wsQCFPrq6+tVrA2VO/ZFPlPW3QDaYzmouMvAa2l4kcoBz7J8Ae1Cv7Ro8rf8H2hXhOU54v3lZ9KrKn/Pcy89pXqFXt6L1/K5pCWkXfRISuiTXmGQ7/XB5TLGS9IZpibmXtBnxJJKlKSZpDtSeZDrwXnmWurHL+dUziWvkcqL3nsheYuMI2E/pPfVzc0VR2exWDRZFqUBDmjHxDc1NWliYQjD5JwzWQehvVQg+DkTUcgYSSo0ssgp+9uRh7wztMLCQjQ1NaFbt2548cUX8c477+CXX35BZWUl3NzcsHTpUlRUVODIkSP45z//qWJRCgsLsXz5cjQ0NKCmpuayjFttbW0oLCzEwoULMWrUKPV5REQEAgICYLfbUVhYiLa2NthsNiWcAsDRo0fx9ttva/ZUfX09li9fjsLCQpUUAHBZtsvKyvDss8/ijTfeUFmfHn30UZhMJpXAoq2tDW+88YYyKrm5ueHFF19E79690dzcjKKiIjidTtxyyy14+OGH1XOnT5+Oxx57DIArzuDqq69GaWkpli9frgr+BQcH44knnkBAQAAAoLq6GsuXL8f999+PIUOGIDMzE8uXL0dFRYWSu5YtWwYPDw/U1NTA398fy5YtQ1xcHI4fP45///vfyiPx3Xffqb488MADmDFjBpxOJ4qKitDc3IyePXvipZdeQnl5OZqampCVlYU333wTixcvVlCoV199FV5eXrj11lvx4IMPwuFw4N///jdOnTqFhIQEvPbaa6quVUxMDJYtW6bGEhoairlz58JsNmP8+PF44oknUFhYqGi61WrF8uXLUVVVpTKQMkuWXMc5c+bgr3/9qxrLxo0bUVNTg8WLF6OkpASzZs1SCQHYMjIysGrVKrS1teHLL79UXjfAFVdx5MgRrFy5Ei0tLaiurtbU6fjuu+9UmtuSkhJlIC8qKsKiRYswdOhQtLa2asbSWdp/BfomMaZgzs+Advc2mQQZnK+vL2JjY1W9C3d3dwQGBipsKi1TUvHw9/dXAba1tbUoKipS1VCZlpSVa6WnQO8ip4AgGbLMLCKZgx5qJZm2ZPx6iJCe0RI/S+ant56T2EtlRR/3oZ9rqYhIwZ3zTkFVZl2RSgqVB3ogJL5Z/l7iyincSauptLjKfrGxj/o9IxUNCgtS2JGwCalocH45V3pBW6+sycPHgGBpbeSLwiLngH8Z9EqGHxYWpgLDqcjIQDi5jlQy+DxCqHhOQkND1fp4eXlpYgfkGnAsUgkzGo0dZurpjI2KGK3o+rMCtCcRoCJAgasjRUOeEULg+GppaYHNZlO0pq6uThOrpQ8w1hsoAC0t6WjvSGGX50Iv9OqNNBI/zybPgbTw8yW9D9Joob+GTSow8r1UPvQKip4WSAWYtE1PD+TZlTRExmaQnkjapI/tkDRFemCkJ6YjA5EU7DkO+bn0fHCs+vWRdE0+U+5ZvQLDc/57684++/j4IDAwUMVXyFS6nB/SKplxj/2gwYHzyNhBJlKhxZZwPUIOWaVcesI62jOduYWEhODaa6/FW2+9pWIyDAYDhg8fjhMnTuCHH37A6NGj4e7ujujoaPj4+GDlypWagqiAC4sfEhICLy8v9OjRAyNGjNBAac6fPw8AKsYDcNHz9PR0AEBKSgqKi4uxZs0a9Z5Jb1auXAkfHx9ER0fDzc0NPXr0UEG9gwYNwqpVq5CXl4fw8HCMGDFCY9hsa2vDqlWr0NLSgoSEBBiNRowYMQKRkZGa/nfr1g39+vVT77t27Yr+/fvDYDBg2LBhiI+PR3l5OVasWIGYmBiEhITAz88Po0ePVkpYdXU1VqxYga5duyI5ORm1tbVYsWKF8ix4enpi9OjRqpq8h4cHRo0aBbPZjNOnT2PNmjXo3r07vv76axw+fFjFv/Tp00cT/wG44LMjRoxQ5zk3Nxf/+te/MHToUISHhyMwMBCjRo2Cu7s7evToobwLe/fuRWFhIQICAjB69Gj07t0bwcHBMJvNGD16tEK1+Pv7Y8yYMfD29kaXLl1UtrC0tDSEhYXBarVixYoVKqDay8sLY8aMgb+/P44dO6bWsX///hgyZIha540bN+LMmTO47rrrYDAY0K9fP5X+uEePHvDz80NmZibefPNN2O12HD9+HCdOnIDBYED37t1hNpuRm5uLN954Q8k3vr6+6NGjB9zc3LBz5058/fXX6n10dDSSk5M168g92tniq64YOhUREQFfX184HA7U1taq4m90uYeFhWmYMNNNhoaGYty4cUhKSlLBvV27dsWFCxdQU1Oj0uDW19fjwoULuHDhAgoKChRR9fPzg4eHB1JTU5GXl4ezZ8/CZDLhL3/5C0JDQzUeDTIuQMvUmSaQ0AgJ0ZAWcxJ3ffCitKzRDS8te4xNAdqFRBJ3MgG62iXUgMoZ+2swuNI3NjQ0KEbJauj8ndPpVLELLS0tmrSxEsvd2NiIuro6jXLB7E3SExQREaECDBlkLyFxHQlG8lnS6ikhHoSHcW0IF5L3o7WT95QKEIUNzg2vl8xc9q+trU1ZODh+eqcoHOifwfmkUFBTU4OMjAyUlpYiICAAMTExuHDhgoIq0DrJLGh5eXmoqKhAa2srunXrhv79+6vUq0wVyL1XV1eH77//Xrn8CwoK4Ofnh7a2NjQ2NiI4OFhZHd3c3C6r+M5aBpyzixcv/n9x/v/HGxUu7gtZbwJojxNiYL3FYkFkZCRiY2PRs2dPpKamqqxTQUFBSgFsaGhAZWUlioqKUFBQgPz8fGRnZ8Nms8HLy0slTbBYLEo59PX1RXh4uBLM+JdWYQrPQLugLuMQKGwC7bECUijkOSNdoreKxg/CuLjmMuGChA7xrEmBnOdB0ippVKFXGWjHDJNu0QjB2AbSJWmEoHdIvpdCuvSssu8UpNkn6XXgmnMu9V5PeY55fwmd4tilAUl6sZn5T84dG/vN3zJmjazPzc1N3Yd0WSoOnEfpJdErFlREm5qaFF9jP+rq6lBeXo6qqipUV1drFCwqwe7u7ggICECfPn3QtWtXVdMlKChIeTtYSDQrKwvFxcVqr7e2tsLd3V3t8ZaWFtTW1qKqqkqTFpcxHbJ1Rgjm/4mCtGbNGnTr1g1Dhgy57F6XLl3C8uXLsWvXLpw4cQKJiYmq6Brbs88+ixkzZqBLly4AgKeeegoPPPAAEhIScPz4cezatQtPPPEEAFeBvMOHD+Nvf/sbAODnn3/G2bNnsWDBApSWlmLMmDHY8zv1Fzpqn332GXx8fPCXv/zlvx63vl26dAlvvPEGXnvttf/re8kWEhKCkpISDB8+HCkpKVixYoUmbuH/trm5uaG0tBQLFizAe++9B6PRiKKiIixZsuR3s0zp24ULF/DRRx9pUtj+p/bggw/iueeeQ2Rk5O96AAMCAlBWVoZJkyZh586d6vNt27bB4XDg1ltvRVlZGe666y5VRJBtxIgR2LlzJ8LCwlBdXY2hQ4diz549iIiIwAsvvICrr74aAwYMUNf37t0bx44dQ3x8/J8mvupKVIgrTm8rISsUdIk3pyAkXcY1NTWqCFFsbCxMJpOCLRQVFSkBjQyXucRJiOkGoyWyoKAA1dXVaGtzpbLtCAJA97tMkUqm5ufnB5PJpHHLA+3WKmldpkLF30thnveXioUe/kBlQ3o3pEWO8wm4oE9kXBwvP6ewQgFbelY4roaGBnh4eCjFQzJJrhnQnsufSpXJZFK1Q2gVbm5uVikTJVSLY2CTUCvOgX4egfYgZ+kFkvMgrfpk2hJ6IVMGGo1GhVmUlksK5xSaeC09OJwDzjMFXFq6peBGz5nT6VSCPbH9FH7Cw8MRFhamcP3V1dWorq5GUVGR8rhR6AsKCkJtbS1sNhv8/PwQFRWlhAUKGXxGY2OjBn7CfU5vSVBQkBJoGMjaGVtQUJCmyFl9fb0mGxDHx3Pq6emp9imFL+5xCpuyzkRzc7PyhtI7ofdUcb1Zq0IPHZTnlEI4zwgFfml5lBA5CefTM1neX3pRpfVf0iBpQJF/Aa1SIz+TVnoaVqSwTpom6Z+kk5KO8ezqPYXSGi6hVhJiyTngeeT8ks5JrwFfnCv+TkIFOSekL9z/vI/eI6anV1QWfs9rBUDNERVL2UibJF3ltbLKt/SgSKWHECo/Pz/VDyqzjMMAXFZixnhwfKTpXCMm9KABSSq63OdMeGKxWBQeXnpeOW/6itSdvQ0dOhTvvfce+vXrpyBCbAsXLtRYgRcvXozevXtjypQpGDx4MGpqatDU1ITExEQUFBRg3rx5GDduHMaMGQMAeP3111V2K8BVy+Ljjz8GAEyYMEGT2nbq1KkaGj19+nS0trbCarWiS5cuKCkpuazvEyZMwMsvv4y+ffuipaUFo0ePxhtvvIG+ffvioYceuky5GjhwID744ANcc801eOaZZxAfH49bb70Vhw4dwrJly343jmHIkCGora1FSkoKdu7cieuuuw7Tp0/HxIkTMWrUKOzatQtff/01VqxYAcAF59mzZw/Wr1+Pn3/+GRMnTkT//v3x4IMPYuDAgfj8889x7tw5/OMf/1CenePHj2P37t3qLEyaNAlLly5Fv379lNLu7e2No0ePYs6cORrh/PfW0W63o0+fPsoL4XA40L9//8sC4Z955hkMGTJEKWVyHUeMGAGbzYb4+Hjs3r0bY8eOVcHhbLNmzcK9996LwYMHw+l04qOPPsL27ds7VDI++eQTlJSU4IknnkBaWhpKS0s163jffffB6XRBgHv06NFhccxffvkFXbp0UTEvhw8fRkpKCqqqqvDMM88oQyzb2bNnkZSUpOqhdJZ2xdApPUGXLngyEVqEHA5XDEVISAhCQ0Ph6ekJp9MJs9mM8PBwuLu7Kzy9ZLB8TmRkpCKaPMAM1DWZXEW7CL2QhJaEvaGhQeHfqqqqVEXWhoYGZc2S8AypLFAgkSkH9XEbkjFLN78eZqWHA/B/KYyQcXHseniCPpOBPsaC8yCte7yewgozyrS0tGggW4C2qrf8Th//IBUB/o7zxe/0OG4qkRRApAVQCg8SviQhKoTT6b0THC+ti4QdMIibQqe7u7umYKEUhAhhIvMtLS1VdTO4JysrK+Hl5YWoqCiVXtdsNiMoKAhBQUEKyuPu7q68Rfzr5uaG4OBgeHt7K6bDKtYANJZoaYWl8G00GhXEh8KRxIR31sbzSqs/95M8J6Qjsrgi09lyXzOAXAqBtMLLgF6g3eIiY5SA9hozvAefL18SjkVFnP9LuqU/53qojRRIpUDP88bPubYyYJdNQh31e0AqUB3RHL2CzsazznmS51pa/PkM6XVg0yspsumho3ooGddNPx+SVshrpdeG99bDKzle0gcZl8NEG3K+uCd4jR5epzeWcA75neyfVEKo3JE2Sm8PlTOZppvGOO4vmThEvmTBQrmPSePJ18iT9dAp/VnsbG3JkiVIT09HWloannvuOeWlKSgowLp165Qwm5CQgOeffx7e3t6oqKjQYOkPHDig8PP5+fkYMmQIZs+ejdzcXLS1teHIkSP47LPP1PVWq1WT5YdFZpcsWQK73a6pa1BUVISUlBQsXLgQBoNBGVE9PT1xzz33ICIiAr169cLf//53dTays7PxwQcfqHW6dOkS3n//fbS1taGsrEwTDwIAxcXFWLduHZqbm7Fv3z58/fXXMBgMiI+Ph9lsVtctWLBAJdZZsmSJSlJgtVrx3nvvoa6uDseOHcPmzZsBuOJHjh07pn7/+eef49dff4XNZsOaNWtQWVmJ06dP45NPPoHT6cS2bduwf/9+OByuatWsXySTDOTk5Kix3XHHHbj11lvR2tqK999/X3mOTCYTFi1ahB49emjWcfr06Zg1axaMRiNuv/12DXzt0qVLqKurUzVIzGYzfv31V3zxxRfqGrmOBQUFuPrqq3HvvffivffeUwq9xWLBkiVLEBERgVOnTmHDhg2K7tTV1amihvr2zTffYM+ePXA6ncjNzUVjY6NmHUtLSxEUFIRFixahqKjoMsgeADQ1NalaHtOmTcMdd9yBvLw8LFiwAImJiUqh4Dq2tLQgNzdXY0jpDO2/qqMhLdbSMkXFg++pVISGhiI0NFR9b7FYEBERoaxlFCiktc5oNKpq4LQQO51OlJaWoq6uDh4eHgqjqLdMkUHabDZYrVbU1NSgpqZGZexgdWCJte4osJPBeVQ2KOzpcc2cDzkPMpZBCjBkQsSoS4bFRiFKCvnSssffS8snvSZ6eJOEAdBKLJ9NZi6hAJJ5y3XVbBhhTZZ/uUek8Axo03pKQUgKzHKumGGFwduS6XMOJMxCxl9wreT1rBFAeJiEqRDe19raisrKSgUtYFCl1WqFp6cnwsPDlaDr5+enUqJS0WAcB4UV7i3isin48HeyKJzey8V7UMkk05Q1YORvOluTsTf6dZGGC9ZLYfArISA8I4QdScGe8yUx9WwU9ijASQiRtEbLe8gEERTiGGguoTE8j1KJYJ/0+0IaBdgvAJrzAGg9pXohXu+B0Cv80pDCZ9Ggon9uRx4N7kOpSMmxSCFdfi6NJsDlMVT68y8F/Y6UFP0Y9R4SGRsiPR5SceCL68p1/D06IgV13kf2nY2/1ytc7I+ejvNzqSzI+hhUNCT9kMlVJKSPv2XRNgDKQEZ6zhcNdjKmkuvdWRWNm266CTExMYiMjMQNN9ygvFC5ubl4+eWXFe0PCQnB9OnT0a1bNwQEBMDLywupqalwc3PDjh07sG7dOnXPXr16Ke9FQkICTp48ibffflt9HxwcjISEBPV9SEgIfHx8cOONNyIgIACBgYFISkpS1yclJWHSpEkwGAyIjY1FeHg4PD09MX36dISHhyM+Ph5Tp06F0WhEdHQ0ampq8NprryExMRG+vr4oLCzEF198odbI19cXKSkpyhBrt9vx0ksvoampCUeOHFECb1ZWlsYjNnHiRKSmpiIgIAA33nij2jO1tbXYvHkz6uvrsXfvXlVU7rvvvsPp06fh7u6O1NRUrF+/Hrt370ZdXR1eeukleHl5IS8vD2+++SZSU1OxceNGfPvtt3Bzc0NqaqqqdcT6ETExMaiqqsJrr70Gu92OYcOGYdCgQWhra8Orr74Km82G6OhoGI1GTJ06FbGxsZp1vOaaazB8+HAYDAZMnTpVEzuTnJysoMwc2w8//IB3331Xs47r1q1Damoq3N3d0b17dwwYMEAlDACgWcdDhw7hjTfeAOAqDBgWFqbGxuyHycnJAICPPvoIu3fvVmPlOv7zn/9UikB0dLTao1FRUaqsQ0pKijI6svXv31+NdfLkyeo5ch09PT2Rmpraoef1z9yuOEajS5cumhgMBpuRAAcEBKChoQFtba50lREREbj66quRmpqKiIgItLW1ISUlBRaLBVlZWSgrK1ME2W63o6ysDCdOnMDZs2dhtVpVylkyycrKSoSEhCAlJQXXXnstamtrleBHyIV0rVOIkMICG4PzaPHXY3SJjzeZTCroXFqiiB8nREYKfoT8SAgErVIUagmfANqthVLRArT553kvurulUkHGT0sYrVwtLS0K28v76tNQ0g0vc/9LQYRCFGEa7DOfL+FKnGuZ8YSWfb2QIAUxOV5ii6XyyO+pvFitVhU0SSWV4yBjJYMnUyaDlrUBpMfEZrOhqKgIZrNZEwB/5swZ9ZnVakVeXh66dOmClJQUlRrx+PHjOHPmDOx2O6ZNm4agoCD1+6uuugqNjY0oLi6G1WqFl5cXLl68iKNHjyInJ0cjiPn6+mqCzAml4vgJ82JgdHZ29pUc2z9dYzpq7hEqwkC7UtrY2AiDwYCQkBCkp6croSIuLg6BgYEIDg5WkD8K/vX19aioqEBOTg7y8vKQm5uLzMxMFeRJpcbpdKpCS0xXzO9koPLvFUSSGan0NXAYoM5zJlPp0sMivyNdkJAteS7ojeR1egu/Hn4k4VzcKxSImUKZxoCO4mIo5DKIXnplKYRzHLLyOQVnZlaStR3YpPLH9eY9pIeEY6QhhHRBjl96LsiLeB8aKui5ZpNWf2n5dzgcKr04aSKv5zOkp5f3YV+lEO9wODTxdTSWVVZWKsMJM5/Rk8E0wFVVVUpA7NGjh9rjISEhCAwMhNPpVDU6ysrKUFhYiOzsbJw8eRK1tbWKV3h6emoUYD8/P2Ws45iA9ngTvaW8M7T/xqMrsf3nzp3DTz/9hKioqD8c98WLF/HOO+/gpZdeUp/JGI0zZ85g27ZtWLhwofr+8ccfx9y5cxEXF3eZ0vzzzz/j3LlzuPfeezt83o4dO2C1WjFr1iyUl5fjpptugtFoxIYNGxASEoKGhgaMHz8eX375JUJCQvD2228jPDxcZcZat24dUlJSMGzYsCuel+7du+P06dNITk5WdTQAV9akTZs24b333sPFixdx1VVX4fTp0+r7Y8eO4aeffsJLL72EoqIiDBo0CL/++uvvxmh8++23KCsrw2233dZhP7Zs2QKTyYRJkyZdcd8BXFGMBtdxy5YtOHfuHLp06XIZVOqPGmNtFi9ejOLiYgwbNgxdu3bFK6+8gvDwcNjtdsyYMQNvvvkmQkND8fXXX8NqteLmm2/u8H6MtZk6dSoqKipw//33/8dUvfrWu3dv/Pbbb4iLi/v/Z4yGtBATkkICJgsmkbEFBgYiKCgIXl5eqKioQEhICJqamlBRUYGSkhJlTSaDLC8vR11dnRKamWWhoaEBFRUV8PX1RXBwMIKCglBeXo7m5mZlCaK7mgzQYrEorwnTWjLAltmriPem5ZSYfrvdrqysFIasVqsSXGnFll4cyewaGxs1RZLYNzIl4u+lsA60w6b0AgSgzZjCwEG6xPWWN6fTFSxOWBBjG5iPnbAzWoAZbO/m5qaEWymsA9B4faTXg/h19pHxHNLSScYtr+NfCftibm4KFawlIS2ynEvG6EhLLeMgOE8Oh0MJThKSxvmipdPpdMLHxweJiYmXxd4kJSUpa2hISAjOnz+vKbzFbCatra04cuQIqqqqEBgYiICAAFRVVaGlpUVlXausrIS7uzsiIiLQvXt3FBYWaiykVqsVDodD7TsKBewvhdG2tjZNSrzO1nx8fDReQe5jACopBAB1BqlYUSCjYk5Pot4zKOFNfM/zQq8GaZjc6zJ2iHSMhTrl/Wl1lqm5pcJIwwVhXRwnBUW5jvqzQxgm70klmueV1zJ2RSa1kLRUNp4F9ocWf3mG5BmUnhdp/aY3h8oh9z/nh8YT0jV6nwifBKChVTJWhcYM9pdNjpuGLfaTtJf3IR0gdFZa62WsjMPhUPxAxvLJrIVcD0kn2R/uAWkYkh4ZLy8vjffVw8MDAQEBGmXSaDRqihdyHJ6ensrwQWsnFSe5Z2k1ZlIEd3d3pQhSceLzaSTjffSxKp21rVq1CiaTCQ8++CAAYMWKFfD391fC/Msvv4yoqCjccccdGDhwIB577DFMmjQJ3bp1+4/0c9SoUUrpY3vzzTfx4YcfAnDFU5BOsb3//vvYtm0bnE4nNm/ejFOnTuH5558HANx6663qrAKuyuSlpaWYP38+AFdcAPdlr169FFymd+/eio7t27cPV111Ferr6/HEE09oFPmFCxd2aBgxGAzYu3cv1qxZo/rOduHCBaSnpyM/Px+PPvooxo8fj/Hjx2PcuHGw2WyoqalBenr6ZUlHbrjhBpV4o2vXrgoiVV1dje7du6tCqfv370drayvuvvvuP/ScybS8bEOGDMHq1asxePBgFaPh5uaGn3/+Ga+99ho+++wzOBwODB06VLOWe/fuxbp16/D+++8DaF9Hm82G9PR05OXl4eGHH8akSZMwbtw4fPXVV/jxxx9VPIq+3XjjjUq579atGy5duoQzZ85g7969sNvtWL9+PRobG9GvXz+0traqdWRjyl8mBXj00UcVvejTp88VK/lyHTdt2oT09HQNDLAztCuGTtHqJL0E0u1PhYPBK8HBwQpuQsJKYkermLRMkMGRwVPhoPXJYrEgNDQUwcHBsFqtCiZD4Z0KAzPHsC/E6BOCQcGaTJYCs3Sjk7mrSTK25yQnnEsf9CeDJjkvMlaB/3O+AK3Qzc+lJ0MGffJ7PfSC1kjOnRQWKBQYDK4CXSUlJUrhYH+lYAFAo2BwDDK4U3oaOC49REBveeRLL5BJWAUFBT5HFi3ktRx7c3OzshDKlx5LrX9J+IDT6VS/MRgMsFgsCgbFOacyajKZNBm+6PGi4MpramtrlfDp4+Oj9gM9L21trqKWYWFh8Pf3V32SFlHue72VQEKEOnOTZ55jkfEq0rMos0VJxQCARkjVw3I417xOQmIYgCuzFEmFgxAVCbPTewH4Xo5F7m3pwegIOiThVh29ly9JS+SZ4bmQNEYqTZxHKtnS69oRxEjCSalk8KxJKz0zK/E3MhZCwjr1kCI9BIp96AhCqZ8bSQM4Ju4HPVxMehNkzA55lqy1w+tkPERHsS565aejl1RgyX/YR1k4z9vbW9XA4DXyt0A7JFgqoxwfn8c4Jcljeb6kAUrWEtLH/XRmWnLw4EEcOHBA837//v3q/ZEjR7B3714AQFZWFnbt2oVvvvkGGRkZsNvtGDduHO6+++4O752Tk4OqqioEBwfjmWeeQWBgICorK5XlPzc3VxPYe++996Jv377KyxwfH4/w8HD1/YgRI9C3b1/1ft++fTh06JB6X1BQgKKiIphMJkybNg0RERGIiorC1KlTYTKZ8Ne//hWTJk1CZmYmHA4HiouLYTQasXDhQnh5eaGkpASXLl2CwWDA448/jt69ewNwre/27dtx4cIFhIeHY+HChUqBNZvNmD59Onx9fXHmzBns2LEDAHD99deje/fuaG1tRUZGBu68806MGDFC9TU3NxdlZWWw2+3IyMjALbfcgvHjx6v3hKnTc5Cfn4/i4mJ4eHhgwYIFCg5kMpkwf/58BROTrbS0FF988YVGEXY6ndi6datG8ZEwMQBqrGFhYXjmmWdQWVmJnj174u6770ZGRgZaW1tVQWgA+P777xVq4ZlnnkFYWBgAV3rchQsXKo+fp6cnbrjhBgQHByMxMVFB4vbt24cffvgB+fn5eOqpp2AymVBUVAR3d3csWLAA5eXlmj06aNAgDB06VEHc6urqEB8fr9YRcHn8Fy5cCG9vb4wfPx533323Zh2bm5uRkZHR6WCP/5WiQcIrCTaFW6loMBCWtTNIZOlKltkfpGuegiKz8NhsNlVQJTQ0FGFhYTCbzairq9PUNJAwGWaloWArcd5+fn4qe40sSkWLnRR2JUOUUACmGZQWdokPlrhxWhI7gkVRMNBbZaUSQQamF2DkNQCUckHmqi9KR09NdXU1GhsbNVAFGeiqt4jKfugDa4F2660cg1xXenKk4qFvUnHlfFEJ4Gf8PcfGzCx1dXVqj3QUbCsFLQmv43OlQMoYAImt5n6iouHv7w+Hw6EyRMhYGXd3d2U9cTqd8PPzU0IPx83YC8ZqUCggDpvzRQgV55ieH/ZVn4miM7WOFCYK8Fw3Khk8u7QES6+DFHw7wtAD7YXrpPJNgwQVDemhk7ApnnfuI/l8eY3+9/yNVDQkLZHKhBSOZQyFXuDtSGjXx3pJbyPfA9AoSXK8UgGTQjafy2tkIDv7KpUMqWjwPvrzJz2ibHqjipwXyWukIimVLtJFvdFFxqpIusLv6G2Qc09aqVfypPGIaywhY/IzCb+TWdQ49xKeKoO/peLKMXEdeBbkuuvhamazWVmzOeeka4TAsWAl6Yd+v3bG9v333yvhOC4uDl999ZWyZAPAnj17sH37dvX+yy+/xDvvvKPeDx48GNOmTVPvAwMDERUVpXlGaGgoXnjhBZU9Mzo6Wn3H9OeAy8o/cOBA9V1xcbEmOHzSpEkYNmwYjEYj4uPj8fHHH+Pbb79FXFyc5nnu7u64++67ER8fj/j4eNx9990wmUwYNWoUxo0bp8bq6+uLyMhI3HPPPRpeYDAYMGPGDAwbNkzFsb7yyis4ePAgQkJCMHv2bOX1t1gsuPfee+Hv749du3bhzTffREJCAm677Tb06dNH3XP69OmasUVGRiIoKAhubm6Ij4/HTTfdhKFDh6qxsT98TwHa3d0d99xzD/r166fKEtx9991ISEiAt7c34uPjYTC4Ct3W1dXh2WefRVNTE0JCQhAREQGHw4GPPvoIGRkZ8PT0RHx8vKJ1Hh4eiI+Px7Jly/Dzzz8jKChIje2aa67BjTfeqPr/22+/YdOmTQCAN954Azt37lSeMFZk9/HxwX333YeePXsiKCgI3t7euPfeexEWFoa0tDTccccdMBqN+Prrr7Fnzx54eHjgnnvuUfuBY9u3bx8+/vhjGAwGxMXFYfLkyWodAVcMUZ8+fTTrGB4ejnvvvRfe3t4YOnQopkyZotYxMzPzshoqnaVdcYwGMWlkoLKia3Nzs4Zwenl5YerUqQgPD1dVTAMDAzUYVQq/FPh+/vlnZGZmorCwEE6nUy06LbyjR49WNTCKiopUwSIKI76+vspzwQBcPYMmdErCHdgPQmx4Dwq2dXV1KiCIcBxCrmQhPKks6C1wQLtgxPtI6ywVNXp+ZCMsS95HWjJtNpvyVNjtdpjNZmXhcjqdKvi9vr4elZWVKihNxoww7S2FMEKg6H3Se3jIBCWRkzAR2VdpRdMHhQOurA70TrS1tWmCfCmgMBMThQbpeZJxDcQ9MwCSmHzCrbieVJJJdKWFlAIK70vlhnEcFRUVcDgc6NmzpxKwrFYr9u3bB29vb1xzzTXo378//P394e7urrJTnTp1CpWVlWqe9+3bh1OnTil8tZubq+ZMW1ubqnArhS+DwaAw8EajEefOnbuSY/una8nJyeocyGr1hLI1NjaqFMLR0dFIS0tDaGioCvoLCQlRkBcaCZjphLj1S5cuIT8/X2XzYOYqPz8/FcgZGBio0oRKIVDvAdBbroF2miSvJR2R1mag3XggYy2kYMq1ZR9kXRo+SyopUtgGtAVFGegpFRE2KnLSms9r7Xb7ZZXuacShMkH6wjNNWBlhtHqaSIu99CTqlSRJF0hDSWOlEYfj4DXSYyE9vPS+EG7BNZMea8LRpFdHb+iRRhKHw6HxQJBmSq+zvJZeBMnbaDThvEoFymAwaBIOVFdXK+EqMjJSQ6+NRheUmBWNa2trceHCBRQWFqrkJ9Ij73A4UFFRoZ7JOZXZ9zpjrJfBYFDY/unTp6OiogKzZs1SAiTggifFx8dj+PDhV3TPZcuWYdSoUcobALgK+RHbf/vtt/9uHY0rbUFBQaqORkxMjML2XymEjXGBDzzwwB9i+1evXn1Z/YX/1FJTU5GRkYG0tDRV/LCjJmM0CgsLMXjw4MtiNPbv3w+LxYKysjKMHz8eu3btUr//5ptv0NDQgOnTp6vPJk6ciM2bNyM0NBTvvfceLBaLEsi5jqNGjfrdWJu+ffvi8OHDiI2Nvcw7om/6eih/1PT1UPTtxx9/RGFhIWbOnPmH9/H29u6wjsYnn3yiibX5T+3111/HsGHDNIrgn6FdiQrxXxXso3Ds4eGhiBcZPgVADw8PREdHY/r06fDx8VECa0xMjCJ+Eq9IxseqjzabDbGxsQCgihyFhobiqquuUjhmi8WiCsuxmF1DQwMMBgN8fHwQGxurYcQUPMkMiNWX8CEpaLJaJieReGMyM+KdZcwGAI0gwmmlJZ0CI4UTXs/7SKseBRbOD9AOaZKChpubG2w2G8rLyxUumXU3pOVSWv9pTWcMB8dlNLoCzWkh+z1Ygt6iLK1i+vdktPIe0mNQVVWlBBiHw6GpayGFKs4BPQIUrhjEKQUPPYyGFkjuQ8AldFFo59h9fX3VmCXOm1bQ8vJyVFdXo6CgAFarFdHR0crVWldXhyNHjqClpQU9e/ZE3759VUrWqKgohIWF4cKFCzh//jxMJleNmPLychw9ehSXLl1CRUWFsnQyVkUGElMwkzA6fUGpztJI4CUUSVrSrVYroqKiEBkZifj4eHTv3l0Fzfr5+SEsLOwyIwcFzPLycmRkZKCwsBAlJSWahBN+fn6Ijo5GZGSkMkowUJznsCMhUg+ZkoK/FIAZu6GHW0lrOcfN/pCOcG0Zv0IawX5Jo4n02kicNr23PFvMUMbvZAwH79GR9Z9JJSSkiGeKwjLTplKhoIGCiqNUOtgf0nyeb46lI1gS6Z9eudNDKHnmO0rwIGFW3F80kHG8vIdUZgBctmZ6gxKTXNCoxhgPzpkcn4yfa21tRW1tLex2u4I0sV80bFVVValCkpGRkQgJCVG0ijFMlZWVsNlsqKurQ2lpqaoxRUix9KCXlpYqfmQ0umr30Evr5ubWKQt/GgwGJeDn5eWhe/fuyM/PV55moD3pRG5uLn788UesXLkSW7ZsgcFgwK5du/D222/j1KlT+OSTTzB27Fi4ubnB398fmZmZ+Oabb/D555/jo48+QlpaGs6fP4/AwEAEBgaqKuGEcZ8/fx6bNm3CoUOHVBG8jz/+GKdPn8bbb7+NXbt24a677sKJEyfg5uaG7t27Izs7G+7u7oiJicGZM2fgdDoxduxYLF68GNdddx1aWlowfPhwvPLKK7juuuuU0ctgcFWYJg9yc3PD7t27sWzZMmzdulWNPTo6Gt7e3gq+NH/+fPTp0we33HILduzYgU8++QTr168H4IIbbd26FevXr0daWpqCS02dOlXVorjzzjtx2223Ydy4cUhJSUFDQwPKysrQrVs3ZGVlqZpTHBtjoLp27Yrc3FwMGzYM//jHP3DdddchOjoaTqcri+jevXvx1FNP4ciRI4iPj8fZs2cRGxsLg8Gg+BvXMTs7G927d0dxcTGampqQkpKCs2fPoq2tDd7e3khNTVW1TvTxKLKx8jjXEXB5ab799lvcdNNNGDBgAO644w6MGTMGKSkpaGxs/F3lJSkpCa2trf8xKJvrpt+jcXFxMJlMCpL3/PPPIzY2VkH6/vGPfyA1NVUpMlFRUfDx8fmvAtr/J9qVqBD/VR0NSZApoJHI8jPAlUFHuucl5EcK3byv0+mEzWZDa2srPD09ERoaqomjYCYfFu+ildhsNsNsNqtgOxLquro6jatfMjRaUskU9dAfSaQlFltaluUE62NX5LwA2tgWCiHsh1Q6pIKhd2lLuJT0LkiFhtdJKzj7K2E3bGSCZLgShqJXLiQMQsKxpJVVCkJ6GMvvWWPl+tjtdo0VlUoLvTwydzzhCYQ3+fj4KAurhClI4YBCl4T3dYSxlooM94MeF05XbVxcnKqVwSxmtGBSuJJxR+yH3W5XsUMUdKWQIhMlyDNCy3hH+bg7S5N7XsIlnU4XfFFmZSJsii8KSHo6xP2tD3ilFRqAMoJwH1Fw471lml0K1RJCI+MPAG2MkR7mI1OpSkGd/ZDWeHk29PAlqdBIesnX70F9JPRI3kdCizgGziWfIekkoDUWsMl7s79yjjgH0tsg6SsVaN5TT7P0dIy/0/eF/ZdzKIV82T+OSdaekB4pSTdkjJ+sEs/nSxid9MxIOiIhU7yWNERCvvTJCUhD5H7Tr31HvIzxiaRtcp14PQ2D+rnqjK1nz57o3bs3nE4nTp8+jaFDh2LatGkwGo148MEHVWY+p9OJffv2aQqc/fTTTygoKEBtbS127dqF5uZmFBcXK0v+gQMHVE2IkydPoqWlBYmJiRg0aJC6R3x8PIYOHQrAZfmWXoDDhw8jMzMTLS0t2LVrF2pqatCjRw/Mnj0bp06dwl/+8hcMHDgQp0+fVmtTWlqKPXv2qDUpLy/XvAegxsq4BGmgBVx7+oEHHkBgYKBGGM3MzMSvv/4KANi/f7+mxsWBAwdw8eJFNdbm5mZcvHgRP//8s7omLy9Pxb9kZWWhqKgIHh4eGDVqlKrZ4eHhgZEjR8JisQCAgnwFBQWhuLhYjSU3Nxd5eXmw2+3YvXs3Bg4ciFGjRuH06dNwOBzIy8uD0+nEY489Bk9PT+Tn56O5uRlz5sxBdnY2+vbti8mTJ+PUqVOYNWsWevXqhcbGRpw4cQK7d+/GpUuXEBISgscff1z1LSgoCI8//jgsFgtKS0s1SgbggsPv2rVL1cyQYy0sLIS3tzfmzJmjYEteXl6YM2cOWlpaNErGtGnTMGHCBLi7u+PRRx9V6Y65bsOGDcO0adPg5uaGv/3tb/D29tZk/Dp79iyOHj2q3p87dw5HjhxR74uKitS63nvvvejXrx86S7virFPS4k7mSegMM/5QKDSbzRpLbEBAADw9PVXQst5ab7fbVXA3BUdmlnI6ncoCL+FSbIQM8N719fWoq6vTxIFQ6ZExBfyejIPEl1Zt/k4yYYm3p8CjD4Ak4+D4WltbNfeh214yecmQJSOi4iHd8MyyRWFFMnEpUEhmLJUtGUxLy6QU/CQumV4fqZAR5y7HKHHi8kWLrV7RkPMlhRRZHZtCIBU3vYeF/eU6UEmQ3hypoHGcFCi5vvIlvU/c75xDAEro8PHxQffu3REcHKwqglssFjV/rNYukwdQeaDgQ3gNhWFaYj08PFSqW4mploKdjOHobI1rxHFQwKKiKWF8hMFxrnx9fZWHiveSArSMT+K+5RnhOWQwOL0HUpE0Go0aAZn35vmX+xxojwEBtGdP9k02CQkkZEdi5Wldl1AcqdDoLe96yIVUTOQ5kc+WVnrp3ZB0QxqVeIbkNbTCy/9lumlpQZf91Qu5+vMlxyC9MXqPr36epRIjFSSpbEgBXBpoONd6BY6fy7XkPQlf1MNAqUxI4wnpuzRAyTHQQML549mQ6YTlXmBfpBeMdETCs6i4c9/TYyJhap25TZkyBSaTSRXdmzRpEkJDQ1W16fLycuTk5CA4OBiLFi1SWboqKirw73//GzabTWVwAqAgx+Xl5XjhhRcAuHh8WFgYSktLMWjQIEyfPh3r1q1DaGgoxowZg6lTp+Ldd9/F+vXrNenzP/roI7S2tsJms2HevHkAXJmnHnroIbzzzju46aabUFxcjB07diAiIgJVVVXIyMjA66+/rmh+dnY2Vq5cidbWVgQGBsJgMKi4j8DAQBiNRlRWVuIf//iHeq7BYMDf/vY32Gw2TUraH3/8UclqLGwXGhqK8vJyLF26FIBr74aHh6O0tBTff/89vv/+e/X7w4cPXwbV9fX1xdy5c7F7926UlJTA29sbTzzxBA4cOIDCwkJ4enpi7ty5OHr0KPbv34/jx48DcAn9TqcT1dXVeOqpp/Dee+8hPT1drSPgqn/x+OOPq4KEMTExmDt3LtavX49x48ahS5cu2LRpEx588EE0NzfjxIkTAFzV3gGgW7dumDdvHjZv3oza2loEBQVh3rx52LJli5KhysrKEBoaquCK3Ae1tbWXKSK+vr544okncP78eTQ0NMBoNGLu3Lk4dOiQpojjjTfeiNraWuzZswdz5szBuXPnNIrE5MmT4e3tje3bt+PRRx9FYWEhLl68iODgYJSUlGDjxo2qTlxpaammYKS+3X///Vi7dq1GEfkzt/8KOkWhwGBwVVPWwzu8vb0RERGBwYMHIzo6Gu7u7rBYLOjTpw/Ky8tRUlKCmpoaleubBN7d3R07d+5ETU0NDAYDYmJi8Ntvv8HhcNXnGDx4MAICAuDt7a2ET6nwBAUFweFwqIreAJSl2GAwwGw2a2AzMkWsXji2211B0zKrB702VEQoEBJPTagAr6cAD7THX0glR2/JY1/4mfQCETsuhXIqKxTO6TJvamrSpM4kcbFarbBaraiqqkJqaiqCgoJgMBhQW1urgSdwnWk5JlOUwaJk2OyDZIb6sUlviMFgUIIlMfkyiJ1WZAqaMsARgFJyqLySITOehAImlQmmX5YWPSqGZMZhYWFKAWA/OY8yOw0rfvr6+qqMI0lJSRgzZgwaGhpQVFSE8+fP47fffoOfnx/i4+MxYsQITeCxj48PMjIyFMQvKioKp06dwoULF5CdnQ03NzdUVVXBaDTCz89PIwzprbR2u11joetMrUePHh0aLChk2e12xMTEICYmBklJSYiNjUVQUJDKOsdrKMwxAN9ms6G6ulpBpxgPU1dXp1Jjd+/eHWazGb6+vjCbzZoMdOxHQ0ODEtB4BthfwmSkZZsCpEzPyu+bmpqUYCfPGC3RbW1tmhof3Ne0cMuzw++kR0AmS7Db7Zel7KXwIy3qem8oDQ5OZ3s8F88jjT8yo51Mv0p6zPWTMRqSPkrFHoC6n2wU7im8U+AyGAwaPkOoK+kkIVnSU9SRJ0fCDklDpAGDjZ5IeQ/ZdwCKHkvjjV5plcoCaRMz8jDlOhUCs9msaHl1dTXa2tqUYJyYmIigoCDlrSC0kgYM1vchD6ivr0d1dbWqoUSaQsU+JiYGZrMZDocDNputU8Z6SeVb8oexY8di69atiIyMRHV1Na677jp8//33iIiIwCuvvIL09HRcd911KCwsxPPPP4+33npL3XPZsmUYOXIkrr76avVZeno6zp49i9TUVGRlZak9eOrUKXz99dd4+umn4XQ68dtvv+HHH39Uwuqvv/6KQ4cO4ZFHHrms3zzTgEu5KS0txeTJkxEQEID33nsPoaGhaG5uxuTJk7FhwwaEhoZi/fr18PHxwYQJEwC4oFnh4eEYPXp0h3OjF+lWrlyJYcOGqcxXr776KsaPH4+rrrpKXZOamorz588jPT39shiNefPm4ZFHHkF8fPwfPus/vQdcUK2GhgYVoK1fxyu5l5zH3xNff+/3zz77LG6//Xakpqbi1KlT+Oqrr/D000+r636vHorB4Moydf78edx3330dPluO5Uq/HzFiBL777jtERESguroaQ4cOxe7duxEZGfmHaZj/aOz/0+1K+nHFHg2j0agsqWQgEl4g4Q/e3t7KktPU1IS6ujpYrVblzdAzXjIVYoJtNpsS4IODg9VAyCAsFosGX0zB3Gg0qrgQCuVGo6uOBNBu1eP3sh9SqCA8pa6uDi0tLZpgbD5Ptra2NpVhiwoN70sLphRm2WTcBQVIzg+tYSwKJiEJVFqoVOghaTIjCxkdBRGn0wmr1arph947w7mihVd6aaTwK61s/E5aYaUHx+FwVYbnPqLgIgURmdeeY5eKAhktn8E4Ha4l953D4dAEWTudLiw/18jDw0OlrpWWXaa75X7k/rVarSgoKEBwcDDMZjP8/f2VIGEwuAq1hYWFqeD02tpa5R1paGiAw+GC/wUHB6OtzRXsTStkUFAQKisrUVJSooShtrY2TZ52p9OphFzOfWdu0npMTHlzczOKiooQHh6u9rs+ew+gzVql36vSykulmIXkCHFjTQ7uaWkEIPae+4JnTu+JlNAZ6ZmTSrbT6dR4Vfm5FPb1MBcqCoDLW0G6I2mP9DbQei49olTgfH19L/PWSAFYnlveh8/gmaPSQiMOhXj2l/yAsVJcNxoApAdSWtFpbOB3khfwHnJdqQzwO66F9LDo6Zm8l6SzhC1yXeX9SV+lBxTQxofoDSgyTobP5D6l8kMaLL1tEtbF2DPuSSobBoNBxRHx3oxVIr3ib6WC5uHhoZRHGmSMRqNKiMEzp4fodqb2yiuvwM3NTQn3TqcTv/zyC4YOHar4/bFjxzB48GDU1tZi6dKlipeOHz8eRUVFSE9Px/vvv49Jkybh9ddfV5XCv/zyS2zZsgWffvopBg4cqOAxPN+33norrFarej9z5kz1TAC45557YLPZLuuzpA+ACwkydOhQZGZmwmQyYcSIEUpx3rdvn4rPWLhwoeaMLFq06DLa0qtXL6xevRrjx4+H1WpFt27dsHbtWkyYMAHLly/HmjVr1LWrVq3CBx98oPn9pUuXMHDgQFy6dAmAKx5gy5YtuPXWW/HRRx/hhx9+AAB88MEH+O2337BixQo1jmnTpuGBBx7A+PHj4XQ6MXHiRMyZMwfjxo3DypUrYbValefl8ccfx/Dhw7F3716MHTtW4wkCgKVLl8Lf3x+PPvpoh3MHuIoNrlmzBhMmTNBk9/rqq6/wySef4PDhw/j4448xdepUVW+Cv3/33XexZcsWzTpGRERg69atuP3227Fhwwbs3r1bXf/Xv/4V9913H8aPH4/77rtP0Tx+f/311+PJJ5/EuHHjNPB0p9OJ1157DU6nE08++eRlY+D/R48exZAhQ9T+OX78OAYNGgSr1YqnnnoKqampmDVrFr7++mt88MEH+PTTTy+7V2doVxyjIV3dQHsGEEnMJZ6ahIxMTAYnSqYoBVagXZA3GFxZdiwWi8aiRUJOjwOtatKqLj0v+jiRjppk5uyfhNzwnhSE9ZAA/X07gjZJzw/QMSyro3nmd1KZ04+FzFKfAUXCxchUWLxPKlp6uAPnRM/w9UyfzFHOnxSkJOSA80QmLfvMcUpcs4REyfgMCugSP02vj7SMSyw2g0BlCmRZjE1vYZdClrQcEDbh4+ODuro6FBUVwWq1oq2tPZsQPSCELABQ35vNZgX7Y5pnPz8/hISEqL5y/8m9QCu+9Oh01kYLOeeallzOmfSY6YVkvcdMKu6SlphMJhW3I1Nay9gsmYZUZoLS7y0JNdHTP7nX5ZnTw3s4Dj1sRa9E6GmKfMnn8BxJRUL2UdIZeY3sl56WSDijnuZIyz7vKe8vnw9oa+zIv/Il6Yk87x39Tq6vnB+pLLGRZvClj+0i1Ij0Qu4B0iP5e34n+6hfO33WLGkY0sfhSCMYeZ2M1+G96BHTp+2WdFManPQB+PJ76VVjsD95YmdtOTk5sNvtmDVrllL2amtrcezYMcycORMpKSkqSUdbWxvi4uKQlpYGwCXMlZWVoaGhAcePH0drayuioqKQnp4OADhz5gxKS0vR1NSEQ4cOYdq0aZo6GKdPn0ZBQQH8/Pwwe/ZsFBUVwWw2Y8aMGer3eXl58Pb2xuzZsxEeHo709HTceeedas+kpKRg5syZOHbsGGpra1FVVYWjR4+qNbFarThy5AgcDgcSExORnJwMo9GoAoX18J76+nocP35cnUuOra2tTRWaA4Dbb78dQUFBGmhVv379MGXKFBw6dAhTpkxBv3790NzcjN9++03V3yL0iR5jji0sLAzl5eU4deqU6ntFRQVOnToFwAUBk4lLMjMzcejQIZw4cUKd57i4OLWOubm5yMrKgtFoxF133YWUlBTNOK+99lqMGTNGrZtsZ86cQXl5ORobG/Hbb79h+vTpmixit9xyCyIiInDy5EnNOra0tOC3335DY2MjSkpKkJmZidmzZyM0NFQzlvPnz8PLy0uzjtXV1Th58iScTifGjx+v8TJlZ2cjJycHbm5uKpUvALWOycnJqK2txdGjRzFz5kykpaXBZrOpPZufn4/MzEw4na4Yj7KyMnTWdsXQqZCQEMUcSHQpTLe2tsLf3x8BAQGIjY3FiBEj4OvrC5PJBD8/PyQlJSEnJ0e5vGlZA9qFq3379qGqqgqtra0wm82orKxEZGSkSkdJIm8ymZTbXlpyqqurlRWHlgsG49LyRIZBiA0ZGQk7CX95eTmcThdsiZVXCeVhlht5P8alSLwtmQmfLb0lMqhVxkQA7TEPkolJaxUFKTKO5uZmWK1W5UqnVV0qAo2Njairq0NlZSWcTidCQkI0Qi8VIpPJhNjYWIVf10MvuHb0KvD30nonrYm8N9dZCgu0iBI+xbTB+oBfGR8iCYveksz+ci7d3NxQW1urUcCkV0DGfFAwYEICzp3dblfzlpOTo4RXp9PlPk9OTlb1Ympra3H48GFYrVZ4enripptuUimaDQYD0tPT0dLSolKwcn81NjaitLQU+/btuyz4kx4fb29vBbWgB0ViQztTi4qK0giZUugxGo3o0qULIiMjERUVheTkZMTGxiI4OBgBAQEICAjQrBktxTU1NYpZZ2dnKy8SPRZMIBEaGqrOKZVNKfRLSCKVIWk04LmUihD3tIQikaTSukyBVSrg0oLN+xHuySa9v6RpbLw30B4LJpV62U8pvErDi6QrVAD1GZxkNW++uA/9/f1VOnAagzgOpsnm8yVNk4oOz7gMgue8ssksgcDl2ezobeEa6OeKBg8ajGQ/aO2XkCvuLyoy5CFSwZNNenQ4t4RNcX8zfS0hflQimpqaVMY7rqvValWem65duyI8PFwpy4zfIkrAarWitrYWtbW1sFqtKvtUbW2tghRyrKSP0mjTWaFTADBmzBisXbsW6enp6kw1NDTg3LlzePrpp7FlyxYEBASgsrISy5cvR2pqqoIf6dvzzz+Pa6+9FsP/33S4hKpVV1fj119/xWeffYZ//vOfAFwxEs3NzQgKCsKxY8cwYsQIDBs2DA899BB69eqlzmBISAjOnDmDCRMmIC0tDYsWLUK3bt1gt9sxffp0Befy8fGBw+FQaZkZxEwr99q1a+Ht7Y0777wT58+fxxNPPKGs8sHBwaqOVEctKChIwegAl5K1Zs0arF69GoGBgaiqqsL8+fMxZcoUDBo0CL/88gu2bt2KV199FUFBQaiurlaGPOk9CAsLU4HtMoBZrlFwcDBqamoUHLiyslLF73Ks+nWkJ9/d3R3nzp3DSy+9hM8++0xla1qxYsVl62gymWCxWFBVVaU590ePHsUHH3yA119/HQDUOi5btkyNTcpebFRGxo4dqxQstttvv12tIz2LHMumTZtQU1OD++67T13v4eGBsLAwHDhwAA899BC+/vpreHh4aNbRZDLh3LlzWLRoETZv3gyLxYLKykolQ1utVrWOjNnRj/V/s12JCnHFigZTzpLgUmkAXEJbYGAggoODkZCQgJEjR6K8vBz+/v4ICwtT+ahJ6GhZIfOtra3F1q1bYTQaVSrLsrIyREVFISkpSaVcldlApPWxsbERtbW1isgT8sJNVFVVBXd3d/j6+qp4DjJZBjcTLsWidqxsTuFS71GRwrXZbIbFYlFpcRknYjQaNdAcPfOT8A0SAymUkDGSAUs4mMRdsz5JTU2Ngp1Joaaurg41NTWorKyEyWRS9U2k1ZiCSUhIiCpoSAZJIcHDw0OjLHJsFAyamppUylgKL4RQMDiXc0/Foa6uTqOQSXe+VFooOPI9hSYpxMigea6rVBykZZVQGQpVMvd9W1sbbDYbampqVFyR2WxGS0uLEgpyc3MRFBSE+Ph4BAcHIzMzEyUlJUrh7dWrF3r06KHmJTU1FWFhYaiqqkJWVhby8/NhsVjQ3NyMiooKHDp0SLNnbTabZs8xaJx9+yP85p+5RUZGqrgYQilpdPD09ERUVBRiY2MRFxeH1NRUWCwWpWSEhIRoBPu6ujolUDEJREZGhlKEeZbMZrOiT4GBgSq9rcwoxCYDpiU8RaZNZTwCoPVkSAgT0K5Q0ErOWkM8t9L6TKFc7k9auKW3jIKw9PSw3zKVqlTYeX/OG5UI/lbChqhU1dTUqCxoTqdTKbq8P4UHGpj0ShONLnqPFJ9JowG/47njGCQNlN4X2V82Kj/S2y0VO70nR3paqYDIWkZyH9Bbr/f+yHWQgeMAFGSS3gP+5TMIjeI8S0+EVA7c3d0RGxuLqKgoFVvEys6EaJaWlqK+vl4pMEVFRepM1NTUoLq6WqN0Go1GxaeampqQkZHxf3uk/8ebNA7QILRhwwZYLBaMHz9eGZGGDRuGnTt3IioqSmVq+j3hTKILAFfQ9NSpU3HVVVdd5pmX2H4Jc5YQRLbf+16+//bbb2G1WnHrrbcCcEG3HA4HbrjhBgDaLI3SQGYymVBcXIx58+apdLX6MV26dAmvvvoqVq1apfrjcDjQtWtXnDx5EikpKcjLy1Nj51jj4+ORlZWFXr164frrr8fDDz+MxMTEDsfWUbNYLCguLsZf/vIXhIeH46233kJ4eDg+//xzNDY24qabbtL08/fmbu3atYiJiVG1JuRcsPXt2xcHDx5EfHy8JnZRzhV/63Q6kZSUhIyMDPTo0eMyz9B/Gpvs6w8//IDCwkLceeedv9u3SZMm4ZNPPkF0dDTq6uo0MrPsG98PGTIEu3btQnR0NJ5//nn07t0bQ4YMUev4008/4fDhw0hMTPzTGBuvRIW44hiN1tZWJezILEUUQGkBIiP19/dHSEgILBaLwqcaDO24esJcKOxLIY9am5eXl7L6SM8AhUgO0s3NDQEBAUq4ZaE5EliHw6EYpdVq1cRxkLmyArnRaER0dLQSQvgMCiQS8kFBVxancjgcmkBmMjcSelr9KUTq01+yEBg3NMfE8UsPgYQe0epJIUIK9FLw4loR2sN7MJ0o14RKmxTCZPA1vTMcD4UhWSNFBu5zrNLiSusj+yHnrCP4B+dWHlCOnYSB30mhh/el0MCxMLMTBScWBZRKEgu7tbW1KeHe19cXAQEBqlgigzq5ftxLvIeHhwdqa2vh4eGB+vp6NQ7uGT8/P0RFRSE7O1uttZxLxgtwDjoztpqNe4tKNIVhqUBLr54+CJxrJDHvjI+SQhXnWXoheLYkLQPa4ySkUMm+8UwTuka6IwUHeW7kOKQQLj9nAHhH0Cr2RZ4N/X5mn9kvmS6YlnoyP3om5PmSNJJ9lgYRKWBR0SGTlYqSFPzoOZR9lHAjvv+92AbOjx4mJZ9D76tcI86dNJ7omzQuSciavJ+Ek7I/pPe8XtJ1SUu5VlQoAKi6HZxzWiVlbBnPPO9B3ibhn3wOMzNKb5ecV3oqyK8lveUe7szpsdmWLFkCd3d3PPXUUwBcGYdkHBcAnDhxAqNHj1bxevqWlpaGt99+WxX9S0pKwtq1a3HzzTfjvffeU7Up9L+96667UFlZqZ61du1aHD16FP/617801wUGBuKLL77AI488gtOnT2sEV/IjAHjyySc1+P5nnnlGc5/nnnsOXl5emD9/Pux2OxYtWoTAwEDMnTsXkyZNUrVQjEYjvvzyS7z77rvYtm0bnE4nbrzxRly6dAkpKSl47733MH36dJSXlyM3NxcjR45ESUnJZUo8AJSUlGDkyJG4ePEiPv30U5Xy9d1338WJEyewatUq1f/Jkyfj3nvvxdSpU9XvbTYbxowZg9OnT8Pd3R0TJkxAa2srFi5cqBkrAAwYMABLlizB5MmTsWDBAvj5+WHevHmw2+148cUXOywMbDC4ijauX78e33//PUaNGoW3334bH374oSrcaLfbcd9992HYsGG444474HA4cNddd2HKlCkYOXIkXnjhBezcuVNVjJfraLfb8fbbb+P8+fN4//33O1zHuXPnajxJr776Kmw2G1555RVs27YNzz77LA4cOIDrr78eNptNI7PIdXz88cfVPU+ePIlRo0ahpqYGy5cvV8WjuY41NTUYNWoUysvL0ZnaFcdoSEFQEmQSNwCaardyM0lLLZm0dKVTOeA19fX1GmiRJNxsktAbDAaV4cfHx0cxdfaPAaAUIiikMEtVTU0N6urqFBPoiOlLV7/ExbIWgnSt6+dKWjzZpHVML3zL38kMKVwHKUBQoZACjNSS5XzLGBoKCXossxRk5Br9kdaqX2/Z9NY0iR+WQpb8TFo5JSNl4/pIxiItrRKuIYUdeS+pLPLFfcb+0gMWGhqKoKAgVR8GgArsZuEtCo3cX1SeqSDK95JwEl7IWCQpIMl9Id93ZkVDCph6wVPG0JCmcM14VmTsgDxnxLTLsyM9AnLO5HPle/6vt8BzP0i6IveqXmjVW6rkuZQCvry39HTIcyf/l32UdImQIb1wrqc9ejrW0RrIPkhvhJ52SI+yFLL1cQyyP2yk2XrFSx9PIH/XkUImaaH8jPMpoa1MYKJXZvVjlR4mCXuTGbTkvfXCvhQgpedK1v4hraNSIfexnvfIfa/3lun3DmmL/FzC1ziGjpSwztYqKipQWloKo9GIW2+9FY2NjZq4gyFDhmDAgAHYv3+/4k2hoaGYMWOGUoSbm5tVTQfApYzn5uaira0Nubm5l6UO9ff3x8yZM5GVlQU/Pz9MmzYNAFBYWIiqqip4e3tj5syZCA4OBuDiMXl5eZokOjNmzEB4eLjmvqdPn0ZjYyNuvfVWGI1GhIWFITQ0VH1fXl6O0tLSy947nU4cPHgQycnJGDduHJxOJ/Lz8zWwpIMHD6KoqAitra3Izc1VY21oaMC+ffvQ1NSE3r17Y8qUKQBcaYN79+6NpqYm7Nu3Dw0NDSgoKMCpU6cwc+ZMNDc3o6qqCl5eXpg5cyZCQkJQV1eH/Px8OJ1OjBs3DoMHD0ZbWxv2798Pq9WK8vJyHDhwAE6nK9ZAehGGDx+OQYMG4dKlS3A4HJeNNSMjA6WlpZgxYwY8PDwwcOBAXH/99QCgxurh4YGEhAQUFxejtrYWgYGBmDlzJnx9fWG1WjVejpqaGmRlZWHfvn24cOGC8nTJdWQrLi5GZWXl/8Pdd4ZJVabbrqrOOUcyNDkLCAoIiIAoKCggKOpxlEEcc3ZUVJzBhIqIIoIKGMAI0iImBAQEAZEkocmhcw7Vubruj7rr67V3NzPMOXPP2Pd7nn6gau/a+4tvXO/7WtYxJSUFkyZNgsPhwN69ey0ewezsbOTm5qKurg6nT59G//790b17d2zZssWc2+TkZNxwww3w8/NrMNaLL74YF110ETZv3oyamhrExsaa2h1cR5fLhZ9++umcULk/ajtv6FS7du0sAWm0UNN7UVdXh44dO6Jt27bo3LkzsrOz0bx5c8TGxsLf3x/p6emGMLOgGa2UR48excqVKxEVFYW4uDgTnxEbG4uoqCj4+fmZFH8UkGnBp5BPAsvNyoBnoD5bkeavV0VDFSHCh+hRIVHWAHMl7oQJkLHTkkim4OPjYwkAp6BF5kThmAIDvQbKaCmYqrveLnxWVVUZKAnnNzg4GFVVVQYTTMGLmUxo+afrnrhquxBCKykVNYUo8BrHxO8o/Oi8USlS6JMqdprdBYAp4EYrJAVxVUSIgeY8MnZHs3EB9d4VzrlWQObn0tJSA28hlpnz7na7cfr0aZSUlMDj8aBly5bYsmWLyTgVERGBQ4cOmXt69uyJfv36mew/vr6+pipqcXExdu/ejYiICLN/jx49in379qGoqKhBhh/1XHHNmR2kqbWUlBSLAEWrbmBgICIjI5GcnIxWrVohMTERiYmJpjhnSEiIqSEDwOyJgoICg09PT09HXl6exROWlJRkUtkyuQT/z1gkNoU40BPIdXC73SYwl8/mHuQ+1lSwPH9aCJD7gGeOWdDUU2EXzklr+C4K6AAs3hz1DvGs6v7VZ/M3OnY96y6Xy8QJqFLHs1ZXV2fqnDA2Qwsq6ljVE6HnlteVl7BxHHYPJZ9LOs/rdq+wnU7oOmkgtipjfL9dWVLYFZ+he1BpHefK5XJZYJwATMIDjdEgRKykpAQOh8PQV8bKAN5KxnFxcWbPMraONCs3N9fQ/cLCQhQUFCAvL89UDieWm3yKfIbn58iRI/+9g/wfbA6Hw8CZa2trcfToUTz66KNYtWqVwfi/9dZbaNasGcaOHYvw8HAjUK9duxadOnVCWVmZgaqxOZ1e6DZpPD+Xlpairq4Obdu2xa5du9C/f3+MHj0af/rTn9CjRw/z+6SkJBw4cADDhg1rgO0HvB6OI0eOYNy4ccZDEBERAZfLhbFjx2LevHlISUnBkiVL4OPjg2nTplkqSZ+rvfHGG2jbti1Gjx5txsr9Y/9sH2tpaSkee+wxjBs3Dv369cOOHTuwatUqU18D8MpPbdq0wdatWzFkyBDs3bsX8fHxOHToEEaOHGlRyNauXYvjx4/jzjvvRHh4OFwu1zmNkADw/vvvw8/PD5MnT0ZERATKy8vh8XgQEhJixj5w4EB8+eWXaN++PZ555hl07NgRo0aNMs/o1asX1q9fjy5duiAzMxNdu3bF5s2b0bNnT+Tk5MDPz8+ifJ2rMa18VVUVwsPDUVZW1gA+deONN+KZZ55B+/bt4Xa7DSS2pKQEISEhxmsJAMuXL4fH48H1119vfn/ZZZfhww8/RLt27RpkJnvjjTfQvn17U4/jlVdeQY8ePUxhRMqpoaGhZo/+Edr59OO8FY3k5GTjQg8PD0deXp7FihQaGoo+ffqgbdu2iI6ORnV1NTp06ICYmBjk5OQgLy/PWIeYXo+W3qNHj+Krr74ygkB+fj5SUlKQlJSE+Ph4ADABh3wGUM8wg4ODDdNzOp1mQcj8SSj4TjJit9sbAExGRObFmBDA642JjIy0VBImxpV90IrGHJN6LNRSR1c5/0/lpDFrJ4UQFSAUFkGsOPtAiBnHX1fnDVQqLS019SrsAgK9MszQQ+Ktf+wHADNvJB76/5CQEAD1ggLHwnHZLfsRERHm99xbWhvA6XQaOBsVTFUOeNDVa0Mlg4V11BrN8bCwI5VNxbWzUSFS67Tb7UZ2draxXu3atQvR0dEGQ3369GmcOHECWVlZaNeuHQYOHIjIyEiz53ifr68vdu/ebawfJIQHDhxAbm6uIXBUzkj8mJbV4XAYd3lTa506dTLWmMDAQOTn5yMiIsJkh0pOTkZKSgqSk5MN9JI1dIB6bw4F//z8fBMEm5mZiRMnThjDA62DfH5oaCjCw8MtdW9UGFUYIoVZQpIIqQOshTj5mUqywhZpGOH+DAoKMtZyBlEDVhiU/ZnqNaYwSjgPFQ3ufY17UAVHaZueE/WeKASLBhiN0aDXmWeVNEPpiN3rQIMJBV2NV1OvLM+l3XjCfjN2TedIvTcOh8PAZWmAUo+Rn5+fpaYO6aXOh3oTtNm9inwX6anGxXB9tD4QaRjPsMvlQl5enqE/Tqc3ho79BGCJj4mMjESLFi2M8Yv7mOudm5trUbYLCgqQn59v4jOocHCNOD7ChbWycVNpDocDn376KXx9fTF+/Hj4+flh2bJlCA8PN0HC6hE6e/Ys/vrXv2LJkiUGfjx37lz079/fUu27W7du+PXXX9GhQwecOnUKHTt2xN69e9GlSxccO3YMQL1xjXvLnvmIfOZcYpVeDw8PR3p6OsaPH48ff/zRPM/X1xdjx47FkiVLkJSU9E+hbjrW06dP46mnnsLixYvhcDhw8uRJvPTSS5g/f77lN126dMFvv/2Gjh074syZM3A6nUbxJa1gu++++3DXXXehU6dOFvhzY2MlBDEsLAzp6ekYM2YM1q9ff86+cz/6+PggMzMTf/7zn1FUVISVK1eiefPmprYa141jVeVFr9s/P/vss7j66qstCuG52s6dO/Hjjz9i7ty5OH78OAYOHNgg0N2+7lOmTMG8efOQnJyMVatWoaioCDfccINlbP+or9p8fHwwePBgfP3112jZsqWJr3K73WYdN2/ejF9++QXt2rX7/zNGQxkgCReJLBUNTTNKwdXPz88EwikGWD0TLIAXHBxshHymreV7NWCOSgOv0YrB4mgUSiik0kqklnf+25iVVIVkCrmaPlXhSgAMYyNjV8s/GTEVH6Ae90scrTJOFTq0hgcJiXo7eKgBGEWHCgevU9CmcB4VFWW+4zrRG0SvgG4cjT3hnJAIqdeFAoFCOoCGsB8ydCqEej+DbRXOQAHEDgHh2qlSo5ZHO36cwgzvobBAYYN7US2YVDwo1NFqq/A8Vha1Q6VCQkIsUDRain18fBAcHIyoqCiTwIDfaZA0FRAdA/eFHUrTlJo97S+VUwBmP9qt4oTqNAYNYqMlmXV8qEzwPRT2+EevFQVPvguoV2ZU+OPeUw+n0hP2me9TuAvPoRpK+Hv+jntaDQ5qQeceVaFY728MEmOnKUD9eVT8P8fAZ9EbokYAFdx59jSWwj4WjpseCJ0fpQ/6f6URusfpjdQ9pHRDlRZ9DhUBPo8CinofOF7Ogb6Xz9V7uVfYd/uz+L2eW6610k71nJDOcY+qoYHf03Oi9JnKg6b55r4mXeTeY/+YkY37qak2tbjX1NTgueeeM+sI1MdpOBwOXH/99SZNKHnwW2+9hRUrVpj7p0+fjkGDBmHs2LHIzs7GLbfcgpEjR2Ls2LGmFkNycjLee+89TJs2DRdddBHGjBmDG2+8EYC3lsSECROMkMlrkydPhsfjweWXX4777rsPAPDwww9jz549KC8vx/jx401xYn9/f3zxxRf4+9//js2bN+Oaa65BVVUVnnzySfj7+2PWrFn45JNP8Nprr6GoqAizZ8/GpEmTLJbxG264AUePHgXgPQc33XQTTpw4gXbt2uGNN97AjTfeiNzcXJw6dcqM1e12IzExEe+++y7+9Kc/IT09HYmJiVi6dCmmT5+OlStX4tdffzVekXHjxuG6667D9ddf30DIfOGFF5CRkYF33nkHfn5+eOmll0w8yz9qTqcT4eHhePLJJ7Ft2zaMHz8eLpcLDz30EOLj400tirvvvhstW7bE/fffj+XLl+Ojjz7C6tWrzbpOnz4dvXr1wowZM7Bs2TIcOXIEM2bMsLzrXOt45513orCwEPn5+bjqqqtw5MgRyzq+8sorOH36NBYvXowvv/wSjz32GDZs2IBJkyahtrYWM2fONDLdp59+ir///e/Ytm0b/P398emnn+Kll15CZWUlnnrqKUyaNMnEFLLde++96Nq1K66++moUFxdblD2uY2FhIcaOHdvkYjTOW9Gg8AzU4+5J/Cl883syQqDeMqfME4BF2KD1WfGvjWVrIlGnoKDaJZUKu/WPv6NlSe9jIyOigMAxqIufgjyJs52x6/3KUEjsVSi3368wKlrpyaztsAr7/NmVIu2DekYcDm9aVBY7pNWXwjDHpQyTa6JCPJ+pzFWZrSoW2hcdO8dB643ui38kVKuwoc+xv5vr09hv1bKr68t54vcUDBUuqIIIhQkAxhKkMDS1rlBAYUV67iV6hzjXFI41MJXPYAYtCnBNtanVnrApfq97zP4HWJVWhc5QsNKKzoDV0keoicbSkF6pZV/fwe+5F/g918xOz1RB5t7mWLnPNC6B3ytt0H2pAqJCLO1zYj/7Okf2M6eCv10BUegY+8jvqHRxDArLUjrI/6ugbYf96TwqM7XTHu2bnm+dC45T15D3cC55FnUu1HijCoI+w950z/FP95sqbZwz+17iuSbPVLqkfFP7RA+aXTEGYOGDGtuiSjUbYW/8t6kqGhMmTMDPP/9swd7TQGFvHo/HWNRjYmIwbNgwpKam4tChQ4iKisKECRPw1VdfmXoR3333HQBYPl9++eU4ffq08RzV1dUZpARbTU2NReCvrq62XK+qqjLpamtrvXU9evfubYK22Wh8ys3NxYYNG3DVVVchOjoaOTk58Hg85nptba0FPhMbG4shQ4YgNTXVApPauHEjAKB169YoKSnB6NGjsW3bNqSlpZmxAt59xrF16tQJAwYMQElJCdxuN4KCgky2M45Fx9a2bVtccMEFAIArrrgCWVlZyMvLg9PptNQfOZ/Wo0cPeDwefP/99xg3bhzGjBljmR/CDwEYpIY2JmPh9f3792Pv3r2YMGECvvnmG5SVlVnGqmMJCwszBkjOja5jWVmZ8ZyWlJSgtrYWmZmZRhGl9yMwMNCsk31da2trLXMXFxeHSy65BKtXrzbxMN9//z0A4IILLkBERATWr19v1jEyMhLh4eENjEp/9Hbe0KmuXbua7BjBwcEmi5PT6TQZn4YOHYqOHTsaRaF169YIDg7GyZMnTcCsr68vysvLTXXWoqIibN26FUeOHEF0dDSio6NRXFxs0toyg0Z8fLzFO0AsPS3jFIoDAwMNPIXEt6KiAuXl5SYFJjNz0JJEIaGmpgbl5eWmgioZWnJysiFktHSroE/vA61jVIZoVWJKVHqBlKES4kEGUlxcjJCQEOPxoODq8dQXerIzShVWmPEI8BJZYiRZDZuHicoa3ekKMVDmHRQU1AAaQAarcS+0wGtfOLcqDKmCosogYLWqqiLCZ3Bedf6U0duVYb5DlQeXy2X2bmVlpSWAVLHdAQEB5h7OZ01NDbKzs5GXlweHw2EC2EJDQ9GxY0fk5OTgzJkzyMzMhK+vL0aNGoWYmBj4+PiYauGRkZFmj2dlZRmIRW1tLdLT05GVlWUIl+6hoKAgFBcXw+PxQnCaKnSKKRI9Ho/JoMN9GBUVhdatW6N9+/ZITk5GTEwMoqOjTbE9tUrT8kvYVG5uroGtBQYGGohJWFiYxbMUFRVlLMCE/2ggrSqTmiIWqK9FoJnwdC9qXQu3223oFRthdByvpnB1OBwWb6pCxPg8Cvpqabfvdz6PVn+eZSotdg+RCrQU/Km0aUyGPTaF3kS+U4OPAwMDzdmnUkJayqZwNI5VDT12Rkp6RcOSGp9Ik9R72xg0Aaj38tKbqYoDlUG7kYLv4XMJg2JaXYWX2hUVh8NhoGiMz9BK4awDwzUPCgpCdna2SSMeHh6OhIQExMbGIjo6GjExMcZzQd6Wm7wpIVQAAQAASURBVJtramnk5eXh1KlTBq5s9+SFhoaitLTU8KvzsTb/0VpZWRmmTJmCtWvXIjAwEC6XC0uXLkVkZKQJatYzT8RD//798f333yMlJQU5OTno1asXNm3ahA4dOiA/P9/MZ3BwsOFrgDd71YoVK/Dcc89Z+kFZqKKiwmIEYwxNTU1No9cBb/G4l19+Ga1btzZebY6FLTAwEKdOncLdd9+NL7/80iAVaCzhZ8AbSLx27Vq0bdvWQPEaG8uhQ4ewYMECzJs3D8HBwSYmQtsjjzyCqVOnonv37gCAe+65BzNmzECnTp3M2FSZmT59Ot56661/dRnPu23evBmXX365ZW4Ar3KpkFY20ieOrWPHjti5cyd69uyJ48ePm3UrLy83NKu8vBxbt27Fhg0b8Ne//vWc62ZvpKfl5eUm7rWqqsrUuwHQYF3ZAgICMHToUKxYsQJt2rRBUVGR6bvL5cK8efPQo0cPDB061Kxjly5dsHnzZnTq1Anp6en/w5n997TzUSHOWy0i4yktLUVubq5x7bjdbhQWFiI2NtbAkBhUS4gKoVEsFESIDzGr6enpFmE4MjLSMI6AgAC4XC4UFhYarGJgYKAJ3i0pKYHL5TKMiURd4xoCAwMRFhaGmJgYJCUlGWy0Pc6Age20UIeHhyM+Pt7AuGjhUyu0PYZCmaCPj4+BhlDTVsGSf5oSkwFFQL1VXl3h7LOvr7eQW2hoqKXqdXBwsNF66bGIiYkx9TEYvBQREWEyKakl3R6DQS8QiZUGXjqdTlOTgGkcNYsKD6FalgnZovBh9x4obppzzLVSwU6tnPoezTBDgYCCoSqDjE2hIhccHGyBO+lcU0gLCgoyNRlCQ0ORmJgIX19fI+Sq94qCFPcDIQ08R4WFhSgtLUVtba2BTdF6UlBQYIG/MT2uWjKbamPAtNvtRnBwMJKSkiyMmcH8XGNCQzinxK4D9edPre+EbPKZCpFzOBymxgBjc8rLy80fIaEa16M1e7j+WpyTe4lQIu5BQi2ZDY+QUO4z0kNVpDUrkN1TqfFcmtRCPRukAwoDpNKsygkFXTv0R63qHIOeb9ZxoPJGmCq9dArnUuVDPX9aW0KLBNKjSAWIZ1YVfaWvapAgbeD7qcipx5YZCbmXOE/qXeSYFN6mUCSPx2Pgk3w+50BpkyqrSisZH6cxcbGxsYiIiDCxHbRSc1xaj4PzZk8Hr3vF4XCYoPHQ0FCjqJO+kuaoZ66ptfj4eHz99dcYOXIkzp49i5CQEEybNg0TJ04097zxxhvIzc1FVlaWKZy6Y8cOJCQkmArLe/bsQXx8PDIzM/Hss89iw4YNALyW6Xvvvdc868ILLzTF+rQ1b94cubm56NKli+X77777Di+++CJiY2ORlZWFCy+8sMFvP/30U7Rr184oAOPGjcOJEycsGQkrKyvRqlUrXHPNNRaY18KFC/HFF19Ynrdt2zYkJiYiPz8fL7/8Mr799ls4HA7s378ft912m7mvV69eeOONN9CxY0fk5uaiVatWDfr28ssvo2/fvpa5ZIXtH3/8EbNnz27wm/+X7eKLL7asI+Cl/QcOHMCtt97a4P4uXbogNzfX1H5LS0tDfHy8Mc61bdsWubm56NixI+6++27s2rULgDcD1syZM5GUlIScnBz07t37n/Zt4sSJOH78OPz8/PDJJ59g8eLFCAkJwdmzZzFy5EiMHj0aZ86cscT1si1cuBD3338/kpKSTParAQMGICsrCzExMXjggQcwatQoyzoyEF+9eU2hnTcGg4IPBUatA0AhjEImhUN6QEgUeagYlEymWVJSgri4OCQmJpoq4GopYmARY0HoISAzoHbPd5eWlhqGTWKsmFh6CMhA6BEBvMyMjJS4VyoTammnMqDPZqP1jsIRGY1a7+1uf6AeE04LB5+lAgf75HTWp/4lnMbuZfHx8UF4eLgZJz0I+mz2W2EAVAwoIJPBU1HgnGs+eqA+ZoQCl8I+9Fl2LwdQnytfCwKyj+wf30ElQGNKVBCwQxZU46YiQiVYPTd2KAzngO9hKuSioiIjgDFOgwX/ysrKDA66sLDQCGkMnKMgGh8fbwJuKTzzGi0jnF+uG9eqqQoIACyBcE6n05IdjvOnyRQa81BRKaHSRk9lUVFRg+QBXEP7OaFCQc+oPdjY4XCYpA08yyq8E4rZGIQRqIdBKRSUyoh6GtQDwTNKpYLX1ZtJGsG+sCnd5VnTfa9KAOeT/bTHYtAQxEaFT8+s0jv1GHK8gDVTH+mmCsg8nxp3x75qcDjnmXRFvcaq1AAwZ0mVMKWHpJucYyqh9nHYIXW6Jtpv0kI73Jaf7R5s5QOMx9KgUdJ0jR/TGijkmapUaVZFGk5ILwj14Lt1zzU1+AUbse27du3CjTfeaMkayfbmm28iNTXVGD7/8pe/oEWLFqbuBuBdbz5r2bJlWLt2LQBvHIB6jOfOnYuNGzcaYf/111/H9u3b8dlnn2Hy5MkNMgA+/vjjKCoqQnFxMW644QYcPnwYI0aMwE033YSbb77Z4j1k27p1K2655RbU1NTg8ccfR11dHZ577jlUVlbipZdespy3119/3chaS5YswZIlS7Bu3TozlsWLFyMiIgIejwczZsywZBajHJeeno7JkycbpUvnbdOmTVi+fDkAYN68edi1axeWLFkCAHj00UeRn5+PyMhIvPfee3A4HGjduvX5LNt/u9Gg+cEHH6C6uhqHDh3Co48+iunTp1vSy7777rtYtWoVNmzYgMmTJ5s4Bl1nwFsjZMqUKTh79iy+/PJL7N27FwAMDKugoABTpkwxCQAiIiKwZMkSPPbYYzh06BDCwsKwdOlSzJw5E5s2bcItt9yC2tpazJ4928iTN954I3bt2gWHw4GbbroJVVVVeOSRRxAQEIBZs2YBqF9H9UwdOnQIN9xwA0pLSy1eWa5jmzZt8Morr+C2225rUkV7z1vR0MqpCksC6vHq+kcYizID3ksGrCn/4uLiLFYptW4D9QyE/WCguQqqjUEB+K8yYh5aMgK7cKDEmuOh5YiNzIvCowoPZFZ2QfVchJ19Y99VGFDvCP/s2GL9DRkcBQY/Pz+LFVihV/zMd6l1k2OwY4bPhSHWNVZYk86rvZHxqVVW50Hnxf5sZexqkVVMuc4vP6uHhNZTvpf7TfeOWheLioqMt4zKIJ9rT3ZA62F5eTnCw8PNXPG5tA57PB6j6KoyzPnTdeB82V3FTakpTIWCHlC/RrofG4PQ8BkaJKseK64NM8HxfHKd9OzY4yh0z9ghMLo+FNYaC3BWOqcxVvyz72M29kmFW75Xn/eP6Ig2u6DNPW4/+1Q09HyrUM/5auzZKozrPKtixrEpLbZDvuzGAO2jXSHjO9SCr3yIZ6OxZypv4W/tc6S/Jc3U/cp11uc2pmCqkUUNRXa6RQie/bdut9sYXQip07gTVWQ1EFyNfwCMV1fnRd/XVBst7vRs7NmzB5mZmXA4HBg5ciT2799vLNUAGpybbt26ITIy0qSZ/f333xEWFoYrr7wSP/74o0Uw5TnQzzSSrF69Gpdccgny8vJw4MABAMBPP/1k7l29ejUAWPapvfXv3x81NTVYs2aN6Sv3y8iRI7F7925kZWWZ+xkLQNpi93Lv2bPH/P/bb79t9J2lpaWmbx07dkRSUhI2bNhg9iibw+FAu3btMHz4cKxbtw4bN25E69atMXbsWFx99dX/ax52h8OB0aNHm/5y3bRxjUtKSrB69WoMGzYMZ86cQVZWFoYOHYoff/zRQOhZjPHo0aMmeB7wxrKkpKSYuWGzrx0/nz171mR/8ng8SExMxIgRI/D111+jrq4OCQkJFgSM7kGuo8PhwIgRI3DgwAGcPXvW8u7IyEgMHDgQP/zwA6qqqtC2bdtz7qM/cjvvGI3IyEi43d7aAlFRUYZwkZh269YNffv2RXJyMqqqqtChQwcDXcjPz0dUVJQhljU13srf6enpOHToED755BO0aNECrVq1Qnx8vIkTIEabUBJaLhITExEfH29gP5WVlcYKzD6qIkKLHFAfuMsDSiGyMagCFSpljnaLkF35UEGXBF8VMbWI8jrjVQilUu8RUG8FoyWM1wkto7JEYYbW0draWgQGBlr6z2rVVKI07z/hRnx+ZGQknE4nysrKzLwp5lMFNgpVdNHzOYp7Zx85VxQMyCg1q48KbfyX0Aun02lidxTOxfcQBkGsKudFLUlqSeQR4LxpqmB+n5eXh4yMDLN2xGFmZGSYGjEJCQkmzWpZWRk6dOiANm3aoFWrVkZgIGwtPj7eYKqzs7Ph5+eHjIwMnD59GkePHkVQUJAJXKPSTk9cVVVVk0xLCXgDMtUaTJgLoWzt2rVDSkoKmjdvbmppcA9RKaFiUVxcjOLiYlP4KC0tzXgz6BnSVLKEO6kRgXuaih8thYQxqsLO35BmsD/cE+r1VAEWgMV7wmvck3aDB5sKOB6PB2VlZZa9rII8oVeqjGp/eDaUpinMk/RRFQgqbipoqzBMTx09DCqcKzP0eOpjxXj+NOuV3aOhHkvSLkKaOH+KzWZMiF0B4LtJLzifFOD5HhXE7d6turo645nXZ9JDpolIAJg5530aiM5nsTAsaxyVlZWhpKQE5eXlxkvHIrIhISFo3rw54uPjkZSUZM6DQu+YwraoqAj5+fk4cuQIcnNzTT2N/Px8o1RTEeG+bYqxXg6HA5988gl8fX0xZcoUpKenm+xIISEhOHr0KO69916sWLEC/v7+DQKGAwICMGfOHFxwwQUYOHCgQVd07twZO3bsMPh3noHGGvlKVVUVtm/fjvXr1+ORRx4BUJ8ljXvK/hmo5+lVVVVITU1FUVGRyWClHq/09HTceuutWLVq1T+dk3ONlWdDrzscXqhlVVUVZs6ciQkTJqBfv37mbGi77777cMcdd6B9+/bw9/fHXXfdhTlz5vzD/vxvtTvvvLNBRXa2Q4cO4Z133sHq1auxe/dudOvWDadOnTLrqutCr+Bdd92Fe+65BykpKUZ+VIOYNq4Tz3hqaiouu+wyuFwuNGvWDFVVVRgzZgzef/99NGvWzCQ94rprsqIzZ87g4Ycfxvvvvw+gft169+6Nn376CW3btrUom3+kdj4qxL+UdSo0NNRo8rSmkOgSAsI/oN5KqRYWwEvAGchZUlJi0toy+Pvs2bOmumZ5eTlOnjxpsNohISGIjY1FTEyMpbYFNwQVDTJVBjOTYFAgAKy50UmAVYEgk6Plm2OgUEDhgZZVfqe58zWIkmNX1z6VDPUWqZWMB4B9pODJ+WXAptPpNEqHChhqLSdGXIM++Rvig8nQKYwoE9bMO2SaZHrKvPiu0NDQBtWaFRLBAE9aREm41WuhCokKcGTICtVQAYoeB1qRKbioMKQ4d1pCVDCj94I4csbrkFBQgQ0KCsLatWvNOlHZ0pTMJGpVVVUoKipCeHi4EZZo8VCsf1FRkRH+amu9GUaYbeJczK8pNLvn08fHxwTnEw9fV1dnIJWMp1LIH9dUlXN6OrkmISEhJnZLDRzcIyT4Wu3abvFXiz1g9cypNVEVB1WUdcwK6eN41HujgeQqaFMx4Hs4FnsgOeeDyoMdSsLfNhZnRTqu3jPOJfuslnmOWxUd+9pVVlZa6LLCA+weGSpuFIg0u5Xds03aqN4ujpvXOY8ck9I8/VPPGJ9F/qV8i+Pm87jXaLBQmkZaxkYYmn2f6Pyp8sn6GpwPJjJhnEZpaamFX6iRhXMWEhJiaFlRURE8Ho+JM+K+4LObarvpppuM0ti6dWtUVlZiyJAh+Oyzz9CjRw9kZ2dj4MCBSE1NRbt27UzFZ6fTid9//x2vvfYaHnnkETgc3grPr7/+Ot58803ExcXB5XJh5syZGDt2rCVWQdvdd9+N2267DV27dsXQoUMt5+27777Drl27cP/99wMA1qxZg0OHDuGuu+4y90ycOBFz5sxB27ZtMWnSJIuw9sEHH8Dj8WDy5Mlo1aqV5eycq/Xv3x/ffPMN2rVrZwL8HQ4vtv+VV17Bli1bsHnzZnTu3Bnp6eno2LEjtm/fjh49euC5557DqlWrkJubi969e1ss/AAwf/58vP322wCAdevWoV+/fuezRP/x1qdPHyMPcV0fffRRXHfddejVqxe2bNmCNWvW4J133sHvv/+Oiy++GAsWLMA777wDj8eD7777Dn369EFOTg7at29voekAsGrVKmRkZGDatGkAgEmTJhl6SRn4m2++QYsWLUx88fDhw7F8+XK0bt0a8+bNQ1xcHMaOHYv27dubdXY6nTh06BBmzZqFpUuXIj4+vtFg8qbUzlvRUOiJv7+/peAQ06bSIu5yuVBQUGCBHFEYJpMgdjIvL894AUi8y8vLERsba96tlm0KEXYmY3eVkwkrA9WxqOXRzrTtiojibPU5brfbkqWJz1PrpHpN7O5TCkHKRO3WQz5f18CeUUUZl8ZdAGhQ3Zr9Zl8591TKVLBSiAHXBoBF6Fdrn3pqtF8UwCoqKizPpIVFLa5sCuVQmIQKSPa51DnlmCmUa2wABQyde90bnGN6bfz8/BAREdEgixDhUpxvZiNjf3NzcxEREYGEhASDcaegwowkFMj4Hs5ZXV2dEZR9fX1N1jNVlJpii46ONgKky+WynCt6GFSwtp8twLo3VPjWa7yuwb58BxULVV4UFqPKLI0WpGGNeTR1f+q51z+7oqxnnk37oMIt+0RPBq/zDPA+fU5jNEjpir6PSrHCbQBremD9o1FA+6vKA+mQCvtqpOBn/pb36nxwbkkzOd9qfND54e91bThOKqTq0dRnqkJh319sKkhq8g9dPzvds4+D4yaf5H5ijR4qa9pfl8tl0mqqgYFGIHo2+GxVtFQB42/0rJ0PBO+P2lT4ZjrTgwcP4o477kBOTg5qa2uRlpaGGTNmoKysDNOmTUOzZs3w9NNP46GHHsK+ffuM8Pfwww/j0KFDxgAKACtXrjQVrymof/755wCAl156CRUVFXjssccAoEFBvdmzZxvFBgBefPFFFBcXIzQ0FAsWLMBzzz2HX375Bffdd59ljQICAvD2229j69at5t326tGA14ofERFhaolMnz4d3bp1w4wZM/Dyyy/j/fffx7p16+DxePDQQw+ZQrDTp09HYWEhACAzMxPTp09Hbm4uqqurcfz4cfz5z39GdnZ2g/fV1NQgLCwMixYtQteuXS0B6//pdssttyAxMRFPPvkk3njjDXz11Vcm1sblcuG6664zmcjuu+8+pKamYt++fQCAmTNnIiMjA/n5+Zg+fTrOnj1rydA1e/ZsxMTEmMQZADBs2DBMmTIFt99+O+bMmYO+ffti8eLFmD59ulHcAwICsHTpUrz22mtwOBy46667MH36dFRVVWH//v244447UFlZicWLFxsP+quvvooPPvgAGzduhMfjwf3334+9e/da9mRTbuetaNgZqFqZKAgpMyE0hYIlv1eGSQuNuiELCwtNUBst0RRUaZWhRV+DgZVJKEaYjEpxvKoA2YX/xpqOlRtOBSB9vzJmFTzUQqjCYmNClCoCOi47UyWD55iAhsKB9kHT1KogxzmjJd4u6Kn1Tcev/QJgMofZhRPdNzpeu+CsGGT73lNFir9RIdFuhaVgoEyewheFRn0n51UhZnwHLYWEMKnCRUVAlT0qmwqHUC8W+0yIl1q3tW/EvdPybt+LTbGpgA5Y05Y2pvQ31nTP2vcUUA+xo3Vc8euKabdnebJ7GwkXVIVCren2/ulnO+xPFQb22f4bHZt9rzT2LP5en8emUEI9x3bBWM+WntnGhHD9VxUuex85Tu0jz45dMdF5sRuLdK/onOuYdM4Aa7wg50GNChTA7WNXRUJphhpddG4VImVfQ6WfSueAehiNejM0OxfpFOeOHhaFqqoRTemP0io9UzouXRu716sptR49esDhcGDfvn245JJLcODAAWRlZeHjjz8291RXVyM7OxsejzemMyoqCgCQm5uLyspKhIWF4cILL8Q333zTABqzd+9eEyQcExNjKSwaHR1tBFRtAQEBGDRoELZt22ZqZgAwdRGYxZJp5HNzcxsYRuLi4lBZWWn5PfvQo0cP/PTTTyYzJ1t4eDhqa2vx8ccfY8KECSbFf79+/fD111+blOEM8E5JSUFsbKz5DHhjNpYvX44BAwYgJycHx48fB+D1lOTn56OqqgpTpkw5z9X532us0/Hkk08iJiamQXan4OBgxMfHA/Cey/3792P//v0AYBQSAJa54Dq63W7s2rXLEnAeGBhojODr16+H0+nEoEGD4HA40KtXL9TW1uLYsWOIi4szHl3GHwNeBe+TTz4BAPz888+mX7GxsQbB4PF400435snint28eXOjcK4/ajvvGI3ExEQLRrmsrMxihRw4cCC6dOmCqKgouFwutGzZ0ljyiGdXJlZTU4OffvoJv/76K2pqatCpUycUFxejoKAAgYGBGDJkCNxut0lf6+vri+joaMTFxSEqKqpBcKUK8JralYRd8fiEJdCqpMVogIZFqHSKNLCHm0chQCrk8llkOCT8dte63XpbXFxsrpEJMpUi+8w+UBkjQ+M9vM/j8Zh6AFw3Vra2W/YdDoex4pJRlZWVmWsaf8F/tcZHRUWFgbqowKwBnI0pZcqU6SVTCBnnxd/f31JrwF4LgJ4tXSdtJSUl5pmESyhMhXElVDIYO6B4cm0MLMvJyUFqairi4uKMtXL//v1o1aoVOnTogA4dOiAiIsIieCQkJMDf3x9FRUXIyMhAXV0d8vLykJaWhl9//dUigHDvEsrlcDiaVMYJbZwj/hGrGhwcjNjYWPTs2RPNmzdHTEwMoqKikJiY2MCIwSxfJSUlKCgoQEZGBrKyspCRkWFR0kNDQ9GyZUuTPphxW0xJS0WDwibTWNuhhqqMKBzU7hlVZZfn1i4UAtbAUI0taAymZRfk+b3b7TYxJ4A1OxvPolrW1SNN+B37QeWL54DjZNpWWvn0OrMdka4odJX3cFzsh17T+ysrK006Yl6nwcIuUGv6bZ59Qpg4X5wXpUucd4XScT44f/zermgpfMvjqY838Xg85mwqnMmuFOm8kV6TPjM+o6SkBPn5+UhLSzNefrfbW1cpKSkJLVu2RKtWrZCYmIjo6GjD20h/KZwWFRXh1KlTJt7rxIkTlhi+iooK0yfSr6bWHA4HPvvsM/j6+uK6665DVlYWpk2bhs8++wwAjIFz8ODB+Pbbb9GsWTMLdOrUqVOYPXs2fvnlF2zbtg2tW7c26UJJv9nsnwHvWXrooYdw2223ISUlxXyfnJyMkydP4uKLLzYeCfZFjbJutxuTJ0/G66+/jpYtW1oygwFeb0pdXR2uvfZa877LL78cK1asQHJysqXgm84JDaiAN1h+y5YtaNWqFR5++GEMHjzYQJ6ef/55jB49Gn369Gkwtt9//x2ffvopnn76aQDAb7/9hu+//x5vvvnmHzaeZ9euXejfv3+DdQNgoUN2I69+1nXmOjocDrz11lsWyJvOs/15X3/9NYqKinD99deb5wP12T3tRkI77dbvz549i5kzZ2LRokVmPHV1dejRowe2b9+Otm3bmiD0/3Q7HxXivD0aJFacLBbRIpErLS1FRUUFIiIijGCr2WAY7At404WVlZXB5XKhoqICQUFBKCkpMQKY0+lEWFiYsa43a9bMEG+n0wmXy2XSt3GTUFmgcK0u4+DgYAtDJ9E9l/uYRB7wblS6pvkXFBRkmWBuUs6NYnLtVirOg9a90LSPAQEByM/PN8w0MjLSwJP4LM07r8oLAxQBaw0PhSdocDiFC7WGcu0oYPC3nHsyNCoEYWFhRpjhnqBQou9XC6jOrwpSVHK4BrT60wKnAgP3F+eGDJdzr14zxrFQCeR1KsEM/iYBcTgcpm4A15EKB4UuFv7zeLxFtbp27YrS0lJzX0xMDEpKSlBYWIiKigrExMQYwYfxJewvIRERERGIiYlBZGSkOW+8h/tG919TbG632+wxoL4ooQo/WmSMY6dgyu8B7x6vqqoyOPaqqiqLwh8bG4uoqCjjyQgLCzNKAw0kgYGBFpiJxiwwFodMhfSCdEmVAVWm1Wqu+1zpD3+nwq2mvwbqlXWNybAba9Q6r8I7UE+zKZzbjSQKpdEYCSoaGrvA8VGo5xyxb0of1VPMe1ThIS3h+2kM4txwvThOjSHh+VbmprSFxhL9TIOErpEqQ5rils9m3/hu7gPOM9+rcCal89xjGrhv92YA9cXXmLQgPDzc1H4hD+Mz+J3ds8RzxXknLJDzTTpFaCz3kh2C25TazTffbAxTbdq0MQqTn58f0tLS8Oijj+Lzzz9HcnKyqVEAeNesW7duJn1+QkKCud61a1ds2rQJPXv2xJkzZ9ChQwds27YNF1xwAU6ePGmesXnzZqxfv75B1evMzEwkJCQYb0RsbCwOHTqEK664Atu3b0dUVBTS0tIwfvx4fP7559iyZQtOnDiByZMnm+rlAExQONs777yDiIgItGjRolElA/B6HlJTU9GpUyfk5+fjt99+Q2JiIoqKivD4449baMszzzyDL774wtSKOHXqlLl20UUXWfb0kCFDcPvtt2P37t3/fFH+Q61nz57Izs5Gt27dkJmZadYRAJ599lm8+uqrAIBffvkFn376KV544QUA3nVcu3Yt3n33XezduxeDBw/G77//btYRQAOvwdSpU/HUU0+hY8eOWLlyJTIyMnD77bcDQINYm/fffx9utxvTpk3DkSNHcMcddyA1NdVcf+uttxAXF4dx48ZZ3uF2u9G5c2eLvLNv3z7MnTsXixcvtuzZptLOG6RJoksrFhkVCScDxSsrK5GdnW0EN40RUEsfmSCvqzWPWUT8/LyVlGNjYxEXF2dRYlRRIEOm0sHiTLTaU3ChxYl9IPRFibYdEkCCTCZMjLBuKLXck2koMyHx12rA7BeD/FRB0gw3nDfAarVUXLPWE2E/2G87Q1KPjj6D1xg/oEKHzpk27gEGkyu+UaFqur5q5VSGTcWH3/Nejp97zw6/4Jwr3KCurs4wEsVmE6rHatJFRUUG/8z5YMIB7kuNG6BSQsFTLd1UPijw2mMNuPf5OwZAO51OREREwMfHxyIsE4NN4YZVTKlQNdWm54w0gOvNeeXYeS7skDqulRaTpNWI88v145nX4nnBwcEICgqy1IbQM0ZFxk4XVMDlv+qx0HtVkFUvIGmHeiZ0Pnj2dD/zfeoJbOy93Os8r/pbjlHfo/2xw2x0PrQvnF89v6Qzas23ey2VZnKtSTdUCVKazsZ7da70eQoV4mc1YKjnQ+eC+1FhRvpsXTedc46B9M/uXdF5sCsn3DPqGVNvOwBTTJJ9sdNkjsFO86mQ6LPIQ9hPeurUU9vU2jvvvIP27dubLGyzZ89Gz549AXj365NPPolff/0VtbW1KC4uxty5czFo0CC0a9cOixcvNoqj2+0tNsy5ysjIwAMPPGDiGLKzs/HAAw8gPz8fV111lakM/uKLL+KLL74wyIOnn34a1113HTweDwoLC/HYY49h6tSpKCsrw0MPPWSUlPLycjz00EM4evQoqqurkZmZiUceeQRpaWmW8ZWVlSElJQULFiyAv78/li5digULFhjh8s9//jMefPBBOJ1OzJs3DwMHDsSJEyfw8MMPGzy/2+1GUVERXn75ZQwcONAoPy+99BKGDBmCtLQ0PPDAA5ZYEsBbFfzyyy83nx988EFMmDABERER/67l+7c3Hx8fREdH49VXX8XIkSPNOj7wwANYt26due/555+3pPt98cUX8fXXX6OwsBAPPPCA8WpxHbVINNsvv/yCp556CnV1dViwYAE++ugjBAUFYeHChWjVqpXFQ/juu+9i6dKlqKnx1kYZPHiwpRDkhx9+aKmofsstt5g6L0VFRUbJ8Xg8eOaZZ7Bp0yY0a9YMc+bMsUDnmkL7l7JOKTEH6oMGaa2htYheDGVIStxJNEn0KGSqVQaAscxQaaA1S5kQmwbukuhScFe3NvurlkF1f6ngwevqkiMTVKZDC6edodj7yblQzK6mhmWf+H/2QYUSZUrKTOzj4/v19/wdG9/F/5Mpar8bw3nb4QacP50bCozK2HXOVcHj9xrErk2fqc+ldY7CENeAgioZCgUaKnZUYimQKXyNe479U68X10AtjVRm1NPEeVALL3/P9zCOgOPgc2nh5d5g/whTswuETa3peeO+55zpfKnwqXPIRsFJ9zafr4K23UBCqy5Qn/mJ1neumwqc6qXQs6T0TPtoF6rtCoL9Nyq4Kg3Sf+1GCx2j0kz771TYtp97fbed9tnv4zywH6TrqiiRjtif0VijAG5/vl7X9VZFT9fVPh/2fWAfg9InXSf1mOhZ1b1oV/rsfdQ517niHtc+c4ykrUozVCng8+xK2bmMN3YFj9/5+/sbxUWVC53zptQ6dOhgILpOpxPt27c3iAaPx4MjR44YJQDwxiRERkaiqKgIHTp0aDDuvn37IiMjAxkZGXjvvffM98XFxeZzTEwM2rRpA8ALbWrWrBn69u2LnTt3onXr1pZ4jVatWpkq7vo8t9uNtLQ0Y6muq6tDWloaXC4XYmNjkZKSgl9++QUejzempEOHDnA6nVi/fj2ioqIwYMAA7NixA0lJSWjbti0GDRqEzp07Izw8HNnZ2ZZ3sbVr187EpvDzrl27UFRUZO5v2bIlYmNjsWvXLrRp08YCkRo/fjy6dev2ryzPf6xdd911yMnJQU5ODt577z307dvXokgRWse2cuVKAF6vYlpamhHs/f390a9fP+zZswdlZWXw8/NDv379sG/fPqSlpRnFkDEeISEh6NChg8mIynX84YcfzLuWLVuGZ599Fs2bNzffZWRkGF4PeMMTeP3CCy/EqVOnTHD+0aNHTQHgDh06NDkjwXnHaHTt2tVUPCaMQwWjiy66CJ06dUJ0dDQqKioQFRVlcLasH6BCWUlJCX777TekpaXB4XAY92dpaSn8/f0xcuRIk85T3f9q2fL1rc/Br7AAZqYCrJW7SXy1bgXzi6vr3M7QFGagljx11fOzwnMAq7BPQVYFIWYO4bv8/PyQl5dnhNbw8HCTf5k4c66DenZ0boB6C5edgSmztdfYoADORkGGf2TI+fn5KCkpQXV1NZKSkgyzowdLx0flkM+i1Y33s9+cBxWqgHphnjhurhUVWa5NZWWl2SNVVVUoLS01KR55v2a4CgoKQnh4uFFiKfxzfxLSQCsis0OUlJSYFJNOp7cKfWFhIdLT03H69Gkz72lpaaitrUWrVq3QrVs3dO3a1WIZDgsLM8yIngwfHx8Tp5GVlWVSUzLlLecvKCjIUu21KTXGetmVYn9/f4SHh6NTp05o0aIFEhISEBMTg2bNmllijAiXKC8vN3UDGKORmZlp4gp8fX0RGRmJtm3bIioqCmFhYQgNDTWeV6BeQQfqFSCNXeB+Iw2iJdhuAeeesgt6Cg9Sy7U2pRMqRNot7Hy+/U+zo6nHTz0obHwOz5R63NQDBMBiDKFCz+ukU/p87asdv8z38j4ajRQip9BaHYddIdK5UGVOFXD1PtMTqf1Umkiazn4TEkovgqavVeMS39uY0Us9cNwbdgMWf8t6MAUFBcjNzcWpU6eQlZVlvBqVlZUICQlBdHQ0EhISkJKSgoSEBERERJjsi6Rt5eXlyM/PNzV/jh8/jszMTABeL4nL5YK/v7+pR+VwOBpUhm4KTZVZu/gSGBiIjIwM3H333fjggw8sv2nsfofDgVOnTuGVV17B3LlzAVgNkfbP3DuPPPIIpk+fjrZt257z2fZ3R0VFITMzE6NGjcKGDRuMgnDVVVchMjISixcvRnx8vAWuw/ddfvnl+OKLL5CQkIDS0lIMGzYMa9euRVJSkvF02JXMf/SZ59XpdOKZZ57BhAkT0Llz5wbzu2fPniajaADeMe7Zswe9e/fGsWPHsGjRIjz//PMNDANA/bq2aNECJ06cQO/evbF3714kJyfj9OnTGDBgAH799VckJCTgzJkzGDx4MLZt22Z+39i6T5w48R+uo7YPP/wQ8fHxGDFihOW6j48PMjIy8OSTT+Ltt9+Gw+HA6dOn8eKLL+L111//t8/Z/7Sdjwpx3tApFv2pq6tPt6VMjROlcRj2ugtkfsHBwXA4HAY2xHvpjVDIBOBd0KCgIEvhLQAWAVIZRXl5uSnmVVZWZgJ8aV3iPUxPqrhdxYuT2QD17m7+XyEFTDFoZ760bKlrX2FOep+60gEvU6U1XouKKTyCUBoyJArftN5y7hQeodADhReRobpcLsMoOUfKyKmMMMCWwjkAizfA7famL1UrXHV1NUpLSy0uQSpMYWFhlpSXak2kIKRrTUVA/6gQqRWbUJmAgADExMQgMTERSUlJiI2NNbEOFEqYrSMyMhIBAQEmDoNMn1AfrreOgXvW19cXERER6Ny5M2JiYhp44jg2Pz8/tG/fHs2bNzdnh4ojlR7CuMLCwkxQcm1tbYOMJE2pca4qKiosgiP3sl2otAsVCi/iGeHe1UBvVaK5tqxTQtiUngfdi+q9VcGSjEmVeioQ6knlH7PkqedL4T9sKgCoUUSFHf5e4YAUfDgONcLwOzYaAQIDAw2MzA7v4zpwHu0JCSjgK2QHqM9wRRpm9xBwvWjYUagiz6t6x9nUks/54d5QY5B6wDh+0jRl9uqBUSVGPVBU3tRrqmOx0xy+X5uuoyYX4Nwqr9T1It1W6CDnT+to0EBB2kxayO84l3w//8+aT6xxFB4eft7n9o/W3nvvPSxbtszy3dChQ3HkyBH069evgfX65ZdfblDtGfDuiQsuuAALFy40323fvh133323+fzzzz/jgQceQOvWrZGZmYkOHTrg9ddfN8HV69atw9P/N3i6sbZ69Wq8/PLLKCwsRIsWLbBlyxYA3kxPLVu2xMaNG7F69WqkpKRY9mtgYCCOHTuGMWPG4Mcff0SrVq2M7LV582a0bNkSRUVFmDt3rql0DXghQZpRadasWSbzFeDN0LRhwwYAwLZt21BdXY1BgwaZ6/feey8yMzORmZlplI+m0p5++mkMHz4cgNcrMG/ePKSkpCAzMxOtW7e23Ltx40b89a9/xdmzZ5GUlGQqu2dlZSE5ORm7d+/GzTffjPXr16NFixamkjfb559/jvnz51u++2frqG3GjBmYOHEifH19cfDgQUyePBmAl5527drVFO/zeDzo3bs3Fi9e/D+foP9QO29Fg0I5hUkyF2VAKjhERkYaDDQFbgZAK+NVhknCrZVmgXpLvz0XPpkCKyir0qBZUmg1Li8vNwIhYC1ixWcqNp9ZPXgvx6L9JsNQgZtCgsZCUECyxzPQisrPVIo0EBuw1sGgEEGrIOePc2Vnpuph4LhUmGFTeAGFLrvyo0KJrgnnUqsJ65pSWVFIgQY22uEc/KzCCN9BIUIFDMBal0RrUvj4+CA8PNxSLV6xyvbq5fzTeB87FEyF3rq6OqO0qCVchTEqRxTSVPBj0Lla+R0Oh1GQGCRtj5Fpio17k+dShWPSE42/0j2kXjoVsClQ8jPPHYV83q/F+ewpb3kP180ujNLIoAqDWq41pkmVAwqhatEHGlqB1PqvyrLuY/WmqidSLfx8n9InPV98rxpNNLbFTs9pfNFkF+yv0hr7dbsVWMeoMXN8Bn+nCqa9L3qP0lFVwPi9Cv88WzT0qEeI69uYosW5J2SxMXgs502b8gqdK50njf9Q7w7ni+93Op0NAtH5HH6n3hZdd84B11XTcys8qym2jz/+GCtWrICvry9efvllXHDBBTh27Bhmz56NM2fOYOLEibjnnnvM/ampqViyZEmjz8rLy7MUL5w3bx6ioqLwxBNPAPAWrNuwYQMKCwsxa9YsTJ8+HZdddpmBSy1cuBDfffcdIiIiTCapQYMG4bnnnoPD4cCSJUuQmpoKj8eDnJwczJgxAzfddBM8Ho+pY1FVVYWSkhK8+uqrxoPgcDgQHx+PgIAApKSk4PHHHzfySJs2bfD4448jKCgIq1atssCm1qxZYxFKv/32W1NwDwB++OEHLFiwAADw+uuvY+3atRbo16ZNm/DGG28gPj6+ycHrRowYgenTpwPwGseHDx+OGTNmYNasWSb2hu3NN9/EunXrUFdXZ2qvDBs2DM8884z5vHPnTrz44ovIzs5GTU0NBgwYgBdeeAFOpxPLli0z8Cu2qqoq5ObmAvDWN/nzn/+MmpoaPP/88zh48CC6dOmCuXPnIiAgwGSJc7vdeOmll7B79260a9cO8+bNQ2VlJa6++mo8+OCDALx79MorrzTV55taO+8YDSXOzAOthLcxq5wSMzIPDZKzM0EyCbrO+V79l0qKpmetqakxOYhpTWdfFBJjt/AB9cxP8e+0XHMc1E5JzPX5fAabuiX5Z4+DUOamz+M9FL44FhWw9eCrt0WbwgQUxsVxU1BWQYoMjAI150SFCf5ehUCdB7UCKqPluPQ7VUpV2NA5Zd+VcasFVD0gamHknKgwFRQUZAQUFeJU+eHasKmgo9fV28J5YbE+Cm12Jl5bW2uytLndbougS+VDBQaFq1AgV2WoqTbOl66vQpjsVnTeC9RDndQDCNQHh+sZUaGS88W10fexT3oW9Pwq/VEBjn2zC9G6hnr2NFBXxwQ0hIKooquKisZx2X/Ls6HNrsCzf5xDNrtSp+eQv7EnvFAjBc+jjkNpkioKduMJzyTnjTRPeY1CDu1CtF2BYl90jZU3sS/aJ+Vf9rVhU2VI77ErePZ9pX3mGqj3RRUO7hcqBL6+viZZhSohyjfttECv6VzrGHTcTbURcu10OtGvXz+kpaVh3bp1RoBOSUlBYmKiuX/9+vUICQlBnz59sGfPHiQlJSEkJASHDh0CADRv3hzh4eE4cOAAli1bhr/85S8YOnQoAGD37t2mrsG2bdtw+eWXW+IY9u3bh5KSEvj7+2PAgAEIDQ1FYmKiyUrFQn9sXbp0QU5ODnx8fNCzZ08cPXoUJSUlcDqduPDCC839dXV12LVrFwoKChAZGYkLL7zQnNvw8HD0798fPj4+loxVPXv2xP79+0368x49eqCiosLUBAG8HhrWcHj//feRnJyMrl274vfffwcA7Ny5E3l5eXjmmWf+h6v0v98GDRqE8PBwE7jfsmVLtGzZEg888AC6deuG4OBgFBQUoFu3bli5ciUiIyPRrVs3U1tD1w2Ape5Gx44d0b9/f/Tt2xcOhwOrVq1CdHQ0evXqhT179sDj8SAqKgqtWrXCnj170LFjR0RERGD79u1YtGgR6urqcPHFF5t1bN26Nfz9/ZGWlmYUw169eqF///7w9fVFmzZt0LFjR9MXwrCbYjvvGA2m26qr8waUlZWVWYTzvn37onPnzkhMTDSWYh7OqKgow5x57ejRo9i6dSuOHj2KVq1aITg4GBkZGcjLy0NiYiLGjBlj8oUz1SgtloTk0J1MgkuPSWNCBgkrYToUqqmg2C2PJPqEJgFowPx5v134I0yL/WIAKr0XZHR2gYfMOz8/33gqKMBy3tg3FdzVyqUpFH18fIxCCNTDJ84FUygrK7MIulQ0OB4/Pz8EBQWZdKLFxcXGKs972V/2kwxSlS5lkhqnoVbDuro6lJSUWDxYTFOpFnC17FKJ4PMocKgAoxZzFZZ8fLxZn3QtGQujlkGmZKZA43K5TBrb48ePIygoCKGhoUhPT0dZWRlCQ0ORlJSELl26GC+cr68vUlJSEBMTg6ysLOzbtw9lZWUICQlBbm4uDh06hPT0dIvF3OGoT0rg4+Pzh8mh/a+2qKgoM+96PkJCQhAfH48ePXqgZcuWiIuLQ2RkJKKioownSNMLV1VVoaysDCdOnMDJkyeRmZmJkpISk00nICAAiYmJaNasmanJER0djfDwcIs3z24N554krVFIVGMKnsIU1ZiigeZcc9IRjl3plO4zoD7bGa32jXncFJrKOeGc+vr6ory83EJfqOAGBQU16B9gpWVOp9NSs0aVefaNzyQ8Un9LZZuCrmakU7rCGAWtV+Lr62s+A/VVoNXjoyl1dT+pEK0GEFWQ1NhRW1tranhwP2jGJ1VilZ4B1ro6vEeVVM6prrlmF+QfYxOLi4uRm5uLoqIiM3fp6ekoLS1FeHg4mjVrhk6dOiEhIQGRkZGIiIgw8KfS0lLk5OQgNzcXxcXFyMrKwsmTJ3Hy5EmLJ530kHPRFOmIw+HAF198AT8/P4wdOxaA18MRERFhyZhkbwMGDMDmzZvRrFkzPPHEExgwYICBP7388ssYPnw4evXq1eB3aWlp+PDDD/Hhhx/iyJEj6Ny5s1FQAK8ism7dOjzwwAP/0jgiIiKQk5ODsWPH4rvvvvuXfttY8/HxQWZmJp544gmD7T979iySk5Oxa9euBul42WbNmoVJkyahU6dO5rvWrVv/Yetm/LO2d+9ek4VMW2PrePXVV2PGjBkNYFWNtS1btuDAgQOYNm2a+W7q1KmYP38+4uPjUV1djUmTJmHx4sWIi4tDVVUVrrrqKixfvhxxcXENMlh99NFHSEhIMFCvptrOR4X4l+pokNBWVFRYquqSWFK4q66uRlFRkSWImb9l+k612jud3hSfxMQTOkQ8Pj0gFCCU2ai13+12o6yszAjpZDqxsbEW/DszCXg83uJLjBnRps+2u94pFANeJkjlQy11GtNQUFBgyW3OuaAw35jrnThdACbojxY+zfvOd1EQ47zoOPh8t9tthGdeq62tNZhq9lHd/AAszEnhDnZGzHfwfs3Dz7lSRYb9sitLZIIKQaJgwr7y2Zw/7QuFCQZR65xrDAT3BJUafu92u40rnXMQEhJiBDdV1ggB8ng8JpaCcRUZGRkWr4rWY3E6nSgqKkJOTg7y8vLg4+Nj9j0FMiZQKC0thcfjMckRVCBtas2e3lkhS9yPJFxcM+5nu4Vb9x73r1qXqVjaPXg8L/zMs6TWcRpGVNlT7xf7o4KkeufYP41Z0L2pZ173rZ4v7kU7vaCBR63Vus+BhkHP/E7XQedYvUekIQp/ouJFxYJQNfVg8Ezoeum4gXoIKGkc15JKDddLoUJcW3o/1LvKpsoQ6QMVLlVK9Ld8vs4Z6b7Gr7Gv6uFSeq+eXzvNUyMV+6lj0GeEhobC5XKhqKjIPD8wMNBiACKMigoR+6V7BYCJEdL9TWOV8oym2qZNm4bLLrsMR48eRY8ePTBjxgxDH3bv3o2ZM2c28CTs2rULrVq1Qm5uLp588klLxp9Zs2YhNTUVZ86cwUUXXWRRwC655BJT3LBFixbIysqyPHfkyJH/tErzlClT8Ne//hW9evUy61FSUoK2bdsiLy8PY8aMwZw5c9CzZ8/zrvjs6+uL3377Dc8++yw++eQTuN1u9OzZ08TweTwe9O3bFz4+PibOafv27Xj33Xfx448/Yt26dRg4cCDmzJmDN954A4A3biE1NbVBjEtTbFu3bsWHH35o4igaW8c333wTS5cuBQB899132Lx5M2bNmgXAC7fbu3cvHn/8cQDAuHHjLEiFTz75BMXFxejfvz8OHz6Mm2++2dQy4Rp+99136NChAyoqKrBw4UIEBQXhpptuAgDceeedFlrx8ssvo3Xr1qZQI5vD4cDOnTsxf/58bN++Hd988w0uvPBCk+ihKbTz9p0qAQsODrZYraurq03VYhI9Mi9fX1+LZZpMhNa3uro6U/Cvrq7OYObVakRGRMsZcfP6FxkZicjISISHhyMkJMQCTWEVVQ2QVvgFlRo+X4WSuro6g+Hn2JTZ8F/Oj93FTqZNIaUxPLRaz+zZlDweb7yBChdkEnymCiZqUSND4bMpDNFLxLlQ2IJa9O1QDzs0QxmcWvw0YFex2wq74txo0L8KWfRGqHCkQjq9A7zX7r1QXDX/tPIzhRFW5tUYHnrudNz0onE83Mf0qvn5+ZnsUZx7WmWp1OhaBQUFWarXs+/sJ1BfH4XrrevUVFtAQADCwsIQFRWFiIgIowza9xLXmX+Nef+4BnZYip5HpSM8V2o40D+NDVHhsrH32pt6BlTA1XOqXjQ7ndC+Uqm2w5W4//ldYxZ3PYdqANC5pEKt7wPqLfwK67HDi1TA5XWF62gf+a/ds6x0lte1Kf0i3bDPD5v9GudfaY0qJLxX15WfOW/2PaLnTj3jPLf2uVZvhSq5qggo/MzudVc6RuGZBiPuJwCW+bevE5/LteZ+0jiWphyjkZ+fjx07duCNN95ATU0NCgoKTEX1t956CwcPHgTg3VtPP/00LrzwQiQlJeHuu+9GcHAwhg4daoJvn3jiCXTv3h2///47XnvtNSOox8fH48UXX0RNTQ1KSkoQGRmJu+++G9HR0Za+5OTkoHv37iamA/B6T2bOnGnWdt++fVi4cKHZIxdccAGeeeYZZGVloaqqCkeOHMGCBQvMmnTp0gUvvvgiXnzxRQOX8fHxwbPPPmu8LnV1dXj77bdNEDPgLRqotRwyMzNx9uxZk13snXfewc6dO5Gfn4/XXnsNxcXFKCkpMWlUly1bhq1bt/4bVug/39577z3s2LHDfL755pvRv39/uN1unD171hjxqDh++OGH+Omnn8z9H3/8sQWWNmnSJIv34fPPP8cXX3yBEydOYN68eTh9+jQqKipMPQ7Aa1xNT0+Hx+PBmjVrLEH7o0aNsigV33//PT777DM4HA489dRT6N+/PwAvXVi8eDH27NmD3NxcvPbaayYpQFNp5y2xaO0AKhoKiVEmQ2JKhUBTnCpRVWLPA0Yojlq+lQlQENcMMsHBwQgNDUVYWBjCwsIQERFhUlmGhoYa5YVWOvXGkGFrQLcqGQAswrEK8xynYvftzIvWOMX7k1HR0ml/p53ZqFJCyyYFI50fFY6VidvhRlRcKBDp+HRcbNo3u3CgyoRdSLHPicIX9F12YY57qDGBWr0xqvwpPEKFSBVU7YJedXU1XC4XysrKTJVxe/VdoN4TpAkG6MlgVipfX18D5+NzCHWrqKiwKAhqUVQLtX6nTb0/TdmbAXjpB7PHhYaGWgp62oUmAJY92lizK/V8lioaKjBqUxqj+0TX6R+5hRV6p0qGej8bExLtSoNdyVcvLftvV7g5XlXu9SzZlQxVsuw0p7HnabIOnVP7OVchW70G9jW0Q5nstETvVcHc4/FY9v651qMxWqPn375XVBjnZ/X+2GmGPse+ZzhXOo9KQ7iWGvBtb3bPkyp2CvVURaOx80J+1hj9J83Tv6bcjh8/jldffdXIJoB3Hb///nsj7DkcDtx5553o3r07oqKicPnllyMgIACdO3fGxRdfDAAYNmwY2rZti9zcXMyZM8coGsHBwRg9erRJlR8SEoLRo0ejd+/epnI0W9u2bTFs2DAA3loVF110EYYPHw6Hw4G2bduisLAQ8+fPN+uSnJyMUaNGoXv37ggNDcXhw4fx2muvGbqVkJCA0aNHY/To0SbWxOl0YsSIEUhKSgLgXf/XX3/dxBCwJScno23btgCATp06ISYmxlxbuHChUTTmzJmD4uJixMfHIyUlBYBXEWFWrKbaAgMD0a1bN7z33nv45ZdfzPeXXHKJGSdbbGysiYPYsmWLBRL3wQcf4IcffoCPjw+6du2KSy+91BIj8fHHH2Pt2rWora3Fd999Z+JinE4nunbtamq7sK1evRrffvstunbtCqfTiV69ehnoHgB88803WL58ORwOB4YPH46WLVuaawsWLMCuXbuQk5ODOXPmnLNC/B+1nTd0ivhnElvmPXc4vNlxkpKSTLAr4BUoWM27urraZDVg+smAgABD/Fq2bIn4+HicOnUKZ8+eRWhoKEJCQgy8weGoz/OuRVWoPFCwI1MODw9HeHi4Ie52HHVYWJjF/RweHm5hsLyPSomd4SujdjqdFmw03dG02mkWK6fTaeBDfIfL5TKpNqk4UEjVzFrsC59LJshGIYJwEWXsCjkg02N/CLei5bC0tNQSx6DMjMK03dqv8RUat8NYGPU4lJWVmbmkNbsxxYLPU6sb50bn024ZVigHvQBk9oWFhWYeaA1XgYrCoNvtNvPAvjHdpHpjcnJyTAVPh8OBmJgYVFVVIScnByUlJSgpKTECXnl5ucl6VVtbi5ycHAQGBhpPYGNCICEUFBzYN2LWm2rTWgXqJSRUTr1aPGPck4T+0WKsihrX3OOpx8LzLHANAwMDjbKue0PnWa3D6uHgHuH/uT/5f55/ConqLaHAR6heY54GNdQohJFzwKZzRiGSCrJ6f1RxpSdBhUsVwPk+fq/CG6FEavABrDVy1AutCgrH5ONTn75VIYLqheHvyWc470ojGIfGZve+6NhIU9XyD9RnNQSsRiT1ZPH9pLVsPIPqnbAbl+rq6iwQLFXC7F4EetTtdIxeftIHjTlTRay6utqSjZF9V4MKPblut9t4X7lO/781X19fbNu2DXfddZdJD8q2e/dug91nsDCAc2LkT548ie7du5vPp0+fNp6P1atX47HHHjPXli1bZtLtLl++HDt27MCQIUMAeAOu7dj+r776Cps2bTpnjMb69est7wZgsh79szZz5kz07t0bAwYMwI8//ojnn38e8+bNO+f9d911FyZNmmQJPG7KrUOHDti3bx/atGljqrIDwJVXXtng3ttuuw233347Wrdujc8//xzr1q3D/fffb7knJiYGe/fuxeDBg00QvbawsDDs2rXLrGNwcDB27tyJSZMmITU11XLv4MGDsXLlSsTFxZ0zg1RdXR0uueSS/8bI/7jtvBWN0NBQw2BpwWWrq6tDeno6YmNjERoaCn9/f4SFhRnYCK0yJPhUHIi7PXXqlCn3TqFC8b4KP9FUfSTAhJuQOZDQUwghEyRTZFAchYLy8nLzzJCQEEudkJqaGsOY7G5vFS7IJMjA+Dyt8qwCBOE3hMlQkFVGRyaocB9lshSW2VfAa4VRJk/NV63iFAT8/f0bBK5rHAAFaBW0QkJCDAMsKCgw9Qk4n1xvMlll0hQS+C4KPwo7UUui7i/+RgVRs4n/L4xBMf9cZ4VykWlTAGWqY65NdXU1KioqLMos5zIsLMz0gevBPcykB2T6wcHBJlUxFTFN5VpTU4P8/HyEhobCx8cHSUlJxnXt6+uL0NBQsy4cM/tqjxNoas3j8VgSOFA5pRLBAGFi5NXSzX3CdeH6MmCanieeY+6JyspK8xsKazyfpEt8ll3AVYGV/6p3QT0RpBVKnxT+w1owAIwRgjTEDiVSGsh5A+o9C4wTUqVdx6i0we7JIQ1WeA37qv3l72m1V4Genl3SBV0n9eDqWdWYBz3nvr6+xujAvqgXSr0PFMztEDfOgyoIbOq10P6o0UCVS6BhVkKl/dx7PJ8KJdOU5f/Is6TwWNJM0qXAwECjFAYEBFgKDtqVYjUa2ZVuX19fREVFoayszLIX6M1uyorGW2+9BR8fHyO8z5s3D+Hh4fiv//ov9OrVy1iX3W43+vTpYypEO51ObN26FfPnz2+giHTq1Alr1qzBJZdcgilTpmDkyJEYOXIkAODuu+/GuHHjcOmll2LUqFEoLy9HcnIyNm3ahCuvvNJiCR8/frwlzmLixIkNvEfjxo3DrFmz0L17d5w5cwYAjIB61113Yd26dQgICMDOnTvx0EMP4ZtvvjG/nTt3LmJiYnDjjTea7/r27Yvly5fjwgsvxBNPPGH47IABA0xBPwDYtGkTkpOTceTIERM4/8orr+D777/HsWPHMHz4cIwZMwYPP/zwf29h/gDt0KFDuPLKK88r0cFbb72Fjz76CABwxRVXWNIcr1y5Env37sUzzzyDdu3anTMmorS0FB07djQ83OVyoXPnzsjJycFll12G1157DX379kVFRQU2bNiAnj174ueff8Zf//rXBoqIvfXs2ROff/45LrroIuTm5qJLly5ITU3FoEGD/v+M0SAho0VOcZ4Unsk0PB4PQkJCTNVlCtDKqHk/s6BoJWYSaXU7K2yF1h4GoAMwQr293gaZlbqfSaipJKhlk9AstYTaLU4KCwLq02aq5VUt7mpRI4MG0IBBq3JB/L/Ovx2PrMqOCmLsI4V4WjxZgE9haLQuckx2K796JGgxtgtXQL3L3j4W/T/nxd5nFYT0M70zKrRw3SiY2IVuFbQcDodR4CjEMhaDgd3cxxTsVYFg9XDGEij8hcJdTEwMEhISLEUkKZSQufA3KnC4XC7k5+ejvLzcWHpVyKWVmdZHrZpuFxybUuOe1bS+uhc1DoaWfI5XhVQ9W7xfi34C9R43FSQpNCqO/lxwFsAqcKoiYIflqSLPd9j3sgq/2hf79415U1Qw51nQvcK+co+q17QxCFZj7+b62L0rDECm8KvwQM1MxaaCsHpaVZjX/a6eU32GQjCVtijN5pkl/Sf/Uf6h46IRRdeTMYMcnx2KxL7q2BSy1hgdV7qp3iWFz3FvMY6Qiif3i8avke9pLKTyYdIL9jcgIMAkpuBzPR6PBap4rj3fFBqDmdk2btxoitKdPHkSI0aMwC233AIAOHXqlDG4eTwerFixwsRwAMADDzyAAQMGoLCwEEuXLkVZWRl+/fVXS42EPXv24IsvvgAAnD17FgUFBXC5XFi6dCkKCwsxYMAAk3XqyiuvNPh6wCvAEqbFdvToUXzwwQc4evSoJfHI+++/bxQPt9uNjz76CCdPnkRKSgpmzpwJf39/bNy4sYEHJCcnB0uXLkVVVRXy8vLgcDjw9NNPo6CgwFLg9fPPP8fSpUstsQK9evXC8OHDsXTpUpSWlmL37t0NAumbUquursbx48dRW1uL4cOH48477wQA3HPPPRj6f1MWs3Xr1s3ESaSnpxuFFPDWI6mrq8ODDz6IU6dO4cYbb8RVV12FoKAgPPXUUyZTVV1dHU6ePIkJEyZg4sSJ8Hg8OHnyJMrLy3HmzBl8/PHHePTRR9GlSxdUVFSYtWdWL6fTiUcffRS9evVCy5Yt8fTTTxt5Ijc3F0uXLjV7xN/fH23btm2QQOiP3v4lRYNCHwBLcLZaAKlIsCozreZAPRxGBfTAwEATY0ErnlYNJjFVaFNFRQVKSkoMtl6tayqQs99q7VImxOtK3Gl1t6edtDNBZaYqINphS3Zmp1lI1LrG32rcBSFZOv/6Wa2uOn4VpFTRKC0tNZZ1Za6qCOm6qrtf4SYcL5VOChQchyoW2i+7NVgVJWXUOsc6RoWRUPBWxQqAETr5vJqaGlNJl8ISA7h1fegtYsXckJAQhISEICIiAtHR0aaatAqyvr6+CA8PR3x8PMLCwuBwOEzch8IklNFzriorK1FQUGC8ZwqJ4Lg1U5laKpuyokGBi4KVKhrckwq9sQvH3E9UEnWt+Vyec3vcggqI3AdaYJJN9wT7wO/Z1KupsC32ze5NUKu4egj1PUqblKbwN6p8cC70d3ZPq76Xwq7SRp0bPb/qRSD91Rgm0g1VNFSpUkFex6Zraqdb6v0hzbIbPDgvCk+rq6szBisK8tp3PU92HsBGpYCeMaX3ahRRD4VCl9hn5ZGqmPB7ervsPErPBJUuKjFcNyIJSMeUN6oyQ7rLDFPsD/tPj7jun6bWUlJSsGbNGqxYsQIOhzcG4ttvv8WHH35o7rn44otx2WWXmc/x8fFo3rw5PB4PVq5ciSNHjphr48aNQ+fOnVFYWIiPPvoILpcLe/fuxZo1a8w9GzduNNmLWrZsiZiYGBQXF2PWrFnIzs5G586dMW7cOADA5Zdfjssuu8wIoiNGjED//v3hdDrRrl07BAUFYf/+/XjxxRdRV1eH5ORkpKSkoHnz5nj++edRWlqKpKQk1NbW4rnnnsOhQ4eQnJyM6667Dn5+fli5cqXxxrRu3RphYWE4ffo0/va3vyE+Ph7h4eGIiYnB9ddfbxATLPo3f/58zJo1y9QbAbxW84suugizZs1Cfn4+fv/9d4si0tSav78/UlJS4Ovri169epmK3GPHjkX37t3h5+eHlJQU+Pn5oWvXrrj66qvNbyMjI826LV68GEePHsU111wDh8Nh1jEgIABTpkxBQkICwsLCTDzMpZdeikGDBsHhcKBdu3YICQnB4cOH8cILL2D8+PFo0aIFAC/dfeGFF5Cbm4vk5GQ4HA5MmDAB7dq1Q3x8PCZPnmwMAhkZGXj22WcRGxtrwhCOHDligbY2hXbedTRatWplEepobSfhT05ORs+ePdGmTRvExsYiJCQEiYmJ8PPzw6lTpxow7tzcXOzduxfHjh1DbW0t2rRpg5KSEuTn56OmpgZjx45FTEyMSWfLgCwK0aGhoUYwYdyHelzUAsg4CwAGx6oMhI1jAawB3iq8UGhXy7zGF1DA5/sZt8LnkeCT4RBuAdQHrhYXF1vwv6wQ7ePjA5fLZYFcALC4aUNDQ1FcXGyEa76DdQeYpYuM2W7B1CrZKmgQ9kW3fmlpKU6cOAG3242oqChERUUhLCzMIpSpB4JzQ+hDTU2N8RJQqHA4HGa+VIAhIya0zi6AqaCglkhmI2Egd3BwsEVQUKXX6XQiLCzMIjwppM5uEa2srLR853A4sGnTJmRkZKCkpASJiYmGmTEIkbFAhPLk5+fD6XQiNDTUeF1KS0tRWFhoiidRmKmsrDR73M/PD8eOHTufY/uHa+3bt7d4GWtrvbVI6uq8GefatWuHtm3bolmzZmjVqpWpGUCGSZx5WVkZsrOzcfbsWWRlZSE/Px+lpaUW6EtISAhSUlIQHx+P6Ohoi2dKBV1ahQEYQwr3O71Naom2e+hUAWerq6uzKNb8TAbCmi161lTgBuqVZqBe0QSsXg9VYEhnKKDS0KB95d5Xyza9QTyLpKk8p4QUqpcDqDeOBAYGGhgg+8BASL6Pa83fqmGjpKTEpK6mgKweafUOc36oqHk8HiNQcw7slcdVgaGRgv1nnxXayGfxfsIpVahXT7B6kLj3aLChN9+edEM9Xep1LSwsxJkzZ4wlmoY1JmHp2LEjmjVrhrCwMJMIRRXrvLw8FBQUoKioCIWFhThw4ABcLpfhf3bDIOlMU2vjx4/HqlWrEBgYiNzcXNx666345JNPznn/4sWL0blzZwwePNgIb0zpytajRw/s2bMHbdq0wZ///GeMHz8enTt3bvCsxmI07G3GjBl4+umnkZSUZM5zTEwMsrOzcdlll2HDhg3m3m+//RYjR45EWVkZ4uLi8Mknn6Curs4oLudqvr6+yMnJwf33348lS5bAx8cHWVlZeOKJJ7Bw4ULLvb169cJvv/2Gli1bGo/JudoTTzyBZ5999h/e0xSaPUaDLSUlBUeOHEHXrl0tGbsA4L777sP999+Pli1bnpfHj3U04uLijPAfGhqK3NzcRmM0tJ1vHQ2Hw4H09PR/Gmvzn2rnM0/n7dGg25bwG7qq6aIlDIQW3VOnTuHMmTPIzc1t4EqnAK71Nkgcq6urERsbaxGQyRjUVU38O5kvU+yykqpaO8k8NOuU/k4tcHwHUM9o7NmGKKyQSasFjMWw+PzKykqTPpXwHLUKUlmidZXPoSVeLd0ULjguejh4zdfX11jri4uLkZ2dbdaNQYl5eXnIzs5GVlYWsrOzLVZCBgyqhVg9EkB9VjAyOfUGqaWTfwr1UBy8JgPgZz8/PzMP/J1aTlUw4xxpLA/Xl/dQoaKnQq+pwkfvG/cv1zUyMtLA/5jhjJZ4QhUolFRUVMDtdiMsLAzJycmW6zq/PDNOp7d2TExMDKKjo+F2uxEREYGIiAgjXBHqxaKXxP03tdR22hQvrhZuZqOi0EdaQ88l59cOa6HwTMGO0BKn02nSC5NeUKDj/5lTXTPJ8fzzXRq/xHXkmaQFXIVa0hu1kDemPGjSBM6FejzUas9nlZWVGTpC5ZNjYX/tnmW1uvO5PHv2d1EgZowRPQo8ozo/TAOtnkL7ZzX4sPHcqfWdcV6kAUqD1DOpnmG795rj04KEHCvPIRUZVbxIP/lbzXjId9CTyWeQn5AGsv9auFThSwoT4/zymeo9UoMUx+R2e2tD1dTUGDqlXgv+q4VT6e0lzedY2W8qL4rdb0qta9euBjZVVVWFfv364ZtvvsHFF1+M3bt3GyW3b9++2Lt3LyIiIvDkk0/ihhtuQF2dN9D2o48+QteuXbFv3z7Ex8cDAA4fPozOnTsjPT0dc+fONZZwwKs4rF27FgBw1VVX4dVXXzXXvvjiiwYxDStWrMDgwYMtntKioiJ069YN27dvB+AVSHfv3o0lS5aga9eu6NevH6qqqvCXv/wFd911l/nd3Llz8dZbb8HPzw/bt2/H6NGjAXhpyUUXXWQgXm63GwMHDsTHH38MwHtONmzYgKlTp+LQoUPo3LkzMjMz8eCDD/5Dj8Vbb71lYlOaYtN1bKydOnUKXbt2xdGjRxtcW7p0KYYNG3ZO4fnaa6/Fzz//bOhOamoq+vfvj5qaGixatAgvv/wyysvL0bt3b6xfvx7Dhg3Drl27jKFM20MPPYQ//elP8PHxwdatWzFu3DhccMEF2LdvH6KiovDUU09hxYoV8Hg8uOSSS9CiRYsmW9/kvIFe6poHYAReEmmgPuCZmV0U36qBunbhlUxMF1ehPxQcyIwVl0pLqMIYFCLBQHRavezQE1WASNiVMbIpllktm5rFiPOk47FfU+WHz2qM0XCO7BhlPp+CkTJ0CgJ8rq+vryWnNq1zjKuhAAagUZiTKoZ8HzMGBQQEmKq0CulSwmrHG1Nw4jqpdZLP1/1BTwbnS/vF/WW3sNI7xM9MEsD1UIHM7vnS99MTxvspQHA8nD+FaAAw8R2Krea66JoHBgYiPDzc9EO9YxTYOH4qo6oQN9VGyzVg9S5y3hX6RsFX4R+6RxUeol4B9cKpkYMJJexeN/4fsEK1VNmgxw2wCrrcU9wzuif0Gvcgf68WcL2f1xX2o33RPtq/s49BrfxKa/W3qnTrOdSm88k/0nOHw2EMAyr8270JdqiUjlU9ROrhUTikzomuA/tnnxulSZw/5V+NzbmOX9eP88f5pNdVaV5jsTwKqeM7dP+xvzov3Mt8n+47PlO98fzMddEYOxqwdE+rV6Wp0hG1Qns8HhOInZOTg7Vr1+LOO+9Eamoq8vLykJqaipqaGhM463A4MGrUKGzZsgXZ2dn46quvUFlZiWHDhqF169Z47733AACdO3dG+/btjec4LS3NKDfDhg3DkSNHTG2KjRs3WoTWm2++GZmZmZY4ih49emDYsGGYN2+emffa2lqkpqZix44dlt9feOGFAGA8Dzt27DD85uuvv7bUaTh8+DBGjx6N6OhofPjhh0hLS8OoUaOQkJCAZcuW4dtvv0Xbtm0xZcoUM7bff//dkghg0KBB6NKlC95++20AQF5engVa1tRaVVWV2RNDhgxB+/btsXjxYkyfPh2///47Nm/ejAMHDuC2227DkSNHsHHjRgDALbfcgpMnT1pqZ/Tq1QuDBw82qYlPnTqFtWvXmjUsLi5GcXExAODnn382yj3fn5mZia+++gq1tbUYP348OnbsiNraWsybNw99+vRBSEiISZNLT2Zqaiqqqqqwd+9ek9Tg6NGj+Omnn3D69On/tXn8d7bz9mjYYSJqaaNQROLMILTQ0FBjgVGmSKJnVwBIAGkds+O2aU1TiEl+fj5cLpcptsbaBrSIlpWVGSs5mU1jze410P7odWXY/FOmqgyN39sD0fk8XtN3qbBARmP/jTJExnGQKWrF7KCgIEvaX19fX2P1pcLB/2uBMA2EJzPjOGkx9vPzQ2RkpMkypszMrqxw7dRDonhx7b9+73DUZ7BRBYWCEu+jIM/5VGYeFBRk9qHuy/DwcISGhhpYGmFhhJZFRUUhODjYeDK4T+mZYIVdjk2VE8IGVfijcsw5DQ4ORlxcHMLCwsxYuJ6MpaFVlAqiVk9uqs2ex1+hJRSW1Cqvllo9cz4+PuYa4U0qXJNu2DH1qjjwX3o6NCYAsAb+KoyITFr3vAqEqrzYPSR2BUIVVe0f//QeNTLY6VhjRhwqquph5b0AGtBkfabSI50v/kYVD/IAe6yLng/to9JMpRUqbCs9bWweVEHge/VenQOFX6nBSj1WdrqrdMbueVPPAf+vHhr+kaaQ/pDO2A0pCqti3JJe0/TsdmVNeYF6f+3B7XYlRnlbU2yxsbGIi4sznxkzMXPmTFx//fVo3749Tp48iccffxxRUVEmC13Lli1x6623onv37khPT8djjz2GkpISDBo0CNddd515Xv/+/TFlyhTzeefOnSYGZPLkyZY0s6+99hp++uknJCcnAwAmTJiAwYMHW/rbpUsX/OlPf4LT6UR8fDxiYmJQWVmJJ598EsXFxZZaF1dccYXxWgBeaNWaNWvgdrvx9NNPY8+ePQgICECLFi3gdDoxfPhwjB8/3tw/dOhQTJgwAYA3ja/T6cTEiRPN9bVr12Lu3LkAvDU3Ro4ciRtvvBEtW7Y0cYfNmjX71xflD9h0Ha+//nr07dvXXLOv43XXXYfRo0ebdQSA7t2745ZbbjG0cefOnXj22WctdM7hcKBFixZYvnw5vvzySzRv3txcO3ToEGbOnImamhqMGjUKM2bMwPTp0+Hv748RI0bgqquuQl1dHWbNmoVff/0VmZmZWLBgASorK7Fy5UosWrTIrHNqaipef/11AN51s9fp+CO3847RSEhIsDClyspKk9GCAmGfPn3QoUMHNGvWzKR/CwsLQ2lpqcGjM/VkRUUF9uzZgwMHDiAvLw/JycnIz89HQUEBgoOD0bt3b4SHhyM4OBjR0dEGM1xeXo7i4mILwQwPD7e4/5m+j65zAEZYJP5XGSnx/EA9Flotq2ol5+80IE+x/kwxqR4KrTMB1FvA6Okh7p7MpLS01KJksbCZr6+v8SiQqRFSxOcSCkLoUkhIiKWvJ06cgMvlgtPpRGxsLKKjoy0ufUJ3KPQyrkEt8nT78x7uC45PvVj2rCx2GAVhSOqNofWZsThcE0Kb+A4Kp+y/y+UygqpWc6egqJ4GNrX6MjWqCkIqfDIjGWFNClsJDAzE1q1bUVVVhaCgIHTu3BmpqakoKCiAv78/xo4di5CQEFRVVcHlcqFFixZo2bIlysvLcfr0aeTk5MDt9lYs3bdvn4GdEaYSEhJiPHz+/v5NNkaDgfW0hFdUVBghzN/fH+3bt0eHDh3QsmVLNG/eHLGxsYiKijKu59ra+mrsJSUlKCgowMmTJ5Genm5gVoB3XaOiotC8eXPExMQYYUPjmyiIqXCpwbr26vIqQCoMR+mO0kgaYoB6DyPvt0MvgXovMfcwhUT1uioEx+59oRALoFGFSA0CrEdCQZZ0khBIhVwyRkAF+vLycgvskRBDhSspNIxzRqFYzzppFechMDDQ4glSLwnfr80eJ2M30PAevo8xLGrg4D0K0+Xv1dil3mUadOzKIpVO/VP4rRrrSCPLysrM37Fjx1BYWGhqLJWVlcHp9MZytWvXDjExMebMkD7W1HirV2dkZCA7OxsFBQXIysrCqVOnTF8Bb2wQ6Uppaakly05TaQ6HAytXroSvry/Gjh0LwFs8LTIyEqNGjbLc6+Pjg+zsbDzyyCM4ePAgfvrpJzRr1sykIj3f9tRTT2Hq1Klo3759o9f/FWz/jz/+iPT0dJOe9ttvv0VRUZFF0dH22WefITg4GFdccYX5btiwYfj222+RkJCAwsLCf2ks2o4dO4b33nsPn376KQ4dOoSOHTti0qRJTTpGY+/evaZeyr/a/tUYDQCIiIgw9VAiIyPxzjvvIDY21hI7e75t0KBB2LBhAxITE5GXl4cLL7wQW7duRfPmzS3pbE+fPo158+Zhzpw5//I7/t3tfObpvM0aKgzwj4SS6WbpNiKUiUSXwjwZLZkvMae0igFeJhMfH4/4+Hj4+/ujtLQUeXl5JpNPWFgY2rVrhy5duiAlJQXJycmWFLm+vr6WiHwyLL6DlmHFexMao8HJtFw1hq3TVKNUGrSiuVqd7O55Mif2yZ5thJuTAg2FAP7Ryq5udQrGtMxSsaFCRyGFsBzFvGugZXFxMQoKCkz9DBV2VPCioEDBC4BFIFeBQYUNeifIeGl54x5RJTAgIMBo7BTOnU6nsWA7nU5LgCOVDM4/hQkyf+5TtTYTOkZrLN9NoUbx93wug5cZa8Dnl5aWIiAgABUVFSZAWbH1ZWVlZr3DwsJM8T6uW2VlJYKCghAVFYWYmBiUlpYabxQ9glSU7dCWptRCQkLMevj4+Jg4moCAAERGRhoaoV40oD5wl8YK9WLY6QhjLxR6Q6GZjQYEtTSrYK8CowrjGluhAibXUS3lpAuaPUy9FXbrND2mmqZU07YqzSKcVAVsNYIoNKKurs6ScIKfNZMa6QMVfI0bU8WfsS2kHyp4812kQ+qJsMOodI3VM0Bvht17xTNKGsC9wTVX5YhrrdkK1RujjFH7znkFrPE/9AjY+8S9ak/5rfFYNHZwXQnl5PsU6sX4FF1XhUPR60x6DtTDjjWhgcbQca54LgAYmtdUoVO7du3C6tWrcc8995jvHnnkEdxxxx0N7nW73Rg6dChWrVqFPXv2oG/fvsjPz8cjjzzSoI5GSkoKdu3ahaSkJNx5552WFK8LFy60ZCcCgKSkJOzatQspKSn44IMPMHr0aHg8Hrz//vuWQPH33nsPM2fONJ///Oc/44knnjCf77jjDjzyyCMICgrCtm3bcMkll2DIkCHYunUrAgMD8eCDD1piNgAvnOrCCy+0pK49V3vooYdMrQgAuPfee/Hpp58C8GZiWrx4MU6ePInevXvj1KlTWLx4MXr37o3evXs3OYPWK6+8YlHYdB2//PJLTJ8+3Vz77LPPcNddd/3TdYyOjsbOnTuN8hIREYEdO3YY70hpaSn69++PrVu34rvvvsPAgQNRXV2NuXPn4pVXXjlnX2fPno2FCxfCx8cHGzZswFVXXYXffvsNffv2Ncrj/v370adPH+Tm5lrWcfTo0aZAZFNo/xIGw46DVXc7rWRlZWWG2ZBoBwUFGRwbmZa69lWAoEVJXetK0DXgV39vZ6RkQurhsD9XLdf6vTb7/Wq153W9z+5Ss8Oi7LAAKiMUNjjPig3mZ7Xiq+VUIQpUChSeRuspn0fGzswlCgtRfLzirnUM2l+FHfD5AIxQoH3VprhiFabt8DWNWdH3q6DGPan/qtW0sT1AZUzHwL3FfijUgp4pFVp03XW89uJ6aoVl/2pqauByuYzngt4WCkecD45Fn9GUFQ0KbxTkqFhzn1GgstfSAWD2vu49FQKB+lgFnUueDfvzVInhetu9Bur95L1qRNBzoffovbxuP9965ukB0WfYn6mWe4X32CE0+h7+X6GGnCftH/e10s7GzndjNE49AzzL3Pe6dvo+Xrd7LeyegMbmwa4ccPyN3Uc6YV8r9To3Nj47jEznrrGmcCj7HtD1o/KgdJbPbwx+xd+pUss+2T3t9vgPNoVQ0StOo0dTbJs2bcKOHTtw/Phx893JkyfRsmVL3HnnnXj77bctBfL2799v/r97924AMNkutZWXl+Onn35CVVUVTp48iZ07d5prrVq1QqdOnXDgwAFMnToVx48fNx6S8vJyNGvWDH369MH+/fuxe/duC5Z+z549yMrKMp/tQcgU5v39/bF582bjZdqyZQvq6upM5iQfH2+Bwh9//BFpaWnYu3cvpk2bho0bN6KkpATXXnstFi1ahIsuugjx8fEmKNz+vuPHjyMwMBAA0K9fPxw8eBBZWVnYvXs3brrpJqSlpWHbtm0AYClg1xTa2bNnLcUTdR137NhhPHwA8Ouvv+LkyZOorKw065ibm4vc3FwAMOtYXV2NTZs2GRm2pqYGmzdvxvDhwxEdHY3vvvvO7CsAJsnC77//3kCZb9WqFcaMGYO3334bhw4dMoWAt27diqysLLhcLuzZswe33XYbtm7davYTYF3HppYt7rwVDbWSkfApYXU6nSbDklq0CDHQCSfxJ1FVi05NTY1x19NLEBISYqlhwGeTiDP1LZvT6bRkqbFjdJWxKbZXBVxlnAp14thVmFerJmAN9tTf8PnqUVGhle+mkMT3UIjlfNkZrQpTQH1wdk1NjfEu8Xl+fn4mPiEqKsoENhLiZFdSdE44Nu0354jNrkDQu6RKHddbhSu7oKXCku4zzYSj1kFVHDk/TB2smXb4++rqagPh4Ryyqjo/k3FTcdZq1SpcNQbn4FxramUVtKiQu91uA4FgEcqKigoEBgY2yCbUmEDV1BrnhetFKAfnU+uc2KFF9FbqvOuZoyLP/aeZ3Th3nEsKfCpQqjeFdEuhdCqs25UE/qvWaLsRQ++l8qrfN0aHeK8Kugrb4pzyX90bOk92hYl0hvNCwUyVMJ0nO+TRLtg2ZmDRs2inl6pE6L+qAPHZehbt42T/1PtFpULPsdIe+3m1/86+L+zrSpps946QnzS2Fv/Iy63vUV7Ic6I0RPmuPXCcnjk7T6DRz953O99sKo2eDHpBc3Jy4PF40KZNGzz88MNYtmyZia3zeDzmup+fH2JiYpCTk4PPPvsMvr6+SExMRE5OjoG13nvvvQCAr776Cl999ZV556BBg3Dttddi6dKlmDZtGr755hv8/PPP5v6xY8fi9ttvx7vvvoslS5ZYUBXLli2z8DZmGaTgGhUVBY/Hg6KiIjz44IOIjIwEADz44IMAvLUdHA4HXC4XHnjgAWRlZSEtLQ1OpxP33nsvioqKcPLkSTz88MP48MMPMXr0aHTr1s0oGj/88AN++uknAEBcXBzWrVuH1atXA/Bm0/r444/x66+/Ii4uDnfccQe++OILo2g05RYXF4f169dj7dq1SExMxHPPPYegoCDExsYiLy8PixYtQmVlJcrKysw6Mo4zNzcXL7/8MgDv2X3hhReQn59vYj7vu+8+fPbZZ2jevLkJ+td1BGC8RoA39KCoqAht27bFww8/jKVLl1o8EuoBczgcuPfee1FZWWlRkrWAZFNr561oaGyAWn35LxUBQhAAGEgEBSa66isqKuB0ejGnYWFhKCoqMho24SwOhwORkZEICwuzMElalulmJ8SJcB9arpn5iu5mTS1K6A1QL9xTWaitrbV4VchkSfQJv1BGT0apnghlMkFBQZbgUzI5O9MhM6DbHwDCw8MtuHIVzHQNVIkh3IsZftT7UVtba8Fk81A4nU6EhIQYQY0WeVVy7JZKrQpOYUUZJwU/Mrjg4GCTlpjjVk+UzgmFMOLeNaWyColqFVWBRSFenNfw8HCjyAKwZI5yOBwWTCWVVcIw+AwV/DW9KJVHVvTk7whhsytErAweEBCAiIgIU8OD2agKCwsN9ppWpcYExqbWWKOB+4XB85qpDqiPNyJ8jkqIwmdCQkJQXl5uAvW1rgFhbOXl5YiIiDBnXwPp7XVcFA5DLxMVDe4TxukAsEBmAJiEBzyfmjpbPZLcp6q08lxSUFR4IvepWrs9Ho+ZS1qpAViUFbsVnnuPijPpEONUOM92T4O9qcKgEB+dO30fY8n4fD2fKpg3Zq3nONhX0jP1vnIfNaa4aCE/rhFpCufY7Xab3/N7XVe7wsA1VlgV+ZI+QxUa0j27skTFmnA/3gfUG210zlSB9fHxQVBQkPH+8XNISAhKS0vNvtKaSeSFhBM25XbZZZfh008/RXx8PMrKyrBx40a0atUKHo8HS5YswU033YTa2lokJiaioKAAffr0waZNm9C8eXNkZ2ejZ8+e+OWXX9CyZUs88MADuPTSS9G7d+9G3/Xyyy8bGMzQoUMbGHsWLlyIt99+Gx6PB19//TW2b99u4E5ffvklDh48iD//+c8AvAJoeno6brrpJgDA8uXLUVRUhMmTJwPwplitq6szAd6LFi1CcHAwrrzySnTo0MEiL3Tp0sV8ZlzBI488Yunbs88+i8GDB6NPnz7YuXMnXn/9dYPtv+iii+DxeNChQweTAvfw4cP/s4X5g7RffvkFb7/9NlatWoUDBw6gQ4cOuOGGG3D99dejY8eOWL9+PVJTU/HXv/7V/Gb69OkNYjRiY2Nx5swZDB48GF27dsXzzz+PxMRES4A9cO51vP7663H06FHcdNNNWLlyJVq3bv0PjYV1dXXo1q1bkzYo2tt5Kxpk5nV1dSgrK7NgjlXwpPWEMAgKWwxAI0NRCBEAC0be4XCYquK8psyNBFQtROwfGYBCH4D6YEDCtpSJqCWKfVVvBgULZVxkEGRSnAeOnQyB77MzUbWiaXam6urqBhViKRCxD2Q8gYGBKC8vN+9hETCmNKSiwPuDgoLQunVro0RQ+FYvgWK/mTGMGGgNslfrvAa9qkeIigfHxgBUzoEKYhR+OMdaNZrromNhDQ9a57Q/xG1TGeRcM2NWXV2d8V5wTinUcQ6IaaaApwIj9572v6ioyGCiS0tL8dtvvxkFhcqqek+4b+xZpcrKylBQUGDGxqBmBoSqpbcpNhobPB6PSVzAzHCBgYGIiooy829XzH19fRvE2ej6c465JmFhYQgJCbHsV4VDqRLB96jBhHNNAZ99UEu7PpfPUMWXZ4HCOA0C9EqQbpCuUIi0W+V5D4Vs7muF86k1m0o+UJ9WlWOiAEylB4Ch0zq3/K2mZ2Xf+Rz9XpUUnlWdj3NZ8YGGAd1awVrhhDo+9ZKod4dGMVV8NJBbvTVK+7kP1Nuiwfzsn95jh9ByfTjHhC1xrKpYqtdXYZFut7eoZ3FxsUlPzj2pBrbGDEF8v72OlBpIuI+bKnSKbcuWLRg2bJhFYeI6/O1vf8Nbb70FACaOYf/+/Rg8eLCBJh06dAiDBg1CXl4e5s2bh6VLl5rn3H777RgyZIgl81SzZs3w6aefYsqUKQ0KwU2ePBnXX389rrrqKtx6662WWke33367BYJ01113obq6GiEhIVi7di3effddbN682Vx/+OGHMXjwYKxfvx6jR4/G448/blFQn3nmGURHR+Ouu+4y4+3Zsyfmz5+PsWPHNqiPMnfuXCxevBiAt9BhVlYWWrdujY8++ggTJkxARkYGTp8+jYsvvhinTp3CLbfcgjFjxuDaa6/9F1bjP9+uuuoqC9ztmmuuQW5uLgoKCnDxxRfj7NmzeOedd0wNkalTpzYIpF+xYgU2btwIj8eDd955BwcPHsTcuXMxePBg7N+/H8ePH8eePXvMeR01ahQefPBBjB49Gvfee6/Fc8XaKlVVVRg+fLiBPnHNuI733nsvvv76a8ybNw9r1qyB0+nEmjVrsHDhQqxatQoOhwOrV6/G+++/b+qujBs3zqRXbgrtvCUWFUCV2QCwEFwSclrXq6qqjOBMS7k+j8ROGZWdIQP1WVtIbLWAEvvA+xngGRQUhPDwcJPaTq3y58JeA/U1M/g8ZSYqfChzUy+Gwj1UeFBGxz/NHmO3IpLx2fuogo8yQMBa00Kz4tBCq8GpfJcGL5JRU/C1rwP7oJY7Mn2Ng7DDDTgeXuO/ClWxQ0EUs63Qq8aez7m1wxl4r86lzrHONYUa7lvGWuia6f6k5ZwpctnH2tpalJaWWtaOHjv2zc/Pz6S95FnhmLmHdByKcVdBrak1FWTr6uqMMksBSYvcaVCywsi4FnrmKYgpXbBnZiKd0jUjnQgNDbWkMOZ+5HMaozsq8OmZVKMFz549C5NaqbWfSgvs9+oetwvHvK+x88Gm50Ov2b3TKpjyXgAWumf/s1v/1fCk8DX7PtD/qxFFx6DPVt6g9FD5g71vKqT9o7nQP+2Dff829hy9zvmjwsw9zT8adtSjxXdxPenlKCsraxA719gf+0LepcYbvof7jLTe7vFqai08PBxdunSB0+lN8aopYY8ePWoKs/n4+GDQoEG49NJLsW3bNlx33XXo0qULgoKC0L17d/j6+iIpKQnt2rUzv8/OzkZaWprlfVVVVSYjIOAtuDdt2jRER0cjKSkJvXv3hsPhQEpKiiU97O+//w4/Pz/ceOONcDgcOHDgAI4ePYq6ujrs378f27dvR11dHf7rv/4LPj4+OHz4MHbs2IH9+/ejrq4OaWlplriDM2fOWOJTAMDlcuHAgQOYMmUKOnXqZL6/4YYbEBISgszMTEybNg2HDx9GixYtcNVVV2H//v245ppr0KtXL1RWVmLbtm2orKxETk6O8Wp89tlnWLRoEZYvX/5vWLH/Ny0vLw9vv/02Nm3aZMnMtHv3bqSnpyMgIADdu3eHv78/4uPj0bFjRwBA+/btTbFGtpiYGHP96NGjSE9PR21tLbZt24aysjJERUWhc+fO5swVFRXhwIED8Hi89Vw0juLw4cM4fPgwHA4HunbtivDwcLRo0QK33HIL/Pz8cObMGVRVVeHWW281meYA79k9ePCgUYj57NzcXFRUVGD//v0WaF5TaOft0aisrDTQA001CVjT/Kn1hgSWgj5z1dNKTjgNhSgSZ1p5gHp4i2Z64v/V8qgu76CgIGOJpuVc4UoUFuxWdxUkVUAgVIzEml4I9tlu2eMcUBAlVIfvUysn07sC9YI1hVtaA5UR0eKvlk2FVfA9Pj4+xivQWBYuZb78rdvtNpWHaeFkdiQ2nSf7XPn7+xsirEpkY3AEflZlwsenPjhYYShs2m9acFUwVwszFVsdJ70I9Lbps7nnmGmHygYFDz6LY+HaKhSltLTUCBOcT6fTaWKNOHfco4QFaTV0CrQVFRXGqkvYmD0OpCk2wku4hyIiIowioUoF55FZhDTjFgVECvlMbsC0qCrgqwVbDRFcD6BegFbMu3od+Fn3KmAtwKbnErAmflBFwa5o8Zl2xVKFV/WE8bPdAk5rP//Uu6OeVjZ9rz3wnooBabHuf107jp00Uvur3gGdX7s3RL039oB9PlM9DNp//l4VCvZVlTiulxoVtNI4+8emY1Hoqc4tmz2uzK5kaFIU9b7QE8c5Zf9V4Sb0jzzLbtDis/hZvX86TsaqEWJMXt3U6UmnTp3wt7/9DZ988gmuv/56hIeHY+3atYiKioLL5UKbNm0we/ZsfPHFF5gwYQI6duyIr776Co8//jjmzJmDnTt3Yvbs2fjqq69w5ZVX4pJLLjE4+G+++QY//vijeReTphD+BHhjJ5577jlTqI2ehL/85S/47bff8PPPPyMqKgolJSXo168fZs6ciY8++gghISEGGcJMWVdffTWeeeYZLF++HG63G3v27GmQaYqN3gnAGxdQXl6Oo0ePYsaMGTh48CDKy8tx6NAhOBwO/PWvf8Vrr72Gn3/+Gc899xzWrFmD0aNHY/jw4Rg8eDB+/fVXBAQEWIKZ16xZg7Vr1yImJgZ/+9vf4Ha70aJFC4t354/UtB6KtsjISFRVVSE2NhbPPfccvv/+e1x22WWYOHEiPv74Y9x///1Yu3Ytdu3ahcjISBQXF2Pw4MG44447sGLFCrz55puGhjW2jm63G7/99hvS0tLgdrtNYLd6swCvHPjMM88gLy8P5eXl+Pvf/45PP/0UixcvxqBBg/DZZ5+hU6dOqKmpQXh4OEpKSnD//fcD8NKf8PBwPPzww+ac6x5sKu2862i0atXKwANKS0tNIFltba3J8x8YGIjk5GQMHz4ckZGRSEpKQkJCApKTk7Fr1y4UFhYaxcPPzw9FRUU4e/Ysvv/+e/j7+6OgoAAlJSWIiorCmDFj0KxZM4SHhzcQUtkolNmx1cSl2i3iZCgej8eSgpdQI6BeQLAzflVo7MHZWpBLhQrAu1EYk8J3aG0Kfkeh2eVyGSYEwAhRZL6NWfcopNFjpDAydSs7HA6TmpVws8TERNN3X19fc1hZ74TQKlrwqTzqOAAvU6uoqLBYNymQ8/+VlZUWrwcFAg1c5LpwnhWqwrHxs+LMAViCuxW2x/niHPDdXFPOIeuXEDalipfD4UB8fLxFgGLtAIfDgdLSUuTm5uLw4cM4evQoQkNDTfaKyMhIXHvttaitrUVwcDAiIyMRGxsLl8uFvLw8ZGVlISgoCJmZmTh79iyOHDmCoqIiE9cUFRWFgoICC3xLs5g0pZaUlGT2BevDaMxGdXU14uPjkZycjM6dO6NFixaIi4tDdHQ0YmJiLNbh8vJyFBYWIjc3Fzk5OThz5gyys7ONohceHo7k5GTExcUhNjbWUk8jODjYrD3PJiEvAIyCyP2nQqsqGwohZbNb4tlYhFFhdCrUAvWCNy3fqtCrkUAhnlQCVOnhHlUPgxoM7EHuhIHyLBKyo2NQr4R6p9UDpMK+0gJNje7n52eBd/r4+JjU6KT1qhS43W5jNAFg1pfXCU/l/Om7VeHUpt4bKv5Op9OSvILvo7Kgnm7+XrPLcb4rKipM8Vjdr6SxCgvjfNODWlJSgjNnzuD06dPIzMy08IHIyEj06dMHzZs3R2RkJMLDw40AXF5ejqKiIuTl5SEnJ8ech4yMDJSUlBiDGr2IVKAPHjx4zrP6R212j5calHx8fJCZmYl7770XH3zwQQODE5V79cSrEs01fvbZZzFu3Dh0794dAEwhQPUW8H16LlXpJ7Z/yJAh+OWXX8z1H374Aenp6bj55pstY+L1822+vr7IzMzEgw8+aGBfOjb758bGar+frXXr1jhy5Ah69eqF33//HS1atPhDV6Wura1Fq1atLFXTjx49ikWLFuGFF174h+vUvHlzHDt2DH369MG+ffvM9a1bt5oCfY2tIwBMmTIF8+fPR2JiIr788ksUFxc3qpApDbKvM/u2ePFipKSkYOjQoeZa37598fPPP6Nly5Z/WJ5/PirEvwT2Lisrg8vlMkGqxPnGxMQYvDGJa21tLbKzs3H8+HGcPHnSwJnsFu3AwEBERER4O/N/rWO08JBpKNyB2PyQkBATu6DXWZRLFQ91OQMwjJQF4BjUSyZDpkxirM9Sy6jCK8hsaAHjH4VUXRBCctiU6XNeNNUvmRTnBai3pvIdZGa6+KoQ8R2VlZUoLCxEUVGRsSBzLel5oVWtpKTECNxUJNhfBnhS4CDzV8HEDvdQwYhClkIeNGiaHhldSyoZKuwpbIYWQDJVuxWV3h3GTrAmAPc1ay/Q+kclsra21hSNJPyMMR6EOOhassgWa78EBQU1gPWEhoaas6JeDBadY5Vyp9MbOM558PPza1IVQe1N10WDnznPgBViR4GSiqNdiKcngbRAazGoJRqopy+6H9RDRuLP86IeS1qVeY4I7VLaYY85UrgLUJ8cQ3H2dviSemw0PomeG+0D55J7Tmti6PyoxdwOdeK86Bxw3uwQMdbw4V7U86leFyqMHL+uJ726qnCpZ7wxqJN6FbhmCllSnsK10jmwKxnsk8LX1GOt88vr/LNDpThv5CMsPKgKHvurHhu+WyGsfKfCm7TwosbK6B+NNJwrvpf7mc/i/Ou+bcqNPGTNmjW45JJLzByPGzcO33//vbln1apVGDVqlFk3rnWHDh3w/fffIzY21jJfy5cvN5AWtmXLlhnF4L333sO0adOQkJCA77//Hm3btrV4iObPn4+77roLhYWFGDVqlIHW8PqDDz6I5557rsFYeP3vf/+7pWDe008/jeeff95yf69evfDNN9/gpptuwrfffmu+VwOpw+HAxx9/jHHjxplrgHVv2A2qy5Ytwy233IKsrCyMGDECJ0+exHXXXYfXX38dl156aYNUuf/pNm/ePAwbNgwjRoxAXl6e5drUqVMRGhqKJUuWmPHedNNNWLJkCQCvZ+jWW29FdnY2RowYgWPHjmHChAkGJnbnnXfi9ddfP+c6vvLKKxgwYACuvvpq1NbW4rHHHsPf/vY3836uo7+/P1JTUzF48OBGPYn8PGfOHNx7771wOp1YuXIlxowZg8OHD2PkyJEoKCgwqZubYvuXYjRIQPmnzJkCJwmu0+kNcC0qKkJZWZlhiMpgFPJApmtnImz8HZkqGTaZnVrT7NY1ZVxqwVQvgV3wsDPrxiAbCoGyw4LscCd139ut6+ybWhvs1ji1aqqwTksF+2237NkZMz0TFFq4rrSs6bzzGXbBx973c1lw7YqGKlx2ocr+HL3fLjQ0JlDou1WR+UfryoxO/GsM3wzUC4gUkLn/+C79HQUwDfCk4Mt32/eG7gm1aCsDoEW1MetTU2oK/+Ef9x+FMnqYKLhTkFLBkkqI3aDA63xOY+df77HvWTY7DbJD8+zGA/1sFyZ5vbGYBt2bKmyy2fe2Ws9VUbfvdfufene5B+1/9rnV/mowsh0KZfdiqofXPp9q1dMzqf+3z6t9brm2jc2xKlWq/Oj5Oddc83u+S/mdnf4qP1T6SQVLvbT2InuqbNv3px3OpUqufZ3t/EPnm4YUevepJOp5a+rQKbaMjAz07dsXw4YNg8PhQHJysqXQbmZmpsUYdO2116JDhw6orq7G2bNn4Xa70adPH1x55ZUAgKysLOzZsweHDh3C1KlTERYWhpMnT+KXX34B4I3fKC4uRm1tLdLT0xvg5XNyclBUVAQfHx80a9YMAQEBSElJwcSJE+FwOLB7925LzMWwYcMwaNAg8zkvL88kBJk8eTJCQ0MbCNHs+08//YTWrVtj5MiRcDgcmDBhAlJSUhATE4MbbrgBRUVFxjupLSIiwgjiPXv2xFVXXWXG3qpVK1x++eXYsGEDXC4XSkpKcOrUKaxfvx6lpaX/3WX6t7fPP/8cqamp2LBhAzZs2IDq6mrLOm7btg179+61VIEvKSkxn7mOVVVVZqylpaXGc/Drr78iLS3Nso7acnNzcfDgQfzyyy+YPHkycnJyLPUtuI4ejwcZGRkoLy9HcnIypkyZYgwCCQkJmDJlivFYxsfHw+PxIDMzE927d8fAgQPN2IqKiv7livZ/lHbe0KmYmBhjUWG8hlq+SHhjY2MxZMgQ9OjRw2zK9u3bIyQkxGx6Bsa63W4UFBTgxx9/hMvlQm5uLkpKSuDr64uxY8eiRYsWiIqKQl1dnYFbaapC9SYQ+61QJKCeYanbW2FTmhqQDJaQHaAeg6uMz16ZVq1XdJWrYEFCRIahTKexZo99YYVoFdz5fDJvKnj83t5nt9tt3POcO8YY8LmKG2bfQ0JCLHhueln4DB0L368WZBUw7DARhY5xLxFnz0J2an1WRYdNFT1lnOoNs+8DXisrKzMCLT0y7HNxcTGKi4uNtTw0NNQicAFeoqUCcHZ2NjIzM5Geno6MjAwUFhYiLCwMLVq0wMiRI83eCwsLQ1JSEgoKClBUVGSCwM6ePYsTJ07g4MGDKCkpMbEEQUFBKCoqsqynBr01pcbaLYGBgQgNDTXnjfNIoSg6OhodO3ZE586dERkZaYgwlWMKjkVFRQY6deDAAeTn55s9QQYfExODyMhIREREIDY21tSR0T1DSzv/7/F4LKmwqaATYsP+qgCv0KLGPKGANQ5LIT7MNkfFivvSvvfVC6J7WxVZ9o/NrrgADWtNaNCwGgD4XL6Xnk+eXT5Ps0wxtkk9LGql1/TipL30aCl9s9MLNt6v58GuQPLZnE/1VlFgUPrNc81nqyHH7XY38EKrYUDfw7GzerrWk1LPLOmaPfCbcGIWDsvOzkZoaCgiIiIQHR2NLl26WKBT9ISWlZWhuLgYBQUFyMrKwpkzZwwEMywszHiFsrKyLPtEC5g1ldYY71yyZAkiIiIwadIkHDt2DA8//DBWrFgBAAgLCzNeR6fTicOHD+PFF1/EokWLzO9nz56NIUOGYODAgea7Tp064eDBg2jfvj3OnDkDPz8/lJWVITQ01CQ6YOPeVnx+XFwc0tLSMGrUKHTq1AnPPPMMUlJSGih4K1euRHFxMf7rv/4LYWFhJpY1MDAQJ06cwPTp0/H1118jKCioUUH/zTffRNu2bXHFFVfg6NGjePbZZ7F3716sW7cOHTt2RHZ2NpxOp6nV5PF40KlTJ/zyyy/o2bMnbrjhBlxzzTXo06cPAG8l8RtvvBE9evRo8K5NmzahR48ecDgc/zHPutvtRmlpKTp27IicnBzL2P7+97+bdSSNr6uraxA3oY1jIaScLSQkBG63N/aC67h9+3ZzPxEQ4eHhOHbsGCZNmoT169eb3wcHB8Pj8Vgyjo0YMQIfffQR2rRpg7KyMhMX1K5dO8yePRtt27bF5ZdfDsCbLaxHjx649NJLAXhlI8Lr/0jtfFSI81Y0IiIiDDxBmSlQb8F3OLy1L3r27In+/fsbwhwbG4t27doZyA4VFU7a1q1b8euvv5rAt5iYGIwYMQLNmjVDWFgYysvLTX7w4OBgSy5+ZYzsU2hoqHdwjnq4DJmLMlhlzspYlIFS4LArHnYLulqw6Kam0K/CKIVVPsPpdBrmRIFD+xYSEmJJhejxeCwYX6fTieLiYktNBw2cV5e/1jMhXEAVHwoStMKVl5cjNjbWBOzznWTI7Kta2nXeKisrLdl22F+1NDIwzp7Lnx4yHSuhd3yHWhapBOlYKBw4HA6TIIBjI4aagkBQUJDJtMX1ZOEk7jF60Rj7QmiU2+2NmaAFo6SkBBUVFUhPT0dISAhatGiBsWPHwuPxICQkBFFRUYiIiDAxBkxTt3PnTpw4cQKZmZlm76gwxH1dUVFhwaI2pdamTRuDaw8LCzNCHIP/fX19ERISgoiICFNtNyEhAdHR0eaPBgIGiufm5iIrKwsHDhwwlVx9fX0RGxuLjh07Ijo6GuHh4QgPD0dsbCzCwsLMfuD+5xlT+FBgYKAlGFwVDe49JZ9q8eI9jXkGAFgMEkD9WWZT4dhusQfq4xRU0FUrvSrqpEHqBVB6qQIvzxONBlQw+LvaWm+aasIHnU6nRejiPRTe+XwqbUoP1PtCQZDj1lpGQL1SBTSscq4xGzo29d7Yobjq6QBg4GyN0SDSDNIZxpPwXLIGDIO/ORZ6+AnHojdUYXOcz7KyMpSWlqKgoABnz55FRkYGcnNzjVcvLCwMCQkJ6NGjBxITExEVFYWoqCgEBwfD5XKhuLgY+fn5JlXp2bNncerUKVRWVhpPRm1trTGecG2aYs2ExhQNKm5aJ4SGgDNnzuDxxx/He++9BwCW62z0gqkyqYrG5MmTMWXKFHTt2hW7d+9GamoqnnzySXPv3XffjXvvvRft2rWz0ATCzHmWtGI5G2lQQEAAMjIycP3112PNmjWWvo4YMQKffvopmjVr1kDZaGzsPH98H2uGpKSk4OzZs8boySQkOnbSqnP11eFwIDExESdPnvyHBtP/V23btm0YMmSI6d8FF1yALVu2oF27dkapqqmpweHDh9G6dWscOnQIPXv2POfzWrZsibS0NPTt29dSIG/Lli3YuXMn7rnnHrOOHo8H0dHROHPmDK644gps3LgRACzX2T7//HN4PB5MmDDBfEcDlCZN4jroOgJo8Pnll1/GoEGD0L9//3/TTP572vmoEOcNnSJ+nkSblXvJPBX/WFRUZAQHPz8/FBcXo7Cw0DAQu1UuNjbWMB+Px4Pi4mJjEWJAL4XwsrIyQ+zp1svLy0NeXp4JeisqKkJRURGKi4tRVlZmGCGZhVoHqfDYMczqPbHjedWCZg/yISMlg7XHLQD1KSkp+CtshNAwurrtVnxq6Hy33WqnlkNi1lXpYf+oabMPahEkpI1rznlRyBnfpdAJp9NphAmdSx2zKocUStQaqi59VfDI8LlvKKgptMDu3VFce11dnVEOXC6XuYdCBPeJriOVDj6fAcTcS2oF1XkHYOCDwcHBiIiIML9hfIbD4TD7m33W88QAf46ReG0t6NUUmyqluudUoKWABjTMFKTV2SmkcQ+r8Mn9pnuXnkwK1aQjSms459wXus8U28710WKN6mnhvlGPHteZ3gNVfPl8frZftxNz0oHGIDCk0Qq1IZ2yQyR1HfQdmtHPjuPmmpCuqqGEc8+sbFTWlAYq9EsVAaUZPLv8vRo69GwodFfpovbFDjHS+BHeQ6WC68nYCK6Bzp0qRNXV1YamaHwi/7gH1FikCpBd4SMdIB+gwhsWFoawsDALHO5cnhwNPme/ARglnnPYmCDZVNqTTz6Jp59+Gn5+fvj8888xYMAAQ18//PBDDBs2DIB3bW+88UYTs+FwOLBkyRJcccUVSElJwZo1axATE2PWH/DGYEyaNMkIkxkZGVixYgVmzJgBwIvdr6qqMlh/AFi9ejVuvfVWsx5XXnklPvroI3N2efYB4PnnnzdVv4F63lZZWYmJEyfiiiuuwMyZM+Hv748VK1bgoosuwq+//oqJEyeivLwcDz/8sCXGg3vS6XRi6dKlGDVqlMVDO336dMyfP98SI8vr7777LiZNmmTGvmjRIlx//fWWvcGK6OxrdXU1srKycMUVV+CKK67A66+//m9a1X/eXnvtNdx3332W/h09ehTjx49Hfn6+ZR2nT5+OcePG4S9/+Yu594YbbrBk7Zo4cSJeeukljB8/HqdOncL48ePx/vvvA/B6dhYuXAgAxqgKAKWlpbj22muxb98+DBs2DJ9++inq6uowa9YsS4Xv2bNnY/bs2ebz448/jqefftr0/eGHH8bs2bPhdrvxySefYNiwYejQoQNSU1MRHh6OGTNm4MUXXzS/X7RokclGtXTp0iZV5+S809vW1VlTutqFTRI+t9ubqUet0bTYKLNQgkuLC6/bg7OVWNPjwANcW+vNekWia8cqq+XIng6Xv2mM2SoT4r98rh17zKaEn02tkuqKZ9MNzPv0X7V62t/p6+trBBoyZ1rddY7s0CVVbpQRs4+cOxXc7NZQVToAWAQaflavkx1qANRnW1CPlM6DKjcqtNn7xPep9d/+PBJy9VjoPrTj7ymsaGwR7+MzCPshY9dYGcLvqDSqwllTU4OCggJTsE69Q/qnwgOt0nbse1Ns6pHiHFOIU2gehWk9NwprBGC5rgKmYt/5Tn2v/lYFMoVYqpCmSpCeI6UTfD4V+cZge/xs9yyQlqnAbr/XTqvYL96nv7PTEb2P9+pvOF8Ky9R3aT/tY9e5Jw3WIHI2e+wE+6dQKbsCqv1mP7hvtKm3tDFFVudIv1ehXw0+dt6giobey77Yz63ua+0D/2/nE3yWKrZsmnREayMBVs+Xeq/ZP9IX9ksNbeeCpjWFph5wGmvYaIwAvGu+bt06y29pZPB4PKYAaLdu3RAXF4f169cbJdblcmHt2rUAvMJsZmYmxo8fjx9++AFxcXFo3ry5eSZpPRuNGI01Pp9t4MCBqKmpwfbt2/Hdd9+hT58+Zn+wr3l5eSboWw0x9sb7o6KiMHToUKxduxbV1dXIzc3FF1980aAavMpZ9rljI10aP348fvzxR8TGxqJDhw5mbtxuN9q0aYMxY8Y02qf/acvLy8OmTZsAAKmpqUhLS8P48ePx9ddfo6qqCiUlJfjmm28wevRoHDlyxASsb9iwocGzaODVz4WFhWYsrVu3xtChQ+FwOPDzzz8D8Bo86b0oKChATU0NvvnmGzN2t9uNcePGITIy0sTmjBkzBr/88gscDgeuvvpqfPXVVwYKy6brqMV9uW/0+ujRo5GWloYtW7YAqM+E11TaeUOnwsPDERkZCafTaZQGbkBCIGiJiYuLwxVXXIHo6GgAQEFBARITE41CwSrKfMbp06excuVKY7nNzs7GyJEj0aFDByQlJVlcgbT26kFj5h5106vlp7q62gTwEjeslmq7dY8ZtPjZHvSo71ZmTUYRFBRkmBFTsvJeVRJ8fb3pZNV7Yg8cZHwGBX8yQjIexXMTIqbZqHSuKPjrJmXfdC6U8bKgmdZxoACgdTOoKKrgBtQnEaC1XgV6eg30flVY7CmFVSCh4M3v+W6FsyhzraqqMgF8amHWsarwxfVQuIfL5TJeII7f5XKhqKgI6enp8PHxMTEDdOEmJiaiffv2uPDCC41iDnir0hLCExAQgIKCAuzduxenT59GRkYGPJ56qBSFa+4nZg5riq1NmzYNzgDXt6KiAhEREQgMDER4eDhatWqFrl27IikpCVFRUQgLC0NkZKSBGmh629zcXJw+fRpHjhwx85WQkIA2bdogOjraxHmEhoYaYY1eEK47CTsFZWJiVeFVD5YaXAhD1EJo9KBwv9qFYxWGuJd5ryZq4PnnO7nfG8vcxEbrrgqlaqXnezlWh8NhgS/xGTxL/I40Tn9PT54qUoxF0BgN9odj4nvV4s8/nXd77IhCpYB6wV29Aqpoaa0ipa12ww/XQeeMll/NLKaGCrfbbTLQ8WyqoG9XAmgIUuWEsRmFhYUmvquoqMik2w4MDERMTAyaN2+O7t27Izo6GqGhoSa9OA0olZWVOHXqFPbu3YuTJ0/i7Nmzhtdy3klvub/tFa6bQuOaArAIjfZGLyaFa/tnbc8//zyGDh2KAQMGWN5DSK3H40Hbtm2xb98+9OnTx9Sp4PU777wTd999Nzp06NBAieT+O5dysHr1apSVleHWW2+14Pn/0Vi0cT/pXPTt2xfr169H+/btLWlRmQ75HwmpNKxpX5o1a2YqqV988cV46KGHLDCxmJgYnDhxAiEhIWasGpD/r7S6ujqLkvbzzz/j6quvNmPv378/fvjhB7Rr1w5FRUVwOr3w87S0NMybNw/z58+3rJt9HbUR1six3nXXXbjnnnvQs2dPg9qJi4vD8ePHMWbMGGzduhXV1dUIDg42Zz08PBynTp3ChAkTsG7dOoSEhOD06dOYOnWq8TK1bNnSsnZBQUGW+m5A4+sIePfh0aNHMWfOHCxYsOC/Naf/L9v5qBDnbdKIiYlBRUUFSktLDRyFWGoKRGRgFPrJgEpLS+F0OhEVFYXExETjArZ7HQAvQyNelcG6JPxkBEqkeQDVq6KQJN5PTHdJSQkKCwtRXFyM0tJSIzzyPk0pSOGYwhAPqB0SQIHDx8fHxLFQmNaMQ8rUORYyYw3sJiMG6i0l9FLwfs6VPeUlCweVlJRYPE4UJDhWwnpYpyEmJgZhYWGWCurEpHP+w8LCjBJHBqdCGC3ztNhQ0QFgfqfQOY5fs4hp8CzTzxKmpJZvKgbcN4rzZwC1XaDQNQRggV7Q6hcQEICQkBCEhYUhNDTUFJtk9jQABtJQUFCAsrIyC/7d5XKhsLDQ7BlCrjgO7pG8vDyLBZvxBlRiuMZsCpsKDg4+32P7h2s+Pj5m39XV1SE/P98wAH9/fwOb1PSbKkASy8r01oTrhYaGmn1MpQSop0ncs+qBUisyzzz3TF1dnYnZ0XsVi8/kChQgKMgRWkX6QlgNBWq+j00VXfUM87NCYlQ51sxc6oEgY+WY+T71EnI8jXlHGMPEsdvPmyriSutIF4OCgkyNB03py3Pt6+tr6CTHr89T+JR6QUiTWL/GrgBSaVHYFqGx6u0ErB4uwrSUNts9InZ6qB4bhVMprSFP0Boi9kbonip1alBgETgafJSP0DBE3lhRUYHi4mLjKaUyR2MGx8p3NCWLqL0tXboUH3744T+85+KLL0ZmZqYxePbr1w/Z2dkNqkEDXijWUKlfAAAdOnRAbm4u2rRpAwA4ceIE4uLiTFxL8+bNkZubi65du2LBggXo0aNHo0LX119/jVdfffWc/Zw4cSJSU1Nx8uTJBpmN2C677DKcPXu20QDsefPm4auvvrJ8t2vXLiQkJDTIUrR7927ceeed5+wLANx///3YuXOn5buMjAwkJCRg3759WLRoEbp27WoZa35+PhITE7Ft2zYsX74cKSkp/+39tX79esTFxZm/F154wbKOO3bsQEJCAnJycvD888/j+++/h8fjQY8ePbBgwQJ07doVubm5aNGiBYCG66jtnnvuwa5du8znBQsWYNSoUcjKykKvXr0AeLNLJSQk4JlnnsGrr76K6OhoZGZmmkxhJSUlaNasmQkEd7lcaNGiBb777jt88803aNGihUXJ8Pf3x4kTJ3DNNddY+tLYOgIwHjdNXtDU2nkrGgwa8vHxMVZ6ZXYU8pxOJwoLCy2BhT4+PigoKDAxGnV1dabeAYAGKW6Dg4NNjAWFL6C+enNZWZnFykeiq7EJduwzma0KB1Q+8vPzUVpaaoRVHhAyIHXLqrBSV1dnydwEwAjQVHTYT7v7XQUgfW5jbn/Ou90iaBbRaU2RqLAnZcgahKjvZF+5ro3FmTidTjM/HJfGr1DIUi8AlSQKPApDocBN4cCe0YVMWjOdqVVS54DWU7uSolZfnRcKGfys8CT2mcXkABgrJ5VDjeXg/ZGRkRZ4FNecyhW9Q+Hh4YiOjkZycrJRzCoqKlBYWGiCywMDA833GsfB8Z2nE/IP2VQYplLH+Q8ODjbBjKyJonETKhwCsDwjPDwcERERJksarWJlZWXmORSc9U89YDz7qiywH6RDVIDKy8st3rGamhoUFxebOihMRKBnWuFZiuNvDArEudIzqt8D9QqYKirqIVDFQD14PBek11wPQnPs2ejoYWDjNTWmcA1V+VcPhV5To4oaADSAWumg0jaFRqrHQBUUpXUKG2Kf1JCk0CnuJ1WeuC9UgVDDihqSOEdULlTBUEgXx2L37OkYaMjhudd4MP2/esM4Hrfbbfiy4vJ9fX0tsKGm3ObOnYs5c+bAz88PH374IS6++GIA3jG+//77GDJkCA4dOoSbb77ZGIiOHDmCm266CcXFxbjtttss+HcqdgDw1ltvYfz48cjIyMDUqVORnZ2N6667DvPnz0d5eTnmzp2LqVOnIi8vD1OnTsXp06dRW1uLoKAgfP7550hJScHll1+Od999Fw6HA7NmzcKiRYsQGhqKTz/9FN26dcMll1yC999/H76+vqiqqsJPP/2EadOmoaamBo899hgeffRR+Pv746OPPsKAAQOwZ88e3HLLLcby3q1bN3zyyScICQnB22+/bam7cfvtt+O5555DRUUFFi1aZNK9Al7BWoXZN998ExMmTEBycjK++OILJCcnY9WqVSYWAPBWLV+4cCHKy8tRV1eHUaNGYf78+WZfjRw5Eu+99x4qKyvx2GOP4dVXX0Vubi4mTpyIa665Bq+++ioqKipw3XXX4bfffsO2bdswZcoUVFdX44UXXsA111xj+XvqqacMzS0vL8fu3bst69i+fXssW7YMERERWLp0KWbOnAnAa2ycOnUq7rvvPrM+ACzraG+pqam49957AXjrYkydOhXp6em46aabcPz4cbOOFRUVeOqpp7Bo0SKUlZXh5ptvxoEDB8xzysvL8cQTT+CBBx4wn0n7uGYPPfQQnnjiCdTW1mLatGnYunWrpS9cR6fTiffeew8jR4401yh7tW7dGp9//jliYmIaPRd/1HbeMRq0jijGk8TSDldRSzSL6GmQW1BQkEklRkEtKirKKChM41ZaWorKykpjQVaYApmbMj02O9xAmRYJvmJZSWRUgCWBppDMph4MMlm1gvH9atFSyIPOU2NufHX5K2ZZlRn7utg/q6WPz1Jmqo19UkFDx6hMvrF32edGC1DpfCtTb2wc2k/9zEZhR/vDMQFe4YfKnwoEbCoY8DP7yu+Uifv6+lqCgTlOXXsqELSq09VKAYLXaMlUpSQ5ORmFhYWWgFMq55wHVb7sgl9TbfTiKO5crbncNxQINSBezwrv5Z6lJT0oKMiCdy0rKzPf0/tHmJPuUe5TFYztcQrcV3re+X/C83gG2D+lIdwXdnrBe9kaO6d6r1ri7edLf6+0RA0XKuzr81SAVsVC6aV+x98qDbGPhefQ3h/13tppu0K7dNx2T4OdZmp8mMI49bzw2UoflDaqkqRnWd+tnmr7swnr0z6roakxo5DOjZ4NNaJxn6sSxT/SHfu80LihjevINWiqbdu2bYiLi8OoUaNMGlM2Gi1pbKBFODQ0FCtXrgQAYxBrrNFQpr9n4hhe5/qrAczhcBgoMo1fAExmorCwMGN40+sAkJ6ejvT0dAAw2RM5lr59+6K2thZffvmluV8NdaSTbDS6al/Dw8MxcOBArF+/3gJLUnijPVkGm46dn7XvOpaffvrJfL9q1SoAXuh8y5Yt8cUXX6Bbt26oqqrCypUrcd1112HVqlU4duwYevfujXXr1qF79+4WWCTgjdH48ssvcemll+Lw4cPGIOBwOLBnzx7LvX5+fqisrDTr3KFDB8THx5vPgNegNWTIEGzatAlpaWlIS0uzrLv+vrF11LElJyejS5cuWLdunWXeAZhYG3pMaHSoq6tDamoq7I33EbmiNHjYsGE4fvy4Zd2bUjvvGI3WrVsDsMJQSCDp5aBgFRYWhosuughdu3ZFQkKCwZR36NABzZo1Q1VVFfbt22esZzU1NdixYweOHTuGrKwsI3h17NgR3bp1Q5s2bSzEVNPzKWMgca2qqjKHmnEQDOLkRlBiHxgYaLE+hoSEmFS6zIUM1HsXXC6XGT83PK1kvE/hBBQa2Qclirzf/n879prEiRYsbaosERvJOeLYtc4Gn8dYkrCwMKMQEnYG1NdH4fuCgoLMu9TKSoZFYkTlS7HcOiYVAEhUFUbEPtrrGugcEEKj1jkVzO3N4XCYuSDDJ9abtRsYI0Nhn1AzCrv8Db13JBpVVVXIy8tDTk4OTp06hTNnzpg93L17d1x88cUGitWsWTM0b94cJ06cwL59+1BeXg6n04mMjAzs2LHDZM7w8fEx7laeE7VWMiVuU2sJCQkIDg42DJAQPxXe6NVMSEhAr1690Lp1ayQmJiImJsZk8aLXp6ioCBUVFXC5XGb+c3NzUVRUZJ4bHR2NxMREJCcnWyzNiiGmAEncLI0gjE9S+I4aOPhbngsK5ZrOlUIN4Ux6BtS7xv1l9yCo1R+AUWypNJFukf6q545NvbS0mKtCwj5p5iU9pxSWFfql8CYKIyogsy8KBeRY6RlUL6d6eBRqZVc++BwqAnyX9gWoD7DWZCEqZOu8s3HOeOY55+qFZgyQKsWqNNGa2ZjCR7rBuaysrERJSYkxrOXn5+PMmTPIzc01hoikpCQ0b94crVu3RnJyMuLj4028kcvlMh75kpISHDt2DPv378fp06eRlZWFqKgoy7oGBQXB5XIZGtcUC4BxPkePHo0VK1YgOTkZLpfLzC/5xqBBg/Dtt9+iRYsW+Pvf/47OnTtj6NChDdLb0mBkN+B07NgRe/fuRZcuXXDs2DHzPZWQZs2ambSoBw8eNB5qoGEa03O1xtKi2n+/fPlyBAcH4+qrr2607wsWLEDr1q0xfPjw805ve67m5+eHRx99FDfccAM6derU6HW78m5v2ncq3iqz2OdG1/Htt99GeHg4xo4d+0/TFAMNU/va13HWrFm4+uqrTXpbHx8ftG/fHnv27EG/fv3w+++/m/vtY7P3XT9z3SZPnozXXnsNzZo1ayB/aHrbxtatsXVqrDkcDpw4cQIvvfQS3njjDfP7xvbsf6Kdjwpx3tApBmFT4FcrDQkZgzgdDoepmeFyuYxFr7S0FMXFxQDqs2VQqPD19YXL5UJeXh4CAwNRWVmJ7OxsnDp1ChUVFcZiyQBkQhQYLE1Gzw1HYbSiosJgurk51NKogYKAV1j9P9T9d3iU5dY2Dp8zaWQmmfReIaGFAKF3EBtgBREBC4K6FbaiYkMsG6zgtoCy7b2igoobEKSISFU6CIQESEid9DIzaZOZ+f6Y97yy7jvRh/383u/d5jqOHMlk7nLVVc+1FmMcCLtgYRYyPVpIqXUS/6yHHFFRIGOi8kJmI1OykoFzTtl3CptkmM3NzRpLlB7SQKs8hVOLxaJiPKSgzwPDFME2m03BQdgHh8OhLCVUJoE2hYsCI8cCtGXCYvphMn7OnYSNyNgbClLsox6eJAPq6B2jNYmCnYROkThJwY41LBiPEhoaioiICISEhGiUCO4l1nzhGrF+C+OSuG9odayurkZTU5PaywxA5twwENnlcqm+MHhOVnC1WCya+CcGBvNdZrP5Qo/tX66RwTAttYxxcbvdKnbDbrcrhYEwSxJlMgPplaQBggYGwHuW6+vrUVVVBavV2k6pkdZ40igJs+S5ZDwG76NQLKFUVNDZHwnVJI2Se5rCt1QIGMtBOibPCpm2vId0QM4D3+tyuTTpnOl6p8BJBVsqUfJ/9DyT5kqIF+GpEvLEueIcsB88U9JgArQZR/hM7gGplDBWhHMvFSi9tV+edanASfrKvnGOJdRIegSk103GSkmDiFTo2G8qOBIGxXFw3kiXmY2OcSRUHAFoigOyXgnjmggv5JxJxYpQYKbqpqGJc9jc3KyJifoji35naVu2bEFiYqKimxdddBGKi4thsVgAAHv37kVcXBxqamqwYMECXHnllTAajThx4gRuv/12AF5eduzYMcydO7fd83NzcxEVFYW8vDzN/3ft2oUnn3wShYWFiI6OxsmTJzF37lwcO3ZMneXbbrsNJ0+ebGdUlM1isaC4uBgX6WJDAGDChAkoKCiAyWTCbbfdpqpHnz17VmH7fX19kZubi7179+Lqq6+G0WhUcLGBAweivLwcMTExAIDjx48jOjpaeU3+qO3btw9+fn4YPHhwh9//9NNPmpStHbWZM2ciNzcXvr6+WLNmjUoRy3bVVVfh/PnzytAj1/GOO+7A9OnT4efnhzNnzuCGG24A4KUXPXv2xGeffaZ5FmM0/mgdn3vuOU0RxocffhhfffUVoqKi8MEHH2hqofz8888aCNrmzZvxwgsvqM8bNmzA8uXLERYWBqvVilGjRmHNmjVIS0vr0Mh5yy234JZbbkGXLl1w/vx5lZVLruOYMWNQWlqqanZ11OiRkzEaR44c0aTt/au3C1Y0JIGlINmRVYB46IqKCthsNmU1DggIQFNTE+rq6uDr66upSQBAVQl2uVzo0qULoqOjYTQalaXYarWitLQUZWVlsNvtmgqoRUVFKp5D5jGXXgrpceB46AWQcRu0PtJyLCEzZBgSiiXd5dISqYcJ8X4+U2a4kdADyRABaDDAFASANi+CtApSwZCKjt7ySuWCigWVBr5bQkgkvIBKAgUw9kFaJGX/AS+TlPEsUqii4KWHC1Ew4F6TVk6+R/5QiZD9outRxkxIIZFjltZgjkcqYRI+JoU73ieDQmmt5nNaW1s1cQP0INF6UV5ejvr6erUfWGWcc0yhhEId9yX3Q2dtEt5GxZLeBYvF0k4ZZowKhWd5toG2QmtUsHkmASgl2OVyKYHbbrdraqlQiGctDdIM+Zv7Ue4XPTxIegKkosK1koYQrrPcY4AWcqTf59JbAEDzLmlskbREng89HWC/5T3sj4x5kkK99AIDUHNHg4g+yx3XQtJduT7SS9JRv7k39IkRZD8k5IrzK70e/OE1km5zffX0Ua4b3yeVGvabRg56liR8RXqD9DRdQsQkTaHhTSIFTCYTgoKClNeZyS6omMizIi2cfCaD7mUgO+dYP6+dqS1YsAD3338/mpqa8P7772PYsGE4deoU5s+fj8bGRsydOxeLFi1CfX29gjgRifDII48oGIzH48GiRYsQGxurBOgVK1bgmmuugdvtRn19vaqzwPb000/D5XJhxYoVqK+vh9vtxrZt27Bo0SK1b37++Wc8/PDDas0vuugivPXWW5oz3tjYiPnz5+Paa6/F/PnzNeM7fvw47rvvPk2cWGtrKx544AHs378fgHcfPfjgg/jpp5+QlJSEjz/+GE8//TR27tyJ/Px83H333Xj++ecxYcIEJCUl4fXXX0dYWJjmPcuXL8fkyZPV5yVLlmDNmjUIDAzE559/rknhC3hrQ3z99dd/ujZ79+7Fgw8+qObogw8+QJcuXfDRRx+hf//+OHz4MO6//35FJzIzM/Haa68p+DGNqxaLRQNDffHFFzVKAwB8+eWXWLZsWYfrCHjppMViweeff46YmBisX78eTzzxBOrr67F48WIFgQKAZ599FmvWrFGfX3jhBVRWVuKNN96AwWDASy+9hM8++wwOhwN33303cnNzMXToULzyyitK7hs2bBjef/99+Pn5Yd68eZg7dy6cTifuu+8+HD58GAA063j69Gncc889aGhowJ133qlRfObMmYOnn34aAGC32zF9+nQ899xzAIDHHnsMmzdv/tN1+Cu1C47RANoYgxTI2KRrmpAGpucjZImZWJxOp1I8yIRoHSczDQ8PR0NDg/KC0PpCLKEsjuTj44OQkBBNsTgprFLg5f/0+GAGZHGMUgglnpvjJdOSjRZnvcAgoQuSwVFYpABAYiSZhh6eAbTBHvTas1QWpFVPz1g9nrY4G31fKeBKbDPfyf6TMbKRqUviKedGenuk50Uyf71wIedCzhvnRr5fCpYyGJJ7SAq1+rnUP597W45Dvk+OUf6f90umTcFEer5YwI9CL2FcfCeVPnpzqHyxL3L+/gwi9ldvHBPPHj/TKyWxwTRoSBiL9HrpA4D1gf48uxQCGxsblbBGowbfw7MuYTByDbj/2FcZs8BrOwrAlucPgLqGNKYjgwDQPiZDnkM+h3tUKj4SBqWnCfo9LM+qXgEwGo3q7OrXTI5LCszSkECDCK+TP7yfv9kP2V+9IqeHTun3kDyPUsHieCQd4b38Lemu/L/0HEsFQD5LP265BnL8He0FOQ9SGeFeJhaeWaNoaCCPk+Pgs0nz5L6R54xzQSWus7bw8HB1Jpg632q14ssvvwQA5bkGvBCqs2fPwuFwYODAgVi/fr2GhpaVlcHHx0dlo4qJidF4jaOjo1UWtWHDhmH79u2wWCzIyMhQ19jtdg0M7cyZM6qeA+CFHcfGxmrG4HQ68eWXX2LgwIEICwuDj48PRo0ahRMnTqCoqAirV6/G6NGjcerUKbhcLvTt2xfffvutOi8ej0cJxpmZmYiLi8N3332HmJgYpKam4osvvsCUKVMUzUtISICPjw/S0tIQFhaGAwcOIDo6Gv369UNpaSl+/fVXFTsQGxuLuLg4+Pr6IikpCQkJCdi3bx82btyIxMREjBw5Env27MHAgQNht9tVnAMAnDt3DufOnQMAVXfCaDQiLi4OAQEBKCwsVGM7efIkunTpgvj4+Hb0aefOnSgrK0NoaCgGDBiApKQkDdx15MiROH/+vMqQ9d133yE2NhaDBg3SzLOvry/i4+Ph6+uL33//XVX/ZvV1toqKCtTX18Pf3x8jR47E7t27ERAQgCFDhgCARrBftWoVsrKyMGjQIMTGxqrzJdc5PDxcnXGr1ap4m9vtxurVq5GRkYGwsDCsWrUKABAaGorIyEj1Dv3nkJAQREVFAYBGQeoM7YJjNHr16qUgRGQEUhhlELfBYEBdXR3MZjOGDRuGvn37olu3bmhpaVHZopKTk5Vl3el0Ijg4GGVlZdi9ezeOHDkCi8WC3r17o7KyElVVVRg0aJCCANGKQ88JU9YSZhIcHKxiCyiMREZGagiwvs6F2WzWxHEwc01AQABCQkIQEhKiGCC1bh4Kp9OpKnhT2QgICNBYCQGtdU3OH4UcXkMBSWZMkV4So9GoMMeEQ+kVFZnr3Wg0or6+XsEFIiMjUVVVhdbWVgQHB2vu9fPzQ1hYmIbJcq6kUkYLLmFCeiYmGWhTU5OCClBglkqqFED0wpa09so+kMkzF7UUuqRF3G63a/DjUiCTUA0ydmbVaG1tVRAE6WlhFhfGSxDrzD7t2LFDQROcTifS0tLQp08f9O7dGw0NDYiPj4fB4K0ITpiV0+lEQ0MDNm3apDwctIabzWYF4QoNDVX7qbPX0ZBVnVtbWxU8hMYFGiB8fX3Ru3dv9O7dG6mpqUhISEBYWJgGekJLJT0S9HBarVbU1taqfWcwGBAfH4+oqCiVAlfubVrnpfVdBiX6+Pio9M5UbOQZJ4yPwh3hioTKEPJCWBLTaEuaID2wPI9A+8J1PH/yrNPbSSWIe1vCCaXhgZ+l0q8X8jknTqdTwXF4Hum95rul1Z+QICmY0yNHRVrCzOR4Oecyox3nWu4Z6f3siLbqaQmhY3qFigYB6YHg2PlZH4/C8cu5lLRcVpfnnqKyIPcUaQrjMerq6lBTU4OioiLY7Xa0tLSoxBGM7YqIiNDAMZlyvqamRgmL58+fR11dnYI5S8NZY2Oj8oqEhoYqK2tnanJdO2rkieQHZWVlWLhwIU6cOIEdO3YgKSlJxbgZDAbk5+dj+fLlWLFihZId9M/zeDzo2rUrsrOz0bdvX2RnZ2uuuf/++7FgwQKkpqZq+KD+edLAQV7PFhQUhNLSUsycORPr169HYGAgSktLVbap1atXIz4+HjabTdEZKTvw8xtvvIGsrCyViUvfli1bhkmTJqm4hSVLlmDatGno06eP5jr2/cEHH8Q999yD7t27o7W1Fffccw8WLlyI5ORk7Nq1C0ePHsXf//53zfpwbD/++CPKy8txyy23aGIqTSYTrFYrbrnlFk2QO8+tnJexY8fixx9/REJCAmpraxVNOX/+PJ5//nlVW0KuO/uuf578zHUlPTh48CB++uknLF++HPn5+RgzZgz279/f4ToCXshXcXExZs+e3S5eVDa5jmvXrlXr9OmnnyImJkaTXaqj1tEe0vflv9kuRIW4YOiUDK40GAwwm81K8Kcblm4vs9mslABqcQaDAUFBQQgJCYHL5VLYc5fLpSyNsbGxSExM1GSKId6dKUGTk5ORnJyMrl27onv37sjMzMSIESOQkZGBxMREBAcHK+sD+0ZMsqyvQAFAejPkNWSUVVVVqK+vVwydeFcK8RTWpWtcwjvIWKXCIKFALpdLBWIzZbDBYFBMSv9sLiwVJQo60qNCwYbZOKR1raqqSllzqCzQS8SCjDKTj4QckLmxL+wv0JYalnuEApXZbFZCg8Fg0MAr2Hd+J/sqhQHpLeMamc1mDTORUALexzWkQOJ0OhVsrKmpSQVfVlZWqjoZVBK4R+RayL4QA833snAf43saGhqUkmoymZTQFxwcjNjYWAW5INFhLJJUPKmI+Pn5oa6uTo1JZvzojI1wM4/Hg7CwMISGhioDgQzAB9oquTIeQgZbut1ujdcIgBKi/P39FVSzuroalZWVqKioUIKXxNMHBgYqaArhVnwG0AapYcwW46Skt0PGCADQMDagrYAe38vxSSiMrEPBIms0OPAcSu8Gz63ZbFZpfhlTIY0ZhFdJr42k5UBb1W5p6SZd54/0jPj4+Kh6MzILCumH9A511KSSIAV4AJp4K45BwsboQZZrIA0mclz67yQMjD9UbCXt5fVcf+5BvWGI6ymNJ5wHqcjQICNpN2OIGAdmMBiU4izpDWM0pCLJH54PKoSEBTKuhfuJ/CUyMlIZ7f5obTpz8/PzQ25uLqZNm9buuwMHDiAuLg4VFRXqfx6PB1lZWXjrrbfQp08fWK1WVX+Bbd++fXjooYeQl5eH2NhY5Obmtnv2W2+9haysLHg8HmzatAnLli1DZGQkSkpKVLxDWFgYiouLMWrUKFx//fXIzc3VeMLtdjtSUlJUBfDGxkZ069YNGzZswLZt25CSkqJSvI4aNQpFRUUK2z9s2DCUlJQgIiICDz/8MCZNmvSHc/T0009j7Nix6vMLL7zQDpKUnJwMq9WKXr164fXXX8dVV10Fq9WKzMxMvPfee6pmyKRJk/Dwww9r7p0xYwZycnLg6+uLadOmYd68eQgKCkJBQQEuvvhiAF4PfmpqKn744QfNve+++67ySrHt2bMHCQkJqKmpwSuvvIL169fD7XajX79++OCDDwB4z+KRI0cwd+5czTo+9thj2LVrl3rWQw89hH379gEAdu7ciccff1x9d/HFF2Px4sWwWq2IjY3FsmXLOlxHtqlTp+Kee+5BcHAwCgsLcVEHsTaAdh0vvvhiFBQUICgoCPPmzcP111//h+sEAAMHDkRpaamKtcnMzITVakV8fPyf3vdXaxesaEimazS2zxoiiT6DZhsaGlBdXY3m5maFjSYUgpAnZu6hsEgCeP78eZSWlipLGq0ETJ0rrycDp4DA78iwqXQAaCe8kxlIyx81Xgrk0k1OAZTWNwqyEk8sYwF4v5rw/wMj431kOmSc0uIpmbuce+a5J3NhI9ORgrXEhRsMBk19Bnmv2+2GzWbTCMrS8sg5o4LFvlGokVZWyUilsCGFCilYSCspr+P8sElvDRkrGbScW1pVKZxRKAKgMrtwH8o4HebsppBLizrXQq4Z8fjcP0ajEQ0NDUrBo+BFht7a2qoYPoUwWfCtoaFBxXhwTiUkwuVyqTWXcTqdsUnLvT4NLOCFUDJDF+eqsbFR0QEJmQTap0SWiQckpE6eB1nATnod6BFlDRUZEC2Ddzuqi8B1k4HJEloEQI2byrEecqMPapZeDP6WyrVUDmTmJXkuJd3SQ31k3IU+HkL2S5539kEq/6R/0nPC+7hnAShlUW/5k54PGTMirY0SWkRDgBwPxy3nTNIi+Q4Zb8h1pLFGPza5voAWPsa+yD3Gs62Hler3olSgZcA8PWOccxmnRTorf2RcFw0TtNYyJkPGNNHaKz0rnbHdfffd7eIaAC8PWbx4sUoV6na7cf/992PXrl1obW1FdXV1OwtsTU0NrrnmGtx5551YuHBhO2/xP//5T2zevBlut1tlBQS80Jh3330XiYmJGi/zv/71L6xduxZ2ux2LFi1CQUEBAK9w/eijj+Ls2bM4fPgwlixZop41ePBgrFy5UiErAK/S9MwzzyAzMxNOp1PT97Nnz+KJJ57AsmXLMGrUKOTl5WHhwoVwOBy47rrrNB4GfWtoaFBJeQCvIMzCkM888wyuvfZaVFdXY+HChSgrK0NTUxPy8/OxcOFClJaWasZaX18Ph8OBoKAgvPPOO0hPT8f+/fvV2Orr65GRkYGXX34ZixcvVsUOgTZYFQvtBQQE4LPPPsP7778PHx8fvPbaaxg+fDi6deuGpUuXwmQyYc2aNXjjjTfUuskaa88//zx27tyJ0tJStY6bNm3CSy+9BMAbGG4ymVT9lJdffhkbN25U6xgcHIxRo0bhhRdeQHV1NVasWNHhOrIxYVBTUxMee+wxDXyMLSsrC2+88YbyUJ4+fRqPP/64konr6+vVtbNnz8YjjzwCg8GA5cuXY+zYsSgsLMSjjz6qruPY5Pp1hnbB1IaxAWQq0iqvxxvzb2LRSYApsFMYoCAnPQ60kpeVlWmKYtHNRYsmBS826TanFRiARnCTbnbp9iaDlgxQb8XnOzhePTOSDFw+l32QFj+6+KSiwefrA1qlpYzzKBkkmYqEckgYgFRi9AI85xVoC46n8Mx+SOupnBPJqCn4yTmVngQpvOgFQzmveuFD/k/uLdl/GRDJ+dXvW7px6TEAtKnq2G9CJKTlUwqDsk9S+COMhMIa3yUZvazOKyvXU3FmZh/AK3BSueZnekWkQt8Zm16AlYIl0AZX4p6lciGLRfK3HiZDmiLhQ9IYIgVPCptSuKdQpzei8D7pLZACt4Qo6T1xcqx6wVV/jfRW6IUh3s8mk0TIa+T5kgq+3uKv/5tnntdybXiNPFcUhPVzIL198qxLmiTPjeyD3tqv9xBIjy37JeeNayBpgf63XEv5nZ7W6OmCHL+eLv8RzdXfJ5UyjkH/I73g0pMkFUJJ9/lM3ic9xcxOJxOa6OerM7ekpCSNsYrN7Xbj008/BQAVR/Hll19qoCaDBg1S8LS+ffvi0KFDiI2NRVhYGN5//31kZWWhoqJCZWhavXo1AK8HPysrC0ePHkVjYyP8/f3Rp08f5U319/fHwIEDsXXrVgQFBaFPnz54//331Xubm5tVatbIyEicPHlS7bmQkBBkZGRo1sVoNKJ3794qi5bBYMCgQYOQl5eH0tJSfPjhh5g2bRoGDRqE2tpa9ey4uDikpaVp5sVkMqFfv344dOiQSoHPwOzo6GhERETgyJEj6NGjBwoLC+F0OnHixAmFSOHn7t27Izg4GPn5+ZrnE+ZqNptx9OhR5OXlYejQocjOzkZISAjS0tJwzz33dBhfGBwcjN69e8NoNKrq2r6+vujVqxfCwsLQ1NSEjIwMDB8+HMePH1ewN64jY2NWrVqFlJQUJCYmqnkvKytT7+zevTt+/PFHtZ7ffPMNAG88CtcxIiICPXv2BAANpIvPi46ORnJysqZyutvtxsmTJ5W3yWg0YsiQIcjJyUFQUJAaW48ePWA0GjXpeRkmcODAAQWPNBgM6NWrF8LDw9HY2IgTJ06gtdVbrC8oKEizpzpLu+AYja5du8JmsylLb3BwsBISiG8nowwICEBDQwMiIiLQvXt3ldWAwcapqanw9/dHXV2dyhYVFBSE4uJinDp1Cps2bUJwcLDCAI8bNw6jRo1SqS/5m0SWQh0FUQqMZOYyPoBYfZmdisoJx2OxWBQzZcpMpuDleGmx4vPYyIABbVE5KWBIQUAf5wC0YUKlYsH7WltblUWV10hPiozPcLvdKs86LbjBwcEaizAt6S6XS1mLpeckJCSknZLG99GyzsxAZHC0+jLWQabNpBdCWvuk8sV5k54z+VmOjWvO76VwTiGVa8Y881IwIF5ZZpIhE2ZfJCab+07iuJ1OJ2w2Gw4dOqQqQzNd8I033oiEhARYLBbExcUhMjISDocDpaWlqKiogMfjQUVFBc6ePauwoJrD+X8EZyrVdXV1an06Y/57AEhPT1dCFfcG9wtTGNMy29raipiYGKSkpCAlJQV9+vRBVFQULBYLgoKCEBYWpmBrhMHV19ejtLQURUVFOHHihKpF4Ovri7S0NPTu3Vvh3KOjoxVMh/uZ+4uCGwVfl8ul4CfcYx1Vq5cCPaGlhDXp07FKZUEaC6R3UC+Id6SsS68X0BZkLDNfEYrDsUqjD8+INN5IqJXH41HWN5ncQVbYBqAxSsjEHKQZPDsUpjkHhASxkdZIiJ18D88dz7GEJ5FG8HvOp8xKx7ng81lHie+lQUKOS3qo5JzL97DvFO5Js0jP2aRCVldXh6qqKhVrWFRUpHDoAQEBCAsLQ9euXREZGYmwsDDExMSoc0KoFGGBRUVF+Omnn1Q6+LCwMOW9o9dWKi1utxsnTpz4v3Ow/x+2jgwtUlnkORo3bhy2bNmC2NhYVFdXA/DymqKiIjzzzDP47bff8OuvvyI5ORklJSXqWWfPnsW7776LZcuWaYw7aWlpOH36NPr166dREtji4+NRUFCA4cOHY8iQIViyZAni4uI0xgf+fdNNN2HlypWIiYlRvFj2nU1+9vf3R3l5OebOnauBF73//vtIT0/HuHHjOpwXj8cLDzt48CBSUlJQVFSEjIwMHD9+HGlpabjtttswbdo09O7dW92XmpqKs2fPon///vj999+RmJiI8+fPAwBWrlypqml3NO+AN5DZarXiyiuvxLZt29oZyaSh4c8+s/n4+KC0tBSLFi3C+++/r1lHxmgAwEsvvYRLLrkEAwYMAOD1YkyePLld/MmfvUs/79J4cfvtt2Pp0qWIi4tTtMBisaCsrAzXXHMNtmzZgqCgIJSVlWHmzJlYt26dunf16tUwmUwqzbLb7cb48eOxadMmxMbGKq+SlBsHDx6Mffv2ITk5GY888gjGjBnTLtj9v90uRIW4YI8GLbZ6yIu0ykucfEBAAGpqalBYWIjq6mrExMQoxiQFe6PRqOI2AgMDFUEltIWKQnNzs2Jazc3NinkHBAQopqj3LlDpIGxIYu2l+9jlcinrEZUVWjcJ5ZAWJQouVAA4fglrkLnm+U6+SyoeeviI9KpIHD+FHQCaOZfVqXm/tNpTueK7pZeI7+VaulyudozIZrNpFADWIpHufAoo3CNUWihsAdAIlnwWLXfys6xXQEZOYUCuL3+kMEQ4jhTaSMQJq5MKHKE0FCgl7Ip95hg5zzJOgP2tr69HcXExwsLClHLEAFNCJOTfxGGbzWa0traivr5eKVocOwP+JcyC69WRJa+zNArn3KPBwcEanDkNAjxDNpsNlZWVMJlMqKmpQUhIiKZauwxEDgwMVBA2wp245jzLVHgpKDM+SsLapLIhITGkGTwfTFUsBVKZdpbKC88ljRLSOs8mvYtS0JDeAr4DaMs8Jr0A3DOkS1KplsYIqSTo0/dy/0lPLhV4/hB+SfighNXKuBIpgDPNqPwf6Ru9KdJbRe8Ef3ekzEhFQ64LBXEZDMp4Lfaf880AdaCNNkuYUkeeC9Idzp/kPaT3XF993zwej4rBoKJARZB1T7hOMl6HSiuNUjwzjBviD41LpKM8c6Q5TU1NiI6Obld5uLO3t956CxaLBbNmzcLvv/+Oxx9/HP/+97+RlJSkgUK53W5kZWUpKEtiYmK74qfDhw9HQ0MD0tPTsXPnTowYMQJTp07F9OnTkZCQgG+++Qbr16/H0qVLNfdZrVYkJiaiqqpKZSpimzlzJp588kn07dsXLpcL33zzDbZs2QKn04lVq1ahvr4eDzzwAE6cOIE777wTmzdvRpcuXXDixAnce++92LBhA1paWtCjRw8N3AYAHnjggQ7X0mAw4PDhw/jXv/6Fjz76CAkJCWqsOTk5SEhIQEVFBf75z3/itdde09xbWFiIhIQEVFZWAgBKSkqQkJAAAKpuCQCsX78eJ06cwLJly3D06FFcf/31+PXXX1FXV4eUlBTU1NRg8uTJePHFF9GnTx98+OGHaGxsxB133AHAqyT5+vpi1qxZmnWcOXOmpj8ulwuZmZmw2Wzt1lG2p556SrMuS5cuxfLly9vNDeBNQbxp06Z26xgbG4tDhw5h0qRJGDBgAB588EH0798fbrcbX3zxBdatW6fxEtpsNqSmpqp95nA40K1bN7z66qu47rrrMHv2bADA3/72N2V0OXHiBB5++GH88MMPSE5ORm1tLZYvX47U1FRcd911OH78OJYtW4Yvv/wSSUlJ2Lx5Mz744ANNnY/O1C7YfypdzJIR0rIi6zsQ+kGiW1lZqRQFX19fVQfDZrNprIZUKljYjESV1X+lBU0K1DKzS3BwMMLCwmCxWBAcHIzg4GBFoKV1UW8h5PdGo1Hh7CUT0gsGHTEgABr4AYUVeb3e0qV/voRh6JUTo9GorPgU0nk/BWagrYAcx0WBS8I+2DfGYkhrId/FcUirrhTUJWSK75GCfkfBixQeJNPkM6WlU6/QSmYur+e86fckBQmuOTH4kmnzO1pb9d4mCoicW3pjCGWioMmYFovFApPJpPoh95zcU1S2eG9ZWZlShj0ej0bxoTIj98pfKePEf9pkLAH3pYxL0nukKBw1NTWpYH09DeBZ4DozdisoKEjBnWSKaqANUqmHKnDNJM1g36TnkWOg8M314bWMHZPrRkWKe00KrlLYlUK1FKglTeD86AO8OS9UAqSXDmifzpmKFM+4zJok75U0QSrxjHnRj0OeIz5XFvGTcFGeM+np7Iju8v8yjkXSWr0yICF08jlyffkMSZ+kQqafIzbZP+ldlzRRGqd4rR4mpYcO0khHgw3PDPe3PgaIMWvE3ZM+0XAhlT+ePQlH7Kzt9ttvx5133qk+r127Fl999RVcLhdeeeUV/P7772hpaUFZWRk8Hg9mzJihrPDl5eVoaGhAXFwcHn30UQQHBwPweiRWrFgBl8ulkABLly5FTU0Ndu3ahddffx1WqxVvvvkm/Pz8sHjxYgDAI488gsmTJ8PtdsNqtcLpdGLnzp144okn1LoeO3YMK1asUGe7qalJCf1fffUV1q5di5aWFrz44osK6+90OvHSSy+puAYfHx88+OCDyMjIQHp6Ol555RUEBgaqhBcGgwHPPfecyjbl8XiwcuVKJCcnY8GCBbBarXC73bjmmmvw0EMPwWq1wuVywW63K4WCzeVywWq1Kl7DsVmtViXsA8DHH3+MjRs3oqGhAf/85z9RWFio3l1WVoaWlhacPHkSL7/8MlwuF77++msFWQK88CVCmeQ6+vj4YNmyZZoA7PLycs2e5TomJSVhxYoVCAkJgc1mQ0BAAFasWIHw8HBcdNFF+Nvf/qbumThxIp544gkA3sBz1lOR62iz2bBs2TKUlpbiwIED+OCDD/DKK68gNTUVjY2Nat3uuece3HzzzQgICMAjjzyCrl27asb++eefY82aNfDz88M///lPpKSkICYmBv/85z/x+uuv4+TJk0hOTsbChQvRpUsXrF+/Hp999hk8Hg9WrFiBw4cPw+l0orS0FCtWrMBPP/2Eqqqqjo7DX779RxFhknlIYdnpdGpqVfAaElqm8iQBZVpbWpZozWIAW3BwsIIUuVwuVFZWKkslny0ZKxkGhQdanyVBlkyTngagzS0uvRnE1sv79XNABiO/lwwdaMvPL/HLcl6k1YwMT97P5+qFI2l5lMoL51LCfWgZ5nP0yoJk7NJyKMcqxyYFIgmj4BzK/srYHQkhkIxdWgKlZ4ZNWnn1+09+LxVhfubYOS6mIaZgxHmREDr5DnonpCBDps1MWkzbbDB4g79JhKUAzR/9WjY3NyvYD59NAVIqrITL6cfeGZvcCxR+pLVeeqikIEVFg95LeTZ4piWkjwaF+vp6zZnXC6Ny/wHt46mkAirhdXIc0oPAvS3/ludRCp9yz+r7wnFJYZHXsUlhnWed79V7IST90beOhGnpxeT3em+JFLBpIZf0UBpXSAv1io/sgzwb0sjCxj5IeisVDakEyfWS45FryedLhUO/PnLfskl6KWmXhFLpPS/6Mcp15jzy2YSdcu/JpBR6ZZP0jOm4eY44Lzxj9KjpUwt31paRkaHWrW/fvti7d6+CnugrUQNAjx49lCDIZjabMWbMGEVbTSYTxo4di4CAAMTFxSEkJASvvfYaMjIycP78eRw5cgRZWVlYs2YNZs6cqTIGDRw4UNHsPn36ICcnR1OvAfB6O3777Tf0798fZ86cgZ+fH5KSknD8+HFNTYTXX39d/e12u7Fnzx6lBBiNRowcORLbt2+H0WjEmDFjMHDgQJw6dQrNzc3o1asXxowZg0OHDsFkMqFHjx74+OOPsWDBAgUlAoBu3bop+E1GRoYqfgx4yxjU1dWhtLRUXR8dHY2oqCicOHECPXv2hN1uV/ErsnjfypUrO1yrnJwcpTz9/vvvig727dsXu3btgsfjQf/+/XH8+HFs3LgRwcHBGDBgAEaNGoWdO3eq52RmZqK0tFQjbCcmJmLw4MF/uI6pqakaZSUlJUV5mz777DPExMSgT58+OHHihFrHlpYW/PLLL7Db7SgvL0dJSQk2b96sia0AgH79+qGsrAy+vr4YPXo0Dh8+jObmZhW/wpokAQEBGDVqFH788Uc0NTVhxIgRePzxx+FwOFTf/fz8kJOTA39/f3g8HuzduxelpaWadYyJiUHPnj1x+vRp9OnTBxUVFe28cX/VdsExGjExMQq+whS2JpMJHo8HdXV1SlkA2oR3xnKkp6djxIgRKs7Bx8cH4eHhykIprX4OhwN79uxBbm4u7Ha70qgnTpyIuLg4BAcHw8fHR1mIGWNAqAIt/rQesj8yQ5QUZqTQT4YtLWdUboA2ZsN3cZy0OPM7Vj3nZwnx4EaWQoq0/BNipIcX0KpPazj7J7PvkMlyPPwsLcKMsyGTM5vNGiunZGS0svP5wcHBqh8UbCwWiwZqIL0Mvr7e1MScR6l4yXgZKfxxbvSQKgpRMgZFWlClEsB5l0oW44OoVEjrKtdEj7/29/dXnjTCmdh3xmuUlJSguLgYdrsdPXv2RG5uLrKzs5GRkYGBAwciPDwcQUFBSEhIUFbH6upqJRjk5eXh2LFjsNvtGuy12WxWle6pyMtzQrxsZ2uxsbEqnWuXLl1UrYDAwEA4nU4UFRVphCyPxxuEHRERgYyMDGRlZSEuLg4REREqVSfnjHj1qqoqlJeXIzs7G0VFRQC8Xk+z2YyePXsiNDRUpWbl34GBgQgKCgLQJhDSg8UzRaUV8AqohOPwHu4t7lmeEQp7Em4poZt6Idbtdqu9Jr2XQFtmKgCq0rH04Ek6wDMtlXzpHZGKhXyX9FjwzDJugWeF8UJcp+joaI3QTcgk50VC/7iX2T+m6OX1UmniXEqlrCNlj7RKejHYmN2J88e14dzLLG5SaSG9kcqmVCpaWlpUDRfJU6TxhWsl6SrjJRjbxuK29fX1qKmpgclkUgp1amoqevXqhYiICISEhMBisWgUi4qKClitVpw/fx45OTkoLS1VdRaYTp6KZ2NjI2pqatS5cjqdmtiEztLkeQkICEBlZSVuv/32/7Fi9X/SnnvuOUyZMgUZGRnIzc3FZ599hs8//xy5ubno3bt3uzoaAJCQkIDCwkIMHTpUEywMeD0w7733HgBg/PjxSExMxL/+9S9ERUV1GCANeOMny8vLMXv2bI0XgM3X1xfl5eV44IEHcObMGfz888+IjY1FZWUlhg4dir179yIxMVGjNOjb2bNn8eGHH+LZZ58FAGRnZ+Prr7/GP/7xD3XNQw89hHvvvRfJyck4dOgQfvnlF+Ud+k/bDz/8gIaGBsyaNQsVFRW46aab4HQ6sWbNGkRFRcFut+Pyyy/HunXrEB0drbIr+fi01UORwdArV65UMTH/m7Zw4ULMmzcPqamp6n9/to5/1n788UfU1tZi+vTp/6u+fPDBB0hPT8f48eNRWlqKJ554AkeOHFHruHDhQowdOxYDBw5EQUEBXnvtNZVR67/ZLkSFuGBFIzExURFSYnwZGEnBm0yCjJm4aRYliYuLg9lsRktLC5KTk1FfX4+6ujpVJp5QkvPnz2P79u0oKyuDw+GAv78/Lr30UqSlpSE+Pl4JebRiyuBog8GgFCIyI8YO8HpaN6Xgy/4y5oPMRWYKIkGQ0CvpoQDaiKBMM0jvg9FoVBWiAS1TozVWzh//L6EJeusimQjf4evrq4LoyRhpEW5oaGjnwaDwzb7IrD3ymQaDFxdMfCaFbdbJoHJHiBzvl8qAjLngeKWFjYIXG3HGZNJcK2lFZUpIu92u1stoNCphh14VfXAtDwjns6mpSV3b2uqtmcD9zrVgUCVjNOx2O4qKilBZWYmuXbvCx8cHZ86cQX5+PrKysjBq1CgkJCSoCqHFxcUqb354eDhycnJw5swZnDp1So2D81pXV6eZD+5LKka03nW2Fh8frwKj3W63JnamqalJYfmltdrX1xchISHo1q0bhg8fjsTERERHR6t4Lgn5sdlsqK6uRnV1tSrcx9S4VFhY3yQuLk7VKDCbzZpqw1yPxsZGRfOk0EglVlqmgTYrOw0Q0ovK9aTxgMI0hVa+RwrCbBLWI/epVLrltdIyz/dLq79MtdqR4M4zwfdImBRpiYRjETLI8yr7DLTFeUivB5UgGotIZ7jHgbZ6ODxzbJJ+mUwmDR2R9JXvlkqWNIoZDAbljScUrSMvEJ/N/5HW0RAhk4voIXn6VOiMb6HgX1BQoOr4SENLYGCgKlbJIpO+vr4KKkXF5OzZsygoKEB+fr7iqZI/yvNVWVmpIMGtra1KEe9MTe51wCub0Hgj2/Dhw/HFF19g4MCBil4ajUbs378fr732Gj7++GMYDAb8+uuveOedd5QiAEDVPyotLUVcXJyKn4mPj0dJSQlaW1uRkJCAPXv2YMKECcjOzoaPjw/i4+NRVlaG6dOnY8GCBRg8eDDcbm/xVVYqZyXy8PDwDuf//fffh8fjwd/+9jckJCSoIrAdtcTERJXwIjo6GsXFxYquxsTEqM99+vTBDz/8gOHDh2sUj/j4eE2aVY5Vpk8lBL2kpASxsbEqS+j+/fsxefJkHDlyRF27Zs0a5Ofn4+mnn8bhw4cxa9YsREREYOnSpRgwYABCQkLg8XjreXFsHo8HUVFRKCoqgsfjQZcuXdTnV155BfHx8So+pra2Fr169cKaNWswZMgQZRDsKDnK7t278cUXX2i8RIA30H3Hjh245JJLUF5ejqCgII3CLddRT3dmzJiBRYsWYeDAge1odFRUlDrfR44cwd///ncYDAb861//QlZWllpDf39/HD58GIsWLcK///1vdX94eLhSHhMSEpQxh+sYEhKCgIAAVUeD6fr/2+1CVIgLjtGQsCigzfJGS5pUMvQMlBYbPYyIRJBEHvAKVBaLRWHqQ0JC4O/vr4qh0QtC4ZlB4wA0Aq20dFMIaWhogM1mUx4DWtXZf5leUOJcpZdAChx6yIGECvB6MnXOnz4HPwVjCTGQz9YLXdIDIr0NeggGg5QJN9FDEKQAwfgKZtWREBTJNPUWf66jXD/JUCX8Qu4Vzq0UlKRyJYUIiceW0AOgrSYK8f5MRiAVGTbp6ZCBrHynhNxInL/E5kuBQRaWo4BSX1+vFCAyc1rSmZ6We4jZqsgc6Q2iIEdliX2Ugbt6hawzNelVktZ7h8OhhHpZTJNCNRU8CnYyCFdCbfgOfZyFwWCA3W5HXV2dOhPyGQA6FL75HGkl537m/uNe1ccoyeQCehiOtHbLc0IBVo/fp6dVT3MkTZaQGDkv8kzJs8t3yvukAsPvZAyX9EZKY4L0iPL/POvyHsZE8f9co47onaRrnCf2Uc6npCFSMZDeGwnX00PK5PPk/dwPcr30sDHeI73CnCcql39kdOF+djgcsNvtStGQcW96eibTeZP2UtGhh4V7jGvHZzscDk12xs4c6wV4YT7PPPOMwuoDXiFx8eLFyMrKQnFxMd566y00NTVhypQpmDt3LjweDz766CMcP34cgHftPv74Y42w/Mgjj2DAgAFKIC8tLUVdXR1CQkIwb948hIeHA/AGAb/xxhuoqqrC6NGj8cgjj6CwsBAtLS04fvw4PvzwQ3g8HsydOxeXX345CgoKUFBQgFmzZuGqq67SKBmZmZl46qmn4Ovri6ioKERGRsLj8aCoqAiNjY3o0aMHnnnmGQQEBGDGjBkqmLqoqAiTJk3C7NmzUVhYCLfbjSlTpuBvf/ub+gwAlZWVqp7DpEmTlEfipptuwqBBgxAZGYmlS5fC6XRi4MCBeOSRR1TfBgwYoIK1p02bhtGjR6OhoQFvvvlmO+jOd999h23btqG5uRlvvfUWioqKkJOTg/feew8ulwsVFRWorKzUjC01NRV33nmn4tdNTU0oKSnBP/7xD1RXVytoWXFxMRwOB6xWK9566y00NDSgpqZGKRmLFi3C6NGjVV8+++wzHDx4UH1+6KGHcMkll6Curg5vvvkmampqUF9fr5SM++67D1deeSVcLpdax+HDh+Pxxx9X5/bkyZP44IMPNHSXraKiQhVEfuedd5Cfn4+8vDy88847mrPmcrnw3nvvtau7cfHFF2PKlCkwGAyYM2cOMjIy0NLSotaxpqZGQdxKSkr+EkrGhbYLVjRkqkSgLY0rCTmZkrRUkaDZbDYNvppM2s/PT0GhZHagoKAgmM1mWCwWhIaGws/PD1VVVaiurlYFUqR1VwbfUUAjUScDYgAcBRpeo49p0Ac2SoIslQzZ9EqGFD44N2RMEpMOQCOI6BmoFCSk90RmhZHMjddyvHTr64tHkQHJefPz81NwNArYsmjgH2F62W85R5J5UwjgZ31QohQI9NZhfTCtZLC8n3Mtg91pbZZrxWeQuVNwZZYgrg8FJCoaMnhbKkkul0sjrNrtdtTU1CgoIBk6vSwMSub4nU5vVWBaOTi33IN8p4ThyLF21ibPlJxHfdVv7hF6PrhOcu1kmmYpzNFjoFfIaAWWNIRCNc+MHgIj9yvQdg718Et6QyXtk95B3ss50CtH/J7rr09+wHnhGZDPk2dHT6OlZwNoM7zolXbeK5V/KcxKGCrPl97QIRVpvWDPNZFZv+R3HXlrO+q/7DfvkUoZm1Q+5LNIf9lHCTPjM/XzIOdSKmt6GsdnSKWMY9TTMNJgGiyYNUoGyss+SwienCsaIPjDrG5cE8LemOlKZpvqSFjqDC0jIwPBwcGIiYnBVVddBV9fXyQkJCA5ORlGoxGTJk1CcnIyqqqq8O9//xtOpxMDBgzAmDFj4PF4A6RZ0A/wxkUcOHAAAQEByMjIwBVXXIHu3bu3e6/ZbMY111yD4OBgREREIDo6Gi+88AIqKiqQlpaGyy+/HIDXYm61WrFy5Up4PB6MGTNGEyMxcuRIDBw4EEajEb169YLZbEZiYiKuuOIKGI1GFBYWqqBqtujoaFx99dXw9fXFoEGDVMA3APTv318J2D169MC4ceMwZswY9X1MTAwCAwPxwgsvICYmBmPHjsX48eMBAJdeeil69uyJoKAgXHPNNQgKCkLPnj1x6aWXqvvT0tJw2WWXAQDGjRuHzMxMOBwOvPDCC+2gd7t378bRo0fhdDqxbt06VFZWIjs7G8uXL4fL5UJycrKmVgTX8eqrr0ZmZqaqGcJ1PHr0KFatWqWeHxcXBz8/P7zwwgvtPFiXX3450tLSEBAQgF69euG9997DuXPnVE2RSy65BD169EBNTQ2WLVvWLrB63Lhx7dLgpqamYuLEier8Hjt2DK+++qo66yEhIaruBhuD+AnZfOWVV5CWlqaSDrhcLixfvhzZ2dkwmUzo1asXjEajWkeDwYAJEyZo4Fzp6elKwe2M7YKhU2azWQlNwcHBCgdKSx4xsIB3IglpoJX24osvRs+ePREVFQWHw6Hy44eFhcFqtaK0tFQtZmNjI3755RcUFhairKxM5RXPzMzE4MGDERMTo5gdBQqz2ayES5erLac8389MPgaDASaTSQmQhHzpmZUkxlQEOF5+T+IvhRRp2SOh/zMLNJUPWnn5XGkZY6YdPpuQNb6XypXE9hPD29TUBJPJ1C7Ggc8OCgpSzJhZgDh2zh37SWiKhKzx8HBO2B+OnYKg3hPjdrsVpI0MlWsghUUp/BOKxj0mBTGXy6WEdu5JmeOeVnHWaOAz5HMI9aPllWNobm5GTU0NzGazykBVV1eHnJwcBYeKiorC2bNn0dTUhC5dumDatGno0aMHIiIiVN2H0tJSVFZWoqamBn5+fti6datyz9bW1mqw01Kh4p7g//39/ZGXl3chx/Yv1yIjI5VQTrgNhVnuB6ANgmMymRRDSUlJQe/evZGWlobExERERkaqGBhCDOklstlsKCwsxOnTp1XWOofDAYPBoOoRdO/eHeHh4aoaeHBwcLvMV1IwlDVm/Pz8lAdDCrLSA8HGPch1pCJrNBrVefR4PJpAdz2d8Xja4EkUMKWCDECjnMo9zPfTi8d9LT16gDbVNaFW3P+E9dHroq81QY+dpIlMGUwYllTaCXWkV1v2g3SKCocelsu9IQ0xcg0ocEsvJGmSNETxeulFAKCBmMp72QfGV1CQaGxs1NB/0h8aK/gswsLoXeAePXv2rDKiEQoWHh6O6Oho9OrVS/FJ7k8Zz1FSUoKcnBwUFRWhuLhYZRZi1jUq1wBUDBLX2Ol0dtpYrylTpmiCqL/66iuEhoZiwoQJ6n/jxo3Dtm3bEBMTc0HZevr164ejR4+ia9eu7QrS6dujjz6KuXPnaoRBtt9++w379+/H3Xff/afPCAkJQXl5Oa6++mps3rz5f+zf/9R8fHxgtVrxxBNPaALiX331VRU4XlBQgJUrV+LFF1/8//y+jtrOnTtx5swZLFiwAOXl5Zg0aRK2bdumvpcxGpWVlbjxxhuxdu1adOnSBRUVFf9jrM17772H3r17Y9SoUX94jVzHO++8E1OmTNHUCPm/2W6++eY/jLX597//DbfbjZkzZ/5hrM348eOxefNmREdHt6tIz2YwGFBcXIxly5a1S0P8V2gXokJcsEfDYrEoKy0FArpxGxsbNYGsfn5+KtOUj48PzGYz6urqVBrc0NBQFTHf0tKCIUOGID09HYGBgaqIWnh4OIxGo0ob6uvrC5vNphQSCTdiCkxpuSfTJtZVWhfJLAilYNCtx+PF75rNZg0mmkI1Xfz8G2irD0Fog1RGqDjoLaJSCNFbsCh0SAsj0GZNZ99puZLCir+/v8Y6TKgWYWMul0uTXYvWNF5PZYXv49xJzwA9B1LR0qeIZdO7/am4cMxURDh+mVWF8y8tiRJeRGGB90uYAf8vBR+Ppy1/PRUMCSnje7iH7XY7ysrKUF1drQLaCW3g2LjvIyIi1NqYTCYkJCQgLCwMQUFBKs0yi/S1trbCbDajtLRUwQGpSFMhpmKkh39JwaezNgm5Y7C19PjQQ0roJM9xa2srKioqYLPZNPtbnoXQ0FCEhIQgMDBQnePw8HD4+/srj1NJSQmsVisqKipQW1urwdbLs0ChUcJ5AC08k4Ivr5HCKgCNEiCVDCr9QJtwL/c1118q1RT4JWyMAi2TbMgfvTdR77GRMQmkXRKG09DQ0M5rzHfLPnE9eZ7+KKOb9OjRqEGljs+SHgGeQ3qeeFal4sT5k14xvYJBugGg3RpJ5UsaxfTGIq57R14o3sexc05kLIse5ivvIR0mDWNiAmZfDA4OVvSVGQel14fxaXwP557f0aghYw8ZfyTrIXSm1r17d2zZskXzv/vuuw9z5syBn58fDh06hMmTJ+O3335Dz549NQKc0WjE3r17cfPNN7d7bnZ2Nrp3764gTWlpacjNzUVKSkq7a99++21VIG/dunVYtGiR+u66667DkiVLEBERgdOnTyMrK6vDcdhsNmRkZGDXrl1/Ot6VK1dq4kf0bdmyZVi1ahVcLheGDBmi8QAAwDPPPINrr70WADB27Fi8++67SE9PR25uLpKSkgB4Lfe5ubkdKk76duutt2LPnj0wGAxYvXq1prbDjBkzsHDhQtTX16N3797Ys2cPrrrqKhw+fBh+fn64/fbbcc8996CxsRF9+/ZV69jc3Iz+/fvjhx9+wOjRo3Hy5EllxGSF8dDQUDz22GOaYOvFixfju+++A+BVcubMmaNZx5dfflmjfM6fPx9bt25Vn++66y78/PPPmvHFxMQgJycHmZmZ7cY+bdo07N+/X9Hm77//XmWr+vDDD/Hqq69qnj1v3jw0NTWhf//+2LRpE8aOHYsTJ06o5Bf79u1Tmb7+qHk8HowcORIff/wxMjIykJOTg9jY2D9fpL9Yu2CJRcIRJMGWkBRaqKRHgQGeZO60iLPYEFN7MvsL32GxWDRVePms6upqZaHRf0dYlGQsZKKSyUn4EfPdS5gQoI0VkK50veCrD5jktRS6CQvTww/YPxmPwP7poVjSiioVBQomevww0GYVlO+WcA/2gwxLPlNCmvRwLcIeJHxCjl8vbPHZfI/ELesDz+W8c4/JPlNwkFZ+aX2WyoacO85zR0ITP1OooWAjLc0ANOOjUFpVVaWSGRAyRbe6yWRSdRwkrIb9stlsyhJKwUEKtSaTSQkk0uNGIaGzNtIHCo/67F+cCwAqZkPC9ii8SYVAr6zSms+sUvRg8lkUAmmc4GfZJz0+v6M9p4fx6JVA6THQx3XJsyb3uIQryT0o4YvSqv9H8Bp9H/WwzI5gQPrYA0kzOE/0Zujjk6TxRA/hAtoC1PX9lrRFeh0kvFQ+h+dDD0PjezuCYurHJ+FG+u/1hgdJ7/+oP/JvqZx2BGHjPEr4GudbFuozm83KS08DjLxe0iyuhd5TQyMflRdJ78nzOmO78cYbkZiYqPnfqFGjMH78eLjdbqxevRp5eXlobGzEuXPn8OCDDyph3+Px4Ntvv0VOTg7i4uLw5JNPKoG2paUFZ86cUca9uro6fPHFF7j11lsxbtw4hIWF4R//+AciIyNRU1OjvEE//PADDh06hODgYDz55JNwu92oqKhAU1MTVq1ahaqqKmRlZeHhhx9W+ygzMxMPPfQQ8vLy0NDQgIyMDDz66KPw8fHBjBkzNML0rl27NMLwlClTcMstt6jP+/btUx4RJgSIj4/Hk08+iaCgIFRWViooVn5+Pmpra9XYmBLZZrPhiy++wKxZs3DRRRepZ8+dOxeXXnopLBYL/vGPfyA2NhanT5/Gd999B4/Hg82bN8PlcuGhhx5Slve4uDg1tsbGRuTn5+Prr7+G2+1GaWkpQkND8eijj+L8+fNwOBxIT0/Ho48+iqKiItjtdpSWluLLL7+E0+nE5MmTMWnSJKxatQrNzc0YNmyYgnEBwIEDB7Bp0yYA3hocp06dUuv497//Hb1790ZBQYG6/vjx4yrt7Lx58xAbG4sdO3bgH//4B2JiYgB4PZpffPEFqqurMXjwYDz44INq3c6ePYs1a9bA7XZj1qxZuOSSS3Du3DkAwE8//aRRGktLS2GxWPDoo4+isLAQDocDJSUl+Oqrr5TBprGxEWfPntXAGA0GAx566CEMHDhQ/S8/P19lrFy1alU72NhfvV2woqEX8CQTNhjaior5+fmhtbVVpb5tbGxUafhoISbTZVGcqqqqdoIZ4QyERBBGw6wykrHx/SS6FM4k3EGPDZbBv9JjIYV1KfCSoUqssF7RkEKsXkj9I1iWFFD0wYZ8N9A+EF1aYKXCx75JYVoKL1JYkYwQQDvLHK+hUsG5kkG2nGcp8Ellh/3hc6SQoVfu9ExbBuvyfimM8B1kvFLgArRWZ2kJ5b7gmlHAJOSMAcfcA5xv9odKcm1trRJ6HQ6HgqJFRESoRAZM4UsBEPAyNBmkr7fkEqIorZ6cX/6vszZ6vuRZkUI/zzVpB9dNeiop6Mr9zOfJgnlBQUEKEhUYGAgAmuu5dvL9UvlkX6RwJhUNvcCrx+PzXEpvGd8vvQfcA/o4Cj2kR3oo9AK57FtHypBkZJzLjt4laQPniT9yraRCLuGYfKakYdI7oFc0JEySfZLj4FmWY5V/y+fpz7geGinpIMfEtZD3yPmSRhI9nZL9kT9yz8j1kMYcfSyQTIBAXsjkHDS4ScWIdEjuYXq5ZB+CgoKU94hQOhqtZOr2ztRmzZqF5ORkmEwmdOvWDQaDAePGjcOll14Kl8uFpUuXorS0FPHx8TAajZg+fTp69OgBwLt2L774In777TdERERg1qxZmiKrbOHh4QgMDMTixYsxbtw49O/fX1UeDw0NRWhoqPJ0vPnmm/jxxx9hMpkwa9YshIeHw2KxICoqCkuWLEFhYSF69OiB6dOnq7VJS0vDzJkz1f5OSUnBjTfeCB8fH4wfP17FUADeCtYSfjR27FhNPMi2bdtUjYfU1FRYLBZERkZi1qxZiu75+/sjLS1N8aDa2lp88sknKqC4qqoKixcvbhdPcs0112DQoEEwm81qbKdOnVLwpnfffRfZ2dm44YYb1HlIT0/HjBkz1Fjz8/OVx0WOlX1JSkrCzTffDF9fX8TExKChoQFPP/00mpqaMGrUKPTo0QNPPfUUGhsbMWLECEyaNEn1b8OGDfjoo4+QlpaGFStWYN++feq7qVOnarwSycnJOHr0qPI6XHvttWhoaMDbb7+NWbNmISwsDIBX0fj0009RVVWFnj17asaWm5uLr776Ch6PB5dddpkmFubTTz/FDz/8gNTUVHV9UlKSZqxnzpzB008/jfj4eA2UMTU1VSm8BoMBN9xwA3r06IGAgACkpaXBx8dHwY4XL17crjr8X71dcIwG6wD4+fkhKipK1c4AvFqZjDPweDwICgqCw+GAx+NBZGQkjEYj4uLikJ6ejgkTJqC2thb19fVwuVxIT09HVlYWqqurUVxcjIqKCrS0tODw4cM4cOAACgsL0bNnTwQHByuim5WVhaCgII2VnBb35uZmDXaa7mc9rpeMRzJpQli4MaRgR8YnM2UxQ5C0gDK9LpkCM2cxCFjPEGWAOJ/LcREGwGsJHSGjbGxsVDUBQkJC4HQ6UVVVpWBC0hpGCAM9SfX19YiOjtbg0snYunTpouAOUhHRW0G5Byg8ylTDVJLIrGX2GgAqraS0RksFiPARzoWEsxD2QOWPXgwpZDU3N6v+MAuXFI4IVaJXCGgfgEnmLy25DQ0NKCgoQGlpqdp35eXlqKmpQbdu3TBkyBBce+21SExMhM1mQ1FREU6dOgWz2azgPtu2bUNRUREcDodKg2q32xW0sLy8HMHBwUq5tlgsmjidjtL5dYYm02Rzn9CySmFJVlE3GAyw2WxoafHWp0lNTUV6ejpSU1ORlpaGmJgYVe2daWqbmppgs9lQVVWF/Px8FBQUIC8vD8ePH0djYyMCAwMREhKChIQEdO/eHZGRkQp2ReGO9KIjbxvQFgeh98rpYTo8H1Re9PAbGQ/FPU1rF2NAJBRQCoqAVsiVSpo8E5xvGl14VmkkkbSIgjH3vDwvfLY0sHAM0mAjoaq0nBMqIL0Oci7lvBD2w6b34PCdUjEjbEjSB84VaZj0NsvreK5Ie0lzJe1hCl7SEQr3pLOSjsnsdzTScB2amppQUVGB6upqlQGtsrJSGYQCAgIQGxuLyMhIREVFoXv37iptpjSm1dfXo7KyEqdOnUJ2djasViuqqqoQHh6u8UDp54r1esjnOmOMBsdy1VVXYfXq1ar+gmyffPIJkpOTNdb5/6Q9//zzmDx5MjIyMjr8/s9iNACvtXzJkiWIi4vT8Lz/TVuzZg1MJhOuuOIKzf99fdvqaHz00UcqRuPxxx/HO++8o7k2KysLhw8fRnJyMgoLC9GnTx/8/vvvSEtLUxb5C2333nsvFi1ahPj4eFyI+Dhz5ky8/fbbiIyMbJcuVt86irX5n9qgQYOwf/9+JCUlqUKCHTXWQ3nqqaf+9Hn/Uz0UBtVL4wrbDTfcgPfffx9RUVF/iDzw9/dHZWUl7rrrLqxatardOso2dOhQ7Nu3DwkJCXj00UdVrM1fqV3IHrhgj4bH41EMpLKyUhMXAUBhY1n4yu325o4OCgpS+cJZSCs/P19h22NjY1Vwt8lkQlhYmGJQFosFMTExCAkJUTAnf39/nD17FlVVVUookdZivZUKgCZ9JhkdGReZsR5PzTHT2kSLJP9Pps/x6xk4mY+Ey5DRy+slRAtoi+sgcyDUhD9kaBQKqAU3NDSguroaBoMBISEhql4A+06mK615LS3ewlt8FscrPQ/Swqu3LpMB04omMz2RmZPpcowyvSUAta4BAQEaCy6fJ4URKkxAW+Evpo6lYsTnyrnlsyg8UpgymUwIDQ1FREQEwsPDERYWhpCQEAQFBalYHmk1ZL7xyspKlJSUIDU1FZmZmejevbuqBRMTE4OoqCjYbDalKLFvnOPm5mZUV1cDgNq7TqdTxUFReWQGNtafoSDNzBydsekhYDITndvt1gThc/4CAwMVlKyxsRG1tbWoqqpSAh8t6jImiGtNZYD7lZZiBtXSg0XvoITHSWgT96X02ElvmExJy3t4jfQqStiL/M0mYUQcLz07kmbRIi1TUUuPit7LCLR5AniO9N4LCfehgCxTPEsapleCJDSJxhMJBSW8RxoL5FxLaBjz9EuYpR6+JGmUVAwAbXpz6ckAtDE2MlOThB5JaJ9cd45T9o3KFQO/eU6pKMsAfQnNomJTV1cHj8ejeB+faTKZ1DmXCp3B0JZam3xN8jMaA6UXQypMMqGAjP/qTO348eMYP348fv75ZwwYMEDBSEaOHInDhw8jODgYixYtwuzZs+Hj44OdO3di6tSpALxnYMeOHZg5c+afvmPFihW45pprNP9LTk7G8ePH0a1bN7z33nsqM9M333yjSQcLAF9++SXGjh2raMXUqVOxc+dOtUevuuoq7Nu3D76+vnjnnXfw8ssv/2FfFixYgLlz52r+N2zYMBw8eBATJ05UQfEulwujR4/G6tWr2z0jOztbVdYGvJb1zMxMvPfee7jrrruQmpqK48ePK8UpISEBx48fR3p6ertnffbZZ7j++utx7Ngx9OvXT/PdJ598oonZALzQsqFDhypZ6bLLLsOBAwfQpUsXvPrqq5o6Fw8++CDuvPPOdu+U65iVlYVjx46puiQnTpxA3759UVZWhkceeUQTcH3fffdpalUYDAbNOsrGdSwrK0Pfvn3x5JNP4oknnmh3zahRo9SZlOsIAJs2bcKQIUM0qIOAgADs379fQb6cTieGDh2KH374AYBX5hs5ciTWrl2LgQMH4tixY8q7cvz4cWRmZqKiogJLly5V1ei3bt2KOXPmtJunv2q74IT8FPSkBUiv0ZHRSCsbBSuXy6VJLxkfH68Ib0NDA6xWq2IIJIAhISGIiYlBbm6uBr/OaqBkdBSmudjMdEJLnI+PD4KCgtT1FLiBNtiQhBJIvDwAzWeOTxJvvWue8SrSEk9GIOEW0hKpVzj4LN7H8VCg4dzR28HPEndLIUC6+KUyxSBcWTxMWvNlcL3eYssmvRT8Xh+von+/tGpKAUIqEmTo3AsSQ623WvK9EjJA4YPzTSVYWjTlmrtcLgVzkpZnKSAEBASooHoGbgJQCmxcXBzi4uJUGjoZtEzvDT0jtNrKTBUcG59PYUQP5+vMjQKrPANyf3DM9DTq0wvr6Yg+1kYmCqAySgWN1em57q2trbDZbGqPsTq5zHYmlYCOBFZ6DqRSLK+Xe03ez/HyR3pCeA709EDCiSQUSXpI5Gc9rZHP0o9J9p2GDPkMvSdYGi9Iozoas4SDSi+W3M9y/Gzys/xbXi+VBs6vhFBJWqL3IsvvJA2T+5M0hXtSzrek19J7RP4llSzZd7mu8h38nspKUFAQTCaT2ovSgCOhujK2jLREwkipHHJs+r3bGdvGjRtRXl4Ou92O3NxczJ8/H5s2bUJlZSU2bdoEp9OpLNtGoxHbtm1TMQoejwfbtm1r58m56KKLkJKSgo8//hh33nknjh49il9//RUAcMcdd+DkyZM4ffo0Nm7cCLvdjm7duiErKwtnzpzBrl27cObMGfWsm2++GVarFXv27MGDDz6IVatWobCwENu2bVP7oaSkBJs3b4bH48Fvv/2GpqYm+Pn54Z577sG///1vGAwGXHXVVXj99dfbpbq96qqr0LdvX2zcuFF5aRMSEjBjxgy88cYbaGxsRGxsLG6++Wa8+eabyih84sQJAN5sXN26dcOHH36IzZs348yZM7Db7Wpsw4cPx7hx49TnwYMHY9CgQQpiVFhYiAMHDuCHH35oVzh27969qKqqQkBAAP7+97/jm2++QUFBgQp2njp1Knr37o0ff/wRLpcLhw4d0tCvAQMGICAgAIWFhbj77ruxbds2nDx5Uq1bYWEhampqsHHjRiXMy7GdOnVKw1NPnz6t6Pt7772HQ4cOweFwqLHJxnVsbW3FiRMn1PtMJhPmzZuHVatWoaSkBE1NTXjwwQfx9ddfa9bx+uuvR3Nzs4oBYXO5XNi8ebNS8jweT7vK8vxcXV2NjRs3qjPb2NiIkydPAoCqoQEA27dv/489Uf/NdsHQKeIdabWXCgUFPRJ/mS3H7XZragtERkYq9w8JZWhoKNxuN4KCghAQEIDa2lo4nd7qxwUFBfjqq68QGxurLM7ctBERESrDT2hoqAbmQ8JLfHxkZKSyfNNSyO/0jIHCvxQQZM59WqsoLFKRIjMl9IqWRipH+uJdZFANDQ2KgUmmRmu4j4+PcpfX1NRoLGyEV5HJsS4J+8EKvnw+4Vb0HoSFhamxOJ1OFRvj5+enXH/SeigFEDYJe5AWXXo0JGOWcRd6/DWVHSnYE4ZGCyPnh/2XVlwZUAq0KZxSIWEQNj1wnMvGxkaEhoYq5ZSCLt/V3NwMk8kEq9UKq9WKmpoapKWlqexU2dnZGD16NDIyMpCWlobQ0FCEh4ejqakJtbW1qKysVNZ4q9WKAwcO4Pz583A6ncqjIXHtUsDm/zg+j8fTKSEPgBeLKhVRCqMANAYMBtbL3OOcA6anzcjIQGZmJoKDg2E2mxEaGoouXbooBaG6uhrl5eWwWq0oKCjAgQMHFC2iEkrYVHh4OLp166bS5RKiKQVxKUzKM0wLsszQpvd+SKWfZ5E0glZ0fWyB3oov67CQvsr3cf6kQYDCLA0HnHfGupCm6Q0cpFsyFoxKM/sloZGyhg3rIxF6Ka9lP3kfPSd6j7G0/Mu54LxJmuN0OhXtoDdCKhNUWD0ejwYKS+MN4/q4lpwjBmbLYptGo1HdQ2+n9PY6nU6EhIRo4Ercvy0tLaivr1de0crKSpSXl2voVVBQELp164a4uDhERkYqLyfnjZDAyspKWK1WnDhxAqdPn0ZNTQ0cDoeKTSLtILyLayqbwWDQCMidpRkMBkRERCh6fvToUTz00EPYsmULoqKiUFxcrFHwACAsLExBXAEgNDQUAQEBCoL65JNPYvTo0ZgwYQL27NmDzz//XFnaf/nlF3z33XdYvny5et68efMwZ84cTJ06FVarVSPcrlu3DkeOHMGKFStw8OBBTJ06FdnZ2QgJCUFJSQmioqLgcrmUV5vNZDLh6NGjmDdvHoxGI/71r3+hf//+MJlMMBgMqKysBODNQhUVFYUZM2aoewcPHow1a9bgqquuQl5eHrp3747vv/8eQ4YMQUtLi2asjz/+OMaPH6+plSHbvffei+nTp6sUsnfddRduu+02DBs2DBs3bsSvv/6KJUuWqOvNZrMaG5vFYsGRI0dwyy234PDhwwgLC0NxcTE+/vhjNDU1Ye7cuYiPj0d1dTU8Hi+8vri4GK+//jqCg4Nx22234dChQ1iyZAnWr1+PqKgolJSUwO32ZtKMjo5Wn/38/BATE4PS0tIO4UxssbGxmnTPAJS8U1paipiYGDQ3N7dTniIiItQ6Hjx4EKGhoTh8+DBmzpypiQn59NNPYbPZ8Pe//x2AN5U7ALVu3LP8HB4eDh8fH1RUVKjPhFEB2j0bHx+voN8XMtb/l+1CVIgLVjTS09NVOkridwFt9WtCB1wulyJ2ZDxAG8E3m8245ZZbEBoaqhjE77//jri4OCQkJMDj8aCmpgZNTU2orKzEN998A5fLhaioKMTGxsLPzw81NTUICwtDQkKCwlxLZkV8NANuIiIiFCyD2FiPx6OCpaTSREYuBWyHw6HBXEtIhsxSJK140uLG+BEyLzJuvo+MikH1FCiYf57CcFVVlSZFKwVi9p/j4bxT2ZDCPxW5Ll26IDo6Wgkn+rgMqfBIaJVk4AaDQQn9ZNTS6iaLy0l4W2trq/IgUJhgTIn0MkkctYQM0F0uoTJM4UimajabNQKUHnpms9mUQCA9B7Qqc32ANktiSUkJysrK4PF40LNnT+Tn5yM7OxulpaW44oor0LNnT6Smpiq3LIWKoqIiGI3e2IqzZ8/i119/VTh3KmcUOgGoOiBSueUZa2lpacekOkuLi4vTVJWWwhrPCJm2yWRCRESEJlDcZrMhNjYWCQkJ6NGjBwYMGIDIyEiEhISo4p58PpXA8vJylJaW4tSpUygoKFCKDOAthBUTE4PY2FhER0cjPDxcxWlI5YbrIT2dMnbLZDJpqshTWQTaAq6lwi1pDftCWsgmlW4qC7yfArzc33qvgmySFjmdTk2qZyoTfIeMMZNQJwkvknFlVORpmGF6VlnbSF+Ph/1lCm32TVrj+V4abuS8cW6l4ieTCwBaAwjPltvtVl5JeZ5krBcblSAK+vzx9fXVQOb4bioowcHB7YwdzJZWU1MDm82mMv9UVVUpGunj44OIiAikp6cjKioKERERKgCU9K+mpgYVFRUoLi7GuXPnsGfPHpUm3mDw1oghrWPCAxmzYTabNV6/C6kv8VdrBoMB3377Lfz8/HD11Ver/19++eVYt24doqOj26ULfffdd5GRkaGE55UrV2L48OEYMmTI/7ofKSkpyMvLU1CeP2sS279lyxYUFxdrMkf9WVu9ejXMZnO7GA198/X1RUlJCZ544glNjMaKFSswZswYDBo06ILe95+2O++8E88++yzi4uI6FH5nzpyJt956C1FRUYoOmEwmVFRU4KabboLT6cSaNWs6jLUBvB6YrVu3IiYmBtXV1Rg+fDh27dqFhIQElJWVYdCgQfjtt9+QlJTUroCgbDk5Ofj88881MRoLFy7EvHnzkJqaiiNHjmDbtm148MEH/y/Mijf1rcfjweTJkwG0X8cvvvgCMTExuOSSSwAAH3zwAdLT0zF27FgAwFtvvYWsrCyMGDECRUVFeOGFF/DLL7/g0KFDSE5O1lSW/2+2C1EhLhg6JWtNMI6AAgEZLBkOBXoyRrvdrgKoaWGxWq1KEWhoaEBkZKQqbkbcqb+/v/JiFBQUqCxUPXv2RHZ2tgbiIC3t9CQAXmEgJCREIxBXVlaq/slARzI+Ciqy/oPEKeuziBiNRk3RKc6LwWBQxfJkxiIprABtRQH5fAr1tCDyB4B6D8fL6t+EiUih2cfHR1n9fXx8FPZZMmDWLZFpYQmZampq0uRup8WNm4uwNCpmEs7A/uqDv6SSQo8WoE2DSwGH3jMKBWTstGrw2S6XSzFyzp/EfssYHv5NjwnHJuEEnAO5Z7gfOV4ASmBzOBxITk5GcnIyLBYLXC4X6uvrlfeI0AaTyYTW1lbU19crgchoNGr2qowroPDNuA3p8eisjZAQKnUcI/HjzDDHeZFnz2azaZRSm80Gm82mMPG0bvF+Kp8hISFwuVwoKytTRSwBqHTa7AcbBVqpKEhBmXuRdJBjYcyE/J+0auu9UjyX0rPG80nPgYyvkMI9abHeEKCHG/KH51EqLTzvQJtnBNDW9pH0h31lP2WcCekNz5acMwkv5dpJOKZUkuRv2Vf+cB71cCoZYyK9OlxX0gL+TZoi43FI46TyR3qj9xyRPlE54RilEivfR68oPeCkCVwT8h5mSiOklWPjPOuTWbCPHo9HJUJgkUAmmpDeFRrMJC3vjI3pVP39/bFt2zY8/fTT2Lt3L4YNG6aE1UGDBuHNN9/EZZddhmeeeUYptADwz3/+U0Ff/6ilpqbim2++USlWr7jiCiU0At70pYMHD0Zubm67e6dNm4Y777wTl19+OTweD9auXYv9+/fD5XLhzjvvRGtrK4KCgrB161bcf//9Gsv48uXL4fF48MADDwDwCsOjR4/G3r17cckll6ChoQGDBw/G66+/jksvvRQPPPAAkpKScMcdd+DSSy9tJ2y/9NJLeOutt/5wnPPmzcNll12G6667Tv0vPj4e69atww033ICRI0di5syZGkUnIiICmzZtwpw5c/Dtt99i3759cLlceO+991BYWIhXXnkFW7duVbC2sWPHKl4/fvx4PP/887jkkktw6tQpAMCIESM0KVt9fX2xdetWvPjii9ixYweGDBmilMfjx49j2LBh+PLLL/Hmm29i/fr1GDJkCCoqKrBgwQIMHToUM2fOxLp16xAfH4+8vDxcf/31mDJlCqqrq5GUlIS1a9di6tSp+Oijj7BhwwYA3hogUtH55JNPcPLkSSxbtkwzXyEhIdiyZQv+/ve/IyoqCo8++iguueQSRWsCAwPx008/4Z133sGOHTsQEBCArVu34pNPPkFzc7Nax8cee0xj2HjqqacwfPhwFc/x/PPPKyPHhAkTFP8aPHhwp0sG8x/FaPC3dGdTIJfYZzIOyVgk7tjtdqOyshJhYWEIDAxUQhyzHQFaZh8SEqIE34aGBuUVoTWbCgjQVk9ButqZ9YPMT28pJ0OTgZO0tvM7CQvoSDDVu/UlI6TgDkADG5AWSTY5hxQceD2FTj5DYvk5dmldZB84NqfTqQKauUZSwJDXSubLOeD7ZR/5W3ozJCxGKoEUzPRNCv+8ThbpkkoDx8W1lO+RFk8JI+G6yMrknFOJd2e/pUAvBRd5Fnx8fOBwOGC32+F0OhEdHa0suYzHqKur0yiCLS0tcDgcSgmRAaWcZ45f7kVpLZbz3hmbtPjTQyUFbGm159oDbcIlzxbr8zAjlwzG5vpIbxcLoAUFBSklWSrcPCvSEyl/k27J/SfPuBTqpZeB+0+/dnKMHBPPBu/ReyWkEMu+SEFdQr34LkmvJZxKGjVk/2QfZawC54bfSyFdKkVS6CftJ03iHmef9EqMHD+fIw1W+rlgk32XTdIuSRskTZHrBrTB9+Se0589ep4kbdCvM/tIOiUTBtAjS4QA30eDlUzFTrqkVzKk0iL5k3ye5A2kzxIO21mDwQEofLqvry9+++03VFVVwWaz4ciRIwCASy+9FAMGDMC+ffvQ2tqKtLQ0hIeHIycnB7Nnz8b+/fvx+++/a54ZERGBGTNmqLSvjY2N2LNnD5qamlBYWIjDhw8D8Fro8/LysG/fPhw6dEjdbzabMXv2bKxevRqlpaUqY9HUqVNRXV2N7du3A/DW0LDb7bBardi3bx9qa2uRnp6Oiy++GO+//z6ys7PVPrvjjjuwdetWHDhwAL/++qvap/X19Uq4P3v2rIIDHTt2DBdddBEiIyOxZs0azJkzBwcPHtR4XEaOHImUlBRV2K+goECN7aabbsKZM2dw5swZ7NmzBw0NDSgpKcHBgwcBeLMqlZSU4NixY9i3b5/KmkY40IkTJ1Rh2n379qGurg41NTWaook1NTXYu3cvDh48iEsvvRRGo1EJ+2wejwf79+9HRUUF7Ha7WlfAqywfOnQIe/fuhdVqhdlsxogRI3D69Gnk5+crGfLQoUPIz89XQjnjOKKiotS6JiQkIDMzE7///ruKk+A6lpWV4dy5cwgMDMTs2bPx3XffwWq1qrERYvXbb7+1Mxzv27cP+/fvx7lz5+Dn56eqxbe0tKh1zM/PR0JCAubNm4cPPvgA58+fh6+vL/bu3YvW1lYUFBQgOjoa8+bNw8cff6wUMbnnOkv7j9LbSsGutbVVwQUCAgJgt9sVYW9paUFoaKhy18o0o0ajNzXjiBEj0K9fPyQnJytCmpCQoFKmHT9+HICXWO/cuRM7d+6En58fEhMTcdFFF6Gqqgr+/v4ICQlBbGyssoA5nU7lkaB3ws/PT33HuhMNDQ2KwUkrvsFgUJjDjqqBS08OGUtjY6P6zJgKKSg2NjYqIZeBwNKaKYUbydzIZOXfLS0tqK2tVXEWUiiQY+f/KOASV1xeXg6HwwFfX19EREQgOTlZA3kAoIk5kQKPn5+fgqfw/1QyAa87VGbdovInIRG0tsln0wMlPQANDQ1wOBwaRhsUFKSBRzQ3N2sgbyzMxjFI4cRoNKqK7/yR3h6uk0yZq58HHx8fldaW65mbm4uysjJMmDABGRkZiIuLUxkj8vPzYbPZ0NzcjC5dusBut+P06dM4dOgQSkpKFLzQaDSq/cixUlkhw+H6A15Cpg8Q7CyNec2bm5tVimWeU5kClT+MeyDkhnQnODgYUVFR6N+/P1JSUhAfH4+oqChYLBaFa3c6naisrITNZkNdXR3y8/Nx9uxZVFdXqzSfhF1GREQgKSlJpcuV2XrYZH0ZnimuCz0pPP8U5qVHjfuIe0kKhvI6qfzoM77xOwrhFHZ5P71DUlmWsCegzTtCwVVW9JYwJCl0A20KL+MgpFAr6VRgYCAsFosaqx5mRW8h+8usc1JgZ7+lYiL7oBfQpYeM9JgKBMfG60grSZdkql72Uwr60iBEeiW9FdJLLI07fA/hgQ6HQ2VfZOFZ0l3u/x49eiA5ORkhISEIDg5WXj6HwwGbzYaamhpVgC03NxcHDx6Ew+FQkDe90iX3rNvtRn19vUIdBAcHd9oYjf+prVy5EomJiZgyZQoA4JVXXkH37t1x7bXX4ujRo3jllVdU7YnIyEg4HA507doV27dvx+DBgzX0lfBNVlLfuXMnNm3ahKVLlyIyMhLV1dVobW1FbGwsjhw5gokTJ2oE43Xr1iE3N1d5KL799luUlpbi7rvvVtdcc801WLFiBXr37q3kpdjYWPzyyy+45557sH79enUtzxYF3eDgYPj6+iph/sUXX0Tfvn0xadIkHD16FCtXrsSHH36I8PBwVFZW4oknnsCll16qIDqy7dmzB99//z1eeOEFAN64AdZ9ArxC7s6dO7FgwQLN2Dtao6ioKNTU1MBoNMJisahYBNmYlveWW25BVFSUikMAoMneGBYWhoqKCuUVDgsLQ2VlJdxuN/r3748ff/wRWVlZsFqt8PX1VWOljBcREYGqqqp20K758+dj9uzZGliZfh0jIyNx/PhxXH311Thw4IBmbDI2B/AaqDnWkJAQAN7Cj/qxsY0ePRpfffUVMjIylJFcKmVZWVnYtGkT+vXrh4aGBpU1jmP7K7QLUSEuWNFITU1VFhkGxpJ4SxcvAGXtlV4A4ufp3cjIyMDQoUORkZGB4OBgWK1WxMbGIikpCYmJidi1a5fCbhcVFeGrr75CQ0MDgoKC0KdPHzgcDoSEhCAxMRG9e/dWC04Pi7RmGgwGlQqTsQE2m00FEcrNx0BGaVmSzIaWOZlOUeKvJeyG0JvAwEB1H+FIvL+pqUljsSOUhJ8JS5DPp9JAGAhTw9Y1t5rTAAEAAElEQVTX1yvhn8yvvLxcU1OEtUsoQKekpGiUKDIiuZ4UCIi7ljVJJOOX9UsAKAZOoYpBadL6Kq2NHo9HHUaZRYjvkdAoegF4P+eQ+5PCBr1d9I4Rf+/n54e6ujqNIFRdXa0E/qCgIE2dAfY5JycH+fn5MJlMcLvdqnjkVVddhZiYGMTHxyMyMhL19fUoLy9HfX09GhoaEBgYiOLiYpw8eRKHDx+G3W5XCrD0+FHw4nip5BoMBjWmgICAThsM3qtXL5VStqGhQZOql3Mg6QaFJ7mnKNyxLk/37t2RmpqKmJgYhIaGKnhWYGAgysrKUFdXh7q6OlXPpKKiArW1tQq6Fh0djeTkZKSnpyM8PFyTGlQKnFSgpZKhj1mQBS0JzSItkh4AQOsN5f7ibxkYz7mRcQuSbknPiV4olr+pfJNG0hBExV56PJgsgZ/1dTToEeLzzWazxrMp49w68rSQb9DIIOmGhCQZjUYNlItKEGm3rCci+yoVJxol9PRKejPoXZDQSKAN9iVpMvsvP0svuny22+1WxgZWBi4rK0NNTY2iASzwFhYWhp49eyImJgZhYWEICgpS557e0+rqauTn5yMvLw+5ubk4d+6c2lOtra3tYtMYi0gFj2PiPDHwtDO1C1E05NroP8v9aDAYcP78ebzyyitYsWJFh57E06dP4/PPP8fTTz8NoC07XlJSEvLy8jBgwADlMejo/j/ri2zy3muuuQaff/454uPjVVE9ts8//xzR0dEqXer777+P9PR0jBs37g/H2r9/fxw4cACpqakoLi5W+1Tf9Gf16NGj2LRpExYuXAigTdFYunQpCgsLMXr0aJWdS7bQ0FBYrVZceeWViIqKwltvvYXo6Oh2UGr21d/fH+Xl5bjjjjuwevVq+Pn5wWq14r777kNBQQG2bNmCuLg4FaOxc+dOJCYmKm+FnLt+/frh8OHD6NatG86fP49evXrhxIkT6NmzZzvFWsp0f7QW+s/h4eEoLS3FhAkT8LOo2A54IXPvvfceoqOjVWG/G2+8EWVlZbj11lvx3XffdTjnbre73Trqv1++fDnuvfdeeDwepKamdqoYjQuuo0FhiOki3W63BhtNAZcwJWaBAtpSBvr6+qoMMcXFxSp7D+sGSCEwLi5OPSM4OBhJSUkIDw+H2+3G+fPnlRBHhs/NIoOyAShmKlMYSosaA9SZXYSZqSTz0//QEkmvhYxtkHnxaWUCoAl6pVWNjF8PqyCj4vOlBY/MkFZDGbQqs0eRObEIGVMmJiUlISkpCbGxsSqjD9/pdDqVwNDa2qoCw6UQr7fSkanrFSI+k/PAeeahkSl6+QypwEkokbQAy3cweJ3rxXdS6bLb7WqdqQjJVJDE1Lvdbk09Ec6hFJacTifq6upQW1uLmpoanD17FufOnYPD4VD1O2JjY1V2NI6b+83pdKK+vl4lImhtbdXEC0gIDQUKzinx8Bx3Z64MLgvSmc1mJTTKZBIUNAml5Jn09fVVnsbW1lY0NDRoiphR6Jf0gHuE1ZaDg4NhsVgQHBys4JoU2lkrhYHSZLo8i1IAlZ427n82vQItPVISqifPPmkG6QrQBl3Sn3/SDr3gIt8t4VLst6RpEmYmle0/EoZkcgzOGxUKKojsO70nVPiZjEIqAJw/mUiDipjesKOnj9Izoa99wjHJfpG2cFyEkJJ+6eGsch65tn9UQZ7v4/16ZY99kl4N0ibSaNIA0jLWemF6W72hic+hd14qOYwBY60ZCZnjdTabDQ6Ho10Wqs7Unn/+efzzn/+Ev78/tmzZgvHjx2Po0KHYvn27gkdKAcjj8aBPnz745ZdfEBISgvvvvx/vvfcePB4PrrvuOlXpmnN111134fPPPwcA3Hjjjcr7AQAff/wx7rrrLpSVlWHs2LFKeI2MjMSOHTvQq1cvTV/ZF4vFgh07dqB///7weLwxNdu3b8fQoUMBeA1IP/30E0aNGoVdu3bhsssug8PhwDPPPIOXXnpJPe+pp57CggUL1OelS5dqvCMPPfQQ3n77bTUej8eD3NxcjBs3DuXl5fB4PEhOTsbOnTsRHx8PwFvBeufOnUhISMCsWbNULY5bb70Vb775pnr2bbfdhuXLl6OqqgoXXXSRgiPpm81mw8UXX4wDBw5g69atmDhxoqKRF110EbZu3aoywY0YMQI//vgjJk+erOBlra2tuOKKK/Djjz/iyJEjGD9+vIKHnThxAuPGjVMJUXr27IkdO3YgMjIS8+fPx5NPPomxY8eqdLDnz5/H2LFj2wnmn376Ke666y5ERUVh586dSEtLU9/pFQ+3240333wTCxcuRH19PcaPH485c+bgySef1Fy3fft2TJgwAS0tLXj00UexaNEiNDc34/LLL8cvv/yC4cOHY/v27TCZTFi8eDFeffVV9S79Oj788MN455131Pevvvoqxo4dq9axM7ULVjSkxY3WMOluJjOTQiSZK7+TxLChoQFVVVXKnWY0ejM7VVZWwm63q5S1FDaioqJUZWUKakwdSmsUGZ3E3kpBQG8ll+ORxa/YZz10QlrZOFagjSnxs3TjEy4hNXcJbdJj+yggkPFKGJWESrAvkuHTOig9GtJqSAutvI5YYc7NH8UHSKGKgp0saKU2lFFbC6EjgaGjxvnTQ0C4BtJaTEGNiiKv0+PyOcdynagk0ZotBQJ5HSFcUrmqq6tTwgkA5UpnWmaLxaKSB1C4oifF19dXxWtIgVhCHqRiwvMj11xCijpr41mVArgU7rhnACgBTxbTA9qs1xTW6CHh3uVZp/JNAY4pcJl6ld41CoJSaZZwE+4h+cOzK9eITY5N7vk/snRy7ED7GhbymfLdcr7k8/X9059F9kt+7kiJ13/WG1vkuvHdfJ+ku1KJkHRN/t1RbJSk5XolS9JNeS6kIiL7LxUVeX6kUibXStIF/dpzbPp4oI6USN4vg8HlnJCm0TND+qb37ujngPEeko/JhByS78jnyv5RieqsrXfv3ujduzfcbjdyc3OV8pSTkwOXy4WxY8di0qRJmnsaGxvV9+Xl5corfODAAZSUlCAsLAxz5sxBUFAQysvLVRzIwYMHERERoYql5efno6KiAr6+vujRowcCAgLQp08fXH/99cjJyVGKY5cuXTBnzhxER0ejR48emDFjBnJzc5Vn2u12IycnR/ERfqahad++fXC73SgpKdEIyTk5Ofj9999hMBhw0003AYAm3qSsrAwFBQXq87Bhw3DppZdi9+7dmDJlCjIzM9HS0oKcnBwl/PNzS0sLqqqq1Ni7du2q0rQC3kxbMTExcDqd2L17d7ssUVdeeSVGjx4Nl8uFPXv2YMSIEcjMzMTBgwcxa9YsJCYmwmazITc3V51X1kNJT09HUFAQEhIScOutt+Lw4cOoqKiAyWRCjx49FH+02WzYs2eP6ntTUxNOnz6N1tZWlJeX48SJE9i9ezeuvfZa9OvXD42Njdi9ezeuuuoqZGVlqb5yHVtbW5GTk4MrrrhCKX2A16s0fPhw9bmgoABlZWVobW3Fnj178Pvvv6O0tBR+fn6YPXs2EhISUFlZiX379sHj8SAxMRGJiYlwu70xG1VVVbDb7cjJyYHb7UZpaakGohcbG4uUlBQYDAbMnDkToaGhah1nzJgBk8mE3bt3Y/fu3Z3u7P5H6W1lfYGysjIFo6EwJQmpJKCBgYGaDEoM6E5JSUHv3r0xceJEZS0yGo3o168f4uPjUVVVhZKSElitVhw9ehQ5OTk4e/YsAC9ukm7icePGwWKxKEGSlVWl8kNvgvQ2SIywtEbxf3SX08rWEQSC3gwyc3p0KKwYjUYVW9Da2pbulcyHTEIyUgoVZEZUEHx9fTVWQQAaIUm+kwof14tjdTgc6jvWdZA57/kcg8GgXPBSaCGOnsyU0CR6Ibp06aKYKueHioC0HErmLwUUrpcMfpRxEtIiKlPhSkswx1ZRUaEZmxQoGBxMC2d9fb1iAK2t3nz1hLExA1RhYSEKCwtRXV2NkJAQnD9/HnFxcejRowfGjBmDAQMGKNhgXl6egp/Qsrpx40bk5ORoiklxrxFGQSVcKlqcO1owCYnrjI3568k0CBOSFmfSFHoRuPaEIlHYczgc6Nu3L7p164bU1FT07NkTERERyhrMPOS0IpeXl6OoqAjFxcUoKSlRRgo/Pz9YLBZV/4QWZabflh4SmdZUCqSkG4zvIA2ShgUJ5+GYpKDL9aeCBLQJ8IyFohLKZBVsejgJ6QCgTdbAPpPW8twxqYE02EjlQS+USyMD96TeAMP/yYx2/L+EpBLSKJVvuR/4TKlk6McrvZ6yv62trXA4HJqaIVJx4Bg4F6RhpOlk6NKj0NraqvgHE0CQFhmNRjU3xLYTuldXVwer1YqSkhL1XMYUMWVz165dERsbq7JO8Rn0fpSXl+PQoUM4e/Ys8vPzUVtbq0kXXVNTo+Fhkj/RCyZj9ijkdqZmMBjw6aefwtfXVwlkDodD41V88803kZiYiKuvvhqhoaEaA1FISIiC08nP6enp2LlzJ/r374/CwkIYjUZVt+vhhx/GtGnTMHjwYFgsFjidTkRERODYsWMYM2YMxo4di3vuuQeZmZkICgqCy+WCyWRCdna2Snv+1FNPYejQoaitrYXRaITJZEJtba3ywEjDFeAVwDsaG5uvry9ycnLw+OOPY/Xq1QgODkZtbW07KMvSpUsxYsQIXHTRRTh+/DjeeecdvP7662pspEXyM9vu3buxdetWLF68GADw888/Y+/evVi0aJG6hsac2tpabNiwAefOncP8+fMBAN999x0qKyuxYMECnDlzBjfffDO2bt2q7uW5dzqdOHPmDObPn4+6ujp8+eWX6N69O+rr6zFq1CisXbsWPXr0UFnUbDbbH64jPx85cgQff/yxqn9y4MABrF69WsWfAFDxYXV1dfjll1+wY8cO5aXYtm0bDh06hEceeQShoaEKdm4wGBAaGqpkBIvFgtzcXMyYMQO7du2C2WxGbW0tvvjiC7hcLpXGmGPtKIUv4I0r6tatG66++mqcPn0azz33HD766CMYDAacPHkSK1aswHvvvQeLxdLhOv+32oX044IVDeLrKOA5nU4FGSHRJiNlTAbgZRBNTU0qWxJ/iPePi4vDlClTkJiYqKxgoaGhCAsLU7AWZg44duwYDh8+jNbWVqWZNjU1oWvXrkhOTlbMmAI9LTkUEMhQGhoaNHERYWFhGqsQAI2SIeFJ9BRI2AGhCBQIpCAtg8z5P70ng4oMFTMK+jK2AWiLP6FFraWlRTESMmlCbrgmPj7ewmcU0umJaGxsRF1dHWw2GyIiIpRi1tjYqPpKpUp6figAejwemEwmZcWXlncJJZBWNofDoWJmKGRJ7KOEYHADy+KCdrtd4+XweDyKmbrdbiVMUOGqq6tTezEwMLBd4DEhTvRWUAlzubzVp6urq1WKSLe7LR6DikllZSUGDhyIUaNGoV+/fkhJSVECrd1uVznqjUZvbMy3334Lq9WqrPSEHba0tCiFiIGfzO5F5ZYBYhQaOgqs6wyNyQe4Pw2GthTCQFutHVp29cIsA/pdLhdqa2sRExODlJQUpKamok+fPkhMTERoaCgsFosqgMQA4bKyMpSVlaG0tBTFxcXKSsh5TkxMRGRkpIJYEXYlM8npvaXSOyljmAjJotIEaNOwSoglz5pUumRNDtICGktIZ2RaWTY5r1IRonAPaDMHkh4zRkMK31Io57MJdZSeHo6JY+A4SRtJo/g3zywb11xmYZOeVc4DDRg0JklvCwBNWnPeSwVdKoiSfugVF7knpQdcet2oAFOYDwkJ0aQ2llmmHA4HysrKUFlZierqalRUVKiiq0ajN0jWZDIhNTUVXbt2RUJCAiwWi6ZgZ21trYJdFhQUYP/+/Th//jzKysoUj5QKo6QbgFdgJZyNUEQaLzprMDj3vK+vL6xWK/72t79hzZo16hq594uLi7Fo0SJ8+OGHMBqNOH/+PJ5//nm8+eabMBgMyMvLw/Lly/Hqq68qegF448mOHz+OXr16IS8vT+3rY8eOYd26dXj88cfV9dzrra2t2Lt3Lw4cOID58+drsilGRUXh/PnzmDhxIuLj47Fy5UrExcXh+++/R21tLW688UYAXuHc7XbjpptugtVqxZw5czrE9nP8LpcLI0eOxLZt25CYmKgyQOnngmfJ7XajV69eOHr0KLp37478/Hykp6fj1KlT6N27t2ZP6Hmy/jMA/O1vf8PTTz+NxMREAGh3PdCWIVMv+6xZswa+vr6YPHmyZq70hkl+fvvtt9G7d29cdNFFf7qOnBtJvzrq+8MPP4y77roL6enpfzjWyMhIFBQU4KKLLsK+ffsQHh6O4uJiTJw4ETt27NCsw9SpU/Huu+8iNjZWA4EFgK+++gomk0lT++XP1knOFcfSr18//Pbbb+jWrVunitG44PS2AJTyQA8GNxEXhoSNMCiJ76eLmcTZZrOhqakJdXV1OHPmDCIiIlSnyWgZmNnc3KwKG0VERKC4uFiVc29tbVVZYyR0hkoChXY+W5+piP0l85OuZWqvUjAiA5XCD5kCMcMcK4UTCgv0PlAIkRAGybgpEPBembWFlkgJuZHF68LDwzWwK64ZXf1kXpwLxqXwndILRIgRFR4KU9LCC7TFF5DpURGSsCmDwaDJisI1lnMrCQ0VLlphJZHkD4UNGQtDqxDXQMZ+MJUyPR7Sa0VPDJU6zhstgW63N2MLFVibzabw/kFBQSp2gBAFWhYpZBUUFKgEBNxvrGvCau4U8Gi1p5Aji6lRSO2sTULWeI6YWYp7UDIn/na5XBrvApXYuro6VFRUoEuXLggNDVVwS641lQQKuPLsUzmlIElvhF7o5JpKSKLe0wdA8x3Xi0YEvWdAwlsAqEQJnBeeKamUsy9sZMA8s3yXFLylkiO9APqYBmkQkB4QCrAci8xexXFKY4F8j4RcyXoOjH+S46CwJj0a0uMjBQaujVQQpBdX7z0hnZYGEXkfx8e+SgWKZ1jGD/F+0kSutaTpEmIlobtAG5/kO2lkoEeM+5T8hJ5dQoWlUYT7nEINlSDyJRrKSAulkst7OmOT8MEpU6bg5MmTGDRoEJ599llcf/31ig8YDAbccMMNSnh2u92YOXMm8vPz1f0333yzgqhwTubMmYOJEydi4sSJWLZsGdatW4dPPvkEgLdAXUVFBaKjo/HFF1/gzjvvxLlz59T+nD9/vgrglnNcU1ODK664AseOHcPp06cxdepUtLa24rHHHtNct2TJEuV5mjx5Mk6ePInBgwfjmWeewdSpU3HfffchPDwcDz/8sLrvxIkTmDRpkqo10bNnT/zrX//C9OnTMX36dPTv3x9z587FJ598grVr12LDhg2YOHGiCqYuKSnBxIkTUVJSgptuugkTJ07ELbfcouZZP+9hYWH46quvcO+99+KHH37A6dOnNdcGBQVhzZo1ePTRR1UGrtbWVjz//PNoampSgfVPP/20OotyDvj3woULER0drYroLV++HEFBQZp17NGjB15//XXcf//9mtSvfEZycjI++OADzJo1C5deeikuueQS3HrrrQCAr7/+Gnv27FFjmzp1KqZNm4YZM2bgtddew+nTp/Hmm2/iyiuvxNy5czFixAj861//Uuuof9cvv/yCKVOmaGKC2Z555hkN3dM3eT2fJ9exuroaZ8+exaRJkzqdofGCYzRk9WU99IVConSfA22Mhim5+D9ONoPyKioqFGzFx8ebAlZCWcxmMwIDAxWkoampCVVVVSqzVWVlpcK/SUscrW+NjY0qlSbT8JL5UBmScRnS/c5+0krFmAaZNlAfjwBoc7jLH6ANUiDnSu8J4DOkosLrOE8ynkQPkyDTpvBORimVEJlhi8KdFA7kOsv+yD7Kd+vhDHyfZMZ8Dn/rtWE5ZxwbrYe0FvMzrYhcN6lEcY64HyhMSIiWjDOiNVQKptKqLOePCkl4eDjCw8OVoED4H/cKFd3m5mbU1tYqIY2eMTZawqTgJ4VQEi0JGeusjftPegdp0WZwoIyz4HxLgwH3tb+/v0qTW1tbq9I+y9SlEhrTEYaea0UaQaw3FRC9VV/fB+lxYJMwI9kPGSemhyaRXrDpP3fU5DmV/dL/zbHL8ym/4xrohWX+6OmA/J6N8Ch+J+dC31fZ5z+6XjbOBc9HR/dI+qjvm4yX0cd1SVoiIaoybkX2QSobco0lbdf3Wb/e7BNpDpUL0jP5DElDmOKWtIX7g+ORcy33OcdIxZNnjLEEnbGNHj0aY8eOhdvtxs8//4zy8nI0NzerFKjDhw/HJZdcAo/Hg19++QWJiYkqS9OuXbsQExODCRMmqM8ypgHwFiguLCzEtm3bUFhYqIGYMSOY2+1GeXk5Wltb0bNnT1x77bUAvBCd06dPt+uz0+nE9u3bkZWVhdTUVOzYsQMejzdxDlOhAt5MT8eOHYPB4E2jGhAQAKfTiYqKCrjdbkXvDAYDpk6ditTUVNTW1mL79u1KmWW8gtvthsPhUIHTVVVVKpnAtm3blIzV0NCAbdu2oaGhQXnzZTOZTJg5c6ZCAbjdbhXfUFxcjF9++QWAt37JsGHDNN/LRu8c27Fjx1BRUYEbbrhB8cTY2FhMnz4dfn5+KrEBW3Z2tqpPsmvXLhQVFansaTt27EB4eDhmzpyJmTNnKpg358LlcsHhcCikAeANFN+9e7f6nJycjFGjRil0CI2D27ZtQ35+voKYbt++XaEM/Pz8MH36dMTGxqK8vBw///yzogcJCQm44YYb4OPjg99//12T9njkyJEYP368Zh0BL32dMmUK0tLSNOtI9MS2bdswceLEdkkH/srtP4rRoAWnsbFRMW9q3qGhocqtLfHXzBrFDBgGg0HFczAdat++fTFx4kRERUXB398fVVVV8PX1VXU1jEZvWtHs7GwcO3YM27dvR2BgIBISEpCYmIiCggKkpaWp1Li0DtLK1dDQgJqaGo2nhBZuZp+R1mgAGssdiTIFPlkhukuXLpr/SZc8BRSphJDgk1nS4k7mSG8MGRWZHpu0pkmLGpUJKlL8MZvNKjCZjIswCaPRiNDQUMU0qTxJAUQK4xIWRlhIfHy8YsCEZ8nn0WtBwVK6Q6WHBfAyTOmdoZeBW5TB63yGhBfQ+i+TA8j830FBQejSpYtirhaLRbPuXHNCpVpbvdlbqqurUVNTg4qKCk0GmrKyMowcORIDBgxA37590b17d5U2lRAJzkNTUxMOHDiAo0ePKuWYfeE5IryQY5FeGgZCUzgJCAjolJAHwJuZhbEvISEhygLLPV5bW6uUa1q2uWZMA0phrLm5GVarFcHBwYiOjkbfvn0xaNAgJCYmIioqSnk3SAdqa2tRVFSkgivPnTunFECPx4Pg4GBVByU8PBwREREIDw/XKLdkIBJ+1BEsSMIweWYpBHBf8HsKg/KcA21ppSlESsWHQr1euJcGDmlEIQxN0iN+1gvFVLyk4YMWcXmNVOI6yigHtEG2ZPYkeebYWC2e+14q9uwblTQ5ds6ZjD/Re0IIhZRrJb0yUlAnHWVGM84FzyDXXUIymdGMCQYaGhqUwmqz2VBSUoKqqipVYNJmsykIk9lsRnJyMuLj4xEXF4fExESNoYlCZWVlJUpLS5GdnY2zZ8+qFLlRUVEwmUzK2+FwODRxQlLplgpzc3OzMrx1tmYwGPDFF1/A19cXN9xwAwCoOEsKzm+//TaSkpJUNeuVK1eiV69eStl46aWXMGTIEIwbNw5ms1mdLZPJBIfDofYJjaTS271//35s2LABS5YsUX265557cO+992Lw4MGw2+3KA0oFhfvFbrdjw4YNKCkpwd/+9jcAwNq1a1FXV6es7KwGDXgDlu+66y58//33AKDSrns83ixWx44dw6JFi7Bq1SoEBgZqkCTkpUSf/NFnzqnZbIbD4VDnVfa9W7du2L9/P0aPHq2UIMoWpNdmsxlr165FXl4eFixY0G5vBQUFqeyAsk2cOBGffvopUlNT4XA4MH78eKxZswbdunVTHpqOGtdNeviff/55zJ8/Hx6PB5mZme0USDnWhoYGTewq13H+/Pno1auXhv7xeso9cmwWi0VVH9+5c6eKuQS8wfEffvghUlNT1Txxj37wwQeIiorClClTcO7cOTz22GP47LPPYDQacfbsWTz99NOabGcvvPACRo4ciTFjxuDUqVN46623FEzsv9kuRIW4YEUjLS1NMR+61OndIAPio0jgpWDOQxwQEKA0S5nm8uabb0ZqaiosFgvq6+vh7++PuLg4xMfHw2w2q+JE+/btw6+//orQ0FCVTtThcCA4OBgRERFKMSG22mAwwGazKUFXCgK0JoWFhWksZnpvhJ6pSqt0cHCwgr5IvDTvJ9PjPaw/IhkoiSTnjgSAyozMoESIAdBmXbXZbMrTYjKZ1Jo0NjaqIHlpVSN0qK6uDjExMRqliTABenHomgfaMIT0bjGuQwrU0vLp4+OjiVGRioY+yNblcmkEEh5IvWdJYji5fyhcMLiRCm1LSwtKS0vR0tKCkJAQhIaGqnXw8fFBSEiIUvq4XswsxaBuprTNzc1FVFQUCgoKUFJSAn9/f1x99dXo2bMnUlNT1fkoLy9HaWmpKthDYXjVqlUK1iGFPvaf88B9WVZWpqm/IuEkvr6+yMvLu5Bj+5dr0dHRaGhogMFgUHUCpGBFusJz1traqoQmKVzSC1ReXq7OfkpKCoYMGYLExETExMQgPDxcZQTz8fGB3W5XmT7y8/NVQUUq+xT8QkNDERERgYiICISGhqqsVaQpfD+DDrl/efakh0MyK3plJIyHTf8/t9tby0VCOfWKAukCz7eEFsq4Lxlfxb7wHgm7kVnW9EHCMgifCjnrzJDeAlD7l881GAzt5oWeRelVkEoR+8dxSOMFx05FjfEweo+vVLwIP5K0QxrDXC6X8hLK1LekgxTgeX6lws907TJmo6mpSSkU1dXVsFqtqK6uVqmxaXAKDAxEVFQUevbsidjYWERHRyv+xXm22+2oqalBaWkpioqKcPjwYZw+fVpBp1gzBoAytHC9AgICVAAr150QNs55bm7u//os/7ca5w+A4gGffvopQkNDFf5deqL/7HNrayvOnj2LFStW4KeffsKBAwfQs2dPnD9/Hj179sTRo0cBAM8++yyeffZZAFD7WwrMPj4+SEpKwqlTpzB69GgMHjwYjz/+uBIw58yZg+eeew5JSUkarxIAJU/x8zfffAO3241p06YpbwblqOLiYtx9990oLy/HunXrkJ6ejoqKCowYMQKbN29GSkoKFi9ejKysLIwZMwaAV6kaPXq0yqD0/PPPY+LEiRg4cKDqf3p6On7//XdkZmZi2rRpuPXWW5XFfMGCBbjnnnvQp08fZZSJiYlBXl4exo4diwMHDiAyMhLnz5/HlVdeia5du2Lp0qVITExUYwoODkZRURFuuOEG/Pjjj5r1JO0jPdV/7qgZjUbk5eVh6dKleOutt9T/pfHhj+5PS0vDiRMnkJWVhauuugp33nknevToodaRRhU2xtZcfPHF6N27t1pHuf5cpylTpuDtt99GQkKCgulzLKtWrYLJZFKeL7knGZdGmaCjPUYEAA24+u//W+1CVIgLjtGQ1jhaGKmhkcmSyfFgUHCVQiOJJ4U9WuWtVivCwsI0li0S7+DgYHTr1k2Vu8/NzUVLS4uqShkUFKSKotntdpXliUxJpg+lB0JapEhwaDWTSoYe4iAtamSYTU1N6h0Sf8zrpUubwoZkxDKYDIDm+ZI4sk8ksuwnrW9ut1tVm6UyU1tbq7Fq0dpGz1N8fLwmDsJsNmuCx/l/qWzwMFOgl5uNgjz7KyETnA8J15CFtKQSoMfpSwGP76NwQCGGVn/2l1ZIKlZSmTIYDJrq4lw/PWSKFhgKIE6nt/5GYmIi4uLiVJYjWkENBgMsFouyLNFSJq3fnBvuBWktJxabGWfIVFmUkVCiztoo9EpBV7/PJbxF7iueFxk4LeOxWlpalCWZEDapzBF+FxoaqqraSk8lvQaEwMk4Hu4NfZpQMjMqgmzc5xSQJQRSwnL0e4Jj83ja6vKwSYJOA4iEH+k9JtIryv5LZUOv7PNe3iP7JmFf+hTA8szyWVQkOGfy/Xr4FMcr+y5pJmO/+EPjjYQ5Se+xNE7IvnFspMmkMTI43cfHR1Vm5hnlc/TCIdeZyhZ5iKSJch1IhxiM7e/vr6kATsWRa6P3+pDWSw9nS0uLUrT4vexLQECABgZIOElnb4888gh8fHwU1n/58uWa+WptbUVmZiaee+453HzzzZg9ezaSkpKwcOFCfPLJJ/jyyy+xYcMGAN6YizNnzqCqqgpTp05VGf2Ki4tVStucnBz17BUrVuDXX3/Fxx9/DMDrLTly5Ag+//xzXH/99Thz5gwqKys1cRtbt25FcXFxh5BIp9OJ0aNH47777sONN96IpUuXqu+ksNza2opbbrkFx44dQ3NzM2644QZUV1fD7Xbj1KlTuOGGG1BfX4+3335bA8X68MMPsXbtWvX5k08+aSfsl5aW4vrrr8fixYtx7tw5zJs3T333/fff49ixYxqoXW1tLaZNm6Y86/X19bjhhhtw7NgxnDlzBiUlJXC5XHjmmWdQX1+P5cuXY8aMGTh06BBGjhyJBx54ADfeeKMmG+nq1avx6quvYteuXXC5XPjyyy/x9ttvo6KiQq0jY1/cbjfuuOMOlRZ3+fLlmD17NqqqqpCYmIiVK1fi9ttv12RnfOedd7B9+3Z8//33uP7661FUVITvvvsOBw8e1Kzj+++/r5mburo6TJs2DadPn0afPn2U3PH888+joqICy5cvV+u0Z88e3HzzzXA6nXjyySfh8XiUgvrSSy9h6NCh+Pbbb3HTTTehsbER/fr1w9NPP42bb75Zw9dbWlpw9913o1u3bio+RcLQOpsMcMExGvpAQ1r5JU6VTRJ63iMhQVIAJbOtqqpCXV2dsvATTlJXV4fm5maEhISoystM/9bU1KQqfvOHFhv5Iy1fJPDEx0rrmwzak54N+Z20wHKsenywZJSANq+7ZIZyPqWbXwoieg+BHt/MxnulZ4aCk8zdLuNNOEYJN5CwFUIB9IXxJIOX45aYdfap3YYzGtsJNnKvSCy79ATxeok11ys5UoCRcC4qDLRSSuFJxtroITtyvhk75HZ7A+xjY2NVIDiVVfZZb0GmUCittHosN88B+yTHo8fhdzSvnaXJcy/3L9C2tnrFlkK03Ms8R9y3jJ+SMVTc31IAZEE0i8WiCoRK66Y8H5IOAG0CIPvJc0DlFGhTICVNYp8lXl/uOb2ALIVa9kdapbl3ZMwA3y3pjp4eybPW0b6SnlY9rZFCs2ycE9I4Cf+SdEx6L/RxEvId8ixzrHp6TuVcH0uhp4vyeXym/L9+Lvijp28y5kHuBe5N+Q49H5F7VsYm6eMzSGulIqan/zREyL0AQCnHhGSwP76+virInOeE8/VnQamdoUn6ftVVV6GsrAx5eXm45pprNIY+aaSTUGi3243Q0FBMnjwZu3btQn5+vjrTnFe73Y7169dj/fr1GkVDWp7l56amJmzYsAFZWVkIDQ3Fli1bAABjx45FREQENm/eDI/Hg9GjR2PQoEHw9fXFNddcg+joaI0Se+DAARQVFeHqq6/uEE5pNBpRVVWFjRs3wul0YuDAgcjIyMCGDRtUJkrOwcSJE9HY2Ihdu3apZ2RnZ6tsSWwOhwPr169HfX09srOzVeE8ADh37hy2bdsGwFtsLzMzE83NzdiwYYPyoLW0tGDDhg2orq5GUVERfvzxRw2/a21txcaNG1WcCcc6bNgw5WmRRlmgLWOnXEfZtmzZgvz8fPW9/hzqG9epoaEB69evx7BhwxAQEKApEkgD+eTJkxEaGorU1FRccskl2LBhA2pqanD+/Hn88MMPGsOhXMfS0lJs2bJFVUTnHr366qtRWlqKX3/9VdM3zgX7HhERgWuvvbZDj0VmZiYuuugiAMCECROQnp7ebox/1fYfpbeVMQxlZWUICQlRHoKGhgbl/pZWI7rVZSYdaTEjM+3ZsycGDhyIvn37IjExEXV1dWhp8VYjT0tLQ48ePWC1WnH48GF89NFHKCoqUgIb01FGRkYiMTER3bp1U7AeChASQ8wsNyT0zFPOjSZjB2iFoqUoPDxcWYwI/2CAFNA+pRoAjRAirYQdWfOoaPGQMnMIFSYqO1KIqK2tVcHuxHdKxUMyR6fTqYJdnU4nEhISEBoaqtaD75F52CUTp5WdWEUKx8TdA+0FIDYJU+K79J4SfcwKFTIyeumx0HsFZH54u92uGHBDQwPq6+sREBCgAvmYslh6MaRwyusZb8Gc9UzRO378eAwcOBAJCQmIjIxEQEAASkpKFIyNdTHq6uqQn5+Pd955R3mKAC9eldlkWltbERERoTx03KdM5cr5oSLjdnuLOHXGlpSUpDDGND6QTvj5+SmrGdcFaDs39MIRSsXsXzyv0dHRGDx4MLp164bk5GQkJSXBYrGovcP31dTUoKSkBCdPnoTValWB5MxyFhQUhNDQUCQkJCAqKgoWiwUhISEwm82qlgf7SE8iDSM8C7Q6S+8d156fWYCU+1dep9//HK9eQZVCpfSaSGWVZ1p6iaVXlX1mpXp6ivgO6e3hZ+aQB6CJdZK0kQI6aQnnQioX7KP+R0IySUd47l0ulyZwmzxIGr30P/Ls0BvC/tCDyXdIpdVut6sMiYzXkNXqCSHl2pB20HNcXl6OsrIyFQTb0tICi8WisqT16NEDXbt2VVA/QlUpgNTW1qK8vBxWqxX5+fn4+eefcf78eeVZJ2a/sbFReUs8Ho96PmsE2Gw22O32dn3tKGj5r96kgSIgIACFhYWYN28eamtr8e233yI5OVmD7ZcxP7JlZWVh9+7d6N69O0pKSpCZmYnffvsNvXr1QkFBgTJMkF7pP/9R27lzJw4ePIj7778fgLcWxalTp3DHHXcAADZt2oTi4mIsWLAAhYWFmDFjBrZt26bOrL+/P6688kq8//77GvgRABQWFmL+/PlYs2YN/P390dTUhLfffhtpaWm49NJLAXgt8/3798e4ceNw9uxZvPjii6q6d5cuXZTgz0aluiOoEXm8y+VNjfzzzz9j586deOihh9T35MOBgYHKy0qZh/SR75ZeZsCb8tXX1xdTp05VYwfaLPakVfTcUaGgp+5Cmlw3KtrNzc04dOgQ1q9fj3/84x+a6+Pj45Gbm4uxY8di0KBBePzxx9G1a9c/NPBZLBYUFhbiuuuuw7Zt22A2m1FUVIQbb7wRGzduRJcuXVBUVIQ77rhDeZb+aE+OHDkSP/74I1JTUzVB64DXIzJq1CiMGDECOTk5eOONN7BixYoLmoP/f7b/q9ApMilChVh7gkQ7IiJCaZ0WiwWVlZUK00zBlooIBQsKeuHh4aiqqkJpaSmioqLUs4hjdTqdKCgoQJcuXdC/f39cfvnl+Prrr1XGB8Jk+D5uSjJis9msUXqIqfV4PB1mkaLw6nQ6FZEOCwtTKUkl0wagYYwMjqJF1GAwKOJOqBAAjVAhFQE9g5VWGB5maWmlosW54vOp5cvnuVwupRTZ7XaUlJRosP98toRssHgcn895oRAvBREKKLxfDwGTApT0aABtVnoSLvlcMhZp3ZFWPVr6iJOUe1ZiH1ljpLGxUaWmBaBqV7Cv7FNzczOqq6tV0cja2loEBgaqoOSIiAhER0cjIiICdrtdzR8FMsZ51NTUqCJOFIxsNpvG2sv4GgozVCzkOFnjRGZx62zNYDAoPD+tilQuCOuQiriEUEkICI0HJpNJMRxfX1/U1dWhqqpKQaQYTyRTRnfp0gVRUVEqcQTfWVZWpgn8ZQyP3Js80xRUZXpRGlOkYMvzzSBdaV1m4CVhfGTqPG8886RJFLC5N6jISAsu+8r7pRWfiriMo9BDiaRXUu89Id1rbW1FaGioRgGUSgTnjM+T6bz5TL1Vj3A6rntHzIt7gfMqoVBynDLWjtfq4UiyL3JN2KSRQnq5mGiC98uCjISmNjU1weFwKCVFwvGampqUsmoymRAaGqrJXCcFNyqsfBbjRKSH3G63q+JdXHNmaaTxiIoejUsyDX1nb83NzUhNTVV8Ly4uThPk7OPjg5ycHDz55JMK6sR27NgxREVFKfpx8uRJREZGqs89evTAgQMH0L9/f5w7dw5du3bF0aNHMWTIEGRnZ/9hny677LI/9TpPnjwZHo8XRh4XF4evvvoKM2fOxKxZswBABQQnJiaioaEBn3/+OQIDA3Hdddeha9euaG5uxpgxY/D9998jNTUV8+fP16zlgw8+qM5Hnz59NDzx6NGjePPNNzUC6qJFizB16lRN1Wy2nTt3YsuWLXjjjTeQnZ2Nyy67TJNCdtOmTTh+/Dieeuop5OXlYeLEiejWrRvefvttAN5g6J9//hnBwcE4f/48ZsyYgc2bN6v7OWa29957DyEhISqO4Y033kBSUhKuvPJKtY7Z2dnYsmUL0tPTL6hwrVzH6dOnY+bMmejXrx9GjRrVoeeDcmhTUxOOHj2Kzz777E/Xs76+HnFxcWoPOhwOFaMBeJE3ycnJ6rO/vz/y8vJw7733YvXq1Zpn7du3DzExMZo9zPbYY4+pde7fv3+Hff+rtgumNC6XN3MHhR2gDbdLtyF/OgoklLEQFMh5ABgMRSsQhTR6SsrLy1UmCJPJhK5du6J3796Ii4uDv7+/SjVXU1OD/Px81NfXq2dLTZ2Hr6WlRVmsGHRHixU3VENDA+rq6pRlm9AhKgfSOkgvAS1Z0jsiMbVud1uwL4UffQwIGbfUdCWTZLVpCdcgw6PwzABFs9nczqpIBuzj4wOz2axJDcrYDZmzndYyQuX4HJmJhzAAKiES/yzHICEjRqNRU8eC68L9xCYtt9J7wvXUW2kl5IB7ICAgAJGRkZrK0hw3g8elVZPvZ9YYppT08fFRlu6UlBSVqEAGjMrEARJCQ5gOrTMUxrj/CAGktSYoKKhdfyR0pLM2CsekCTJGw+FwwOFwKKWf3ikaKihIk+5I2A73F881lRL92eJZYSwSrcuM36LA2dTUpLxiPA8yPbF8tx7eIt/JPU9LudzfUjGQay29DPJZ8ixIz6MeuiThiVKZANrqTejnRtJiBs/Ld8jny3POedAba6RnAdDCADlW/i2hSuy79OjIOdbPr4SL6b0knOM/wzOzn/KZEoLJxmdzPmQ2ML0HV0LepMIiM3mxaChTtsvq3tLzAkCjqMg4Na4r+RL3LL3bVVVVGvrNcfyZ0NRZ2sMPP4xHH30UgJdX33vvvVi0aJES0O655x4sWbIEbrcb99xzj4IKGQwGvPvuu5gwYQJSU1Px0UcfKeNbUlISPvroI0RGRgLw1paYM2eOEmbLy8sxZ84cFBcXY/LkyXjjjTcAAC+++CJuvfVWhIeH4+uvv0ZCQoJmzz3xxBPKowBAU726oaEBL7zwgnoWALz66qt45ZVX1FhWrlyJV155RV3vcrlw8uRJ3H777YpeyviJ2bNnKys9ZRG2Bx54AD/88AMSExOxevVqREdH49tvv1Vz+eqrr2L69Omavn/11Veorq7GnDlzkJ2djUsuuQTvvPMODAYDnn76aXz00Uew2WyYM2cOcnJysGvXLtx666249dZbcfLkSdWP22+/XdWe8Pf3x6effoq+ffsq4+0nn3yCw4cP48UXX1Tvf/vtt7F06VLNOp45cwa33Xab8lp169YNX3/9NcLDwzV75PXXX8eUKVM06/j999/jkUceAeCNsbj++usRGRmJr7/+GikpKQDaEvKQFnMdnnnmGcydO1c9f/HixaoCOteFjZ8HDx6Mzz//XEMLW1tbMW/ePOzbtw+ZmZn48ssvYTabMW/ePCxZskSjZKSnp+Prr79GaGioZp1feeUVTJo0CZ2lXbBHQ2+FJgMA2rBtciL5WzIKCRHS4+nIiKXVhtASCV8CvDg25o6uqalR72EV1pSUFCXM0XommRvQFkgt4VxUBACo99FyKqEPfA7/53K11VaQrkG321sQjMKwhP+QmVLwkjhlaVGUMATpBeJ8Sqs/x8t3k9nJa1g5loJ+RUWFEtwoTJDJ8b1SwJGMXL+28hpCM6QXQ/adz5Dj4njkPOv3jISL6OeBChQVOVqmKDzJZ/HQSqumFGLp5ZCwOo/Hg6CgIERHRytYAr1psoI554UCmSxSx3HLv41Go4IJcq/IOdF7dDqzoNDR2nG9paIPaHHYUsHS73tauRmQz6xBpB/Smi4x/hT0pMeRZ87hcKChoUGDt6eiIAuXSuu2FOzlfpb7UwqQ8ozItdXfJ88Fz51UciQki/3paM75fP4mDFHSYz7L4/FogvHZ5BzKRjoqFQn5fv3e13s5+R37Jr0aehqgpxFUIGWTCglbR3RFvk+un16RJI0AoIlT0wvukq5Lr4SEl1KppZIh9x77I4PvyV9IY/QeP667VMRlv7iHpQGItLSzNsKfjUYjLr74YnTt2lUFCgNQ8DaPx4N///vfmnuDgoIUfCw4OFizNwlvTU9PR2xsLNasWYPRo0ejtLQUpaWlsNlsygNHDywVRd7v4+PNQJWeno7t27erGAA/Pz+MHz8eBw4cQEBAADIyMvDTTz+p+AkfHx9cfPHFOHLkCAwGAy677DL89NNP2LdvHyIjI9VnBiR/8803ALzYfZPJhN9++02NXQb99+rVC+Hh4dizZ48KgE9JSVFjZYIejiUjIwNjx47FL7/8orwP9MLTKMc4WXqAPR6P+t7pdKKmpgY//fQTPB4P4uLi0KdPH6xduxYDBw5EbGwsTp06BYvFoqEVwcHBOHjwIE6dOoXLL79cZQFj4zqGhoaqTKK9evXC0KFDNeuoX2ebzaaqxp88eVIpP7I4Ju9PSUlB165d8fPPP2P48OGor69X19NYyGYymVSK3IsvvhgHDx5EVVWVZh0JbwS8UD0fHx8cPHgQa9euRb9+/TB06FCYzWYF70pISMDll1+uaqL4+PggKCionfeRY+ss7YJjNGJiYpQ12G63w+VyISQkRFlSJEaY+aPJROlxIGHg9WRObrcbwcHBiImJQWpqKsaPH4/AwEDlIQgLC0NYWJgS1iMiInDy5En89ttv2L17N4qKihAeHq5c60OHDkV6ejqCg4MVIaYFlX2WWFwJFWKO5JKSEjQ2NsLPzw+xsbEaaychG7KOAw+cw+HQWDE9Hi9eNiwsTPVHVsSW8SBkpLLQDK1gnFf2XQpa0gonIV1kJGQ0LpcLdXV1igk1Nzfj1KlTMBi8eaLj4uLU2tBqR4bLVI4UrjlGxpPwf3IshGlw3Z1OpyYVJfeHnrl3pNRKoYG/JcSEgdp8f2trK6xWq1LUAgMD0draqvFeSQWQQhfx2G63G5WVlSgsLERZWZna84MHD8bgwYPRr18/xMbGqne53W4Fk6Bi7OPjg1OnTmH//v04ffq0gneZTCbNnjMajcpTSEGN+9NgMCglSc6F1Wr93534/3JLSEjQeLEkjEoPg6EVmNZeKeRTWJOMil66pKQkpKamIjMzE7GxsTCZTAoySWgKIWpFRUUoKipCYWEhCgoKUFNTA7vdjubmZvTo0QMpKSkIDQ1VtToiIyNVliDGQ3FfEd5CrwrQZtDw9fVV8SJkGjKDld4DCLRZn2m4oXBFOJI0XsimV9wJq+E8d6RUUEDVnzG+n+eL/ZIGGpkpzd/fX5Plj14RGegshV290Ynv1HuJ+F7eR8FcPpP0mWvLJoX4jowe5F0Ukjwej6IT5EHSiECvLtdPxhq53W5V94L1d4qKilTGOn9/f7U/U1NT0atXL0RGRqrnsVaQ3W5HfX09KisrUVFRgXPnziE7OxuHDx/W4OD9/f0V76HhhHuLxhYqXS6XC6GhoQDavC5FRUXt9t1fvcn9HhAQgNLSUtx5551KmJTX+fr6Kjqr/8xGuUYqjM888wyuvfZa9OvXDydPnsSqVavwxRdf4OTJk+jfv/+fQqcA4K677sITTzyBlJQUpUyHhISoIO+EhAS8/PLLiI+PV/0JCgpCSUkJZs6cCaPRiM8++0xBwSZMmIA1a9YgPj4er7/+uqbg4Lvvvou0tDRcfPHFHY7l5ZdfxujRozFs2DD1Pc+Q0+nEkiVLMHXqVPTt2xeAF0o1e/Zs9OzZUxkb4uLicPbsWQwbNgzHjh1T9Hjnzp04duwYFi9ejIKCAlx22WVIS0vDiy++iISEBLS2tmL69Ol4/fXXER8fjzVr1qChoQEzZswAAA2/Zt8uvvhifPfddypWV79uw4cPx/bt25GSkoLHHnsMw4YNw4gRI9qtgZwLIk/kd9K4yu/uu+8+3H///ejWrRt2796NAwcO4N5779VAUeX1gDdUoLi4GFOmTMHWrVs160jFzs/PD59//jm6dOmCa665BoA3+5dcRwAYNWoUtmzZguTkZFRWVv7pHv2rtAtRIS5Y0SCzJVElsSMTYq50Lhzd8STMFBj0MQMej0cFHrJg1iWXXILw8HDFRAMCAlBeXo6GhgYEBARgypQpcLu9FUHXrFmD3NxcFXdBwj9gwABVfCs4OFhBNAj7IV5Vn+UH8KYz40ZiQSZ+19raqiygZJgyEwit0zKDAcdgNptVkTJuGBngTWYlYUDE1lKZkFZeX19f5dqjtYqQLM4/lT9C3wgr4/vr6uqU0MS54brabDalkZtMJgQHB2sw8mTI3EJcd1r7gLbMERT2KQTx8FOol8oF95BMY0rPDNeN1lM+k/tI7i2OmcK/n5+fcmlS4OS1tFZT0fB4PNi3b5/yZjQ2NiIlJQVDhw7FwIED0a9fP4SEhCiPRk1NDfLy8toJyidPnsTBgwdRUlKi9pn06Mh4A35mjBG9crSISGGouLj4Qo7tX64x1omVmGXtFa41/+7SpYvycOqhhPyR8RdUHlNTU5Geno6MjAwkJycr2kUhi3vYZDKhsrISBQUFyM3NRWFhofJOEe4WFxcHi8UCi8WiyTTGgHGuB5Ui/maKXXkuuVe5t0lDOvJkEo7EuaDATmMK6QoFbRlL0pE3VCoa0isoz4v0uOg9laQxvJ7eJyoipK8SCiVrY1BJkjEcEmaoZ6DsAw0ApLWcG5mkgtfze2nk4fxxrHqPE9dHKj1cUxqipEdAD51i/wCvUtnY2IjKykqlaNBYQWUzLCxMKRq0nrIoJHkHIZu1tbWoqKhAcXExzp07hzNnziA3N1ftC19fX5hMJjVezpM0BLFfvr6+KrGIjMk5f/78/8cT/f++6RXrkJAQDRSbbdSoUfj+++/RvXt31NTUYOjQodi4cSN69eqFiooK9awTJ05g5cqVGngTFT+bzab4ntPpRHBwsPJq/Fmjwl1fX48ff/wRp0+fxn333QeLxYIvv/wSVqsV9913n6ZKNsdC6LnZbFbwIHpgya8NBoO6jmeroaEBOTk5ePbZZ/HRRx9pxuLr6wu73Y7jx4/jnXfewZYtW7Bv3z5kZWXBarWqsQJQyTZsNhv27NmDrVu3YvHixbBYLLDb7bjjjjvwwAMPoFevXjCbzYoH8nvyZY6N8Uh1dXUqXpaGmE8//RR+fn649dZbcebMGdx7771Yt26dZuz6daSVv76+XsFeOyo8eezYMXz44YfYsGEDfvvtNwwcOBDnzp1Dt27dcOjQIQwbNgyTJk3Cbbfdhn79+qmxs/4M4yqDgoKQnZ2NSZMmoWfPnnjqqafQvXt3DU/iuvHMyc9dunTB2bNn8eCDD2LdunVq3fTrCEAzNum5PHXqFJYvX65iX/5K7UJUiAuO0WAAqgy+lrhemc+eRJnMikKltNBJoZK/HQ6HKqhFDwgJJwsb9e/fX7lFk5KSkJWV1a5gUnl5OYqKilBdXQ0ASvAnA2FmIOIkZf+JB6eAzTgHPl8KNnSLSygAGSSvB9qKWFHJktAZCY+RbnD+MG5CarMUABobG1VaYArI0iJL5sZ5JDOTHgsGINLqJ70f/v7+CAwM1PSd60fhSmZm4dzZ7XbVJ+mZkGlHpeteQsckE+f8UsmgEEhGTKgdrcjE0HMu2X8WYpMQDkIQOB/0NHC8DHQH2gS3pKQkhIeHK5clLdkOhwOVlZWa/U5iymwvXFtpQZXKJvcZ54PX8UzJedcz1M7U6KKWQfMyaw4Js0wdTOGa9QekEEwhkXulubkZNpsNVVVVqK2t1ewLwDvXwcHBCAsLU14Oi8WC8PBw5fHgeeC+ZpwVlQh6LmQ8iTQCyPMrvRVUZmWskh6uA7QJxBKGI+ND9E0mFZAwMWlll8oE38H3yPSrUmCnoC9/k8bxjHFfy3dLb6uEcsrP+vdTqCcNkT9ScdArN3JsVA4kjaWnRgrj+rOkT40s42M4TtIHCXXSe124TtLTQ8++nA8Ga0srJX9k4Dk9RXa7XRNYLoVc9lEqYHJ87KNcZ/KCzgS90Lf77rsPCxYsAKA1DPr4+OCtt97CqFGjcObMGdx///1KkMvLy8N9990Hm82GW265BU899RQ8Hg+efPJJbN++HUlJSfj4448RGRmJpqYmBAUF4ZNPPlFJOtxub0HX5557DjfccAMiIiLw8ccfIykpSdO3JUuWYMaMGUrQXr58Ob744gt4PB7U1dVh+fLleP/99+F0OvHBBx8gMzMTQ4cOxTvvvKPg2v369cPLL7+s4TUUvG+99VYVGwBA0SSPx4PHHntMQbEMBgNee+01jB07VgniS5YswbZt21BaWor58+ejsrISV155JZ544gn1vIkTJ2LJ/6l6vnTpUqxdu1b13eVyYceOHXj88cfh+f9x99/xUdZZ+zh+TTLpk2TSCykkIRB66IgooIgFC1jBXSui7rOC67oWFtRde8EGumLva0FB7DRBinQILQkQ0nubmfQ2M78/5rkO5x7iLvt8n8/nY37v1ysvmHbf97ud9znXuc45brdQi/XnEyZMwDPPPCNyj0l1AMhZzfb222/j9ddfR3d3N+6//37s378fPT2eDHi9zeMtt9yCv/71r3A4HKIDsW8vvfQSLrroIrn2I488gvXr16O6uhoLFiwQ47Kurg4LFixAdXW1GFFsBKu4Dtrb29Hc3Iy7774bRUVF2LFjBx588EG4XC4sWrQId9xxh6xBHQvjcDiwYMEC/OlPf0J3dzf+8pe/4JdffkFGRgbefvttBAYGoq2tDcnJyXj33XcREhKC2267DQ8++CCamprwyiuvYNq0aQA8snLJkiX4+eefkZqaivfff/+0eJTfejvjGA2iUFrQA8Yqq96Hpnbde7vzvTmp/v7+Qj9gNim27u5uREZGIjY2FnFxcYKWR0dHIzMzE6GhoYKM8s/hcEgNDir8RHp0QLFGykh5oYKg62xo40CjXz09PQZETFPE+J4O2uSBo+kDvD/Hgq95OOjfAqdSnVKp4j01JYDxGlrpoGKg76ODEPXzeAetU4Hm4aw560TIOOf6Ob37ymfxVhTZ9Njxflxzui4B+Yua4uF2uw390dx+vX59fX1l3qiM8L5BQUHo6OhAY2Oj8DFJAYuLi0NQUBC6ujzFIrUi6B1wSiFMRZVNU0o0XYzGjTY4OX7cN1pB7atNxwB5UwyBUzQXTbnjmvXz8zPEHHFdaZlDrxeD+HXsjNPpFG8K10NQUJAYHsHBwfJdpoHkOqfSRy+bpi9ppFyvf90HykYABtnI1/pfXk8r1pSR2vvHNUIFUxvvek/x+957nM+kEzTo73jLcK3QUs4AOO1ffS8tN3Ufdb90nR/uRV5PAyV8xt7GTXtqtJGlx7k3o0sr+NoY0GtK71mdsUnvf44j+0K5SAqX9kqR6609RtrzrD3t2sBlUhENkHg3joXea9qo0wZfb3S9vtKio6MFvJw0aRKOHTsGp9OJ7OxspKWlwWKxoKamBh999JH8pq6uTl5HRUUhISEBACTWISMjA/379zfIm9TUVJjNZqSmpiImJgZ79+5FYmIiioqKYDab0b9//9MMtoSEBEN63erqagPiXl1dLYViU1NTJaYiJSVF1nRQUBBSUlLg4+ODrKwsmM1mHDlyBICnUrVOqQ946DsjRozA6tWrkZqaiuzsbBw8eBBJSUmSHGbMmDH49ttv0dnZieDgYEmTbLVakZiYKNcKDw9Hv379AADffPMNkpKSMGHCBOzatQuAJ8uSN+XOz89PaFWhoaFISUmRz2JiYpCZmYkdO3bA7XYjOjoagwYNwo4dO7B582YAnnVbWloqRiGBPe95jImJEcoy4AmWtlgsyMnJkXTm7Ov3338vYPKHH34IADKPfO1wOCSV7b59+9Da2gpfX1+ZF8ATvM/7+/r6orS0FG63pzq6BhuGDRuGnp4eodXFxsaKrPnkk09kbZBOB3gAfL6Ojo5GfHy89D00NFSuTVrggAED0L9//z63d8+YOpWUlGQ42DV67J1WlIqdViR4EFJI0kNCARgRESHKWlxcHGbOnCnCpK2tDVarFSkpKejfv7/ch7EUK1aswIkTJ+ByuWCxWKTqb2RkJAYPHozMzMzTFGCtRLBPzEYVFhYmyCazZelMOOSCkzeo0WmiRRTmPT09BqSU36EyqelHRP01Ks6DyRspZDFD5lqmgDKbzeJK5bhr2gJRdk3j0ehcUFCQoPr6gOfBS5eepk/wgGf6Xx5oPj6nctQTSdYbhAo/FSbGeFAJ1Yhne3u75KHXHiD2s6urS9YV76G51Fx/XJN8dh7qRDLdbrdkpxg6dCgcDocUJbruuuvE8PT19cXo0aPFw1FRUYHy8nJRlJxOJw4cOIDDhw8jPz9fFFc93kSCuGY0KqeVRvZH15/oq9Sp2NhY2VM0AqgwcN1YLBbxKnAdUG5owy02NlbGhEhxc3MzAgMDERkZiZEjR2L48OGIj49HVFSUHND0lHA9NzY2oqamBgcOHEB1dbV4s3hf1ttJSEhAcHAwgoODpX4ODRbKDi1X+Pwafaf3lHV+uBZ8fX0leJXrn33je/Q+0lAiys4Dkd/T+4Hv0wgHYNhbWqnlPACnKEp8FgY9ci22tbUZPAbaoHC5TtWAoawjgk6Zp5Nn6Gxt2ujXVDFNWdKUMQAyzpTDnBNtkOngak1v1cBTbwY85RGfQ9cLATznBlHa9vZ2OBwO1NXVwW63S/Ey9iU4OBhxcXEYPnw4oqOjERYWhoiICInRoFe/sbFR5E5FRQWOHz+OiooKlJWVoby8XCjAjBHi2GkDmc/e0NAgc0kPigYxioqK/o/s8/+TTXvG/P39UVdXh3nz5sFms2HNmjVISEgwVJAGjEbwr11TG2nerx9++GFcf/31yMrKMvxOU+f0a22wbt26Fbm5uYJ+s0r4TTfddNrve3vWzz//HEFBQVLAj2tbt8mTJ2Pjxo3o168fHn30UWRnZ2PSpEnyeXZ2Nvbs2YO0tDRUVFRg6NChyMnJwYABA1BcXNxrX9juvfde3HXXXUhLSwMALFiwAPfff7/BMIqMjERFRQXOP/98Q3FAAJgzZw5ee+01xMXFoaurC7Nnz8b777+PuLg4A6ukpqYG8+fPN6R89R4L79fLli3DhAkTMGHCBPnNiBEjsH//fmRkZEg9lN7mkbIoOTkZhYWFGD16tGTFYtPzCAC33HILnn76aSQmJp42zz/88APsdjvmzp3b6zz11hfNbPlXa9T7er+2Dv5ftDN5hjM2NOgeJIWBG5kbksGvPEh5eFGAa/ej1WqVw8bt9gRJ0c3IyZ85cyYGDBiA2NhYBAQEwGaziQIxbNgwhISEoLOzEzabDRs3bsTKlStRW1sLX19fJCQkoKqqCiaTCdHR0bjkkksQGhoqsQsMPCIi5evrC4fDIW57Ik48yHkw8bAid4/oEGsoUDkiWkHEnHxNnQ2Ci4objQe6j4+PoZYEf68VTy5snVKY06gRAW4CKkfk8xLp5RzxHvyOVji8qR+McSCCR5et9lxoz5JWDKKiogwIAI0LLdw0dYxjSM8NC9pRsdRKGBUM3oubmEH7jIXR3g5eVxvNzc3NqKysFDdlZWUlWlpaMGTIEMydOxeJiYmSrcPt9mTaYBpkKpc+Pj6oqanBxo0bUVlZiaamJvk+x4kFhLSByjHV8050rbm5WQRmT0/PacV8+kobPHiwyAWdFQ04tfc1guyNsjPQ1+l0Sp0LVq8HgPr6evj6+iI0NBTDhw/H0KFDkZSUhMTERCQnJws9jsa0plqRB19fXy/piun5CgoKQmpqqmSqslqtYhABECPROyZIe2xoeNNTqI0M7YHU1FSNRutYKl6HgAABDeCUh8cb8WYiB653zeXX9YEAYyZBvUb5jFTO6QXW8oaUTE0B1EkgeD3OJVODU54yQYD2Imjgg4Y5+0o5zj7xGdg4hnxWUpPYf62s8xl02mB6rrSXV3tDCN7wr6GhAXV1dWhoaEBtba0YpMHBwUhJScGIESMQExMDq9WK8PBwg2ejq6sLDQ0NsNlssNlsKCsrw5EjR1BZWSnF/7TX1tug5NhyTvU5xHtomlVfDQZ///33YTabMW/ePDE0Vq9ejfDwcDQ0NOCVV15Bv379MGvWLAAe+tLAgQMxc+bMXq/56KOP4txzz8XU/668vHjxYlxyySU4++yzAUDOEZvNJr9JSkrC/v37MW3aNBw9ehTx8fE4dOgQZsyYgVGjRuG+++7DsGHDEBYWJmcl4PEYcJ7y8/Pxu9/9TpB9wIO+u91uqTFBumlHRwfy8/Nx7733YvXq1YbnN5vNsFqtUkPIbDYLQAZ49lJERAQaGxvx0EMP4aKLLsJll12GxsZGuFwuZGRkYOfOnZg4cSJOnjxpuDblJanopK43Njbixx9/xJEjR3DfffchKioKdrvdwEYBIOnaeWYxFX9DQ4PM4+9+9ztERUUZQFDAk6L2X80j08D/Wl8XLVr0q/O4detWrF27Fk8++SQiIyNhs9lOM7RuueUWmUeXyyU6HPty9dVX45lnnkFWVhaCg4NFJuTn5+O2227D2rVr5VrvvPMOgoKCMHfuXACe1L1XXnklenp6MHjwYDz66KNIS0vDZZddZngGk8mEQ4cOYdmyZXjzzTcBADk5OXjzzTfx6quv4v91OxMT4oypU95eAO3q7ezsFHSFwpIPwEPf5XKJgNacY5PJJIc5m5+fH2praxEVFSXcaeCUq59Uhvr6elRUVCAkJARDhw5FUFAQqqqq4HK5kJKSIsGZRUVFSEtLE54+PQ58RlJcmC43ISHB4KXQ2ZfoDtdKrq6MCxjpAZoWwn91EKgeX404ErHnGGtFnpuB6Kavr68ob6RX8NmJyFGp1dY5AFFyNK2Lz6P7rFMMa4VDu415H/aZHiHtUdCeL46DNhp0MLCmXGkji+Oo44H0QUpFh3PldDplnLTSoeubcGwYX8IEBt3dnsKD8fHxQt0LCgpCeXm5gU+uDTC3242KigrU1NQYarroAHnuA2+6B9eMVvjoReM6oCLWVxsVbB28T+WQihOVPyrXWmnX1Drt9aFCy+ZwOGCz2RAeHi4KJpVtb6qQj48PwsPDERkZaVBEGXjb09MDm81mUEQ5n5rOA/SOjnLdUVHmWteovKZ/edOWtFdRyxS9l/V32TR9UssFxmHxj8/kfW+9N/TzcAyprAPo1dvA/vP5tOHCfrDfwKlsdTwXKDcoL/nXmwdC90+fKZRhmial9x/n2ZvCRA+Kph9xTHk/7s/Ozk4xnPh/7e2mZ1kX5qMnRlPrSJeiAdbY2GgwenWWP46LHmfuH210Ef3UqZ61TO2L7eOPPxZP1cKFC7F3715kZmbij3/8I/7yl7/giy++kCQaALB69WoD3WjOnDlIS0vDU089BQD4/vvvDWj2+vXrceLECXnd1taGkJAQrFixAn//+99RVVUFu92OJUuWoKqqCoBnnUVHR8NsNmPXrl14+umn4XK5cPvtt6OqqspA15kwYQJuuukmPPbYY5g5cyYyMzNFidR0LwC4/vrr4e/vj1dffRWPPfaYPKePjw+WLl2Kzz//HDt37pQsRb0FRicmJmLx4sVYtGgRfvzxR9jtdjz++ONYsmQJJk2ahPPPPx9LlizBwoULsXHjRkNKYJ5xgMcAO3HiBD7//HMAwIoVK1BfXw+3223IkqQbEwctX74cK1aswNGjR0VR5zwCQENDA+bPn4+goCAsW7YMAHqdxzFjxuC1117D/fffj+bmZqSkpODpp5/Ggw8+CLvdjvj4eDz00ENYvHhxr/Oo65MUFxfD5XLJs0+fPh3Tp0+XuiJ6HgFgzJgxuPLKK3HffffB5XIhJycHjz32GJxOJ2666Sa0t7fj3Xffxd/+9jehUPn5+eH555/HsWPHDGvs888/x/79++Fyeei+q1atQlhYGEwmE5577jl89dVX2LZtG9xuN5555hkMGjQIixcvxhNPPIHnnntOqHR9of2P6mh4GxoUlhoR0geN2WwWIUchSGOEh4Dhocxm1NTUIDY2FklJSQCM9S+IoDkcDtTX1yMwMBAZGRno6upCXV0dOjs7ER8fL3z6srIyWK1WA92GByuDw7XSqGtwaEWA48DKzXyesLAww0GsUXsqE5qSAMCAHhLt5O84nvw+DzqNfGlUioosaR4areS/VFR1DQBej2Pi7TbmfNEwpDGjv6MVBM6rpjfRANN91X1h0+ult3Xn7SbUhgn7QGOKaK5WyKig6zWrM2bxWqQsMM0x4Mn+ERsbKzQVX19fiQPQni5teFdXV4uXo7ex5NrxVhjYL62cUeHmWtJobV9seq2ziCT3Jr2NNDS4VvT+0QHIen653ji/drtdkhLwgCFlikoWFWN6mZjBhulItXLc1NQkBgapQG6327C+eS09h1TstKdNywo+j7dy7r0/9B7Q3+W9tEKsFUkNWLAv2nDxNiS0UaIBFX0/bXToZ+zNyNCKLZVzAIZn0X/ewArHSBtD+szQ/XW5jDFyeq1prwcNLW0oaNCCBgbHTe9P73vSM6eDuHVNJa6R3oLJteFG2cA/Zglk0gHKd+0FJtDDMfGmV2gZTbnI8enLbd26dbBarRg1ahTef/99uFwuTJgwASNGjICvryeblgbBtmzZAovFgrFjxyInJwdJSUkYNGiQfL5z504EBQVh/PjxyMnJQWVl5WnVmQMCAjBq1CiMGzcO+/fvR3l5Od544w35vKurC7t370ZLSwvy8/Ol/kJmZuZpnPqIiAgMGjQICxcuxAsvvGCghHt7FPr37y8G5qFDh8SrYjKZMHz4cPz000+wWCzIyspCTk6OYW9kZ2ejqqoKQUFBGDVqFPz8/LBr1y7U1dWJAZOQkIDExETcfffdWLlypcSu9NYGDRqEtrY2+Pn5YeTIkVi/fj1aW1thNpuRnZ0tNOH09HQAHo8NPfrZ2dkSD5eRkYH9+/cbqoSzr0lJSRg7diwOHDjQ6zw2Njbi2muvlXMwODgY2dnZ8pp99ff3x+7du3Ho0CGMHz8eBw8elLMXgBhL/v7+yM7OxuHDhxETE4OhQ4fKd3TdDcAT2zNq1CiMHz8eR48eRUFBAYqKijB69GiMGTNG6NMHDx6UOB0fHx+MHDkSq1evlpoqAFBcXIyKigoxSOjV8vHxwfDhw7F9+3b57kcffYR77rkHo0aNAuAx0PpSO2PqVGxsrAFRJzIIQJRdIighISGCNtPFTquUt9PUGH9/fzQ2NgoNgsJ93LhxmDx5MpKTkwVdNpk8RVVSUlLQ1taG6upqVFdXw9fXFwcPHsT69evR2NiIwYMHiyclNzcXw4YNQ0pKCvr16ydp1kj5YrYEAMKVJoUIgCgUGgkHjBW59SGnU67qWAgaBkwTrOk8MiFKWWJ/SUUjAk9ll98ntYsUKh6eTLXodntqc9D40rSVlpYW4Yu7XC6plUFvgy4UxefRwaN0lerDm4pzeHg4QkJCREHneGpvBSu+c2zII9bjxnz2NpvtNGNLF7CiACY6rmt/BAQEICwsTMaHFDKuSWav2Lt3L8rLyxEVFYXi4mJYLBakpKTgsssuQ0ZGhlDw2traYLfbJesGjdPu7m40NDTghx9+QGVlpSgpzGTS1NQEh8Mh65zPzuI/VCZIQezp8RSPCwoKQmtrq3jQKisrz3iT/5ZaVlaWQeELDw8XJY+ZzFjIqaWlRRQorlnWoiGFk3uZ65qxDQzgHzZsGAYNGiSBnnFxcRIcyboWXV1daG5uRl1dHWw2G6qrq1FWVibxGnw2l8uF8PBwWK1WSUnKe9PrApxSaIm48tm1kkdFTyt9OoaBgIo2ToiKk7rAcfCmLAIw7EOtoHOfagWXAIK+l6a0cZ1qeQTAkBTC6XQaakvwX52AgmOj55PUNdap4eekLFE518YdFWZvqhl/rwP+2X8GU+sseQxAdzqd8ppeFFLr/Pz8JFEA5QifgTKXcqClpUVqaFRXV8u5EhkZifj4eMTHxyM9PR2JiYkiG3m26Di0xsZGNDQ0oLq6GgcPHkR1dbWhvg+byWQSOcJEKszSRXqZrnFFQIoxiAD+bT2I32Kj4TRz5kypbk1QiGv1/fffR0pKCqZNmybvTZo0CVu3bkViYiJqamoM3weAkSNHYv/+/UhPT8ftt9+OWbNmYejQoacZ/YcPH8Y333yDv/71r6ddw/s5vd/X7/H/GqwDgDVr1sDlcmH27NmG3wYGBqK6uhp33nknPv30U8M1Jk+ejE2bNiEhIUHQeZPJhPLycjz77LNYtmxZr8/4P3leAIiPj0d5eTnOPvts7Nq1C9HR0aiqqsK0adOQmZmJt99+GwBwwQUXYOPGjYZrzp49Gx988AFiY2PFU6KvPWPGDHz99deIi4vDsmXLTpvHM3lu/X5WVhZyc3MxcOBAFBQUGMbd7XYjJSUFRUVFGDVqlHgcfm2e3G43IiMjUV1djRkzZmDz5s0IDw9HTU0NLr/8cqxbtw4WiwW1tbW47rrr8M033/QKGrndbnz88ceIi4vD9OnT5X3gdCrSr/3+t9LO5FnOGNbQhwvTv1Lp7OjoEB6r0+mUnNY8HL0HxuVyiReASgErLbvdbglebmhowIkTJ4RrSuXLYrGIIpuRkYHzzz8fw4YNw+jRozF58mR0d3fj2LFjqKqqQnh4OEaOHImmpiaxIGlctLS0wGTyVPOMjIxEVFQUoqOj5TBhelQdfBcQECA58gGI4kPkiIqyNrJ4ePJAZm0Afp9KMek9AIQGogMqidozWFBnJuEBS8WFBpyuf9Ld3W1QjLXBob0onFO675l2kmll6UHo6elBaGioKG4cK/KYNW2DSpe354Njp2kHGpmjctDR0SEKjF5THC8qZVTm6+vr5b4AJPUvUXIqO9ogLi8vl2B8KgmxsbEYMmQIsrKyYLVahbLX1NRkMOio7NTU1KCgoABlZWWCcFIJYRY0Gqda8aQnTivKOsZJe1+8PYB9qXG9ccy5l6j86uxzOqUrFU+uI6ay9fPzQ2dnp4ytljtNTU2isNHDxMBj7SXkPklMTBQZEBsbK1QLggjd3d1S24ABv7ymjjfx9T2VLILrnYq6phyxzzp2g94EHZ/B/aaDkPlc+gBk055bHSehaz9QNnmj79pbQLnDPeZNFaKHzTuBhE7SoNNTa3lD4IjPresSaY8IFWvKQe3p0N4/ng+MBaGHgPKXz8Xxo0Gn5QtjLXR1eQIa2qjRmaW8M+ERvGEGOR30HRMTg9jYWDnr2D+eqYxVqaurQ2VlJcrKyuSvtrZWUpdzPJ1OJ+rr6yUuLzw8HBaLReaI/Q4NDUV0dDQSEhIkPpL7pC+3DRs2ICMjQ/o/ZcoUFBUVISwsDHfffTeuvvpq+Pr64tChQ8KL123EiBEoLS1FfHw8HnnkEbz88stITk5GeXk5nn76aUz973iNbdu2SSpdADjvvPOEcgV4vCs6RSrgSUGbk5Nj2K8REREoKSnBpEmTMHv2bOTl5cHPz1PITdfwuOWWWzBv3jzD9aZNm4b8/HyMHTsWa9asAeDREY4ePYrrrrsOu3fvRkpKisRRAJ79MXr0aKSkpODHH3/8l2OZnp6O8vJy8USwbdy4EUuWLEFiYiLKy8sxbNgwAEBtbS2Sk5Oxf/9+AEBjYyNSUlKwaNEiTJgwAUlJSUhKSsLWrVtPu9cPP/yAgQMHipExffp0nDx5UijyP//8M9LS0tDU1PRv5/GJJ57A999/32ufdu7ciQULFqCgoABJSUkoKirCPffcI8HqmzZtwqJFi1BRUYHk5GTxXMTHx6O0tBQjR440zONXX32FpUuXwmazITU1Fb/88gsATxautLQ0/PzzzwA84GlGRgbWrVuHGTNmoKCgQOjO06ZNQ2FhISwWC+666y5cd9118rzPP/88Vq1aZehDdnY2SktLERsbC8CT2aq8vNyQJawvtDM2NDQ/OigoyHBQM2BXZzPRQY46JSQRGB3E6X3A8h4OhwMFBQWoq6szBAh5H/Kkp1gsFsTHx6Nfv35wu92iIIeEhIiSUF1dLcYQYy0Y9G2xWIQ+ob0GgJECwOejMsFDTStAdKH3Rj3gtTR9gQemPiSplBCN48FMvq2uWEvFjUgWFX3tndAxATxs9LwRWaOiAsBQh0DHkWj00ZtCoY1CXkcf+sApShSNFwaocxy4dngdrhH9p40OKqBUAviM+vl0al7dZyryrABOZSEwMFDS6dG45RgxnkcXz+rp6UFjYyMqKysNSp7JZDIEuRFhZT+pmHKu6CGk4gxAeN404vpq4z6g4cDGOdR7AjgV56VlBVF9TYOhoarpNz4+PuIJa2xsFFlBRZnKrqa3BAUFwWKxSJpE1vEAILKru7sbTU1NQrnkngdgyCSlAQQ+t3eqam+EX9NiNPVKyx8dm6KpRrppI0WPvZbbvV1bx1DwXvp3mkqmP9cUNO2t8c4oxmv3Rpnyfk7tUeH72rDQ46M9OdxXen9xL5Iiqve+ph1pmpEGMthvAka69gvnX3u+uFcJGNEzo9cAx4VyS1cjr6urE+NYx3hw/9ML63KdSvvNM0LXhfL39xfDTtcGohHYF9uLL76IYcOGISMjA3/+85/Fk1hUVITnn39evExWqxVLly7FO++8g0OHDqGoqAh//vOfxZNdXV2NpUuXorm5GZs2bcKbb76JyspKqTbNugtvvvkmduzYIfevq6uTawDAe++9h02bNiE8PBwvvvgikpOTceDAAbz66quG/dfe3o6lS5eitLQUeXl5ePnll+F0OrFy5UqsWbMG/v7+eO6554TK9OyzzwrAy76VlJSIgu5yubBs2TIcPnwYXV1dqK6uxt///ndMnDhR7llTU4Ovv/7aUMDv8ssvxwMPPGAY08bGRjz33HNobGzEjBkz8NBDDwHwBDBv3rwZTU1NePbZZ8UTFBISgvvuu08UXpfLhaqqKrz33nv48ssvUVlZicrKSsybNw833HADAgIC8NxzzyErKwsdHR2or6/Hs88+i2HDhqGgoADLly/HY489huzsbHR2dqKqqgputxt2ux0NDQ1wuVx45ZVXTssKtWHDBnzwwQcAPDVCpkyZgsTERLz44otYuXIlduzYgZ6eHmEX7NixQ2Jh3n77bWzZsgVOpxOVlZW45557cMUVV6C5uRlLly5FdXW1YR4//vhj/Pjjj3C73aiqqsL8+fPxu9/9Tl53dnYiOzsbTz31lACHBQUFePHFF+UcLyoqwgsvvCCJjCIiIvD8888jKCgIP/zwA3bu3Innn39ejK6qqiosXbpUjOmamho8++yzpxV6/K23/yhGAzgVtOzNf6fiptE1CmkdTM1rUYhTWdQVokk3aW9vR21tLWprawXN9PHxkQxUugig2WxGeHi4VF11OByCYlssFlE07XY74uLi5F5UYiiY6VGhEqMPcd0vXfBIK+/6EOZ4cYzYNDKpOdH6Pvw9jS/Np+Y929vbZVzYWPhNK+y8nkb3iJzrgEWNZvKaNGRofNANz3HRiiF/q9NRagVAKxMAhCqjvRgWi8VAM+Nv/f390draKkoF/4gIUwHkOLKPpBHo9Lbe1BDSN5qammRMOjo6kJCQgKioKISHhxuMThpIDIwngtve3g6bzYaGhgaDYgNAPDJUeugF5PgEBQUJyujj4yPeNl7D6XTKXunLMRqcHypg3Dv8V+8DKoXsMxUnrm96ebTizXmm4cA00NrQoELn7SWg8UNaFSmUNAB0GlV60GgAce64HvTe0x4G733G5waM9TW0Mu39x3EEjKi+t6vfG+TQsslbRngr9oyL0E3LOH0PfU/9Pe1B0HFk3mCDt0Glx0MbEey3Dmj2vr8eO02v0n9aPlPZ51gQoNGeIN5XB27r4O/eAsC194geHxoZ2khyOp2GNalponV1dWhqajKck1zXnEuevZSD3d3d4hlnH3QMmTae+qpn9LzzzsPXX38Np9OJKVOmSAxoaWkpXnnlFQwZMgSVlZWwWq2YMmUKpk2bJnz5l19+GQDQr18/BAUFyestW7bI9c866ywDNZVKur+/P7KysnDs2DHxpg4ePBhffvklOjo6EBsbi2nTpuGtt97CoUOHRCkeMGAAOjo6UFVVhc2bN8Nms6G8vFxoa8wgFRwcjClTpuDbb7+FyWTClClTZO0XFxfjlVdewdChQ1FeXg673Q6Xy4WtW7dKMLrJZMI555yDgwcPGsbr559/RlBQEEaMGIG8vDxkZGRg/Pjx8nlcXBysViteeuklDBo0CBMnThRj5aOPPkJMTAySkpJkrDgW06ZNw9atW9HT0yPp1nVqWsCDyNtsNpjNZkydOhVfffUVAI9snzJlCvLy8rBp0ya8+uqr+Pnnn8XzYjKZZB67urqQkZGBt956CwkJCRgwYAAKCgoAwBDzcPbZZ+PkyZNC4brkkktQWVmJgIAADBo0CPn5+di5cyd27twJwJPdKyYmBllZWcjPz8e4cePgdrvx3XffYdOmTcJaYED2559/jsjISAwdOhRHjx7FqFGjxPBii42NNcxbYWEhXn31VQwdOhRlZWUoLi7G8uXL5fuRkZGYOnUq/Pz8sH79etTV1eGdd96RM76mpgYvv/wyhgwZgtraWtTV1eHll19GVlaWZLfrC+2MYzQyMzMNaBsAURABD+9Tu9ZNJpN4Behu1kg6U4GZTCaEhYWhublZFFmi0larFbGxsUhNTcWYMWMQFhYm2WdYL8FsNiMhIUEK7TgcDnz88cdYvXo17Ha75L/n4eTj44OMjAzExMQgODhYUs/qg5mHDgAxevi6p6cHdXV1hqJLGlX39fU1oEvAqYKE5HRbrVbh/OrvaBoFF2pzc7MBDWSBPirRtHx5GFmtVoML3zvwkf3o7u6W6ppWq1XiSqhc0Yiqrq42eFs0DYGKF6lBra2tUleCiqT3YU8Um25/rg0i0ORD09VIhZL8Z2Yuo7eKz8T+entESHdjemGOR3NzsxzC9JyRu03KzcUXX4zMzEwkJycjNTUVkZGR8hxVVVVS1I/G3d69eyU4rKWlxaAQUUmmYkNUCoBhTvz9/RESEgK73Q6LxSJroby8XGJdAgMDcezYsf9sp/9GGvOPBwQEIDIyEiaTSfYL61BoL1xra6uso66uLlnvbIxborLFVJAAxBMXHBwsSkBWVpakFiWtUKeZpfFrt9uRl5eH0tJS2Gw2NDU1ScwIcGq/RUZGwmq1Ijo6GjExMUJT1AaBpvF4e2G4j9hHNn0fbXhrLx+9Jr3FaXBteyvVNBR0vRsaTnrvUCHnMwAw0J20ws7/awVXp6MlCMD768yFHB9NQXK5XAIkcZ3oWiGUJ9rA1IaHjhXx8fERudfZ2YnW1lbxRDJBQENDg8gfJiKgB4GymvfkGmGmOXpk+fxMb6vrnaSnpyM+Ph7R0dESI8TnJfre2tqKlpYW1NfXIzc3FydPnhSlKT4+XgwUVqzmmDocDoNx1dnZKd5RPz8/Q9pP7gkavv7+/oaMPH2leXvvdPP390dNTQ0WLlwoWZ56a8uXL8fEiRMxbty4M77vgAEDcOLECQwePBj5+flITU1FcXExRo4ceRrSrtv27duRm5uLBx54ADU1NbjggguwWaWzPdPGmiF33nknPvnkE5jNZtTW1uLPf/6zwWPRW8vOzsb+/fuRmpqKsrIyw2ePPvoorr32WlG4P//8czz88MPy+V/+8hcsXLjQUISPbefOncjJycGdd975H/cH8BhZZrP5tJSuzDy6cOFClJaWYsOGDYiLi8MzzzyDIUOGSLraM2lZWVnIy8tDZmamGChsDzzwAP7whz8YgvH79euHsrIyjB8/HuPGjcPf/vY3yUQ6b948PPPMM4iLizsNiPm1FhQUhNraWtx8881SHPI/aTz/n376acnGVVpaimXLlmHp0qX/8fX+t9uZmBD/UXpbreDSfcsbUYABpyr40t3DGAd+PzQ01MDPb2pqMngHGJDd3t6OoqIi1NbWIikpyUC3oqJmNpths9kQHx+PsLAwhIeHY/DgwSgsLMSxY8dQXV0tdCw+K6lPgYGBBnSef3wujTzydVdXl1CsmJmEnwOnAiQpyL0pRUS2iKrzfnwOvqcVcyLxvLfdbhflXCsSvr6+sNvt8n0ABnoKGw9hHo5UTLy9GRrpo0JERYNeAAZSsqAXFSGz2SzUMo02UtGhOx8wVvUGPMZce3u7GJU62JRzQqSQnhlmCaISRpRZe1zImwYgecBrampQUVGBo0ePIjExUQqRjR07FtnZ2UhJSUFcXBxiY2OlWCEPfRq7TqcTdrtdFAMawprfzXmh8kODiWPf0NAgSg7nlu+FhIQYUlNqhbSvNQbY6pgub1qKpl1arVaDIUGKEQOCCVjQC8UkDxxrIn89PT0oKytDTEyMgc7EmCKdcpoxNFFRUZKemKBKW1ubKMjd3d0iRxgnor24WtE2mU7Fb3H9EqTQ3juN7nvHJGnlXaPtVIx1rBj3skb5tVLOtaafh7FevAcbvTmaGtQbiMQ+c2y8Za72avJ5dHwIn4t7W1OvNA1Rxxdwrumh0Y3X5z21x5WNY0f5TuNP0480AMV1poPLNYWOa4L7Ni4uTmJ/wsPDDampOTc61svhcKC2thb19fVoaWmBxWIxJFrhucmxiIqKkvHXlGXKPJ7LnBPWNOB53tdbQEAA9u/fj/vvvx8OhwPvvvsuJk+eLEXodBs9ejRWrlyJCRMm4JFHHjFQUJnm9ZJLLpH3MjIysGHDBkydOhWzZs3C1VdfLQXvAKC8vFxez5s3D/Pnz8dZZ52Fr776CocOHRL60VVXXSVsigEDBpyGgvfWLrjgAixbtgyjR4+Wc7KrqwvDhw+X1LA9PT0YOXKkobYHG7McTZw4EbW1tcjNzUV6enqvSUReeOEFvP766wCA888/X/Q2tjfeeEOCz7/55hvs3bsXf//73wF4Ars1rf3zzz9HSUkJ7rvvPsM1QkJCsH//ftxxxx0IDAzECy+8gNGjR0sRQ+/W3d2NkSNHoqGhAT09PRgwYADsdjsefPBBOX937dqF5cuXY//+/fj6669x1lln9Tq2J0+eRFpaGsrLy3H33Xdj9uzZmPrf8TcrVqzA5s2bUVRUhIsvvhj5+fmorq5GWloaqqqqcOzYMXz77beyVz777DOsW7euVyPjgw8+gMPhwIIFCwAAb731FlwuF+644w4MHTpUgvT9/f2xb98+LF68GLW1tfjwww8xduxYLFmyBKmpqZgzZw727NmDpUuXIjc3F6tXr8all15qAAUmTZrUp+hTZxyjoQMAiepo9Ms78FBnRNHeAe+0hPqgo1DXvH4ebA6HQ1A3CklNfyIyVVdXh/j4eGRlZaFfv34APPxDIlBULklF0V4FTcfR1weMcRW6X968YQAGlFEjlsHBwYbfsP/eBoNG+mSiFErIa1Hx5nfpgtcHqkav+EyaqsJ50FQHTbnga2/qltvtiYHhvAAQpI3Pqw840g5oBJEfrA0QriOuJe/x5fc5H1RCvBFW7UHg2mVwJykKpNPU1NTAZrMhLCxMPDNOpxPx8fGCVrNfVJZ0ILLJZEJbWxtOnDiByspKNDc3S6YoTUsDTtUu4NrW1B3OC9c8kw/QmOX/qWj11cb5I7qv44a4TrXSSONDVz8GTq9l4Ha7xSunvWTAKYoeUxezwCJRZMZr6YBok8kkqRjDwsJE8dVGDe9LAELLDT7br3kmuC/ZD21M6fWujS6Ojw6c1mPgndzBW7Z4yxlvqpIGGPS1NYVUK//cE97eG2040jjR9Eodq6XXBceEsTjaK6ANDe/sXt7yjEaMt7dDjyf7obNT6fOL4817anCE+59ZojR9iuPO5wwPDzfE+2jDkWuJHi5ShXle0cPCe9ODwkQmPT09huvpOeB+ogfQ7XafFiup04b2tfb73/8eN954I3p6evDOO+/g5MmTKC8vx5tvvomCggJRln18fLBkyRKMHTsWNTU1ePPNN9He3o7GxkahHAHAjh07TkOb7XY73njjDTQ1NWHfvn34+OOPUVxcLPLX6XTK68OHD+Ojjz6C2+3G6tWrsW3bNoSEhOCJJ56Av7+/xBmUlJTI2RcQEIDHHnsMAwYMAOBRQB999FFkZWWhqKgI77zzjsi7gQMH4tFHH0VNTY3BECgrKzNk3Fq8eDHGjx+P2tpavPHGG2htbcWll16K22+/HcXFxXA6nbjoootw9913yzXGjh2La6+9FgBQUVFh8ILdfffdmDRpkhR2XLVqlQRBA54YgoyMDCxevBgmkwlff/01Nm/ejMDAQDz22GPIyMgA4JHBb7/9NkpLSxESEoLU1FSYTCahxpvNZjzyyCMSbO7r64ubbroJmZmZSEhIwG233YaAgABJ7uF2u/Hhhx/i6NGjqK+vx5tvvinjEh8fjyeffBJWqxUAYLVacfvttyM8PBz79u0To+lPf/oTJk6ciLy8PLzxxhsSSO90OlFSUiLZCLUHqKWlRV7fdtttuOaaa+Sz77//3pBh68cff8S6devgdrtRWlqKK664AjfddBOcTifee+89FBQUoKqqCm+99RY6OzuxefNmfP3113C73fjggw+Ql5eH2tpavPnmm7jqqqswfPhwuXZ5efn/fxoa+iDkgauD/zRypw9GHfTmHRCokSLddMAeXzc2NsJms0luax7w/F5zczPq6+tRU1MjOaqZf5pZQDo6OtDa2ora2loJxtWeFm1MaIWDnwGnOOb6gNV0BSKk2tAgQke0VXsKdD/YF61w8I+HDnnkOiCWB5aOddDKq0YE9aHEfvHe2tDwfkaiblqJ4HiSCsNDjWsAgEHB0F6hzs5Owzzrcda/1+tEK5Vs7L9GT3kdbbBSoWRGmYaGBtTX18Nms6G7uxtRUVFiZAQGBiI2NlayuDATEo0MBuETFW5ubsaxY8dgt9tlDrypKUQZtRKqefsaUacRTNoXAIOh0Ve51WzaO+FtaOi0vxxHXb/F29DQ1yCVj5nYuOZIVWPGKM67NjSoPPJZ3G43LBYLIiIihD6jARNeHzgVR8XPvOWc3n9abnjvf+99x+9QMSZo0VtdGvZbGxrexoYGJXozNrRSrZ+J16cc0ntTg03a26TlhE4j631/PZeUcVSC6b32zhal4yb0uPUGnmjwhuPKZ/YGYHhu6do/2viioUHQipnkeL5oA49eS52Vj33h+GkQhgwAJjqhfNF0Sxrc9CDT+62Nbs4/jSwaE3weDcjo2Je+1iZMmICzzjoLTqcTzz//PPLz81FcXIxnn33WUCvBZDLh0ksvRf/+/VFRUYGnn35aFNLAwEAMGjQIZrMZGzdulJSsABAdHY3Q0FA89dRTsNls2LZtG1asWAHAU+shJiYGZrMZgwYNQkBAAHbv3o0VK1Zg0KBB+PTTT7Fr1y4MGzYMs2fPRmRkZK998PPzw6xZsxAXFwfAAwxeccUVSEhIQEFBAZYuXYq0tDRYLBbExsZi1qxZsvaDg4MxaNAgDBo0COHh4dLXmTNnYuLEifD395e+Dh8+HJdccon0dfjw4ZJSFQCGDBmCGTNmSN+io6Pls/PPP9+g4G7atAkHDx6Er68vBg0ahMDAQKSlpWHmzJkwmUz46KOP8N1338Hf3x+zZs1CTEwMAI+c/uqrr1BTU4Pm5mYcP378NBlw+eWXG+qmXXbZZUhNTUV0dDSuvPJKBAQEID4+HikpKXC73Vi2bBn27duH6upqPPXUU4bK67NnzxZ9JDQ0FFdeeSVCQkIM83j++edjyJAhaGtrw6pVqySOh/MaGBgIq9UqhmBKSgri4+Plmc855xwD9W7btm3YvXu3vP7iiy/wxRdfyOuJEyfi7LPPljWbm5uLkpISPPPMM+jo6MB3330nxurLL7+MnJwc2Gw2rF69GtOmTROjDfB4235tXf0W2xnHaKSlpYmCQMVIo3V0O/v4+MBqtUq6PvLO6bblIaJT4jY1NRkQM1IR2Hx8fBAaGop+/fohMzMTU6ZMEUoDaSqs8+F2uzFixAi4XC7s3bsXK1euxKFDh+ByuRAcHIyIiAi0tLRg5MiRSE9PR2pqqijLvJd2pzudTgn6dTqdIqQYR8BDiUJbU8g0FUtXDtfGGb9Hxau9vd3Ax9aHncvlkgqxVMJYFA7wbBCr1WpQQrSB6HJ5OOzk+jY0NKCjo0PmhbEZzNHf09NzWoyIyWQyIHgAhOvOvunsSXosOH40NEgT0igiD8SQkBChc7A4nlbsSGMhb5xGkOYnc+1x3DQPvLOzE0ePHhWPRU9PD/Lz85GZmYmsrCyMGTMGo0aNgsVigcvlQlFREZqamsQrwnvZbDYUFRXhn//8J6KiogTpZGY2KklUAjkXTHHJolxWq1WoYkTXqdzyd9rT482z7SstIyPDUGTMx8eTGYqUEBp12jDl2mAtEe39CggIMKxxUgG5jjWCHhQUhKSkJKSmpiI9PV1SFoeEhCAkJEQokQAEZW5ra0Ntba1QMblnmOGNhkh0dLTURqCByLVI+QjAoFRqRVFTDAEIYKE9Wxpl10o2lXcd28W9TwWc+5F7h9/hb7WSTJoe58jbk6QNJ/ZJF6bk/tPULg1ucO9zjDl39NYQiddjp0Et3k/HDJKqpeWsN8qvARIasEwXrvtHbzGfhZQpKvmM2WE62pqaGplzPjMzB/Xv3x8pKSmGukwcT2avY92MyspK7N69GydOnICPj4+sJdb4YXyeHt/g4GDpB712TPHL/vCZfH190dTUZPDU9EU5otfS/5c2ZswY7NmzB8nJyUKHYnvyyScxe/ZsDB48+LTfHT16FF9//TVWrFhhiNH4V9z+/0kLCgpCfX09brzxxtO8LdOmTcNPP/0EwJMOV8dorFixAqNGjcKECRPkvezsbBw4cAApKSn/cs6PHTuGzz//XGhf3m3//v3YunUrnnrqKVRWVuKss87Crl27/m1fQkNDUV9fj1mzZuGHH374t9//tfbWW29h8ODB/1GMxr9r3rE2vzaPW7duRV5eHm677bZer7N27VrY7XZDytr/r238+PHYuXMn+vXrZ/DAlZaWYvny5Xjuuef+1+71P21nYkKcMaRBNA2AoQ4DhT4zILEIVnt7OwICAmC1WkVg8zBlYTIqkDxESDlhtL/Oud7Y2IiSkhLk5uZKJoGQkBD4+fmhvLwcXV1dCAoKQkREBIqLi9HU1ITY2FhMnz4dYWFhgkBRWd6/fz+2bduGvLw8CULVqJ9GwLT7msorD3V9QJnNZlgsFoSEhCAiIgIRERGnKQyaowuc8pDoA7CpqQl2u11oHszIRc8IDRweNFQgmGVLK9WMdeBBSC8E6Tk0GHiAaUoY+cpUOjo6OlBeXo7a2lp0dHQgIiIC0dHRMj6a7sAYHo0ielNlGDOhkVQif97oJ+l6OpUj89z7+vrCYrEIcsjMMdHR0RLIqRV4FjKkgujv74+CggIEBwcjOjoaiYmJSElJEf4zs794e2eam5tRXFyM3NxcBAcHi+HF2BGXyyW8a8Z+EKXs7u5GfX09Ojo6pOAjU/xSOSB/ncoWlRNthPe1Rs+Ny+WptM24F+4pXajR6XSKIh8UFCReMCra7e3tqKurMxgf3GsBAQES0MukDW1tbaipqUFpaSkKCwvltwRFCDjwOlRcw8PD0a9fP8TExCAqKkpqbYSGhooxznoGusK0NoB0fR3KDI2sm0wmQ9ICjbJTKedvuF/4f15De0foxaFRpOln+mDgHtWACfe/N/0JOGV4aM+Ft1GjvXn6M29vKI1IDdgwtoF98q7HoylHHEvvlMUEA3Qdmt48O9yPlNlMa0wQiTFEmu5JhZ9JIWpqaqRIHkEpnoNWqxVhYWGy/rSXhVQpxnfQ0Kirq5M50rKGc00jgvKsoaHBEIsWEREh8YM03PW4M3EFjei+3KZOnYpDhw4hODgYL730Et5++234+flhz549uOKKKwB4DPZdu3bh6quvPu33R44cQVZWVq+8/hdeeAEXX3yxvL7rrrukivXMmTPx/PPPo6KiAoMGDZLEHDU1NcjKysLhw4fxySefYNKkSQYjw2q1Ijc3FxMmTMDll1+OvXv3ivF58cUXY//+/QY6W0dHB0aOHIm1a9fi3HPPxeHDhwWl37lzp3g0vGsvPPLIIwZKDwDk5eVh0KBBBmW1t3bhhRfipZdeQlJSEo4dOyZofnx8PPLz87F48WI8/vjjqKurQ1ZWlmS4ioiIQG5uLsaMGWO43rvvvovnn38era2tGDp0qGT3CgwMxMGDBw2eFQC9zuPYsWORm5sLq9WKxYsXY86cOfL9hx56SIywLVu24Oabb/6X/dPzyHam8zhnzhwsXrz4V+fx1ltvxZ/+9CfDtc8//3wcPHhQ9D49j88//zzef/99+Pr6Yvfu3VKg0cfHBzt27MB1112HQ4cOYfDgwVi1ahVuvPFGue7UqVPx1ltv/cu+/pbaGRsaWrB7u8+1C1Z7NjS6xoOWn/F9otzao8Egav7xXkQXa2pqRPFmVhIeBvyur68vwsLCkJycjKysLFGIaRABkMwy5LtSkdMud40i07Wv86ADpyMsNAI0Us8D0hsN9EYVvXOya6SRfdZB8VSsqJzQ4KABQeOI7nx9byoPfCbOBw9rHtJsmhLArFkM7NV8an4XgAGB1RQKKl1aIdEBsfy//r5GKLnutIIUGhpqKBzG72guti4GFxMTI14TwJNtIi4uDuHh4eKFYB9o6HAMzGYzGhoaUFNTI4iotyKjDS3GWvB9zeln3zR/OjAwUNYjudjcd325jkZ3d7co9dxz3CNUhgCIEa3rDmiUnXKBMsPHx0eQcm/6nA7KpoJGY57FRXlPvWdJdwwNDRWjNCIiQuh0nBO9Z7zrKmjUnc/EpulQfD5NrdKyx7vpfmvqqqbPcHw1jco7FkRfTwdBa5mu6VVs2kuhFVkq5pRl3pQifq5lqqaHaQoTn0HTMDVFVNPktLFB2UkF3JtG5m2gBQQEiEFKeco1pz0v/ONc67oV/A4AMVooIznH2gOkC3KymjgDX7XM1fPH9ykfCB7RoGAfOHY0WLwpzHr8+3KrqqrCl19+iZ6eHvzyyy/YsmULXC4XvvrqK5SUlCA1NRWLFi3C2rVrUVRUBMCzlu+55x6MHTsWnZ2dOH78uMic2NhYLFmyBOHh4aivrzcElB89elQKwxUXF2PgwIH4wx/+gOPHj6OzsxMTJkzAH//4R3nN5CAAcPPNN+PSSy9FZ2cnVq5ciZqaGpSUlGD16tUyp2VlZVi1apW8HjhwIB544AGUlJSgpaUFlZWV+OKLL+RZ4+LicNVVV6G0tPQ0rj7BFLYZM2bghhtuMPQ1JiYGS5YskTgGtuLiYgwePBi33HILPv/8c7m2n5+fGCppaWlYuHAhjh8/jrlz5+Liiy+WvjHgme3nn3/Gzp07YTabcc011wj1yOl04ssvv5TYD7be5rG+vh4rV66UWldlZWUyj76+vli/fj0AT6D68ePH5VoLFizA2WefjaioKCxZsgQRERGGeWQLCQnBtddeKxS0oKAgXHvttYiMjJQMk4sXL4bb7UZNTc2vzuO5556LiRMnws/PDw8++CAGDBiAiooKWaMADPO4c+dObN68WeJ6uEbdbjfWrFmDkydPoqOjA8eOHcPq1atx/PhxxMfHY8mSJUL/7SvtjKlTiYmJBp4us/oQDaMCT4WKSAwNANKliKpobq3dbhdKD13XurgQET8qJeeddx7OOussqS5ONI+eBKKQbrcbTU1N2LJlC7Zs2YLKykp0dHQgPj5egoI7Ozsxbdo0xMXFSbyGPhx8fX0NtJeenh6DMAdg4BGTosX/kzJG9z8RJo3y0zjQqCoPW6L1fCYaLTTaSGPSvHQuas3/p4GkKV6kAFCZCgsLA4DTDiP2g657os/k/PZmZNC4JH2F+eH1Adve3i5GGTP90NDkOBHlZ/VtHVhK+goAoSNxbJj5S1O26BVj0FdmZibq6+uFFz1x4kRJZ9uvXz9ER0fDZPKkxSwrK0NdXZ2sST8/P+zbtw8nTpxASUkJHA6HITgdOJWpjXuEWX30WmbjnuC8A57CUPSgsbYLDT3vNH19pSUlJcnepqJMBNs7c1hQUJCB/sQ1o9cXueykYBHJ5dqjPOL+IG8+Ojoaw4cPx8CBAxEXFyepR/kb7j1S9Gw2GwoLC1FeXo6GhgbYbDZZx5zT6OhooeIxAJgeNp1amV41GqDeCjXlYkhIiAG0oAygAqxRclLsNOdfK6d6XWnlk8au9ipqb4D24um9Rg+oyWSSvU1ZTFRWAzE0Xrj2tZHB/cJxJ4VTx/1p7wo9CDQ6mJiE19Tym+CLBgG4fvh/nQaYFDv2k7KV8V1UPOg5bm1tFfluNntqOQ0ZMgTJycmIj49HfHy80CJNJpMEmPK3drsdubm5KCoqQmlpKRoaGiQLIxMQcL3wXkzJDECAP2046oKCBIH4HaYEZ//OJAPSb62dqYE0YcIEfPTRRxg3bpzIhJqaGuzcuRPLly+X9LfJyclwOBxITEzE999/j3POOQdtbW0IDg4+jVIFeACpG2+8EZdffjnmzp2LyspK3HDDDbjtttswadIkJCQkoLOzU7JDffHFFzhx4gQWLVp0Wj9SU1PFK6bb9OnTsWzZMowZM8bwWWpqKurr6zF8+HB88MEHGDduHBwOBwICApCQkIDS0tLTqFoPP/wwxo0bh8suuwzJycloampCTEwM1q1bhylTpqCsrAx+fn7o168fKioqcMcdd+Caa67BlClT0K9fP2GnbN++HZdeeinGjx+PP/zhD5g4cSK+/PJL5OXlYfHixXI/i8WCqKgolJSUyHshISHYt28f7rjjDuzZswcxMTEoLS3tlXYTFBSEuLi4XvtCwLi3eQQ8dSmCgoJQUVGBLVu24JNPPsG6deuwfv16TJs2DSUlJTCbzUhKSkJFRQW6u7vRr18/bN26FfPnz8eBAwfg5+eHX375BbNnz8ahQ4cQFRWFXbt24dprr5VK6L21999/H01NTXjggQewb98+3HXXXdi4cSNMJhNSUlJQV1dn8CIS6Kyurpaq7owz+bU2ZMgQfPvttzj77LP/rXfq/1Y7ExPiPzI0dH54nV6WPFIt3HW2ESpami9NPjoPAK10uVwuxMfHo7u7WzIBUZGla33GjBlIT09HTEwMWltbBQENCwsTOgyFa1lZGX755Rfs27cPBw4cQP/+/eWZa2trYbVacfbZZyM5OVmKEdLFTOqKPph4yLOvGt3kxtBjQSWAvH6N4tIY0u7v+vp6UajoyqeyYrFYDKkpeQhRgee8EDmmosbnYlpKUj54eIWEhCAyMhJhYWFiQAIeYcig2ZaWFiQkJBgUIypm2gOhqQmaQqJRbCKLRKxpHPIaTqcTFotFDDC73S4KiuZqay8NlR2NwNLo7ejoQGVlJQ4ePIiWlhakp6fDz88PRUVFUrNi1qxZyMzMREpKCtLS0qR2gsPhQGVlpfCf/fz84HA4sHHjRpSVlaGxsdEQ18P5ZupVUtO0EU6jk3NFg0IjjTRMuA5pwHZ1df1mhMx/2mJjY4X6Rc+M9lhwb/CPyhIVLtKnuN7oJaOHQ8sYi8UiMUukqlEWhYaGYsiQIcjKykJKSoqkMQ4NDTVQr6iIdnR0oK6uDoWFhaiqqkJVVZUYRtx7rLsQHh4uGcuoNBMpByDrkcaw5sxzzzCrmY4V0BmnaKhpr4c3ZYiVoznWNAwAGDwgpD1Soec401tDiqI23GmccD2TAqfjTbwNDa0cM0MYjS0aCbqPmq7LcWacBGOzdGyd9u7oIG+uL01/1eOtZZYGaQhQMOCbxkZlZSVqa2vR2toqIFFwcDCsVivS09MxcOBAJCYmIiYmBjExMYZMaowJIZW3qqoKBw8eRFlZGSorK1FeXm446zhnBFsYw8g10tzcLAAN5aOmu5KKyTGk955zQmW4L7X/iSfmzTff7LX+gslkQmlpKV544QW8+OKL8v6TTz6JWbNmYciQIadd69diNNh2796NPXv24I9//OO/fKbw8HDU1tbisssuO43O01vzrqOh2+TJk7F582bEx8ef5lXQ7dfqLwwcOBDHjh3DoEGDDF6Bw4cP44cffsD999//b5+P7eabb8aLL76I2NjYXjMkXnXVVfjggw8QHR19moEFeDww33zzDWJjYyVAm+3X5pHt+eefx/nnn4/s7OxffT7veihsOTk52LhxI+69994z7OmZtV+ro/HPf/4TcXFxuPDCC8+4Hspvsf2vxmh40xQAYzYV7ZplESFvCgl/w8NDo2g8SAMDAxEeHm4IuAwKChJFn62wsBAVFRUG3jaVWeY2N5lMSEhIQHJyMjIzMzFo0CCkpqaio6MDUVFRSE1NRWpqKpqampCXlyd5iskfdzgcogTSXa+rORM5pDLJg0jTlTTlSqdS1OgskUUqlgxGBIzB5ZpqpFFFndkkNDRUaB+xsbESx0KKAO9F5YzudsYcMJ6ACFptbS0cDgecTieioqLkWcxmM8LCwk5TkrQCQ1SNwkaPHRU+KlaaDkNvCQ0e74BGAKdRqai88N4aNXW5POmRDxw4gLCwMAwaNAihoaE4duwYenp6EB8fjyFDhiAmJkYygzU3N8Nut6OhoQGNjY3o7Ow0BJmzMF9DQ4PMPdHglpYW+Pj4wOFwwOFwiGJNDxqNDiKlnHf9R3Q0ODhYEGvuob6clpJ1KBiTor0/2oDQtBiHwyEIMtcvvQws0kgjjv8n9ayurg7V1dVobGyEv78/IiIiJIamvLxcqG+66BqVda5pghtMd0sjggYJ793Q0CB/DocDdXV18tza00B5R5qfNtz1Pb09p5p6pOPGtLHlTcXjHtH0H01X1PRDKvaUVfw9P+f1+RvuLcbcAaeC0Fm8jhmSSOWkYaD3Nv8oS7wpS94Z3JzOUymmaRjwc54v2sjR9Dmz2WwwJPVvdMYpbXBRrjc3N6O6uhqlpaWG9LOaXhcZGYmIiAihcWrKlDZ06B2h0WK328VgYbY7GhnaCKNcpEGuaw9xHbAwoQawGMvCdaeLova1dujQIUybNs3w3nPPPYcVK1bAbDZj+/btpxWA+9vf/mbguLO53W5ccMEFpxX3e/nllzFr1qxe7z979my8/PLLqKysxPDhww2KOQBcf/31eOKJJxAZGYmcnByMHDkSgMewOHDgAMaOHQvAU4x3zJgx+OWXXzBjxgzs3LkT/v7+WLZsGV5++WUEBARg165dEsfQ3d2NSZMmCfXH19cXW7duxRVXXIH9+/dLFe6HHnoIH3/8sTzPAw88gM8++wyAR4nXyuz3338vqW+HDx9+Wv2R2bNn44UXXpDXn332GR544IHTxiQiIgIHDhzA6NGjsWbNGpx99tno7u7GG2+8gaefftrw3fXr12P8+PFCWT733HOxZ88eBAcHA/DQp8aMGYPm5mY88cQTeOedd+S33vO4aNEig9G1dOlSQzzOwoULpRo5W2lpKYYPHy5UJbarr776NAPsuuuuw6ZNm2QPz549G1u2bBE9ZObMmdi+fTvMZjNWrFhhCM7mPHZ0dGDixIlC8WJ74IEHMG/ePPT09GDy5MlYs2YNRo8ejQMHDiAiIgIAMGzYMBw8eNCQCawvtjM2NLz5vZoqo+MueMh5Hzxa4dZUHk2TAU5lJ/LmEmvXudPpRE1NDaqqqlBbWysHE4UpecH8LoM5U1NTkZSUhK6uLimIxMJczJd88uRJA71HH4C9HdxUhsjN1hx+Kk2avsCDGjiVrUV7fjRKpxEtHhY6Swsbefvk7NJ4IKWLaCYPWY45C87RKKipqUFNTQ3q6+slvzVTLfI6PJR56GkqV29cZo4laV06KJPPro0HrQRx3dBwIrLNdaQVI461RvNImXE4HCguLhZlIDAwUAq5MYA/MTERFotFPCw0WBksDHgUfCrIlZWVkjGLY8G1z7GmgcDx4joiGsk51xQa9rs3tJaKVl9uVGBpPGq+ug6i5t7SiQ84xxwzZoni/qecoOzR2dF4fTYNJthsNjQ2NsqcU3548/lp1IeFhSEsLEzWvgZPmPmHtRB0vAafTcc3aLCG72tEXssc77guHRuhPSva+NDyU8tYXk8j/lqp1Yq3znKk//jc/B7nobcYFe8YCe/m7dFh3zR1S8tbHYuh6Wv6Olpma1qXzt6lKbKaKqpjO3im0LOq1xjgQSxDQ0MRHh4uRgIzRnFc2Sf2g4HlDofDkGbZ+1m8jSn+n2PI9cR51vfiOuez0sjgGaDjcPpS27BhA+rq6gzvHT58GAcOHIDb7cbmzZtP8/hWVFSgra0Nf/rTn0ShZTvnnHMMVa/nzZuH1NRUMSBuueUWTJw4UT4/fvy4FAI+cuSIKMwWiwX33HOP4XzYsGGDoPLd3d3YsGED7HY7srKy8Ic//AG5ubloaWlBTU0NNm3aBJfLhYMHDyInJwculwubNm1CbW0tAM8emTp1qsQ5sK8VFRWIiIjA9OnT4e/vj7y8PEOa1WPHjmHnzp0AgPz8fGRlZYmyvn37diQkJOD666/HkSNHcP3112PSpEkICwvDPffcA5vNhurqarnWzp07JWgaAObOnYvp06ejq6sLGzdulDIEJ0+exN133436+nocOXIE/v7+WLhwIfr374/Y2Ficd955ck7X19fjp59+krXd0tKCI0eOwOVy4ciRI9izZ8+vzmN+fr6hr1VVVbDb7bjnnnsQFhaGEydO4NixY7jnnntwzz33YOLEiejq6sKRI0cwZ84cTJ48GaGhobjnnnuEFsl5jIuLQ1lZGbZs2SJ7qLKyEj///LO8rq6uljiLAwcO4PDhw/IsnEe3242jR49KvIuvry/++Mc/Ijg4GMXFxfDx8cGUKVOQmJiIxsZG/PTTT5g3bx6GDRuGpqYmbNiwQcDKmJgY3HPPPbBYLOhL7YypUzrXs06VSKEZFhYmypOOVyC6FRYWZjA2ePjxT2c4Ilqj6UE8oIik+/r6Ijk5GUOHDsW4ceMMBziR4PDwcOFN19TU4NixY9i6dSvWrl2L9vZ2hIaGIjU1FT4+PqiqqkJXVxfCwsIwYcIEQb0Y1KcD61hvobegVH9/f1FmaYix6YNPo4uaD00FnQYLx439Cw4OFroSlRPtRdCVi4n86YOT1C0eWgxkbm1tRWVlJSwWi4x9d7enym1UVJSkAaWRGRgYKK95IPv6+gpdw+l0oqmpSSgIjD/QtT54IOtYF41w0hBg/1taWqRoI1Fk3VpbW+V3XItNTU2oqqpCTk6OZMRwOBwoKChAT08PLBYLEhISMGHCBKSlpSE2NhbBwcGSXcZutwuvOTAwEM3NzaiqqsKGDRvQ2NhooJC0trYaKDI60xSzSGnFmt9zu92GbDI03HUSAW2U+Pr6nhZE11daSkqK8FSDg4MNFdOpABGF9fX1RVRUlEHpZLIGX19fREZGGjyIVESpOLNSNxU3t9uNmJgYuN1uoUQOHDgQKSkpSE1NxZAhQxAZGSkZzMLDww3IfXt7O2w2G+rr61FWVoYTJ07AbreLQU3PBWmcqampCA8PF+MkPDzcoNBTzvHABU73HPNfl8tlMLSBU0HYbNpI0l5kALJ+tWKtjRl6hCnn+FsNqGhlFjAmCNGxJ83NzXJPehEIbnAvaA+Dpp5y3RNk0PRLIvUcc+5/bRDx2jpWh//X406Z5G2M6YByxp4QcLHZbKiqqpK1CHhkutVqRUxMDGJjY5GRkYGUlBTJSsa55fPTCK2trUV1dTXy8/Nx8uRJVFZWSpprbbQ1NTVJPCSpU/Ri6M95D12AlueM9qowGyD7oIOG+0ozmUyIjIyEyWQS6ldERAR8fHx6pYIlJCTA4XBgyJAhWLVqFUaPHi30IpPJhD179uD111/Hm2++CcCjTH/00Ud45ZVXAABbt27FqlWrhFoVGxsrhmd8fDxqamrEM75nzx5ceumlko3p19rs2bPx5JNPYsSIEQgLC4PL5fq3wb3+/v44dOgQnnnmGXzzzTeor6+Xvg0bNgxffPEFsrOz0djYCD8/P8TExKC6uvq0c3LJkiU477zzcN555wEA7rvvPlx22WU499xzsWXLFnzzzTf49NNP8csvv2D69Ok4duwYfH19ER8fj9raWqEexsfH491338WBAwfw17/+VWhb1KMOHTqE3//+99i2bRssFgsOHTqEefPmITAwEMuWLcPw4cPFSOM8cW/r106nE9HR0dKXsWPHGubRu69ZWVlYv349zjrrLJSXl2PAgAHYtGkTABgocps2bcK6devwwQcfYOfOnbjggguQn59/2jyyr+wbW3R0NJxOT4xxQkKCsBv+XeM8PvDAA1izZg3MZjMOHjyIZcuW4csvv0RjYyNycnLw7LPP4qOPPgLgyfrV3NyMtLQ0/PDDD7jssstQUFDwb2M6/m+0/9UYjQEDBgiNJzg4GDabzcCfNZvNQjeg0aE9Hpp7DUCUTh74VqtVPAOkllBh4CHHg5fvhYWFISUlBXPmzEF4eLiBfsSKvhEREYiPj0dYWBiqq6uxc+dObNiwAbt374bNZkNAQADGjBmDyMhItLS0iEU6fvx44RgmJCSIAk66i6YE8WCgEUHlgvQNbXCQMkOjiYc0gwN54HvTZHgAUgnSrm/ScZxOpxy0nBdemxQBCh2iXi0tLZKBh5QAXQeDRXKioqIMRgj7QsXIbPbkvidViMgjEc6uri75nHQMVtzmunC73XJvGjI0lLRiRsOUY0clRyO3jEE5cOAA7HY7oqOjMWDAAOHYM3VwdnY2hg0bhmHDhiErK0vGnzSampoa8X74+fmhtrYWxcXF2LBhg6xdPq+OR9HBthxvjeAz3kIH0pIiSGGmY4MiIyMNCRL6ooIAeAIvaZQGBQUZDpaQkBA5dGloMCDa7XZLjAWNbcZ9UQlraGgw7EXS17iOfXx8JJUwlTKLxYK4uDikpqZixIgR6N+/PyIjI2UPa7ojaTw2mw3l5eUoKChAZWUl7Ha7JDpgDE1bW5vEPIWGhiIyMhLx8fFCpwFOeSg0VUo3TS3s6uoSjyKVTSrjwKnq5/TgeAdCa8OCnk1vNF/LHSr73FuamkXkkUCTrqzOYGe9B3QBPhoV2lNEWaA9NrwH76NpTKR38rc6gQQNQy1HvClCuh80jnTCAcZmUJmsqalBZWWl3JNJOChv4+LikJSUhISEBPGaMzOZyWQypONmvBcpfYcOHUJ5eTkcDge6uroQEREhhgHj9Wh8d3V1CUWW9Xd8fHwM96LiwTXuLUO5xujNIV24LzWTyYRVq1bBbDbj8ssvBwB8+umnsFqtuOiiiwzfZbDtgw8+iLffftvAttDX8zbK/9Xrw4cP49tvv8Vrr72G4uJiZGdnS4xGb9f/V/1wu9348ccfYbfbDWlb/9VvPv74Y8TGxuKiiy5CTU0N7r33Xrz33nuGe48fPx47duxAUlJSr/F8v9ZH/b7+f1JSEkpKSjBmzBjk5OQgPj4e5eXlmDx5Mnbu3Ino6GhUVVVh2rRp2LZt278dR+/PAgICUFdXh3nz5mHlypUS8H333XejtLQU69evR3x8vCRy0b8fM2YMdu/ejeTkZFRWVv7qvQGc8fPo17GxsaisrJS+sv30008oLy/HggULUFNTg8svv/yMYm1+7X5vv/02BgwYgHPPPfe0ZysvL8czzzyDZcuWwcfHB0VFRVi+fPlpVK//F+1M1vsZ+06bm5tFqaytrTUcFFSkNZpLeghTTpIepZVPHdDIg4poZUtLi+SlJ3eViDcFcUtLC4qLi3H06FEJsON1tKeEwdW+vr6Ii4tDXFwcIiIipGZDTU0NOjo6YLFYMHjwYLS1teH48eMoKiqSA9ebhkHBTSVBZ2Kiy5wKE9Op0kugaWVms9ngHicSr+MSNBWACremI2nFyntD+fj4IDIyEnFxcYiPjxdU9deMhfDwcDkc6XFhwSi+R261RjhJn9Losfao+Pj4iAIEnIo30d4v0gloxLFYYE9Pj9CYGhsbhVPPOBKNilCBam5uxvfff4+Kigr4+npSHRcWFqKsrAz19fVwuVwYMmQIhg4dioEDByIpKUl+C0A45kQoLBYLOjs7UVhYiMOHD4vCRKTbm27j3S+z2WwwUFnwi4aoRly5xrkXLBYLHA6HQYHqq83pdIq3kGte0yc1Wm0ymUShoneC482UoNXV1UKDpPFAQ4QIN+eByjQAMVg7OjqkHgIz/rBOAmUVG8ffYrEYuPghISFSz4ZzZzKZpBgb+8QYKM49AQIqylqOaKVZexc0jYiHkaar6n2n16WOldPUK43ua7qRjgfR65kGok4MosECDWbwWXUcnbe3hnOqn4MGj6YaedPBNFWX+4QAi76P9tjwTKJxQXlOYIFpa0n/aGhokDgb0mh1nAdBn/j4eMTFxSEmJka82dqjr1PiMgi8rq5OqiRTzvH8YD9ID6S8DQoKQnh4uBilXHsA5DzQKW/dbrfUmOE5TZALOJXevC+2+++/H3/+85/l9eLFi7Fw4UKYzWb89NNPUgPD6XTiggsuwJo1a+S7P/74o6HOxLfffou5c+fK66+++gq7d+/G559/DuB0RWru3Ll49dVXUV1djfHjxxuMNX736quvxtq1a2EymfDWW28ZsjJ5f3fhwoWnfX7uuedi69atok9NmjQJ27dvR1BQEJYsWYI//vGP6Onpwfnnn49vv/32tOc8evQoJkyYIJ6bQYMGYffu3cJM4XdXr16NefPmGXQS/XyffPIJFi5ciNraWkyYMEHoZA0NDZg4caIAs3a7HWeddRb++Mc/4uGHH5bfv/rqq3jsscfk9UsvvYSnn376tDHt6urC1KlTsWHDBgAe79x5552H77//Hvv378ekSZPgcDiwePFi7N69Gzt37pTK2Hl5eZgwYQLef/99zJ07FwMGDMCuXbuQkJAAwFPtfNeuXejXrx8Aj9G0e/dupKamnjYXgIeetGvXLkkE0NjYiIkTJ+LIkSOGZ/7DH/6Ahx9+GC0tLTj77LPFCAkKCsLWrVsxefJkw/effvppvPTSS3K/xx57DP/4xz/k9aOPPorbb7+913m45JJL8OmnnwLwAC+XX365eDv6QvuPYjT0QaY54xS+OruHpn3ogwGACEEKbl6br7XLnAKYh5YOYuTBwbS1AORwJvpIVIpIT0REBJKTk5GWlob4+Hi4XC7hZ9NFRtdoXV0dGhoaDLEDOtCQApwHmXcwKw0tHmo8aDR/mX0iRYHoJekjwKnUlN6BoTycvccNMHK/+T4VAn3oa3oBD0g/P08RKwa+UomiAqPjXzT3mgaljgnhs1NBZiCidxAjjUw9FjpWh1QGjqWOidEUkqamJlRWVuLYsWNoamqSzEM9PT2oqqqS7DgWiwVpaWlISkpCTEyMFITTwapEqTmmDodDgoa9lSWuOY4HjUBNNeG1zWazHPKaIsU54ff0e1SyaPT21aa9Pj09PYY0plqx5TqncUBqEvcU97ZGfbjmtOzQnlEdFKyNFl0gk/Qj/unYCk3tYYa7sLAwie3R3j5WhqfHkGuHcSBaadY8evZFy07Nt9dxGd5xDPr3mhakx97b2NBN0xa939fyRa9L4FS9D17XWx5y/L3jJ/hMmn5Jo8n7Plq2aRoW55Kfecdd6PgvTU/1jvfQtC/OF5NBaAok58Hf3x8hISFSwJFeMIJM7D9gTGxArwbXAedde2e8wSLKTfKy+bn3utFUZgJQvDb/r1OQ9+XCnwUFBeju7sb8+fPh5+eHkydP4vjx43C73cjJyTEg39nZ2YZg2oMHD0rcA+AJLvd+vW/fPhw9elTeGzt2rBgjQ4cORb9+/dDV1YW9e/caPLJ33nknYmJiUFtbK/SpY8eOoaSkBAEBAbjjjjtw5513Ytq0afD398ftt9+OtrY2nDx5En5+fpg/fz5SU1Nht9tx4MABuFwuzJgxA5MnT8b+/fvhcrlQWFiIlpYW3H777cjLy8OwYcNwxRVXwGQy4eabb8aQIUPQ2tqKvXv3yjnT1taGvXv3oqurCxMnTsS1114LwFO0kF4AAJgzZ44EqwNAbm4uysrKpK+kvXZ3d2Pv3r2YMWMGzj33XPT09GDv3r3Yu3evIaD8+PHjKCwsNMybfn3hhRfi4osvhtvtxv79+4U+ZjKZMGrUKImf3bt3L5xOJ0pKSrBv375e+7Z3717U1taivb0d+/btMyRh0a87Ozuxb98+A0AJANdccw0mTZokfbvwwgsxefJk+Pn5YcyYMQgJCUFWVhZuvvlmmEwmDBo0CGlpaXA6ndi3b5/EYLhcLmFS6FZYWGhIS19YWIjjx4/DZDLhlltuQUBAAOx2O+644w4xMGNjY3HHHXfgxIkTyMzMlED3gwcPGmJnfuvtjKlTMTEx0nnmoyc66F1DoaurCxaLxeBdCAoKEkU6NDQULS0tgjYxDSsVBB3gR0S3tbUVAITLzYPUx8cH/fv3xyWXXILExEQEBgbKAiICFBAQgJiYGKGrHDt2DDk5OThy5Ah2796N1tZWZGZmIjU1FSkpKSguLkZhYSFaW1sRFRWFSZMmST78oKAgyaRC17nmHOuxoMtf8415EOp0k0S6GX/Q2toqAdCkiPAwplKqFXjmcSe9AjAWApTJ/u8DqrW1VVBOxmc0NTUJesdCZXFxcYIIc255gOkAUB7szHjFQ6ypqUkEkz7stGKhOeGazsFgdd6zq6sLLS0topCSJ8qxIVWvpKREqnVHRkYiIyMDgYGBqK6uRm5urlBv+vfvjylTpiAjI0PWRltbmwR1s+iRNs5ycnJQVFSEqqoqMRiBU0g3BQvpM1xvgMe4oIHi5+dnSEvJ8WL8THNzs4ECBHgMKK28/ha4mf+TlpCQYDCkOO40NGmE0uvm7+8vVCSbzSYGMZV6TYvh3uL60gAH1wq9QfQ0dXR0IDAwUOpqZGdnIyEhAVFRUQgPD0dUVJTsQV6bSmlpaSkqKioEna6qqhIEOSgoCI2NjTLnUVFR6NevnxglrLGha0dwvXB/a0NCG1TAqZoTOn5HU/5Ic6XSr4Og6ZXgeOlkCmx6DHlv7yQUmi7FVKsEVtxutyFFq3dtC+2pAU7RT7V810aRt3eG/3Is2BcaADpuA4AhiQZBKJ04hAY8U9nq1NYmk0ky2nF8Q0JCEB4ejsTERKmZwWKOOpsYqXbMQFdXV4djx46htrYWjY2NUvSTY6DTkXPd68J/jAOjwcIx5vzoFNBMr62TkjBlOOfJWxnqC41pT6dMmYJ33nkHWVlZcmZarVahWAKec+fo0aN47LHHJBNTVFSUnLn/qjEOrLGxEX/+859x9dVXY8KECRLn+fjjjxu+Hx8fj4MHD+LCCy9ETk6OvG+1WsV7f/ToUZjNZqxcuRIPPvggcnNzccMNN2Dz5s0ICQlBfn4+brvtNmzatAlhYWGor6/HO++8g/DwcNxxxx1oaGiA2+3G5MmT8fnnn2Pw4MF49NFHkZaWhlmzZuHo0aN46qmn8MEHH/xqXx955BGcf/75OPfcc0/r844dO7BmzRpDpijqIQ0NDYiIiJA0yQDwww8/4OjRo/jLX/5i6GtLSwuio6Nht9vF+xoVFSXgHdt7770HX19f3HDDDYiOjhYZ4ufnh9zcXPz1r3/FypUr5fs8M+12+2l9+7V51fOo9SEAwpRpbGzEpk2bsHXrVvHIrF27Fjk5OXjuuedw5MgRXHrppRg0aBAeeughDB06FCtXrkRVVRXuuuuuf9tX9s3tdiMsLEzmEfDoBrm5uXj44YdRVFSEr776CkOHDkVjYyOys7Px448/YuTIkbjrrrswadIknH/++b+6Zv9ftDMxIc7Y0MjIyBDhTmWShgEFPg9xGgUagWPBKrqDKRgYGE7hqikOPABJv9E8Yx0AGhAQgClTpmDIkCHIyMgwBB8zODslJQWxsbGIiIhAbm4uDh48iPLycpSWlmLdunUwmUyIjY3FsGHDMGDAAOTl5aG4uBhFRUUYPny4pD9lX6gYEQnXtCkABve85ttxHKhsWK1WuN1uSZ9ImhWNEXKXNZpKZEuj4tqw8FYQND1K0y9ID6JxyOBt8tNDQ0MNxhJRYo3CEm3Tyh+Vu56eHkk/ytc6LoWcYh72RO2YMUvT1pqbm8WQ0ooRUefu7m4UFhYiLy8PbW1tiImJkaI2FRUVKCgogM1mE+Vg7NixmDx5snhrGGNCmlZVVZVw/n18fGCz2fDdd9/BbrfLIU0Dj/PqbQzT40Ilh+umo6NDqtlrxFqnw6SSQUoLAIOR1Rfz3wOeejxUoln/hsZraGioABT0LlFRJIWvp6dHFDmuYwBCj+O6AiBKMBXurq4uREZGitcjICBA4pf8/PwQGxuLkSNHon///khNTUVCQoLUwtBVlwmGVFZWoqKiAjabDTabDSdOnBAEnM+ks5elp6cjPj5eUp+ykBtTpPLQ4/rWHg3uW/aFBRzpAfLz8xPEnOOkDRTy+LXB4R1bpqmQ9BZz/dGg55+Wv1TYtbeTwAH/aDzxT3v6dB9oNHjXuiDlUCfO0PJNgzjM+MS9ZDKZDOmFGbfGfUwvJWNA6uvr0djYiK6uLokf0fdkAHhUVBSSkpKQnJyMsLAwWSPMauRyuQzUv8bGRhQXFyMvL09SLjOYm4ad9p5wDWiPqM7eR8+K9tKQWsXv8zzlOISEhMictbW1GZD8vtKcTieuuuoqrFmzRoAaAEKRYoA0m7e3raysDI8//jhee+21f3mfrKwsHDlyBIMGDUJhYaHsEX0976afh23r1q3Iy8vD7bffbgDbCKTq7/P15Zdfjo8//hhxcXHo6OjAhRdeiC+//BJxcXGSbIHf1R5Q/Wwmk6dGyLPPPovly5fLPfT3vVtvfbv33nuxYMEC9O/fH3v37sXWrVtxzz339Pr9zZs34+TJk7j33ntRVVWFSy65BJs2bUJoaChqampw5ZVX4scffzTcD4DEZMyfP18Mi96e5Y033sDgwYOl0CDn0WQyoaSkBC+88ILQk3qbR1ZrZ7v//vtx5513Ij09/bT76df/aqxDQ0NRXV2NK664AuvXr4fFYkFVVRXmzp2Lb7/9FkFBQaipqcFNN92E9vZ2rFq1yjCPv3Yv7zXxr+bt/2U7ExPijKlTOuUrFSQWevP1PVUArqWlRbJk8IAKCQkx8ITb29vlICCaS6HJIGqtgFGh1dmQ2traJP6AgUQ1NTWGwlFutycWpL6+XhDHqqoqWK1WJCYmIiUlBZmZmcjKyoLFYkFjYyN2796NEydOwM/PD4mJiUhLS0NeXh5yc3NRUlICk8kk2WV44Gp6AGMLmDmIhhK9L+wvD2PGJdCYIpefgaWMyeDCI7VKewI0DYAxC7o6rEYDNcqpuctutydYOTo6GmFhYQYlmkoJUbWenh7J5kRaGiuaa9oEkX5tOHAj6XgIzrVOuch4D31AAB63p91uR3l5OcrLy8UTk5ubi127dsFqtWLw4MGCLhUUFKC6uho9PT0IDg5GZmYmJkyYgGnTpiExMVEyeIWGhqKtrU142Q6HQ4Lf29vbceLECTEa+Zz01ukNx71Bo0Oj1VRUGJej55FGWVtbmyHzFCmKVLz7OuWB80jDgtx3evbq6+vFqOTaZqIEKqn0bjU3N4tCSqOUiC5jooBT2Y8CAwPR0NAgBo6mJ9F4q66uRnV1tWQaokzTqW+5f0NCQoReGBERIZmG/P39xYjXPPjq6mrxgOhsWeyPzuCks9JpD5CmGumDxzvtNfeWjq/QBiuNVm/6JteeltfaA6kVJO1x4zPSq8s6EFy/gNHLyufWoIOmP2lAQVNHKU/oJeGfjmuhjNPACOUj5a1OgsE4CZvNhsrKSklNygNex1MFBwcjNjYWSUlJSE1NRXp6utBLdZprKvI0mpkti3WJ6DHRHgjW8OF65xlAQ41rnTEiISEhInNDQkIQHR0Ni8ViiHvR6ce7u7sNMVB9NdZr6tSp2LZtGyZNmoR169YhKCgIf//73zF37lxccMEFaGlpweLFi7Fs2TIAnvPlvvvuwz/+8Q+4XC5ceeWVSEtLkyxTAHDXXXfh3XffNdynpKQEU6dORUVFBW6++WbxiLz//vvCpQeAd999F3fddRcAnGZk8NrPPvusfK6NZKfTieeffx5///vfDb/ftm0bLrzwQtELmBVJV5bWOsGwYcOwefNmhIaGyrXdbjeuuuoqQ5G4L7/8EnPmzJE9+dlnn+Gmm26Szz/88EPceuutSEhIwJYtW9C/f398+umnQtm57bbbDEaLy+XCZZddhm+++QY+Pj5YuHAhnnrqKTQ3N2P69Ok4cOAAAA+96YILLsCuXbsAeFgw69evx6RJk2R/XnTRRZIditd2u93Izs7G5s2bERYWhmeffRZ33XWXzCNrZLjdblx99dWIi4uTefz888/x+9//3jCPt9xyi6Huxj//+U/cfffd2LJlC9LS0nDNNddg1apVhvt7j7XFYsHmzZsxYsQIXHDBBfjyyy9xySWXYO7cufj73/+OtrY2XHjhhfjll18AeHSWiy66CFu2bMGuXbtkHh955BExilwuFx588EEsX77csIaysrLw008/ISoqCm63G+np6diyZQtiY2NPW2e/5Wb+91851TRXGDjlStcBwjx4eChSsdIcaW42ouxEFDVvV/NV+VtNrdIHnY+PjyicNTU1SEtLO406xMBhFgRkhhvAM5ldXV1S7beurk5cdFarFWVlZairqxMvA1POAhCXqPf48NnowvfmNHP8tKWqOf98T1u3RLo0h5qbgWPqzVvXCjGz92i6BX8LQGpIaAWChgX7qg0VrdhomoO+v3eKXxpovSkr3vQyPQ6kf5COxeB+Zrmpr69HZGSkKHr0ShAJ9ff3R2JiItLT09GvXz/5HhU0FobTweU+Pj5oa2tDXV0dSktLDYXDuOb1fGjUl2PF62jUlgY21w0NDSK53BPeyhff66uFtoBTSr+Oa2EfuZ7YbyK6ek0R1PBG37kOuSYYGK7lhjdlTyP03AcOhwMNDQ1S6ZnGMa+tjXN6MGkURERECDpOgxE4ZTSTXsR9TgOcqHRHR0evKJ6WpdqY9ZYD/K6mi/Ff7VX1jrHQ73EcNJDhHTOgDTT9nqYC6DnmnHt7MPT69n4ODTJw/NgXngF6XviZjr/RY+Md50VlXnucCJrwLNN9onc8ODgY0dHRktVQU/208cT76eJ5TJGrjWl+n/ehYarlsl6/2quv9wypUjRAue4CAgIMWQk5lt7z35fatm3bMG3aNGRnZ6OwsBAulwvV1dVobm7G9u3bAQC1tbUGha2urk4Mq127dmHgwIHIyMiQzxsaGgwpw0ePHo2UlBRRZG02m2T6KysrQ79+/USJLy8vR1xcHK655hoDzScwMBDXXnstfvjhB9TV1SEgIADXXnst1q9fj+DgYEyYMAGfffaZAHZsLEZIpXvq1Kkwm80SKA14aFoXXHABPvvsM4wbNw5jxoxBYWEhnE4nJk6ciKioKHz33XfYvXs3xo8fjzFjxuCbb75BSUmJgS5XWlpqSKtbVlaGxsZGYQh0d3ejoqICFRUVADxZAwnaXnPNNfj+++/R1NSE4uJiuN1uQ4X07du3Y8aMGejo6MCWLVtkbgDP+i4qKhLDye12Y+fOnZgyZQr8/f2xceNGXHfdddi1axfa29ulbzrGgUYL2+7du5GRkSFyt7S0VAxrZsLS83jFFVegrKwMv/zyCwoLC9HV5am5VVJSYriu9zw6nU4UFhZKxsiCggJs3boVY8eOFQ85jQwA8tp7HpmEiM3buzhx4kSMGzcOhYWF0qeuri7D677Szpg61b9/f4lLoOJMRZlBnSwAR+WOQs1isQjFibzV1tZWUWxbWloM7man0ymvvYUj3dEa/aZSEhMTg4yMDJx33nkGNCsoKEhQx6ioKMTExKC4uFgqp9bV1WHbtm0oKChAXV0drFaruML9/f1RVlYm1Z5jYmIwbtw4g0LO+BKtnPMQpRKpx4Of+fv7S65+Uq26u7slw1BgYCCioqKkCi2/r3n9FKZEIilEOCaBgYHC1ybdQitpTU1NouAQYSZiSISQz08vEhXz+vp6REVFyffJMfdWgLT3RHtXmB2KSD4P7uDgYMkIxuxSNCyKiorEY2Q2m2VuzGYzJkyYIFmpWHiQaGF0dDQuuOACyQwTExODmJgYg8eHgf8cP7fbjYqKChQWFmL79u2y/uh9MJvNhiwvpPTpWiFcI6wsznGg549z19raaijOSCRSu1PZZ1Y174stJSVFxguAQbFl/7QSqoOmadjxPRqkwKl6Bi6XS9YMi+rx+owdoILPtcxmtVolo1R8fDxGjRqF2NhYhIeHS9Vneua4j1mdvKWlRapG19TUoLa2VjwDpMF1d3cLKh8WFobk5GTx4HJvkzroHUANnPL8AZCaGhp8IahAbxD5wSaTSfj6lNNE/nltghBaGdfGi0a/NRWJfzSu9NrVBgff45xr0IOeXO21oIFHJJ79p9zSgIW3p4a/1UcbaVKUNTabTWRPe3u7xNO4XC6hF/H3pJsFBwcjMjISaWlpsFgsslbo1deUM44l1wU9JcePH8fRo0fFwKY8CAkJgdlsRmlpqSFWh2ck54T0Uq77+vp6iZm0WCxoamqSWhptbW1ISEiQeDJNheN9dSBwX2kmkwkffvgh/Pz8/mVKWLPZLBn79FoIDw8XzyQAScevPcUPPfQQLr30UkyYMAFhYWGyr8LCwtDU1ISFCxfi1ltvxYgRIwB4MhAtWLAAQ4cOlXvFxMQgPz8f1157LXbs2IGAgAAcP34cs2fPRmJiIp577jkMGDDgtOxf//znP2E2m3H77bfDbrfj/fffR1BQkARwA8DkyZOxevVqZGZm4rHHHkNGRgYuueQSAMCLL76I4cOHS0Xxp556CpMmTcKUKVMAnKLoauqObpRHTU1NohMw7uHnn3/GL7/8ghdffBF5eXmYMWMGDh8+jKCgIKGrkU7tcDjw1Vdfoa6uDvPnzwcAyZKmDSvOk91ul3iUOXPm4MSJE4aq5gBkb7S0tMg89vT0IDQ09LR5DgsLExaJN00pLCwMO3fuxOrVq7Fo0SJD/00mT4kC0so5jxdffDFycnIMffVulBMOh+O0vnrPo+5Lb6+95/G32s7EhDhjQyM5OVlQOgopXViIsQdUCijsTSYPF1oHf1Pp5gHR0tIi2Tp4uDY1NYnSyaBDCkhtiPDxiRRarVb8/ve/R1pamgSsd3Z2Sj57KiO60qvb7YbD4UBpaSl27tyJ2tpaWK1W4eEmJyejoaEBFRUVyMnJwbRp0zBo0CBYrVa0tbWJsu12uwUd0Mq17iuNDXppIiIiEBgYKIcgKWhU0KOjow3ejejoaDFMGKyoKWhdXV2CsmuqCeeFtT2oRNTX1wvdITQ0VJ6dB79Wfqm40KPQ0dFhoDOQYw+cCoQn2kilwWazyWFHGh2rb3McOTZBQUFCL6BCyPoXDQ0NOHHiBCwWC5KTk5GdnY3q6moUFhaKkUHFMj4+Huecc46sYT8/P2RmZsJqtUohveLiYpSWlooXymw2o6KiAkePHkVBQQEaGhoMihLRQqLZXNs8wIKDgwUZJQrJPdLV1SVCkgqTyWQypHXl2mXgLuNFAA+q8e+KO/1WW0pKighgzgUAoUhZrVbD2tBotY7N0vFeOm5A154gAEIjkIU2KTO4dnVcTEhICEJDQxEVFSWoZnR0NKKiomR907D28fERJFwj1qymy+JWzJgFwDDnAwcOlOtRdjFzkbcXmIHflAWkNurAavaZcpjKLPe3Hkctu3ldTdXSWe9oNFAW6JgAHaPBOaHs194+HQSuPbW8h04soQt30rDXgAXHUXuE2Q+32y31aDTAw33Z0tIidU/oyaDBQbCFyRoYU8bAfavVin79+iE2NlaeMyoqSgLrSXEkDY7AT2FhIYqKilBWVoaioiKp30T5oZ+3qakJsbGxBkBHJ0ehF57zrUGbwMBAtLS0GAxUAkUcK70Wurq6+mQ9Hq4foHeqEtu5556LH3/8EUlJSZKFysfHByUlJXjyySeF219YWIiXXnoJL7/8svxWe+1zc3PxySef4J///Cfy8vIwYsQIHD9+3ABU6O/r5ufnh40bNyI3Nxd33nmnUDwBY/ZB3Xx9fXH55ZfjvffeQ0JCghhAuq/ck2R3mEwmQ7zav3r91FNP4eKLL0Z2dnav47Zo0SLccsstGDhwIA4cOIAff/xRlHHS151Op/Rl3rx5ePTRR5GcnAyn04kbb7wRS5cuRWJiIgCjd+7rr79GW1ubwUC8+OKL8dlnnyExMVHoyaT9aRkBACtWrMDgwYMxbdo0mcddu3Zhx44dSEtLMxjOJ06cQGpqKvLz88UgBDzxxnl5efDz88PTTz99mqGRmJiIoqIiTJo0Cfv27ZN57Onpwc0334wnn3wSSUlJva69q6++Gm+88QYSEhLwySefCH2N86Dn8cMPP0RsbCwuvPBCAMCbb74pQHlv8/ZbbWdiQpwxdYoIlD6oiaroPOdEq7TAo8JJVzGvRUNEV4cmCkyll8qzpklQcPLgogAlGrp9+3aYzWYkJCQIZ7alpUUOIqY8JVJZUFAAk8mEmJgYnHXWWfjpp5/kIOrs7BSEkdc7efIkOjo6kJSUhLS0NOkXvRBU5rlJvKkDgJGqwL4RtfZGD0k/Y+wLD3oqonzN5yBViOPDBc5noICixa4pEDovO70BRDiojFA51AqLNiJ1P9k3CmIG+zIbDw9bKiu8HgADfUMfrPX19SgsLISfnx+ioqLg6+uL/Px81NTUoLq6WihQZrMZgwYNQnp6OlJSUpCeni486tDQULS3t6OsrAwNDQ3iYaAxUFhYiG3btqG2tlaupb0dHCN6G0wmk7iBiTp2dHTIvFIRJrqu0WbOCdcD+04lyul0ilHrdrtlb/TFpoEG0vy8PYOAZ21FRkaKgaepI3pPdXZ2CkhB5VhTNHkwEq2m8cfxBmA4+Ht6esTdTrSaRjefg+tA7wsq7ewjjXNmLaIiyufy9fWV+SQiR4OEhyy9kUS8teHKvmmKj97rpHZpwEMb+JryqOPHvONC+B1vpYW0Nn7Pm/6oZQp/Ry8NjRBNX6NR4k3pIaClY1L4e8BIV/WOZ6FRz9gMghpMvNHU1ISmpiaDnGFRPh8fH/FoR0REIDw8HOHh4YiIiEBERIQhXTc9ljpdLoGs2tpaVFRUoKqqSioLa4+eTobARB+a/kSDkv1rbm6W93iu8XlpWLP/3uOi51l7nfti81byFi9eDIvFYlAaDx8+jMsvv9yAZrtcLvzud79DUVERAM/avuGGGwwG14cffojvvvtO6hbMnz9f4jtnzpyJRx55BBs2bMDbb78NwKP8HjhwAK+//joAYPny5Th+/DiWL1+O7u5u/PnPfzakhWXz9/fHmjVr8NBDD4lCy75t27YNs2bNErkFeJTdlStX4uWXX8amTZvkWk6nE8OGDcPSpUtx3XXXweFwYNCgQVi2bBnmzp2LxsZGZGRk4LXXXsPvfvc7vP3224a6IoCntsQ777yDW2+9FZ988gm2bt0KwOOpqa+vR0xMDD7++GP84Q9/wMmTJxEREYFPP/0Ud999N3744QfU1tbi+++/x7333osNGzbg1ltvxbfffosHH3wQERERuPvuu3HNNdfg4Ycfhsvlie/94osv8NRTT2HPnj244oor0N7ejgcffFDmsbu7G/fffz9iY2Mlq9WyZcvEO8t5tNvtmDlzJhoaGnDHHXdg1KhRuPPOO3HLLbcgKCjI4D0BgMrKSsycORMAZB3oeXzvvfcwc+ZMQ30UjvXatWuFrscWEhKClStX4qGHHsLWrVtx1VVXobu7G48++qhBBi5evBgBAQFSM+WZZ54R2uXKlSuxdetWvP766/Dx8cFnn32G9957D9999x1MJhM++eQTfPrppzh8+LDMY11dHfpKO2NDQx8oGtnVipFG3PRhoQ98rVRrBVQbE1TaqZRRMHpfV6N4mtdbWVmJkpISmM1myXIDnAokJtITGBgoQZzkWkdFRUm2KaJf9HAEBQUhLi5O8qtTkUxMTBQXNw90AAalSSuUwKl0r3x+rQToRkoE3+fBQTSR96HyxENLxz7wOxrBJEWCz6wRTa0o0bDQKCSvo5UYjr+m++i+aYWcz0rlkL/XVAodJM3nJ72psbERnZ2diI+Ph5+fH9rb24Wu4nA40NPjqYibmJiIgQMHCkWFQd9EZolAM4EAlbnm5mYUFRWhsrJSvCLeWXf4bBxrrkdtfOm9oOeQ3gsqzdrjxevQAKRRQpqVRvP6YtNrAjDWaKCypPn5fM11xXWtFX49rhqw0AkVuF69qSN6v3J9875cH/TY6b3C5+ZaDgoKkkBMzjn5tzSISKmhYu10OqXCtDYSqDD2Fsuk11pv//LZ9Dphv7y/z756ezH0mtSGiTfNSlOWKN/Z9LrX+10bZHw+7/nwBmT0vbwpUZxrLRM1nbS7u9tQe0cXzSNVVV+H8ohzzloZrJlCSieNTK5Z7/ou9DbX1dWhrq4OdrtdMgrq/UuDiPLOuxCi3jecG+3p0Z4jHUPH/mgDkb/ThmZfbgkJCZg0aRLWrFkj3HgfHx9cccUV2L9/P0pKSoQPP3bsWISGhmLTpk3YsmULAE9NrenTp+Pbb781pES12+3IzMzERRddhB9//BHbt2/H0KFDMXXqVPzwww+46KKLkJqaipkzZ+K7775DU1MTkpKSJCia6e8DAgJw+eWX46effjJkCZw6dSra29tx+PBh2Gw2WYNmsxlXXHEFduzYgcrKSmzZsgVXXHEF9uzZg/LycrjdbtjtdgNIdcUVVyAnJwc9PT3CFhgzZgzGjRsnrwEY2ATBwcEIDw8H4PEmlJSUiCfW6XQiLCxMPmcRuqioKNhsNpx//vkIDQ1FcXGxFLKsqKhAU1MT5s6dK7IrPDxcPu/q6hLmCNP++vv7S1/8/PwQGRkJk8kk88hGjyNbbm6u/J/zCEDmmXWRAA9F7uTJk6ioqMC1116L77//XkoYcB55rgKeFPJM3sDrZWRkYOjQofj6668BQCiQvzaPNTU1kq6axTbZmpubERUVhauvvhpfffWVFAD09fWVuinFxcW45ppr5DkiIyMxffp0kS16HvtSO2PqVFxcnBzWFIbsONFeCjOt9FPY8TtUxvhHZZOcN6LjPFAoXHlw8tAmNYUKs3cO+vj4eAwePBjjx49HeHg43G638HRDQ0MlW0xsbKwol3RT5+TkSICQ3W5HYGAg4uPjpTAT4EFLKisrERwcjAsvvBCxsbFC52J/qUzoDDDeBpZG5ohQaf6xpibx4NOZdQAYFC/SeLxjBjQdh1Sljo4OWK1WUWpaWloEVSNVQ9O8NPWHhzkPTnKEOc/0OhHlo8GikWQAhoBMbaxpBZCtsrIS+/fvl5ielJQUQSeZCMDHx8O/TEtLw+TJk2Ve6EkjGtnZ2Yn8/HwZQ6KXNTU1KCwsxJYtW4TzqVFkUne0e1d7+7im9WFOTxcVA8YRaMqEdqtybLXBGhAQIEpnQECAAYnpSy0pKcmgaJLf7na7JU5JZ4RiTRHuE62MM7mDrlmg5YLNZhN5Rd4/jQ8tf9i45jnuqampSE5OlloJ0dHRku6Wh7WuM8NYDKY0ZYpk1lCw2WwGsIWxPFQwY2Nj5Tnp5SXSTQOZcoV1fbSc1IaIjnfRXmjt+dXymfKH7/Vm/OpinFSuuR/097j/GXPBP+21897bmu7pjcprjwPlopYxOqEDPaXcRz09PZItjPRUejK0J1rX5aAnixS6uLg4SXEcHBwMi8Uini7KbF1TpLm5GQ0NDaivr5eaTLrqvK4rAsBwjZiYGAP9lHKZVDGuNR1/SPmms+IBEMOLe8RsNqOxsdFAaeur1CkAuOiii/DRRx9h6NChaGhokDVXWFiI+++/35BdaPny5Rg8eLCB756dnY0tW7Zg0KBBqKqqEq9qa2srHn74YVx22WVSvG7RokWYO3cuJk2ahLa2Ntx9992YP3++VI++6667sGDBAmRlZcm53a9fPxw8eBAzZszAvn37JJ32d999h8rKSolbADxGRlRUFI4ePYobbrgB69atQ0REBI4ePYrbb7/d4IGgzOnu7kZRUREWLVpkqBL98ssvY+TIkZg6daroC6Sbt7a24tFHH8WFF16I8ePH4/Dhw/jwww8lKxbgSfl6ww03YPjw4ZJggMbN1q1bsWPHDtx///2GZ2ffAOD3v/89nnrqKaSlpYlMZZ0oUix14zympqae5n1gX9vb2xESEiJxRuwLz2f9mu3o0aN455138N133yEvLw+ZmZm45pprMGfOHIwcORJ79uzB999/j7/97W8ICQkRb6a/v788x/z587Fo0SIMGDBAPInUGXqbR90+++wzuN1uA03sggsuwKeffor+/fvLWaSN3IkTJ+LHH39ERkYGGhoaMGbMGGzatAmZmZliwPzW2pmYEGdsaAwYMMDAWyXVRB8Q2pDQ3FDSeugmIi2Bn/Gw4/XoLtR51Um/4gEdFhZmoAzpehzcVFarFQkJCZgzZ44BLXM6nZJVCvAomampqUJJKS0txdGjR5Gfn4/du3cjJycHMTExCAsLg9lsxrBhw2C1WtHZ2Yn9+/ejqqpKqo0PGTLEkNqRqTC96RwaoSS6TToBaV4mk4eDTgNF86SpDOhKwyx0wz9mVdIZnADIIcu54rgDnuwbVLoZH8DDmIWwNOdXZ0DSyKnL5YLdbj+Nw0qvC5VrFsupqalBT08P4uLihKNOWkBbWxuqq6uxZs0aDBgwQAJ0c3NzhV/d1NQEHx8fDB06FOnp6cjMzJSiWjo2p7OzU+Jx7Ha7VHY2m80oKChAfn4+SkpKZFNzjgIDAw3xJdqYoAGgExgwJonKgh4bAFL0i2uQNUuYFpN7iciwjnny9fXtk0GcgIf/So54a2urKGzAqYJjrG1C3jwBDRp0lCOkp1ApJrefeykqKkoM2c7OTlit1tO8IdqjEBERgaCgIFFaAwMDERcXh/j4eKFJMtuQDg7Xa4Fz3t7eDpvNhsbGRthsNkG6CBAQpWOfGhsb5ZBjoVEqtFRMAwMDpVYD9yZwSrlnv/38/ET5781gB2DwXAIQmUtZrNFxKgvcvzQaqDR4e0xouOj4GC1/Nf1Ne3q1t1NTu2jU8PlI/aSRQ08EPRXeXgp6M1pbW8XgY8YwFiLjdVkHg0ZlREQEQkNDxZPBdNiakmez2QzB5rW1teLJKCsrQ0VFhRiVlCWcA557NI5ZoE+fiUynrs9R0tB41vFMoJxgemU9pgEBAVL4k56Yqqqq/90N/n+hcc0SVDpx4gTuvPNOSeOqKcZs9MrrYnEEcOh5HDZsGHbv3o3BgwejoqJCQE3Ao0wPHDgQ+/btw6hRo1BQUGD4nOczX99+++1YsmQJsrKy0N7ejptuuglPPPEEUlJSRF7odXrVVVdhxYoVyMzMRFNTEy655BJ88MEHGDBgAOx2u2EPv//++4iNjcXFF18sRn9vfWXmqKVLl2Lbtm3YsWMHBg4ciOrqanl2Ust1HAD1hc7OTuzduxfr1q3DX//6VwCnMivq78+bNw9/+9vfkJqaKmcd9bjvv/8edXV1uOuuu1BWVoZrr70W69atM8wnwSDvSt0A8NZbbyElJQUXX3wxSktLsXjxYuTn52Pjxo1IS0tDbW0tRo8ejW3btmHAgAGGc5F9Y120zMxMFBcXn9b3xMRE5OfnY/z48TjvvPNw9913Y8CAAXKuc143bdqEvLw8/Nd//ZeMhfc86qbjD3vrq57HXxsL7zX6W2xnYkKcMXWKVjGVHeb8pptZZ7Xg5PDw0Aiwy+US4cnfU5nVrmseRp2dnYiIiDBsJC4UHu5U1gHPJqGgpqK7Y8cOjBgxQpQ5Hx8fiQcg9UYXaAoKCkJycrIIaKfTk/aSlR3r6urgdnuqGI8aNQphYWFobW1Ffn4+ioqKkJ6ejtTUVMTFxRkKJPEZtaeA/dIInKY9MGCWxhZwir+vFSYuUNI49OEOnKInkBdPhYGB5YwNYfYHbsSwsDBBxOh1ILqoaRDsAykrnHOz2SyUBXpQeD1fX18Jom1tbYWPjw8aGhpEqQI8HEoe3Onp6UhISIDJZJJCacww1NPTgzFjxiArKwv9+/dHeno6AIhywMwidrtdlD8mCOB9d+zYgbq6Ogka5txr3rf2xmjalOZC0xgnAsn0pdrTRIFNDwUD3slf5aFIhFIbNWeIDfwmmzbGzGYz6uvrRXEiCEAFietHFzQDIHufBhhlhaZAAR4KBOeI1+e4Uy7peXO73bJHiRi2trYaEgHoexDh4jOwX9wbpOJpzwnXOpVGrimr1SqeRsaGUeayaCf7wn5TVmj6GdF9TXlkf4BTFByuTf1aezM0ZUvTrIBTdB+iid4oO+/XG92HFB/+ls+k6VFavnHMqTjyTGAfdUC6TlurDSWn0ynjTvCCxi1j8DinrBLPyvBWq1WMDJ4XlMVMyMHkGG1tbbDb7ZIi1GazwW63i5LAe3Z0dMjZ2NPTI+udfdUeJu3tpCzRlFs9TlzHlMFOp1OMGM5bWFiY7CNNG+lrbdGiRfD19cWTTz6Jm2++Weo1AJ7x/dOf/oSYmBjhw3d3dyMrKwtPPfUUbrnlFtjtdqSnp+P555/HrbfeisbGRpSWlmLOnDmora0VMAwAXnvtNfz888/45ptvMGfOHJSXlxs+B4yeaABYt24dHA4H/vnPf+LBBx/ETz/9hNtuu03mFfDIwg8++ACPP/44duzYgZtvvhlNTU1wuVzYu3cvbrzxRjgcDrhcLowePRp//etfccMNN2DZsmUC1nR2dmLBggVISEgQY+C2225Deno67rvvPtx5551SSHTOnDmor683PLt3TaZ//OMf2Lp1KzZu3Ig33ngDr7zyCnbv3i2fUwexWq1499138cADD2DDhg2oqKgw0LTYx8cff1z25u9//3tDxXQ9j7rKenZ2NpYsWYIbb7wR//jHP0Q/vO2223D06FE0Nzfj+uuvx7Jly/D+++/jl19+wdy5c7F06VJ8+umnQnNi38rLyzFr1ixUVVWhp6cHMTExeO2117BgwQIpXTBnzhyUlpbiu+++Q11dHVavXo2//OUvKCgokL489NBDkhGKrxsbG/Hiiy8CAP72t7+htbUVzz33nKw5wGOQfPjhh3jxxRexc+dOkQd6HgHIPC5ZsgQffPABPv74Y6xdu9ZgZKSnp+OFF17Abbfdhvr6evSVdsaGhuY2a0S3N/e296Gk+aE87FkBlU27ximE+b5GyfkdnR6Q99GUCKZa7OnpQV5eHsLDw5GQkCDeBnpD+Hsql0S5oqOj5VCpr68Xhddms6Gurg49PT1S+C8+Ph7V1dXo6upCfX29KCktLS1ISkoSyhEAOfi0wPGub8GxYj819YqorxYWdPtrz4fmP/NfKiLAKQRGK87AqcOH16YBoCkZWgnU3G3tYaISRWWNBaoAD3eRxhDTfnJum5qaDM9aWloqnrSEhAT4+Hgy/VRXVxsoWZGRkRgwYIAYeHT5kqLgdDqFZtXe3i6UNFIrTp48ibKyMlFiNfVMrxMqdDQmdN85J+Spag8EFWteR69bopF6Djkees9x3fRlQ4PzSkWQh5Jet5p7DkBQWm0UADAYbNrQoBLGOeK4ajoRPSJ63PU1OD/MAKe9Jvw9jW5dbVs3Gu6UlTpmg31kY/C33kMtLS2nxSgAMMgOrg+93th/3Q+uM/6eRor2QPB59HNR/moaH1/TyPCmuerfai8J17VuOp6ATccs8I+eYR27xXGiUaaNDY0008Oo97buG702LEDL4G/KKWYZ08AZYzF0EUBNmWpoaBBvK5+DXgR9pnHsvYEhjo2eR0251bGLGqzyjm3THi2uD32W99U2fvx42dvfffcdAE862fHjx2Pt2rWG9Q4AI0eOxLhx4wzveX+nqalJlFTdpk+fjpqaGnzzzTeGdRoUFITp06fj559/RnR0NDIyMrB+/XoAkBiGuXPnAvCwJBhMvmPHDgQGBmLixIkwm80466yzsHv3bunH+PHj4XK58O233/b6rPv27UN0dDQuvfRS6av3XOq97nJ5klLovoWGhmLq1KnYuHGj0EM3bdpkOG98fHzw008/Geh15557LhobG1FeXm64J2XG2WefjdbWVjEodD0J9ichIQHZ2dlYu3at4Vl14zPs379f3vvhhx/k/19//TWuuuoq+Pj4wOFwYM2aNZg1a5YY01OmTMGGDRuQmpqKhIQErFmzBtOmTUNFRYVkKGVrb2/H119/jSlTpqC+vh5r164VulP//v0xcOBArFu3Dtu2bUNKSgouvPBCrFu3rtcx133hPB46dEjGMzY2FuPGjcPatWsNCQD4ey2vdbvgggskOL238fqtt/+ojgZzi+tDmwND5Y2CT1d+BmCgkfj4+EiqXC38vNFeGg6s38BDGDiF7pAepLn9LLxCZSUoKEiqgI8ePRrR0dGGQ5HucFYxJ32np6cH1dXVyMnJQWFhIQoLC5GTkwOn0ymZi9LT0xERESF0C7vdjqqqKqn1MXz4cGRmZhrSVpKuwQM7MjJSDg9dxImHWUJCgmTWoZFBzjHRPRpwERERwlmnW08jrvX19YJ0apRRo74sWsfUiQyCpDLDedLBsQAkaw7H1ul0orGxEc3NzULxYlA96SCcU+bhp1HS0NAgRZciIiLQv39/AB4PT21tLcrKyhASEgKXy4Xo6GgMGzYMI0aMQHJyMoKCgoRuEx4eDrPZLMi03W6X91mMr6SkBLt27RL+MpVGs9ksAWL0OpBWZrfbhWrndDpFiaGRzeQCHFdmAuPcAZA51R4iKn9M60uFVtdAcblchuJSfamlpKSI55MxTDQyiNZq3r9Oscw1ApxK06qVRu2hADxGsw6c7OnpESqS2WwWzx3gkU/h4eGGtevn5yd0RIvFgoSEBKSkpCAxMVEOMAIXjJvQ8SXe6YhramrQ1tYGh8OBiooKQbvpXSBAween1zUwMFCy5DHbERF2NnoyuX4AyLgBEP465TXHRcc7aMOY8kEbCzrDFg1nb+OFngoNdNC7pzPK6dgY9p2yhF4KPRf0iPK5uecop6h49/T0SJApr8HfcJ1xzWnPMcc3MjIScXFxEhPj6+upA8W+0+PGmAzer6GhQYq+McMUg4J1gVedKYggDKmCHG+OGQABzDgflOXaI6aTgrAmEueWxiu9YNrbHBAQIIXY+lIzmUz4/PPPYTabceWVV8r706dPx6pVqzBgwADU19cbjILXXnsNWVlZUgyP48218a9eHz58GJ9++ik+/fRTHDlyBKNGjUJ+fj5SUlJw7NgxjBs3Dueddx7+9Kc/YejQoTIHpNxQnwkLC0NJSQlmzpyJfv364aWXXkJSUhJWrVoFu92OG264AQDwxRdfwGQy4frrrzfEYVIeAZ5A5G+++QaZmZlSRK63cWLq3tdee81AG8vKysKBAwcwePBg3HTTTbjqqqswYsSI06hR3q937tyJbdu2SRYo4BR1qn///li3bh1OnjyJ22+/XTJKAjA8+6xZs/D2228jKSlJrqu/5/2a3n3dL39/f7me9+thw4Zhz549yMrKwrx583D55ZcjOztb5vGJJ57otW+7d+/Gpk2b8MADD8h5P3/+fCxZsgT9+/eHy+XCzTffLBQ4DVJ4e4Z+bR5nzJiBlStXIikpSfSA7u5uAT0pZ/X1/tU8/hba/2qMRr9+/QyvKawByOHIuAcdv0EUi0KRgpFoIA8sokRUrhhkzM80chYaGorg4GBBsGgkUImjECWViHnGw8LCkJiYKNxGHmytra1yLz8/P6SkpCA4OFiQrNLSUlRWVqK2thalpaXYvXu3KONdXV2Ij49HWloaEhIShDNaWloqmSa6u7uFztOvXz/hB1PhJmqqx5Z84urqasTExMhhZDKZZOy6urrQ2Ngoh5ifnx+Sk5Nl3LXh5e3a5e95XeBUEDsNgNraWlRWVgq9LTExUQoGsl/afQzAwP+tra1FcXGxZF1xOp2IiopCREQErFYrAI9bs7GxEQ6Hw1Dt2+VyYejQobLpyK2urq4WpT4wMBDnnHMOMjMzkZKSgv79+4uyyMJ9OuC2o6NDjL2uri4UFBTgyJEjKC8vR3NzsxgafAYqbew/lSXAU8WTNRzcbjesVqvEZfj5+UmchTbomIVCr1ui+FyDXJOBgYFwOBwICAhAbGysCN6eHk/xpL6oIACeA057POkxc7lcolgT0NB7kvtfB87pGCMe6lrRNps9NRCo1NlsNoOyy+/rOAKNGlMOUTnLzMxEXFwcrFYrYmNjMXjwYERGRkoSA8oMzispXzQQbTabZFGpq6tDcXGxVIjmIUPll4HeOpNReHg4QkJCpKCljpdgPzTdidfgM2iZoOmpGuDg2GlqrI4nA07JCd3oQeK86ftQHvD/3GNU9rVnS3t1ddyC3W4XSpn2gLpcnhgMpiTmftdjyfnhc/I8YCyMn58fYmJiDAVdeT7wjNLPR5CMwf+VlZUoLy+X9Np1dXVoaGhAZ2cnYmJiDJ5RbwYAs/sQ8NGp3+mJ0WOn68iwjzTSaYACpzyv9C4T8NAeOlJJ+1qjfARgoJX4+voiLCwMR44cwV/+8hdDMLgOKgaA559/HuPGjcO5554LwFNbYurUqTjrrLMAAI888ogEg9M73t3dLUHDHHsdRJyamor9+/dj6tSpGDNmjAQRr1u3Dnl5eViwYIGkd6eOwoQWui+BgYGYOXMmXnvtNSmUrIOIm5ub4evrKQFw+PDh04LBdQsJCcHjjz+OsWPH4pxzzpH3fXxOBVBThrS1tWH//v1YtWqVUJl27dqFdevW4aGHHgIAAby0squDwUlzCgwMRFFREa688krExMTg5ZdflgBo/f1PPvkEZrMZ11xzDQBP/El4eDhmzZoFAHjjjTeQnJxsiGOYMGEC1q1bh8zMTNTW1v7LoH7dN+/AdgaDP/LIIwAgn0dFReHYsWOYOnUqDh8+bAgO188OANdddx1eeOEFAeJ1620eSc9sbW3FO++8g5iYGMyePVuC+gsKCgzB4P9uHn8L7X81RgMw0kaIFhKZZJwGqQSavkChyAMIOCXwe3p6ZPHyHt51JMxmT+VIKgwUmDxQKEDYYQY680Ak/YgI2bZt2zBkyBDExMTIIaS5sSzuw3iP+Ph4BAYGIjIyElarVQq81dTUyAFot9slE014eDgyMzMlToBBoHl5eTh+/DgiIyMlhVx0dLSMKwBDZpzIyEipzNnT48myw6Ji5CozFz//GADt4+Mj/GMqbJqaoakTmltJbxM3DZH/9vZ2VFZWSpAsjTguNCJxjY2NEpxdV1cnh6tOmdfc3IyqqipJCcesOvHx8QYPCQBUV1cLd72hoUGEWUJCgtTHYAVQrk2uLZvNJmtBx6M4HA7U1NTg6NGjqKyslDmkMUGDggc8rwuc4n3S4OIapoePCrP7v2N4iISyr0S6vCs768xKVAI0vYzGYl+nTmkaIZUtXX+Hc6WVbY2yc8xpCPK7GjnXdCK9HhgzwTHk93UsgPbYaYW3s7MTRUVFBq8qg07prQQ8c0eFj8/EeQ0LCxOFEIDUZyHP3xvFooFFLxtpfuT1U+4SmeMap0ezvb1d1q/2UHh7HTRN01uB5e90zJ033YoGAceLHh1taNDbAECUDQ2IaIqVDvTu7vbU+2lubhajj40Z7RwOh1BfAYgnguuF9/dWyrW81rEY7Cf7qqlhXE+kaNlsNpSXl6O6uhqNjY2SxjM4OFg8Svo8o2ygzNUeOo6Djofkuak9rfTw83m41nj+cr3xO1znvBfHSSfy6GuNSrmfnx/efPNNrFixAjt37oTD4cBdd92FAwcOICsrCw899BDuuOMOA7cegPDfAU9cQkVFBf72t7/J56tXr0ZFRYXUimBykJaWFixduhT79u3DJ598gpaWFjzzzDM4cuQIvvjiC8yfPx/FxcVy/rlcLjz11FNwOBywWCx4/fXX8cQTTyA3N1f2g3egb0dHB3bu3In/+q//Eplw5MgR3HnnnWIoOZ2euNGFCxfiwIEDGDRoEB555BHccccdkmjCZDJh6dKlKC0txWOPPQYAePXVV/HDDz8gJycHS5cuxcKFC1FbWyv7avHixSgrK0NMTAyWL1+Ot956C7t370ZUVBReeeUVLFq0CMXFxfKsjz32GEpKSvDZZ5/hn//8J/7+978jPj4e8+bNwx//+Efk5eWhsLAQCxYskP5q2verr75q6Pvrr78ucuGNN95Afn6+wWAEgIKCAtx+++2SxrakpATz588Xo9nl8mRqW758OTZs2CAZu5588kls27YNX3zxBQDg4YcfNgB23B92u13mUZcRAIBzzjkH119/Pe644w64XC7s3LkTd911l+ypiRMn4s4778T8+fN7ncfhw4fj3nvvxfz58/HGG2+I7st5bGlpwe23346WlhbcdtttSE1NxUMPPYTW1la8//77BvpYX2pnbGhQ+ePBr/loPKiJNJESwYNIIzJEU6hgUOBS2eChBsBwkGnXMhU7fp/X4zPRtc/fkh/c3d2NxsZGHD16VOgs9ABoVz2zlPCQYvYZprbkgtYHg8PhEKXAx8dHlAoe0PX19WhsbER9fb3BY9Hc3GyIweDhHxgYKFWDeahzc2q+NAONOd7cxNrdyGw8+tAEjOk8Na+X1yMyoKlaNPBIF9GeGKJ9On2kRqN9fHzkM4fDIbQCZvEJDQ010F8aGhpQV1cnCBIzNcXHxyMzMxPDhg2TVKP0pDBFqo4JIcLrcnkSBNTX16O0tBQlJSXynKTDse+azqR/z7GiEqEpL1SM2Qc9p95xF1rxA05l+mDTtTOY778vGxhsGqzga641egA4phocAE6NkZYhmidL44DjTOFPFJzuacCYuYb/53MAp+pw6OdmbQStgFOuREREGJ6Xc66pQlSA+ZxRUVHy3ET1OcfeshA4lSGL1DOCCZQBAESJJuWA8kfTXHltGsG8J0EhygK9VtkvjpX2Qujva46x3jv62pRbmrbJz5xOp8gaGpSMrSJFSgNK9A5S5nGN8Lm95Q+TfTAWj2CPjsXQgf00wLjm6Hlvbm6WGkv19fWw2WwGryyNC21EsX8acNPjyu9ynoFT9Dc9njTayAbQZ4A+K3gP9p8eXY57Xw4GHzlyJEwmE44ePSopy6OiopCdnY1vvvkGPT09GDFiBKKiomTNhoeHY9y4cdiyZYuB+x8REYGNGzeK4TF58mSUl5dj7dq1uPbaa+Hr6yuJSLZv3y56AFt4eDiCg4PR3t6Ozz77DADQ2NiIgoICAMDGjRsBQNIl0xA0m82YMmUKcnJy0NDQAF9fX5x77rk4fPgwKioqRCEGgKqqKnzxxReYOnUq8vLy0NnZidGjR0tfhw0bJhRs3SIiIrB27VrJ9ETqsq+vr+H7wcHBOPvss/Hzzz+jra0N8fHxiIyMxPr161FcXIyYmBgpjssMm1u2bBEvK4FRgh8WiwWff/65rOOKigpMmzZN+sqmEyEAp2I6zGZPwdbu7m6J2zz33HNx4sQJoUHzXLDZbDLuAwcORGRkJHbu3Cl9ZeProKAgTJ48GVu2bDGk0504cSIaGxtRUlIi6ZIBj0w999xzsWfPHgGcAWDMmDHo7OzE6tWr5RoBAQGIiIgA4Alqd7vdMo8jRozA+PHjER0djfPPPx/79u1DZ2cnzjvvPJlHq9WKxsZGuFwuhISEICUlBdOnT8eWLVtOC6TvS+0/itGg14AKmQ6y9XbNU3ACkFzeAOQg4XfMZjOsViuqqqrkMObioHCngqk5shS6dMvzYOUhpg0Xp9Mp6XAZuxEaGooBAwbgnHPOQXp6utyL8SVaOU9OTkb//v0RHByM2tpa7Nu3D/X19aiqqsLBgwdRWFgoh6DZbMaoUaME0WINDxpWLLyjFffGxkbDYR8eHi4eAKZYJOUjMjLSEDirXen0aGgjkLEBHDtNXwgKCpJ0kDRQ2G+tRGk6AuedyjWRVV9fT+Yvoms+Pj6SN5uHcmFhIWpqaiTYvl+/fkhNTTWk9aWXiNW9iSRxTQwfPhxpaWlIT09HWlqaxMJERUVJClpypZuamkS4mEyeAn15eXlCcygvLzfQT7RCQUpPaGiooA5EPAAIQq6pEDzE6WkCTgUs02DUaI53ACjHjYo41y7jTXQ2Ki2s+1IbPny4rCUi2/ROhIaGirJO451eCZPJJFQ1AhRUBInk06VNSiNRYCqkVOQYH8TMalSGW1tbJcMT+fNcH/w+Y3T8/f0RHR2NxMREid2IjY0VVDwkJETSYWuZyHs1NzdLoSsGEdPzSTlEz6VWcvXeZsFNKp/Misex0sYOlWiudYIIVMq9Od7BwcEGvjpBBcoprk2t3GrvCedEc90po3g9bXBrGcbzRNPWWPCOMVxa/mujSgf5c03Qo01PIj3FFotFPBp8JnprKRN19i+im6S0klra0tKC+vp68axwDXG+6YmmkcTPNcimQQh6qRkfoj1M9Ozz/NDxgfQ2NzU1iZzmntKpc3l+RkRE4NixY/8Xd///TjOZTPjiiy9gNpsxa9Ys0UOmT5+Or776ComJiXA4HDL+lLcTJ07E5s2bkZKSgtra2l+9/okTJ/DWW2/hmWeekfceeughXH/99Rg8ePC/fT4NOPBs04AS13lYWBiqqqpw1VVXYf369QgKCkJVVRXmzp0rgdP8PuDZk6Wlpfiv//ov1NTU4Pvvv0e/fv1gs9l6vXdvr3/tWdPT05Gbm4shQ4aIgfRrv7/rrrvwwAMPICUlRc4uxrTpvgKnYrGCg4NRWVmJq6++GuvWrZN56W0e9e/feecdCcCuqKjAokWLkJeX96vz+Nxzz+G8887DmDFjTpsHeiYyMjKQl5eHESNGID8/X367b98+/PTTT3jxxRdRVFSESZMm4cCBA4iPj0dhYSGmTp0qBQzNZjN+/PFHlJeX4+abbzb0lfO1atUquN1uXHXVVQA8dTWCg4Nx1VVXoaqqqtd5PPvss7FhwwYkJyejvr4eY8eOxfbt25Gamora2lrRd//dvP7fbGdiQpyxoaFzxvPg56Gp61hQwQROIYlUWPWDRUREiFIQGhoqCBYPWMZoUEmm0k+0UAtnHjSkSGgUUdcf0MilLn514YUXYsCAAbBarfD19ZUAYLqdqUjQYrfb7RJb4HQ6sW3bNhw7dkziB3jgBQYGIjk5Ga2traKQREREiPLd3t4uMQgMdCWHlgprUVGRQYFlEDoVBhoi3EhMeaa5xZwPBlSaTCZYrVZDXAnTrPFAoiDUFVe10qcVGCKe5JOT/mC327Fv3z5UV1eju7sb48aNk0D7wMBAJCQkoLy8XKpz22w2oV3R4+Hj44Po6GgMGDAAY8aMQVRUFCwWC0JDQzFs2DBUV1cLbSIqKkpSSzY3N0sNkubmZhQUFCAnJ0eC0kmPo6eABhZrA4SFhUkhSD8/P7S0tCAyMlIO+NbWVkRERIgiEh0dbfDQ8V9SNADA4XAIhYPGMeeKaYyDgoLEQKMCB0AQMwry32rxnn/XWEeDip/dbjegYFxrPT09Qn3je0xbTTnDgHsq2zo4mHxqCn2OufZ4hIWFiYzRaZ55fRqP2gOjm7+/P/r374+YmBgxnFnjhXFkpDZR6eacd3R0yDqnAl1XV4empiYDVY4yr7OzUzjHOpGC90GjDVnKABoq/B3HnxQryklNF/IOOOTvue+5x3l/ygIaFvQwAqf2GO+l42EoOzSNkJQozqfNZkNZWZkg/YxT0WCVPg8IetHQ5PizUGt0dLSASRaLBWFhYQavA4M76S3hfOhnqaurE480PRl6T7PxDOE1AgMDpSglx0IrZ+w/90dPT49h3VssFrS0tMh4ahqhj48P7HY7rFarUKnILqBBxrGnp6avFuzjmdbT04OCggIsWLAA3377LSwWi4CJZ599Nr766isMHDgQNptN4hr4+a81pkLXVEYaoKTr/Ku2ceNGHDhwAM888wzy8/Nx8cUXY/fu3YiIiMDx48cxe/ZsbNu2DYAHZf/ggw9QX1+PW2+9VdJcE5w9efKkUKY+/vhjjBkzBjU1NbIWdF98fHxw/PhxPP7443jvvfdgMpmQm5uLl156Ca+//nqvz3rgwAF8/PHHeOGFFxAWFibpddm2b9+ODRs2SBwDAEk/73A4sH79ehw5cgSPP/44jh07hksvvVSUcQD46quvUFdXh/nz50sc48UXX4wVK1ZgwIABBio95/Grr76S33Oft7S0IDw8XGTAr80j6cbNzc04ePAg3nvvPXz33XfYtWsXxowZg8LCQmGcePeVNHXGwzU3N+Pmm2/Ggw8+iAkTJsj5zXm88cYbxQMEAF9++SUcDgduvfVWAJA1Sq+Jfk1Kuvc8eq9R/frJJ5/EpEmTMGXKFBw+fBhvvPEGli9f/m/X4//p9r9qaERFRRmC6lhFmlQGs9ksQr6npwehoaGiLBFxA05liwJO1ebQlUy10KVCa7FYhN9OAUo+vVbseW9N6SH9gIimzmrAAzEhIQHDhg1D//79JRMCUTXGD4SFhSEyMlKC4pmurra2VtKtVldXo6ioSBR6egiCg4MREREhfxoJJ5eaiJem4wAQmhYVXDbNqeYCJsWGiCKVZ5fLhdbWVtTX1ws6T3QsIiJCNjO9QJwrGhFEIRlzoNE3zhPgMSxbWlqkyi29HDzk+/XrZwjip7Ci4s5sXZy7wMBApKSkID4+Hv369UNSUhJCQkIQFBQEi8WCIUOGSJaXiooKWK1WOBwOMW59fX1RUVGByspKnDx5UvJo8498aU1Z81bsORc88AGId4NrnopSbzQevQY1Ik1DmoF1Wmmi8akpE6GhoVLrxcfHp896NJKSkgxxFUTwuc/YaKhqWUB0msqyn9+p4p5UfAEYFEZNv9TeAa18hYSEwGKxiDzRdBYa4JRxVGj5OZMbxMTEID4+XoyO6OhohIeHiyeBiR80BZSxGaQbkh7U3t4Oh8MhMoT0Ia4JrknN/SeNSlNAdQwVYPSgaYWfn7GvlKOa0sA50p5S7U31NjQ0fYtZ5bw91r1RcLVRRQorx4dyiEaMN43RmxZKj0ZISIhQM3XxPX7OuDsaEyaTSWQyvbas7k5Pdl1dncyXzjJEGaHlDAEMgmA6EQTlLeeF1+L40ZDmHPMs0waWzjpFMIoUTsYR0dPP+DOOH1Nm9qVmMpmwYMEC+Pj44JVXXsHcuXOxbds2Q+wAAMTHx+Piiy/GWWedhY8++ghbtmyRz37/+98jPT0djz76KABgzpw5GDJkCB5++GEAngJ6o0ePljocuj322GM4ePCggdo0Y8YMXHTRRfjzn/+MSy+99P/X3nuHR1lnbcD3TArpmfRGCiRAgABRQkcxggIiKoso4K6r68paQXQVsaGoqFgBG6iLwKoIKi7sCkjvTUJADARCgJDey6SQMvP9Md99cp5JXNn9fN9XvivnunJBMs88z/Nrp97nHJSWliI9PR2333471q9fj+LiYnTq1AmTJ0/Gxo0bERcXhz/84Q+YPn06Ro4cicbGRmzfvh0AMH36dNjtdrz//vuYMmUKdu3ahebmZowcORJffPFFm6RjPS+TJ09GWloaLl68iOeeew4HDhzAjh07DJ57wOHwefnll3Hw4EHs3bsXR48elc/Gjx+Pq666Ck888QRuueUW5Obm4ocffgDg6BWRmZkpeRPjxo1DWVkZjhw5gsmTJ2PDhg3o1q0bbr/9djzyyCMYNWoU+vXrh7i4OEyfPh3Nzc2IjY3FVVddhS+++AIPPPBAm3X09vbGI488gunTpxv6Gi1YsACrV69Gfn4+Zs+ejZkzZ6K6uhpxcXF47rnn8OijjxqKG9x66604deoUzp8/jwkTJuDrr7+W/BXS2LFjce211+Lxxx9vs46Ao3hJcnIyVq5cidmzZ6OgoABffPGFrGNhYaFhDzQ2NmLfvn1YtGgR3n//fQDA/fffj4cffrjdalGJiYn461//iunTp6Ourg4JCQmYPXs2ZsyYgYkTJyImJkbyawYOHIjw8HCsXbsWkyZNwokTJ3D8+PF298L/Jl2KCXHJORo6YVnXrifz08Kivr5evDBUtuiBoXDXYX7tpdQJnRQ8WqHVERUtCHlvClEaLHoSnL1GfJcLFy4IxILYQApxCkpWTSIUh4KC4Ws/Pz9ERERIiJDwH1YiYlWUuro6BAQEwMfHB15eXgbMMpUnncwYEhICu721UhHHRUW5oqJCsO0AxBPWqVMnMSAoDOlNB1oVOZPJJKF2KjScRwphrjPHrQWoNgApTOlR1tAvQr6Yx0FvNa+lkULjzGKxICQkBImJiQgNDUVQUJDAALRXmJEgNmfUyn1ZWRmys7Nx4cIF6btBLzGjcRyr9rgSSsLwpMY9c19yj9Fz2tDQYGi+oxUfZ0PE+TOtFBIaROVDJ8v+/yFHQ2PoOSa9p7kuzjkY2uCjEcd5pDJOA1Mr0jp/SUOYqIjRwCQ/If9pL1pA0uvGfcf1JMSG7649/s57Tucq0HtKyJKbmxuqq6vFkaKfwTFzz/BefJbeo0Brc0RtGHMtOB7ej9/h+ea86IgOYU00hAjPJESJ46VCSwOOPFY7k7hWOsdMR6b4f66Ndipph4cem67A5OnpCT8/PwQHB0tfDBqq+kxy7WhIsoIVi0owD6O0tFT6KZEfE47H5+roCPe0Xica2gAMhhrhaLpyGfc250nzEspLDaFwNsKIQtDzyznU/Opyo/DwcDnfrLhksVjQu3dv7N+/Hy0tLSgsLMSyZcvwu9/9Dn5+fvLdQYMGoW/fvoKlB4Dg4GBER0fL70FBQYiJiZHfCY384YcfEBcX16a8uMViQbdu3TB8+HBs374dvr6+SElJwZkzZwwJ3FlZWaivr4efnx+6du0Kk8mE77//HsHBwRg8eDAOHDiAyMhI0Z10Nanly5fL//39/ZGUlIQDBw4IrPvYsWNiACQmJiIhIQGrV682RGFSUlJQUFAANzc3JCQk4Omnn0Z+fj48PT3Rv39/HD58GIGBgYiNjQUAQ3QBAKKjo6U646BBg7Bz507JM+VYfX19ER8fDwDYuHEjWlpacO2114rucf78eZw/f77ddUxMTMTAgQMRHx9vyFkEHPB9Pz8/lJeXIyEhQT7v1KkTEhIS4Orqiri4OFgsFqSnp+Orr75CbGwsEhIS8Omnn8p9PDw8kJKSgrS0NAQGBkrpfK4jf09OTkZNTQ1WrlwpYyfvW7ZsGQBH75b4+HipzgU4zmB8fLw4lOPj42E2m5GYmAgXFxdkZGRg0KBBOHPmDLy8vAxj1b+HhISgc+fO8m6lpaVirKxevRqXE/1H5W3J0HXVJ+LYKVypKHl5eRlqxDvjdlmWleFtKsz0NhFCRGZKCJT2jpHBEm5CRs3KLDRMNH4YcDTm0YYJE569vb3RpUsXDBs2TA4vKzDRCAkODkZISAiam5tRXV2NvLw8NDY2IjY2FoGBgbh48SIOHz6MgoICFBYW4uTJkyIsGH1wdXVFaGgoQkNDRRAwWtDQ0CARHIbcQkNDDcKCSjvLE+pO22fOnJH/m81mgT4xyUnjrv39/SX3hkrCkSNHxDPn5+eHxMREQ88OCn67vbVKEIn5JSxfy+RbKl/EVjMRnpU56JVmF/LQ0FDExMRINIMKQlBQEGw2m0DPXF1dJYJSU1NjKGdcUlKCI0eOICsrCxUVFaKA0FNJhU8byN7e3mJENTU1ISQkRPZbXV0dAgMDpQKXrihDeAIZpi5jSYWnU6dOqK6uNuTD6KRVJtgDEGWDkAgPDw+pakbj7nIub6sVfa4NI5fErzNnRyf5ent7o7CwUP7GMDkNRTZiovFnsVgM/WCYBEmPfWNjo+w51k0HYID+Eb9PXqOND55HetkZyaCR3L17d4SEhEgfGpY3Box43sbGRjEqdBlOFkLQBRY0rJJKNh0m3C8aNkPeRaiQVjYtFouMDYAYBmazo0mpjnhYrVZZF84nYZLNzc0CK+Va6P2t8xIAY6M87VzSZ0NXYKNxoo0/vhv3Bc+FzWYzNNvz9vaGr69vmzLEjHYxOsCxkMcxqlJRUYFz586hrKwMVVVVUkyCxhIdG+ynQnlDnllRUQFvb2/hxzpvpKGhQZoLkh+x8Ih2LlFR5LsT6sV1LSsrE4dPYGCgOEq4vwFHtJs9lLQh9uOPP/7PHfb/IdIORqA14f66667D2rVrER4eLgnEzs4Cs9mM3NxcvPjii/jggw/+7TP095977jnccccd6NGjR5truecjIiKQk5ODwYMHIyUlRSoqDRs2DPv370dgYCAKCgowevRoiV7w+5MmTcKSJUsQGhoqfMhZNdOOi9TUVKxfvx4RERF44403kJCQgNTUVHHM8tzl5ORg/vz5ArHJycnBokWLpHs1371bt244ceIEevbsiVOnTv3sXPD60NBQ5ObmYtiwYTh48CCCg4ORn5+Pa6+9VmBh+n3bm7P2Pvviiy9gsVgM5Wx5Pefk51RWs9mM119/HampqbjyyisBOKJPt9xyC/r06SPvHh8fj8zMTPTu3dsQ6eHnvP/+/ftx6NAhPPzwwz/7vDvuuAMLFixAWFjYz0aaONZVq1bB09MTEydORFFREe6//34xYpyf3d733377bVx11VVISUn5t9f/b9OvCp2KjIw05Dq4u7uLMUCMv/YCeXt7i3JNOAjQCoEgI9UCicLEZDIJRp5KHWvs8//aq6M9nUwI1d57lkRlJIPKPRXNgIAACZ3b7Y78kT59+qBr167o2rUrWlpaREE1m82C6/Xw8IDdbjdEb2w2G6qrq0VolZaW4ujRo4LrpSDVCV5RUVGwWCzw8/MTo4dCifkqVCbYV4EKE400GkQ0yCho7Ha7lF1keVjmOISHh8u707BhroC3tzeCg4MFC24ymeDv7y/QEg2dotLBnAYegtraWtjtdlGU6AWi4k5vM+BQHHr06IGkpCRERkYiNDQU4eHhksTPCFhLi6O2PDvvai/nxYsXUVhYKFCpwsJC2aPEy7NJGz2QJEIadGIwvcL8GwDxtGt8O/evcx6ShigwckOlmQoJzwSTWLk3qBDoKAh/zGYzzp49eynH9jdH9BYxMVfD02w2m8D9CF/kPtfefOZ28QyzOhsAUeAI39Rnh2eDZ51eY55nrWjqhHKuLw1/rhcVPirJNG58fX0RHByMuLg4icaFhYWJQsxzQ6OC7w20dopuamoSjznHxLwlKsDcUzqqoCNGOmpAZxB5lO75QaWe0UryXR1V1nuzpaUFlZWVsn95TtjYTucp0ZGjo4bMu+MP9zvHT6OGPLW2tlacJ3a7HdXV1QLB4rmmccmqL3RYeXl5SeK39pDSKaV5EY3PyspK4ZklJSWSB1ZXVwer1SoGq81mE5goo/pmswOCSgOC8k8bv3TscD1oWHH9dLNb3WOJUWXON9eOzT1bWloElkYDm8Y7c544Rt6DOX2XE2lDo1OnTjh58iQeeeQRfPfdd7BYLCgtLcXChQsRHR0t/Rg0BQUFicPo52j27Nm44YYbpGcBzwtz+zRt27YNu3btwvPPP4+goCBUVFRIIj4AmXeTyYTg4GBUVlYaZMWXX36Jixcv4tFHH0VpaSlWrFgBu92OO++8U64ZOXIkPv30U/Ts2VNg6xyrj48Phg0bhqVLl6J379546qmnkJSUhDFjxrQZq7OhcejQIXz++edYuHAhAgMDJe9U04MPPog///nPuOKKK7Blyxbs3bsXc+bMkbG2NzZ/f39kZGTg9ttvF8ODdOONN+Ldd98VJ6YmNresqqqSvw0dOlQiKzNnzsRnn33WZg1MJhPS09OxYsUKfPLJJ5Igzwat5eXlOHDgAL766iu89dZb7Y6V60j4XEBAAJqamtqURiZ98803qKiowKxZs372HI0ZMwZLlixBYmKi8IiqqioEBwdLyW5XV1dkZGTgueeeE8ODlJycjA0bNqBfv36So1dRUYEjR47g448/blMe+P+CflXoFBkzb0ohAsCA56WXSUNBiE/nZ1QKaX17enqKNw6AwVsGtCZ1U+AxVK2FtIYu0EPKSdAKG8PmJCqufNempiaUlJTgp59+kg6zERERAmuiwcGIjNlsRnR0tHit6uvrpYqUh4cHunTpAhcXF0RERIgSXFBQIJVIaAgwOqExzhSUFOAU8FScAIgCxPlkxIBzS2VEV4ihcCwrK5PKNlQC6JH18vIyQIh0giSv5ZzoXAIAknNBTxsFNJV8KlPEuAcGBooHmJEhf39/hIWFyXjsdrvA17hXaHja7Y4qXtnZ2Th9+jSKi4sFqsa9QQ8k31vDUbiOvA6ACHHODT2/uiSo/o5WmuhZ1ffVpX65Z509wFx7vgPPGL3VzhCZy5GsVqtEMvXcc/+Qb+ioB9eJfIQRNzotyMD1uabRoCNXnGedIM3nUcHWFYuo7Or5pmFJ5wmdAtwfjMLQocH7NjU1SRlVrZTq3CAaNfqd+a4sy0hDg9AqwotYSYhj4Xxx3M75Hc7QMO3oofFEI4PnS+c2OTd64znne2oYFpuSkb/rSJCGuun3Jj+nQs6zYjY7io3Qu08jUxsehKXy7zRGOM/kP5on8jlMyi8rKxPnjE721rINaK3IBbRCcrlPmCenHRbNzc0GyJXJ5Ki2piOgPP9cP+43PlfnpDQ0NMjeY/SKuTpcL+a9MdrLd3VWKC8nmjZtGlxcHL0W5s2bh+PHj4vsBhyQHx8fH7n+jjvuQHh4ON58803Jb4uJicGsWbPw1FNPoaqqClFRUXjqqafw7LPPYuvWrYacD0YWAUcFqmPHjkl/hg8++AC5ubnw9fXF888/j/nz5+P8+fNtlGi73Y6SkhLMmDEDlZWVAr9ZsWIFkpKS8NRTT+GJJ57A559/3ma8p0+fxquvvopXXnkFy5YtQ3V1NR5++GHMmjVLEp9feOEF1NXVYd26ddi3bx8A4K9//Ss2btwoEZRnnnkGx44dk/u+8847OHnyJFpaWmTuAIcxNnfuXLz00kvYs2eP5DZ8+OGHKCgogM1mk+uHDx+O8ePH48knn8QjjzyC0tJSfPnll3jxxRdx7tw5pKSkYMqUKZg1axaam5uRkZGBl19+2QDHJE2aNAmenp5499135W/nzp2TZPTDhw8brr/tttvQpUsXvPbaa3jzzTdx9OhRQxWu2tpacRAFBgZKgRA9VtIHH3yAHj16YN68eXjqqacM9yENHToUt9xyC5588kksW7ZMEBULFy7E+++/Dy8vL/z+97/HE088gebmZpw8eRIvvfSSwNJ79eqFuXPn4oknnsDUqVPh5+eHhQsX4pVXXpHytSaTCa+++irWrVuHU6dO4YUXXkBNTY2h6uXbb79tWMffOv3XfTQ0HEl78/X1VKq0t5geSo1jJmxBCzYqa3wGhaPGGwOtPR+0wNYKBJPNtTeN78X3ZYlUUkNDA/Ly8iRfobm5GZGRkfDx8RHmr+FZzOkg7pVePeZ7+Pr6IjQ0VLyagANvR68gDQUKDMKoqOyyM7hOftV9LLTAbG5uFsXc1dVVKljQQ6oFkNVqlfehByY0NFTuzeRFjlnDprRhQQHHexIaxYZ9zE2hAKTXzsfHBzExMZLoHRkZKeVkaRCSwbMilzYOCFWqq6tDaWkpTp06hbNnz0riKPcrFUzC37gHOJcag669h4REaQy1Vor0fqcSoqFQzAHg2eHnXCvdfZhGIs8R15V7Veck/BbCpf8t6ZwtoFVpAmCAQ2plkLyB11HpJMSM9+L88nNW9eDcacNB8xoqX1Ta9TlmSW/Nh2hYkLhXuOZWq1W8ydrQsNlssFgs4rHm/iIPJX8kT+R+4Lx5e3vLD/cxBSn3sZ473tPZkNX7iNfpCILmJc6GBr31zt5gbYgQ7se9y14fGi6m14RnQkc4+Q78XUPEyP8Ji2IVKv7QSUOIEI0M7gWOi8YoI0ZMwi8pKUFpaalUBaupqZFoJA0m3kMbbs6wSb3GHCejHNq5o0tfA63RLX1OuM+0nKXRoouLkEdrfqELSzg7QC5X6tatm5yPjz76CPHx8ejevTtOnz6Nfv36Gfo1JCcnY8CAAWJ4JCcnIz8/H15eXhgwYIDIfk9PTwwYMACdOnXCgQMHcODAgXafnZSUZIhsrFq1CoADrz9gwAB4e3sjJCQEUVFRSE9PF889DZfExERDWdZTp04hKioKY8eOhdlsbtOULT4+HiaTCUuWLMGmTZukj0NKSoqcqYKCAnz44YcAgB07dgBwnJnk5GSkp6fDy8sLvXr1wsqVKw1JyUePHkVZWRk6deqEPn364Mcff5SI/4ABA+Dl5YX09HRRglevXo3Q0FD069dPEshDQkJwxRVXwGQyoWfPnsjNzUVjY6O8T69evdC/f3/Zo9nZ2cjOzgYAJCQkAICU1I2PjzcUBQEcUb1Dhw7hyJEjiI6ORmJiIk6ePCnr2qVLF6SkpCAjIwOFhYXw9PRE7969cezYMcNYCRNMSkqSJOrw8HCEhobi2LFj+Omnn9ClSxcMHjzY8HxXV1ckJyfj5MmThrH+4x//QFBQEFJSUjBgwACpNti/f3/hfefOncOSJUvkXhaLRdata9euCAoKgt1ux9KlSwFAitwMGDAA+/fvx+7du/HBBx+gX79+cHNzQ11dHTIyMgz5OpcDXTJ0Kjo6WgQ+Q7wUTM3NrZ2UqViyRFxLS4uhlrnJZBIFVneYpkeMteG1UCQeVdc5J9SE1X+0V4l9Nwh58vb2lupHfn5+kgRNgcVGRgAEysUIAb8zYMAAdO/eHZGRkZIEzgROFxdH+V5PT0/06tVLyvWyukhYWJgkEVZVVSE/Px8nTpxAZmamVBICWpMFmaRGod7S0gIfHx8EBwdLmWF67jhXrO9+7tw5Q/UXVjGpqanB2bNn4ebmJo16qBCxaR4jCbrUKz2DfCbXiEKWv7Nc59mzZ0Vo1tTUiCLI9/Pz80NYWBhiYmIwYMAAKa/r6ekpHYC9vLwQEhKC0NBQZGVlSSSExo9W8M+dO4cLFy4gOzsbBQUFYghwzxFSADgMSmK3ue5asddKf2Njo+xN7lntiWaZY3pNKysrpZSth4cHSkpKDAZRp06dDIoc55aGCM8Pcdu6oRmjL9z3Npvtss3R6Natmxi+jY2NhsIHjMBRIFFRpoHMBDkaepWVldJXgOdGw5vodaOhGBERIWvL9SRESEcDmMjM/Q5A1oDvROWVPISKvy5VzdwAFkXQ5W/9/Pzg6+srhoVOotZ5BFqB9fDwENx+bW2tFJygMszmkzp6x3llvhT3r24mSqcGcxboKNDPrq+vF0OMXnPyGMKFyJtZQYz7n1XnNGyMc8f9rBV0Rk4ZQXdxcYG3t7cYDXSEsFcJi2vw+TzTNNhYLpr7juPUPzqCUVBQgPLycuGPjN74+PggJCSkTcSdqTeMPQAAZnZJREFUTicmijKCw6gd1wJobcRJPsM54J5knh6hVJQB3I+ca1JlZaUk1NpsNkNDSaDVCAZaq+XpaH97nt3fOuk9RFq5ciUsFgvGjx+P4uJizJgxA8uXL4eLiwsKCwvx5JNP4pNPPoHZbEZeXh5eeuklgZ1omOG/e+Z/Ypjdf//9mDNnDiIjI6Xq07333mu4D/+/YcMGVFZWYvLkye1+vnr1anh5eWHcuHGXNDfO3wccxlVaWhpiY2Nx4cIFuT4rKwuffvopVq1ahZMnTyIxMRGnTp1qd7z82/Tp0/Hkk08iKirqZ+fkUufrm2++gZubG8aPH/+z14wYMQKbN29GWFgYXnvtNSQmJmLEiBGyjsylAIDHHnsMW7ZsQXp6Orp06SJJ56Q5c+bgjjvuQPfu3QEAs2bNwn333YcuXbogPT0dW7ZswV//+lfDu4eFhSEvLw/Dhw83lO4FHJGyd999F6GhoT+bo/FL86E/Gz58OLZt24aIiAiBY5lMJuTm5iIiIgJHjhxB//79f/Y5/xd0Ket8yYZGWFiYKGwUDmSYbNqmPWX0pNELRO+5xlJTKNIzob2FuoKM1WoViAxhBGTyWjnUWG+gFdfO52mcPZVxMnNCu1gJitfx78Qgx8bGon///lLFhJAiXeM9ODhYulVToWhsdDQgO3HiBHx9faXJ05kzZ5CVlSVwHwCGkm5UenSzrcjISKkuQsHE5n9FRUUSiqcyW1tbK/Ps4eGB8PBwNDU1ITMzE7W1tQgJCZFoTUREhHguqWT5+/tL1IlGFQWjhh4UFRVJQjU9hR4eHhJhSkpKQnx8PCIiIhAaGorg4GCEhoZKid1Tp07h8OHDaGlpQWBgIEJDQ6WvAA0ivldlZSVOnz6NjIwMqTldV1cn+4tCm3uMkSLtcaUyw7nWXmoatVSu+D16TKlc0CBlfxTuOxcXF8F+83s01PW54N7kPuMP/05lqaWlRfJp6uvr21Q9uVwoICBAzp6Li4vk2dCrffHiRYPCSo8so3iErjU3O4oxcH8BkLwwKsB0BlDZ8vb2lrmmYqtzyLif2dNDX8f76QR0/k4jnNcSrlJWVmaoMBcVFQU/Pz/JkYqJiREFmYqp5oGECOmEd/IkRvcaGxslcZyQSEJz6BCw2+0GqKU2lsljbTabQBz5o6MfjFQShsmKToSL0UAizwoODjYou4QvmUwm4W9cd61wAxCjQOcp+fr6yp5gTg3nkXAt7puGhgZYrVaRA4Tm0ggEIDybDVPZKJS5GcwDIjSJUVh/f3+B29Kg1NBJoDXqz7XUuTEaisd1Zt4hy7aTuPe1Uw9ohf9yrNqxxPLedJBxHFo+akipxsJfLtSeoREQEACz2VFpMCIiAi+88AICAwNx6623IiIiQvKbAIcXu6amBrW1tTCZTDh06BAWL16Mjz76qN3n7dq1C3FxcTh37hyuuuoqbNu2Dd9//z1eeeWVn31HRtsKCwsRHBwseyQ9PR1TpkxBWFgY5s2bh759+xqS+48dO4b77rsPJpMJ77//Pvr06SO9fH6ppLnZbEZ6ejrmz5+P48ePY926dejfvz+Ki4vh5uaG0NBQrFu3DkuWLJFoQ1hYmORwhIWFoaioCA8++CAmT56MIUOGYMuWLdi2bRs++eQTHDx4EGPHjsXZs2dlbO3Rbbfdhueffx59+/bF3//+d5SVleHBBx8E4ICJ1dfXY9q0aQAgxQv+3djYHLWwsFCaoJaWlso6NjY2IjQ0FACkLHhYWBgKCwvbwAPZ34h9qHx8fODt7Y2ioiKEhobizjvvxOTJkzFgwADJk3n88cdxww03oKioqE2JWk9PT2k4/dlnn6G6uhr333+/4RoWXZg+fXqbaNWwYcPw2WefoV+/ftKglmOdN28eevfujfHjxyM8PFwcEr8158CvmqPBKjg6RM8HUAjqcD+NDQorjYUGIJhzhqK1QNehfwopho2dB6eNCo3B1lh6nWRObygNG3o7+UziujV8hUKaycZpaWno3LkzQkJCEBISIkKQJVo1tp/KDMP5FotFBLz+W2lpKcrLy6WfBAUZvWH0LjLKohsZ0tvKOeW88P21h4PvzOTACxcuCHykoaFBSt9RQFqtVlHW2PWWhgUhEfwuPZP0+rIpHxWD+Ph4dOnSBUFBQfD395cqMDQGamtr4evrK9/XyVoUqkVFRSgoKEBubq50VqZRQyVAw+6o+HAduHcIIeO8AEZ4Et+JPzSctfLKPUsGoCEVVJI07l17ErleWhnjejlDHIBW4/5yJ39/fwOMSY9Le9rJUwgf5J5jsQJCpKjgUSmjV4k4fw3LaW5ultwO7W2nksvoCKOi3NNcSw254voArSWJgVaID/kWFXgqrIxYEd5HaCUFoIbscXx6PPzcy8tL+Aqv8/HxEUODnnXuX/JVAIZcGMDY2I+fE27KedJJ44R46SRsQjWB1pw9XcVK54s4n0v+X+cs6aaCjIhwHXT/HzYP0x3eydu5XpwHOki0QcWIEHMxKioqBNKq54PrrOUejTkaxxpupx0G/C75ko7+670IQCLv/K7e45rf63nlvcjXdPSEz+YzaLQ7R5cuR0pMTMRdd92F5557zoCnLygowJo1awSCU1BQgEmTJiEsLAzvvvuuQUG22+346KOPkJaWJn+bPXs29u/fj23btgEAPv30U1gsFnEELlu2zNA9e+bMmThz5gzWrl0LAJgxYwZycnKwadMmvP7663j33Xdx/vx5eHh44J133pGmsu+99x5aWlrwu9/9Dg0NDVi5ciUWLlyIM2fOwGQyYdGiRRJNBBz7cO7cuVi9ejXS09NhNpvx/PPPY+3atfjhhx9gt9vxwQcf4NixYyguLsZbb70lEV5WKvzwww+RlpaGsLAwPP7445g3b57kXzBKfuDAAfne8uXLcfbsWVitVrzzzjvSN4yfT58+HXl5ediwYQPmzJmD999/Hz/99BPee+892Gw2fPXVVwaY5TfffGPIzSgvL0diYiIef/xxPPfcc2hsbET37t1xzz33YM6cOVKUo7CwEM8//zz++c9/4uDBgwBgWEcd4Y+IiMDMmTPx8ssvY/DgwejTpw/mz58PAFKd8o033sDrr7+OXr16YejQoXj55ZdRXFyMbdu2SeEZwAEte//995Gbm4sHH3wQRUVFhtKy5LccG3WiuXPnYvny5cjIyEBzczMWLVqEoUOHIioqCkuXLpV1zMnJwYIFC8QB0tjYiPz8fADA5s2bBZ42bdo0bN26FWfPnsUbb7yBF1988bJyElyyoeGcE6GjCBQYGpOqce0U1kCrYdDc3CzMWQtuKnkam87IiTM+XUdPNH5VCwOtHFBw6YgF0MrMqQDyHXhvbShZrVZkZmZKcnNDQwPi4uJE+DIUz/dhtIFRAV9fX1EQfHx8EBQUJJ57lk60WCwoKSlBeXm5QMMoqFgKU1dCYgdsemc5XqAV70vlIigoCBaLBc3NzQgLC0NBQYHME+EohG1cvHhROtpy7FRciGumcs/30EqDv78/unXrhpCQEAQFBSEoKAidO3eGr6+vQJuodLW0tEgpSK4NPaXcLyUlJTh//rxApRjBoBJF+JWGjvD7NAK0l5F7jj/aoGAVLxKvoaDWBhHLiFJpJiNl1Ekb6Fph0IYKDS4qxHpfUiFwNowuR6Iyr414nUBLJQporWZEQ4CeWKAVdqNzDbim+kefa1bD05563sfFxUUipRTuuoQsDXugdf61Ys2cG5475jxxPVl8gf/nteyrc/HiRVgsFjEc6LXWUWI9bp0HRiPAx8dHjBpCRml8kRdxnsmntaechh6NXSqvOnrNKA8dCeRpNJo1zFV3Etf8X1dso3wgb9brzX/ZoFPDsljpUFcFo9zRkUM6RLgODQ0Nkh+nczAqKytRU1MjkRudiK0NSt5f5xAx+qJzXbiP9Rxzb1AGtee0o+zjHiX/0utG4vfozOAPcw40v9BONn3GLmcKDQ3FqFGjMHfuXISFhcHf3x92ux0nTpxo4znu3bs34uPjpdpRcXGx5Fk4d82+5pprYDabceHCBWRlZWHXrl2orKyUvIrdu3cbGr8NGzZMol6JiYlITU3FoUOHsGPHDlx//fXi6Q4LC8Pbb7+NuLg4WK1WSXhOSUlBTU0NPvvsM2zevFmaQS5cuFByHliSf+TIkdi9ezfS09NhMpkwcuRIpKWlISMjA126dMGSJUvQ0uKoPrZp0yZcvHgRERERCAoKAgAsXboUTU1NiI+Px+jRo/HOO+/A1dUVfn5+Yjzt379fIELLli1DcHAwIiMj8eabbwJwJIqHhITg5MmTGDRoEDIzM7F161aMHj0aK1euRFpaGn766ScAkKaGZrMZPXv2xKZNm2A2m9GrVy+cOHECdrvdsI6NjY0IDg7Gddddh5deesmAEkhNTcWRI0dk3p3XEXBEq5KTkzF69Gi89dZb6N69O4YNGwYA6N69u8CdR48ejffffx9du3bF1VdfjZdffhmAA4qtq0wdPXoUx48fR69evZCamoq8vDz8+OOPOHnyJOLi4tDc3Iz8/Hz07NkTGzduhNVqhZeXF0aNGoUNGzaIobFw4UK89dZb6N+/P5YtW2ZYx7fffhuAo4WEm5ub5PJs3rxZxn7NNdfgzJkzKC0txfXXX4833njjsjI0Lhk6xcpLZOiVlZXiZaLCRMbJJF0qfGTaDPFSISDp6jBAazlaCqSqqipD1RISmbe/v7+B2bq7u8tmYdIxDyrzAZjQSOgGBRuFI+E4VMrJWJg/wXF6e3sjMTERXbt2RWhoqHjlCMey2+3igSMmm2Ftend1c6qamhoUFxdLN9rs7GyUlJQIHlt7wajUUkF2dXWVuQJaFSB6BCMiIhAXFycwqKKiIuzcuRNhYWGCqdbNshid0UJPG1N1dXWIiIhASEiIQETsdkd54ODgYAQEBCAyMlKSNd3d3REaGirVT1ghgvukpcXREJEKOwUkE7137twpXuHq6mpYLBYADoFLhsTEWFdXV9kXVDprampE+a+rqxOYl93uqBBWW1srWHCr1SoKFe/r7+8ve489CbTAZvIxDQkKfGKj6W1kngzLCTc0NCAyMhIAZGxcD0I2dE+aTp06GTxqlxOxsZJW3ggx43zRmNAJxIRK0dMNQIxg7XUn9JERMq43+UtAQIBAm9hkkfAkKu88V9zjhF3x7PJaDaFpbGwUyCJhd7rqVEVFhSjW7u7u8PT0RGBgoORHBQUFSfSPirW/v788D4CU5abyqyMe+p15fnW1OeZv6IRrbRgQ+snoJA0z8lgNl2xqahIDg1AyrXybTK3VwKjUsmO2doJwHIxg6JwoGiI0aHQVKVaY0hA23pNOIg2R4+8sC3zhwgUxLBjRYElynTOlo4mUfdwT2tmmk+YBiFxgDhAbnPHdCaNkhK2iokKMIZPJZHBMsDAGebkufkCi/KIsYTIvS4PryCjz1Ah5dsawXw7UnqPlyy+/xG233SYwmp9TwsxmM/Lz8/Hiiy/+29Kg8+bNwy233IJevXrh9OnT+Pvf/44XXngBAPDTTz9h7dq1mD17tuE7UVFRuHDhAgYOHCidtEn33HMPXnvtNYSFhWHTpk3Iy8vDH/7wB8M1Pj4+KCkpwW233YZ169bB09MTxcXFuOuuu/D111//2zlho8Dw8HCUlpZi4MCB2L9/P6KiovDkk09i+vTpAByVtnSOBuDoNXHbbbe12yMEgHSujo2NlRyN2bNnS8uDSyFfX1+UlJRgwoQJ8PLywvLlyxEcHPxvSwz/O/q5dXzzzTelG7kzOa9jezRz5kw8+uijiImJkbGFhoaioKAAV111FXr27NlmHR966CEUFxdj/Pjx0rTvv6G//e1vSEhIwNVXX/1f3+P/gn7VHA0mg9NgKCsrMySnBgQEiDJFLxs98VarVRQzwiF0JQwABkVWRxYIV6GgondND5JMWQtR7SUjxp7KBqv9aMgFn088PBk08b8sdUsjRTdnYqJ2cHAwOnfujJ49exqgTTSk+HwKTA8PD/j6+iIqKgoARLknrpberZqaGgnREU/J3hT0zFEpoMBixMFisUhJUSrKNASJ6abQbmpqQnl5uSSEE/9LJZ75Juz5ERQUJNhyNtrTsIqQkBDBp7JyVGNjo+Ciz507J55YNtliDkhtbS1ycnJQWVmJ/Px8nD17VuaEncWpzFHIent7t6luxX3DKkDagCCungoZ54WKjy7ny0gL9wn3Ab2GLDhAbyRhZlRyafDRK8umg/TaA60QDaA1csj5AWDoDK+rllxOFB8fL4oUPaw6esl5BlohPTrxVlfqopFBY5hdafk9XQiAyiPnnn15dASBa08nicnU2juDybjkeTrKQUgX+ynwPNBIpGGroXh0GFgsFvj7+0vpRSaK64aejFjova7zVTg+7l9GLzR0knAwrRTr/CoaGnw/GhX6fXVkhXyUDg5nft7c3NobgmvmDAfkuGhgamcP0Br9Zi18HQVhtEDnP3AeGMVgDgXL0xIaVVpaKhj9mpoalJSUyL2ptJP/e3t7y1zpqAj3FY1ADUXi3zjndGAwR9HHx8cAN+U862gM1415RRwjYYR0WOjqgC4uLigpKTFULKQc5Nro6o8uLi6GEq6XC2VnZ+O+++7Dpk2b5G+hoaHw8fGBzWZDTk4ObDYbBg0ahBUrVmDgwIGYNWsWEhISMGnSJMTGxqKiokKa+u3ZswcfffQR9u3bh3/961+4+uqrxRGVm5uL6OhoWK1WgWd17txZoum7du3CuHHjcPLkSbi4uCA2NhZ5eXlSKIbEwhDnz5+XPEhi7T/99FNUV1djxowZiI2NRXFxMYYOHYpFixZh4sSJOH/+vPAhwFGSNigoCHfffTcOHjyIl19+Gf/6178QHh4uY+/UqROioqJw/vx5WCwWDBkyBOvWrWvX0KCT0PnvJH9/fyQmJuLzzz/H+PHjkZubC4vFgpycHMM9Dh48iClTphiMrBUrVqCsrAwzZ85EbGwsioqKYDabERISgvPnz/+ikvrGG28gMjISU6dOBQC88sor7a4jiY052yuW4ryOGzduxObNm6WnCMfq7+9vGJvZbEZMTAwKCgrg7u7eZh3LysoQExODoqKiNobTqFGjsGDBAqSkpPyiURUSEgI3NzcUFhbiwIEDeOutt5CRkYGvv/4aQ4YM+c3lZpAuxYS4ZOgUBQ29YLpajMYo66RFChadYA1AYFUaXw20hoKp9GqhRkgQB0ZPEj2hVOJdXV0l6YvCl829+H0KIwDiMeP9WPWISh8FKL9LWJSGwBBSxKoldXV1BoVB909obm4WfLCLiwusVqsom5w3V1dXaZZXX18v+TH84Xyzpr6uEMOcCgonQnxo4FDQcCw6agFAKkVR4adyRcOI0Rg/Pz+EhITIPNCLp/MR+P6EJujGWIx8ADB4RSsrK0UZYMi4oqJCYCecQwpKricjaxTQNDz1ntU4cg3705EhDaOiMqdx33oduX+45/l/vac0rppYdnpg+Wy73S4eco6HRi6VHp24rg2Sy42sVqso+Mxt0YYGDTnuEw2D0uulvfXcf86ezk6dOqGmpkbuyXXW+4Pf0R5lzV+0Q0IXhuB60vjV66kT+8k3uJeoYJKvsMcBE4EZ5WKUgcmK9ELTgaOVYf5oqB33Gt+DkBltaHBeW1ocVe0IC+R8cE54pjlvep/zXzqhtHOFn3GteT3nnOeCZ4Wfk/gujCZoWKyWM9rg4PmiY8NqtUrkgnDXsrIyQySKSrmGSfFenBPKGp57zit5p96rdCzwdz8/PzG0mM+hI/q8J6MoGmrFcXJNddEC7Uzj9xj90dBAHfXiHtW5e5cbLV26FDk5OejRowcmT56MV155Bddccw08PT2lNwXgwPAvXboUFy9exJ49e5CZmQkAhiiO3W7Hl19+iYyMDJSXl2Pp0qWSu0OF1FkBnzBhAk6ePImDBw9i6dKlAt1paWmRsq0DBw5Eamoq5s+fL9Gj5uZmvPDCC1i6dClCQ0Mxbdo0vPrqq9i4caPkY9Lwy8nJweeff47Jkydj5cqVyMjIkOfv2LFD+l99/vnnyMrKQlhYGO6++2689tprGDVqFCIiIgQSVlZWhh9++AHPPvtsm0jPzJkzkZaWhuPHj+PFF1/EO++8g6SkJFx55ZUC6WERG461urpalPtp06ahsLAQmzdvxqeffipJ1qQNGzYgISEBTz/9NF555RWRYY2NjXj22Wel2pWmhIQE/P73v8err76KXbt2SXd7ALKOJpMJkydPxvbt2w1liDWMaubMmTh8+DB27tzZ7jquWbMGmZmZ8Pf3x2OPPYb33nsPRUVFsNlsmDt3LhYvXoy8vDx4enri7rvvxscff4wLFy4IumXMmDGorq7G119/bTDY3d3dMXv2bKxcuRI5OTlYsWKFIS+FNGnSJPj4+Ehp2+HDhyMwMBB/+9vf8PnnnyMzMxMlJSX49NNPxUiJiIjA/fffjzfeeMNgYP3W6ZIjGsHBwQa8rU6s1UoX0CogyNj8/PwMzUboqdUKhvbiuLm5GcpUAhBPJ59FZZtJwGSqbm5uAntiiJ3VJXgvNzc3qY9PrzdhBzU1NQgKChLvIJM4+Ux6pp3D1RQqgKOLOsuzxsfHIzQ0VJRpu7218Rzg8H4xQZxQAyrxwcHBKC0tNQhFGnkUQLoCDeDoQkrvJL2uTLo2m82ora2VdbLbHc0CNSaYc0wB7uXlhaCgIPj6+kqEiop0SEhIm7rwrEwDQLrgEp6gyzZSEdJGYVVVFfLy8lBYWIiCggKUlZWJUsaymrpSjfaoMglTG6hNTU2CyddJlpw3whBaWlpkznhNY2OjVLgAIOVrnZVibXRquBcAQx8Svi8VSMJ0qLz4+vqKsNGRMhrFbObIPf5z3qffOrEcKc9DaWmpzHFTU5PMuc1mE4OMipWLi4tUltIwIF0iWzegZAliGiLkX9qTT7gTvck6EZ1nX3uqWcCBXnZ6yxkl4/4j3IqRDSqKNDQI5dQRCS8vL+kszigfIx6EIhJ+42zIAjDwEN6XZ003cdO8m88m/+P+45kjT9MKto7W0vBwzgfQ+S2UGRr2pg0GZxgin6sdTbqoAnkQlWnmvXBfVFdXo7a2VowMwpcYwSgtLTX0miAsi+9FKCbfSe815phoWUiIFCNGnA/OZUhIiERZbDZHLxVtQLPZIiM72pBuL4eJ/N5kMkkRAR3hY/dwJqZqeBf/z/fRuQaXC3Xr1g0FBQUYMGAAFi1ahEGDBmHevHnw8/PDn/70J8THxwv8GAC6du2K0tJSNDY2onPnzjh79qyUif85PhoUFAQfHx+cP38ecXFxqK6uFiX2u+++w5YtWyRnAXDIOovFIgrnlClTMH36dAwbNgyRkZGyN/ft24e77roLYWFhmDNnDgYPHoympib4+PggLCwM2dnZBufEwYMH8cQTT2DXrl2IjIxEdna2wUgl9e/fH3//+98xdOhQzJw5E7169cKtt96KuLg4aV4bHR2Nc+fOGZTeLVu24KuvvsL69evx/fff4/rrr8fYsWMxadIkXHvttQAc0Qp/f3+cO3cOMTExUuUOcPTVOHHihHTT7ty5M5qaHI0Tu3btiry8PIwcORJz587FoEGDRDfw8PDAwYMH8eijj2Lfvn2IiIjAmTNnYLfbMXz4cCxevBiDBw827E+uI3nn7t278d577+Grr75CdHQ0zp49K7wNcHT6XrlyJf72t78hNjYW58+fl3yq2NhY5OTkoLGxEZGRkdi+fTvGjx+PzMxMhIWFYefOnZg4cSKOHz+OoKAg7NmzB5MnT5Yk/K5du+LVV1/F2bNn8fjjjwNwQOcIUz1w4ACmT5+Obdu2wWQyoWvXrmLEcB3feOMNBAUF4Y9//CMAR7QmNjYWU6dORdeuXaVABQBZx8jISKxZswYjRoz42apf/9v0q0KnAgMDpSZ5UFAQiouLDcKAeRf8XXtdiTvVCiY93i4uLqJskgkSmqRzNIBWL5JOFqUApxKvoQIUDvTkUUEmxhiAAWZD4VFdXS1KHpv5aaw4BSQVFL4L35+JilRKunbtKtWeoqOjpbqMzWZDdXW1/A5A8hSYJB4cHAybzYbS0lLk5eXBZrNJYxjmeBQWFoowotKjDxznjkoIFVoPDw8EBASI8tPQ0IDQ0FCDMgJAwv6BgYGoqqpCcXExGhsb0aNHDxQXFyMvL0/K0xUWFooQJ+xMezKpyBGbX1xcjLKyMhQVFUkPDl2Kkso5a8nTIOCcBwQEyP7y9PQ0rD8T6Tm3WvH38PBAaWkpvLy8ZE505Kyurg5+fn4GuAiFuIuLC4qLiw3QK0bBdJlKreBqjzgVaa04ce35bjS0eeb4PMIfWGP7cqPw8HDJg3FxcUFpaalBcWMPAipdVqtV9jbnlHyG8DcqVeXl5ZKTQQONCqnZ7CiRqpUsKqxAa64IjXNWBuKPzWZDYGCgrBHzKHg/wh2pAFKh9vf3l2hdYGCgAZJERZbngVBF9sghzt5isUhBBTalCwgIkL2sDW/yWB154fxSGdVGAa8BWqsU6Rwzzh2NPkKmCMvRkRTyQBpsen/rcuLkx+QTumGdzjPRXnd9TnTkwGazSRUcq9WK6upqlJWVSWSI8EYWnWD0l+/NXBOdQM2KXTQYNeSB+4P/p6OJ59tqtcLPz0/G29jYaEjSb2lpadNAEIDAA3nGyVd06WSdp0W+4gwjY5lyUmVlpcGg5Hvy/PxW4Ri/RBMmTMC3337b5u9ubm4oKSnBww8/jBUrVsDFxQVFRUWYNWsWTpw4IQr7M888gyFDhiAlJaXd+8+bNw8TJkxAz549/2tsP2nPnj04ceIE/vznP//s92+77TZ88sknCAkJEaiyprFjx+Lbb79FcHDwJRuHJpMJeXl5ePXVV7Fz504cOXKkXejUL9Hjjz+Ohx9+GDExMUhLS8OuXbswY8aMdq/dtWsXsrKyMHPmTBQXF2Ps2LHYsmXLv73/jTfeiNWrVyMkJMSQiK1Jr+Mnn3xi+Kx///44dOgQoqOj24VMJSYm4sSJE+jWrRuysrKQkJCA06dPo3fv3oZI0aVSUFAQioqKMGrUKGz/fzuuAw4oVmVlJW6//XbD9Z6enigtLcWdd96Jurq6X1xHFxdH75dnnnkGixcvNqzjwoUL/+P3/Z+mX9XQCAkJEQbHZFmt+DPBjQYHhQmVfRoWNAro2dV4eSoIFHRkpiw3SA8SS51SoLMnAp/NCAXf11lQagavmTaVEo1NZjUYCnMqkTr8TEWBfyOul5AYANIZPDo6GuHh4dLRVleMItabAoy5AEwKpbJApSk0NBQWiwUVFRUS7aDR1NLiSEAuKCiQNaMSxkgPoxRMhGRPAnrM6OWjgeLt7S3RiYsXL0qJTVbJ8PT0RGFhoUSLAgMDxVAiRCYvL08q+uTk5CAjI0OSqAkjozLGXgZUaKigayiNVo6oYBFqRG8gryXUhYqP1Wo1lCbWc8x31A0iNbGDNL3EuliBNkx4Jti9l+eF96ZSQAWIGHWd5Mz5p/e2oaHhF2uq/1YpODjYYPzpAgxUMHkOqRBqfLxWoumt5e/MNeD86+8DrT08aMyaTCZDwnZjY6P06+E1/J3rzOpHOtGZ+5HwPfI97hGOlblP3McaqgdAYF4kwqa8vLwkqsG8JEaFaFzw/+SvGuKknTEaasWIrnMXckY4tNFOzDkjGoQacR4AiPHE/+scGZ235ww54/N0yWkamIQsuru7G5K72V+HP7psMHuK6LNfWloqURYN3TOZTPDz85PcB90rhGOwWq2GctyMAuu104YxDVE6MCwWixi3AAyRIjoOtGHIM+Di4iLlOLnXyL+4vhrmS3nFXDzOFeUSDRcttzUW/XIhVmL6OaW0e/fuKCwsFGhJQkICSkpK0NjYiNjYWJw+fRrBwcEYMGAA5s+fj2uuuQZ33303Bg8ejAkTJgCAlK3Pzs5GfHw8qqqqDM6dP//5z5gyZQpGjhyJNWvW4NSpU/joo4+kSMeUKVPwwAMPYMSIEYiOjpbS+KTx48fj2WefxbBhwySyGhERgdOnT+Pdd9+FzWbDX//6V+zduxezZ8/Gnj170LlzZ5w+fRo2myP/5OOPP8awYcMwa9YsxMTEtEku59gJ5+7SpQuysrLwyCOP4Oqrr8ZNN93U7vzdd999mDhxIq677joADidzYGAgsrKyEBcXh/r6+jYQKVJsbKwUmunevTtycnIMaJbFixejoaHBYKj4+PggKioKp06dwptvvglfX1/ce++9AIBXX30VERER+OMf/yjr6Az/8vDwQFxcHLKysvDYY4/JOm7evBmrVq3Cp59+iq5du+LMmTMS0YiPj0d2djbuvPNOWUdnWrVqFX788Ue8+OKLhr+7uLigW7dubcYWHR0Nm82GiooK7N27FzNnzpSIRvfu3ZGfnw+bzSbrOH/+fMm1AYCXX35Z1jEhIUGq43Ed//KXv6Br166YOHFiu3P/f0WXYkL8R300NLRGK30UWjrUTY+XzsfQXn8t7IDWUrNAaxUqEu9FRkyFQzN6PpeGgsb8UhjqcLYWoto75ubmaPPOexEfrIWghs3QoNEKsDPUqrm5WXpCMA+BHtH4+HiJLlAAsspIY2MjSktLxdNFhZiKKt+HiahMRiV8pLm5uU2TRIbjARgiB5wnGi1ULij4mpqakJubK0oXx0Hhxw3n5eVlUKr5XvT4EsJQXV2NrKwsFBUVSbRJJ0I6r6vZbJY1oOLkXKKW60gFisKciiOT8DWkw3nv8B5McKWSqyNY/J0QJxp4znlHVIY1rErvN41Hp+LmHLXjeaMi5Dw3lxtxXOQN2vDn+LXCywiohn7wMxrTdD5wD/NanQsAQJwXvAeNTO5vAGJoNjc3S/RK7zG+D6OuhFDx/Wks8ozqMtm6Eh6NdI6FUDndIJLPZqUjlsXt1KmTlILmvbThQYNDV6Vi8ruGfOoIMOE23J8kXqOrz/GHfJXvqfe/jhZpw5tRDg2X5d/ItzknzlEY8qu6ujqBlDKaofkMP9P8XkOhnInvQ+NCyyJGIJyjsnxP50g3jV8NPWMkgrBbHVHSUS2eBc4dHU0azsf97GwwMtJCo03DpfQc6zm4RB/jb46I6U9ISMCECRPwzjvvyPk1m80YN24cNm3ahOPHjwOAKP9RUVFiSHz//fc4cuQI1qxZg4aGBhw7dsygNJaUlEi058yZMwAcjor77rsPS5YswenTp7FhwwYAwNatW5GVlWWoBJidnY2dO3di1qxZWL58OcLCwjB58mQsWLAAdrsdFy5cwD//+U/YbDZMnjwZDQ0NEqHZv3+/nI1169YhLy8PYWFhuPnmm/HOO++gsdHRtO3bb79FU1MT0tLSYLPZ8OSTT2LBggWGCNzYsWOxb98+/PDDDzJvx48fl/m6//77cezYMZw4cQJ/+ctfsHjxYmRmZmLjxo1yj/LycoGNORcP+OMf/4iioiKZi6uuugoVFRX417/+Jc/r1asXxo4di3feeQd79+4V5yAATJw4EWazWXpTHDx40NC08vDhw2I4t1dp8frrr5feFAAM67hx40ZkZmbC19cXEyZMwIcffoiKigr4+PhgwoQJbdYRcOTWDBw4EO+++y62bduGvLw8eHl5Yfr06VixYgXy8vLQ0tIiY+vduzeuv/56LFiwQCJF7u7uWLt2LQoKCgx79MYbb4Sbmxu+/vprzJw5E7W1tThx4oQ8Oy0tTfKHOFYaWYsWLcKOHTsuyypxwH8Q0ejWrRuAVvyos0JPxY9KGhOPea325vIaeqh0JAOAlLGk8sXnkuGyIgQACW9rL1FdXZ0hYZM16+k1o8fYOVmYimFpaakopRSwFFz0CFFZ18naVHwZ/aFCArRGTgh9MJvN8PT0RHJyMlxcXMT7T2+lzWaT7uFUZujh4rg8PT0RGxtr8OYR58138/b2ls64ZWVlaGlpkc7lAESIu7o6ysGeP39ehJzd7shzqaurk67j/v7+olgQmkRlg0oehWNxcbGhwU9ZWZnMSUVFBbKyssTLR4VIJ/fyHbh/WFGK88iEeh2NolLo7u4uc8+19/f3lz1GKAJx41QQ9Z6gAaWjJVREWAWInkpCA3VUQxvn9fX18n8aGrpniFaAeLZowDY1NcHT01MMRcLlLkeKiIiQcXHfOXtsyUd4/oh/1zyA/ILngVE6HUXiGdQKGY1wAFJSmHlErBRGY5rQKA0JpTe+qqpKKq/psriE0ZhMJqlUR2VXn/+goCDDe9FQra6ulmpwLJFKo4qdwtmVm+Vlvby8EB4eLvcm9Iq8inPNCla8J41d5tEBrWdFJw3zTJPn6f1Pp4o2WnRUWDuj9BnnWaIXHmgtx825Jr9mVT06RaxWK4qKisTQYC4GzyFzNDTMlYYo942OaHCem5ubJRGf88aKUdx3zMfQJZh1fgsjZPqsaucGCxQArUYt0ArDYoRDnwu+GyMuOjKlE+nJf6xWq8GRpvNbGI3i+rOP0uVEJpOj8ezIkSPxwgsv4IorrhCZWlFRgcOHD2POnDlYs2YNAEfeQGVlJXr06CFlYp977jmsXLkS4eHhojy6ubkhMjISubm5kvuSn5+PyMhI1NbWwmKxYMeOHRg1apRB6Y2IiJAS1oADHsqI6aFDhzBp0iQkJCRg9uzZ6N+/v1RiZFR6xYoVqK6uxkMPPYTo6GiUlJSIvOjcuTPKysowcOBALFmyBFdeeaUhktO5c2dUVFSgZ8+eWLVqlUDB3N3dUVRUhAMHDuCDDz4QRRxw5JN4enqioKAAO3fuxOrVq7Fu3Tps374d11xzTRtjgtUmCUsibDs3NxfffvstcnJyMH/+fOTm5mL16tU4d+4cZs2ahc6dO6O4uBjXX389Xn75ZfTv3x+NjY3C/3Jzc7F48WK4urriT3/60yWtfWBgINzd3UX+zZ07F1dccQXGjx+PqKgoVFdXS7n43NxctLS0ID4+Hlu2bMGIESNw/vx5xMbGtruOgCNSde+992Lw4MEICwtrs46HDx82XD9hwgTMnTsXV155pehtjHyFhIRg1KhRmDNnDq644gq89dZb8PT0xLRp03D48GE8/fTT0uSR1KlTJ4SFhSE3Nxc2mw39+vXDt99+i0GDBkmH94iICNmzvwX6VaFTUVFRwqTpIdJRDK0UUhjrW1P5tNlsBqWLzJeQIe3RpFCkgknvHZVNKgBscEdLWQtoRiN8fX3R1NSEqqoqqd9MrxOhVlRwWdlDY5BpyLi5uSE4OBh2uyNZtLCwUIQNw/Dag0RDiMqNxWKRhnv0xFosFsFj9+/fHyEhIWJccB40nEMLDn9/f5SXl0vpUypKFH6ESwEOJSIuLk6Sz+12OyoqKgz9HOjlpZKmqzlRUFFg04On8eYVFRVS3eXUqVPYvXs33Nzc4Ofnh8rKSkM0i5V3aHCyahUFOvcElbT6+no5zDohUns/aTB4eXmhpqZGxs6ogPaSms1mqbBVW1sr5XXpKffz8xMvKfM7dCUq5xK4uuEb0FpP38PDA7m5ufD39xdllIJNe3kJo9P4eH7u5+cnc+zu7o7Tp09fyrH9zVFoaKg4HKjo0ZlAQ4MKMr3XQGvHbe2lZ9RNe+B16Wod4QIge5lzSGNb52FoGKWOTvI+VERbWlqk8STXiQ4MRiX4N95LN59ibxvenxAbfp+V6zSEhxEZRlLYGZvQKV9fXzFCoqKiJEpKPsO97kwuLi6GXkScT803eJ12cmhIoY5Wc+9qHshzqa/Xz2MVPc4bHRm6gzd5VENDg1SxY5lvwjkJHdXvC7Q2E+U66KgADQHONSvz6cgNZRENJEadmpsdlQp1FImyB2jtHEzIG3kX54iFTJxlK/cYjTuSzvGw2WwS/eY9dDRMyx1NNMiZI3e5kclkwpo1a+Dq6orx48cDcPTRsFgsGD16tOHa/wTb37dvXxw9ehRdunTBtGnTfraPhjOlp6djy5YteOyxxwA4vPKHDh3Cgw8+2O71W7du/VX6aNCZ9+ijj+LTTz+Vv3/44Ye44oorMGjQoHafP3/+fIwdOxZ9+vRp93NnYh+NmJgYAGjTR+Mvf/kLXnzxRURERIjuZrFYfjZHY+LEif91H42PPvoIvXr1kiZ8JJPJhJycHLz11lvYsmWLrOP/l/LNv7SOzuSco7F27VrYbDbccsstl/xM534ozpScnIy0tDTExsb+ZgrC/KqGRmRkpCgF7u7uogCQORLCQEVJe6WpOAKtzdUIN6DyruE79OpojLrO8aAnmx434ndJNET4bHoU6YWrrKw0eKQYbqbiycpMFOo6pO5c3pbvpRP96IGisCAcisoz0ApjomLASjKsp0+jigmo7LnBRn/0Tup7aegWFS3tbdWJrFS6tXLA+SazYAhTQy2oIPA7FRUVKCkpQVlZGSoqKiQHgwZRaWmpQdjp96SnnvhhGpH6mbrxWFBQkEEhoHeaiiv3EJ+jIWPsF6ITTFlSlu/H6JfdbkdZWRmCgoLEk6rhPTrSxnfhHuKZYJNI7j3uE85vQUGBRM0YfaH3kutHhaOpqQkhISGyB5ubmy9LBQFwCFMqyDTUNFSMe4HroqNCGjJHmB7Lw2qvvfO9qOxybZ17N/D+XD+gtV+PPvvkKfwbr6HzJCAgQLzJvE7nMdCIYGSF0VXtXefedHV1lWIEPG/OjeK8vLxEueV9GNkJDAw0zDP3LfmMbnpH54o2MHRuCeeS68TrNZxIGyXcszqHTd9XK8WMUuheQex1Q6OCyjwNuPr6enFocP111Shd3lVDWvn+lEHkldwjpKqqKkNiPdcDaDU4CYOl4anLDrMRqI6aMsLEKAn3sKenp+EM0AGiUQDkGZQvuoIYnRvcy+Q3fEdGfgEIyoARflbhutzIZDIhLi4OAJCfn4/t27dj6dKlqK+vx4wZM5Camgqr1Yr+/ftjyZIlmD59Ok6cOCG5NZs2bcKSJUuwdu1adO/eHRkZGZK/0qNHD5w4cQJBQUHw9/fHyZMnkZiYiMrKSri5uWHt2rWYOHEiRowYgUmTJuGGG25Ajx49UFNTg/z8fACOHJG6ujrk5ubKO0+YMAEzZszAyJEjERcXh6YmR8+qbdu24eGHH8b+/fthNpuRlJSEc+fOSY+P3r1748KFC0hKSsLbb7+N1NRUPPPMMwgKCsK0adOQlJSE/Px8xMTE4JNPPsHIkSNlr2lvfWJiIr744guMGTMGLi4u8PX1RWZmJtatW4e1a9fio48+kmvvvfde3HLLLRg3bhwASCGbjIwMfPHFF7hw4QKWLVsm3b/ZbJRQNcCxF5OSknDmzBlDBGbRokXw8PDAokWL8OOPPxpk8fbt2/H888/j+++/h6urK7Zt24bXXnsNBQUFWLx4MUaOHCmFdrKzs2Udv/zySwAOiBbzRbmOGqb1cxQREYHvvvsOt99+OwYOHIg//OEPGD16dLvrCDhy7bZs2YL77rvP0DOka9euaGlpEXgT92hBQQG2b9+OZ555pt3EeBcXF2zduhVvvvkmtmzZgoSEBBw/fhyzZ89Gjx49DAapp6enYc/+FuhSTIhLztHgzYg/ZURBCxlnDKlmklrg0EPE+7W0tAjDbG5uFoNCC12gtQyu9gDxuToETSWT0RH+8F6asetn8H7ay6ob71BZ1DXjtSKhveea4VPp16VX9fN5PQWXhoLQE+fl5SVNf/z9/QXKQ6+59rZpvL+uHKMr6ehqSTqKQygQFRsKYf6wgR+9jWyoV1xcLAoAx6PhRjqvh+PXUCUm83It+TeNBXfeS4SbaMy3VhiYzMtx6z3Cd9HKh84DoSLB96Aiq0kroTQ2uS+o9Oqxa6an979Wgqkg6HfSfV5+K+HS/5Y0TIqGgIa/6QRi7VXWzgqdu+D8uT5f3Av6bOr9zM/4TtqpwPtrrzudBpq08ac90dqrrSMoVDJ1vhcFBteb78K5cXNzEz7Axn+MxnCu+L5Uxhml4/7nWOnA0A0Iucd0pEk7avS78TOdk8Szrt+Dc8d51vKARKOhvr5ekrsZQdTfZ4SREExGMbRhrw1M7cTS88P70QjU0SIdUWAXeJ0zxHFw/+h76dwX7gnKBkavuAfpFKNM4H4GWqNC+jyQn2uIHXkE9w0/p+GkCxhw/1AOaaNWY+EvN6Kn2tXVFTt37sShQ4dQX1+PHTt2yL6orKzEzp07MXDgQFRWVqK8vBx2ux179uwRh9jRo0cBAFdffTWio6Px2WefAXAoh4SVDR48GCdPnkRWVha2b98uzWT3798PAEhJScGFCxfE0Dh16hQACExm1apVKCgowK5du2C325GcnAyr1YodO3Zgx44dArmy2Ww4duwYbrrpJtjtdqxfvx4jRozAxo0bUVZWJmPLyMiQJq5U7i0WC7Zv3y65lM5UU1OD7du34+LFi0hKSkKXLl2QmZmJAwcOtMH9nz9/XsYGOLqJJycnIyMjA4cPH8aZM2dw9uxZzJgxA19++SUKCwsFBjZp0iRUVlZi06ZNOHr0KCZMmICGhgasX78eAPDjjz+isbERx44dMzzTZrNh586dKC4uRkxMDCZOnIiDBw+isLAQlZWV2LFjB5qamtCjRw8EBgbizJkzso4kXUGK6zp06FAkJCRg+fLlspaDBw8GACxfvhzl5eVoaGjA9u3bYbVaceHCBezbtw+AI+JVWFgo83nbbbehvLwce/fuxY4dO1BZWYnExESkpqZi8eLFyM7ORkJCAu6//34sWbJE9qibmxt27tyJkpISxMXFYfz48fjwww/l/NrtduzevRt9+/ZFp06dJF8lMzNT4N/33nsvDh06hPT0dBnb5USXHNEIDw83KMY+Pj6i3BHWQgWQShwVbQo+Mluz2WzwzNjtdum1UVdXJ4dIk3NOhfZ0AhDPFhXbgIAAgTexHj6ZN7t6kjlrDL5WZO12RyUkwo/43oR+OSvwVGB0PX6NR2aEgYonIx8au0/hqL2uVAiYv0HFs6nJ0XcgIiICPj4+YqxpiAOVWEZOaBwymTs0NFRyBYh3ZuK2Fvpcp4CAAMkHIWSroKAAFRUVhqRQCjgq7BR6GhfOdaNQZlRJQy2A1qpXOhoBQOB03B96vhklIEyM0DIAhs+JM2c1Ct23Q0PwmABPLyeVDa4roWEAZOwWi0Vw1QCEsXDv6MgfYWP80REUrjehN+7u7u2W8bscKDo6WtaVVda4V93d3VFVVWUw3nVUgLlQNDjZXFIr9PQuM3lerxmbRwLGLtSMdGrDkHxD7zlnrL9zRFCXXyZP4X29vb0FikcoEGFeQKuDhAUh/Pz8ZH+y6lRVVRVqamrQ2NiI4OBgyUPQ70jFNzg4WM4QI3e65DbhhTyLhHHps0uHhXaYaKOf/zLJmeePsFRtsGsjg/cjj3E2IrSxRN7J3iPMpWGVOEZldIlwFxcXA7SJa0T4GZ0qOpLL9SKkTTtXdMNX7Yig4s5nAq0RW64t5RnX1cvLC+Xl5QanA+dN7yOuRX19Pby8vMSA43lhtITQYe4f8noAwk/pZCHUVTtjzp49+z9wyv9nifuoU6dOCAgIkNxBs9lsaNgGONbl2LFjeOmll/DFF18AcMA3GTULDg5GUVERnnvuOaSmpmLy5MkoLi6WsxMSEoJvv/0Wq1atMvTN0LR582bs3r0bc+fORVhYGEpLS+XeR44cwYQJEwye76+++goFBQWYPn06wsLC2uyHTz/9FHa7Hffffz+OHz+OGTNmYPPmzTJWrmNgYCCKioqk91BZWRnCwsIEmsexOcPnnn32WVx77bVITU1tM5aQkBAx/kNDQ1FSUoJ7770X99xzj6EUcHh4OPLz8zFkyBBDw7w1a9bg3LlzmDlzJgAHpK2srAwPPPBAu3PHpr0VFRUICwtDVVUVBg0ahOXLl6NPnz6orq42jPX1119H9+7dpWKWn5+f5NW2R7Nnz8aYMWMwYsQIAMBjjz0mELerr7663QRz0vr165Geno7Zs2cDcEChTp06hb/+9a9yzcSJE/HKK69g5MiRKCoqwujRo/HWW28hKSlJnEEVFRUIDw9HZWUlhgwZgqVLlyIpKUl0RO7Zt99+G3FxcVKwgGQymXDkyBEsWrSoDfzvt0D/I9ApAAYFnwxMe2eY88DPqVgznE58L5VK5ixogUABpxUOeuGpdDh7nfkuQUFBopS5ujoaHIWEhABwlB/09vaG1WoVr47Oe6DiTy8Rf6dgoVLEd9dNkfScUEjTG6c9VDrSoufH3d1dGCa9kwyvM+E7MDDQEBWgUePj44OgoCAZA4VzXV0dQkJC4OLiqHrDxl+sDsV6/Xa7o3vpuXPnUFNTI42taLQxj6S0tFS8a1TauG7EItNYIvzH398fXl5eKCwshI+PjxhjLDFLYR8YGChzSEid9lxS8dD4fhpO2iDQUQRfX18R2NoA1PA8GgWEQfCH3wUg+S5cw5KSEkO5Tje31iaTVNQI1eL+oteVBqw+R+0l1nIuaVBpGM3lamhERERIRI7QO8Dh9WEzPkYjqCxSAfT29pY51nPBdeCeZ4I09zy90O7u7sJrGDEEWg0/Z4WPET0qoc4lYr28vAzGiHMpURoENFhZVYpwPIvFImvO8869zl4MumocvfPa4UHFHGjNNQFao8A6Okweoyt0kS/5+vq2C4/S12jlWne35tzy/5w7riOdCdzXdKSQr7L0OPkV94TOcyCv4RmnHCFv0WWNNeyQChwdDVwnnuOmpiaEhobK+7JiHp1ldMjQcGBUl7/zHhyXc9Ta29tbKn5xjsjXycdYTZDvyN5ANO6cSzbzPfg88jQaWITGmkwmQ9ls8i/OAfNfLjfivhg3bhxWr16N0NBQfPzxx7BYLBgzZky71+s1ycvLw9y5c3Hw4EEcPHgQ0dHRyM/PR79+/ZCWloauXbvi/PnzSExMREZGBrp37/5vFVLePzIyEhcuXMCgQYPEsNDPdn5/Pz8/FBUV4aabbsL333/f5nOuv91ux5gxY7BmzRqEhoaipqYGqamp2LBhA8LDw/Hmm28iPj4e1157LfLz8/Hss88iLS0N+/fvR3R0dLsJ/+29F+Co6LVq1Sr87W9/Q3Z2Nvr27Yvjx4+3uf7nDA397u397kzffPMNXF1dMWnSJJSUlOCee+7B6tWrDc8bMWIENm3ahPDwcJSXlxs+Y8PGgQMH/uL6/Nw7/iffa+87FosFhYWFso687h//+AdsNhumTp2K4uJi3HnnnVizZo18/tlnnyEsLAyjRo36xff6uWf/FuhXNTR69OhhwMuTgVPo8oEUJkx8BRwe2JqaGlHQ6d3WHjHiWvkMjZ2mQNKeNu1RJvaUzJtRAzJxdqQGWkvzUhBT+NAA6NSpk6FaEask8HfiXvXnWrnmM6nwEhpDoc0qJvw7IypUdJy9D0zupped80nBxudRCXJ1dRXlB4AkNmqFmHkpdrvdUJmG/Sx0dRdCCdg9l94erov2GhL7y71AJYLRFHoE+a41NTUiuD09PQ1VpXTXbm3EaigG9wENTaAViqJ7YHBMzJPRe7a2thY2m028mPSKEsZGZm8yORL9tbfVGV5Co5QKiGYO3N8au80x6VLNek9oY5sGOA0rhukvNwoODjbAVBih4DzoqAIVQiZ8MwLBCJndbhc8vjMPorJotVoNZ41N/xgVpUFLZV53mGZETsOpnAtNAK18wXnvk1dQ2afSyXNMw4O8x9fXV4wrrdTy3XROhYZhXbx4Ufo36Ep+3J8mk6MSkYZHcd40pNQ5d4v3IO/QkDbncrg8Z5wr9sShrND8HGit1sT8KfJS8h2dE8MzpA1LzifXs7a2Vgw+ev0pT2pra+WMcqw0KmmYAq0RK15DvqGhdhwH8108PT2lzxPHwKpR5LU635BnWsP9+B1GWxhh5rzSmcax6TnRhiPPCPm2p6enGBPk48w1oGF3KRj23xpxH1ksFiQkJIhxMGzYMPz5z3/G2LFjYbVakZycjPfeew9jx46VnhoAcOWVVyIvLw9WqxVJSUlIS0sTGHGfPn1w5MgRkev9+vVDenq64PYnTZqEL774Ajt37sQHH3xgeC83NzdceeWVOH78uOgQpJtuugkPPvggbrjhBsM56t+/Px544AFcuHAB8+bNw8aNG/HUU09h9+7dhu/7+/ujR48e+OGHHySCnpiYiMOHDyM2NhYeHh7IyMhA//79ceHCBdTW1srYHnroISQnJ0sH6vYoJiYGq1atwssvvyyJ5HPnzsWxY8faTdjmWH/66ac2xuq4cePwyCOPYMyYMWhpacF1112HJ598EmPGjMH8+fPR0NAgUYLu3bvDZDLh1KlT6N+/P7Kzs9tEpXx9fdGzZ0+kpaWhubkZSUlJWLJkCW688Ub4+fkhJSUFjz32GG666SZMnToVKSkp7fYUcSbndVyxYgV++OEHLFiwQK6ZOHEi7rnnHowbN07OnJ+fH9avX4+HH34YaWlpso6ZmZmGHh/du3cHAJw+fRr9+/fHvffei7q6Oon2JCQkwNXVFadPn8b69euxYMEC5Obm4v3338e4cePwl7/8Bd27d8c999xjuOeyZctw8803/2byNC/FhLjkHA2NgdawDqC13KHGPWuli0ydCrEWpPw+exZQKadw0s/UcAUKco2z5zXMweD3NAaWypyOuGhBArRVaPXvzt+hV10rgVSOyMQ17EdXzeE9tBDkuEk07Pi+GnfsbIhpjybnlZ/x3ShctZGkhSoNFQq35ua25Sf1/fh3etq0QkHDTc8f9w7XgUKfc6EVdO2F0vfkmIlV55prBdP5Wr1u/DsVGXrP+V3udf09bVxT+dIHTEMFNTad49A9QqhE6XXUyjD3Ce+rK/FouMblSPSMcx24b7g3nXMo6DwgcU9RudLzRKKRr6NXvEbzD6DVONTRAo3P5/f4Hlx7DXEk/9F7iw4M8iGOlXPAfaGjb9qTTn7B0r46squ99JxLbRi0571j9LM9OBDnhRE/GlEaKsUoK88s4Ty8N7/HMVVXVxt6/ThHPHQZWK3scq41H+OccZ51ZIlzxagh54wwUd6T+4BroXmnlhF0SnFf8XodOdWQK/IhvpuO3PD7nCNtnGreSZmn+aTm+Rpey/NCogHIazS0jWvN9eP1fCdWr7pcqbKyEkeOHMEf//hH7NixA4cOHcLAgQNl7q1WK44ePSq/sxfFihUrRHnW3vi6ujocOHAAU6ZMwYkTJ5Ceni6fFxYWSu+EkydPSnR+6tSp+Oqrr1BeXg43Nzf069cP2dnZiI2NRUpKiuQGlJWVISsrC3/+85+xbt065Ofnw8XFBX379kVJSQlycnJgs9lw9OhRMYpcXFxw5513Ytu2bTh37hwOHjwo7+rj44N+/frh6NGjyM7OBuDYU71790ZtbS2Ki4vl3fPy8hATE4Np06bhs88+Q1JSEmJiYrB69WpMmTIFmZmZyMvLQ3p6Og4ePIjmZkee7IEDBzBp0iTk5OTIvSZOnIiCggLs3btX/paYmIiBAwdixYoVsNsdhVR0YnhFRYUkfmdnZyMqKgp33303li9fjlOnTiEmJgZ/+tOfsGzZMlxzzTVwc3PDxo0bceedd2L37t3IysoyjL2urg5Hjx5FU1MTzp07J/PW1NSEvLw8xMXFYdq0afj888/Rq1cvxMXFYdWqVQAceRfdunXDypUrZR1Jp06daoMUKC0tlaR3UktLC44ePSoGVktLi7xf165dMWLECBlbbGysjC0tLc0gu2NjY+Hp6YlTp07h+PHjKCsrQ21tLY4dO4bm5mbk5uYanDNDhw7FwIEDZayXE11yRCMhIUGY18WLF6WkH5U8YswBCCSGnjyGcAnToeCk0KFXjh4lb29vVFRUCAbXbrcbBACVEQpZPpvCm5U+dHUgbdiYzWbU1tYK/IFCCjB6u/hDb7T2/lFAUPjT8+Xt7Q1fX1/U1NSIZ439Fmw2GywWiwEGRo+qhvMwMkCFlOOiQKQyT6WNm7e52dEY0M/Pz7AuJC00KZw4T0CrECPGk9Eg50gADQJ6A/VY+GzA2HBNK8zaiNDwIho5zEugEOea8Z2ovNXV1SEqKgouLi4oLy83CHxGFWh06JKy/D7zhLSR6KxYAQ5Fpb6+3pBAy+/RmNYGMruSawPQbm9tisY109/X+5eGBktXsr8D94qnpycyMzP/qwP/f03R0dEyVka1qKjSWUGlSJ83DZljpErDMbVyx6gB96Z2Guj18PPzM/Th4LO0AcDrWViAfWSoIOuzwWgdI3ccA73LQKuxy2cRLsPKROSJjJqxZwMrSVF5ZMUtEo1uoBXKqCMa3EeM1hDPTyKfIn8gb9f8sLa2Vvamn5+fGOfc1zopmb0snKGuFy9elH4/HAvfkevK6zTclERDU0emmRPCtWpoaDBEbAiL4vxr/nDx4kUpa03Il4+Pj+QJEuJI3shiGHwnyiPOV2Njo0RJuedYHYyGHfeEyWQyyCpXV1eUl5fDy8tL9oB2sDHnQ6+bp6enRIZ5NnTeWm1trfBMwGHwMcrn7u5+2fbRIHXq1AmZmZmYMWMG/vGPfwBw9FpgbqGmlJQU/Otf/0JSUpLIZu2BdnFxQUBAALZs2YKlS5di0aJFsFgsqKioEL4UEBCAyspKtLS0oHPnzjhy5AiuueYa/PTTTwgPD8ePP/6I6667DldccQWeeOIJ9O7dW/ZCQEAATpw4gYkTJ2LPnj1S+Wnq1KnYu3cvfHx8DN58Dw8PnDp1Cg888AD++c9/ytisVisGDhyI1atXo2fPnpIbaDabceLECcybNw/Lli2TZ9bX1yMhIQHbtm1Dv379MG3aNIwcORJXXXUVzpw5g6VLl+Kll15qd6537dqFLVu24PnnnwfgKM27b98+PPPMMwgMDERVVRXuuOMOPPXUU+jZs2cbRAbLslMp9/Pzw7hx4/Daa6+hR48eqK+vx/XXX49PPvkEiYmJWLBgAfz9/TF16lScPHkSs2fPFiOB60rEDJP7SRxrfHw8tm/fjiuvvBJ33nknxo0bh6FDhwIAnnjiCdx6660GqJXzuvL3qqoqA+9huXHnCI7JZEJgYCCqq6txww03YMGCBejRowcuXryIMWPG4KOPPkKPHj3kzNOQXLx4MUJDQyUng3xI70lXV1cZ60svvYQhQ4bg2muvRUBAgOS4/V/TrwqdYmMq7Zkhw2VeBh/KxdLYaOYxUICzr0VLS4scMHqrGBGgZ5LhZ2KW6SEGWj1SOvFYwxvsdkcuAXHZ3t7eKC0tlXdiqBkwdvKmwsFkPCo5xC5zbPw7PVwUOPREmkwmWCwWw1h1jxFdopZeb7474RQ6ssCkayqihAVwXbThArQmKVLR9/T0lHr6NGSYXEnh7u3tLcYYhTaNAM4t4PCoEnLERlkcE+ePRhaNBCorei457yzFqRUAKkVUGHSUgVASfqajYB4eHvDx8RGDgNASwhnc3d2FUXFvaMWRcAagFWtNr6EWdNrzqCMonTp1kn2ljSTub93skIarTmymwkSYCwAZS0tLi3StvdyoS5cuqKioEHgkI2rE/bOsI9eQ+1Yrv2TY3L9Aa5SJ4WQ6JWpqauQsATDsPW14Ao6ypjQEaLxoD7Xd7sgj4nnX+SFcV23Y0Jmi+YrG5TOKADiEDA0DCjPnyA6VUpaxZQSS32OJZMIONZySZ4D305A1GnqaL+l5BloTi3mthrkyQkF4JQ0GKtZsdkqYFJUplvUGHEKc55lzoKO4jLJq54GOHvG80lBgjg7HwrPDvaSbO1osFjHobDab9BJqbGxEeXk5bDYbgoKC4ObmJrAu8gkXFxdUVlYayhbrvaadUdyXNBiampqkmhj5SlNTk0HG6Z5L5F06Gq9zmhgpprOHkVUdRWevBsK02qtQ9FsnzX+BVgQC93xBQQEeeeQR/P3vf2/zXe6thQsXYtCgQYZeE3379sXhw4fRo0cPnD17Fj169MDx48fRo0cPnDlzBvHx8cjMzERSUpJEN3g/5/trB1J7z3d+91tvvRUfffQRwsLC2lS65Njc3R2N6h544AGsXLmyzb2crzeZTNJMb9GiRXK9jiD/kqGho57695CQEFy4cAHDhw/HoUOH2h0r4OgtUVxcLFCmdevWoaGhAZMnTzZE9HV0GzBGn8mz8/Pz8eSTT+LEiRPYsWMHYmJiUFRUJPc4d+4c3n77bbzzzjvtjtX5WaSYmBicOXMG/fv3x7FjxxAZGYnz589jyJAhhiT+nTt3Sgd1TX5+figsLMTNN9+MzZs3G+6vn7dq1Sp4enpK7xc9VgD4+OOPkZCQgGuuuUbunZKSgr179yImJgbFxcUylrNnz+Ldd9/92QIF/5t0KSaE+Rev+H9JK9XOoXV666gQamiAq6srAgMDhdlSoaLgZZidApKKJj1cvr6+MJkcjY18fHykeoz2rttsNoOiCRg7TeskXA2VYAIehb0OkVPQsoma/uH12ivFjaVDWlQ4aZDZbDaxVvnuVPLd3NxkjBRgLi4uKCwslK63WgjqSI2OJFHJoCJEo8LVtTUxuqqqCiUlJdK5GIBUuuG8aE+knlMNBaACQCHNjsTMAdHVXcj4dN8QzUSoGDDaQyWGChGNPQp4QgI4r56enlKBAmhNruV6enp6ShECKh5MJKVRxGdz/JxPviv3DZV9bXwCEEORxiNhY/Sea+iGM/xDw06YV6MVFg3V4/MuR6LA5Nxyv7GSmS6xzKIErEZED75W3NlJu6qqShgx5429Zpqbm6UsLPciANkfAMTDrT3XAORdvby8pK8F9x7PGXML2ERSG1BUzC0Wizg2AEiVJW2kcj/zTFLYurq6Cl9gxKO6uloUa+LwtTFAiKSunuU8FirkPj4+8Pf3l5wHRkE5Vh1JZFRHV7LjGdLk6+sr9fzJR3itxWKBxWKBt7c3vL29pdCEhv14eXlJ8nJ1dXUbg4tRKz1X7u7uCAgIEGWckUrteAgICBDPIYmeS/JgDWHivJPIe7lHKe84j1wvGgU6Wsz3ZXlbvgOvI1/Qhi0NDLu9NU9PO/j8/f0BtHZRp4zieHXkpq6uDoWFhTJn7SmGlxMNHjwY33//veQHAY55uPnmm7Fp0ybDtY8//jgWLVokY164cCHuv/9+wzVnzpzBqFGj8NZbb2Hq1KnIycnByJEjJR8uPz8fI0eORE5ODqZMmYIvv/xS7jdp0iR89dVXBsicnt9x48Zh7dq1BqXM29sbGzZsQP/+/bF9+3bceOONbXJmuA8Ah25x0003YevWrfIZAMyaNQsLFy5sc73dbsekSZPwzTffICEhAZs3b0ZISAimTZuGjz/+GAAwdepUrFixQp63YsUK3H333fL7xx9/jGnTphneZ8KECfj4449x/fXX48SJE7jxxhvx9ddfC98dM2YM1q1bBxcXFzz++ON4+eWX5fuzZ8/GnDlz4Orqio0bN2L48OGGudJwv2+//RbXXXed4bnr169HRkYGrrvuOnFYdO/eHVu3bsVDDz2Er776yjA3Gibb3roAQFFREUaNGoUzZ84AcMClRo0ahczMTNx8882SwD19+nS8+eab8PPzw5YtW5CcnAzAwcvHjBmDH374ASNGjMA///lP0UOGDh2K7777Dh4eHnj++eclNwUAnn76abzxxhvy++uvv45HHnkEZrMZ3377LcaPH4/MzEwZqx7LlClTJNJzOdAlGxo6gVhvZnqzKTA1tEd7fzXGmUaIjgYAxsRy/q4Zt2b42jOocdK8HxVtKm8aCqUPO9+Fz6YCzXtTsOj78746sqJxv4xekChc+Hz9frpsL40h5/nlDyMmnF8Kfj2fhJ9QKdVzSwWE8AntqdBrrA+7M46bxqL2PmiFW0OXNESK80rByb3kHBXSwptJovyhR5vj1PtEewW1F5Z7UysWVFQ5Jt5Lw8M4t3pv8e+cR86x3u86ssTzoCF82mhoDwZHw0t7RLR3Vxt7lyM5Oyy4Z3hONZ6ca6TPAv/lXOmz5Jykr//P72lIlMa6A63eQK4tYMzZoNFN3kB+xDPN9yevcu6voDH8jAiQN/C9qEgCrTlEhFUReso9wfvw3HC/63tqo13vY+f9yOiQTizW1/M7+kdHohgpdc4RoLOJ19NA0VEPPlO/P7+roWsa9qX5pI4kUr7o9ePaMhLEvaDXmt8lD9RQWZ1fwvXW/J/RZUabtOOCsoUFB/TZ1rxV/+gxa37HSlear+p55rVcc8op8kDtHLycnRWkhoYG5ObmwmazYfjw4UhNTYXJZEJUVFSbHiFUSu+44w54eHjAYrEgLCwMgKOZXmJiImpra7Fjxw6cPXtW+rrs2LFDItvs01FXV2do0Ddu3Dj06NFDfh87diwGDhwIDw8P3HHHHQgJCYHVam2D/7fZbMjNzUVDQwNKSkqwe/duWdfOnTtjypQpBrnFsXl5eSEsLAxTpkyRyLxzYnBQUBDuuOMOHD16FHl5eWhqcvTXaGlpQWVlpUQCnPtoFBQUIDY2VrpZs4+FJlan3LFjB2pqamC1WpGfny/vzt8BR7nc4OBguLq6YsqUKSgvL0dGRgbsdjtyc3Pbhf5ERERg6tSpKC0tFecSx85GuDt37hTdprGxERcuXMDu3buRm5sLi8WC3//+9/D29kZycrJEEABH1Ormm2+W33v37o0bbrgBO3bsQG1tLRITEzFu3Lg2YwMcOT6Er3Ld4uLiMHHiROzevRsVFRWor69Hbm6uzIX+PSMjA+Xl5ZgyZQpcXV1RWlqKkpISmEwm3HbbbWhqakJ6ejrsdjvy8/NhtVpRU1ODHTt2oLGxESkpKRg7diwAYP/+/b+ZzuCXQpcMnYqPjzcozB4eHuJhIYbY29tbvAs6yY/VVHhoCIOiYGe+BJk0GS0ZJz15FBqMXOjkcoYbTSZHhRUqd/xdCxri7fk5ca8UMFarFQEBAXJvvguFJe9PLHJgYKAot/Ty61A5381sNouXn+/a0tIiGF6z2SyYbXrEdITIZrNJaV4AIrhZQ5oRH0KYWH6TCgCFFyE5VFxYXcnb21siLlSsqPgDELiGVqgpuHWEg/Oq15zv7+fnJ+vFvAyuOzGSVEaIV+b4WZkKaK28pQ0WGnJa4eDcE6/Pd6MQZqSE76ojYYRe0aNLjzEA2VO647s2sugJ1nAHvUcvXryIgIAAQ4iXUBiNu9awFo6psbHxsoVORUZGGpQqLy8vg+fc19dXzpyedxqpxK9TqddnnVEqKrqEFhIyQy8/81x0VMnFxUUiA1oR1IY9IWw0HtnTQr+fxWIRyBchluSFhFfq/U8llCV/GeXw8/MTI8fNzQ3BwcGwWq3izbdarYYojIZi0gOulWoNweM+5nsDrZEbrkNVVZWUetWREuYp6TwFbUxzzvgc7m3yBcLCtMKtCzrwWdwXrKjFyDP3jI74+fj4oLKyUox0wonIH51hYFTKaOyxhwiNAfJ1Rgh0wQzmPbCpX11dnSF3htexgh7lDu/BHBW+H9eMvItrwWg7nV2EpTU1NUmpXxpwrPDFe+rxAxC+wmgz92RTU9NlWb1On0XKg6VLl8Lf3x+33347srKyMGvWLKxcudLwvYEDB2LDhg3o0aMHnnjiCQwYMACpqanIyMjAu+++i/fee0+upZOJ+XE05gHHfuMZ9vPzw4YNG7B161Y8/fTTAIBt27YhLS0Nr776KjIzMzFmzBgcOXIEHh4eqKmpkaqTWskmT6LsHjt2LJYuXYquXbvKNc3NzThz5gyeeuoplJaWYsWKFUhISDAYAtS1+vTpg82bNyMxMVGMip8jPVbAEf35/e9/j379+slY6fxwHruWg9yXeiz/+Mc/UFJSgkceeQTZ2dmYOnUqNm/ebHi+q6ujuATHnpqailWrViE+Pt6QGH/69Gk8//zz+Oyzz+Dt7S15wpQDHHv37t2lAd5dd92FG2+8UXIynn/+edxxxx3o1q0bAGDmzJn405/+hD59+gAAHnzwQTz00EPo1asXvLy8JBLp6+uLNWvW4KeffsKMGTPg6+uL2tpaTJw4EW+88Qbi4+NF3ut1JLFP3DXXXIPPP/8cXbt2NehxWVlZePrpp7Fy5UoZG/kZjatXXnkFQ4cOxdVXX/1v1/N/my7FhLhkQ6Nnz54GGAyZo2awVA6YHKc9vfS4AK0dm+mdqaurM0RMGhoaBLJEeJHujtzY2GhQFGisuLq6CkZWK/NUQinwCYugZ0gLZQozX19fyVOgskPYB5UIKpRAazUPQhro3aKQ09AcHUXx9PQU5YqHmbkdFH70UlExoZewU6dO0tCGxh2ANt2+KbyZCEg8sa7cQ6FOY8putwsToVGokxP5HuzCS4MIaPUWEuZGhYTRF64blT4diaivr5d3YklkZ+8gc23Y9Mxmc+D42WyI78KStnrOqHBSceL8cv65zlw/GtP8Pr2HxFFzD1dVVSEgIEDmjgoh0OrJZp6HLnwAtHpIaXARnqeNPACSoMqqIpcjsbkU50TDGjnPPC8NDQ0IDg6WuWhoaEBISIjwGFdXV/HU02hn3wntJCDV1dUZDFE2xQNaHRT0bnNP6Igm0Fq5jnyA3mPmBnBP8f8AxLjXUVU6E3i2mAtG5ZzQPZ3Hw6IXPM86AZrKpo4+cFyap/KeusgDo7Tc3ywJSx7O88KoBeGtem9qCCAdEvycZ43rQUWYa8NxMcKjI17k5Zr36OgxANkH9PrrHjd6bHwP52RtOk90tFDvk+rqavj7+0sOlTMUw8/PT6ItnFfem3xYR501dKmmpkaU5qamJpSXl8Pf398wn3of0Ghk4np9fT38/PwMxSd05TYd/eWa6YiTrrpzuZDJZMLq1avh6uoqibQ0qnhmtRGtv+fm5iaOSs6j1mVIc+fOxc0334x+/fohIyMDn3/+ueQxpKenY926dfjoo49w+vRpDBkyxFDdik5IwhWbmppw1113Yd68eejcuTPWr1+PvLw8A0Tp1ltvxeLFixEZGSn7ke+6cuVKeHl54aabboK7uzs++eQThIWFtYFaubi4ICcnB3PmzMEnn3wi3/8lmjNnDm699VZRtvXcpKWlYePGjQL5OXDgAHbv3o358+fj3LlzGDFiBPr27YsXXngBMTExaGlpwZ133onXX38dUVFR8gyuC/UfTWPHjsXKlSsRGRkpTuf23p3rNHDgQGzbtg2xsbF46qmnMGjQIAwdOhTZ2dl45513sHDhwnbXGWhraDh/rtEVe/bsweHDh/Hiiy8iJycH1113Hfbt2wd/f39cuHABN9xwA3bt2iXGfXvrCDggvPn5+bjnnnvw7bfftjs27tkhQ4Zg06ZNiImJQWlpKVJSUrBr1y506dIFJSUlhnf9rdCvamhERkYaQrZaWee/ZO5ms9mQYE2Fj9dRSGi4iQ4J09qnd46KNzcFhSs9aMzZoJLs7e0tSnOnTp1QUVEhzFZj9umtowLP96qoqBClUSv9fL7GeXNMVGh1AjCxwToKQFw4mRI9/Nq7p++hFVwqKZxLCmcdHdKHi4JHR0R05IUeZOZ1UFmhElVVVSXeBiruusoUlUIq31SctSGl4QBUQrhuNHhooNrtdgPmmKRhY1VVVRKNoSHA+aO3m7Cn6upqUWR1aVrOC4Wu3W6XvAiuNWvQa/gIoVxms1k8zjqng4nxGjrHfaP3MPcpc5Wcu9fraAyVE2dl6XIKm2oKCAgwRA+513ie6AEGHPtb50WQqLRzXTUETUN9NNGhoWFJoaGhhv3D/cr9zWgU0NpA0pnv0RDVScbMO9CN9BgtocJIJwWLK+iIVXNzs5w9DUv08/OTeSEP1BAaZ+GlP9cQJU3aIaGrTpWVlYnAJwyFRoY24pw9+bwn14if0xOqv8MzTo+xjmRdvHhRvPmUBZw7jkvvCR2xqKysNBizlE10WLBJKwCRVVpRp0LD5HUaMLwnnRt0GrBgBx0G3M+aD/LdeH5J/A5zTogW8PLykspcnDOuu5Zlzc3NhvdxHrfmWTpiw3e/HB0WJpMJycnJMJvN+PHHH7Fq1Sq8/fbb2LlzJ1xdXbFy5Up88MEHKCoqwmuvvYbJkyejpqYGiYmJkn/hDAeaNm0akpOTpYN1QkICwsPDsXv3bgwfPhwFBQWC4R8+fDiKi4uRk5ODESNGYO/evaipqUFQUBD+/ve/Y8aMGTh16pTh/p07d0ZKSgqmTZuGb775Bvv37zeUgA0PD8eAAQMwbdo0vPTSSzhw4ADc3d2xatUqbN68Gfv27cOxY8fw5ZdfYseOHdizZ48hUZmUmpqK06dPw9vbG2+//TZ+//vft+lLcc8992DgwIGS1KzHCjjw/6NHj8Zdd92FYcOGobS0VKocDh06FOXl5cjOzkZqair2798PX19fdOvWDdu2bQMAREVFITExEVu3bhWe4OnpiVWrVuGFF15o894hISFITk7G1q1b0dLSgiuvvBIvvPACJk+ejPvvvx+hoaF44okn5HqLxYJBgwZh27Zt6Nq1KywWC/bv349rrrkGZ8+eFSjYsmXL8N133+HLL7+U78bHxyMiIgK7d+/G4sWLsX//fixdulQ+nzBhAn73u9/hD3/4AwYPHozq6mpkZWUhNTUVBw4ckD40qamp+OGHH9CvXz889NBDuP3229Hc3Izw8HAkJSVh69atBvjmtddeix9//BFFRUVwc3PDl19+iYULF6KiogIvvfQSpkyZIkiaAQMGYNu2bWhqaoK/vz+GDBmCe++9F8uXL8fx48fx7rvv4g9/+MPPdkP/36ZLMSEuOUeDiiCZpvYIkXFqz7bGnvLvGtOur9HKOxV07UGkcsgB6bwMDRnQXg0NgXE2aJxJvx+FOknnQGijSkNkNPab99CQHmcFSP/QWNCKDu+tDRuOke/J99FRI61w6bl1xgDreeB9dOheNofy+HOt+XeNZwdaPQPOuGl+TxtqzuvC96Un7ufmS8Os9CHmvNH41AavVsh0xEBj9LXxq9+XnznPB5Vj/nCfAK3RHO4jvZacZx0R4zN0HhEhG/pdGB3T73Y5EiEgOpLEOdR4ZK6r7sVATzr3rFY4de6FhirpPA/uMa246TUgP+FnhMpoDzHv5ezN1/uVUQH9LF6nv8N/eW41H9LnlnuYhpJWynl//XyOQ/NcfY51dFXfR9+P13EttFGt513nE+g9TIOBf9frwPNAfsBIgF5bZ76g4WE8A+QpmmcArQnbQGtSqN4f3E8aXqQjDvre2vB35o98D+f9S8NTFzbg+PWcU1bqdadTR+cXaQOU/3LN9bs58w2db0L+R4NLO50uR0pPT0daWhoASCEEEuGHRDpouU1nWnJyMlJTU+U7hKoBjmRmFxcXUbx3794NNzc3jB49GgCkqEhDQwM2btyIQYMGoW/fvnJ/DTWeMGECAgMDkZubi82bN8NqtWLfvn0GI2P48OGIjY3Fpk2bDN/nWH744QccPnwYdrsdtbW1OHz4cBtlPSgoCBMmTMCePXskF0NH7iwWCyZOnChGNiFnY8aMgaurq6FBIKNlALBnzx5DKfW9e/fi5MmTMJlMsldzc3PFyABaoVAcG2FLVqsVgwcPxuDBgw3v7uLiIjBNAPLudLbRkTx+/HjExcWhsrISGzduxHXXXYempiacPHkSEydOxP79+3H+/Hn4+/tj4sSJANDG+XLmzBkZqy4EpMfOfcB1bmxsxMaNG8U4bWpqwvfff4/y8nJ5V1JhYSE2b94Mm82GoUOHYvDgwbDZbNi8ebNA2LiO5AGErvfv3x99+/bFpk2bMHr0aMTFxaGqqgobN25EeXm55KfpPX250H8U0XCGEDgrwzwkfn5+sljae8wEQDJoMmMPDw9UVVVJVRIyYDJkMmMKfHqTtRAiFMtms0nOBRk7hSw9/hoCA0C6rWrvHQ0cLy8vlJeXi6ecB5XQCDIuMnvCwigkeSCB9j2RFByEJTBJm9EYk8kkPUW8vLxQVFQkAoNCw9kApPC02WyCC7XZbIJZ10o41wWA4Ed5X4bleS9GIDw9PWE2m1FRUQGz2WxYV74rlXFiswn10goHFW7OAT2LTBatrKyUzzkWvhuFOKMYlZWV8PPzk6pcNptNqmpRyEZERMBqtUrFn4CAAINip2FX/v7+UoKSRhFzfMxms8wp97KGvXBO+WwNxSGzCAsLEwOTZSwJ+6MHmOV8NcMDHIz89OnT/99P//8B9enTR84hoR+6l4uuHEelDHAY2QEBAYbqUczh4JnivDlHTKk41tXVibeblZf0uvA8a4cB4X08wzRUCWnRScq6pCv5jo5eaIOF55/VqAi/0pAd5hRp45w8l7BEoDVHQueC0WNN6CP5Gp9P77m7u6NXEedZGxA8tzoXwMXFBRaLBW5ubgaDjmvBqAcrOZGXMXoHtCZX830IN9XOIkZXmpubUVpaKjBS5oKwPC35BuFF5LstLS2i8LPyHw0u/o37jgqD2dyag0IIrclkkig0+b9eIw8PD4H+Ag5lqqKiQvYK5RB5F+ed8pM5d7xnYGCgnAEaEYwmM7dRV63Sxl5zc7OUUue7VVVVoa6uDh4eHggNDZXqaOS9/38ob/vf0KJFizBkyBCkpKS0+ezs2bNYsmQJXnnlFfmbhtz89NNPWLt2rcCJjh49ii1btuDRRx813Cc0NBQFBQW46qqrsHfv3p99Fyqxt99++389nuHDh2P79u0IDw9v19OdnJyMI0eOICYmxhAN/6Xytj9H4eHhyM/Px5AhQwxNDwHgrrvuwttvv43Q0FCsX78excXFmDp1KgDgu+++Q11dHW699Va5/sYbb8Tq1aslab49cnFxQVFREWbNmoVPPvkEZrMZ+fn5ePHFF7F//34cOnQI0dHRyMvLQ9++fXH06FF06dIF586d+4/GpengwYM4dOgQHnzwwf/q+2vXroXNZpPE+l+ipUuXSnnbwsJCPPPMM1i8ePF/9ez/TboUE+KSDY0O6qAO6qAO6qAO6qAO6qAO6qBLpUuGTnVQB3VQB3VQB3VQB3VQB3VQB10qdRgaHdRBHdRBHdRBHdRBHdRBHfSrU4eh0UEd1EEd1EEd1EEd1EEd1EG/OnUYGh3UQR3UQR3UQR3UQR3UQR30q1OHodFBHdRBHdRBHdRBHdRBHdRBvzp1GBod1EEd1EEd1EEd1EEd1EEd9KtTh6HRQR3UQR3UQR3UQR3UQR3UQb86dRgaHdRBHdRBHdRBHdRBHdRBHfSrU4eh0UEd1EEd1EEd1EEd1EEd1EG/Ov0/6NgKWcBnn/gAAAAASUVORK5CYII="
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(torch.abs(rss_target), cmap='gray')\n",
+ "plt.title('Fully-sampled RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(torch.abs(masked_rss_target), cmap='gray')\n",
+ "plt.title('Undersampled 5x RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(mask_5x.squeeze(), cmap='gray')\n",
+ "plt.title(f'Poisson 2D 5x CC359', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "***\n",
+ "# ATOMMIC Undersampling\n",
+ "***"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Equispaced 1D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.816238Z",
+ "end_time": "2024-03-05T17:23:11.826352Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the undersampling masker\n",
+ "masker = create_masker('equispaced1d', 0.08, 4)\n",
+ "# apply the masker\n",
+ "masked_kspace, mask, acc = utils.apply_mask(kspace, masker)\n",
+ "# apply the IFFT\n",
+ "masked_imspace = fft.ifft2(masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "masked_imspace = masked_imspace / torch.max(torch.abs(masked_imspace))\n",
+ "# compute the RSS target\n",
+ "masked_imspace_rss_target = utils.rss_complex(masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:11.836657Z",
+ "end_time": "2024-03-05T17:23:12.102029Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(masked_imspace_rss_target, cmap='gray')\n",
+ "plt.title(f'Equispaced 1D {acc:1.0f}x RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(mask.repeat(1, masked_kspace.shape[1], 1, 1).squeeze(), cmap='gray')\n",
+ "plt.title(f'Equispaced 1D {acc:1.0f}x', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Equispaced 2D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:12.101744Z",
+ "end_time": "2024-03-05T17:23:12.118483Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the undersampling masker\n",
+ "masker = create_masker('equispaced2d', 0.08, 4)\n",
+ "# apply the masker\n",
+ "masked_kspace, mask, acc = utils.apply_mask(kspace, masker)\n",
+ "# apply the IFFT\n",
+ "masked_imspace = fft.ifft2(masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "masked_imspace = masked_imspace / torch.max(torch.abs(masked_imspace))\n",
+ "# compute the RSS target\n",
+ "masked_imspace_rss_target = utils.rss_complex(masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:12.123674Z",
+ "end_time": "2024-03-05T17:23:12.945174Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(masked_imspace_rss_target, cmap='gray')\n",
+ "plt.title(f'Equispaced 2D {acc:1.0f}x RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(mask.squeeze(), cmap='gray')\n",
+ "plt.title(f'Equispaced 2D {acc:1.0f}x', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Gaussian 1D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:12.927200Z",
+ "end_time": "2024-03-05T17:23:12.945643Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the undersampling masker\n",
+ "masker = create_masker('gaussian1d', 0.7, 4)\n",
+ "# apply the masker\n",
+ "masked_kspace, mask, acc = utils.apply_mask(kspace, masker, shift=True, center_scale=0.02)\n",
+ "# apply the IFFT\n",
+ "masked_imspace = fft.ifft2(masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "masked_imspace = masked_imspace / torch.max(torch.abs(masked_imspace))\n",
+ "# compute the RSS target\n",
+ "masked_imspace_rss_target = utils.rss_complex(masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:12.927503Z",
+ "end_time": "2024-03-05T17:23:13.128783Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(masked_imspace_rss_target, cmap='gray')\n",
+ "plt.title(f'Gaussian 1D {acc:1.0f}x RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(mask.repeat(1, masked_kspace.shape[1], 1, 1).squeeze(), cmap='gray')\n",
+ "plt.title(f'Gaussian 1D {acc:1.0f}x', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Gaussian 2D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:13.129087Z",
+ "end_time": "2024-03-05T17:23:13.188032Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the undersampling masker\n",
+ "masker = create_masker('gaussian2d', 0.7, 4)\n",
+ "# apply the masker\n",
+ "masked_kspace, mask, acc = utils.apply_mask(kspace, masker, center_scale=0.02)\n",
+ "# apply the IFFT\n",
+ "masked_imspace = fft.ifft2(masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "masked_imspace = masked_imspace / torch.max(torch.abs(masked_imspace))\n",
+ "# compute the RSS target\n",
+ "masked_imspace_rss_target = utils.rss_complex(masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:13.190325Z",
+ "end_time": "2024-03-05T17:23:13.469859Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(masked_imspace_rss_target, cmap='gray')\n",
+ "plt.title(f'Gaussian 2D {acc:1.0f}x RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(mask.squeeze(), cmap='gray')\n",
+ "plt.title(f'Gaussian 2D {acc:1.0f}x', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Poisson 2D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:13.469066Z",
+ "end_time": "2024-03-05T17:23:14.404046Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the undersampling masker\n",
+ "masker = create_masker('poisson2d', 0.7, 4)\n",
+ "# apply the masker\n",
+ "masked_kspace, mask, acc = utils.apply_mask(kspace, masker)\n",
+ "# apply the IFFT\n",
+ "masked_imspace = fft.ifft2(masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "masked_imspace = masked_imspace / torch.max(torch.abs(masked_imspace))\n",
+ "# compute the RSS target\n",
+ "masked_imspace_rss_target = utils.rss_complex(masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:14.409832Z",
+ "end_time": "2024-03-05T17:23:14.689750Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(masked_imspace_rss_target, cmap='gray')\n",
+ "plt.title(f'Poisson 2D {acc:1.0f}x RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(mask.squeeze(), cmap='gray')\n",
+ "plt.title(f'Poisson 2D {acc:1.0f}x', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Random 1D"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:14.690038Z",
+ "end_time": "2024-03-05T17:23:14.697562Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the undersampling masker\n",
+ "masker = create_masker('random1d', 0.08, 4)\n",
+ "# apply the masker\n",
+ "masked_kspace, mask, acc = utils.apply_mask(kspace, masker)\n",
+ "# apply the IFFT\n",
+ "masked_imspace = fft.ifft2(masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "masked_imspace = masked_imspace / torch.max(torch.abs(masked_imspace))\n",
+ "# compute the RSS target\n",
+ "masked_imspace_rss_target = utils.rss_complex(masked_imspace, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 30,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:14.702540Z",
+ "end_time": "2024-03-05T17:23:14.986621Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(10, 10))\n",
+ "plt.subplot(1, 3, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 2)\n",
+ "plt.imshow(masked_imspace_rss_target, cmap='gray')\n",
+ "plt.title(f'Random 1D {acc:1.0f}x RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 3, 3)\n",
+ "plt.imshow(mask.repeat(1, masked_kspace.shape[1], 1, 1).squeeze(), cmap='gray')\n",
+ "plt.title(f'Random 1D {acc:1.0f}x', fontsize=14)\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Poisson 2D 20% Partial Fourier"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:14.984759Z",
+ "end_time": "2024-03-05T17:23:15.780445Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "# initialize the undersampling masker\n",
+ "masker = create_masker('poisson2d', 0.7, 4)\n",
+ "# apply the masker\n",
+ "masked_kspace, mask, _ = utils.apply_mask(kspace, masker)\n",
+ "masked_kspace_partial_fourier, mask_partial_fourier, acc = utils.apply_mask(kspace, masker, partial_fourier_percentage=0.2)\n",
+ "# apply the IFFT\n",
+ "masked_imspace = fft.ifft2(masked_kspace, fft_centered, fft_normalization, spatial_dims)\n",
+ "masked_imspace_partial_fourier = fft.ifft2(masked_kspace_partial_fourier, fft_centered, fft_normalization, spatial_dims)\n",
+ "# normalize the image for consistent visualization\n",
+ "masked_imspace = masked_imspace / torch.max(torch.abs(masked_imspace))\n",
+ "masked_imspace_partial_fourier = masked_imspace_partial_fourier / torch.max(torch.abs(masked_imspace_partial_fourier))\n",
+ "# compute the RSS target\n",
+ "masked_imspace_rss_target = utils.rss_complex(masked_imspace, coil_dim)\n",
+ "masked_imspace_partial_fourier_rss_target = utils.rss_complex(masked_imspace_partial_fourier, coil_dim)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "metadata": {
+ "ExecuteTime": {
+ "start_time": "2024-03-05T17:23:15.784790Z",
+ "end_time": "2024-03-05T17:23:16.211005Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": "",
+ "image/png": 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"
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=(15, 15))\n",
+ "plt.subplot(1, 5, 1)\n",
+ "plt.imshow(rss_target, cmap='gray')\n",
+ "plt.title('Fully-sampled RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 5, 2)\n",
+ "plt.imshow(masked_imspace_rss_target, cmap='gray')\n",
+ "plt.title(f'Poisson 2D {acc:1.0f}x RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 5, 3)\n",
+ "plt.imshow(mask.squeeze(), cmap='gray')\n",
+ "plt.title(f'Poisson 2D {acc:1.0f}x')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 5, 4)\n",
+ "plt.imshow(masked_imspace_partial_fourier_rss_target, cmap='gray')\n",
+ "plt.title(f'Poisson 2D {acc:1.0f}x \\n 20% Partial Fourier RSS')\n",
+ "plt.axis('off')\n",
+ "plt.subplot(1, 5, 5)\n",
+ "plt.imshow(mask_partial_fourier.squeeze(), cmap='gray')\n",
+ "plt.title(f'Poisson 2D {acc:1.0f}x \\n 20% Partial Fourier')\n",
+ "plt.axis('off')\n",
+ "plt.show()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.0"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/tutorials/03_ATOMMIC_Upload_Model_On_HF.ipynb b/tutorials/03_ATOMMIC_Upload_Model_On_HF.ipynb
new file mode 100644
index 00000000..f7b0fa2e
--- /dev/null
+++ b/tutorials/03_ATOMMIC_Upload_Model_On_HF.ipynb
@@ -0,0 +1,837 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "outputs": [],
+ "source": [
+ "\"\"\"\n",
+ "You can run either this notebook locally (if you have all the dependencies and a GPU) or on Google Colab.\n",
+ "\n",
+ "Instructions for setting up Colab are as follows:\n",
+ "1. Open a new Python 3 notebook.\n",
+ "2. Import this notebook from GitHub (File -> Upload Notebook -> \"GITHUB\" tab -> copy/paste GitHub URL)\n",
+ "3. Connect to an instance with a GPU (Runtime -> Change runtime type -> select \"GPU\" for hardware accelerator)\n",
+ "4. Run this cell to set up dependencies.\n",
+ "\"\"\"\n",
+ "# If you're using Google Colab and not running locally, run this cell.\n",
+ "\n",
+ "## Install dependencies\n",
+ "!apt-get install sox libsndfile1 ffmpeg\n",
+ "!pip install wget\n",
+ "!pip install text-unidecode\n",
+ "\n",
+ "# ### Install ATOMMIC\n",
+ "BRANCH = 'main'\n",
+ "!python -m pip install git@github.com:wdika/atommic.git"
+ ],
+ "metadata": {
+ "collapsed": false
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-10-04T15:18:54.830038Z",
+ "start_time": "2023-10-04T15:18:48.840015Z"
+ },
+ "collapsed": true,
+ "id": "J6d04-VRjC-O"
+ },
+ "outputs": [],
+ "source": [
+ "### Install Hugging Face Hub\n",
+ "!python -m pip install huggingface_hub\n",
+ "!python -m pip install evaluate"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aS-Y5O_oGBTc"
+ },
+ "source": [
+ "# ATOMMIC models on Hugging Face Hub\n",
+ "\n",
+ "This guide will briefly show how to upload ATOMMIC models to Hugging Face programmatically.\n",
+ "\n",
+ "This enables community members to share their ATOMMIC models (any model!) with all users of ATOMMIC!\n",
+ "\n",
+ "**Note**: While in this tutorial we showcase a reconstruction model, there is no particular restriction to any domain - all ATOMMIC models (.atommic files) of every domain can be uploaded and shared in the same way."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Us3UlvwCiEZi"
+ },
+ "source": [
+ "# Login to Hugging Face\n",
+ "\n",
+ "Use the notebook login, and access your user access token (or create one to upload models to Hugging Face).\n",
+ "\n",
+ "For more information, visit the User Access Token section - https://huggingface.co/docs/hub/security-tokens"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-10-04T15:18:55.046849Z",
+ "start_time": "2023-10-04T15:18:54.829715Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "from huggingface_hub import notebook_login\n",
+ "\n",
+ "# allow to enter token manually if not in notebook\n",
+ "notebook_login()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "dgZbTPcFiaml"
+ },
+ "outputs": [],
+ "source": [
+ "!git config --global credential.helper store"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "s-FiNn1eiFAl"
+ },
+ "source": [
+ "# Prepare a model to upload to HF\n",
+ "\n",
+ "In this example, we will upload an ATOMMIC REC model to Hugging Face for simplicity and to showcase the method.\n",
+ "\n",
+ "**You can swap out this REC model for any model that you restore via `restore_from()` and follow the same steps to upload your own models !**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5KnVl-M0ax14"
+ },
+ "outputs": [],
+ "source": [
+ "from omegaconf import OmegaConf, open_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "ZEDpkIinbwmm"
+ },
+ "outputs": [],
+ "source": [
+ "import atommic.collections.reconstruction as atommic_rec"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mLuQo1vnHVcP"
+ },
+ "source": [
+ "# Model Name\n",
+ "\n",
+ "ATOMMIC adheres to strict requirements when naming a model for upload to Hugging Face Hub. \n",
+ "\n",
+ "It is **mandatory** to share the model name across the model card, the ATOMMIC file itself. Otherwise ATOMMIC model from Hugging Face will fail to restore correctly."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "MRO2f9fhHywJ"
+ },
+ "source": [
+ "## Naming Convention\n",
+ "\n",
+ "ATOMMIC model names can vary based on the task. Following the standard guidelines when naming models, we do not expect the same level of strictness for community contributions.\n",
+ "\n",
+ "Here are some common guidelines we encourage (but do not enforce) users to follow : \n",
+ "\n",
+ "- `Task name`: Usually a short 3-4 character representation of the task that the model performs.\n",
+ " - `mtl` = MultiTask Learning (MTL)\n",
+ " - `qmri` = quantitative MRI (qMRI)\n",
+ " - `rec` = Reconstruction (REC)\n",
+ " - `seg` = Segmentation (SEG)\n",
+ "\n",
+ "- `Model Identifier`: Since models vary so drastically across domains, there is a lot of flexibility here. We try to adhere to naming conventions in literature as much as possible. For example, you can attach `model architecture` (REC/UNet), `training loss` (REC/SSIM), and `model size` (small, large, etc.).\n",
+ "\n",
+ "- `Optional: Additional Modifiers`: These are additional identifiers such as dataset name (cc359 for Calgary-Campinas 359), etc. It can be set on a case-by-case basis.\n",
+ "\n",
+ "All these name segments are jointed by `_`.\n",
+ "\n",
+ "-----\n",
+ "\n",
+ "As an example of the following model we will try today : \n",
+ "\n",
+ "`{task name}_{model identifier}_[OPTIONAL modifiers]` = `rec_unet_small`"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "BjLstKWnPzWV"
+ },
+ "source": [
+ "**Set the MODEL_NAME carefully** !"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "UzHjXDbckU0M"
+ },
+ "outputs": [],
+ "source": [
+ "MODEL_NAME = \"REC_UNet_CC359_12_channel_poisson2d_5x_10x_NNEstimationCSM\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "qibj1RwvKjSQ"
+ },
+ "source": [
+ "-----\n",
+ "**Restore a ATOMMIC Model**\n",
+ "\n",
+ "Here, we restore a model from a local .atommic file using `restore_from()`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "path_to_local_model = input(\"Please enter the (local) path to the pre-trained model file : \")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "id": "MsC3pE65d_z2"
+ },
+ "outputs": [],
+ "source": [
+ "model, _ = atommic_rec.nn.UNet.restore_from(path_to_local_model)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "y1AkXPFVKfC2"
+ },
+ "source": [
+ "# Create a Hugging Face Model\n",
+ "\n",
+ "Now that we have an ATOMMIC model and have logged into Hugging Face with our user API key, we can begin by creating a new repository and uploading our model."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "iv17qFG7KzlL"
+ },
+ "source": [
+ "-----\n",
+ "\n",
+ "After the model has been restored, create an HfApi object to interact with the model repository."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "aJUXCOTjKy-2"
+ },
+ "outputs": [],
+ "source": [
+ "from huggingface_hub import HfApi\n",
+ "api = HfApi()\n",
+ "username = api.whoami()['name']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "DKRlMeaEkeAH"
+ },
+ "outputs": [],
+ "source": [
+ "try:\n",
+ " api.create_repo(repo_id=MODEL_NAME)\n",
+ " print(\"Successfully created repository !\")\n",
+ "except Exception as e:\n",
+ " print(\"Repository is possibly already created. Refer to error here - \\n\\n\", e)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "N2-deSyTlCdS"
+ },
+ "outputs": [],
+ "source": [
+ "from huggingface_hub import Repository"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "aTa4RqDYLGMI"
+ },
+ "source": [
+ "Note two essential names - \n",
+ "\n",
+ "- `hf_model_name`: A string name that is the composite of your `username` and `MODEL_NAME` as set above. This name is used for multiple purposes, so keep track of it.\n",
+ "\n",
+ "- `model_filename`: The actual filename of the ATOMMIC model that will be uploaded to Hugging Face. Note that this filename is explicitly set to `{MODEL_NAME}.atommic`. If this model filename is altered, then the model cannot correctly be restored by ATOMMIC when downloaded from Hugging Face Hub, so please be careful."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xhTTMNpBskMS",
+ "scrolled": true
+ },
+ "outputs": [],
+ "source": [
+ "local_dir = f'model-{MODEL_NAME}/'\n",
+ "hf_model_name = f'{username}/{MODEL_NAME}'\n",
+ "\n",
+ "commit_message = \"Upload model\"\n",
+ "model_filename = f'{MODEL_NAME}.atommic'\n",
+ "\n",
+ "with Repository(local_dir=local_dir, clone_from=hf_model_name, repo_type='model').commit(commit_message):\n",
+ " model.save_to(model_filename)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "BhvNp8MYvxLi"
+ },
+ "outputs": [],
+ "source": [
+ "print(\"Finished uploading model to :\", hf_model_name)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Qrs-MlW9vVbH"
+ },
+ "source": [
+ "## Test if the model works \n",
+ "\n",
+ "Now that we uploaded the model, let's try to use it in ATOMMIC !\n",
+ "\n",
+ "The only change required between normally calling `from_pretrained(model_name)` is to call **`from_pretrained({username}/{filename})`**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "collapsed": true,
+ "id": "NyuyyRv5snkr"
+ },
+ "outputs": [],
+ "source": [
+ "hf_model_name = f'{username}/{MODEL_NAME}'\n",
+ "hf_model, _ = atommic_rec.nn.UNet.from_pretrained(hf_model_name)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Yhi922WVv4G_"
+ },
+ "outputs": [],
+ "source": [
+ "print(\"Successfully used HF model -\", hf_model_name)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "9gG1ElJywEJT"
+ },
+ "source": [
+ "# Model Card\n",
+ "\n",
+ "Now that we have uploaded the model, we are nearly 50% done!\n",
+ "\n",
+ "The next step is to update the model card to have some helpful information regarding the uploaded model and its scores compared to other models.\n",
+ "\n",
+ "You can do this in two ways, manually (by clicking the link below) or programmatically fill in part of the model card by following the instructions below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "aZJRKoxhwBLr"
+ },
+ "outputs": [],
+ "source": [
+ "hf_url = f'https://huggingface.co/{username}/{MODEL_NAME}'\n",
+ "print(f\"Visit {hf_url} to manually edit your model card\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZlA4hNq6w4rH"
+ },
+ "source": [
+ "-----\n",
+ "\n",
+ "Here, we are going to setup some variables for our model card.\n",
+ "\n",
+ "First up are the tags:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "QxKtPynWyUWX"
+ },
+ "outputs": [],
+ "source": [
+ "TAGS = [\n",
+ " \"image-reconstruction\",\n",
+ " \"UNet\",\n",
+ " \"ATOMMIC\", # required for library identification\n",
+ " \"pytorch\", # required, for toolkit identification\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Fh7rYWEMM0Vz"
+ },
+ "source": [
+ "-----\n",
+ "\n",
+ "Next, we list down all the datasets that were used to train the model.\n",
+ "\n",
+ "By convention, try to search if the dataset already exists on Hugging Face Datasets - it is usually listed at the top and in lower case.\n",
+ "\n",
+ "If you train on datasets that don't yet exist in Hugging Face Datasets, you can still add them but try to differentiate them by using capitalized names."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "qy-5aDAgzuGD"
+ },
+ "outputs": [],
+ "source": [
+ "# Replace all spaces with `-`\n",
+ "DATASETS = [\n",
+ " \"CC359\",\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "_0w1X_z4NN5-"
+ },
+ "source": [
+ "-----\n",
+ "Now we create an automated template based on a config for the top portion of the readme file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "O88WFyPJwjJD"
+ },
+ "outputs": [],
+ "source": [
+ "from dataclasses import dataclass, field\n",
+ "from typing import List, Optional, Dict, Any\n",
+ "\n",
+ "@dataclass\n",
+ "class ATOMMICHuggingFaceModelConfig:\n",
+ " language: List[str]\n",
+ " license: str\n",
+ "\n",
+ " library_name: str = \"atommic\"\n",
+ " datasets: List[str] = field(default_factory=lambda: DATASETS)\n",
+ " thumbnail: Optional[str] = None\n",
+ " tags: List[str] = field(default_factory=lambda: TAGS)\n",
+ " model_index: Any = field(default_factory=lambda: [dict(name=MODEL_NAME, results=[])])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "BpInrBdNxxZ3"
+ },
+ "outputs": [],
+ "source": [
+ "config = ATOMMICHuggingFaceModelConfig(language=['en'], license=\"cc-by-4.0\") # choose appropriate license here\n",
+ "config = OmegaConf.structured(config)\n",
+ "\n",
+ "with open_dict(config):\n",
+ " # Update `model_index` to `model-index`\n",
+ " model_index = config.pop('model_index')\n",
+ " config['model-index'] = model_index\n",
+ "\n",
+ " # Replace all spaces with `-` in datasets\n",
+ " normalized_datasets = [ds_name.replace(\" \", \"-\") for ds_name in config['datasets']]\n",
+ " config['datasets'] = OmegaConf.create(normalized_datasets)\n",
+ "\n",
+ "print(OmegaConf.to_yaml(config))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0TECX8QrC6FY"
+ },
+ "source": [
+ "## Markdown Template\n",
+ "\n",
+ "Now that we have an auto-generated header for our readme, next, we write down some template markdown for the actual contents of the markdown.\n",
+ "\n",
+ "You can edit the code here directly if you want, or if you prefer the GUI to see the actual changes in real-time, you can finish uploading this model card and then edit the readme file on the Hugging Face webpage itself."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "SSmm7_OiC9Ex"
+ },
+ "outputs": [],
+ "source": [
+ "hf_model_name = f'{username}/{MODEL_NAME}'\n",
+ "\n",
+ "TEMPLATE = f\"\"\"\n",
+ "## Model Overview\n",
+ "\n",
+ "UNet for 5x & 10x accelerated MRI Reconstruction on the CC359 dataset\n",
+ "\n",
+ "\n",
+ "## ATOMMIC: Training\n",
+ "\n",
+ "To train, fine-tune or play with the model you will need to install [ATOMMIC](https://github.com/wdika/atommic). We recommend you install it after you've installed latest Pytorch version.\n",
+ "```\n",
+ "pip install atommic['all']\n",
+ "``` \n",
+ "\n",
+ "## How to Use this Model\n",
+ "\n",
+ "The model is available for use in ATOMMIC, and can be used as a pre-trained checkpoint for inference or for fine-tuning on another dataset.\n",
+ "\n",
+ "Corresponding configuration YAML files can be found [here](https://github.com/wdika/atommic/tree/main/projects/REC/CC359/conf).\n",
+ "\n",
+ "### Automatically instantiate the model\n",
+ "\n",
+ "```python\n",
+ "import atommic.collections.reconstruction.nn as atommic_rec_nn\n",
+ "atommic_rec_model = atommic_rec_nn.unet.UNet.from_pretrained(\"{hf_model_name}\")\n",
+ "```\n",
+ "\n",
+ "### Usage\n",
+ "\n",
+ "You need to download the CC359 dataset to effectively use this model. Check the [CC359](https://github.com/wdika/atommic/blob/main/projects/REC/CC359/README.md) page for more information.\n",
+ "\n",
+ "\n",
+ "## Model Architecture\n",
+ "```base\n",
+ "model_name: UNet\n",
+ "channels: 64\n",
+ "pooling_layers: 4\n",
+ "in_channels: 2\n",
+ "out_channels: 2\n",
+ "padding_size: 11\n",
+ "dropout: 0.0\n",
+ "normalize: true\n",
+ "norm_groups: 2\n",
+ "dimensionality: 2\n",
+ "reconstruction_loss:\n",
+ " l1: 0.1\n",
+ " ssim: 0.9\n",
+ "```\n",
+ "\n",
+ "## Training\n",
+ "```base\n",
+ "optim:\n",
+ " name: adamw\n",
+ " lr: 1e-4\n",
+ " betas:\n",
+ " - 0.9\n",
+ " - 0.999\n",
+ " weight_decay: 0.0\n",
+ " sched:\n",
+ " name: CosineAnnealing\n",
+ " min_lr: 0.0\n",
+ " last_epoch: -1\n",
+ " warmup_ratio: 0.1\n",
+ "\n",
+ "trainer:\n",
+ " strategy: ddp_find_unused_parameters_false\n",
+ " accelerator: gpu\n",
+ " devices: 1\n",
+ " num_nodes: 1\n",
+ " max_epochs: 50\n",
+ " precision: 16-mixed\n",
+ " enable_checkpointing: false\n",
+ " logger: false\n",
+ " log_every_n_steps: 50\n",
+ " check_val_every_n_epoch: -1\n",
+ " max_steps: -1\n",
+ "```\n",
+ "\n",
+ "## Performance\n",
+ "\n",
+ "To compute the targets using the raw k-space and the chosen coil combination method, accompanied with the chosen coil sensitivity maps estimation method, you can use [targets](https://github.com/wdika/atommic/tree/main/projects/REC/CC359/conf/targets) configuration files.\n",
+ "\n",
+ "Evaluation can be performed using the [evaluation](https://github.com/wdika/atommic/blob/main/tools/evaluation/reconstruction.py) script for the reconstruction task.\n",
+ "\n",
+ "Results\n",
+ "-------\n",
+ "\n",
+ "Evaluation against RSS targets\n",
+ "------------------------------\n",
+ "5x: MSE = 0.001429 +/- 0.001373 NMSE = 0.02208 +/- 0.02319 PSNR = 28.85 +/- 4.169 SSIM = 0.8487 +/- 0.07037\n",
+ "\n",
+ "10x: MSE = 0.002108 +/- 0.002 NMSE = 0.03273 +/- 0.03417 PSNR = 27.2 +/- 4.197 SSIM = 0.8095 +/- 0.09149\n",
+ "\n",
+ "\n",
+ "## Limitations\n",
+ "\n",
+ "This model was trained on the CC359 using automatic coil sensitivity maps estimation and might differ from the results reported on the challenge leaderboard.\n",
+ "\n",
+ "\n",
+ "## References\n",
+ "\n",
+ "[1] [ATOMMIC](https://github.com/wdika/atommic)\n",
+ "\n",
+ "[2] Beauferris, Y., Teuwen, J., Karkalousos, D., Moriakov, N., Caan, M., Yiasemis, G., Rodrigues, L., Lopes, A., Pedrini, H., Rittner, L., Dannecker, M., Studenyak, V., Grรถger, F., Vyas, D., Faghih-Roohi, S., Kumar Jethi, A., Chandra Raju, J., Sivaprakasam, M., Lasby, M., โฆ Souza, R. (2022). Multi-Coil MRI Reconstruction ChallengeโAssessing Brain MRI Reconstruction Models and Their Generalizability to Varying Coil Configurations. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.919186\n",
+ "\"\"\""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "KPa53S_5NzNp"
+ },
+ "source": [
+ "-----\n",
+ "\n",
+ "Below, we will upload this model card in a temporary file called **`\"readme_template.md\"`**. This is done to prevent overwriting of the \"final\" model card that the user may have manually edited.\n",
+ "\n",
+ "Once this step is finished, **please copy the contents of this file, create a README.md file and paste the contents into it**."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "0vk5KK4gzpSU"
+ },
+ "outputs": [],
+ "source": [
+ "local_dir = f'model-{MODEL_NAME}/'\n",
+ "hf_model_name = f'{username}/{MODEL_NAME}'\n",
+ "\n",
+ "commit_message = \"Upload config\"\n",
+ "filename = 'readme_template.md'\n",
+ "\n",
+ "with Repository(local_dir=local_dir, clone_from=hf_model_name, repo_type='model').commit(commit_message):\n",
+ " with open(filename, 'w') as f:\n",
+ " f.write(\"---\\n\")\n",
+ " f.write(OmegaConf.to_yaml(config))\n",
+ " f.write(\"\\n---\\n\\n\")\n",
+ " f.write(TEMPLATE)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dfXoihCQmWDa"
+ },
+ "source": [
+ "-----\n",
+ "\n",
+ "Please visit the URL below to copy the contents of the `readme_template.md` file into your `README.md` file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "but-5LuLTHFd"
+ },
+ "outputs": [],
+ "source": [
+ "hf_url = f'https://huggingface.co/{username}/{MODEL_NAME}'\n",
+ "print(f\"Visit {hf_url} to edit your model card from the generated template file `{filename}`\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "5vPEnlE62dGU"
+ },
+ "source": [
+ "## Evaluation Results\n",
+ "\n",
+ "Now that we have both the model checkpoint and the readme uploaded to the Hub, we can optionally add some evaluation results to the card as well!\n",
+ "\n",
+ "However, HF doesn't support (yet) the image-reconstruction task and logging metrics is not possible. You can log metrics for segmentation, if logging a segmentation model."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install cchardet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "rkXMtapA0YzH"
+ },
+ "outputs": [],
+ "source": [
+ "import evaluate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5A4g3SGf4d0V"
+ },
+ "outputs": [],
+ "source": [
+ "hf_model_name = f'{username}/{MODEL_NAME}'\n",
+ "\n",
+ "# evaluate.push_to_hub(\n",
+ "# model_id=hf_model_name,\n",
+ "# task_type=\"segmentation\",\n",
+ "# dataset_type=\"\",\n",
+ "# dataset_name=\"\",\n",
+ "# metric_type=\"\",\n",
+ "# metric_name=\"\",\n",
+ "# dataset_split=\"\",\n",
+ "# dataset_config=\"\",\n",
+ "# metric_value=1.0,\n",
+ "# )"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "f3YYa7liO_m3"
+ },
+ "source": [
+ "-----\n",
+ "\n",
+ "Done! Now we have a model checkpoint, a model card as well as evaluation results all set up for the ATOMMIC model on Hugging Face!\n",
+ "\n",
+ "To add more metrics, you can copy-paste the above cell and repeat the procedure for as many metrics as needed!"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Optionally you might want to remove any generated dirs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "ExecuteTime": {
+ "end_time": "2023-10-04T16:02:32.958527Z",
+ "start_time": "2023-10-04T16:02:32.915152Z"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import os"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# remove dir with MODEL_NAME in tutorials dir\n",
+ "os.system(f\"rm -rf tutorials/{MODEL_NAME}\")\n",
+ "# remove .ipynb checkpoints\n",
+ "os.system(f\"rm -rf tutorials/.ipynb_checkpoints\")"
+ ]
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "collapsed_sections": [],
+ "name": "Publish_NeMo_Model_On_Hugging_Face_Hub.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
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+}
+