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Updates docs for release (openvinotoolkit#936)
<!-- Contributing guide: https://github.com/openvinotoolkit/datumaro/blob/develop/CONTRIBUTING.md --> ### Summary * Added docs workflow for stable release * Updated some docstrings * Fixed some docs build errors <!-- Resolves openvinotoolkit#111 and openvinotoolkit#222. Depends on openvinotoolkit#1000 (for series of dependent commits). This PR introduces this capability to make the project better in this and that. - Added this feature - Removed that feature - Fixed the problem openvinotoolkit#1234 --> ### How to test <!-- Describe the testing procedure for reviewers, if changes are not fully covered by unit tests or manual testing can be complicated. --> ### Checklist <!-- Put an 'x' in all the boxes that apply --> - [ ] I have added unit tests to cover my changes.​ - [ ] I have added integration tests to cover my changes.​ - [ ] I have added the description of my changes into [CHANGELOG](https://github.com/openvinotoolkit/datumaro/blob/develop/CHANGELOG.md).​ - [ ] I have updated the [documentation](https://github.com/openvinotoolkit/datumaro/tree/develop/docs) accordingly ### License - [ ] I submit _my code changes_ under the same [MIT License](https://github.com/openvinotoolkit/datumaro/blob/develop/LICENSE) that covers the project. Feel free to contact the maintainers if that's a concern. - [ ] I have updated the license header for each file (see an example below). ```python # Copyright (C) 2023 Intel Corporation # # SPDX-License-Identifier: MIT ```
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‎.github/workflows/docs_stable.yml

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name: Build Docs for releases
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on:
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workflow_dispatch: # run on request (no need for PR)
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release:
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types: [published]
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jobs:
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Build-Docs:
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runs-on: ubuntu-20.04
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permissions:
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contents: write
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steps:
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- name: Checkout repository
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uses: actions/checkout@v3
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with:
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fetch-depth: 0 # otherwise, you will failed to push refs to dest repo
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- name: Set up Python
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uses: actions/setup-python@v4
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with:
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python-version: "3.10"
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- name: Install dependencies
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run: python -m pip install tox
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- name: Build-Docs
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run: |
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echo RELEASE_VERSION=${GITHUB_REF#refs/*/} >> $GITHUB_ENV
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tox -e build-doc
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# - name: Deploy
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# uses: peaceiris/actions-gh-pages@v3
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# with:
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# github_token: ${{ secrets.GITHUB_TOKEN }}
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# publish_dir: ./public
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# destination_dir: ${{ env.RELEASE_VERSION }}
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# force_orphan: true

‎docs/contributing.md ‎contributing.md

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‎datumaro/cli/util/project.py

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@@ -153,7 +153,7 @@ def split_local_revpath(revpath: str) -> Tuple[Revision, str]:
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A local revpath is a path to a revision withing the current project.
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The syntax is:
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- [ <revision> : ] [ <target> ]
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- [ <revision> : ] [ <target> ]
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At least one part must be present.
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Returns: (revision, build target)

‎datumaro/plugins/data_formats/common_semantic_segmentation.py

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class CommonSemanticSegmentationWithSubsetDirsImporter(CommonSemanticSegmentationImporter):
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"""It supports the following subset sub-directory structure for CommonSemanticSegmentation.
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```
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Dataset/
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└─ <split: train,val, ...>
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├── dataset_meta.json # a list of labels
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├── images/
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│ ├── <img1>.png
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│ ├── <img2>.png
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│ └── ...
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└── masks/
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├── <img1>.png
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├── <img2>.png
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└── ...
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.. code-block::
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Dataset/
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└─ <split: train,val, ...>
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├── dataset_meta.json # a list of labels
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├── images/
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│ ├── <img1>.png
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│ ├── <img2>.png
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│ └── ...
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└── masks/
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├── <img1>.png
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├── <img2>.png
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└── ...
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Then, the imported dataset will have train, val, ... CommonSemanticSegmentation subsets.
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```
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"""

‎datumaro/plugins/data_formats/datumaro_binary/mapper/__init__.py

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# ruff: noqa: F405
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from .annotation import *
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from .common import *
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from .common import DictMapper, FloatListMapper, IntListMapper, Mapper, StringMapper
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from .dataset_item import *
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from .media import *
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‎datumaro/plugins/data_formats/imagenet.py

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"""TorchVision's ImageFolder style importer.
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For example, it imports the following directory structure.
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root
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├── label_0
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│ ├── label_0_1.jpg
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│ └── label_0_2.jpg
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└── label_1
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└── label_1_1.jpg
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.. code-block:: text
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root
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├── label_0
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│ ├── label_0_1.jpg
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│ └── label_0_2.jpg
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└── label_1
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└── label_1_1.jpg
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"""
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@classmethod
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"""TorchVision ImageFolder style importer.
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For example, it imports the following directory structure.
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root
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├── train
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│ ├── label_0
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│ │ ├── label_0_1.jpg
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│ │ └── label_0_2.jpg
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│ └── label_1
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│ └── label_1_1.jpg
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├── val
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│ ├── label_0
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│ │ ├── label_0_1.jpg
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│ │ └── label_0_2.jpg
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│ └── label_1
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│ └── label_1_1.jpg
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└── test
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├── label_0
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│ ├── label_0_1.jpg
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│ └── label_0_2.jpg
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└── label_1
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└── label_1_1.jpg
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.. code-block::
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root
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├── train
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│ ├── label_0
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│ │ ├── label_0_1.jpg
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│ │ └── label_0_2.jpg
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│ └── label_1
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│ └── label_1_1.jpg
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├── val
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│ ├── label_0
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│ │ ├── label_0_1.jpg
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│ │ └── label_0_2.jpg
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│ └── label_1
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│ └── label_1_1.jpg
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└── test
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├── label_0
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│ ├── label_0_1.jpg
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│ └── label_0_2.jpg
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└── label_1
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└── label_1_1.jpg
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Then, it will have three subsets: train, val, and test and they have label_0 and label_1 labels.
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"""

‎datumaro/plugins/sampler/random_sampler.py

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class RandomSampler(Transform, CliPlugin):
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"""
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r"""
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Sampler that keeps no more than required number of items in the dataset.|n
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|n
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Notes:|n

‎docs/source/conf.py

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suppress_warnings = [
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# "myst.xref_missing",
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"myst.iref_ambiguous"
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"myst.iref_ambiguous",
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"autosectionlabel.*",
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]
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autosummary_generate = True # Turn on sphinx.ext.autosummary

‎docs/source/docs/level-up/advanced_skills/index.rst

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Advanced Skills
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###########
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###############
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.. panels::
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‎docs/source/docs/level-up/basic_skills/index.rst

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Basic Skills
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###########
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############
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.. panels::
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‎docs/source/docs/level-up/intermediate_skills/index.rst

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Intermediate Skills
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###########
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###################
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.. panels::
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‎docs/source/docs/reference/plugins/sampler/algorithm/datumaro.plugins.sampler.algorithm.entropy.rst

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--------------
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.. automodule:: datumaro.plugins.sampler.algorithm.entropy
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.. autoclass:: SampleEntropy
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.. automethod:: __init__
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.. automethod:: get_sample
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.. automethod:: _get_sample_mixed
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.. automethod:: _rank_images
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:members:
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:undoc-members:
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:show-inheritance:

‎docs/source/docs/reference/plugins/sampler/algorithm/datumaro.plugins.sampler.algorithm.rst

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:members:
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:undoc-members:
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:show-inheritance:
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:private-members:

‎docs/source/docs/user-manual/extending.md

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# Extending
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There are few ways to extend and customize Datumaro behavior, which is
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supported by plugins. Check [our contribution guide](/docs/contributing) for
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details on plugin implementation. In general, a plugin is a Python module.
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supported by plugins. Check [our contribution guide](https://github.com/openvinotoolkit/datumaro/blob/develop/contributing.md)
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for details on plugin implementation. In general, a plugin is a Python module.
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It must be put into a plugin directory:
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- `<project_dir>/.datumaro/plugins` for project-specific plugins
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- `<datumaro_dir>/plugins` for global plugins

‎notebooks/09_encrypt_dataset.ipynb

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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Re-export again to any dataset format with no encryption\n",
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"## Re-export again to any dataset format with no encryption\n",
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"\n",
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"Because the `DatumaroBinary` format is encrypted, it cannot be easily used for your purposes. In this time, we re-export it to any dataset format for the future usage. For example, COCO format is used for the export."
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]

‎requirements.txt

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-r requirements-default.txt
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opencv-python-headless>=4.1.0.25
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# docs
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markupsafe>=2.0.1
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nbconvert>=7.2.3
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ipython>=8.4.0

‎tox.ini

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isolated_build = true
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skip_missing_interpreters = true
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[testenv]
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deps =
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-r{toxinidir}/requirements.txt
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[testenv:pre-commit]
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basepython = python3
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deps =

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