Feature/inferrer class

.github/workflows/tests.yml

@@ -0,0 +1,39 @@
+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: tests
+
+on:
+  push:
+    branches: ["main"]
+  pull_request:
+    branches: ["main"]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10"] #TODO add 3.11 support (for 3.12 torch is not available yet)
+
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v3
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install flake8 pytest
+          pip install -e .
+      - name: Lint with flake8
+        run: |
+          # stop the build if there are Python syntax errors or undefined names
+          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+      - name: Test with pytest
+        run: |
+          pytest

.gitignore

@@ -26,7 +26,6 @@ share/python-wheels/
 .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.
@@ -130,5 +129,4 @@ dmypy.json
 
 .vscode
 poetry.lock
-
-.DS_Store
+.DS_Store

README.md

@@ -1,5 +1,6 @@
 [![PyPI version panoptica](https://badge.fury.io/py/brainles-aurora.svg)](https://pypi.python.org/pypi/brainles-aurora/)
 [![Documentation Status](https://readthedocs.org/projects/brainles-aurora/badge/?version=latest)](http://brainles-aurora.readthedocs.io/?badge=latest)
+[![tests](https://github.com/BrainLesion/AURORA/actions/workflows/tests.yml/badge.svg)](https://github.com/BrainLesion/AURORA/actions/workflows/tests.yml)
 
 # AURORA
 

brainles_aurora/aux.py

@@ -1,4 +1,4 @@
-from path import Path
+from pathlib import Path
 import os
 
 

brainles_aurora/inferer/constants.py

@@ -0,0 +1,45 @@
+from enum import Enum
+
+
+class InferenceMode(str, Enum):
+    """Enum representing different modes of inference based on available image inputs."""
+
+    T1_T1C_T2_FLA = "t1-t1c-t2-fla"
+    T1_T1C_FLA = "t1-t1c-fla"
+    T1_T1C = "t1-t1c"
+    T1C_FLA = "t1c-fla"
+    T1C_O = "t1c-o"
+    FLA_O = "fla-o"
+    T1_O = "t1-o"
+
+
+class ModelSelection(str, Enum):
+    """Enum representing different strategies for model selection."""
+
+    BEST = "best"
+    LAST = "last"
+    VANILLA = "vanilla"
+
+
+class DataMode(str, Enum):
+    """Enum representing different modes for handling input and output data.
+
+    Enum Values:
+        NIFTI_FILE (str): Input data is provided as NIFTI file paths/ output is writte to NIFTI files.
+        NUMPY (str): Input data is provided as NumPy arrays/ output is returned as NumPy arrays.
+    """
+
+    NIFTI_FILE = "NIFTI_FILEPATH"
+    NUMPY = "NP_NDARRAY"
+
+
+# booleans indicate presence of files in order: T1 T1C T2 FLAIR
+IMGS_TO_MODE_DICT = {
+    (True, True, True, True): InferenceMode.T1_T1C_T2_FLA,
+    (True, True, False, True): InferenceMode.T1_T1C_FLA,
+    (True, True, False, False): InferenceMode.T1_T1C,
+    (False, True, False, True): InferenceMode.T1C_FLA,
+    (False, True, False, False): InferenceMode.T1C_O,
+    (False, False, False, True): InferenceMode.FLA_O,
+    (True, False, False, False): InferenceMode.T1_O,
+}

brainles_aurora/inferer/dataclasses.py

@@ -0,0 +1,68 @@
+import logging
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+
+from brainles_aurora.inferer.constants import DataMode, ModelSelection
+
+
+@dataclass
+class BaseConfig:
+    """Base configuration for the Aurora model inferer.
+
+    Attributes:
+        output_mode (DataMode, optional): Output mode for the inference results. Defaults to DataMode.NIFTI_FILE.
+        output_folder (str | Path, optional): Output folder for the results. Defaults to "aurora_output".
+        log_level (int | str, optional): Logging level. Defaults to logging.INFO.
+        segmentation_file_name (str, optional): File name for the segmentation result. Defaults to "segmentation.nii.gz". (The segmentation will be saved in output_folder/{timestamp}/segmentation_file_name)
+        t1 (str | Path | np.ndarray | None, optional): Path or NumPy array for T1 image. Defaults to None.
+        t1c (str | Path | np.ndarray | None, optional): Path or NumPy array for T1 contrast-enhanced image. Defaults to None.
+        t2 (str | Path | np.ndarray | None, optional): Path or NumPy array for T2 image. Defaults to None.
+        fla (str | Path | np.ndarray | None, optional): Path or NumPy array for FLAIR image. Defaults to None.
+    """
+
+    output_mode: DataMode = DataMode.NIFTI_FILE
+    output_folder: str | Path = "aurora_output"
+    segmentation_file_name: str | None = "segmentation.nii.gz"
+    log_level: int | str = logging.INFO
+    t1: str | Path | np.ndarray | None = None
+    t1c: str | Path | np.ndarray | None = None
+    t2: str | Path | np.ndarray | None = None
+    fla: str | Path | np.ndarray | None = None
+
+
+@dataclass
+class AuroraInfererConfig(BaseConfig):
+    """Configuration for the Aurora model inferer.
+
+    Attributes:
+        output_mode (DataMode, optional): Output mode for the inference results. Defaults to DataMode.NIFTI_FILE.
+        output_folder (str | Path, optional): Output folder for the results. Defaults to "aurora_output".
+        segmentation_file_name (str, optional): File name for the segmentation result. Defaults to "segmentation.nii.gz". (The segmentation will be saved in output_folder/{timestamp}/segmentation_file_name)
+        log_level (int | str, optional): Logging level. Defaults to logging.INFO.
+        t1 (str | Path | np.ndarray | None, optional): Path or NumPy array for T1 image. Defaults to None.
+        t1c (str | Path | np.ndarray | None, optional): Path or NumPy array for T1 contrast-enhanced image. Defaults to None.
+        t2 (str | Path | np.ndarray | None, optional): Path or NumPy array for T2 image. Defaults to None.
+        fla (str | Path | np.ndarray | None, optional): Path or NumPy array for FLAIR image. Defaults to None.
+        output_whole_network (bool, optional): Whether to output the whole network results. Defaults to False.
+        output_metastasis_network (bool, optional): Whether to output the metastasis network results. Defaults to False.
+        tta (bool, optional): Whether to apply test-time augmentations. Defaults to True.
+        sliding_window_batch_size (int, optional): Batch size for sliding window inference. Defaults to 1.
+        workers (int, optional): Number of workers for data loading. Defaults to 0.
+        threshold (float, optional): Threshold for binarizing the model outputs. Defaults to 0.5.
+        sliding_window_overlap (float, optional): Overlap ratio for sliding window inference. Defaults to 0.5.
+        crop_size (Tuple[int, int, int], optional): Crop size for sliding window inference. Defaults to (192, 192, 32).
+        model_selection (ModelSelection, optional): Model selection strategy. Defaults to ModelSelection.BEST.
+    """
+
+    output_whole_network: bool = False
+    output_metastasis_network: bool = False
+    tta: bool = True
+    sliding_window_batch_size: int = 1
+    workers: int = 0
+    threshold: float = 0.5
+    sliding_window_overlap: float = 0.5
+    crop_size: Tuple[int, int, int] = (192, 192, 32)
+    model_selection: ModelSelection = ModelSelection.BEST

brainles_aurora/download.py → brainles_aurora/inferer/download.py

@@ -1,10 +1,10 @@
 # copied from https://github.com/Nordgaren/Github-Folder-Downloader/blob/master/gitdl.py
 import os
-from github import Github, Repository, ContentFile
-import requests
-
 import shutil as sh
 
+import requests
+from github import ContentFile, Github, Repository
+
 
 def download(c: ContentFile, out: str):
     r = requests.get(c.download_url)