Add basic GitHub action for testing nnunet inference

.github/workflows/run_tests_nnunet.yml

@@ -0,0 +1,49 @@
+name: Run nnunet tests on all OS
+on:
+  push:
+    paths-ignore:
+      - '**.md'
+jobs:
+
+  run-tests:
+    strategy:
+      matrix:
+        # os: [ubuntu-latest, windows-latest, macos-latest]  # fails on windows until https://github.com/MIC-DKFZ/nnUNet/issues/2396 is resolved
+        os: [ubuntu-latest, macos-latest]
+        python-version: ["3.10"]
+    runs-on: ${{ matrix.os }}
+
+    steps:
+    - uses: actions/checkout@v4
+
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+
+    - name: Download and extract weights and download example file
+      # use python instead of curl to avoid the need to install curl (more difficult for macos)
+      run: |
+        mkdir -p $HOME/github_actions_nnunet/results
+        python -c "import urllib.request; urllib.request.urlretrieve('https://github.com/wasserth/TotalSegmentator/releases/download/v2.0.0-weights/Dataset300_body_6mm_1559subj.zip', '$HOME/github_actions_nnunet/results/tmp_download_file.zip')"
+        unzip -o $HOME/github_actions_nnunet/results/tmp_download_file.zip -d $HOME/github_actions_nnunet/results
+        rm $HOME/github_actions_nnunet/results/tmp_download_file.zip
+
+    - name: Install dependencies on Ubuntu
+      if: runner.os == 'Linux'
+      run: |
+        python -m pip install --upgrade pip
+        pip install pytest Cython
+        pip install torch==2.4.0 -f https://download.pytorch.org/whl/cpu
+        pip install .
+
+    - name: Install dependencies on Windows / MacOS
+      if: runner.os == 'Windows' || runner.os == 'macOS'
+      run: |
+        python -m pip install --upgrade pip
+        pip install pytest Cython
+        pip install torch==2.4.0
+        pip install .
+
+    - name: Run test script
+      run: python nnunetv2/tests/integration_tests/run_nnunet_inference.py

.gitignore

@@ -99,7 +99,7 @@ ENV/
 *.txt
 .idea/*
 *.png
-*.nii.gz
+# *.nii.gz  # nifti files needed for example_data for github actions tests
 *.nii
 *.tif
 *.bmp

nnunetv2/tests/integration_tests/run_nnunet_inference.py

@@ -0,0 +1,46 @@
+import os
+import shutil
+import subprocess
+from pathlib import Path
+
+import nibabel as nib
+import numpy as np
+
+
+def dice_score(y_true, y_pred):
+    intersect = np.sum(y_true * y_pred)
+    denominator = np.sum(y_true) + np.sum(y_pred)
+    f1 = (2 * intersect) / (denominator + 1e-6)
+    return f1
+
+
+def run_tests_and_exit_on_failure():
+    """
+    Runs inference of a simple nnU-Net for CT body segmentation on a small example CT image 
+    and checks if the output is correct.
+    """
+    # Set nnUNet_results env var
+    weights_dir = Path.home() / "github_actions_nnunet" / "results"
+    os.environ["nnUNet_results"] = str(weights_dir)
+
+    # Copy example file
+    os.makedirs("nnunetv2/tests/github_actions_output", exist_ok=True)
+    shutil.copy("nnunetv2/tests/example_data/example_ct_sm.nii.gz", "nnunetv2/tests/github_actions_output/example_ct_sm_0000.nii.gz")
+
+    # Run nnunet
+    subprocess.call(f"nnUNetv2_predict -i nnunetv2/tests/github_actions_output -o nnunetv2/tests/github_actions_output -d 300 -tr nnUNetTrainer -c 3d_fullres -f 0 -device cpu", shell=True)
+
+    # Check if the nnunet segmentation is correct
+    img_gt = nib.load(f"nnunetv2/tests/example_data/example_ct_sm_T300_output.nii.gz").get_fdata()
+    img_pred = nib.load(f"nnunetv2/tests/github_actions_output/example_ct_sm.nii.gz").get_fdata()
+    dice = dice_score(img_gt, img_pred)
+    images_equal = dice > 0.99  # allow for a small difference in the segmentation, otherwise the test will fail often
+    assert images_equal, f"The nnunet segmentation is not correct (dice: {dice:.5f})."
+
+    # Clean up
+    shutil.rmtree("nnunetv2/tests/github_actions_output")
+    shutil.rmtree(Path.home() / "github_actions_nnunet")
+
+
+if __name__ == "__main__":
+    run_tests_and_exit_on_failure()