Merge branch 'develop' into add-slide-data

Author: shaneahmed

.github/workflows/conda-env-create.yml

@@ -33,7 +33,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     timeout-minutes: 20
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - name: Copy requirements files
         shell: bash
         run: |
@@ -48,3 +48,4 @@ jobs:
         run: |
           micromamba info
           micromamba list
+          python -c "import torch, numpy; print(torch.__version__, numpy.__version__)"

.github/workflows/mypy-type-check.yml

@@ -33,6 +33,7 @@ jobs:
         sudo apt update
         sudo apt-get install -y libopenslide-dev openslide-tools libopenjp2-7 libopenjp2-tools
         python -m pip install --upgrade pip
+        pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
         pip install -r requirements/requirements_dev.txt
 
     - name: Perform type checking

.github/workflows/pip-install.yml

@@ -55,6 +55,8 @@ jobs:
     - name: OpenJPEG Version Information
       continue-on-error: true  # This -h option has exit code 1 for some reason
       run: opj_dump -h
+    - name: Install CPU-only PyTorch
+      run: python -m pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
     - name: pip Install From GitHub Repo on Linux and macOS
       if: runner.os != 'Windows'
       run: python -m pip install git+https://github.com/TissueImageAnalytics/tiatoolbox@${GITHUB_SHA}

.github/workflows/python-package.yml

@@ -30,7 +30,8 @@ jobs:
         sudo apt update
         sudo apt-get install -y libopenjp2-7 libopenjp2-tools
         python -m pip install --upgrade pip
-        python -m pip install ruff==0.14.3 pytest pytest-cov pytest-runner
+        python -m pip install ruff==0.14.7 pytest pytest-cov pytest-runner
+        pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
         pip install -r requirements/requirements.txt
     - name: Cache tiatoolbox static assets
       uses: actions/cache@v4
@@ -107,6 +108,7 @@ jobs:
       run: |
         sudo apt-get install -y libopenslide-dev libopenjp2-7
         python -m pip install --upgrade pip
+        pip install torch torchvision --index-url https://download.pytorch.org/whl/cpu
         pip install -r requirements/requirements.txt
         pip install build
     - name: Build package

.pre-commit-config.yaml

@@ -60,7 +60,7 @@ repos:
       - id: rst-inline-touching-normal # Detect mistake of inline code touching normal text in rst.
   - repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.14.3
+    rev: v0.14.7
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]

requirements/requirements.txt

@@ -32,7 +32,7 @@ SimpleITK>=2.2.1
 sphinx>=5.3.0
 tifffile>=2022.10.10, <=2025.5.10
 timm>=1.0.3
-torch>=2.1.0
+torch>=2.5.0
 torchvision>=0.15.0
 tqdm>=4.64.1
 umap-learn>=0.5.3

requirements/requirements_dev.txt

@@ -10,7 +10,7 @@ pytest>=7.2.0
 pytest-cov>=4.0.0
 pytest-runner>=6.0
 pytest-xdist[psutil]
-ruff==0.14.3  # This will be updated by pre-commit bot to latest version
+ruff==0.14.7  # This will be updated by pre-commit bot to latest version
 toml>=0.10.2
 twine>=4.0.1
 wheel>=0.37.1

tests/models/test_arch_sccnn.py

@@ -39,20 +39,22 @@ def test_functionality(remote_sample: Callable) -> None:
         units="mpp",
         coord_space="resolution",
     )
-    batch = torch.from_numpy(patch)[None]
     model = _load_sccnn(name="sccnn-crchisto")
+    patch = model.preproc(patch)
+    batch = torch.from_numpy(patch)[None]
     output = model.infer_batch(
         model,
         batch,
         device=select_device(on_gpu=env_detection.has_gpu()),
     )
     output = model.postproc(output[0])
-    assert np.all(output == [[8, 7]])
+    np.testing.assert_array_equal(output, np.array([[8, 7]]))
+
     model = _load_sccnn(name="sccnn-conic")
     output = model.infer_batch(
         model,
         batch,
         device=select_device(on_gpu=env_detection.has_gpu()),
     )
     output = model.postproc(output[0])
-    assert np.all(output == [[7, 8]])
+    np.testing.assert_array_equal(output, np.array([[7, 8]]))

tiatoolbox/data/pretrained_model.yaml

@@ -863,14 +863,14 @@ sccnn-crchisto:
         class: semantic_segmentor.IOSegmentorConfig
         kwargs:
             input_resolutions:
-                - { "units": "mpp", "resolution": 0.5 }
+                - { "units": "mpp", "resolution": 0.25 }
             output_resolutions:
-                - { "units": "mpp", "resolution": 0.5 }
+                - { "units": "mpp", "resolution": 0.25 }
             tile_shape: [ 2048, 2048 ]
             patch_input_shape: [ 31, 31 ]
             patch_output_shape: [ 13, 13 ]
             stride_shape: [ 8, 8 ]
-            save_resolution: { 'units': 'mpp', 'resolution': 0.5 }
+            save_resolution: { 'units': 'mpp', 'resolution': 0.25 }
 
 sccnn-conic:
     hf_repo_id: TIACentre/TIAToolbox_pretrained_weights
@@ -886,14 +886,14 @@ sccnn-conic:
         class: semantic_segmentor.IOSegmentorConfig
         kwargs:
             input_resolutions:
-                - { "units": "mpp", "resolution": 0.5 }
+                - { "units": "mpp", "resolution": 0.25 }
             output_resolutions:
-                - { "units": "mpp", "resolution": 0.5 }
+                - { "units": "mpp", "resolution": 0.25 }
             tile_shape: [ 2048, 2048 ]
             patch_input_shape: [ 31, 31 ]
             patch_output_shape: [ 13, 13 ]
             stride_shape: [ 8, 8 ]
-            save_resolution: { 'units': 'mpp', 'resolution': 0.5 }
+            save_resolution: { 'units': 'mpp', 'resolution': 0.25 }
 
 nuclick_original-pannuke:
     hf_repo_id: TIACentre/TIAToolbox_pretrained_weights

tiatoolbox/models/architecture/sccnn.py

@@ -239,7 +239,7 @@ def spatially_constrained_layer2(
         return sc2 * out_map_threshold
 
     @staticmethod
-    def preproc(image: torch.Tensor) -> torch.Tensor:
+    def preproc(image: np.ndarray) -> np.ndarray:
         """Transforming network input to desired format.
 
         This method is model and dataset specific, meaning that it can be replaced by
@@ -309,7 +309,6 @@ def spatially_constrained_layer1(
             sigmoid2 = sigmoid[:, 2:3, :, :]
             return sigmoid0, sigmoid1, sigmoid2
 
-        input_tensor = self.preproc(input_tensor)
         l1 = self.layer["l1"]["conv1"](input_tensor)
         p1 = self.layer["pool1"](l1)
         l2 = self.layer["l2"]["conv1"](p1)