Merge branch 'dev-define-engines-abc' into dev-add-grandQC

Author: shaneahmed

.github/workflows/python-package.yml

@@ -30,7 +30,7 @@ jobs:
         sudo apt update
         sudo apt-get install -y libopenjp2-7 libopenjp2-tools
         python -m pip install --upgrade pip
-        python -m pip install ruff==0.13.3 pytest pytest-cov pytest-runner
+        python -m pip install ruff==0.14.3 pytest pytest-cov pytest-runner
         pip install -r requirements/requirements.txt
     - name: Cache tiatoolbox static assets
       uses: actions/cache@v4

.pre-commit-config.yaml

@@ -23,7 +23,7 @@ repos:
         - mdformat-black
         - mdformat-myst
   - repo: https://github.com/executablebooks/mdformat
-    rev: 0.7.22
+    rev: 1.0.0
     hooks:
     - id: mdformat
       # Optionally add plugins
@@ -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: v0.13.3
+    rev: v0.14.3
     hooks:
       - id: ruff
         args: [--fix, --exit-non-zero-on-fix]

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==0.13.3  # This will be updated by pre-commit bot to latest version
+ruff==0.14.3  # 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: io_config.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: io_config.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)