🆕 Add GrandQC Tissue Segmentation Model

tests/models/test_arch_grandqc.py

@@ -0,0 +1,70 @@
+"""Unit test package for GrandQC Tissue Model."""
+
+import numpy as np
+import torch
+
+from tiatoolbox.models.architecture import (
+    fetch_pretrained_weights,
+    get_pretrained_model,
+)
+from tiatoolbox.models.architecture.grandqc import GrandQCModel
+from tiatoolbox.models.engine.io_config import IOSegmentorConfig
+from tiatoolbox.utils.misc import select_device
+from tiatoolbox.wsicore.wsireader import VirtualWSIReader
+
+ON_GPU = False
+
+
+def test_functional_grandqc() -> None:
+    """Test for GrandQC model."""
+    # test fetch pretrained weights
+    pretrained_weights = fetch_pretrained_weights("grandqc_tissue_detection_mpp10")
+    assert pretrained_weights is not None
+
+    # test creation
+    model = GrandQCModel(num_output_channels=2)
+    assert model is not None
+
+    # load pretrained weights
+    pretrained = torch.load(pretrained_weights, map_location="cpu")
+    model.load_state_dict(pretrained)
+
+    # test get pretrained model
+    model, ioconfig = get_pretrained_model("grandqc_tissue_detection_mpp10")
+    assert isinstance(model, GrandQCModel)
+    assert isinstance(ioconfig, IOSegmentorConfig)
+    assert model.num_output_channels == 2
+    assert model.decoder_channels == (256, 128, 64, 32, 16)
+
+    # test inference
+    generator = np.random.default_rng(1337)
+    test_image = generator.integers(0, 256, size=(2048, 2048, 3), dtype=np.uint8)
+    reader = VirtualWSIReader.open(test_image)
+    read_kwargs = {"resolution": 0, "units": "level", "coord_space": "resolution"}
+    batch = np.array(
+        [
+            reader.read_bounds((0, 0, 512, 512), **read_kwargs),
+            reader.read_bounds((512, 512, 1024, 1024), **read_kwargs),
+        ],
+    )
+    batch = torch.from_numpy(batch)
+    output = model.infer_batch(model, batch, device=select_device(on_gpu=ON_GPU))
+    assert output.shape == (2, 512, 512, 2)
+
+
+def test_grandqc_preproc_postproc() -> None:
+    """Test GrandQC preproc and postproc functions."""
+    model = GrandQCModel(num_output_channels=2)
+
+    generator = np.random.default_rng(1337)
+    # test preproc
+    dummy_image = generator.integers(0, 256, size=(512, 512, 3), dtype=np.uint8)
+    preproc_image = model.preproc(dummy_image)
+    assert preproc_image.shape == dummy_image.shape
+    assert preproc_image.dtype == np.float64
+
+    # test postproc
+    dummy_output = generator.random(size=(512, 512, 2), dtype=np.float32)
+    postproc_image = model.postproc(dummy_output)
+    assert postproc_image.shape == (512, 512)
+    assert postproc_image.dtype == np.int64

tiatoolbox/data/pretrained_model.yaml

@@ -815,7 +815,7 @@ mapde-crchisto:
             threshold_abs: 250
             num_classes: 1
     ioconfig:
-        class: semantic_segmentor.IOSegmentorConfig
+        class: io_config.IOSegmentorConfig
         kwargs:
             input_resolutions:
                 - { "units": "mpp", "resolution": 0.5 }
@@ -837,7 +837,7 @@ mapde-conic:
             threshold_abs: 205
             num_classes: 1
     ioconfig:
-        class: semantic_segmentor.IOSegmentorConfig
+        class: io_config.IOSegmentorConfig
         kwargs:
             input_resolutions:
                 - { "units": "mpp", "resolution": 0.5 }
@@ -860,7 +860,7 @@ sccnn-crchisto:
             threshold_abs: 0.20
             patch_output_shape: [ 13, 13 ]
     ioconfig:
-        class: semantic_segmentor.IOSegmentorConfig
+        class: io_config.IOSegmentorConfig
         kwargs:
             input_resolutions:
                 - { "units": "mpp", "resolution": 0.25 }
@@ -883,7 +883,7 @@ sccnn-conic:
             threshold_abs: 0.05
             patch_output_shape: [ 13, 13 ]
     ioconfig:
-        class: semantic_segmentor.IOSegmentorConfig
+        class: io_config.IOSegmentorConfig
         kwargs:
             input_resolutions:
                 - { "units": "mpp", "resolution": 0.25 }
@@ -903,7 +903,7 @@ nuclick_original-pannuke:
             num_input_channels: 5
             num_output_channels: 1
     ioconfig:
-        class: semantic_segmentor.IOSegmentorConfig
+        class: io_config.IOSegmentorConfig
         kwargs:
             input_resolutions:
                 - {'units': 'baseline', 'resolution': 0.25}
@@ -925,7 +925,7 @@ nuclick_light-pannuke:
             decoder_block: [3,3]
             skip_type: "add"
     ioconfig:
-        class: semantic_segmentor.IOSegmentorConfig
+        class: io_config.IOSegmentorConfig
         kwargs:
             input_resolutions:
                 - {'units': 'baseline', 'resolution': 0.25}
@@ -934,3 +934,21 @@ nuclick_light-pannuke:
             patch_input_shape: [128, 128]
             patch_output_shape: [128, 128]
             save_resolution: {'units': 'baseline', 'resolution': 1.0}
+
+grandqc_tissue_detection_mpp10:
+    hf_repo_id: TIACentre/GrandQC_Tissue_Detection
+    architecture:
+        class: grandqc.GrandQCModel
+        kwargs:
+            num_output_channels: 2
+    ioconfig:
+        class: io_config.IOSegmentorConfig
+        kwargs:
+            input_resolutions:
+                - {'units': 'mpp', 'resolution': 10.0}
+            output_resolutions:
+                - {'units': 'mpp', 'resolution': 10.0}
+            patch_input_shape: [512, 512]
+            patch_output_shape: [512, 512]
+            stride_shape: [256, 256]
+            save_resolution: {'units': 'mpp', 'resolution': 10.0}