💥 API: Change API for SCCNN input

docs/pretrained.rst

@@ -241,6 +241,28 @@ input output configuration:
 .. collapse:: Model names
 
     - hovernet_original-consep
+
+
+.. collapse:: Input Output Configuration Details
+
+   .. code-block:: python
+
+        from tiatoolbox.models import IOSegmentorConfig
+        ioconfig = IOSegmentorConfig(
+            input_resolutions=[
+                {'units': 'mpp', 'resolution': 0.25}
+            ],
+            output_resolutions=[
+                {'units': 'mpp', 'resolution': 0.25}
+            ],
+            tile_shape=[2048, 2048]
+            patch_input_shape=(252, 252),
+            patch_output_shape=(252, 252),
+            stride_shape=(150, 150),
+            save_resolution={'units': 'mpp', 'resolution': 0.25}
+        )
+
+.. collapse:: Model names
     - micronet_hovernet-consep
 
 Kumar Dataset
@@ -275,6 +297,87 @@ input output configuration:
 
     - hovernet_original_kumar
 
+Nucleus Detection
+^^^^^^^^^^^^^^^^^
+
+CRCHisto Dataset
+--------------
+
+We provide the following models trained using the `CRCHisto dataset <https://warwick.ac.uk/fac/cross_fac/tia/data/crchistolabelednucleihe//>`_, which uses the following
+input output configuration:
+
+.. collapse:: Input Output Configuration Details
+
+   .. code-block:: python
+
+        from tiatoolbox.models import IOPatchPredictorConfig
+        ioconfig = IOPatchPredictorConfig(
+            patch_input_shape=(31, 31),
+            stride_shape=(8, 8),
+            input_resolutions=[{"resolution": 0.25, "units": "mpp"}]
+        )
+
+
+.. collapse:: Model names
+
+    - sccnn-crchisto
+
+.. collapse:: Input Output Configuration Details
+
+   .. code-block:: python
+
+        from tiatoolbox.models import IOPatchPredictorConfig
+        ioconfig = IOPatchPredictorConfig(
+            patch_input_shape=(252, 252),
+            stride_shape=(150, 150),
+            input_resolutions=[{"resolution": 0.25, "units": "mpp"}]
+        )
+
+
+.. collapse:: Model names
+
+    - mapde-crchisto
+
+
+CoNIC Dataset
+--------------
+
+We provide the following models trained using the `CoNIC dataset <https://conic-challenge.grand-challenge.org/>`_, which uses the following
+input output configuration:
+
+.. collapse:: Input Output Configuration Details
+
+   .. code-block:: python
+
+        from tiatoolbox.models import IOPatchPredictorConfig
+        ioconfig = IOPatchPredictorConfig(
+            patch_input_shape=(31, 31),
+            stride_shape=(8, 8),
+            input_resolutions=[{"resolution": 0.25, "units": "mpp"}]
+        )
+
+
+.. collapse:: Model names
+
+    - sccnn-conic
+
+.. collapse:: Input Output Configuration Details
+
+   .. code-block:: python
+
+        from tiatoolbox.models import IOPatchPredictorConfig
+        ioconfig = IOPatchPredictorConfig(
+            patch_input_shape=(252, 252),
+            stride_shape=(150, 150),
+            input_resolutions=[{"resolution": 0.25, "units": "mpp"}]
+        )
+
+
+.. collapse:: Model names
+
+    - mapde-conic
+
+
 Multi-Task Segmentation
 ^^^^^^^^^^^^^^^^^^^^^^^
 

tiatoolbox/data/pretrained_model.yaml

@@ -777,44 +777,66 @@ mapde-crchisto:
     architecture:
         class: mapde.MapDe
         kwargs:
+            input_resolutions:
+                - { "units": "mpp", "resolution": 0.25 }
             num_input_channels: 3
             min_distance: 4
             threshold_abs: 250
             num_classes: 1
+            tile_shape: [ 2048, 2048 ]
+            patch_input_shape: [ 252, 252 ]
+            stride_shape: [ 150, 150 ]
 
 mapde-conic:
     url: https://tiatoolbox.dcs.warwick.ac.uk/models/detection/mapde-conic.pth
     architecture:
         class: mapde.MapDe
         kwargs:
+            input_resolutions:
+                - { "units": "mpp", "resolution": 0.25 }
             num_input_channels: 3
             min_distance: 3
             threshold_abs: 205
             num_classes: 1
+            tile_shape: [ 2048, 2048 ]
+            patch_input_shape: [ 252, 252 ]
+            stride_shape: [ 150, 150 ]
 
 sccnn-crchisto:
     url: https://tiatoolbox.dcs.warwick.ac.uk/models/detection/sccnn-crchisto.pth
     architecture:
         class: sccnn.SCCNN
         kwargs:
+            input_resolutions:
+                - { "units": "mpp", "resolution": 0.25 }
             num_input_channels: 3
             out_height: 13
             out_width: 13
             radius: 12
             min_distance: 6
             threshold_abs: 0.20
+            tile_shape: [ 2048, 2048 ]
+            patch_input_shape: [ 31, 31 ]
+            patch_output_shape: [ 13, 13 ]
+            stride_shape: [ 8, 8 ]
 
 sccnn-conic:
     url: https://tiatoolbox.dcs.warwick.ac.uk/models/detection/sccnn-conic.pth
     architecture:
         class: sccnn.SCCNN
         kwargs:
+            input_resolutions:
+                - { "units": "mpp", "resolution": 0.25 }
             num_input_channels: 3
             out_height: 13
             out_width: 13
             radius: 12
             min_distance: 5
             threshold_abs: 0.05
+            tile_shape: [ 2048, 2048 ]
+            patch_input_shape: [ 31, 31 ]
+            patch_output_shape: [ 13, 13 ]
+            stride_shape: [ 8, 8 ]
 
 nuclick_original-pannuke:
     url: https://tiatoolbox.dcs.warwick.ac.uk/models/seg/nuclick_original-pannuke.pth

tiatoolbox/models/architecture/sccnn.py

@@ -90,13 +90,14 @@ class SCCNN(ModelABC):
     def __init__(
         self,
         num_input_channels: int = 3,
-        out_height: int = 13,
-        out_width: int = 13,
+        patch_output_shape: Tuple[int, int] = (13, 13),
         radius: int = 12,
         min_distance: int = 6,
         threshold_abs: float = 0.20,
     ) -> None:
         super().__init__()
+        out_height = patch_output_shape[0]
+        out_width = patch_output_shape[1]
         self.in_ch = num_input_channels
         self.out_height = out_height
         self.out_width = out_width