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Refactor HoVerNet post-processing (#5618)
Fixes #5616 Fixes #5617 ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Breaking change (fix or new feature that would cause existing functionality to change). - [x] New tests added to cover the changes. - [x] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [x] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [x] In-line docstrings updated. - [x] Documentation updated, tested `make html` command in the `docs/` folder. Signed-off-by: Behrooz <3968947+drbeh@users.noreply.github.com>
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‎docs/source/apps.rst‎

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@@ -155,6 +155,8 @@ Applications
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:members:
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.. autoclass:: HoVerNetNuclearTypePostProcessing
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:members:
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.. autoclass:: HoVerNetInstanceMapPostProcessing
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:members:
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.. automodule:: monai.apps.pathology.transforms.post.dictionary
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.. autoclass:: GenerateSuccinctContourd
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:members:
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.. autoclass:: GenerateWatershedMarkersd
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:members:
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.. autoclass:: HoVerNetInstanceMapPostProcessingd
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:members:
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.. autoclass:: HoVerNetNuclearTypePostProcessingd
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:members:
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‎monai/apps/pathology/transforms/__init__.py‎

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GenerateSuccinctContour,
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GenerateWatershedMarkers,
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GenerateWatershedMask,
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HoVerNetInstanceMapPostProcessing,
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HoVerNetNuclearTypePostProcessing,
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Watershed,
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)
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GenerateWatershedMaskD,
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GenerateWatershedMaskd,
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GenerateWatershedMaskDict,
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HoVerNetInstanceMapPostProcessingD,
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HoVerNetInstanceMapPostProcessingd,
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HoVerNetInstanceMapPostProcessingDict,
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HoVerNetNuclearTypePostProcessingD,
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HoVerNetNuclearTypePostProcessingd,
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HoVerNetNuclearTypePostProcessingDict,

‎monai/apps/pathology/transforms/post/__init__.py‎

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GenerateSuccinctContour,
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GenerateWatershedMarkers,
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GenerateWatershedMask,
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HoVerNetInstanceMapPostProcessing,
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HoVerNetNuclearTypePostProcessing,
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Watershed,
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)
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GenerateWatershedMaskD,
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GenerateWatershedMaskd,
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GenerateWatershedMaskDict,
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HoVerNetInstanceMapPostProcessingD,
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HoVerNetInstanceMapPostProcessingd,
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HoVerNetInstanceMapPostProcessingDict,
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HoVerNetNuclearTypePostProcessingD,
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HoVerNetNuclearTypePostProcessingd,
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HoVerNetNuclearTypePostProcessingDict,

‎monai/apps/pathology/transforms/post/array.py‎

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‎monai/apps/pathology/transforms/post/dictionary.py‎

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‎monai/transforms/post/array.py‎

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@@ -360,7 +360,7 @@ class RemoveSmallObjects(Transform):
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Data should be one-hotted.
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Args:
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min_size: objects smaller than this size are removed.
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min_size: objects smaller than this size (in pixel) are removed.
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connectivity: Maximum number of orthogonal hops to consider a pixel/voxel as a neighbor.
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Accepted values are ranging from 1 to input.ndim. If ``None``, a full
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connectivity of ``input.ndim`` is used. For more details refer to linked scikit-image

‎tests/test_generate_distance_map.py‎

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for p in TEST_NDARRAYS:
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EXCEPTION_TESTS.append([{}, p(np.random.rand(2, 5, 5)), p(np.random.rand(1, 5, 5)), ValueError])
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EXCEPTION_TESTS.append([{}, p(np.random.rand(1, 5, 5)), p(np.random.rand(2, 5, 5)), ValueError])
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for p in TEST_NDARRAYS:
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TESTS.append([{}, p(np.random.rand(1, 5, 5)), p(np.random.rand(1, 5, 5)), (1, 5, 5)])
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TESTS.append(
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[{"smooth_fn": GaussianSmooth(sigma=0.4)}, p(np.random.rand(1, 5, 5)), p(np.random.rand(1, 5, 5)), (1, 5, 5)]

‎tests/test_generate_distance_mapd.py‎

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for p in TEST_NDARRAYS:
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EXCEPTION_TESTS.append(
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[{"keys": "mask", "border_key": "border"}, p(np.random.rand(2, 5, 5)), p(np.random.rand(1, 5, 5)), ValueError]
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[
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{"mask_key": "mask", "border_key": "border"},
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p(np.random.rand(2, 5, 5)),
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p(np.random.rand(1, 5, 5)),
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ValueError,
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]
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)
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EXCEPTION_TESTS.append(
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[{"keys": "mask", "border_key": "border"}, p(np.random.rand(1, 5, 5)), p(np.random.rand(2, 5, 5)), ValueError]
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[
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{"mask_key": "mask", "border_key": "border"},
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p(np.random.rand(1, 5, 5)),
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p(np.random.rand(2, 5, 5)),
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ValueError,
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]
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)
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for p in TEST_NDARRAYS:
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TESTS.append(
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[{"keys": "mask", "border_key": "border"}, p(np.random.rand(1, 5, 5)), p(np.random.rand(1, 5, 5)), (1, 5, 5)]
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)
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TESTS.append([{}, p(np.random.rand(1, 5, 5)), p(np.random.rand(1, 5, 5)), (1, 5, 5)])
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TESTS.append(
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[
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{"keys": "mask", "border_key": "border", "smooth_fn": GaussianSmooth(sigma=0.4)},
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{"mask_key": "mask", "border_key": "border", "smooth_fn": GaussianSmooth(sigma=0.4)},
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p(np.random.rand(1, 5, 5)),
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p(np.random.rand(1, 5, 5)),
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(1, 5, 5),
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@parameterized.expand(TESTS)
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def test_value2(self, argments, mask, border_map, expected_shape):
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result = GenerateDistanceMapd(**argments)({"mask": mask, "border": border_map})
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self.assertEqual(result["dist"].shape, expected_shape)
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self.assertEqual(result["dist_map"].shape, expected_shape)
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if __name__ == "__main__":

‎tests/test_generate_instance_border.py‎

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np.random.RandomState(123)
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for p in TEST_NDARRAYS:
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EXCEPTION_TESTS.append(
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[
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{"kernel_size": 3, "remove_small_objects": False},
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p(np.random.rand(1, 5, 5, 5)),
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p(np.random.rand(2, 5, 5)),
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ValueError,
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]
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)
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EXCEPTION_TESTS.append([{"kernel_size": 3}, p(np.random.rand(1, 5, 5, 5)), p(np.random.rand(2, 5, 5)), ValueError])
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EXCEPTION_TESTS.append([{"kernel_size": 3}, p(np.random.rand(1, 5, 5)), p(np.random.rand(1, 5, 5)), ValueError])
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EXCEPTION_TESTS.append([{"kernel_size": 3}, p(np.random.rand(2, 5, 5)), p(np.random.rand(2, 5, 5)), ValueError])
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EXCEPTION_TESTS.append(
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[
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{"kernel_size": 3, "remove_small_objects": False},
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p(np.random.rand(1, 5, 5)),
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p(np.random.rand(1, 5, 5)),
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ValueError,
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]
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)
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EXCEPTION_TESTS.append(
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[
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{"kernel_size": 3, "remove_small_objects": False},
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p(np.random.rand(2, 5, 5)),
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p(np.random.rand(2, 5, 5)),
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ValueError,
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]
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)
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for p in TEST_NDARRAYS:
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TESTS.append(
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[
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{"kernel_size": 3, "remove_small_objects": False},
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p(np.random.rand(1, 5, 5)),
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p(np.random.rand(2, 5, 5)),
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(1, 5, 5),
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]
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)
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TESTS.append(
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[
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{"kernel_size": 3, "remove_small_objects": False},
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p(np.random.rand(1, 5, 5)),
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p(np.random.rand(2, 5, 5)),
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(1, 5, 5),
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]
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)
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TESTS.append([{"kernel_size": 3}, p(np.random.rand(1, 5, 5)), p(np.random.rand(2, 5, 5)), (1, 5, 5)])
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TESTS.append([{"kernel_size": 3}, p(np.random.rand(1, 5, 5)), p(np.random.rand(2, 5, 5)), (1, 5, 5)])
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class TestGenerateInstanceBorder(unittest.TestCase):
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@parameterized.expand(EXCEPTION_TESTS)
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def test_value(self, argments, mask, hover_map, exception_type):
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def test_value(self, arguments, mask, hover_map, exception_type):
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with self.assertRaises(exception_type):
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GenerateInstanceBorder(**argments)(mask, hover_map)
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GenerateInstanceBorder(**arguments)(mask, hover_map)
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@parameterized.expand(TESTS)
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def test_value2(self, argments, mask, hover_map, expected_shape):
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result = GenerateInstanceBorder(**argments)(mask, hover_map)
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def test_value2(self, arguments, mask, hover_map, expected_shape):
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result = GenerateInstanceBorder(**arguments)(mask, hover_map)
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self.assertEqual(result.shape, expected_shape)
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‎tests/test_generate_instance_borderd.py‎

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for p in TEST_NDARRAYS:
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EXCEPTION_TESTS.append(
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[
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{"keys": "mask", "kernel_size": 3, "remove_small_objects": True, "min_size": 10},
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p(np.random.rand(1, 5, 5, 5)),
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p(np.random.rand(2, 5, 5)),
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ValueError,
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]
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[{"mask_key": "mask", "kernel_size": 3}, p(np.random.rand(1, 5, 5, 5)), p(np.random.rand(2, 5, 5)), ValueError]
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)
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EXCEPTION_TESTS.append(
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[
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{"keys": "mask", "kernel_size": 3, "remove_small_objects": True, "min_size": 10},
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p(np.random.rand(1, 5, 5)),
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p(np.random.rand(1, 5, 5)),
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ValueError,
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]
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[{"mask_key": "mask", "kernel_size": 3}, p(np.random.rand(1, 5, 5)), p(np.random.rand(1, 5, 5)), ValueError]
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)
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EXCEPTION_TESTS.append(
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[
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{"keys": "mask", "kernel_size": 3, "remove_small_objects": True, "min_size": 10},
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p(np.random.rand(2, 5, 5)),
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p(np.random.rand(2, 5, 5)),
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ValueError,
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]
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[{"mask_key": "mask", "kernel_size": 3}, p(np.random.rand(2, 5, 5)), p(np.random.rand(2, 5, 5)), ValueError]
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)
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for p in TEST_NDARRAYS:
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TESTS.append(
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[
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{"keys": "mask", "kernel_size": 3, "remove_small_objects": False, "min_size": 10},
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p(np.random.rand(1, 5, 5)),
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p(np.random.rand(2, 5, 5)),
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(1, 5, 5),
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]
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[{"mask_key": "mask", "kernel_size": 3}, p(np.random.rand(1, 5, 5)), p(np.random.rand(2, 5, 5)), (1, 5, 5)]
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)
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TESTS.append(
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[
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{"keys": "mask", "kernel_size": 3, "remove_small_objects": True, "min_size": 10},
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p(np.random.rand(1, 5, 5)),
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p(np.random.rand(2, 5, 5)),
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(1, 5, 5),
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]
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[{"mask_key": "mask", "kernel_size": 3}, p(np.random.rand(1, 5, 5)), p(np.random.rand(2, 5, 5)), (1, 5, 5)]
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)
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