Change name of label filter class (Project-MONAI#2678)

Signed-off-by: Sebastian Penhouet <sebastian.penhouet@airamed.de>

docs/source/transforms.rst

@@ -356,9 +356,9 @@ Post-processing
     :members:
     :special-members: __call__
 
-`Filter`
+`LabelFilter`
 """""""""""""""""""""""""""""""
-.. autoclass:: Filter
+.. autoclass:: LabelFilter
     :members:
     :special-members: __call__
 
@@ -967,9 +967,9 @@ Post-processing (Dict)
     :members:
     :special-members: __call__
 
-`Filterd`
+`LabelFilterd`
 """"""""""""""""""""""""""""""""
-.. autoclass:: Filterd
+.. autoclass:: LabelFilterd
     :members:
     :special-members: __call__
 

monai/transforms/__init__.py

@@ -195,8 +195,8 @@
     Activations,
     AsDiscrete,
     FillHoles,
-    Filter,
     KeepLargestConnectedComponent,
+    LabelFilter,
     LabelToContour,
     MeanEnsemble,
     ProbNMS,
@@ -213,15 +213,15 @@
     FillHolesD,
     FillHolesd,
     FillHolesDict,
-    FilterD,
-    Filterd,
-    FilterDict,
     InvertD,
     Invertd,
     InvertDict,
     KeepLargestConnectedComponentD,
     KeepLargestConnectedComponentd,
     KeepLargestConnectedComponentDict,
+    LabelFilterD,
+    LabelFilterd,
+    LabelFilterDict,
     LabelToContourD,
     LabelToContourd,
     LabelToContourDict,

monai/transforms/post/array.py

@@ -31,8 +31,8 @@
     "Activations",
     "AsDiscrete",
     "FillHoles",
-    "Filter",
     "KeepLargestConnectedComponent",
+    "LabelFilter",
     "LabelToContour",
     "MeanEnsemble",
     "ProbNMS",
@@ -292,7 +292,7 @@ def __call__(self, img: torch.Tensor) -> torch.Tensor:
         return output
 
 
-class Filter:
+class LabelFilter:
     """
     This transform filters out labels and can be used as a processing step to view only certain labels.
 
@@ -303,7 +303,7 @@ class Filter:
 
     For example:
 
-    Use Filter with applied_labels=[1, 5, 9]::
+    Use LabelFilter with applied_labels=[1, 5, 9]::
 
         [1, 2, 3]         [1, 0, 0]
         [4, 5, 6]    =>   [0, 5 ,0]
@@ -312,7 +312,7 @@ class Filter:
 
     def __init__(self, applied_labels: Union[Sequence[int], int]) -> None:
         """
-        Initialize the Filter class with the labels to filter on.
+        Initialize the LabelFilter class with the labels to filter on.
 
         Args:
             applied_labels (Union[Sequence[int], int]): Label(s) to filter on.
@@ -416,7 +416,7 @@ def __call__(self, img: NdarrayTensor) -> NdarrayTensor:
             img_arr = np.squeeze(img, axis=channel_axis)
             output = get_filled_holes(img_arr, self.connectivity)
             if self.applied_labels:
-                output = Filter(self.applied_labels)(output)
+                output = LabelFilter(self.applied_labels)(output)
             output = np.expand_dims(output, axis=channel_axis)
             output = img_arr + output
             return output

monai/transforms/post/dictionary.py

@@ -29,8 +29,8 @@
     Activations,
     AsDiscrete,
     FillHoles,
-    Filter,
     KeepLargestConnectedComponent,
+    LabelFilter,
     LabelToContour,
     MeanEnsemble,
     ProbNMS,
@@ -53,15 +53,15 @@
     "FillHolesD",
     "FillHolesDict",
     "FillHolesd",
-    "FilterD",
-    "FilterDict",
-    "Filterd",
     "InvertD",
     "InvertDict",
     "Invertd",
     "KeepLargestConnectedComponentD",
     "KeepLargestConnectedComponentDict",
     "KeepLargestConnectedComponentd",
+    "LabelFilterD",
+    "LabelFilterDict",
+    "LabelFilterd",
     "LabelToContourD",
     "LabelToContourDict",
     "LabelToContourd",
@@ -216,9 +216,9 @@ def __call__(self, data: Mapping[Hashable, torch.Tensor]) -> Dict[Hashable, torc
         return d
 
 
-class Filterd(MapTransform):
+class LabelFilterd(MapTransform):
     """
-    Dictionary-based wrapper of :py:class:`monai.transforms.Filter`.
+    Dictionary-based wrapper of :py:class:`monai.transforms.LabelFilter`.
     """
 
     def __init__(
@@ -236,7 +236,7 @@ def __init__(
 
         """
         super().__init__(keys, allow_missing_keys)
-        self.converter = Filter(applied_labels)
+        self.converter = LabelFilter(applied_labels)
 
     def __call__(self, data: Mapping[Hashable, NdarrayTensor]) -> Dict[Hashable, NdarrayTensor]:
         d = dict(data)
@@ -692,9 +692,9 @@ def get_saver(self):
 ActivationsD = ActivationsDict = Activationsd
 AsDiscreteD = AsDiscreteDict = AsDiscreted
 FillHolesD = FillHolesDict = FillHolesd
-FilterD = FilterDict = Filterd
 InvertD = InvertDict = Invertd
 KeepLargestConnectedComponentD = KeepLargestConnectedComponentDict = KeepLargestConnectedComponentd
+LabelFilterD = LabelFilterDict = LabelFilterd
 LabelToContourD = LabelToContourDict = LabelToContourd
 MeanEnsembleD = MeanEnsembleDict = MeanEnsembled
 ProbNMSD = ProbNMSDict = ProbNMSd

tests/test_filter.py → tests/test_label_filter.py

@@ -15,7 +15,7 @@
 import torch
 from parameterized import parameterized
 
-from monai.transforms import Filter
+from monai.transforms import LabelFilter
 from tests.utils import allclose, clone
 
 grid_1 = torch.tensor(
@@ -102,10 +102,10 @@
 INVALID_CASES = [ITEST_CASE_1]
 
 
-class TestFillHoles(unittest.TestCase):
+class TestLabelFilter(unittest.TestCase):
     @parameterized.expand(VALID_CASES)
     def test_correct_results(self, _, args, input_image, expected):
-        converter = Filter(**args)
+        converter = LabelFilter(**args)
         if isinstance(input_image, torch.Tensor) and torch.cuda.is_available():
             result = converter(clone(input_image).cuda())
             assert allclose(result, expected.cuda())
@@ -116,7 +116,7 @@ def test_correct_results(self, _, args, input_image, expected):
     @parameterized.expand(INVALID_CASES)
     def test_raise_exception(self, _, args, input_image, expected_error):
         with self.assertRaises(expected_error):
-            converter = Filter(**args)
+            converter = LabelFilter(**args)
             if isinstance(input_image, torch.Tensor) and torch.cuda.is_available():
                 _ = converter(clone(input_image).cuda())
             else: